omicverse.single.pathway_enrichment¶

omicverse.single.pathway_enrichment(adata, pathways_dict, organism='Human', group_by='louvain', cutoff=0.05, logfc_threshold=2, pvalue_type='adjust', plot=True)[source]¶

Perform pathway enrichment analysis on gene expression data.

Parameters:

adata (anndata.AnnData) – AnnData with rank_genes_groups results and cluster labels.
pathways_dict (dict) – Mapping from pathway/set name to member genes.
organism (str) – Organism name used by enrichment backend.
group_by (str) – adata.obs key used as grouping variable.
cutoff (float) – Significance cutoff for pathway filtering.
logfc_threshold (float) – Minimum log2 fold-change threshold for DE genes.
pvalue_type (str) – P-value type used for final filtering: 'adjust' or 'raw'.
plot (bool) – Whether to draw per-cluster barplots for enriched pathways.

Returns:

pd.DataFrame – Enrichment result table with cluster labels and pathway statistics.
Examples – >>> # Perform pathway enrichment analysis on adata using pathways_dict >>> res = pathway_enrichment(adata, pathways_dict)
Reference – The code for pathway_enrichment() function was adapted from scanpy workflows: https://scanpy-tutorials.readthedocs.io/en/latest/pbmc3k.html#Gene-set-enrichment-analysis.