TopGO - CTL04
Date: July 18, 2025
Condition_1 <- params$Condition_1
Condition_2 <- params$Condition_2Top GO Analysis for LivX Inhibitor
1. Environment Set Up
library(RNASeqBulkExploratory)
library(DT)
library(ggplot2)
library(AnnotationDbi)
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library(org.Hs.eg.db)
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library(RColorBrewer)
library(viridis)
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library(topGO)
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library(SummarizedExperiment)
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## Loading required package: GenomicRanges
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library(sechm)
source("../../plotGenesInTerm_v2.R")Dataset <- params$Dataset
logFcTh <- params$logFcTh
FdrTh <- params$FdrTh
OutputFolder <- ifelse(is.null(params$OutputFolder), getwd(), params$OutputFolder)
if (dir.exists(OutputFolder) == FALSE) {
dir.create(OutputFolder, recursive=TRUE)
}2. Data Upload
- Summarized Experiment object containing expression data used for DEA
and gene and sample metadata
- DEA object, containing results of the differential expression
2.1 Load Data from DEA
# List with differential expression results
DEA <- readRDS(params$DEAFile)
#SE object coming from DEA, but not containing specific contrast results
SE_DEA <- readRDS(params$SEFile)2.2 Add DEA results to SE
if(! identical(rownames(SE_DEA), row.names(DEA[[Condition_1]][[Condition_2]]$Res))){
stop('Expression data in SE and results from differential espression analysis are inconsistent.')
}
## Loading required package: DESeq2
rowData(SE_DEA) <- cbind(rowData(SE_DEA)[,1:6], DEA[[Condition_1]][[Condition_2]]$Res)
# Column names must be set to be compliant with the required format to be recognized by ORA
names(rowData(SE_DEA))[which(names(rowData(SE_DEA))=='log2FoldChange')] <- 'logFC'
names(rowData(SE_DEA))[which(names(rowData(SE_DEA))=='padj')] <- 'FDR'
#metadata(SE_DEA_Prel)$annotation <- 'hsa'14305 genes in 21 samples have been testes for differential expression.
Imposing a threshold of 1 on the Log2FC and 0.01 on the FDR (as specified in parameters), 351 genes are selected: 149 up-regulated genes and 202 down-regulated genes.
4. RESULTS VISUALIZATION
4.1 Volcano plot
The results of the differential expression analysis are visualized by Volcano plot. An interactive version is included in the html (only genes with FDR < threshold), while a static version is saved.
plotVolcanoSE(SE=SE_DEA, FdrTh=FdrTh, logFcTh=logFcTh, FdrCeil=1e-10, logFcCeil=4)4.3 Heatmap for significant genes
Heatmaps for DEGs, showing scaled vst values.
DEGs <- dplyr::filter(data.frame(rowData(SE_DEA)), FDR < FdrTh & abs(logFC) > logFcTh)
ScaledCols <- c('darkblue', "purple","white","lightgoldenrod1", 'goldenrod1')
colData(SE_DEA)$Condition <- factor(colData(SE_DEA)$Condition, levels=c("CTL", "DMSO", "AhHyd_Ag", "AhHyd_Inh", "Andr_Ag", "Andr_Inh", "Estr_Ag", "Estr_Inh", "GC_Ag", "GC_Inh", "LivX_Ag", "LivX_Inh", "Ret_Ag", "Ret_Inh", "Thyr_Ag", "Thyr_Inh" ))
metadata(SE_DEA)$anno_colors <- list(Condition = c('DMSO' = 'grey30', 'CTL' = 'azure3',
'AhHyd_Ag'='#F8766D', 'AhHyd_Inh'='#F8766D50',
'Andr_Ag'='#fccb17', 'Andr_Inh'='#C49A0050',
"Estr_Ag"= '#53B400', "Estr_Inh"= '#53B40050',
'GC_Ag' = '#00C094', 'GC_Inh' = '#00C09450',
'LivX_Ag' = '#00B6EB', 'LivX_Inh' = '#00B6EB50',
'Ret_Ag' = '#A58AFF', 'Ret_Inh' = '#A58AFF50',
'Thyr_Ag' = '#FB61D7', 'Thyr_Inh' = '#FB61D750'
))
sechm(SE_DEA, features=DEGs$GeneName, assayName="vst", gaps_at="Condition", show_rownames=FALSE,
top_annotation=c('Condition'), hmcols=ScaledCols, show_colnames=TRUE,
do.scale=TRUE, breaks=0.85)
5. TOPGO for Gene Ontology Enrichment analysis
Gene ontology enrichment analysis is performed on the set of 351 genes using TopGO with Fisher statistics and weight01 algorithm.
