Differential expression using edgeR

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

Value

A matrix of differentially expressed genes and related statistics.

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

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