topGO_Analysis
Thomas Källman
27 Apr 2015
I am using topGO and the annFUN.org in that package to collect GO terms for enrichment using ensembl gene names. The analysis is done using the GO main category BP (Biological Process), but if needed one can of course also do it for MF and/or CC. I am not sure how to best summarise the data over the different contrasts and if all contrast are interesting to look at. The analysis described below focus on a single contrast, but it is easy enough to do it for all other contrasts as well, by just changing the input object.
# Function for creating named vector suitable for topGO analysis from Limma results. Note that the up and down alteres p-values so that only the up- respectively down- regulated once are retained as significant and the other class will have p-values of 1.
library(topGO)## Loading required package: graph
## Loading required package: Biobase
## Loading required package: BiocGenerics
## Loading required package: parallel
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## Attaching package: 'BiocGenerics'
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## The following objects are masked from 'package:parallel':
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## clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
## clusterExport, clusterMap, parApply, parCapply, parLapply,
## parLapplyLB, parRapply, parSapply, parSapplyLB
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## The following object is masked from 'package:stats':
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## xtabs
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## The following objects are masked from 'package:base':
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## anyDuplicated, append, as.data.frame, as.vector, cbind,
## colnames, do.call, duplicated, eval, evalq, Filter, Find, get,
## intersect, is.unsorted, lapply, Map, mapply, match, mget,
## order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,
## rbind, Reduce, rep.int, rownames, sapply, setdiff, sort,
## table, tapply, union, unique, unlist, unsplit
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## Welcome to Bioconductor
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## 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: GenomeInfoDb
## Loading required package: S4Vectors
## Loading required package: IRanges
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## Attaching package: 'AnnotationDbi'
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## The following object is masked from 'package:GenomeInfoDb':
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## species
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## Loading required package: DBI
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## Loading required package: SparseM
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## Attaching package: 'SparseM'
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## The following object is masked from 'package:base':
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## backsolve##
## groupGOTerms: GOBPTerm, GOMFTerm, GOCCTerm environments built.##
## Attaching package: 'topGO'
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## The following objects are masked from 'package:IRanges':
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## members, score, score<-library(limma)##
## Attaching package: 'limma'
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## The following object is masked from 'package:BiocGenerics':
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## plotMAlimmaTopGenes <- function(marray.lm, p.val = 0.05, direction = c("both", "up", "down")) {
result.table.allgenes <- topTableF(marray.lm, adjust="BH", number=length(marray.lm$genes[, 1]))
if (direction == "both") {
# Use all significant genes in analysis
# selected.genes <- result.table.allgenes[result.table.allgenes$adj.P.Val < p.val, ]
result.table.allgenes[abs(result.table.allgenes[,3]) < 0.75, ncol(result.table.allgenes)] <- 1
genes.pval <- setNames(result.table.allgenes$adj.P.Val, result.table.allgenes$ensembl_gene_id)
} else if (direction == "up") {
# Use the significant up-regulated genes in analysis, whereas the significant down-regulated genes get adjusted p-values of 1
result.table.allgenes[result.table.allgenes[,3] < 0, ncol(result.table.allgenes)] <- 1
# selected.genes <- result.table.allgenes[result.table.allgenes$adj.P.Val < p.val, ]
genes.pval <- setNames(result.table.allgenes$adj.P.Val, result.table.allgenes$ensembl_gene_id)
} else {
# Use the significant down-regulated genes in analysis, whereas the significant up-regulated genes get adjusted p-values of 1
result.table.allgenes[result.table.allgenes[,3] > 0, ncol(result.table.allgenes)] <- 1
# selected.genes <- result.table.allgenes[result.table.allgenes$adj.P.Val < p.val, ]
genes.pval <- setNames(result.table.allgenes$adj.P.Val, result.table.allgenes$ensembl_gene_id)
}
}
topDiffGenes <- function(allScore) {
return(allScore < 0.05)
}The code below will use the linear results from the earlier limma analysis and add GO terms and do the test of enrichment test using Fischer exact test as implemented in the topGO package. Please check the manual for more info on the created object.
# read in data from earlier analysis, but remove contrasts that iis within treatment and just over time.
load(file = "FitLM.rdata")
rm(fit.CeO2.time)
rm(fit.NAC.time)
rm(fit.SDC.time)
# note that this will analyse all significant, whereas dir = "up"/"down" will do the enrichment test for the up or downregulated genes sep. This will take care of cut-off for fold change and only retain genes where the absolute value of the foldchange is larger than 0.75.
