runEdgeR: runEdgeR

In UMMS-Biocore/debrowser: Interactive Differential Expresion Analysis Browser

runEdgeR

Description

Run EdgeR algorithm on the selected conditions. Output is to be used for the interactive display.

Usage

runEdgeR(
  data = NULL,
  metadata = NULL,
  columns = NULL,
  conds = NULL,
  params = NULL
)

Arguments

data,	A matrix that includes all the expression raw counts, rownames has to be the gene, isoform or region names/IDs
metadata,	metadata of the matrix of expression raw counts
columns,	is a vector that includes the columns that are going to be analyzed. These columns has to match with the given data.
conds,	experimental conditions. The order has to match with the column order
params,	normfact: Calculate normalization factors to scale the raw library sizes. Values can be "TMM","RLE","upperquartile","none". dispersion: either a numeric vector of dispersions or a character string indicating that dispersions should be taken from the data object. If a numeric vector, then can be either of length one or of length equal to the number of genes. Allowable character values are "common", "trended", "tagwise" or "auto". Default behavior ("auto" is to use most complex dispersions found in data object. testType: exactTest or glmLRT. exactTest: Computes p-values for differential abundance for each gene between two digital libraries, conditioning on the total count for each gene. The counts in each group as a proportion of the whole are assumed to follow a binomial distribution. glmLRT: Fit a negative binomial generalized log-linear model to the read counts for each gene. Conduct genewise statistical tests for a given coefficient or coefficient contrast.

Value

edgeR results

Examples

    x <- runEdgeR()

UMMS-Biocore/debrowser documentation built on Feb. 9, 2024, 6:15 p.m.