SplicingGraphs (version 1.6.0)

txpath-methods: Extract the transcript paths of a splicing graph

Description

txpath extracts the transcript paths of the splicing graph of a given gene from a SplicingGraphs object.

Usage

txpath(x, as.matrix=FALSE)
## Related utility:
txweight(x)
txweight(x) <- value

Arguments

x

A SplicingGraphs object of length 1 or a GRangesList object for txpath.

A SplicingGraphs object for txweight.

as.matrix

TODO

value

A numeric vector containing the weights to assign to each transcript in x.

Value

A named list-like object with one list element per transcript in the gene. Each list element is an integer vector that describes the path of the transcript i.e. the Splicing Site ids that it goes thru.

Details

TODO

See Also

This man page is part of the SplicingGraphs package. Please see ?`SplicingGraphs-package` for an overview of the package and for an index of its man pages.

Other topics related to this man page and documented in other packages:

• The GRangesList class defined in the GenomicRanges package.

The GAlignments and GAlignmentPairs classes defined in the GenomicAlignments package.

findOverlaps-methods in the GenomicRanges package.

encodeOverlaps-methods in the GenomicAlignments package.

The ScanBamParam function defined in the Rsamtools package.

Examples

## ---------------------------------------------------------------------
## 1. Make SplicingGraphs object 'sg' from toy gene model (see
##    '?SplicingGraphs')
## ---------------------------------------------------------------------
example(SplicingGraphs)
sg

## 'sg' has 1 element per gene and 'names(sg)' gives the gene ids.
names(sg)

## ---------------------------------------------------------------------
## 2. txpath()
## ---------------------------------------------------------------------
## Note that the list elements in the returned IntegerList object
## always consist of an even number of Splicing Site ids in ascending
## order.
txpath(sg["geneB"])
txpath(sg["geneD"])
strand(sg)

txpath(sg["geneD"], as.matrix=TRUE)  # splicing matrix

## ---------------------------------------------------------------------
## 3. txweight()
## ---------------------------------------------------------------------
txweight(sg)
plot(sg["geneD"])

txweight(sg) <- 5
txweight(sg)
plot(sg["geneD"])  # FIXME: Edges not rendered with correct width!
plot(sgraph(sg["geneD"], as.igraph=TRUE))  # need to use this for now

txweight(sg)[8:11] <- 5 * (4:1)
txweight(sg)
plot(sgraph(sg["geneD"], tx_id.as.edge.label=TRUE, as.igraph=TRUE))

## ---------------------------------------------------------------------
## 4. An advanced example
## ---------------------------------------------------------------------
## [TODO: Counting "unambiguous compatible hits" per transcript should be
##  supported by countReads(). Simplify the code below when countReads()
##  supports this.]
## Here we show how to find "unambiguous compatible hits" between a set
## of RNA-seq reads and a set of transcripts, that is, hits that are
## compatible with the splicing of exactly 1 transcript. Then we set the
## transcript weights based on the number of unambiguous compatible hits
## they received and we finally plot some splicing graphs that show
## the weighted transcripts.
## Note that the code we use to find the unambiguous compatible hits
## uses findOverlaps() and encodeOverlaps() defined in the IRanges and
## GenomicRanges packages. It only requires that the transcripts are
## represented as a GRangesList object and the reads as a GAlignments
## (single-end) or GAlignmentPairs (paired-end) object, and therefore is
## not specific to SplicingGraphs.

## First we load toy reads (single-end) from a BAM file. We filter out
## secondary alignments, reads not passing quality controls, and PCR or
## optical duplicates (see ?scanBamFlag in the Rsamtools package for
## more information):
library(Rsamtools)
flag0 <- scanBamFlag(isNotPrimaryRead=FALSE,
                     isNotPassingQualityControls=FALSE,
                     isDuplicate=FALSE)
param0 <- ScanBamParam(flag=flag0)
gal <- readGAlignments(toy_reads_bam(), use.names=TRUE, param=param0)
gal

## Put the reads in a GRangesList object:
grl <- grglist(gal, order.as.in.query=TRUE)

## Put the transcripts in a GRangesList object (made of exons grouped
## by transcript):
ex_by_tx <- unlist(sg)

## Most of the times the RNA-seq protocol is unstranded so the strand
## reported in the BAM file (and propagated to 'grl') for each alignment
## is meaningless. Thus we should call findOverlaps() with
## 'ignore.strand=TRUE':
ov0 <- findOverlaps(grl, ex_by_tx, ignore.strand=TRUE)

## Encode the overlaps (this compare the fragmentation of the reads with
## the splicing of the transcripts):
ovenc0 <- encodeOverlaps(grl, ex_by_tx, hits=ov0,
                         flip.query.if.wrong.strand=TRUE)
ov0_is_compat <- isCompatibleWithSplicing(ovenc0)

## Keep compatible overlaps only:
ov1 <- ov0[ov0_is_compat]

## Only keep overlaps that are compatible with exactly 1 transcript:
ov2 <- ov1[queryHits(ov1) %in% which(countQueryHits(ov1) == 1L)]
nhit_per_tx <- countSubjectHits(ov2)
names(nhit_per_tx) <- names(txweight(sg))
nhit_per_tx

txweight(sg) <- 2 * nhit_per_tx
plot(sgraph(sg["geneA"], tx_id.as.edge.label=TRUE, as.igraph=TRUE))
plot(sgraph(sg["geneB"], tx_id.as.edge.label=TRUE, as.igraph=TRUE))