diffSpliceDGE {edgeR} | R Documentation

## 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.

### 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.

### Value

`diffSpliceDGE` produces an object of class `DGELRT` containing the component `design` from `glmfit` plus the following new components:

`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.

Some components of the output depend on whether `glmfit` is produced by `glmFit` or `glmQLFit`. If `glmfit` is produced by `glmFit`, then the following components are returned in the output object:

`exon.LR` | numeric vector of LR-statistics for exons.
`gene.LR` | numeric vector of LR-statistics for gene-level test.

If `glmfit` is produced by `glmQLFit`, then the following components are returned in the output object:

`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.

The information and testing results for both exons and genes are sorted by geneid and by exonid within gene.

### Author(s)

Yunshun Chen and Gordon Smyth

### Examples

    # 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)

[Package edgeR version 3.36.0 Index]