decidetestsDGE: Multiple Testing Across Genes and Contrasts

In hiraksarkar/edgeR_fork: Empirical Analysis of Digital Gene Expression Data in R

Description

Identify which genes are significantly differentially expressed from an edgeR fit object containing p-values and test statistics.

Usage

decideTestsDGE(object, adjust.method="BH", p.value=0.05, lfc=0)
## S3 method for class 'DGELRT'
decideTests(object, adjust.method="BH", p.value=0.05, lfc=0, ...)

Arguments

object	DGEExact, DGELRT or glmQLFTest object from which p-values and log-fold-changes can be extracted.
adjust.method	character string specifying p-value adjustment method. Possible values are "none", "BH", "fdr" (equivalent to "BH"), "BY" and "holm". See p.adjust for details.
p.value	numeric value between 0 and 1 giving the required family-wise error rate or false discovery rate.
lfc	numeric, minimum absolute log2-fold-change required.
...	other arguments are not used.

Details

This function applies a multiple testing procedure and significance level cutoff to the genewise tests contained in object.

Value

An object of class TestResults. This is essentially a single-column integer matrix with elements -1, 0 or 1 indicating whether each gene is classified as significantly down-regulated, not significant or significant up-regulated for the comparison contained in object. To be considered significant, genes have to have adjusted p-value below p.value and log2-fold-change greater than lfc.

If object contains F-tests or LRTs for multiple contrasts, then the genes are simply classified as significant (1) or not significant. In this case, the log2-fold-change theshold lfc has to be achieved by at least one of the contrastsf or a gene to be significant.

Note

Although this function enables users to set p-value and lfc cutoffs simultaneously, this combination criterion not usually recommended. Unless the fold changes and p-values are very highly correlated, the addition of a fold change cutoff can increase the family-wise error rate or false discovery rate above the nominal level. Users wanting to use fold change thresholding should considering using glmTreat instead and leaving lfc at the default value when using decideTestsDGE.

Author(s)

Davis McCarthy, Gordon Smyth and the edgeR team

See Also

decideTests and TestResults in the limma package.

Examples

ngenes <- 100
x1 <- rnorm(6)
x2 <- rnorm(6)
design <- cbind(Intercept=1,x1,x2)
beta <- matrix(0,ngenes,3)
beta[,1] <- 4
beta[1:20,2] <- rnorm(20)
mu <- 2^(beta %*% t(design))
y <- matrix(rnbinom(ngenes*6,mu=mu,size=10),ngenes,6)
fit <- glmFit(y,design,dispersion=0.1)
lrt <- glmLRT(fit,coef=2:3)
res <- decideTests(lrt,p.value=0.1)
summary(res)
lrt <- glmLRT(fit,coef=2)
res <- decideTests(lrt,p.value=0.1)
summary(res)

hiraksarkar/edgeR_fork documentation built on Dec. 20, 2021, 3:52 p.m.