CeO2.time1.pval.both <- limmaTopGenes(fit.CeO2.time1, dir="both")
go.MF.CeO2.time1 <- new("topGOdata", description="GO annotation CeO2 time 1", ontology="MF", allGenes = CeO2.time1.pval.both, geneSel = topDiffGenes, nodeSize = 10, annot = annFUN.org, mapping="org.Mm.eg.db", ID = "Ensembl")##
## Building most specific GOs .....## Loading required package: org.Mm.eg.db## ( 3874 GO terms found. )
##
## Build GO DAG topology .......... ( 4287 GO terms and 5292 relations. )
##
## Annotating nodes ............... ( 13656 genes annotated to the GO terms. )result.fischer.Ceo2.time1 <- runTest(go.MF.CeO2.time1, algorithm = "classic", statistic = "fisher")##
## -- Classic Algorithm --
##
## the algorithm is scoring 873 nontrivial nodes
## parameters:
## test statistic: fisherall.res.CeO2.time1 <- GenTable(go.MF.CeO2.time1, classic = result.fischer.Ceo2.time1, topNodes = 20)
all.res.CeO2.time1## GO.ID Term Annotated
## 1 GO:0005509 calcium ion binding 369
## 2 GO:0017154 semaphorin receptor activity 10
## 3 GO:0005198 structural molecule activity 336
## 4 GO:0030414 peptidase inhibitor activity 88
## 5 GO:0061134 peptidase regulator activity 117
## 6 GO:0005201 extracellular matrix structural constitu... 24
## 7 GO:0032403 protein complex binding 745
## 8 GO:0005102 receptor binding 874
## 9 GO:0005515 protein binding 5679
## 10 GO:0004857 enzyme inhibitor activity 193
## 11 GO:0004872 receptor activity 566
## 12 GO:0005507 copper ion binding 41
## 13 GO:0003735 structural constituent of ribosome 125
## 14 GO:0005178 integrin binding 69
## 15 GO:0038023 signaling receptor activity 449
## 16 GO:0004888 transmembrane signaling receptor activit... 388
## 17 GO:0035591 signaling adaptor activity 27
## 18 GO:0071889 14-3-3 protein binding 17
## 19 GO:0003774 motor activity 95
## 20 GO:0001948 glycoprotein binding 68
## Significant Expected classic
## 1 36 14.48 4.6e-07
## 2 5 0.39 2.0e-05
## 3 28 13.19 0.00015
## 4 12 3.45 0.00016
## 5 14 4.59 0.00019
## 6 6 0.94 0.00026
## 7 49 29.24 0.00026
## 8 55 34.30 0.00034
## 9 260 222.90 0.00057
## 10 18 7.58 0.00060
## 11 38 22.22 0.00088
## 12 7 1.61 0.00097
## 13 13 4.91 0.00125
## 14 9 2.71 0.00144
## 15 31 17.62 0.00167
## 16 27 15.23 0.00291
## 17 5 1.06 0.00360
## 18 4 0.67 0.00371
## 19 10 3.73 0.00405
## 20 8 2.67 0.00499showSigOfNodes(go.MF.CeO2.time1, score(result.fischer.Ceo2.time1), firstSigNodes = 10, useInfo = 'all')## Loading required package: Rgraphviz
## Loading required package: grid
##
## Attaching package: 'grid'
##
## The following object is masked from 'package:topGO':
##
## depth
## $dag
## A graphNEL graph with directed edges
## Number of Nodes = 21
## Number of Edges = 22
##
## $complete.dag
## [1] "A graph with 21 nodes."CeO2.time4.pval.both <- limmaTopGenes(fit.CeO2.time4, dir="both")
go.MF.CeO2.time4 <- new("topGOdata", description="GO annotation CeO2 time 4", ontology="MF", allGenes = CeO2.time4.pval.both, geneSel = topDiffGenes, nodeSize = 10, annot = annFUN.org, mapping="org.Mm.eg.db", ID = "Ensembl")##
## Building most specific GOs ..... ( 3874 GO terms found. )
##
## Build GO DAG topology .......... ( 4287 GO terms and 5292 relations. )
##
## Annotating nodes ............... ( 13656 genes annotated to the GO terms. )result.fischer.CeO2.time4 <- runTest(go.MF.CeO2.time4, algorithm = "classic", statistic = "fisher")##
## -- Classic Algorithm --
##
## the algorithm is scoring 680 nontrivial nodes
## parameters:
## test statistic: fisherallRes.CeO2.time4 <- GenTable(go.MF.CeO2.time4, classic = result.fischer.CeO2.time4, topNodes = 20)
allRes.CeO2.time4## GO.ID Term Annotated
