TopGO - CTL04
Date: July 18, 2025
Condition_1 <- params$Condition_1
Condition_2 <- params$Condition_2Top GO Analysis for Ret 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), 787 genes are selected: 147 up-regulated genes and 640 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 787 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: 787 genes
- down-regulated genes: 640 genes of interest
- up-regulated genes: 147 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$TopGO))!=0, TRUE, FALSE)5.2 TopGO analysis: Biological Process
On the basis of the analysis settings, the enrichment for Biological Process IS performed.
Biological Process Analysis for ALL modulated genes: 787 genes
BPann <- topGO::annFUN.org(whichOnto="BP", feasibleGenes=names(GeneVectors$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 4171 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: 12 nodes to be scored (18 eliminated genes)
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## Level 15: 22 nodes to be scored (69 eliminated genes)
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## Level 14: 40 nodes to be scored (235 eliminated genes)
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## Level 13: 62 nodes to be scored (502 eliminated genes)
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## Level 12: 122 nodes to be scored (1176 eliminated genes)
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## Level 11: 260 nodes to be scored (3089 eliminated genes)
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## Level 10: 436 nodes to be scored (4826 eliminated genes)
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## Level 9: 561 nodes to be scored (6383 eliminated genes)
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## Level 8: 625 nodes to be scored (7974 eliminated genes)
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## Level 7: 700 nodes to be scored (9667 eliminated genes)
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## Level 6: 613 nodes to be scored (11091 eliminated genes)
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## Level 5: 387 nodes to be scored (11836 eliminated genes)
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## Level 4: 215 nodes to be scored (12243 eliminated genes)
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## Level 3: 93 nodes to be scored (12434 eliminated genes)
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## Level 2: 18 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: 640 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 3775 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: 12 nodes to be scored (18 eliminated genes)
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## Level 15: 21 nodes to be scored (69 eliminated genes)
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## Level 14: 34 nodes to be scored (235 eliminated genes)
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## Level 13: 53 nodes to be scored (487 eliminated genes)
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## Level 12: 105 nodes to be scored (1078 eliminated genes)
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## Level 11: 234 nodes to be scored (3025 eliminated genes)
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## Level 10: 381 nodes to be scored (4761 eliminated genes)
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## Level 9: 498 nodes to be scored (6254 eliminated genes)
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## Level 8: 560 nodes to be scored (7779 eliminated genes)
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## Level 7: 628 nodes to be scored (9504 eliminated genes)
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## Level 6: 561 nodes to be scored (11019 eliminated genes)
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## Level 5: 369 nodes to be scored (11784 eliminated genes)
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## Level 4: 208 nodes to be scored (12233 eliminated genes)
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## Level 3: 88 nodes to be scored (12431 eliminated genes)
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## Level 2: 18 nodes to be scored (12507 eliminated genes)
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## Level 1: 1 nodes to be scored (12546 eliminated genes)GOTable(ResBPDown$ResSel, maxGO=20)Biological Process Analysis for UP-REGULATED genes: 147 genes
ResBPUp <- topGOResults(Genes=GeneVectors$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 2766 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 16: 1 nodes to be scored (0 eliminated genes)
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## Level 15: 5 nodes to be scored (0 eliminated genes)
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## Level 14: 14 nodes to be scored (25 eliminated genes)
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## Level 13: 28 nodes to be scored (145 eliminated genes)
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## Level 12: 58 nodes to be scored (609 eliminated genes)
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## Level 11: 128 nodes to be scored (2388 eliminated genes)
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## Level 10: 238 nodes to be scored (4061 eliminated genes)
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## Level 9: 334 nodes to be scored (5477 eliminated genes)
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## Level 8: 409 nodes to be scored (6984 eliminated genes)
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## Level 7: 493 nodes to be scored (8760 eliminated genes)
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## Level 6: 468 nodes to be scored (10566 eliminated genes)
