TopGO_BPanalysis
Thomas Källman
28 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 BP 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
##
## 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
##
## 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: 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.BP.CeO2.time1 <- new("topGOdata", description="GO annotation CeO2 time 1", ontology="BP", 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## ( 8852 GO terms found. )
##
## Build GO DAG topology .......... ( 12170 GO terms and 28213 relations. )
##
## Annotating nodes ............... ( 13785 genes annotated to the GO terms. )result.fischer.Ceo2.time1 <- runTest(go.BP.CeO2.time1, algorithm = "classic", statistic = "fisher")##
## -- Classic Algorithm --
##
## the algorithm is scoring 3490 nontrivial nodes
## parameters:
## test statistic: fisherall.res.CeO2.time1 <- GenTable(go.BP.CeO2.time1, classic = result.fischer.Ceo2.time1, topNodes = 20)
all.res.CeO2.time1## GO.ID Term Annotated
## 1 GO:0007155 cell adhesion 697
## 2 GO:0022610 biological adhesion 702
## 3 GO:0048731 system development 2673
## 4 GO:0048468 cell development 1351
## 5 GO:0030154 cell differentiation 2359
## 6 GO:0009888 tissue development 1209
## 7 GO:0000904 cell morphogenesis involved in different... 506
## 8 GO:0006928 cellular component movement 1044
## 9 GO:0044707 single-multicellular organism process 3827
## 10 GO:0032501 multicellular organismal process 3926
## 11 GO:0007166 cell surface receptor signaling pathway 1632
## 12 GO:0048513 organ development 1970
## 13 GO:2000026 regulation of multicellular organismal d... 1072
## 14 GO:0048666 neuron development 641
## 15 GO:0031175 neuron projection development 555
## 16 GO:0007156 homophilic cell adhesion 52
## 17 GO:0022008 neurogenesis 944
## 18 GO:0030182 neuron differentiation 793
## 19 GO:0007167 enzyme linked receptor protein signaling... 541
## 20 GO:0007399 nervous system development 1290
## Significant Expected classic
## 1 59 27.86 3.4e-08
## 2 59 28.06 4.4e-08
## 3 154 106.84 4.4e-07
## 4 89 54.00 1.3e-06
## 5 137 94.29 1.6e-06
## 6 81 48.32 2.2e-06
## 7 43 20.23 2.5e-06
## 8 72 41.73 3.2e-06
## 9 201 152.97 3.4e-06
## 10 205 156.93 3.8e-06
## 11 101 65.23 3.9e-06
## 12 117 78.74 4.2e-06
## 13 73 42.85 4.3e-06
## 14 50 25.62 4.4e-06
## 15 45 22.18 5.0e-06
## 16 11 2.08 5.1e-06
## 17 66 37.73 5.5e-06
## 18 58 31.70 5.5e-06
## 19 44 21.62 5.9e-06
## 20 83 51.56 7.9e-06showSigOfNodes(go.BP.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 = 24
## Number of Edges = 35
##
## $complete.dag
## [1] "A graph with 24 nodes."CeO2.time4.pval.both <- limmaTopGenes(fit.CeO2.time4, dir="both")
go.BP.CeO2.time4 <- new("topGOdata", description="GO annotation CeO2 time 4", ontology="BP", allGenes = CeO2.time4.pval.both, geneSel = topDiffGenes, nodeSize = 10, annot = annFUN.org, mapping="org.Mm.eg.db", ID = "Ensembl")##
## Building most specific GOs ..... ( 8852 GO terms found. )
##
## Build GO DAG topology .......... ( 12170 GO terms and 28213 relations. )
##
## Annotating nodes ............... ( 13785 genes annotated to the GO terms. )result.fischer.CeO2.time4 <- runTest(go.BP.CeO2.time4, algorithm = "classic", statistic = "fisher")##
## -- Classic Algorithm --
##
## the algorithm is scoring 3106 nontrivial nodes
## parameters:
## test statistic: fisherallRes.CeO2.time4 <- GenTable(go.BP.CeO2.time4, classic = result.fischer.CeO2.time4, topNodes = 20)
allRes.CeO2.time4## GO.ID Term Annotated
