TopGO - CTL04
Date: July 18, 2025
Condition_1 <- params$Condition_1
Condition_2 <- params$Condition_2Top GO Analysis for AhHyd 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), 363 genes are selected: 229 up-regulated genes and 134 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 363 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: 363 genes
- down-regulated genes: 134 genes of interest
- up-regulated genes: 229 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$TopGO))!=0, TRUE, FALSE)5.2 TopGO analysis: Biological Process
On the basis of the analysis settings, the enrichment for Biological Process IS performed.
Biological Process Analysis for ALL modulated genes: 363 genes
BPann <- topGO::annFUN.org(whichOnto="BP", feasibleGenes=names(GeneVectors$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 --
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## the algorithm is scoring 3583 nontrivial nodes
## parameters:
## test statistic: fisher
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## 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: 8 nodes to be scored (18 eliminated genes)
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## Level 15: 13 nodes to be scored (38 eliminated genes)
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## Level 14: 29 nodes to be scored (182 eliminated genes)
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## Level 13: 58 nodes to be scored (373 eliminated genes)
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## Level 12: 105 nodes to be scored (987 eliminated genes)
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## Level 11: 202 nodes to be scored (2894 eliminated genes)
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## Level 10: 343 nodes to be scored (4702 eliminated genes)
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## Level 9: 467 nodes to be scored (6046 eliminated genes)
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## Level 8: 546 nodes to be scored (7754 eliminated genes)
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## Level 7: 611 nodes to be scored (9591 eliminated genes)
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## Level 6: 538 nodes to be scored (10986 eliminated genes)
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## Level 5: 354 nodes to be scored (11776 eliminated genes)
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## Level 4: 200 nodes to be scored (12212 eliminated genes)
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## Level 3: 88 nodes to be scored (12412 eliminated genes)
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## Level 2: 17 nodes to be scored (12508 eliminated genes)
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## Level 1: 1 nodes to be scored (12546 eliminated genes)Biological Process Analysis for DOWN-REGULATED genes: 134 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: 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: 10 nodes to be scored (38 eliminated genes)
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## Level 14: 18 nodes to be scored (138 eliminated genes)
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## Level 13: 35 nodes to be scored (305 eliminated genes)
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## Level 12: 56 nodes to be scored (727 eliminated genes)
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## Level 11: 107 nodes to be scored (2590 eliminated genes)
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## Level 10: 181 nodes to be scored (4192 eliminated genes)
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## Level 9: 279 nodes to be scored (5384 eliminated genes)
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## Level 8: 378 nodes to be scored (6845 eliminated genes)
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## Level 7: 476 nodes to be scored (8507 eliminated genes)
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## Level 6: 439 nodes to be scored (10560 eliminated genes)
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## Level 5: 302 nodes to be scored (11618 eliminated genes)
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## Level 4: 172 nodes to be scored (12186 eliminated genes)
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## Level 3: 73 nodes to be scored (12404 eliminated genes)
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## Level 2: 17 nodes to be scored (12508 eliminated genes)
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## Level 1: 1 nodes to be scored (12540 eliminated genes)GOTable(ResBPDown$ResSel, maxGO=20)Biological Process Analysis for UP-REGULATED genes: 229 genes
ResBPUp <- topGOResults(Genes=GeneVectors$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 3009 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: 4 nodes to be scored (18 eliminated genes)
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## Level 15: 9 nodes to be scored (23 eliminated genes)
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## Level 14: 18 nodes to be scored (69 eliminated genes)
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## Level 13: 45 nodes to be scored (307 eliminated genes)
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## Level 12: 88 nodes to be scored (869 eliminated genes)
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## Level 11: 162 nodes to be scored (2705 eliminated genes)
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## Level 10: 273 nodes to be scored (4367 eliminated genes)
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## Level 9: 376 nodes to be scored (5463 eliminated genes)
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## Level 8: 441 nodes to be scored (7245 eliminated genes)
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## Level 7: 505 nodes to be scored (9348 eliminated genes)
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## Level 6: 472 nodes to be scored (10752 eliminated genes)
