New_contrasts
Thomas Källman, BILS
3 Jun 2015
This first section of R-code extract some of the contrasts that was of interest, but was not done earlier.
library(topGO)## Loading required package: graph
## Loading required package: Biobase
## 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 object is masked from 'package:stats':
##
## xtabs
##
## The following objects are masked from 'package:base':
##
## 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
##
## Attaching package: 'AnnotationDbi'
##
## The following object is masked from 'package:GenomeInfoDb':
##
## species
##
## Loading required package: DBI
##
## 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 objects are masked from 'package:IRanges':
##
## members, score, score<-library(limma)##
## Attaching package: 'limma'
##
## The following object is masked from 'package:BiocGenerics':
##
## plotMAdesign<-read.table("../designMatrix.txt", header=T)
lev <- c("con.0d","con.1d","CeO2.1d","SDC.1d","NAC.1d","con.4d","CeO2.4d","SDC.4d","NAC.4d", "con.7d","CeO2.7d","SDC.7d","NAC.7d")
f<- factor(design$Name, levels=lev)
des <- model.matrix(~0+f)
colnames(des) <- lev
load(file = "Fit.rdata") # reads in earlier analysis results
control.7_4 <- makeContrasts("con.7d-con.4d", levels=lev)
fit.control.7_4 <- contrasts.fit(fit, control.7_4)
fit.control.7_4 <- eBayes(fit.control.7_4)
fit.control.7_4$genes <- gene.list.retained
topTableF(fit.control.7_4, adjust="BH")## ensembl_gene_id external_gene_name con.7d.con.4d
## ENSMUSG00000031375 ENSMUSG00000031375 Bgn 3.136833
## ENSMUSG00000039748 ENSMUSG00000039748 Exo1 -2.479832
## ENSMUSG00000017716 ENSMUSG00000017716 Birc5 -2.353609
## ENSMUSG00000023015 ENSMUSG00000023015 Racgap1 -1.961801
## ENSMUSG00000028678 ENSMUSG00000028678 Kif2c -2.284767
## ENSMUSG00000001864 ENSMUSG00000001864 Aif1l -1.943947
## ENSMUSG00000069910 ENSMUSG00000069910 Spdl1 -1.935958
## ENSMUSG00000030346 ENSMUSG00000030346 Rad51ap1 -2.282057
## ENSMUSG00000023505 ENSMUSG00000023505 Cdca3 -2.347342
## ENSMUSG00000030677 ENSMUSG00000030677 Kif22 -2.370468
## AveExpr F P.Value adj.P.Val
## ENSMUSG00000031375 5.332354 808.9531 1.340702e-23 2.364461e-19
## ENSMUSG00000039748 5.043317 711.8636 8.839100e-23 7.794318e-19
## ENSMUSG00000017716 6.039535 685.8608 1.527683e-22 8.980741e-19
## ENSMUSG00000023015 7.400599 633.0405 4.949165e-22 2.141194e-18
## ENSMUSG00000028678 5.620743 624.2695 6.070519e-22 2.141194e-18
## ENSMUSG00000001864 5.574066 589.1575 1.414537e-21 4.157795e-18
## ENSMUSG00000069910 4.788107 581.8255 1.698084e-21 4.278202e-18
## ENSMUSG00000030346 4.596470 568.6560 2.370795e-21 5.226418e-18
## ENSMUSG00000023505 5.524965 553.4214 3.520397e-21 6.801947e-18
## ENSMUSG00000030677 5.671283 546.9897 4.172814e-21 6.801947e-18results.control.7_4 <- decideTests(fit.control.7_4, adjust.method="BH")
write.fit(fit.control.7_4, results=results.control.7_4, file="control7_4.txt", digits=30, dec=",", adjust = "BH")
control.4_1 <- makeContrasts("con.4d-con.1d", levels=lev)
fit.control.4_1 <- contrasts.fit(fit, control.4_1)
fit.control.4_1 <- eBayes(fit.control.4_1)
fit.control.4_1$genes <- gene.list.retained
topTableF(fit.control.4_1, adjust="BH")## ensembl_gene_id external_gene_name con.4d.con.1d
## ENSMUSG00000026185 ENSMUSG00000026185 Igfbp5 3.343576
## ENSMUSG00000018411 ENSMUSG00000018411 Mapt 2.237047
## ENSMUSG00000052229 ENSMUSG00000052229 Gpr17 2.595538
## ENSMUSG00000030257 ENSMUSG00000030257 Srgap3 1.491618
## ENSMUSG00000041482 ENSMUSG00000041482 Piezo2 2.310039
## ENSMUSG00000024803 ENSMUSG00000024803 Ankrd1 -2.325763
## ENSMUSG00000019997 ENSMUSG00000019997 Ctgf -3.832182
## ENSMUSG00000007097 ENSMUSG00000007097 Atp1a2 4.257575
## ENSMUSG00000020908 ENSMUSG00000020908 Myh3 3.196655
## ENSMUSG00000042961 ENSMUSG00000042961 Egflam 1.524425
## AveExpr F P.Value adj.P.Val
## ENSMUSG00000026185 6.848453 979.5128 7.850641e-25 1.384539e-20
## ENSMUSG00000018411 5.959252 441.8348 9.133987e-20 8.054350e-16
## ENSMUSG00000052229 6.365094 385.1910 6.496782e-19 3.819242e-15
## ENSMUSG00000030257 5.387324 372.9599 1.027839e-18 4.531743e-15
## ENSMUSG00000041482 4.530825 337.9162 4.151035e-18 1.464153e-14
## ENSMUSG00000024803 4.747288 311.4302 1.306247e-17 3.839495e-14
## ENSMUSG00000019997 5.398320 306.7180 1.616745e-17 4.073273e-14
## ENSMUSG00000007097 3.622022 285.6973 4.347676e-17 9.584453e-14
## ENSMUSG00000020908 2.631798 257.3631 1.840999e-16 3.607540e-13
## ENSMUSG00000042961 5.772126 253.1419 2.310477e-16 4.074757e-13results.control.4_1 <- decideTests(fit.control.4_1, adjust.method="BH")
write.fit(fit.control.4_1, results=results.control.4_1, file="control4_1.txt", digits=30, dec=",", adjust = "BH")
control.1_0 <- makeContrasts("con.1d-con.0d", levels=lev)
fit.control.1_0 <- contrasts.fit(fit, control.1_0)
fit.control.1_0 <- eBayes(fit.control.1_0)
fit.control.1_0$genes <- gene.list.retained
topTableF(fit.control.1_0, adjust="BH")## ensembl_gene_id external_gene_name con.1d.con.0d
## ENSMUSG00000025880 ENSMUSG00000025880 Smad7 -2.767593
## ENSMUSG00000024063 ENSMUSG00000024063 Lbh 1.552172
## ENSMUSG00000022324 ENSMUSG00000022324 Matn2 2.065396
## ENSMUSG00000018166 ENSMUSG00000018166 Erbb3 1.543640
## ENSMUSG00000033066 ENSMUSG00000033066 Gas7 2.008864
## ENSMUSG00000025885 ENSMUSG00000025885 Myo5b 1.634908
## ENSMUSG00000033105 ENSMUSG00000033105 Lss 1.592018
## ENSMUSG00000001123 ENSMUSG00000001123 Lgals9 2.236096
## ENSMUSG00000033208 ENSMUSG00000033208 S100b 2.575458
## ENSMUSG00000063415 ENSMUSG00000063415 Cyp26b1 -5.640826
## AveExpr F P.Value adj.P.Val
## ENSMUSG00000025880 3.0921165 697.6810 1.188398e-22 2.095858e-18
## ENSMUSG00000024063 8.9655336 478.6905 2.881580e-20 1.701464e-16
## ENSMUSG00000022324 8.5644917 478.5445 2.894303e-20 1.701464e-16
## ENSMUSG00000018166 8.9954200 453.8158 6.217474e-20 2.741284e-16
## ENSMUSG00000033066 9.6781861 425.6469 1.560393e-19 4.669246e-16
## ENSMUSG00000025885 7.1434780 421.6533 1.786007e-19 4.669246e-16
## ENSMUSG00000033105 7.8363905 420.5657 1.853296e-19 4.669246e-16
## ENSMUSG00000001123 6.1899384 390.0798 5.428817e-19 1.083735e-15
## ENSMUSG00000033208 6.8653808 389.5719 5.530513e-19 1.083735e-15
## ENSMUSG00000063415 -0.8231852 372.8367 1.032672e-18 1.821221e-15results.control.1_0 <- decideTests(fit.control.1_0, adjust.method="BH")
write.fit(fit.control.1_0, results=results.control.1_0, file="control1_0.txt", digits=30, dec=",", adjust = "BH")This second section does the TopGO analysis for the contrasts addressed, treating up and down regulated genes seperately. NB! This code is anything, but nicely written and I will update it using functions and loops instead of this mess, but there was not enough time to finalize that.