## 1 GO:0004872 receptor activity 566
## 2 GO:0001968 fibronectin binding 24
## 3 GO:0004888 transmembrane signaling receptor activit... 388
## 4 GO:0004930 G-protein coupled receptor activity 183
## 5 GO:0038023 signaling receptor activity 449
## 6 GO:0022843 voltage-gated cation channel activity 81
## 7 GO:0008201 heparin binding 86
## 8 GO:0005244 voltage-gated ion channel activity 107
## 9 GO:0022832 voltage-gated channel activity 107
## 10 GO:0005515 protein binding 5679
## 11 GO:0005539 glycosaminoglycan binding 115
## 12 GO:0019838 growth factor binding 98
## 13 GO:0050839 cell adhesion molecule binding 121
## 14 GO:0004871 signal transducer activity 672
## 15 GO:0060089 molecular transducer activity 672
## 16 GO:0005509 calcium ion binding 369
## 17 GO:0005520 insulin-like growth factor binding 21
## 18 GO:0005178 integrin binding 69
## 19 GO:0016208 AMP binding 11
## 20 GO:0005261 cation channel activity 154
## Significant Expected classic
## 1 34 15.00 7.4e-06
## 2 6 0.64 3.0e-05
## 3 25 10.29 4.0e-05
## 4 15 4.85 0.00011
## 5 26 11.90 0.00016
## 6 9 2.15 0.00028
## 7 9 2.28 0.00045
## 8 10 2.84 0.00054
## 9 10 2.84 0.00054
## 10 181 150.54 0.00065
## 11 10 3.05 0.00096
## 12 9 2.60 0.00116
## 13 10 3.21 0.00141
## 14 31 17.81 0.00193
## 15 31 17.81 0.00193
## 16 20 9.78 0.00203
## 17 4 0.56 0.00203
## 18 7 1.83 0.00228
## 19 3 0.29 0.00260
## 20 11 4.08 0.00268showSigOfNodes(go.MF.CeO2.time4, score(result.fischer.CeO2.time4), firstSigNodes = 10, useInfo = 'all')
## $dag
## A graphNEL graph with directed edges
## Number of Nodes = 31
## Number of Edges = 39
##
## $complete.dag
## [1] "A graph with 31 nodes."CeO2.time7.pval.both <- limmaTopGenes(fit.CeO2.time7, dir="both")
go.MF.CeO2.time7 <- new("topGOdata", description="GO annotation CeO2 time 7", ontology="MF", allGenes = CeO2.time7.pval.both, geneSel = topDiffGenes, nodeSize = 10, annot = annFUN.org, mapping="org.Mm.eg.db", ID = "Ensembl")##
## Building most specific GOs ..... ( 3874 GO terms found. )
##
## Build GO DAG topology .......... ( 4287 GO terms and 5292 relations. )
##
## Annotating nodes ............... ( 13656 genes annotated to the GO terms. )result.fischer.CeO2.time7 <- runTest(go.MF.CeO2.time7, algorithm = "classic", statistic = "fisher")##
## -- Classic Algorithm --
##
## the algorithm is scoring 956 nontrivial nodes
## parameters:
## test statistic: fisherallRes.CeO2.time7 <- GenTable(go.MF.CeO2.time7, classic = result.fischer.CeO2.time7, topNodes = 20)
allRes.CeO2.time7## GO.ID Term Annotated
## 1 GO:0005515 protein binding 5679
## 2 GO:0004871 signal transducer activity 672
## 3 GO:0060089 molecular transducer activity 672
## 4 GO:0004872 receptor activity 566
## 5 GO:0038023 signaling receptor activity 449
## 6 GO:0005102 receptor binding 874
## 7 GO:0019838 growth factor binding 98
## 8 GO:0050431 transforming growth factor beta binding 15
## 9 GO:0008083 growth factor activity 82
## 10 GO:0004888 transmembrane signaling receptor activit... 