TopGO - CTL04
Date: July 18, 2025
Condition_1 <- params$Condition_1
Condition_2 <- params$Condition_2Top GO Analysis for Thyr AgvsInh
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), 324 genes are selected: 191 up-regulated genes and 133 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 324 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: 324 genes
- down-regulated genes: 133 genes of interest
- up-regulated genes: 191 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$TopGO))!=0, TRUE, FALSE)5.2 TopGO analysis: Biological Process
On the basis of the analysis settings, the enrichment for Biological Process IS performed.
Biological Process Analysis for ALL modulated genes: 324 genes
BPann <- topGO::annFUN.org(whichOnto="BP", feasibleGenes=names(GeneVectors$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 3433 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 18: 1 nodes to be scored (0 eliminated genes)
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## Level 17: 3 nodes to be scored (0 eliminated genes)
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## Level 16: 4 nodes to be scored (18 eliminated genes)
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## Level 15: 15 nodes to be scored (48 eliminated genes)
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## Level 14: 28 nodes to be scored (91 eliminated genes)
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## Level 13: 49 nodes to be scored (373 eliminated genes)
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## Level 12: 94 nodes to be scored (984 eliminated genes)
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## Level 11: 186 nodes to be scored (2811 eliminated genes)
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## Level 10: 332 nodes to be scored (4645 eliminated genes)
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## Level 9: 451 nodes to be scored (5892 eliminated genes)
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## Level 8: 510 nodes to be scored (7595 eliminated genes)
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## Level 7: 590 nodes to be scored (9597 eliminated genes)
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## Level 6: 517 nodes to be scored (10924 eliminated genes)
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## Level 5: 353 nodes to be scored (11720 eliminated genes)
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## Level 4: 195 nodes to be scored (12199 eliminated genes)
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## Level 3: 86 nodes to be scored (12426 eliminated genes)
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## Level 2: 18 nodes to be scored (12506 eliminated genes)
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## Level 1: 1 nodes to be scored (12546 eliminated genes)Biological Process Analysis for DOWN-REGULATED genes: 133 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 2552 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 18: 1 nodes to be scored (0 eliminated genes)
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## Level 17: 3 nodes to be scored (0 eliminated genes)
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## Level 16: 4 nodes to be scored (18 eliminated genes)
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## Level 15: 10 nodes to be scored (48 eliminated genes)
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## Level 14: 19 nodes to be scored (91 eliminated genes)
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## Level 13: 33 nodes to be scored (294 eliminated genes)
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## Level 12: 68 nodes to be scored (861 eliminated genes)
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## Level 11: 132 nodes to be scored (2629 eliminated genes)
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## Level 10: 234 nodes to be scored (4381 eliminated genes)
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## Level 9: 316 nodes to be scored (5500 eliminated genes)
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## Level 8: 367 nodes to be scored (7005 eliminated genes)
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## Level 7: 428 nodes to be scored (8974 eliminated genes)
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## Level 6: 396 nodes to be scored (10423 eliminated genes)
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## Level 5: 288 nodes to be scored (11514 eliminated genes)
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## Level 4: 163 nodes to be scored (12146 eliminated genes)
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## Level 3: 73 nodes to be scored (12393 eliminated genes)
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## Level 2: 16 nodes to be scored (12502 eliminated genes)
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## Level 1: 1 nodes to be scored (12543 eliminated genes)GOTable(ResBPDown$ResSel, maxGO=20)Biological Process Analysis for UP-REGULATED genes: 191 genes
ResBPUp <- topGOResults(Genes=GeneVectors$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 2717 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 17: 1 nodes to be scored (0 eliminated genes)
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## Level 16: 1 nodes to be scored (0 eliminated genes)
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## Level 15: 10 nodes to be scored (15 eliminated genes)
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## Level 14: 20 nodes to be scored (38 eliminated genes)
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## Level 13: 36 nodes to be scored (271 eliminated genes)
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## Level 12: 63 nodes to be scored (758 eliminated genes)
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## Level 11: 119 nodes to be scored (2538 eliminated genes)
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## Level 10: 226 nodes to be scored (4134 eliminated genes)