control.7_4.pval.up <- limmaTopGenes(fit.control.7_4, dir = 'up')
go.MF.control.7_4.up <- new("topGOdata", description="GO annotation control 7 vs 4", ontology="MF", allGenes = control.7_4.pval.up, 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.weight01.control.7_4.up <- runTest(go.MF.control.7_4.up, algorithm = 'weight01', statistic = "fisher")##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 1192 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 15: 2 nodes to be scored (0 eliminated genes)
##
## Level 14: 1 nodes to be scored (0 eliminated genes)
##
## Level 13: 3 nodes to be scored (19 eliminated genes)
##
## Level 12: 3 nodes to be scored (19 eliminated genes)
##
## Level 11: 10 nodes to be scored (53 eliminated genes)
##
## Level 10: 25 nodes to be scored (86 eliminated genes)
##
## Level 9: 80 nodes to be scored (237 eliminated genes)
##
## Level 8: 134 nodes to be scored (719 eliminated genes)
##
## Level 7: 198 nodes to be scored (2474 eliminated genes)
##
## Level 6: 293 nodes to be scored (3308 eliminated genes)
##
## Level 5: 201 nodes to be scored (5027 eliminated genes)
##
## Level 4: 160 nodes to be scored (7188 eliminated genes)
##
## Level 3: 65 nodes to be scored (9347 eliminated genes)
##
## Level 2: 16 nodes to be scored (10008 eliminated genes)
##
## Level 1: 1 nodes to be scored (10686 eliminated genes)all.res.control.7_4.up <- GenTable(go.MF.control.7_4.up, Weight01 = result.weight01.control.7_4.up, topNodes = table(result.weight01.control.7_4.up@score < 0.01)[2])
showSigOfNodes(go.MF.control.7_4.up, score(result.weight01.control.7_4.up), 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 = 34
## Number of Edges = 39
##
## $complete.dag
## [1] "A graph with 34 nodes."control.7_4.pval.down <- limmaTopGenes(fit.control.7_4, dir = 'down')
go.MF.control.7_4.down <- new("topGOdata", description="GO annotation control 7 vs 4", ontology="MF", allGenes = control.7_4.pval.down, 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.weight01.control.7_4.down <- runTest(go.MF.control.7_4.down, algorithm = 'weight01', statistic = "fisher")##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 1196 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 15: 1 nodes to be scored (0 eliminated genes)
##
## Level 14: 1 nodes to be scored (0 eliminated genes)
##
## Level 13: 3 nodes to be scored (10 eliminated genes)
##
## Level 12: 2 nodes to be scored (19 eliminated genes)
##
## Level 11: 8 nodes to be scored (53 eliminated genes)
##
## Level 10: 25 nodes to be scored (71 eliminated genes)
##
## Level 9: 79 nodes to be scored (203 eliminated genes)
##
## Level 8: 134 nodes to be scored (720 eliminated genes)
##
## Level 7: 204 nodes to be scored (2489 eliminated genes)
##
## Level 6: 293 nodes to be scored (3315 eliminated genes)
##
## Level 5: 203 nodes to be scored (5054 eliminated genes)
##
## Level 4: 161 nodes to be scored (7191 eliminated genes)
##
## Level 3: 65 nodes to be scored (9352 eliminated genes)
##
## Level 2: 16 nodes to be scored (10012 eliminated genes)
##
## Level 1: 1 nodes to be scored (10686 eliminated genes)all.res.control.7_4.down <- GenTable(go.MF.control.7_4.down, Weight01 = result.weight01.control.7_4.down, topNodes = table(result.weight01.control.7_4.down@score < 0.01)[2])
showSigOfNodes(go.MF.control.7_4.down, score(result.weight01.control.7_4.down), firstSigNodes = 10, useInfo = 'all')
## $dag
## A graphNEL graph with directed edges
## Number of Nodes = 43
## Number of Edges = 58
##
## $complete.dag
## [1] "A graph with 43 nodes."control.7_4.pval.up <- limmaTopGenes(fit.control.7_4, dir = 'up')
go.BP.control.7_4.up <- new("topGOdata", description="GO annotation control 7 vs 4", ontology="BP", allGenes = control.7_4.pval.up, 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.BP.weight01.control.7_4.up <- runTest(go.BP.control.7_4.up, algorithm = 'weight01', statistic = "fisher")##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 4426 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 19: 1 nodes to be scored (0 eliminated genes)
##
## Level 18: 2 nodes to be scored (0 eliminated genes)
##
## Level 17: 7 nodes to be scored (10 eliminated genes)
##
## Level 16: 14 nodes to be scored (45 eliminated genes)
##
## Level 15: 25 nodes to be scored (148 eliminated genes)
##
## Level 14: 56 nodes to be scored (320 eliminated genes)
##
## Level 13: 112 nodes to be scored (609 eliminated genes)
##
## Level 12: 208 nodes to be scored (1112 eliminated genes)
##
## Level 11: 371 nodes to be scored (1975 eliminated genes)
##
## Level 10: 500 nodes to be scored (3701 eliminated genes)
##
## Level 9: 614 nodes to be scored (5023 eliminated genes)
##
## Level 8: 649 nodes to be scored (6885 eliminated genes)
##
## Level 7: 647 nodes to be scored (8051 eliminated genes)
##
## Level 6: 552 nodes to be scored (9003 eliminated genes)
##
## Level 5: 391 nodes to be scored (9709 eliminated genes)
##
## Level 4: 203 nodes to be scored (10318 eliminated genes)
##
## Level 3: 53 nodes to be scored (10558 eliminated genes)
##
## Level 2: 20 nodes to be scored (10811 eliminated genes)
##
## Level 1: 1 nodes to be scored (10919 eliminated genes)all.res.BP.control.7_4.up <- GenTable(go.BP.control.7_4.up, Weight01 = result.BP.weight01.control.7_4.up, topNodes = table(result.BP.weight01.control.7_4.up@score < 0.01)[2])
showSigOfNodes(go.BP.control.7_4.up, score(result.BP.weight01.control.7_4.up), firstSigNodes = 10, useInfo = 'all')
## $dag
## A graphNEL graph with directed edges
## Number of Nodes = 170
## Number of Edges = 360
##
## $complete.dag
## [1] "A graph with 170 nodes."control.7_4.pval.down <- limmaTopGenes(fit.control.7_4, dir = 'down')
go.BP.control.7_4.down <- new("topGOdata", description="GO annotation control 7 vs 4", ontology="BP", allGenes = control.7_4.pval.down, 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.BP.weight01.control.7_4.down <- runTest(go.BP.control.7_4.down, algorithm = 'weight01', statistic = "fisher")##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 4426 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 19: 1 nodes to be scored (0 eliminated genes)
##
## Level 18: 2 nodes to be scored (0 eliminated genes)
##
## Level 17: 7 nodes to be scored (10 eliminated genes)
##
## Level 16: 12 nodes to be scored (45 eliminated genes)
##
## Level 15: 23 nodes to be scored (148 eliminated genes)
##
## Level 14: 56 nodes to be scored (305 eliminated genes)
##
## Level 13: 111 nodes to be scored (600 eliminated genes)
##
## Level 12: 204 nodes to be scored (1116 eliminated genes)
##
## Level 11: 370 nodes to be scored (1999 eliminated genes)
##
## Level 10: 508 nodes to be scored (3706 eliminated genes)
##
## Level 9: 629 nodes to be scored (5072 eliminated genes)
##
## Level 8: 648 nodes to be scored (6922 eliminated genes)
##
## Level 7: 642 nodes to be scored (8058 eliminated genes)
##
## Level 6: 550 nodes to be scored (9023 eliminated genes)
##
## Level 5: 389 nodes to be scored (9705 eliminated genes)
##
## Level 4: 201 nodes to be scored (10316 eliminated genes)
##
## Level 3: 52 nodes to be scored (10557 eliminated genes)
##
## Level 2: 20 nodes to be scored (10811 eliminated genes)
##
## Level 1: 1 nodes to be scored (10919 eliminated genes)all.res.BP.control.7_4.down <- GenTable(go.BP.control.7_4.down, Weight01 = result.BP.weight01.control.7_4.down, topNodes = table(result.BP.weight01.control.7_4.down@score < 0.01)[2])
showSigOfNodes(go.BP.control.7_4.down, score(result.BP.weight01.control.7_4.down), firstSigNodes = 10, useInfo = 'all')
## $dag
## A graphNEL graph with directed edges
## Number of Nodes = 129
## Number of Edges = 248
##
## $complete.dag
## [1] "A graph with 129 nodes."control.7_4.pval.up <- limmaTopGenes(fit.control.7_4, dir = 'up')
go.CC.control.7_4.up <- new("topGOdata", description="GO annotation control 7 vs 4", ontology="CC", allGenes = control.7_4.pval.up, geneSel = topDiffGenes, nodeSize = 10, annot = annFUN.org, mapping="org.Mm.eg.db", ID = "Ensembl")##
## Building most specific GOs ..... ( 1194 GO terms found. )
##
## Build GO DAG topology .......... ( 1420 GO terms and 2773 relations. )
##
## Annotating nodes ............... ( 13908 genes annotated to the GO terms. )result.CC.weight01.control.7_4.up <- runTest(go.CC.control.7_4.up, algorithm = 'weight01', statistic = "fisher")##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 550 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 16: 1 nodes to be scored (0 eliminated genes)
##
## Level 15: 8 nodes to be scored (0 eliminated genes)
##
## Level 14: 21 nodes to be scored (15 eliminated genes)
##
## Level 13: 22 nodes to be scored (103 eliminated genes)
##
## Level 12: 34 nodes to be scored (438 eliminated genes)
##
## Level 11: 74 nodes to be scored (855 eliminated genes)
##
## Level 10: 73 nodes to be scored (1663 eliminated genes)
##
## Level 9: 56 nodes to be scored (3266 eliminated genes)
##
## Level 8: 57 nodes to be scored (4552 eliminated genes)
##
## Level 7: 40 nodes to be scored (5040 eliminated genes)
##
## Level 6: 38 nodes to be scored (9281 eliminated genes)
##
## Level 5: 48 nodes to be scored (9458 eliminated genes)
##
## Level 4: 47 nodes to be scored (10565 eliminated genes)
##
## Level 3: 20 nodes to be scored (11504 eliminated genes)
##
## Level 2: 10 nodes to be scored (11717 eliminated genes)
##
## Level 1: 1 nodes to be scored (11729 eliminated genes)all.res.CC.control.7_4.up <- GenTable(go.CC.control.7_4.up, Weight01 = result.CC.weight01.control.7_4.up, topNodes = table(result.CC.weight01.control.7_4.up@score < 0.01)[2])
showSigOfNodes(go.CC.control.7_4.up, score(result.CC.weight01.control.7_4.up), firstSigNodes = 10, useInfo = 'all')
