pathway-ssgsea

You compute per-cell pathway activity scores for spatial transcriptomics data by applying ssGSEA to a single-slice gene expression dataset, which outputs per-cell scores stored in adata.obs as ssgsea_<pathway> columns for spatial visualization.

Yes, you can use custom gene sets for single-sample enrichment on an AnnData object. The tool also supports standard libraries like MSigDB Hallmark, KEGG, and GO Biological Process for scoring pathway activation.

Yes, ssGSEA spatial transcriptomics scoring supports both human and mouse species. You must ensure species compatibility when applying the gene set library to your single-slice gene expression dataset.

ssGSEA reveals spatial variation in pathway activation by calculating per-cell pathway scores across tissues, allowing you to visualize distinct pathway activation patterns directly on the spatial canvas of your transcriptomics slice.

The ssGSEA results are stored directly within the AnnData object after processing. Per-cell pathway scores are saved in adata.obs as ssgsea_<pathway> columns, with additional analysis parameters recorded in adata.uns.