GO Enrichment result list object, such as returned by compute.go.enrichment.
up.generations
Integer (optional). Number of generations above significant nodes to include in the subgraph.
only.topics
Integer vector (optional). If not NULL, vector of topics that should be included in the plot (otherwise all topic enrichment sets are used).
file.output
String (optional). If not NULL, pathname of file to write the plot to.
p.val.threshold
Numeric (optional). P-value treshold to use to select which terms should be plotted.
only.unique
Only display terms that are only significant for one of the topics.
topic.colors
RGB colour vector (optional). Colors to use for each topic.
Value
An igraph object with the annotated GO DAG.
Examples
# Load pre-computed LDA model for skeletal myoblast RNA-Seq data from HSMMSingleCell package:
data(HSMM_lda_model)
# Load GO mapping database for 'homo sapiens':
library(org.Hs.eg.db)
# Compute GO enrichment sets for each topic:
go.results = compute.go.enrichment(HSMM_lda_model, org.Hs.eg.db, bonferroni.correct=TRUE)
go.dag.subtree = ct.plot.go.dag(go.results, up.generations = 2)
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)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(cellTree)
Loading required package: topGO
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: graph
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: GO.db
Loading required package: AnnotationDbi
Loading required package: stats4
Loading required package: IRanges
Loading required package: S4Vectors
Attaching package: 'S4Vectors'
The following objects are masked from 'package:base':
colMeans, colSums, expand.grid, rowMeans, rowSums
Loading required package: SparseM
Attaching package: 'SparseM'
The following object is masked from 'package:base':
backsolve
groupGOTerms: GOBPTerm, GOMFTerm, GOCCTerm environments built.
Attaching package: 'topGO'
The following object is masked from 'package:IRanges':
members
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/cellTree/ct.plot.go.dag.Rd_%03d_medium.png", width=480, height=480)
> ### Name: ct.plot.go.dag
> ### Title: Gene Ontology enrichment sets plotting
> ### Aliases: ct.plot.go.dag
>
> ### ** Examples
>
> # Load pre-computed LDA model for skeletal myoblast RNA-Seq data from HSMMSingleCell package:
> data(HSMM_lda_model)
>
> # Load GO mapping database for 'homo sapiens':
> library(org.Hs.eg.db)
>
> ## No test:
> # Compute GO enrichment sets for each topic:
> go.results = compute.go.enrichment(HSMM_lda_model, org.Hs.eg.db, bonferroni.correct=TRUE)
Loading required namespace: maptpx
Computing GO enrichment for topic: 1
Building most specific GOs .....
( 9195 GO terms found. )
Build GO DAG topology ..........
( 13026 GO terms and 31134 relations. )
Annotating nodes ...............
( 9573 genes annotated to the GO terms. )
-- Weight01 Algorithm --
the algorithm is scoring 5491 nontrivial nodes
parameters:
test statistic: KS
score order: decreasing
Level 19: 3 nodes to be scored (0 eliminated genes)
Level 18: 4 nodes to be scored (0 eliminated genes)
Level 17: 5 nodes to be scored (19 eliminated genes)
Level 16: 17 nodes to be scored (36 eliminated genes)
Level 15: 43 nodes to be scored (87 eliminated genes)
Level 14: 113 nodes to be scored (240 eliminated genes)
Level 13: 181 nodes to be scored (564 eliminated genes)
Level 12: 301 nodes to be scored (1429 eliminated genes)
Level 11: 499 nodes to be scored (2651 eliminated genes)
Level 10: 614 nodes to be scored (3975 eliminated genes)
Level 9: 761 nodes to be scored (5622 eliminated genes)
Level 8: 767 nodes to be scored (6918 eliminated genes)
Level 7: 789 nodes to be scored (7746 eliminated genes)
Level 6: 654 nodes to be scored (8461 eliminated genes)
Level 5: 436 nodes to be scored (8806 eliminated genes)
Level 4: 221 nodes to be scored (9114 eliminated genes)
Level 3: 62 nodes to be scored (9223 eliminated genes)
Level 2: 20 nodes to be scored (9334 eliminated genes)
Level 1: 1 nodes to be scored (9384 eliminated genes)
Computing GO enrichment for topic: 2
Building most specific GOs .....
( 9195 GO terms found. )
Build GO DAG topology ..........
( 13026 GO terms and 31134 relations. )
Annotating nodes ...............
( 9573 genes annotated to the GO terms. )
-- Weight01 Algorithm --
the algorithm is scoring 5491 nontrivial nodes
parameters:
test statistic: KS
score order: decreasing
Level 19: 3 nodes to be scored (0 eliminated genes)
Level 18: 4 nodes to be scored (0 eliminated genes)
Level 17: 5 nodes to be scored (19 eliminated genes)
Level 16: 17 nodes to be scored (36 eliminated genes)
Level 15: 43 nodes to be scored (87 eliminated genes)
Level 14: 113 nodes to be scored (240 eliminated genes)
Level 13: 181 nodes to be scored (564 eliminated genes)
Level 12: 301 nodes to be scored (1429 eliminated genes)
Level 11: 499 nodes to be scored (2651 eliminated genes)
Level 10: 614 nodes to be scored (3975 eliminated genes)
Level 9: 761 nodes to be scored (5622 eliminated genes)
Level 8: 767 nodes to be scored (6918 eliminated genes)
Level 7: 789 nodes to be scored (7746 eliminated genes)
Level 6: 654 nodes to be scored (8461 eliminated genes)
Level 5: 436 nodes to be scored (8806 eliminated genes)
Level 4: 221 nodes to be scored (9114 eliminated genes)
Level 3: 62 nodes to be scored (9223 eliminated genes)
Level 2: 20 nodes to be scored (9334 eliminated genes)
Level 1: 1 nodes to be scored (9384 eliminated genes)
Computing GO enrichment for topic: 3
Building most specific GOs .....