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## Level 5: 318 nodes to be scored (11716 eliminated genes)
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## Level 4: 171 nodes to be scored (12206 eliminated genes)
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## Level 3: 83 nodes to be scored (12414 eliminated genes)
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## Level 2: 17 nodes to be scored (12503 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: 787 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 652 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 11: 7 nodes to be scored (0 eliminated genes)
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## Level 10: 11 nodes to be scored (0 eliminated genes)
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## Level 9: 28 nodes to be scored (152 eliminated genes)
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## Level 8: 44 nodes to be scored (1181 eliminated genes)
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## Level 7: 81 nodes to be scored (3118 eliminated genes)
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## Level 6: 116 nodes to be scored (3569 eliminated genes)
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## Level 5: 150 nodes to be scored (5013 eliminated genes)
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## Level 4: 147 nodes to be scored (7941 eliminated genes)
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## Level 3: 51 nodes to be scored (10340 eliminated genes)
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## Level 2: 16 nodes to be scored (11180 eliminated genes)
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## Level 1: 1 nodes to be scored (12865 eliminated genes)Molecular Function Enrichment for DOWN-REGULATED genes: 640 genes
ResMFDown <- topGOResults(Genes=GeneVectors$DEGenesDown, gene2GO=MFann, ontology='MF',
desc=NULL, nodeSize=15, algorithm='weight01', statistic='fisher',
EnTh=params$GoEnTh, PvalTh=params$GoPvalTh, minTerms=12, geneTh=4,
saveRes=TRUE, outDir=OutputFolder, fileName='MFDown')
## Gene vector contains levels: 0,1
##
## Building most specific GOs .....
## ( 4060 GO terms found. )
##
## Build GO DAG topology ..........
## ( 4530 GO terms and 5903 relations. )
##
## Annotating nodes ...............
## ( 12999 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 584 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 11: 7 nodes to be scored (0 eliminated genes)
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## Level 10: 11 nodes to be scored (0 eliminated genes)
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## Level 9: 26 nodes to be scored (152 eliminated genes)
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## Level 8: 40 nodes to be scored (1181 eliminated genes)
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## Level 7: 67 nodes to be scored (3101 eliminated genes)
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## Level 6: 102 nodes to be scored (3539 eliminated genes)
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## Level 5: 133 nodes to be scored (4867 eliminated genes)
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## Level 4: 135 nodes to be scored (7751 eliminated genes)
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## Level 3: 47 nodes to be scored (10198 eliminated genes)
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## Level 2: 15 nodes to be scored (11116 eliminated genes)
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## Level 1: 1 nodes to be scored (12861 eliminated genes)GOTable(ResMFDown$ResSel, maxGO=20)Molecular Function Analysis for UP-REGULATED genes: 147 genes
ResMFUp <- topGOResults(Genes=GeneVectors$DEGenesUp, gene2GO=MFann, ontology='MF',
desc=NULL, nodeSize=15, algorithm='weight01', statistic='fisher',
EnTh=params$GoEnTh, PvalTh=params$GoPvalTh, minTerms=12, geneTh=4,
saveRes=TRUE, outDir=OutputFolder, fileName='MFUp')
## Gene vector contains levels: 0,1
##
## Building most specific GOs .....
## ( 4060 GO terms found. )
##
## Build GO DAG topology ..........
## ( 4530 GO terms and 5903 relations. )
##
## Annotating nodes ...............
## ( 12999 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 420 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 10: 2 nodes to be scored (0 eliminated genes)
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## Level 9: 10 nodes to be scored (0 eliminated genes)
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## Level 8: 21 nodes to be scored (967 eliminated genes)
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## Level 7: 52 nodes to be scored (2820 eliminated genes)
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## Level 6: 78 nodes to be scored (3262 eliminated genes)
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## Level 5: 101 nodes to be scored (4574 eliminated genes)
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## Level 4: 103 nodes to be scored (7410 eliminated genes)
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## Level 3: 40 nodes to be scored (9993 eliminated genes)
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## Level 2: 12 nodes to be scored (10956 eliminated genes)
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## Level 1: 1 nodes to be scored (12852 eliminated genes)GOTable(ResMFUp$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$GOdata, SE_DEA, nterms=8, ngenes=12, plotTitle='Genes in Term - Up DEGs', Interactive=FALSE, fillCol='red')
5.4 TopGO analysis: Cellular Component
On the basis of the analysis settings, the enrichment for Cellular Component IS performed.
