| Title: | Create, manipulate, visualize splicing graphs, and assign RNA-seq reads to them |
|---|---|
| Description: | This package allows the user to create, manipulate, and visualize splicing graphs and their bubbles based on a gene model for a given organism. Additionally it allows the user to assign RNA-seq reads to the edges of a set of splicing graphs, and to summarize them in different ways. |
| Authors: | D. Bindreither, M. Carlson, M. Morgan, H. Pagès |
| Maintainer: | H. Pagès <[email protected]> |
| License: | Artistic-2.0 |
| Version: | 1.47.0 |
| Built: | 2024-12-21 05:59:48 UTC |
| Source: | https://github.com/bioc/SplicingGraphs |
The SplicingGraphs package allows the user to create, manipulate, and visualize splicing graphs and their bubbles based on a gene model for a given organism. Additionally it allows the user to assign RNA-seq reads to the edges of a set of splicing graphs, and to summarize them in different ways.
See the Splicing graphs and RNA-seq data vignette in the package
for a gentle introduction to its use.
To access the vignette, do browseVignettes("SplicingGraphs"), then
click on the link to the PDF version.
The SplicingGraphs package is a reincarnation of an internal project, the SpliceGraph package, originally written by D. Bindreither, M. Carlson, and M. Morgan. SpliceGraph was never released as part of Bioconductor.
With respect to the old SpliceGraph, the scope of the new SplicingGraphs package has been reduced to focus only on the following functionalities: creating/manipulating/plotting splicing graphs, computing the bubbles and AS codes, and assigning/counting reads.
In addition to this, the old SpliceGraph package also had facilities for performing some downstream statistical analysis. They were covered in its vignette under the following topics/sections:
Experimental design
Significant altered alternative splice events
Modification of GLM employed in the DEXSeq package
Differential edge expression analysis
Comparison to the classic DEXSeq analysis
The SplicingGraphs vignette doesn't cover any of this and the new package provides no facilities for doing this type of downstream statistical analysis.
Author and maintainer: H. Pagès <[email protected]>
The SplicingGraphs package is a complete revamp (design and implementation) of the old SpliceGraph package (see Note above).
Heber, S., Alekseyev, M., Sze, S., Tang, H., and Pevzner, P. A. Splicing graphs and EST assembly problem Bioinformatics Date: Jul 2002 Vol: 18 Pages: S181-S188
Sammeth, M. (2009) Complete Alternative Splicing Events Are Bubbles in Splicing Graphs J. Comput. Biol. Date: Aug 2009 Vol: 16 Pages: 1117-1140
The man pages in the SplicingGraphs package are:
The SplicingGraphs class.
plotTranscripts for plotting a set of transcripts
along genomic coordinates.
sgedgesByGene for extracting the edges and their
ranges from a SplicingGraphs object.
txpath for extracting the transcript paths of a
splicing graph.
sgedges for extracting the edges (and nodes) of a
splicing graph.
sgraph for extracting a splicing graph as a
plottable graph-like object.
rsgedgesByGene for extracting the reduced edges
and their ranges from a SplicingGraphs object.
bubbles for computing the bubbles of a splicing graph.
assignReads for assigning reads to the edges of a
SplicingGraphs object.
countReads for summarizing the reads assigned to
a SplicingGraphs object.
toy_genes_gff for details about the toy data included
in this package.
if (interactive()) { ## Access the "Splicing graphs and RNA-seq data" vignette with: browseVignettes("SplicingGraphs") }if (interactive()) { ## Access the "Splicing graphs and RNA-seq data" vignette with: browseVignettes("SplicingGraphs") }
assignReads assigns reads to the exonic and intronic edges of a
SplicingGraphs object.
removeReads removes all the reads assigned to the exonic and
intronic edges of a SplicingGraphs object.
assignReads(sg, reads, sample.name=NA) removeReads(sg)assignReads(sg, reads, sample.name=NA) removeReads(sg)
sg |
A SplicingGraphs object. |
reads |
A GAlignments,
GAlignmentPairs, or
GRangesList object, containing the
reads to assign to the exons and introns in |
sample.name |
A single string containing the name of the sample where the reads are coming from. |
TODO
For assignReads: the supplied SplicingGraphs object with
the reads assigned to it.
For removeReads: the supplied SplicingGraphs object with
all reads previously assigned with assignReads removed from it.
The read names are typically imported from the BAM file by calling
readGAlignments (or
readGAlignmentPairs) with
use.names=TRUE. This extracts the "query names" from the
file (stored in the QNAME field), and makes them the names of the
returned object.
The reads object must have unique names on it. The presence of
duplicated names generally indicates one (or both) of the following
situations:
(a) reads contains paired-end reads that have not been
paired;
(b) some of the reads are secondary alignments.
If (a): you can find out whether reads in a BAM file are single- or
paired-end with the quickBamFlagSummary utility
from the Rsamtools package. If they're paired-end, load them
with readGAlignmentPairs
instead of readGAlignments, and that
will pair them.
If (b): you can filter out secondary alignments by passing
'isSecondaryAlignment=FALSE' to scanBamFlag
when preparing the ScanBamParam object used to load
the reads. For example:
flag0 <- scanBamFlag(isSecondaryAlignment=FALSE,
isNotPassingQualityControls=FALSE,
isDuplicate=FALSE)
param0 <- ScanBamParam(flag=flag0)
reads <- readGAlignments("path/to/BAM/file", use.names=TRUE,
param=param0)
This will filter out records that have flag 0x100 (secondary alignment)
set to 1. See ?scanBamFlag in the Rsamtools
package for more information.
See the SAM Specs on the SAMtools project page at
http://samtools.sourceforge.net/ for a description of the
SAM/BAM flags.
H. Pagès
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, as well as
the readGAlignments and
readGAlignmentPairs functions
defined in the GenomicAlignments package.
The quickBamFlagSummary and
ScanBamParam functions defined in the
Rsamtools package.
