TopGO - CTL08
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'14850 genes in 20 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), 394 genes are selected: 155 up-regulated genes and 239 down-regulated genes.
4. RESULTS VISUALIZATION
4.1 Volcano plot
plotVolcanoSE(SE=SE_DEA, FdrTh=FdrTh, logFcTh=logFcTh, FdrCeil=1e-10, logFcCeil=4)4.2 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)$anno_colors <- list(Condition = c('DMSO' = 'grey30', 'CTL' = 'azure3',
'AhHyd_Ag'='#F8766D', 'AhHyd_Inh'='#F8766D50',
'Andr_Ag'='#fccb17', 'Andr_Inh'='#C49A0050',
"Estr_Ag"= '#53B400', "Estr_Inh"= '#53B40050',
'GC_Ag' = '#00C094', 'GC_Inh' = '#00C09450',
'LivX_Ag' = '#00B6EB', 'LivX_Inh' = '#00B6EB50',
'Ret_Ag' = '#A58AFF', 'Ret_Inh' = '#A58AFF50',
'Thyr_Ag' = '#FB61D7', 'Thyr_Inh' = '#FB61D750'
), SeqRun = c("20210310" = "orange", "20210724" = "green4", "20220422" =
"#66ACB7"))
sechm(SE_DEA, features=DEGs$GeneName, assayName="vst", gaps_at="Condition", show_rownames=FALSE,
top_annotation=c('Condition', 'SeqRun'), hmcols=ScaledCols, show_colnames=TRUE,
do.scale=TRUE, breaks=0.85, column_title = "Scaled Vst Values")
5. TOPGO for Gene Ontology Enrichment analysis
Gene ontology enrichment analysis is performed on the set of 394 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
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: 14850 genes
- modulated genes: 394 genes
- down-regulated genes: 239 genes of interest
- up-regulated genes: 155 genes of interest
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$TopGO))!=0, TRUE, FALSE)5.2 TopGO analysis: Biological Process
On the basis of the analysis settings, the enrichment for Biological Process IS performed.
Biological Process Analysis for ALL modulated genes: 394 genes
BPann <- topGO::annFUN.org(whichOnto="BP", feasibleGenes=names(GeneVectors$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 .....
## ( 11591 GO terms found. )
##
## Build GO DAG topology ..........
## ( 15045 GO terms and 33988 relations. )
##
## Annotating nodes ...............
## ( 13128 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 3649 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 17: 2 nodes to be scored (0 eliminated genes)
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## Level 16: 5 nodes to be scored (0 eliminated genes)
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## Level 15: 9 nodes to be scored (27 eliminated genes)
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## Level 14: 32 nodes to be scored (83 eliminated genes)
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## Level 13: 68 nodes to be scored (214 eliminated genes)
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## Level 12: 104 nodes to be scored (1111 eliminated genes)
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## Level 11: 231 nodes to be scored (3080 eliminated genes)
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## Level 10: 370 nodes to be scored (4916 eliminated genes)
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## Level 9: 492 nodes to be scored (6608 eliminated genes)
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## Level 8: 553 nodes to be scored (8332 eliminated genes)
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## Level 7: 610 nodes to be scored (10178 eliminated genes)
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## Level 6: 521 nodes to be scored (11349 eliminated genes)
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## Level 5: 355 nodes to be scored (12202 eliminated genes)
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## Level 4: 192 nodes to be scored (12678 eliminated genes)
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## Level 3: 86 nodes to be scored (12879 eliminated genes)
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## Level 2: 18 nodes to be scored (12959 eliminated genes)
##
## Level 1: 1 nodes to be scored (13003 eliminated genes)Biological Process Analysis for DOWN-REGULATED genes: 239 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 .....
## ( 11591 GO terms found. )
##
## Build GO DAG topology ..........
## ( 15045 GO terms and 33988 relations. )
##
## Annotating nodes ...............
## ( 13128 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 3132 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: 9 nodes to be scored (27 eliminated genes)
##
## Level 14: 28 nodes to be scored (83 eliminated genes)
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## Level 13: 56 nodes to be scored (214 eliminated genes)
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## Level 12: 88 nodes to be scored (1035 eliminated genes)
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## Level 11: 189 nodes to be scored (2851 eliminated genes)
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## Level 10: 302 nodes to be scored (4605 eliminated genes)
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## Level 9: 412 nodes to be scored (6088 eliminated genes)
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## Level 8: 470 nodes to be scored (7854 eliminated genes)
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## Level 7: 517 nodes to be scored (9591 eliminated genes)
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## Level 6: 460 nodes to be scored (11025 eliminated genes)
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## Level 5: 322 nodes to be scored (11996 eliminated genes)
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## Level 4: 175 nodes to be scored (12636 eliminated genes)
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## Level 3: 79 nodes to be scored (12865 eliminated genes)
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## Level 2: 17 nodes to be scored (12954 eliminated genes)
##
## Level 1: 1 nodes to be scored (13003 eliminated genes)GOTable(ResBPDown$ResSel, maxGO=20)Biological Process Analysis for UP-REGULATED genes: 155 genes
ResBPUp <- topGOResults(Genes=GeneVectors$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 .....