For each specified domain of the ontology:
- Enrichment analysis on all DEGs or splitted in down- and up-regulated
5.1 Selection of modulated genes and generation of gene vectors
I generate vectors for the gene universe, all modulated genes, up-regulated genes and down-regulated genes in the format required by TopGo.
GeneVectors <- topGOGeneVectors(SE_DEA, FdrTh=FdrTh, logFcTh=logFcTh)## Gene vector contains levels: 0,1
## Gene vector contains levels: 0,1
## Gene vector contains levels: 0,1Therefore:
- universe genes: 14305 genes
- modulated genes: 351 genes
- down-regulated genes: 202 genes of interest
- up-regulated genes: 149 genes of interest
Then I set parameters according to the gene ontology domains to be evaluated. By default, Biological Process and Molecular Function domains are interrogated.
BpEval <- ifelse(length(grep('BP', params$TopGO))!=0, TRUE, FALSE)
MfEval <- ifelse(length(grep('MF', params$TopGO))!=0, TRUE, FALSE)
CcEval <- ifelse(length(grep('CC', params$TopGO))!=0, TRUE, FALSE)5.2 TopGO analysis: Biological Process
On the basis of the analysis settings, the enrichment for Biological Process IS performed.
Biological Process Analysis for ALL modulated genes: 351 genes
BPann <- topGO::annFUN.org(whichOnto="BP", feasibleGenes=names(GeneVectors$DEGenes),
mapping="org.Hs.eg.db", ID="symbol") %>% inverseList()
# Wrapper function for topGO analysis
ResBPAll <- topGOResults(Genes=GeneVectors$DEGenes, gene2GO=BPann, ontology='BP',
desc=NULL, nodeSize=15, algorithm='weight01', statistic='fisher',
EnTh=params$GoEnTh, PvalTh=params$GoPvalTh, minTerms=12, geneTh=4,
saveRes=TRUE, outDir=paste0(OutputFolder), fileName='BPAll')
## Gene vector contains levels: 0,1
##
## Building most specific GOs .....
## ( 11416 GO terms found. )
##
## Build GO DAG topology ..........
## ( 14877 GO terms and 33562 relations. )
##
## Annotating nodes ...............
## ( 12665 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 3552 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 18: 1 nodes to be scored (0 eliminated genes)
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## Level 17: 2 nodes to be scored (0 eliminated genes)
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## Level 16: 5 nodes to be scored (18 eliminated genes)
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## Level 15: 12 nodes to be scored (48 eliminated genes)
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## Level 14: 25 nodes to be scored (119 eliminated genes)
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## Level 13: 52 nodes to be scored (324 eliminated genes)
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## Level 12: 97 nodes to be scored (973 eliminated genes)
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## Level 11: 199 nodes to be scored (2866 eliminated genes)
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## Level 10: 348 nodes to be scored (4587 eliminated genes)
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## Level 9: 462 nodes to be scored (5963 eliminated genes)
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## Level 8: 529 nodes to be scored (7847 eliminated genes)
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## Level 7: 622 nodes to be scored (9635 eliminated genes)
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## Level 6: 542 nodes to be scored (10931 eliminated genes)
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## Level 5: 350 nodes to be scored (11764 eliminated genes)
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## Level 4: 201 nodes to be scored (12235 eliminated genes)
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## Level 3: 87 nodes to be scored (12430 eliminated genes)
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## Level 2: 17 nodes to be scored (12506 eliminated genes)
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## Level 1: 1 nodes to be scored (12545 eliminated genes)Biological Process Analysis for DOWN-REGULATED genes: 202 genes
# Wrapper function for topGO analysis
ResBPDown <- topGOResults(Genes=GeneVectors$DEGenesDown, gene2GO=BPann, ontology='BP',
desc=NULL, nodeSize=15, algorithm='weight01', statistic='fisher',
EnTh=params$GoEnTh, PvalTh=params$GoPvalTh, minTerms=12, geneTh=4,
saveRes=TRUE, outDir=paste0(OutputFolder), fileName='BPDown')
