edgeRTest: Differential expression using edgeR

In Coolgenome/iTALK: Characterize and Illustrate Intercellular Communication

Description

Identifies differentially expressed genes between two groups of cells using edgeR

Usage

edgeRTest(sub_data, min_gene_expressed, min_valid_cells,
  contrast = unique(sub_data$compare_group), calcNormMethod = "TMM",
  trend.method = "locfit", tagwise = TRUE, robust = FALSE)

Arguments

sub_data	Count data removed cell_type and selected certain two compare_group
min_gene_expressed	Genes expressed in minimum number of cells
min_valid_cells	Minimum number of genes detected in the cell
contrast	String vector specifying the contrast to be tested against the log2-fold-change threshold
calcNormMethod	normalization method to be used
trend.method	method for estimating dispersion trend. Possible values are "none", "movingave", "loess" and "locfit" (default).
tagwise	logical, should the tagwise dispersions be estimated
robust	logical, should the estimation of prior.df be robustified against outliers

Details

This test does not support pre-processed genes. To use this method, please install edgeR, using the instructions at http://bioconductor.org/packages/release/bioc/html/edgeR.html

Value

A matrix of differentially expressed genes and related statistics.

References

McCarthy, J. D, Chen, Yunshun, Smyth, K. G (2012). “Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation.” Nucleic Acids Research, 40(10), 4288-4297.

Robinson MD, McCarthy DJ, Smyth GK (2010). “edgeR: a Bioconductor package for differential expression analysis of digital gene expression data.” Bioinformatics, 26(1), 139-140. https://github.com/cole-trapnell-lab/monocle-release

Coolgenome/iTALK documentation built on Aug. 3, 2019, 3:12 p.m.