omicverse.bulk.pyGSEA

omicverse.bulk.pyGSEA#

class omicverse.bulk.pyGSEA(gene_rnk, pathways_dict, processes=1, permutation_num=1000, outdir='./enrichr_gsea', cutoff=0.5, organism='Human', backend='numpy', weight=1.0, min_size=15, max_size=500, progress=True)[source]#

Gene Set Enrichment Analysis (GSEA) wrapper for ranked gene lists.

Parameters:

  • gene_rnk (pd.DataFrame) – Ranked gene table used for enrichment scoring.

  • pathways_dict (dict|str) – Gene sets — a prepared dict, a .gmt/.txt path, or an Enrichr library name (resolved and auto-downloaded internally).

  • processes (int, optional, default=8) – Number of worker processes.

  • permutation_num (int, optional, default=100) – Number of permutations for enrichment significance.

  • outdir (str, optional, default='./enrichr_gsea') – Output directory for reports and plots.

  • cutoff (float, optional, default=0.5) – Significance/score threshold for result filtering.

Returns:

Initializes GSEA analysis settings.

Return type:

None

Examples

>>> # Initialize GSEA object
Parameters:

  • organism (str (default: 'Human'))

  • backend (str (default: 'numpy'))

  • weight (float (default: 1.0))

  • min_size (int (default: 15))

  • max_size (int (default: 500))

  • progress (bool (default: True))

__init__(gene_rnk, pathways_dict, processes=1, permutation_num=1000, outdir='./enrichr_gsea', cutoff=0.5, organism='Human', backend='numpy', weight=1.0, min_size=15, max_size=500, progress=True)[source]#

Initialize pyGSEA with ranked genes and pathway libraries.

Parameters:

  • gene_rnk (pandas.DataFrame) – Ranked gene table equivalent to GSEA .rnk input.

  • pathways_dict (dict) – Dictionary of pathway collections/gene sets.

  • processes (int, optional) – Number of parallel worker processes.

  • permutation_num (int, optional) – Number of permutations for null distribution estimation.

  • outdir (str, optional) – Output directory for GSEA artifacts.

  • cutoff (float, optional) – Internal GSEA cutoff threshold.

  • organism (str (default: 'Human'))

  • backend (str (default: 'numpy'))

  • weight (float (default: 1.0))

  • min_size (int (default: 15))

  • max_size (int (default: 500))

  • progress (bool (default: True))

Methods

__init__(gene_rnk, pathways_dict[, ...])

Initialize pyGSEA with ranked genes and pathway libraries.

enrichment([format, pval, seed])

Run GSEA and return filtered enrichment results.

plot_enrichment([num, node_size, cax_loc, ...])

Plot top GSEA terms as bubble enrichment chart.

plot_gsea([term_num, gene_set_title, ...])

Plot running-enrichment curve for one selected GSEA term.
Contents