## Gene vector contains levels: 0,1
##
## Building most specific GOs .....
## ( 11416 GO terms found. )
##
## Build GO DAG topology ..........
## ( 14877 GO terms and 33562 relations. )
##
## Annotating nodes ...............
## ( 12665 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 3002 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 18: 1 nodes to be scored (0 eliminated genes)
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## Level 17: 1 nodes to be scored (0 eliminated genes)
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## Level 16: 2 nodes to be scored (18 eliminated genes)
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## Level 15: 7 nodes to be scored (23 eliminated genes)
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## Level 14: 19 nodes to be scored (42 eliminated genes)
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## Level 13: 42 nodes to be scored (189 eliminated genes)
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## Level 12: 80 nodes to be scored (829 eliminated genes)
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## Level 11: 150 nodes to be scored (2646 eliminated genes)
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## Level 10: 256 nodes to be scored (4279 eliminated genes)
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## Level 9: 369 nodes to be scored (5592 eliminated genes)
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## Level 8: 438 nodes to be scored (7332 eliminated genes)
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## Level 7: 534 nodes to be scored (9054 eliminated genes)
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## Level 6: 487 nodes to be scored (10737 eliminated genes)
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## Level 5: 328 nodes to be scored (11673 eliminated genes)
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## Level 4: 188 nodes to be scored (12217 eliminated genes)
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## Level 3: 82 nodes to be scored (12419 eliminated genes)
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## Level 2: 17 nodes to be scored (12504 eliminated genes)
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## Level 1: 1 nodes to be scored (12544 eliminated genes)GOTable(ResBPDown$ResSel, maxGO=20)Biological Process Analysis for UP-REGULATED genes: 149 genes
ResBPUp <- topGOResults(Genes=GeneVectors$DEGenesUp, gene2GO=BPann, ontology='BP',
desc=NULL, nodeSize=15, algorithm='weight01', statistic='fisher',
EnTh=params$GoEnTh, PvalTh=params$GoPvalTh, minTerms=12, geneTh=4,
saveRes=TRUE, outDir=OutputFolder, fileName='BPUp')
## Gene vector contains levels: 0,1
##
## Building most specific GOs .....
## ( 11416 GO terms found. )
##
## Build GO DAG topology ..........
## ( 14877 GO terms and 33562 relations. )
##
## Annotating nodes ...............
## ( 12665 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 2410 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 17: 1 nodes to be scored (0 eliminated genes)
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## Level 16: 3 nodes to be scored (0 eliminated genes)
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## Level 15: 6 nodes to be scored (25 eliminated genes)
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## Level 14: 11 nodes to be scored (77 eliminated genes)
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## Level 13: 27 nodes to be scored (201 eliminated genes)
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## Level 12: 52 nodes to be scored (587 eliminated genes)
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## Level 11: 117 nodes to be scored (2525 eliminated genes)
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## Level 10: 216 nodes to be scored (4167 eliminated genes)
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## Level 9: 298 nodes to be scored (5240 eliminated genes)
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## Level 8: 350 nodes to be scored (6907 eliminated genes)
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## Level 7: 422 nodes to be scored (8882 eliminated genes)
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## Level 6: 381 nodes to be scored (10469 eliminated genes)
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## Level 5: 275 nodes to be scored (11508 eliminated genes)
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## Level 4: 159 nodes to be scored (12120 eliminated genes)
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## Level 3: 74 nodes to be scored (12389 eliminated genes)
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## Level 2: 17 nodes to be scored (12501 eliminated genes)
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## Level 1: 1 nodes to be scored (12544 eliminated genes)GOTable(ResBPUp$ResSel, maxGO=20)Result visualization: Barplot
topGOBarplotAll(TopGOResAll=ResBPAll$ResSel, TopGOResDown=ResBPDown$ResSel, TopGOResUp=ResBPUp$ResSel,
terms=8, pvalTh=0.01, plotTitle=NULL)
Top Terms associated Genes
All
plotGenesInTerm_v2(ResBPAll$ResSel, ResBPAll$GOdata, SE_DEA, nterms=8, ngenes=12, plotTitle=NULL, Interactive=FALSE)
Down
plotGenesInTerm_v2(ResBPDown$ResSel, ResBPDown$GOdata, SE_DEA, nterms=8, ngenes=12, plotTitle='Genes in Term - Down DEGs', Interactive=FALSE, fillCol='blue')
Up
plotGenesInTerm_v2(ResBPUp$ResSel, ResBPUp$GOdata, SE_DEA, nterms=8, ngenes=12, plotTitle='Genes in Term - Up DEGs', Interactive=FALSE, fillCol='red')
5.3 TopGO analysis: Molecular Function
On the basis of the analysis settings, the enrichment for Molecular Function IS performed.
