TopGO - CTL08
Date: July 18, 2025
Condition_1 <- params$Condition_1
Condition_2 <- params$Condition_2Top GO Analysis for LivX 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), 614 genes are selected: 440 up-regulated genes and 174 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 614 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: 614 genes
- down-regulated genes: 174 genes of interest
- up-regulated genes: 440 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$TopGO))!=0, TRUE, FALSE)5.2 TopGO analysis: Biological Process
On the basis of the analysis settings, the enrichment for Biological Process IS performed.
Biological Process Analysis for ALL modulated genes: 614 genes
BPann <- topGO::annFUN.org(whichOnto="BP", feasibleGenes=names(GeneVectors$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 3924 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 18: 1 nodes to be scored (0 eliminated genes)
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## Level 17: 5 nodes to be scored (0 eliminated genes)
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## Level 16: 11 nodes to be scored (18 eliminated genes)
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## Level 15: 20 nodes to be scored (85 eliminated genes)
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## Level 14: 43 nodes to be scored (211 eliminated genes)
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## Level 13: 67 nodes to be scored (448 eliminated genes)
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## Level 12: 109 nodes to be scored (1306 eliminated genes)
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## Level 11: 223 nodes to be scored (3150 eliminated genes)
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## Level 10: 398 nodes to be scored (4970 eliminated genes)
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## Level 9: 529 nodes to be scored (6433 eliminated genes)
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## Level 8: 599 nodes to be scored (8402 eliminated genes)
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## Level 7: 667 nodes to be scored (10301 eliminated genes)
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## Level 6: 572 nodes to be scored (11430 eliminated genes)
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## Level 5: 368 nodes to be scored (12215 eliminated genes)
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## Level 4: 204 nodes to be scored (12695 eliminated genes)
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## Level 3: 89 nodes to be scored (12881 eliminated genes)
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## Level 2: 18 nodes to be scored (12958 eliminated genes)
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## Level 1: 1 nodes to be scored (13003 eliminated genes)Biological Process Analysis for DOWN-REGULATED genes: 174 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 2437 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 17: 2 nodes to be scored (0 eliminated genes)
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## Level 16: 4 nodes to be scored (0 eliminated genes)
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## Level 15: 6 nodes to be scored (27 eliminated genes)
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## Level 14: 20 nodes to be scored (67 eliminated genes)
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## Level 13: 33 nodes to be scored (181 eliminated genes)
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## Level 12: 56 nodes to be scored (637 eliminated genes)
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## Level 11: 133 nodes to be scored (2539 eliminated genes)
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## Level 10: 231 nodes to be scored (4323 eliminated genes)
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## Level 9: 310 nodes to be scored (5652 eliminated genes)
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## Level 8: 361 nodes to be scored (7472 eliminated genes)
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## Level 7: 394 nodes to be scored (9657 eliminated genes)
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## Level 6: 374 nodes to be scored (11029 eliminated genes)
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## Level 5: 270 nodes to be scored (11980 eliminated genes)
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## Level 4: 152 nodes to be scored (12606 eliminated genes)
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## Level 3: 73 nodes to be scored (12861 eliminated genes)
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## Level 2: 17 nodes to be scored (12955 eliminated genes)
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## Level 1: 1 nodes to be scored (12991 eliminated genes)GOTable(ResBPDown$ResSel, maxGO=20)Biological Process Analysis for UP-REGULATED genes: 440 genes
ResBPUp <- topGOResults(Genes=GeneVectors$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 3262 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 18: 1 nodes to be scored (0 eliminated genes)
