From 56e554d7fa8eee21bf1b88a3f554eefcc05e96a7 Mon Sep 17 00:00:00 2001 From: hechth Date: Thu, 10 Jul 2025 15:01:15 +0200 Subject: [PATCH 01/19] initial comit adding all files --- tools/batch_correction/.shed.yml | 8 + tools/batch_correction/batch_correction.xml | 400 +++++++++++++ .../batch_correction/batch_correction_3Lfct.R | 541 ++++++++++++++++++ .../batch_correction_3Llauncher.R | 146 +++++ .../batch_correction_3Lwrapper.R | 139 +++++ .../batch_correction_all_loess_script.R | 296 ++++++++++ .../batch_correction_all_loess_wrapper.R | 250 ++++++++ tools/batch_correction/determine_bc.xml | 231 ++++++++ .../static/images/batch_correction.png | Bin 0 -> 65271 bytes .../images/determine_batch_correction.png | Bin 0 -> 40187 bytes .../input-batchcorrection-dataMatrix.tsv | 114 ++++ ...ection-sampleMetadata-customSampleType.tsv | 109 ++++ .../input-batchcorrection-sampleMetadata.tsv | 109 ++++ ...input-batchcorrection-variableMetadata.tsv | 114 ++++ .../input-determinebc-dataMatrix.tsv | 11 + .../input-determinebc-sampleMetadata.tsv | 217 +++++++ .../output-batchcorrection-dataMatrix-3L.tsv | 114 ++++ .../output-batchcorrection-dataMatrix.tsv | 114 ++++ .../output-determinebc-preNormSummary.txt | 11 + 19 files changed, 2924 insertions(+) create mode 100644 tools/batch_correction/.shed.yml create mode 100644 tools/batch_correction/batch_correction.xml create mode 100644 tools/batch_correction/batch_correction_3Lfct.R create mode 100644 tools/batch_correction/batch_correction_3Llauncher.R create mode 100644 tools/batch_correction/batch_correction_3Lwrapper.R create mode 100644 tools/batch_correction/batch_correction_all_loess_script.R create mode 100755 tools/batch_correction/batch_correction_all_loess_wrapper.R create mode 100644 tools/batch_correction/determine_bc.xml create mode 100644 tools/batch_correction/static/images/batch_correction.png create mode 100644 tools/batch_correction/static/images/determine_batch_correction.png create mode 100644 tools/batch_correction/test-data/input-batchcorrection-dataMatrix.tsv create mode 100644 tools/batch_correction/test-data/input-batchcorrection-sampleMetadata-customSampleType.tsv create mode 100644 tools/batch_correction/test-data/input-batchcorrection-sampleMetadata.tsv create mode 100644 tools/batch_correction/test-data/input-batchcorrection-variableMetadata.tsv create mode 100644 tools/batch_correction/test-data/input-determinebc-dataMatrix.tsv create mode 100644 tools/batch_correction/test-data/input-determinebc-sampleMetadata.tsv create mode 100644 tools/batch_correction/test-data/output-batchcorrection-dataMatrix-3L.tsv create mode 100644 tools/batch_correction/test-data/output-batchcorrection-dataMatrix.tsv create mode 100644 tools/batch_correction/test-data/output-determinebc-preNormSummary.txt diff --git a/tools/batch_correction/.shed.yml b/tools/batch_correction/.shed.yml new file mode 100644 index 000000000..177304d7a --- /dev/null +++ b/tools/batch_correction/.shed.yml @@ -0,0 +1,8 @@ +name: batchcorrection +owner: melpetera +description: '[W4M][Metabolomics][LC-MS] Correction of data intensities for signal drift and batch-effects.' +homepage_url: http://workflow4metabolomics.org +long_description: 'Instrumental drift and offset differences between batches have been described in LC-MS experiments when the number of samples is large and/or multiple batches of acquisition are needed. Recently a normalization strategy relying on the measurements of a "pooled" (or QC) sample injected periodically has been described: for each variable, a regression model is fitted to the values of the "pool" and subsequently used to adjust the intensities of the samples of interest (van der Kloet et al, 2009; Dunn et al, 2011). The current repository contains two modules: "Determine batch correction" and "Batch correction". The "Batch correction" module provides two strategies for normalization: variables can be either first checked to assess which of them should be corrected (in that case the "Determine Batch Correction" module provides the information about the correction which will be applied), or all variables can be corrected ("all loess" options). In the latter case, it is possible to fit the model on the samples instead of the pools. Output figures and files are provided to assess the quality of the normalization.' +remote_repository_url: https://github.com/workflow4metabolomics/batch_correction.git +categories: +- Metabolomics \ No newline at end of file diff --git a/tools/batch_correction/batch_correction.xml b/tools/batch_correction/batch_correction.xml new file mode 100644 index 000000000..372a1396a --- /dev/null +++ b/tools/batch_correction/batch_correction.xml @@ -0,0 +1,400 @@ + + Corrects intensities for signal drift and batch-effects + + + macros.xml + + + + r-batch + r-ade4 + bioconductor-pcamethods + bioconductor-ropls + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + span_condition['method'] == 'all_loess_pool' or span_condition['method'] == 'all_loess_sample' + + + + + + + + + + + + + + + + + + + + + + + + + + + + +.. class:: infomark + +**Authors** + | Jean-Francois Martin - PF MetaToul-AXIOM ; INRAE ; MetaboHUB (for original version of this tool and overall development of the R script) + | Melanie Petera - PFEM ; INRAE ; MetaboHUB (for R wrapper and R script improvement regarding "linear/lowess/loess" methods) + | Marion Landi - FLAME ; PFEM (for original xml interface and R wrapper) + | Franck Giacomoni - PFEM ; INRAE ; MetaboHUB (for original xml interface and R wrapper) + | Etienne Thevenot - LIST/LADIS ; CEA ; MetaboHUB (for R script and wrapper regarding "all loess pool" and "all loess sample" methods) + +--------------------------------------------------- + +.. class:: infomark + +**Please cite** If you use this tool, please cite: + +when using the **linear**, **lowess** or **loess** methods: + | `F.M. Van Der Kloet, I. Bobeldijk, E.R. Verheij, R.H. Jellema. (2009). "Analytical error reduction using single point calibration for accurate and precise metabolomic phenotyping." Journal of Proteome Research p5132-5141 <http://www.ncbi.nlm.nih.gov/pubmed/19754161>`_ + +when using the **all loess pool** or **all loess sample** method: + | `Dunn et al (2011). Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry. Nature Protocols, 6:1060-1083 <http://dx.doi.org/10.1038/nprot.2011.335>`_ + | Cleveland et al (1997). In Statistical Models in S; Chambers JM. and Hastie TJ. Ed.; Chapman et Hall: London; pp. 309-376 + | Etienne A. Thevenot, Aurelie Roux, Ying Xu, Eric Ezan, and Christophe Junot (2015). Analysis of the human adult urinary metabolome variations with age, body mass index and gender by implementing a comprehensive workflow for univariate and OPLS statistical analyses. *Journal of Proteome Research*, **14**:3322-3335 (http://dx.doi.org/10.1021/acs.jproteome.5b00354). + +--------------------------------------------------- + +.. class:: infomark + +**Tool updates** + +See the **NEWS** section at the bottom of this page + +--------------------------------------------------- + +================ +Batch_correction +================ + +| + +----------- +Description +----------- + +| **Instrumental drift** and **offset differences** between batches have been described in **LC-MS** experiments when the number of samples is large and/or multiple batches of acquisition are needed. +| Recently a normalization strategy relying on the measurements of a **pooled** (or QC) sample injected periodically has been described: for each variable, a **regression model** is fitted to the values of the **pool** and subsequently used to adjust the intensities of the samples of interest (van der Kloet et al, 2009; Dunn et al, 2011). +| +| The current tool implements **two strategies** which differ in the way the regression model is applied to the variables (either depending on variable quality metrics, or 'loess' model for all variables) and also in the generated figure. +| + + +----------------- +Workflow position +----------------- + +.. image:: batch_correction.png + :width: 800 + + +----------- +Input files +----------- + ++----------------------------+------------+ +| Parameter : num + label | Format | ++============================+============+ +| 1 : Data Matrix file | tabular | ++----------------------------+------------+ +| 2 : Sample metadata file | tabular | ++----------------------------+------------+ +| 3 : Variable metadata file | tabular | ++----------------------------+------------+ + + | + +Data Matrix file must contain the intensity values of variables. + | First line must contain all the samples' names + | First column must contain all the variables' ID + + +Sample metadata file must contain at least the three following columns: + | - a batch column (default to "*batch*") to identify the batches of analyses + | - an injection order column (default to "*injectionOrder*") composed of integers defining the injection order of samples + | - a sample type column (default to "*sampleType*") indicating if a sample is a biological one ("*sample*"), a QC-pool ("*pool*") or a blank ("*blank*") + | *Default values* can be changed according to your data coding using the customisation parameters in the "**Sample metadata file coding parameters**" section. + + +**Notes concerning your design:** + | - the 3 mandatory columns must not contain NA + | - your data should contain at least 3 QC-pools in each batch for intra-batch **linear** adjustment and 8 for **lo(w)ess** adjustment (minimum of 5 for **all loess** methods) + + + +---------- +Parameters +---------- + + +Sample metadata file coding parameters + | Enables to give the names of columns in the sample metadata table that contain the injection order, the batches and the sample types. + | Also enables to specify the sample type coding used in the sampletype column. + | + +Type of regression model + | To choose between *linear*, *lowess*, *loess*, *all loess pool*, and *all loess sample* strategies + | **- Option 1** (**linear**, **lowess**, and **loess** methods): before the normalisation of each variable, some quality metrics are computed (see the "Determine Batch Correction" module); depending on the result, the variable can be normalized or not, with either the **linear**, **lowess** or **loess** model. + | **- Option 2** (**all loess pool** and **all loess sample**): each variable is normalized by using the 'loess' model; + | in the case **all loess pool** is chosen and the number of pool observations is below 5, the linear method is used (for all variables) and a warning is generated; + | if the pool intensities are not representative of the samples (which can be viewed on the figure where both trends are shown), the case **all loess sample** enables using the sample intensities (instead of the pool intensities) as the reference for the loess curve. + | In all "option 2" cases: the **median intensity of the reference observations** (either 'pool' or 'sample') is used as the scaling factor after the initial intensities have been divided by the loess predictions. + | + +Span + | Smoothing parameter, advanced option for *lo(w)ess* and *all loess* methods + | In case of a loess fit, the **span** parameter (between 0 and 1) controls the smoothing + | (the higher the smoother; higher values are prefered to avoid overfitting; Cleveland et al, 1997). + | + +Unconsistant values + | available for regression model *linear*, *lowess* and *loess* + | Controls what is done regarding negative or infinite values that can be generated during regression estimation. + | *Prevent it* will change the normalisation term leading to an unconsistant value to prevent it: + | when intra batch denominator term is below 1, it is turned to the minimum >1 one obtained in the concerned batch. + | *Consider it as a missing value* will switch concerned intensities to NA; + | this option implies that concerned ions will not be considered in PCA display. + | *Consider it as a null intensity* will switch concerned intensities to 0. + | + +Factor of interest + | available for regression model *linear*, *lowess* and *loess* + | Name of the factor (column header) that will be used as a categorical variable for design plots (often a biological factor ; if none, put the batch column name). + | This factor does not affect correction calculation. + | + +Level of details for plots + | available for regression model *linear*, *lowess* and *loess* + | *basic*: Sum of intensities + PCA + CV boxplot (before and after correction) + | *standard*: 'basic' plots + before/after-correction plots of intensities over injection order, and design effects for each ion + | *complete*: 'standard' plots + QC-pool regression plots per batch with samples' intensities over injection order + | This factor is not used by the *all loess* methods where a unique figure is generated showing the sum of intensities along injection order, and the first 4 PCA scores. + | + + +------------ +Output files +------------ + +Batch_correction_$method_rdata.rdata ('all_loess' methods only) + | binary data + | Download, open R and use the 'load' function; objects are in the 'res' list + | + +Batch_correction_$method_graph.pdf + | graphical output + | For the *linear* and *lo(w)ess* methods, content depends on level of details chosen + | + +Batch_correction_$method_variableMetadata.tabular + | tsv output + | Identical to the variable metadata input file, with x more columns (where x is the number of batches) in case of *linear*, *lowess* and *loess* methods + | + +Batch_correction_$method_dataMatrix.tabular + | tsv output (tabulated) + | Same formatting as the data matrix file; contains corrected intensities + | + + +--------------------------------------------------- + +---------------------- +Additional information +---------------------- + +.. class:: infomark + +Refer to the corresponding "W4M HowTo" page: + | `MS data processing - Filters and normalisation <https://download.workflow4metabolomics.org/docs/w4e2018/2018-10_EC_W4E%20-%20Dataprocessing_Filter_and_normalisation.pdf>`_ + | + +See also the reference history: + | `W4M00001_Sacurine-statistics (DOI:10.15454/1.4811121736910142E12) <http://dx.doi.org/10.15454/1.4811121736910142E12>`_ + + +--------------------------------------------------- + +---- +NEWS +---- + +CHANGES IN VERSION 3.0.0 +======================== + +NEW FEATURES + +| - Specific names for the 'sampleType', 'injectionOrder', and 'batch' from sampleMetadata are now available in a dedicated parameter section +| - Addition of a sum of ions before/after plot for linear/lowess/loess methods +| - Addition of a third option in "Null values" parameter (renamed "unconsistant values") in linear/lowess/loess methods +| - linear/lowess/loess methods now handle NA in intensities and allow "blank" samples in the dataset +| + +INTERNAL MODIFICATIONS + +| - XML optimisation using macros +| - Output name changes +| - linear/lowess/loess methods: disabling of RData output +| - linear/lowess/loess methods: split of tool-linked code and script-linked one +| - linear/lowess/loess methods: adjustments in the normalisation process to match matters linked to NA acceptance +| - linear/lowess/loess methods: better handling of special characters in IDs and column names +| + +CHANGES IN VERSION 2.2.4 +======================== + +INTERNAL MODIFICATIONS + +Fixed bug for pool selection ("all_loess" methods) + +CHANGES IN VERSION 2.2.2 +======================== + +INTERNAL MODIFICATIONS + +Fixed bug for color plot ("all_loess" methods) + +CHANGES IN VERSION 2.2.0 +======================== + +NEW FEATURE + +Specific names for the 'sampleType', 'injectionOrder', and 'batch' from sampleMetadata can be selected by the user (for compatibility with the MTBLS downloader) + +CHANGES IN VERSION 2.1.2 +======================== + +INTERNAL MODIFICATIONS + +Minor modifications in config file + +CHANGES IN VERSION 2.1.0 +======================== + +INTERNAL MODIFICATIONS + +For PCA figure display only (**all_loess** options): missing values are set to the minimum value before PCA computation is performed (with svd) + +Additional running and installation tests added with planemo, conda, and travis + +BUG FIX + +Variables with NA or 0 values in all reference samples are discarded before applying the **all_loess** normalization + +INTERNAL MODIFICATIONS + +Modifications of the **all_loess_wrapper** file to handle the recent **ropls** package versions (i.e. 1.3.15 and above) which use S4 classes + + + + + + 10.1021/pr900499r + 10.1038/nprot.2011.335 + @ARTICLE{Cleveland91, + author = {Cleveland et al}, + year = {1991}, + journal = {Statistical Models in S, Chambers JM. and Hastie TJ. Ed., Chapman et Hall: London}, + title = {Local Regression Models}, + pages = {309-376}, + editor = {Chambers JM. and Hastie TJ. Ed.}, + publisher = {Chapman et Hall: London}, + chapter = {8} + } + 10.1021/acs.jproteome.5b00354 + + + + diff --git a/tools/batch_correction/batch_correction_3Lfct.R b/tools/batch_correction/batch_correction_3Lfct.R new file mode 100644 index 000000000..53adcd399 --- /dev/null +++ b/tools/batch_correction/batch_correction_3Lfct.R @@ -0,0 +1,541 @@ +# Author: jfmartin +# Modified by : mpetera +############################################################################### +# Correction of analytical effects inter and intra batch on intensities using quality control pooled samples (QC-pools) +# according to the algorithm mentioned by Van der Kloet (J Prot Res 2009). +# Parameters : a dataframe of Ions intensities and an other of samples? metadata which must contains at least the three following columns : +# "batch" to identify the batches of analyses ; need at least 3 QC-pools for linear adjustment and 8 for lo(w)ess adjustment +# "injectionOrder" integer defining the injection order of all samples : QC-pools and analysed samples +# "sampleType" indicates if defining a sample with "sample" or a QC-pool with "pool" +# NO MISSING DATA are allowed +# Version 0.91 insertion of ok_norm function to assess correction feasibility +# Version 0.92 insertion of slope test in ok_norm +# Version 0.93 name of log file define as a parameter of the correction function +# Version 0.94 Within a batch, test if all QCpools or samples values = 0. Definition of an error code in ok_norm function (see function for details) +# Version 0.99 include non linear lowess correction. +# Version 1.00 the corrected result matrix is return transposed in Galaxy +# Version 1.01 standard deviation=0 instead of sum of value=0 is used to assess constant data in ok_norm function. Negative values in corrected matrix are converted to 0. +# Version 1.02 plotsituation create a result file with the error code of non execution of correction set by function ok_norm +# Version 1.03 fix bug in plot with "reg" option. suppression of ok_norm=4 condition if ok_norm function +# Version 2.00 Addition of loess function, correction indicator, plots ; modification of returned objects' format, some plots' displays and ok_norm ifelse format +# Version 2.01 Correction for pools negative values earlier in norm_QCpool +# Version 2.10 Script refreshing ; vocabulary adjustment ; span in parameters for lo(w)ess regression ; conditionning for third line ACP display ; order in loess display +# Version 2.11 ok1 and ok2 permutation (ok_norm) ; conditional display of regression (plotsituation) ; grouping of linked lignes + conditioning (normX) ; conditioning for CVplot +# Version 2.20 acplight function added from previous toolBox.R [# Version 1.01 "NA"-coding possibility added in acplight function] +# Version 2.30 addition of suppressWarnings() for known and controlled warnings ; suppression of one useless "cat" message ; change in Rdata names ; 'batch(es)' in cat +# Version 2.90 change in handling of generated negative and Inf values +# Version 2.91 Plot improvement +# Version 3.00 - handling of sample tags' parameters +# - accepting sample types beyond "pool" and "sample" +# - dealing with NA +# - changes in the normalisation strategy regarding mean values to adjust for NA or 0 values +# - changes in the normalisation strategy regarding unconsistant values (negative or Inf) + +ok_norm=function(qcp,qci,spl,spi,method,normref=NA,valimp="0") { + # Function used for one ion within one batch to determine whether or not batch correction is possible + # ok_norm values : + # 0 : no preliminary-condition problem + # 1 : standard deviation of QC-pools or samples = 0 + # 2 : insufficient number of QC-pools within a batch (n=3 for linear, n=8 for lowess or loess) + # 2.5 : less than 2 samples within a batch + # 3 : significant difference between QC-pools' and samples' means + # 4 : denominator =0 when on 1 pool per batch <> 0 + # 5 : (linear regression only) the slopes ratio ?QC-pools/samples? is lower than -0.2 + # 6 : (linear regression only) none of the pool or sample could be corrected if negative and infinite values are turned into NA + # Parameters: + # qcp: intensity of a given ion for pools + # qci: injection numbers for pools + # spl: intensity of a given ion for samples + # spi: injection numbers for samples + # method: to provide specific checks for "linear" + + ok=0 + if (method=="linear") {minQC=3} else {minQC=8} + if (length(qcp[!is.na(qcp)]) 0 + if (method=="linear" & length(which(((penteB*qci)+ordori)==0))>0 ){ ok=6 + } else { + # different sloop between samples and pools + if (method=="linear" & penteB/penteS < -0.20) { ok=5 + } else { + # + if (method=="linear" & !is.na(normref) & valimp=="NA") { + denom = (penteB * c(spi,qci) + ordori) + normval = c(spl,qcp)*normref / denom + if(length(which((normval==Inf)|(denom<1)))==length(normval)){ok=6} + } + }}}}}} + ok_norm=ok +} + +plotsituation <- function (x, nbid,outfic="plot_regression.pdf", outres="PreNormSummary.txt",fact="batch",span="none", + sm_meta=list(batch="batch", injectionOrder="injectionOrder", sampleType="sampleType", + sampleTag=list(pool="pool",blank="blank",sample="sample"))) { + # Checks for all ions in every batch if linear or lo(w)ess correction is possible. + # Uses ok_norm function and creates a file (PreNormSummary.txt) with the corresponding error codes. + # Also creates a pdf file with plots of linear and lo(w)ess regression lines. + # Parameters: + # x: dataframe with ions in columns and samples in rows ; x is the result of concatenation of sample metadata file and ions file + # nbid: number of samples description columns (id and factors) with at least : "batch","injectionOrder","sampleType" + # outfic: name of regression plots pdf file + # outres: name of summary table file + # fact: factor to be used as categorical variable for plots and PCA + # span: span value for lo(w)ess regression; "none" for linear or default values + # sm_meta: list of information about sample metadata coding + indfact=which(dimnames(x)[[2]]==fact) + indtypsamp=which(dimnames(x)[[2]]==sm_meta$sampleType) + indbatch=which(dimnames(x)[[2]]==sm_meta$batch) + indinject=which(dimnames(x)[[2]]==sm_meta$injectionOrder) + lastIon=dim(x)[2] + nbi=lastIon-nbid # Number of ions = total number of columns - number of identifying columns + nbb=length(levels(x[[sm_meta$batch]])) # Number of batch = number of levels of "batch" comlumn (factor) + nbs=length(x[[sm_meta$sampleType]][x[[sm_meta$sampleType]] %in% sm_meta$sampleTag$sample])# Number of samples = number of rows with "sample" value in sampleType + pdf(outfic,width=27,height=7*ceiling((nbb+2)/3)) + cat(nbi," ions ",nbb," batch(es) \n") + cv=data.frame(matrix(0,nrow=nbi,ncol=2))# initialisation de la dataset qui contiendra les CV + pre_bilan=matrix(0,nrow=nbi,ncol=3*nbb) # dataset of ok_norm function results + for (p in 1:nbi) {# for each ion + par (mfrow=c(ceiling((nbb+2)/3),3),ask=F,cex=1.2) + labion=dimnames(x)[[2]][p+nbid] + indpool=which(x[[sm_meta$sampleType]] %in% sm_meta$sampleTag$pool) # QCpools subscripts in x + pools1=x[indpool,p+nbid]; cv[p,1]=sd(pools1,na.rm=TRUE)/mean(pools1,na.rm=TRUE)# CV before correction + for (b in 1:nbb) {# for each batch... + xb=data.frame(x[(x[[sm_meta$batch]]==levels(x[[sm_meta$batch]])[b]),c(indtypsamp,indinject,p+nbid)]) + indpb = which(xb[[sm_meta$sampleType]] %in% sm_meta$sampleTag$pool)# QCpools subscripts in the current batch + indsp = which(xb[[sm_meta$sampleType]] %in% sm_meta$sampleTag$sample)# samples subscripts in the current batch + normLinearTest=ok_norm(xb[indpb,3],xb[indpb,2], xb[indsp,3],xb[indsp,2],method="linear",normref=mean(xb[c(indpb,indsp),3],na.rm=TRUE),valimp="NA") + normLoessTest=ok_norm(xb[indpb,3],xb[indpb,2], xb[indsp,3],xb[indsp,2],method="loess") + normLowessTest=ok_norm(xb[indpb,3],xb[indpb,2], xb[indsp,3],xb[indsp,2],method="lowess") + pre_bilan[ p,3*b-2]=normLinearTest + pre_bilan[ p,3*b-1]=normLoessTest + pre_bilan[ p,3*b]=normLowessTest + if(length(indpb)>1){ + if(span=="none"){span1<-1 ; span2<-2*length(indpool)/nbs}else{span1<-span ; span2<-span} + if(normLoessTest!=2){resloess=loess(xb[indpb,3]~xb[indpb,2],span=span1,degree=2,family="gaussian",iterations=4,surface="direct")} + if(length(which(!(is.na(xb[indsp,3]))))>1){resloessSample=loess(xb[indsp,3]~xb[indsp,2],span=2*length(indpool)/nbs,degree=2,family="gaussian",iterations=4,surface="direct") } + if(normLowessTest!=2){reslowess=lowess(xb[indpb,2],xb[indpb,3],f=span2)} + if(length(which(!(is.na(xb[indsp,3]))))>1){reslowessSample=lowess(xb[indsp,2],xb[indsp,3])} + liminf=min(xb[,3],na.rm=TRUE);limsup=max(xb[,3],na.rm=TRUE) + firstinj=min(xb[,2],na.rm=TRUE);lastinj=max(xb[,2],na.rm=TRUE) + plot(xb[indsp,2],xb[indsp,3],pch=16, main=paste(labion,"batch ",b),ylab="intensity",xlab="injection order",ylim=c(liminf,limsup),xlim=c(firstinj,lastinj)) + if(nrow(xb)>(length(indpb)+length(indsp))){points(xb[-c(indpb,indsp),2], xb[-c(indpb,indsp),3],pch=18,col="grey")} + points(xb[indpb,2], xb[indpb,3],pch=5) + if(normLoessTest!=2){points(cbind(resloess$x,resloess$fitted)[order(resloess$x),],type="l",col="green3")} + if(length(which(!(is.na(xb[indsp,3]))))>1){points(cbind(resloessSample$x,resloessSample$fitted)[order(resloessSample$x),],type="l",col="green3",lty=2)} + if(normLowessTest!=2){points(reslowess,type="l",col="red")}; if(length(which(!(is.na(xb[indsp,3]))))>1){points(reslowessSample,type="l",col="red",lty=2)} + abline(lsfit(xb[indpb,2],xb[indpb,3]),col="blue") + if(length(which(!(is.na(xb[indsp,3]))))>1){abline(lsfit(xb[indsp,2],xb[indsp,3]),lty=2,col="blue")} + legend("topleft",c("pools","samples"),lty=c(1,2),bty="n") + legend("topright",c("linear","lowess","loess"),lty=1,col=c("blue","red","green3"),bty="n") + } else { + plot.new() + legend("center","Plot only available when the\nbatch contains at least 2 pools.") + } + } + # series de plot avant correction + minval=min(x[p+nbid],na.rm=TRUE);maxval=max(x[p+nbid],na.rm=TRUE) + plot( x[[sm_meta$injectionOrder]], x[,p+nbid],col=x[[sm_meta$batch]],ylim=c(minval,maxval),ylab=labion, + main=paste0("before correction (CV for pools = ",round(cv[p,1],2),")"),xlab="injection order") + suppressWarnings(plot.design( x[c(indtypsamp,indbatch,indfact,p+nbid)],main="factors effect before correction")) + } + dev.off() + pre_bilan=data.frame(pre_bilan) + labion=dimnames(x)[[2]][nbid+1:nbi] + for (i in 1:nbb) { + dimnames(pre_bilan)[[2]][3*i-2]=paste("batch",i,"linear") + dimnames(pre_bilan)[[2]][3*i-1]=paste("batch",i,"loess") + dimnames(pre_bilan)[[2]][3*i]=paste("batch",i,"lowess") + } + bilan=data.frame(labion,pre_bilan) + write.table(bilan,file=outres,sep="\t",row.names=F,quote=F) +} + + +normlowess=function (xb,detail="no",vref=1,b,span=NULL,valneg="none",sm_meta=list(batch="batch", injectionOrder="injectionOrder", sampleType="sampleType", + sampleTag=list(pool="pool",blank="blank",sample="sample")),min_norm=1){ + # Correction function applied to 1 ion in 1 batch. + # Uses a lowess regression computed on QC-pools in order to correct samples intensity values + # xb: dataframe for 1 ion in columns and samples in rows. + # vref: reference value (average of ion) + # b: batch subscript + # detail: level of detail in the outlog file + # span: span value for lo(w)ess regression; NULL for default values + # valneg: to determine what to do with generated negative and Inf values + # sm_meta: list of information about sample metadata coding + # min_norm: minimum value accepted for normalisation term (denominator); should be strictly positive + indpb = which(xb[[sm_meta$sampleType]] %in% sm_meta$sampleTag$pool) # pools subscripts of current batch + indsp = which(xb[[sm_meta$sampleType]] %in% sm_meta$sampleTag$sample) # samples of current batch subscripts + labion=dimnames(xb)[[2]][3] + newval=xb[[3]] # initialisation of corrected values = intial values + ind <- 0 # initialisation of correction indicator + normTodo=ok_norm(xb[indpb,3],xb[indpb,2], xb[indsp,3],xb[indsp,2],method="lowess") + #cat("batch:",b," dim xb=",dim(xb)," ok=",normTodo,"\n") + if (normTodo==0) { + if(length(span)==0){span2<-2*length(indpb)/length(indsp)}else{span2<-span} + reslowess=lowess(xb[indpb,2],xb[indpb,3],f=span2) # lowess regression with QC-pools + if(length(which(reslowess$y=min_norm],na.rm=TRUE) + reslowess$y[toajust] <- mindenom + } } } + for(j in 1:nrow(xb)) { + if (j %in% indpb) { + newval[j]=(vref*xb[j,3]) / (reslowess$y[which(indpb==j)]) + } else { # for samples other than pools, the correction value "corv" correspond to the nearest QCpools + corv= reslowess$y[which(abs(reslowess$x-xb[j,2])==min(abs(reslowess$x-xb[j,2]),na.rm=TRUE))] + if (length(corv)>1) {corv=corv[1]} + newval[j]=(vref*xb[j,3]) / corv + } + if((!is.na(newval[j]))&(newval[j]<0)){newval[j]<-0} + } + if (detail=="reg") { + liminf=min(xb[,3],na.rm=TRUE);limsup=max(xb[,3],na.rm=TRUE) + firstinj=min(xb[,2],na.rm=TRUE);lastinj=max(xb[,2],na.rm=TRUE) + plot(xb[indsp,2],xb[indsp,3],pch=16,main=paste(labion,"batch ",b),ylab="intensity",xlab="injection order",ylim=c(liminf,limsup),xlim=c(firstinj,lastinj)) + if(nrow(xb)>(length(indpb)+length(indsp))){points(xb[-c(indpb,indsp),2], xb[-c(indpb,indsp),3],pch=18)} + points(xb[indpb,2], xb[indpb,3],pch=5) + points(reslowess,type="l",col="red") + } + ind <- 1 + } else {# if ok_norm != 0 , we perform a correction based on batch pool or sample average + if((length(which(!is.na(xb[indpb,3])))>0)&(length(which(xb[indpb,3]>0))>0)){ + moypool=mean(xb[indpb,3],na.rm=TRUE) + newval = (vref*xb[,3])/moypool + } else { + moysamp=mean(xb[indsp,3],na.rm=TRUE) + if((!is.na(moysamp))&(moysamp>0)){ + cat("Warning: no pool value >0 detected in batch",b,"of ion",labion,": sample mean used as normalisation term.\n") + newval = (vref*xb[,3])/moysamp + } else { + dev.off() + stop(paste("\n- - - -\nNo pool nor sample value >0 in batch",b,"of ion",labion,"- correction process aborted.\n- - - -\n")) + } + } + } + newval <- list(norm.ion=newval,norm.ind=ind) + return(newval) +} + +normlinear <- function (xb,detail="no",vref=1,b,valneg="none",sm_meta=list(batch="batch", injectionOrder="injectionOrder", sampleType="sampleType", + sampleTag=list(pool="pool",blank="blank",sample="sample")),min_norm=1){ + # Correction function applied to 1 ion in 1 batch. + # Uses a linear regression computed on QC-pools in order to correct samples intensity values + # xb: dataframe with ions in columns and samples in rows; x is a result of concatenation of sample metadata file and ion file + # detail: level of detail in the outlog file + # vref: reference value (average of ion) + # b: which batch it is + # valneg: to determine what to do with generated negative and Inf values + # sm_meta: list of information about sample metadata coding + # min_norm: minimum value accepted for normalisation term (denominator); should be strictly positive + indpb = which(xb[[sm_meta$sampleType]] %in% sm_meta$sampleTag$pool)# pools subscripts of current batch + indsp = which(xb[[sm_meta$sampleType]] %in% sm_meta$sampleTag$sample)# samples of current batch subscripts + labion=dimnames(xb)[[2]][3] + newval=xb[[3]] # initialisation of corrected values = intial values + ind <- 0 # initialisation of correction indicator + normTodo=ok_norm(xb[indpb,3],xb[indpb,2], xb[indsp,3],xb[indsp,2],method="linear",normref=vref,valimp=valneg) + if (normTodo==0) { + ind <- 1 + reslsfit=lsfit(xb[indpb,2],xb[indpb,3]) # linear regression for QCpools + reslsfitSample=lsfit(xb[indsp,2],xb[indsp,3]) # linear regression for samples + ordori=reslsfit$coefficients[1] + pente=reslsfit$coefficients[2] + if (detail=="reg") { + liminf=min(xb[,3],na.rm=TRUE);limsup=max(xb[,3],na.rm=TRUE) + firstinj=min(xb[,2],na.rm=TRUE);lastinj=max(xb[,2],na.rm=TRUE) + plot(xb[indsp,2],xb[indsp,3],pch=16, + main=paste(labion,"batch ",b),ylab="intensity",xlab="injection order",ylim=c(liminf,limsup),xlim=c(firstinj,lastinj)) + if(nrow(xb)>(length(indpb)+length(indsp))){points(xb[-c(indpb,indsp),2], xb[-c(indpb,indsp),3],pch=18)} + points(xb[indpb,2], xb[indpb,3],pch=5) + abline(reslsfit) + abline(reslsfitSample,lty=2) + } + # correction with rescaling of ion global intensity (vref) + newval = (vref*xb[,3]) / (pente * (xb[,2]) + ordori) + newval[which((pente * (xb[,2]) + ordori)=min_norm],na.rm=TRUE) + newval[toajust] <- vref * (xb[,3][toajust]) / mindenom + } + } + } + } else {# if ok_norm != 0 , we perform a correction based on batch pool or sample average + if((length(which(!is.na(xb[indpb,3])))>0)&(length(which(xb[indpb,3]>0))>0)){ + moypool=mean(xb[indpb,3],na.rm=TRUE) + newval = (vref*xb[,3])/moypool + } else { + moysamp=mean(xb[indsp,3],na.rm=TRUE) + if((!is.na(moysamp))&(moysamp>0)){ + cat("Warning: no pool value >0 detected in batch",b,"of ion",labion,": sample mean used as normalisation term.\n") + newval = (vref*xb[,3])/moysamp + } else { + dev.off() + stop(paste("\n- - - -\nNo pool nor sample value >0 in batch",b,"of ion",labion,"- correction process aborted.\n- - - -\n")) + } + } + } + newval <- list(norm.ion=newval,norm.ind=ind) + return(newval) +} + + +normloess <- function (xb,detail="no",vref=1,b,span=NULL,valneg="none",sm_meta=list(batch="batch", injectionOrder="injectionOrder", sampleType="sampleType", + sampleTag=list(pool="pool",blank="blank",sample="sample")),min_norm=1){ + # Correction function applied to 1 ion in 1 batch. + # Uses a loess regression computed on QC-pools in order to correct samples intensity values. + # xb: dataframe for 1 ion in columns and samples in rows. + # detail: level of detail in the outlog file. + # vref: reference value (average of ion) + # b: batch subscript + # span: span value for lo(w)ess regression; NULL for default values + # valneg: to determine what to do with generated negative and Inf values + # sm_meta: list of information about sample metadata coding + # min_norm: minimum value accepted for normalisation term (denominator); should be strictly positive + indpb = which(xb[[sm_meta$sampleType]] %in% sm_meta$sampleTag$pool) # pools subscripts of current batch + indsp = which(xb[[sm_meta$sampleType]] %in% sm_meta$sampleTag$sample) # samples of current batch subscripts + indbt = which(xb[[sm_meta$sampleType]] %in% c(sm_meta$sampleTag$sample,sm_meta$sampleTag$pool))# batch subscripts of samples and QCpools + labion=dimnames(xb)[[2]][3] + newval=xb[[3]] # initialisation of corrected values = intial values + ind <- 0 # initialisation of correction indicator + normTodo=ok_norm(xb[indpb,3],xb[indpb,2], xb[indsp,3],xb[indsp,2],method="loess") + if (normTodo==0) { + if(length(span)==0){span1<-1}else{span1<-span} + resloess=loess(xb[indpb,3]~xb[indpb,2],span=span1,degree=2,family="gaussian",iterations=4,surface="direct") # loess regression with QCpools + corv=predict(resloess,newdata=xb[,2]) + if(length(which(corv=min_norm],na.rm=TRUE) + corv[toajust] <- mindenom + } + } + } + newvalps=(vref*xb[indbt,3]) / corv[indbt] # to check if correction generates outlier values + refthresh=max(c(3*(quantile(newvalps,na.rm=TRUE)[4]),1.3*(xb[indbt,3])),na.rm=TRUE) + if(length(which(newvalps>refthresh))>0){ # if outliers + # in this case no modification of initial value + newval <- xb[,3] + } else { + newval=(vref*xb[,3]) / corv + newval[newval<0] <- 0 + ind <- 1 # confirmation of correction + } + if ((detail=="reg")&(ind==1)) { # plot + liminf=min(xb[,3],na.rm=TRUE);limsup=max(xb[,3],na.rm=TRUE) + firstinj=min(xb[,2],na.rm=TRUE);lastinj=max(xb[,2],na.rm=TRUE) + plot(xb[indsp,2],xb[indsp,3],pch=16,main=paste(labion,"batch ",b),ylab="intensity",xlab="injection order",ylim=c(liminf,limsup),xlim=c(firstinj,lastinj)) + if(nrow(xb)>(length(indpb)+length(indsp))){points(xb[-c(indpb,indsp),2], xb[-c(indpb,indsp),3],pch=18)} + points(xb[indpb,2], xb[indpb,3],pch=5) + points(cbind(resloess$x,resloess$fitted)[order(resloess$x),],type="l",col="red") + } + } + if (ind==0) {# if ok_norm != 0 or if correction creates outliers, we perform a correction based on batch pool or sample average + if((length(which(!is.na(xb[indpb,3])))>0)&(length(which(xb[indpb,3]>0))>0)){ + moypool=mean(xb[indpb,3],na.rm=TRUE) + newval = (vref*xb[,3])/moypool + } else { + moysamp=mean(xb[indsp,3],na.rm=TRUE) + if((!is.na(moysamp))&(moysamp>0)){ + cat("Warning: no pool value >0 detected in batch",b,"of ion",labion,": sample mean used as normalisation term.\n") + newval = (vref*xb[,3])/moysamp + } else { + dev.off() + stop(paste("\n- - - -\nNo pool nor sample value >0 in batch",b,"of ion",labion,"- correction process aborted.\n- - - -\n")) + } + } + } + newval <- list(norm.ion=newval,norm.ind=ind) + return(newval) +} + + + +norm_QCpool <- function (x, nbid, outlog, fact, metaion, detail="no", NormMoyPool=FALSE, NormInt=FALSE, method="linear",span="none",valNull="0", + sm_meta=list(batch="batch", injectionOrder="injectionOrder", sampleType="sampleType", + sampleTag=list(pool="pool",blank="blank",sample="sample")),min_norm=1) { + ### Correction applying linear or lo(w)ess correction function on all ions for every batch of a dataframe. + # x: dataframe with ions in column and samples' metadata + # nbid: number of sample description columns (id and factors) with at least "batch", "injectionOrder", "sampleType" + # outlog: name of regression plots and PCA pdf file + # fact: factor to be used as categorical variable for plots + # metaion: dataframe of ions' metadata + # detail: level of detail in the outlog file. detail="no" ACP + boxplot of CV before and after correction. + # detail="plot" with plot for all batch before and after correction. + # detail="reg" with added plots with regression lines for all batches. + # NormMoyPool: not used + # NormInt: not used + # method: regression method to be used to correct : "linear" or "lowess" or "loess" + # span: span value for lo(w)ess regression; "none" for linear or default values + # valNull: to determine what to do with negatively estimated intensities + # sm_meta: list of information about sample metadata coding + # min_norm: minimum value accepted for normalisation term (denominator); should be strictly positive + indfact=which(dimnames(x)[[2]]==fact) + indtypsamp=which(dimnames(x)[[2]]==sm_meta$sampleType) + indbatch=which(dimnames(x)[[2]]==sm_meta$batch) + indinject=which(dimnames(x)[[2]]==sm_meta$injectionOrder) + lastIon=dim(x)[2] + indpool=which(x[[sm_meta$sampleType]] %in% sm_meta$sampleTag$pool)# QCpools subscripts in all batches + valref=apply(as.matrix(x[indpool,(nbid+1):(lastIon)]),2,mean,na.rm=TRUE) # reference value for each ion used to still have the same rought size of values + nbi=lastIon-nbid # number of ions + nbb=length(levels(x[[sm_meta$batch]])) # Number of batch(es) = number of levels of factor "batch" (can be =1) + Xn=data.frame(x[,c(1:nbid)],matrix(0,nrow=nrow(x),ncol=nbi))# initialisation of the corrected dataframe (=initial dataframe) + dimnames(Xn)=dimnames(x) + cv=data.frame(matrix(NA,nrow=nbi,ncol=2))# initialisation of dataframe containing CV before and after correction + dimnames(cv)[[2]]=c("avant","apres") + if (detail!="reg" && detail!="plot" && detail!="no") {detail="no"} + pdf(outlog,width=27,height=20) + cat(nbi," ions ",nbb," batch(es) \n") + if (detail=="plot") {if(nbb<6){par(mfrow=c(3,3),ask=F,cex=1.5)}else{par(mfrow=c(4,4),ask=F,cex=1.5)}} + res.ind <- matrix(NA,ncol=nbb,nrow=nbi,dimnames=list(dimnames(x)[[2]][-c(1:nbid)],paste("norm.b",1:nbb,sep=""))) + for (p in 1:nbi) {# for each ion + labion=dimnames(x)[[2]][p+nbid] + pools1=x[indpool,p+nbid] + if(length(which(pools1[!(is.na(pools1))]>0))<2){ # if not enough pools >0 -> no normalisation + war.note <- paste("Warning: less than 2 pools with values >0 in",labion,"-> no normalisation for this ion.") + cat(war.note,"\n") + Xn[,p+nbid] <- x[,p+nbid] + res.ind[p,] <- rep(0,nbb) + if (detail=="reg" || detail=="plot" ) { + par(mfrow=c(2,2),ask=F,cex=1.5) + plot.new() + legend("center",war.note) + minval=min(x[p+nbid],na.rm=TRUE);maxval=max(x[p+nbid],na.rm=TRUE) + plot( x[[sm_meta$injectionOrder]], x[,p+nbid],col=x[[sm_meta$batch]],ylab=labion,ylim=c(minval,maxval), + main="No correction",xlab="injection order") + points(x[[sm_meta$injectionOrder]][indpool],x[indpool,p+nbid],col="maroon",pch=16,cex=1) + } + } else { + if (detail == "reg") {if(nbb<6){par(mfrow=c(3,3),ask=F,cex=1.5)}else{par(mfrow=c(4,4),ask=F,cex=1.5)}} + if (detail == "plot") {par(mfrow=c(2,2),ask=F,cex=1.5)} + cv[p,1]=sd(pools1,na.rm=TRUE)/mean(pools1,na.rm=TRUE)# CV before correction + for (b in 1:nbb) {# for every batch + indbt = which(x[[sm_meta$batch]]==(levels(x[[sm_meta$batch]])[b])) # subscripts of all samples + sub=data.frame(x[(x[[sm_meta$batch]]==levels(x[[sm_meta$batch]])[b]),c(indtypsamp,indinject,p+nbid)]) + if (method=="linear") { res.norm = normlinear(sub,detail,valref[p],b,valNull,sm_meta,min_norm) + } else { if (method=="loess"){ res.norm <- normloess(sub,detail,valref[p],b,span,valNull,sm_meta,min_norm) + } else { if (method=="lowess"){ res.norm <- normlowess(sub,detail,valref[p],b,span,valNull,sm_meta,min_norm) + } else {stop("\n--\nNo valid 'method' argument supplied.\nMust be 'linear','loess' or 'lowess'.\n--\n")} + }} + Xn[indbt,p+nbid] = res.norm[[1]] + res.ind[p,b] <- res.norm[[2]] + } + # Post correction CV calculation + pools2=Xn[indpool,p+nbid] + cv[p,2]=sd(pools2,na.rm=TRUE)/mean(pools2,na.rm=TRUE) + if (detail=="reg" || detail=="plot" ) { + # plot before and after correction + minval=min(cbind(x[p+nbid],Xn[p+nbid]),na.rm=TRUE);maxval=max(cbind(x[p+nbid],Xn[p+nbid]),na.rm=TRUE) + plot( x[[sm_meta$injectionOrder]], x[,p+nbid],col=x[[sm_meta$batch]],ylab=labion,ylim=c(minval,maxval), + main=paste0("before correction (CV for pools = ",round(cv[p,1],2),")"),xlab="injection order") + points(x[[sm_meta$injectionOrder]][indpool],x[indpool,p+nbid],col="maroon",pch=16,cex=1) + plot(Xn[[sm_meta$injectionOrder]],Xn[,p+nbid],col=x[[sm_meta$batch]],ylab="",ylim=c(minval,maxval), + main=paste0("after correction (CV for pools = ",round(cv[p,2],2),")"),xlab="injection order") + points(Xn[[sm_meta$injectionOrder]][indpool],Xn[indpool,p+nbid],col="maroon",pch=16,cex=1) + suppressWarnings(plot.design( x[c(indtypsamp,indbatch,indfact,p+nbid)],main="factors effect before correction")) + suppressWarnings(plot.design(Xn[c(indtypsamp,indbatch,indfact,p+nbid)],main="factors effect after correction")) + } + } + } + + if (detail=="reg" || detail=="plot" || detail=="no") { + if (nbi > 3) { + # Sum of ions before/after plot + par(mfrow=c(1,2),ask=F,cex=1.2) + xsum <- rowSums(x[,(nbid+1):lastIon],na.rm=TRUE) + Xnsum <- rowSums(Xn[,(nbid+1):lastIon],na.rm=TRUE) + plot(x[[sm_meta$injectionOrder]],xsum,col=x[[sm_meta$batch]],ylab="sum of variables' intensities",xlab="injection order", + ylim=c(min(c(xsum,Xnsum),na.rm=TRUE),max(c(xsum,Xnsum),na.rm=TRUE)),main="Sum of intensities\nBefore correction") + points(x[[sm_meta$injectionOrder]][indpool],xsum[indpool],col="maroon",pch=16,cex=1.2) + plot(x[[sm_meta$injectionOrder]],Xnsum,col=x[[sm_meta$batch]],ylab="sum of variables' intensities",xlab="injection order", + ylim=c(min(c(xsum,Xnsum),na.rm=TRUE),max(c(xsum,Xnsum),na.rm=TRUE)),main="Sum of intensities\nAfter correction") + points(x[[sm_meta$injectionOrder]][indpool],Xnsum[indpool],col="maroon",pch=16,cex=1.2) + # PCA Plot before/after, normed only and ions plot + par(mfrow=c(3,4),ask=F,cex=1.2) + acplight(x[,c(indtypsamp,indbatch,indtypsamp,indfact,(nbid+1):lastIon)],"uv",TRUE) + norm.ion <- which(colnames(Xn)%in%(rownames(res.ind)[which(rowSums(res.ind)>=1)])) + acplight(Xn[,c(indtypsamp,indbatch,indtypsamp,indfact,(nbid+1):lastIon)],"uv",TRUE,norm.ion) + if(length(norm.ion)>0){acplight(Xn[,c(indtypsamp,indbatch,indtypsamp,indfact,norm.ion)],"uv",TRUE)} + # Before/after boxplot + par(mfrow=c(1,2),ask=F,cex=1.2) + cvplot=cv[!is.na(cv[[1]])&!is.na(cv[[2]]),] + if(nrow(cvplot)>0){ + boxplot(cvplot[[1]],ylim=c(min(cvplot),max(cvplot)),main="CV of pools before correction") + boxplot(cvplot[[2]],ylim=c(min(cvplot),max(cvplot)),main="CV of pools after correction") + } + dev.off() + } + } + if (nbi<=3) {dev.off()} + # transposed matrix is return (format of the initial matrix with ions in rows) + Xr=Xn[,-c(1:nbid)]; dimnames(Xr)[[1]]=Xn[[1]] + Xr=t(Xr) ; Xr <- data.frame(ions=rownames(Xr),Xr) + + res.norm[[1]] <- Xr ; res.norm[[2]] <- data.frame(metaion,res.ind) ; res.norm[[3]] <- x[,c(1:nbid)] + names(res.norm) <- c("dataMatrix","variableMetadata","sampleMetadata") + return(res.norm) +} + + + + + +acplight <- function(ids, scaling="uv", indiv=FALSE,indcol=NULL) { + suppressPackageStartupMessages(library(ade4)) + suppressPackageStartupMessages(library(pcaMethods)) + # Make a PCA and plot scores and loadings. + # First column must contain samples' identifiers. + # Columns 2 to 4 contain factors to colour the plots. + for (i in 1:3) { + idss <- data.frame(ids) + idss[,i+1] <- as.character(idss[,i+1]) + idss[which(is.na(idss[,i+1])),i+1] <- "no_modality" + idss[which(idss[,i+1]=="NA"),i+1] <- "no_modality" + idss[which(idss[,i+1]==""),i+1] <- "no_modality" + classe=as.factor(idss[[i+1]]) + idsample=as.character(idss[[1]]) + colour=1:length(levels(classe)) + ions=as.matrix(idss[,5:dim(idss)[2]]) + # Removing ions containing NA (not compatible with standard PCA) + ions=t(na.omit(t(ions))) + if(i==1){if(ncol(ions)!=(ncol(idss)-4)){cat("Note:",(ncol(idss)-4)-ncol(ions),"ions were ignored for PCA display due to NA in intensities.\n")}} + # Scaling choice: "uv","none","pareto" + object=suppressWarnings(prep(ions, scale=scaling, center=TRUE)) + if(i==1){if(length(which(apply(ions,2,var)==0))>0){cat("Warning: there are",length(which(apply(ions,2,var)==0)),"constant ions.\n")}} + # ALGO: nipals,svdImpute, Bayesian, svd, probalistic=F + result <- pca(object, center=F, method="svd", nPcs=2) + # ADE4 : to plot samples' ellipsoid for each class + s.class(result@scores, classe, cpoint = 1,xax=1,yax=2,col=colour,sub=sprintf("Scores - PCs %sx%s",1,2), possub="bottomright") + #s.label(result@loadings,label = ions, cpoint = 0, clabel=0.4, xax=1,yax=2,sub="Loadings",possub="bottomright") + if(i==1){resulti <- result} + } + if(indiv) { + colour <- rep("darkblue",length(resulti@loadings)) ; if(!is.null(indcol)) {colour[-c(indcol)] <- "red"} + plot(resulti@loadings,col=colour,main="Loadings",xaxt="n",yaxt="n",pch=20, + xlab=bquote(PC1-R^2==.(resulti@R2[1])),ylab=bquote(PC2 - R^2 == .(resulti@R2[2]))) + abline(h=0,v=0)} +} + + diff --git a/tools/batch_correction/batch_correction_3Llauncher.R b/tools/batch_correction/batch_correction_3Llauncher.R new file mode 100644 index 000000000..c7afb1fea --- /dev/null +++ b/tools/batch_correction/batch_correction_3Llauncher.R @@ -0,0 +1,146 @@ +############################################################################################################### +# batch_correction_3Llauncher # +# # +# Authors: Jean-Francois MARTIN / Melanie Petera # +# Starting date: 04-08-2020 # +# Based on batch_correction_wrapper.R version 2.91 # +# Version 1: 02-10-2020 # +# - split of tool-linked code and script-linked one # +# - handling of sample tags' parameters # +# - accepting samples beyond pools and samples # +# - dealing with special characters in IDs and column names # +# - adding a min.norm argument to the function # +# # +# Input files: dataMatrix.txt, sampleMetadata.txt, variableMetadata.txt (BC only) # +# Output files: graph.pdf, corrected table (BC only), diagnostic table (DBC only), variableMetadata (BC only) # +# # +############################################################################################################### + +meth3L <- function(idsample,iddata,sample_type_col_name,injection_order_col_name,batch_col_name,sample_type_tags, + factbio,analyse,metaion,detail,method,outlog,span,valnull, + rdata_output,dataMatrix_out,variableMetadata_out,out_graph_pdf,out_preNormSummary,min.norm){ + +## Import function +tab.import <- function(tested.file,tabtype){ + tab.res <- tryCatch(read.table(tested.file,header=TRUE,sep='\t',check.names=FALSE,comment.char = ''), error=conditionMessage) + if(length(tab.res)==1){ + stop(paste("Could not import the",tabtype,"file. There may be issues in your table integrity.\nCorresponding R error message:\n",tab.res)) + }else{ + tab.comp <- tryCatch(read.table(tested.file,header=TRUE,sep='\t',check.names=FALSE,comment.char = '',quote=""), error=conditionMessage) + if((length(tab.comp)!=1)&&(dim(tab.res)!=dim(tab.comp))){ # wrong original import due to quotes inside a column name + return(tab.comp) + }else{ return(tab.res) } + } +} + +## Reading of input files +idsample=tab.import(idsample,"sampleMetadata") +iddata=tab.import(iddata,"dataMatrix") + +### Table match check +table.check <- match2(iddata,idsample,"sample") +if(length(table.check)>1){check.err(table.check)} + +### StockID +samp.id <- stockID(iddata,idsample,"sample") +iddata<-samp.id$dataMatrix ; idsample<-samp.id$Metadata ; samp.id<-samp.id$id.match + +### Checking mandatory variables +mand.check <- "" +for(mandcol in c(sample_type_col_name, injection_order_col_name, batch_col_name)){ + if(!