388
## 11 GO:0005200 structural constituent of cytoskeleton 41
## 12 GO:0001078 RNA polymerase II core promoter proximal... 59
## 13 GO:0042802 identical protein binding 894
## 14 GO:0005516 calmodulin binding 122
## 15 GO:0008022 protein C-terminus binding 159
## 16 GO:0015081 sodium ion transmembrane transporter act... 62
## 17 GO:0047485 protein N-terminus binding 93
## 18 GO:0005539 glycosaminoglycan binding 115
## 19 GO:0004896 cytokine receptor activity 45
## 20 GO:0005509 calcium ion binding 369
## Significant Expected classic
## 1 409 318.97 1.1e-11
## 2 69 37.74 7.1e-07
## 3 69 37.74 7.1e-07
## 4 60 31.79 1.4e-06
## 5 48 25.22 1.3e-05
## 6 79 49.09 1.5e-05
## 7 17 5.50 2.9e-05
## 8 6 0.84 9.9e-05
## 9 14 4.61 0.00017
## 10 39 21.79 0.00030
## 11 9 2.30 0.00037
## 12 11 3.31 0.00038
## 13 74 50.21 0.00045
## 14 17 6.85 0.00046
## 15 20 8.93 0.00059
## 16 11 3.48 0.00060
## 17 14 5.22 0.00065
## 18 16 6.46 0.00068
## 19 9 2.53 0.00076
## 20 36 20.73 0.00088showSigOfNodes(go.MF.CeO2.time7, score(result.fischer.CeO2.time7), firstSigNodes = 10, useInfo = 'all')
## $dag
## A graphNEL graph with directed edges
## Number of Nodes = 13
## Number of Edges = 14
##
## $complete.dag
## [1] "A graph with 13 nodes."SDC.time1.pval.both <- limmaTopGenes(fit.SDC.time1, dir="both")
go.MF.SDC.time1 <- new("topGOdata", description="GO annotation SDC time 1", ontology="MF", allGenes = SDC.time1.pval.both, geneSel = topDiffGenes, nodeSize = 10, annot = annFUN.org, mapping="org.Mm.eg.db", ID = "Ensembl")##
## Building most specific GOs ..... ( 3874 GO terms found. )
##
## Build GO DAG topology .......... ( 4287 GO terms and 5292 relations. )
##
## Annotating nodes ............... ( 13656 genes annotated to the GO terms. )result.fischer.SDC.time1 <- runTest(go.MF.SDC.time1, algorithm = "classic", statistic = "fisher")##
## -- Classic Algorithm --
##
## the algorithm is scoring 678 nontrivial nodes
## parameters:
## test statistic: fisherall.res.SDC.time1 <- GenTable(go.MF.SDC.time1, classic = result.fischer.SDC.time1, topNodes = 20)
all.res.SDC.time1## GO.ID Term Annotated
## 1 GO:0005509 calcium ion binding 369
## 2 GO:0005507 copper ion binding 41
## 3 GO:0015078 hydrogen ion transmembrane transporter a... 64
## 4 GO:0036442 hydrogen-exporting ATPase activity 19
## 5 GO:0003735 structural constituent of ribosome 125
## 6 GO:0043236 laminin binding 20
## 7 GO:0022891 substrate-specific transmembrane transpo... 493
## 8 GO:0019829 cation-transporting ATPase activity 52
## 9 GO:0042625 ATPase activity, coupled to transmembran... 53
## 10 GO:0008553 hydrogen-exporting ATPase activity, phos... 11
## 11 GO:0001968 fibronectin binding 24
## 12 GO:0022857 transmembrane transporter activity 546
## 13 GO:0015075 ion transmembrane transporter activity 461
## 14 GO:0050840 extracellular matrix binding 40
## 15 GO:0022892 substrate-specific transporter activity 581
## 16 GO:0008324 cation transmembrane transporter activit... 351
## 17 GO:0022804 active transmembrane transporter activit... 