## --------------------------------------------------------------------- ## 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. Load toy reads ## --------------------------------------------------------------------- ## 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): flag0 <- scanBamFlag(isSecondaryAlignment=FALSE, isNotPassingQualityControls=FALSE, isDuplicate=FALSE) param0 <- ScanBamParam(flag=flag0) gal <- readGAlignments(toy_reads_bam(), use.names=TRUE, param=param0) gal ## --------------------------------------------------------------------- ## 3. Assign the reads to the exons and introns in 'sg' ## --------------------------------------------------------------------- ## The same read can be assigned to more than 1 exon or intron (e.g. a ## junction read with 1 gap can be assigned to 2 exons and 1 intron). sg <- assignReads(sg, gal, sample.name="TOYREADS") ## See the assignments to the splicing graph edges. edge_by_tx <- sgedgesByTranscript(sg, with.hits.mcols=TRUE) edge_data <- mcols(unlist(edge_by_tx)) colnames(edge_data) head(edge_data) edge_data[ , c("sgedge_id", "TOYREADS.hits")] edge_by_gene <- sgedgesByGene(sg, with.hits.mcols=TRUE) mcols(unlist(edge_by_gene)) ## See the assignments to the reduced splicing graph edges. redge_by_gene <- rsgedgesByGene(sg, with.hits.mcols=TRUE) mcols(unlist(redge_by_gene)) ## --------------------------------------------------------------------- ## 4. Summarize the reads assigned to 'sg' and eventually remove them ## --------------------------------------------------------------------- ## See '?countReads'.## --------------------------------------------------------------------- ## 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. Load toy reads ## --------------------------------------------------------------------- ## 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): flag0 <- scanBamFlag(isSecondaryAlignment=FALSE, isNotPassingQualityControls=FALSE, isDuplicate=FALSE) param0 <- ScanBamParam(flag=flag0) gal <- readGAlignments(toy_reads_bam(), use.names=TRUE, param=param0) gal ## --------------------------------------------------------------------- ## 3. Assign the reads to the exons and introns in 'sg' ## --------------------------------------------------------------------- ## The same read can be assigned to more than 1 exon or intron (e.g. a ## junction read with 1 gap can be assigned to 2 exons and 1 intron). sg <- assignReads(sg, gal, sample.name="TOYREADS") ## See the assignments to the splicing graph edges. edge_by_tx <- sgedgesByTranscript(sg, with.hits.mcols=TRUE) edge_data <- mcols(unlist(edge_by_tx)) colnames(edge_data) head(edge_data) edge_data[ , c("sgedge_id", "TOYREADS.hits")] edge_by_gene <- sgedgesByGene(sg, with.hits.mcols=TRUE) mcols(unlist(edge_by_gene)) ## See the assignments to the reduced splicing graph edges. redge_by_gene <- rsgedgesByGene(sg, with.hits.mcols=TRUE) mcols(unlist(redge_by_gene)) ## --------------------------------------------------------------------- ## 4. Summarize the reads assigned to 'sg' and eventually remove them ## --------------------------------------------------------------------- ## See '?countReads'.
bubbles computes the bubbles of the splicing graph of a given gene
from a SplicingGraphs object.
bubbles(x) ASCODE2DESCbubbles(x) ASCODE2DESC
x |
A SplicingGraphs object of length 1. |
TODO
TODO
H. Pagès
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.
example(SplicingGraphs) # create SplicingGraphs object 'sg' sg ## 'sg' has 1 element per gene and 'names(sg)' gives the gene ids. names(sg) plot(sgraph(sg["geneA"], tx_id.as.edge.label=TRUE)) bubbles(sg["geneA"]) plot(sgraph(sg["geneB"], tx_id.as.edge.label=TRUE)) bubbles(sg["geneB"]) plot(sgraph(sg["geneD"], tx_id.as.edge.label=TRUE)) bubbles(sg["geneD"])example(SplicingGraphs) # create SplicingGraphs object 'sg' sg ## 'sg' has 1 element per gene and 'names(sg)' gives the gene ids. names(sg) plot(sgraph(sg["geneA"], tx_id.as.edge.label=TRUE)) bubbles(sg["geneA"]) plot(sgraph(sg["geneB"], tx_id.as.edge.label=TRUE)) bubbles(sg["geneB"]) plot(sgraph(sg["geneD"], tx_id.as.edge.label=TRUE)) bubbles(sg["geneD"])
countReads counts the reads assigned to a SplicingGraphs object.
The counting can be done by splicing graph edge, reduced splicing
graph edge, transcript, or gene.
reportReads is similar to countReads but returns right before
the final counting step, that is, the returned DataFrame contains the reads
instead of their counts.
countReads(x, by=c("sgedge", "rsgedge", "tx", "gene")) reportReads(x, by=c("sgedge", "rsgedge", "tx", "gene"))countReads(x, by=c("sgedge", "rsgedge", "tx", "gene")) reportReads(x, by=c("sgedge", "rsgedge", "tx", "gene"))
x |
A SplicingGraphs object. |
by |
Can be |
countReads and reportReads allow summarization of the reads
at different levels of resolution. The level of resolution is determined
by the type of feature that one chooses via the by argument.
The supported resolutions are (from highest to lowest resolution):
by="sgedge" for summarization at the splicing graph edge
level (i.e. at the exons/intron level);
by="rsgedge" for summarization at the reduced
splicing graph edge level;
by="tx" for summarization at the transcript level;
by="gene" for summarization at the gene level.
There is a parent-child relationship between the features corresponding to a given level of resolution (the parent features) and those corresponding to a higher level of resolution (the child features).
For example, in the case of the 2 first levels of resolution listed above, the parent-child relationship is the following: the parent features are the reduced splicing graph edges, the child features are the splicing graph edges, and each parent feature is obtained by merging one or more child features together. Similarly, transcripts can be seen as parent features of reduced splicing graph edges, and genes as parent features of transcripts. Note that, the rsgedge/sgedge and gene/tx relationships are one-to-many, but the tx/rsgedge relationship is many-to-many because a given edge can belong to more than one transcript.
Finally the parent-child relationships between 2 arbitrary levels of resolution is defined by combining the relationships between consecutive levels. All possible parent-child relationships are summarized in the following table:
| to: sgedge | to: rsgedge | to: tx
--------------+--------------+--------------+------------
from: rsgedge | one-to-many | |
from: tx | many-to-many | many-to-many |
from: gene | one-to-many | one-to-many | one-to-many
An important distinction needs to be made between a read that hits a given feature multiple times and a read that hits more than one feature.
If the former, the read is counted/reported only once for that feature. For example, when summarizing at the transcript level, a read is counted/reported only once for a given transcript, even if that read hits more than one splicing graph edge (or reduced splicing graph edge) associated with that transcript.
If the latter, the read is said to be ambiguous. An ambiguous read is currently counted/reported for each feature where it has a hit. This is a temporary situation: in the near future the user will be offered options to handle ambiguous reads in different ways.
A read might be ambiguous at one level of resolution but not at the other.
Also the number of ambiguous reads is typically affected by the level
of resolution. However, even though higher resolution generally means
more ambiguous reads, this is only true when the switch from one level
of resolution to the other implies a parent-child relationship between
features that is one-to-many.
So, based on the above table, this is always true, except when
switching from using by="tx" to using by="sgedge" or
by="rsgedge". In those cases, the switch can produce more
ambiguities but it can also produce less.
A DataFrame object with one row per:
unique splicing graph edge, if by="sgedge";
unique reduced splicing graph edge, if by="rsgedge";
transcript if by="tx";
gene if by="gene".
And with one column per sample (containing the counts for that sample for
countReads, and the reads for that sample for reportReads),
plus the two following left columns:
if by="sgedge": "sgedge_id", containing the
global splicing graph edge ids, and "ex_or_in",
containing the type of edge (exon or intron);
if by="rsgedge": "rsgedge_id", containing the
global reduced splicing graph edge ids, and
"ex_or_in", containing the type of edge (exon, intron,
or mixed);
if by="tx": "tx_id" and "gene_id";
if by="gene": "gene_id" and "tx_id".
For countReads, each column of counts is of type integer and
is named after the corresponding sample.
For reportReads, each column of reads is a CharacterList object
and its name is the name of the corresponding sample with the
".hits" suffix added to it.
In both cases, the name of the sample is the name that was passed to
assignReads when the reads of a given sample were initially
assigned. See ?assignReads for more information.
H. Pagès
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.
