spliceVariants: Identify Genes with Splice Variants

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

Description

Identify genes exhibiting evidence for splice variants (alternative exon usage/transcript isoforms) from exon-level count data using negative binomial generalized linear models.

Usage

spliceVariants(y, geneID, dispersion=NULL, group=NULL, estimate.genewise.disp=TRUE,
               trace=FALSE)

Arguments

y	either a matrix of exon-level counts or a DGEList object with (at least) elements counts (table of counts summarized at the exon level) and samples (data frame containing information about experimental group, library size and normalization factor for the library size). Each row of y should represent one exon.
geneID	vector of length equal to the number of rows of y, which provides the gene identifier for each exon in y. These identifiers are used to group the relevant exons into genes for the gene-level analysis of splice variation.
dispersion	scalar (in future a vector will also be allowed) supplying the negative binomial dispersion parameter to be used in the negative binomial generalized linear model.
group	factor supplying the experimental group/condition to which each sample (column of y) belongs. If NULL (default) the function will try to extract if from y, which only works if y is a DGEList object.
estimate.genewise.disp	logical, should genewise dispersions (as opposed to a common dispersion value) be computed if the dispersion argument is NULL?
trace	logical, whether or not verbose comments should be printed as function is run. Default is FALSE.

Details

This function can be used to identify genes showing evidence of splice variation (i.e. alternative splicing, alternative exon usage, transcript isoforms). A negative binomial generalized linear model is used to assess evidence, for each gene, given the counts for the exons for each gene, by fitting a model with an interaction between exon and experimental group and comparing this model (using a likelihood ratio test) to a null model which does not contain the interaction. Genes that show significant evidence for an interaction between exon and experimental group by definition show evidence for splice variation, as this indicates that the observed differences between the exon counts between the different experimental groups cannot be explained by consistent differential expression of the gene across all exons. The function topTags can be used to display the results of spliceVariants with genes ranked by evidence for splice variation.

Value

spliceVariants returns a DGEExact object, which contains a table of results for the test of differential splicing between experimental groups (alternative exon usage), a data frame containing the gene identifiers for which results were obtained and the dispersion estimate(s) used in the statistical models and testing.

Author(s)

Davis McCarthy, Gordon Smyth

See Also

estimateExonGenewiseDisp for more information about estimating genewise dispersion values from exon-level counts. DGEList for more information about the DGEList class. topTags for more information on displaying ranked results from spliceVariants. estimateCommonDisp and related functions for estimating the dispersion parameter for the negative binomial model.

Examples

# generate exon counts from NB, create list object
y<-matrix(rnbinom(40,size=1,mu=10),nrow=10)
d<-DGEList(counts=y,group=rep(1:2,each=2))
genes <- rep(c("gene.1","gene.2"), each=5)
disp <- 0.2
spliceVariants(d, genes, disp)

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