run_bidirectional_enrichment: Run Gene Set Enrichment Analysis on Loadings

In eisascience/scCustFx: single-cell custom fxs Package

Run Gene Set Enrichment Analysis on Loadings

Description

This function performs gene set enrichment analysis for both positive and negative loadings using the clusterProfiler package with gene sets obtained via msigdbr. It extracts the top topN genes in both directions for a specified component and then uses enricher for enrichment analysis. If any significant enrichment is detected (based on the adjusted p-value cutoff), a dotplot is displayed.

Usage

run_bidirectional_enrichment(
  loadings,
  comp,
  topN,
  species = "Homo sapiens",
  category = "H",
  pvalueCutoff = 0.05,
  doPlot = F
)

Arguments

loadings	A numeric matrix or data frame of gene loadings with genes as row names. Rows represent genes and columns represent different components.
comp	Numeric. The component number (row index) to analyze.
topN	Numeric. The number of top genes (from both positive and negative directions) to consider.
species	Character. The species to query from msigdbr. Default is "Homo sapiens".
category	Character. The gene set category to use (e.g., "H" for Hallmark). Default is "H".
pvalueCutoff	Numeric. The p-value cutoff to consider enrichment as significant. Default is 0.05.

Value

A list with two elements:

enrichedPos

An object of class enrichResult with enrichment results for positively loaded genes.

enrichedNeg

An object of class enrichResult with enrichment results for negatively loaded genes.

Examples

## Not run: 
  # Assuming results$loadings[[1]] is a numeric matrix with gene symbols as row names:
  loadings <- results$loadings[[1]]
  enrichmentResults <- run_bidirectional_enrichment(loadings, comp = 2, topN = 100)

## End(Not run)

eisascience/scCustFx documentation built on June 2, 2025, 3:59 a.m.