## --------------------------------------------------------------------- ## 1. Make SplicingGraphs object 'sg' from toy gene model and assign toy ## reads to it (see '?assignReads') ## --------------------------------------------------------------------- example(assignReads) ## --------------------------------------------------------------------- ## 2. Summarize the reads by splicing graph edge ## --------------------------------------------------------------------- countReads(sg) reportReads(sg) ## --------------------------------------------------------------------- ## 3. Summarize the reads by reduced splicing graph edge ## --------------------------------------------------------------------- countReads(sg, by="rsgedge") reportReads(sg, by="rsgedge") ## --------------------------------------------------------------------- ## 4. Summarize the reads by transcript ## --------------------------------------------------------------------- countReads(sg, by="tx") reportReads(sg, by="tx") ## --------------------------------------------------------------------- ## 5. Summarize the reads by gene ## --------------------------------------------------------------------- countReads(sg, by="gene") reportReads(sg, by="gene") ## --------------------------------------------------------------------- ## 6. A close look at ambiguous reads ## --------------------------------------------------------------------- resolutions <- c("sgedge", "rsgedge", "tx", "gene") reported_reads <- lapply(resolutions, function(by) { reported_reads <- reportReads(sg, by=by) unlist(reported_reads$TOYREADS.hits) }) ## The set of reported reads is the same at all levels of resolution: unique_reported_reads <- lapply(reported_reads, unique) stopifnot(identical(unique_reported_reads, rep(unique_reported_reads[1], 4))) ## Extract ambigous reads for each level of resolution: ambiguous_reads <- lapply(reported_reads, function(x) unique(x[duplicated(x)])) names(ambiguous_reads) <- resolutions ambiguous_reads ## Reads that are ambiguous at the "rsgedge" level must also be ## ambiguous at the "sgedge" level: stopifnot(all(ambiguous_reads$rsgedge %in% ambiguous_reads$sgedge)) ## However, there is no reason why reads that are ambiguous at the ## "tx" level should also be ambiguous at the "sgedge" or "rsgedge" ## level! ## --------------------------------------------------------------------- ## 7. Remove the reads from 'sg'. ## --------------------------------------------------------------------- sg <- removeReads(sg) countReads(sg)## --------------------------------------------------------------------- ## 1. Make SplicingGraphs object 'sg' from toy gene model and assign toy ## reads to it (see '?assignReads') ## --------------------------------------------------------------------- example(assignReads) ## --------------------------------------------------------------------- ## 2. Summarize the reads by splicing graph edge ## --------------------------------------------------------------------- countReads(sg) reportReads(sg) ## --------------------------------------------------------------------- ## 3. Summarize the reads by reduced splicing graph edge ## --------------------------------------------------------------------- countReads(sg, by="rsgedge") reportReads(sg, by="rsgedge") ## --------------------------------------------------------------------- ## 4. Summarize the reads by transcript ## --------------------------------------------------------------------- countReads(sg, by="tx") reportReads(sg, by="tx") ## --------------------------------------------------------------------- ## 5. Summarize the reads by gene ## --------------------------------------------------------------------- countReads(sg, by="gene") reportReads(sg, by="gene") ## --------------------------------------------------------------------- ## 6. A close look at ambiguous reads ## --------------------------------------------------------------------- resolutions <- c("sgedge", "rsgedge", "tx", "gene") reported_reads <- lapply(resolutions, function(by) { reported_reads <- reportReads(sg, by=by) unlist(reported_reads$TOYREADS.hits) }) ## The set of reported reads is the same at all levels of resolution: unique_reported_reads <- lapply(reported_reads, unique) stopifnot(identical(unique_reported_reads, rep(unique_reported_reads[1], 4))) ## Extract ambigous reads for each level of resolution: ambiguous_reads <- lapply(reported_reads, function(x) unique(x[duplicated(x)])) names(ambiguous_reads) <- resolutions ambiguous_reads ## Reads that are ambiguous at the "rsgedge" level must also be ## ambiguous at the "sgedge" level: stopifnot(all(ambiguous_reads$rsgedge %in% ambiguous_reads$sgedge)) ## However, there is no reason why reads that are ambiguous at the ## "tx" level should also be ambiguous at the "sgedge" or "rsgedge" ## level! ## --------------------------------------------------------------------- ## 7. Remove the reads from 'sg'. ## --------------------------------------------------------------------- sg <- removeReads(sg) countReads(sg)
plotTranscripts uses the Gviz package to plot the exon
structure of a set of transcripts along genomic coordinates.
plotTranscripts(x, reads=NULL, from=NA, to=NA, max.plot.reads=200)plotTranscripts(x, reads=NULL, from=NA, to=NA, max.plot.reads=200)
x |
A GRangesList object containing the genomic ranges
of a set of exons grouped by transcript. Alternatively, |
reads |
A GAlignments or GAlignmentPairs object containing single-end or paired-end reads. |
from, to
|
Single numeric values, giving the range of genomic coordinates to plot
the tracks in. By default (i.e. |
max.plot.reads |
The maximum number of reads that will be plotted. When the number of
reads that fall in the region being plotted is very large, plotting them
all would take a long time and result in a plot that is not very useful.
If that number is greater than |
H. Pagès
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:
plotTranscripts is based on the plotTracks
function defined in the Gviz package.
The GRangesList class defined in the GenomicRanges package.
The GAlignments and GAlignmentPairs classes defined in the GenomicAlignments package.
The TxDb class defined in the GenomicFeatures package.
## --------------------------------------------------------------------- ## A. PLOT TRANSCRIPTS ## --------------------------------------------------------------------- example(SplicingGraphs) # create SplicingGraphs object 'sg' sg ## 'sg' has 1 element per gene and 'names(sg)' gives the gene ids. names(sg) ## The transcripts of a given gene can be extracted with [[. The result ## is an *unnamed* GRangesList object containing the exons grouped by ## transcript: sg[["geneD"]] plotTranscripts(sg[["geneD"]]) # requires the Gviz package ## The transcripts of all the genes can be extracted with unlist(). The ## result is a *named* GRangesList object containing the exons grouped ## by transcript. The names on the object are the gene ids: ex_by_tx <- unlist(sg) ex_by_tx plotTranscripts(ex_by_tx) ## --------------------------------------------------------------------- ## B. PLOT TRANSCRIPTS AND READS ## --------------------------------------------------------------------- gal <- readGAlignments(toy_reads_bam(), use.names=TRUE) plotTranscripts(sg[["geneA"]], reads=gal) plotTranscripts(ex_by_tx, reads=gal) plotTranscripts(ex_by_tx, reads=gal, from=1, to=320) plotTranscripts(ex_by_tx, reads=gal[21:26], from=1, to=320)## --------------------------------------------------------------------- ## A. PLOT TRANSCRIPTS ## --------------------------------------------------------------------- example(SplicingGraphs) # create SplicingGraphs object 'sg' sg ## 'sg' has 1 element per gene and 'names(sg)' gives the gene ids. names(sg) ## The transcripts of a given gene can be extracted with [[. The result ## is an *unnamed* GRangesList object containing the exons grouped by ## transcript: sg[["geneD"]] plotTranscripts(sg[["geneD"]]) # requires the Gviz package ## The transcripts of all the genes can be extracted with unlist(). The ## result is a *named* GRangesList object containing the exons grouped ## by transcript. The names on the object are the gene ids: ex_by_tx <- unlist(sg) ex_by_tx plotTranscripts(ex_by_tx) ## --------------------------------------------------------------------- ## B. PLOT TRANSCRIPTS AND READS ## --------------------------------------------------------------------- gal <- readGAlignments(toy_reads_bam(), use.names=TRUE) plotTranscripts(sg[["geneA"]], reads=gal) plotTranscripts(ex_by_tx, reads=gal) plotTranscripts(ex_by_tx, reads=gal, from=1, to=320) plotTranscripts(ex_by_tx, reads=gal[21:26], from=1, to=320)
rsgedgesByGene and rsgedgesByTranscript are analog to
sgedgesByGene and sgedgesByTranscript,
but operate on the reduced splicing graphs, that is, the
graphs in SplicingGraphs object x are reduced before
the edges and their ranges are extracted. The reduced graphs are
obtained by removing the uninformative nodes from it. See Details
section below.