Molecular Function Enrichment for ALL modulated genes: 351 genes
MFann <- topGO::annFUN.org(whichOnto='MF', feasibleGenes=names(GeneVectors$DEGenes),
mapping='org.Hs.eg.db', ID='symbol') %>% inverseList()
# Wrapper function for topGO analysis
ResMFAll <- topGOResults(Genes=GeneVectors$DEGenes, gene2GO=MFann, ontology='MF',
desc=NULL, nodeSize=15, algorithm='weight01', statistic='fisher',
EnTh=params$GoEnTh, PvalTh=params$GoPvalTh, minTerms=12, geneTh=4,
saveRes=TRUE, outDir=OutputFolder, fileName='MFAll')
## Gene vector contains levels: 0,1
##
## Building most specific GOs .....
## ( 4060 GO terms found. )
##
## Build GO DAG topology ..........
## ( 4530 GO terms and 5903 relations. )
##
## Annotating nodes ...............
## ( 12999 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 529 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 11: 3 nodes to be scored (0 eliminated genes)
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## Level 10: 9 nodes to be scored (0 eliminated genes)
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## Level 9: 17 nodes to be scored (70 eliminated genes)
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## Level 8: 26 nodes to be scored (1140 eliminated genes)
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## Level 7: 59 nodes to be scored (2998 eliminated genes)
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## Level 6: 89 nodes to be scored (3410 eliminated genes)
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## Level 5: 128 nodes to be scored (4821 eliminated genes)
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## Level 4: 134 nodes to be scored (7640 eliminated genes)
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## Level 3: 48 nodes to be scored (10249 eliminated genes)
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## Level 2: 15 nodes to be scored (11112 eliminated genes)
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## Level 1: 1 nodes to be scored (12880 eliminated genes)Molecular Function Enrichment for DOWN-REGULATED genes: 202 genes
ResMFDown <- topGOResults(Genes=GeneVectors$DEGenesDown, gene2GO=MFann, ontology='MF',
desc=NULL, nodeSize=15, algorithm='weight01', statistic='fisher',
EnTh=params$GoEnTh, PvalTh=params$GoPvalTh, minTerms=12, geneTh=4,
saveRes=TRUE, outDir=OutputFolder, fileName='MFDown')
## Gene vector contains levels: 0,1
##
## Building most specific GOs .....
## ( 4060 GO terms found. )
##
## Build GO DAG topology ..........
## ( 4530 GO terms and 5903 relations. )
##
## Annotating nodes ...............
## ( 12999 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 451 nontrivial nodes
## parameters:
## test statistic: fisher
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## Level 11: 2 nodes to be scored (0 eliminated genes)
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## Level 10: 7 nodes to be scored (0 eliminated genes)
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## Level 9: 16 nodes to be scored (55 eliminated genes)
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## Level 8: 24 nodes to be scored (1118 eliminated genes)
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## Level 7: 52 nodes to be scored (2985 eliminated genes)
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## Level 6: 73 nodes to be scored (3380 eliminated genes)
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## Level 5: 109 nodes to be scored (4604 eliminated genes)
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## Level 4: 110 nodes to be scored (7211 eliminated genes)
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## Level 3: 44 nodes to be scored (10110 eliminated genes)
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## Level 2: 13 nodes to be scored (10962 eliminated genes)
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## Level 1: 1 nodes to be scored (12865 eliminated genes)GOTable(ResMFDown$ResSel, maxGO=20)Molecular Function Analysis for UP-REGULATED genes: 149 genes
ResMFUp <- topGOResults(Genes=GeneVectors$DEGenesUp, gene2GO=MFann, ontology='MF',
desc=NULL, nodeSize=15, algorithm='weight01', statistic='fisher',
EnTh=params$GoEnTh, PvalTh=params$GoPvalTh, minTerms=12, geneTh=4,
saveRes=TRUE, outDir=OutputFolder, fileName='MFUp')