## 1 GO:0044707 single-multicellular organism process 3827
## 2 GO:0051239 regulation of multicellular organismal p... 1592
## 3 GO:0032501 multicellular organismal process 3926
## 4 GO:0023052 signaling 3366
## 5 GO:0044700 single organism signaling 3366
## 6 GO:0007165 signal transduction 3060
## 7 GO:0007154 cell communication 3437
## 8 GO:0007166 cell surface receptor signaling pathway 1632
## 9 GO:0003008 system process 880
## 10 GO:0001503 ossification 293
## 11 GO:0048731 system development 2673
## 12 GO:0009888 tissue development 1209
## 13 GO:0050896 response to stimulus 4726
## 14 GO:0009653 anatomical structure morphogenesis 1689
## 15 GO:0022612 gland morphogenesis 95
## 16 GO:0051716 cellular response to stimulus 3869
## 17 GO:0050793 regulation of developmental process 1395
## 18 GO:0030509 BMP signaling pathway 82
## 19 GO:0048583 regulation of response to stimulus 2131
## 20 GO:0060429 epithelium development 711
## Significant Expected classic
## 1 156 103.27 1.8e-09
## 2 81 42.96 9.0e-09
## 3 156 105.95 1.2e-08
## 4 138 90.83 2.3e-08
## 5 138 90.83 2.3e-08
## 6 128 82.58 3.2e-08
## 7 139 92.75 4.9e-08
## 8 80 44.04 5.9e-08
## 9 52 23.75 7.0e-08
## 10 26 7.91 1.0e-07
## 11 112 72.13 3.4e-07
## 12 62 32.63 5.5e-07
## 13 172 127.54 9.1e-07
## 14 78 45.58 1.0e-06
## 15 13 2.56 1.5e-06
## 16 146 104.41 1.6e-06
## 17 67 37.65 1.9e-06
## 18 12 2.21 1.9e-06
## 19 91 57.51 3.1e-06
## 20 41 19.19 3.6e-06showSigOfNodes(go.BP.CeO2.time4, score(result.fischer.CeO2.time4), firstSigNodes = 10, useInfo = 'all')
## $dag
## A graphNEL graph with directed edges
## Number of Nodes = 19
## Number of Edges = 27
##
## $complete.dag
## [1] "A graph with 19 nodes."CeO2.time7.pval.both <- limmaTopGenes(fit.CeO2.time7, dir="both")
go.BP.CeO2.time7 <- new("topGOdata", description="GO annotation CeO2 time 7", ontology="BP", allGenes = CeO2.time7.pval.both, geneSel = topDiffGenes, nodeSize = 10, annot = annFUN.org, mapping="org.Mm.eg.db", ID = "Ensembl")##
## Building most specific GOs ..... ( 8852 GO terms found. )
##
## Build GO DAG topology .......... ( 12170 GO terms and 28213 relations. )
##
## Annotating nodes ............... ( 13785 genes annotated to the GO terms. )result.fischer.CeO2.time7 <- runTest(go.BP.CeO2.time7, algorithm = "classic", statistic = "fisher")##
## -- Classic Algorithm --
##
## the algorithm is scoring 3761 nontrivial nodes
## parameters:
## test statistic: fisherallRes.CeO2.time7 <- GenTable(go.BP.CeO2.time7, classic = result.fischer.CeO2.time7, topNodes = 20)
allRes.CeO2.time7## GO.ID Term Annotated
## 1 GO:0044707 single-multicellular organism process 3827
## 2 GO:0032501 multicellular organismal process 3926
## 3 GO:0048731 system development 2673
## 4 GO:0048856 anatomical structure development 3206
## 5 GO:0007275 multicellular organismal development 3069
## 6 GO:0044699 single-organism process 8653
## 7 GO:0007154 cell communication 3437
## 8 GO:0032502 developmental process 3587
## 9 GO:0048513 organ development 1970
## 10 GO:0030154 cell differentiation 2359
## 11 GO:0044767 single-organism developmental process 3564
## 12 GO:0023052 signaling 3366
## 13 GO:0044700 