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## Level 5: 325 nodes to be scored (11663 eliminated genes)
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## Level 4: 190 nodes to be scored (12175 eliminated genes)
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## Level 3: 82 nodes to be scored (12394 eliminated genes)
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## Level 2: 16 nodes to be scored (12507 eliminated genes)
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## Level 1: 1 nodes to be scored (12545 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: 363 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 528 nontrivial nodes
## parameters:
## test statistic: fisher
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## Level 11: 5 nodes to be scored (0 eliminated genes)
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## Level 10: 5 nodes to be scored (0 eliminated genes)
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## Level 9: 13 nodes to be scored (102 eliminated genes)
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## Level 8: 27 nodes to be scored (1068 eliminated genes)
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## Level 7: 60 nodes to be scored (2926 eliminated genes)
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## Level 6: 96 nodes to be scored (3391 eliminated genes)
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## Level 5: 125 nodes to be scored (4675 eliminated genes)
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## Level 4: 132 nodes to be scored (7723 eliminated genes)
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## Level 3: 48 nodes to be scored (10242 eliminated genes)
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## Level 2: 16 nodes to be scored (11127 eliminated genes)
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## Level 1: 1 nodes to be scored (12864 eliminated genes)Molecular Function Enrichment for DOWN-REGULATED genes: 134 genes
ResMFDown <- topGOResults(Genes=GeneVectors$DEGenesDown, gene2GO=MFann, ontology='MF',
desc=NULL, nodeSize=15, algorithm='weight01', statistic='fisher',
EnTh=params$GoEnTh, PvalTh=params$GoPvalTh, minTerms=12, geneTh=4,
saveRes=TRUE, outDir=OutputFolder, fileName='MFDown')
## Gene vector contains levels: 0,1
##
## Building most specific GOs .....
## ( 4060 GO terms found. )
##
## Build GO DAG topology ..........
## ( 4530 GO terms and 5903 relations. )
##
## Annotating nodes ...............
## ( 12999 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 362 nontrivial nodes
## parameters:
## test statistic: fisher
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## Level 11: 2 nodes to be scored (0 eliminated genes)
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## Level 10: 3 nodes to be scored (0 eliminated genes)
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## Level 9: 10 nodes to be scored (55 eliminated genes)
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## Level 8: 17 nodes to be scored (1018 eliminated genes)
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## Level 7: 34 nodes to be scored (2867 eliminated genes)
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## Level 6: 57 nodes to be scored (3253 eliminated genes)
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## Level 5: 89 nodes to be scored (4260 eliminated genes)
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## Level 4: 99 nodes to be scored (6955 eliminated genes)
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## Level 3: 38 nodes to be scored (9862 eliminated genes)
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## Level 2: 12 nodes to be scored (10958 eliminated genes)
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## Level 1: 1 nodes to be scored (12859 eliminated genes)GOTable(ResMFDown$ResSel, maxGO=20)Molecular Function Analysis for UP-REGULATED genes: 229 genes
ResMFUp <- topGOResults(Genes=GeneVectors$DEGenesUp, gene2GO=MFann, ontology='MF',
desc=NULL, nodeSize=15, algorithm='weight01', statistic='fisher',
EnTh=params$GoEnTh, PvalTh=params$GoPvalTh, minTerms=12, geneTh=4,
saveRes=TRUE, outDir=OutputFolder, fileName='MFUp')
## Gene vector contains levels: 0,1
##
## Building most specific GOs .....
## ( 4060 GO terms found. )
##
## Build GO DAG topology ..........
## ( 4530 GO terms and 5903 relations. )
##
## Annotating nodes ...............
## ( 12999 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 435 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 11: 5 nodes to be scored (0 eliminated genes)
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## Level 10: 5 nodes to be scored (0 eliminated genes)
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## Level 9: 12 nodes to be scored (102 eliminated genes)
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## Level 8: 22 nodes to be scored (1068 eliminated genes)
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## Level 7: 48 nodes to be scored (2648 eliminated genes)
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## Level 6: 80 nodes to be scored (3045 eliminated genes)
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## Level 5: 97 nodes to be scored (4486 eliminated genes)
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## Level 4: 108 nodes to be scored (7536 eliminated genes)
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## Level 3: 43 nodes to be scored (9927 eliminated genes)
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## Level 2: 14 nodes to be scored (10945 eliminated genes)
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## Level 1: 1 nodes to be scored (12859 eliminated genes)GOTable(ResMFUp$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$GOdata, SE_DEA, nterms=8, ngenes=12, plotTitle='Genes in Term - Up DEGs', Interactive=FALSE, fillCol='red')
5.4 TopGO analysis: Cellular Component
On the basis of the analysis settings, the enrichment for Cellular Component IS performed.
