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Differential splicing
limma can also detectgenes that how evidence of di_erential splicing between conditions. One can
test for di_erentialsplicing associated with any contrast for a linear model.
In this case, the matrixof counts should be at the exon level, with a row for each exon. For
example,
> dge <-DGEList(counts=counts)
> dge$genes$GeneID<- GeneID
where counts is amatrix of exon-level counts, and GeneID identi_es which gene each exon belongs
to. Then _lter andnormalize:
> A <-rowSums(dge$counts)
> dge <-dge[A>10,,keep.lib.sizes=FALSE]
> dge <-calcNormFactors(dge)
Then apply the voomtransformation and _t a linear model:
> v <-voom(dge,design,plot=TRUE)
> fit <-lmFit(v,design)
Now we can test fordi_erential splicing associated with any coe_cient in the linear model. First
run the di_Splicefunction:
> ex <- diffSplice(fit,geneid="GeneID")
Then
>topSplice(ex,coef=2,level="gene")
will _nd genes that showevidence of di_erential splicing associated with the second coe_cient in
the linear model. Theoutput is similar that from the limma topTable function. More detail can be
obtained by
72
>topSplice(ex,coef=2,level="exon")
which will showindividual exons that are enriched or depleted relative to other exons in thesame
gene. To display thepattern of exons in the top genes:
> plotSplice(ex)
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