TSPCsg {SplicingGraphs} | R Documentation

## TSPC splicing graphs

### Description

TODO

### Examples

    ## 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)

[Package SplicingGraphs version 1.20.0 Index]