1. Environment Setup Ensure you have all the required libraries installed. This notebook primarily uses scanpy, so ensure it's installed in your environment. You can also check the environment summary for a list of packages and versions.
2. Data Import Load your spatial data. The notebook assumes the data is in a specific format, so make sure to adjust paths and file names as necessary.
3. Data Preprocessing Filter out cells based on specific criteria, e.g., total counts below a certain threshold.
4. Data Integration This step ensures the data from different sources or batches is integrated seamlessly. The notebook provides techniques to correct for batch effects and generate integrated embeddings that can be used for downstream analysis.
5. Dimensionality Reduction Visualize the high-dimensional data using UMAP. This will help in understanding the spatial distribution and clustering of the data.
6. Spatial Mapping of Gene Expressions Designate the gene of interest and use "plot_spatial_for_gene" function to plot gene expressions across FFPE tissue samples.