isoformToGeneExp {IsoformSwitchAnalyzeR} | R Documentation |
Extract a data.frame with (mean) gene expression, isoform expression or Isoform Fraction values for all conditions (columns) from a switchAnalyzeRlist.
isoformToGeneExp( isoRepExpWithGeneId, showProgress = TRUE, quiet = FALSE )
isoRepExpWithGeneId |
A isoform expression matrix which in addition to expression columns contain two colmns 'isoform_id' and 'gene_id' indicating which isoforms are a part of which gene. |
showProgress |
A logic indicating whether to make a progress bar (if TRUE) or not (if FALSE). Default is TRUE. |
quiet |
A logic indicating whether to avoid printing progress messages. Default is FALSE |
This is just specialized function that is a lot faster than general pourpose functions such as ddplyr.
This function returns a data.frame where the first column is the gene id followed by the gene expression in all samples.
Kristoffer Vitting-Seerup
Vitting-Seerup et al. The Landscape of Isoform Switches in Human Cancers. Mol. Cancer Res. (2017).
### Construct data needed from example data in cummeRbund package ### (The recomended way of analyzing Cufflinks/Cuffdiff datat is via importCufflinksCummeRbund ### This is jus an easy way to get some example data ). cuffDB <- prepareCuffExample() isoRepCount <- repCountMatrix(isoforms(cuffDB)) isoRepCount$isoform_id <- rownames(isoRepCount) ### add gene info localAnnotaion <- import(system.file("extdata/chr1_snippet.gtf", package="cummeRbund"))[,c('transcript_id','gene_id')] colnames(localAnnotaion@elementMetadata)[1] <- 'isoform_id' isoRepCount$gene_id <- localAnnotaion$gene_id[match( isoRepCount$isoform_id , localAnnotaion$isoform_id )] ### Summarize to gene level geneRepCount <- isoformToGeneExp(isoRepCount)