## Gene vector contains levels: 0,1
##
## Building most specific GOs .....
## ( 4060 GO terms found. )
##
## Build GO DAG topology ..........
## ( 4530 GO terms and 5903 relations. )
##
## Annotating nodes ...............
## ( 12999 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 317 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 11: 2 nodes to be scored (0 eliminated genes)
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## Level 10: 3 nodes to be scored (0 eliminated genes)
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## Level 9: 11 nodes to be scored (49 eliminated genes)
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## Level 8: 13 nodes to be scored (956 eliminated genes)
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## Level 7: 21 nodes to be scored (2904 eliminated genes)
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## Level 6: 47 nodes to be scored (3209 eliminated genes)
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## Level 5: 76 nodes to be scored (4227 eliminated genes)
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## Level 4: 94 nodes to be scored (6846 eliminated genes)
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## Level 3: 37 nodes to be scored (9618 eliminated genes)
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## Level 2: 12 nodes to be scored (10712 eliminated genes)
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## Level 1: 1 nodes to be scored (12870 eliminated genes)GOTable(ResMFUp$ResSel, maxGO=20)Result visualization: Barplot
topGOBarplotAll(TopGOResAll=ResMFAll$ResSel, TopGOResDown=ResMFDown$ResSel, TopGOResUp=ResMFUp$ResSel,
terms=8, pvalTh=0.01, plotTitle=NULL)
Top Terms associated Genes
All
plotGenesInTerm_v2(ResMFAll$ResSel, ResMFAll$GOdata, SE_DEA, nterms=8, ngenes=12, plotTitle=NULL, Interactive=FALSE)
Down
plotGenesInTerm_v2(ResMFDown$ResSel, ResMFDown$GOdata, SE_DEA, nterms=8, ngenes=12, plotTitle='Genes in Term - Down DEGs', Interactive=FALSE, fillCol='blue')
Up
plotGenesInTerm_v2(ResMFUp$ResSel, ResMFUp$GOdata, SE_DEA, nterms=8, ngenes=12, plotTitle='Genes in Term - Up DEGs', Interactive=FALSE, fillCol='red')
5.4 TopGO analysis: Cellular Component
On the basis of the analysis settings, the enrichment for Cellular Component IS performed.
Cellular Component Enrichment for ALL modulated genes: 351 genes
CCann <- topGO::annFUN.org(whichOnto='CC', feasibleGenes=names(GeneVectors$DEGenes),
mapping='org.Hs.eg.db', ID='symbol') %>% inverseList()
# Wrapper function for topGO analysis
ResCCAll <- topGOResults(Genes=GeneVectors$DEGenes, gene2GO=CCann, ontology='CC',
desc=NULL, nodeSize=15, algorithm='weight01', statistic='fisher',
EnTh=params$GoEnTh, PvalTh=params$GoPvalTh, minTerms=12, geneTh=4,
saveRes=TRUE, outDir=OutputFolder, fileName='CCAll')
