TopGO - CTL08
Date: July 18, 2025
Condition_1 <- params$Condition_1
Condition_2 <- params$Condition_2Top GO Analysis for Thyr Agonist
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), 164 genes are selected: 142 up-regulated genes and 22 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 164 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: 164 genes
- down-regulated genes: 22 genes of interest
- up-regulated genes: 142 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$TopGO))!=0, TRUE, FALSE)5.2 TopGO analysis: Biological Process
On the basis of the analysis settings, the enrichment for Biological Process IS performed.
Biological Process Analysis for ALL modulated genes: 164 genes
BPann <- topGO::annFUN.org(whichOnto="BP", feasibleGenes=names(GeneVectors$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$GoPvalTh, minTerms=12, geneTh=4,
saveRes=TRUE, outDir=paste0(OutputFolder), fileName='BPAll')
## Gene vector contains levels: 0,1
##
## Building most specific GOs .....
## ( 11591 GO terms found. )
##
## Build GO DAG topology ..........
## ( 15045 GO terms and 33988 relations. )
##
## Annotating nodes ...............
## ( 13128 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 2623 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 16: 7 nodes to be scored (0 eliminated genes)
##
## Level 15: 15 nodes to be scored (0 eliminated genes)
##
## Level 14: 24 nodes to be scored (142 eliminated genes)
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## Level 13: 47 nodes to be scored (375 eliminated genes)
##
## Level 12: 71 nodes to be scored (861 eliminated genes)
##
## Level 11: 140 nodes to be scored (2778 eliminated genes)
##
## Level 10: 222 nodes to be scored (4397 eliminated genes)
##
## Level 9: 313 nodes to be scored (5580 eliminated genes)
##
## Level 8: 392 nodes to be scored (7156 eliminated genes)
##
## Level 7: 447 nodes to be scored (9115 eliminated genes)
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## Level 6: 411 nodes to be scored (10894 eliminated genes)
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## Level 5: 280 nodes to be scored (11972 eliminated genes)
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## Level 4: 163 nodes to be scored (12592 eliminated genes)
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## Level 3: 74 nodes to be scored (12828 eliminated genes)
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## Level 2: 16 nodes to be scored (12928 eliminated genes)
##
## Level 1: 1 nodes to be scored (13000 eliminated genes)Biological Process Analysis for DOWN-REGULATED genes: 22 genes
# Wrapper function for topGO analysis
ResBPDown <- topGOResults(Genes=GeneVectors$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 289 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 13: 2 nodes to be scored (0 eliminated genes)
##
## Level 12: 5 nodes to be scored (0 eliminated genes)
##
## Level 11: 9 nodes to be scored (802 eliminated genes)
##
## Level 10: 13 nodes to be scored (2337 eliminated genes)
##
## Level 9: 17 nodes to be scored (2991 eliminated genes)
##
## Level 8: 24 nodes to be scored (3203 eliminated genes)
##
## Level 7: 36 nodes to be scored (3655 eliminated genes)
##
## Level 6: 48 nodes to be scored (4648 eliminated genes)
##
## Level 5: 53 nodes to be scored (6667 eliminated genes)
##
## Level 4: 41 nodes to be scored (10336 eliminated genes)
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## Level 3: 29 nodes to be scored (12038 eliminated genes)
##
## Level 2: 11 nodes to be scored (12716 eliminated genes)
##
## Level 1: 1 nodes to be scored (12910 eliminated genes)GOTable(ResBPDown$ResSel, maxGO=20)Biological Process Analysis for UP-REGULATED genes: 142 genes
ResBPUp <- topGOResults(Genes=GeneVectors$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 2560 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 16: 7 nodes to be scored (0 eliminated genes)