## $dag
## A graphNEL graph with directed edges
## Number of Nodes = 40
## Number of Edges = 66
##
## $complete.dag
## [1] "A graph with 40 nodes."control.7_4.pval.down <- limmaTopGenes(fit.control.7_4, dir = 'down')
go.CC.control.7_4.down <- new("topGOdata", description="GO annotation control 7 vs 4", ontology="CC", allGenes = control.7_4.pval.down, geneSel = topDiffGenes, nodeSize = 10, annot = annFUN.org, mapping="org.Mm.eg.db", ID = "Ensembl")##
## Building most specific GOs ..... ( 1194 GO terms found. )
##
## Build GO DAG topology .......... ( 1420 GO terms and 2773 relations. )
##
## Annotating nodes ............... ( 13908 genes annotated to the GO terms. )result.CC.weight01.control.7_4.down <- runTest(go.CC.control.7_4.down, algorithm = 'weight01', statistic = "fisher")##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 580 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 16: 1 nodes to be scored (0 eliminated genes)
##
## Level 15: 8 nodes to be scored (0 eliminated genes)
##
## Level 14: 25 nodes to be scored (15 eliminated genes)
##
## Level 13: 24 nodes to be scored (103 eliminated genes)
##
## Level 12: 36 nodes to be scored (480 eliminated genes)
##
## Level 11: 80 nodes to be scored (886 eliminated genes)
##
## Level 10: 75 nodes to be scored (1703 eliminated genes)
##
## Level 9: 55 nodes to be scored (3415 eliminated genes)
##
## Level 8: 60 nodes to be scored (4562 eliminated genes)
##
## Level 7: 42 nodes to be scored (5032 eliminated genes)
##
## Level 6: 43 nodes to be scored (9294 eliminated genes)
##
## Level 5: 46 nodes to be scored (9456 eliminated genes)
##
## Level 4: 53 nodes to be scored (10558 eliminated genes)
##
## Level 3: 20 nodes to be scored (11497 eliminated genes)
##
## Level 2: 11 nodes to be scored (11718 eliminated genes)
##
## Level 1: 1 nodes to be scored (11729 eliminated genes)all.res.CC.control.7_4.down <- GenTable(go.CC.control.7_4.down, Weight01 = result.CC.weight01.control.7_4.down, topNodes = table(result.CC.weight01.control.7_4.down@score < 0.01)[2])
showSigOfNodes(go.CC.control.7_4.down, score(result.CC.weight01.control.7_4.down), firstSigNodes = 10, useInfo = 'all')
## $dag
## A graphNEL graph with directed edges
## Number of Nodes = 44
## Number of Edges = 76
##
## $complete.dag
## [1] "A graph with 44 nodes."control.4_1.pval.up <- limmaTopGenes(fit.control.4_1, dir = 'up')
go.MF.control.4_1.up <- new("topGOdata", description="GO annotation control 4 vs 1", ontology="MF", allGenes = control.4_1.pval.up, 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.weight01.control.4_1.up <- runTest(go.MF.control.4_1.up, algorithm = 'weight01', statistic = "fisher")##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 1142 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 15: 1 nodes to be scored (0 eliminated genes)
##
## Level 14: 1 nodes to be scored (0 eliminated genes)
##
## Level 13: 2 nodes to be scored (11 eliminated genes)
##
## Level 12: 3 nodes to be scored (19 eliminated genes)
##
## Level 11: 9 nodes to be scored (53 eliminated genes)
##
## Level 10: 22 nodes to be scored (86 eliminated genes)
##
## Level 9: 79 nodes to be scored (229 eliminated genes)
##
## Level 8: 128 nodes to be scored (691 eliminated genes)
##
## Level 7: 191 nodes to be scored (2478 eliminated genes)
##
## Level 6: 280 nodes to be scored (3269 eliminated genes)
##
## Level 5: 190 nodes to be scored (4970 eliminated genes)
##
## Level 4: 157 nodes to be scored (7140 eliminated genes)
##
## Level 3: 62 nodes to be scored (9327 eliminated genes)
##
## Level 2: 16 nodes to be scored (10009 eliminated genes)
##
## Level 1: 1 nodes to be scored (10683 eliminated genes)all.res.control.4_1.up <- GenTable(go.MF.control.4_1.up, Weight01 = result.weight01.control.4_1.up, topNodes = table(result.weight01.control.4_1.up@score < 0.01)[2])
showSigOfNodes(go.MF.control.4_1.up, score(result.weight01.control.4_1.up), firstSigNodes = 10, useInfo = 'all')
## $dag
## A graphNEL graph with directed edges
## Number of Nodes = 25
## Number of Edges = 24
##
## $complete.dag
## [1] "A graph with 25 nodes."control.4_1.pval.down <- limmaTopGenes(fit.control.4_1, dir = 'down')
go.MF.control.4_1.down <- new("topGOdata", description="GO annotation control 4 vs 1", ontology="MF", allGenes = control.4_1.pval.down, 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.weight01.control.4_1.down <- runTest(go.MF.control.4_1.down, algorithm = 'weight01', statistic = "fisher")##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 1091 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 15: 2 nodes to be scored (0 eliminated genes)
##
## Level 14: 1 nodes to be scored (0 eliminated genes)
##
## Level 13: 3 nodes to be scored (19 eliminated genes)
##
## Level 12: 3 nodes to be scored (19 eliminated genes)
##
## Level 11: 8 nodes to be scored (53 eliminated genes)
##
## Level 10: 22 nodes to be scored (86 eliminated genes)
##
## Level 9: 74 nodes to be scored (203 eliminated genes)
##
## Level 8: 127 nodes to be scored (686 eliminated genes)
##
## Level 7: 187 nodes to be scored (2466 eliminated genes)
##
## Level 6: 245 nodes to be scored (3290 eliminated genes)
##
## Level 5: 187 nodes to be scored (4996 eliminated genes)
##
## Level 4: 151 nodes to be scored (7157 eliminated genes)
##
## Level 3: 64 nodes to be scored (9307 eliminated genes)
##
## Level 2: 16 nodes to be scored (9992 eliminated genes)
##
## Level 1: 1 nodes to be scored (10687 eliminated genes)all.res.control.4_1.down <- GenTable(go.MF.control.4_1.down, Weight01 = result.weight01.control.4_1.down, topNodes = table(result.weight01.control.4_1.down@score < 0.01)[2])
showSigOfNodes(go.MF.control.4_1.down, score(result.weight01.control.4_1.down), firstSigNodes = 10, useInfo = 'all')
## $dag
## A graphNEL graph with directed edges
## Number of Nodes = 55
## Number of Edges = 73
##
## $complete.dag
## [1] "A graph with 55 nodes."control.4_1.pval.up <- limmaTopGenes(fit.control.4_1, dir = 'up')
go.BP.control.4_1.up <- new("topGOdata", description="GO annotation control 4 vs 1", ontology="BP", allGenes = control.4_1.pval.up, 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.BP.weight01.control.4_1.up <- runTest(go.BP.control.4_1.up, algorithm = 'weight01', statistic = "fisher")##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 4184 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 19: 1 nodes to be scored (0 eliminated genes)
##
## Level 18: 2 nodes to be scored (0 eliminated genes)
##
## Level 17: 4 nodes to be scored (10 eliminated genes)
##
## Level 16: 11 nodes to be scored (45 eliminated genes)
##
## Level 15: 23 nodes to be scored (129 eliminated genes)
##
## Level 14: 51 nodes to be scored (298 eliminated genes)
##
## Level 13: 100 nodes to be scored (593 eliminated genes)
##
## Level 12: 198 nodes to be scored (1093 eliminated genes)
##
## Level 11: 341 nodes to be scored (1947 eliminated genes)
##
## Level 10: 472 nodes to be scored (3703 eliminated genes)
##
## Level 9: 579 nodes to be scored (4961 eliminated genes)
##
## Level 8: 610 nodes to be scored (6841 eliminated genes)
##
## Level 7: 607 nodes to be scored (8011 eliminated genes)
##
## Level 6: 528 nodes to be scored (9000 eliminated genes)
##
## Level 5: 384 nodes to be scored (9698 eliminated genes)
##
## Level 4: 201 nodes to be scored (10318 eliminated genes)
##
## Level 3: 51 nodes to be scored (10558 eliminated genes)
##
## Level 2: 20 nodes to be scored (10811 eliminated genes)
##
## Level 1: 1 nodes to be scored (10918 eliminated genes)all.res.BP.control.4_1.up <- GenTable(go.BP.control.4_1.up, Weight01 = result.BP.weight01.control.4_1.up, topNodes = table(result.BP.weight01.control.4_1.up@score < 0.01)[2])
showSigOfNodes(go.BP.control.4_1.up, score(result.BP.weight01.control.4_1.up), firstSigNodes = 10, useInfo = 'all')
## $dag
## A graphNEL graph with directed edges
## Number of Nodes = 115
## Number of Edges = 204
##
## $complete.dag
## [1] "A graph with 115 nodes."control.4_1.pval.down <- limmaTopGenes(fit.control.4_1, dir = 'down')
go.BP.control.4_1.down <- new("topGOdata", description="GO annotation control 4 vs 1", ontology="BP", allGenes = control.4_1.pval.down, 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.BP.weight01.control.4_1.down <- runTest(go.BP.control.4_1.down, algorithm = 'weight01', statistic = "fisher")##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 4235 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 19: 1 nodes to be scored (0 eliminated genes)