## ( 11591 GO terms found. )
##
## Build GO DAG topology ..........
## ( 15045 GO terms and 33988 relations. )
##
## Annotating nodes ...............
## ( 13128 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 2158 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 16: 2 nodes to be scored (0 eliminated genes)
##
## Level 15: 2 nodes to be scored (0 eliminated genes)
##
## Level 14: 10 nodes to be scored (37 eliminated genes)
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## Level 13: 32 nodes to be scored (95 eliminated genes)
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## Level 12: 54 nodes to be scored (604 eliminated genes)
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## Level 11: 102 nodes to be scored (2695 eliminated genes)
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## Level 10: 162 nodes to be scored (4428 eliminated genes)
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## Level 9: 235 nodes to be scored (5736 eliminated genes)
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## Level 8: 290 nodes to be scored (7123 eliminated genes)
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## Level 7: 388 nodes to be scored (9210 eliminated genes)
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## Level 6: 364 nodes to be scored (10855 eliminated genes)
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## Level 5: 274 nodes to be scored (12064 eliminated genes)
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## Level 4: 153 nodes to be scored (12620 eliminated genes)
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## Level 3: 72 nodes to be scored (12851 eliminated genes)
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## Level 2: 17 nodes to be scored (12956 eliminated genes)
##
## Level 1: 1 nodes to be scored (12994 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: 394 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 .....
## ( 4114 GO terms found. )
##
## Build GO DAG topology ..........
## ( 4579 GO terms and 5967 relations. )
##
## Annotating nodes ...............
## ( 13476 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 577 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 11: 5 nodes to be scored (0 eliminated genes)
##
## Level 10: 8 nodes to be scored (0 eliminated genes)
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## Level 9: 19 nodes to be scored (134 eliminated genes)
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## Level 8: 35 nodes to be scored (1154 eliminated genes)
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## Level 7: 68 nodes to be scored (3134 eliminated genes)
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## Level 6: 113 nodes to be scored (3622 eliminated genes)
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## Level 5: 133 nodes to be scored (5058 eliminated genes)
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## Level 4: 132 nodes to be scored (8189 eliminated genes)
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## Level 3: 47 nodes to be scored (10601 eliminated genes)
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## Level 2: 16 nodes to be scored (11517 eliminated genes)
##
## Level 1: 1 nodes to be scored (13357 eliminated genes)Molecular Function Enrichment for DOWN-REGULATED genes: 239 genes
ResMFDown <- topGOResults(Genes=GeneVectors$DEGenesDown, gene2GO=MFann, ontology='MF',
desc=NULL, nodeSize=15, algorithm='weight01', statistic='fisher',
EnTh=params$GoEnTh, PvalTh=params$GoPvalTh, minTerms=12, geneTh=4,
saveRes=TRUE, outDir=OutputFolder, fileName='MFDown')
## Gene vector contains levels: 0,1
##
## Building most specific GOs .....
## ( 4114 GO terms found. )
##
## Build GO DAG topology ..........
## ( 4579 GO terms and 5967 relations. )
##
## Annotating nodes ...............
## ( 13476 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 464 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 11: 4 nodes to be scored (0 eliminated genes)
##
## Level 10: 7 nodes to be scored (0 eliminated genes)
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## Level 9: 14 nodes to be scored (118 eliminated genes)
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## Level 8: 27 nodes to be scored (1131 eliminated genes)
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## Level 7: 55 nodes to be scored (3010 eliminated genes)
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## Level 6: 86 nodes to be scored (3493 eliminated genes)
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## Level 5: 108 nodes to be scored (4847 eliminated genes)
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## Level 4: 113 nodes to be scored (7758 eliminated genes)
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## Level 3: 37 nodes to be scored (10330 eliminated genes)
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## Level 2: 12 nodes to be scored (11382 eliminated genes)
##
## Level 1: 1 nodes to be scored (13329 eliminated genes)GOTable(ResMFDown$ResSel, maxGO=20)Molecular Function Analysis for UP-REGULATED genes: 155 genes
ResMFUp <- topGOResults(Genes=GeneVectors$DEGenesUp, gene2GO=MFann, ontology='MF',
desc=NULL, nodeSize=15, algorithm='weight01', statistic='fisher',
EnTh=params$GoEnTh, PvalTh=params$GoPvalTh, minTerms=12, geneTh=4,
saveRes=TRUE, outDir=OutputFolder, fileName='MFUp')
