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Last data update: 2014.03.03 |
Extract the reduced edges and their ranges from a SplicingGraphs objectDescription
UsagersgedgesByGene(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) Arguments
DetailsTODO: Explain graph reduction. ValueFor TODO: Explain values returned by the other function. Author(s)H. Pages See AlsoThis man page is part of the SplicingGraphs package.
Please see 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. 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). Results
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
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> library(SplicingGraphs)
Loading required package: GenomicFeatures
Loading required package: BiocGenerics
Loading required package: parallel
Attaching package: 'BiocGenerics'
The following objects are masked from 'package:parallel':
clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
clusterExport, clusterMap, parApply, parCapply, parLapply,
parLapplyLB, parRapply, parSapply, parSapplyLB
The following objects are masked from 'package:stats':
IQR, mad, xtabs
The following objects are masked from 'package:base':
Filter, Find, Map, Position, Reduce, anyDuplicated, append,
as.data.frame, cbind, colnames, do.call, duplicated, eval, evalq,
get, grep, grepl, intersect, is.unsorted, lapply, lengths, mapply,
match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank,
rbind, rownames, sapply, setdiff, sort, table, tapply, union,
unique, unsplit
Loading required package: S4Vectors
Loading required package: stats4
Attaching package: 'S4Vectors'
The following objects are masked from 'package:base':
colMeans, colSums, expand.grid, rowMeans, rowSums
Loading required package: IRanges
Loading required package: GenomeInfoDb
Loading required package: GenomicRanges
Loading required package: AnnotationDbi
Loading required package: Biobase
Welcome to Bioconductor
Vignettes contain introductory material; view with
'browseVignettes()'. To cite Bioconductor, see
'citation("Biobase")', and for packages 'citation("pkgname")'.
Loading required package: GenomicAlignments
Loading required package: SummarizedExperiment
Loading required package: Biostrings
Loading required package: XVector
Loading required package: Rsamtools
Loading required package: Rgraphviz
Loading required package: graph
Attaching package: 'graph'
The following object is masked from 'package:Biostrings':
complement
Loading required package: grid
Attaching package: 'Rgraphviz'
The following objects are masked from 'package:IRanges':
from, to
The following objects are masked from 'package:S4Vectors':
from, to
Warning messages:
1: replacing previous import 'IRanges::from' by 'Rgraphviz::from' when loading 'SplicingGraphs'
2: replacing previous import 'IRanges::to' by 'Rgraphviz::to' when loading 'SplicingGraphs'
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/SplicingGraphs/rsgedgesByGene-methods.Rd_%03d_medium.png", width=480, height=480)
> ### Name: rsgedgesByGene-methods
> ### Title: Extract the reduced edges and their ranges from a SplicingGraphs
> ### object
> ### Aliases: rsgedgesByGene-methods uninformativeSSids
> ### uninformativeSSids,ANY-method uninformativeSSids,DataFrame-method
> ### rsgedgesByTranscript rsgedgesByTranscript,SplicingGraphs-method
> ### rsgedgesByGene rsgedgesByGene,SplicingGraphs-method rsgedges sgedges2
> ### rsgraph sgraph2
>
> ### ** Examples
>
> ## ---------------------------------------------------------------------
> ## 1. Make SplicingGraphs object 'sg' from toy gene model (see
> ## '?SplicingGraphs')
> ## ---------------------------------------------------------------------