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## Level 9: 326 nodes to be scored (5312 eliminated genes)
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## Level 8: 397 nodes to be scored (6957 eliminated genes)
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## Level 7: 490 nodes to be scored (8975 eliminated genes)
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## Level 6: 444 nodes to be scored (10533 eliminated genes)
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## Level 5: 314 nodes to be scored (11467 eliminated genes)
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## Level 4: 171 nodes to be scored (12178 eliminated genes)
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## Level 3: 81 nodes to be scored (12401 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 (12546 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: 324 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 511 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 11: 4 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 (89 eliminated genes)
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## Level 8: 31 nodes to be scored (1092 eliminated genes)
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## Level 7: 59 nodes to be scored (2990 eliminated genes)
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## Level 6: 86 nodes to be scored (3504 eliminated genes)
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## Level 5: 121 nodes to be scored (4832 eliminated genes)
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## Level 4: 123 nodes to be scored (7653 eliminated genes)
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## Level 3: 49 nodes to be scored (10200 eliminated genes)
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## Level 2: 14 nodes to be scored (11074 eliminated genes)
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## Level 1: 1 nodes to be scored (12882 eliminated genes)Molecular Function Enrichment for DOWN-REGULATED genes: 133 genes
ResMFDown <- topGOResults(Genes=GeneVectors$DEGenesDown, gene2GO=MFann, ontology='MF',
desc=NULL, nodeSize=15, algorithm='weight01', statistic='fisher',
EnTh=params$GoEnTh, PvalTh=params$GoPvalTh, minTerms=12, geneTh=4,
saveRes=TRUE, outDir=OutputFolder, fileName='MFDown')
## Gene vector contains levels: 0,1
##
## Building most specific GOs .....
## ( 4060 GO terms found. )
##
## Build GO DAG topology ..........
## ( 4530 GO terms and 5903 relations. )
##
## Annotating nodes ...............
## ( 12999 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 315 nontrivial nodes
## parameters:
## test statistic: fisher
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## Level 11: 2 nodes to be scored (0 eliminated genes)
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## Level 10: 2 nodes to be scored (0 eliminated genes)
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## Level 9: 6 nodes to be scored (49 eliminated genes)
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## Level 8: 9 nodes to be scored (934 eliminated genes)
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## Level 7: 25 nodes to be scored (2658 eliminated genes)
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## Level 6: 54 nodes to be scored (2880 eliminated genes)
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## Level 5: 75 nodes to be scored (4012 eliminated genes)
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## Level 4: 92 nodes to be scored (6692 eliminated genes)
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## Level 3: 37 nodes to be scored (9540 eliminated genes)
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## Level 2: 12 nodes to be scored (10805 eliminated genes)
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## Level 1: 1 nodes to be scored (12872 eliminated genes)GOTable(ResMFDown$ResSel, maxGO=20)Molecular Function Analysis for UP-REGULATED genes: 191 genes
ResMFUp <- topGOResults(Genes=GeneVectors$DEGenesUp, gene2GO=MFann, ontology='MF',
desc=NULL, nodeSize=15, algorithm='weight01', statistic='fisher',
EnTh=params$GoEnTh, PvalTh=params$GoPvalTh, minTerms=12, geneTh=4,
saveRes=TRUE, outDir=OutputFolder, fileName='MFUp')
## Gene vector contains levels: 0,1
##
## Building most specific GOs .....
## ( 4060 GO terms found. )
##
## Build GO DAG topology ..........
## ( 4530 GO terms and 5903 relations. )
##
## Annotating nodes ...............
## ( 12999 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 423 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 11: 3 nodes to be scored (0 eliminated genes)
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## Level 10: 6 nodes to be scored (0 eliminated genes)
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## Level 9: 14 nodes to be scored (74 eliminated genes)
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## Level 8: 27 nodes to be scored (1086 eliminated genes)
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## Level 7: 47 nodes to be scored (2975 eliminated genes)
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## Level 6: 67 nodes to be scored (3437 eliminated genes)
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## Level 5: 103 nodes to be scored (4486 eliminated genes)
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## Level 4: 98 nodes to be scored (7119 eliminated genes)
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## Level 3: 44 nodes to be scored (9978 eliminated genes)
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## Level 2: 13 nodes to be scored (10855 eliminated genes)
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## Level 1: 1 nodes to be scored (12865 eliminated genes)GOTable(ResMFUp$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$GOdata, SE_DEA, nterms=8, ngenes=12, plotTitle='Genes in Term - Up DEGs', Interactive=FALSE, fillCol='red')
5.4 TopGO analysis: Cellular Component
On the basis of the analysis settings, the enrichment for Cellular Component IS performed.