##
## Level 18: 2 nodes to be scored (0 eliminated genes)
##
## Level 17: 5 nodes to be scored (10 eliminated genes)
##
## Level 16: 13 nodes to be scored (45 eliminated genes)
##
## Level 15: 24 nodes to be scored (139 eliminated genes)
##
## Level 14: 51 nodes to be scored (318 eliminated genes)
##
## Level 13: 105 nodes to be scored (600 eliminated genes)
##
## Level 12: 193 nodes to be scored (1087 eliminated genes)
##
## Level 11: 344 nodes to be scored (1962 eliminated genes)
##
## Level 10: 474 nodes to be scored (3673 eliminated genes)
##
## Level 9: 590 nodes to be scored (5034 eliminated genes)
##
## Level 8: 630 nodes to be scored (6839 eliminated genes)
##
## Level 7: 620 nodes to be scored (8013 eliminated genes)
##
## Level 6: 526 nodes to be scored (9011 eliminated genes)
##
## Level 5: 383 nodes to be scored (9703 eliminated genes)
##
## Level 4: 200 nodes to be scored (10309 eliminated genes)
##
## Level 3: 53 nodes to be scored (10552 eliminated genes)
##
## Level 2: 20 nodes to be scored (10811 eliminated genes)
##
## Level 1: 1 nodes to be scored (10919 eliminated genes)all.res.BP.control.4_1.down <- GenTable(go.BP.control.4_1.down, Weight01 = result.BP.weight01.control.4_1.down, topNodes = table(result.BP.weight01.control.4_1.down@score < 0.01)[2])
showSigOfNodes(go.BP.control.4_1.down, score(result.BP.weight01.control.4_1.down), firstSigNodes = 10, useInfo = 'all')
## $dag
## A graphNEL graph with directed edges
## Number of Nodes = 156
## Number of Edges = 276
##
## $complete.dag
## [1] "A graph with 156 nodes."control.4_1.pval.up <- limmaTopGenes(fit.control.4_1, dir = 'up')
go.CC.control.4_1.up <- new("topGOdata", description="GO annotation control 4 vs 1", ontology="CC", allGenes = control.4_1.pval.up, geneSel = topDiffGenes, nodeSize = 10, annot = annFUN.org, mapping="org.Mm.eg.db", ID = "Ensembl")##
## Building most specific GOs ..... ( 1194 GO terms found. )
##
## Build GO DAG topology .......... ( 1420 GO terms and 2773 relations. )
##
## Annotating nodes ............... ( 13908 genes annotated to the GO terms. )result.CC.weight01.control.4_1.up <- runTest(go.CC.control.4_1.up, algorithm = 'weight01', statistic = "fisher")##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 529 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 15: 5 nodes to be scored (0 eliminated genes)
##
## Level 14: 19 nodes to be scored (0 eliminated genes)
##
## Level 13: 16 nodes to be scored (77 eliminated genes)
##
## Level 12: 27 nodes to be scored (414 eliminated genes)
##
## Level 11: 72 nodes to be scored (784 eliminated genes)
##
## Level 10: 71 nodes to be scored (1593 eliminated genes)
##
## Level 9: 53 nodes to be scored (3359 eliminated genes)
##
## Level 8: 58 nodes to be scored (4550 eliminated genes)
##
## Level 7: 41 nodes to be scored (5025 eliminated genes)
##
## Level 6: 38 nodes to be scored (9286 eliminated genes)
##
## Level 5: 48 nodes to be scored (9458 eliminated genes)
##
## Level 4: 49 nodes to be scored (10563 eliminated genes)
##
## Level 3: 20 nodes to be scored (11504 eliminated genes)
##
## Level 2: 11 nodes to be scored (11718 eliminated genes)
##
## Level 1: 1 nodes to be scored (11729 eliminated genes)all.res.CC.control.4_1.up <- GenTable(go.CC.control.4_1.up, Weight01 = result.CC.weight01.control.4_1.up, topNodes = table(result.CC.weight01.control.4_1.up@score < 0.01)[2])
showSigOfNodes(go.CC.control.4_1.up, score(result.CC.weight01.control.4_1.up), firstSigNodes = 10, useInfo = 'all')
## $dag
## A graphNEL graph with directed edges
## Number of Nodes = 46
## Number of Edges = 74
##
## $complete.dag
## [1] "A graph with 46 nodes."control.4_1.pval.down <- limmaTopGenes(fit.control.4_1, dir = 'down')
go.CC.control.4_1.down <- new("topGOdata", description="GO annotation control 4 vs 1", ontology="CC", allGenes = control.4_1.pval.down, geneSel = topDiffGenes, nodeSize = 10, annot = annFUN.org, mapping="org.Mm.eg.db", ID = "Ensembl")##
## Building most specific GOs ..... ( 1194 GO terms found. )
##
## Build GO DAG topology .......... ( 1420 GO terms and 2773 relations. )
##
## Annotating nodes ............... ( 13908 genes annotated to the GO terms. )result.CC.weight01.control.4_1.down <- runTest(go.CC.control.4_1.down, algorithm = 'weight01', statistic = "fisher")##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 515 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 16: 1 nodes to be scored (0 eliminated genes)
##
## Level 15: 7 nodes to be scored (0 eliminated genes)
##
## Level 14: 17 nodes to be scored (15 eliminated genes)
##
## Level 13: 18 nodes to be scored (93 eliminated genes)
##
## Level 12: 28 nodes to be scored (377 eliminated genes)
##
## Level 11: 71 nodes to be scored (785 eliminated genes)
##
## Level 10: 67 nodes to be scored (1657 eliminated genes)
##
## Level 9: 53 nodes to be scored (3332 eliminated genes)
##
## Level 8: 57 nodes to be scored (4526 eliminated genes)
##
## Level 7: 36 nodes to be scored (5025 eliminated genes)
##
## Level 6: 40 nodes to be scored (9278 eliminated genes)
##
## Level 5: 44 nodes to be scored (9451 eliminated genes)
##
## Level 4: 44 nodes to be scored (10558 eliminated genes)
##
## Level 3: 20 nodes to be scored (11492 eliminated genes)
##
## Level 2: 11 nodes to be scored (11718 eliminated genes)
##
## Level 1: 1 nodes to be scored (11729 eliminated genes)all.res.CC.control.4_1.down <- GenTable(go.CC.control.4_1.down, Weight01 = result.CC.weight01.control.4_1.down, topNodes = table(result.CC.weight01.control.4_1.down@score < 0.01)[2])
showSigOfNodes(go.CC.control.4_1.down, score(result.CC.weight01.control.4_1.down), firstSigNodes = 10, useInfo = 'all')
## $dag
## A graphNEL graph with directed edges
## Number of Nodes = 51
## Number of Edges = 83
##
## $complete.dag
## [1] "A graph with 51 nodes."control.1_0.pval.up <- limmaTopGenes(fit.control.1_0, dir = 'up')
go.MF.control.1_0.up <- new("topGOdata", description="GO annotation control 1 vs 0", ontology="MF", allGenes = control.1_0.pval.up, 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.weight01.control.1_0.up <- runTest(go.MF.control.1_0.up, algorithm = 'weight01', statistic = "fisher")##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 1190 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 15: 1 nodes to be scored (0 eliminated genes)
##
## Level 14: 1 nodes to be scored (0 eliminated genes)
##
## Level 13: 2 nodes to be scored (11 eliminated genes)
##
## Level 12: 3 nodes to be scored (19 eliminated genes)
##
## Level 11: 10 nodes to be scored (53 eliminated genes)
##
## Level 10: 24 nodes to be scored (86 eliminated genes)
##
## Level 9: 80 nodes to be scored (237 eliminated genes)
##
## Level 8: 130 nodes to be scored (716 eliminated genes)
##
## Level 7: 199 nodes to be scored (2482 eliminated genes)
##
## Level 6: 291 nodes to be scored (3286 eliminated genes)
##
## Level 5: 205 nodes to be scored (5026 eliminated genes)
##
## Level 4: 162 nodes to be scored (7207 eliminated genes)
##
## Level 3: 65 nodes to be scored (9356 eliminated genes)
##
## Level 2: 16 nodes to be scored (10012 eliminated genes)
##
## Level 1: 1 nodes to be scored (10686 eliminated genes)all.res.control.1_0.up <- GenTable(go.MF.control.1_0.up, Weight01 = result.weight01.control.1_0.up, topNodes = table(result.weight01.control.1_0.up@score < 0.01)[2])
showSigOfNodes(go.MF.control.1_0.up, score(result.weight01.control.1_0.up), firstSigNodes = 10, useInfo = 'all')
## $dag
## A graphNEL graph with directed edges
## Number of Nodes = 27
## Number of Edges = 31
##
## $complete.dag
## [1] "A graph with 27 nodes."control.1_0.pval.down <- limmaTopGenes(fit.control.1_0, dir = 'down')
go.MF.control.1_0.down <- new("topGOdata", description="GO annotation control 1 vs 0", ontology="MF", allGenes = control.1_0.pval.down, 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.weight01.control.1_0.down <- runTest(go.MF.control.1_0.down, algorithm = 'weight01', statistic = "fisher")##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 1157 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 15: 2 nodes to be scored (0 eliminated genes)
##
## Level 14: 1 nodes to be scored (0 eliminated genes)
##
## Level 13: 3 nodes to be scored (19 eliminated genes)
##
## Level 12: 3 nodes to be scored (19 eliminated genes)
##
## Level 11: 7 nodes to be scored (53 eliminated genes)
##
## Level 10: 25 nodes to be scored (86 eliminated genes)
##
## Level 9: 77 nodes to be scored (211 eliminated genes)
##
## Level 8: 128 nodes to be scored (720 eliminated genes)
##
## Level 7: 195 nodes to be scored (2474 eliminated genes)
##
## Level 6: 282 nodes to be scored (3270 eliminated genes)
##
## Level 5: 198 nodes to be scored (5010 eliminated genes)
##
## Level 4: 157 nodes to be scored (7164 eliminated genes)
##
## Level 3: 62 nodes to be scored (9343 eliminated genes)
##
## Level 2: 16 nodes to be scored (10010 eliminated genes)
##
## Level 1: 1 nodes to be scored (10687 eliminated genes)all.res.control.1_0.down <- GenTable(go.MF.control.1_0.down, Weight01 = result.weight01.control.1_0.down, topNodes = table(result.weight01.control.1_0.down@score < 0.01)[2])
showSigOfNodes(go.MF.control.1_0.down, score(result.weight01.control.1_0.down), firstSigNodes = 10, useInfo = 'all')
## $dag
## A graphNEL graph with directed edges
## Number of Nodes = 47
## Number of Edges = 60
##
## $complete.dag
## [1] "A graph with 47 nodes."control.1_0.pval.up <- limmaTopGenes(fit.control.1_0, dir = 'up')
go.BP.control.1_0.up <- new("topGOdata", description="GO annotation control 1 vs 0", ontology="BP", allGenes = control.1_0.pval.up, 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.BP.weight01.control.1_0.up <- runTest(go.BP.control.1_0.up, algorithm = 'weight01', statistic = "fisher")##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 4427 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 18: 2 nodes to be scored (0 eliminated genes)