single organism signaling 3366
## 14 GO:0007165 signal transduction 3060
## 15 GO:0046903 secretion 574
## 16 GO:0009653 anatomical structure morphogenesis 1689
## 17 GO:0051239 regulation of multicellular organismal p... 1592
## 18 GO:0050896 response to stimulus 4726
## 19 GO:0007166 cell surface receptor signaling pathway 1632
## 20 GO:0048869 cellular developmental process 2536
## Significant Expected classic
## 1 297 212.66 7.3e-12
## 2 301 218.16 2.2e-11
## 3 220 148.53 9.4e-11
## 4 248 178.15 1.9e-09
## 5 237 170.54 6.4e-09
## 6 552 480.83 1.5e-08
## 7 257 190.99 2.3e-08
## 8 266 199.32 2.5e-08
## 9 164 109.47 2.6e-08
## 10 189 131.08 3.0e-08
## 11 264 198.04 3.2e-08
## 12 251 187.04 4.9e-08
## 13 251 187.04 4.9e-08
## 14 232 170.04 5.3e-08
## 15 64 31.90 6.7e-08
## 16 143 93.85 9.4e-08
## 17 136 88.46 1.2e-07
## 18 330 262.61 1.2e-07
## 19 138 90.69 1.8e-07
## 20 196 140.92 2.4e-07showSigOfNodes(go.BP.CeO2.time7, score(result.fischer.CeO2.time7), firstSigNodes = 10, useInfo = 'all')
## $dag
## A graphNEL graph with directed edges
## Number of Nodes = 15
## Number of Edges = 21
##
## $complete.dag
## [1] "A graph with 15 nodes."SDC.time1.pval.both <- limmaTopGenes(fit.SDC.time1, dir="both")
go.BP.SDC.time1 <- new("topGOdata", description="GO annotation SDC time 1", ontology="BP", allGenes = SDC.time1.pval.both, geneSel = topDiffGenes, nodeSize = 10, annot = annFUN.org, mapping="org.Mm.eg.db", ID = "Ensembl")##
## Building most specific GOs ..... ( 8852 GO terms found. )
##
## Build GO DAG topology .......... ( 12170 GO terms and 28213 relations. )
##
## Annotating nodes ............... ( 13785 genes annotated to the GO terms. )result.fischer.SDC.time1 <- runTest(go.BP.SDC.time1, algorithm = "classic", statistic = "fisher")##
## -- Classic Algorithm --
##
## the algorithm is scoring 2857 nontrivial nodes
## parameters:
## test statistic: fisherall.res.SDC.time1 <- GenTable(go.BP.SDC.time1, classic = result.fischer.SDC.time1, topNodes = 20)
all.res.SDC.time1## GO.ID Term Annotated
## 1 GO:0006119 oxidative phosphorylation 31
## 2 GO:0060740 prostate gland epithelium morphogenesis 25
## 3 GO:0060512 prostate gland morphogenesis 26
## 4 GO:0006935 chemotaxis 291
## 5 GO:0010035 response to inorganic substance 219
## 6 GO:0042330 taxis 292
## 7 GO:0030850 prostate gland development 40
## 8 GO:0071621 granulocyte chemotaxis 40
## 9 GO:0071241 cellular response to inorganic substance 58
## 10 GO:0022612 gland morphogenesis 95
## 11 GO:0097530 granulocyte migration 43
## 12 GO:0060560 developmental growth involved in morphog... 137
## 13 GO:0048589 developmental growth 254
## 14 GO:0007155 cell adhesion 697
## 15 GO:0022610 biological adhesion 702
## 16 GO:0050921 positive regulation of chemotaxis 62
## 17 GO:0050795 regulation of behavior 123
## 18 GO:0030593 neutrophil chemotaxis 32
## 19 GO:1990266 neutrophil migration 32
## 20 GO:0097529 myeloid leukocyte migration 66
## Significant Expected classic
## 1 6 0.78 0.00011
## 2 5 0.63 0.00034
## 3 5 0.65 0.00042
## 4 18 7.33 0.00042
## 5 15 5.51 0.00044
## 6 18 7.35 0.00044
## 7 6 1.01 0.00045
## 8 6 1.01 0.00045
## 9 7 1.46 0.00060
## 10 9 2.39 0.00064
## 11 6 1.08 0.00068
## 12 11 3.45 0.00069
## 13 16 6.39 0.00072
## 14 32 17.55 0.00076
## 15 32 17.67 0.00085
## 16 7 1.56 0.00090
## 17 10 3.10 0.00109
