Test for Differential Splicing
Given a linear model fit at the exon level, test for differences in exon retention between experimental conditions.
diffSplice(fit, geneid, exonid=NULL, robust=FALSE, verbose=TRUE)
fit |
an |
geneid |
gene identifiers. Either a vector of length |
exonid |
exon identifiers. Either a vector of length |
robust |
logical, should the estimation of the empirical Bayes prior parameters be robustified against outlier sample variances? |
verbose |
logical, if |
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.
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 |
t |
numeric matrix of moderated t-statistics, of same dimensions as |
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 |
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.simes.p.value |
numeric matrix of Simes adjusted p-values, one row for each gene. |
gene.genes |
data.frame of gene annotation. |
Gordon Smyth and Charity Law
A summary of functions available in LIMMA for RNA-seq analysis is given in 11.RNAseq.
## Not run: v <- voom(dge,design) fit <- lmFit(v,design) ex <- diffSplice(fit,geneid="EntrezID") topSplice(ex) plotSplice(ex) ## End(Not run)
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