edgeR_tidiers: Tidiers for edgeR's differential expression objects

In StoreyLab/biobroom: Turn Bioconductor objects into tidy data frames

Description

Tidy, augment and glance methods for turning edgeR objects into tidy data frames, where each row represents one observation and each column represents one column.

Usage

## S3 method for class 'DGEExact'
tidy(x, ...)

## S3 method for class 'DGEList'
tidy(x, addSamples = FALSE, ...)

## S3 method for class 'DGEList'
augment(x, data = NULL, ...)

## S3 method for class 'DGEExact'
glance(x, alpha = 0.05, p.adjust.method = "fdr", ...)

Arguments

x	DGEExact, DGEList object
...	extra arguments (not used)
addSamples	Merge information from samples. Default is FALSE.
data	merge data to augment. This is particularly useful when merging gene names or other per-gene information. Default is NULL.
alpha	Confidence level to test for significance
p.adjust.method	Method for adjusting p-values to determine significance; can be any in p.adjust.methods

Value

tidy defaults to tidying the counts in the dataset:

gene	gene ID
sample	sample ID
count	number of reads in this gene in this sample

If addSamples = TRUE, it also merges this with the sample information present in x$samples.

augment returns per-gene information (DGEList only)

glance returns one row with the columns (DGEExact only)

significant	number of significant genes using desired adjustment method and confidence level
comparison	The pair of groups compared by edgeR, delimited by /

Examples

if (require("edgeR")) {
    library(Biobase)
    data(hammer)
    hammer.counts <- exprs(hammer)[, 1:4]
    hammer.treatment <- phenoData(hammer)$protocol[1:4]

    y <- DGEList(counts=hammer.counts,group=hammer.treatment)
    y <- calcNormFactors(y)
    y <- estimateCommonDisp(y)
    y <- estimateTagwiseDisp(y)
    et <- exactTest(y)

    head(tidy(et))
    head(glance(et))
}

StoreyLab/biobroom documentation built on May 9, 2019, 3:09 p.m.