(mandcol%in%colnames(idsample))){ + mand.check <- c(mand.check,"\nError: no '",mandcol,"' column in sample metadata.\n", + "Note: column names are case-sensitive.\n") + } +} +if(length(mand.check)>1){ + mand.check <- c(mand.check,"\nFor more information, see the help section or:", + "\n http://workflow4metabolomics.org/sites/", + "workflow4metabolomics.org/files/files/w4e-2016-data_processing.pdf\n") + check.err(mand.check) +} + +if(analyse == "batch_correction") { + ## Reading of Metadata Ions file + metaion=read.table(metaion,header=T,sep='\t',check.names=FALSE,comment.char = '') + ## Table match check + table.check <- c(table.check,match2(iddata,metaion,"variable")) + ## StockID + var.id <- stockID(iddata,metaion,"variable") + iddata<-var.id$dataMatrix ; metaion<-var.id$Metadata ; var.id<-var.id$id.match +} + +### Formating +idsample[[1]]=make.names(idsample[[1]]) +dimnames(iddata)[[1]]=iddata[[1]] + +### Transposition of ions data +idTdata=t(iddata[,2:dim(iddata)[2]]) +idTdata=data.frame(dimnames(idTdata)[[1]],idTdata) + +### Merge of 2 files (ok even if the two dataframe are not sorted on the same key) +ids=merge(idsample, idTdata, by.x=1, by.y=1) + +ids[[batch_col_name]]=as.factor(ids[[batch_col_name]]) +nbid=dim(idsample)[2] + +### Checking the number of sample and pool + +# least 2 samples +if(length(which(ids[[sample_type_col_name]] %in% sample_type_tags$sample))<2){ + table.check <- c(table.check,"\nError: less than 2 samples specified in sample metadata.", + "\nMake sure this is not due to errors in your ",sample_type_col_name," coding.\n") +} + +# least 2 pools per batch for all batchs +B <- rep(0,length(levels(ids[[batch_col_name]]))) +for(nbB in 1:length(levels(ids[[batch_col_name]]))){ + B[nbB]<-length(which(ids[which(ids[[batch_col_name]]==(levels(ids[[batch_col_name]])[nbB])),,drop=FALSE][[sample_type_col_name]] %in% sample_type_tags$pool)) +} +if(length(which(B>1))==0){ + table.check <- c(table.check,"\nError: less than 2 pools specified in at least one batch in sample metadata.", + "\nMake sure this is not due to errors in your ",sample_type_col_name," coding.\n") +} + +### Checking the unicity of samples and variables +uni.check <- function(tested.tab,tabtype,err.obj){ + unicity <- duplicated(tested.tab[,1]) + if(sum(unicity)>0){ + #Sending back an explicit error + duptable <- t(t(table(tested.tab[,1][unicity])+1)) + err.obj <- c(err.obj,paste0("\n-------\nError: your '",tabtype,"' IDs contain duplicates:\n"), + paste(rownames(duptable),duptable,sep=": ",collapse="\n"), + "\nSince identifiers are meant to be unique, please check your data.\n-------\n") + } + return(err.obj) +} +table.check <- uni.check(ids,"sample",table.check) +if(analyse == "batch_correction"){table.check <- uni.check(metaion,"variable",table.check)} + +## error check +check.err(table.check) + + +### BC/DBC-specific processing + +# Gathering mandatory information in a single object +sm.meta <- list(batch=batch_col_name, injectionOrder=injection_order_col_name, sampleType=sample_type_col_name, sampleTag=sample_type_tags) + +if(analyse == "batch_correction") { + ## Launch + res = norm_QCpool(ids,nbid,outlog,factbio,metaion,detail,FALSE,FALSE,method,span,valnull,sm.meta,min.norm) + ## Get back original IDs + var.id <- reproduceID(res[[1]],res[[2]],"variable",var.id) + res[[1]] <- var.id$dataMatrix ; res[[2]] <- var.id$Metadata + samp.id <- reproduceID(res[[1]],res[[3]],"sample",samp.id) + res[[1]] <- samp.id$dataMatrix ; res[[3]] <- samp.id$Metadata + ## Save files + save(res, file=rdata_output) + write.table(res[[1]], file=dataMatrix_out, sep = '\t', row.names=FALSE, quote=FALSE) + write.table(res[[2]], file=variableMetadata_out, sep = '\t', row.names=FALSE, quote=FALSE) +}else{ + ## Launch + plotsituation(ids,nbid,out_graph_pdf,out_preNormSummary,factbio,span,sm.meta) +} + +}#end of meth3L \ No newline at end of file diff --git a/tools/batch_correction/batch_correction_3Lwrapper.R b/tools/batch_correction/batch_correction_3Lwrapper.R new file mode 100644 index 000000000..5ca70f07d --- /dev/null +++ b/tools/batch_correction/batch_correction_3Lwrapper.R @@ -0,0 +1,139 @@ +#!/usr/bin/env Rscript + +################################################################################################ +# batch_correction_wrapper # +# # +# Authors: Marion LANDI / Jean-Francois MARTIN / Melanie Petera # +# User: Galaxy # +# Original data: -- # +# Starting date: 22-07-2014 # +# Version 1: 22-07-2014 # +# Version 2: 08-12-2014 # +# Version 2.1: 09-01-2015 modification in Error message of sample matching # +# Version 2.2: 16-03-2015 inclusion of miniTools' functions for special characters # +# Version 2.90: 18-08-2015 new parameter valnull # +# Version 2.91: 25-08-2016 error message improvment # +# Version 3: 02-10-2020 # +# - split of tool-linked code and script-linked one # +# - addition of args print and sessionInfo() # +# - adjustment of sample tags' parameters to 3L methods # +# - addition of the min.norm argument in meth3L() call # +# # +# Input files: dataMatrix.txt, sampleMetadata.txt, variableMetadata.txt (BC only) # +# Output files: graph.pdf, corrected table (BC only), diagnostic table (DBC only), # +# variableMetadata (BC only) # +# # +################################################################################################ + + +library(batch) #necessary for parseCommandArgs function + +##------------------------------ +## test help option +##------------------------------ + +# Prog. constants +argv.help <- commandArgs(trailingOnly = FALSE) +script.path <- sub("--file=", "", argv.help[grep("--file=", argv.help)]) +prog.name <- basename(script.path) + +# Test Help +if (length(grep('-h', argv.help)) > 0) { + cat("Usage: Rscript ", + prog.name, + "{args} \n", + "parameters: \n", + "\tanalyse {val}: must be set to \"batch_correction\"", + "\tdataMatrix {file}: set the input data matrix file (mandatory) \n", + "\tsampleMetadata {file}: set the input sample metadata file (mandatory) \n", + "\tvariableMetadata {file}: set the input variable metadata file (mandatory) \n", + "\tmethod {opt}: set the method; can set to \"linear\", \"lowess\" or \"loess\" (mandatory) \n", + "\tspan {condition}: set the span condition; set to \"none\" if method is set to \"linear\" (mandatory) \n", + "\tref_factor {value}: set the ref_factor value; (if span value is set to NULL, optional) \n", + "\tdetail {value}: set the detail value; (if span value is set to NULL, optional) \n", + "\tdataMatrix_out {file}: set the output data matrix file (mandatory) \n", + "\tvariableMetadata_out {file}: set the output variable metadata file (mandatory) \n", + "\tgraph_output {file}: set the output graph file (mandatory) \n", + "\trdata_output {file}: set the output Rdata file (mandatory) \n", + "\tbatch_col_name {val}: the column name for batch. Default value is \"batch\".\n", + "\tinjection_order_col_name {val}: the column name for the injection order. Default value is \"injectionOrder\".\n", + "\tsample_type_col_name {val}: the column name for the sample types. Default value is \"sampleType\".\n", + "\tsample_type_tags {val}: the tags used inside the sample type column, defined as key/value pairs separated by commas (example: blank=blank,pool=pool,sample=sample).\n", + "\n") + quit(status = 0) +} + +##------------------------------ +## init. params +##------------------------------ + +args = parseCommandArgs(evaluate=FALSE) #interpretation of arguments given in command line as an R list of objects + + +cat('\nJob starting time:\n',format(Sys.time(), "%a %d %b %Y %X"), +'\n\n--------------------------------------------------------------------', +'\nParameters used:\n\n') +print(args) +cat('--------------------------------------------------------------------\n\n') + + +# Set default col names +if ( ! 'batch_col_name' %in% names(args)) + args[['batch_col_name']] <- 'batch' +if ( ! 'injection_order_col_name' %in% names(args)) + args[['injection_order_col_name']] <- 'injectionOrder' +if ( ! 'sample_type_col_name' %in% names(args)) + args[['sample_type_col_name']] <- 'sampleType' +if ( ! 'sample_type_tags' %in% names(args)) + args[['sample_type_tags']] <- 'blank=blank,pool=pool,sample=sample' + +# Parse sample type tags +sample.type.tags <- list() +for (kv in strsplit(strsplit(args$sample_type_tags, ',')[[1]], '=')) + sample.type.tags[[kv[[1]]]] <- kv[-1] +if ( ! all(c('pool', 'blank', 'sample') %in% names(sample.type.tags))) + stop("All tags pool, blank and sample must be defined in option sampleTypeTags.") +args$sample_type_tags <- sample.type.tags + +##------------------------------ +## init. functions +##------------------------------ + +source_local <- function(...){ + argv <- commandArgs(trailingOnly = FALSE) + base_dir <- dirname(substring(argv[grep("--file=", argv)], 8)) + for(i in 1:length(list(...))){source(paste(base_dir, list(...)[[i]], sep="/"))} +} +#Import the different functions +source_local("batch_correction_3Lfct.R","batch_correction_3Llauncher.R","easyrlibrary-lib/RcheckLibrary.R","easyrlibrary-lib/miniTools.R") + +# Specificities of BC and DBC +if(args$analyse == "batch_correction") { + args$out_graph_pdf <- NULL + args$out_preNormSummary <- NULL +}else{ + args$variableMetadata <- NULL + args$rdata_output <- NULL + args$dataMatrix_out <- NULL + args$variableMetadata_out <- NULL + args$graph_output <- NULL + args$method <- NULL + args$detail <- NULL + args$valnull <- NULL +} + +# Launch tool +meth3L(idsample=args$sampleMetadata, iddata=args$dataMatrix, sample_type_col_name=args$sample_type_col_name, injection_order_col_name=args$injection_order_col_name, + batch_col_name=args$batch_col_name, sample_type_tags=args$sample_type_tags, factbio=args$ref_factor, analyse=args$analyse, metaion=args$variableMetadata, + detail=args$detail, method=args$method, outlog=args$graph_output, span=args$span, valnull=args$valnull, rdata_output=args$rdata_output, + dataMatrix_out=args$dataMatrix_out, variableMetadata_out=args$variableMetadata_out, out_graph_pdf=args$out_graph_pdf, out_preNormSummary=args$out_preNormSummary, + min.norm=1) + + +cat('\n\n--------------------------------------------------------------------', +'\nInformation about R (version, Operating System, attached or loaded packages):\n\n') +sessionInfo() +cat('--------------------------------------------------------------------\n', +'\nJob ending time:\n',format(Sys.time(), "%a %d %b %Y %X")) + +rm(args) diff --git a/tools/batch_correction/batch_correction_all_loess_script.R b/tools/batch_correction/batch_correction_all_loess_script.R new file mode 100644 index 000000000..28f84c10c --- /dev/null +++ b/tools/batch_correction/batch_correction_all_loess_script.R @@ -0,0 +1,296 @@ +loessF <- function(datVn, qcaVi, preVi, spnN) { + + if(length(qcaVi) < 5) { + + return(predict(lm(datVn[qcaVi] ~ qcaVi), + newdata = data.frame(qcaVi = preVi))) + + } else { + + return(predict(loess(datVn[qcaVi] ~ qcaVi, + control = loess.control(surface = "direct"), + span = spnN), + newdata = data.frame(qcaVi = preVi))) + + } + + ## Note: + ## the surface = 'direct' argument allows extrapolation + +} ## loessF + +plotBatchF <- function(datMN, samDF.arg, spnN.arg) { + + maiC <- switch(gsub("MN", "", deparse(substitute(datMN))), + raw = "Raw", + nrm = "Normalized") + + colVc <- c(samp = "green4", + biol = "green4", + pool = "red", + blan = "black", + other = "yellow") + + par(font = 2, font.axis = 2, font.lab = 2, lwd = 2, pch = 18) + + layout(matrix(c(1, 1, 2, 3), nrow = 2), + widths = c(0.7, 0.3)) + + obsNamVc <- rownames(datMN) + + obsColVc <- sapply(substr(samDF.arg[, args$sample_type_col_name], 1, 4), + function(typC) + ifelse(typC %in% names(colVc), colVc[typC], colVc["other"])) + + ## Graphic 1: Sum of intensities for each sample + + par(mar = c(3.6, 3.6, 3.1, 0.6)) + + batTab <- table(samDF.arg[, args$batch_col_name]) + + sumVn <- rowSums(datMN, na.rm = TRUE) + + plot(sumVn, + cex = 1.2, + col = obsColVc, + pch = 18, + xaxs = "i", + xlab = "", + ylab = "") + + mtext("Injection order", + line = 2.2, + side = 1) + mtext("Sum of variable intensities", + line = 2.2, + side = 2) + + mtext(maiC, cex = 1.2, line = 1.5, side = 3) + + abline(v = cumsum(batTab) + 0.5, + col = "red") + + mtext(names(batTab), + at = batTab / 2 + c(0, cumsum(batTab[-length(batTab)]))) + + obsColVuc <- obsColVc[sort(unique(names(obsColVc)))] + + text(rep(batTab[1], times = length(obsColVuc)), + par("usr")[3] + (0.97 - length(obsColVuc) * 0.03 + 1:length(obsColVuc) * 0.03) * diff(par("usr")[3:4]), + col = obsColVuc, + font = 2, + labels = names(obsColVuc), + pos = 2) + + for(batC in names(batTab)) { + + batSeqVi <- which(samDF.arg[, args$batch_col_name] == batC) + batPooVi <- intersect(batSeqVi, + which(samDF.arg[, args$sample_type_col_name] == "pool")) + batSamVi <- intersect(batSeqVi, + which(samDF.arg[, args$sample_type_col_name] == "sample")) + if(length(batPooVi)) + lines(batSeqVi, + loessF(sumVn, batPooVi, batSeqVi, spnN=spnN.arg), + col = colVc["pool"]) + if (length(batSamVi)) + lines(batSeqVi, + loessF(sumVn, batSamVi, batSeqVi, spnN=spnN.arg), + col = colVc["samp"]) + + } + + ## Graphics 2 and 3 (right): PCA score plots of components 1-4 + + radVn <- seq(0, 2 * pi, length.out = 100) + epsN <- .Machine[["double.eps"]] ## [1] 2.22e-16 + + pcaMN <- datMN + + if(any(is.na(pcaMN))) { + minN <- min(pcaMN, na.rm = TRUE) + pcaMN[is.na(pcaMN)] <- minN + } + + pcaLs <- opls(pcaMN, predI = 4, algoC = "svd", printL = FALSE, plotL = FALSE) + tMN <- getScoreMN(pcaLs) + vRelVn <- pcaLs@modelDF[, "R2X"] + + n <- nrow(tMN) + hotN <- 2 * (n - 1) * (n^2 - 1) / (n^2 * (n - 2)) + + hotFisN <- hotN * qf(0.95, 2, n - 2) + + pcsLs <- list(c(1, 2), c(3, 4)) + + par(mar = c(3.6, 3.6, 0.6, 1.1)) + + for(pcsN in 1:length(pcsLs)) { + + pcsVn <- pcsLs[[pcsN]] + + tcsMN <- tMN[, pcsVn] + + micMN <- solve(cov(tcsMN)) + + n <- nrow(tMN) + hotN <- 2 * (n - 1) * (n^2 - 1) / (n^2 * (n - 2)) + + hotFisN <- hotN * qf(0.95, 2, n - 2) + + hotVn <- apply(tcsMN, + 1, + function(x) 1 - pf(1 / hotN * t(as.matrix(x)) %*% micMN %*% as.matrix(x), 2, n - 2)) + + obsHotVi <- which(hotVn < 0.05) + + xLabC <- paste("t", + pcsVn[1], + "(", + round(vRelVn[pcsVn[1]] * 100), + "%)", + sep = "") + + yLabC <- paste("t", + pcsVn[2], + "(", + round(vRelVn[pcsVn[2]] * 100), + "%)", + sep = "") + + xLimVn <- c(-1, 1) * max(sqrt(var(tcsMN[, 1]) * hotFisN), max(abs(tcsMN[, 1]))) + yLimVn <- c(-1, 1) * max(sqrt(var(tcsMN[, 2]) * hotFisN), max(abs(tcsMN[, 2]))) + + plot(tcsMN, + main = "", + type = "n", + xlab = "", + ylab = "", + xlim = xLimVn, + ylim = yLimVn) + + mtext(xLabC, + line = 2.2, + side = 1) + mtext(yLabC, + line = 2.2, + side = 2) + + par(lwd = 1) + + abline(v = axTicks(1), + col = "grey") + + abline(h = axTicks(2), + col = "grey") + + abline(v = 0) + abline(h = 0) + + lines(sqrt(var(tcsMN[, 1]) * hotFisN) * cos(radVn), + sqrt(var(tcsMN[, 2]) * hotFisN) * sin(radVn)) + + points(tcsMN, + col = obsColVc, + pch = 18) + + if(length(obsHotVi)) + text(tcsMN[obsHotVi, 1], + tcsMN[obsHotVi, 2], + col = obsColVc[obsHotVi], + labels = obsNamVc[obsHotVi], + pos = 3) + + } ## for(pcsN in 1:length(pcsLs)) { + + return(invisible(list(sumVn = sumVn, + tcsMN = tcsMN))) + +} ## plotBatchF + +shiftBatchCorrectF <- function(rawMN.arg, + samDF.arg, + refC.arg, + spnN.arg) { + + cat("\nReference observations are: ", refC.arg, "\n") + + ## computing median off all pools (or samples) for each variable + + refMeaVn <- apply(rawMN.arg[samDF.arg[, args$sample_type_col_name] == refC.arg, ], + 2, + function(feaRefVn) mean(feaRefVn, na.rm = TRUE)) + + ## splitting data and sample metadata from each batch + + batRawLs <- split(as.data.frame(rawMN.arg), + f = samDF.arg[, args$batch_col_name]) + batRawLs <- lapply(batRawLs, function(inpDF) as.matrix(inpDF)) + + batSamLs <- split(as.data.frame(samDF.arg), + f = samDF.arg[, args$batch_col_name]) + + ## checking extrapolation: are there pools at the first and last observations of each batch + + if(refC.arg == args$sample_type_tags$pool) { + pooExtML <- matrix(FALSE, nrow = 2, ncol = length(batRawLs), + dimnames = list(c("first", "last"), names(batRawLs))) + for(batC in names(batSamLs)) { + batSamTypVc <- batSamLs[[batC]][, args$sample_type_col_name] + pooExtML["first", batC] <- head(batSamTypVc, 1) == args$sample_type_tags$pool + pooExtML["last", batC] <- tail(batSamTypVc, 1) == args$sample_type_tags$pool + } + if(!all(c(pooExtML))) { + cat("\nWarning: Pools are missing at the first and/or last position of the following batches:\n") + pooExtBatVi <- which(!apply(pooExtML, 2, all)) + for(i in 1:length(pooExtBatVi)) + cat(names(pooExtBatVi)[i], ": ", + paste(rownames(pooExtML)[!pooExtML[, pooExtBatVi[i]]], collapse = ", "), "\n", sep = "") + cat("Extrapolating loess fits for these batches may result in inaccurate modeling!\n") + } + } + + ## normalizing + + nrmMN <- NULL ## normalized data matrix to be computed + + cat("\nProcessing batch:") + + for(batC in names(batRawLs)) { ## processing each batch individually + + cat("\n", batC) + + batRawMN <- batRawLs[[batC]] + batSamDF <- batSamLs[[batC]] + + batAllVi <- 1:nrow(batRawMN) + + batRefVi <- which(batSamDF[, args$sample_type_col_name] == refC.arg) + + if(length(batRefVi) < 5) + cat("\nWarning: less than 5 '", refC.arg, "'; linear regression will be performed instead of loess regression for this batch\n", sep="") + + ## prediction of the loess fit + + batLoeMN <- apply(batRawMN, + 2, + function(rawVn) loessF(rawVn, batRefVi, batAllVi, spnN=spnN.arg)) + + ## normalization + + batLoeMN[batLoeMN <= 0] <- NA + + batNrmMN <- batRawMN / batLoeMN + + nrmMN <- rbind(nrmMN, + batNrmMN) + + } + + cat("\n") + + nrmMN <- sweep(nrmMN, MARGIN = 2, STATS = refMeaVn, FUN = "*") + + return(nrmMN) + +} ## shiftBatchCorrectF diff --git a/tools/batch_correction/batch_correction_all_loess_wrapper.R b/tools/batch_correction/batch_correction_all_loess_wrapper.R new file mode 100755 index 000000000..1903b388a --- /dev/null +++ b/tools/batch_correction/batch_correction_all_loess_wrapper.R @@ -0,0 +1,250 @@ +#!/usr/bin/env Rscript + +library(batch) ## necessary for parseCommandArgs function + +##------------------------------ +## test help option +##------------------------------ + +# Prog. constants +argv.help <- commandArgs(trailingOnly = FALSE) +script.path <- sub("--file=", "", argv.help[grep("--file=", argv.help)]) +prog.name <- basename(script.path) + +# Test Help +if (length(grep('-h', argv.help)) > 0) { + cat("Usage: Rscript ", + prog.name, + "{args} \n", + "parameters: \n", + "\tdataMatrix {file}: set the input data matrix file (mandatory) \n", + "\tsampleMetadata {file}: set the input sample metadata file (mandatory) \n", + "\tvariableMetadata {file}: set the input variable metadata file (mandatory) \n", + "\tmethod {opt}: set the method; can set to \"all_loess_pool\" or \"all_loess_sample\" (mandatory) \n", + "\tspan {condition}: set the span condition; (mandatory) \n", + "\tdataMatrix_out {file}: set the output data matrix file (mandatory) \n", + "\tvariableMetadata_out {file}: set the output variable metadata file (mandatory) \n", + "\tgraph_output {file}: set the output graph file (mandatory) \n", + "\trdata_output {file}: set the output Rdata file (mandatory) \n", + "\tbatch_col_name {val}: the column name for batch. Default value is \"batch\".\n", + "\tinjection_order_col_name {val}: the column name for the injection order. Default value is \"injectionOrder\".\n", + "\tsample_type_col_name {val}: the column name for the sample types. Default value is \"sampleType\".\n", + "\tsample_type_tags {val}: the tags used inside the sample type column, defined as key/value pairs separated by commas (example: blank=blank,pool=pool,sample=sample).\n", + "\n") + quit(status = 0) +} + +##------------------------------ +## init. params +##------------------------------ + +args = parseCommandArgs(evaluate=FALSE) #interpretation of arguments given in command line as an R list of objects + +# Set default col names +if ( ! 'batch_col_name' %in% names(args)) + args[['batch_col_name']] <- 'batch' +if ( ! 'injection_order_col_name' %in% names(args)) + args[['injection_order_col_name']] <- 'injectionOrder' +if ( ! 'sample_type_col_name' %in% names(args)) + args[['sample_type_col_name']] <- 'sampleType' +if ( ! 'sample_type_tags' %in% names(args)) + args[['sample_type_tags']] <- 'blank=blank,pool=pool,sample=sample' + +# Parse sample type tags +sample.type.tags <- list() +for (kv in strsplit(strsplit(args$sample_type_tags, ',')[[1]], '=')) + sample.type.tags[[kv[[1]]]] <- kv[[2]] +if ( ! all(c('pool', 'blank', 'sample') %in% names(sample.type.tags))) + stop("All tags pool, blank and sample must be defined in option sampleTypeTags.") +args$sample_type_tags <- sample.type.tags + +##------------------------------ +## init. functions +##------------------------------ + +source_local <- function(fname){ + argv <- commandArgs(trailingOnly = FALSE) + base_dir <- dirname(substring(argv[grep("--file=", argv)], 8)) + source(paste(base_dir, fname, sep="/")) +} + +## Import the different functions +source_local("batch_correction_all_loess_script.R") + +argVc <- unlist(args) + +## argVc["method"] is either 'all_loess_pool' or 'all_loess_sample' +## alternative version developped by CEA +## all variables are treated with loess +## the reference observations for loess are either 'pool' +## ('all_loess_pool') or 'sample' ('all_loess_sample') + + +##------------------------------ +## Initializing +##------------------------------ + +## options +##-------- + +strAsFacL <- options()$stringsAsFactors +options(stringsAsFactors = FALSE) + +## libraries +##---------- + +suppressMessages(library(ropls)) + +if(packageVersion("ropls") < "1.4.0") + stop("Please use 'ropls' versions of 1.4.0 and above") + +## constants +##---------- + +modNamC <- "Batch correction" ## module name + +## log file +##--------- + +## sink(argVc["information"]) ## not implemented + +cat("\nStart of the '", modNamC, "' Galaxy module call: ", + format(Sys.time(), "%a %d %b %Y %X"), "\n", sep="") + +## loading +##-------- + +rawMN <- t(as.matrix(read.table(argVc["dataMatrix"], + header = TRUE, + comment.char = '', + row.names = 1, + sep = "\t"))) + +samDF <- read.table(argVc["sampleMetadata"], + header = TRUE, + comment.char = '', + row.names = 1, + sep = "\t") + +varDF <- read.table(argVc["variableMetadata"], + check.names = FALSE, + header = TRUE, + comment.char = '', + row.names = 1, + sep = "\t") ## not used; for compatibility only + +refC <- tolower(gsub("all_loess_", "", argVc["method"])) + +spnN <- as.numeric(argVc["span"]) + +## checking +##--------- + +stopifnot(refC %in% c('pool', 'sample')) +refC <- args$sample_type_tags[[refC]] + +if(refC == args$sample_type_tags$pool && + !any(args$sample_type_tags$pool %in% samDF[, args$sample_type_col_name])) + stop("No 'pool' found in the 'sampleType' column; use the samples as normalization reference instead") + +refMN <- rawMN[samDF[, args$sample_type_col_name] == refC, ] +refNasZerVl <- apply(refMN, 2, + function(refVn) + all(sapply(refVn, + function(refN) {is.na(refN) || refN == 0}))) + +if(sum(refNasZerVl)) { + + refNasZerVi <- which(refNasZerVl) + cat("The following variables have 'NA' or 0 values in all reference samples; they will be removed from the data:\n", sep = "") + rawMN <- rawMN[, !refNasZerVl, drop = FALSE] + varDF <- varDF[!refNasZerVl, , drop = FALSE] + +} + +##------------------------------ +## Computation +##------------------------------ + + +## ordering (batch and injection order) +##------------------------------------- + +samDF[, "ordIniVi"] <- 1:nrow(rawMN) +ordBatInjVi <- order(samDF[, args$batch_col_name], samDF[, args$injection_order_col_name]) +rawMN <- rawMN[ordBatInjVi, ] +samDF <- samDF[ordBatInjVi, ] + +## signal drift and batch-effect correction +##----------------------------------------- + +nrmMN <- shiftBatchCorrectF(rawMN, + samDF, + refC, + spnN) + +## figure +##------- + +cat("\nPlotting\n") + +pdf(argVc["graph_output"], onefile = TRUE, width = 11, height = 7) +plotBatchF(rawMN, samDF, spnN) +plotBatchF(nrmMN, samDF, spnN) +dev.off() + +## returning to initial order +##--------------------------- + +ordIniVi <- order(samDF[, "ordIniVi"]) +nrmMN <- nrmMN[ordIniVi, ] +samDF <- samDF[ordIniVi, ] +samDF <- samDF[, colnames(samDF) != "ordIniVi", drop=FALSE] + + +##------------------------------ +## Ending +##------------------------------ + + +## saving +##------- + +datMN <- nrmMN + +datDF <- cbind.data.frame(dataMatrix = colnames(datMN), + as.data.frame(t(datMN))) +write.table(datDF, + file = argVc["dataMatrix_out"], + quote = FALSE, + row.names = FALSE, + sep = "\t") + +varDF <- cbind.data.frame(variableMetadata = rownames(varDF), + varDF) ## not modified; for compatibility only +write.table(varDF, + file = argVc["variableMetadata_out"], + quote = FALSE, + row.names = FALSE, + sep = "\t") + + +res <- list(dataMatrix_raw = rawMN, + dataMatrix_normalized = nrmMN, + sampleMetadata = samDF) +save(res, + file = argVc["rdata_output"]) ## for compatibility + +## closing +##-------- + +cat("\nEnd of '", modNamC, "' Galaxy module call: ", + as.character(Sys.time()), "\n", sep = "") + +## sink() + +options(stringsAsFactors = strAsFacL) + + +rm(argVc) + diff --git a/tools/batch_correction/determine_bc.xml b/tools/batch_correction/determine_bc.xml new file mode 100644 index 000000000..7e0bb4992 --- /dev/null +++ b/tools/batch_correction/determine_bc.xml @@ -0,0 +1,231 @@ + + to choose between linear, lowess and loess methods + + + macros.xml + + + + r-batch + r-ade4 + bioconductor-pcamethods + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +.. class:: infomark + +**Authors** + | Jean-Francois Martin - PF MetaToul-AXIOM ; INRAE ; MetaboHUB (for original version of this tool and overall development of the R script) + +.. class:: infomark + +**Contributors** + | Melanie Petera - PFEM ; INRAE ; MetaboHUB (for R wrapper and R script improvement) + +.. class:: infomark + +**Wrapping** + | Marion Landi - FLAME ; PFEM (for original xml interface and R wrapper) + | Franck Giacomoni - PFEM ; INRAE ; MetaboHUB (for original xml interface and R wrapper) + +--------------------------------------------------- + +.. class:: infomark + +**Please cite** If you use this tool, please cite: + | `F.M. van der Kloet, I. Bobeldijk, E.R. Verheij, R.H. Jellema. (2009). "Analytical error reduction using single point calibration for accurate and precise metabolomic phenotyping." Journal of Proteome Research p5132-5141 <http://www.ncbi.nlm.nih.gov/pubmed/19754161>`_ + +--------------------------------------------------- + +========================== +Determine_batch_correction +========================== + +| + +----------- +Description +----------- + +| + +Generates outputs to help to determine what type of regression to use between linear or non-linear (lowess or loess) functions for batch correction +using quality control pooled samples (QC-pools) with correction algorithm as described by Van Der Kloet (J Prot Res 2009). + + +Warning: this module does *not* deliver which choice should be made in model type; +it only provides information to help users in determining which choice may be appropriate, based on their own expertise. + + +This tool is meant to be used prior to the Batch_correction tool to help to make a choice in parameters, +but it is not a requirement and thus can be avoided. + +| + +----------------- +Workflow position +----------------- + +.. image:: determine_batch_correction.png + :width: 600 + + +----------- +Input files +----------- + ++--------------------------+-----------+ +| Parameter : num + label | Format | ++==========================+===========+ +| 1 : Data matrix file | tabular | ++--------------------------+-----------+ +| 2 : Sample metadata file | tabular | ++--------------------------+-----------+ + +| + +The data matrix file must contain the intensity values of variables. + | First line must contain all the samples' names. + | First column must contain all the variables' ID. + + +The sample metadata file must contain at least the three following columns: + | - a batch column (default to "*batch*") to identify the batches of analyses + | - an injection order column (default to "*injectionOrder*") composed of integers defining the injection order of samples + | - a sample type column (default to "*sampleType*") indicating if a sample is a biological one ("*sample*"), a QC-pool ("*pool*") or a blank ("*blank*") + | *Default values* can be changed according to your data coding using the customisation parameters in the "**Sample metadata file coding parameters**" section. + + +**Notes concerning your design:** + | - the 3 mandatory columns must not contain NA + | - your data should contain at least 3 QC-pools in each batch for intra-batch **linear** adjustment and 8 for **lo(w)ess** adjustment + + + +---------- +Parameters +---------- + +Sample metadata file coding parameters + | Enables to give the names of columns in the sample metadata table that contain the injection order, the batches and the sample types. + | Also enables to specify the sample type coding used in the sampletype column. + | + +Factor of interest + | Name of the factor (column header) that will be used as a categorical variable for design plots (often a biological factor ; if none, put the batch column name). + | This factor does not affect correction calculation. + | + +Advanced options + | Should only be put at "show" if you have good understanding of the span parameter and are willing to adjust it. + | + +Span + | - When advanced option is at "hide" (default): + | default is 1 for loess regression and is two times the ratio between number of pools and number of samples for lowess regression. + | - When advanced option is at "show": + | filled value is used for lowess and loess regressions. + + +------------ +Output files +------------ + +Determine_BC_preNormSummary.tabular + | tabular output + | Meaning of results of diagnosis analysis + | 0 - no preliminary-condition problem + | 1 - standard deviation of QC-pools or samples = 0 + | 2 - insufficient number of QC-pools within a batch (n=3 for linear, n=8 for lowess or loess) + | 2.5 - less than 2 samples within a batch + | 3 - significant difference between QC-pools' and samples' means + | 4 - denominator =0 when on 1 pool per batch non-zero + | 5 - (linear regression only) the slopes ratio “QC-pools/samples” is lower than -0.2 + | 6 - (linear regression only) none of the pool or sample could be corrected if negative and infinite values are turned into NA + | + +Determine_BC_graph.pdf + | graphical output + | One page per ion. Plots regression curves for all methods allowed and plot.design results regarding the factor of interest. + + +--------------------------------------------------- + +---------------------- +Additional information +---------------------- + + +.. class:: warningmark + +Refer to the corresponding "W4M HowTo" page: + | `MS data processing - Filters and normalisation <https://download.workflow4metabolomics.org/docs/w4e2018/2018-10_EC_W4E%20-%20Dataprocessing_Filter_and_normalisation.pdf>`_ + | + | + + + + + + + + 10.1021/pr900499r + + + + + diff --git a/tools/batch_correction/static/images/batch_correction.png b/tools/batch_correction/static/images/batch_correction.png new file mode 100644 index 0000000000000000000000000000000000000000..c3c964e4050dce5d026fc2e797594f478eaa8889 GIT binary patch literal 65271 zcmeFZc|4T;|39kR-EO&6$bM^~5|XT0iYyT_w(LnF`!;r_60(z!b;<~1&%O<1O}1q0 zjI6`h_pyvQ?~D3;`Yhky@0`bZoX7d&oPW||%ynI__v`h1?axc#eN_eO6U-+lC@84! zD&B!nP#g`RprHKo*dO4Nme*>v6co=W?%uhr={7JoeEjX1Zj8tef0Ktt{MqRGZ@av> zv@pit>Z^>MP47L6dS$f9>n3#PP-qz)sxzyezM<^U@!K*(jwsHzXG7JG9|~kSapKjB z3bW#cRlpHyZ@W#P)BPzOogeRQ?kqCe z8|3*$3~U#CYOYRxX{J=}m2Rw;2fOf5P(N4J;u?zQn53 zR89_cgu2$D%uu)g^l*ir?`ln6g{7b$mcy%LEUPe&s(7>2&XuFuPS?y7d6weV@-Y>s zUhIQ^rxjlQKBZ3t!!(N>0`sC6Z%Y}K|x&1dgORjgbEDT8qWSfuG`zT%h@Fufk+avEzcuZiy z1imkjA@nqAlS*Y{5|NzBeUidjC1$Y(5g;F0yVWjdGw37m@Yzen%O41s+CyR$$sWxg zVr?5Ew&O9U>6&e7A-(q>R~3R!vo2G$_Fpa3+P$t+@2?l?Jh4Bdg58^a1=qr%S|x&%aM(8uWc zF?eovRi}wSS3hZeOV@5+{XI2M0vsV|^$`IW-FhX$qdQYeB|%8pA1l9gA7~^9F%({K zBs4+$%#xTe#VrSBX&s~(Al)C30Q32Ra?wG(_-nHFmATICZ zBf=k2O-Cp$shT#198VW9E$Np(n+gQc9{rIhaVMOquvwNceY&MSKe>Vg9)xAMZ@j40 zhwr1HkO3Nj$uYweLT>}J{QB+tX1?h&hbV3-<^Xl<|8tpBh~ilU(C|y_{v}F^)D(ZG zYAap72>tWn4N&u6u#@O$7>jX-^P3h&SG!?gLMq%G=Ba~Wjn{~GBT;E40clQ)#GiqikyWwq=4=KpL1;s1zK`Tq8;uR#GX6`_`(<5JIVMVHbVTY#M zxZHC&Uvw%VKYxi;LT_hkk#;!rwCFceTUc%!BTJTRWohUYi+?n0Ne*RcqJ{JGBl3uR zXomf>WR}Si6wOfeDm$J{(76v3!c^?>);x(R(U(A%1#5LP)JCv%Uz0c`B!K z)K$4>OZFuQ0YJN&80@sr*3)k_ccVUd?`Vq*GEyF4{g$nJ1OIZqWbnJJrHHB~@VH*i zbzkEwmt=)-Me791JEwK#MpZO^3F+Ydt<)|GxO*o`x6a>geZg?Gb#Z-0qx#8I(v>Se zjp!bLec#?MAT-E1jCl%K^cXPUmE5es(r}Mf)ap%-L~MbXS)G4Vu|=fR1;rcAjXZ;P zWRsEV(Et}SGhd!7wI_5^hs~CAaG1QOBYSTdcq?LJOZj-LMlw1_wX}4s3)^Z9&KoH3 zczrbQSX%W?5~sGpNnHK@Q605ZwTWkbcV03zc^K8yoTImHJtTUyK;LZg!~6hm?%rib zn$6{{XNO!}=G=w`REEn7R(6GLj0Syn=ES54y^Gs6rIH2it4ofxiJqF;$pe|)8p9RG zf%Hm>Nm{0xN^J~N9cRtPrt-MB*pheO%Z=aJj0rrwebGn4dRU?>Ox57p1%V=s6z{GH zDoK4V(}hG+eFZC)8sioGYQP1TnTU)lYDH+pxqg9ie`f#k40lQ{jyO>(d&i4|nq206 z)mn~}Ddy8!DFoKn?ljvjJ( z=(zprb~Cc>CX}rp5JhWyC)^11XboPiWE%_eA!+MG(UDV?W0QKr^w|P^ht&tIHA<#V z%~`83-*`LpLAjk?m4KaDDjF1P+{nn@wI`X}9gEm*?a3*`+u`y}T zC{e>@RWpmxt^3=ZuAxxpM@zzSEE-vHPB>A4m89V){nx9)Z$~c|81EgHJvB+~)QZ@C zH_>#22?>Lo7{rUr2`jl8A9VFVBlQSf-i2L#+`G^0(b3V=EHrwR?N&WT*~6uGqY`d9 zzr_{LhU6M9&SEdKk5=6EJ)-R7*Phh-vcUwsa}%!jt zGo~5Q!p!9~(P$QE%iCiZF?fC5#Bn8aLfKV?YZy;^cg<)ga_*a|m`rzxm6X7OX7oMf zMbS%Qe{Qf3eqbM{)><&Ate`$yx>->LA8Bugo4ZRvIVVV*0N8aB2gOdn|K z$z6?6Ua_2ZyJ#{Q&Q^{)H`4EI_>#ooeG0Xqs4AA~Y57R2d_Usze9)9{n~SNqHa!u_ z(k50dZ?TxBBCRJ_-$Zy$NycXB5J$WANC`Q|opwZ?zIJeM$Os-R3FVJ(FRuk*=z8ts z$zHzeK7%%e_>ruqU26jJGJdfPl4};O%NC7iiES(!aMzdduR=Cu7~biywc;dGZp50-SpezoBhJY#3Iz`(p*eX zIbsm5BW|93tzEJPqa0SeZXM_5M&j`cACg-ChBj`FteYBohGb3pTFD};7=m-ce5jSJ z%%iSWBh=tz9jYZPNjtKt;u6ssI>Jg}9I#I(cjd|0#vyn6sm3(}mVzcaubd}9gETH* zDptgOw$8g6+C@b5SV?8uc8V@bAI%j(xKXkQe-XezMJSZcl&ab16A9W#%$`}z;Q(wu zL#gAm^w9kg20!==P6f8`Zfs_LhRDeQdOssha5_4!-BgA63Vk%<0 zAz2a?$XH^NfI)A<31`ABPwjbrji0M5FsYjpLlS!C0m5tb<9*!AAXirUJC(hBO6JE} z#iQ{0hMU~xuPDEURB|;gNL_F$?p=uTnEo>F6h2-xA8&V|K}WL5>STw|=nqT$NZR!g zYEip%)>xl(T1_^;cCE?T@3`fi?xHq4tuFuT-1(Ecma!8Eme@)5MbGg0QvvIWLkKrc z!CqEaz^0%lafTS3W*o^@V~VFm&%iuDH33x*=%VfAfr#xPOBsToeyJ@8O6^KD&rOP6 z-|h}!D)4ACJT0wOnJr3U@DsZ5B|t}O(qMF2=(*kY+umjPkkI34CPo!=wN@h=5AT5* za#5}!QpUtuPQo?f9?gHtEzmWB(WHt>tn{Aadh2OhVc{a0WE{}Q4eVOQEbY*?R%$Zuk zf6d7K!L@Ol?9qlPL8mrOIJrb@irn0ZR-m1Z-wG4rYhnH4BKz|@zp`l-m+Q=_^%(y= zBwZ6+DNDP1y>M}+PvHELB&azgkej~M-saBp9w4zdZpyVX!OVv;uH`;8tb7vkLw)t# zMU@_duSp->`#Iq^e!$;~A!C>17jY^*xDy3cw|!085=MsJ*CBgD?9V}E80Y#nD1442 z;pZOM2**e2$ix66?qYiQef7E{cQ%#haE^7QFC@J0 zTPock!rkktaHRv)pN=2RWBI!>+r)8ssjB<%P;q?uRD{P!`xf7+;dF=ejc%!6frC)2 zWY+C8tT8Uh-jyQP&a^O#eR^lMX9oUlppxxL0QP+5-3nP!F@ntDGHq`V%m7NkmYye- zGmidPF@^?uw&}G<@n*=%Gkf=b7I+x><`cAX* zN$M!q(X8?@9#kR!8`Zol|FkE-&8md|4l>xxkV>V=6ES@^|9LRgmgQ}S75|UJ{uSh< z`ew>ggZS;t-j5hTT6%t@#y`RXHjv-Tgm!KG&;vgOqXm`Oy{9p*M#xBQ>4EZ9bdFxJ9rn_~ zUK;Hn?>@NTys`vUt>R-|UA-=y?d2SpN8zlodcFCFI?cXxhuSA4dys3YXG4FetH<>A zFf9Ec_{Vf)C0D>p+y2LfYhIrtK!%BmsBq{%;=l7s{yrUya=-b|t6?|t8Um-6d_wqDdvHoDM$CQtA{W~mG?|fA65i0A1 z3hIY&HP_|#xL(`E*ouC+u~EyCt#`J8#(w9?SlW#^;#WzmA6_7iXSN ztzhBykBxJZ`0H2vdQN#%_^zR*v&moyOXAi>@p0>9;Hlpf;B|p7qp^R%Hf@JQ@i0#z z8)kMJUG0*J?fUX(5!;|CD9C#jHk*S(SBiw{IlT>S?9E+ZLg}#K9zYGzar8-QwAzjm zp_9y<=-H1dQ48CXcsvL{e^o(TdosDkIAH>%($X7z->UB!N&|U;yK&yI>j(Yk;egb% zMCsHtp7?QrY%)rXli$80EU+ZHTfVVV@_41N8mUxUuvbgbdwU@Kko

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1578174 6926178 11960517 1487469 10079986 483011 1634611 644542 229454 493678 2758609 5983950 6445722 483373 5116692 4341054 2652660 3285128 9214051 1725760 380224 5878072 9679727 1935469 3354872 3491777 3903044 3098324 3901596 16486928 7354595 5928774 6276978 1069606 431358 423052 3159131 1092630 1042776 1771261 4726444 1151587 1737898 8026262 1297907 7275379 6723651 6868065 2627565 5745052 859014 5306284 2535047 5307520 269522 21581115 391810 4323746 3412789 1672193 4095309 3737559 1781359 1792450 291267 2119319 26261958 5040629 529740 362314 1887159 3808365 115792 12919439 1980086 743883 5533137 1538355 380500 2777958 +met_011 785442 60478 60202 224880 55462 100060 42986 NA 130475 NA 675924 4903004 396065 380956 NA 392119 9854 106252 99264 637406 312148 33714 690355 247213 216205 67547 36430 21057 131148 574095 544490 99843 NA NA NA 144958 565976 53797 139064 306347 686592 404783 NA 79308 147421 66507 NA 73762 198084 525400 482320 402890 NA 308122 3847455 106160 64832 716412 494328 417494 174319 88831 4044643 71642 1122075 NA 71395 131196 433963 92619 3073860 1131592 55550 132765 57931 786643 264705 171375 55880 465617 43560 33298 293164 NA 38935 117848 274209 144362 251705 396383 1976437 155386 71656 62054 273108 159751 179756 64850 94359 366996 126976 47026 142714 138801 72099 37766 102870 334806 +met_012 966828 422417 214842 1194887 421456 1901281 258409 NA 839552 1106235 878410 491735 4269851 283059 519567 810110 81046 656910 571446 839770 767967 832013 160535 583306 882564 1492712 61676 840394 321103 823730 899697 139924 545929 1294920 1130207 825318 236651 375197 251330 1506027 770762 751443 358250 183505 466680 234995 363012 22319 266856 754857 1427392 226903 284836 209380 1195077 4620106 222481 1374989 411419 729779 435834 640528 260978 242145 1428986 620975 355664 302626 731010 577113 269282 1025669 1939260 299290 550611 613659 640041 414264 218912 656206 624452 300278 471693 705508 736267 605046 661164 776176 307460 629006 2060619 411678 93072 633718 338884 264054 157504 676603 1805777 566963 910158 108224 250755 387572 303497 125823 689546 451727 +met_013 714912 791249 604575 589454 1074418 3070098 2119240 7137 890118 386826 930174 1000597 733434 233617 1360212 1037357 176066 772653 1884339 751699 1209307 255309 525705 1839142 659431 515303 159998 122897 1308292 804084 1176095 311879 1090860 1448865 1490176 473518 1314978 370766 658153 2072868 797182 241909 609394 363945 638321 364544 610599 90246 304093 689520 535665 940965 1084750 563914 300670 250836 847567 963190 994193 618534 541323 428765 214996 219920 308257 480191 443707 227943 328803 214748 175739 395171 87076 356367 663958 835129 406727 1311985 309483 421842 617353 320188 159318 432346 121762 313052 342490 369635 325067 279331 312419 170664 207936 381802 507274 489883 75053 222493 340858 231135 120305 217904 278230 150750 163974 297904 633609 195582 +met_014 2192373 2373274 309675 2067019 156561 1335591 2561776 2628 1367751 2883908 2189352 1399096 1059588 1752409 2350095 1961794 449838 1848751 1702954 2184962 5270167 2111154 3409472 3651287 1223133 483357 176636 359017 5356208 2108275 7326500 1999235 2297084 1110000 2780288 948330 648616 544135 1920152 3729873 2014850 2085972 1185999 831100 5866712 904382 1817319 272422 NA 1794888 1073283 715155 877347 1288101 767352 165713 1619548 312374 4411684 1757101 1347974 5483326 105622 NA 880374 4773821 1852051 4012697 1506395 747420 860522 1784675 269957 1034751 2756790 2327842 1403518 NA 226200 1520585 583978 1399246 741899 865840 118051 1041124 1160379 1246515 3709009 1297454 2801529 4143776 1740145 877177 1951698 723309 205685 2724339 483101 1223618 232499 811131 1998752 479220 2297602 901182 195086 857346 +met_015 650957 607290 344037 1227529 1072694 576358 501563 NA 502469 591694 628810 843246 499456 685211 2294708 25129 NA 396924 1183224 585823 1211863 95856 1144957 787735 689605 479871 NA 73159 21384 617342 1458756 347990 445231 1570717 1136540 395902 148198 309567 739570 265275 566263 220112 529228 346333 620592 381877 266345 NA NA 557575 279088 1668394 414413 NA 82640 96029 371246 387503 604542 487548 313124 271980 77109 NA 327966 684942 1063651 397266 470555 381094 163296 311328 170466 373177 511493 294494 457952 53349 184101 455759 235010 231678 234265 877479 85416 91934 643917 413352 960793 418237 1018189 344346 53396 309733 1051458 800181 29708 436994 409034 451321 28816 378301 2906 145014 594244 406030 416940 409468 +met_016 3728543 5271810 2396071 9589731 5114332 980903 4150829 31072 593696 1881520 3546584 4217005 1971560 3884824 9657866 4530178 1402006 3635446 3074964 3248352 1352072 7480839 512684 11155460 12682407 1675744 478909 916605 2614207 2990150 723349 2298819 1304874 2646610 4645701 1226585 358662 2008421 3038019 1348614 2687620 843385 1453715 898170 7128463 1363450 8420472 1421312 2814699 2482355 699541 1473779 1709188 1380690 325067 337051 1819782 698009 3424228 2146596 176414 2969708 1196100 781640 761994 2162716 3141233 2643394 1688375 1037309 1082009 948821 644108 296384 1491277 4266847 376457 3953926 440850 1609610 1122880 591212 2149989 2065993 149791 2075448 568954 1977753 2892161 1233190 818414 1106932 296057 1800273 2704726 842085 799040 750455 772005 1177812 243149 1124326 1157891 1921771 779319 406666 348015 769630 +met_017 4821103 2435403 2318732 4245753 3748189 4349935 4689943 NA 2451934 3331274 4202823 1370470 1761558 1754097 2700769 426851 1421552 3508482 1312895 3480628 153921 640917 4404841 2251667 4439211 3342751 1543974 1098969 858681 3440833 6434298 1877831 602319 195656 151869 2383960 2486404 2254876 1908070 705181 2902467 1052946 1271786 2219297 1805955 3250971 1844215 495613 1812866 3086559 1835313 2882274 3433802 2034058 3943305 1112063 3043290 3146250 1149854 3119954 1959167 2538248 931529 1334514 2458891 2782916 4005410 3855724 3184232 2002086 1822977 1588408 519939 3609550 2113979 4260220 7162992 2063593 1974086 3128563 2511623 3723824 3208944 3304203 292819 5146262 2436561 2378318 2980413 2487889 2835818 1141876 1331599 2511176 2512983 2690201 1226346 2457264 1783679 2374533 578362 1931922 7306894 751094 2056881 2013244 3440995 1986099 +met_018 3845845 1125035 3713026 4269132 6333108 5776736 8166664 NA 4171324 7988966 3714898 2876103 3849785 2430197 5422776 4662180 957971 4677780 2992946 3762550 1969501 1716169 6312832 4616472 5606941 3117587 346932 60487 10814109 3864794 4470255 1102389 5587587 4534492 3512890 3328218 1802985 1680743 2781704 8525375 3682738 3099285 1746654 788594 7427151 554576 6269287 5357 1140956 3709283 3307003 2315279 5493432 3053170 6192947 1432019 1076046 1464520 4824344 3147257 1994147 4573157 2230984 991725 1315331 4629949 2133538 3895081 2562451 1401708 1399516 3477358 648304 3496616 3434817 3068024 2848307 2263251 1598976 2740585 1773213 5880906 3627568 4706634 319862 3713057 1596358 2055649 3499900 2304312 2797584 1688276 967096 1358564 1437973 2728284 153100 1214379 1735479 2046537 783838 2422950 2074712 174935 841044 3060876 1269234 2031300 +met_019 5358981 1852069 2817716 13425015 6947343 9222617 9099809 NA 2007588 7787991 5015511 2251853 8420137 3275473 1831063 1018399 1118722 4098937 5116422 4752971 7501400 3802804 3861141 4796229 8889990 4018242 1056423 1778148 8605488 4674493 11002636 1309019 3295768 4003774 7986281 3554994 1607508 2115776 4253223 12066534 4281759 1970354 2927447 2831393 6021370 1974441 807466 651417 2493515 4180516 3116490 2176577 4888627 2196961 8476471 2528441 3630897 4682288 2689475 3796573 1696309 2923614 616268 2459265 4009301 7179730 3180600 4284691 3426119 6686427 2717812 4102734 4042429 1710961 1394798 8008701 1179149 3868258 2073126 3241222 5200014 2987295 6798742 3795870 516069 7273064 2569684 4363849 3392370 2862745 2413851 3631103 730630 2997575 2888571 2034137 894761 2900228 1749975 2510749 1092769 1391664 2705004 968069 2659929 1570759 4909668 2120155 +met_020 376727 558206 452456 684769 811993 1237434 2024650 59378 843116 425348 569489 680002 462590 261614 1086047 2150276 170780 587597 1690881 619922 1288773 511404 593075 1177122 476312 519660 101142 138098 693096 681209 1126823 499025 1237860 2135820 828884 454240 997945 166067 1676554 1730833 558241 229712 697829 170024 965976 338241 704863 94190 155578 629936 292129 893964 472098 347876 332458 146373 903792 728107 421968 607762 291200 341419 148894 171930 164280 253783 447534 122062 219240 172104 195005 269045 140185 268742 203012 672259 348201 328573 170828 202060 290122 245991 84278 292948 65677 236954 329675 185635 156592 211279 289833 106413 106902 198000 322821 208822 146461 124009 160929 167014 57739 225505 170062 108220 64332 287259 259570 165915 +met_021 791149 660956 341904 1567870 1327941 712706 674132 NA 617537 655997 727450 935308 541886 864815 2729263 31117 8699 438681 1470543 692208 1477338 85072 1251052 833682 817020 587321 5039 120855 33258 618630 1835142 376618 525836 1682514 1232387 441300 212594 357377 912678 275265 617032 222079 568340 433492 688010 379197 301587 4586 NA 576374 351101 1793570 434035 4703 118688 116337 437463 448917 595367 551755 312709 263131 118862 32997 409710 611048 837015 442475 417624 374604 171133 257577 150829 329983 427291 279155 446230 15583 172201 432252 239956 147483 192023 654753 65681 53180 585086 403571 740845 422602 1639724 294225 37612 273156 546205 770099 72131 378102 349769 349563 27926 378938 NA 15250 260854 288504 289160 252000 +met_022 717661 1074674 1053130 470247 521228 141457 315018 NA 375766 215001 578705 1202624 423604 418645 200320 226647 91793 2799516 350468 563598 850445 873695 711073 535147 292570 277092 120513 522397 495888 530745 719010 176789 202170 612553 567576 326593 62121 398881 424545 332295 500045 545217 983058 4041853 392344 912560 608316 45829 104453 529543 159508 369095 1460098 263150 44633 329668 2241426 377053 213404 457278 300502 527770 37907 77360 1103612 1302545 1060525 288958 528834 593810 295636 85515 535728 257044 347618 354228 914941 3320840 325200 466768 134035 159685 1163397 267130 80379 648474 1027243 192482 1859746 459580 638605 388737 67230 639054 449460 395205 1087405 664116 493277 470489 113896 147651 266705 922588 384589 582386 693787 398914 +met_023 1198343 2990133 3007328 2211039 2275944 1232879 1306785 NA 1340386 1491497 1383251 7626498 561214 1043718 754326 1066984 87553 1332514 6885438 1409603 940022 1097478 341960 4183801 529091 2118345 465776 266454 709026 1290204 453619 125443 1751723 480027 4382126 120404 361744 582154 916286 1646763 1149840 244615 695215 217962 2786697 3178144 154683 23837 1172846 1216139 463226 2119886 333539 2375022 225204 1897492 369852 596586 1172643 909821 1299489 520981 75183 1022220 1243021 2587846 2060262 2138614 1063708 229744 188650 1008907 223690 3329536 455120 486754 1802765 5603827 2281845 976508 1040916 1154191 2696195 474992 224476 429688 1015476 312237 2683955 888410 44435 154754 357451 649277 459104 282643 174424 128223 376053 866228 557089 1101878 1126394 153400 767169 534183 449284 803526 +met_024 16032278 14936988 8033606 12588739 15513840 26462423 27143962 18829 9970907 23846414 16510821 16842315 11790038 7304590 17486391 20755862 4460837 14246803 27477125 16220853 27768410 8554938 17186780 56208841 22324609 14058372 3167035 3015384 21081827 14697234 29010089 6206646 20865107 26003200 22833177 8418608 12391544 7250829 19366320 21105604 13827437 12035630 13395311 8450688 20293343 11361602 15299578 1366191 4289310 13598987 11425397 20310595 19715917 8236349 7852032 4755976 8385880 15186902 11243022 12462786 10369892 6611827 1781915 2988499 4246851 13094738 15792917 12059370 10484953 14363177 5567819 16525838 1939040 10475011 14302713 17962839 14881974 17317964 4920828 10855788 12224283 7722189 13580469 18832254 994413 14438127 9533519 9015535 14704865 8708281 10293424 8708380 3281132 11166962 10104254 9161854 3602659 9122643 8001536 8105192 2534757 6099879 5273866 2462544 10053531 4359819 10044986 6470875 +met_025 4604239 1480158 748805 13999568 6006627 3662509 5207024 3823 1183383 2000660 4202211 3228241 4177390 2958747 1362676 5028201 530152 3454784 4523323 4049546 12046491 1844607 5832995 2597793 7415702 1990117 755147 1571123 6764574 3877167 13468473 1184323 2097410 4014463 8825706 4501006 994023 1260526 2735667 6916403 3784603 1026110 1293773 1872377 3528525 1784286 1211795 343526 1402745 3304306 2841948 2021501 4695037 1505070 4519839 736794 1282724 2990922 2367859 3108800 1628786 1869765 551801 1743444 3792041 6887103 2886252 4054228 3012004 3960451 1379595 2224271 2402140 770706 2745879 6698651 1266929 858261 1122287 2739607 4974371 3178671 6975447 4855873 366738 6848155 1829657 3109782 2509516 2369553 612533 4833219 797057 2821520 3120072 1430257 1446108 2568798 3832649 2213224 324820 517627 1785816 1750448 2640988 1466110 3114520 1824761 +met_026 337451 239138 131693 354044 105480 376839 143451 NA 262542 93041 334301 155344 198194 88103 200856 87315 135981 371674 341599 283841 439703 513935 972532 461574 180250 91705 192104 223480 445509 261418 169971 286471 239314 112895 262893 159132 165484 183936 256519 210371 182599 270423 69419 115027 231044 203461 116551 128766 429241 202459 91145 69469 229878 162068 294390 154594 71607 48019 286453 168071 215128 168717 48520 186837 208748 204031 108586 145761 157936 128718 67016 142263 157301 78271 824554 232236 75552 306409 146464 174743 106881 280654 222574 277808 22251 542255 78048 156431 663504 137845 173891 142710 785999 303568 13250 136592 NA 305324 69648 110489 82416 43667 214303 NA NA 131275 54230 90987 +met_027 2250930 1436433 1622845 2211403 1441333 2930173 1998616 2755 1436602 2767550 2347107 2533061 1314209 699730 1799272 2297696 261782 1215235 5933660 2420406 1803746 1579528 2132469 3230502 3321092 1231513 351935 574052 2021275 2316278 2768553 374876 1515499 2883699 2161680 1682608 775782 564460 1484764 3774954 2086312 1072067 2339610 1130946 1365368 1217005 831862 176919 586477 2048071 1421136 1611255 2320060 1028277 2070417 