rsgedges extracts the edges of the reduced splicing graph of
a given gene from a SplicingGraphs object.
rsgraph extracts the reduced splicing graph for a given gene
from a SplicingGraphs object, and returns it as a plottable
graph-like object.
rsgedgesByGene(x, with.hits.mcols=FALSE, keep.dup.edges=FALSE) rsgedgesByTranscript(x, with.hits.mcols=FALSE) rsgedges(x) rsgraph(x, tx_id.as.edge.label=FALSE, as.igraph=FALSE) ## Related utility: uninformativeSSids(x)rsgedgesByGene(x, with.hits.mcols=FALSE, keep.dup.edges=FALSE) rsgedgesByTranscript(x, with.hits.mcols=FALSE) rsgedges(x) rsgraph(x, tx_id.as.edge.label=FALSE, as.igraph=FALSE) ## Related utility: uninformativeSSids(x)
x |
A SplicingGraphs object. Must be of length 1 for |
with.hits.mcols |
Whether or not to include the hits metadata columns in the
returned object. See |
keep.dup.edges |
Not supported yet. |
tx_id.as.edge.label |
Whether or not to use the transcript ids as edge labels. |
as.igraph |
TODO |
TODO: Explain graph reduction.
For rsgedgesByGene: A GRangesList object
named with the gene ids and where the reduced splicing graph edges are
grouped by gene.
TODO: Explain values returned by the other function.
H. Pagès
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.
## --------------------------------------------------------------------- ## 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. rsgedgesByGene() ## --------------------------------------------------------------------- edges_by_gene <- rsgedgesByGene(sg) edges_by_gene ## 'edges_by_gene' has the length and names of 'sg', that is, the names ## on it are the gene ids and are guaranteed to be unique. ## Extract the reduced edges and their ranges for a given gene: edges_by_gene[["geneA"]] ## Note that edge with global reduced edge id "geneA:1,2,4,5" is a mixed ## edge obtained by combining together edges "geneA:1,2" (exon), ## "geneA:2,4" (intron), and "geneA:4,5" (exon), during the graph ## reduction. stopifnot(identical(edges_by_gene["geneB"], rsgedgesByGene(sg["geneB"]))) ## --------------------------------------------------------------------- ## 3. sgedgesByTranscript() ## --------------------------------------------------------------------- #edges_by_tx <- rsgedgesByTranscript(sg) # not ready yet! #edges_by_tx ## --------------------------------------------------------------------- ## 4. rsgedges(), rsgraph(), uninformativeSSids() ## --------------------------------------------------------------------- plot(sgraph(sg["geneB"])) uninformativeSSids(sg["geneB"]) plot(rsgraph(sg["geneB"])) rsgedges(sg["geneB"]) ## --------------------------------------------------------------------- ## 5. Sanity checks ## --------------------------------------------------------------------- ## TODO: Do the same kind of sanity checks that are done for sgedges() ## vs sgedgesByGene() vs sgedgesByTranscript() (in man page for sgedges).## --------------------------------------------------------------------- ## 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. rsgedgesByGene() ## --------------------------------------------------------------------- edges_by_gene <- rsgedgesByGene(sg) edges_by_gene ## 'edges_by_gene' has the length and names of 'sg', that is, the names ## on it are the gene ids and are guaranteed to be unique. ## Extract the reduced edges and their ranges for a given gene: edges_by_gene[["geneA"]] ## Note that edge with global reduced edge id "geneA:1,2,4,5" is a mixed ## edge obtained by combining together edges "geneA:1,2" (exon), ## "geneA:2,4" (intron), and "geneA:4,5" (exon), during the graph ## reduction. stopifnot(identical(edges_by_gene["geneB"], rsgedgesByGene(sg["geneB"]))) ## --------------------------------------------------------------------- ## 3. sgedgesByTranscript() ## --------------------------------------------------------------------- #edges_by_tx <- rsgedgesByTranscript(sg) # not ready yet! #edges_by_tx ## --------------------------------------------------------------------- ## 4. rsgedges(), rsgraph(), uninformativeSSids() ## --------------------------------------------------------------------- plot(sgraph(sg["geneB"])) uninformativeSSids(sg["geneB"]) plot(rsgraph(sg["geneB"])) rsgedges(sg["geneB"]) ## --------------------------------------------------------------------- ## 5. Sanity checks ## --------------------------------------------------------------------- ## TODO: Do the same kind of sanity checks that are done for sgedges() ## vs sgedgesByGene() vs sgedgesByTranscript() (in man page for sgedges).
sgedges (resp. sgnodes) extracts the edges (resp. the nodes)
of the splicing graph of a given gene from a SplicingGraphs object.
sgedges(x, txweight=NULL, keep.dup.edges=FALSE) sgnodes(x) outdeg(x) indeg(x)sgedges(x, txweight=NULL, keep.dup.edges=FALSE) sgnodes(x) outdeg(x) indeg(x)
x |
A SplicingGraphs object of length 1. |
txweight |
TODO |
keep.dup.edges |
If |
TODO
TODO
H. Pagès
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.