##
## Level 15: 15 nodes to be scored (0 eliminated genes)
##
## Level 14: 24 nodes to be scored (142 eliminated genes)
##
## Level 13: 47 nodes to be scored (375 eliminated genes)
##
## Level 12: 69 nodes to be scored (861 eliminated genes)
##
## Level 11: 136 nodes to be scored (2778 eliminated genes)
##
## Level 10: 216 nodes to be scored (4372 eliminated genes)
##
## Level 9: 305 nodes to be scored (5499 eliminated genes)
##
## Level 8: 378 nodes to be scored (7039 eliminated genes)
##
## Level 7: 435 nodes to be scored (8978 eliminated genes)
##
## Level 6: 401 nodes to be scored (10729 eliminated genes)
##
## Level 5: 278 nodes to be scored (11938 eliminated genes)
##
## Level 4: 160 nodes to be scored (12580 eliminated genes)
##
## Level 3: 72 nodes to be scored (12823 eliminated genes)
##
## Level 2: 16 nodes to be scored (12926 eliminated genes)
##
## Level 1: 1 nodes to be scored (12991 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: 164 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 341 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 11: 4 nodes to be scored (0 eliminated genes)
##
## Level 10: 8 nodes to be scored (0 eliminated genes)
##
## Level 9: 16 nodes to be scored (90 eliminated genes)
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## Level 8: 23 nodes to be scored (1174 eliminated genes)
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## Level 7: 36 nodes to be scored (3065 eliminated genes)
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## Level 6: 57 nodes to be scored (3508 eliminated genes)
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## Level 5: 78 nodes to be scored (4430 eliminated genes)
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## Level 4: 74 nodes to be scored (7029 eliminated genes)
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## Level 3: 33 nodes to be scored (9971 eliminated genes)
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## Level 2: 11 nodes to be scored (11024 eliminated genes)
##
## Level 1: 1 nodes to be scored (13327 eliminated genes)Molecular Function Enrichment for DOWN-REGULATED genes: 22 genes
ResMFDown <- topGOResults(Genes=GeneVectors$DEGenesDown, gene2GO=MFann, ontology='MF',
desc=NULL, nodeSize=15, algorithm='weight01', statistic='fisher',
EnTh=params$GoEnTh, PvalTh=params$GoPvalTh, minTerms=12, geneTh=4,
saveRes=TRUE, outDir=OutputFolder, fileName='MFDown')
## Gene vector contains levels: 0,1
##
## Building most specific GOs .....
## ( 4114 GO terms found. )
##
## Build GO DAG topology ..........
## ( 4579 GO terms and 5967 relations. )
##
## Annotating nodes ...............
## ( 13476 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 71 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 9: 2 nodes to be scored (0 eliminated genes)
##
## Level 8: 3 nodes to be scored (0 eliminated genes)
##
## Level 7: 5 nodes to be scored (1583 eliminated genes)
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## Level 6: 8 nodes to be scored (1655 eliminated genes)
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## Level 5: 13 nodes to be scored (1734 eliminated genes)
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## Level 4: 19 nodes to be scored (3860 eliminated genes)
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## Level 3: 13 nodes to be scored (7457 eliminated genes)
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## Level 2: 7 nodes to be scored (9497 eliminated genes)
##
## Level 1: 1 nodes to be scored (12738 eliminated genes)GOTable(ResMFDown$ResSel, maxGO=20)Molecular Function Analysis for UP-REGULATED genes: 142 genes
ResMFUp <- topGOResults(Genes=GeneVectors$DEGenesUp, gene2GO=MFann, ontology='MF',
desc=NULL, nodeSize=15, algorithm='weight01', statistic='fisher',
EnTh=params$GoEnTh, PvalTh=params$GoPvalTh, minTerms=12, geneTh=4,
saveRes=TRUE, outDir=OutputFolder, fileName='MFUp')