454712 1089427 2255368 2186969 1996087 1625629 1743307 161619 665745 4803070 3649696 3293603 1075822 1671522 1273018 656881 1389690 349842 747085 2098959 1880866 2093600 3142304 569603 1687392 2614360 569516 1241882 5786230 214950 2937948 1637761 2613417 1757844 1393085 1196456 4459313 347621 3500693 1732461 2402126 451680 632510 1136530 1332439 130507 810754 725144 2205393 2004142 1125066 709144 1029735 +met_028 1342382 1575026 600914 843118 690910 2953079 983830 4774 602174 2102743 867368 814522 454501 313879 2590268 698037 166396 1062771 1680388 959273 1680007 527029 539474 724637 1801413 1179526 186699 225276 1279410 966823 816622 163347 2786798 668231 1066316 1425432 1140647 340711 1304180 738560 850095 978487 502774 631631 1318448 941867 998921 163127 393569 807139 739740 323405 834844 572297 1736714 326550 484590 655641 915836 888105 1154005 859248 165915 261986 3084129 843012 632786 622889 1097986 873904 12361 2060359 621118 541740 578473 822581 1425697 1493448 644370 1236185 612450 418594 761778 481198 420312 854232 1488335 871301 1448584 1088997 986969 1712592 287924 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NA 10532530 6083042 15330338 18300220 10381995 6775200 18338212 12219016 2640179 9095868 17966689 14282244 42601014 12743075 15317688 23633853 38319356 8886700 12719346 15665537 21412050 13613421 19695308 4276699 9905666 21358813 33512214 6821979 6875578 4446798 25877019 16377840 12108796 5968562 4961466 8286116 14420053 7689786 8692657 693185 3256123 12638355 4899039 9557133 10702425 5661755 12468510 1642709 10245501 12530004 9511953 10574173 8369060 4800275 1467152 3708234 4758377 12313787 18069489 12443528 11097380 5100336 2956962 9471982 1493639 4519599 4732329 29610015 15684186 17897401 2539077 10376836 8949448 9663585 18484191 13859039 751423 23232727 14264998 16295947 3887988 9126898 7845474 8238760 3501955 7223657 8624873 7399474 3635483 16621131 8874905 8315877 895109 5538822 9994456 2851607 16905160 6418248 7987640 7221992 +met_031 1693955 1350873 441101 720164 205935 6232540 292240 NA 2042354 5111194 1630747 654773 260235 113852 7998652 558252 56627 567235 440472 1671518 2389553 141667 355907 858457 5155501 6753638 132280 126598 390440 1584203 376822 278355 2702321 4279904 474124 3061113 1149061 833752 596788 300727 1512845 7405588 286513 277951 3431522 275429 1883533 28342 341775 1533530 4371896 177208 173557 159672 4237446 324497 218223 1947969 356170 1408813 5443380 4005638 322129 49382 453013 625480 234390 420681 1184368 808147 160182 3202563 496884 255037 396557 657502 855069 539843 204051 1164728 313711 137639 129363 161389 23776 1161919 3140284 1858027 273246 961528 235786 3356118 309929 2764411 707041 408679 284508 833686 5891318 956502 330301 139520 261747 3233346 152027 84497 84821 444352 +met_032 1687436 1327191 482363 2626653 1737284 632465 1270979 NA 390343 203483 1938259 6647760 2495064 1811611 2650533 4773443 177386 387012 677635 1805860 460232 1169018 420212 2788576 90560 243580 81997 4001147 385458 1693049 1268903 379160 1193294 3057856 2761768 1538023 1670686 646899 424302 6016819 1638922 788163 619121 1812329 1415358 4764833 856753 261700 300022 1593894 3191637 1009268 1449991 492131 257104 73115 225552 4390926 1011450 1382981 1021477 769354 112295 44388 2175061 965745 940518 2208789 1223698 54992 405783 335695 3782355 1159166 630205 575851 2396077 396162 1747538 1153530 2280240 626451 65581 931429 262861 400527 3758150 1419565 455451 1064058 28139 448160 509897 2417119 1647107 573586 1395045 192544 3298607 1064775 432684 714508 965175 695907 1108821 179327 1505795 940918 +met_033 404126 229387 246732 495438 458999 739333 350568 NA 330232 155057 416459 52993 317248 328619 330066 108690 109686 335235 382859 302862 352792 336346 544700 415546 374615 337498 82197 66583 385699 303232 710881 190588 396744 190115 253078 176786 249379 136409 541059 221847 270413 146609 257646 176619 310604 314169 221940 11720 180707 263315 174008 226313 291109 175475 210371 86368 200669 318310 214428 186936 255829 103260 45779 89182 62054 234630 228615 168381 163878 196463 103182 92351 36596 121218 104859 170809 237488 192046 87427 145850 126984 211099 199070 277152 38435 220863 111540 143482 148208 112504 134058 79038 50613 104417 93098 138633 8565 123087 74655 99018 28762 52762 76281 19806 110633 65274 77529 64346 +met_034 2565674 10775564 202970 1263880 1007314 1841773 238321 NA 484647 1762143 2600938 2917722 1036864 654657 8606215 691410 1107304 1797882 616802 2689618 619579 1400934 494131 7314582 3971612 538607 256701 475577 1109846 2646193 354020 814835 2799028 472250 731499 1065191 885369 415542 382725 7864367 2547839 955850 18355720 140683 3524337 2131412 6425537 183653 1029280 2631140 1258516 1266858 10303865 875299 244475 336115 944565 445116 1054613 2459228 3980481 2092637 2150059 217219 1169554 128909 8566725 774686 1562154 1775531 313326 948077 226506 273674 NA 4695875 2749271 3142867 676603 1897116 380696 637865 492247 566598 752544 838299 773400 737190 652803 1658098 528147 1069139 NA 1591788 2129952 638766 1216868 1233059 3587011 1456699 86508 2293689 770063 852874 8245262 1232647 496410 1235831 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1269030 1228557 983714 762300 436245 182301 190612 391834 1231435 1442917 1428094 967530 935033 496561 1126645 224761 871384 695433 699815 2890435 731777 715827 858207 1615432 1263821 1180323 2210160 45185 1021020 545042 1082739 802425 831354 36838 1126365 366457 698722 801967 1179748 274197 915446 569060 817413 98196 785392 572562 209097 1012555 482017 1134921 693172 +met_038 9501430 677457 4376578 32265288 10349957 7407003 11085628 369578 8322147 5767769 8815097 893135 4894874 4754761 22801241 10171977 3275031 1379426 9186045 9563855 23218930 457458 19766857 3795717 8236695 5633928 745804 81445 6937233 9402928 26363350 2250500 18531103 5261812 6212477 4445575 3515332 10080088 16275380 3647887 8575743 4597352 6826457 6920585 8323683 2872766 7035752 3901 3205685 8161771 5473670 12365702 9582148 5074389 5278253 6794366 4482627 3676422 10218681 7559597 6717247 1209774 594625 189370 1189080 9061916 7076483 5778008 6172523 11032495 3057450 4875248 2667344 4121072 7807104 15171920 5674073 266842 2566408 5712647 4205850 4942607 19601860 2796145 2484114 1403229 2636250 7520508 3540476 4737596 8219944 1884544 2610478 1568541 7946607 2044631 141925 10012208 5242011 4512357 2220858 3640892 769083 84248 2923157 2679339 7406584 3489127 +met_039 1254051 281508 245108 273952 569419 730609 678094 NA 494815 1301913 1192639 1165415 917601 318679 265972 5405773 150121 692922 6800706 1203006 731979 213201 477009 700751 975835 515123 98124 242969 691184 1150293 491614 200763 730555 280490 666883 430953 482566 511421 857346 1907337 1057871 858737 301492 346707 1155191 291161 1837109 227172 132684 941402 415416 549092 820199 1371112 663848 232251 732002 438599 324964 929766 711158 358932 66450 140161 163880 1095900 393762 315811 747526 712240 187860 1636001 100936 212452 705208 401497 469354 420377 249127 654246 4034189 267738 348434 824831 64745 416552 204742 467086 337514 623068 261646 2776410 176421 339748 1069025 1813567 46620 330671 287603 550025 69129 186687 244306 30249 187273 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254521 341843 41663 391911 799965 141622 61044 142993 56264 489267 169008 295265 58546 180961 148614 230389 278210 134266 NA 126340 243261 195423 207095 531089 107702 211278 29193 144878 334016 202936 252522 31143 142686 4208 49443 21953 253027 361869 351277 197331 133511 58768 284831 15512 173505 92997 249678 480287 300312 7558 200343 91303 300709 421112 520229 1809 336086 60356 114298 666859 167964 513058 343977 24498 442912 89609 70676 4176 79846 147925 144500 5008 147269 256171 71762 229074 200381 68895 120784 +met_049 8773017 954774 NA 518612 49891220 85510343 26871458 1981876 850073 164060 8600336 630781 30081798 76653 8008 39943 5950440 7040263 138258 8183260 9132 11753114 28657944 1169390 29190451 10841717 1777804 1305536 14203562 7945530 3161630 1288789 451277 23084770 146993 195989 11389821 54245 936950 2129319 7636029 402061 36132 726734 93687176 314737 30097873 4455 690225 8064023 3572777 NA 2206491 3046082 7931947 10138 36338610 16358767 5640490 7167964 NA 659754 NA 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NA 32468 28421 42002 20687 5155 55486 57706 53381 23677 4475 NA 60673 14626 70260 11271 35414 7629 NA NA 24627 5294 33362 NA 30039 5386 7088 19800 46120 38125 NA NA 4806 33383 31030 37184 33089 14254 12352 NA 26579 22485 28114 15522 22773 12102 NA NA NA 30703 48894 30691 7330 35862 5017 21352 NA 13674 16361 19769 44612 50261 4771 25664 28927 19144 28549 24703 NA 35845 8831 28162 36115 16693 40838 12482 2801 21653 12521 49724 NA 25765 6331 19266 2464 11461 13857 NA 14954 17701 22776 14809 +met_053 738984 2424680 530000 353461 274130 420709 580470 56056 256754 1544972 683537 3641961 1766279 204773 3139551 457672 113679 280458 492638 709934 400375 124165 508815 452818 269260 256017 70083 2271800 379190 643984 497496 112987 891800 450372 868049 630132 149617 188677 270319 435230 619624 1278883 169927 114981 381982 225307 884418 376384 154280 540399 391628 212064 297317 230642 185172 123682 161384 419543 308295 556160 175876 669413 252797 51359 1797649 121044 159100 146796 421507 188370 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334970 754726 176912 75952 387467 166441 493955 352876 289977 47393 410282 269523 658630 320968 343207 737456 410302 59383 265859 197416 349345 198660 171037 250661 315370 48930 281771 170571 163952 110000 204407 228319 292236 +met_086 971949 1614846 368356 1628172 3244652 279540 572433 2726 435191 452277 915087 1613511 2326319 797681 1168103 1231557 257867 887179 760931 869119 3011253 689608 1808605 1069205 1648705 606125 220902 529622 829089 969005 1539241 760695 848858 2320581 1294843 766475 200598 939229 943730 760434 907761 445498 371523 667730 583126 496614 731364 142369 10991 829856 1251987 1065528 468600 629796 1246803 486994 343725 646307 1379653 776598 1178728 466834 854524 217358 535155 174760 296792 793376 426410 403497 302318 572481 509493 637030 503467 351180 1046810 458108 381259 601356 869955 96257 393684 139095 104331 359997 460735 312751 356222 311484 281561 541115 59438 210317 572395 192357 454988 1047600 342338 392372 223793 138848 302764 323145 171546 83421 226707 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90264 391599 308157 132845 324026 200460 +met_101 4301373 965289 995068 3172360 3636413 3402503 4910757 1191743 1381233 1864080 4583425 1341576 2317062 1601289 5528392 3903664 689081 1137451 3273548 4690022 6473473 394575 3638255 2301190 3509539 2965875 282679 231533 1932379 4826466 8580593 837721 3291207 1914753 2254667 1777291 1295621 1321296 3328682 1424367 4666747 868046 2167666 1742428 2626144 1122030 3162310 118325 843288 4606369 4530326 4574076 2241580 1526093 1578749 1273599 1637253 5065540 2567292 4654747 2345706 870649 449895 419864 971978 1971155 4988547 2147491 3891233 1368078 1368531 2481298 684247 2649088 2319357 2249799 4358983 1352322 796807 3987798 1823028 770040 2555838 1178526 435916 1231494 1408554 2390755 1616588 3590749 2332931 1097159 1021206 1171670 3474799 1865044 622538 2013568 2065482 3806933 1006136 950871 804492 604206 1138285 1058431 2838268 3204413 +met_102 10474724 18640156 9398818 12886582 10029602 404122 13887842 4523 2970811 2191610 10419660 24457520 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8510941 2893996 7413022 1511557 10515620 4396182 7548310 2778862 4293948 2135677 6709445 17695990 2728922 2320772 1748006 3588390 6268035 3388469 3546667 575799 1874812 2599158 2366620 2223432 3065515 3760704 4250450 1537301 3269603 1884664 3186768 2676759 4740682 3360723 1890952 1333556 4282757 2437706 4242677 3448659 2255406 2219518 2488255 7001739 1053513 3727167 2216831 11146096 6598371 11419599 1652485 2216659 1550371 2458285 4365534 10530571 497195 3776148 3096812 2300228 3181050 2074355 8719387 1846325 2247449 7641709 2162142 3728097 1374523 3692619 3450453 2072358 576496 3045713 3447958 1664055 2536040 2129666 2070735 1582002 +met_104 1412908 1698846 1196489 1494334 2781797 7628559 4544074 NA 3319179 5031977 2496155 3539222 1580251 1091267 3153361 4199392 1257060 6002057 5316779 2789978 2883601 2516364 1849538 5604429 2579540 2087949 1023990 953262 5944523 2766852 2502641 2327034 9231105 3671542 4049005 2066506 4158248 918541 5529651 7318566 2728445 1643155 2798724 1519221 4670158 1721368 3922807 731655 1121066 2467939 1355536 1552355 2410306 3219073 2456800 1583501 3260358 1821908 1062135 2446421 1974808 1494826 1692802 1153312 1948898 1079904 1998289 1177663 1053291 652984 1285081 4140644 685953 896366 602373 2715429 998320 2085301 388158 791967 528570 652394 393978 1288961 717578 607605 1887230 318038 749493 912558 2494536 740476 797783 752892 1813333 1209404 675236 438924 774860 728744 283281 1233185 219075 745177 1032992 2217710 439491 1187853 +met_105 12793419 23535415 5344558 20982120 28603692 12613245 2105851 NA 17896560 3273597 11654374 8053253 19077140 6718561 2125404 20436336 1561579 4345459 13572127 11096877 43456706 29050074 4039518 12844173 5515698 3693486 1675219 1778334 14110683 10980791 5723130 2468088 22765367 21807244 26191263 2174708 9968311 9455273 11742901 13674507 10102691 26735420 5446256 6860083 8792188 6882331 4058995 864977 9905729 9377076 9190570 7238562 9074498 11920193 6219582 2432861 4382472 4146521 16494470 8851715 15667875 4647054 2524653 4475693 2027567 5397137 18037533 4318924 9698270 4765807 5352799 10759203 2628835 8088165 9318567 9488186 3413383 7490570 6629679 9158994 8237636 20034342 7003374 10919250 1203445 15833338 3282196 7383811 11448944 8368154 3485556 6881496 4546484 13294326 2721520 14345510 1658543 16300387 12903263 7872453 2941299 2126782 13153041 1597338 12279232 3375221 16960658 4206774 +met_106 561083 251794 988646 993586 1082292 544028 482220 292549 729939 3096237 562671 454545 506657 770250 231489 1173873 284818 967486 373105 546727 454256 521907 6518 465878 1344684 684010 6174 158257 1296234 496902 407582 1833652 5795 81322 450301 479471 3981317 255719 286557 493690 516410 1033241 18188 115631 462403 NA 1321324 710212 71417 498318 109296 450752 1120516 201769 246269 206818 546895 342009 398993 483822 180275 892482 187882 444773 241782 54860 423583 100924 473596 461916 868297 1156708 227223 156518 469387 335408 27191 865315 83815 447614 403960 649910 1200658 1276946 67878 1066444 106858 336559 85424 434147 163495 463186 1429550 198076 170206 128685 152415 2623 185380 392053 115329 682683 487417 317608 67265 511166 137348 399312 +met_107 201582 75410 95388 223372 299101 576764 328772 NA 193337 268202 179759 392882 274922 NA 115736 36052 90324 276254 212282 189272 142054 171324 165306 301091 480057 198236 NA 4221 382700 182721 358989 12438 201540 322952 52636 25559 176565 17162 86193 202267 144552 63397 115468 52602 391431 54720 121582 NA 29665 141500 213104 66684 359238 64145 180454 NA 217455 255074 378585 162391 87821 176146 20257 36925 6203 262054 255942 420848 151634 52791 30697 154052 NA 166229 114644 204447 118902 646098 NA 138880 45920 162491 171430 472754 NA 389655 101701 228612 450240 142415 79067 154946 68627 195301 64307 208891 3210 42905 98563 101516 NA 128307 120150 10855 120630 228377 58521 87268 +met_108 3611050 337281 1568496 829839 787242 1184795 1382222 NA 200938 729889 1080156 1081094 141312 1178130 7147341 44857 732843 110684 113797 810384 1255280 127231 956735 354295 1293048 707058 433284 845693 543469 716759 700772 979416 820758 709255 1852263 32010 127736 2812608 10064 393720 657528 639572 892471 758360 341676 100966 214990 18945 161975 645484 928350 270958 1176416 267300 88930 275130 805771 1738257 95137 728854 147464 223930 261243 49451 1007337 857047 1618812 2407238 3274776 514652 1615173 1152733 767664 8086876 3798948 2185760 6553555 46318 7021342 2349035 1688759 2143951 11726529 2373218 1223659 1111505 4464462 1916635 1237563 2632954 1239801 6734170 2053650 1820519 2090701 1281098 2321166 3041016 4801228 2316764 456626 1043066 430170 5699919 3120767 933288 3381137 2187363 +met_109 28704630 33586179 24692441 28786031 33702648 30944680 28399727 2656016 20383142 43557757 28591838 35548272 25334421 23782385 33496403 27502756 21304507 36008666 31066457 28168027 32541069 28514582 26973491 31448100 34659066 28767624 25600071 15859089 27835970 27390922 41203737 20498119 24452585 19528858 38827573 19668360 36226070 20834484 23959078 24861056 26613170 19267218 29521876 24702382 22611875 27200384 24013133 8211817 19321158 25053327 21309585 26111146 22752518 23678180 14152377 18368970 20070120 25344880 23708432 24308059 21954805 17310020 15790465 18113923 15887568 21212232 24847089 26846871 21379272 16591716 20216215 26495026 9555821 25623659 22300532 19302590 32540833 38044859 18732489 20874060 25502870 19077479 27777050 38317810 11205446 23184858 22056757 19844415 20832666 18822607 22767829 20416588 14459562 18594833 20083706 24380208 11525928 16313321 14407791 17369758 14005534 14258891 13852000 11087898 24969299 15806268 16899211 15920144 +met_110 935810 216794 1250664 961929 322675 358002 748600 1860981 1018669 502991 684506 319659 372075 435408 271584 134703 303433 660710 497665 545837 562326 294841 768892 810960 1051193 334380 415293 274518 1024104 541427 301845 328083 257992 599182 440881 228402 425882 556153 473391 796559 433075 637036 502610 252290 878377 318451 806407 240703 431264 364408 432337 490946 495207 557849 481367 240800 239131 552620 296097 325675 299775 538890 129963 202343 159717 524611 659238 837602 401479 347983 286154 405225 113652 522101 168009 185467 508620 217008 189382 300949 181027 293385 593884 707190 86552 667494 119244 290894 850008 307178 409093 223301 101862 205697 38427 209271 69666 182736 247482 273165 123847 387771 325480 44799 104296 295313 155240 212170 +met_111 1043674 783394 569047 2884257 2457038 2025231 2245847 NA 576988 636282 1109803 511108 1986572 950755 453817 673562 345902 1199314 673490 1088285 1650159 548818 673318 1350162 1955525 740092 273505 467277 2262955 1064977 2870714 199436 422478 991340 2525718 798722 179006 619100 874665 3040034 996244 329299 679032 733260 2162571 528686 219519 138708 740267 940086 736191 703043 1639741 433247 2081907 705395 1089016 1249615 789518 854281 424305 719392 258294 813349 666412 1123188 524519 910137 742428 1482493 1050502 1019270 846205 398868 409318 832274 122817 1372087 902737 753444 1261485 1138611 1159395 680881 111575 2005973 748730 797783 815843 601216 897182 1159520 207899 606512 1051255 493530 299954 651714 315358 558515 255169 338669 832857 218229 649477 415127 1274096 494555 +met_112 6419071 2797128 4299074 11909792 6115103 3096009 8035572 NA 3259089 6395447 5049944 4099603 7256757 5080926 3738872 2566634 1719671 4297409 5324570 4873112 7648125 1910687 5439846 4340784 7283405 3178504 1547866 3074074 6191537 4916409 8943211 1784458 4200543 4136622 7786416 4378240 2608290 2992852 4462941 9689440 4286272 2686538 2745539 4160961 7108278 2547671 2212921 992136 3451416 4253840 4661221 2464180 4009317 3451266 9045261 2681403 3260592 5840008 2611730 4002414 3128586 4061269 875155 2993219 4222945 4810052 3599437 6386359 4087475 5853004 2845191 5015241 5255266 2977940 4272008 7251435 3019276 3081894 2290972 3841186 3298124 3942297 7512978 3936963 796120 7537291 3841541 4596420 5044950 3496643 2341361 6304734 1369143 4345921 3364143 3282371 2017426 2744678 5320910 3371583 1340912 2186105 3792760 2150395 4931386 2886649 3159267 2835997 +met_113 1351348 1546452 704498 1178543 1910776 2419982 1730728 NA 1518760 1292032 1333917 1256258 974162 519797 2540508 976266 328769 1916054 1273384 1345103 2028673 740657 1759018 2037601 1797824 1183570 281005 210403 2013482 1157284 2890251 473682 1397064 922962 1608440 785419 836119 552727 996652 1314640 1080507 941223 1069492 492963 1146686 413720 1146636 106708 428730 1153669 682704 1685395 1027646 673104 762161 230163 984144 741438 962147 954327 887638 721180 215275 364352 353548 991113 1975188 1308883 919371 525484 489779 945717 86415 409383 910262 1155717 1527056 1552890 374555 863254 831837 632228 1534291 1054678 104907 1181832 570191 1181392 710672 765250 157618 799343 352504 730991 12561534 1127759 317084 828816 772629 761105 256288 491981 517417 208846 2334220 563581 849860 654635 diff --git a/tools/batch_correction/test-data/input-batchcorrection-sampleMetadata-customSampleType.tsv b/tools/batch_correction/test-data/input-batchcorrection-sampleMetadata-customSampleType.tsv new file mode 100644 index 000000000..5ddeda35c --- /dev/null +++ b/tools/batch_correction/test-data/input-batchcorrection-sampleMetadata-customSampleType.tsv @@ -0,0 +1,109 @@ + injectionOrder MySampType batch +QC1_001 1 lot b1 +HU_022 2 echant b1 +HU_076 3 echant b1 +HU_166 4 echant b1 +HU_114 5 echant b1 +HU_190 6 echant b1 +HU_139 7 echant b1 +HU_096 8 echant b1 +HU_058 9 echant b1 +HU_031 10 echant b1 +QC1_002 11 lot b1 +HU_039 12 echant b1 +HU_087 13 echant b1 +HU_143 14 echant b1 +HU_162 15 echant b1 +HU_102 16 echant b1 +HU_188 17 echant b1 +HU_095 18 echant b1 +HU_062 19 echant b1 +QC1_003 20 lot b1 +HU_063 21 echant b1 +HU_011 22 echant b1 +HU_074 23 echant b1 +HU_122 24 echant b1 +HU_142 25 echant b1 +HU_030 26 echant b1 +HU_187 27 echant b1 +HU_167 28 echant b1 +HU_113 29 echant b1 +QC1_004 30 lot b1 +HU_081 31 echant b1 +HU_014 32 echant b1 +HU_206 33 echant b1 +HU_119 34 echant b1 +HU_033 35 echant b1 +HU_067 36 echant b1 +HU_140 37 echant b1 +HU_164 38 echant b1 +HU_193 39 echant b1 +HU_112 40 echant b1 +QC1_005 41 lot b1 +HU_097 42 echant b1 +HU_056 43 echant b1 +HU_195 44 echant b1 +HU_135 45 echant b1 +HU_159 46 echant b1 +HU_070 47 echant b1 +HU_038 48 echant b1 +HU_026 49 echant b1 +QC1_006 50 lot b1 +HU_098 51 echant b1 +HU_115 52 echant b1 +HU_137 53 echant b1 +HU_019 54 echant b1 +HU_073 55 echant b1 +HU_168 56 echant b1 +HU_191 57 echant b1 +HU_040 58 echant b1 +HU_046 59 echant b1 +QC1_007 60 lot b1 +HU_103 61 echant b1 +HU_015 62 echant b1 +HU_053 63 echant b1 +HU_121 64 echant b1 +HU_183 65 echant b1 +HU_199 66 echant b1 +HU_042 67 echant b1 +HU_075 68 echant b1 +QC1_008 69 lot b1 +HU_203 70 echant b1 +HU_024 71 echant b1 +HU_082 72 echant b1 +HU_169 73 echant b1 +HU_129 74 echant b1 +HU_057 75 echant b1 +HU_035 76 echant b1 +HU_194 77 echant b1 +HU_145 78 echant b1 +HU_104 79 echant b1 +QC1_009 80 lot b1 +HU_177 81 echant b1 +HU_025 82 echant b1 +HU_080 83 echant b1 +HU_117 84 echant b1 +HU_136 85 echant b1 +HU_197 86 echant b1 +HU_036 87 echant b1 +HU_054 88 echant b1 +HU_085 89 echant b1 +QC1_010 90 lot b1 +HU_160 91 echant b1 +HU_092 92 echant b1 +HU_023 93 echant b1 +HU_124 94 echant b1 +HU_147 95 echant b1 +HU_205 96 echant b1 +HU_084 97 echant b1 +HU_120 98 echant b1 +HU_198 99 echant b1 +QC1_011 100 lot b1 +HU_045 101 echant b1 +HU_029 102 echant b1 +HU_069 103 echant b1 +HU_155 104 echant b1 +HU_106 105 echant b1 +HU_146 106 echant b1 +HU_128 107 echant b1 +QC1_012 108 lot b1 diff --git a/tools/batch_correction/test-data/input-batchcorrection-sampleMetadata.tsv b/tools/batch_correction/test-data/input-batchcorrection-sampleMetadata.tsv new file mode 100644 index 000000000..a62f26ce1 --- /dev/null +++ b/tools/batch_correction/test-data/input-batchcorrection-sampleMetadata.tsv @@ -0,0 +1,109 @@ + injectionOrder sampleType batch +QC1_001 1 pool b1 +HU_022 2 sample b1 +HU_076 3 sample b1 +HU_166 4 sample b1 +HU_114 5 sample b1 +HU_190 6 sample b1 +HU_139 7 sample b1 +HU_096 8 sample b1 +HU_058 9 sample b1 +HU_031 10 sample b1 +QC1_002 11 pool b1 +HU_039 12 sample b1 +HU_087 13 sample b1 +HU_143 14 sample b1 +HU_162 15 sample b1 +HU_102 16 sample b1 +HU_188 17 sample b1 +HU_095 18 sample b1 +HU_062 19 sample b1 +QC1_003 20 pool b1 +HU_063 21 sample b1 +HU_011 22 sample b1 +HU_074 23 sample b1 +HU_122 24 sample b1 +HU_142 25 sample b1 +HU_030 26 sample b1 +HU_187 27 sample b1 +HU_167 28 sample b1 +HU_113 29 sample b1 +QC1_004 30 pool b1 +HU_081 31 sample b1 +HU_014 32 sample b1 +HU_206 33 sample b1 +HU_119 34 sample b1 +HU_033 35 sample b1 +HU_067 36 sample b1 +HU_140 37 sample b1 +HU_164 38 sample b1 +HU_193 39 sample b1 +HU_112 40 sample b1 +QC1_005 41 pool b1 +HU_097 42 sample b1 +HU_056 43 sample b1 +HU_195 44 sample b1 +HU_135 45 sample b1 +HU_159 46 sample b1 +HU_070 47 sample b1 +HU_038 48 sample b1 +HU_026 49 sample b1 +QC1_006 50 pool b1 +HU_098 51 sample b1 +HU_115 52 sample b1 +HU_137 53 sample b1 +HU_019 54 sample b1 +HU_073 55 sample b1 +HU_168 56 sample b1 +HU_191 57 sample b1 +HU_040 58 sample b1 +HU_046 59 sample b1 +QC1_007 60 pool b1 +HU_103 61 sample b1 +HU_015 62 sample b1 +HU_053 63 sample b1 +HU_121 64 sample b1 +HU_183 65 sample b1 +HU_199 66 sample b1 +HU_042 67 sample b1 +HU_075 68 sample b1 +QC1_008 69 pool b1 +HU_203 70 sample b1 +HU_024 71 sample b1 +HU_082 72 sample b1 +HU_169 73 sample b1 +HU_129 74 sample b1 +HU_057 75 sample b1 +HU_035 76 sample b1 +HU_194 77 sample b1 +HU_145 78 sample b1 +HU_104 79 sample b1 +QC1_009 80 pool b1 +HU_177 81 sample b1 +HU_025 82 sample b1 +HU_080 83 sample b1 +HU_117 84 sample b1 +HU_136 85 sample b1 +HU_197 86 sample b1 +HU_036 87 sample b1 +HU_054 88 sample b1 +HU_085 89 sample b1 +QC1_010 90 pool b1 +HU_160 91 sample b1 +HU_092 92 sample b1 +HU_023 93 sample b1 +HU_124 94 sample b1 +HU_147 95 sample b1 +HU_205 96 sample b1 +HU_084 97 sample b1 +HU_120 98 sample b1 +HU_198 99 sample b1 +QC1_011 100 pool b1 +HU_045 101 sample b1 +HU_029 102 sample b1 +HU_069 103 sample b1 +HU_155 104 sample b1 +HU_106 105 sample b1 +HU_146 106 sample b1 +HU_128 107 sample b1 +QC1_012 108 pool b1 diff --git a/tools/batch_correction/test-data/input-batchcorrection-variableMetadata.tsv b/tools/batch_correction/test-data/input-batchcorrection-variableMetadata.tsv new file mode 100644 index 000000000..0a8e6961d --- /dev/null +++ b/tools/batch_correction/test-data/input-batchcorrection-variableMetadata.tsv @@ -0,0 +1,114 @@ + number MSI_level +met_001 1 1 +met_002 2 1 +met_003 3 1 +met_004 4 1 +met_005 5 2 +met_006 6 1 +met_007 7 1 +met_008 8 1 +met_009 9 1 +met_010 10 1 +met_011 11 2 +met_012 12 1 +met_013 13 1 +met_014 14 1 +met_015 15 1 +met_016 16 1 +met_017 17 2 +met_018 18 1 +met_019 19 1 +met_020 20 1 +met_021 21 1 +met_022 22 2 +met_023 23 1 +met_024 24 2 +met_025 25 2 +met_026 26 1 +met_027 27 1 +met_028 28 1 +met_029 29 1 +met_030 30 2 +met_031 31 1 +met_032 32 2 +met_033 33 1 +met_034 34 1 +met_035 35 2 +met_036 36 1 +met_037 37 2 +met_038 38 2 +met_039 39 2 +met_040 40 2 +met_041 41 1 +met_042 42 1 +met_043 43 2 +met_044 44 2 +met_045 45 1 +met_046 46 1 +met_047 47 2 +met_048 48 1 +met_049 49 1 +met_050 50 1 +met_051 51 2 +met_052 52 2 +met_053 53 1 +met_054 54 2 +met_055 55 2 +met_056 56 2 +met_057 57 2 +met_058 58 1 +met_059 59 2 +met_060 60 2 +met_061 61 2 +met_062 62 2 +met_063 63 2 +met_064 64 1 +met_065 65 1 +met_066 66 2 +met_067 67 2 +met_068 68 2 +met_069 69 1 +met_070 70 2 +met_071 71 2 +met_072 72 2 +met_073 73 1 +met_074 74 1 +met_075 75 1 +met_076 76 2 +met_077 77 2 +met_078 78 2 +met_079 79 2 +met_080 80 2 +met_081 81 2 +met_082 82 1 +met_083 83 1 +met_084 84 1 +met_085 85 1 +met_086 86 1 +met_087 87 1 +met_088 88 2 +met_089 89 2 +met_090 90 1 +met_091 91 1 +met_092 92 1 +met_093 93 2 +met_094 94 2 +met_095 95 2 +met_096 96 1 +met_097 97 1 +met_098 98 2 +met_099 99 2 +met_100 100 2 +met_101 101 1 +met_102 102 1 +met_103 103 1 +met_104 104 1 +met_105 105 2 +met_106 106 1 +met_107 107 2 +met_108 108 2 +met_109 109 2 +met_110 110 1 +met_111 111 2 +met_112 112 2 +met_113 113 1 diff --git a/tools/batch_correction/test-data/input-determinebc-dataMatrix.tsv b/tools/batch_correction/test-data/input-determinebc-dataMatrix.tsv new file mode 100644 index 000000000..9d9458b40 --- /dev/null +++ b/tools/batch_correction/test-data/input-determinebc-dataMatrix.tsv @@ -0,0 +1,11 @@ +"" "QC1_001" "HU_neg_017" "HU_neg_018" "HU_neg_020" "HU_neg_021" "HU_neg_027" "HU_neg_028" "HU_neg_032" "HU_neg_034" "QC1_002" "HU_neg_037" "HU_neg_041" "HU_neg_043" "HU_neg_044" "HU_neg_047" "HU_neg_048" "QC1_003" "HU_neg_049" "HU_neg_050" "HU_neg_051" "HU_neg_052" "HU_neg_055" "HU_neg_059" "HU_neg_060" "HU_neg_061" "QC1_004" "HU_neg_064" "HU_neg_065" "HU_neg_066" "HU_neg_068" "HU_neg_071" "HU_neg_072" "HU_neg_077" "HU_neg_078" "QC1_005" "HU_neg_079" "HU_neg_083" "HU_neg_086" "HU_neg_088" "HU_neg_089" "HU_neg_090" "HU_neg_091" "HU_neg_093" "HU_neg_094" "QC1_006" "HU_neg_099" "HU_neg_100" "HU_neg_101" "HU_neg_105" "HU_neg_107" "HU_neg_108" "HU_neg_109" "HU_neg_110" "HU_neg_111" "QC1_007" "HU_neg_116" "HU_neg_118" "HU_neg_123" "HU_neg_125" "HU_neg_126" "HU_neg_127" "QC1_008" "HU_neg_130" "HU_neg_131" "HU_neg_132" "HU_neg_133" "HU_neg_134" "HU_neg_138" "QC1_009" "HU_neg_144" "HU_neg_148" "HU_neg_149" "HU_neg_150" "QC1_010" "HU_neg_152" "HU_neg_154" "HU_neg_156" "HU_neg_157" "HU_neg_158" "HU_neg_163" "HU_neg_170" "HU_neg_171" "QC1_011" "HU_neg_172" "HU_neg_173" "HU_neg_174" "HU_neg_175" "HU_neg_178" "HU_neg_179" "HU_neg_180" "HU_neg_181" "HU_neg_182" "QC1_012" "HU_neg_184" "HU_neg_185" "HU_neg_186" "HU_neg_189" "HU_neg_192" "HU_neg_196" "QC1_013" "HU_neg_200" "HU_neg_201" "HU_neg_202" "HU_neg_204" "HU_neg_207" "HU_neg_208" "HU_neg_209" "QC1_014" "QC1_001_b2" "HU_neg_022_b2" "HU_neg_076_b2" "HU_neg_166_b2" "HU_neg_114_b2" "HU_neg_190_b2" "HU_neg_139_b2" "HU_neg_096_b2" "HU_neg_058_b2" "HU_neg_031_b2" "QC1_002_b2" "HU_neg_039_b2" "HU_neg_087_b2" "HU_neg_143_b2" "HU_neg_162_b2" "HU_neg_102_b2" "HU_neg_188_b2" "HU_neg_095_b2" "HU_neg_062_b2" "QC1_003_b2" "HU_neg_063_b2" "HU_neg_011_b2" "HU_neg_074_b2" "HU_neg_122_b2" "HU_neg_142_b2" "HU_neg_030_b2" "HU_neg_187_b2" "HU_neg_167_b2" "HU_neg_113_b2" "QC1_004_b2" "HU_neg_081_b2" "HU_neg_014_b2" "HU_neg_206_b2" "HU_neg_119_b2" "HU_neg_033_b2" "HU_neg_067_b2" "HU_neg_140_b2" "HU_neg_164_b2" "HU_neg_193_b2" "HU_neg_112_b2" "QC1_005_b2" "HU_neg_097_b2" "HU_neg_056_b2" "HU_neg_195_b2" "HU_neg_135_b2" "HU_neg_159_b2" "HU_neg_070_b2" "HU_neg_038_b2" "HU_neg_026_b2" "QC1_006_b2" "HU_neg_098_b2" "HU_neg_115_b2" "HU_neg_137_b2" "HU_neg_019_b2" "HU_neg_073_b2" "HU_neg_168_b2" "HU_neg_191_b2" "HU_neg_040_b2" "HU_neg_046_b2" "QC1_007_b2" "HU_neg_103_b2" "HU_neg_015_b2" "HU_neg_053_b2" "HU_neg_121_b2" "HU_neg_183_b2" "HU_neg_199_b2" "HU_neg_042_b2" "HU_neg_075_b2" "QC1_008_b2" "HU_neg_203_b2" "HU_neg_024_b2" "HU_neg_082_b2" "HU_neg_169_b2" "HU_neg_129_b2" "HU_neg_057_b2" "HU_neg_035_b2" "HU_neg_194_b2" "HU_neg_145_b2" "HU_neg_104_b2" "QC1_009_b2" "HU_neg_177_b2" "HU_neg_025_b2" "HU_neg_080_b2" "HU_neg_117_b2" "HU_neg_136_b2" "HU_neg_197_b2" "HU_neg_036_b2" "HU_neg_054_b2" "HU_neg_085_b2" "QC1_010_b2" "HU_neg_160_b2" "HU_neg_092_b2" "HU_neg_023_b2" "HU_neg_124_b2" "HU_neg_147_b2" "HU_neg_205_b2" "HU_neg_084_b2" "HU_neg_120_b2" "HU_neg_198_b2" "QC1_011_b2" "HU_neg_045_b2" "HU_neg_029_b2" "HU_neg_069_b2" "HU_neg_155_b2" "HU_neg_106_b2" "HU_neg_146_b2" "HU_neg_128_b2" "QC1_012_b2" +"M53T345" 42779.3721121841 15074.1968224961 27606.1785823935 19852.1617880405 52602.4518881409 0 79597.5040896563 17656.125836332 14442.9490502211 138869.981260119 32761.0927210332 0 122734.450361837 0 0 166910.294593527 156294.989437752 202048.894588891 109383.253873075 0 62456.6025907225 39365.5200417267 111628.134234755 119125.737447368 33089.1407089231 101475.563434966 0 50574.1059385567 97324.7117260035 45957.8534136242 0 81739.3213397075 68762.047406909 58300.6616291019 77304.0740671995 39891.7167254028 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506217.390394149 327121.396308575 285836.715770459 81508.8189953141 NA 350590.114112736 736195.630773545 1149396.01880679 431567.888589194 513858.972304564 418313.452334391 180157.65574264 345203.008729499 189952.615782566 417873.93483512 575525.388811945 332940.496000638 520170.731899033 60877.6279505304 211038.651923654 524787.670310421 271796.860841707 269103.227507003 273264.292556722 1027822.13472102 100459.182751578 108557.088458484 763317.121721107 491868.17425664 1147549.91377612 501343.994798637 1224816.19681426 415645.561062133 64666.2582859362 376315.090903388 1281458.17725854 978140.622198296 36419.8596526614 537207.953192374 504171.053349797 557704.52500672 35694.416863633 469677.110595825 3615.76145811209 180801.882696336 742323.117526482 508121.457671656 522648.952763088 514078.378289401 +met_016 2261006.82236912 3217885.74693034 1472285.00657083 5932170.10731837 3185261.86251954 615128.578868965 2621158.93359211 19759.7729794841 380248.207380955 1213776.76725214 2304646.58336891 2760578.27270082 1300307.94692543 2581587.894673 6467193.73695289 3057099.60973302 953553.502022519 2492273.92296165 2125023.80059426 2263164.70083646 949787.998314931 5299028.67265199 366235.992497697 8037317.70285592 9216918.69591018 1228573.50181887 354246.724194343 684140.709114361 1969090.16022889 2273181.76171613 555084.247409951 1780906.25703327 1020673.33030083 2090489.28863649 3706024.39894417 988357.990781182 291960.187531956 1651878.9276004 2525016.96173698 1132866.44244134 2282153.56713268 724034.949254305 1261950.95945116 788545.208136719 6330627.01694867 1225049.37817267 7655932.60926507 1307931.23367561 2622103.26062346 2341490.85978569 668246.375845999 1426015.60405375 1675357.48333513 1371155.81762858 327263.071225525 344165.953239807 1884543.95918143 733003.501750467 3646002.26146248 2317326.48404083 193085.763648154 3295565.08103403 1345915.42789872 891947.04908786 881908.366834644 2539078.85567954 3741561.07118204 3194969.55186593 2071124.3045081 1291699.55310497 1368000.72275099 1218231.23938436 840008.79862559 392691.095313368 2007793.14828837 5838845.90178574 523710.591633684 5593184.6702624 634273.637585765 2355940.27119377 1672397.74456006 896229.821218319 3318121.50889044 3246964.68501211 239796.496507639 3385292.48391934 945825.109801808 3351819.86253658 4998449.18366241 2174103.2318378 1472305.08430844 2032646.00592078 555108.774998643 3447892.59887747 5293061.74025668 1684492.45385022 1634474.03613492 1570375.89667523 1653286.96847577 2582498.34424934 546093.482741857 2587738.53962076 2732386.77443516 4652061.80452205 1936254.86893419 1037611.07548103 912437.668568051 2074764.61972837 +met_017 4821103 2435403 2318732 4245753 3748189 4349935 4689943 NA 2451934 3331274 4202823 1370470 1761558 1754097 2700769 426851 1421552 3508482 1312895 3480628 153921 640917 4404841 2251667 4439211 3342751 1543974 1098969 858681 3440833 6434298 1877831 602319 195656 151869 2383960 2486404 2254876 1908070 705181 2902467 1052946 1271786 2219297 1805955 3250971 1844215 495613 1812866 3086559 1835313 2882274 3433802 2034058 3943305 1112063 3043290 3146250 1149854 3119954 1959167 2538248 931529 1334514 2458891 2782916 4005410 3855724 3184232 2002086 1822977 1588408 519939 3609550 2113979 4260220 7162992 2063593 1974086 3128563 2511623 3723824 3208944 3304203 292819 5146262 2436561 2378318 2980413 2487889 2835818 1141876 1331599 2511176 2512983 2690201 1226346 2457264 1783679 2374533 578362 1931922 7306894 751094 2056881 2013244 3440995 1986099 +met_018 3189068.13824707 930278.040048712 3062182.5787137 3512210.22604092 5198487.14740168 4731980.83767569 6677081.00655291 NA 3399554.4437436 6502242.96432166 3020125.77694267 2335979.58273115 3124412.82337732 1971158.25455437 4396736.21111668 3779262.76399297 776533.55123391 3792432.9450736 2427323.42168661 3053107.09514829 1599290.08505887 1394834.02465643 5136391.97451523 3760931.46204017 4574484.31919117 2547681.54482059 284027.611580559 49618.8725487234 8890434.27319509 3184816.59251059 3693128.32431839 913227.536350302 4642247.80940707 3778942.76516404 2937119.12727546 2792298.05522566 1518143.53125639 1420596.07422274 2360516.07881831 7264654.51021365 3151789.44507766 2664486.30249075 1508714.59571527 684521.904667005 6480063.99478949 486448.152528935 5529879.32596103 4752.87573136388 1018527.07622864 3332848.96865841 2992000.01059951 2110269.94288927 5046718.09856548 2828571.5738874 5788507.26489658 1350745.27507381 1024520.74882811 1407682.10235773 4681298.30939501 3082848.82289657 1971685.82194117 4563852.5869271 2247143.65410527 1008180.28410104 1349574.55029033 4794711.34265834 2230110.96924608 4109627.64125026 2729135.94836036 1507082.86513225 1519129.73827135 3810937.01605552 717388.937443973 3907022.14564106 3875714.92797184 3496113.11061276 3278083.66058667 2630875.08139341 1877462.10682681 3250583.80260673 2124685.60543689 7119022.66882412 4436705.90568128 5816354.50210551 399415.289827066 4685341.72125829 2035697.96817302 2649298.61578368 4558917.09939268 3033865.5371605 3723159.80045571 2271273.90973341 1315275.3896764 1867975.77477958 1998979.51177184 3834736.62525006 217585.929840466 1745179.95207796 2522061.97095888 3007639.95712717 1164987.18366678 3642048.85520513 3154151.86451505 268993.007429348 1308089.74614091 4815408.23883786 2019810.15494969 3269913.03666126 +met_019 3897653.38971062 1352811.18621835 2067043.08384845 9891292.48483335 5141111.60615796 6854992.31016711 6793826.87266572 NA 1512383.46586352 5893717.70784188 3813047.24735281 1719915.46957392 6461206.50732416 2525303.50222824 1418423.74923515 792687.914362612 874997.448695442 3221627.37437223 4041190.66860259 3772826.83025173 5984431.12263333 3049184.36469462 3111824.84157115 3885436.78692186 7239399.10425002 3289422.14224206 869410.115300195 1471225.18049236 7158668.29305888 3909829.28184535 9253532.70844886 1107045.00838947 2802885.83567237 3424275.18349068 6869336.71626878 3075394.74409809 1398709.17202965 1851725.32676902 3744361.13785684 10685987.4226928 3814577.66782458 1765956.18772971 2639703.04961551 2568710.64237482 5496393.4366589 1813480.00873909 746274.269033505 605843.614903063 2333816.19084483 3937965.97115663 2954904.45321536 2077575.87495583 4698611.73452563 2126746.01181295 8266022.70395572 2483832.52637292 3593086.61282779 4667700.62752581 2701008.84312066 3841490.57838103 1729449.0741807 3003785.76674061 638145.675565263 2566933.24870883 4218873.44263096 7617553.07253054 3402969.77525401 4623526.76272761 3729282.72501438 7342620.00813203 3011453.82834838 4587715.47583569 4562460.08003945 1949388.74492262 1604502.45123766 9303191.8206926 1383409.17379352 4584394.09179467 2482278.89660077 3921635.98922499 6358775.15817 3692632.69292923 8496813.15066293 4797232.7949376 659665.434302325 9404952.25595775 3362259.21281736 5778631.09772256 4547328.45040819 3885352.33672929 3317817.34537726 5055656.13447211 1030716.61681454 4285720.98448145 4186602.46587189 2989511.55383628 1333794.39866849 4386320.71330555 2686058.38600045 3912333.2744824 1729212.30462625 2237100.18546689 4418748.29357153 1607581.72921911 4491935.31332922 2698594.36537475 8584592.67956446 3774479.43401431 +met_020 380460.662530204 545043.809688284 428060.63092774 628969.873202076 725429.87466462 1077126.41728487 1719848.61604982 49295.9572323184 685060.521232811 338701.433412276 444971.923414127 521972.238827728 349231.554914656 194457.915761107 795623.49103355 1554089.29785085 121885.861776506 414502.517085271 1179981.11696259 428335.787186172 882406.982516911 347257.755672827 399700.077615497 787982.643923043 316945.28930176 343978.159871503 66646.6193578285 90652.7571298145 453568.325191447 444724.914941863 734400.337495777 324914.642858664 805735.394651856 1390800.7572855 540357.391170123 296668.087819409 653439.971668872 109098.127941938 1105897.09807873 1147235.53342694 372106.225659587 154112.029274189 471610.078652277 115855.070920421 664278.606254665 234973.768302979 495177.973958203 66989.0624080028 112147.429502614 460770.920287776 217064.816833352 675394.732597931 362866.474495026 272112.86247148 265425.824130158 119649.914938069 757028.691671476 625030.160145377 371092.42079731 547226.923559283 268278.699258314 321674.196117464 143406.819262747 169236.950986558 165240.404152451 260832.746766526 470012.882648704 131007.07351727 240511.864777452 193020.222990609 223645.337186303 315616.631295092 168261.897472301 330145.931609031 255342.134602234 866000.82097745 459566.752785138 444475.754945979 236939.554098071 287467.777747412 423535.780999503 368637.815927329 129698.867830011 463151.298810215 106714.554752985 395838.496305391 566430.631232052 328161.23489477 284917.987981237 395804.933587855 559234.11075959 211544.396656032 219021.369017209 418200.165586387 703095.470064642 469101.546046875 339424.652946772 296540.975468291 397137.027545414 425384.199243966 151792.24324229 611923.970005419 476317.204651986 312828.499800305 191898.130289251 884031.804926983 823896.972797341 542959.132329054 +met_021 544395.063036637 457583.423964441 238149.711962312 1098780.06154983 936356.950003889 505641.277987247 481231.916575154 NA 446326.548731241 477084.551974982 532365.747382305 688787.41900836 401580.541961547 644960.501980169 2048386.47477181 23503.5090498123 6612.79561286682 335627.952916975 1132384.06363148 536504.032162794 1152525.85151867 66804.6030981732 988915.033913575 663381.366539849 654471.172312522 473635.795450179 4091.10898788827 98788.3967375868 27371.5466732553 512641.407543922 1531262.75858915 316443.553187286 444916.191677483 1433628.24342794 1057527.63113824 381383.1268355 185044.804066609 313303.539311042 805902.349772003 244823.975379045 552788.674783904 200408.462277306 516631.371493922 396937.988566926 634612.261935163 352331.894391746 282275.218552416 4323.80824676051 NA 551428.270739642 338387.834929822 1741565.06438245 424672.891427297 4637.72103447745 118026.216674003 116668.664753012 442195.485597974 457196.07522785 610791.998797998 570169.864082036 325515.503445489 275949.713461391 125602.579838509 35140.5833768946 439822.741588247 661354.770045084 913575.207088308 487132.55182237 463860.899479115 419870.84975637 193604.085611706 294185.246656632 173950.88422037 384375.536782964 502810.526341134 331922.252809105 536233.572169708 18929.7444697866 211505.856359293 536923.027249265 301502.570589574 187492.075077537 247043.934663221 852664.917265638 86600.8817709459 71009.4666229194 791368.803261011 553068.491927806 1028957.14647291 595013.37263808 2341040.28656172 426071.439350868 55261.1840255577 407311.185798424 826854.500349706 1183911.47207 112652.563369825 600105.985427598 564365.142260229 573629.281876729 46624.7282334128 643962.531481922 NA 26885.5126583707 468745.72776051 528696.691742251 540684.06634708 481066.575987173 +met_022 544077.651139656 822914.596176267 814417.877393707 367221.970187314 410976.420926772 112602.651495342 253129.195397263 NA 307559.92142082 177571.762580103 482232.474414016 1010970.96434718 359187.510667535 358014.892665059 172748.397386334 197066.860702444 80460.9777875907 2473476.78742414 312074.448928389 505706.831781882 768826.67251867 795658.6691447 652223.32248486 494309.947497758 272099.35956161 259429.223661286 113566.43088544 495404.694123108 473158.576028736 509437.649178146 694125.988394637 171621.149985714 197313.115949933 600918.306960209 559544.104022623 323488.382003759 61806.1184873077 398541.691474507 425876.666190441 334581.402132516 505229.39927194 552623.285922353 999291.989057668 4119215.34390167 400758.5861508 933930.275846996 623549.066897212 47034.9869538633 107300.033418218 544329.494538898 164043.937846926 379802.895869542 1503788.55936316 271400.546466944 46113.8080811862 341211.634050901 2323468.55805459 391385.431883543 221804.197791903 475910.055973526 313186.005294841 550873.101221294 39629.7952473015 81013.5062758282 1157809.61463423 1369091.12974675 1116905.6609068 304945.006541613 559282.338724759 629385.541733208 314062.71593035 91059.1086909299 571846.76785269 275061.146994552 372943.272111538 381043.485078595 986889.22620308 3592027.39857342 352769.890112544 507837.167592554 146270.741268245 174804.578952807 1277614.66212208 294314.711370799 88855.367292181 719316.707587499 1143463.81059807 215028.776512652 2085227.13533808 517238.313522229 721487.521681997 440917.198158734 76560.9411653242 730741.406932885 516106.040127178 455756.151970404 1259519.57646548 772686.159151635 576552.194196359 552496.726181275 134389.733754802 175072.368537704 317821.72434349 1105046.40207872 463063.425707931 704981.419844904 844442.148741467 488265.038649675 +met_023 1010654.40402613 2517437.72953248 2528089.50984 1856290.62116976 1908716.48071379 1033048.94627065 1094242.31254035 NA 1121539.33934192 1247871.19796693 1157423.37447724 6383220.53891933 469942.626689717 874536.97392185 632570.000797207 895646.657690885 73578.8319228479 1121319.88566802 5802794.25628624 1189930.12844222 794972.7898863 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215325.197421846 258087.123763798 81975.9766041141 323769.843506551 311796.876952531 324271.573983145 277941.469844941 32170.6221220612 66854.1295593002 187263.81223851 221938.394378374 518212.862982978 81309.7534426995 373639.937516776 329091.516761553 371972.286712933 120372.771967892 118682.345308905 50066.6977019993 94314.9986814818 934763.387161659 235923.75617326 87126.6145357083 497149.557870348 66280.4460438143 249172.821232175 165840.33003965 209878.789881438 17668.433842529 108443.503835071 222482.272380562 235962.126367135 164454.456744079 406930.601952988 105966.68978985 264867.834258671 42759.5957362792 207203.720192909 262923.010400774 124791.999973639 267494.006077342 63900.7591245786 328454.122952477 129819.344646126 132732.562228615 229923.251385911 235464.069388138 110593.105493913 219615.170414527 133832.026275209 78095.5406517576 175458.841371521 259482.58376368 69855.4048971026 143788.377857736 44697.9581849473 476543.176749062 135515.522753179 74717.1730497046 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2490258.25699844 1569798.28724701 1359050.33052147 888229.590370069 2540556.77509054 924120.062324909 897534.57821386 288344.687020174 2975610.68334469 1229271.16333887 1327587.23810098 1387555.41393671 1047889.53422985 6264615.44907611 935263.511602511 209648.348483169 4283131.97930825 3096871.786365 584679.583633969 2907496.64888378 619889.912650968 1389580.29932316 1399540.69982984 3404702.97407197 2398461.3980123 2301260.25600563 533215.105824989 315309.059518747 846593.568733793 2773384.66136999 1850585.09497394 1401417.31216544 1281583.69486307 30303.2210238652 2520093.88774222 941278.421543383 1005033.99721433 2405613.94488181 3179733.34946406 502375.103659848 611061.609203505 1818424.52151544 1399691.87233854 696953.677962189 1977397.39465172 851959.308646051 280532.836747977 832520.538865161 1633100.7307123 1898343.75594558 1420395.42231599 +met_081 302805.934339317 198268.110356067 508780.368626825 26013.7393118613 49235.7760988675 50823.6488256171 11782.7414731473 NA 17346.8826187513 5845.18177310912 279022.45157333 132623.606701876 133673.498689374 NA 518186.925767248 167876.217240311 4026.92884291118 120029.18348942 190186.921837744 291615.606653763 150684.535371361 221059.544923577 640745.723306251 252921.222681471 64094.5467539563 4349.23852953978 26546.6586474349 182004.620589231 25653.5743994641 281253.387766029 430692.893372175 16434.9447440763 39138.8107647899 602060.561974313 475935.837557407 1523038.69603938 1324803.89943051 1274395.97855355 961842.940749584 154367.003937568 291010.885850994 142016.204793706 85450.0539677439 120596.908441097 25600.4605284529 270967.763138811 132480.497071723 5298.46950486991 93452.419844568 263658.903527012 1902359.54590334 40776.2084739182 17597.6433292518 208718.503471733 109709.925992754 13920.0091698174 8284.5803497451 647286.86534774 107622.389217047 373880.617033298 12383.8990212413 108158.637936051 19856.3399111607 52194.4069745796 85981.5169935776 5162.45157142305 5941.64623778389 20889.8915724979 245640.013643355 2354418.76017085 24346.8606483235 6508.94181996301 43496.9867116622 161034.046764367 221567.035439131 431586.444710143 8880.53801633617 105625.940071322 6879.61268666415 253797.68668466 1430978.27614384 55661.5622876469 679368.851749934 157846.119238671 14832.0042387536 1149146.87035326 29605.349699632 69517.1998969656 2440892.81435987 308628.62098272 70695.0216853404 89477.6955032979 659457.838110551 112806.382253783 43899.7325329574 110411.387956553 418015.219303301 146288.334941198 40587.0228320363 322193.577158025 347653.807594148 94151.0415671175 47526.6735340501 688071.892792621 102352.587158494 178719.106162974 10398.6434564243 272045.205433086 +met_082 335820 38185 143437 676162 889040 971358 770773 NA 562169 527857 325434 67565 105855 314576 269507 629427 234820 611221 994602 298716 993592 331307 819151 1125036 849481 625426 177300 NA 971682 279127 865103 228315 742011 NA 243990 153883 526853 432373 207985 520444 239544 92204 313549 365333 875425 358634 182934 NA NA 267574 384247 561433 696678 300817 370111 253930 466799 467352 606943 230511 562880 105531 79947 167310 6958 548991 892786 533895 262636 384126 248580 147498 42961 345068 436829 647872 580617 656135 128323 232975 553222 451099 657701 582331 31789 677913 70922 456759 510671 250065 NA 105429 256463 448370 147630 545639 18099 360109 337324 189218 111580 295602 308203 4691 240444 310209 355551 185756 +met_083 302490.28121761 195662.877970767 212017.049321212 469518.01864376 325581.315386093 326104.568370665 231395.836613758 2135.22126096082 289990.105600591 263832.853024249 350523.643196708 324755.571803271 352864.832137056 265189.794984174 347933.054858485 363271.323468316 170377.530589464 366037.828555114 566218.371546762 324488.638946722 510606.125756931 207061.902194651 394404.137049736 419018.202294243 618294.952552136 466509.216835276 214022.226609145 195688.874897733 378298.589307785 295785.311493246 658198.815693363 216207.76126528 197079.19508079 427561.870258401 734705.168301533 429363.10333584 385285.557871984 254874.575341649 403923.774836681 274430.336388833 298671.198276473 256249.389149052 