Cellular Component Enrichment for ALL modulated genes: 787 genes
CCann <- topGO::annFUN.org(whichOnto='CC', feasibleGenes=names(GeneVectors$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$GoPvalTh, minTerms=12, geneTh=4,
saveRes=TRUE, outDir=OutputFolder, fileName='CCAll')
## Gene vector contains levels: 0,1
##
## Building most specific GOs .....
## ( 1732 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1926 GO terms and 3253 relations. )
##
## Annotating nodes ...............
## ( 13224 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 492 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 13: 1 nodes to be scored (0 eliminated genes)
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## Level 12: 2 nodes to be scored (0 eliminated genes)
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## Level 11: 16 nodes to be scored (30 eliminated genes)
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## Level 10: 47 nodes to be scored (59 eliminated genes)
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## Level 9: 67 nodes to be scored (605 eliminated genes)
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## Level 8: 76 nodes to be scored (2492 eliminated genes)
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## Level 7: 74 nodes to be scored (4668 eliminated genes)
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## Level 6: 71 nodes to be scored (8199 eliminated genes)
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## Level 5: 59 nodes to be scored (9782 eliminated genes)
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## Level 4: 39 nodes to be scored (11640 eliminated genes)
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## Level 3: 37 nodes to be scored (12705 eliminated genes)
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## Level 2: 2 nodes to be scored (13071 eliminated genes)
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## Level 1: 1 nodes to be scored (13169 eliminated genes)
#write.table(ResCCAll$ResAll, file=paste0(OutputFolder, 'TopGO/CCAllResults.txt'), sep='\t', row.names=FALSE)Cellular Component Enrichment for DOWN-REGULATED genes: 640 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$GoPvalTh, minTerms=12, geneTh=4,
saveRes=TRUE, outDir=OutputFolder, fileName='CCDown')
## Gene vector contains levels: 0,1
##
## Building most specific GOs .....
## ( 1732 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1926 GO terms and 3253 relations. )
##
## Annotating nodes ...............
## ( 13224 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 458 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 13: 1 nodes to be scored (0 eliminated genes)
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## Level 12: 2 nodes to be scored (0 eliminated genes)
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## Level 11: 13 nodes to be scored (30 eliminated genes)
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## Level 10: 40 nodes to be scored (59 eliminated genes)
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## Level 9: 62 nodes to be scored (508 eliminated genes)
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## Level 8: 72 nodes to be scored (2216 eliminated genes)
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## Level 7: 70 nodes to be scored (4622 eliminated genes)
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## Level 6: 67 nodes to be scored (8161 eliminated genes)
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## Level 5: 55 nodes to be scored (9772 eliminated genes)
##
## Level 4: 36 nodes to be scored (11635 eliminated genes)
##
## Level 3: 37 nodes to be scored (12704 eliminated genes)
##
## Level 2: 2 nodes to be scored (13071 eliminated genes)
##
## Level 1: 1 nodes to be scored (13169 eliminated genes)GOTable(ResCCDown$ResSel, maxGO=20)Cellular Component Analysis for UP-REGULATED genes: 147 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$GoPvalTh, minTerms=12, geneTh=4,
saveRes=TRUE, outDir=OutputFolder, fileName='CCUp')
## Gene vector contains levels: 0,1
##
## Building most specific GOs .....
## ( 1732 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1926 GO terms and 3253 relations. )
##
## Annotating nodes ...............
## ( 13224 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 345 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 11: 8 nodes to be scored (0 eliminated genes)
##
## Level 10: 30 nodes to be scored (0 eliminated genes)
##
## Level 9: 38 nodes to be scored (291 eliminated genes)
##
## Level 8: 52 nodes to be scored (2105 eliminated genes)
##
## Level 7: 59 nodes to be scored (4098 eliminated genes)
##
## Level 6: 51 nodes to be scored (7975 eliminated genes)
##
## Level 5: 47 nodes to be scored (9736 eliminated genes)
##
## Level 4: 28 nodes to be scored (11601 eliminated genes)
##
## Level 3: 29 nodes to be scored (12686 eliminated genes)
##
## Level 2: 2 nodes to be scored (13068 eliminated genes)
##
## Level 1: 1 nodes to be scored (13163 eliminated genes)GOTable(ResCCUp$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 19:41:03 2025"