diffSpliceDGE: Test for Differential Exon Usage

Description

Given a negative binomial generalized log-linear model fit at the exon level, test for differential exon usage between experimental conditions.

Usage

diffSpliceDGE(glmfit, coef=ncol(glmfit$design), contrast=NULL, geneid, exonid=NULL, prior.count=0.125, verbose=TRUE)

Arguments

glmfit

an DGEGLM fitted model object produced by glmFit or glmQLFit. Rows should correspond to exons.

coef

integer indicating which coefficient of the generalized linear model is to be tested for differential exon usage. Defaults to the last coefficient.

contrast

numeric vector specifying the contrast of the linear model coefficients to be tested for differential exon usage. Length must equal to the number of columns of design. If specified, then takes precedence over coef.

geneid

gene identifiers. Either a vector of length nrow(glmfit) or the name of the column of glmfit$genes containing the gene identifiers. Rows with the same ID are assumed to belong to the same gene.

exonid

exon identifiers. Either a vector of length nrow(glmfit) or the name of the column of glmfit$genes containing the exon identifiers.

prior.count

average prior count to be added to observation to shrink the estimated log-fold-changes towards zero.

verbose

logical, if TRUE some diagnostic information about the number of genes and exons is output.

Value

comparison: character string describing the coefficient being tested.
coefficients: numeric vector of coefficients on the natural log scale. Each coefficient is the difference between the log-fold-change for that exon versus the log-fold-change for the entire gene which contains that exon.
genes: data.frame of exon annotation.
genecolname: character string giving the name of the column of genes containing gene IDs.
exoncolname: character string giving the name of the column of genes containing exon IDs.
exon.df.test: numeric vector of testing degrees of freedom for exons.
exon.p.value: numeric vector of p-values for exons.
gene.df.test: numeric vector of testing degrees of freedom for genes.
gene.p.value: numeric vector of gene-level testing p-values.
gene.Simes.p.value: numeric vector of Simes' p-values for genes.
gene.genes: data.frame of gene annotation.
exon.LR: numeric vector of LR-statistics for exons.
gene.LR: numeric vector of LR-statistics for gene-level test.
exon.F: numeric vector of F-statistics for exons.
gene.df.prior: numeric vector of prior degrees of freedom for genes.
gene.df.residual: numeric vector of residual degrees of freedom for genes.
gene.F: numeric vector of F-statistics for gene-level test.

Details

This function tests for differential exon usage for each gene for a given coefficient of the generalized linear model.

Testing for differential exon usage is equivalent to testing whether the exons in each gene have the same log-fold-changes as the other exons in the same gene. At exon-level, the log-fold-change of each exon is compared to the log-fold-change of the entire gene which contains that exon. At gene-level, two different tests are provided. One is converting exon-level p-values to gene-level p-values by the Simes method. The other is using exon-level test statistics to conduct gene-level tests.

Examples

Run this code

# Gene exon annotation
Gene <- paste("Gene", 1:100, sep="")
Gene <- rep(Gene, each=10)
Exon <- paste("Ex", 1:10, sep="")
Gene.Exon <- paste(Gene, Exon, sep=".")
genes <- data.frame(GeneID=Gene, Gene.Exon=Gene.Exon)

group <- factor(rep(1:2, each=3))
design <- model.matrix(~group)
mu <- matrix(100, nrow=1000, ncol=6)
# knock-out the first exon of Gene1 by 90%
mu[1,4:6] <- 10
# generate exon counts
counts <- matrix(rnbinom(6000,mu=mu,size=20),1000,6)

y <- DGEList(counts=counts, lib.size=rep(1e6,6), genes=genes)
gfit <- glmFit(y, design, dispersion=0.05)

ds <- diffSpliceDGE(gfit, geneid="GeneID")
topSpliceDGE(ds)
plotSpliceDGE(ds)