## Gene vector contains levels: 0,1
##
## Building most specific GOs .....
## ( 4114 GO terms found. )
##
## Build GO DAG topology ..........
## ( 4579 GO terms and 5967 relations. )
##
## Annotating nodes ...............
## ( 13476 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 332 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 11: 4 nodes to be scored (0 eliminated genes)
##
## Level 10: 8 nodes to be scored (0 eliminated genes)
##
## Level 9: 16 nodes to be scored (90 eliminated genes)
##
## Level 8: 23 nodes to be scored (1174 eliminated genes)
##
## Level 7: 35 nodes to be scored (3065 eliminated genes)
##
## Level 6: 56 nodes to be scored (3508 eliminated genes)
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## Level 5: 77 nodes to be scored (4411 eliminated genes)
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## Level 4: 71 nodes to be scored (6927 eliminated genes)
##
## Level 3: 31 nodes to be scored (9930 eliminated genes)
##
## Level 2: 10 nodes to be scored (11004 eliminated genes)
##
## Level 1: 1 nodes to be scored (13327 eliminated genes)GOTable(ResMFUp$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$GOdata, SE_DEA, nterms=8, ngenes=12, plotTitle='Genes in Term - Up DEGs', Interactive=FALSE, fillCol='red')
5.4 TopGO analysis: Cellular Component
On the basis of the analysis settings, the enrichment for Cellular Component IS performed.
Cellular Component Enrichment for ALL modulated genes: 164 genes
CCann <- topGO::annFUN.org(whichOnto='CC', feasibleGenes=names(GeneVectors$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$GoPvalTh, minTerms=12, geneTh=4,
saveRes=TRUE, outDir=OutputFolder, fileName='CCAll')
## Gene vector contains levels: 0,1
##
## Building most specific GOs .....
## ( 1740 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1936 GO terms and 3272 relations. )
##
## Annotating nodes ...............
## ( 13716 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 304 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 11: 7 nodes to be scored (0 eliminated genes)
##
## Level 10: 21 nodes to be scored (0 eliminated genes)
##
## Level 9: 34 nodes to be scored (442 eliminated genes)
##
## Level 8: 49 nodes to be scored (1628 eliminated genes)
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## Level 7: 44 nodes to be scored (3585 eliminated genes)
##
## Level 6: 44 nodes to be scored (8033 eliminated genes)
##
## Level 5: 48 nodes to be scored (9717 eliminated genes)
##
## Level 4: 26 nodes to be scored (11871 eliminated genes)
##
## Level 3: 28 nodes to be scored (13134 eliminated genes)
##
## Level 2: 2 nodes to be scored (13533 eliminated genes)
##
## Level 1: 1 nodes to be scored (13657 eliminated genes)Cellular Component Enrichment for DOWN-REGULATED genes: 22 genes
# Wrapper function for topGO analysis
ResCCDown <- topGOResults(Genes=GeneVectors$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 79 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 11: 1 nodes to be scored (0 eliminated genes)
##
## Level 10: 2 nodes to be scored (0 eliminated genes)
##
## Level 9: 3 nodes to be scored (109 eliminated genes)
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## Level 8: 10 nodes to be scored (292 eliminated genes)
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## Level 7: 9 nodes to be scored (770 eliminated genes)
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## Level 6: 11 nodes to be scored (5701 eliminated genes)
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## Level 5: 16 nodes to be scored (7934 eliminated genes)
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## Level 4: 13 nodes to be scored (10810 eliminated genes)
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## Level 3: 11 nodes to be scored (12908 eliminated genes)
##
## Level 2: 2 nodes to be scored (13486 eliminated genes)
##
## Level 1: 1 nodes to be scored (13627 eliminated genes)GOTable(ResCCDown$ResSel, maxGO=20)Cellular Component Analysis for UP-REGULATED genes: 142 genes
# Wrapper function for topGO analysis
ResCCUp <- topGOResults(Genes=GeneVectors$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 292 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 11: 6 nodes to be scored (0 eliminated genes)
##
## Level 10: 21 nodes to be scored (0 eliminated genes)
##
## Level 9: 32 nodes to be scored (347 eliminated genes)
##
## Level 8: 44 nodes to be scored (1628 eliminated genes)
##
## Level 7: 43 nodes to be scored (3542 eliminated genes)
##
## Level 6: 43 nodes to be scored (7979 eliminated genes)
##
## Level 5: 48 nodes to be scored (9707 eliminated genes)
##
## Level 4: 26 nodes to be scored (11871 eliminated genes)
##
## Level 3: 26 nodes to be scored (13134 eliminated genes)
##
## Level 2: 2 nodes to be scored (13533 eliminated genes)
##
## Level 1: 1 nodes to be scored (13657 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 17:54:19 2025"