## Gene vector contains levels: 0,1
##
## Building most specific GOs .....
## ( 1732 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1926 GO terms and 3253 relations. )
##
## Annotating nodes ...............
## ( 13224 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 394 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 11: 9 nodes to be scored (0 eliminated genes)
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## Level 10: 30 nodes to be scored (0 eliminated genes)
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## Level 9: 49 nodes to be scored (422 eliminated genes)
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## Level 8: 56 nodes to be scored (1908 eliminated genes)
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## Level 7: 71 nodes to be scored (4216 eliminated genes)
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## Level 6: 62 nodes to be scored (7973 eliminated genes)
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## Level 5: 49 nodes to be scored (9744 eliminated genes)
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## Level 4: 33 nodes to be scored (11618 eliminated genes)
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## Level 3: 32 nodes to be scored (12697 eliminated genes)
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## Level 2: 2 nodes to be scored (13070 eliminated genes)
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## Level 1: 1 nodes to be scored (13169 eliminated genes)
#write.table(ResCCAll$ResAll, file=paste0(OutputFolder, 'TopGO/CCAllResults.txt'), sep='\t', row.names=FALSE)Cellular Component Enrichment for DOWN-REGULATED genes: 202 genes
# Wrapper function for topGO analysis
ResCCDown <- topGOResults(Genes=GeneVectors$DEGenesDown, gene2GO=CCann, ontology='CC',
desc=NULL, nodeSize=15, algorithm='weight01', statistic='fisher',
EnTh=params$GoEnTh, PvalTh=params$GoPvalTh, minTerms=12, geneTh=4,
saveRes=TRUE, outDir=OutputFolder, fileName='CCDown')
## Gene vector contains levels: 0,1
##
## Building most specific GOs .....
## ( 1732 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1926 GO terms and 3253 relations. )
##
## Annotating nodes ...............
## ( 13224 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 342 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 11: 9 nodes to be scored (0 eliminated genes)
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## Level 10: 28 nodes to be scored (0 eliminated genes)
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## Level 9: 43 nodes to be scored (422 eliminated genes)
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## Level 8: 44 nodes to be scored (1838 eliminated genes)
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## Level 7: 59 nodes to be scored (4126 eliminated genes)
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## Level 6: 52 nodes to be scored (7796 eliminated genes)
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## Level 5: 46 nodes to be scored (9577 eliminated genes)
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## Level 4: 29 nodes to be scored (11575 eliminated genes)
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## Level 3: 29 nodes to be scored (12695 eliminated genes)
##
## Level 2: 2 nodes to be scored (13068 eliminated genes)
##
## Level 1: 1 nodes to be scored (13169 eliminated genes)GOTable(ResCCDown$ResSel, maxGO=20)Cellular Component Analysis for UP-REGULATED genes: 149 genes
# Wrapper function for topGO analysis
ResCCUp <- topGOResults(Genes=GeneVectors$DEGenesUp, gene2GO=CCann, ontology='CC',
desc=NULL, nodeSize=15, algorithm='weight01', statistic='fisher',
EnTh=params$GoEnTh, PvalTh=params$GoPvalTh, minTerms=12, geneTh=4,
saveRes=TRUE, outDir=OutputFolder, fileName='CCUp')