##
## Level 17: 3 nodes to be scored (0 eliminated genes)
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## Level 16: 8 nodes to be scored (18 eliminated genes)
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## Level 15: 16 nodes to be scored (64 eliminated genes)
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## Level 14: 29 nodes to be scored (184 eliminated genes)
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## Level 13: 50 nodes to be scored (385 eliminated genes)
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## Level 12: 82 nodes to be scored (1040 eliminated genes)
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## Level 11: 153 nodes to be scored (2913 eliminated genes)
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## Level 10: 290 nodes to be scored (4696 eliminated genes)
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## Level 9: 409 nodes to be scored (5982 eliminated genes)
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## Level 8: 491 nodes to be scored (7708 eliminated genes)
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## Level 7: 588 nodes to be scored (9726 eliminated genes)
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## Level 6: 515 nodes to be scored (11296 eliminated genes)
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## Level 5: 336 nodes to be scored (12156 eliminated genes)
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## Level 4: 189 nodes to be scored (12674 eliminated genes)
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## Level 3: 83 nodes to be scored (12862 eliminated genes)
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## Level 2: 18 nodes to be scored (12953 eliminated genes)
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## Level 1: 1 nodes to be scored (13002 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: 614 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 643 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 11: 6 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: 21 nodes to be scored (126 eliminated genes)
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## Level 8: 40 nodes to be scored (1173 eliminated genes)
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## Level 7: 77 nodes to be scored (3134 eliminated genes)
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## Level 6: 113 nodes to be scored (3712 eliminated genes)
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## Level 5: 157 nodes to be scored (5206 eliminated genes)
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## Level 4: 152 nodes to be scored (8208 eliminated genes)
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## Level 3: 52 nodes to be scored (10664 eliminated genes)
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## Level 2: 15 nodes to be scored (11572 eliminated genes)
##
## Level 1: 1 nodes to be scored (13354 eliminated genes)Molecular Function Enrichment for DOWN-REGULATED genes: 174 genes
ResMFDown <- topGOResults(Genes=GeneVectors$DEGenesDown, gene2GO=MFann, ontology='MF',
desc=NULL, nodeSize=15, algorithm='weight01', statistic='fisher',
EnTh=params$GoEnTh, PvalTh=params$GoPvalTh, minTerms=12, geneTh=4,
saveRes=TRUE, outDir=OutputFolder, fileName='MFDown')
## Gene vector contains levels: 0,1
##
## Building most specific GOs .....
## ( 4114 GO terms found. )
##
## Build GO DAG topology ..........
## ( 4579 GO terms and 5967 relations. )
##
## Annotating nodes ...............
## ( 13476 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 385 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 11: 1 nodes to be scored (0 eliminated genes)
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## Level 10: 1 nodes to be scored (0 eliminated genes)
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## Level 9: 7 nodes to be scored (16 eliminated genes)
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## Level 8: 20 nodes to be scored (958 eliminated genes)
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## Level 7: 47 nodes to be scored (2868 eliminated genes)
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## Level 6: 75 nodes to be scored (3420 eliminated genes)
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## Level 5: 82 nodes to be scored (4669 eliminated genes)
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## Level 4: 94 nodes to be scored (7355 eliminated genes)
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## Level 3: 43 nodes to be scored (9921 eliminated genes)
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## Level 2: 14 nodes to be scored (11285 eliminated genes)
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## Level 1: 1 nodes to be scored (13351 eliminated genes)GOTable(ResMFDown$ResSel, maxGO=20)Molecular Function Analysis for UP-REGULATED genes: 440 genes
ResMFUp <- topGOResults(Genes=GeneVectors$DEGenesUp, gene2GO=MFann, ontology='MF',
desc=NULL, nodeSize=15, algorithm='weight01', statistic='fisher',
EnTh=params$GoEnTh, PvalTh=params$GoPvalTh, minTerms=12, geneTh=4,
saveRes=TRUE, outDir=OutputFolder, fileName='MFUp')
