diffSplice {limma} | R Documentation

## Test for Differential Splicing

### Description

Given a linear model fit at the exon level, test for differences in exon retention between experimental conditions.

### Usage

    diffSplice(fit, geneid, exonid=NULL, robust=FALSE, verbose=TRUE)

### Arguments

`fit` | an `MArrayLM` fitted model object produced by `lmFit` or `contrasts.fit`. Rows should correspond to exons.
`geneid` | gene identifiers. Either a vector of length `nrow(fit)` or the name of the column of `fit$genes` containing the gene identifiers. Rows with the same ID are assumed to belong to the same gene.
`exonid` | exon identifiers. Either a vector of length `nrow(fit)` or the name of the column of `fit$genes` containing the exon identifiers.
`robust` | logical, should the estimation of the empirical Bayes prior parameters be robustified against outlier sample variances?
`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 and for each column of `fit`.

Testing for differential exon usage is equivalent to testing whether the log-fold-changes in the `fit` differ between exons for the same gene. Two different tests are provided. The first is an F-test for differences between the log-fold-changes. The other is a series of t-tests in which each exon is compared to the average of all other exons for the same gene. The exon-level t-tests are converted into a genewise test by adjusting the p-values for the same gene by Simes method. The minimum adjusted p-value is then used for each gene.

This function can be used on data from an exon microarray or can be used in conjunction with voom for exon-level RNA-seq counts.

### Value

An object of class `MArrayLM` containing both exon level and gene level tests. Results are sorted by geneid and by exonid within gene.

`coefficients` | numeric matrix of coefficients of same dimensions as `fit`. Each coefficient is the difference between the log-fold-change for that exon versus the average log-fold-change for all other exons for the same gene.
`t` | numeric matrix of moderated t-statistics, of same dimensions as `fit`.
`p.value` | numeric vector of p-values corresponding to the t-statistics
`genes` | data.frame of exon annotation
`genecolname` | character string giving the name of the column of `genes` containing gene IDs
`gene.F` | numeric matrix of moderated F-statistics, one row for each gene.
`gene.F.p.value` | numeric matrix of p-values corresponding to `gene.F`
`gene.simes.p.value` | numeric matrix of Simes adjusted p-values, one row for each gene.
`gene.genes` | data.frame of gene annotation.

### Author(s)

Gordon Smyth and Charity Law

### See Also

`topSplice`, `plotSplice`

A summary of functions available in LIMMA for RNA-seq analysis is given in 11.RNAseq.

### Examples

    ## Not run:
    v <- voom(dge,design)
    fit <- lmFit(v,design)
    ex <- diffSplice(fit,geneid="EntrezID")
    topSplice(ex)
    plotSplice(ex)

    ## End(Not run)

[Package limma version 3.38.3 Index]