193
## 18 GO:0008137 NADH dehydrogenase (ubiquinone) activity 26
## 19 GO:0050136 NADH dehydrogenase (quinone) activity 26
## 20 GO:0003954 NADH dehydrogenase activity 27
## Significant Expected classic
## 1 21 9.27 0.00043
## 2 6 1.03 0.00051
## 3 7 1.61 0.00108
## 4 4 0.48 0.00112
## 5 10 3.14 0.00121
## 6 4 0.50 0.00138
## 7 24 12.38 0.00155
## 8 6 1.31 0.00185
## 9 6 1.33 0.00204
## 10 3 0.28 0.00223
## 11 4 0.60 0.00279
## 12 25 13.71 0.00285
## 13 22 11.58 0.00306
## 14 5 1.00 0.00310
## 15 26 14.59 0.00321
## 16 18 8.82 0.00339
## 17 12 4.85 0.00353
## 18 4 0.65 0.00378
## 19 4 0.65 0.00378
## 20 4 0.68 0.00435showSigOfNodes(go.MF.SDC.time1, score(result.fischer.SDC.time1), firstSigNodes = 10, useInfo = 'all')
## $dag
## A graphNEL graph with directed edges
## Number of Nodes = 41
## Number of Edges = 49
##
## $complete.dag
## [1] "A graph with 41 nodes."SDC.time4.pval.both <- limmaTopGenes(fit.SDC.time4, dir="both")
go.MF.SDC.time4 <- new("topGOdata", description="GO annotation SDC time 4", ontology="MF", allGenes = SDC.time4.pval.both, geneSel = topDiffGenes, nodeSize = 10, annot = annFUN.org, mapping="org.Mm.eg.db", ID = "Ensembl")##
## Building most specific GOs ..... ( 3874 GO terms found. )
##
## Build GO DAG topology .......... ( 4287 GO terms and 5292 relations. )
##
## Annotating nodes ............... ( 13656 genes annotated to the GO terms. )result.fischer.SDC.time4 <- runTest(go.MF.SDC.time4, algorithm = "classic", statistic = "fisher")##
## -- Classic Algorithm --
##
## the algorithm is scoring 648 nontrivial nodes
## parameters:
## test statistic: fisherallRes.SDC.time4 <- GenTable(go.MF.SDC.time4, classic = result.fischer.SDC.time4, topNodes = 20)
allRes.SDC.time4## GO.ID Term Annotated
## 1 GO:0001968 fibronectin binding 24
## 2 GO:0005509 calcium ion binding 369
## 3 GO:0008083 growth factor activity 82
## 4 GO:0051183 vitamin transporter activity 13
## 5 GO:0043395 heparan sulfate proteoglycan binding 14
## 6 GO:0004872 receptor activity 566
## 7 GO:0005539 glycosaminoglycan binding 115
## 8 GO:0061134 peptidase regulator activity 117
## 9 GO:0030414 peptidase inhibitor activity 88
## 10 GO:0043394 proteoglycan binding 19
## 11 GO:0004930 G-protein coupled receptor activity 183
## 12 GO:0005179 hormone activity 39
## 13 GO:0005520 insulin-like growth factor binding 21
## 14 GO:0004620 phospholipase activity 69
## 15 GO:0005178 integrin binding 69
## 16 GO:0052689 carboxylic ester hydrolase activity 101
## 17 GO:0004867 serine-type endopeptidase inhibitor acti... 49
## 18 GO:0005044 scavenger receptor activity 26
## 19 GO:0005125 cytokine activity 78
## 20 GO:0016298 lipase activity 78
## Significant Expected classic
## 1 5 0.41 4.3e-05
## 2 17 6.24 0.00018
## 3 7 1.39 0.00047
## 4 3 0.22 0.00121
## 5 3 0.24 0.00152
## 6 20 9.57 0.00152
## 7 7 1.95 0.00338
## 8 7 1.98 0.00372
## 9 6 1.49 0.00375
## 10 3 0.32 0.00379
## 11 9 3.10 0.00397
## 12 4 0.66 0.00412
## 13 3 0.36 0.00507
## 14 5 1.17 0.00619
## 15 5 1.17 0.00619
## 16 6 1.71 0.00733
## 17 4 0.83 0.00932
## 18 3 0.44 0.00932
## 19 5 1.32 0.01030
## 20 5 1.32 0.01030showSigOfNodes(go.MF.SDC.time4, score(result.fischer.SDC.time4), firstSigNodes = 10, useInfo = 'all')
## $dag
## A graphNEL graph with directed edges
## Number of Nodes = 24
## Number of Edges = 27
##
## $complete.dag
## [1] "A graph with 24 nodes."SDC.time7.pval.both <- limmaTopGenes(fit.SDC.time7, dir="both")
go.MF.SDC.time7 <- new("topGOdata", description="GO annotation SDC time 7", ontology="MF", allGenes = SDC.time7.pval.both, geneSel = topDiffGenes, nodeSize = 10, annot = annFUN.org, mapping="org.Mm.eg.db", ID = "Ensembl")##