## 18 5 0.81 0.00113
## 19 5 0.81 0.00113
## 20 7 1.66 0.00131showSigOfNodes(go.BP.SDC.time1, score(result.fischer.SDC.time1), firstSigNodes = 10, useInfo = 'all')
## $dag
## A graphNEL graph with directed edges
## Number of Nodes = 93
## Number of Edges = 168
##
## $complete.dag
## [1] "A graph with 93 nodes."SDC.time4.pval.both <- limmaTopGenes(fit.SDC.time4, dir="both")
go.BP.SDC.time4 <- new("topGOdata", description="GO annotation SDC time 4", ontology="BP", allGenes = SDC.time4.pval.both, geneSel = topDiffGenes, nodeSize = 10, annot = annFUN.org, mapping="org.Mm.eg.db", ID = "Ensembl")##
## Building most specific GOs ..... ( 8852 GO terms found. )
##
## Build GO DAG topology .......... ( 12170 GO terms and 28213 relations. )
##
## Annotating nodes ............... ( 13785 genes annotated to the GO terms. )result.fischer.SDC.time4 <- runTest(go.BP.SDC.time4, algorithm = "classic", statistic = "fisher")##
## -- Classic Algorithm --
##
## the algorithm is scoring 2925 nontrivial nodes
## parameters:
## test statistic: fisherallRes.SDC.time4 <- GenTable(go.BP.SDC.time4, classic = result.fischer.SDC.time4, topNodes = 20)
allRes.SDC.time4## GO.ID Term Annotated
## 1 GO:0003008 system process 880
## 2 GO:0009888 tissue development 1209
## 3 GO:0061448 connective tissue development 160
## 4 GO:0044057 regulation of system process 255
## 5 GO:0051239 regulation of multicellular organismal p... 1592
## 6 GO:0044707 single-multicellular organism process 3827
## 7 GO:0060429 epithelium development 711
## 8 GO:0006936 muscle contraction 148
## 9 GO:0048729 tissue morphogenesis 426
## 10 GO:0009653 anatomical structure morphogenesis 1689
## 11 GO:0090257 regulation of muscle system process 110
## 12 GO:0043270 positive regulation of ion transport 116
## 13 GO:0032501 multicellular organismal process 3926
## 14 GO:0006937 regulation of muscle contraction 77
## 15 GO:0001503 ossification 293
## 16 GO:0003012 muscle system process 188
## 17 GO:0009605 response to external stimulus 1027
## 18 GO:0001656 metanephros development 51
## 19 GO:0051480 cytosolic calcium ion homeostasis 129
## 20 GO:0043269 regulation of ion transport 348
## Significant Expected classic
## 1 42 15.26 1.7e-09
## 2 50 20.96 4.7e-09
## 3 16 2.77 1.8e-08
## 4 19 4.42 9.9e-08
## 5 56 27.60 1.4e-07
## 6 103 66.35 2.1e-07
## 7 33 12.33 2.2e-07
## 8 14 2.57 2.8e-07
## 9 24 7.39 3.9e-07
## 10 57 29.28 4.3e-07
## 11 12 1.91 4.4e-07
## 12 12 2.01 7.8e-07
## 13 103 68.07 8.0e-07
## 14 10 1.34 8.1e-07
## 15 19 5.08 8.5e-07
## 16 15 3.26 9.7e-07
## 17 40 17.81 1.0e-06
## 18 8 0.88 2.4e-06
## 19 12 2.24 2.4e-06
## 20 20 6.03 2.8e-06showSigOfNodes(go.BP.SDC.time4, score(result.fischer.SDC.time4), firstSigNodes = 10, useInfo = 'all')
## $dag
## A graphNEL graph with directed edges
## Number of Nodes = 19
## Number of Edges = 25
##
## $complete.dag
## [1] "A graph with 19 nodes."SDC.time7.pval.both <- limmaTopGenes(fit.SDC.time7, dir="both")
go.BP.SDC.time7 <- new("topGOdata", description="GO annotation SDC time 7", ontology="BP", allGenes = SDC.time7.pval.both, geneSel = topDiffGenes, nodeSize = 10, annot = annFUN.org, mapping="org.Mm.eg.db", ID = "Ensembl")##