## --------------------------------------------------------------------- ## 1. Make SplicingGraphs object 'sg' from toy gene model (see ## '?SplicingGraphs') ## --------------------------------------------------------------------- example(SplicingGraphs) # create SplicingGraphs object 'sg' sg ## 'sg' has 1 element per gene and 'names(sg)' gives the gene ids. names(sg) ## --------------------------------------------------------------------- ## 2. Basic usage ## --------------------------------------------------------------------- sgedges(sg["geneD"]) sgnodes(sg["geneD"]) outdeg(sg["geneD"]) indeg(sg["geneD"]) ## --------------------------------------------------------------------- ## 3. Sanity checks ## --------------------------------------------------------------------- check_way1_vs_way2 <- function(res1, res2) { edges1 <- res1[res1$ex_or_in != "", ] # remove artificial edges edges2 <- mcols(unlist(res2, use.names=FALSE)) stopifnot(identical(edges1, edges2)) } for (i in seq_along(sg)) { sgi <- sg[i] ## After removal of the artificial edges, the edges returned ## by 'sgedges()' should be the same as those returned ## by 'sgedgesByGene()' on a SplicingGraphs object of length 1. check_way1_vs_way2( sgedges(sgi), sgedgesByGene(sgi)) ## After removal of the artificial edges, the edges returned ## by 'sgedges( , keep.dup.edges=TRUE)' should be the same as ## those returned by 'sgedgesByGene( , keep.dup.edges=TRUE)' or by ## 'sgedgesByTranscript()' on a SplicingGraphs object of length 1. res1 <- DataFrame(sgedges(sgi, keep.dup.edges=TRUE)) check_way1_vs_way2( res1, sgedgesByGene(sgi, keep.dup.edges=TRUE)) check_way1_vs_way2( res1, sgedgesByTranscript(sgi)) }## --------------------------------------------------------------------- ## 1. Make SplicingGraphs object 'sg' from toy gene model (see ## '?SplicingGraphs') ## --------------------------------------------------------------------- example(SplicingGraphs) # create SplicingGraphs object 'sg' sg ## 'sg' has 1 element per gene and 'names(sg)' gives the gene ids. names(sg) ## --------------------------------------------------------------------- ## 2. Basic usage ## --------------------------------------------------------------------- sgedges(sg["geneD"]) sgnodes(sg["geneD"]) outdeg(sg["geneD"]) indeg(sg["geneD"]) ## --------------------------------------------------------------------- ## 3. Sanity checks ## --------------------------------------------------------------------- check_way1_vs_way2 <- function(res1, res2) { edges1 <- res1[res1$ex_or_in != "", ] # remove artificial edges edges2 <- mcols(unlist(res2, use.names=FALSE)) stopifnot(identical(edges1, edges2)) } for (i in seq_along(sg)) { sgi <- sg[i] ## After removal of the artificial edges, the edges returned ## by 'sgedges()' should be the same as those returned ## by 'sgedgesByGene()' on a SplicingGraphs object of length 1. check_way1_vs_way2( sgedges(sgi), sgedgesByGene(sgi)) ## After removal of the artificial edges, the edges returned ## by 'sgedges( , keep.dup.edges=TRUE)' should be the same as ## those returned by 'sgedgesByGene( , keep.dup.edges=TRUE)' or by ## 'sgedgesByTranscript()' on a SplicingGraphs object of length 1. res1 <- DataFrame(sgedges(sgi, keep.dup.edges=TRUE)) check_way1_vs_way2( res1, sgedgesByGene(sgi, keep.dup.edges=TRUE)) check_way1_vs_way2( res1, sgedgesByTranscript(sgi)) }
sgedgesByGene and sgedgesByTranscript both extract the
edges and their ranges of all the genes from a SplicingGraphs object.
They return them in a GRangesList object named
with the gene ids, and where the items are grouped by gene (for
sgedgesByGene) or by transcript (for sgedgesByTranscript).
Alternatively, intronsByTranscript extracts the intronic edges
and their ranges of all the genes from a SplicingGraphs object.
It returns them in a GRangesList object named
with the gene ids, and where the items are grouped by transcript.
sgedgesByGene(x, with.exon.mcols=FALSE, with.hits.mcols=FALSE, keep.dup.edges=FALSE) sgedgesByTranscript(x, with.exon.mcols=FALSE, with.hits.mcols=FALSE) ## S4 method for signature 'SplicingGraphs' intronsByTranscript(x)sgedgesByGene(x, with.exon.mcols=FALSE, with.hits.mcols=FALSE, keep.dup.edges=FALSE) sgedgesByTranscript(x, with.exon.mcols=FALSE, with.hits.mcols=FALSE) ## S4 method for signature 'SplicingGraphs' intronsByTranscript(x)
x |
A SplicingGraphs object. |
with.exon.mcols |
Whether or not to include the exon metadata columns in the
returned object. Those columns are named: |
with.hits.mcols |
Whether or not to include the hits metadata columns in the
returned object. See |
keep.dup.edges |
If |
A GRangesList object named with the gene ids and
where the items are grouped by gene (for sgedgesByGene), or by
transcript (for sgedgesByTranscript and intronsByTranscript).
In the latter case (i.e. grouping by transcript), the names are not unique.
The items that are being grouped are the splicing graph edges of type
exon and intron (no artificial edges) for sgedgesByGene and
sgedgesByTranscript, and the introns for intronsByTranscript.
When the grouping is by transcript (i.e. for sgedgesByTranscript
and intronsByTranscript, items are ordered by their position
from 5' to 3'.
About duplicated edges: A given edge can typically be shared by more than
1 transcript within the same gene, therefore sgedgesByTranscript
typically returns an object where the same global edge id shows
up in more than 1 group. However, the same global edge id is never
shared across genes. By default sgedgesByGene removes duplicated
edges, unless keep.dup.edges=TRUE is used.
H. Pagès
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.
## --------------------------------------------------------------------- ## 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. sgedgesByGene() ## --------------------------------------------------------------------- edges_by_gene <- sgedgesByGene(sg) edges_by_gene ## 'edges_by_gene' has the length and names of 'sg', that is, the names ## on it are the gene ids and are guaranteed to be unique. ## Extract the edges and their ranges for a given gene: edges_by_gene[["geneB"]] ## Note that edge with global edge id "geneB:3,4" is an intron that ## belongs to transcripts B1 and B2. edges_by_gene0 <- sgedgesByGene(sg, keep.dup.edges=TRUE) edges_by_gene0[["geneB"]] ## Note that edge "geneB:3,4" now shows up twice, once for transcript ## B1, and once for transcript B2. ## Keep the "exon metadata columns": sgedgesByGene(sg, with.exon.mcols=TRUE) ## Note that those cols are set to NA for intronic edges. ## --------------------------------------------------------------------- ## 3. sgedgesByTranscript() ## --------------------------------------------------------------------- edges_by_tx <- sgedgesByTranscript(sg) edges_by_tx ## 'edges_by_tx' is typically longer than 'sg'. ## IMPORTANT NOTE: One caveat here is that the names on 'edges_by_tx' ## are the gene ids, not the transcript ids, and thus are typically NOT ## unique! ## Select elements of a given gene: edges_by_tx["geneB"] # not a good idea edges_by_tx[names(edges_by_tx) %in% "geneB"] # much better :-) ## Note that edge with global edge id "geneB:3,4" is an intron that ## belongs to transcripts B1 and B2. ## Keep the "exon metadata columns": sgedgesByTranscript(sg, with.exon.mcols=TRUE) ## Note that those cols are set to NA for intronic edges. ## --------------------------------------------------------------------- ## 4. intronsByTranscript() ## --------------------------------------------------------------------- in_by_tx <- intronsByTranscript(sg) in_by_tx ## 'in_by_tx' has the length and names of 'edges_by_tx'. The same ## recommendation applies for selecting elements of a given set of ## genes: in_by_tx[c("geneB", "geneD")] # not a good idea in_by_tx[names(in_by_tx) %in% c("geneB", "geneD")] # much better :-) ## --------------------------------------------------------------------- ## 5. Comparing the outputs of unlist(), intronsByTranscript(), and ## sgedgesByTranscript() ## --------------------------------------------------------------------- ex_by_tx <- unlist(sg) in_by_tx <- intronsByTranscript(sg) edges_by_tx <- sgedgesByTranscript(sg) ## A sanity check: stopifnot(identical(elementNROWS(in_by_tx) + 1L, elementNROWS(ex_by_tx))) ## 'edges_by_tx' combines 'ex_by_tx' and 'in_by_tx' in a single ## GRangesList object. Sanity check: stopifnot(identical(elementNROWS(edges_by_tx), elementNROWS(ex_by_tx) + elementNROWS(in_by_tx)))## --------------------------------------------------------------------- ## 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. sgedgesByGene() ## --------------------------------------------------------------------- edges_by_gene <- sgedgesByGene(sg) edges_by_gene ## 'edges_by_gene' has the length and names of 'sg', that is, the names ## on it are the gene ids and are guaranteed to be unique. ## Extract the edges and their ranges for a given gene: edges_by_gene[["geneB"]] ## Note that edge with global edge id "geneB:3,4" is an intron that ## belongs to transcripts B1 and B2. edges_by_gene0 <- sgedgesByGene(sg, keep.dup.edges=TRUE) edges_by_gene0[["geneB"]] ## Note that edge "geneB:3,4" now shows up twice, once for transcript ## B1, and once for transcript B2. ## Keep the "exon metadata columns": sgedgesByGene(sg, with.exon.mcols=TRUE) ## Note that those cols are set to NA for intronic edges. ## --------------------------------------------------------------------- ## 3. sgedgesByTranscript() ## --------------------------------------------------------------------- edges_by_tx <- sgedgesByTranscript(sg) edges_by_tx ## 'edges_by_tx' is typically longer than 'sg'. ## IMPORTANT NOTE: One caveat here is that the names on 'edges_by_tx' ## are the gene ids, not the transcript ids, and thus are typically NOT ## unique! ## Select elements of a given gene: edges_by_tx["geneB"] # not a good idea edges_by_tx[names(edges_by_tx) %in% "geneB"] # much better :-) ## Note that edge with global edge id "geneB:3,4" is an intron that ## belongs to transcripts B1 and B2. ## Keep the "exon metadata columns": sgedgesByTranscript(sg, with.exon.mcols=TRUE) ## Note that those cols are set to NA for intronic edges. ## --------------------------------------------------------------------- ## 4. intronsByTranscript() ## --------------------------------------------------------------------- in_by_tx <- intronsByTranscript(sg) in_by_tx ## 'in_by_tx' has the length and names of 'edges_by_tx'. The same ## recommendation applies for selecting elements of a given set of ## genes: in_by_tx[c("geneB", "geneD")] # not a good idea in_by_tx[names(in_by_tx) %in% c("geneB", "geneD")] # much better :-) ## --------------------------------------------------------------------- ## 5. Comparing the outputs of unlist(), intronsByTranscript(), and ## sgedgesByTranscript() ## --------------------------------------------------------------------- ex_by_tx <- unlist(sg) in_by_tx <- intronsByTranscript(sg) edges_by_tx <- sgedgesByTranscript(sg) ## A sanity check: stopifnot(identical(elementNROWS(in_by_tx) + 1L, elementNROWS(ex_by_tx))) ## 'edges_by_tx' combines 'ex_by_tx' and 'in_by_tx' in a single ## GRangesList object. Sanity check: stopifnot(identical(elementNROWS(edges_by_tx), elementNROWS(ex_by_tx) + elementNROWS(in_by_tx)))
Extract the splicing graph for a given gene from a SplicingGraphs object and return it as a plottable graph-like object.
sgraph(x, keep.dup.edges=FALSE, tx_id.as.edge.label=FALSE, as.igraph=FALSE) ## PLotting: ## S4 method for signature 'SplicingGraphs,missing' plot(x, y, ...) slideshow(x)sgraph(x, keep.dup.edges=FALSE, tx_id.as.edge.label=FALSE, as.igraph=FALSE) ## PLotting: ## S4 method for signature 'SplicingGraphs,missing' plot(x, y, ...) slideshow(x)
x |
TODO |
keep.dup.edges |
If |
tx_id.as.edge.label |
Whether or not to use the transcript ids as edge labels. |
as.igraph |
TODO |
y |
TODO |
... |
Additional arguments. |
TODO
TODO
H. Pagès
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.
example(SplicingGraphs) # create SplicingGraphs object 'sg' sg ## 'sg' has 1 element per gene and 'names(sg)' gives the gene ids. names(sg) graphA <- sgraph(sg["geneA"], tx_id.as.edge.label=TRUE) if (interactive()) { ## Edges are labeled with the transcript ids (or names), in blue. ## The orange arrows are edges corresponding to exons: plot(graphA) ## Note that plot() works directly on a SplicingGraphs object of ## length 1: plot(sg["geneB"]) ## Slideshow of the graphs: slideshow(sg) }example(SplicingGraphs) # create SplicingGraphs object 'sg' sg ## 'sg' has 1 element per gene and 'names(sg)' gives the gene ids. names(sg) graphA <- sgraph(sg["geneA"], tx_id.as.edge.label=TRUE) if (interactive()) { ## Edges are labeled with the transcript ids (or names), in blue. ## The orange arrows are edges corresponding to exons: plot(graphA) ## Note that plot() works directly on a SplicingGraphs object of ## length 1: plot(sg["geneB"]) ## Slideshow of the graphs: slideshow(sg) }
The SplicingGraphs class is a container for storing splicing graphs together with the gene model that they are based on.
## Constructor: SplicingGraphs(x, grouping=NULL, min.ntx=2, max.ntx=NA, check.introns=TRUE) ## SplicingGraphs basic API: ## S4 method for signature 'SplicingGraphs' length(x) ## S4 method for signature 'SplicingGraphs' names(x) ## S4 method for signature 'SplicingGraphs' seqnames(x) ## S4 method for signature 'SplicingGraphs' strand(x) ## S4 method for signature 'SplicingGraphs,ANY,ANY,ANY' x[i, j, ... , drop=TRUE] ## S4 method for signature 'SplicingGraphs,ANY,ANY' x[[i, j, ...]] ## S4 method for signature 'SplicingGraphs' elementNROWS(x) ## S4 method for signature 'SplicingGraphs' unlist(x, recursive=TRUE, use.names=TRUE) ## S4 method for signature 'SplicingGraphs' seqinfo(x)## Constructor: SplicingGraphs(x, grouping=NULL, min.ntx=2, max.ntx=NA, check.introns=TRUE) ## SplicingGraphs basic API: ## S4 method for signature 'SplicingGraphs' length(x) ## S4 method for signature 'SplicingGraphs' names(x) ## S4 method for signature 'SplicingGraphs' seqnames(x) ## S4 method for signature 'SplicingGraphs' strand(x) ## S4 method for signature 'SplicingGraphs,ANY,ANY,ANY' x[i, j, ... , drop=TRUE] ## S4 method for signature 'SplicingGraphs,ANY,ANY' x[[i, j, ...]] ## S4 method for signature 'SplicingGraphs' elementNROWS(x) ## S4 method for signature 'SplicingGraphs' unlist(x, recursive=TRUE, use.names=TRUE) ## S4 method for signature 'SplicingGraphs' seqinfo(x)
x |
For For the methods in the SplicingGraphs basic API: A SplicingGraphs object. |
grouping |
An optional object that represents the grouping by gene of the top-level
elements (i.e. the transcripts) in |
min.ntx, max.ntx
|
Single integers (or |
check.introns |
If |
i, j, ..., drop
|
A SplicingGraphs object is a list-like object and therefore it can be
subsetted like a list. When subsetting with |
recursive, use.names
|
A SplicingGraphs object is a list-like object and therefore it can be
unlisted with |
The Splicing graph theory only applies to genes that have all the exons
of all their transcripts on the same chromosome and strand. In particular,
in its current form, the splicing graph theory cannot describe
trans-splicing events. The SplicingGraphs constructor will reject
genes that do not satisfy this.