##
## Level 17: 7 nodes to be scored (0 eliminated genes)
##
## Level 16: 14 nodes to be scored (45 eliminated genes)
##
## Level 15: 25 nodes to be scored (148 eliminated genes)
##
## Level 14: 53 nodes to be scored (320 eliminated genes)
##
## Level 13: 108 nodes to be scored (609 eliminated genes)
##
## Level 12: 204 nodes to be scored (1085 eliminated genes)
##
## Level 11: 371 nodes to be scored (1957 eliminated genes)
##
## Level 10: 512 nodes to be scored (3707 eliminated genes)
##
## Level 9: 621 nodes to be scored (5051 eliminated genes)
##
## Level 8: 649 nodes to be scored (6914 eliminated genes)
##
## Level 7: 644 nodes to be scored (8037 eliminated genes)
##
## Level 6: 551 nodes to be scored (9014 eliminated genes)
##
## Level 5: 390 nodes to be scored (9709 eliminated genes)
##
## Level 4: 202 nodes to be scored (10319 eliminated genes)
##
## Level 3: 53 nodes to be scored (10558 eliminated genes)
##
## Level 2: 20 nodes to be scored (10811 eliminated genes)
##
## Level 1: 1 nodes to be scored (10919 eliminated genes)all.res.BP.control.1_0.up <- GenTable(go.BP.control.1_0.up, Weight01 = result.BP.weight01.control.1_0.up, topNodes = table(result.BP.weight01.control.1_0.up@score < 0.01)[2])
showSigOfNodes(go.BP.control.1_0.up, score(result.BP.weight01.control.1_0.up), firstSigNodes = 10, useInfo = 'all')## Warning in arrows(head_from[1], head_from[2], head_to[1], head_to[2], col
## = edgeColor, : zero-length arrow is of indeterminate angle and so skipped
## $dag
## A graphNEL graph with directed edges
## Number of Nodes = 136
## Number of Edges = 282
##
## $complete.dag
## [1] "A graph with 136 nodes."control.1_0.pval.down <- limmaTopGenes(fit.control.1_0, dir = 'down')
go.BP.control.1_0.down <- new("topGOdata", description="GO annotation control 1 vs 0", ontology="BP", allGenes = control.1_0.pval.down, 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.BP.weight01.control.1_0.down <- runTest(go.BP.control.1_0.down, algorithm = 'weight01', statistic = "fisher")##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 4370 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 19: 1 nodes to be scored (0 eliminated genes)
##
## Level 18: 2 nodes to be scored (0 eliminated genes)
##
## Level 17: 6 nodes to be scored (10 eliminated genes)
##
## Level 16: 12 nodes to be scored (45 eliminated genes)
##
## Level 15: 25 nodes to be scored (141 eliminated genes)
##
## Level 14: 56 nodes to be scored (313 eliminated genes)
##
## Level 13: 112 nodes to be scored (609 eliminated genes)
##
## Level 12: 203 nodes to be scored (1124 eliminated genes)
##
## Level 11: 366 nodes to be scored (1998 eliminated genes)
##
## Level 10: 496 nodes to be scored (3694 eliminated genes)
##
## Level 9: 613 nodes to be scored (5062 eliminated genes)
##
## Level 8: 640 nodes to be scored (6875 eliminated genes)
##
## Level 7: 632 nodes to be scored (8040 eliminated genes)
##
## Level 6: 543 nodes to be scored (9018 eliminated genes)
##
## Level 5: 387 nodes to be scored (9703 eliminated genes)
##
## Level 4: 202 nodes to be scored (10305 eliminated genes)
##
## Level 3: 53 nodes to be scored (10553 eliminated genes)
##
## Level 2: 20 nodes to be scored (10811 eliminated genes)
##
## Level 1: 1 nodes to be scored (10919 eliminated genes)all.res.BP.control.1_0.down <- GenTable(go.BP.control.1_0.down, Weight01 = result.BP.weight01.control.1_0.down, topNodes = table(result.BP.weight01.control.1_0.down@score < 0.01)[2])
showSigOfNodes(go.BP.control.1_0.down, score(result.BP.weight01.control.1_0.down), firstSigNodes = 10, useInfo = 'all')
## $dag
## A graphNEL graph with directed edges
## Number of Nodes = 87
## Number of Edges = 140
##
## $complete.dag
## [1] "A graph with 87 nodes."control.1_0.pval.up <- limmaTopGenes(fit.control.1_0, dir = 'up')
go.CC.control.1_0.up <- new("topGOdata", description="GO annotation control 1 vs 0", ontology="CC", allGenes = control.1_0.pval.up, geneSel = topDiffGenes, nodeSize = 10, annot = annFUN.org, mapping="org.Mm.eg.db", ID = "Ensembl")##
## Building most specific GOs ..... ( 1194 GO terms found. )
##
## Build GO DAG topology .......... ( 1420 GO terms and 2773 relations. )
##
## Annotating nodes ............... ( 13908 genes annotated to the GO terms. )result.CC.weight01.control.1_0.up <- runTest(go.CC.control.1_0.up, algorithm = 'weight01', statistic = "fisher")##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 550 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 16: 1 nodes to be scored (0 eliminated genes)
##
## Level 15: 6 nodes to be scored (0 eliminated genes)
##
## Level 14: 18 nodes to be scored (15 eliminated genes)
##
## Level 13: 19 nodes to be scored (84 eliminated genes)
##
## Level 12: 34 nodes to be scored (407 eliminated genes)
##
## Level 11: 74 nodes to be scored (825 eliminated genes)
##
## Level 10: 74 nodes to be scored (1673 eliminated genes)
##
## Level 9: 56 nodes to be scored (3377 eliminated genes)
##
## Level 8: 59 nodes to be scored (4551 eliminated genes)
##
## Level 7: 41 nodes to be scored (5036 eliminated genes)
##
## Level 6: 40 nodes to be scored (9293 eliminated genes)
##
## Level 5: 47 nodes to be scored (9456 eliminated genes)
##
## Level 4: 49 nodes to be scored (10565 eliminated genes)
##
## Level 3: 20 nodes to be scored (11504 eliminated genes)
##
## Level 2: 11 nodes to be scored (11718 eliminated genes)
##
## Level 1: 1 nodes to be scored (11729 eliminated genes)all.res.CC.control.1_0.up <- GenTable(go.CC.control.1_0.up, Weight01 = result.CC.weight01.control.1_0.up, topNodes = table(result.CC.weight01.control.1_0.up@score < 0.01)[2])
showSigOfNodes(go.CC.control.1_0.up, score(result.CC.weight01.control.1_0.up), firstSigNodes = 10, useInfo = 'all')
## $dag
## A graphNEL graph with directed edges
## Number of Nodes = 46
## Number of Edges = 75
##
## $complete.dag
## [1] "A graph with 46 nodes."control.1_0.pval.down <- limmaTopGenes(fit.control.1_0, dir = 'down')
go.CC.control.1_0.down <- new("topGOdata", description="GO annotation control 1 vs 0", ontology="CC", allGenes = control.1_0.pval.down, geneSel = topDiffGenes, nodeSize = 10, annot = annFUN.org, mapping="org.Mm.eg.db", ID = "Ensembl")##
## Building most specific GOs ..... ( 1194 GO terms found. )
##
## Build GO DAG topology .......... ( 1420 GO terms and 2773 relations. )
##
## Annotating nodes ............... ( 13908 genes annotated to the GO terms. )result.CC.weight01.control.1_0.down <- runTest(go.CC.control.1_0.down, algorithm = 'weight01', statistic = "fisher")##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 570 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 16: 1 nodes to be scored (0 eliminated genes)
##
## Level 15: 8 nodes to be scored (0 eliminated genes)
##
## Level 14: 24 nodes to be scored (15 eliminated genes)
##
## Level 13: 23 nodes to be scored (103 eliminated genes)
##
## Level 12: 35 nodes to be scored (470 eliminated genes)
##
## Level 11: 77 nodes to be scored (875 eliminated genes)
##
## Level 10: 74 nodes to be scored (1711 eliminated genes)
##
## Level 9: 55 nodes to be scored (3396 eliminated genes)
##
## Level 8: 61 nodes to be scored (4549 eliminated genes)
##
## Level 7: 40 nodes to be scored (5030 eliminated genes)
##
## Level 6: 41 nodes to be scored (9296 eliminated genes)
##
## Level 5: 47 nodes to be scored (9456 eliminated genes)
##
## Level 4: 52 nodes to be scored (10558 eliminated genes)
##
## Level 3: 20 nodes to be scored (11504 eliminated genes)
##
## Level 2: 11 nodes to be scored (11718 eliminated genes)
##
## Level 1: 1 nodes to be scored (11729 eliminated genes)all.res.CC.control.1_0.down <- GenTable(go.CC.control.1_0.down, Weight01 = result.CC.weight01.control.1_0.down, topNodes = table(result.CC.weight01.control.1_0.down@score < 0.01)[2])
showSigOfNodes(go.CC.control.1_0.down, score(result.CC.weight01.control.1_0.down), firstSigNodes = 10, useInfo = 'all')
## $dag
## A graphNEL graph with directed edges
## Number of Nodes = 44
## Number of Edges = 77
##
## $complete.dag
## [1] "A graph with 44 nodes."The last section reads in earlier results and runs the topGO analysis for these contrasts
source('topgofunctions.R')
load(file = "FitLM.rdata")
rm(fit.CeO2.time)
rm(fit.NAC.time)
rm(fit.SDC.time)
NAC.time7.pval.up <- limmaTopGenes(fit.NAC.time7, dir="up")
go.MF.NAC.time7.up <- new("topGOdata", description="GO annotation NAC time 7", ontology="MF", allGenes = NAC.time7.pval.up, 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.MF.NAC.time7.up <- runTest(go.MF.NAC.time7.up, algorithm = 'weight01', statistic = "fisher")##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 765 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 13: 2 nodes to be scored (0 eliminated genes)
##
## Level 12: 1 nodes to be scored (0 eliminated genes)
##
## Level 11: 5 nodes to be scored (53 eliminated genes)
##
## Level 10: 12 nodes to be scored (53 eliminated genes)
##
## Level 9: 49 nodes to be scored (171 eliminated genes)
##
## Level 8: 80 nodes to be scored (505 eliminated genes)
##
## Level 7: 105 nodes to be scored (2290 eliminated genes)
##
## Level 6: 146 nodes to be scored (3004 eliminated genes)
##
## Level 5: 161 nodes to be scored (4636 eliminated genes)
##
## Level 4: 133 nodes to be scored (6948 eliminated genes)
##
## Level 3: 55 nodes to be scored (9233 eliminated genes)
##
## Level 2: 15 nodes to be scored (9961 eliminated genes)
##
## Level 1: 1 nodes to be scored (10681 eliminated genes)allRes.MF.NAC.time7.up <- GenTable(go.MF.NAC.time7.up, Weight01 = result.MF.NAC.time7.up, topNodes = table(result.MF.NAC.time7.up@score < 0.01)[2])
NAC.time7.pval.down <- limmaTopGenes(fit.NAC.time7, dir="down")