399592.119689275 361274.594389735 338795.191472946 367616.448254614 435108.265985572 78271.6868947275 363050.663039898 362276.591117789 319217.664604072 290399.991771016 562598.899555644 302993.005644574 215399.271870995 113690.88532319 328904.204410123 404481.646605152 264647.01381884 318812.615379121 400294.811641733 283468.879502722 140120.239200256 131889.099084178 143015.044238447 360183.731435886 389639.954488149 320724.990224729 290985.777803656 308220.392030133 144221.95570918 269423.406891437 130758.702876459 155574.734707736 306921.345817873 539512.76834608 390287.734094569 296011.436766899 175488.084156899 339469.739444202 391504.22987442 387406.452764711 488053.787600288 651462.109779484 218014.974114667 346160.087928398 448247.535269292 408756.413527642 511603.406791658 306300.676854067 372687.269667132 286743.783303265 165532.17413033 426885.120308903 364526.265250104 410496.1285927 228640.011188027 509185.867803005 479969.388977488 313148.785804567 138795.681206006 228593.846011672 423969.569955952 257894.092838902 230446.749928205 315729.858958826 428559.372002787 330552.147993284 +met_084 1586223.73397367 603218.233724442 1039664.66875421 1336491.21006042 2098068.87354096 2186591.83717174 3122187.71098696 NA 1568069.66426025 2923556.87518794 1539853.93851536 1449741.63652538 1892044.56970272 766597.752524585 868482.42062711 2132406.61222431 510854.985038913 1951915.03412423 2326698.07311378 1647979.53661189 1347469.8707442 967019.826304132 1703587.41055447 2274214.30649625 1743286.8314767 1284260.20832479 258821.522097706 404259.736459925 2004826.2056637 1490099.78500955 2575924.86404597 807993.807805688 2006228.19449597 1852035.04965689 1356768.7205114 1305978.74076454 1220287.31952254 997090.851083892 1357412.04238461 2472315.54313975 1614954.08949353 802374.124336859 1063940.77017024 1008525.98504383 2830588.89748004 686174.182533472 1178947.88288179 250803.008065538 1457875.52441996 1640504.88725525 2159771.40850613 1419419.80225219 2159858.969382 1981565.13742231 4260192.13213954 1041734.05232463 1318328.37824323 2596877.94073541 1727144.39261462 1557839.99299476 938175.530766688 1626359.90961402 616227.507071332 449025.624265825 685366.643356613 883739.324894012 1786628.3282443 1782494.70251877 1441155.77848133 1588082.20793368 1141704.40563427 2602713.12639246 503542.839618198 2527464.50060344 1977992.83038549 2313296.44251768 2365429.66014482 1511657.87571084 806265.513091982 1716736.30799622 1431935.88435279 856712.406211081 1273319.61991103 1518436.28339906 280976.708754515 2036179.48970519 1103984.74601602 2209100.69598736 2304386.72697207 1533310.05416524 1490734.16844253 2066778.0993009 764043.265877034 2636713.59983725 1794166.72305958 2546623.47986866 1093609.15028956 1603853.64442092 2401835.22736959 1827854.6669052 660157.429431202 2497470.85189658 1579197.10908408 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181562.646391228 313054.703957764 849912.644344986 464093.919772824 446342.665322824 340028.075627124 654957.799027104 593165.999060618 755046.213923065 607793.078311266 432003.172826181 1312819.31417693 586001.864313764 497698.592425822 801533.955702843 1184590.61313804 133977.631593131 560446.789032358 202653.753638699 155666.603345149 550447.465867832 722459.832289469 503307.184852501 588803.890458638 529253.148662505 492221.343564687 974185.482985487 110308.182381137 402775.029207574 1132427.32800297 393606.510598175 964145.658785616 2302049.26399296 781236.764381376 931355.620073745 553466.995272439 358439.104111557 817500.464757449 914644.803777643 510233.765206878 261441.865637833 750911.093487349 518835.666297818 +met_087 131965.972735192 97724.2619328014 102611.562748677 136034.187324963 342712.490924076 122497.985737143 185350.197462582 NA 160072.872358258 140015.601644896 162039.081506423 219771.470601804 259880.333633715 71600.5138019742 298847.957460391 176445.720136264 NA 78144.1468798725 178038.551183971 149315.588192819 409192.704636694 44372.8259733729 154265.903040319 224785.314982887 126283.506467159 64419.6966661603 4446.44830255133 45589.1652690723 56888.839607604 143520.146660449 73815.6994114806 111479.573126253 151088.826438278 214114.244830249 250729.668767004 99208.488242453 79642.6555248607 138576.369039701 76133.9657998515 215484.79783855 149885.077877731 124712.907676846 66137.3626873529 93826.0342036053 105554.190092932 37863.4423709673 177574.338893587 10193.1000900656 85826.458939816 123626.336381494 133587.762362518 192459.083590221 150893.410318906 99322.8330603218 257428.88839326 11333.9308789248 60191.1191984258 272989.279094218 132677.180137248 150260.914028848 165827.751855038 143702.010386926 93021.0339783607 3588.5441384188 122335.242594937 230087.821171637 96321.193732546 173222.075098019 156646.934269244 80678.1944481282 42925.6879842744 195342.48438844 72321.3572146009 177314.630158041 167553.671795147 178025.047828465 268711.111487844 57616.463936392 120919.773348822 155425.967528471 86954.5565949913 53447.9887405126 40037.3367292991 169053.253410038 55038.4700535131 105864.439643486 162684.259111456 46300.7035602892 223092.088668303 127832.845078914 75209.5088807512 198784.233483975 61045.4314364947 145085.549156358 290220.471451161 107656.280210869 114811.530918273 523313.365985681 169010.204446685 150083.040311 63103.5875359216 136841.958149501 75742.3462357972 26909.5939914595 108306.521058793 156136.929116422 231544.77745666 144591.424405918 +met_088 1114156.10844461 718962.434703991 251169.755693155 2430966.15744992 771935.303694082 500878.108207795 1506532.21606952 NA 260003.887477074 1193923.07210796 1063550.99794682 733833.757082309 481311.958165321 2038122.90237624 729304.130362747 672943.036858587 351460.073363752 1281410.2455841 3049080.96794124 1122272.03337669 1662091.44944421 1966742.526825 2131860.0994331 1202189.74831586 893982.58112865 1158051.41917858 402108.223598413 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1429618.16242526 426713.576343358 3537443.80194433 992615.016091958 6011436.42239215 2084282.80758756 235561.218398303 1814375.41573414 1220073.40767394 1334265.04080617 864437.094113292 2043318.90729368 863308.223491655 1084953.78077351 244367.002935613 456691.988851544 982720.29282082 605015.907263426 1092129.66245419 471213.982618589 1042487.47812902 1152435.00046088 +met_089 787339.312018695 670815.716121031 729515.586587312 930898.818185093 409307.728431572 470262.852068709 913192.483067085 NA 844463.147046143 788964.624915218 715776.481914173 467860.676709611 745831.35406529 548743.081063763 719412.148907683 378773.779807552 316600.480961303 808323.91768562 1108146.76825309 676050.02066195 690379.908303285 260021.039027926 1184197.73031207 837770.320605675 1267859.66027529 649954.738271925 390752.52294793 767955.389389823 850384.90437754 774586.30180831 459681.500164326 523148.970758145 994518.250999669 411471.453669388 956881.262596719 606199.6912887 444289.877411212 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9960302.11543546 4917540.14021325 5551143.17729386 11218264.5425964 2756798.9021014 8533728.58703471 9895445.50786236 10144186.058344 3675512.38638005 8126412.37561777 7249058.88868021 10097138.8381322 9159541.34551803 22476545.591712 7930710.1897242 12485886.4345125 1390116.64276228 18483239.5657518 3873816.23693402 8814930.32023822 13831512.6276293 10235517.2397478 4318635.60803256 8641311.42714934 5789391.62852438 17176443.5306407 3569843.62548976 19116056.4875753 2246681.3030274 22461970.235754 18101047.6225948 11251363.4818958 4286286.68449666 3162931.22202763 19981098.7685465 2481100.42550222 19522245.8275916 5498703.89880394 28348102.1835471 7222977.39900191 +met_106 479735.800454594 215870.307903844 849884.640627909 856436.666426712 935413.705722231 471464.272378989 419024.749352418 254893.393938796 637691.387755469 2712193.44590509 494199.046109341 400298.142879349 447382.041802602 681950.127835593 205497.433064335 1044843.30407355 254185.358748953 865725.212578393 334748.131853498 491823.190008697 409723.12706085 471991.889984752 5910.27992531077 423564.224854296 1225807.38611134 625203.528747011 5658.28921307501 145426.69365149 1194348.39809737 459080.828918446 377580.157873206 1703303.88256162 5397.78060415341 75955.9992901471 421749.857177877 450316.783998851 3749660.46696676 241514.365111356 271400.560623284 468897.719344468 491864.984455012 986921.293847098 17421.9210128408 111074.415700169 445435.259922637 NA 1279943.50996921 689842.639869287 69552.5909281785 486547.470160394 106973.005264334 442173.830373413 1101518.77584671 198742.538961906 243079.939065982 204622.036165817 542467.835814557 340173.031374824 398011.599681722 484111.287588897 180955.71242281 898776.773083835 189838.732359473 450933.564255829 245978.352948085 56007.9765310637 433985.21803249 103775.220992168 488753.756707371 478462.494984047 902767.779260369 1207187.18216799 238049.278324562 164612.764898934 495605.652817309 355554.633695728 28940.6337037717 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161292.306643301 1587484.10783621 308275.89974379 4383001.41804118 1757407.563014 1357396.38659822 5560070.6874909 107863.643505138 NA 912430.261828198 4985759.70754324 1949562.78021475 4258206.97571243 1611843.84586971 806548.43809298 936692.831389327 1959979.77225689 299179.687472651 1157461.3989634 3113121.20629743 2654340.88535823 1616300.23108706 NA 265872.04480127 1806211.61056395 701177.037984812 1698619.4941964 910788.596160385 1075186.28026224 148318.138787428 1323773.85570399 1493514.1258369 1624500.67287462 4895663.88151516 1734999.18192543 3796491.6227334 5692387.68425876 2423985.98788057 1239421.43263197 2798191.81420242 1052624.68168328 303944.945180658 4089407.39891433 736911.481964469 1897492.30164651 366689.638425409 1301689.55680529 3265271.52420551 797360.463495392 3895654.30808391 1557909.93171011 344057.76405268 1543482.89302654 +met_015 529458.619988772 495036.941329857 281080.916192286 1005231.34725591 880525.30431383 474257.342981324 413739.841372801 NA 416625.463732812 491913.727701207 524194.137158541 704910.490402123 418706.991292945 576099.13824966 1935027.61605427 21254.4063282977 NA 337825.051894781 1010296.03452311 501849.476933716 1041632.04844655 82672.7223663106 990929.022037204 684182.767173983 601117.008327458 419833.919385959 NA 64489.9013066341 18922.9425279658 548438.05419246 1301104.62194316 311637.429261772 400356.387078146 1418282.74966085 1030570.91130408 360523.597330471 135539.410471399 284367.170931067 682385.695744897 245865.257913198 527222.45992207 205882.538818473 497330.19457958 327001.483898672 588770.02719376 364065.743247185 255185.249523841 NA NA 542611.369773165 273105.871989176 1642073.21025122 410339.019789765 NA 82910.9909289859 97041.8548303656 377846.143649046 397130.281121622 623695.501474941 506217.390394149 327121.396308575 285836.715770459 81508.8189953142 NA 350590.114112736 736195.630773545 1149396.01880679 431567.888589194 513858.972304564 418313.452334391 180157.65574264 345203.008729499 189952.615782566 417873.93483512 575525.388811945 332940.496000638 520170.731899033 60877.6279505303 211038.651923654 524787.67031042 271796.860841707 269103.227507003 273264.292556722 1027822.13472102 100459.182751579 108557.088458484 763317.121721107 491868.17425664 1147549.91377612 501343.994798637 1224816.19681426 415645.561062133 64666.2582859362 376315.090903388 1281458.17725854 978140.622198296 36419.8596526614 537207.953192374 504171.053349797 557704.52500672 35694.416863633 469677.110595825 3615.76145811209 180801.882696336 742323.117526482 508121.457671657 522648.952763088 514078.378289401 +met_016 2261006.82236912 3217885.74693034 1472285.00657083 5932170.10731836 3185261.86251954 615128.578868965 2621158.93359211 19759.7729794841 380248.207380955 1213776.76725214 2304646.58336891 2760578.27270082 1300307.94692543 2581587.894673 6467193.73695289 3057099.60973302 953553.502022519 2492273.92296165 2125023.80059426 2263164.70083647 949787.99831493 5299028.67265199 366235.992497697 8037317.70285591 9216918.69591017 1228573.50181887 354246.724194343 684140.709114361 1969090.16022889 2273181.76171612 555084.247409951 1780906.25703327 1020673.33030083 2090489.28863649 3706024.39894417 988357.990781181 291960.187531956 1651878.9276004 2525016.96173698 1132866.44244134 2282153.56713268 724034.949254304 1261950.95945116 788545.208136719 6330627.01694867 1225049.37817267 7655932.60926507 1307931.23367561 2622103.26062346 2341490.85978569 668246.375845999 1426015.60405375 1675357.48333513 1371155.81762858 327263.071225525 344165.953239807 1884543.95918143 733003.501750468 3646002.26146248 2317326.48404083 193085.763648154 3295565.08103403 1345915.42789872 891947.049087861 881908.366834643 2539078.85567954 3741561.07118204 3194969.55186593 2071124.3045081 1291699.55310497 1368000.72275099 1218231.23938436 840008.79862559 392691.095313368 2007793.14828837 5838845.90178574 523710.591633684 5593184.6702624 634273.637585765 2355940.27119377 1672397.74456006 896229.82121832 3318121.50889044 3246964.68501211 239796.496507639 3385292.48391935 945825.109801808 3351819.86253658 4998449.18366241 2174103.2318378 1472305.08430844 2032646.00592078 555108.774998643 3447892.59887747 5293061.74025668 1684492.45385022 1634474.03613491 1570375.89667523 1653286.96847577 2582498.34424934 546093.482741857 2587738.53962076 2732386.77443516 4652061.80452205 1936254.86893419 1037611.07548103 912437.668568051 2074764.61972837 +met_017 3254293.34440216 1665903.19869495 1607138.24581532 2981494.86693231 2666417.48689976 3134477.5599142 3422723.4831346 NA 1834787.96351897 2523677.32261007 3222907.0538807 1063638.23970792 1383474.98942734 1393823.80518663 2170947.40371291 347033.326880065 1168729.05427065 2916402.92257831 1103199.93318471 2955931.82652454 132087.483593327 555654.148950128 3857282.76500541 1991181.4073371 3963439.79163599 3012534.97211266 1404199.38194934 1008396.6555009 794746.542966719 3211456.79370407 6054374.93432332 1780899.02865228 575580.307782375 188342.261922386 147222.509552027 2326623.06976217 2442239.27140403 2228392.20383433 1896591.19909249 704762.582252538 2915534.40484888 1062682.88798989 1289099.41414391 2258283.81409448 1844005.603313 3329240.2697306 1893152.39530245 509687.216849016 1866557.97480998 3179704.75347238 1890608.08321573 2967683.5617838 3533337.49672562 2091973.83003033 4053025.07851718 1142296.43151512 3124709.12527526 3230308.14788113 1181142.9473897 3208132.34097763 2017619.37580684 2619149.32031037 963499.31877851 1384065.86691051 2557908.01130991 2904549.1542395 4195370.23415387 4053954.60690056 3361456.1273483 2122522.64987396 1941302.43855273 1699462.0971961 559029.018388985 3900887.11158231 2296869.53383234 4654698.41098447 7871876.3766396 2281574.95177065 2196386.44160565 3503704.06309786 2831963.96653942 4228514.86197165 3670666.89117803 3808522.1021572 340191.134720304 6028108.56432051 2878511.03394668 2834671.17160336 3585064.5066891 3021273.04223012 3478017.18015441 1414907.27746867 1667650.32707325 3179829.6259009 3218772.13697444 3486941.77132161 1609253.31939291 3265983.85276515 2402352.89010592 3242436.04799689 801105.962496546 2715876.46185711 10431015.6477772 1089469.92649803 3033349.6947739 3020504.69463286 5255663.03193147 3090362.92969184 +met_018 3189068.13824707 930278.040048711 3062182.5787137 3512210.22604092 5198487.14740168 4731980.83767569 6677081.00655291 NA 3399554.4437436 6502242.96432166 3020125.77694267 2335979.58273115 3124412.82337732 1971158.25455437 4396736.21111668 3779262.76399297 776533.55123391 3792432.9450736 2427323.42168661 3053107.09514829 1599290.08505887 1394834.02465643 5136391.97451523 3760931.46204017 4574484.31919117 2547681.54482059 284027.611580559 49618.8725487235 8890434.27319508 3184816.59251059 3693128.32431839 913227.536350302 4642247.80940707 3778942.76516404 2937119.12727546 2792298.05522566 1518143.53125639 1420596.07422274 2360516.07881831 7264654.51021365 3151789.44507766 2664486.30249075 1508714.59571527 684521.904667005 6480063.99478948 486448.152528936 5529879.32596103 4752.87573136388 1018527.07622864 3332848.96865841 2992000.01059951 2110269.94288927 5046718.09856549 2828571.5738874 5788507.26489658 1350745.27507381 1024520.74882811 1407682.10235773 4681298.30939501 3082848.82289657 1971685.82194117 4563852.5869271 2247143.65410527 1008180.28410104 1349574.55029033 4794711.34265835 2230110.96924608 4109627.64125026 2729135.94836036 1507082.86513224 1519129.73827135 3810937.01605552 717388.937443973 3907022.14564106 3875714.92797184 3496113.11061276 3278083.66058666 2630875.08139341 1877462.10682681 3250583.80260673 2124685.60543689 7119022.66882412 4436705.90568128 5816354.50210551 399415.289827066 4685341.72125829 2035697.96817302 2649298.61578368 4558917.09939268 3033865.5371605 3723159.80045571 2271273.90973341 1315275.3896764 1867975.77477958 1998979.51177184 3834736.62525006 217585.929840466 1745179.95207796 2522061.97095887 3007639.95712717 1164987.18366678 3642048.85520513 3154151.86451505 268993.007429348 1308089.74614091 4815408.23883785 2019810.15494969 3269913.03666126 +met_019 3897653.38971062 1352811.18621835 2067043.08384845 9891292.48483335 5141111.60615796 6854992.31016711 6793826.87266572 NA 1512383.46586351 5893717.70784188 3813047.24735281 1719915.46957391 6461206.50732416 2525303.50222824 1418423.74923515 792687.914362612 874997.448695442 3221627.37437223 4041190.66860259 3772826.83025174 5984431.12263332 3049184.36469462 3111824.84157114 3885436.78692185 7239399.10425002 3289422.14224206 869410.115300195 1471225.18049236 7158668.29305888 3909829.28184535 9253532.70844886 1107045.00838947 2802885.83567237 3424275.18349068 6869336.71626878 3075394.74409809 1398709.17202965 1851725.32676902 3744361.13785684 10685987.4226928 3814577.66782459 1765956.18772971 2639703.04961552 2568710.64237482 5496393.4366589 1813480.00873909 746274.269033505 605843.614903064 2333816.19084483 3937965.97115663 2954904.45321536 2077575.87495583 4698611.73452563 2126746.01181295 8266022.70395572 2483832.52637292 3593086.61282779 4667700.62752581 2701008.84312066 3841490.57838103 1729449.0741807 3003785.76674061 638145.675565264 2566933.24870883 4218873.44263096 7617553.07253054 3402969.77525401 4623526.76272761 3729282.72501438 7342620.00813203 3011453.82834838 4587715.47583569 4562460.08003945 1949388.74492262 1604502.45123766 9303191.8206926 1383409.17379352 4584394.09179467 2482278.89660077 3921635.98922498 6358775.15817 3692632.69292923 8496813.15066293 4797232.7949376 659665.434302325 9404952.25595775 3362259.21281736 5778631.09772257 4547328.45040819 3885352.33672929 3317817.34537726 5055656.13447211 1030716.61681454 4285720.98448145 4186602.46587189 2989511.55383628 1333794.39866849 4386320.71330555 2686058.38600045 3912333.2744824 1729212.30462625 2237100.18546689 4418748.29357153 1607581.72921911 4491935.31332922 2698594.36537475 8584592.67956446 3774479.43401431 +met_020 380460.662530203 545043.809688284 428060.630927739 628969.873202076 725429.874664621 1077126.41728487 1719848.61604982 49295.9572323184 685060.521232811 338701.433412276 444971.923414127 521972.238827728 349231.554914656 194457.915761107 795623.491033551 1554089.29785085 121885.861776506 414502.517085271 1179981.11696259 428335.787186172 882406.98251691 347257.755672827 399700.077615496 787982.643923043 316945.28930176 343978.159871503 66646.6193578285 90652.7571298145 453568.325191447 444724.914941863 734400.337495777 324914.642858664 805735.394651856 1390800.7572855 540357.391170123 296668.087819409 653439.971668872 109098.127941938 1105897.09807873 1147235.53342694 372106.225659587 154112.029274189 471610.078652277 115855.070920421 664278.606254665 234973.768302979 495177.973958203 66989.0624080028 112147.429502614 460770.920287776 217064.816833352 675394.732597932 362866.474495026 272112.86247148 265425.824130158 119649.914938069 757028.691671476 625030.160145377 371092.42079731 547226.923559283 268278.699258314 321674.196117464 143406.819262747 169236.950986558 165240.404152451 260832.746766526 470012.882648704 131007.07351727 240511.864777452 193020.222990609 223645.337186303 315616.631295092 168261.897472301 330145.931609031 255342.134602234 866000.82097745 459566.752785138 444475.754945979 236939.554098071 287467.777747412 423535.780999503 368637.81592733 129698.867830012 463151.298810215 106714.554752985 395838.496305391 566430.631232052 328161.23489477 284917.987981237 395804.933587855 559234.110759591 211544.396656032 219021.369017209 418200.165586387 703095.470064643 469101.546046874 339424.652946772 296540.975468292 397137.027545414 425384.199243967 151792.24324229 611923.970005418 476317.204651986 312828.499800305 191898.130289251 884031.804926982 823896.972797342 542959.132329054 +met_021 544395.063036636 457583.423964441 238149.711962312 1098780.06154983 936356.95000389 505641.277987247 481231.916575154 NA 446326.548731241 477084.551974981 532365.747382306 688787.419008359 401580.541961547 644960.501980169 2048386.47477181 23503.5090498123 6612.79561286682 335627.952916975 1132384.06363148 536504.032162795 1152525.85151867 66804.6030981732 988915.033913575 663381.366539849 654471.172312522 473635.795450179 4091.10898788827 98788.3967375868 27371.5466732553 512641.407543922 1531262.75858915 316443.553187286 444916.191677483 1433628.24342794 1057527.63113824 381383.1268355 185044.804066609 313303.539311042 805902.349772003 244823.975379045 552788.674783904 200408.462277306 516631.371493922 396937.988566926 634612.261935163 352331.894391746 282275.218552416 4323.80824676052 NA 551428.270739642 338387.834929822 1741565.06438245 424672.891427297 4637.72103447745 118026.216674003 116668.664753012 442195.485597974 457196.075227851 610791.998797998 570169.864082036 325515.503445489 275949.713461391 125602.579838509 35140.5833768946 439822.741588246 661354.770045084 913575.207088308 487132.551822369 463860.899479115 419870.84975637 193604.085611706 294185.246656632 173950.88422037 384375.536782964 502810.526341134 331922.252809105 536233.572169708 18929.7444697866 211505.856359293 536923.027249265 301502.570589574 187492.075077537 247043.934663222 852664.917265638 86600.8817709459 71009.4666229194 791368.803261011 553068.491927806 1028957.14647291 595013.372638081 2341040.28656172 426071.439350868 55261.1840255578 407311.185798424 826854.500349706 1183911.47207 112652.563369825 600105.985427598 564365.142260229 573629.28187673 46624.7282334128 643962.531481922 NA 26885.5126583707 468745.727760511 528696.691742251 540684.06634708 481066.575987173 +met_022 544077.651139656 822914.596176267 814417.877393706 367221.970187313 410976.420926772 112602.651495342 253129.195397262 NA 307559.92142082 177571.762580104 482232.474414016 1010970.96434718 359187.510667535 358014.892665059 172748.397386334 197066.860702444 80460.9777875907 2473476.78742414 312074.448928389 505706.831781882 768826.67251867 795658.6691447 652223.32248486 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696953.677962189 1977397.39465172 851959.308646051 280532.836747978 832520.538865161 1633100.7307123 1898343.75594558 1420395.42231599 +met_081 302805.934339317 198268.110356067 508780.368626824 26013.7393118612 49235.7760988675 50823.6488256171 11782.7414731473 NA 17346.8826187513 5845.18177310912 279022.45157333 132623.606701876 133673.498689374 NA 518186.925767248 167876.217240311 4026.92884291118 120029.18348942 190186.921837744 291615.606653763 150684.535371361 221059.544923577 640745.723306251 252921.222681471 64094.5467539562 4349.23852953978 26546.6586474349 182004.620589231 25653.5743994641 281253.387766029 430692.893372175 16434.9447440763 39138.8107647899 602060.561974313 475935.837557406 1523038.69603938 1324803.89943051 1274395.97855355 961842.940749584 154367.003937568 291010.885850994 142016.204793706 85450.053967744 120596.908441097 25600.4605284529 270967.763138811 132480.497071723 5298.46950486991 93452.419844568 263658.903527012 1902359.54590334 40776.2084739182 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539512.76834608 390287.734094569 296011.436766899 175488.084156899 339469.739444202 391504.22987442 387406.452764711 488053.787600289 651462.109779484 218014.974114667 346160.087928398 448247.535269292 408756.413527643 511603.406791658 306300.676854067 372687.269667131 286743.783303265 165532.17413033 426885.120308903 364526.265250104 410496.1285927 228640.011188026 509185.867803005 479969.388977487 313148.785804567 138795.681206007 228593.846011672 423969.569955952 257894.092838902 230446.749928205 315729.858958826 428559.372002787 330552.147993284 +met_084 1586223.73397367 603218.233724441 1039664.66875421 1336491.21006042 2098068.87354096 2186591.83717174 3122187.71098696 NA 1568069.66426025 2923556.87518794 1539853.93851536 1449741.63652538 1892044.56970272 766597.752524585 868482.42062711 2132406.61222431 510854.985038912 1951915.03412423 2326698.07311378 1647979.53661189 1347469.8707442 967019.826304132 1703587.41055447 2274214.30649625 1743286.8314767 1284260.20832479 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260003.887477074 1193923.07210796 1063550.99794682 733833.757082309 481311.958165321 2038122.90237624 729304.130362747 672943.036858587 351460.073363752 1281410.2455841 3049080.96794124 1122272.03337669 1662091.44944421 1966742.526825 2131860.0994331 1202189.74831586 893982.58112865 1158051.41917858 402108.223598413 214276.484634403 2290672.64733941 1086455.37737022 1049989.6023926 303745.419466866 443333.459109463 963610.516696698 1114393.36849389 315015.168097275 127390.110377145 238123.451685537 985741.297155707 1528245.82787371 1052513.75128992 254284.981779738 2782595.33769961 2052187.73314568 1648117.77502738 1146858.85939891 2076301.79871278 222731.770697336 897010.554609965 1142767.78793222 1175566.62629062 147605.410451921 1334477.85944374 674257.455767311 238272.320081859 83535.8446922161 338575.310758172 238252.269619172 373820.121743556 1080305.84828162 647712.740705505 1310223.72832642 235283.643524588 688048.806749499 332199.290812502 2839113.17196381 1700626.4863834 1463264.37730141 1112948.7568569 831865.778620376 1806201.20972594 652752.439435275 303839.754161771 17886.3913487886 569632.612206131 4619261.55926605 554251.623493179 1349817.66630686 420473.92491991 1136259.90546501 1005407.24991569 1138806.12537123 1431081.99547944 2150539.00207346 277995.739510929 5197172.52331334 1429618.16242526 426713.576343358 3537443.80194433 992615.016091959 6011436.42239216 2084282.80758756 235561.218398302 1814375.41573414 1220073.40767394 1334265.04080617 864437.094113291 2043318.90729368 863308.223491654 1084953.78077351 244367.002935613 456691.988851544 982720.29282082 605015.907263426 1092129.66245419 471213.98261859 1042487.47812902 1152435.00046088 +met_089 787339.312018696 670815.71612103 729515.586587312 930898.818185092 409307.728431572 470262.852068709 913192.483067085 NA 844463.147046142 788964.624915218 715776.481914173 467860.676709611 745831.354065289 548743.081063763 719412.148907683 378773.779807552 316600.480961303 808323.91768562 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875771.501944033 403461.492354442 1568966.83957551 751403.630730766 358967.800839651 533331.136206212 274215.114572557 952059.984434005 535463.221190266 866726.591517779 1316679.62612104 438804.876083318 539543.133894599 1084498.49798105 1574773.63499868 597622.682446737 221497.369896048 379590.645665412 1844298.69664545 870937.442830778 2356166.75195693 164311.529003331 349459.801477914 823428.847301445 2107084.32750251 669378.01382843 150733.348803707 523911.746625199 744024.061805947 2599962.04012818 856833.824741266 284883.086861231 591043.736673909 642323.237630858 1907003.50488254 469450.994570684 196339.885602822 125002.9600286 672404.048261206 860929.784369575 679927.541258647 654929.477989829 1540739.95309163 410562.903519911 1989289.6989242 679559.976464001 1057930.49563655 1224273.21983297 780153.428304949 851459.59548852 426594.142639599 729634.719366778 264295.457609759 839697.679793717 694220.121072861 1180742.63583948 556486.258026036 974617.45493391 802529.952244419 1617802.85361309 1157452.28225789 1134009.02893168 950763.050806536 452631.049787301 469184.972536049 963758.139805117 143691.091476743 1622088.74186515 1078521.16384775 909801.613197758 1539788.40006757 1405057.03024695 1446597.07759849 859101.558136012 142383.112819682 2589391.88056521 977785.384695409 1054180.33235713 1090979.33590343 813748.203006604 1229309.49062481 1608619.81571132 292076.848877472 863039.358844594 1515390.49822307 720835.420170901 443982.812394315 977783.054388112 479678.805085766 861454.511172265 399179.581453245 537468.992122009 1341168.53297045 356665.05278084 1077581.84947797 699378.392150834 2180156.83550866 859740.908081678 +met_112 4538260.07332924 1997603.63759386 3101105.23740388 8676705.07693322 4499120.50413992 2300186.26663146 6028064.84593985 NA 2491991.86068611 4936389.92875914 3934368.63868677 3223602.52806592 5758560.31329564 4068603.841081 3020876.08310117 2092201.98947378 1414128.60450223 3564589.34919401 4454519.51899205 4111395.21282403 6506628.04141497 1638924.06694788 4704057.12477128 3783706.68101197 6398699.60280182 2814042.19687001 1380804.4721669 2762753.71246615 5605197.20586148 4482681.39654663 8211280.3603205 1649600.0418054 3908924.69185977 3874350.9274596 7338556.35476537 4151528.10101192 2487779.1011633 2870763.03704328 4304219.62588264 9393649.7874239 4176141.32094111 2629914.41305345 2699719.51441013 4108774.30717426 7046808.49106602 2534899.22779241 2209294.01128173 993612.490501395 3466629.03422472 4284484.51427465 4707978.58556783 2496536.33382371 4076646.32542001 3524390.53123507 9275598.64644259 2759480.08076153 3366878.58611063 6050319.56436356 2714865.54936974 4174949.54971015 3275325.72458992 4267918.90420684 923327.926460122 3170959.29947981 4492717.85645683 5139744.91372233 3863471.57733796 6886498.45100012 4428439.83785168 6371944.02729372 3112777.74911802 5514644.81005529 5808375.07378356 3308682.79888993 4771946.46729538 8144361.80783535 3409974.53680361 3500477.92909192 2617203.28223743 4414062.2820156 3812798.14081105 4585421.2045072 8793172.67675604 4637139.66088434 943790.950209665 8994466.91015319 4615134.45758878 5559998.73571828 6145353.55124427 4289798.20115878 2893421.75793062 7849317.9231938 1717519.10857095 5494016.27783925 4286547.30565284 4216165.66988365 2612752.98298915 3584586.08196358 7009045.08682862 4480360.79775925 1797914.42775286 2958118.63205771 5180419.78048592 2965412.74263722 6867343.34574981 4060369.23068331 4489646.44698815 4072790.12794517 +met_113 1008565.5752504 1158567.63903145 529830.796502162 889814.888535702 1448388.21917358 1841757.04389807 1322567.71170675 NA 1170273.11749264 999795.60192758 1036645.09275775 980543.484756615 763711.435143932 409322.220596871 2009596.78032114 775776.680204633 262459.955623623 1536763.6397872 1026146.72109141 1089130.8837249 1650573.81412147 605567.758060381 1445318.28337213 1682617.78861332 1492151.26331285 987383.80646001 235646.175796624 177370.54091637 1706438.13091351 986114.199693531 2476280.35702388 408095.520330724 1210422.14154099 804240.996639918 1409699.96479546 692439.380275386 741562.342086506 493209.987837817 894850.612050541 1187806.75778047 982531.920495763 861473.583014974 985392.332655452 457279.162538855 1071029.24363867 389141.615930624 1086237.1583117 101822.549704857 412116.995986363 1117205.58522017 666034.618948003 1656278.0882703 1017055.12392107 670681.845781125 764619.55232811 232557.596158996 1001461.99426945 759811.792469226 992902.332619325 991708.040046475 928839.397680866 759923.801127517 228428.464322441 389332.254437437 380456.756714177 1074125.13525503 2155924.49102891 1438928.10254248 1018035.38268366 586120.199558703 550305.983321682 1070444.71972962 98540.2137253375 470324.959918036 1053661.68418953 1347955.77809265 1794698.05572542 1839129.91193994 447039.267881417 1038365.31754997 1008450.35781633 772534.824283954 1889746.64026194 1309455.69768566 131302.851803826 1491241.59783436 725369.21239059 1515316.3946072 919122.079000583 997990.2905651 207287.048524534 1060150.0355142 471509.506822373 986177.685439062 17093346.8989243 1547984.63462982 439051.348344737 1157749.50227074 1088849.75286227 1082199.05099702 367690.017886928 712227.420096786 755882.247545083 307899.905156455 3473142.11220125 846374.452146134 1288272.54990407 1001712.70994295 diff --git a/tools/batch_correction/test-data/output-determinebc-preNormSummary.txt b/tools/batch_correction/test-data/output-determinebc-preNormSummary.txt new file mode 100644 index 000000000..b132c17a8 --- /dev/null +++ b/tools/batch_correction/test-data/output-determinebc-preNormSummary.txt @@ -0,0 +1,11 @@ +labion batch.1.linear batch.1.loess batch.1.lowess batch.2.linear batch.2.loess batch.2.lowess +M53T345 0 0 0 0 0 0 +M58T69 0 0 0 0 0 0 +M59T246 0 0 0 5 0 0 +M59T62 0 0 0 3 3 3 +M59T270 0 0 0 0 0 0 +M62T56 0 0 0 0 0 0 +M64T348 0 0 0 0 0 0 +M64T379 0 0 0 0 0 0 +M65T347 5 0 0 5 0 0 +M65T379 0 0 0 0 0 0 From 9bbc1d38d928a7bd204408864bf14532bc253991 Mon Sep 17 00:00:00 2001 From: hechth Date: Thu, 10 Jul 2025 15:02:19 +0200 Subject: [PATCH 02/19] forgot macro file --- tools/batch_correction/macros.xml | 44 +++++++++++++++++++++++++++++++ 1 file changed, 44 insertions(+) create mode 100644 tools/batch_correction/macros.xml diff --git a/tools/batch_correction/macros.xml b/tools/batch_correction/macros.xml new file mode 100644 index 000000000..7924563da --- /dev/null +++ b/tools/batch_correction/macros.xml @@ -0,0 +1,44 @@ + + + + +

+ + + + + + +
+ + + + batch_col_name "$smcust.batch_col_name" + injection_order_col_name "$smcust.injection_order_col_name" + sample_type_col_name "$smcust.sample_type_col_name" + sample_type_tags "blank=$smcust.sampleTypeTagBlank,pool=$smcust.sampleTypeTagPool,sample=$smcust.sampleTypeTagSample" + + + + + + + + + + + + + + + + + + + + + + + + + From 534191fc3f3b025ab2442beb12fc23123dbf463d Mon Sep 17 00:00:00 2001 From: hechth Date: Thu, 10 Jul 2025 15:04:27 +0200 Subject: [PATCH 03/19] fixed linter --- tools/batch_correction/batch_correction.xml | 6 +++--- tools/batch_correction/determine_bc.xml | 2 +- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/tools/batch_correction/batch_correction.xml b/tools/batch_correction/batch_correction.xml index 372a1396a..a4e0068d5 100644 --- a/tools/batch_correction/batch_correction.xml +++ b/tools/batch_correction/batch_correction.xml @@ -1,4 +1,4 @@ - + Corrects intensities for signal drift and batch-effects @@ -98,7 +98,7 @@ - + @@ -106,7 +106,7 @@ - + diff --git a/tools/batch_correction/determine_bc.xml b/tools/batch_correction/determine_bc.xml index 7e0bb4992..92a3b4724 100644 --- a/tools/batch_correction/determine_bc.xml +++ b/tools/batch_correction/determine_bc.xml @@ -1,4 +1,4 @@ - + to choose between linear, lowess and loess methods From a4433ceeec75bf8fd32332e81711a36c917523e6 Mon Sep 17 00:00:00 2001 From: hechth Date: Thu, 10 Jul 2025 15:10:39 +0200 Subject: [PATCH 04/19] styling R files --- .../batch_correction/batch_correction_3Lfct.R | 1024 ++++++++++------- .../batch_correction_3Llauncher.R | 276 ++--- .../batch_correction_3Lwrapper.R | 162 +-- .../batch_correction_all_loess_script.R | 293 +++-- .../batch_correction_all_loess_wrapper.R | 254 ++-- 5 files changed, 1140 insertions(+), 869 deletions(-) diff --git a/tools/batch_correction/batch_correction_3Lfct.R b/tools/batch_correction/batch_correction_3Lfct.R index 53adcd399..05c0b6e8f 100644 --- a/tools/batch_correction/batch_correction_3Lfct.R +++ b/tools/batch_correction/batch_correction_3Lfct.R @@ -8,13 +8,13 @@ # "injectionOrder" integer defining the injection order of all samples : QC-pools and analysed samples # "sampleType" indicates if defining a sample with "sample" or a QC-pool with "pool" # NO MISSING DATA are allowed -# Version 0.91 insertion of ok_norm function to assess correction feasibility +# Version 0.91 insertion of ok_norm function to assess correction feasibility # Version 0.92 insertion of slope test in ok_norm # Version 0.93 name of log file define as a parameter of the correction function # Version 0.94 Within a batch, test if all QCpools or samples values = 0. Definition of an error code in ok_norm function (see function for details) -# Version 0.99 include non linear lowess correction. +# Version 0.99 include non linear lowess correction. # Version 1.00 the corrected result matrix is return transposed in Galaxy -# Version 1.01 standard deviation=0 instead of sum of value=0 is used to assess constant data in ok_norm function. Negative values in corrected matrix are converted to 0. +# Version 1.01 standard deviation=0 instead of sum of value=0 is used to assess constant data in ok_norm function. Negative values in corrected matrix are converted to 0. # Version 1.02 plotsituation create a result file with the error code of non execution of correction set by function ok_norm # Version 1.03 fix bug in plot with "reg" option. suppression of ok_norm=4 condition if ok_norm function # Version 2.00 Addition of loess function, correction indicator, plots ; modification of returned objects' format, some plots' displays and ok_norm ifelse format @@ -31,59 +31,81 @@ # - changes in the normalisation strategy regarding mean values to adjust for NA or 0 values # - changes in the normalisation strategy regarding unconsistant values (negative or Inf) -ok_norm=function(qcp,qci,spl,spi,method,normref=NA,valimp="0") { - # Function used for one ion within one batch to determine whether or not batch correction is possible - # ok_norm values : - # 0 : no preliminary-condition problem - # 1 : standard deviation of QC-pools or samples = 0 - # 2 : insufficient number of QC-pools within a batch (n=3 for linear, n=8 for lowess or loess) - # 2.5 : less than 2 samples within a batch - # 3 : significant difference between QC-pools' and samples' means - # 4 : denominator =0 when on 1 pool per batch <> 0 - # 5 : (linear regression only) the slopes ratio ?QC-pools/samples? is lower than -0.2 - # 6 : (linear regression only) none of the pool or sample could be corrected if negative and infinite values are turned into NA - # Parameters: - # qcp: intensity of a given ion for pools - # qci: injection numbers for pools - # spl: intensity of a given ion for samples - # spi: injection numbers for samples - # method: to provide specific checks for "linear" - - ok=0 - if (method=="linear") {minQC=3} else {minQC=8} - if (length(qcp[!is.na(qcp)]) 0 + # 5 : (linear regression only) the slopes ratio ?QC-pools/samples? is lower than -0.2 + # 6 : (linear regression only) none of the pool or sample could be corrected if negative and infinite values are turned into NA + # Parameters: + # qcp: intensity of a given ion for pools + # qci: injection numbers for pools + # spl: intensity of a given ion for samples + # spi: injection numbers for samples + # method: to provide specific checks for "linear" + + ok <- 0 + if (method == "linear") { + minQC <- 3 } else { - cvp= sd(qcp,na.rm=TRUE)/mean(qcp,na.rm=TRUE); cvs=sd(spl,na.rm=TRUE)/mean(spl,na.rm=TRUE) - rttest=t.test(qcp,y=spl) - reslsfit=lsfit(qci, qcp) - reslsfitSample=lsfit(spl, spi) - ordori=reslsfit$coefficients[1] - penteB=reslsfit$coefficients[2] - penteS=reslsfitSample$coefficients[2] - # Significant difference between samples and pools - if (rttest$p.value < 0.01) { ok=3 + minQC <- 8 + } + if (length(qcp[!is.na(qcp)]) < minQC) { + ok <- 2 } else { - # to avoid denominator =0 when on 1 pool per batch <> 0 - if (method=="linear" & length(which(((penteB*qci)+ordori)==0))>0 ){ ok=6 - } else { - # different sloop between samples and pools - if (method=="linear" & penteB/penteS < -0.20) { ok=5 + if (length(spl[!is.na(spl)]) < 2) { + ok <- 2.5 } else { - # - if (method=="linear" & !is.na(normref) & valimp=="NA") { - denom = (penteB * c(spi,qci) + ordori) - normval = c(spl,qcp)*normref / denom - if(length(which((normval==Inf)|(denom<1)))==length(normval)){ok=6} - } - }}}}}} - ok_norm=ok + if (sd(qcp, na.rm = TRUE) == 0 | sd(spl, na.rm = TRUE) == 0) { + ok <- 1 + } else { + cvp <- sd(qcp, na.rm = TRUE) / mean(qcp, na.rm = TRUE) + cvs <- sd(spl, na.rm = TRUE) / mean(spl, na.rm = TRUE) + rttest <- t.test(qcp, y = spl) + reslsfit <- lsfit(qci, qcp) + reslsfitSample <- lsfit(spl, spi) + ordori <- reslsfit$coefficients[1] + penteB <- reslsfit$coefficients[2] + penteS <- reslsfitSample$coefficients[2] + # Significant difference between samples and pools + if (rttest$p.value < 0.01) { + ok <- 3 + } else { + # to avoid denominator =0 when on 1 pool per batch <> 0 + if (method == "linear" & length(which(((penteB * qci) + ordori) == 0)) > 0) { + ok <- 6 + } else { + # different sloop between samples and pools + if (method == "linear" & penteB / penteS < -0.20) { + ok <- 5 + } else { + # + if (method == "linear" & !is.na(normref) & valimp == "NA") { + denom <- (penteB * c(spi, qci) + ordori) + normval <- c(spl, qcp) * normref / denom + if (length(which((normval == Inf) | (denom < 1))) == length(normval)) { + ok <- 6 + } + } + } + } + } + } + } + } + ok_norm <- ok } -plotsituation <- function (x, nbid,outfic="plot_regression.pdf", outres="PreNormSummary.txt",fact="batch",span="none", - sm_meta=list(batch="batch", injectionOrder="injectionOrder", sampleType="sampleType", - sampleTag=list(pool="pool",blank="blank",sample="sample"))) { +plotsituation <- function(x, nbid, outfic = "plot_regression.pdf", outres = "PreNormSummary.txt", fact = "batch", span = "none", + sm_meta = list( + batch = "batch", injectionOrder = "injectionOrder", sampleType = "sampleType", + sampleTag = list(pool = "pool", blank = "blank", sample = "sample") + )) { # Checks for all ions in every batch if linear or lo(w)ess correction is possible. # Uses ok_norm function and creates a file (PreNormSummary.txt) with the corresponding error codes. # Also creates a pdf file with plots of linear and lo(w)ess regression lines. @@ -95,214 +117,279 @@ plotsituation <- function (x, nbid,outfic="plot_regression.pdf", outres="PreNorm # fact: factor to be used as categorical variable for plots and PCA # span: span value for lo(w)ess regression; "none" for linear or default values # sm_meta: list of information about sample metadata coding - indfact=which(dimnames(x)[[2]]==fact) - indtypsamp=which(dimnames(x)[[2]]==sm_meta$sampleType) - indbatch=which(dimnames(x)[[2]]==sm_meta$batch) - indinject=which(dimnames(x)[[2]]==sm_meta$injectionOrder) - lastIon=dim(x)[2] - nbi=lastIon-nbid # Number of ions = total number of columns - number of identifying columns - nbb=length(levels(x[[sm_meta$batch]])) # Number of batch = number of levels of "batch" comlumn (factor) - nbs=length(x[[sm_meta$sampleType]][x[[sm_meta$sampleType]] %in% sm_meta$sampleTag$sample])# Number of samples = number of rows with "sample" value in sampleType - pdf(outfic,width=27,height=7*ceiling((nbb+2)/3)) - cat(nbi," ions ",nbb," batch(es) \n") - cv=data.frame(matrix(0,nrow=nbi,ncol=2))# initialisation de la dataset qui contiendra les CV - pre_bilan=matrix(0,nrow=nbi,ncol=3*nbb) # dataset of ok_norm function results - for (p in 1:nbi) {# for each ion - par (mfrow=c(ceiling((nbb+2)/3),3),ask=F,cex=1.2) - labion=dimnames(x)[[2]][p+nbid] - indpool=which(x[[sm_meta$sampleType]] %in% sm_meta$sampleTag$pool) # QCpools subscripts in x - pools1=x[indpool,p+nbid]; cv[p,1]=sd(pools1,na.rm=TRUE)/mean(pools1,na.rm=TRUE)# CV before correction - for (b in 1:nbb) {# for each batch... - xb=data.frame(x[(x[[sm_meta$batch]]==levels(x[[sm_meta$batch]])[b]),c(indtypsamp,indinject,p+nbid)]) - indpb = which(xb[[sm_meta$sampleType]] %in% sm_meta$sampleTag$pool)# QCpools subscripts in the current batch - indsp = which(xb[[sm_meta$sampleType]] %in% sm_meta$sampleTag$sample)# samples subscripts in the current batch - normLinearTest=ok_norm(xb[indpb,3],xb[indpb,2], xb[indsp,3],xb[indsp,2],method="linear",normref=mean(xb[c(indpb,indsp),3],na.rm=TRUE),valimp="NA") - normLoessTest=ok_norm(xb[indpb,3],xb[indpb,2], xb[indsp,3],xb[indsp,2],method="loess") - normLowessTest=ok_norm(xb[indpb,3],xb[indpb,2], xb[indsp,3],xb[indsp,2],method="lowess") - pre_bilan[ p,3*b-2]=normLinearTest - pre_bilan[ p,3*b-1]=normLoessTest - pre_bilan[ p,3*b]=normLowessTest - if(length(indpb)>1){ - if(span=="none"){span1<-1 ; span2<-2*length(indpool)/nbs}else{span1<-span ; span2<-span} - if(normLoessTest!=2){resloess=loess(xb[indpb,3]~xb[indpb,2],span=span1,degree=2,family="gaussian",iterations=4,surface="direct")} - if(length(which(!(is.na(xb[indsp,3]))))>1){resloessSample=loess(xb[indsp,3]~xb[indsp,2],span=2*length(indpool)/nbs,degree=2,family="gaussian",iterations=4,surface="direct") } - if(normLowessTest!=2){reslowess=lowess(xb[indpb,2],xb[indpb,3],f=span2)} - if(length(which(!(is.na(xb[indsp,3]))))>1){reslowessSample=lowess(xb[indsp,2],xb[indsp,3])} - liminf=min(xb[,3],na.rm=TRUE);limsup=max(xb[,3],na.rm=TRUE) - firstinj=min(xb[,2],na.rm=TRUE);lastinj=max(xb[,2],na.rm=TRUE) - plot(xb[indsp,2],xb[indsp,3],pch=16, main=paste(labion,"batch ",b),ylab="intensity",xlab="injection order",ylim=c(liminf,limsup),xlim=c(firstinj,lastinj)) - if(nrow(xb)>(length(indpb)+length(indsp))){points(xb[-c(indpb,indsp),2], xb[-c(indpb,indsp),3],pch=18,col="grey")} - points(xb[indpb,2], xb[indpb,3],pch=5) - if(normLoessTest!=2){points(cbind(resloess$x,resloess$fitted)[order(resloess$x),],type="l",col="green3")} - if(length(which(!(is.na(xb[indsp,3]))))>1){points(cbind(resloessSample$x,resloessSample$fitted)[order(resloessSample$x),],type="l",col="green3",lty=2)} - if(normLowessTest!=2){points(reslowess,type="l",col="red")}; if(length(which(!(is.na(xb[indsp,3]))))>1){points(reslowessSample,type="l",col="red",lty=2)} - abline(lsfit(xb[indpb,2],xb[indpb,3]),col="blue") - if(length(which(!(is.na(xb[indsp,3]))))>1){abline(lsfit(xb[indsp,2],xb[indsp,3]),lty=2,col="blue")} - legend("topleft",c("pools","samples"),lty=c(1,2),bty="n") - legend("topright",c("linear","lowess","loess"),lty=1,col=c("blue","red","green3"),bty="n") - } else { - plot.new() - legend("center","Plot only available when the\nbatch contains at least 2 pools.") - } + indfact <- which(dimnames(x)[[2]] == fact) + indtypsamp <- which(dimnames(x)[[2]] == sm_meta$sampleType) + indbatch <- which(dimnames(x)[[2]] == sm_meta$batch) + indinject <- which(dimnames(x)[[2]] == sm_meta$injectionOrder) + lastIon <- dim(x)[2] + nbi <- lastIon - nbid # Number of ions = total number of columns - number of identifying columns + nbb <- length(levels(x[[sm_meta$batch]])) # Number of batch = number of levels of "batch" comlumn (factor) + nbs <- length(x[[sm_meta$sampleType]][x[[sm_meta$sampleType]] %in% sm_meta$sampleTag$sample]) # Number of samples = number of rows with "sample" value in sampleType + pdf(outfic, width = 27, height = 7 * ceiling((nbb + 2) / 3)) + cat(nbi, " ions ", nbb, " batch(es) \n") + cv <- data.frame(matrix(0, nrow = nbi, ncol = 2)) # initialisation de la dataset qui contiendra les CV + pre_bilan <- matrix(0, nrow = nbi, ncol = 3 * nbb) # dataset of ok_norm function results + for (p in 1:nbi) { # for each ion + par(mfrow = c(ceiling((nbb + 2) / 3), 3), ask = F, cex = 1.2) + labion <- dimnames(x)[[2]][p + nbid] + indpool <- which(x[[sm_meta$sampleType]] %in% sm_meta$sampleTag$pool) # QCpools subscripts in x + pools1 <- x[indpool, p + nbid] + cv[p, 1] <- sd(pools1, na.rm = TRUE) / mean(pools1, na.rm = TRUE) # CV before correction + for (b in 1:nbb) { # for each batch... + xb <- data.frame(x[(x[[sm_meta$batch]] == levels(x[[sm_meta$batch]])[b]), c(indtypsamp, indinject, p + nbid)]) + indpb <- which(xb[[sm_meta$sampleType]] %in% sm_meta$sampleTag$pool) # QCpools subscripts in the current batch + indsp <- which(xb[[sm_meta$sampleType]] %in% sm_meta$sampleTag$sample) # samples subscripts in the current batch + normLinearTest <- ok_norm(xb[indpb, 3], xb[indpb, 2], xb[indsp, 3], xb[indsp, 2], method = "linear", normref = mean(xb[c(indpb, indsp), 3], na.rm = TRUE), valimp = "NA") + normLoessTest <- ok_norm(xb[indpb, 3], xb[indpb, 2], xb[indsp, 3], xb[indsp, 2], method = "loess") + normLowessTest <- ok_norm(xb[indpb, 3], xb[indpb, 2], xb[indsp, 3], xb[indsp, 2], method = "lowess") + pre_bilan[p, 3 * b - 2] <- normLinearTest + pre_bilan[p, 3 * b - 1] <- normLoessTest + pre_bilan[p, 3 * b] <- normLowessTest + if (length(indpb) > 1) { + if (span == "none") { + span1 <- 1 + span2 <- 2 * length(indpool) / nbs + } else { + span1 <- span + span2 <- span + } + if (normLoessTest != 2) { + resloess <- loess(xb[indpb, 3] ~ xb[indpb, 2], span = span1, degree = 2, family = "gaussian", iterations = 4, surface = "direct") + } + if (length(which(!(is.na(xb[indsp, 3])))) > 1) { + resloessSample <- loess(xb[indsp, 3] ~ xb[indsp, 2], span = 2 * length(indpool) / nbs, degree = 2, family = "gaussian", iterations = 4, surface = "direct") + } + if (normLowessTest != 2) { + reslowess <- lowess(xb[indpb, 2], xb[indpb, 3], f = span2) + } + if (length(which(!(is.na(xb[indsp, 3])))) > 1) { + reslowessSample <- lowess(xb[indsp, 2], xb[indsp, 3]) + } + liminf <- min(xb[, 3], na.rm = TRUE) + limsup <- max(xb[, 3], na.rm = TRUE) + firstinj <- min(xb[, 2], na.rm = TRUE) + lastinj <- max(xb[, 2], na.rm = TRUE) + plot(xb[indsp, 2], xb[indsp, 3], pch = 16, main = paste(labion, "batch ", b), ylab = "intensity", xlab = "injection order", ylim = c(liminf, limsup), xlim = c(firstinj, lastinj)) + if (nrow(xb) > (length(indpb) + length(indsp))) { + points(xb[-c(indpb, indsp), 2], xb[-c(indpb, indsp), 3], pch = 18, col = "grey") + } + points(xb[indpb, 2], xb[indpb, 3], pch = 5) + if (normLoessTest != 2) { + points(cbind(resloess$x, resloess$fitted)[order(resloess$x), ], type = "l", col = "green3") + } + if (length(which(!