( 9195 GO terms found. )
Build GO DAG topology ..........
( 13026 GO terms and 31134 relations. )
Annotating nodes ...............
( 9573 genes annotated to the GO terms. )
-- Weight01 Algorithm --
the algorithm is scoring 5491 nontrivial nodes
parameters:
test statistic: KS
score order: decreasing
Level 19: 3 nodes to be scored (0 eliminated genes)
Level 18: 4 nodes to be scored (0 eliminated genes)
Level 17: 5 nodes to be scored (19 eliminated genes)
Level 16: 17 nodes to be scored (36 eliminated genes)
Level 15: 43 nodes to be scored (87 eliminated genes)
Level 14: 113 nodes to be scored (240 eliminated genes)
Level 13: 181 nodes to be scored (564 eliminated genes)
Level 12: 301 nodes to be scored (1429 eliminated genes)
Level 11: 499 nodes to be scored (2651 eliminated genes)
Level 10: 614 nodes to be scored (3975 eliminated genes)
Level 9: 761 nodes to be scored (5622 eliminated genes)
Level 8: 767 nodes to be scored (6918 eliminated genes)
Level 7: 789 nodes to be scored (7746 eliminated genes)
Level 6: 654 nodes to be scored (8461 eliminated genes)
Level 5: 436 nodes to be scored (8806 eliminated genes)
Level 4: 221 nodes to be scored (9114 eliminated genes)
Level 3: 62 nodes to be scored (9223 eliminated genes)
Level 2: 20 nodes to be scored (9334 eliminated genes)
Level 1: 1 nodes to be scored (9384 eliminated genes)
Computing GO enrichment for topic: 4
Building most specific GOs .....
( 9195 GO terms found. )
Build GO DAG topology ..........
( 13026 GO terms and 31134 relations. )
Annotating nodes ...............
( 9573 genes annotated to the GO terms. )
-- Weight01 Algorithm --
the algorithm is scoring 5491 nontrivial nodes
parameters:
test statistic: KS
score order: decreasing
Level 19: 3 nodes to be scored (0 eliminated genes)
Level 18: 4 nodes to be scored (0 eliminated genes)
Level 17: 5 nodes to be scored (19 eliminated genes)
Level 16: 17 nodes to be scored (36 eliminated genes)
Level 15: 43 nodes to be scored (87 eliminated genes)
Level 14: 113 nodes to be scored (240 eliminated genes)
Level 13: 181 nodes to be scored (564 eliminated genes)
Level 12: 301 nodes to be scored (1429 eliminated genes)
Level 11: 499 nodes to be scored (2651 eliminated genes)
Level 10: 614 nodes to be scored (3975 eliminated genes)
Level 9: 761 nodes to be scored (5622 eliminated genes)
Level 8: 767 nodes to be scored (6918 eliminated genes)
Level 7: 789 nodes to be scored (7746 eliminated genes)
Level 6: 654 nodes to be scored (8461 eliminated genes)
Level 5: 436 nodes to be scored (8806 eliminated genes)
Level 4: 221 nodes to be scored (9114 eliminated genes)
Level 3: 62 nodes to be scored (9223 eliminated genes)
Level 2: 20 nodes to be scored (9334 eliminated genes)
Level 1: 1 nodes to be scored (9384 eliminated genes)
Computing GO enrichment for topic: 5
Building most specific GOs .....
( 9195 GO terms found. )
Build GO DAG topology ..........
( 13026 GO terms and 31134 relations. )
Annotating nodes ...............
( 9573 genes annotated to the GO terms. )
-- Weight01 Algorithm --
the algorithm is scoring 5491 nontrivial nodes
parameters:
test statistic: KS
score order: decreasing
Level 19: 3 nodes to be scored (0 eliminated genes)
Level 18: 4 nodes to be scored (0 eliminated genes)
Level 17: 5 nodes to be scored (19 eliminated genes)
Level 16: 17 nodes to be scored (36 eliminated genes)
Level 15: 43 nodes to be scored (87 eliminated genes)
Level 14: 113 nodes to be scored (240 eliminated genes)
Level 13: 181 nodes to be scored (564 eliminated genes)
Level 12: 301 nodes to be scored (1429 eliminated genes)
Level 11: 499 nodes to be scored (2651 eliminated genes)
Level 10: 614 nodes to be scored (3975 eliminated genes)
Level 9: 761 nodes to be scored (5622 eliminated genes)
Level 8: 767 nodes to be scored (6918 eliminated genes)
Level 7: 789 nodes to be scored (7746 eliminated genes)
Level 6: 654 nodes to be scored (8461 eliminated genes)
Level 5: 436 nodes to be scored (8806 eliminated genes)
Level 4: 221 nodes to be scored (9114 eliminated genes)
Level 3: 62 nodes to be scored (9223 eliminated genes)
Level 2: 20 nodes to be scored (9334 eliminated genes)
Level 1: 1 nodes to be scored (9384 eliminated genes)
>
> go.dag.subtree = ct.plot.go.dag(go.results, up.generations = 2)
>
> ## End(No test)
>
>
>
>
>
> dev.off()
null device
1
>
providing 
.