runEscape: Enrichment calculation for single-cell workflows

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

Enrichment calculation for single-cell workflows

Description

Run the escape-based gene-set enrichment calculation with Seurat or SingleCellExperiment pipelines

Usage

runEscape(
  input.data,
  gene.sets = NULL,
  method = "ssGSEA",
  groups = 1000,
  min.size = 5,
  normalize = FALSE,
  make.positive = FALSE,
  new.assay.name = "escape",
  BPPARAM = SerialParam(),
  ...
)

Arguments

input.data	The count matrix, Seurat, or Single-Cell Experiment object.
gene.sets	Gene sets can be a list, output from getGeneSets, or the built-in gene sets in the escape package escape.gene.sets.
method	Select the method to calculate enrichment, AUCell, GSVA, ssGSEA or UCell.
groups	The number of cells to separate the enrichment calculation.
min.size	Minimum number of gene necessary to perform the enrichment calculation
normalize	Whether to divide the enrichment score by the number of genes TRUE or report unnormalized FALSE.
make.positive	During normalization shift enrichment values to a positive range TRUE for downstream analysis or not TRUE (default). Will only be applied if normalize = TRUE.
new.assay.name	The new name of the assay to append to the single-cell object containing the enrichment scores.
BPPARAM	A BiocParallel::bpparam() object that for parallelization.
...	pass arguments to AUCell GSVA, ssGSEA or UCell call

Value

Seurat or Single-Cell Experiment object with escape enrichment scores in the assay slot.

Examples

GS <- list(Bcells = c("MS4A1", "CD79B", "CD79A", "IGH1", "IGH2"),
           Tcells = c("CD3E", "CD3D", "CD3G", "CD7","CD8A"))
pbmc_small <- SeuratObject::pbmc_small
pbmc_small <- runEscape(pbmc_small, 
                        gene.sets = GS, 
                        min.size = NULL)

ncborcherding/escape documentation built on Nov. 6, 2024, 1:43 p.m.