> example(SplicingGraphs)
SplcnG> ## ---------------------------------------------------------------------
SplcnG> ## 1. Load a toy gene model as a TxDb object
SplcnG> ## ---------------------------------------------------------------------
SplcnG>
SplcnG> library(GenomicFeatures)
SplcnG> suppressWarnings(
SplcnG+ toy_genes_txdb <- makeTxDbFromGFF(toy_genes_gff())
SplcnG+ )
Import genomic features from the file as a GRanges object ... OK
Prepare the 'metadata' data frame ... OK
Make the TxDb object ... OK
SplcnG> ## ---------------------------------------------------------------------
SplcnG> ## 2. Compute all the splicing graphs (1 graph per gene) and return them
SplcnG> ## in a SplicingGraphs object
SplcnG> ## ---------------------------------------------------------------------
SplcnG>
SplcnG> ## Extract the exons grouped by transcript:
SplcnG> ex_by_tx <- exonsBy(toy_genes_txdb, by="tx", use.names=TRUE)
SplcnG> ## Extract the transcripts grouped by gene:
SplcnG> tx_by_gn <- transcriptsBy(toy_genes_txdb, by="gene")
SplcnG> sg <- SplicingGraphs(ex_by_tx, tx_by_gn)
SplcnG> sg
SplicingGraphs object with 5 gene(s) and 13 transcript(s)
SplcnG> ## Alternatively 'sg' can be constructed directly from the TxDb
SplcnG> ## object:
SplcnG> sg2 <- SplicingGraphs(toy_genes_txdb) # same as 'sg'
SplcnG> sg2
SplicingGraphs object with 5 gene(s) and 13 transcript(s)
SplcnG> ## Note that because SplicingGraphs objects have a slot that is an
SplcnG> ## environment (for caching the bubbles), they cannot be compared with
SplcnG> ## 'identical()' (will always return FALSE). 'all.equal()' should be
SplcnG> ## used instead:
SplcnG> stopifnot(isTRUE(all.equal(sg2, sg)))
SplcnG> ## 'sg' has 1 element per gene and 'names(sg)' gives the gene ids:
SplcnG> length(sg)
[1] 5
SplcnG> names(sg)
[1] "geneA" "geneB" "geneC" "geneD" "geneE"
SplcnG> ## ---------------------------------------------------------------------
SplcnG> ## 3. Basic manipulation of a SplicingGraphs object
SplcnG> ## ---------------------------------------------------------------------
SplcnG>
SplcnG> ## Basic accessors:
SplcnG> seqnames(sg)
geneA geneB geneC geneD geneE
chrX chrX chrX chrX chrX
Levels: chrX
SplcnG> strand(sg)
geneA geneB geneC geneD geneE
+ - + + +
Levels: + - *
SplcnG> seqinfo(sg)
Seqinfo object with 1 sequence from an unspecified genome; no seqlengths:
seqnames seqlengths isCircular genome
chrX NA NA <NA>
SplcnG> ## Number of transcripts per gene:
SplcnG> elementNROWS(sg)
geneA geneB geneC geneD geneE
2 2 3 4 2
SplcnG> ## The transcripts of a given gene can be extracted with [[. The result
SplcnG> ## is an *unnamed* GRangesList object containing the exons grouped by
SplcnG> ## transcript:
SplcnG> sg[["geneD"]]
GRangesList object of length 4:
[[1]]
GRanges object with 2 ranges and 5 metadata columns:
seqnames ranges strand | exon_id exon_name exon_rank start_SSid
<Rle> <IRanges> <Rle> | <integer> <character> <integer> <integer>
[1] chrX [601, 630] + | 10 Dx2 1 1
[2] chrX [666, 675] + | 12 Dx4 2 5
end_SSid
<integer>
[1] 3
[2] 6
[[2]]
GRanges object with 2 ranges and 5 metadata columns:
seqnames ranges strand | exon_id exon_name exon_rank start_SSid
[1] chrX [601, 620] + | 9 Dx1 1 1
[2] chrX [651, 700] + | 11 Dx3 2 4
end_SSid
[1] 2
[2] 8
[[3]]
GRanges object with 3 ranges and 5 metadata columns:
seqnames ranges strand | exon_id exon_name exon_rank start_SSid
[1] chrX [601, 620] + | 9 Dx1 1 1
[2] chrX [666, 675] + | 12 Dx4 2 5
[3] chrX [691, 700] + | 13 Dx5 3 7
end_SSid
[1] 2
[2] 6
[3] 8
...
<1 more element>
-------
seqinfo: 1 sequence from an unspecified genome; no seqlengths
SplcnG> ## See '?plotTranscripts' for how to plot those transcripts.
