| topSplice {limma} | R Documentation |
Top table ranking the most differentially spliced genes or exons.
topSplice(fit, coef = ncol(fit), test = "simes", number = 10, FDR=1, sort.by = "p")
fit |
|
coef |
the coefficient (column) of fit for which differentially splicing is assessed. |
test |
character string specifying which statistical test to apply.
Possible values are |
number |
integer, maximum number of rows to output. |
FDR |
numeric, only show exons or genes with false discovery rate less than this cutoff. |
sort.by |
character string specifying which column to sort results by.
Possible values for |
Ranks genes or exons by evidence for differential splicing. The F-statistic tests for any differences in exon usage between experimental conditions. The exon-level t-statistics test for differences between each exon and all other exons for the same gene.
The Simes processes the exon-level p-values to give an overall call of differential splicing for each gene. It returns the minimum Simes-adjusted p-values for each gene.
The F-tests are likely to be powerful for genes in which several exons are differentially splices. The Simes p-values is likely to be more powerful when only a minority of the exons for a gene are differentially spliced. The exon-level t-tests are not recommended for formal error rate control.
A data.frame with any annotation columns found in fit plus the following columns
logFC |
log2-fold change of exon vs other exons for the same gene (if |
t |
moderated t-statistic (if |
F |
moderated F-statistic (if |
P.Value |
p-value |
FDR |
false discovery rate |
Gordon Smyth
A summary of functions available in LIMMA for RNA-seq analysis is given in 11.RNAseq.
# See diffSplice