## Gene vector contains levels: 0,1
##
## Building most specific GOs .....
## ( 1732 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1926 GO terms and 3253 relations. )
##
## Annotating nodes ...............
## ( 13224 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 271 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 11: 2 nodes to be scored (0 eliminated genes)
##
## Level 10: 9 nodes to be scored (0 eliminated genes)
##
## Level 9: 24 nodes to be scored (145 eliminated genes)
##
## Level 8: 42 nodes to be scored (991 eliminated genes)
##
## Level 7: 44 nodes to be scored (3439 eliminated genes)
##
## Level 6: 49 nodes to be scored (7803 eliminated genes)
##
## Level 5: 41 nodes to be scored (9622 eliminated genes)
##
## Level 4: 27 nodes to be scored (11598 eliminated genes)
##
## Level 3: 30 nodes to be scored (12685 eliminated genes)
##
## Level 2: 2 nodes to be scored (13070 eliminated genes)
##
## Level 1: 1 nodes to be scored (13169 eliminated genes)GOTable(ResCCUp$ResSel, maxGO=20)Result visualization: Barplot
topGOBarplotAll(TopGOResAll=ResCCAll$ResSel, TopGOResDown=ResCCDown$ResSel, TopGOResUp=ResCCUp$ResSel,
terms=8, pvalTh=0.01, plotTitle=NULL)
Top Terms associated Genes
All
plotGenesInTerm_v2(ResCCAll$ResSel, ResCCAll$GOdata, SE_DEA, nterms=8, ngenes=12, plotTitle=NULL, Interactive=FALSE)
Down
plotGenesInTerm_v2(ResCCDown$ResSel, ResCCDown$GOdata, SE_DEA, nterms=8, ngenes=12, plotTitle='Genes in Term - Down DEGs', Interactive=FALSE, fillCol='blue')
Up
plotGenesInTerm_v2(ResCCUp$ResSel, ResCCUp$GOdata, SE_DEA, nterms=8, ngenes=12, plotTitle='Genes in Term - Up DEGs', Interactive=FALSE, fillCol='red')
6. Savings
SessionInfo <- sessionInfo()
Date <- date()
#
save.image(paste0(OutputFolder, Dataset, 'FunctionalAnalysisWorkspace.RData'))SessionInfo## R version 4.2.1 (2022-06-23)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.4 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] DESeq2_1.38.3 sechm_1.6.0
## [3] SummarizedExperiment_1.28.0 GenomicRanges_1.50.2
## [5] GenomeInfoDb_1.34.9 MatrixGenerics_1.10.0
## [7] matrixStats_0.63.0 dplyr_1.1.0
## [9] tidyr_1.3.0 data.table_1.14.8
## [11] topGO_2.50.0 SparseM_1.81
## [13] GO.db_3.16.0 graph_1.76.0
## [15] viridis_0.6.2 viridisLite_0.4.1
## [17] RColorBrewer_1.1-3 gridExtra_2.3
## [19] org.Hs.eg.db_3.16.0 AnnotationDbi_1.60.0
## [21] IRanges_2.32.0 S4Vectors_0.36.1
## [23] Biobase_2.58.0 BiocGenerics_0.44.0
## [25] ggplot2_3.4.1 DT_0.27
## [27] RNASeqBulkExploratory_0.2.1
##
## loaded via a namespace (and not attached):
## [1] Rtsne_0.16 colorspace_2.1-0 rjson_0.2.21
## [4] ellipsis_0.3.2 circlize_0.4.15 XVector_0.38.0
## [7] GlobalOptions_0.1.2 clue_0.3-64 rstudioapi_0.14
## [10] farver_2.1.1 bit64_4.0.5 fansi_1.0.4
## [13] codetools_0.2-19 doParallel_1.0.17 cachem_1.0.7
## [16] geneplotter_1.76.0 knitr_1.42 jsonlite_1.8.4
## [19] annotate_1.76.0 cluster_2.1.4 png_0.1-8
## [22] compiler_4.2.1 httr_1.4.5 lazyeval_0.2.2
## [25] Matrix_1.5-3 fastmap_1.1.1 cli_3.6.1
## [28] htmltools_0.5.4 tools_4.2.1 gtable_0.3.1
## [31] glue_1.6.2 GenomeInfoDbData_1.2.9 V8_4.2.2
## [34] Rcpp_1.0.10 jquerylib_0.1.4 vctrs_0.6.2
## [37] Biostrings_2.66.0 iterators_1.0.14 crosstalk_1.2.0
## [40] xfun_0.37 stringr_1.5.0 lifecycle_1.0.3
## [43] XML_3.99-0.13 ca_0.71.1 zlibbioc_1.44.0
## [46] scales_1.2.1 TSP_1.2-2 parallel_4.2.1
## [49] ComplexHeatmap_2.14.0 yaml_2.3.7 curl_5.0.0
## [52] memoise_2.0.1 sass_0.4.5 stringi_1.7.12
## [55] RSQLite_2.3.0 highr_0.10 randomcoloR_1.1.0.1
## [58] foreach_1.5.2 seriation_1.4.1 BiocParallel_1.32.5
## [61] shape_1.4.6 rlang_1.1.1 pkgconfig_2.0.3
## [64] bitops_1.0-7 evaluate_0.20 lattice_0.20-45
## [67] purrr_1.0.1 labeling_0.4.2 htmlwidgets_1.6.1
## [70] bit_4.0.5 tidyselect_1.2.0 magrittr_2.0.3
## [73] R6_2.5.1 generics_0.1.3 DelayedArray_0.24.0
## [76] DBI_1.1.3 pillar_1.8.1 withr_2.5.0
## [79] KEGGREST_1.38.0 RCurl_1.98-1.10 tibble_3.2.1
## [82] crayon_1.5.2 utf8_1.2.3 plotly_4.10.1
## [85] rmarkdown_2.20 GetoptLong_1.0.5 locfit_1.5-9.7
## [88] grid_4.2.1 blob_1.2.3 digest_0.6.31
## [91] xtable_1.8-4 munsell_0.5.0 registry_0.5-1
## [94] bslib_0.4.2Date## [1] "Fri Jul 18 19:50:40 2025"