gseaEnrichment: Classical GSEA-style Running-Enrichment Plot

In ncborcherding/escape: Easy single cell analysis platform for enrichment

Classical GSEA-style Running-Enrichment Plot

Description

Produces the familiar two-panel GSEA graphic—running enrichment score (RES) plus a “hit” rug—for a **single gene-set** evaluated across multiple biological groups (clusters, conditions, samples, ...).

Usage

gseaEnrichment(
  input.data,
  gene.set.use,
  gene.sets,
  group.by = NULL,
  summary.fun = "mean",
  p = 1,
  nperm = 1000,
  rug.height = 0.02,
  digits = 2,
  BPPARAM = NULL,
  palette = "inferno"
)

Arguments

input.data	A Seurat object or a SingleCellExperiment.
gene.set.use	Character(1). Name of the gene set to display.
gene.sets	A named list of character vectors, the result of [getGeneSets()], or the built-in data object [escape.gene.sets].
group.by	Metadata column. Defaults to the Seurat/SCE 'ident' slot when 'NULL'.
summary.fun	Method used to collapse expression within each group **before** ranking: one of '"mean"' (default), '"median"', '"max"', '"sum"', or '"geometric"'.
p	Weighting exponent in the KS statistic (classical GSEA uses 'p = 1').
nperm	Integer >= 0. Gene-label permutations per group (default 1000). '0' value will skip NES/*p* calculation.
rug.height	Vertical spacing of the hit rug as a fraction of the y-axis (default '0.02').
digits	Number of decimal places displayed for ES in the legend (default '2').
BPPARAM	A BiocParallel parameter object describing the parallel backend.
palette	Character. Any palette from hcl.pals.

Details

**Algorithm (Subramanian _et al._, PNAS 2005)** 1. Within every group, library-size-normalise counts to CPM. 2. Collapse gene expression with 'summary.fun' (mean/median/…). 3. Rank genes (descending) to obtain one ordered list per group. 4. Compute the weighted Kolmogorov–Smirnov running score (weight = \|stat\|^*p*). 5. ES = maximum signed deviation of the curve.

Value

A single 'patchwork'/'ggplot2' object

See Also

escape.matrix, densityEnrichment

Examples

pbmc_small <- SeuratObject::pbmc_small

gs <- list(Bcells = c("MS4A1", "CD79B", "CD79A", "IGH1", "IGH2"),
           Tcells = c("CD3E", "CD3D", "CD3G", "CD7","CD8A"))

gseaEnrichment(pbmc_small,
               gene.set.use = "Bcells",
               gene.sets    = gs,
               group.by     = "groups",
               summary.fun  = "mean",
               digits       = 3)

ncborcherding/escape documentation built on June 12, 2025, 1 p.m.