performNormalization: Perform Normalization on Enrichment Data

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

Perform Normalization on Enrichment Data

Description

This function allows users to normalize the enrichment calculations by accounting for single-cell dropout and producing positive values for downstream differential enrichment analyses. A positive range values is useful for several downstream analyses, like differential evaluation for log2-fold change, but will alter the original enrichment values.

Usage

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

Arguments

sc.data	Single-cell object or matrix used in the gene set enrichment calculation in escape.matrix or runEscape.
enrichment.data	The enrichment results from escape.matrix or runEscape (optional)
assay	Name of the assay to normalize if using a single-cell object
gene.sets	The gene set library to use to extract the individual gene set information from
make.positive	Shift enrichment values to a positive range TRUE for downstream analysis or not TRUE (default).
scale.factor	A vector to use for normalizing enrichment scores per cell.
groups	the number of cells to calculate normalization on at once. chunks matrix into groups sized chunks. Useful in case of memory issues.

Value

Single-cell object or matrix of normalized enrichment scores

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)
                        
pbmc_small <- performNormalization(pbmc_small, 
                                   assay = "escape", 
                                   gene.sets = GS)

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