## Building most specific GOs ..... ( 8852 GO terms found. )
##
## Build GO DAG topology .......... ( 12170 GO terms and 28213 relations. )
##
## Annotating nodes ............... ( 13785 genes annotated to the GO terms. )result.fischer.SDC.time7 <- runTest(go.BP.SDC.time7, algorithm = "classic", statistic = "fisher")##
## -- Classic Algorithm --
##
## the algorithm is scoring 2346 nontrivial nodes
## parameters:
## test statistic: fisherallRes.SDC.time7 <- GenTable(go.BP.SDC.time7, classic = result.fischer.SDC.time7, topNodes = 20)
allRes.SDC.time7## GO.ID Term Annotated
## 1 GO:0051239 regulation of multicellular organismal p... 1592
## 2 GO:0010628 positive regulation of gene expression 1014
## 3 GO:0040011 locomotion 935
## 4 GO:0042221 response to chemical 1795
## 5 GO:0008284 positive regulation of cell proliferatio... 531
## 6 GO:0009888 tissue development 1209
## 7 GO:0030154 cell differentiation 2359
## 8 GO:0035914 skeletal muscle cell differentiation 55
## 9 GO:0007155 cell adhesion 697
## 10 GO:0022610 biological adhesion 702
## 11 GO:0016337 single organismal cell-cell adhesion 230
## 12 GO:0008283 cell proliferation 1184
## 13 GO:0042127 regulation of cell proliferation 947
## 14 GO:0048870 cell motility 784
## 15 GO:0051674 localization of cell 784
## 16 GO:0006836 neurotransmitter transport 93
## 17 GO:0048145 regulation of fibroblast proliferation 66
## 18 GO:0048144 fibroblast proliferation 67
## 19 GO:0006928 cellular component movement 1044
## 20 GO:0048731 system development 2673
## Significant Expected classic
## 1 40 18.25 1.1e-06
## 2 29 11.62 3.8e-06
## 3 27 10.72 7.3e-06
## 4 41 20.57 8.4e-06
## 5 19 6.09 1.1e-05
## 6 31 13.86 1.6e-05
## 7 48 27.04 2.6e-05
## 8 6 0.63 3.8e-05
## 9 21 7.99 4.7e-05
## 10 21 8.05 5.2e-05
## 11 11 2.64 6.9e-05
## 12 29 13.57 7.0e-05
## 13 25 10.85 7.4e-05
## 14 22 8.99 8.7e-05
## 15 22 8.99 8.7e-05
## 16 7 1.07 9.4e-05
## 17 6 0.76 0.00011
## 18 6 0.77 0.00012
## 19 26 11.97 0.00014
## 20 50 30.64 0.00016showSigOfNodes(go.BP.SDC.time7, score(result.fischer.SDC.time7), firstSigNodes = 10, useInfo = 'all')
## $dag
## A graphNEL graph with directed edges
## Number of Nodes = 46
## Number of Edges = 73
##
## $complete.dag
## [1] "A graph with 46 nodes."NAC.time1.pval.both <- limmaTopGenes(fit.NAC.time1, dir="both")
go.BP.NAC.time1 <- new("topGOdata", description="GO annotation NAC time 1", ontology="BP", allGenes = NAC.time1.pval.both, geneSel = topDiffGenes, nodeSize = 10, annot = annFUN.org, mapping="org.Mm.eg.db", ID = "Ensembl")##
## Building most specific GOs ..... ( 8852 GO terms found. )
##
## Build GO DAG topology .......... ( 12170 GO terms and 28213 relations. )
##
## Annotating nodes ............... ( 13785 genes annotated to the GO terms. )result.fischer.NAC.time1 <- runTest(go.BP.NAC.time1, algorithm = "classic", statistic = "fisher")##
## -- Classic Algorithm --
##
## the algorithm is scoring 3484 nontrivial nodes
## parameters:
## test statistic: fisherall.res.NAC.time1 <- GenTable(go.BP.NAC.time1, classic = result.fischer.NAC.time1, topNodes = 20)
all.res.NAC.time1## GO.ID Term Annotated
