runEdgeR {debrowser} | R Documentation

## runEdgeR

### Description

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

### Usage

    runEdgeR(data = NULL, columns = NULL, conds = NULL, normfact = c("TMM",
      "RLE", "upperquartile", "none"), dispersion = 0, testType = c("glmLRT",
      "exactTest"), rowsum.filter = 10)

### Arguments

`data,` | A matrix that includes all the expression raw counts, rownames has to be the gene, isoform or region names/IDs
`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
`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.
`rowsum.filter,` | regions/genes/isoforms with total count (across all samples) below this value will be filtered out

### Value

edgeR results

### Examples

        x <- runEdgeR()

[Package debrowser version 1.6.8 Index]