(is.na(xb[indsp, 3])))) > 1) { + points(cbind(resloessSample$x, resloessSample$fitted)[order(resloessSample$x), ], type = "l", col = "green3", lty = 2) + } + if (normLowessTest != 2) { + points(reslowess, type = "l", col = "red") + } + if (length(which(!(is.na(xb[indsp, 3])))) > 1) { + points(reslowessSample, type = "l", col = "red", lty = 2) + } + abline(lsfit(xb[indpb, 2], xb[indpb, 3]), col = "blue") + if (length(which(!(is.na(xb[indsp, 3])))) > 1) { + abline(lsfit(xb[indsp, 2], xb[indsp, 3]), lty = 2, col = "blue") + } + legend("topleft", c("pools", "samples"), lty = c(1, 2), bty = "n") + legend("topright", c("linear", "lowess", "loess"), lty = 1, col = c("blue", "red", "green3"), bty = "n") + } else { + plot.new() + legend("center", "Plot only available when the\nbatch contains at least 2 pools.") + } } # series de plot avant correction - minval=min(x[p+nbid],na.rm=TRUE);maxval=max(x[p+nbid],na.rm=TRUE) - plot( x[[sm_meta$injectionOrder]], x[,p+nbid],col=x[[sm_meta$batch]],ylim=c(minval,maxval),ylab=labion, - main=paste0("before correction (CV for pools = ",round(cv[p,1],2),")"),xlab="injection order") - suppressWarnings(plot.design( x[c(indtypsamp,indbatch,indfact,p+nbid)],main="factors effect before correction")) + minval <- min(x[p + nbid], na.rm = TRUE) + maxval <- max(x[p + nbid], na.rm = TRUE) + plot(x[[sm_meta$injectionOrder]], x[, p + nbid], + col = x[[sm_meta$batch]], ylim = c(minval, maxval), ylab = labion, + main = paste0("before correction (CV for pools = ", round(cv[p, 1], 2), ")"), xlab = "injection order" + ) + suppressWarnings(plot.design(x[c(indtypsamp, indbatch, indfact, p + nbid)], main = "factors effect before correction")) } dev.off() - pre_bilan=data.frame(pre_bilan) - labion=dimnames(x)[[2]][nbid+1:nbi] + pre_bilan <- data.frame(pre_bilan) + labion <- dimnames(x)[[2]][nbid + 1:nbi] for (i in 1:nbb) { - dimnames(pre_bilan)[[2]][3*i-2]=paste("batch",i,"linear") - dimnames(pre_bilan)[[2]][3*i-1]=paste("batch",i,"loess") - dimnames(pre_bilan)[[2]][3*i]=paste("batch",i,"lowess") + dimnames(pre_bilan)[[2]][3 * i - 2] <- paste("batch", i, "linear") + dimnames(pre_bilan)[[2]][3 * i - 1] <- paste("batch", i, "loess") + dimnames(pre_bilan)[[2]][3 * i] <- paste("batch", i, "lowess") } - bilan=data.frame(labion,pre_bilan) - write.table(bilan,file=outres,sep="\t",row.names=F,quote=F) + bilan <- data.frame(labion, pre_bilan) + write.table(bilan, file = outres, sep = "\t", row.names = F, quote = F) } -normlowess=function (xb,detail="no",vref=1,b,span=NULL,valneg="none",sm_meta=list(batch="batch", injectionOrder="injectionOrder", sampleType="sampleType", - sampleTag=list(pool="pool",blank="blank",sample="sample")),min_norm=1){ - # Correction function applied to 1 ion in 1 batch. - # Uses a lowess regression computed on QC-pools in order to correct samples intensity values - # xb: dataframe for 1 ion in columns and samples in rows. - # vref: reference value (average of ion) - # b: batch subscript - # detail: level of detail in the outlog file - # span: span value for lo(w)ess regression; NULL for default values - # valneg: to determine what to do with generated negative and Inf values - # sm_meta: list of information about sample metadata coding - # min_norm: minimum value accepted for normalisation term (denominator); should be strictly positive - indpb = which(xb[[sm_meta$sampleType]] %in% sm_meta$sampleTag$pool) # pools subscripts of current batch - indsp = which(xb[[sm_meta$sampleType]] %in% sm_meta$sampleTag$sample) # samples of current batch subscripts - labion=dimnames(xb)[[2]][3] - newval=xb[[3]] # initialisation of corrected values = intial values - ind <- 0 # initialisation of correction indicator - normTodo=ok_norm(xb[indpb,3],xb[indpb,2], xb[indsp,3],xb[indsp,2],method="lowess") - #cat("batch:",b," dim xb=",dim(xb)," ok=",normTodo,"\n") - if (normTodo==0) { - if(length(span)==0){span2<-2*length(indpb)/length(indsp)}else{span2<-span} - reslowess=lowess(xb[indpb,2],xb[indpb,3],f=span2) # lowess regression with QC-pools - if(length(which(reslowess$y=min_norm],na.rm=TRUE) - reslowess$y[toajust] <- mindenom - } } } - for(j in 1:nrow(xb)) { - if (j %in% indpb) { - newval[j]=(vref*xb[j,3]) / (reslowess$y[which(indpb==j)]) - } else { # for samples other than pools, the correction value "corv" correspond to the nearest QCpools - corv= reslowess$y[which(abs(reslowess$x-xb[j,2])==min(abs(reslowess$x-xb[j,2]),na.rm=TRUE))] - if (length(corv)>1) {corv=corv[1]} - newval[j]=(vref*xb[j,3]) / corv - } - if((!is.na(newval[j]))&(newval[j]<0)){newval[j]<-0} - } - if (detail=="reg") { - liminf=min(xb[,3],na.rm=TRUE);limsup=max(xb[,3],na.rm=TRUE) - firstinj=min(xb[,2],na.rm=TRUE);lastinj=max(xb[,2],na.rm=TRUE) - plot(xb[indsp,2],xb[indsp,3],pch=16,main=paste(labion,"batch ",b),ylab="intensity",xlab="injection order",ylim=c(liminf,limsup),xlim=c(firstinj,lastinj)) - if(nrow(xb)>(length(indpb)+length(indsp))){points(xb[-c(indpb,indsp),2], xb[-c(indpb,indsp),3],pch=18)} - points(xb[indpb,2], xb[indpb,3],pch=5) - points(reslowess,type="l",col="red") - } - ind <- 1 - } else {# if ok_norm != 0 , we perform a correction based on batch pool or sample average - if((length(which(!is.na(xb[indpb,3])))>0)&(length(which(xb[indpb,3]>0))>0)){ - moypool=mean(xb[indpb,3],na.rm=TRUE) - newval = (vref*xb[,3])/moypool - } else { - moysamp=mean(xb[indsp,3],na.rm=TRUE) - if((!is.na(moysamp))&(moysamp>0)){ - cat("Warning: no pool value >0 detected in batch",b,"of ion",labion,": sample mean used as normalisation term.\n") - newval = (vref*xb[,3])/moysamp - } else { - dev.off() - stop(paste("\n- - - -\nNo pool nor sample value >0 in batch",b,"of ion",labion,"- correction process aborted.\n- - - -\n")) - } + span2 <- span + } + reslowess <- lowess(xb[indpb, 2], xb[indpb, 3], f = span2) # lowess regression with QC-pools + if (length(which(reslowess$y < min_norm)) != 0) { # to handle cases where 0= min_norm], na.rm = TRUE) + reslowess$y[toajust] <- mindenom + } + } + } + for (j in 1:nrow(xb)) { + if (j %in% indpb) { + newval[j] <- (vref * xb[j, 3]) / (reslowess$y[which(indpb == j)]) + } else { # for samples other than pools, the correction value "corv" correspond to the nearest QCpools + corv <- reslowess$y[which(abs(reslowess$x - xb[j, 2]) == min(abs(reslowess$x - xb[j, 2]), na.rm = TRUE))] + if (length(corv) > 1) { + corv <- corv[1] + } + newval[j] <- (vref * xb[j, 3]) / corv + } + if ((!is.na(newval[j])) & (newval[j] < 0)) { + newval[j] <- 0 + } + } + if (detail == "reg") { + liminf <- min(xb[, 3], na.rm = TRUE) + limsup <- max(xb[, 3], na.rm = TRUE) + firstinj <- min(xb[, 2], na.rm = TRUE) + lastinj <- max(xb[, 2], na.rm = TRUE) + plot(xb[indsp, 2], xb[indsp, 3], pch = 16, main = paste(labion, "batch ", b), ylab = "intensity", xlab = "injection order", ylim = c(liminf, limsup), xlim = c(firstinj, lastinj)) + if (nrow(xb) > (length(indpb) + length(indsp))) { + points(xb[-c(indpb, indsp), 2], xb[-c(indpb, indsp), 3], pch = 18) + } + points(xb[indpb, 2], xb[indpb, 3], pch = 5) + points(reslowess, type = "l", col = "red") + } + ind <- 1 + } else { # if ok_norm != 0 , we perform a correction based on batch pool or sample average + if ((length(which(!is.na(xb[indpb, 3]))) > 0) & (length(which(xb[indpb, 3] > 0)) > 0)) { + moypool <- mean(xb[indpb, 3], na.rm = TRUE) + newval <- (vref * xb[, 3]) / moypool + } else { + moysamp <- mean(xb[indsp, 3], na.rm = TRUE) + if ((!is.na(moysamp)) & (moysamp > 0)) { + cat("Warning: no pool value >0 detected in batch", b, "of ion", labion, ": sample mean used as normalisation term.\n") + newval <- (vref * xb[, 3]) / moysamp + } else { + dev.off() + stop(paste("\n- - - -\nNo pool nor sample value >0 in batch", b, "of ion", labion, "- correction process aborted.\n- - - -\n")) + } + } } - } - newval <- list(norm.ion=newval,norm.ind=ind) - return(newval) + newval <- list(norm.ion = newval, norm.ind = ind) + return(newval) } -normlinear <- function (xb,detail="no",vref=1,b,valneg="none",sm_meta=list(batch="batch", injectionOrder="injectionOrder", sampleType="sampleType", - sampleTag=list(pool="pool",blank="blank",sample="sample")),min_norm=1){ - # Correction function applied to 1 ion in 1 batch. - # Uses a linear regression computed on QC-pools in order to correct samples intensity values - # xb: dataframe with ions in columns and samples in rows; x is a result of concatenation of sample metadata file and ion file - # detail: level of detail in the outlog file - # vref: reference value (average of ion) - # b: which batch it is - # valneg: to determine what to do with generated negative and Inf values - # sm_meta: list of information about sample metadata coding - # min_norm: minimum value accepted for normalisation term (denominator); should be strictly positive - indpb = which(xb[[sm_meta$sampleType]] %in% sm_meta$sampleTag$pool)# pools subscripts of current batch - indsp = which(xb[[sm_meta$sampleType]] %in% sm_meta$sampleTag$sample)# samples of current batch subscripts - labion=dimnames(xb)[[2]][3] - newval=xb[[3]] # initialisation of corrected values = intial values - ind <- 0 # initialisation of correction indicator - normTodo=ok_norm(xb[indpb,3],xb[indpb,2], xb[indsp,3],xb[indsp,2],method="linear",normref=vref,valimp=valneg) - if (normTodo==0) { - ind <- 1 - reslsfit=lsfit(xb[indpb,2],xb[indpb,3]) # linear regression for QCpools - reslsfitSample=lsfit(xb[indsp,2],xb[indsp,3]) # linear regression for samples - ordori=reslsfit$coefficients[1] - pente=reslsfit$coefficients[2] - if (detail=="reg") { - liminf=min(xb[,3],na.rm=TRUE);limsup=max(xb[,3],na.rm=TRUE) - firstinj=min(xb[,2],na.rm=TRUE);lastinj=max(xb[,2],na.rm=TRUE) - plot(xb[indsp,2],xb[indsp,3],pch=16, - main=paste(labion,"batch ",b),ylab="intensity",xlab="injection order",ylim=c(liminf,limsup),xlim=c(firstinj,lastinj)) - if(nrow(xb)>(length(indpb)+length(indsp))){points(xb[-c(indpb,indsp),2], xb[-c(indpb,indsp),3],pch=18)} - points(xb[indpb,2], xb[indpb,3],pch=5) - abline(reslsfit) - abline(reslsfitSample,lty=2) - } - # correction with rescaling of ion global intensity (vref) - newval = (vref*xb[,3]) / (pente * (xb[,2]) + ordori) - newval[which((pente * (xb[,2]) + ordori) (length(indpb) + length(indsp))) { + points(xb[-c(indpb, indsp), 2], xb[-c(indpb, indsp), 3], pch = 18) + } + points(xb[indpb, 2], xb[indpb, 3], pch = 5) + abline(reslsfit) + abline(reslsfitSample, lty = 2) + } + # correction with rescaling of ion global intensity (vref) + newval <- (vref * xb[, 3]) / (pente * (xb[, 2]) + ordori) + newval[which((pente * (xb[, 2]) + ordori) < min_norm)] <- -1 # to handle cases where 0= min_norm], na.rm = TRUE) + newval[toajust] <- vref * (xb[, 3][toajust]) / mindenom + } + } + } + } else { # if ok_norm != 0 , we perform a correction based on batch pool or sample average + if ((length(which(!is.na(xb[indpb, 3]))) > 0) & (length(which(xb[indpb, 3] > 0)) > 0)) { + moypool <- mean(xb[indpb, 3], na.rm = TRUE) + newval <- (vref * xb[, 3]) / moypool } else { - mindenom <- (pente * (xb[,2]) + ordori) - mindenom <- min(mindenom[mindenom>=min_norm],na.rm=TRUE) - newval[toajust] <- vref * (xb[,3][toajust]) / mindenom + moysamp <- mean(xb[indsp, 3], na.rm = TRUE) + if ((!is.na(moysamp)) & (moysamp > 0)) { + cat("Warning: no pool value >0 detected in batch", b, "of ion", labion, ": sample mean used as normalisation term.\n") + newval <- (vref * xb[, 3]) / moysamp + } else { + dev.off() + stop(paste("\n- - - -\nNo pool nor sample value >0 in batch", b, "of ion", labion, "- correction process aborted.\n- - - -\n")) + } } - } - } - } else {# if ok_norm != 0 , we perform a correction based on batch pool or sample average - if((length(which(!is.na(xb[indpb,3])))>0)&(length(which(xb[indpb,3]>0))>0)){ - moypool=mean(xb[indpb,3],na.rm=TRUE) - newval = (vref*xb[,3])/moypool - } else { - moysamp=mean(xb[indsp,3],na.rm=TRUE) - if((!is.na(moysamp))&(moysamp>0)){ - cat("Warning: no pool value >0 detected in batch",b,"of ion",labion,": sample mean used as normalisation term.\n") - newval = (vref*xb[,3])/moysamp - } else { - dev.off() - stop(paste("\n- - - -\nNo pool nor sample value >0 in batch",b,"of ion",labion,"- correction process aborted.\n- - - -\n")) - } } - } - newval <- list(norm.ion=newval,norm.ind=ind) - return(newval) + newval <- list(norm.ion = newval, norm.ind = ind) + return(newval) } -normloess <- function (xb,detail="no",vref=1,b,span=NULL,valneg="none",sm_meta=list(batch="batch", injectionOrder="injectionOrder", sampleType="sampleType", - sampleTag=list(pool="pool",blank="blank",sample="sample")),min_norm=1){ - # Correction function applied to 1 ion in 1 batch. +normloess <- function(xb, detail = "no", vref = 1, b, span = NULL, valneg = "none", sm_meta = list( + batch = "batch", injectionOrder = "injectionOrder", sampleType = "sampleType", + sampleTag = list(pool = "pool", blank = "blank", sample = "sample") + ), min_norm = 1) { + # Correction function applied to 1 ion in 1 batch. # Uses a loess regression computed on QC-pools in order to correct samples intensity values. # xb: dataframe for 1 ion in columns and samples in rows. # detail: level of detail in the outlog file. @@ -312,230 +399,301 @@ normloess <- function (xb,detail="no",vref=1,b,span=NULL,valneg="none",sm_meta=l # valneg: to determine what to do with generated negative and Inf values # sm_meta: list of information about sample metadata coding # min_norm: minimum value accepted for normalisation term (denominator); should be strictly positive - indpb = which(xb[[sm_meta$sampleType]] %in% sm_meta$sampleTag$pool) # pools subscripts of current batch - indsp = which(xb[[sm_meta$sampleType]] %in% sm_meta$sampleTag$sample) # samples of current batch subscripts - indbt = which(xb[[sm_meta$sampleType]] %in% c(sm_meta$sampleTag$sample,sm_meta$sampleTag$pool))# batch subscripts of samples and QCpools - labion=dimnames(xb)[[2]][3] - newval=xb[[3]] # initialisation of corrected values = intial values + indpb <- which(xb[[sm_meta$sampleType]] %in% sm_meta$sampleTag$pool) # pools subscripts of current batch + indsp <- which(xb[[sm_meta$sampleType]] %in% sm_meta$sampleTag$sample) # samples of current batch subscripts + indbt <- which(xb[[sm_meta$sampleType]] %in% c(sm_meta$sampleTag$sample, sm_meta$sampleTag$pool)) # batch subscripts of samples and QCpools + labion <- dimnames(xb)[[2]][3] + newval <- xb[[3]] # initialisation of corrected values = intial values ind <- 0 # initialisation of correction indicator - normTodo=ok_norm(xb[indpb,3],xb[indpb,2], xb[indsp,3],xb[indsp,2],method="loess") - if (normTodo==0) { - if(length(span)==0){span1<-1}else{span1<-span} - resloess=loess(xb[indpb,3]~xb[indpb,2],span=span1,degree=2,family="gaussian",iterations=4,surface="direct") # loess regression with QCpools - corv=predict(resloess,newdata=xb[,2]) - if(length(which(corv=min_norm],na.rm=TRUE) - corv[toajust] <- mindenom + if (valneg == "0") { + corv[toajust] <- -1 + } else { + mindenom <- min(corv[corv >= min_norm], na.rm = TRUE) + corv[toajust] <- mindenom + } } - } - } - newvalps=(vref*xb[indbt,3]) / corv[indbt] # to check if correction generates outlier values - refthresh=max(c(3*(quantile(newvalps,na.rm=TRUE)[4]),1.3*(xb[indbt,3])),na.rm=TRUE) - if(length(which(newvalps>refthresh))>0){ # if outliers - # in this case no modification of initial value - newval <- xb[,3] - } else { - newval=(vref*xb[,3]) / corv - newval[newval<0] <- 0 - ind <- 1 # confirmation of correction } - if ((detail=="reg")&(ind==1)) { # plot - liminf=min(xb[,3],na.rm=TRUE);limsup=max(xb[,3],na.rm=TRUE) - firstinj=min(xb[,2],na.rm=TRUE);lastinj=max(xb[,2],na.rm=TRUE) - plot(xb[indsp,2],xb[indsp,3],pch=16,main=paste(labion,"batch ",b),ylab="intensity",xlab="injection order",ylim=c(liminf,limsup),xlim=c(firstinj,lastinj)) - if(nrow(xb)>(length(indpb)+length(indsp))){points(xb[-c(indpb,indsp),2], xb[-c(indpb,indsp),3],pch=18)} - points(xb[indpb,2], xb[indpb,3],pch=5) - points(cbind(resloess$x,resloess$fitted)[order(resloess$x),],type="l",col="red") + newvalps <- (vref * xb[indbt, 3]) / corv[indbt] # to check if correction generates outlier values + refthresh <- max(c(3 * (quantile(newvalps, na.rm = TRUE)[4]), 1.3 * (xb[indbt, 3])), na.rm = TRUE) + if (length(which(newvalps > refthresh)) > 0) { # if outliers + # in this case no modification of initial value + newval <- xb[, 3] + } else { + newval <- (vref * xb[, 3]) / corv + newval[newval < 0] <- 0 + ind <- 1 # confirmation of correction } - } - if (ind==0) {# if ok_norm != 0 or if correction creates outliers, we perform a correction based on batch pool or sample average - if((length(which(!is.na(xb[indpb,3])))>0)&(length(which(xb[indpb,3]>0))>0)){ - moypool=mean(xb[indpb,3],na.rm=TRUE) - newval = (vref*xb[,3])/moypool - } else { - moysamp=mean(xb[indsp,3],na.rm=TRUE) - if((!is.na(moysamp))&(moysamp>0)){ - cat("Warning: no pool value >0 detected in batch",b,"of ion",labion,": sample mean used as normalisation term.\n") - newval = (vref*xb[,3])/moysamp + if ((detail == "reg") & (ind == 1)) { # plot + liminf <- min(xb[, 3], na.rm = TRUE) + limsup <- max(xb[, 3], na.rm = TRUE) + firstinj <- min(xb[, 2], na.rm = TRUE) + lastinj <- max(xb[, 2], na.rm = TRUE) + plot(xb[indsp, 2], xb[indsp, 3], pch = 16, main = paste(labion, "batch ", b), ylab = "intensity", xlab = "injection order", ylim = c(liminf, limsup), xlim = c(firstinj, lastinj)) + if (nrow(xb) > (length(indpb) + length(indsp))) { + points(xb[-c(indpb, indsp), 2], xb[-c(indpb, indsp), 3], pch = 18) + } + points(xb[indpb, 2], xb[indpb, 3], pch = 5) + points(cbind(resloess$x, resloess$fitted)[order(resloess$x), ], type = "l", col = "red") + } + } + if (ind == 0) { # if ok_norm != 0 or if correction creates outliers, we perform a correction based on batch pool or sample average + if ((length(which(!is.na(xb[indpb, 3]))) > 0) & (length(which(xb[indpb, 3] > 0)) > 0)) { + moypool <- mean(xb[indpb, 3], na.rm = TRUE) + newval <- (vref * xb[, 3]) / moypool } else { - dev.off() - stop(paste("\n- - - -\nNo pool nor sample value >0 in batch",b,"of ion",labion,"- correction process aborted.\n- - - -\n")) + moysamp <- mean(xb[indsp, 3], na.rm = TRUE) + if ((!is.na(moysamp)) & (moysamp > 0)) { + cat("Warning: no pool value >0 detected in batch", b, "of ion", labion, ": sample mean used as normalisation term.\n") + newval <- (vref * xb[, 3]) / moysamp + } else { + dev.off() + stop(paste("\n- - - -\nNo pool nor sample value >0 in batch", b, "of ion", labion, "- correction process aborted.\n- - - -\n")) + } } - } } - newval <- list(norm.ion=newval,norm.ind=ind) + newval <- list(norm.ion = newval, norm.ind = ind) return(newval) } -norm_QCpool <- function (x, nbid, outlog, fact, metaion, detail="no", NormMoyPool=FALSE, NormInt=FALSE, method="linear",span="none",valNull="0", - sm_meta=list(batch="batch", injectionOrder="injectionOrder", sampleType="sampleType", - sampleTag=list(pool="pool",blank="blank",sample="sample")),min_norm=1) { - ### Correction applying linear or lo(w)ess correction function on all ions for every batch of a dataframe. - # x: dataframe with ions in column and samples' metadata - # nbid: number of sample description columns (id and factors) with at least "batch", "injectionOrder", "sampleType" - # outlog: name of regression plots and PCA pdf file - # fact: factor to be used as categorical variable for plots - # metaion: dataframe of ions' metadata - # detail: level of detail in the outlog file. detail="no" ACP + boxplot of CV before and after correction. - # detail="plot" with plot for all batch before and after correction. - # detail="reg" with added plots with regression lines for all batches. - # NormMoyPool: not used - # NormInt: not used - # method: regression method to be used to correct : "linear" or "lowess" or "loess" - # span: span value for lo(w)ess regression; "none" for linear or default values - # valNull: to determine what to do with negatively estimated intensities - # sm_meta: list of information about sample metadata coding - # min_norm: minimum value accepted for normalisation term (denominator); should be strictly positive - indfact=which(dimnames(x)[[2]]==fact) - indtypsamp=which(dimnames(x)[[2]]==sm_meta$sampleType) - indbatch=which(dimnames(x)[[2]]==sm_meta$batch) - indinject=which(dimnames(x)[[2]]==sm_meta$injectionOrder) - lastIon=dim(x)[2] - indpool=which(x[[sm_meta$sampleType]] %in% sm_meta$sampleTag$pool)# QCpools subscripts in all batches - valref=apply(as.matrix(x[indpool,(nbid+1):(lastIon)]),2,mean,na.rm=TRUE) # reference value for each ion used to still have the same rought size of values - nbi=lastIon-nbid # number of ions - nbb=length(levels(x[[sm_meta$batch]])) # Number of batch(es) = number of levels of factor "batch" (can be =1) - Xn=data.frame(x[,c(1:nbid)],matrix(0,nrow=nrow(x),ncol=nbi))# initialisation of the corrected dataframe (=initial dataframe) - dimnames(Xn)=dimnames(x) - cv=data.frame(matrix(NA,nrow=nbi,ncol=2))# initialisation of dataframe containing CV before and after correction - dimnames(cv)[[2]]=c("avant","apres") - if (detail!="reg" && detail!="plot" && detail!="no") {detail="no"} - pdf(outlog,width=27,height=20) - cat(nbi," ions ",nbb," batch(es) \n") - if (detail=="plot") {if(nbb<6){par(mfrow=c(3,3),ask=F,cex=1.5)}else{par(mfrow=c(4,4),ask=F,cex=1.5)}} - res.ind <- matrix(NA,ncol=nbb,nrow=nbi,dimnames=list(dimnames(x)[[2]][-c(1:nbid)],paste("norm.b",1:nbb,sep=""))) - for (p in 1:nbi) {# for each ion - labion=dimnames(x)[[2]][p+nbid] - pools1=x[indpool,p+nbid] - if(length(which(pools1[!(is.na(pools1))]>0))<2){ # if not enough pools >0 -> no normalisation - war.note <- paste("Warning: less than 2 pools with values >0 in",labion,"-> no normalisation for this ion.") - cat(war.note,"\n") - Xn[,p+nbid] <- x[,p+nbid] - res.ind[p,] <- rep(0,nbb) - if (detail=="reg" || detail=="plot" ) { - par(mfrow=c(2,2),ask=F,cex=1.5) - plot.new() - legend("center",war.note) - minval=min(x[p+nbid],na.rm=TRUE);maxval=max(x[p+nbid],na.rm=TRUE) - plot( x[[sm_meta$injectionOrder]], x[,p+nbid],col=x[[sm_meta$batch]],ylab=labion,ylim=c(minval,maxval), - main="No correction",xlab="injection order") - points(x[[sm_meta$injectionOrder]][indpool],x[indpool,p+nbid],col="maroon",pch=16,cex=1) - } - } else { - if (detail == "reg") {if(nbb<6){par(mfrow=c(3,3),ask=F,cex=1.5)}else{par(mfrow=c(4,4),ask=F,cex=1.5)}} - if (detail == "plot") {par(mfrow=c(2,2),ask=F,cex=1.5)} - cv[p,1]=sd(pools1,na.rm=TRUE)/mean(pools1,na.rm=TRUE)# CV before correction - for (b in 1:nbb) {# for every batch - indbt = which(x[[sm_meta$batch]]==(levels(x[[sm_meta$batch]])[b])) # subscripts of all samples - sub=data.frame(x[(x[[sm_meta$batch]]==levels(x[[sm_meta$batch]])[b]),c(indtypsamp,indinject,p+nbid)]) - if (method=="linear") { res.norm = normlinear(sub,detail,valref[p],b,valNull,sm_meta,min_norm) - } else { if (method=="loess"){ res.norm <- normloess(sub,detail,valref[p],b,span,valNull,sm_meta,min_norm) - } else { if (method=="lowess"){ res.norm <- normlowess(sub,detail,valref[p],b,span,valNull,sm_meta,min_norm) - } else {stop("\n--\nNo valid 'method' argument supplied.\nMust be 'linear','loess' or 'lowess'.\n--\n")} - }} - Xn[indbt,p+nbid] = res.norm[[1]] - res.ind[p,b] <- res.norm[[2]] +norm_QCpool <- function(x, nbid, outlog, fact, metaion, detail = "no", NormMoyPool = FALSE, NormInt = FALSE, method = "linear", span = "none", valNull = "0", + sm_meta = list( + batch = "batch", injectionOrder = "injectionOrder", sampleType = "sampleType", + sampleTag = list(pool = "pool", blank = "blank", sample = "sample") + ), min_norm = 1) { + ### Correction applying linear or lo(w)ess correction function on all ions for every batch of a dataframe. + # x: dataframe with ions in column and samples' metadata + # nbid: number of sample description columns (id and factors) with at least "batch", "injectionOrder", "sampleType" + # outlog: name of regression plots and PCA pdf file + # fact: factor to be used as categorical variable for plots + # metaion: dataframe of ions' metadata + # detail: level of detail in the outlog file. detail="no" ACP + boxplot of CV before and after correction. + # detail="plot" with plot for all batch before and after correction. + # detail="reg" with added plots with regression lines for all batches. + # NormMoyPool: not used + # NormInt: not used + # method: regression method to be used to correct : "linear" or "lowess" or "loess" + # span: span value for lo(w)ess regression; "none" for linear or default values + # valNull: to determine what to do with negatively estimated intensities + # sm_meta: list of information about sample metadata coding + # min_norm: minimum value accepted for normalisation term (denominator); should be strictly positive + indfact <- which(dimnames(x)[[2]] == fact) + indtypsamp <- which(dimnames(x)[[2]] == sm_meta$sampleType) + indbatch <- which(dimnames(x)[[2]] == sm_meta$batch) + indinject <- which(dimnames(x)[[2]] == sm_meta$injectionOrder) + lastIon <- dim(x)[2] + indpool <- which(x[[sm_meta$sampleType]] %in% sm_meta$sampleTag$pool) # QCpools subscripts in all batches + valref <- apply(as.matrix(x[indpool, (nbid + 1):(lastIon)]), 2, mean, na.rm = TRUE) # reference value for each ion used to still have the same rought size of values + nbi <- lastIon - nbid # number of ions + nbb <- length(levels(x[[sm_meta$batch]])) # Number of batch(es) = number of levels of factor "batch" (can be =1) + Xn <- data.frame(x[, c(1:nbid)], matrix(0, nrow = nrow(x), ncol = nbi)) # initialisation of the corrected dataframe (=initial dataframe) + dimnames(Xn) <- dimnames(x) + cv <- data.frame(matrix(NA, nrow = nbi, ncol = 2)) # initialisation of dataframe containing CV before and after correction + dimnames(cv)[[2]] <- c("avant", "apres") + if (detail != "reg" && detail != "plot" && detail != "no") { + detail <- "no" + } + pdf(outlog, width = 27, height = 20) + cat(nbi, " ions ", nbb, " batch(es) \n") + if (detail == "plot") { + if (nbb < 6) { + par(mfrow = c(3, 3), ask = F, cex = 1.5) + } else { + par(mfrow = c(4, 4), ask = F, cex = 1.5) } - # Post correction CV calculation - pools2=Xn[indpool,p+nbid] - cv[p,2]=sd(pools2,na.rm=TRUE)/mean(pools2,na.rm=TRUE) - if (detail=="reg" || detail=="plot" ) { - # plot before and after correction - minval=min(cbind(x[p+nbid],Xn[p+nbid]),na.rm=TRUE);maxval=max(cbind(x[p+nbid],Xn[p+nbid]),na.rm=TRUE) - plot( x[[sm_meta$injectionOrder]], x[,p+nbid],col=x[[sm_meta$batch]],ylab=labion,ylim=c(minval,maxval), - main=paste0("before correction (CV for pools = ",round(cv[p,1],2),")"),xlab="injection order") - points(x[[sm_meta$injectionOrder]][indpool],x[indpool,p+nbid],col="maroon",pch=16,cex=1) - plot(Xn[[sm_meta$injectionOrder]],Xn[,p+nbid],col=x[[sm_meta$batch]],ylab="",ylim=c(minval,maxval), - main=paste0("after correction (CV for pools = ",round(cv[p,2],2),")"),xlab="injection order") - points(Xn[[sm_meta$injectionOrder]][indpool],Xn[indpool,p+nbid],col="maroon",pch=16,cex=1) - suppressWarnings(plot.design( x[c(indtypsamp,indbatch,indfact,p+nbid)],main="factors effect before correction")) - suppressWarnings(plot.design(Xn[c(indtypsamp,indbatch,indfact,p+nbid)],main="factors effect after correction")) + } + res.ind <- matrix(NA, ncol = nbb, nrow = nbi, dimnames = list(dimnames(x)[[2]][-c(1:nbid)], paste("norm.b", 1:nbb, sep = ""))) + for (p in 1:nbi) { # for each ion + labion <- dimnames(x)[[2]][p + nbid] + pools1 <- x[indpool, p + nbid] + if (length(which(pools1[!(is.na(pools1))] > 0)) < 2) { # if not enough pools >0 -> no normalisation + war.note <- paste("Warning: less than 2 pools with values >0 in", labion, "-> no normalisation for this ion.") + cat(war.note, "\n") + Xn[, p + nbid] <- x[, p + nbid] + res.ind[p, ] <- rep(0, nbb) + if (detail == "reg" || detail == "plot") { + par(mfrow = c(2, 2), ask = F, cex = 1.5) + plot.new() + legend("center", war.note) + minval <- min(x[p + nbid], na.rm = TRUE) + maxval <- max(x[p + nbid], na.rm = TRUE) + plot(x[[sm_meta$injectionOrder]], x[, p + nbid], + col = x[[sm_meta$batch]], ylab = labion, ylim = c(minval, maxval), + main = "No correction", xlab = "injection order" + ) + points(x[[sm_meta$injectionOrder]][indpool], x[indpool, p + nbid], col = "maroon", pch = 16, cex = 1) + } + } else { + if (detail == "reg") { + if (nbb < 6) { + par(mfrow = c(3, 3), ask = F, cex = 1.5) + } else { + par(mfrow = c(4, 4), ask = F, cex = 1.5) + } + } + if (detail == "plot") { + par(mfrow = c(2, 2), ask = F, cex = 1.5) + } + cv[p, 1] <- sd(pools1, na.rm = TRUE) / mean(pools1, na.rm = TRUE) # CV before correction + for (b in 1:nbb) { # for every batch + indbt <- which(x[[sm_meta$batch]] == (levels(x[[sm_meta$batch]])[b])) # subscripts of all samples + sub <- data.frame(x[(x[[sm_meta$batch]] == levels(x[[sm_meta$batch]])[b]), c(indtypsamp, indinject, p + nbid)]) + if (method == "linear") { + res.norm <- normlinear(sub, detail, valref[p], b, valNull, sm_meta, min_norm) + } else { + if (method == "loess") { + res.norm <- normloess(sub, detail, valref[p], b, span, valNull, sm_meta, min_norm) + } else { + if (method == "lowess") { + res.norm <- normlowess(sub, detail, valref[p], b, span, valNull, sm_meta, min_norm) + } else { + stop("\n--\nNo valid 'method' argument supplied.\nMust be 'linear','loess' or 'lowess'.\n--\n") + } + } + } + Xn[indbt, p + nbid] <- res.norm[[1]] + res.ind[p, b] <- res.norm[[2]] + } + # Post correction CV calculation + pools2 <- Xn[indpool, p + nbid] + cv[p, 2] <- sd(pools2, na.rm = TRUE) / mean(pools2, na.rm = TRUE) + if (detail == "reg" || detail == "plot") { + # plot before and after correction + minval <- min(cbind(x[p + nbid], Xn[p + nbid]), na.rm = TRUE) + maxval <- max(cbind(x[p + nbid], Xn[p + nbid]), na.rm = TRUE) + plot(x[[sm_meta$injectionOrder]], x[, p + nbid], + col = x[[sm_meta$batch]], ylab = labion, ylim = c(minval, maxval), + main = paste0("before correction (CV for pools = ", round(cv[p, 1], 2), ")"), xlab = "injection order" + ) + points(x[[sm_meta$injectionOrder]][indpool], x[indpool, p + nbid], col = "maroon", pch = 16, cex = 1) + plot(Xn[[sm_meta$injectionOrder]], Xn[, p + nbid], + col = x[[sm_meta$batch]], ylab = "", ylim = c(minval, maxval), + main = paste0("after correction (CV for pools = ", round(cv[p, 2], 2), ")"), xlab = "injection order" + ) + points(Xn[[sm_meta$injectionOrder]][indpool], Xn[indpool, p + nbid], col = "maroon", pch = 16, cex = 1) + suppressWarnings(plot.design(x[c(indtypsamp, indbatch, indfact, p + nbid)], main = "factors effect before correction")) + suppressWarnings(plot.design(Xn[c(indtypsamp, indbatch, indfact, p + nbid)], main = "factors effect after correction")) + } } - } } - if (detail=="reg" || detail=="plot" || detail=="no") { + if (detail == "reg" || detail == "plot" || detail == "no") { if (nbi > 3) { # Sum of ions before/after plot - par(mfrow=c(1,2),ask=F,cex=1.2) - xsum <- rowSums(x[,(nbid+1):lastIon],na.rm=TRUE) - Xnsum <- rowSums(Xn[,(nbid+1):lastIon],na.rm=TRUE) - plot(x[[sm_meta$injectionOrder]],xsum,col=x[[sm_meta$batch]],ylab="sum of variables' intensities",xlab="injection order", - ylim=c(min(c(xsum,Xnsum),na.rm=TRUE),max(c(xsum,Xnsum),na.rm=TRUE)),main="Sum of intensities\nBefore correction") - points(x[[sm_meta$injectionOrder]][indpool],xsum[indpool],col="maroon",pch=16,cex=1.2) - plot(x[[sm_meta$injectionOrder]],Xnsum,col=x[[sm_meta$batch]],ylab="sum of variables' intensities",xlab="injection order", - ylim=c(min(c(xsum,Xnsum),na.rm=TRUE),max(c(xsum,Xnsum),na.rm=TRUE)),main="Sum of intensities\nAfter correction") - points(x[[sm_meta$injectionOrder]][indpool],Xnsum[indpool],col="maroon",pch=16,cex=1.2) + par(mfrow = c(1, 2), ask = F, cex = 1.2) + xsum <- rowSums(x[, (nbid + 1):lastIon], na.rm = TRUE) + Xnsum <- rowSums(Xn[, (nbid + 1):lastIon], na.rm = TRUE) + plot(x[[sm_meta$injectionOrder]], xsum, + col = x[[sm_meta$batch]], ylab = "sum of variables' intensities", xlab = "injection order", + ylim = c(min(c(xsum, Xnsum), na.rm = TRUE), max(c(xsum, Xnsum), na.rm = TRUE)), main = "Sum of intensities\nBefore correction" + ) + points(x[[sm_meta$injectionOrder]][indpool], xsum[indpool], col = "maroon", pch = 16, cex = 1.2) + plot(x[[sm_meta$injectionOrder]], Xnsum, + col = x[[sm_meta$batch]], ylab = "sum of variables' intensities", xlab = "injection order", + ylim = c(min(c(xsum, Xnsum), na.rm = TRUE), max(c(xsum, Xnsum), na.rm = TRUE)), main = "Sum of intensities\nAfter correction" + ) + points(x[[sm_meta$injectionOrder]][indpool], Xnsum[indpool], col = "maroon", pch = 16, cex = 1.2) # PCA Plot before/after, normed only and ions plot - par(mfrow=c(3,4),ask=F,cex=1.2) - acplight(x[,c(indtypsamp,indbatch,indtypsamp,indfact,(nbid+1):lastIon)],"uv",TRUE) - norm.ion <- which(colnames(Xn)%in%(rownames(res.ind)[which(rowSums(res.ind)>=1)])) - acplight(Xn[,c(indtypsamp,indbatch,indtypsamp,indfact,(nbid+1):lastIon)],"uv",TRUE,norm.ion) - if(length(norm.ion)>0){acplight(Xn[,c(indtypsamp,indbatch,indtypsamp,indfact,norm.ion)],"uv",TRUE)} + par(mfrow = c(3, 4), ask = F, cex = 1.2) + acplight(x[, c(indtypsamp, indbatch, indtypsamp, indfact, (nbid + 1):lastIon)], "uv", TRUE) + norm.ion <- which(colnames(Xn) %in% (rownames(res.ind)[which(rowSums(res.ind) >= 1)])) + acplight(Xn[, c(indtypsamp, indbatch, indtypsamp, indfact, (nbid + 1):lastIon)], "uv", TRUE, norm.ion) + if (length(norm.ion) > 0) { + acplight(Xn[, c(indtypsamp, indbatch, indtypsamp, indfact, norm.ion)], "uv", TRUE) + } # Before/after boxplot - par(mfrow=c(1,2),ask=F,cex=1.2) - cvplot=cv[!is.na(cv[[1]])&!is.na(cv[[2]]),] - if(nrow(cvplot)>0){ - boxplot(cvplot[[1]],ylim=c(min(cvplot),max(cvplot)),main="CV of pools before correction") - boxplot(cvplot[[2]],ylim=c(min(cvplot),max(cvplot)),main="CV of pools after correction") + par(mfrow = c(1, 2), ask = F, cex = 1.2) + cvplot <- cv[!is.na(cv[[1]]) & !is.na(cv[[2]]), ] + if (nrow(cvplot) > 0) { + boxplot(cvplot[[1]], ylim = c(min(cvplot), max(cvplot)), main = "CV of pools before correction") + boxplot(cvplot[[2]], ylim = c(min(cvplot), max(cvplot)), main = "CV of pools after correction") } dev.off() } } - if (nbi<=3) {dev.off()} - # transposed matrix is return (format of the initial matrix with ions in rows) - Xr=Xn[,-c(1:nbid)]; dimnames(Xr)[[1]]=Xn[[1]] - Xr=t(Xr) ; Xr <- data.frame(ions=rownames(Xr),Xr) - - res.norm[[1]] <- Xr ; res.norm[[2]] <- data.frame(metaion,res.ind) ; res.norm[[3]] <- x[,c(1:nbid)] - names(res.norm) <- c("dataMatrix","variableMetadata","sampleMetadata") - return(res.norm) + if (nbi <= 3) { + dev.off() + } + # transposed matrix is return (format of the initial matrix with ions in rows) + Xr <- Xn[, -c(1:nbid)] + dimnames(Xr)[[1]] <- Xn[[1]] + Xr <- t(Xr) + Xr <- data.frame(ions = rownames(Xr), Xr) + + res.norm[[1]] <- Xr + res.norm[[2]] <- data.frame(metaion, res.ind) + res.norm[[3]] <- x[, c(1:nbid)] + names(res.norm) <- c("dataMatrix", "variableMetadata", "sampleMetadata") + return(res.norm) } -acplight <- function(ids, scaling="uv", indiv=FALSE,indcol=NULL) { +acplight <- function(ids, scaling = "uv", indiv = FALSE, indcol = NULL) { suppressPackageStartupMessages(library(ade4)) suppressPackageStartupMessages(library(pcaMethods)) - # Make a PCA and plot scores and loadings. - # First column must contain samples' identifiers. - # Columns 2 to 4 contain factors to colour the plots. - for (i in 1:3) { - idss <- data.frame(ids) - idss[,i+1] <- as.character(idss[,i+1]) - idss[which(is.na(idss[,i+1])),i+1] <- "no_modality" - idss[which(idss[,i+1]=="NA"),i+1] <- "no_modality" - idss[which(idss[,i+1]==""),i+1] <- "no_modality" - classe=as.factor(idss[[i+1]]) - idsample=as.character(idss[[1]]) - colour=1:length(levels(classe)) - ions=as.matrix(idss[,5:dim(idss)[2]]) - # Removing ions containing NA (not compatible with standard PCA) - ions=t(na.omit(t(ions))) - if(i==1){if(ncol(ions)!=(ncol(idss)-4)){cat("Note:",(ncol(idss)-4)-ncol(ions),"ions were ignored for PCA display due to NA in intensities.\n")}} - # Scaling choice: "uv","none","pareto" - object=suppressWarnings(prep(ions, scale=scaling, center=TRUE)) - if(i==1){if(length(which(apply(ions,2,var)==0))>0){cat("Warning: there are",length(which(apply(ions,2,var)==0)),"constant ions.\n")}} - # ALGO: nipals,svdImpute, Bayesian, svd, probalistic=F - result <- pca(object, center=F, method="svd", nPcs=2) - # ADE4 : to plot samples' ellipsoid for each class - s.class(result@scores, classe, cpoint = 1,xax=1,yax=2,col=colour,sub=sprintf("Scores - PCs %sx%s",1,2), possub="bottomright") - #s.label(result@loadings,label = ions, cpoint = 0, clabel=0.4, xax=1,yax=2,sub="Loadings",possub="bottomright") - if(i==1){resulti <- result} - } - if(indiv) { - colour <- rep("darkblue",length(resulti@loadings)) ; if(!is.null(indcol)) {colour[-c(indcol)] <- "red"} - plot(resulti@loadings,col=colour,main="Loadings",xaxt="n",yaxt="n",pch=20, - xlab=bquote(PC1-R^2==.(resulti@R2[1])),ylab=bquote(PC2 - R^2 == .(resulti@R2[2]))) - abline(h=0,v=0)} + # Make a PCA and plot scores and loadings. + # First column must contain samples' identifiers. + # Columns 2 to 4 contain factors to colour the plots. + for (i in 1:3) { + idss <- data.frame(ids) + idss[, i + 1] <- as.character(idss[, i + 1]) + idss[which(is.na(idss[, i + 1])), i + 1] <- "no_modality" + idss[which(idss[, i + 1] == "NA"), i + 1] <- "no_modality" + idss[which(idss[, i + 1] == ""), i + 1] <- "no_modality" + classe <- as.factor(idss[[i + 1]]) + idsample <- as.character(idss[[1]]) + colour <- 1:length(levels(classe)) + ions <- as.matrix(idss[, 5:dim(idss)[2]]) + # Removing ions containing NA (not compatible with standard PCA) + ions <- t(na.omit(t(ions))) + if (i == 1) { + if (ncol(ions) != (ncol(idss) - 4)) { + cat("Note:", (ncol(idss) - 4) - ncol(ions), "ions were ignored for PCA display due to NA in intensities.\n") + } + } + # Scaling choice: "uv","none","pareto" + object <- suppressWarnings(prep(ions, scale = scaling, center = TRUE)) + if (i == 1) { + if (length(which(apply(ions, 2, var) == 0)) > 0) { + cat("Warning: there are", length(which(apply(ions, 2, var) == 0)), "constant ions.\n") + } + } + # ALGO: nipals,svdImpute, Bayesian, svd, probalistic=F + result <- pca(object, center = F, method = "svd", nPcs = 2) + # ADE4 : to plot samples' ellipsoid for each class + s.class(result@scores, classe, cpoint = 1, xax = 1, yax = 2, col = colour, sub = sprintf("Scores - PCs %sx%s", 1, 2), possub = "bottomright") + # s.label(result@loadings,label = ions, cpoint = 0, clabel=0.4, xax=1,yax=2,sub="Loadings",possub="bottomright") + if (i == 1) { + resulti <- result + } + } + if (indiv) { + colour <- rep("darkblue", length(resulti@loadings)) + if (!is.null(indcol)) { + colour[-c(indcol)] <- "red" + } + plot(resulti@loadings, + col = colour, main = "Loadings", xaxt = "n", yaxt = "n", pch = 20, + xlab = bquote(PC1 - R^2 == .(resulti@R2[1])), ylab = bquote(PC2 - R^2 == .(resulti@R2[2])) + ) + abline(h = 0, v = 0) + } } - - diff --git a/tools/batch_correction/batch_correction_3Llauncher.R b/tools/batch_correction/batch_correction_3Llauncher.R index c7afb1fea..373a3d894 100644 --- a/tools/batch_correction/batch_correction_3Llauncher.R +++ b/tools/batch_correction/batch_correction_3Llauncher.R @@ -16,131 +16,151 @@ # # ############################################################################################################### -meth3L <- function(idsample,iddata,sample_type_col_name,injection_order_col_name,batch_col_name,sample_type_tags, - factbio,analyse,metaion,detail,method,outlog,span,valnull, - rdata_output,dataMatrix_out,variableMetadata_out,out_graph_pdf,out_preNormSummary,min.norm){ - -## Import function -tab.import <- function(tested.file,tabtype){ - tab.res <- tryCatch(read.table(tested.file,header=TRUE,sep='\t',check.names=FALSE,comment.char = ''), error=conditionMessage) - if(length(tab.res)==1){ - stop(paste("Could not import the",tabtype,"file. There may be issues in your table integrity.\nCorresponding R error message:\n",tab.res)) - }else{ - tab.comp <- tryCatch(read.table(tested.file,header=TRUE,sep='\t',check.names=FALSE,comment.char = '',quote=""), error=conditionMessage) - if((length(tab.comp)!=1)&&(dim(tab.res)!=dim(tab.comp))){ # wrong original import due to quotes inside a column name - return(tab.comp) - }else{ return(tab.res) } - } -} - -## Reading of input files -idsample=tab.import(idsample,"sampleMetadata") -iddata=tab.import(iddata,"dataMatrix") - -### Table match check -table.check <- match2(iddata,idsample,"sample") -if(length(table.check)>1){check.err(table.check)} - -### StockID -samp.id <- stockID(iddata,idsample,"sample") -iddata<-samp.id$dataMatrix ; idsample<-samp.id$Metadata ; samp.id<-samp.id$id.match - -### Checking mandatory variables -mand.check <- "" -for(mandcol in c(sample_type_col_name, injection_order_col_name, batch_col_name)){ - if(!(mandcol%in%colnames(idsample))){ - mand.check <- c(mand.check,"\nError: no '",mandcol,"' column in sample metadata.\n", - "Note: column names are case-sensitive.\n") - } -} -if(length(mand.check)>1){ - mand.check <- c(mand.check,"\nFor more information, see the help section or:", - "\n http://workflow4metabolomics.org/sites/", - "workflow4metabolomics.org/files/files/w4e-2016-data_processing.pdf\n") - check.err(mand.check) -} - -if(analyse == "batch_correction") { - ## Reading of Metadata Ions file - metaion=read.table(metaion,header=T,sep='\t',check.names=FALSE,comment.char = '') - ## Table match check - table.check <- c(table.check,match2(iddata,metaion,"variable")) - ## StockID - var.id <- stockID(iddata,metaion,"variable") - iddata<-var.id$dataMatrix ; metaion<-var.id$Metadata ; var.id<-var.id$id.match -} - -### Formating -idsample[[1]]=make.names(idsample[[1]]) -dimnames(iddata)[[1]]=iddata[[1]] - -### Transposition of ions data -idTdata=t(iddata[,2:dim(iddata)[2]]) -idTdata=data.frame(dimnames(idTdata)[[1]],idTdata) - -### Merge of 2 files (ok even if the two dataframe are not sorted on the same key) -ids=merge(idsample, idTdata, by.x=1, by.y=1) - -ids[[batch_col_name]]=as.factor(ids[[batch_col_name]]) -nbid=dim(idsample)[2] - -### Checking the number of sample and pool - -# least 2 samples -if(length(which(ids[[sample_type_col_name]] %in% sample_type_tags$sample))<2){ - table.check <- c(table.check,"\nError: less than 2 samples specified in sample metadata.", - "\nMake sure this is not due to errors in your ",sample_type_col_name," coding.\n") -} - -# least 2 pools per batch for all batchs -B <- rep(0,length(levels(ids[[batch_col_name]]))) -for(nbB in 1:length(levels(ids[[batch_col_name]]))){ - B[nbB]<-length(which(ids[which(ids[[batch_col_name]]==(levels(ids[[batch_col_name]])[nbB])),,drop=FALSE][[sample_type_col_name]] %in% sample_type_tags$pool)) -} -if(length(which(B>1))==0){ - table.check <- c(table.check,"\nError: less than 2 pools specified in at least one batch in sample metadata.", - "\nMake sure this is not due to errors in your ",sample_type_col_name," coding.\n") -} - -### Checking the unicity of samples and variables -uni.check <- function(tested.tab,tabtype,err.obj){ - unicity <- duplicated(tested.tab[,1]) - if(sum(unicity)>0){ - #Sending back an explicit error - duptable <- t(t(table(tested.tab[,1][unicity])+1)) - err.obj <- c(err.obj,paste0("\n-------\nError: your '",tabtype,"' IDs contain duplicates:\n"), - paste(rownames(duptable),duptable,sep=": ",collapse="\n"), - "\nSince identifiers are meant to be unique, please check your data.\n-------\n") - } - return(err.obj) -} -table.check <- uni.check(ids,"sample",table.check) -if(analyse == "batch_correction"){table.check <- uni.check(metaion,"variable",table.check)} - -## error check -check.err(table.check) - - -### BC/DBC-specific processing - -# Gathering mandatory information in a single object -sm.meta <- list(batch=batch_col_name, injectionOrder=injection_order_col_name, sampleType=sample_type_col_name, sampleTag=sample_type_tags) - -if(analyse == "batch_correction") { - ## Launch - res = norm_QCpool(ids,nbid,outlog,factbio,metaion,detail,FALSE,FALSE,method,span,valnull,sm.meta,min.norm) - ## Get back original IDs - var.id <- reproduceID(res[[1]],res[[2]],"variable",var.id) - res[[1]] <- var.id$dataMatrix ; res[[2]] <- var.id$Metadata - samp.id <- reproduceID(res[[1]],res[[3]],"sample",samp.id) - res[[1]] <- samp.id$dataMatrix ; res[[3]] <- samp.id$Metadata - ## Save files - save(res, file=rdata_output) - write.table(res[[1]], file=dataMatrix_out, sep = '\t', row.names=FALSE, quote=FALSE) - write.table(res[[2]], file=variableMetadata_out, sep = '\t', row.names=FALSE, quote=FALSE) -}else{ - ## Launch - plotsituation(ids,nbid,out_graph_pdf,out_preNormSummary,factbio,span,sm.meta) -} - -}#end of meth3L \ No newline at end of file +meth3L <- function(idsample, iddata, sample_type_col_name, injection_order_col_name, batch_col_name, sample_type_tags, + factbio, analyse, metaion, detail, method, outlog, span, valnull, + rdata_output, dataMatrix_out, variableMetadata_out, out_graph_pdf, out_preNormSummary, min.norm) { + ## Import function + tab.import <- function(tested.file, tabtype) { + tab.res <- tryCatch(read.table(tested.file, header = TRUE, sep = "\t", check.names = FALSE, comment.char = ""), error = conditionMessage) + if (length(tab.res) == 1) { + stop(paste("Could not import the", tabtype, "file. There may be issues in your table integrity.\nCorresponding R error message:\n", tab.res)) + } else { + tab.comp <- tryCatch(read.table(tested.file, header = TRUE, sep = "\t", check.names = FALSE, comment.char = "", quote = ""), error = conditionMessage) + if ((length(tab.comp) != 1) && (dim(tab.res) != dim(tab.comp))) { # wrong original import due to quotes inside a column name + return(tab.comp) + } else { + return(tab.res) + } + } + } + + ## Reading of input files + idsample <- tab.import(idsample, "sampleMetadata") + iddata <- tab.import(iddata, "dataMatrix") + + ### Table match check + table.check <- match2(iddata, idsample, "sample") + if (length(table.check) > 1) { + check.err(table.check) + } + + ### StockID + samp.id <- stockID(iddata, idsample, "sample") + iddata <- samp.id$dataMatrix + idsample <- samp.id$Metadata + samp.id <- samp.id$id.match + + ### Checking mandatory variables + mand.check <- "" + for (mandcol in c(sample_type_col_name, injection_order_col_name, batch_col_name)) { + if (!(mandcol %in% colnames(idsample))) { + mand.check <- c( + mand.check, "\nError: no '", mandcol, "' column in sample metadata.\n", + "Note: column names are case-sensitive.\n" + ) + } + } + if (length(mand.check) > 1) { + mand.check <- c( + mand.check, "\nFor more information, see the help section or:", + "\n http://workflow4metabolomics.org/sites/", + "workflow4metabolomics.org/files/files/w4e-2016-data_processing.pdf\n" + ) + check.err(mand.check) + } + + if (analyse == "batch_correction") { + ## Reading of Metadata Ions file + metaion <- read.table(metaion, header = T, sep = "\t", check.names = FALSE, comment.char = "") + ## Table match check + table.check <- c(table.check, match2(iddata, metaion, "variable")) + ## StockID + var.id <- stockID(iddata, metaion, "variable") + iddata <- var.id$dataMatrix + metaion <- var.id$Metadata + var.id <- var.id$id.match + } + + ### Formating + idsample[[1]] <- make.names(idsample[[1]]) + dimnames(iddata)[[1]] <- iddata[[1]] + + ### Transposition of ions data + idTdata <- t(iddata[, 2:dim(iddata)[2]]) + idTdata <- data.frame(dimnames(idTdata)[[1]], idTdata) + + ### Merge of 2 files (ok even if the two dataframe are not sorted on the same key) + ids <- merge(idsample, idTdata, by.x = 1, by.y = 1) + + ids[[batch_col_name]] <- as.factor(ids[[batch_col_name]]) + nbid <- dim(idsample)[2] + + ### Checking the number of sample and pool + + # least 2 samples + if (length(which(ids[[sample_type_col_name]] %in% sample_type_tags$sample)) < 2) { + table.check <- c( + table.check, "\nError: less than 2 samples specified in sample metadata.", + "\nMake sure this is not due to errors in your ", sample_type_col_name, " coding.\n" + ) + } + + # least 2 pools per batch for all batchs + B <- rep(0, length(levels(ids[[batch_col_name]]))) + for (nbB in 1:length(levels(ids[[batch_col_name]]))) { + B[nbB] <- length(which(ids[which(ids[[batch_col_name]] == (levels(ids[[batch_col_name]])[nbB])), , drop = FALSE][[sample_type_col_name]] %in% sample_type_tags$pool)) + } + if (length(which(B > 1)) == 0) { + table.check <- c( + table.check, "\nError: less than 2 pools specified in at least one batch in sample metadata.", + "\nMake sure this is not due to errors in your ", sample_type_col_name, " coding.\n" + ) + } + + ### Checking the unicity of samples and variables + uni.check <- function(tested.tab, tabtype, err.obj) { + unicity <- duplicated(tested.tab[, 1]) + if (sum(unicity) > 0) { + # Sending back an explicit error + duptable <- t(t(table(tested.tab[, 1][unicity]) + 1)) + err.obj <- c( + err.obj, paste0("\n-------\nError: your '", tabtype, "' IDs contain duplicates:\n"), + paste(rownames(duptable), duptable, sep = ": ", collapse = "\n"), + "\nSince identifiers are meant to be unique, please check your data.