## Building most specific GOs ..... ( 3874 GO terms found. )
##
## Build GO DAG topology .......... ( 4287 GO terms and 5292 relations. )
##
## Annotating nodes ............... ( 13656 genes annotated to the GO terms. )result.fischer.SDC.time7 <- runTest(go.MF.SDC.time7, algorithm = "classic", statistic = "fisher")##
## -- Classic Algorithm --
##
## the algorithm is scoring 625 nontrivial nodes
## parameters:
## test statistic: fisherallRes.SDC.time7 <- GenTable(go.MF.SDC.time7, classic = result.fischer.SDC.time7, topNodes = 20)
allRes.SDC.time7## GO.ID Term Annotated
## 1 GO:0008083 growth factor activity 82
## 2 GO:0043565 sequence-specific DNA binding 525
## 3 GO:0001077 RNA polymerase II core promoter proximal... 115
## 4 GO:0001071 nucleic acid binding transcription facto... 655
## 5 GO:0003700 sequence-specific DNA binding transcript... 655
## 6 GO:0000982 RNA polymerase II core promoter proximal... 167
## 7 GO:0000977 RNA polymerase II regulatory region sequ... 229
## 8 GO:0070412 R-SMAD binding 20
## 9 GO:0001012 RNA polymerase II regulatory region DNA ... 235
## 10 GO:0001228 RNA polymerase II transcription regulato... 150
## 11 GO:0000981 sequence-specific DNA binding RNA polyme... 294
## 12 GO:0004872 receptor activity 566
## 13 GO:0005102 receptor binding 874
## 14 GO:0000975 regulatory region DNA binding 425
## 15 GO:0001067 regulatory region nucleic acid binding 425
## 16 GO:0000976 transcription regulatory region sequence... 271
## 17 GO:0019838 growth factor binding 98
## 18 GO:0016653 oxidoreductase activity, acting on NAD(P... 11
## 19 GO:0023023 MHC protein complex binding 11
## 20 GO:0023026 MHC class II protein complex binding 11
## Significant Expected classic
## 1 7 0.94 4.3e-05
## 2 16 6.04 0.00036
## 3 7 1.32 0.00036
## 4 18 7.53 0.00053
## 5 18 7.53 0.00053
## 6 8 1.92 0.00069
## 7 9 2.63 0.00133
## 8 3 0.23 0.00147
## 9 9 2.70 0.00158
## 10 7 1.72 0.00173
## 11 10 3.38 0.00213
## 12 15 6.51 0.00222
## 13 20 10.05 0.00245
## 14 12 4.89 0.00370
## 15 12 4.89 0.00370
## 16 9 3.12 0.00412
## 17 5 1.13 0.00543
## 18 2 0.13 0.00675
## 19 2 0.13 0.00675
## 20 2 0.13 0.00675showSigOfNodes(go.MF.SDC.time7, score(result.fischer.SDC.time7), firstSigNodes = 10, useInfo = 'all')
## $dag
## A graphNEL graph with directed edges
## Number of Nodes = 24
## Number of Edges = 28
##
## $complete.dag
## [1] "A graph with 24 nodes."NAC.time1.pval.both <- limmaTopGenes(fit.NAC.time1, dir="both")
go.MF.NAC.time1 <- new("topGOdata", description="GO annotation NAC time 1", ontology="MF", allGenes = NAC.time1.pval.both, geneSel = topDiffGenes, nodeSize = 10, annot = annFUN.org, mapping="org.Mm.eg.db", ID = "Ensembl")##
## Building most specific GOs ..... ( 3874 GO terms found. )
##
## Build GO DAG topology .......... ( 4287 GO terms and 5292 relations. )
##
## Annotating nodes ............... ( 13656 genes annotated to the GO terms. )result.fischer.NAC.time1 <- runTest(go.MF.NAC.time1, algorithm = "classic", statistic = "fisher")##
## -- Classic Algorithm --
##
## the algorithm is scoring 794 nontrivial nodes
## parameters:
## test statistic: fisherall.res.NAC.time1 <- GenTable(go.MF.NAC.time1, classic = result.fischer.NAC.time1, topNodes = 20)
all.res.NAC.time1## GO.ID Term Annotated