## Gene vector contains levels: 0,1
##
## Building most specific GOs .....
## ( 4114 GO terms found. )
##
## Build GO DAG topology ..........
## ( 4579 GO terms and 5967 relations. )
##
## Annotating nodes ...............
## ( 13476 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 544 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 11: 6 nodes to be scored (0 eliminated genes)
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## Level 10: 9 nodes to be scored (0 eliminated genes)
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## Level 9: 21 nodes to be scored (126 eliminated genes)
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## Level 8: 36 nodes to be scored (1173 eliminated genes)
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## Level 7: 57 nodes to be scored (3134 eliminated genes)
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## Level 6: 87 nodes to be scored (3670 eliminated genes)
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## Level 5: 135 nodes to be scored (4913 eliminated genes)
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## Level 4: 130 nodes to be scored (7824 eliminated genes)
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## Level 3: 49 nodes to be scored (10504 eliminated genes)
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## Level 2: 13 nodes to be scored (11446 eliminated genes)
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## Level 1: 1 nodes to be scored (13341 eliminated genes)GOTable(ResMFUp$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$GOdata, SE_DEA, nterms=8, ngenes=12, plotTitle='Genes in Term - Up DEGs', Interactive=FALSE, fillCol='red')
5.4 TopGO analysis: Cellular Component
On the basis of the analysis settings, the enrichment for Cellular Component IS performed.
Cellular Component Enrichment for ALL modulated genes: 614 genes
CCann <- topGO::annFUN.org(whichOnto='CC', feasibleGenes=names(GeneVectors$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 477 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 13: 1 nodes to be scored (0 eliminated genes)
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## Level 12: 1 nodes to be scored (0 eliminated genes)
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## Level 11: 11 nodes to be scored (30 eliminated genes)
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## Level 10: 41 nodes to be scored (42 eliminated genes)
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## Level 9: 64 nodes to be scored (540 eliminated genes)
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## Level 8: 74 nodes to be scored (2450 eliminated genes)
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## Level 7: 73 nodes to be scored (4791 eliminated genes)
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## Level 6: 75 nodes to be scored (8422 eliminated genes)
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## Level 5: 61 nodes to be scored (10098 eliminated genes)
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## Level 4: 40 nodes to be scored (12039 eliminated genes)
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## Level 3: 33 nodes to be scored (13172 eliminated genes)
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## Level 2: 2 nodes to be scored (13554 eliminated genes)
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## Level 1: 1 nodes to be scored (13659 eliminated genes)Cellular Component Enrichment for DOWN-REGULATED genes: 174 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 290 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 11: 8 nodes to be scored (0 eliminated genes)
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## Level 10: 23 nodes to be scored (0 eliminated genes)
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## Level 9: 35 nodes to be scored (379 eliminated genes)
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## Level 8: 47 nodes to be scored (1950 eliminated genes)
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## Level 7: 44 nodes to be scored (4098 eliminated genes)
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## Level 6: 39 nodes to be scored (7899 eliminated genes)
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## Level 5: 36 nodes to be scored (9640 eliminated genes)
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## Level 4: 30 nodes to be scored (11892 eliminated genes)
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## Level 3: 25 nodes to be scored (13127 eliminated genes)
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## Level 2: 2 nodes to be scored (13552 eliminated genes)
##
## Level 1: 1 nodes to be scored (13659 eliminated genes)GOTable(ResCCDown$ResSel, maxGO=20)Cellular Component Analysis for UP-REGULATED genes: 440 genes
# Wrapper function for topGO analysis
ResCCUp <- topGOResults(Genes=GeneVectors$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 415 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 13: 1 nodes to be scored (0 eliminated genes)
##
## Level 12: 1 nodes to be scored (0 eliminated genes)
##
## Level 11: 8 nodes to be scored (30 eliminated genes)
##
## Level 10: 34 nodes to be scored (42 eliminated genes)
##
## Level 9: 52 nodes to be scored (409 eliminated genes)
##
## Level 8: 65 nodes to be scored (2150 eliminated genes)
##
## Level 7: 63 nodes to be scored (4578 eliminated genes)
##
## Level 6: 67 nodes to be scored (8310 eliminated genes)
##
## Level 5: 56 nodes to be scored (10060 eliminated genes)
##
## Level 4: 33 nodes to be scored (12014 eliminated genes)
##
## Level 3: 32 nodes to be scored (13170 eliminated genes)
##
## Level 2: 2 nodes to be scored (13553 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 18:25:47 2025"