## Gene vector contains levels: 0,1
##
## Building most specific GOs .....
## ( 4114 GO terms found. )
##
## Build GO DAG topology ..........
## ( 4579 GO terms and 5967 relations. )
##
## Annotating nodes ...............
## ( 13476 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 362 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 11: 1 nodes to be scored (0 eliminated genes)
##
## Level 10: 3 nodes to be scored (0 eliminated genes)
##
## Level 9: 12 nodes to be scored (16 eliminated genes)
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## Level 8: 20 nodes to be scored (1060 eliminated genes)
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## Level 7: 37 nodes to be scored (2966 eliminated genes)
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## Level 6: 64 nodes to be scored (3383 eliminated genes)
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## Level 5: 81 nodes to be scored (4555 eliminated genes)
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## Level 4: 89 nodes to be scored (7302 eliminated genes)
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## Level 3: 40 nodes to be scored (10073 eliminated genes)
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## Level 2: 14 nodes to be scored (11293 eliminated genes)
##
## Level 1: 1 nodes to be scored (13345 eliminated genes)GOTable(ResMFUp$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$GOdata, SE_DEA, nterms=8, ngenes=12, plotTitle='Genes in Term - Up DEGs', Interactive=FALSE, fillCol='red')
5.4 TopGO analysis: Cellular Component
On the basis of the analysis settings, the enrichment for Cellular Component IS performed.
Cellular Component Enrichment for ALL modulated genes: 394 genes
CCann <- topGO::annFUN.org(whichOnto='CC', feasibleGenes=names(GeneVectors$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$GoPvalTh, minTerms=12, geneTh=4,
saveRes=TRUE, outDir=OutputFolder, fileName='CCAll')
## Gene vector contains levels: 0,1
##
## Building most specific GOs .....
## ( 1740 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1936 GO terms and 3272 relations. )
##
## Annotating nodes ...............
## ( 13716 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 476 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 12: 1 nodes to be scored (0 eliminated genes)
##
## Level 11: 16 nodes to be scored (0 eliminated genes)
##
## Level 10: 44 nodes to be scored (17 eliminated genes)
##
## Level 9: 72 nodes to be scored (640 eliminated genes)
##
## Level 8: 72 nodes to be scored (2492 eliminated genes)
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## Level 7: 72 nodes to be scored (4925 eliminated genes)
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## Level 6: 68 nodes to be scored (8431 eliminated genes)
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## Level 5: 53 nodes to be scored (10078 eliminated genes)
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## Level 4: 40 nodes to be scored (12032 eliminated genes)
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## Level 3: 35 nodes to be scored (13171 eliminated genes)
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## Level 2: 2 nodes to be scored (13557 eliminated genes)
##
## Level 1: 1 nodes to be scored (13659 eliminated genes)Cellular Component Enrichment for DOWN-REGULATED genes: 239 genes
# Wrapper function for topGO analysis
ResCCDown <- topGOResults(Genes=GeneVectors$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 .....
## ( 1740 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1936 GO terms and 3272 relations. )
##
## Annotating nodes ...............
## ( 13716 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 398 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 12: 1 nodes to be scored (0 eliminated genes)
##
## Level 11: 9 nodes to be scored (0 eliminated genes)
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## Level 10: 32 nodes to be scored (17 eliminated genes)
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## Level 9: 57 nodes to be scored (344 eliminated genes)
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## Level 8: 60 nodes to be scored (2158 eliminated genes)
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## Level 7: 61 nodes to be scored (4679 eliminated genes)
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## Level 6: 58 nodes to be scored (8255 eliminated genes)
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## Level 5: 48 nodes to be scored (9962 eliminated genes)
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## Level 4: 36 nodes to be scored (11994 eliminated genes)
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## Level 3: 33 nodes to be scored (13165 eliminated genes)
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## Level 2: 2 nodes to be scored (13557 eliminated genes)
##
## Level 1: 1 nodes to be scored (13659 eliminated genes)GOTable(ResCCDown$ResSel, maxGO=20)Cellular Component Analysis for UP-REGULATED genes: 155 genes
# Wrapper function for topGO analysis
ResCCUp <- topGOResults(Genes=GeneVectors$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 .....
## ( 1740 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1936 GO terms and 3272 relations. )
##
## Annotating nodes ...............
## ( 13716 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 338 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 11: 11 nodes to be scored (0 eliminated genes)
##
## Level 10: 28 nodes to be scored (0 eliminated genes)
##
## Level 9: 40 nodes to be scored (527 eliminated genes)
##
## Level 8: 45 nodes to be scored (1552 eliminated genes)
##
## Level 7: 51 nodes to be scored (3549 eliminated genes)
##
## Level 6: 53 nodes to be scored (8029 eliminated genes)
##
## Level 5: 46 nodes to be scored (9895 eliminated genes)
##
## Level 4: 32 nodes to be scored (11969 eliminated genes)
##
## Level 3: 29 nodes to be scored (13157 eliminated genes)
##
## Level 2: 2 nodes to be scored (13554 eliminated genes)
##
## Level 1: 1 nodes to be scored (13659 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 18:02:30 2025"