go.MF.NAC.time7.down <- new("topGOdata", description="GO annotation NAC time 7", ontology="MF", allGenes = NAC.time7.pval.down, 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.MF.NAC.time7.down <- runTest(go.MF.NAC.time7.down, algorithm = 'weight01', statistic = "fisher")##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 888 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 13: 1 nodes to be scored (0 eliminated genes)
##
## Level 12: 2 nodes to be scored (0 eliminated genes)
##
## Level 11: 7 nodes to be scored (52 eliminated genes)
##
## Level 10: 18 nodes to be scored (71 eliminated genes)
##
## Level 9: 48 nodes to be scored (194 eliminated genes)
##
## Level 8: 92 nodes to be scored (596 eliminated genes)
##
## Level 7: 144 nodes to be scored (2288 eliminated genes)
##
## Level 6: 217 nodes to be scored (2945 eliminated genes)
##
## Level 5: 150 nodes to be scored (4614 eliminated genes)
##
## Level 4: 141 nodes to be scored (7032 eliminated genes)
##
## Level 3: 53 nodes to be scored (9225 eliminated genes)
##
## Level 2: 14 nodes to be scored (9945 eliminated genes)
##
## Level 1: 1 nodes to be scored (10648 eliminated genes)allRes.MF.NAC.time7.down <- GenTable(go.MF.NAC.time7.down, Weight01 = result.MF.NAC.time7.down, topNodes = table(result.MF.NAC.time7.down@score < 0.01)[2])
NAC.time7.pval.up <- limmaTopGenes(fit.NAC.time7, dir="up")
go.BP.NAC.time7.up <- new("topGOdata", description="GO annotation NAC time 7", ontology="BP", allGenes = NAC.time7.pval.up, 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.BP.NAC.time7.up <- runTest(go.BP.NAC.time7.up, algorithm = 'weight01', statistic = "fisher")##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 3744 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 18: 1 nodes to be scored (0 eliminated genes)
##
## Level 17: 3 nodes to be scored (0 eliminated genes)
##
## Level 16: 10 nodes to be scored (40 eliminated genes)
##
## Level 15: 23 nodes to be scored (129 eliminated genes)
##
## Level 14: 39 nodes to be scored (289 eliminated genes)
##
## Level 13: 87 nodes to be scored (595 eliminated genes)
##
## Level 12: 166 nodes to be scored (957 eliminated genes)
##
## Level 11: 287 nodes to be scored (1782 eliminated genes)
##
## Level 10: 416 nodes to be scored (3464 eliminated genes)
##
## Level 9: 507 nodes to be scored (4685 eliminated genes)
##
## Level 8: 552 nodes to be scored (6376 eliminated genes)
##
## Level 7: 545 nodes to be scored (7768 eliminated genes)
##
## Level 6: 485 nodes to be scored (8909 eliminated genes)
##
## Level 5: 355 nodes to be scored (9644 eliminated genes)
##
## Level 4: 196 nodes to be scored (10297 eliminated genes)
##
## Level 3: 51 nodes to be scored (10539 eliminated genes)
##
## Level 2: 20 nodes to be scored (10801 eliminated genes)
##
## Level 1: 1 nodes to be scored (10919 eliminated genes)allRes.BP.NAC.time7.up <- GenTable(go.BP.NAC.time7.up, Weight01 = result.BP.NAC.time7.up, topNodes = table(result.BP.NAC.time7.up@score < 0.01)[2])
NAC.time7.pval.down <- limmaTopGenes(fit.NAC.time7, dir="down")
go.BP.NAC.time7.down <- new("topGOdata", description="GO annotation NAC time 7", ontology="BP", allGenes = NAC.time7.pval.down, 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.BP.NAC.time7.down <- runTest(go.BP.NAC.time7.down, algorithm = 'weight01', statistic = "fisher")##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 3406 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 19: 1 nodes to be scored (0 eliminated genes)
##
## Level 18: 2 nodes to be scored (0 eliminated genes)
##
## Level 17: 5 nodes to be scored (10 eliminated genes)
##
## Level 16: 13 nodes to be scored (45 eliminated genes)
##
## Level 15: 19 nodes to be scored (138 eliminated genes)
##
## Level 14: 30 nodes to be scored (319 eliminated genes)
##
## Level 13: 68 nodes to be scored (544 eliminated genes)
##
## Level 12: 144 nodes to be scored (888 eliminated genes)
##
## Level 11: 253 nodes to be scored (1666 eliminated genes)
##
## Level 10: 369 nodes to be scored (3413 eliminated genes)
##
## Level 9: 454 nodes to be scored (4615 eliminated genes)
##
## Level 8: 491 nodes to be scored (6363 eliminated genes)
##
## Level 7: 496 nodes to be scored (7722 eliminated genes)
##
## Level 6: 468 nodes to be scored (8808 eliminated genes)
##
## Level 5: 339 nodes to be scored (9644 eliminated genes)
##
## Level 4: 183 nodes to be scored (10292 eliminated genes)
##
## Level 3: 50 nodes to be scored (10530 eliminated genes)
##
## Level 2: 20 nodes to be scored (10797 eliminated genes)
##
## Level 1: 1 nodes to be scored (10913 eliminated genes)allRes.BP.NAC.time7.down <- GenTable(go.BP.NAC.time7.down, Weight01 = result.BP.NAC.time7.down, topNodes = table(result.BP.NAC.time7.down@score < 0.01)[2])
NAC.time7.pval.up <- limmaTopGenes(fit.NAC.time7, dir="up")
go.CC.NAC.time7.up <- new("topGOdata", description="GO annotation NAC time 7", ontology="CC", allGenes = NAC.time7.pval.up, geneSel = topDiffGenes, nodeSize = 10, annot = annFUN.org, mapping="org.Mm.eg.db", ID = "Ensembl")##
## Building most specific GOs ..... ( 1194 GO terms found. )
##
## Build GO DAG topology .......... ( 1420 GO terms and 2773 relations. )
##
## Annotating nodes ............... ( 13908 genes annotated to the GO terms. )result.CC.NAC.time7.up <- runTest(go.CC.NAC.time7.up, algorithm = 'weight01', statistic = "fisher")##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 415 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 15: 3 nodes to be scored (0 eliminated genes)
##
## Level 14: 11 nodes to be scored (0 eliminated genes)
##
## Level 13: 13 nodes to be scored (50 eliminated genes)
##
## Level 12: 18 nodes to be scored (333 eliminated genes)
##
## Level 11: 51 nodes to be scored (740 eliminated genes)
##
## Level 10: 55 nodes to be scored (1551 eliminated genes)
##
## Level 9: 43 nodes to be scored (3149 eliminated genes)
##
## Level 8: 49 nodes to be scored (4334 eliminated genes)
##
## Level 7: 33 nodes to be scored (4960 eliminated genes)
##
## Level 6: 29 nodes to be scored (9259 eliminated genes)
##
## Level 5: 38 nodes to be scored (9443 eliminated genes)
##
## Level 4: 43 nodes to be scored (10559 eliminated genes)
##
## Level 3: 19 nodes to be scored (11503 eliminated genes)
##
## Level 2: 9 nodes to be scored (11718 eliminated genes)
##
## Level 1: 1 nodes to be scored (11729 eliminated genes)allRes.CC.NAC.time7.up <- GenTable(go.CC.NAC.time7.up, Weight01 = result.CC.NAC.time7.up, topNodes = table(result.CC.NAC.time7.up@score < 0.01)[2])
NAC.time7.pval.down <- limmaTopGenes(fit.NAC.time7, dir="down")
go.CC.NAC.time7.down <- new("topGOdata", description="GO annotation NAC time 7", ontology="CC", allGenes = NAC.time7.pval.down, geneSel = topDiffGenes, nodeSize = 10, annot = annFUN.org, mapping="org.Mm.eg.db", ID = "Ensembl")##
## Building most specific GOs ..... ( 1194 GO terms found. )
##
## Build GO DAG topology .......... ( 1420 GO terms and 2773 relations. )
##
## Annotating nodes ............... ( 13908 genes annotated to the GO terms. )result.CC.NAC.time7.down <- runTest(go.CC.NAC.time7.down, algorithm = 'weight01', statistic = "fisher")##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 416 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 15: 5 nodes to be scored (0 eliminated genes)
##
## Level 14: 10 nodes to be scored (0 eliminated genes)
##
## Level 13: 14 nodes to be scored (56 eliminated genes)
##
## Level 12: 20 nodes to be scored (295 eliminated genes)
##
## Level 11: 49 nodes to be scored (755 eliminated genes)
##
## Level 10: 55 nodes to be scored (1569 eliminated genes)
##
## Level 9: 46 nodes to be scored (3057 eliminated genes)
##
## Level 8: 46 nodes to be scored (4362 eliminated genes)
##
## Level 7: 32 nodes to be scored (4993 eliminated genes)
##
## Level 6: 32 nodes to be scored (9248 eliminated genes)
##
## Level 5: 38 nodes to be scored (9440 eliminated genes)
##
## Level 4: 41 nodes to be scored (10561 eliminated genes)
##
## Level 3: 18 nodes to be scored (11504 eliminated genes)
##
## Level 2: 9 nodes to be scored (11717 eliminated genes)
##
## Level 1: 1 nodes to be scored (11729 eliminated genes)allRes.CC.NAC.time7.down <- GenTable(go.CC.NAC.time7.down, Weight01 = result.CC.NAC.time7.down, topNodes = table(result.CC.NAC.time7.down@score < 0.01)[2])
CeO2.time7.pval.up <- limmaTopGenes(fit.CeO2.time7, dir="up")
go.MF.CeO2.time7.up <- new("topGOdata", description="GO annotation CeO2 time 7", ontology="MF", allGenes = CeO2.time7.pval.up, 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.MF.CeO2.time7.up <- runTest(go.MF.CeO2.time7.up, algorithm = 'weight01', statistic = "fisher")##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 1100 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 15: 2 nodes to be scored (0 eliminated genes)
