TME_classification: TME_classification

In miccec/scTHI: Indentification of significantly activated ligand-receptor interactions across clusters of cells from single-cell RNA sequencing data

TME_classification

Description

The function allows the user to classify non-tumor cells in tumor microenvironment. It implements the Mann-Whitney-Wilcoxon Gene Set Test (MWW-GST) algorithm and tests for each cell the enrichment of a collection of signatures of different cell types.

Usage

TME_classification(expMat, minLenGeneSet = 10,
  alternative = "two.sided", pvalFilter = FALSE, fdrFilter = TRUE,
  pvalCutoff = 0.01, nesCutoff = 0.58, nNES = 1)

Arguments

expMat	Gene expression matrix where rows are genes presented with Hugo Symbols and columns are cells. Gene expression values should be normalized counts.
minLenGeneSet	Minimum gene set length
alternative	a character string specifying the alternative hypothesis of wilcoxon test, must be one of "two.sided" (default), "greater" or "less".
pvalFilter	Logical, if TRUE results will be filtered for p-Value. Defoult is FALSE.
fdrFilter	Logical, if TRUE results will be filtered for FDR.
pvalCutoff	Numeric p-Value (or FDR) threshold. Gene set with p-Value (or FDR) greater than pvalCutoff will be discarded (default is 0.01).
nesCutoff	Numeric threshold. Gene set with NES greater than nesCutoff will be discarded (default is 0.58)
nNES	Default is 0.58, so each cell is classified with a specific phenotype based on the first significant enriched gene set.

Value

A list with two items: Class (character) and ClassLegend (character)

Examples

library(scTHI.data)
data(scExample)
Class <- TME_classification(scExample)

miccec/scTHI documentation built on April 21, 2023, 12:40 p.m.