performNormalization: Perform Normalization on Enrichment Data

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

Perform Normalization on Enrichment Data

Description

Scales each enrichment value by the **number of genes from the set that are expressed** in that cell (non‑zero counts). Optionally shifts results into a positive range and/or applies a natural‑log transform for compatibility with log‑based differential tests.

Usage

performNormalization(
  input.data,
  enrichment.data = NULL,
  assay = "escape",
  gene.sets = NULL,
  make.positive = FALSE,
  scale.factor = NULL,
  groups = NULL
)

Arguments

input.data	raw‐counts matrix ('genes × cells'), a Seurat object, or a SingleCellExperiment. Gene identifiers must match those in 'gene.sets'.
enrichment.data	Output of escape.matrix or a single‑cell object previously processed by runEscape.
assay	Name of the assay holding enrichment scores when 'input.data' is a single‑cell object. Ignored otherwise.
gene.sets	A named list of character vectors, the result of [getGeneSets()], or the built-in data object [escape.gene.sets]. List names become column names in the result.
make.positive	Logical; if 'TRUE' shifts each column so its minimum is zero.
scale.factor	Optional numeric vector overriding gene‑count scaling (length = #cells). Use when you want external per‑cell normalization factors.
groups	Integer >= 1. Number of cells per processing chunk. Larger values reduce overhead but increase memory usage. Default **1000**.

Value

If 'input.data' is an object, the same object with a new assay "<assay>_normalized". Otherwise a matrix of normalized scores.

Examples

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

pbmc <- SeuratObject::pbmc_small |>
  runEscape(gene.sets = gs,
            min.size = NULL)
                        
pbmc <- performNormalization(pbmc, 
                             assay = "escape", 
                             gene.sets = gs)

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