TME_classification: TME_classification

View source: R/TME_classification.R

TME_classificationR Documentation

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.