TSPCsg {SplicingGraphs} | R Documentation |
TODO
## Load SplicingGraphs object 'TSPCsg': filepath <- system.file("extdata", "TSPCsg.rda", package="SplicingGraphs") load(filepath) TSPCsg ## 'TSPCsg' has 1 element per gene and 'names(sg)' gives the gene ids. names(TSPCsg) ## 1 splicing graph per gene. (Note that gene MUC16 was dropped ## because transcripts T-4 and T-5 in this gene both have their ## 2nd exon *inside* their 3rd exon. Splicing graph theory doesn't ## apply in that case.) ## Extract the edges of a given graph: TSPCsgedges <- sgedges(TSPCsg["LGSN"]) TSPCsgedges ## Plot the graph for a given gene: plot(TSPCsg["LGSN"]) # or 'plot(sgraph(TSPCsgedges))' ## The reads from all samples have been assigned to 'TSPCsg'. ## Use countReads() to summarize by splicing graph edge: counts <- countReads(TSPCsg) dim(counts) counts[ , 1:5] ## You can subset 'TSPCsg' by 1 or more gene ids before calling ## countReads() in order to summarize only for those genes: DAPL1counts <- countReads(TSPCsg["DAPL1"]) dim(DAPL1counts) DAPL1counts[ , 1:5] ## Use 'by="rsgedge"' to summarize by *reduced* splicing graph edge: DAPL1counts2 <- countReads(TSPCsg["DAPL1"], by="rsgedge") dim(DAPL1counts2) DAPL1counts2[ , 1:5] ## No reads assigned to genes KIAA0319L or TREM2 because no ## BAM files were provided for those genes: KIAA0319Lcounts <- countReads(TSPCsg["KIAA0319L"]) KIAA0319Lcountsums <- sapply(KIAA0319Lcounts[ , -(1:2)], sum) stopifnot(all(KIAA0319Lcountsums == 0)) TREM2counts <- countReads(TSPCsg["TREM2"]) TREM2countsums <- sapply(TREM2counts[ , -(1:2)], sum) stopifnot(all(TREM2countsums == 0)) ## Plot all the splicing graphs: slideshow(TSPCsg)