\n-------\n" + ) + } + return(err.obj) + } + table.check <- uni.check(ids, "sample", table.check) + if (analyse == "batch_correction") { + table.check <- uni.check(metaion, "variable", table.check) + } + + ## error check + check.err(table.check) + + + ### BC/DBC-specific processing + + # Gathering mandatory information in a single object + sm.meta <- list(batch = batch_col_name, injectionOrder = injection_order_col_name, sampleType = sample_type_col_name, sampleTag = sample_type_tags) + + if (analyse == "batch_correction") { + ## Launch + res <- norm_QCpool(ids, nbid, outlog, factbio, metaion, detail, FALSE, FALSE, method, span, valnull, sm.meta, min.norm) + ## Get back original IDs + var.id <- reproduceID(res[[1]], res[[2]], "variable", var.id) + res[[1]] <- var.id$dataMatrix + res[[2]] <- var.id$Metadata + samp.id <- reproduceID(res[[1]], res[[3]], "sample", samp.id) + res[[1]] <- samp.id$dataMatrix + res[[3]] <- samp.id$Metadata + ## Save files + save(res, file = rdata_output) + write.table(res[[1]], file = dataMatrix_out, sep = "\t", row.names = FALSE, quote = FALSE) + write.table(res[[2]], file = variableMetadata_out, sep = "\t", row.names = FALSE, quote = FALSE) + } else { + ## Launch + plotsituation(ids, nbid, out_graph_pdf, out_preNormSummary, factbio, span, sm.meta) + } +} # end of meth3L diff --git a/tools/batch_correction/batch_correction_3Lwrapper.R b/tools/batch_correction/batch_correction_3Lwrapper.R index 5ca70f07d..e117ead0a 100644 --- a/tools/batch_correction/batch_correction_3Lwrapper.R +++ b/tools/batch_correction/batch_correction_3Lwrapper.R @@ -26,11 +26,11 @@ ################################################################################################ -library(batch) #necessary for parseCommandArgs function +library(batch) # necessary for parseCommandArgs function -##------------------------------ +## ------------------------------ ## test help option -##------------------------------ +## ------------------------------ # Prog. constants argv.help <- commandArgs(trailingOnly = FALSE) @@ -38,102 +38,120 @@ script.path <- sub("--file=", "", argv.help[grep("--file=", argv.help)]) prog.name <- basename(script.path) # Test Help -if (length(grep('-h', argv.help)) > 0) { - cat("Usage: Rscript ", - prog.name, - "{args} \n", - "parameters: \n", - "\tanalyse {val}: must be set to \"batch_correction\"", - "\tdataMatrix {file}: set the input data matrix file (mandatory) \n", - "\tsampleMetadata {file}: set the input sample metadata file (mandatory) \n", - "\tvariableMetadata {file}: set the input variable metadata file (mandatory) \n", - "\tmethod {opt}: set the method; can set to \"linear\", \"lowess\" or \"loess\" (mandatory) \n", - "\tspan {condition}: set the span condition; set to \"none\" if method is set to \"linear\" (mandatory) \n", - "\tref_factor {value}: set the ref_factor value; (if span value is set to NULL, optional) \n", - "\tdetail {value}: set the detail value; (if span value is set to NULL, optional) \n", - "\tdataMatrix_out {file}: set the output data matrix file (mandatory) \n", - "\tvariableMetadata_out {file}: set the output variable metadata file (mandatory) \n", - "\tgraph_output {file}: set the output graph file (mandatory) \n", - "\trdata_output {file}: set the output Rdata file (mandatory) \n", - "\tbatch_col_name {val}: the column name for batch. Default value is \"batch\".\n", - "\tinjection_order_col_name {val}: the column name for the injection order. Default value is \"injectionOrder\".\n", - "\tsample_type_col_name {val}: the column name for the sample types. Default value is \"sampleType\".\n", - "\tsample_type_tags {val}: the tags used inside the sample type column, defined as key/value pairs separated by commas (example: blank=blank,pool=pool,sample=sample).\n", - "\n") - quit(status = 0) +if (length(grep("-h", argv.help)) > 0) { + cat( + "Usage: Rscript ", + prog.name, + "{args} \n", + "parameters: \n", + "\tanalyse {val}: must be set to \"batch_correction\"", + "\tdataMatrix {file}: set the input data matrix file (mandatory) \n", + "\tsampleMetadata {file}: set the input sample metadata file (mandatory) \n", + "\tvariableMetadata {file}: set the input variable metadata file (mandatory) \n", + "\tmethod {opt}: set the method; can set to \"linear\", \"lowess\" or \"loess\" (mandatory) \n", + "\tspan {condition}: set the span condition; set to \"none\" if method is set to \"linear\" (mandatory) \n", + "\tref_factor {value}: set the ref_factor value; (if span value is set to NULL, optional) \n", + "\tdetail {value}: set the detail value; (if span value is set to NULL, optional) \n", + "\tdataMatrix_out {file}: set the output data matrix file (mandatory) \n", + "\tvariableMetadata_out {file}: set the output variable metadata file (mandatory) \n", + "\tgraph_output {file}: set the output graph file (mandatory) \n", + "\trdata_output {file}: set the output Rdata file (mandatory) \n", + "\tbatch_col_name {val}: the column name for batch. Default value is \"batch\".\n", + "\tinjection_order_col_name {val}: the column name for the injection order. Default value is \"injectionOrder\".\n", + "\tsample_type_col_name {val}: the column name for the sample types. Default value is \"sampleType\".\n", + "\tsample_type_tags {val}: the tags used inside the sample type column, defined as key/value pairs separated by commas (example: blank=blank,pool=pool,sample=sample).\n", + "\n" + ) + quit(status = 0) } -##------------------------------ +## ------------------------------ ## init. params -##------------------------------ +## ------------------------------ -args = parseCommandArgs(evaluate=FALSE) #interpretation of arguments given in command line as an R list of objects +args <- parseCommandArgs(evaluate = FALSE) # interpretation of arguments given in command line as an R list of objects -cat('\nJob starting time:\n',format(Sys.time(), "%a %d %b %Y %X"), -'\n\n--------------------------------------------------------------------', -'\nParameters used:\n\n') +cat( + "\nJob starting time:\n", format(Sys.time(), "%a %d %b %Y %X"), + "\n\n--------------------------------------------------------------------", + "\nParameters used:\n\n" +) print(args) -cat('--------------------------------------------------------------------\n\n') +cat("--------------------------------------------------------------------\n\n") # Set default col names -if ( ! 'batch_col_name' %in% names(args)) - args[['batch_col_name']] <- 'batch' -if ( ! 'injection_order_col_name' %in% names(args)) - args[['injection_order_col_name']] <- 'injectionOrder' -if ( ! 'sample_type_col_name' %in% names(args)) - args[['sample_type_col_name']] <- 'sampleType' -if ( ! 'sample_type_tags' %in% names(args)) - args[['sample_type_tags']] <- 'blank=blank,pool=pool,sample=sample' +if (!"batch_col_name" %in% names(args)) { + args[["batch_col_name"]] <- "batch" +} +if (!"injection_order_col_name" %in% names(args)) { + args[["injection_order_col_name"]] <- "injectionOrder" +} +if (!"sample_type_col_name" %in% names(args)) { + args[["sample_type_col_name"]] <- "sampleType" +} +if (!"sample_type_tags" %in% names(args)) { + args[["sample_type_tags"]] <- "blank=blank,pool=pool,sample=sample" +} # Parse sample type tags sample.type.tags <- list() -for (kv in strsplit(strsplit(args$sample_type_tags, ',')[[1]], '=')) +for (kv in strsplit(strsplit(args$sample_type_tags, ",")[[1]], "=")) { sample.type.tags[[kv[[1]]]] <- kv[-1] -if ( ! all(c('pool', 'blank', 'sample') %in% names(sample.type.tags))) +} +if (!all(c("pool", "blank", "sample") %in% names(sample.type.tags))) { stop("All tags pool, blank and sample must be defined in option sampleTypeTags.") +} args$sample_type_tags <- sample.type.tags -##------------------------------ +## ------------------------------ ## init. functions -##------------------------------ +## ------------------------------ -source_local <- function(...){ +source_local <- function(...) { argv <- commandArgs(trailingOnly = FALSE) base_dir <- dirname(substring(argv[grep("--file=", argv)], 8)) - for(i in 1:length(list(...))){source(paste(base_dir, list(...)[[i]], sep="/"))} + for (i in 1:length(list(...))) { + source(paste(base_dir, list(...)[[i]], sep = "/")) + } } -#Import the different functions -source_local("batch_correction_3Lfct.R","batch_correction_3Llauncher.R","easyrlibrary-lib/RcheckLibrary.R","easyrlibrary-lib/miniTools.R") +# Import the different functions +source_local("batch_correction_3Lfct.R", "batch_correction_3Llauncher.R", "easyrlibrary-lib/RcheckLibrary.R", "easyrlibrary-lib/miniTools.R") # Specificities of BC and DBC -if(args$analyse == "batch_correction") { - args$out_graph_pdf <- NULL - args$out_preNormSummary <- NULL -}else{ - args$variableMetadata <- NULL - args$rdata_output <- NULL - args$dataMatrix_out <- NULL - args$variableMetadata_out <- NULL - args$graph_output <- NULL - args$method <- NULL - args$detail <- NULL - args$valnull <- NULL +if (args$analyse == "batch_correction") { + args$out_graph_pdf <- NULL + args$out_preNormSummary <- NULL +} else { + args$variableMetadata <- NULL + args$rdata_output <- NULL + args$dataMatrix_out <- NULL + args$variableMetadata_out <- NULL + args$graph_output <- NULL + args$method <- NULL + args$detail <- NULL + args$valnull <- NULL } # Launch tool -meth3L(idsample=args$sampleMetadata, iddata=args$dataMatrix, sample_type_col_name=args$sample_type_col_name, injection_order_col_name=args$injection_order_col_name, - batch_col_name=args$batch_col_name, sample_type_tags=args$sample_type_tags, factbio=args$ref_factor, analyse=args$analyse, metaion=args$variableMetadata, - detail=args$detail, method=args$method, outlog=args$graph_output, span=args$span, valnull=args$valnull, rdata_output=args$rdata_output, - dataMatrix_out=args$dataMatrix_out, variableMetadata_out=args$variableMetadata_out, out_graph_pdf=args$out_graph_pdf, out_preNormSummary=args$out_preNormSummary, - min.norm=1) - - -cat('\n\n--------------------------------------------------------------------', -'\nInformation about R (version, Operating System, attached or loaded packages):\n\n') +meth3L( + idsample = args$sampleMetadata, iddata = args$dataMatrix, sample_type_col_name = args$sample_type_col_name, injection_order_col_name = args$injection_order_col_name, + batch_col_name = args$batch_col_name, sample_type_tags = args$sample_type_tags, factbio = args$ref_factor, analyse = args$analyse, metaion = args$variableMetadata, + detail = args$detail, method = args$method, outlog = args$graph_output, span = args$span, valnull = args$valnull, rdata_output = args$rdata_output, + dataMatrix_out = args$dataMatrix_out, variableMetadata_out = args$variableMetadata_out, out_graph_pdf = args$out_graph_pdf, out_preNormSummary = args$out_preNormSummary, + min.norm = 1 +) + + +cat( + "\n\n--------------------------------------------------------------------", + "\nInformation about R (version, Operating System, attached or loaded packages):\n\n" +) sessionInfo() -cat('--------------------------------------------------------------------\n', -'\nJob ending time:\n',format(Sys.time(), "%a %d %b %Y %X")) +cat( + "--------------------------------------------------------------------\n", + "\nJob ending time:\n", format(Sys.time(), "%a %d %b %Y %X") +) rm(args) diff --git a/tools/batch_correction/batch_correction_all_loess_script.R b/tools/batch_correction/batch_correction_all_loess_script.R index 28f84c10c..2b0f7cf55 100644 --- a/tools/batch_correction/batch_correction_all_loess_script.R +++ b/tools/batch_correction/batch_correction_all_loess_script.R @@ -1,46 +1,50 @@ loessF <- function(datVn, qcaVi, preVi, spnN) { - - if(length(qcaVi) < 5) { - + if (length(qcaVi) < 5) { return(predict(lm(datVn[qcaVi] ~ qcaVi), - newdata = data.frame(qcaVi = preVi))) - + newdata = data.frame(qcaVi = preVi) + )) } else { - - return(predict(loess(datVn[qcaVi] ~ qcaVi, - control = loess.control(surface = "direct"), - span = spnN), - newdata = data.frame(qcaVi = preVi))) - + return(predict( + loess(datVn[qcaVi] ~ qcaVi, + control = loess.control(surface = "direct"), + span = spnN + ), + newdata = data.frame(qcaVi = preVi) + )) } ## Note: ## the surface = 'direct' argument allows extrapolation - } ## loessF plotBatchF <- function(datMN, samDF.arg, spnN.arg) { - maiC <- switch(gsub("MN", "", deparse(substitute(datMN))), - raw = "Raw", - nrm = "Normalized") - - colVc <- c(samp = "green4", - biol = "green4", - pool = "red", - blan = "black", - other = "yellow") + raw = "Raw", + nrm = "Normalized" + ) + + colVc <- c( + samp = "green4", + biol = "green4", + pool = "red", + blan = "black", + other = "yellow" + ) par(font = 2, font.axis = 2, font.lab = 2, lwd = 2, pch = 18) layout(matrix(c(1, 1, 2, 3), nrow = 2), - widths = c(0.7, 0.3)) + widths = c(0.7, 0.3) + ) obsNamVc <- rownames(datMN) - obsColVc <- sapply(substr(samDF.arg[, args$sample_type_col_name], 1, 4), - function(typC) - ifelse(typC %in% names(colVc), colVc[typC], colVc["other"])) + obsColVc <- sapply( + substr(samDF.arg[, args$sample_type_col_name], 1, 4), + function(typC) { + ifelse(typC %in% names(colVc), colVc[typC], colVc["other"]) + } + ) ## Graphic 1: Sum of intensities for each sample @@ -51,53 +55,66 @@ plotBatchF <- function(datMN, samDF.arg, spnN.arg) { sumVn <- rowSums(datMN, na.rm = TRUE) plot(sumVn, - cex = 1.2, - col = obsColVc, - pch = 18, - xaxs = "i", - xlab = "", - ylab = "") + cex = 1.2, + col = obsColVc, + pch = 18, + xaxs = "i", + xlab = "", + ylab = "" + ) mtext("Injection order", - line = 2.2, - side = 1) + line = 2.2, + side = 1 + ) mtext("Sum of variable intensities", - line = 2.2, - side = 2) + line = 2.2, + side = 2 + ) mtext(maiC, cex = 1.2, line = 1.5, side = 3) - abline(v = cumsum(batTab) + 0.5, - col = "red") + abline( + v = cumsum(batTab) + 0.5, + col = "red" + ) mtext(names(batTab), - at = batTab / 2 + c(0, cumsum(batTab[-length(batTab)]))) + at = batTab / 2 + c(0, cumsum(batTab[-length(batTab)])) + ) obsColVuc <- obsColVc[sort(unique(names(obsColVc)))] text(rep(batTab[1], times = length(obsColVuc)), - par("usr")[3] + (0.97 - length(obsColVuc) * 0.03 + 1:length(obsColVuc) * 0.03) * diff(par("usr")[3:4]), - col = obsColVuc, - font = 2, - labels = names(obsColVuc), - pos = 2) - - for(batC in names(batTab)) { - + par("usr")[3] + (0.97 - length(obsColVuc) * 0.03 + 1:length(obsColVuc) * 0.03) * diff(par("usr")[3:4]), + col = obsColVuc, + font = 2, + labels = names(obsColVuc), + pos = 2 + ) + + for (batC in names(batTab)) { batSeqVi <- which(samDF.arg[, args$batch_col_name] == batC) - batPooVi <- intersect(batSeqVi, - which(samDF.arg[, args$sample_type_col_name] == "pool")) - batSamVi <- intersect(batSeqVi, - which(samDF.arg[, args$sample_type_col_name] == "sample")) - if(length(batPooVi)) - lines(batSeqVi, - loessF(sumVn, batPooVi, batSeqVi, spnN=spnN.arg), - col = colVc["pool"]) - if (length(batSamVi)) - lines(batSeqVi, - loessF(sumVn, batSamVi, batSeqVi, spnN=spnN.arg), - col = colVc["samp"]) - + batPooVi <- intersect( + batSeqVi, + which(samDF.arg[, args$sample_type_col_name] == "pool") + ) + batSamVi <- intersect( + batSeqVi, + which(samDF.arg[, args$sample_type_col_name] == "sample") + ) + if (length(batPooVi)) { + lines(batSeqVi, + loessF(sumVn, batPooVi, batSeqVi, spnN = spnN.arg), + col = colVc["pool"] + ) + } + if (length(batSamVi)) { + lines(batSeqVi, + loessF(sumVn, batSamVi, batSeqVi, spnN = spnN.arg), + col = colVc["samp"] + ) + } } ## Graphics 2 and 3 (right): PCA score plots of components 1-4 @@ -107,7 +124,7 @@ plotBatchF <- function(datMN, samDF.arg, spnN.arg) { pcaMN <- datMN - if(any(is.na(pcaMN))) { + if (any(is.na(pcaMN))) { minN <- min(pcaMN, na.rm = TRUE) pcaMN[is.na(pcaMN)] <- minN } @@ -125,8 +142,7 @@ plotBatchF <- function(datMN, samDF.arg, spnN.arg) { par(mar = c(3.6, 3.6, 0.6, 1.1)) - for(pcsN in 1:length(pcsLs)) { - + for (pcsN in 1:length(pcsLs)) { pcsVn <- pcsLs[[pcsN]] tcsMN <- tMN[, pcsVn] @@ -138,114 +154,138 @@ plotBatchF <- function(datMN, samDF.arg, spnN.arg) { hotFisN <- hotN * qf(0.95, 2, n - 2) - hotVn <- apply(tcsMN, - 1, - function(x) 1 - pf(1 / hotN * t(as.matrix(x)) %*% micMN %*% as.matrix(x), 2, n - 2)) + hotVn <- apply( + tcsMN, + 1, + function(x) 1 - pf(1 / hotN * t(as.matrix(x)) %*% micMN %*% as.matrix(x), 2, n - 2) + ) obsHotVi <- which(hotVn < 0.05) xLabC <- paste("t", - pcsVn[1], - "(", - round(vRelVn[pcsVn[1]] * 100), - "%)", - sep = "") + pcsVn[1], + "(", + round(vRelVn[pcsVn[1]] * 100), + "%)", + sep = "" + ) yLabC <- paste("t", - pcsVn[2], - "(", - round(vRelVn[pcsVn[2]] * 100), - "%)", - sep = "") + pcsVn[2], + "(", + round(vRelVn[pcsVn[2]] * 100), + "%)", + sep = "" + ) xLimVn <- c(-1, 1) * max(sqrt(var(tcsMN[, 1]) * hotFisN), max(abs(tcsMN[, 1]))) yLimVn <- c(-1, 1) * max(sqrt(var(tcsMN[, 2]) * hotFisN), max(abs(tcsMN[, 2]))) plot(tcsMN, - main = "", - type = "n", - xlab = "", - ylab = "", - xlim = xLimVn, - ylim = yLimVn) + main = "", + type = "n", + xlab = "", + ylab = "", + xlim = xLimVn, + ylim = yLimVn + ) mtext(xLabC, - line = 2.2, - side = 1) + line = 2.2, + side = 1 + ) mtext(yLabC, - line = 2.2, - side = 2) + line = 2.2, + side = 2 + ) par(lwd = 1) - abline(v = axTicks(1), - col = "grey") + abline( + v = axTicks(1), + col = "grey" + ) - abline(h = axTicks(2), - col = "grey") + abline( + h = axTicks(2), + col = "grey" + ) abline(v = 0) abline(h = 0) - lines(sqrt(var(tcsMN[, 1]) * hotFisN) * cos(radVn), - sqrt(var(tcsMN[, 2]) * hotFisN) * sin(radVn)) + lines( + sqrt(var(tcsMN[, 1]) * hotFisN) * cos(radVn), + sqrt(var(tcsMN[, 2]) * hotFisN) * sin(radVn) + ) points(tcsMN, - col = obsColVc, - pch = 18) + col = obsColVc, + pch = 18 + ) - if(length(obsHotVi)) + if (length(obsHotVi)) { text(tcsMN[obsHotVi, 1], - tcsMN[obsHotVi, 2], - col = obsColVc[obsHotVi], - labels = obsNamVc[obsHotVi], - pos = 3) - + tcsMN[obsHotVi, 2], + col = obsColVc[obsHotVi], + labels = obsNamVc[obsHotVi], + pos = 3 + ) + } } ## for(pcsN in 1:length(pcsLs)) { - return(invisible(list(sumVn = sumVn, - tcsMN = tcsMN))) - + return(invisible(list( + sumVn = sumVn, + tcsMN = tcsMN + ))) } ## plotBatchF shiftBatchCorrectF <- function(rawMN.arg, samDF.arg, refC.arg, spnN.arg) { - cat("\nReference observations are: ", refC.arg, "\n") ## computing median off all pools (or samples) for each variable - refMeaVn <- apply(rawMN.arg[samDF.arg[, args$sample_type_col_name] == refC.arg, ], - 2, - function(feaRefVn) mean(feaRefVn, na.rm = TRUE)) + refMeaVn <- apply( + rawMN.arg[samDF.arg[, args$sample_type_col_name] == refC.arg, ], + 2, + function(feaRefVn) mean(feaRefVn, na.rm = TRUE) + ) ## splitting data and sample metadata from each batch batRawLs <- split(as.data.frame(rawMN.arg), - f = samDF.arg[, args$batch_col_name]) + f = samDF.arg[, args$batch_col_name] + ) batRawLs <- lapply(batRawLs, function(inpDF) as.matrix(inpDF)) batSamLs <- split(as.data.frame(samDF.arg), - f = samDF.arg[, args$batch_col_name]) + f = samDF.arg[, args$batch_col_name] + ) ## checking extrapolation: are there pools at the first and last observations of each batch - if(refC.arg == args$sample_type_tags$pool) { - pooExtML <- matrix(FALSE, nrow = 2, ncol = length(batRawLs), - dimnames = list(c("first", "last"), names(batRawLs))) - for(batC in names(batSamLs)) { + if (refC.arg == args$sample_type_tags$pool) { + pooExtML <- matrix(FALSE, + nrow = 2, ncol = length(batRawLs), + dimnames = list(c("first", "last"), names(batRawLs)) + ) + for (batC in names(batSamLs)) { batSamTypVc <- batSamLs[[batC]][, args$sample_type_col_name] pooExtML["first", batC] <- head(batSamTypVc, 1) == args$sample_type_tags$pool pooExtML["last", batC] <- tail(batSamTypVc, 1) == args$sample_type_tags$pool } - if(!all(c(pooExtML))) { + if (!all(c(pooExtML))) { cat("\nWarning: Pools are missing at the first and/or last position of the following batches:\n") pooExtBatVi <- which(!apply(pooExtML, 2, all)) - for(i in 1:length(pooExtBatVi)) + for (i in 1:length(pooExtBatVi)) { cat(names(pooExtBatVi)[i], ": ", - paste(rownames(pooExtML)[!pooExtML[, pooExtBatVi[i]]], collapse = ", "), "\n", sep = "") + paste(rownames(pooExtML)[!pooExtML[, pooExtBatVi[i]]], collapse = ", "), "\n", + sep = "" + ) + } cat("Extrapolating loess fits for these batches may result in inaccurate modeling!\n") } } @@ -256,7 +296,7 @@ shiftBatchCorrectF <- function(rawMN.arg, cat("\nProcessing batch:") - for(batC in names(batRawLs)) { ## processing each batch individually + for (batC in names(batRawLs)) { ## processing each batch individually cat("\n", batC) @@ -267,14 +307,17 @@ shiftBatchCorrectF <- function(rawMN.arg, batRefVi <- which(batSamDF[, args$sample_type_col_name] == refC.arg) - if(length(batRefVi) < 5) - cat("\nWarning: less than 5 '", refC.arg, "'; linear regression will be performed instead of loess regression for this batch\n", sep="") + if (length(batRefVi) < 5) { + cat("\nWarning: less than 5 '", refC.arg, "'; linear regression will be performed instead of loess regression for this batch\n", sep = "") + } ## prediction of the loess fit - batLoeMN <- apply(batRawMN, - 2, - function(rawVn) loessF(rawVn, batRefVi, batAllVi, spnN=spnN.arg)) + batLoeMN <- apply( + batRawMN, + 2, + function(rawVn) loessF(rawVn, batRefVi, batAllVi, spnN = spnN.arg) + ) ## normalization @@ -282,9 +325,10 @@ shiftBatchCorrectF <- function(rawMN.arg, batNrmMN <- batRawMN / batLoeMN - nrmMN <- rbind(nrmMN, - batNrmMN) - + nrmMN <- rbind( + nrmMN, + batNrmMN + ) } cat("\n") @@ -292,5 +336,4 @@ shiftBatchCorrectF <- function(rawMN.arg, nrmMN <- sweep(nrmMN, MARGIN = 2, STATS = refMeaVn, FUN = "*") return(nrmMN) - } ## shiftBatchCorrectF diff --git a/tools/batch_correction/batch_correction_all_loess_wrapper.R b/tools/batch_correction/batch_correction_all_loess_wrapper.R index 1903b388a..7f8c599fa 100755 --- a/tools/batch_correction/batch_correction_all_loess_wrapper.R +++ b/tools/batch_correction/batch_correction_all_loess_wrapper.R @@ -2,9 +2,9 @@ library(batch) ## necessary for parseCommandArgs function -##------------------------------ +## ------------------------------ ## test help option -##------------------------------ +## ------------------------------ # Prog. constants argv.help <- commandArgs(trailingOnly = FALSE) @@ -12,60 +12,68 @@ script.path <- sub("--file=", "", argv.help[grep("--file=", argv.help)]) prog.name <- basename(script.path) # Test Help -if (length(grep('-h', argv.help)) > 0) { - cat("Usage: Rscript ", - prog.name, - "{args} \n", - "parameters: \n", - "\tdataMatrix {file}: set the input data matrix file (mandatory) \n", - "\tsampleMetadata {file}: set the input sample metadata file (mandatory) \n", - "\tvariableMetadata {file}: set the input variable metadata file (mandatory) \n", - "\tmethod {opt}: set the method; can set to \"all_loess_pool\" or \"all_loess_sample\" (mandatory) \n", - "\tspan {condition}: set the span condition; (mandatory) \n", - "\tdataMatrix_out {file}: set the output data matrix file (mandatory) \n", - "\tvariableMetadata_out {file}: set the output variable metadata file (mandatory) \n", - "\tgraph_output {file}: set the output graph file (mandatory) \n", - "\trdata_output {file}: set the output Rdata file (mandatory) \n", - "\tbatch_col_name {val}: the column name for batch. Default value is \"batch\".\n", - "\tinjection_order_col_name {val}: the column name for the injection order. Default value is \"injectionOrder\".\n", - "\tsample_type_col_name {val}: the column name for the sample types. Default value is \"sampleType\".\n", - "\tsample_type_tags {val}: the tags used inside the sample type column, defined as key/value pairs separated by commas (example: blank=blank,pool=pool,sample=sample).\n", - "\n") - quit(status = 0) +if (length(grep("-h", argv.help)) > 0) { + cat( + "Usage: Rscript ", + prog.name, + "{args} \n", + "parameters: \n", + "\tdataMatrix {file}: set the input data matrix file (mandatory) \n", + "\tsampleMetadata {file}: set the input sample metadata file (mandatory) \n", + "\tvariableMetadata {file}: set the input variable metadata file (mandatory) \n", + "\tmethod {opt}: set the method; can set to \"all_loess_pool\" or \"all_loess_sample\" (mandatory) \n", + "\tspan {condition}: set the span condition; (mandatory) \n", + "\tdataMatrix_out {file}: set the output data matrix file (mandatory) \n", + "\tvariableMetadata_out {file}: set the output variable metadata file (mandatory) \n", + "\tgraph_output {file}: set the output graph file (mandatory) \n", + "\trdata_output {file}: set the output Rdata file (mandatory) \n", + "\tbatch_col_name {val}: the column name for batch. Default value is \"batch\".\n", + "\tinjection_order_col_name {val}: the column name for the injection order. Default value is \"injectionOrder\".\n", + "\tsample_type_col_name {val}: the column name for the sample types. Default value is \"sampleType\".\n", + "\tsample_type_tags {val}: the tags used inside the sample type column, defined as key/value pairs separated by commas (example: blank=blank,pool=pool,sample=sample).\n", + "\n" + ) + quit(status = 0) } -##------------------------------ +## ------------------------------ ## init. params -##------------------------------ +## ------------------------------ -args = parseCommandArgs(evaluate=FALSE) #interpretation of arguments given in command line as an R list of objects +args <- parseCommandArgs(evaluate = FALSE) # interpretation of arguments given in command line as an R list of objects # Set default col names -if ( ! 'batch_col_name' %in% names(args)) - args[['batch_col_name']] <- 'batch' -if ( ! 'injection_order_col_name' %in% names(args)) - args[['injection_order_col_name']] <- 'injectionOrder' -if ( ! 'sample_type_col_name' %in% names(args)) - args[['sample_type_col_name']] <- 'sampleType' -if ( ! 'sample_type_tags' %in% names(args)) - args[['sample_type_tags']] <- 'blank=blank,pool=pool,sample=sample' +if (!"batch_col_name" %in% names(args)) { + args[["batch_col_name"]] <- "batch" +} +if (!"injection_order_col_name" %in% names(args)) { + args[["injection_order_col_name"]] <- "injectionOrder" +} +if (!"sample_type_col_name" %in% names(args)) { + args[["sample_type_col_name"]] <- "sampleType" +} +if (!"sample_type_tags" %in% names(args)) { + args[["sample_type_tags"]] <- "blank=blank,pool=pool,sample=sample" +} # Parse sample type tags sample.type.tags <- list() -for (kv in strsplit(strsplit(args$sample_type_tags, ',')[[1]], '=')) - sample.type.tags[[kv[[1]]]] <- kv[[2]] -if ( ! all(c('pool', 'blank', 'sample') %in% names(sample.type.tags))) - stop("All tags pool, blank and sample must be defined in option sampleTypeTags.") +for (kv in strsplit(strsplit(args$sample_type_tags, ",")[[1]], "=")) { + sample.type.tags[[kv[[1]]]] <- kv[[2]] +} +if (!all(c("pool", "blank", "sample") %in% names(sample.type.tags))) { + stop("All tags pool, blank and sample must be defined in option sampleTypeTags.") +} args$sample_type_tags <- sample.type.tags -##------------------------------ +## ------------------------------ ## init. functions -##------------------------------ +## ------------------------------ -source_local <- function(fname){ +source_local <- function(fname) { argv <- commandArgs(trailingOnly = FALSE) base_dir <- dirname(substring(argv[grep("--file=", argv)], 8)) - source(paste(base_dir, fname, sep="/")) + source(paste(base_dir, fname, sep = "/")) } ## Import the different functions @@ -80,95 +88,107 @@ argVc <- unlist(args) ## ('all_loess_pool') or 'sample' ('all_loess_sample') -##------------------------------ +## ------------------------------ ## Initializing -##------------------------------ +## ------------------------------ ## options -##-------- +## -------- strAsFacL <- options()$stringsAsFactors options(stringsAsFactors = FALSE) ## libraries -##---------- +## ---------- suppressMessages(library(ropls)) -if(packageVersion("ropls") < "1.4.0") +if (packageVersion("ropls") < "1.4.0") { stop("Please use 'ropls' versions of 1.4.0 and above") +} ## constants -##---------- +## ---------- modNamC <- "Batch correction" ## module name ## log file -##--------- +## --------- ## sink(argVc["information"]) ## not implemented cat("\nStart of the '", modNamC, "' Galaxy module call: ", - format(Sys.time(), "%a %d %b %Y %X"), "\n", sep="") + format(Sys.time(), "%a %d %b %Y %X"), "\n", + sep = "" +) ## loading -##-------- +## -------- rawMN <- t(as.matrix(read.table(argVc["dataMatrix"], - header = TRUE, - comment.char = '', - row.names = 1, - sep = "\t"))) + header = TRUE, + comment.char = "", + row.names = 1, + sep = "\t" +))) samDF <- read.table(argVc["sampleMetadata"], - header = TRUE, - comment.char = '', - row.names = 1, - sep = "\t") + header = TRUE, + comment.char = "", + row.names = 1, + sep = "\t" +) varDF <- read.table(argVc["variableMetadata"], - check.names = FALSE, - header = TRUE, - comment.char = '', - row.names = 1, - sep = "\t") ## not used; for compatibility only + check.names = FALSE, + header = TRUE, + comment.char = "", + row.names = 1, + sep = "\t" +) ## not used; for compatibility only refC <- tolower(gsub("all_loess_", "", argVc["method"])) spnN <- as.numeric(argVc["span"]) ## checking -##--------- +## --------- -stopifnot(refC %in% c('pool', 'sample')) +stopifnot(refC %in% c("pool", "sample")) refC <- args$sample_type_tags[[refC]] -if(refC == args$sample_type_tags$pool && - !any(args$sample_type_tags$pool %in% samDF[, args$sample_type_col_name])) +if (refC == args$sample_type_tags$pool && + !any(args$sample_type_tags$pool %in% samDF[, args$sample_type_col_name])) { stop("No 'pool' found in the 'sampleType' column; use the samples as normalization reference instead") +} refMN <- rawMN[samDF[, args$sample_type_col_name] == refC, ] -refNasZerVl <- apply(refMN, 2, - function(refVn) - all(sapply(refVn, - function(refN) {is.na(refN) || refN == 0}))) - -if(sum(refNasZerVl)) { - +refNasZerVl <- apply( + refMN, 2, + function(refVn) { + all(sapply( + refVn, + function(refN) { + is.na(refN) || refN == 0 + } + )) + } +) + +if (sum(refNasZerVl)) { refNasZerVi <- which(refNasZerVl) cat("The following variables have 'NA' or 0 values in all reference samples; they will be removed from the data:\n", sep = "") rawMN <- rawMN[, !refNasZerVl, drop = FALSE] varDF <- varDF[!refNasZerVl, , drop = FALSE] - } -##------------------------------ +## ------------------------------ ## Computation -##------------------------------ +## ------------------------------ ## ordering (batch and injection order) -##------------------------------------- +## ------------------------------------- samDF[, "ordIniVi"] <- 1:nrow(rawMN) ordBatInjVi <- order(samDF[, args$batch_col_name], samDF[, args$injection_order_col_name]) @@ -176,15 +196,17 @@ rawMN <- rawMN[ordBatInjVi, ] samDF <- samDF[ordBatInjVi, ] ## signal drift and batch-effect correction -##----------------------------------------- +## ----------------------------------------- -nrmMN <- shiftBatchCorrectF(rawMN, - samDF, - refC, - spnN) +nrmMN <- shiftBatchCorrectF( + rawMN, + samDF, + refC, + spnN +) ## figure -##------- +## ------- cat("\nPlotting\n") @@ -194,52 +216,63 @@ plotBatchF(nrmMN, samDF, spnN) dev.off() ## returning to initial order -##--------------------------- +## --------------------------- ordIniVi <- order(samDF[, "ordIniVi"]) nrmMN <- nrmMN[ordIniVi, ] samDF <- samDF[ordIniVi, ] -samDF <- samDF[, colnames(samDF) != "ordIniVi", drop=FALSE] +samDF <- samDF[, colnames(samDF) != "ordIniVi", drop = FALSE] -##------------------------------ +## ------------------------------ ## Ending -##------------------------------ +## ------------------------------ ## saving -##------- +## ------- datMN <- nrmMN -datDF <- cbind.data.frame(dataMatrix = colnames(datMN), - as.data.frame(t(datMN))) +datDF <- cbind.data.frame( + dataMatrix = colnames(datMN), + as.data.frame(t(datMN)) +) write.table(datDF, - file = argVc["dataMatrix_out"], - quote = FALSE, - row.names = FALSE, - sep = "\t") - -varDF <- cbind.data.frame(variableMetadata = rownames(varDF), - varDF) ## not modified; for compatibility only + file = argVc["dataMatrix_out"], + quote = FALSE, + row.names = FALSE, + sep = "\t" +) + +varDF <- cbind.data.frame( + variableMetadata = rownames(varDF), + varDF +) ## not modified; for compatibility only write.table(varDF, - file = argVc["variableMetadata_out"], - quote = FALSE, - row.names = FALSE, - sep = "\t") - - -res <- list(dataMatrix_raw = rawMN, - dataMatrix_normalized = nrmMN, - sampleMetadata = samDF) + file = argVc["variableMetadata_out"], + quote = FALSE, + row.names = FALSE, + sep = "\t" +) + + +res <- list( + dataMatrix_raw = rawMN, + dataMatrix_normalized = nrmMN, + sampleMetadata = samDF +) save(res, - file = argVc["rdata_output"]) ## for compatibility + file = argVc["rdata_output"] +) ## for compatibility ## closing -##-------- +## -------- cat("\nEnd of '", modNamC, "' Galaxy module call: ", - as.character(Sys.time()), "\n", sep = "") + as.character(Sys.time()), "\n", + sep = "" +) ## sink() @@ -247,4 +280,3 @@ options(stringsAsFactors = strAsFacL) rm(argVc) - From 2ffc5180777926dc0b2ab89ffdaa7b106aa17167 Mon Sep 17 00:00:00 2001 From: hechth Date: Thu, 10 Jul 2025 15:10:50 +0200 Subject: [PATCH 05/19] fixing linter --- tools/batch_correction/batch_correction.xml | 8 ++------ tools/batch_correction/determine_bc.xml | 6 +----- 2 files changed, 3 insertions(+), 11 deletions(-) diff --git a/tools/batch_correction/batch_correction.xml b/tools/batch_correction/batch_correction.xml index a4e0068d5..e5e883e23 100644 --- a/tools/batch_correction/batch_correction.xml +++ b/tools/batch_correction/batch_correction.xml @@ -11,12 +11,8 @@ bioconductor-pcamethods bioconductor-ropls - - - - - - r-ade4 bioconductor-pcamethods - - - - - Date: Thu, 10 Jul 2025 15:45:12 +0200 Subject: [PATCH 06/19] trying to fix planemo test bug --- tools/batch_correction/batch_correction.xml | 20 +++++++++----------- tools/batch_correction/determine_bc.xml | 12 ++++-------- tools/batch_correction/macros.xml | 9 +++++++++ 3 files changed, 22 insertions(+), 19 deletions(-) diff --git a/tools/batch_correction/batch_correction.xml b/tools/batch_correction/batch_correction.xml index e5e883e23..0fcb8d741 100644 --- a/tools/batch_correction/batch_correction.xml +++ b/tools/batch_correction/batch_correction.xml @@ -5,13 +5,11 @@ macros.xml - - r-batch - r-ade4 - bioconductor-pcamethods + bioconductor-ropls - - + r-w4mrutils + + - - - + + + @@ -94,7 +92,7 @@ - + @@ -102,7 +100,7 @@ - + diff --git a/tools/batch_correction/determine_bc.xml b/tools/batch_correction/determine_bc.xml index 3355bc457..1036545b6 100644 --- a/tools/batch_correction/determine_bc.xml +++ b/tools/batch_correction/determine_bc.xml @@ -5,11 +5,7 @@ macros.xml - - r-batch - r-ade4 - bioconductor-pcamethods - + - - + + - + diff --git a/tools/batch_correction/macros.xml b/tools/batch_correction/macros.xml index 7924563da..ce9e6ed1f 100644 --- a/tools/batch_correction/macros.xml +++ b/tools/batch_correction/macros.xml @@ -1,6 +1,15 @@ + + + r-batch + r-ade4 + bioconductor-pcamethods + + + +
From cd604c7c3d3ce8f2ad6f2a4c638b0f7e66aa0e72 Mon Sep 17 00:00:00 2001 From: hechth Date: Thu, 10 Jul 2025 15:49:32 +0200 Subject: [PATCH 07/19] switched profile back to 23 --- tools/batch_correction/batch_correction.xml | 2 +- tools/batch_correction/determine_bc.xml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/tools/batch_correction/batch_correction.xml b/tools/batch_correction/batch_correction.xml index 0fcb8d741..49676a178 100644 --- a/tools/batch_correction/batch_correction.xml +++ b/tools/batch_correction/batch_correction.xml @@ -1,4 +1,4 @@ - + Corrects intensities for signal drift and batch-effects diff --git a/tools/batch_correction/determine_bc.xml b/tools/batch_correction/determine_bc.xml index 1036545b6..ee2e8f595 100644 --- a/tools/batch_correction/determine_bc.xml +++ b/tools/batch_correction/determine_bc.xml @@ -1,4 +1,4 @@ - + to choose between linear, lowess and loess methods From 0b04fc650acb54a2630396a1baa2584934dc138a Mon Sep 17 00:00:00 2001 From: hechth Date: Thu, 10 Jul 2025 16:19:29 +0200 Subject: [PATCH 08/19] updated dependencies --- tools/batch_correction/batch_correction.xml | 2 +- tools/batch_correction/batch_correction_3Lwrapper.R | 3 ++- tools/batch_correction/determine_bc.xml | 4 +++- tools/batch_correction/macros.xml | 5 ++--- 4 files changed, 8 insertions(+), 6 deletions(-) diff --git a/tools/batch_correction/batch_correction.xml b/tools/batch_correction/batch_correction.xml index 49676a178..1b357702b 100644 --- a/tools/batch_correction/batch_correction.xml +++ b/tools/batch_correction/batch_correction.xml @@ -6,7 +6,7 @@ - bioconductor-ropls + bioconductor-ropls r-w4mrutils diff --git a/tools/batch_correction/batch_correction_3Lwrapper.R b/tools/batch_correction/batch_correction_3Lwrapper.R index e117ead0a..76cc3be0f 100644 --- a/tools/batch_correction/batch_correction_3Lwrapper.R +++ b/tools/batch_correction/batch_correction_3Lwrapper.R @@ -27,6 +27,7 @@ library(batch) # necessary for parseCommandArgs function +library(W4MRUtils) ## ------------------------------ ## test help option @@ -117,7 +118,7 @@ source_local <- function(...) { } } # Import the different functions -source_local("batch_correction_3Lfct.R", "batch_correction_3Llauncher.R", "easyrlibrary-lib/RcheckLibrary.R", "easyrlibrary-lib/miniTools.R") +source_local("batch_correction_3Lfct.R", "batch_correction_3Llauncher.R") # Specificities of BC and DBC if (args$analyse == "batch_correction") { diff --git a/tools/batch_correction/determine_bc.xml b/tools/batch_correction/determine_bc.xml index ee2e8f595..930b30b1c 100644 --- a/tools/batch_correction/determine_bc.xml +++ b/tools/batch_correction/determine_bc.xml @@ -5,7 +5,9 @@ macros.xml - + + libgfortran + - r-batch - r-ade4 - bioconductor-pcamethods + r-ade4 + bioconductor-pcamethods From 49d389ae51ef331fbee52403e4657952d8cbb922 Mon Sep 17 00:00:00 2001 From: hechth Date: Thu, 10 Jul 2025 17:26:50 +0200 Subject: [PATCH 09/19] fixed deps and commandline arg parsing using W4MRUtils --- tools/batch_correction/batch_correction.xml | 2 +- tools/batch_correction/batch_correction_3Lwrapper.R | 3 +-- tools/batch_correction/batch_correction_all_loess_wrapper.R | 4 ++-- tools/batch_correction/macros.xml | 2 +- 4 files changed, 5 insertions(+), 6 deletions(-) diff --git a/tools/batch_correction/batch_correction.xml b/tools/batch_correction/batch_correction.xml index 1b357702b..216dd8f31 100644 --- a/tools/batch_correction/batch_correction.xml +++ b/tools/batch_correction/batch_correction.xml @@ -6,7 +6,7 @@ - bioconductor-ropls + bioconductor-ropls r-w4mrutils diff --git a/tools/batch_correction/batch_correction_3Lwrapper.R b/tools/batch_correction/batch_correction_3Lwrapper.R index 76cc3be0f..5237303a4 100644 --- a/tools/batch_correction/batch_correction_3Lwrapper.R +++ b/tools/batch_correction/batch_correction_3Lwrapper.R @@ -26,7 +26,6 @@ ################################################################################################ -library(batch) # necessary for parseCommandArgs function library(W4MRUtils) ## ------------------------------ @@ -70,7 +69,7 @@ if (length(grep("-h", argv.help)) > 0) { ## init. params ## ------------------------------ -args <- parseCommandArgs(evaluate = FALSE) # interpretation of arguments given in command line as an R list of objects +args <- parse_args() # interpretation of arguments given in command line as an R list of objects cat( diff --git a/tools/batch_correction/batch_correction_all_loess_wrapper.R b/tools/batch_correction/batch_correction_all_loess_wrapper.R index 7f8c599fa..1d6285ede 100755 --- a/tools/batch_correction/batch_correction_all_loess_wrapper.R +++ b/tools/batch_correction/batch_correction_all_loess_wrapper.R @@ -1,6 +1,6 @@ #!/usr/bin/env Rscript -library(batch) ## necessary for parseCommandArgs function +library(W4MRUtils) ## necessary for parseCommandArgs function ## ------------------------------ ## test help option @@ -40,7 +40,7 @@ if (length(grep("-h", argv.help)) > 0) { ## init. params ## ------------------------------ -args <- parseCommandArgs(evaluate = FALSE) # interpretation of arguments given in command line as an R list of objects +args <- parse_args() # interpretation of arguments given in command line as an R list of objects # Set default col names if (!"batch_col_name" %in% names(args)) { diff --git a/tools/batch_correction/macros.xml b/tools/batch_correction/macros.xml index 1327c27da..7f9b13cf0 100644 --- a/tools/batch_correction/macros.xml +++ b/tools/batch_correction/macros.xml @@ -4,7 +4,7 @@ r-ade4 - bioconductor-pcamethods + bioconductor-pcamethods From c3a0184b0c6fb80d344aeaff2cee25e8b4d8ed1a Mon Sep 17 00:00:00 2001 From: hechth Date: Thu, 10 Jul 2025 17:34:54 +0200 Subject: [PATCH 10/19] updated use of W4MRUtils and moved it to shared deps --- tools/batch_correction/batch_correction.xml | 1 - tools/batch_correction/batch_correction_3Llauncher.R | 10 +++++----- .../batch_correction_all_loess_script.R | 2 +- tools/batch_correction/macros.xml | 1 + 4 files changed, 7 insertions(+), 7 deletions(-) diff --git a/tools/batch_correction/batch_correction.xml b/tools/batch_correction/batch_correction.xml index 216dd8f31..5eb5bce53 100644 --- a/tools/batch_correction/batch_correction.xml +++ b/tools/batch_correction/batch_correction.xml @@ -7,7 +7,6 @@ bioconductor-ropls - r-w4mrutils 1) { - check.err(table.check) + check_err(table.check) } ### StockID - samp.id <- stockID(iddata, idsample, "sample") + samp.id <- stock_id(iddata, idsample, "sample") iddata <- samp.id$dataMatrix idsample <- samp.id$Metadata samp.id <- samp.id$id.match @@ -66,7 +66,7 @@ meth3L <- function(idsample, iddata, sample_type_col_name, injection_order_col_n "\n http://workflow4metabolomics.org/sites/", "workflow4metabolomics.org/files/files/w4e-2016-data_processing.pdf\n" ) - check.err(mand.check) + check_err(mand.check) } if (analyse == "batch_correction") { @@ -75,7 +75,7 @@ meth3L <- function(idsample, iddata, sample_type_col_name, injection_order_col_n ## Table match check table.check <- c(table.check, match2(iddata, metaion, "variable")) ## StockID - var.id <- stockID(iddata, metaion, "variable") + var.id <- stock_id(iddata, metaion, "variable") iddata <- var.id$dataMatrix metaion <- var.id$Metadata var.id <- var.id$id.match @@ -137,7 +137,7 @@ meth3L <- function(idsample, iddata, sample_type_col_name, injection_order_col_n } ## error check - check.err(table.check) + check_err(table.check) ### BC/DBC-specific processing diff --git a/tools/batch_correction/batch_correction_all_loess_script.R b/tools/batch_correction/batch_correction_all_loess_script.R index 2b0f7cf55..89f13d903 100644 --- a/tools/batch_correction/batch_correction_all_loess_script.R +++ b/tools/batch_correction/batch_correction_all_loess_script.R @@ -129,7 +129,7 @@ plotBatchF <- function(datMN, samDF.arg, spnN.arg) { pcaMN[is.na(pcaMN)] <- minN } - pcaLs <- opls(pcaMN, predI = 4, algoC = "svd", printL = FALSE, plotL = FALSE) + pcaLs <- opls(pcaMN, predI = 4, algoC = "svd") tMN <- getScoreMN(pcaLs) vRelVn <- pcaLs@modelDF[, "R2X"] diff --git a/tools/batch_correction/macros.xml b/tools/batch_correction/macros.xml index 7f9b13cf0..b80d4d432 100644 --- a/tools/batch_correction/macros.xml +++ b/tools/batch_correction/macros.xml @@ -5,6 +5,7 @@ r-ade4 bioconductor-pcamethods + r-w4mrutils From a78fc8c55b20301e5f277ea93846e9c0741574c0 Mon Sep 17 00:00:00 2001 From: hechth Date: Thu, 10 Jul 2025 17:52:17 +0200 Subject: [PATCH 11/19] fixed wrong function calls --- tools/batch_correction/batch_correction_3Llauncher.R | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tools/batch_correction/batch_correction_3Llauncher.R b/tools/batch_correction/batch_correction_3Llauncher.R index 42b7f6655..a1de5a7aa 100644 --- a/tools/batch_correction/batch_correction_3Llauncher.R +++ b/tools/batch_correction/batch_correction_3Llauncher.R @@ -149,10 +149,10 @@ meth3L <- function(idsample, iddata, sample_type_col_name, injection_order_col_n ## Launch res <- norm_QCpool(ids, nbid, outlog, factbio, metaion, detail, FALSE, FALSE, method, span, valnull, sm.meta, min.norm) ## Get back original IDs - var.id <- reproduceID(res[[1]], res[[2]], "variable", var.id) + var.id <- reproduce_id(res[[1]], res[[2]], "variable", var.id) res[[1]] <- var.id$dataMatrix res[[2]] <- var.id$Metadata - samp.id <- reproduceID(res[[1]], res[[3]], "sample", samp.id) + samp.id <- reproduce_id(res[[1]], res[[3]], "sample", samp.id) res[[1]] <- samp.id$dataMatrix res[[3]] <- samp.id$Metadata ## Save files From 1a3ef42b773278e5ec4a8f7058b11c983b3c0f9f Mon Sep 17 00:00:00 2001 From: hechth Date: Thu, 10 Jul 2025 17:59:45 +0200 Subject: [PATCH 12/19] updated testdata --- .../output-batchcorrection-dataMatrix-3L.tsv | 200 ++++++++-------- .../output-batchcorrection-dataMatrix.tsv | 226 +++++++++--------- 2 files changed, 213 insertions(+), 213 deletions(-) diff --git a/tools/batch_correction/test-data/output-batchcorrection-dataMatrix-3L.tsv b/tools/batch_correction/test-data/output-batchcorrection-dataMatrix-3L.tsv index e40ea9b14..005a3d403 100644 --- a/tools/batch_correction/test-data/output-batchcorrection-dataMatrix-3L.tsv +++ b/tools/batch_correction/test-data/output-batchcorrection-dataMatrix-3L.tsv @@ -1,114 +1,114 @@ QC1_001 HU_022 HU_076 HU_166 HU_114 HU_190 HU_139 HU_096 HU_058 HU_031 QC1_002 HU_039 HU_087 HU_143 HU_162 HU_102 HU_188 HU_095 HU_062 QC1_003 HU_063 HU_011 HU_074 HU_122 HU_142 HU_030 HU_187 HU_167 HU_113 QC1_004 HU_081 HU_014 HU_206 HU_119 HU_033 HU_067 HU_140 HU_164 HU_193 HU_112 QC1_005 HU_097 HU_056 HU_195 HU_135 HU_159 HU_070 HU_038 HU_026 QC1_006 HU_098 HU_115 HU_137 HU_019 HU_073 HU_168 HU_191 HU_040 HU_046 QC1_007 HU_103 HU_015 HU_053 HU_121 HU_183 HU_199 HU_042 HU_075 QC1_008 HU_203 HU_024 HU_082 HU_169 HU_129 HU_057 HU_035 HU_194 HU_145 HU_104 QC1_009 HU_177 HU_025 HU_080 HU_117 HU_136 HU_197 HU_036 HU_054 HU_085 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5299028.67265199 366235.992497697 8037317.70285592 9216918.69591018 1228573.50181887 354246.724194343 684140.709114361 1969090.16022889 2273181.76171612 555084.247409951 1780906.25703327 1020673.33030083 2090489.28863649 3706024.39894417 988357.990781182 291960.187531956 1651878.9276004 2525016.96173698 1132866.44244134 2282153.56713268 724034.949254305 1261950.95945116 788545.208136719 6330627.01694867 1225049.37817267 7655932.60926507 1307931.23367561 2622103.26062345 2341490.85978569 668246.375845998 1426015.60405375 1675357.48333513 1371155.81762858 327263.071225525 344165.953239807 1884543.95918143 733003.501750468 3646002.26146248 2317326.48404083 193085.763648154 3295565.08103403 1345915.42789872 891947.04908786 881908.366834643 2539078.85567954 3741561.07118204 3194969.55186593 2071124.3045081 1291699.55310497 1368000.72275099 1218231.23938436 840008.79862559 392691.095313368 2007793.14828837 5838845.90178574 523710.591633684 5593184.6702624 634273.637585764 2355940.27119377 1672397.74456006 896229.82121832 3318121.50889045 3246964.6850121 239796.496507639 3385292.48391934 945825.109801808 3351819.86253658 4998449.18366241 2174103.2318378 1472305.08430844 2032646.00592078 555108.774998643 3447892.59887747 5293061.74025668 1684492.45385022 1634474.03613491 1570375.89667523 1653286.96847577 2582498.34424934 546093.482741858 2587738.53962076 2732386.77443516 4652061.80452205 1936254.86893419 1037611.07548103 912437.668568051 2074764.61972837 met_017 4821103 2435403 2318732 4245753 3748189 4349935 4689943 NA 2451934 3331274 4202823 1370470 1761558 1754097 2700769 426851 1421552 3508482 1312895 3480628 153921 640917 4404841 2251667 4439211 3342751 1543974 1098969 858681 3440833 6434298 1877831 602319 195656 151869 2383960 2486404 2254876 1908070 705181 2902467 1052946 1271786 2219297 1805955 3250971 1844215 495613 1812866 3086559 1835313 2882274 3433802 2034058 3943305 1112063 3043290 3146250 1149854 3119954 1959167 2538248 931529 1334514 2458891 2782916 4005410 3855724 3184232 2002086 1822977 1588408 519939 3609550 2113979 4260220 7162992 2063593 1974086 3128563 2511623 3723824 3208944 3304203 292819 5146262 2436561 2378318 2980413 2487889 2835818 1141876 1331599 2511176 2512983 2690201 1226346 2457264 1783679 2374533 578362 1931922 7306894 751094 2056881 2013244 3440995 1986099 -met_018 3189068.13824707 930278.040048712 3062182.5787137 3512210.22604092 5198487.14740168 4731980.83767569 6677081.00655291 NA 3399554.4437436 6502242.96432166 3020125.77694267 2335979.58273115 3124412.82337732 1971158.25455437 4396736.21111668 3779262.76399297 776533.55123391 3792432.9450736 2427323.42168661 3053107.09514829 1599290.08505887 1394834.02465643 5136391.97451523 3760931.46204017 4574484.31919117 2547681.54482059 284027.611580559 49618.8725487234 8890434.27319509 3184816.59251059 3693128.32431839 913227.536350302 4642247.80940707 3778942.76516404 2937119.12727546 2792298.05522566 1518143.53125639 1420596.07422274 2360516.07881831 7264654.51021365 3151789.44507766 2664486.30249075 1508714.59571527 684521.904667005 6480063.99478949 486448.152528935 5529879.32596103 4752.87573136388 1018527.07622864 3332848.96865841 2992000.01059951 2110269.94288927 5046718.09856548 2828571.5738874 5788507.26489658 1350745.27507381 1024520.74882811 1407682.10235773 4681298.30939501 3082848.82289657 1971685.82194117 4563852.5869271 2247143.65410527 1008180.28410104 1349574.55029033 4794711.34265834 2230110.96924608 4109627.64125026 2729135.94836036 1507082.86513225 1519129.73827135 3810937.01605552 717388.937443973 3907022.14564106 3875714.92797184 3496113.11061276 3278083.66058667 2630875.08139341 1877462.10682681 3250583.80260673 2124685.60543689 7119022.66882412 4436705.90568128 5816354.50210551 399415.289827066 4685341.72125829 2035697.96817302 2649298.61578368 4558917.09939268 3033865.5371605 3723159.80045571 2271273.90973341 1315275.3896764 1867975.77477958 1998979.51177184 3834736.62525006 217585.929840466 1745179.95207796 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3937965.97115663 2954904.45321536 2077575.87495583 4698611.73452563 2126746.01181295 8266022.70395572 2483832.52637292 3593086.61282779 4667700.62752581 2701008.84312066 3841490.57838103 1729449.0741807 3003785.76674061 638145.675565263 2566933.24870883 4218873.44263096 7617553.07253054 3402969.77525401 4623526.76272761 3729282.72501438 7342620.00813203 3011453.82834838 4587715.47583569 4562460.08003945 1949388.74492262 1604502.45123766 9303191.8206926 1383409.17379352 