edgeR (version 3.8.6)

treatDGE: Testing for Differential Expression Relative to a Threshold

Description

Conduct genewise statistical tests for a given coefficient or coefficient contrast relative to a specified threshold.

Usage

treatDGE(glmfit, coef=ncol(glmfit$design), contrast=NULL, lfc=0)

Arguments

glmfit

a DGEGLM object, usually output from glmFit.

coef

integer or character vector indicating which coefficients of the linear model are to be tested equal to zero. Values must be columns or column names of design. Defaults to the last coefficient. Ignored if contrast is specified.

contrast

numeric vector specifying the contrast of the linear model coefficients to be tested against the log2-fold change threshold. Length must equal to the number of columns of design. If specified, then takes precedence over coef.

lfc

numeric scalar specifying the absolute value of the log2-fold change threshold above which differential expression is to be considered.

Value

treatDGE produces an object of class DGELRT with the same components as for glmfit plus the following:

lfc

absolute value of the specified log2-fold change threshold.

table

data frame with the same rows as glmfit containing the log2-fold changes, average log2-counts per million and p-values, ready to be displayed by topTags..

comparison

character string describing the coefficient or the contrast being tested.

The data frame table contains the following columns:

logFC

log2-fold change of expression between conditions being tested.

logCPM

average log2-counts per million, the average taken over all libraries.

PValue

p-values.

Details

treatDGE implements a two-sided modified likelihood ratio test.

Examples

ngenes <- 100
n1 <- 3
n2 <- 3
nlibs <- n1+n2
mu <- 100
phi <- 0.1
group <- c(rep(1,n1), rep(2,n2))
design <- model.matrix(~as.factor(group))

### 4-fold change for the first 5 genes
i <- 1:5
fc <- 4
mu <- matrix(mu, ngenes, nlibs)
mu[i, 1:n1] <- mu[i, 1:n1]*fc

counts <- matrix(rnbinom(ngenes*nlibs, mu=mu, size=1/phi), ngenes, nlibs)
d <- DGEList(counts=counts,lib.size=rep(1e6, nlibs), group=group)

gfit <- glmFit(d, design, dispersion=phi)
tr <- treatDGE(gfit, coef=2, lfc=1)
topTags(tr)