## 1 GO:0001968 fibronectin binding 24
## 2 GO:0005515 protein binding 5679
## 3 GO:0008201 heparin binding 86
## 4 GO:0002020 protease binding 72
## 5 GO:0005539 glycosaminoglycan binding 115
## 6 GO:0005201 extracellular matrix structural constitu... 24
## 7 GO:0005518 collagen binding 48
## 8 GO:0043236 laminin binding 20
## 9 GO:1901681 sulfur compound binding 148
## 10 GO:0019838 growth factor binding 98
## 11 GO:0008083 growth factor activity 82
## 12 GO:0004866 endopeptidase inhibitor activity 83
## 13 GO:0001071 nucleic acid binding transcription facto... 655
## 14 GO:0003700 sequence-specific DNA binding transcript... 655
## 15 GO:0038024 cargo receptor activity 39
## 16 GO:0050840 extracellular matrix binding 40
## 17 GO:0003690 double-stranded DNA binding 102
## 18 GO:0030414 peptidase inhibitor activity 88
## 19 GO:0061135 endopeptidase regulator activity 89
## 20 GO:0022843 voltage-gated cation channel activity 81
## Significant Expected classic
## 1 9 0.78 3.2e-08
## 2 234 185.06 1.3e-06
## 3 13 2.80 3.9e-06
## 4 11 2.35 2.0e-05
## 5 13 3.75 9.3e-05
## 6 6 0.78 9.5e-05
## 7 8 1.56 0.00014
## 8 5 0.65 0.00037
## 9 13 4.82 0.00110
## 10 10 3.19 0.00132
## 11 9 2.67 0.00136
## 12 9 2.70 0.00148
## 13 36 21.34 0.00151
## 14 36 21.34 0.00151
## 15 6 1.27 0.00152
## 16 6 1.30 0.00173
## 17 10 3.32 0.00179
## 18 9 2.87 0.00224
## 19 9 2.90 0.00242
## 20 8 2.64 0.00480showSigOfNodes(go.MF.NAC.time1, score(result.fischer.NAC.time1), firstSigNodes = 10, useInfo = 'all')
## $dag
## A graphNEL graph with directed edges
## Number of Nodes = 19
## Number of Edges = 21
##
## $complete.dag
## [1] "A graph with 19 nodes."NAC.time4.pval.both <- limmaTopGenes(fit.NAC.time4, dir="both")
go.MF.NAC.time4 <- new("topGOdata", description="GO annotation NAC time 4", ontology="MF", allGenes = NAC.time4.pval.both, geneSel = topDiffGenes, nodeSize = 10, annot = annFUN.org, mapping="org.Mm.eg.db", ID = "Ensembl")##
## Building most specific GOs ..... ( 3874 GO terms found. )
##
## Build GO DAG topology .......... ( 4287 GO terms and 5292 relations. )
##
## Annotating nodes ............... ( 13656 genes annotated to the GO terms. )result.fischer.NAC.time4 <- runTest(go.MF.NAC.time4, algorithm = "classic", statistic = "fisher")##
## -- Classic Algorithm --
##
## the algorithm is scoring 524 nontrivial nodes
## parameters:
## test statistic: fisherallRes.NAC.time4 <- GenTable(go.MF.NAC.time4, classic = result.fischer.NAC.time4, topNodes = 20)
allRes.NAC.time4## GO.ID Term Annotated
## 1 GO:0003690 double-stranded DNA binding 102
## 2 GO:0005520 insulin-like growth factor binding 21
## 3 GO:0008083 growth factor activity 82
## 4 GO:0004866 endopeptidase inhibitor activity 83
## 5 GO:0008201 heparin binding 86
## 6 GO:0030414 peptidase inhibitor activity 88
## 7 GO:0061135 endopeptidase regulator activity 89
## 8 GO:0005539 glycosaminoglycan binding 115
## 9 GO:0004867 serine-type endopeptidase inhibitor acti... 49
## 10 GO:1901681 sulfur compound binding 148
## 11 GO:0019838 growth factor binding 98
## 12 GO:0017017 MAP kinase tyrosine/serine/threonine pho... 36
## 13 GO:0033549 MAP kinase phosphatase activity 37
## 14 GO:0043566 structure-specific DNA binding 224
## 15 GO:0048156 tau protein binding 11
## 16 GO:0005515 protein binding 5679
## 17 GO:0061134 peptidase regulator activity 117
## 18 GO:0004857 enzyme inhibitor activity 193
## 19 GO:0019955 cytokine binding 56
