## 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)
Results
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
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Type 'demo()' for some demos, 'help()' for on-line help, or
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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/TSPCsg.Rd_%03d_medium.png", width=480, height=480)
> ### Name: TSPCsg
> ### Title: TSPC splicing graphs
> ### Aliases: TSPCsg TSPC
>
> ### ** Examples
>
> ## Load SplicingGraphs object 'TSPCsg':
> filepath <- system.file("extdata", "TSPCsg.rda", package="SplicingGraphs")
> load(filepath)
> TSPCsg
SplicingGraphs object with 9 gene(s) and 33 transcript(s)
>
> ## 'TSPCsg' has 1 element per gene and 'names(sg)' gives the gene ids.
> names(TSPCsg)
[1] "BAI1" "CYB561" "DAPL1" "ITGB8" "KIAA0319L" "LGSN"
[7] "MKRN3" "ST14" "TREM2"
>
> ## 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
DataFrame with 19 rows and 5 columns
from to sgedge_id ex_or_in tx_id
<character> <character> <character> <factor> <CharacterList>
1 R 1 LGSN:R,1 T-1,T-2,T-3,...
2 1 5 LGSN:1,5 ex T-1,T-4n
3 5 6 LGSN:5,6 in T-1,T-2,T-4n,...
4 6 8 LGSN:6,8 ex T-1,T-2,T-4n,...
5 8 11 LGSN:8,11 in T-1,T-2
... ... ... ... ... ...
15 6 7 LGSN:6,7 ex T-3
16 7 9 LGSN:7,9 in T-3
17 9 10 LGSN:9,10 ex T-3,T-4n,T-5n
18 10 11 LGSN:10,11 in T-3,T-4n,T-5n
19 8 9 LGSN:8,9 in T-4n,T-5n
>
> ## 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)
[1] 249 56
> counts[ , 1:5]
DataFrame with 249 rows and 5 columns
sgedge_id ex_or_in OVCAR3__OVCAR3 SOC_10764_474070 SOC_11199_480891
<character> <factor> <integer> <integer> <integer>
1 BAI1:1,2 ex 0 0 0
2 BAI1:2,3 in 0 0 0
3 BAI1:3,4 ex 0 0 0
4 BAI1:4,5 in 0 0 0
5 BAI1:5,6 ex 0 1 0
... ... ... ... ... ...
245 TREM2:9,10 ex 0 0 0
246 TREM2:6,9 in 0 0 0
247 TREM2:2,3 in 0 0 0
248 TREM2:3,4 ex 0 0 0
249 TREM2:4,5 in 0 0 0
>
> ## 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)
[1] 21 56
> DAPL1counts[ , 1:5]
DataFrame with 21 rows and 5 columns
sgedge_id ex_or_in OVCAR3__OVCAR3 SOC_10764_474070 SOC_11199_480891
<character> <factor> <integer> <integer> <integer>
1 DAPL1:1,2 ex 2 757 4
2 DAPL1:2,3 in 1 296 1
3 DAPL1:3,4 ex 3 1040 8
4 DAPL1:4,5 in 2 451 7
5 DAPL1:5,6 ex 3 964 16
... ... ... ... ... ...
17 DAPL1:18,19 ex 2 3 0
18 DAPL1:6,7 in 0 10 0
19 DAPL1:7,8 ex 0 10 0
20 DAPL1:8,9 in 0 0 0
21 DAPL1:6,17 in 0 0 0
>
> ## Use 'by="rsgedge"' to summarize by *reduced* splicing graph edge:
> DAPL1counts2 <- countReads(TSPCsg["DAPL1"], by="rsgedge")
> dim(DAPL1counts2)
[1] 11 56
> DAPL1counts2[ , 1:5]
DataFrame with 11 rows and 5 columns
rsgedge_id ex_or_in OVCAR3__OVCAR3 SOC_10764_474070 SOC_11199_480891
<character> <factor> <integer> <integer> <integer>
1 DAPL1:1,2,3 mixed 2 757 4
2 DAPL1:3,4,5,6 mixed 4 1399 17
3 DAPL1:6,9 in 1 349 9
4 DAPL1:9,10 ex 1 1506 29
5 DAPL1:6,11,12 mixed 0 11 2
6 DAPL1:12,15,16 mixed 0 0 0
7 DAPL1:6,13,14,17 mixed 0 2 0
8 DAPL1:17,19 ex 2 3 0
9 DAPL1:12,18,19 mixed 2 3 0
10 DAPL1:6,7,8,9 mixed 0 10 0
11 DAPL1:6,17 in 0 0 0
>
> ## 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)
Plotting splicing graph for gene "BAI1" (2 transcript(s)). Press <Enter> for next...
Plotting splicing graph for gene "CYB561" (6 transcript(s)). Press <Enter> for next...
Plotting splicing graph for gene "DAPL1" (6 transcript(s)). Press <Enter> for next...
Plotting splicing graph for gene "ITGB8" (2 transcript(s)). Press <Enter> for next...
Plotting splicing graph for gene "KIAA0319L" (2 transcript(s)). Press <Enter> for next...
Plotting splicing graph for gene "LGSN" (5 transcript(s)). Press <Enter> for next...dev.off()
Plotting splicing graph for gene "MKRN3" (4 transcript(s)). Press <Enter> for next...Plotting splicing graph for gene "ST14" (2 transcript(s)). Press <Enter> for next...Plotting splicing graph for gene "TREM2" (4 transcript(s)). Press <Enter> for next...>
providing 
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