SplcnG>
SplcnG> ## The transcripts of all the genes can be extracted with unlist(). The
SplcnG> ## result is a *named* GRangesList object containing the exons grouped
SplcnG> ## by transcript. The names on the object are the gene ids:
SplcnG> ex_by_tx <- unlist(sg)
SplcnG> ex_by_tx
GRangesList object of length 13:
$geneA
GRanges object with 1 range and 5 metadata columns:
seqnames ranges strand | exon_id exon_name exon_rank start_SSid
<Rle> <IRanges> <Rle> | <integer> <character> <integer> <integer>
[1] chrX [11, 50] + | 2 Ax2 1 1
end_SSid
<integer>
[1] 3
$geneA
GRanges object with 2 ranges and 5 metadata columns:
seqnames ranges strand | exon_id exon_name exon_rank start_SSid
[1] chrX [11, 40] + | 1 Ax1 1 1
[2] chrX [71, 100] + | 3 Ax3 2 4
end_SSid
[1] 2
[2] 5
$geneB
GRanges object with 2 ranges and 5 metadata columns:
seqnames ranges strand | exon_id exon_name exon_rank start_SSid
[1] chrX [251, 300] - | 23 Bx1 1 3
[2] chrX [201, 230] - | 20 Bx2 2 6
end_SSid
[1] 1
[2] 4
...
<10 more elements>
-------
seqinfo: 1 sequence from an unspecified genome; no seqlengths
> sg
SplicingGraphs object with 5 gene(s) and 13 transcript(s)
>
> ## 'sg' has 1 element per gene and 'names(sg)' gives the gene ids.
> names(sg)
[1] "geneA" "geneB" "geneC" "geneD" "geneE"
>
> ## ---------------------------------------------------------------------
> ## 2. rsgedgesByGene()
> ## ---------------------------------------------------------------------
> edges_by_gene <- rsgedgesByGene(sg)
> edges_by_gene
GRangesList object of length 5:
$geneA
GRanges object with 2 ranges and 5 metadata columns:
seqnames ranges strand | from to rsgedge_id
<Rle> <IRanges> <Rle> | <character> <character> <character>
[1] chrX [11, 50] + | 1 3 geneA:1,3
[2] chrX [11, 100] + | 1 5 geneA:1,2,4,5
ex_or_in tx_id
<factor> <CharacterList>
[1] ex A1
[2] mixed A2
$geneB
GRanges object with 5 ranges and 5 metadata columns:
seqnames ranges strand | from to rsgedge_id ex_or_in tx_id
[1] chrX [251, 300] - | 1 3 geneB:1,3 ex B1
[2] chrX [231, 250] - | 3 4 geneB:3,4 in B1,B2
[3] chrX [201, 230] - | 4 6 geneB:4,6 ex B1
[4] chrX [251, 270] - | 2 3 geneB:2,3 ex B2
[5] chrX [216, 230] - | 4 5 geneB:4,5 ex B2
$geneC
GRanges object with 5 ranges and 5 metadata columns:
seqnames ranges strand | from to rsgedge_id ex_or_in tx_id
[1] chrX [401, 415] + | 1 2 geneC:1,2 ex C1,C2
[2] chrX [416, 480] + | 2 8 geneC:2,7,8 mixed C1
[3] chrX [416, 480] + | 2 9 geneC:2,5,6,9 mixed C2
[4] chrX [481, 500] + | 9 10 geneC:9,10 ex C2,C3
[5] chrX [421, 480] + | 3 9 geneC:3,4,9 mixed C3
...
<2 more elements>
-------
seqinfo: 1 sequence from an unspecified genome; no seqlengths
> ## '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"]]
GRanges object with 2 ranges and 5 metadata columns:
seqnames ranges strand | from to rsgedge_id
<Rle> <IRanges> <Rle> | <character> <character> <character>
[1] chrX [11, 50] + | 1 3 geneA:1,3
[2] chrX [11, 100] + | 1 5 geneA:1,2,4,5
ex_or_in tx_id
<factor> <CharacterList>
[1] ex A1
[2] mixed A2
-------
seqinfo: 1 sequence from an unspecified genome; no seqlengths
> ## 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"])
[1] "1" "6" "2" "5"
>
> plot(rsgraph(sg["geneB"]))
> rsgedges(sg["geneB"])
DataFrame with 5 rows and 5 columns
from to rsgedge_id ex_or_in tx_id
<character> <character> <character> <factor> <CharacterList>
1 R 3 geneB:R,1,3 mixed B1
2 3 4 geneB:3,4 in B1,B2
3 4 L geneB:4,6,L mixed B1
4 R 3 geneB:R,2,3 mixed B2
5 4 L geneB:4,5,L mixed B2
>
> ## ---------------------------------------------------------------------
> ## 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).
>
>
>
>
>
> dev.off()
null device
1
>
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Created & Maintained by Osamu Ogasawara (osamu.ogasawara@gmail.com) and