## 1 GO:0044707 single-multicellular organism process 3827
## 2 GO:0032501 multicellular organismal process 3926
## 3 GO:0051239 regulation of multicellular organismal p... 1592
## 4 GO:0032502 developmental process 3587
## 5 GO:0048731 system development 2673
## 6 GO:0009888 tissue development 1209
## 7 GO:0044767 single-organism developmental process 3564
## 8 GO:0007275 multicellular organismal development 3069
## 9 GO:0050793 regulation of developmental process 1395
## 10 GO:0048856 anatomical structure development 3206
## 11 GO:0003008 system process 880
## 12 GO:0048513 organ development 1970
## 13 GO:2000026 regulation of multicellular organismal d... 1072
## 14 GO:0009653 anatomical structure morphogenesis 1689
## 15 GO:0050896 response to stimulus 4726
## 16 GO:0030154 cell differentiation 2359
## 17 GO:0045595 regulation of cell differentiation 1015
## 18 GO:0010941 regulation of cell death 1038
## 19 GO:0042221 response to chemical 1795
## 20 GO:0051094 positive regulation of developmental pro... 649
## Significant Expected classic
## 1 199 123.82 7.8e-15
## 2 199 127.02 1.2e-13
## 3 106 51.51 1.3e-13
## 4 184 116.05 7.8e-13
## 5 149 86.48 8.1e-13
## 6 86 39.12 1.5e-12
## 7 182 115.31 1.8e-12
## 8 161 99.29 1.0e-11
## 9 92 45.13 1.5e-11
## 10 164 103.73 4.6e-11
## 11 66 28.47 9.9e-11
## 12 113 63.74 2.8e-10
## 13 74 34.68 2.9e-10
## 14 100 54.65 7.7e-10
## 15 214 152.91 9.3e-10
## 16 127 76.32 9.4e-10
## 17 70 32.84 1.0e-09
## 18 71 33.58 1.1e-09
## 19 104 58.08 1.1e-09
## 20 52 21.00 1.3e-09showSigOfNodes(go.BP.NAC.time1, score(result.fischer.NAC.time1), firstSigNodes = 10, useInfo = 'all')
## $dag
## A graphNEL graph with directed edges
## Number of Nodes = 14
## Number of Edges = 19
##
## $complete.dag
## [1] "A graph with 14 nodes."NAC.time4.pval.both <- limmaTopGenes(fit.NAC.time4, dir="both")
go.BP.NAC.time4 <- new("topGOdata", description="GO annotation NAC time 4", ontology="BP", allGenes = NAC.time4.pval.both, geneSel = topDiffGenes, nodeSize = 10, annot = annFUN.org, mapping="org.Mm.eg.db", ID = "Ensembl")##
## Building most specific GOs ..... ( 8852 GO terms found. )
##
## Build GO DAG topology .......... ( 12170 GO terms and 28213 relations. )
##
## Annotating nodes ............... ( 13785 genes annotated to the GO terms. )result.fischer.NAC.time4 <- runTest(go.BP.NAC.time4, algorithm = "classic", statistic = "fisher")##
## -- Classic Algorithm --
##
## the algorithm is scoring 3094 nontrivial nodes
## parameters:
## test statistic: fisherallRes.NAC.time4 <- GenTable(go.BP.NAC.time4, classic = result.fischer.NAC.time4, topNodes = 20)
allRes.NAC.time4## GO.ID Term Annotated
## 1 GO:0050793 regulation of developmental process 1395
## 2 GO:0051239 regulation of multicellular organismal p... 1592
## 3 GO:2000026 regulation of multicellular organismal d... 1072
## 4 GO:0044707 single-multicellular organism process 3827
## 5 GO:0048731 system development 2673
## 6 GO:0032501 multicellular organismal process 3926
## 7 GO:0009888 tissue development 1209
## 8 GO:0007275 multicellular organismal development 3069
## 9 GO:0045595 regulation of cell differentiation 1015
## 10 GO:0048856 anatomical structure development 3206
## 11 GO:0070887 cellular response to chemical stimulus 1205
## 12 GO:0009605 response to external stimulus 1027
## 13 GO:0051093 negative regulation of developmental pro... 513
## 14 GO:0003008 system process 880
## 15 GO:1901342 regulation of vasculature development 140
## 16 GO:0048514 blood vessel morphogenesis 364
## 17 GO:0048519 negative regulation of biological proces... 