Introduction

Module overview

Module	submodule	module description	input files
geneSetEnrichment	gsea	Implements the classical Gene Set Enrichment Analysis (GSEA) method. It identifies pathways or functional modules significantly associated with phenotypes (e.g., treatment vs control) by evaluating the enrichment of gene sets in a ranked gene list derived from expression profiles and phenotype labels.	Stereo-seq h5ad file
geneSetEnrichment	gsva	Performs Gene Set Variation Analysis (GSVA), an unsupervised non-parametric method. It transforms a gene expression matrix into a gene set activity matrix, calculating enrichment scores for gene sets in individual samples. Useful for discovering sample-specific pathway activities (e.g., cancer subtype analysis).	Stereo-seq h5ad file
geneSetEnrichment	prerank	Conducts enrichment analysis on pre-ranked gene lists (e.g., ranked by logFC or p-values from differential expression analysis) without requiring raw expression data. Ideal for flexible scenarios (e.g., custom ranking metrics) to evaluate gene set enrichment directly.	csv format file with gene list and log2FC value
geneSetEnrichment	enrichr	Enrichment analysis on input gene lists. Supports multiple functional databases (e.g., GO, KEGG, WikiPathways) for rapid annotation of small gene sets, providing insights into pathway associations.	csv format file with gene list and log2FC value

Reference

• Fang, Z., Liu, X., & Peltz, G. (2023). GSEApy: a comprehensive package for performing gene set enrichment analysis in Python. Bioinformatics, 39(1), btac757.