4584394.09179467 2482278.89660077 3921635.98922499 6358775.15817 3692632.69292923 8496813.15066293 4797232.7949376 659665.434302325 9404952.25595775 3362259.21281736 5778631.09772256 4547328.45040819 3885352.33672929 3317817.34537726 5055656.13447211 1030716.61681454 4285720.98448145 4186602.46587189 2989511.55383628 1333794.39866849 4386320.71330555 2686058.38600045 3912333.2744824 1729212.30462625 2237100.18546689 4418748.29357153 1607581.72921911 4491935.31332922 2698594.36537475 8584592.67956446 3774479.43401431 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371092.42079731 547226.923559283 268278.699258314 321674.196117464 143406.819262747 169236.950986558 165240.404152451 260832.746766526 470012.882648704 131007.07351727 240511.864777452 193020.222990609 223645.337186303 315616.631295092 168261.897472301 330145.931609031 255342.134602234 866000.82097745 459566.752785138 444475.754945979 236939.554098071 287467.777747412 423535.780999503 368637.815927329 129698.867830011 463151.298810215 106714.554752985 395838.496305391 566430.631232052 328161.23489477 284917.987981237 395804.933587855 559234.11075959 211544.396656032 219021.369017209 418200.165586387 703095.470064642 469101.546046875 339424.652946772 296540.975468291 397137.027545414 425384.199243966 151792.24324229 611923.970005419 476317.204651986 312828.499800305 191898.130289251 884031.804926983 823896.972797341 542959.132329054 -met_021 544395.063036637 457583.423964441 238149.711962312 1098780.06154983 936356.950003889 505641.277987247 481231.916575154 NA 446326.548731241 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178573031.738247 256228287.896186 145366371.867039 298773162.752934 296783005.780204 351840731.616052 246897129.19726 426396924.06014 148949668.060712 280753499.119689 145593913.840241 231679837.815121 434915101.180222 170189192.431326 100075294.817796 361462504.703628 233671452.056102 272129406.578202 265528393.0969 271612963.820075 405107973.67047 215358013.980854 207681293.947235 265570194.749545 224838343.133752 305661686.231061 235528150.205653 303707127.491713 280249073.686981 298615290.812873 208567176.997485 251878709.266332 291912187.251942 247391354.641997 331917570.134021 274741906.023219 502808200.787889 270684748.756448 -met_047 840050.457887697 400701.514224266 168085.731011586 1556033.39986252 1324896.96725389 3365148.01240911 4983680.07104642 NA 874432.482149454 2154214.00831687 941876.624528012 775495.031541292 673101.393276869 272544.723586544 1103822.69884074 105984.815391831 216448.077165354 2230641.12813539 579355.347543631 1005929.49698317 979111.102470136 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534917.031010639 559454.341448613 985205.877924709 1303523.44830691 NA 2414038.02642317 597433.924063208 2298853.51887236 2058423.16034106 896023.092855524 1942411.83708894 530937.284826265 403409.396298642 1832025.32066013 531128.614448813 3104394.88933885 146718.785317482 449282.652423731 1072123.88628863 1124646.69878179 211971.392110781 782901.303281416 2159484.3518059 138076.419364063 629260.680279705 956572.439067101 66213.7869983923 728951.238827932 -met_048 240957.190722653 156980.020172149 110791.822609995 338727.351702483 77433.0854485737 559855.922717964 663646.718477668 NA 148776.528959855 362322.075092577 204992.903528332 273342.15877003 399925.836541905 39391.299700686 80897.2275017666 173678.889758347 92759.3168525677 409127.568769047 167620.497802401 228941.610723045 174202.320072809 211543.680642428 163825.30283826 225630.353487189 242832.481475897 196714.003139366 19541.6862774957 6328.0675868197 219966.436373116 210514.069822698 283466.621693577 34640.7842190637 326760.549393896 668897.87755637 118769.879646077 51350.3749165711 120663.713041293 47630.7831051111 415556.09346401 144027.772173344 252482.843363615 50236.6750537125 155823.710684027 128426.129249619 199811.658230433 242169.48801487 117308.002252849 NA 111241.481206052 215081.978622115 173566.246685717 184863.80726124 476834.357946212 97344.7163014495 192387.011416015 26791.5046737685 134027.640944733 311512.803959328 190811.432242206 239384.873126308 29766.8245823353 137518.763140468 4089.88101155944 48467.4839390827 21707.7887105572 252427.954719035 364292.273085954 356911.686306052 202398.805958023 138268.808733765 61466.4701734175 300935.055043866 16559.2901433413 187187.471440722 101421.33272414 275323.496878322 535643.542717349 338822.048539576 8628.67846319179 231508.637953535 106820.303915349 356298.66201208 505464.907386396 632766.870943206 2230.37251278788 420161.960793634 76534.3139386057 147058.510603343 870868.014438869 222718.990859336 691021.184300012 470765.667961785 34082.3035591554 626638.886688271 128984.752661198 103547.083513377 6230.24903219474 121362.628796491 229180.031791296 228313.788804827 8074.08491629961 242410.419952144 430763.18848488 123350.777980046 402759.027275407 360616.775160121 127001.937990385 228241.999471836 -met_049 7353829.92602082 803303.511251271 NA 439553.010297129 42439237.3048515 73000059.5761717 23022002.6585898 1703968.84424696 733433.121938992 142040.364920617 7471619.56226249 549861.7734647 26311132.3643945 67268.3651499787 7050.77430173465 35283.3208319297 5273255.41249589 6258992.54609874 123303.801923465 7320958.98714415 8194.97613158321 10579364.6052156 25873854.2828898 1058935.45530836 26511202.4823306 9875284.74054441 1623987.73734412 1195967.36469818 13047989.377176 7319294.18636275 2920394.04227294 1193658.87209249 419076.534249134 21493688.1281943 137214.668055824 183415.910481369 10685778.6362767 51017.0125665426 883322.417386003 2012203.33497139 7232806.98526305 381696.450779382 34378.1142598622 692953.695411187 89519896.3754906 301346.607937918 28873351.5975071 4281.6388601064 664515.403659337 7776097.1312788 3450233.19345414 NA 2136117.83132744 2952123.45457288 7688770.24891376 9819.2666838959 35157854.3618208 15810352.2254938 5447306.92881872 6920557.71250231 NA 637636.048930935 NA 1635606.4394931 512418.396643562 5276443.36058539 5963073.25460535 12950044.0864539 6988948.06073265 893075.049219096 204461.368997304 2332433.25151238 79851.0799542069 936507.106217963 82992.2479542522 313181.038580603 277571.366278653 85269194.5182606 301761.69100155 8618802.73363884 95055.868273808 2193940.43594964 478506.20817081 851749.495203909 13201.7572747005 98748367.8471276 6877.71036010893 36665904.7504419 35849873.3125741 7373431.04817102 34791611.5858109 15769359.3325639 143751.92018533 31826763.2627227 86029.7729955846 23830.6204578814 44292.2729234307 421709.710700861 34904008.3057322 7867042.09178522 NA 9168839.80431342 2114943.70932627 243463.974537251 2178493.03554102 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62984.770595431 136167.90828554 478963.648668903 140066.89722957 381094.609073518 91361.6587143137 140791.468862386 188637.813918487 33925.576691111 154509.659839663 35212.2529933417 230117.34968102 351972.78537837 361012.515471932 333722.575782755 314972.59163911 -met_073 4131322.52161362 2724935.70151468 3078019.08985278 5482865.62846659 4025718.701987 7596498.75964651 7335608.677094 NA 4054218.77653801 1820165.37796284 4374702.80842555 4126853.51948032 4975042.57661529 3290767.75253457 3818273.1728171 7093463.93450578 1467921.09614982 6450807.32081785 7750345.33938031 4293838.8319705 4854690.63901766 2171074.44299838 5906076.49074469 7882333.58388036 8651819.20806779 3242728.52402399 1123468.74253133 1299424.83901416 7671364.61344823 4323922.54589973 4877770.09128078 1262892.18677044 5784317.18421445 1232480.70449362 10183344.3898889 4846615.01417282 3394168.58804754 2954632.43392408 5962047.96025414 6050806.2662331 4364211.16930674 4555512.07557241 2282480.60456958 4264178.19488617 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392556.636382747 223532.457637465 285935.111360028 360257.106631046 417356.633548898 179670.393854552 107572.885527058 500333.989033099 177676.906816535 673949.585016382 333064.906171839 1354316.37223643 563566.819259882 727640.601003522 482269.077708666 499386.439378991 283961.951753291 175438.893802675 637203.290531295 374207.569703301 229677.530896475 385201.308283197 409579.702255737 656742.369211811 394688.949641628 407329.870465799 412902.889513996 209413.556093722 132384.564120857 647226.335403178 884650.905653462 423910.559283909 343440.92169508 1078911.86922316 820391.354767411 703026.523658637 517860.41281459 352124.126705894 155171.748149866 764118.504386998 270272.49230272 220531.729288413 1239648.67961216 461612.105502382 -met_076 381321.37319453 825950.325751731 140438.702717657 127378.833493833 688379.23640266 179054.699200241 328177.499091831 2398.55328175046 76132.8518943836 16732.2514777193 379359.299591474 464367.196632894 184175.818150936 242788.400109913 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306921.345817873 539512.76834608 390287.734094569 296011.436766899 175488.084156899 339469.739444202 391504.22987442 387406.452764711 488053.787600288 651462.109779484 218014.974114667 346160.087928398 448247.535269292 408756.413527642 511603.406791658 306300.676854067 372687.269667132 286743.783303265 165532.17413033 426885.120308903 364526.265250104 410496.1285927 228640.011188027 509185.867803005 479969.388977488 313148.785804567 138795.681206006 228593.846011672 423969.569955952 257894.092838902 230446.749928205 315729.858958826 428559.372002787 330552.147993284 -met_084 1586223.73397367 603218.233724442 1039664.66875421 1336491.21006042 2098068.87354096 2186591.83717174 3122187.71098696 NA 1568069.66426025 2923556.87518794 1539853.93851536 1449741.63652538 1892044.56970272 766597.752524585 868482.42062711 2132406.61222431 510854.985038913 1951915.03412423 2326698.07311378 1647979.53661189 1347469.8707442 967019.826304132 1703587.41055447 2274214.30649625 1743286.8314767 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131295.722232269 269741.358639035 586069.768134643 396465.286661839 467849.194530996 134285.488250822 420110.167527886 343190.039145886 515273.40996906 187609.373030457 333220.328048447 57155.0966938127 668764.575878107 480649.084732283 816839.543984204 318535.985339668 935893.345579615 477683.154811726 764788.458084076 797338.528668671 262501.02521973 670848.588186786 353401.46615717 551941.775333207 106285.408205287 506776.977681639 125616.620193642 91284.958017834 441054.904585089 218180.992444511 518822.114943625 477965.014871938 472204.849485675 299818.403518265 307812.83080134 263841.640790283 248382.203967945 1170013.75218859 268018.180108623 389362.162653407 886667.186570188 210092.3472121 91187.1940106482 470362.810830494 204327.018553695 613317.113958483 443218.942981742 368490.054475502 60941.2282962989 533929.862368477 355036.879073088 878349.465601189 433420.281831502 469353.325733779 1021531.70425011 575795.148718484 84440.873511191 383130.472107733 288377.859530301 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594244.854427832 116639.087772855 9083.21705796267 692144.127900184 1054531.49777193 907061.810968971 403555.527256857 549269.29164658 1102986.17368765 437415.362541631 313448.234836861 598231.295634868 1295811.65523689 739951.065331355 1139185.59773373 457619.981251462 849683.980538895 219261.442333455 547784.140371507 181562.646391228 313054.703957764 849912.644344986 464093.919772824 446342.665322824 340028.075627124 654957.799027104 593165.999060618 755046.213923065 607793.078311266 432003.172826181 1312819.31417693 586001.864313764 497698.592425822 801533.955702843 1184590.61313804 133977.631593131 560446.789032358 202653.753638699 155666.603345149 550447.465867832 722459.832289469 503307.184852501 588803.890458638 529253.148662505 492221.343564687 974185.482985487 110308.182381137 402775.029207574 1132427.32800297 393606.510598175 964145.658785616 2302049.26399296 781236.764381376 931355.620073745 553466.995272439 358439.104111557 817500.464757449 914644.803777643 510233.765206878 261441.865637833 750911.093487349 518835.666297818 -met_087 131965.972735192 97724.2619328014 102611.562748677 136034.187324963 342712.490924076 122497.985737143 185350.197462582 NA 160072.872358258 140015.601644896 162039.081506423 219771.470601804 259880.333633715 71600.5138019742 298847.957460391 176445.720136264 NA 78144.1468798725 178038.551183971 149315.588192819 409192.704636694 44372.8259733729 154265.903040319 224785.314982887 126283.506467159 64419.6966661603 4446.44830255133 45589.1652690723 56888.839607604 143520.146660449 73815.6994114806 111479.573126253 151088.826438278 214114.244830249 250729.668767004 99208.488242453 79642.6555248607 138576.369039701 76133.9657998515 215484.79783855 149885.077877731 124712.907676846 66137.3626873529 93826.0342036053 105554.190092932 37863.4423709673 177574.338893587 10193.1000900656 85826.458939816 123626.336381494 133587.762362518 192459.083590221 150893.410318906 99322.8330603218 257428.88839326 11333.9308789248 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1506532.21606952 NA 260003.887477074 1193923.07210796 1063550.99794682 733833.757082309 481311.958165321 2038122.90237624 729304.130362747 672943.036858587 351460.073363752 1281410.2455841 3049080.96794124 1122272.03337669 1662091.44944421 1966742.526825 2131860.0994331 1202189.74831586 893982.58112865 1158051.41917858 402108.223598413 214276.484634403 2290672.64733941 1086455.37737022 1049989.6023926 303745.419466866 443333.459109463 963610.516696698 1114393.36849389 315015.168097275 127390.110377145 238123.451685537 985741.297155707 1528245.82787371 1052513.75128992 254284.981779738 2782595.3376996 2052187.73314568 1648117.77502738 1146858.85939891 2076301.79871278 222731.770697336 897010.554609965 1142767.78793222 1175566.62629062 147605.410451921 1334477.85944374 674257.455767311 238272.320081859 83535.8446922161 338575.310758172 238252.269619172 373820.121743556 1080305.84828162 647712.740705506 1310223.72832642 235283.643524587 688048.806749499 332199.290812502 2839113.17196381 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429363.10333584 385285.557871984 254874.575341649 403923.774836681 274430.336388833 298671.198276473 256249.389149053 399592.119689275 361274.594389735 338795.191472946 367616.448254614 435108.265985572 78271.6868947275 363050.663039898 362276.591117789 319217.664604072 290399.991771016 562598.899555644 302993.005644574 215399.271870996 113690.88532319 328904.204410122 404481.646605152 264647.01381884 318812.615379121 400294.811641733 283468.879502723 140120.239200256 131889.099084178 143015.044238447 360183.731435886 389639.954488149 320724.990224729 290985.777803656 308220.392030133 144221.955709181 269423.406891437 130758.702876459 155574.734707736 306921.345817873 539512.768346079 390287.734094569 296011.436766899 175488.084156899 339469.739444202 391504.22987442 387406.452764712 488053.787600289 651462.109779483 218014.974114667 346160.087928398 448247.535269292 408756.413527642 511603.406791658 306300.676854067 372687.269667131 286743.783303265 165532.17413033 426885.120308904 364526.265250104 410496.1285927 228640.011188027 509185.867803005 479969.388977488 313148.785804567 138795.681206007 228593.846011672 423969.569955952 257894.092838902 230446.749928205 315729.858958826 428559.372002787 330552.147993284 +met_084 1586223.73397367 603218.233724441 1039664.66875421 1336491.21006042 2098068.87354095 2186591.83717174 3122187.71098696 NA 1568069.66426025 2923556.87518794 1539853.93851536 1449741.63652538 1892044.56970271 766597.752524585 868482.42062711 2132406.61222431 510854.985038912 1951915.03412423 2326698.07311378 1647979.53661189 1347469.8707442 967019.826304131 1703587.41055447 2274214.30649625 1743286.8314767 1284260.20832479 258821.522097706 404259.736459925 2004826.2056637 1490099.78500955 2575924.86404597 807993.807805688 2006228.19449597 1852035.04965689 1356768.7205114 1305978.74076454 1220287.31952254 997090.851083893 1357412.04238461 2472315.54313975 1614954.08949353 802374.124336859 1063940.77017024 1008525.98504383 2830588.89748004 686174.182533472 1178947.88288179 250803.008065538 1457875.52441996 1640504.88725525 2159771.40850613 1419419.80225219 2159858.969382 1981565.13742231 4260192.13213954 1041734.05232463 1318328.37824323 2596877.94073541 1727144.39261462 1557839.99299476 938175.530766688 1626359.90961402 616227.507071333 449025.624265825 685366.643356612 883739.324894013 1786628.3282443 1782494.70251877 1441155.77848133 1588082.20793368 1141704.40563427 2602713.12639246 503542.839618198 2527464.50060344 1977992.83038549 2313296.44251768 2365429.66014482 1511657.87571084 806265.513091982 1716736.30799622 1431935.88435279 856712.406211082 1273319.61991103 1518436.28339906 280976.708754516 2036179.48970519 1103984.74601602 2209100.69598736 2304386.72697207 1533310.05416524 1490734.16844253 2066778.0993009 764043.265877034 2636713.59983725 1794166.72305958 2546623.47986866 1093609.15028956 1603853.64442092 2401835.22736959 1827854.6669052 660157.429431202 2497470.85189658 1579197.10908407 600051.015330059 813995.599060063 1543401.42787065 2062903.45704993 1352001.28103063 +met_085 485305.713796082 113374.576756088 173908.274174162 680084.908653724 842624.651931713 401954.262376303 641426.406280863 NA 220950.737496989 799327.40395065 509660.913291497 341628.501856658 1003291.93698417 146087.676039225 446561.02178908 184030.571380669 239797.955784593 191928.830075187 661012.666168852 486163.372314444 81309.029736508 63096.5623650373 1614161.86096076 546682.379704561 1203554.1417024 285978.627087475 28106.8124464964 107275.064861868 254303.821765427 513190.409766574 484349.77746209 376117.62569386 462981.535104919 72122.8762405188 NA 619936.518829922 131295.722232269 269741.358639035 586069.768134643 396465.286661839 467849.194530996 134285.488250822 420110.167527886 343190.039145886 515273.40996906 187609.373030457 333220.328048447 57155.0966938127 668764.575878106 480649.084732282 816839.543984203 318535.985339669 935893.345579615 477683.154811726 764788.458084076 797338.528668671 262501.025219729 670848.588186786 353401.46615717 551941.775333207 106285.408205287 506776.977681639 125616.620193642 91284.958017834 441054.904585089 218180.992444511 518822.114943625 477965.014871938 472204.849485675 299818.403518265 307812.83080134 263841.640790283 248382.203967945 1170013.75218859 268018.180108623 389362.162653407 886667.186570188 210092.3472121 91187.1940106482 470362.810830494 204327.018553695 613317.113958483 443218.942981742 368490.054475501 60941.2282962989 533929.862368477 355036.879073088 878349.46560119 433420.281831502 469353.325733779 1021531.70425011 575795.148718484 84440.873511191 383130.472107733 288377.859530301 517367.107677456 298332.740914076 260500.933723977 387273.957387239 494366.338434242 77837.0769926239 454964.326508472 279604.49696268 272899.643541671 185958.8662874 351034.581304827 398400.122471037 518237.86743277 +met_086 706071.154060075 1169719.34254175 266111.016039498 1173381.12553656 2333189.66806834 200617.421001346 410102.16762445 1950.0074410367 310909.506312044 322777.566888062 652540.498664118 1149913.55379478 1657345.26564075 568232.498186632 832212.177248291 877742.164126443 183895.694854279 633220.074650943 543702.166789257 621831.346663572 2157862.82741096 495071.46644032 1301080.5869397 770942.813317901 1191819.94419157 439381.821947663 160619.1173484 386355.014532751 606945.301910954 712043.907975013 1135595.79684352 563595.993040348 631736.732413642 1735181.43054305 973005.777244634 578960.928535136 152346.815037025 717358.333031614 725057.617273878 587825.803071508 706193.198242479 348871.878218432 292940.391908779 530244.311669187 466479.041271683 400316.743567346 594244.854427832 116639.087772855 9083.21705796267 692144.127900184 1054531.49777193 907061.810968971 403555.527256857 549269.29164658 1102986.17368765 437415.36254163 313448.234836861 598231.295634869 1295811.65523689 739951.065331356 1139185.59773373 457619.981251462 849683.980538895 219261.442333455 547784.140371506 181562.646391228 313054.703957764 849912.644344986 464093.919772824 446342.665322824 340028.075627124 654957.799027104 593165.999060618 755046.213923065 607793.078311266 432003.172826181 1312819.31417693 586001.864313764 497698.592425822 801533.955702842 1184590.61313804 133977.631593131 560446.789032358 202653.753638699 155666.603345149 550447.465867832 722459.832289468 503307.184852501 588803.890458638 529253.148662505 492221.343564687 974185.482985487 110308.182381137 402775.029207574 1132427.32800297 393606.510598174 964145.658785616 2302049.26399296 781236.764381376 931355.620073746 553466.99527244 358439.104111557 817500.464757449 914644.803777642 510233.765206878 261441.865637833 750911.093487348 518835.666297818 +met_087 131965.972735192 97724.2619328014 102611.562748677 136034.187324964 342712.490924076 122497.985737143 185350.197462581 NA 160072.872358258 140015.601644896 162039.081506423 219771.470601804 259880.333633715 71600.5138019741 298847.957460391 176445.720136264 NA 78144.1468798725 178038.551183971 149315.588192818 409192.704636694 44372.8259733728 154265.903040319 224785.314982887 126283.506467159 64419.6966661603 4446.44830255133 45589.1652690723 56888.839607604 143520.146660449 73815.6994114806 111479.573126253 151088.826438278 214114.244830249 250729.668767004 99208.488242453 79642.6555248607 138576.369039702 76133.9657998516 215484.79783855 149885.077877731 124712.907676847 66137.3626873529 93826.0342036053 105554.190092932 37863.4423709673 177574.338893588 10193.1000900656 85826.458939816 123626.336381494 133587.762362518 192459.083590221 150893.410318906 99322.8330603218 257428.88839326 11333.9308789248 60191.1191984258 272989.279094218 132677.180137248 150260.914028848 165827.751855038 143702.010386926 93021.0339783608 3588.5441384188 122335.242594937 230087.821171637 96321.193732546 173222.075098019 156646.934269244 80678.1944481282 42925.6879842744 195342.48438844 72321.3572146009 177314.630158041 167553.671795147 178025.047828465 268711.111487844 57616.463936392 120919.773348822 155425.96752847 86954.5565949912 53447.9887405126 40037.3367292991 169053.253410037 55038.4700535131 105864.439643486 162684.259111456 46300.7035602892 223092.088668303 127832.845078914 75209.5088807511 198784.233483975 61045.4314364947 145085.549156358 290220.471451161 107656.280210869 114811.530918273 523313.365985681 169010.204446685 150083.040310999 63103.5875359217 136841.958149501 75742.3462357972 26909.5939914595 108306.521058793 156136.929116423 231544.777456659 144591.424405918 +met_088 1114156.10844461 718962.434703991 251169.755693155 2430966.15744992 771935.303694081 500878.108207795 1506532.21606952 NA 260003.887477074 1193923.07210796 1063550.99794682 733833.757082309 481311.958165321 2038122.90237624 729304.130362748 672943.036858587 351460.073363752 1281410.2455841 3049080.96794124 1122272.03337669 1662091.44944421 1966742.526825 2131860.0994331 1202189.74831586 893982.58112865 1158051.41917858 402108.223598413 214276.484634403 2290672.64733941 1086455.37737022 1049989.6023926 303745.419466866 443333.459109463 963610.516696698 1114393.36849389 315015.168097275 127390.110377145 238123.451685537 985741.297155707 1528245.82787371 1052513.75128992 254284.981779738 2782595.3376996 2052187.73314568 1648117.77502738 1146858.8593989 2076301.79871278 222731.770697336 897010.554609965 1142767.78793222 1175566.62629062 147605.410451921 1334477.85944374 674257.455767311 238272.320081859 83535.8446922161 338575.310758172 238252.269619172 373820.121743556 1080305.84828162 647712.740705506 1310223.72832642 235283.643524588 688048.806749499 332199.290812502 2839113.17196381 1700626.4863834 1463264.37730141 1112948.7568569 831865.778620376 1806201.20972594 652752.439435276 303839.754161771 17886.3913487886 569632.612206131 4619261.55926605 554251.623493179 1349817.66630686 420473.92491991 1136259.90546501 1005407.24991569 1138806.12537123 1431081.99547944 2150539.00207345 277995.739510929 5197172.52331334 1429618.16242526 426713.576343358 3537443.80194433 992615.016091958 6011436.42239215 2084282.80758756 235561.218398303 1814375.41573414 1220073.40767394 1334265.04080617 864437.094113292 2043318.90729368 863308.223491655 1084953.78077351 244367.002935613 456691.988851544 982720.292820819 605015.907263426 1092129.66245419 471213.98261859 1042487.47812902 1152435.00046088 +met_089 787339.312018696 670815.71612103 729515.586587312 930898.818185093 409307.728431572 470262.852068709 913192.483067085 NA 844463.147046143 788964.624915218 715776.481914172 467860.676709611 745831.354065289 548743.081063763 719412.148907684 378773.779807552 316600.480961303 808323.91768562 1108146.76825309 676050.020661949 690379.908303285 260021.039027926 1184197.73031207 837770.320605676 1267859.66027529 649954.738271926 390752.52294793 767955.389389824 850384.90437754 774586.30180831 459681.500164326 523148.970758146 994518.250999669 411471.453669388 956881.262596719 606199.6912887 444289.877411212 585994.184878185 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1623183.05578127 1962803.7026734 met_091 3951874 7370628 305105 3672597 288835 410788 384800 66641 77540 NA 4140064 950309 2543407 2803087 367570 1833503 497944 344716 1514237 4200099 6065165 1348016 593194 647065 NA NA 950773 5123257 2332860 3726471 14996434 4500386 1105540 416317 2252126 20697234 150105 8499549 2965834 803335 3478144 121858 59625 288346 303527 4824029 378550 478019 4434449 3361938 575071 178738 111398 1142241 2262486 541090 406282 138102 1644606 2880750 172636 449761 8766 3313334 2210672 980659 NA 92981 2414553 297234 1760229 126911 17562646 NA 1876505 3177295 3754807 NA 5709321 2362465 NA 1839242 10171553 129309 671350 5322435 2083142 1546649 26809244 2062201 10659039 NA 93555 155940 NA NA 848242 23940564 128764 1850944 670327 670519 103830 106104 541638 100128 53347 1384794 -met_092 3801442.95932062 3667565.99059773 3138902.15564695 3709005.8180296 4177170.87095162 5758723.39968003 4369678.17851446 31477.3678222748 3189726.79138056 888630.264680606 3858732.32325046 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4025400.50155597 NA 2795184.93154025 4141714.34598263 2011028.69312427 2794894.42631373 1224804.25862257 831183.391052526 2363089.37843426 3099736.95747309 914947.430824078 4312134.38893287 3774206.87739331 1958754.546174 2004101.91129876 1732824.2718201 1263051.37185104 3798714.46842688 1736831.45316898 1397658.18561644 682012.09249399 632220.574083108 3928929.17828011 1823820.07404412 1646530.21965963 1529270.89731248 6064314.28607768 2413014.99135873 2664300.0301733 1362505.96544549 2749336.35419924 609519.041098081 3685713.10131358 4904095.24568689 1839685.32404676 1115843.84153983 1916021.36560425 1049582.29720056 3259469.08434175 1215070.06831569 2803882.917363 530221.44620432 824791.070799242 1845779.18058369 1031823.52828055 1203686.04357494 1904488.42842796 2591773.12517541 2020887.17895303 1335246.33069634 2822095.13974518 1619769.33173801 969734.322486527 2292432.02552438 1897887.26607935 1472363.96795839 1707836.9204261 1191197.22208335 2059896.02010614 1167668.61113668 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3675512.38638005 8126412.37561777 7249058.88868021 10097138.8381322 9159541.34551803 22476545.5917121 7930710.1897242 12485886.4345125 1390116.64276228 18483239.5657518 3873816.23693402 8814930.32023822 13831512.6276293 10235517.2397478 4318635.60803256 8641311.42714934 5789391.62852438 17176443.5306407 3569843.62548976 19116056.4875753 2246681.30302739 22461970.235754 18101047.6225948 11251363.4818958 4286286.68449666 3162931.22202763 19981098.7685465 2481100.42550222 19522245.8275916 5498703.89880395 28348102.183547 7222977.39900191 +met_106 479735.800454594 215870.307903844 849884.640627909 856436.666426712 935413.705722231 471464.272378989 419024.749352418 254893.393938796 637691.387755469 2712193.44590509 494199.046109341 400298.142879349 447382.041802602 681950.127835593 205497.433064335 1044843.30407355 254185.358748953 865725.212578393 334748.131853498 491823.190008697 409723.12706085 471991.889984752 5910.27992531078 423564.224854296 1225807.38611134 625203.528747011 5658.28921307502 145426.69365149 1194348.39809737 459080.828918445 377580.157873206 1703303.88256162 5397.78060415341 75955.9992901471 421749.857177877 450316.783998851 3749660.46696676 241514.365111356 271400.560623284 468897.719344467 491864.984455012 986921.293847099 17421.9210128408 111074.415700169 445435.259922637 NA 1279943.50996921 689842.639869286 69552.5909281784 486547.470160394 106973.005264334 442173.830373414 1101518.77584671 198742.538961906 243079.939065982 204622.036165817 542467.835814556 340173.031374824 398011.599681723 484111.287588897 180955.71242281 898776.773083835 189838.732359473 450933.564255829 245978.352948085 56007.9765310637 433985.21803249 103775.220992168 488753.756707371 478462.494984047 902767.779260369 1207187.18216799 238049.278324562 164612.764898934 495605.652817309 355554.633695728 28940.6337037717 924761.349004762 89944.1404118622 482363.061620445 437171.679904949 706373.836552483 1310669.86518269 1400117.37424977 74759.1921933037 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546794.034408443 154630.007912095 716259.495046856 232410.999067075 258706.605619237 715416.857945325 307804.065457706 270880.828107514 434092.014738147 263323.936179642 430380.128500764 878603.768044785 1055152.44244212 130243.257168092 1013064.07004337 182537.019331596 449147.252328361 1323831.16093734 482583.86583938 648331.295727069 357008.865990431 164299.871621097 334745.844694145 63097.7324624238 346742.088956954 116485.345872499 308363.275047107 421510.220146948 469630.244071362 214944.539087649 679473.815523522 575874.754905382 80044.5334481536 188212.074199603 538318.614357879 285891.884100972 394813.373467562 +met_111 829000.289975951 621330.988744866 450740.709470865 2282077.67453648 1942259.22615969 1599738.15705569 1773016.86671848 NA 455251.31893667 502025.800582396 875771.501944032 403461.492354442 1568966.8395755 751403.630730766 358967.800839651 533331.136206211 274215.114572557 952059.984434005 535463.221190266 866726.591517778 1316679.62612104 438804.876083317 539543.1338946 1084498.49798105 1574773.63499868 597622.682446737 221497.369896049 379590.645665412 1844298.69664545 870937.442830778 2356166.75195693 164311.529003331 349459.801477914 823428.847301445 2107084.32750251 669378.01382843 150733.348803707 523911.746625199 744024.061805947 2599962.04012817 856833.824741265 284883.086861231 591043.736673909 642323.237630858 1907003.50488254 469450.994570684 196339.885602822 125002.960028599 672404.048261205 860929.784369575 679927.541258646 654929.477989829 1540739.95309163 410562.903519911 1989289.6989242 679559.976464 1057930.49563655 1224273.21983297 780153.428304949 851459.595488521 426594.142639599 729634.719366778 264295.457609758 839697.679793716 694220.12107286 1180742.63583948 556486.258026036 974617.45493391 802529.952244419 1617802.85361309 1157452.28225789 1134009.02893168 950763.050806536 452631.049787301 469184.972536049 963758.139805117 143691.091476743 1622088.74186515 1078521.16384775 909801.613197758 1539788.40006757 1405057.03024695 1446597.07759849 859101.558136011 142383.112819682 2589391.88056521 977785.384695409 1054180.33235713 1090979.33590343 813748.203006604 1229309.49062481 1608619.81571132 292076.848877472 863039.358844594 1515390.49822307 720835.420170902 443982.812394315 977783.054388113 479678.805085767 861454.511172265 399179.581453246 537468.992122009 1341168.53297046 356665.052780839 1077581.84947797 699378.392150834 2180156.83550866 859740.908081677 +met_112 4538260.07332924 1997603.63759386 3101105.23740388 8676705.07693322 4499120.50413991 2300186.26663146 6028064.84593985 NA 2491991.86068611 4936389.92875914 3934368.63868677 3223602.52806592 5758560.31329564 4068603.84108099 3020876.08310118 2092201.98947378 1414128.60450223 3564589.34919401 4454519.51899205 4111395.21282403 6506628.04141497 1638924.06694788 4704057.12477128 3783706.68101197 6398699.60280182 2814042.19687001 1380804.4721669 2762753.71246615 5605197.20586148 4482681.39654662 8211280.36032051 1649600.0418054 3908924.69185977 3874350.9274596 7338556.35476538 4151528.10101193 2487779.1011633 2870763.03704328 4304219.62588264 9393649.78742389 4176141.32094111 2629914.41305345 2699719.51441013 4108774.30717426 7046808.49106602 2534899.22779241 2209294.01128173 993612.490501394 3466629.03422472 4284484.51427465 4707978.58556783 2496536.33382371 4076646.32542001 3524390.53123507 9275598.64644259 2759480.08076152 3366878.58611063 6050319.56436356 2714865.54936974 4174949.54971015 3275325.72458992 4267918.90420685 923327.926460122 3170959.29947981 4492717.85645683 5139744.91372233 3863471.57733796 6886498.45100012 4428439.83785168 6371944.02729372 3112777.74911802 5514644.81005529 5808375.07378356 3308682.79888993 4771946.46729538 8144361.80783534 3409974.53680361 3500477.92909193 2617203.28223743 4414062.2820156 3812798.14081105 4585421.20450721 8793172.67675603 4637139.66088434 943790.950209665 8994466.91015318 4615134.45758878 5559998.73571828 6145353.55124428 4289798.20115878 2893421.75793062 7849317.9231938 1717519.10857095 5494016.27783926 4286547.30565284 4216165.66988365 2612752.98298915 3584586.08196358 7009045.08682862 4480360.79775925 1797914.42775286 2958118.63205771 5180419.78048592 2965412.74263722 6867343.34574981 4060369.23068331 4489646.44698815 4072790.12794517 met_113 1351348 1546452 704498 1178543 1910776 2419982 1730728 NA 1518760 1292032 1333917 1256258 974162 519797 2540508 976266 328769 1916054 1273384 1345103 2028673 740657 1759018 2037601 1797824 1183570 281005 210403 2013482 1157284 2890251 473682 1397064 922962 1608440 785419 836119 552727 996652 1314640 1080507 941223 1069492 492963 1146686 413720 1146636 106708 428730 1153669 682704 1685395 1027646 673104 762161 230163 984144 741438 962147 954327 887638 721180 215275 364352 353548 991113 1975188 1308883 919371 525484 489779 945717 86415 409383 910262 1155717 1527056 1552890 374555 863254 831837 632228 1534291 1054678 104907 1181832 570191 1181392 710672 765250 157618 799343 352504 730991 12561534 1127759 317084 828816 772629 761105 256288 491981 517417 208846 2334220 563581 849860 654635 diff --git a/tools/batch_correction/test-data/output-batchcorrection-dataMatrix.tsv b/tools/batch_correction/test-data/output-batchcorrection-dataMatrix.tsv index dd5b7ae8d..6094f1318 100644 --- a/tools/batch_correction/test-data/output-batchcorrection-dataMatrix.tsv +++ b/tools/batch_correction/test-data/output-batchcorrection-dataMatrix.tsv @@ -1,114 +1,114 @@ dataMatrix QC1_001 HU_022 HU_076 HU_166 HU_114 HU_190 HU_139 HU_096 HU_058 HU_031 QC1_002 HU_039 HU_087 HU_143 HU_162 HU_102 HU_188 HU_095 HU_062 QC1_003 HU_063 HU_011 HU_074 HU_122 HU_142 HU_030 HU_187 HU_167 HU_113 QC1_004 HU_081 HU_014 HU_206 HU_119 HU_033 HU_067 HU_140 HU_164 HU_193 HU_112 QC1_005 HU_097 HU_056 HU_195 HU_135 HU_159 HU_070 HU_038 HU_026 QC1_006 HU_098 HU_115 HU_137 HU_019 HU_073 HU_168 HU_191 HU_040 HU_046 QC1_007 HU_103 HU_015 HU_053 HU_121 HU_183 HU_199 HU_042 HU_075 QC1_008 HU_203 HU_024 HU_082 HU_169 HU_129 HU_057 HU_035 HU_194 HU_145 HU_104 QC1_009 HU_177 HU_025 HU_080 HU_117 HU_136 HU_197 HU_036 HU_054 HU_085 QC1_010 HU_160 HU_092 HU_023 HU_124 HU_147 HU_205 HU_084 HU_120 HU_198 QC1_011 HU_045 HU_029 HU_069 HU_155 HU_106 HU_146 HU_128 QC1_012 -met_001 25803075.7934692 22245824.2847269 33362554.2191462 33928934.4345858 38807014.0133696 3717738.65900104 31219859.2933296 6291.31363707351 15131230.5632725 8756547.00256203 25856701.6650235 59056979.1889069 29050997.1320749 13803382.5181896 58821588.2685604 25572860.8589489 4186241.20057895 24196855.2209286 8227264.66573771 25428773.6793063 14045498.2763384 9414858.01334612 10423851.438681 44437723.8111921 2879783.59900579 20152143.5369082 1704640.52100938 19249095.9866807 8052735.84029288 25401278.6667381 6425412.11717836 4483638.76404864 51444905.2526968 13543480.6520335 37044229.888368 10617590.3611776 9120431.09064855 15241202.9006152 2261599.30730485 58759423.0611775 26517342.9796032 30719184.4007157 33192502.7071917 22068493.5017958 33303157.4776968 34172633.7472131 18579133.8342916 1736830.6196864 5469159.08340882 26028583.3459301 37001411.617135 20692573.3989206 13816252.639442 34870261.9550916 23892218.629954 1872945.84405521 15426635.7603488 18162831.6252458 26102769.1872127 25361346.9657174 21929943.0837876 18078624.5109582 6840325.5118984 4298370.91012195 31509926.1754072 8295287.27846444 17172834.9582154 39707893.629551 25130983.0149321 6717913.82352372 25692029.3204722 20419104.2428675 30597753.5063953 34746369.1892372 21024347.449302 48531513.7571104 23734467.4540775 33698127.9888684 32655993.753639 25846652.7556179 20639166.0088837 16756757.7249078 13706617.4203876 17909520.861204 10313589.4233512 3496125.9541859 60226584.6603481 27630414.5917129 31789128.6314396 25532528.4031233 504295.340317393 22972884.1487623 19702043.0659849 43164491.123306 33921240.1948793 25973242.5446075 23840326.3610582 1969203.08687009 33932726.7881634 26901619.9379972 13650110.0854526 44632793.1211613 37583231.5063693 20143805.8172678 48458875.6355475 26748923.8912103 68078552.6187057 24997128.5325153 -met_002 18733622.4121993 9610714.57965993 24275813.6999091 25847057.3745696 24947283.7601687 1423097.122946 20742232.7047708 45452.5705507721 10215310.6474844 5672975.2543946 18050231.7934152 43362560.6525127 22514809.2661231 10349051.5414914 48192375.6131067 16695513.6708099 2675773.81516617 18930420.3286806 3921078.03495049 19234141.2742445 8425289.97232181 4805183.87633365 8700758.02537088 26978338.4524019 689420.717332801 11168038.570606 1063787.26606082 18897743.6167054 3482719.94279234 18869997.188431 1524961.04944888 2820831.69737027 36792629.0197586 19875448.8680594 30621776.9377938 8020404.73929573 3508706.85006505 9712180.2063126 2476600.44425196 36796698.0155182 18383172.8975158 20030292.2095366 28420783.034872 15738173.1376331 18707849.3261424 26475198.0402705 12514954.7774325 1827949.32969507 10372154.4272829 18847233.3554353 27866491.9445067 13948235.1453217 8412641.28884851 28087583.1927401 17915114.1791026 1115150.68457987 15600658.6295136 12481472.3346767 16615464.9590619 18318989.4193656 13282366.2522553 20247449.1816865 6307817.92544131 2473501.72891451 21331736.790259 4248183.31283758 8831209.30602942 30167422.2374754 18446675.1074117 4870390.45834935 17272393.7800738 12115844.3854106 37838671.3549049 24578647.3453827 12576530.043839 38821007.8916729 14918987.221712 16276773.2164009 21233018.154724 19136021.9919222 9400183.58057162 12542982.1328679 6306912.91844588 12911814.5121544 6689485.86631368 674602.07844137 51043256.677418 23737519.6779297 23295262.7648037 18768418.0053805 1334583.73597841 19352280.2354814 16788721.3602632 22060509.8618984 37497312.2871331 17115387.3066589 19369297.961345 369783.176344102 20464081.235087 19252786.192493 9349280.31420263 31503254.5224025 27107438.2824431 20461599.222412 32465411.2134117 23787399.9801255 41488770.391049 17919239.652186 -met_003 4778904.708469 8321593.73262689 6282725.73154528 8817230.06922691 8841714.31416625 167737.839250301 12316743.2839794 NA 3995300.67738337 1009370.52206468 5542708.88074106 8952961.27091115 8005777.96662427 2698703.75749928 12887397.7860527 5901775.53443865 400092.30764924 4350262.11937583 905408.443286938 5194285.85613204 4083459.69093816 870514.145102677 2152849.55410428 13721776.0793011 78085.1990410156 4418793.73026172 121739.740426593 3132567.12407928 704165.255444612 5228586.36094714 917647.220915657 1109555.2269576 16164083.8160006 6673520.7320792 7626607.49643956 1840255.27665882 1315510.39130809 455940.621960695 957981.918193957 20087046.6215634 4984459.07289544 4999014.81604525 5724028.47611399 3751993.47183535 5970279.09777181 11456972.0477201 4617363.99971197 686229.567647423 312565.982394466 5172504.15717413 6061875.52164521 5512944.50534057 2875130.24124018 4430542.51136985 4550828.94744674 301086.689358867 2477637.18828612 4437744.63846804 7413480.05332944 5519298.49263623 3169167.54411178 2570397.49437179 2534494.78138446 429679.4475561 4625727.25831355 910188.442678784 2411442.68721674 7304379.85950291 4604590.73484601 1226866.74944348 4180121.85011741 6186243.53141144 4972057.68341644 10996305.1926989 6782121.60266908 9169335.67646044 8959049.24702056 6887272.85451964 7165027.07379546 5086546.49950148 7433806.53196334 2648138.54528904 1064180.00757449 3046623.32394915 1423175.18076544 101444.02506851 20595399.5264515 5684987.69930753 7389624.81132536 4802172.93254546 NA 3742993.2286086 7618618.16740444 8761112.14128071 12305325.5011163 7199260.81082526 3399786.17550035 53933.1582194023 6685340.19504491 5229269.72153939 4764628.24509895 14279587.4422643 9873478.69537573 2633488.71827336 18461150.8665865 6328721.45684095 20073659.9111122 5303542.29707729 -met_004 17794293.8735081 22807733.5569224 15854633.1275134 621036.333350336 23308516.4255639 438510.033333387 34394210.2957845 13984.9449948478 19100798.2487551 1958469.2883676 18084633.8107825 28868204.6832861 23239671.8815024 9970223.41562386 29910459.704486 22729058.475211 3570592.16571126 16917035.6449875 6629644.65777113 18672724.1060418 15256740.9689841 1898468.85963789 12313809.8912313 38291537.6021229 588524.060762819 9742155.88663981 761659.074489974 18299195.7303403 2703857.62856548 18587769.8453956 2535906.89560089 4864427.52379535 24251065.1237797 30444188.5374637 23253155.2502713 5244221.3842484 3261605.2394005 15344097.9501382 5370384.91508075 48985205.2456935 18230431.566831 13411734.3057951 20737647.0295497 12768408.0415166 12469574.0809307 34197615.7067577 16966865.5820297 3782491.20888798 12582750.6609255 18191913.2717147 24912339.6846156 22826165.423208 10598224.9278058 15942459.7019378 8348695.92276633 3173318.72954067 14382665.6436416 21001913.3488662 22543480.8308715 18879436.9780274 10955220.8592855 9485271.91668875 9664713.86640465 4134442.77618546 20145716.5820375 3049626.860899 8594055.6747 20363115.3578053 16652608.2900284 5414927.23100655 14087166.8056463 12112683.2409828 19673033.3184226 31173838.4499138 14686940.6692255 27631583.0901255 17867235.1436744 14253081.3245111 17016435.6679223 17503659.1474941 19394322.8715885 8092649.36229238 6548796.86891195 17026257.4092062 9385049.15741126 313040.776128002 32841551.9945153 17039025.5355054 20142884.2980857 18070968.5160268 1358899.59301611 7355087.47260012 24029987.6304125 10406398.7532898 15370791.2614373 15847371.6900899 19504650.2455573 105998.88695721 11402096.3417354 18048278.2494685 15748993.7765525 25832591.287735 17081160.9240173 12072500.5246618 30214029.6913737 17461216.2043165 36251745.119605 18642351.0728471 -met_005 161114.123767047 133933.322873204 98274.7108468394 642602.05576694 223550.047127051 73669.8089822882 58240.410351863 NA 6402.23205527168 493397.513724768 173994.793370765 102280.819054213 180405.724457533 149751.927826541 205785.759680191 209017.249235244 38457.7256850032 20904.2334514936 646068.22480545 182522.858625917 334190.157268916 133805.514560403 159273.093775931 325247.39145051 220573.746536674 218122.579161605 192465.075685404 94435.2957892673 74730.6029521196 171418.157412875 59497.8328065126 648904.71905497 736405.619481317 520018.432794255 154745.944678534 23741.5835598408 180065.422813155 33346.0160571652 33073.8060771722 296007.811246745 144848.279768575 34128.4812909014 104731.030400812 69062.7450330251 136239.785722945 205603.600823339 123335.970110136 14541.9516526035 104989.751441048 176400.7295433 348561.074074567 231140.018853612 143124.821271607 83459.0970274129 64455.2548181335 51043.3059126698 23842.2031091841 79469.7682780039 105601.203664546 166710.131346811 308189.711625666 71573.1489236432 45038.4527203613 26277.4488423653 109651.621092218 467524.727967013 276473.721564033 165067.452527302 186121.492162992 45086.5934385701 114188.879064367 194010.877084368 68242.1843097468 131938.685358732 178491.851696905 331603.174600713 8917.74638251547 172692.112575852 161054.095479596 180527.633626521 215362.388607462 132423.218439289 354119.625640746 325329.337182286 71844.6515702429 201180.945378485 166866.828415336 372923.176602767 268810.032308667 134350.028049889 343433.393537604 149582.884788516 15683.7719574737 162958.452083867 67199.6897999027 48671.8952121108 384956.390578337 210532.148930015 433712.380566361 149740.980741513 39250.2144818339 60566.7003975278 121067.191842176 414436.606841064 338738.127147468 61577.5107793952 969651.038764883 195897.952868552 -met_006 168401.448609155 107965.676888357 149288.073589089 136733.298414474 183716.97833119 643921.116452808 213363.279841997 11807.317629387 111651.552792782 762760.234306497 179317.730643487 191920.860972427 253587.080073918 93424.6278840279 175102.726144898 199242.324571987 107233.218521601 151913.483548142 212074.027251427 156372.69643496 302253.589066086 100688.957926041 130650.192202441 169905.789488199 215559.640654701 132489.92800686 105175.563624186 56017.6334179509 153416.039625891 163573.321519854 449210.090999202 98000.9331403652 301102.353878542 228834.66865642 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380958.784043939 174900.390437086 276428.206724337 83735.6606639501 132326.9358512 152498.872459477 185577.767973574 64066.925338417 146535.995924542 187826.885350955 113130.122052879 98608.2311335189 139928.631677491 304885.451975076 167757.277049048 -met_007 711603.127731167 140184.319402708 126134.050904435 35543.958132699 15703.60989418 33061.7747885109 13529.6204627212 NA 65655.6284111555 11218.708392893 708914.693494713 143591.571590001 100842.204908433 45966.2592182104 106509.189671565 27227.8755036977 222677.831860209 77334.4498928933 35007.9703982837 742817.566932689 65449.5081069204 301213.742469578 3277714.76082375 206213.095624315 28641.2849127379 29215.7409813784 239455.749880535 249231.407031193 419221.502566564 754227.867942741 50266.7993810497 198440.448356778 21136.4189894167 55671.4334193576 215813.201052646 401323.024016204 62142.2707220722 124273.016826572 113194.274041496 168459.256266547 703615.569726189 339407.486844264 58701.8188897489 148430.499949209 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243285403.372989 221899510.214254 454025880.048574 252270222.492537 112669272.601032 307939072.354269 300490417.531419 285183969.995183 379425706.950049 165384200.681924 517692189.621809 451427613.380544 203537683.467533 172450800.539093 65653962.5046915 111904173.951328 453068229.709485 282125179.788227 253490153.381404 185286695.687657 290573468.864473 408556869.372545 362954013.950761 105306970.564675 191998628.23823 238370744.348788 309879913.367117 309355205.595706 277229780.35036 225721143.525558 195921233.162759 87132361.3889471 331029081.997463 163476962.448989 199171379.829233 66060486.1802818 189654210.132671 282062595.723303 225184244.330004 337860833.187817 272459810.374944 281294463.422557 252704740.513722 284531049.535981 197303078.865593 214992098.534968 377847426.755098 289878114.176437 220121294.861714 235108172.589423 165098274.987771 177973763.853289 298923054.506179 224548999.171659 311454112.63323 295323067.918253 259874627.18212 185243333.857123 178573031.738248 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1159205.0735271 1506739.10549218 3189160.60975602 1806504.97868543 139612.253191598 52386.7035682354 1813985.00400746 819035.046218433 4027228.11198144 227586.49935546 1283478.07877277 903467.987652998 220085.009026913 531803.943316847 2132985.01823786 369522.033496401 1064065.42605435 589017.491820765 934059.557655971 172181.876873326 882458.278473882 460049.035049899 966800.742635745 800507.284838186 365660.060272929 24195.303445845 274006.498157475 892697.55149921 795983.325893115 1618704.94841386 1273404.05034815 486233.79622812 380792.103985893 138937.831875908 677803.662562022 1900336.9482197 1221302.11272231 976094.119925966 1293009.1864395 550112.478281424 196084.818014036 298290.84764384 227925.731569465 867575.933452986 978224.750143395 884056.959810643 802743.573596009 759298.345509729 286900.253418187 600042.746097936 85906.3500195155 1742767.63581513 418023.497383931 1726544.02602895 999457.168824183 2723084.69691474 444377.495153722 873641.507406809 534917.031010639 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626638.886688272 128984.752661198 103547.083513376 6230.24903219473 121362.628796491 229180.031791296 228313.788804828 8074.0849162996 242410.419952144 430763.18848488 123350.777980046 402759.027275407 360616.775160121 127001.937990385 228241.999471836 -met_049 7353829.92602082 803303.511251271 NA 439553.010297128 42439237.3048514 73000059.5761718 23022002.6585898 1703968.84424696 733433.121938992 142040.364920617 7471619.56226249 549861.7734647 26311132.3643945 67268.3651499787 7050.77430173465 35283.3208319297 5273255.41249589 6258992.54609874 123303.801923465 7320958.98714415 8194.97613158321 10579364.6052156 25873854.2828898 1058935.45530836 26511202.4823306 9875284.74054441 1623987.73734412 1195967.36469818 13047989.377176 7319294.18636275 2920394.04227294 1193658.87209249 419076.534249134 21493688.1281943 137214.668055824 183415.910481369 10685778.6362767 51017.0125665427 883322.417386003 2012203.33497139 7232806.98526305 381696.450779382 34378.1142598622 692953.695411187 89519896.3754906 301346.607937919 28873351.5975071 4281.6388601064 664515.403659337 7776097.13127879 3450233.19345414 NA 2136117.83132745 2952123.45457288 7688770.24891376 9819.2666838959 35157854.3618207 15810352.2254938 5447306.92881873 6920557.71250231 NA 637636.048930935 NA 1635606.43949311 512418.396643562 5276443.36058539 5963073.25460536 12950044.0864539 