##
## Level 14: 1 nodes to be scored (0 eliminated genes)
##
## Level 13: 3 nodes to be scored (19 eliminated genes)
##
## Level 12: 2 nodes to be scored (19 eliminated genes)
##
## Level 11: 6 nodes to be scored (53 eliminated genes)
##
## Level 10: 19 nodes to be scored (68 eliminated genes)
##
## Level 9: 74 nodes to be scored (210 eliminated genes)
##
## Level 8: 121 nodes to be scored (665 eliminated genes)
##
## Level 7: 181 nodes to be scored (2474 eliminated genes)
##
## Level 6: 265 nodes to be scored (3297 eliminated genes)
##
## Level 5: 192 nodes to be scored (4988 eliminated genes)
##
## Level 4: 156 nodes to be scored (7164 eliminated genes)
##
## Level 3: 61 nodes to be scored (9335 eliminated genes)
##
## Level 2: 16 nodes to be scored (10011 eliminated genes)
##
## Level 1: 1 nodes to be scored (10685 eliminated genes)allRes.MF.CeO2.time7.up <- GenTable(go.MF.CeO2.time7.up, Weight01 = result.MF.CeO2.time7.up, topNodes = table(result.MF.CeO2.time7.up@score < 0.01)[2])
CeO2.time7.pval.down <- limmaTopGenes(fit.CeO2.time7, dir="down")
go.MF.CeO2.time7.down <- new("topGOdata", description="GO annotation CeO2 time 7", ontology="MF", allGenes = CeO2.time7.pval.down, 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.MF.CeO2.time7.down <- runTest(go.MF.CeO2.time7.down, algorithm = 'weight01', statistic = "fisher")##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 1135 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 13: 2 nodes to be scored (0 eliminated genes)
##
## Level 12: 3 nodes to be scored (0 eliminated genes)
##
## Level 11: 10 nodes to be scored (53 eliminated genes)
##
## Level 10: 22 nodes to be scored (86 eliminated genes)
##
## Level 9: 77 nodes to be scored (237 eliminated genes)
##
## Level 8: 132 nodes to be scored (672 eliminated genes)
##
## Level 7: 192 nodes to be scored (2467 eliminated genes)
##
## Level 6: 279 nodes to be scored (3288 eliminated genes)
##
## Level 5: 188 nodes to be scored (4973 eliminated genes)
##
## Level 4: 151 nodes to be scored (7152 eliminated genes)
##
## Level 3: 62 nodes to be scored (9307 eliminated genes)
##
## Level 2: 16 nodes to be scored (9999 eliminated genes)
##
## Level 1: 1 nodes to be scored (10687 eliminated genes)allRes.MF.CeO2.time7.down <- GenTable(go.MF.CeO2.time7.down, Weight01 = result.MF.CeO2.time7.down, topNodes = table(result.MF.CeO2.time7.down@score < 0.01)[2])
CeO2.time7.pval.up <- limmaTopGenes(fit.CeO2.time7, dir="up")
go.BP.CeO2.time7.up <- new("topGOdata", description="GO annotation CeO2 time 7", ontology="BP", allGenes = CeO2.time7.pval.up, 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.BP.CeO2.time7.up <- runTest(go.BP.CeO2.time7.up, algorithm = 'weight01', statistic = "fisher")##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 4207 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 18: 1 nodes to be scored (0 eliminated genes)
##
## Level 17: 5 nodes to be scored (0 eliminated genes)
##
## Level 16: 11 nodes to be scored (40 eliminated genes)
##
## Level 15: 20 nodes to be scored (141 eliminated genes)
##
## Level 14: 51 nodes to be scored (290 eliminated genes)
##
## Level 13: 103 nodes to be scored (578 eliminated genes)
##
## Level 12: 195 nodes to be scored (1087 eliminated genes)
##
## Level 11: 343 nodes to be scored (1972 eliminated genes)
##
## Level 10: 477 nodes to be scored (3662 eliminated genes)
##
## Level 9: 593 nodes to be scored (4977 eliminated genes)
##
## Level 8: 625 nodes to be scored (6866 eliminated genes)
##
## Level 7: 604 nodes to be scored (8025 eliminated genes)
##
## Level 6: 526 nodes to be scored (9016 eliminated genes)
##
## Level 5: 379 nodes to be scored (9690 eliminated genes)
##
## Level 4: 201 nodes to be scored (10311 eliminated genes)
##
## Level 3: 52 nodes to be scored (10549 eliminated genes)
##
## Level 2: 20 nodes to be scored (10811 eliminated genes)
##
## Level 1: 1 nodes to be scored (10919 eliminated genes)allRes.BP.CeO2.time7.up <- GenTable(go.BP.CeO2.time7.up, Weight01 = result.BP.CeO2.time7.up, topNodes = table(result.BP.CeO2.time7.up@score < 0.01)[2])
CeO2.time7.pval.down <- limmaTopGenes(fit.CeO2.time7, dir="down")
go.BP.CeO2.time7.down <- new("topGOdata", description="GO annotation CeO2 time 7", ontology="BP", allGenes = CeO2.time7.pval.down, 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.BP.CeO2.time7.down <- runTest(go.BP.CeO2.time7.down, algorithm = 'weight01', statistic = "fisher")##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 4092 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 19: 1 nodes to be scored (0 eliminated genes)
##
## Level 18: 2 nodes to be scored (0 eliminated genes)
##
## Level 17: 7 nodes to be scored (10 eliminated genes)
##
## Level 16: 13 nodes to be scored (45 eliminated genes)
##
## Level 15: 24 nodes to be scored (148 eliminated genes)
##
## Level 14: 45 nodes to be scored (304 eliminated genes)
##
## Level 13: 96 nodes to be scored (600 eliminated genes)
##
## Level 12: 186 nodes to be scored (1042 eliminated genes)
##
## Level 11: 338 nodes to be scored (1898 eliminated genes)
##
## Level 10: 460 nodes to be scored (3651 eliminated genes)
##
## Level 9: 562 nodes to be scored (4966 eliminated genes)
##
## Level 8: 596 nodes to be scored (6804 eliminated genes)
##
## Level 7: 598 nodes to be scored (7972 eliminated genes)
##
## Level 6: 517 nodes to be scored (8967 eliminated genes)
##
## Level 5: 375 nodes to be scored (9678 eliminated genes)
##
## Level 4: 198 nodes to be scored (10298 eliminated genes)
##
## Level 3: 53 nodes to be scored (10549 eliminated genes)
##
## Level 2: 20 nodes to be scored (10811 eliminated genes)
##
## Level 1: 1 nodes to be scored (10919 eliminated genes)allRes.BP.CeO2.time7.down <- GenTable(go.BP.CeO2.time7.down, Weight01 = result.BP.CeO2.time7.down, topNodes = table(result.BP.CeO2.time7.down@score < 0.01)[2])
CeO2.time7.pval.up <- limmaTopGenes(fit.CeO2.time7, dir="up")
go.CC.CeO2.time7.up <- new("topGOdata", description="GO annotation CeO2 time 7", ontology="CC", allGenes = CeO2.time7.pval.up, geneSel = topDiffGenes, nodeSize = 10, annot = annFUN.org, mapping="org.Mm.eg.db", ID = "Ensembl")##
## Building most specific GOs ..... ( 1194 GO terms found. )
##
## Build GO DAG topology .......... ( 1420 GO terms and 2773 relations. )
##
## Annotating nodes ............... ( 13908 genes annotated to the GO terms. )result.CC.CeO2.time7.up <- runTest(go.CC.CeO2.time7.up, algorithm = 'weight01', statistic = "fisher")##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 528 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 15: 5 nodes to be scored (0 eliminated genes)
##
## Level 14: 18 nodes to be scored (0 eliminated genes)
##
## Level 13: 17 nodes to be scored (70 eliminated genes)
##
## Level 12: 35 nodes to be scored (420 eliminated genes)
##
## Level 11: 72 nodes to be scored (794 eliminated genes)
##
## Level 10: 72 nodes to be scored (1705 eliminated genes)
##
## Level 9: 52 nodes to be scored (3391 eliminated genes)
##
## Level 8: 56 nodes to be scored (4557 eliminated genes)
##
## Level 7: 40 nodes to be scored (5006 eliminated genes)
##
## Level 6: 38 nodes to be scored (9280 eliminated genes)
##
## Level 5: 42 nodes to be scored (9454 eliminated genes)
##
## Level 4: 49 nodes to be scored (10562 eliminated genes)
##
## Level 3: 20 nodes to be scored (11499 eliminated genes)
##
## Level 2: 11 nodes to be scored (11718 eliminated genes)
##
## Level 1: 1 nodes to be scored (11729 eliminated genes)allRes.CC.CeO2.time7.up <- GenTable(go.CC.CeO2.time7.up, Weight01 = result.CC.CeO2.time7.up, topNodes = table(result.CC.CeO2.time7.up@score < 0.01)[2])
CeO2.time7.pval.down <- limmaTopGenes(fit.CeO2.time7, dir="down")
go.CC.CeO2.time7.down <- new("topGOdata", description="GO annotation CeO2 time 7", ontology="CC", allGenes = CeO2.time7.pval.down, geneSel = topDiffGenes, nodeSize = 10, annot = annFUN.org, mapping="org.Mm.eg.db", ID = "Ensembl")##
## Building most specific GOs ..... ( 1194 GO terms found. )
##
## Build GO DAG topology .......... ( 1420 GO terms and 2773 relations. )
##
## Annotating nodes ............... ( 13908 genes annotated to the GO terms. )result.CC.CeO2.time7.down <- runTest(go.CC.CeO2.time7.down, algorithm = 'weight01', statistic = "fisher")##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 529 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 16: 1 nodes to be scored (0 eliminated genes)
##
## Level 15: 8 nodes to be scored (0 eliminated genes)
##
## Level 14: 22 nodes to be scored (15 eliminated genes)
##
## Level 13: 21 nodes to be scored (103 eliminated genes)
##
## Level 12: 30 nodes to be scored (451 eliminated genes)
##
## Level 11: 68 nodes to be scored (843 eliminated genes)
##
## Level 10: 73 nodes to be scored (1639 eliminated genes)
##
## Level 9: 55 nodes to be scored (3303 eliminated genes)
##
## Level 8: 57 nodes to be scored (4548 eliminated genes)
##
## Level 7: 39 nodes to be scored (5036 eliminated genes)
##
## Level 6: 36 nodes to be scored (9281 eliminated genes)
##
## Level 5: 42 nodes to be scored (9451 eliminated genes)
##
## Level 4: 48 nodes to be scored (10556 eliminated genes)
##
## Level 3: 19 nodes to be scored (11497 eliminated genes)
##
## Level 2: 9 nodes to be scored (11718 eliminated genes)
##
## Level 1: 1 nodes to be scored (11729 eliminated genes)allRes.CC.CeO2.time7.down <- GenTable(go.CC.CeO2.time7.down, Weight01 = result.CC.CeO2.time7.down, topNodes = table(result.CC.CeO2.time7.down@score < 0.01)[2])
SDC.time7.pval.up <- limmaTopGenes(fit.SDC.time7, dir="up")
go.MF.SDC.time7.up <- new("topGOdata", description="GO annotation SDC time 7", ontology="MF", allGenes = SDC.time7.pval.up, 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.MF.SDC.time7.up <- runTest(go.MF.SDC.time7.up, algorithm = 'weight01', statistic = "fisher")##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 634 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 15: 1 nodes to be scored (0 eliminated genes)