Cellular Component Enrichment for ALL modulated genes: 363 genes
CCann <- topGO::annFUN.org(whichOnto='CC', feasibleGenes=names(GeneVectors$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$GoPvalTh, minTerms=12, geneTh=4,
saveRes=TRUE, outDir=OutputFolder, fileName='CCAll')
## Gene vector contains levels: 0,1
##
## Building most specific GOs .....
## ( 1732 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1926 GO terms and 3253 relations. )
##
## Annotating nodes ...............
## ( 13224 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 403 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 12: 1 nodes to be scored (0 eliminated genes)
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## Level 11: 14 nodes to be scored (0 eliminated genes)
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## Level 10: 39 nodes to be scored (17 eliminated genes)
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## Level 9: 47 nodes to be scored (581 eliminated genes)
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## Level 8: 51 nodes to be scored (2353 eliminated genes)
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## Level 7: 65 nodes to be scored (4289 eliminated genes)
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## Level 6: 64 nodes to be scored (7962 eliminated genes)
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## Level 5: 52 nodes to be scored (9662 eliminated genes)
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## Level 4: 34 nodes to be scored (11594 eliminated genes)
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## Level 3: 33 nodes to be scored (12706 eliminated genes)
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## Level 2: 2 nodes to be scored (13072 eliminated genes)
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## Level 1: 1 nodes to be scored (13169 eliminated genes)
#write.table(ResCCAll$ResAll, file=paste0(OutputFolder, 'TopGO/CCAllResults.txt'), sep='\t', row.names=FALSE)Cellular Component Enrichment for DOWN-REGULATED genes: 134 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$GoPvalTh, minTerms=12, geneTh=4,
saveRes=TRUE, outDir=OutputFolder, fileName='CCDown')
## Gene vector contains levels: 0,1
##
## Building most specific GOs .....
## ( 1732 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1926 GO terms and 3253 relations. )
##
## Annotating nodes ...............
## ( 13224 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 293 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 11: 3 nodes to be scored (0 eliminated genes)
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## Level 10: 19 nodes to be scored (0 eliminated genes)
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## Level 9: 28 nodes to be scored (173 eliminated genes)
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## Level 8: 35 nodes to be scored (1686 eliminated genes)
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## Level 7: 49 nodes to be scored (3825 eliminated genes)
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## Level 6: 52 nodes to be scored (7749 eliminated genes)
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## Level 5: 46 nodes to be scored (9345 eliminated genes)
##
## Level 4: 30 nodes to be scored (11505 eliminated genes)
##
## Level 3: 28 nodes to be scored (12683 eliminated genes)
##
## Level 2: 2 nodes to be scored (13072 eliminated genes)
##
## Level 1: 1 nodes to be scored (13169 eliminated genes)GOTable(ResCCDown$ResSel, maxGO=20)Cellular Component Analysis for UP-REGULATED genes: 229 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$GoPvalTh, minTerms=12, geneTh=4,
saveRes=TRUE, outDir=OutputFolder, fileName='CCUp')
## Gene vector contains levels: 0,1
##
## Building most specific GOs .....
## ( 1732 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1926 GO terms and 3253 relations. )
##
## Annotating nodes ...............
## ( 13224 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 358 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 12: 1 nodes to be scored (0 eliminated genes)
##
## Level 11: 11 nodes to be scored (0 eliminated genes)
##
## Level 10: 34 nodes to be scored (17 eliminated genes)
##
## Level 9: 43 nodes to be scored (443 eliminated genes)
##
## Level 8: 47 nodes to be scored (2261 eliminated genes)
##
## Level 7: 57 nodes to be scored (4151 eliminated genes)
##
## Level 6: 56 nodes to be scored (7883 eliminated genes)
##
## Level 5: 44 nodes to be scored (9632 eliminated genes)
##
## Level 4: 30 nodes to be scored (11579 eliminated genes)
##
## Level 3: 32 nodes to be scored (12691 eliminated genes)
##
## Level 2: 2 nodes to be scored (13070 eliminated genes)
##
## Level 1: 1 nodes to be scored (13169 eliminated genes)GOTable(ResCCUp$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 20:11:02 2025"