The first argument of the SplicingGraphs constructor, x,
can be either a GRangesList object or a
TxDb object.
When x is a GRangesList object, it must
contain the exons of one or more genes grouped by transcripts. More
precisely, each top-level element in x must contain the genomic
ranges of the exons for a particular transcript. Typically x
will be obtained from a TxDb object
txdb with exonsBy(txdb, by="tx",
use.names=TRUE).
grouping is an optional argument that is only supported
when x is a GRangesList object.
It represents the grouping by gene of the top-level elements (i.e. the
transcripts) in GRangesList object x.
It can be either:
Missing (i.e. NULL). In that case, all the transcripts in
x are considered to belong to the same gene and the
SplicingGraphs object returned by SplicingGraphs will be
unnamed.
A list of integer or character vectors, or an
IntegerList, or a CharacterList
object, of length the number of genes to process, and where
grouping[[i]] is a vector of valid subscripts in
x pointing to all the transcripts of the i-th gene.
A factor, character vector, or integer vector, of the same length
as x and 1 level per gene.
A named GRangesList object containing
transcripts grouped by genes i.e. each top-level element in
grouping contains the genomic ranges of the transcripts
for a particular gene. In that case, the grouping is inferred from
the tx_id (or alternatively tx_name) metadata column of
unlist(grouping) and all the values in that column must
be in names(x).
If x was obtained with
exonsBy(txdb, by="tx", use.names=TRUE),
then the GRangesList object used for grouping
would typically be obtained with
transcriptsBy(txdb, by="gene").
A data.frame or DataFrame with 2 character vector
columns: a gene_id column (factor, character vector, or integer
vector), and a tx_id (or alternatively tx_name) column. In that
case, x must be named and all the values in the tx_id
(or tx_name) column must be in names(x).
For SplicingGraphs: a SplicingGraphs object with 1 element per gene.
For length: the number of genes in x, which is also the number
of splicing graphs in x.
For names: the gene ids. Note that the names on a SplicingGraphs
object are always unique and cannot be modified.
For seqnames: a named factor of the length of x containing
the name of the chromosome for each gene.
For strand: a named factor of the length of x containing
the strand for each gene.
For elementNROWS: the number of transcripts per gene.
For seqinfo: the seqinfo of the GRangesList
or TxDb object that was used to construct
the SplicingGraphs object.
H. Pagès
Heber, S., Alekseyev, M., Sze, S., Tang, H., and Pevzner, P. A. Splicing graphs and EST assembly problem Bioinformatics Date: Jul 2002 Vol: 18 Pages: S181-S188
Sammeth, M. (2009) Complete Alternative Splicing Events Are Bubbles in Splicing Graphs J. Comput. Biol. Date: Aug 2009 Vol: 16 Pages: 1117-1140
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 exonsBy and
transcriptsBy functions, and
the TxDb class, defined in the
GenomicFeatures package.
The GRangesList class defined in the GenomicRanges package.
The IntegerList and CharacterList classes defined in the IRanges package.
The DataFrame class defined in the S4Vectors package.
## --------------------------------------------------------------------- ## 1. Load a toy gene model as a TxDb object ## --------------------------------------------------------------------- library(txdbmaker) suppressWarnings( toy_genes_txdb <- makeTxDbFromGFF(toy_genes_gff()) ) ## --------------------------------------------------------------------- ## 2. Compute all the splicing graphs (1 graph per gene) and return them ## in a SplicingGraphs object ## --------------------------------------------------------------------- ## Extract the exons grouped by transcript: ex_by_tx <- exonsBy(toy_genes_txdb, by="tx", use.names=TRUE) ## Extract the transcripts grouped by gene: tx_by_gn <- transcriptsBy(toy_genes_txdb, by="gene") sg <- SplicingGraphs(ex_by_tx, tx_by_gn) sg ## Alternatively 'sg' can be constructed directly from the TxDb ## object: sg2 <- SplicingGraphs(toy_genes_txdb) # same as 'sg' sg2 ## Note that because SplicingGraphs objects have a slot that is an ## environment (for caching the bubbles), they cannot be compared with ## 'identical()' (will always return FALSE). 'all.equal()' should be ## used instead: stopifnot(isTRUE(all.equal(sg2, sg))) ## 'sg' has 1 element per gene and 'names(sg)' gives the gene ids: length(sg) names(sg) ## --------------------------------------------------------------------- ## 3. Basic manipulation of a SplicingGraphs object ## --------------------------------------------------------------------- ## Basic accessors: seqnames(sg) strand(sg) seqinfo(sg) ## Number of transcripts per gene: elementNROWS(sg) ## The transcripts of a given gene can be extracted with [[. The result ## is an *unnamed* GRangesList object containing the exons grouped by ## transcript: sg[["geneD"]] ## See '?plotTranscripts' for how to plot those transcripts. ## The transcripts of all the genes can be extracted with unlist(). The ## result is a *named* GRangesList object containing the exons grouped ## by transcript. The names on the object are the gene ids: ex_by_tx <- unlist(sg) ex_by_tx## --------------------------------------------------------------------- ## 1. Load a toy gene model as a TxDb object ## --------------------------------------------------------------------- library(txdbmaker) suppressWarnings( toy_genes_txdb <- makeTxDbFromGFF(toy_genes_gff()) ) ## --------------------------------------------------------------------- ## 2. Compute all the splicing graphs (1 graph per gene) and return them ## in a SplicingGraphs object ## --------------------------------------------------------------------- ## Extract the exons grouped by transcript: ex_by_tx <- exonsBy(toy_genes_txdb, by="tx", use.names=TRUE) ## Extract the transcripts grouped by gene: tx_by_gn <- transcriptsBy(toy_genes_txdb, by="gene") sg <- SplicingGraphs(ex_by_tx, tx_by_gn) sg ## Alternatively 'sg' can be constructed directly from the TxDb ## object: sg2 <- SplicingGraphs(toy_genes_txdb) # same as 'sg' sg2 ## Note that because SplicingGraphs objects have a slot that is an ## environment (for caching the bubbles), they cannot be compared with ## 'identical()' (will always return FALSE). 'all.equal()' should be ## used instead: stopifnot(isTRUE(all.equal(sg2, sg))) ## 'sg' has 1 element per gene and 'names(sg)' gives the gene ids: length(sg) names(sg) ## --------------------------------------------------------------------- ## 3. Basic manipulation of a SplicingGraphs object ## --------------------------------------------------------------------- ## Basic accessors: seqnames(sg) strand(sg) seqinfo(sg) ## Number of transcripts per gene: elementNROWS(sg) ## The transcripts of a given gene can be extracted with [[. The result ## is an *unnamed* GRangesList object containing the exons grouped by ## transcript: sg[["geneD"]] ## See '?plotTranscripts' for how to plot those transcripts. ## The transcripts of all the genes can be extracted with unlist(). The ## result is a *named* GRangesList object containing the exons grouped ## by transcript. The names on the object are the gene ids: ex_by_tx <- unlist(sg) ex_by_tx
TODO
toy_genes_gff() toy_reads_sam() toy_reads_bam() toy_overlaps()toy_genes_gff() toy_reads_sam() toy_reads_bam() toy_overlaps()
H. Pagès
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.