6988948.06073265 893075.049219096 204461.368997305 2332433.25151239 79851.0799542069 936507.106217962 82992.2479542522 313181.038580603 277571.366278653 85269194.5182606 301761.691001551 8618802.73363883 95055.868273808 2193940.43594964 478506.20817081 851749.495203909 13201.7572747005 98748367.8471277 6877.71036010893 36665904.7504419 35849873.3125741 7373431.04817103 34791611.5858109 15769359.3325639 143751.92018533 31826763.2627227 86029.7729955846 23830.6204578814 44292.2729234307 421709.710700861 34904008.3057321 7867042.09178523 NA 9168839.80431342 2114943.70932627 243463.974537251 2178493.03554102 58600464.0494915 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334748.131853498 491823.190008697 409723.12706085 471991.889984752 5910.27992531078 423564.224854296 1225807.38611134 625203.528747011 5658.28921307501 145426.69365149 1194348.39809737 459080.828918446 377580.157873206 1703303.88256162 5397.78060415341 75955.999290147 421749.857177877 450316.783998851 3749660.46696676 241514.365111356 271400.560623284 468897.719344468 491864.984455012 986921.293847099 17421.9210128409 111074.415700169 445435.259922636 NA 1279943.50996921 689842.639869287 69552.5909281785 486547.470160394 106973.005264334 442173.830373414 1101518.77584671 198742.538961906 243079.939065982 204622.036165817 542467.835814556 340173.031374824 398011.599681723 484111.287588896 180955.71242281 898776.773083835 189838.732359473 450933.56425583 245978.352948085 56007.9765310637 433985.21803249 103775.220992168 488753.756707371 478462.494984047 902767.779260369 1207187.18216799 238049.278324562 164612.764898934 495605.652817309 355554.633695728 28940.6337037717 924761.349004761 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8817230.06922691 8841714.31416624 167737.839250301 12316743.2839794 NA 3995300.67738338 1009370.52206468 5542708.88074105 8952961.27091114 8005777.96662426 2698703.75749928 12887397.7860527 5901775.53443865 400092.30764924 4350262.11937583 905408.443286938 5194285.85613204 4083459.69093816 870514.145102676 2152849.55410428 13721776.0793011 78085.1990410156 4418793.73026172 121739.740426593 3132567.12407928 704165.255444612 5228586.36094714 917647.220915658 1109555.2269576 16164083.8160006 6673520.7320792 7626607.49643956 1840255.27665882 1315510.39130809 455940.621960695 957981.918193958 20087046.6215634 4984459.07289544 4999014.81604525 5724028.47611399 3751993.47183535 5970279.09777181 11456972.0477201 4617363.99971197 686229.567647423 312565.982394466 5172504.15717413 6061875.52164521 5512944.50534057 2875130.24124018 4430542.51136985 4550828.94744674 301086.689358866 2477637.18828612 4437744.63846804 7413480.05332943 5519298.49263623 3169167.54411178 2570397.49437179 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9970223.41562386 29910459.704486 22729058.475211 3570592.16571126 16917035.6449875 6629644.65777113 18672724.1060417 15256740.9689841 1898468.85963789 12313809.8912313 38291537.6021229 588524.060762819 9742155.88663981 761659.074489974 18299195.7303403 2703857.62856548 18587769.8453956 2535906.89560089 4864427.52379535 24251065.1237797 30444188.5374637 23253155.2502713 5244221.3842484 3261605.2394005 15344097.9501382 5370384.91508076 48985205.2456934 18230431.5668311 13411734.3057951 20737647.0295497 12768408.0415166 12469574.0809307 34197615.7067577 16966865.5820297 3782491.20888797 12582750.6609255 18191913.2717147 24912339.6846156 22826165.423208 10598224.9278058 15942459.7019378 8348695.92276633 3173318.72954067 14382665.6436417 21001913.3488662 22543480.8308715 18879436.9780274 10955220.8592855 9485271.91668875 9664713.86640465 4134442.77618546 20145716.5820375 3049626.860899 8594055.67469999 20363115.3578053 16652608.2900284 5414927.23100655 14087166.8056463 12112683.2409828 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260832.746766526 470012.882648703 131007.07351727 240511.864777452 193020.222990609 223645.337186303 315616.631295092 168261.897472301 330145.931609031 255342.134602234 866000.82097745 459566.752785138 444475.754945979 236939.554098071 287467.777747412 423535.780999503 368637.81592733 129698.867830012 463151.298810215 106714.554752985 395838.496305391 566430.631232052 328161.23489477 284917.987981237 395804.933587855 559234.110759591 211544.396656032 219021.369017209 418200.165586387 703095.470064642 469101.546046875 339424.652946772 296540.975468292 397137.027545414 425384.199243967 151792.24324229 611923.970005418 476317.204651986 312828.499800304 191898.130289251 884031.804926982 823896.972797342 542959.132329054 +met_021 544395.063036636 457583.423964441 238149.711962312 1098780.06154983 936356.950003888 505641.277987248 481231.916575154 NA 446326.548731241 477084.551974981 532365.747382305 688787.419008359 401580.541961547 644960.501980169 2048386.47477181 23503.5090498123 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1143463.81059807 215028.776512653 2085227.13533808 517238.313522229 721487.521681997 440917.198158735 76560.9411653242 730741.406932885 516106.040127178 455756.151970404 1259519.57646548 772686.159151635 576552.194196359 552496.726181275 134389.733754802 175072.368537704 317821.724343489 1105046.40207872 463063.425707931 704981.419844905 844442.148741467 488265.038649675 +met_023 1010654.40402613 2517437.72953248 2528089.50984001 1856290.62116976 1908716.48071378 1033048.94627065 1094242.31254035 NA 1121539.33934192 1247871.19796693 1157423.37447724 6383220.53891933 469942.626689716 874536.973921849 632570.000797207 895646.657690885 73578.8319228479 1121319.88566802 5802794.25628624 1189930.12844222 794972.789886301 929972.222359882 290388.651757604 3561032.37739808 451446.747632695 1812238.04464257 399585.623691837 229267.156991528 611987.215028245 1117311.34096879 394202.468848733 109412.125649338 1533756.4021975 421999.881763896 3868781.53511045 106773.531615841 322294.045492896 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732160.834080589 1283957.2981304 3015644.60771521 2404944.74731153 3180874.54461803 1679042.76586238 1942108.01311363 577570.349356006 1839142.45792721 4031949.39946886 7381821.15508508 3118865.59108539 4417300.82166465 3309345.00991305 4388545.50205198 1541947.01484262 2507844.31632932 2732500.02935438 884615.205001785 3180581.97741164 7831209.13464989 1495091.67237981 1022510.89354556 1350037.03661145 3328010.35408949 6103122.64796672 3939503.42994689 8734057.68886952 6143662.0697448 468923.065265628 8850677.98853401 2390581.11832056 4108376.37874832 3352852.91050678 3202250.01224351 837461.64682662 6686568.0569306 1116027.1271929 3999234.76318503 4477744.66950906 2078763.80694594 2129053.44939227 3831882.02977095 5794052.74633756 3391707.1746281 504727.97641818 815772.861090478 2855269.55402529 2840158.39764046 4349830.68396292 2451995.806202 5290923.93896333 3149772.76390793 +met_026 193523.628442304 138828.09814849 77396.400973991 210652.875018949 63541.0059798457 229844.580738935 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563686.38147085 1581155.10896944 2917460.14532511 2125937.9292923 674384.804796753 579896.881059228 575126.641652627 2381660.60944149 438554.863659916 2514528.51640659 7759.14729272956 494275.661405366 2166445.96338315 1314862.23360969 1026783.59077678 1734010.89826092 935008.816790477 1326490.36872839 415516.250423134 743453.339957462 1272539.29052151 1986541.88302487 2334039.25498515 1171430.49973655 809523.095532988 562629.017946377 199114.177508843 192412.655673047 711510.155696493 1382344.32256962 1260556.32080184 1644702.22808442 905564.8885613 1204802.78079127 1880710.51089456 323237.685450554 1542516.98239263 1933099.0462446 2024825.29498812 2405110.14366132 696925.353887634 717580.685014496 2116916.97450266 1592585.07663646 788529.782685133 1286636.11844038 1261001.95937494 203105.432556938 1441054.84041586 1105332.5642128 1410159.03689161 1404135.99426428 2028845.51092603 1738303.88548618 910413.136782485 1018928.82750385 1119453.80823351 1490300.9779229 1541214.98678231 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1832347.55243455 337175.844583964 1342175.6538948 5219473.53865163 3866495.4256064 313094.023300333 48343.7656954178 446835.533136463 621815.404309635 234935.898190137 425285.959662017 1208069.63240498 832023.056796547 166518.859785466 3362943.3581102 527252.988918064 273578.662369266 430205.108623647 721667.701705515 949936.793738033 607297.724121668 232544.140528195 1345304.49137798 367415.691088046 163534.73470338 156003.584433856 197641.087115264 29583.6466685084 1469731.06597792 4040443.00046395 2433152.33681825 364417.010843155 1306822.95734966 326797.292376864 4746934.5012175 447692.089388433 4081348.5670426 1067802.40638515 631910.608672855 450815.757233698 1355087.48477183 9833172.79393245 1641230.995565 583332.454168629 253935.331775204 491642.768143198 6276974.11988048 305528.794893749 176104.403465974 183680.726958731 1001917.18730779 +met_032 1350210.07842471 1060591.42686714 385048.055434892 2094856.07248377 1384573.01179711 503798.558896774 1012080.11219641 NA 310794.358271051 162049.284201995 1544187.4627071 5299184.70602414 1990383.87440571 1446495.93204205 2118644.66032865 3820363.89765686 142172.583962945 310684.786808101 544961.847439026 1455140.65073158 371641.826587274 946177.446500994 340957.94037813 2268683.27850136 73886.6060294211 199337.356861122 67319.9407895734 3296181.41248685 318689.680976918 1405108.35796697 1057315.80469967 317265.754800564 1002911.8232826 2581898.6908841 2343207.35696638 1311549.37489076 1432235.78785004 557643.364816893 367875.769114239 5248174.03425446 1438558.81986128 696354.462643524 550752.026668553 1623714.46824692 1277500.95760228 4334100.58075202 785596.166792582 241976.84820966 279816.400153776 1499779.65477388 3030240.01133134 966787.383093823 1400920.93983088 479353.586514699 252617.288663696 72525.6806327059 225863.205423805 4438131.73893015 1031668.04851849 1423213.59389337 1060378.65749004 805515.296245868 118570.0628113 47261.9551911301 2335195.88759734 1045446.89786145 1026554.12113345 2430726.43728963 1357745.19590004 61518.1066883228 457672.794934008 381735.78506393 4336468.55177364 1339906.65976478 734453.891640165 676620.087149958 2838476.09717018 473155.459859293 2104263.9075953 1400360.9478448 2790763.68946705 772953.998031263 81575.5333701577 1167984.1435075 332282.317697135 510380.3411104 4827286.53617892 1837957.26277748 594368.556138943 1399570.98988845 37302.1354336103 598731.234723993 686486.315797812 3279240.86519342 2251628.06832384 790032.743057587 1935874.00447511 269172.242721574 4645258.08139173 1510365.93240435 618165.653497063 1028049.96882795 1398455.05091454 1015289.88879523 1628758.48867143 265189.448252987 2241555.97719322 1409826.16206232 +met_033 221984.667075762 127204.850174731 138138.283357436 280063.697131421 261989.341477011 426129.961449454 204047.069303672 NA 196050.931885664 92977.4208207669 252244.883813468 32423.5724784438 196093.300848784 205214.704528596 208257.297860926 69295.6037845424 70667.2824364661 218274.795878876 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293943.382444528 136493.095733886 232328.14722748 206443.372904004 350367.507973488 337412.688711656 479873.817968875 68003.0380561337 399447.209517038 206275.924467392 271423.473592215 286885.562173614 222921.375599695 272011.81989856 164290.352882886 107818.829796105 228055.954716126 208562.30650738 318699.755001265 20214.5347109632 298386.563510527 185983.511151976 253632.500800216 75791.6800201069 143114.215407636 213106.265919159 57025.2283225128 328496.996541424 200015.925946819 245348.733586734 210461.222086675 +met_034 2182386.83332266 9120411.0602109 170986.72110505 1059993.54808901 841276.253477399 1532126.66921798 197520.919772767 NA 399005.840918311 1446451.63666756 2129148.03475802 2382518.17149301 844759.834594052 532287.787181639 6985019.45122819 560292.543693681 896128.691388532 1453413.54061414 498193.585167912 2171035.11189141 499914.955682198 1130158.72795087 398643.975265576 5902729.58021625 3206633.80850453 435183.734426376 207608.595371697 385083.437290268 899937.257107867 2149245.60263497 288076.027032971 664452.3588111 2287792.69811084 386987.967088458 601113.669672476 877990.098691755 732163.871170466 344845.208347334 318805.530116456 6577157.50007408 2139878.76439583 806415.441217048 15559797.8039688 119853.887068981 3018452.79752315 1835669.37822682 5566560.89261377 160090.317309668 903108.004124699 2324612.57336277 1120050.52601564 1136201.30172089 9316294.86896039 798112.989280024 224995.16320505 312376.511313557 886414.138773321 421711.809910688 1008512.5333787 2373298.78572157 3876135.2729625 2056072.24515907 2131430.83072618 217275.03244487 1180466.1375003 131304.557624471 8806961.37117334 803915.444218836 1636614.25075585 1878265.58512253 334735.0228939 1023044.28324431 246914.682305309 301430.640890105 NA 5282675.88808735 3126450.28199142 3613483.40771735 786628.035782912 2230663.29478869 452785.173042503 767512.83019025 599311.191203497 698114.251859071 938502.801478172 1058340.18178588 988607.935307869 954254.955052317 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219615.170414527 133832.026275209 78095.5406517575 175458.841371521 259482.58376368 69855.4048971026 143788.377857736 44697.9581849473 476543.176749062 135515.522753179 74717.1730497046 35409.8535196925 117597.004291088 215402.515490954 68077.8351563419 74725.9649256029 274274.537433444 54318.7354670246 242107.37515679 831974.500728793 67354.8464508289 320184.268466532 201269.62067175 236478.532278232 466652.72197375 62984.770595431 136167.90828554 478963.648668903 140066.89722957 381094.609073518 91361.6587143137 140791.468862386 188637.813918487 33925.576691111 154509.659839663 35212.2529933417 230117.34968102 351972.78537837 361012.515471931 333722.575782755 314972.591639109 +met_073 4131322.52161361 2724935.70151468 3078019.08985278 5482865.62846659 4025718.701987 7596498.75964651 7335608.677094 NA 4054218.77653802 1820165.37796284 4374702.80842555 4126853.51948032 4975042.57661529 3290767.75253457 3818273.1728171 7093463.93450578 1467921.09614982 6450807.32081785 7750345.33938031 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832520.538865161 1633100.7307123 1898343.75594558 1420395.42231599 +met_081 302805.934339317 198268.110356067 508780.368626824 26013.7393118613 49235.7760988675 50823.6488256171 11782.7414731473 NA 17346.8826187513 5845.18177310912 279022.45157333 132623.606701876 133673.498689374 NA 518186.925767248 167876.217240311 4026.92884291118 120029.18348942 190186.921837744 291615.606653763 150684.535371361 221059.544923577 640745.723306251 252921.222681471 64094.5467539563 4349.23852953978 26546.6586474349 182004.620589231 25653.5743994641 281253.387766029 430692.893372175 16434.9447440763 39138.8107647899 602060.561974313 475935.837557407 1523038.69603938 1324803.89943051 1274395.97855355 961842.940749584 154367.003937568 291010.885850994 142016.204793706 85450.0539677439 120596.908441097 25600.460528453 270967.763138811 132480.497071723 5298.46950486991 93452.419844568 263658.903527012 1902359.54590334 40776.2084739182 17597.6433292518 208718.503471733 109709.925992754 13920.0091698174 8284.5803497451 647286.86534774 107622.389217047 373880.617033298 12383.8990212413 108158.637936051 19856.3399111607 52194.4069745796 85981.5169935776 5162.45157142305 5941.64623778388 20889.8915724979 245640.013643355 2354418.76017085 24346.8606483235 6508.94181996302 43496.9867116623 161034.046764367 221567.035439131 431586.444710143 8880.53801633617 105625.940071322 6879.61268666415 253797.68668466 1430978.27614384 55661.5622876469 679368.851749934 157846.119238671 14832.0042387536 1149146.87035326 29605.349699632 69517.1998969656 2440892.81435987 308628.62098272 70695.0216853404 89477.6955032979 659457.838110551 112806.382253783 43899.7325329574 110411.387956553 418015.219303301 146288.334941198 40587.0228320363 322193.577158026 347653.807594148 94151.0415671175 47526.67353405 688071.892792621 102352.587158494 178719.106162974 10398.6434564243 272045.205433086 +met_082 253218.69898939 29030.6799656207 109946.263015421 522520.634307006 692603.678094653 762833.70318542 610153.630800482 NA 452089.638606847 427813.428900055 265798.153655734 55607.200018062 87782.7336909709 262832.493823713 226853.531957233 533713.51545227 200562.444037152 525808.891983679 861701.096383756 260618.423420368 872880.597568069 293047.173651209 729441.365988234 1008488.59838681 766468.698319531 567950.290993794 162029.360019255 NA 899040.762222504 259822.04868523 810058.536751505 215036.806278287 702867.935674942 NA 233706.761352984 148197.502836416 510089.345525097 420801.865040287 203455.44541242 511664.844433809 236661.290723428 91533.0218366424 312733.072829811 366057.618892391 881089.225466061 362523.262486538 185694.009467069 NA NA 274716.209701245 395777.351229489 579920.470492916 721340.107710929 312070.70899309 384233.425090955 263507.489102907 484098.2732568 484428.509435745 629043.014905958 238988.737002756 584065.061051488 109640.868169138 83196.5824634543 174452.819750488 7271.37352352368 575150.004692504 937870.704251709 562493.405960816 277562.412592048 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510606.125756931 207061.902194651 394404.137049736 419018.202294242 618294.952552136 466509.216835276 214022.226609145 195688.874897733 378298.589307785 295785.311493246 658198.815693363 216207.76126528 197079.19508079 427561.870258402 734705.168301533 429363.10333584 385285.557871984 254874.57534165 403923.774836681 274430.336388833 298671.198276473 256249.389149053 399592.119689275 361274.594389735 338795.191472946 367616.448254614 435108.265985572 78271.6868947275 363050.663039898 362276.591117789 319217.664604072 290399.991771016 562598.899555644 302993.005644574 215399.271870996 113690.88532319 328904.204410122 404481.646605152 264647.01381884 318812.615379121 400294.811641733 283468.879502723 140120.239200256 131889.099084178 143015.044238447 360183.731435886 389639.954488149 320724.990224729 290985.777803656 308220.392030133 144221.955709181 269423.406891437 130758.702876459 155574.734707736 306921.345817873 539512.76834608 390287.734094569 296011.4367669 175488.084156899 339469.739444202 391504.22987442 387406.452764712 488053.787600289 651462.109779483 218014.974114667 346160.087928398 448247.535269292 408756.413527643 511603.406791658 306300.676854067 372687.269667131 286743.783303265 165532.17413033 426885.120308903 364526.265250104 410496.1285927 228640.011188026 509185.867803006 479969.388977487 313148.785804567 138795.681206007 228593.846011672 423969.569955952 257894.092838902 230446.749928205 315729.858958826 428559.372002787 330552.147993284 +met_084 1586223.73397367 603218.233724441 1039664.66875421 1336491.21006043 2098068.87354095 2186591.83717174 3122187.71098696 NA 1568069.66426025 2923556.87518794 1539853.93851536 1449741.63652538 1892044.56970271 766597.752524585 868482.42062711 2132406.61222431 510854.985038912 1951915.03412423 2326698.07311378 1647979.53661189 1347469.8707442 967019.826304131 1703587.41055447 2274214.30649625 1743286.83147669 1284260.20832479 258821.522097706 404259.736459925 2004826.2056637 1490099.78500955 2575924.86404597 807993.807805688 2006228.19449597 1852035.04965689 1356768.7205114 1305978.74076454 1220287.31952254 997090.851083893 1357412.04238461 2472315.54313975 1614954.08949353 802374.124336859 1063940.77017024 1008525.98504383 2830588.89748004 686174.182533472 1178947.88288179 250803.008065538 1457875.52441996 1640504.88725525 2159771.40850613 1419419.80225219 2159858.969382 1981565.13742231 4260192.13213954 1041734.05232463 1318328.37824323 2596877.94073541 1727144.39261462 1557839.99299476 938175.530766688 1626359.90961402 616227.507071332 449025.624265825 685366.643356613 883739.324894012 1786628.3282443 1782494.70251877 1441155.77848133 1588082.20793368 1141704.40563427 2602713.12639246 503542.839618199 2527464.50060344 1977992.83038549 2313296.44251768 2365429.66014482 1511657.87571084 806265.513091981 1716736.30799622 1431935.88435279 856712.406211082 1273319.61991103 1518436.28339906 280976.708754515 2036179.48970519 1103984.74601602 2209100.69598736 2304386.72697208 1533310.05416524 1490734.16844253 2066778.0993009 764043.265877034 2636713.59983724 1794166.72305958 2546623.47986866 1093609.15028956 1603853.64442092 2401835.22736959 1827854.66690521 660157.429431202 2497470.85189658 1579197.10908407 600051.015330059 813995.599060063 1543401.42787065 2062903.45704993 1352001.28103063 +met_085 485305.713796081 113374.576756088 173908.274174162 680084.908653725 842624.651931713 401954.262376303 641426.406280863 NA 220950.73749699 799327.40395065 509660.913291498 341628.501856658 1003291.93698417 146087.676039225 446561.02178908 184030.571380669 239797.955784593 191928.830075187 661012.666168852 486163.372314444 81309.029736508 63096.5623650373 1614161.86096076 546682.379704561 1203554.1417024 285978.627087475 28106.8124464964 107275.064861868 254303.821765427 513190.409766574 484349.77746209 376117.62569386 462981.53510492 72122.8762405188 NA 619936.518829922 131295.722232269 269741.358639036 586069.768134643 396465.286661839 467849.194530996 134285.488250822 420110.167527886 343190.039145886 515273.40996906 187609.373030457 333220.328048447 57155.0966938127 668764.575878106 480649.084732282 816839.543984203 318535.985339669 935893.345579615 477683.154811726 764788.458084076 797338.528668671 262501.02521973 670848.588186786 353401.46615717 551941.775333207 106285.408205287 506776.977681639 125616.620193642 91284.958017834 441054.904585089 218180.992444511 518822.114943625 477965.014871938 472204.849485675 299818.403518265 307812.83080134 263841.640790283 248382.203967945 1170013.75218859 268018.180108623 389362.162653407 886667.186570188 210092.3472121 91187.1940106481 470362.810830494 204327.018553695 613317.113958483 443218.942981742 368490.054475502 60941.2282962989 533929.862368477 355036.879073088 878349.46560119 433420.281831502 469353.325733779 1021531.70425011 575795.148718484 84440.873511191 383130.472107733 288377.859530301 517367.107677456 298332.740914076 260500.933723977 387273.957387239 494366.338434242 77837.0769926239 454964.326508472 279604.49696268 272899.643541671 185958.8662874 351034.581304827 398400.122471037 518237.867432771 +met_086 706071.154060074 1169719.34254175 266111.016039498 1173381.12553656 2333189.66806834 200617.421001346 410102.16762445 1950.0074410367 310909.506312044 322777.566888062 652540.498664118 1149913.55379478 1657345.26564075 568232.498186632 832212.17724829 877742.164126443 183895.694854279 633220.074650943 543702.166789257 621831.346663572 2157862.82741096 495071.46644032 1301080.5869397 770942.813317901 1191819.94419157 439381.821947663 160619.1173484 386355.014532751 606945.301910954 712043.907975013 1135595.79684352 563595.993040348 631736.732413643 1735181.43054305 973005.777244634 578960.928535136 152346.815037025 717358.333031614 725057.617273879 587825.803071508 706193.19824248 348871.878218432 292940.391908779 530244.311669187 466479.041271683 400316.743567346 594244.854427832 116639.087772855 9083.21705796267 692144.127900184 1054531.49777193 907061.810968972 403555.527256857 549269.29164658 1102986.17368765 437415.362541631 313448.234836861 598231.295634869 1295811.65523689 739951.065331356 1139185.59773373 457619.981251462 849683.980538895 219261.442333455 547784.140371506 181562.646391228 313054.703957764 849912.644344986 464093.919772825 446342.665322824 340028.075627124 654957.799027104 593165.999060618 755046.213923065 607793.078311265 432003.172826182 1312819.31417693 586001.864313764 497698.592425822 801533.955702842 1184590.61313804 133977.631593131 560446.789032357 202653.753638699 155666.603345149 550447.465867832 722459.832289469 503307.184852501 588803.890458638 529253.148662505 492221.343564687 974185.482985487 110308.182381137 402775.029207574 1132427.32800297 393606.510598175 964145.658785616 2302049.26399296 781236.764381374 931355.620073747 553466.995272439 358439.104111557 817500.464757449 914644.803777641 510233.765206877 261441.865637833 750911.093487348 518835.666297818 +met_087 131965.972735192 97724.2619328014 102611.562748677 136034.187324963 342712.490924076 122497.985737143 185350.197462581 NA 160072.872358258 140015.601644896 162039.081506423 219771.470601804 259880.333633715 71600.5138019741 298847.957460391 176445.720136264 NA 78144.1468798725 178038.551183971 149315.588192818 409192.704636694 44372.8259733728 154265.903040319 224785.314982887 126283.506467159 64419.6966661603 4446.44830255132 45589.1652690723 56888.839607604 143520.146660449 73815.6994114806 111479.573126253 151088.826438278 214114.244830249 250729.668767004 99208.488242453 79642.6555248607 138576.369039702 76133.9657998516 215484.79783855 149885.077877731 124712.907676846 66137.3626873529 93826.0342036053 105554.190092932 37863.4423709673 177574.338893588 10193.1000900656 85826.458939816 123626.336381494 133587.762362518 192459.083590221 150893.410318906 99322.8330603218 257428.88839326 11333.9308789248 60191.1191984258 272989.279094218 132677.180137248 150260.914028848 165827.751855038 143702.010386926 93021.0339783608 3588.5441384188 122335.242594937 230087.821171637 96321.193732546 173222.075098019 156646.934269244 80678.1944481282 42925.6879842743 195342.48438844 72321.3572146009 177314.630158041 167553.671795147 178025.047828465 268711.111487844 57616.463936392 120919.773348822 155425.96752847 86954.5565949912 53447.9887405126 40037.3367292991 169053.253410038 55038.4700535131 105864.439643486 162684.259111456 46300.7035602892 223092.088668303 127832.845078914 75209.5088807512 198784.233483975 61045.4314364947 145085.549156358 290220.471451161 107656.280210869 114811.530918273 523313.365985682 169010.204446685 150083.040311 63103.5875359217 136841.958149501 75742.3462357972 26909.5939914595 108306.521058793 156136.929116422 231544.777456659 144591.424405918 +met_088 1114156.10844461 718962.434703991 251169.755693155 2430966.15744992 771935.303694081 500878.108207795 1506532.21606952 NA 260003.887477074 1193923.07210796 1063550.99794682 733833.757082308 481311.958165321 2038122.90237623 729304.130362748 672943.036858587 351460.073363752 1281410.2455841 3049080.96794124 1122272.03337669 1662091.44944421 1966742.526825 2131860.0994331 1202189.74831586 893982.58112865 1158051.41917858 402108.223598413 214276.484634403 2290672.64733941 1086455.37737022 1049989.6023926 303745.419466866 443333.459109463 963610.516696698 1114393.36849389 315015.168097275 127390.110377145 238123.451685537 985741.297155708 1528245.82787371 1052513.75128992 254284.981779738 2782595.3376996 2052187.73314568 1648117.77502738 1146858.8593989 2076301.79871278 222731.770697336 897010.554609965 1142767.78793222 1175566.62629062 147605.410451921 1334477.85944373 674257.455767311 238272.320081859 83535.8446922161 338575.310758172 238252.269619172 373820.121743556 1080305.84828162 647712.740705506 1310223.72832642 235283.643524588 688048.806749499 332199.290812502 2839113.17196381 1700626.4863834 1463264.37730141 1112948.7568569 831865.778620376 1806201.20972594 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1619769.33173802 969734.322486527 2292432.02552438 1897887.26607935 1472363.96795839 1707836.9204261 1191197.22208335 2059896.02010614 1167668.61113668 2209836.3799749 1331692.07711026 1217713.9285361 771722.863556919 1552414.44772752 5112403.47259856 865558.38143545 1155840.42711364 793694.550588007 3655646.03067983 1373066.39435302 2929808.86105747 557023.426978581 1160655.89366737 790964.731161082 996635.138960015 614282.834486936 2050637.66341388 1164492.07555484 1005436.05868775 3183112.22933765 546522.875925676 1311554.41543493 1625301.45686237 4519189.22872019 1363634.37044928 1492390.77401303 1429589.24043615 3492063.25483232 2360055.11168603 1333976.19715791 876990.236095822 1564188.32275423 1484655.34159318 581775.479367065 2549967.35097927 455542.316573134 1556208.14529625 2163734.81777799 4652892.4578312 922316.556058003 2489979.68846815 +met_105 9597289.94689704 17775786.1877401 4063961.94442271 16062149.4486709 22043374.2100533 9785192.17635333 1644527.72955446 NA 14160450.4460325 2607082.69255055 9341647.10218758 6496704.62393387 15488313.5357806 5489306.09907518 1747485.69907334 16907762.3436931 1299982.15827414 3639805.04499129 11437666.5126208 9408375.4453566 37065646.9357045 24925202.2772838 3486362.74359992 11149997.6362597 4815793.19611356 3243207.21120414 1479285.18918058 1579086.43847924 12598591.2008803 9857277.68628061 5165036.11016821 2239148.24156424 20760822.3123328 19988619.1084059 24127604.8894296 2013248.5351025 9272971.00948804 8837590.14193245 11027032.4998799 12899679.9089735 9573009.43324273 25445004.7698943 5205682.41277029 6584660.38469973 8473959.02974484 6659974.46468484 3943369.11080716 843593.049736784 9697666.82265808 9214708.0234089 9065306.82434424 7166802.30482907 9018824.95388598 11892956.0577038 6223827.5680508 2438173.09935524 4394597.5467602 4158257.46445385 16541558.4680658 8879678.89636791 15729023.6514073 4670957.90566615 2542047.89177542 4516545.35364553 2051566.0180459 5478071.90661227 18372821.8792767 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28940.6337037717 924761.349004761 89944.1404118622 482363.061620445 437171.679904949 706373.836552482 1310669.86518268 1400117.37424977 74759.1921933037 1179895.16454218 118770.554529293 375825.562772123 95841.9801276573 489430.754809949 185210.553704108 527293.172027224 1635538.04241568 227765.151724782 196722.655106735 149507.136967426 178011.068346065 3079.88222354712 218850.150774505 465381.314234406 137662.380509278 819485.093541267 588437.962396002 385658.119814096 82156.9650793635 628050.703419479 169771.992538275 496593.020201788 +met_107 152180.191191269 57297.3493643439 72944.1060497764 171912.382098385 231669.440408359 449584.539407147 257904.915867669 NA 153584.687974014 214392.75792403 144590.677596849 317979.34641048 223881.386775447 NA 95404.7157890226 29898.519480339 75356.8223238459 231849.44103255 179211.75053974 160720.791888549 121324.048357963 147161.331737008 142797.171171266 261551.216724255 419325.616603172 174105.11894003 NA 3747.0942454325 341520.035375096 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255972.397440834 203628.508698265 674154.061671363 853963.306570161 1093809.40240916 528564.486991671 461894.541792851 382956.541060571 546794.034408443 154630.007912095 716259.495046856 232410.999067075 258706.605619237 715416.857945324 307804.065457706 270880.828107514 434092.014738147 263323.936179642 430380.128500764 878603.768044785 1055152.44244212 130243.257168092 1013064.07004337 182537.019331596 449147.252328361 1323831.16093734 482583.86583938 648331.295727069 357008.865990431 164299.871621097 334745.844694145 63097.7324624238 346742.088956954 116485.345872499 308363.275047107 421510.220146948 469630.244071362 214944.539087649 679473.815523521 575874.754905382 80044.5334481536 188212.074199603 538318.614357879 285891.884100972 394813.373467562 +met_111 829000.289975951 621330.988744865 450740.709470865 2282077.67453648 1942259.22615969 1599738.15705569 1773016.86671848 NA 455251.31893667 502025.800582396 875771.501944033 403461.492354442 1568966.83957551 751403.630730765 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452631.049787301 469184.972536049 963758.139805117 143691.091476743 1622088.74186515 1078521.16384775 909801.613197758 1539788.40006757 1405057.03024695 1446597.07759849 859101.558136011 142383.112819682 2589391.88056521 977785.384695409 1054180.33235713 1090979.33590343 813748.203006604 1229309.49062481 1608619.81571132 292076.848877472 863039.358844594 1515390.49822307 720835.420170902 443982.812394315 977783.054388113 479678.805085767 861454.511172266 399179.581453245 537468.992122009 1341168.53297045 356665.052780839 1077581.84947797 699378.392150834 2180156.83550866 859740.908081679 +met_112 4538260.07332923 1997603.63759386 3101105.23740388 8676705.07693322 4499120.50413991 2300186.26663147 6028064.84593985 NA 2491991.86068611 4936389.92875914 3934368.63868677 3223602.52806592 5758560.31329564 4068603.84108099 3020876.08310118 2092201.98947378 1414128.60450223 3564589.34919401 4454519.51899206 4111395.21282403 6506628.04141497 1638924.06694788 4704057.12477128 3783706.68101197 6398699.60280182 2814042.19687001 1380804.4721669 2762753.71246615 5605197.20586148 4482681.39654662 8211280.36032051 1649600.0418054 3908924.69185977 3874350.9274596 7338556.35476537 4151528.10101193 2487779.1011633 2870763.03704328 4304219.62588265 9393649.78742389 4176141.32094111 2629914.41305345 2699719.51441013 4108774.30717426 7046808.49106602 2534899.22779241 2209294.01128173 993612.490501395 3466629.03422472 4284484.51427465 4707978.58556783 2496536.33382371 4076646.32542001 3524390.53123507 9275598.64644259 2759480.08076152 3366878.58611063 6050319.56436356 2714865.54936974 4174949.54971015 3275325.72458992 4267918.90420685 923327.926460122 3170959.29947981 4492717.85645683 5139744.91372233 3863471.57733796 6886498.45100012 4428439.83785168 6371944.02729372 3112777.74911802 5514644.81005529 5808375.07378356 3308682.79888993 4771946.46729538 8144361.80783535 3409974.53680361 3500477.92909193 2617203.28223743 4414062.2820156 3812798.14081104 4585421.20450721 8793172.67675603 4637139.66088434 943790.950209665 8994466.91015318 4615134.45758878 5559998.73571828 6145353.55124428 4289798.20115877 2893421.75793062 7849317.9231938 1717519.10857095 5494016.27783926 4286547.30565284 4216165.66988365 2612752.98298915 3584586.08196358 7009045.08682862 4480360.79775925 1797914.42775286 2958118.63205771 5180419.78048592 2965412.74263722 6867343.34574981 4060369.23068331 4489646.44698815 4072790.12794517 +met_113 1008565.5752504 1158567.63903145 529830.796502162 889814.888535702 1448388.21917357 1841757.04389807 1322567.71170675 NA 1170273.11749264 999795.60192758 1036645.09275775 980543.484756615 763711.435143931 409322.220596871 2009596.78032114 775776.680204633 262459.955623623 1536763.6397872 1026146.72109141 1089130.8837249 1650573.81412147 605567.75806038 1445318.28337213 1682617.78861332 1492151.26331285 987383.80646001 235646.175796624 177370.54091637 1706438.13091351 986114.199693531 2476280.35702388 408095.520330724 1210422.14154099 804240.996639918 1409699.96479546 692439.380275387 741562.342086506 493209.987837817 894850.612050542 1187806.75778047 982531.920495764 861473.583014974 985392.332655452 457279.162538855 1071029.24363867 389141.615930624 1086237.1583117 101822.549704857 412116.995986363 1117205.58522017 666034.618948003 1656278.0882703 1017055.12392107 670681.845781125 764619.552328111 232557.596158996 1001461.99426945 759811.792469226 992902.332619325 991708.040046476 928839.397680867 759923.801127518 228428.464322441 389332.254437437 380456.756714177 1074125.13525503 2155924.49102891 1438928.10254248 1018035.38268366 586120.199558702 550305.983321681 1070444.71972962 98540.2137253375 470324.959918036 1053661.68418953 1347955.77809265 1794698.05572542 1839129.91193994 447039.267881417 1038365.31754997 1008450.35781633 772534.824283955 1889746.64026194 1309455.69768566 131302.851803826 1491241.59783436 725369.212390591 1515316.3946072 919122.079000584 997990.290565099 207287.048524534 1060150.0355142 471509.506822373 986177.685439062 17093346.8989243 1547984.63462982 439051.348344737 1157749.50227074 1088849.75286227 1082199.05099703 367690.017886928 712227.420096786 755882.247545083 307899.905156454 3473142.11220125 846374.452146134 1288272.54990407 1001712.70994295 From a211b74f1eed98264c5ea53929a078fa497b3c32 Mon Sep 17 00:00:00 2001 From: hechth Date: Thu, 10 Jul 2025 18:18:08 +0200 Subject: [PATCH 13/19] moved changes to dedicated changelog --- tools/batch_correction/CHANGELOG.md | 48 +++++++++++++ tools/batch_correction/batch_correction.xml | 74 +-------------------- 2 files changed, 49 insertions(+), 73 deletions(-) create mode 100644 tools/batch_correction/CHANGELOG.md diff --git a/tools/batch_correction/CHANGELOG.md b/tools/batch_correction/CHANGELOG.md new file mode 100644 index 000000000..9eaac67f7 --- /dev/null +++ b/tools/batch_correction/CHANGELOG.md @@ -0,0 +1,48 @@ + +# Changelog + +All notable changes to this project will be documented in this file. + +The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/), and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html). + +## [3.0.0] - 2025-07-10 +### Added +- Specific names for the 'sampleType', 'injectionOrder', and 'batch' from sampleMetadata are now available in a dedicated parameter section. +- Addition of a sum of ions before/after plot for linear/lowess/loess methods. +- Addition of a third option in "Null values" parameter (renamed "unconsistant values") in linear/lowess/loess methods. +- linear/lowess/loess methods now handle NA in intensities and allow "blank" samples in the dataset. + +### Changed +- XML optimisation using macros. +- Output name changes. +- linear/lowess/loess methods: disabling of RData output. +- linear/lowess/loess methods: split of tool-linked code and script-linked one. +- linear/lowess/loess methods: adjustments in the normalisation process to match matters linked to NA acceptance. +- linear/lowess/loess methods: better handling of special characters in IDs and column names. + +## [2.2.4] - 2024-xx-xx +### Fixed +- Fixed bug for pool selection ("all_loess" methods). + +## [2.2.2] - 2024-xx-xx +### Fixed +- Fixed bug for color plot ("all_loess" methods). + +## [2.2.0] - 2024-xx-xx +### Added +- Specific names for the 'sampleType', 'injectionOrder', and 'batch' from sampleMetadata can be selected by the user (for compatibility with the MTBLS downloader). + +## [2.1.2] - 2024-xx-xx +### Changed +- Minor modifications in config file. + +## [2.1.0] - 2024-xx-xx +### Changed +- For PCA figure display only (**all_loess** options): missing values are set to the minimum value before PCA computation is performed (with svd). +- Additional running and installation tests added with planemo, conda, and travis. + +### Fixed +- Variables with NA or 0 values in all reference samples are discarded before applying the **all_loess** normalization. + +### Changed +- Modifications of the **all_loess_wrapper** file to handle the recent **ropls** package versions (i.e. 1.3.15 and above) which use S4 classes. \ No newline at end of file diff --git a/tools/batch_correction/batch_correction.xml b/tools/batch_correction/batch_correction.xml index 5eb5bce53..366a40656 100644 --- a/tools/batch_correction/batch_correction.xml +++ b/tools/batch_correction/batch_correction.xml @@ -123,6 +123,7 @@ | Marion Landi - FLAME ; PFEM (for original xml interface and R wrapper) | Franck Giacomoni - PFEM ; INRAE ; MetaboHUB (for original xml interface and R wrapper) | Etienne Thevenot - LIST/LADIS ; CEA ; MetaboHUB (for R script and wrapper regarding "all loess pool" and "all loess sample" methods) + | Helge Hecht - RECETOX ; Masaryk University ; for maintenance --------------------------------------------------- @@ -297,79 +298,6 @@ Refer to the corresponding "W4M HowTo" page: See also the reference history: | `W4M00001_Sacurine-statistics (DOI:10.15454/1.4811121736910142E12) <http://dx.doi.org/10.15454/1.4811121736910142E12>`_ - ---------------------------------------------------- - ----- -NEWS ----- - -CHANGES IN VERSION 3.0.0 -======================== - -NEW FEATURES - -| - Specific names for the 'sampleType', 'injectionOrder', and 'batch' from sampleMetadata are now available in a dedicated parameter section -| - Addition of a sum of ions before/after plot for linear/lowess/loess methods -| - Addition of a third option in "Null values" parameter (renamed "unconsistant values") in linear/lowess/loess methods -| - linear/lowess/loess methods now handle NA in intensities and allow "blank" samples in the dataset -| - -INTERNAL MODIFICATIONS - -| - XML optimisation using macros -| - Output name changes -| - linear/lowess/loess methods: disabling of RData output -| - linear/lowess/loess methods: split of tool-linked code and script-linked one -| - linear/lowess/loess methods: adjustments in the normalisation process to match matters linked to NA acceptance -| - linear/lowess/loess methods: better handling of special characters in IDs and column names -| - -CHANGES IN VERSION 2.2.4 -======================== - -INTERNAL MODIFICATIONS - -Fixed bug for pool selection ("all_loess" methods) - -CHANGES IN VERSION 2.2.2 -======================== - -INTERNAL MODIFICATIONS - -Fixed bug for color plot ("all_loess" methods) - -CHANGES IN VERSION 2.2.0 -======================== - -NEW FEATURE - -Specific names for the 'sampleType', 'injectionOrder', and 'batch' from sampleMetadata can be selected by the user (for compatibility with the MTBLS downloader) - -CHANGES IN VERSION 2.1.2 -======================== - -INTERNAL MODIFICATIONS - -Minor modifications in config file - -CHANGES IN VERSION 2.1.0 -======================== - -INTERNAL MODIFICATIONS - -For PCA figure display only (**all_loess** options): missing values are set to the minimum value before PCA computation is performed (with svd) - -Additional running and installation tests added with planemo, conda, and travis - -BUG FIX - -Variables with NA or 0 values in all reference samples are discarded before applying the **all_loess** normalization - -INTERNAL MODIFICATIONS - -Modifications of the **all_loess_wrapper** file to handle the recent **ropls** package versions (i.e. 1.3.15 and above) which use S4 classes - From 36667ce1fec705d223a577611a3a44be2eac8c9c Mon Sep 17 00:00:00 2001 From: hechth Date: Thu, 10 Jul 2025 18:20:39 +0200 Subject: [PATCH 14/19] fixed lint on test --- tools/batch_correction/batch_correction.xml | 12 ++++++++---- tools/batch_correction/determine_bc.xml | 13 +++++-------- 2 files changed, 13 insertions(+), 12 deletions(-) diff --git a/tools/batch_correction/batch_correction.xml b/tools/batch_correction/batch_correction.xml index 366a40656..f81d6e1db 100644 --- a/tools/batch_correction/batch_correction.xml +++ b/tools/batch_correction/batch_correction.xml @@ -95,16 +95,20 @@ - - + + + + - - + + + + diff --git a/tools/batch_correction/determine_bc.xml b/tools/batch_correction/determine_bc.xml index 930b30b1c..8523ce2b7 100644 --- a/tools/batch_correction/determine_bc.xml +++ b/tools/batch_correction/determine_bc.xml @@ -214,12 +214,9 @@ Refer to the corresponding "W4M HowTo" page: - - - - 10.1021/pr900499r - - - - + + + + 10.1021/pr900499r + From 1614d2c6904d475f733af0ae771e02e67e8f55bf Mon Sep 17 00:00:00 2001 From: hechth Date: Thu, 10 Jul 2025 18:35:33 +0200 Subject: [PATCH 15/19] lint --- tools/batch_correction/batch_correction.xml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tools/batch_correction/batch_correction.xml b/tools/batch_correction/batch_correction.xml index f81d6e1db..d82362dce 100644 --- a/tools/batch_correction/batch_correction.xml +++ b/tools/batch_correction/batch_correction.xml @@ -296,7 +296,7 @@ Additional information .. class:: infomark Refer to the corresponding "W4M HowTo" page: - | `MS data processing - Filters and normalisation <https://download.workflow4metabolomics.org/docs/w4e2018/2018-10_EC_W4E%20-%20Dataprocessing_Filter_and_normalisation.pdf>`_ + | `MS data processing - Filters and normalisation <https://nextcloud.inrae.fr/s/XwW9S8d2XMbFyXR>`_ | See also the reference history: From a87254b4b130be287f9536c465bb44c3f4ded97b Mon Sep 17 00:00:00 2001 From: hechth Date: Thu, 10 Jul 2025 18:44:09 +0200 Subject: [PATCH 16/19] lint more urls --- tools/batch_correction/batch_correction.xml | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/tools/batch_correction/batch_correction.xml b/tools/batch_correction/batch_correction.xml index d82362dce..bb4107e65 100644 --- a/tools/batch_correction/batch_correction.xml +++ b/tools/batch_correction/batch_correction.xml @@ -136,12 +136,12 @@ **Please cite** If you use this tool, please cite: when using the **linear**, **lowess** or **loess** methods: - | `F.M. Van Der Kloet, I. Bobeldijk, E.R. Verheij, R.H. Jellema. (2009). "Analytical error reduction using single point calibration for accurate and precise metabolomic phenotyping." Journal of Proteome Research p5132-5141 <http://www.ncbi.nlm.nih.gov/pubmed/19754161>`_ + | `F.M. Van Der Kloet, I. Bobeldijk, E.R. Verheij, R.H. Jellema. (2009). "Analytical error reduction using single point calibration for accurate and precise metabolomic phenotyping." Journal of Proteome Research p5132-5141 <https://www.ncbi.nlm.nih.gov/pubmed/19754161>`_ when using the **all loess pool** or **all loess sample** method: - | `Dunn et al (2011). Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry. Nature Protocols, 6:1060-1083 <http://dx.doi.org/10.1038/nprot.2011.335>`_ + | `Dunn et al (2011). Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry. Nature Protocols, 6:1060-1083 <https://www.nature.com/articles/nprot.2011.335>`_ | Cleveland et al (1997). In Statistical Models in S; Chambers JM. and Hastie TJ. Ed.; Chapman et Hall: London; pp. 309-376 - | Etienne A. Thevenot, Aurelie Roux, Ying Xu, Eric Ezan, and Christophe Junot (2015). Analysis of the human adult urinary metabolome variations with age, body mass index and gender by implementing a comprehensive workflow for univariate and OPLS statistical analyses. *Journal of Proteome Research*, **14**:3322-3335 (http://dx.doi.org/10.1021/acs.jproteome.5b00354). + | Etienne A. Thevenot, Aurelie Roux, Ying Xu, Eric Ezan, and Christophe Junot (2015). Analysis of the human adult urinary metabolome variations with age, body mass index and gender by implementing a comprehensive workflow for univariate and OPLS statistical analyses. *Journal of Proteome Research*, **14**:3322-3335 (https://pubs.acs.org/doi/10.1021/acs.jproteome.5b00354). --------------------------------------------------- From 0250b53a97b6b8f50707832da735df69abb6b7ec Mon Sep 17 00:00:00 2001 From: hechth Date: Thu, 10 Jul 2025 19:13:48 +0200 Subject: [PATCH 17/19] lint --- tools/batch_correction/batch_correction.xml | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/tools/batch_correction/batch_correction.xml b/tools/batch_correction/batch_correction.xml index bb4107e65..d33d92cf4 100644 --- a/tools/batch_correction/batch_correction.xml +++ b/tools/batch_correction/batch_correction.xml @@ -109,10 +109,12 @@ - - - - +
+ + + + +
From 212421bc6dda8413ac5a261a39b4816701223fe8 Mon Sep 17 00:00:00 2001 From: hechth Date: Fri, 11 Jul 2025 11:15:48 +0200 Subject: [PATCH 18/19] adressed comments from PR review --- tools/batch_correction/batch_correction.xml | 12 ++++++------ tools/batch_correction/determine_bc.xml | 2 +- 2 files changed, 7 insertions(+), 7 deletions(-) diff --git a/tools/batch_correction/batch_correction.xml b/tools/batch_correction/batch_correction.xml index d33d92cf4..36443da43 100644 --- a/tools/batch_correction/batch_correction.xml +++ b/tools/batch_correction/batch_correction.xml @@ -11,7 +11,7 @@ - + @@ -73,10 +73,10 @@ - + - + diff --git a/tools/batch_correction/determine_bc.xml b/tools/batch_correction/determine_bc.xml index 8523ce2b7..1aa66eab2 100644 --- a/tools/batch_correction/determine_bc.xml +++ b/tools/batch_correction/determine_bc.xml @@ -10,7 +10,7 @@ Date: Thu, 17 Jul 2025 08:54:12 +0200 Subject: [PATCH 19/19] updated links for linting --- tools/batch_correction/batch_correction.xml | 2 +- tools/batch_correction/determine_bc.xml | 8 ++++---- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/tools/batch_correction/batch_correction.xml b/tools/batch_correction/batch_correction.xml index 36443da43..de77941bc 100644 --- a/tools/batch_correction/batch_correction.xml +++ b/tools/batch_correction/batch_correction.xml @@ -143,7 +143,7 @@ when using the **linear**, **lowess** or **loess** methods: when using the **all loess pool** or **all loess sample** method: | `Dunn et al (2011). Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry. Nature Protocols, 6:1060-1083 <https://www.nature.com/articles/nprot.2011.335>`_ | Cleveland et al (1997). In Statistical Models in S; Chambers JM. and Hastie TJ. Ed.; Chapman et Hall: London; pp. 309-376 - | Etienne A. Thevenot, Aurelie Roux, Ying Xu, Eric Ezan, and Christophe Junot (2015). Analysis of the human adult urinary metabolome variations with age, body mass index and gender by implementing a comprehensive workflow for univariate and OPLS statistical analyses. *Journal of Proteome Research*, **14**:3322-3335 (https://pubs.acs.org/doi/10.1021/acs.jproteome.5b00354). + | Etienne A. Thevenot, Aurelie Roux, Ying Xu, Eric Ezan, and Christophe Junot (2015). Analysis of the human adult urinary metabolome variations with age, body mass index and gender by implementing a comprehensive workflow for univariate and OPLS statistical analyses. *Journal of Proteome Research*, **14**:3322-3335 (https://doi.org/10.1021/acs.jproteome.5b00354). --------------------------------------------------- diff --git a/tools/batch_correction/determine_bc.xml b/tools/batch_correction/determine_bc.xml index 1aa66eab2..6e9a266bb 100644 --- a/tools/batch_correction/determine_bc.xml +++ b/tools/batch_correction/determine_bc.xml @@ -27,16 +27,16 @@ - + - + - + @@ -207,7 +207,7 @@ Additional information .. class:: warningmark Refer to the corresponding "W4M HowTo" page: - | `MS data processing - Filters and normalisation <https://download.workflow4metabolomics.org/docs/w4e2018/2018-10_EC_W4E%20-%20Dataprocessing_Filter_and_normalisation.pdf>`_ + | `MS data processing - Filters and normalisation <https://nextcloud.inrae.fr/s/qLkNZRf84QQ5YLY>`_ | |