##
## Level 14: 1 nodes to be scored (0 eliminated genes)
##
## Level 13: 3 nodes to be scored (10 eliminated genes)
##
## Level 12: 1 nodes to be scored (19 eliminated genes)
##
## Level 11: 3 nodes to be scored (53 eliminated genes)
##
## Level 10: 10 nodes to be scored (53 eliminated genes)
##
## Level 9: 36 nodes to be scored (93 eliminated genes)
##
## Level 8: 58 nodes to be scored (386 eliminated genes)
##
## Level 7: 92 nodes to be scored (2198 eliminated genes)
##
## Level 6: 148 nodes to be scored (2848 eliminated genes)
##
## Level 5: 122 nodes to be scored (4473 eliminated genes)
##
## Level 4: 100 nodes to be scored (6775 eliminated genes)
##
## Level 3: 44 nodes to be scored (8963 eliminated genes)
##
## Level 2: 14 nodes to be scored (9767 eliminated genes)
##
## Level 1: 1 nodes to be scored (10671 eliminated genes)allRes.MF.SDC.time7.up <- GenTable(go.MF.SDC.time7.up, Weight01 = result.MF.SDC.time7.up, topNodes = table(result.MF.SDC.time7.up@score < 0.01)[2])
SDC.time7.pval.down <- limmaTopGenes(fit.SDC.time7, dir="down")
go.MF.SDC.time7.down <- new("topGOdata", description="GO annotation SDC time 7", ontology="MF", allGenes = SDC.time7.pval.down, 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.MF.SDC.time7.down <- runTest(go.MF.SDC.time7.down, algorithm = 'weight01', statistic = "fisher")##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 755 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 13: 1 nodes to be scored (0 eliminated genes)
##
## Level 12: 3 nodes to be scored (0 eliminated genes)
##
## Level 11: 6 nodes to be scored (52 eliminated genes)
##
## Level 10: 16 nodes to be scored (86 eliminated genes)
##
## Level 9: 41 nodes to be scored (181 eliminated genes)
##
## Level 8: 87 nodes to be scored (619 eliminated genes)
##
## Level 7: 138 nodes to be scored (2245 eliminated genes)
##
## Level 6: 185 nodes to be scored (2972 eliminated genes)
##
## Level 5: 118 nodes to be scored (4464 eliminated genes)
##
## Level 4: 103 nodes to be scored (6883 eliminated genes)
##
## Level 3: 44 nodes to be scored (9071 eliminated genes)
##
## Level 2: 12 nodes to be scored (9803 eliminated genes)
##
## Level 1: 1 nodes to be scored (10605 eliminated genes)allRes.MF.SDC.time7.down <- GenTable(go.MF.SDC.time7.down, Weight01 = result.MF.SDC.time7.down, topNodes = table(result.MF.SDC.time7.down@score < 0.01)[2])
SDC.time7.pval.up <- limmaTopGenes(fit.SDC.time7, dir="up")
go.BP.SDC.time7.up <- new("topGOdata", description="GO annotation SDC time 7", ontology="BP", allGenes = SDC.time7.pval.up, 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.BP.SDC.time7.up <- runTest(go.BP.SDC.time7.up, algorithm = 'weight01', statistic = "fisher")##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 3005 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 17: 2 nodes to be scored (0 eliminated genes)
##
## Level 16: 5 nodes to be scored (0 eliminated genes)
##
## Level 15: 7 nodes to be scored (121 eliminated genes)
##
## Level 14: 15 nodes to be scored (227 eliminated genes)
##
## Level 13: 54 nodes to be scored (419 eliminated genes)
##
## Level 12: 108 nodes to be scored (707 eliminated genes)
##
## Level 11: 201 nodes to be scored (1522 eliminated genes)
##
## Level 10: 314 nodes to be scored (3176 eliminated genes)
##
## Level 9: 396 nodes to be scored (4442 eliminated genes)
##
## Level 8: 454 nodes to be scored (6274 eliminated genes)
##
## Level 7: 456 nodes to be scored (7543 eliminated genes)
##
## Level 6: 415 nodes to be scored (8764 eliminated genes)
##
## Level 5: 324 nodes to be scored (9566 eliminated genes)
##
## Level 4: 181 nodes to be scored (10275 eliminated genes)
##
## Level 3: 52 nodes to be scored (10542 eliminated genes)
##
## Level 2: 20 nodes to be scored (10807 eliminated genes)
##
## Level 1: 1 nodes to be scored (10919 eliminated genes)allRes.BP.SDC.time7.up <- GenTable(go.BP.SDC.time7.up, Weight01 = result.BP.SDC.time7.up, topNodes = table(result.BP.SDC.time7.up@score < 0.01)[2])
SDC.time7.pval.down <- limmaTopGenes(fit.SDC.time7, dir="down")
go.BP.SDC.time7.down <- new("topGOdata", description="GO annotation SDC time 7", ontology="BP", allGenes = SDC.time7.pval.down, 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.BP.SDC.time7.down <- runTest(go.BP.SDC.time7.down, algorithm = 'weight01', statistic = "fisher")##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 2905 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 19: 1 nodes to be scored (0 eliminated genes)
##
## Level 18: 2 nodes to be scored (0 eliminated genes)
##
## Level 17: 3 nodes to be scored (10 eliminated genes)
##
## Level 16: 10 nodes to be scored (45 eliminated genes)
##
## Level 15: 16 nodes to be scored (96 eliminated genes)
##
## Level 14: 25 nodes to be scored (275 eliminated genes)
##
## Level 13: 57 nodes to be scored (522 eliminated genes)
##
## Level 12: 110 nodes to be scored (846 eliminated genes)
##
## Level 11: 203 nodes to be scored (1537 eliminated genes)
##
## Level 10: 288 nodes to be scored (3253 eliminated genes)
##
## Level 9: 378 nodes to be scored (4480 eliminated genes)
##
## Level 8: 411 nodes to be scored (6223 eliminated genes)
##
## Level 7: 434 nodes to be scored (7561 eliminated genes)
##
## Level 6: 405 nodes to be scored (8658 eliminated genes)
##
## Level 5: 311 nodes to be scored (9538 eliminated genes)
##
## Level 4: 180 nodes to be scored (10245 eliminated genes)
##
## Level 3: 50 nodes to be scored (10530 eliminated genes)
##
## Level 2: 20 nodes to be scored (10804 eliminated genes)
##
## Level 1: 1 nodes to be scored (10913 eliminated genes)allRes.BP.SDC.time7.down <- GenTable(go.BP.SDC.time7.down, Weight01 = result.BP.SDC.time7.down, topNodes = table(result.BP.SDC.time7.down@score < 0.01)[2])
SDC.time7.pval.up <- limmaTopGenes(fit.SDC.time7, dir="up")
go.CC.SDC.time7.up <- new("topGOdata", description="GO annotation SDC time 7", ontology="CC", allGenes = SDC.time7.pval.up, geneSel = topDiffGenes, nodeSize = 10, annot = annFUN.org, mapping="org.Mm.eg.db", ID = "Ensembl")##
## Building most specific GOs ..... ( 1194 GO terms found. )
##
## Build GO DAG topology .......... ( 1420 GO terms and 2773 relations. )
##
## Annotating nodes ............... ( 13908 genes annotated to the GO terms. )result.CC.SDC.time7.up <- runTest(go.CC.SDC.time7.up, algorithm = 'weight01', statistic = "fisher")##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 317 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 14: 4 nodes to be scored (0 eliminated genes)
##
## Level 13: 3 nodes to be scored (0 eliminated genes)
##
## Level 12: 11 nodes to be scored (201 eliminated genes)
##
## Level 11: 34 nodes to be scored (431 eliminated genes)
##
## Level 10: 39 nodes to be scored (1298 eliminated genes)
##
## Level 9: 36 nodes to be scored (2779 eliminated genes)
##
## Level 8: 41 nodes to be scored (3884 eliminated genes)
##
## Level 7: 23 nodes to be scored (4704 eliminated genes)
##
## Level 6: 21 nodes to be scored (9229 eliminated genes)
##
## Level 5: 37 nodes to be scored (9403 eliminated genes)
##
## Level 4: 40 nodes to be scored (10529 eliminated genes)
##
## Level 3: 18 nodes to be scored (11500 eliminated genes)
##
## Level 2: 9 nodes to be scored (11717 eliminated genes)
##
## Level 1: 1 nodes to be scored (11729 eliminated genes)allRes.CC.SDC.time7.up <- GenTable(go.CC.SDC.time7.up, Weight01 = result.CC.SDC.time7.up, topNodes = table(result.CC.SDC.time7.up@score < 0.01)[2])
SDC.time7.pval.down <- limmaTopGenes(fit.SDC.time7, dir="down")
go.CC.SDC.time7.down <- new("topGOdata", description="GO annotation SDC time 7", ontology="CC", allGenes = SDC.time7.pval.down, geneSel = topDiffGenes, nodeSize = 10, annot = annFUN.org, mapping="org.Mm.eg.db", ID = "Ensembl")##
## Building most specific GOs ..... ( 1194 GO terms found. )
##
## Build GO DAG topology .......... ( 1420 GO terms and 2773 relations. )
##
## Annotating nodes ............... ( 13908 genes annotated to the GO terms. )result.CC.SDC.time7.down <- runTest(go.CC.SDC.time7.down, algorithm = 'weight01', statistic = "fisher")##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 344 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 15: 1 nodes to be scored (0 eliminated genes)
##
## Level 14: 7 nodes to be scored (0 eliminated genes)
##
## Level 13: 9 nodes to be scored (29 eliminated genes)
##
## Level 12: 11 nodes to be scored (249 eliminated genes)
##
## Level 11: 35 nodes to be scored (580 eliminated genes)
##
## Level 10: 49 nodes to be scored (1477 eliminated genes)
##
## Level 9: 41 nodes to be scored (2772 eliminated genes)
##
## Level 8: 43 nodes to be scored (4305 eliminated genes)
##
## Level 7: 29 nodes to be scored (4937 eliminated genes)
##
## Level 6: 25 nodes to be scored (9236 eliminated genes)
##
## Level 5: 32 nodes to be scored (9434 eliminated genes)
##
## Level 4: 34 nodes to be scored (10545 eliminated genes)
##
## Level 3: 18 nodes to be scored (11489 eliminated genes)
##
## Level 2: 9 nodes to be scored (11717 eliminated genes)
##
## Level 1: 1 nodes to be scored (11729 eliminated genes)allRes.CC.SDC.time7.down <- GenTable(go.CC.SDC.time7.down, Weight01 = result.CC.SDC.time7.down, topNodes = table(result.CC.SDC.time7.down@score < 0.01)[2])
save(list=ls(pattern = "allRes."), file = "TopGO.rdata")```