Cellular Component Enrichment for ALL modulated genes: 324 genes
CCann <- topGO::annFUN.org(whichOnto='CC', feasibleGenes=names(GeneVectors$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$GoPvalTh, minTerms=12, geneTh=4,
saveRes=TRUE, outDir=OutputFolder, fileName='CCAll')
## Gene vector contains levels: 0,1
##
## Building most specific GOs .....
## ( 1732 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1926 GO terms and 3253 relations. )
##
## Annotating nodes ...............
## ( 13224 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 399 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 11: 8 nodes to be scored (0 eliminated genes)
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## Level 10: 26 nodes to be scored (0 eliminated genes)
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## Level 9: 50 nodes to be scored (436 eliminated genes)
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## Level 8: 63 nodes to be scored (1785 eliminated genes)
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## Level 7: 65 nodes to be scored (4363 eliminated genes)
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## Level 6: 65 nodes to be scored (8089 eliminated genes)
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## Level 5: 53 nodes to be scored (9749 eliminated genes)
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## Level 4: 32 nodes to be scored (11636 eliminated genes)
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## Level 3: 34 nodes to be scored (12705 eliminated genes)
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## Level 2: 2 nodes to be scored (13072 eliminated genes)
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## Level 1: 1 nodes to be scored (13169 eliminated genes)
#write.table(ResCCAll$ResAll, file=paste0(OutputFolder, 'TopGO/CCAllResults.txt'), sep='\t', row.names=FALSE)Cellular Component Enrichment for DOWN-REGULATED genes: 133 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$GoPvalTh, minTerms=12, geneTh=4,
saveRes=TRUE, outDir=OutputFolder, fileName='CCDown')
## Gene vector contains levels: 0,1
##
## Building most specific GOs .....
## ( 1732 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1926 GO terms and 3253 relations. )
##
## Annotating nodes ...............
## ( 13224 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 285 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 11: 5 nodes to be scored (0 eliminated genes)
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## Level 10: 13 nodes to be scored (0 eliminated genes)
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## Level 9: 27 nodes to be scored (265 eliminated genes)
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## Level 8: 40 nodes to be scored (1185 eliminated genes)
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## Level 7: 47 nodes to be scored (3526 eliminated genes)
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## Level 6: 51 nodes to be scored (7674 eliminated genes)
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## Level 5: 40 nodes to be scored (9462 eliminated genes)
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## Level 4: 30 nodes to be scored (11577 eliminated genes)
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## Level 3: 29 nodes to be scored (12685 eliminated genes)
##
## Level 2: 2 nodes to be scored (13070 eliminated genes)
##
## Level 1: 1 nodes to be scored (13169 eliminated genes)GOTable(ResCCDown$ResSel, maxGO=20)Cellular Component Analysis for UP-REGULATED genes: 191 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$GoPvalTh, minTerms=12, geneTh=4,
saveRes=TRUE, outDir=OutputFolder, fileName='CCUp')
## Gene vector contains levels: 0,1
##
## Building most specific GOs .....
## ( 1732 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1926 GO terms and 3253 relations. )
##
## Annotating nodes ...............
## ( 13224 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 327 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 11: 5 nodes to be scored (0 eliminated genes)
##
## Level 10: 19 nodes to be scored (0 eliminated genes)
##
## Level 9: 37 nodes to be scored (256 eliminated genes)
##
## Level 8: 50 nodes to be scored (1267 eliminated genes)
##
## Level 7: 53 nodes to be scored (3561 eliminated genes)
##
## Level 6: 52 nodes to be scored (7951 eliminated genes)
##
## Level 5: 47 nodes to be scored (9629 eliminated genes)
##
## Level 4: 29 nodes to be scored (11548 eliminated genes)
##
## Level 3: 32 nodes to be scored (12681 eliminated genes)
##
## Level 2: 2 nodes to be scored (13070 eliminated genes)
##
## Level 1: 1 nodes to be scored (13164 eliminated genes)GOTable(ResCCUp$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 19:15:29 2025"