2851
## 18 GO:0030154 cell differentiation 2359
## 19 GO:0032502 developmental process 3587
## 20 GO:0051094 positive regulation of developmental pro... 649
## Significant Expected classic
## 1 64 24.79 5.1e-13
## 2 69 28.29 7.1e-13
## 3 54 19.05 1.5e-12
## 4 118 68.02 6.4e-12
## 5 93 47.51 7.4e-12
## 6 118 69.78 3.9e-11
## 7 54 21.49 1.5e-10
## 8 98 54.55 2.4e-10
## 9 48 18.04 3.0e-10
## 10 100 56.98 5.3e-10
## 11 52 21.42 1.2e-09
## 12 47 18.25 1.4e-09
## 13 31 9.12 2.4e-09
## 14 42 15.64 3.6e-09
## 15 16 2.49 3.7e-09
## 16 25 6.47 7.5e-09
## 17 89 50.67 8.9e-09
## 18 78 41.93 9.8e-09
## 19 104 63.75 1.3e-08
## 20 34 11.53 1.4e-08showSigOfNodes(go.BP.NAC.time4, score(result.fischer.NAC.time4), firstSigNodes = 10, useInfo = 'all')
## $dag
## A graphNEL graph with directed edges
## Number of Nodes = 21
## Number of Edges = 33
##
## $complete.dag
## [1] "A graph with 21 nodes."NAC.time7.pval.both <- limmaTopGenes(fit.NAC.time7, dir="both")
go.BP.NAC.time7 <- new("topGOdata", description="GO annotation NAC time 7", ontology="BP", allGenes = NAC.time7.pval.both, geneSel = topDiffGenes, nodeSize = 10, annot = annFUN.org, mapping="org.Mm.eg.db", ID = "Ensembl")##
## Building most specific GOs ..... ( 8852 GO terms found. )
##
## Build GO DAG topology .......... ( 12170 GO terms and 28213 relations. )
##
## Annotating nodes ............... ( 13785 genes annotated to the GO terms. )result.fischer.NAC.time7 <- runTest(go.BP.NAC.time7, algorithm = "classic", statistic = "fisher")##
## -- Classic Algorithm --
##
## the algorithm is scoring 3285 nontrivial nodes
## parameters:
## test statistic: fisherallRes.NAC.time7 <- GenTable(go.BP.NAC.time7, classic = result.fischer.NAC.time7, topNodes = 20)
allRes.NAC.time7## GO.ID Term Annotated
## 1 GO:0003008 system process 880
## 2 GO:0044707 single-multicellular organism process 3827
## 3 GO:0032501 multicellular organismal process 3926
## 4 GO:0022610 biological adhesion 702
## 5 GO:0007155 cell adhesion 697
## 6 GO:0050877 neurological system process 556
## 7 GO:0009653 anatomical structure morphogenesis 1689
## 8 GO:0048731 system development 2673
## 9 GO:0009888 tissue development 1209
## 10 GO:0006811 ion transport 859
## 11 GO:0007610 behavior 439
## 12 GO:0051239 regulation of multicellular organismal p... 1592
## 13 GO:0007154 cell communication 3437
## 14 GO:0023052 signaling 3366
## 15 GO:0044700 single organism signaling 3366
## 16 GO:0050806 positive regulation of synaptic transmis... 55
## 17 GO:0009887 organ morphogenesis 616
## 18 GO:0007275 multicellular organismal development 3069
## 19 GO:0048729 tissue morphogenesis 426
## 20 GO:2000026 regulation of multicellular organismal d... 1072
## Significant Expected classic
## 1 71 29.05 1.4e-12
## 2 192 126.32 1.2e-11
## 3 195 129.59 1.9e-11
## 4 59 23.17 2.8e-11
## 5 58 23.01 6.2e-11
## 6 48 18.35 9.6e-10
## 7 99 55.75 5.0e-09
## 8 138 88.23 9.0e-09
## 9 77 39.91 1.2e-08
## 10 60 28.35 2.5e-08
## 11 38 14.49 5.6e-08
## 12 91 52.55 8.2e-08
## 13 163 113.44 9.5e-08
## 14 160 111.10 1.2e-07
## 15 160 111.10 1.2e-07
## 16 12 1.82 1.7e-07
## 17 46 20.33 1.8e-07
## 18 148 101.30 1.9e-07
## 19 36 14.06 2.3e-07
## 20 67 35.38 2.5e-07showSigOfNodes(go.BP.NAC.time7, score(result.fischer.NAC.time7), firstSigNodes = 10, useInfo = 'all')
## $dag
## A graphNEL graph with directed edges
## Number of Nodes = 20
## Number of Edges = 25
##
## $complete.dag
## [1] "A graph with 20 nodes."