## 20 GO:0071889 14-3-3 protein binding 17
## Significant Expected classic
## 1 10 1.79 1.2e-05
## 2 5 0.37 2.6e-05
## 3 8 1.44 9.4e-05
## 4 8 1.46 0.00010
## 5 8 1.51 0.00013
## 6 8 1.55 0.00016
## 7 8 1.56 0.00017
## 8 9 2.02 0.00019
## 9 6 0.86 0.00021
## 10 10 2.60 0.00029
## 11 8 1.72 0.00033
## 12 5 0.63 0.00039
## 13 5 0.65 0.00044
## 14 12 3.94 0.00061
## 15 3 0.19 0.00080
## 16 124 99.81 0.00094
## 17 8 2.06 0.00107
## 18 10 3.39 0.00220
## 19 5 0.98 0.00296
## 20 3 0.30 0.00304showSigOfNodes(go.MF.NAC.time4, score(result.fischer.NAC.time4), firstSigNodes = 10, useInfo = 'all')
## $dag
## A graphNEL graph with directed edges
## Number of Nodes = 26
## Number of Edges = 30
##
## $complete.dag
## [1] "A graph with 26 nodes."NAC.time7.pval.both <- limmaTopGenes(fit.NAC.time7, dir="both")
go.MF.NAC.time7 <- new("topGOdata", description="GO annotation NAC time 7", ontology="MF", allGenes = NAC.time7.pval.both, geneSel = topDiffGenes, nodeSize = 10, annot = annFUN.org, mapping="org.Mm.eg.db", ID = "Ensembl")##
## Building most specific GOs ..... ( 3874 GO terms found. )
##
## Build GO DAG topology .......... ( 4287 GO terms and 5292 relations. )
##
## Annotating nodes ............... ( 13656 genes annotated to the GO terms. )result.fischer.NAC.time7 <- runTest(go.MF.NAC.time7, algorithm = "classic", statistic = "fisher")##
## -- Classic Algorithm --
##
## the algorithm is scoring 787 nontrivial nodes
## parameters:
## test statistic: fisherallRes.NAC.time7 <- GenTable(go.MF.NAC.time7, classic = result.fischer.NAC.time7, topNodes = 20)
allRes.NAC.time7## GO.ID Term Annotated
## 1 GO:0004872 receptor activity 566
## 2 GO:0050839 cell adhesion molecule binding 121
## 3 GO:0001968 fibronectin binding 24
## 4 GO:0015081 sodium ion transmembrane transporter act... 62
## 5 GO:0005178 integrin binding 69
## 6 GO:0019838 growth factor binding 98
## 7 GO:0008201 heparin binding 86
## 8 GO:0015370 solute:sodium symporter activity 22
## 9 GO:0050840 extracellular matrix binding 40
## 10 GO:0004888 transmembrane signaling receptor activit... 388
## 11 GO:0005539 glycosaminoglycan binding 115
## 12 GO:1901681 sulfur compound binding 148
## 13 GO:0015075 ion transmembrane transporter activity 461
## 14 GO:0015291 secondary active transmembrane transport... 110
## 15 GO:0022891 substrate-specific transmembrane transpo... 493
## 16 GO:0005102 receptor binding 874
## 17 GO:0022857 transmembrane transporter activity 546
## 18 GO:0038023 signaling receptor activity 449
## 19 GO:0005515 protein binding 5679
## 20 GO:0005231 excitatory extracellular ligand-gated io... 21
## Significant Expected classic
## 1 43 18.57 2.5e-07
## 2 17 3.97 4.2e-07
## 3 8 0.79 5.8e-07
## 4 12 2.03 6.5e-07
## 5 12 2.26 2.2e-06
## 6 14 3.21 3.6e-06
## 7 13 2.82 4.2e-06
## 8 7 0.72 4.3e-06
## 9 9 1.31 4.5e-06
## 10 31 12.73 4.6e-06
## 11 15 3.77 5.1e-06
## 12 17 4.86 7.1e-06
## 13 34 15.12 9.0e-06
## 14 14 3.61 1.4e-05
## 15 35 16.17 1.5e-05
## 16 52 28.67 2.1e-05
## 17 37 17.91 2.4e-05
## 18 32 14.73 3.3e-05
## 19 228 186.31 3.3e-05
## 20 6 0.69 4.3e-05showSigOfNodes(go.MF.NAC.time7, score(result.fischer.NAC.time7), firstSigNodes = 10, useInfo = 'all')
## $dag
## A graphNEL graph with directed edges
## Number of Nodes = 36
## Number of Edges = 44
##
## $complete.dag
## [1] "A graph with 36 nodes."