The makeTxDbFromGFF
function in the txdbmaker package.
The TxDb class defined in the GenomicFeatures package.
## --------------------------------------------------------------------- ## A. LOAD THE TOY GENE MODEL AS A TxDb OBJECT AND PLOT IT ## --------------------------------------------------------------------- toy_genes_gff() ## Note that you can display the content of the file with: cat(readLines(toy_genes_gff()), sep="\n") library(txdbmaker) suppressWarnings( txdb <- makeTxDbFromGFF(toy_genes_gff()) ) ## Plot all the transcripts in the gene model: plotTranscripts(txdb) ## --------------------------------------------------------------------- ## B. LOAD THE TOY READS AS A GAlignments OBJECT AND PLOT THEM ## --------------------------------------------------------------------- ## The reads are single-end reads. They are assumed to come from an ## RNA-seq experiment and to have been aligned to the exact same ## reference genome that the above toy gene model is based on. toy_reads_sam() toy_reads_bam() gal <- readGAlignments(toy_reads_bam(), use.names=TRUE) plotTranscripts(txdb, reads=gal) plotTranscripts(txdb, reads=gal, from=1, to=320) ## --------------------------------------------------------------------- ## C. FIND THE OVERLAPS BETWEEN THE TOY READS AND THE TOY GENE MODEL ## --------------------------------------------------------------------- grl <- grglist(gal, order.as.in.query=TRUE) ex_by_tx <- exonsBy(txdb, by="tx", use.names=TRUE) ## Most of the times the RNA-seq protocol is unstranded so the strand ## reported in the BAM file for each alignment is meaningless. Thus we ## should call findOverlaps() with 'ignore.strand=TRUE': ov0 <- findOverlaps(grl, ex_by_tx, ignore.strand=TRUE) ## Sort and put the overlaps in a data.frame to make them easier to ## read: ov0 <- sort(ov0) df0 <- data.frame(QNAME=names(grl)[queryHits(ov0)], tx_id=names(ex_by_tx)[subjectHits(ov0)], stringsAsFactors=FALSE) head(df0) ## These overlaps have been manually checked and included in the ## SplicingGraphs package. They can be loaded with the toy_overlaps() ## helper: toy_ov <- toy_overlaps() head(toy_ov) stopifnot(identical(df0, toy_ov[ , 1:2])) ## --------------------------------------------------------------------- ## D. DETECT THE OVERLAPS THAT ARE COMPATIBLE WITH THE GENE MODEL ## --------------------------------------------------------------------- ## First we encode the overlaps: ovenc0 <- encodeOverlaps(grl, ex_by_tx, hits=ov0, flip.query.if.wrong.strand=TRUE) ovenc0 ## Each encoding tells us whether the corresponding overlap is ## compatible or not with the gene model: ov0_is_comp <- isCompatibleWithSplicing(ovenc0) head(ov0_is_comp) ## Overlap compatibility has also been manually checked and included in ## the table returned by toy_overlaps(): stopifnot(identical(ov0_is_comp, toy_ov[ , 3]))## --------------------------------------------------------------------- ## A. LOAD THE TOY GENE MODEL AS A TxDb OBJECT AND PLOT IT ## --------------------------------------------------------------------- toy_genes_gff() ## Note that you can display the content of the file with: cat(readLines(toy_genes_gff()), sep="\n") library(txdbmaker) suppressWarnings( txdb <- makeTxDbFromGFF(toy_genes_gff()) ) ## Plot all the transcripts in the gene model: plotTranscripts(txdb) ## --------------------------------------------------------------------- ## B. LOAD THE TOY READS AS A GAlignments OBJECT AND PLOT THEM ## --------------------------------------------------------------------- ## The reads are single-end reads. They are assumed to come from an ## RNA-seq experiment and to have been aligned to the exact same ## reference genome that the above toy gene model is based on. toy_reads_sam() toy_reads_bam() gal <- readGAlignments(toy_reads_bam(), use.names=TRUE) plotTranscripts(txdb, reads=gal) plotTranscripts(txdb, reads=gal, from=1, to=320) ## --------------------------------------------------------------------- ## C. FIND THE OVERLAPS BETWEEN THE TOY READS AND THE TOY GENE MODEL ## --------------------------------------------------------------------- grl <- grglist(gal, order.as.in.query=TRUE) ex_by_tx <- exonsBy(txdb, by="tx", use.names=TRUE) ## Most of the times the RNA-seq protocol is unstranded so the strand ## reported in the BAM file for each alignment is meaningless. Thus we ## should call findOverlaps() with 'ignore.strand=TRUE': ov0 <- findOverlaps(grl, ex_by_tx, ignore.strand=TRUE) ## Sort and put the overlaps in a data.frame to make them easier to ## read: ov0 <- sort(ov0) df0 <- data.frame(QNAME=names(grl)[queryHits(ov0)], tx_id=names(ex_by_tx)[subjectHits(ov0)], stringsAsFactors=FALSE) head(df0) ## These overlaps have been manually checked and included in the ## SplicingGraphs package. They can be loaded with the toy_overlaps() ## helper: toy_ov <- toy_overlaps() head(toy_ov) stopifnot(identical(df0, toy_ov[ , 1:2])) ## --------------------------------------------------------------------- ## D. DETECT THE OVERLAPS THAT ARE COMPATIBLE WITH THE GENE MODEL ## --------------------------------------------------------------------- ## First we encode the overlaps: ovenc0 <- encodeOverlaps(grl, ex_by_tx, hits=ov0, flip.query.if.wrong.strand=TRUE) ovenc0 ## Each encoding tells us whether the corresponding overlap is ## compatible or not with the gene model: ov0_is_comp <- isCompatibleWithSplicing(ovenc0) head(ov0_is_comp) ## Overlap compatibility has also been manually checked and included in ## the table returned by toy_overlaps(): stopifnot(identical(ov0_is_comp, toy_ov[ , 3]))
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)## 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)
txpath extracts the transcript paths of the splicing graph
of a given gene from a SplicingGraphs object.
txpath(x, as.matrix=FALSE) ## Related utility: txweight(x) txweight(x) <- valuetxpath(x, as.matrix=FALSE) ## Related utility: txweight(x) txweight(x) <- value
x |
A SplicingGraphs object of length 1
or a GRangesList object for A SplicingGraphs object for |
as.matrix |
TODO |
value |
A numeric vector containing the weights to assign to each
transcript in |
TODO
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.
H. Pagès
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.
## --------------------------------------------------------------------- ## 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): flag0 <- scanBamFlag(isSecondaryAlignment=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))## --------------------------------------------------------------------- ## 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): flag0 <- scanBamFlag(isSecondaryAlignment=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))