Source: https://github.com/markziemann/enrichment_recipe

Introduction

This guide is a Rmarkdown script that conducts differential expression and enrichment analysis, which are very popular workflows for transcriptome data.

The goal of this work is to understand how ClusterProfiler manages the background list, as we have observed some weird behaviour. In particular we see that when we provide a custom background, those genes do not appear in the final analysis unless they are also included in the gene list.

In the code chunk below called libs, you can add and remove required R library dependancies. Check that the libraries listed here match the Dockerfile, otherwise you might get errors.

suppressPackageStartupMessages({
  library("getDEE2")
  library("DESeq2")
  library("kableExtra")
  library("clusterProfiler")
  library("fgsea")
  library("eulerr")
  library("gplots")
})

For this guide I will be using bulk RNA-seq data from a previous study, which is deposited at NCBI GEO and SRA under accession numbers: GSE55123 and SRP038101 (Lund et al, 2014). The experiment is designed to investigate the effect of Azacitidine treatment on AML3 cells.

The raw data have been processed by the DEE2 project, and the summary gene expression counts are available at the dee2.io website, and programmatically with the getDEE2 bioconductor package (Ziemann et al, 2019).

Alternatively, you could fetch data from another resource like NCBI GEO, Zenodo or from the host storage drive.

mdat <- getDEE2Metadata("hsapiens")

# get sample sheet
ss <- subset(mdat,SRP_accession=="SRP038101")

# fetch the whole set of RNA-seq data
x <- getDEE2("hsapiens",ss$SRR_accession , metadata=mdat, legacy=TRUE)
## For more information about DEE2 QC metrics, visit
##     https://github.com/markziemann/dee2/blob/master/qc/qc_metrics.md
mx <- x$GeneCounts
rownames(mx) <- paste(rownames(mx),x$GeneInfo$GeneSymbol)
dim(mx)
## [1] 58302     6
# aza no filtering
ss$trt <- grepl("Treated",ss$Experiment_title)

ss %>%
  kbl(caption="sample sheet for Aza treatment in AML3 cells") %>%
  kable_paper("hover", full_width = F)
sample sheet for Aza treatment in AML3 cells
SRR_accession QC_summary SRX_accession SRS_accession SRP_accession Experiment_title GEO_series trt
156267 SRR1171523 PASS SRX472607 SRS559064 SRP038101 GSM1329859: Untreated.1; Homo sapiens; RNA-Seq FALSE
156268 SRR1171524 WARN(3,4) SRX472608 SRS559066 SRP038101 GSM1329860: Untreated.2; Homo sapiens; RNA-Seq FALSE
156269 SRR1171525 WARN(3,4) SRX472609 SRS559065 SRP038101 GSM1329861: Untreated.3; Homo sapiens; RNA-Seq FALSE
156270 SRR1171526 WARN(3,4) SRX472610 SRS559068 SRP038101 GSM1329862: Treated.1; Homo sapiens; RNA-Seq TRUE
156271 SRR1171527 WARN(3,4) SRX472611 SRS559067 SRP038101 GSM1329863: Treated.2; Homo sapiens; RNA-Seq TRUE
156272 SRR1171528 WARN(3,4) SRX472612 SRS559069 SRP038101 GSM1329864: Treated.3; Homo sapiens; RNA-Seq TRUE

Data quality control

QC is important, even if you are using public transcriptome data. For RNA-seq it is a good idea to show the number of reads for each sample.

par(mar=c(5,7,5,1))
barplot(rev(colSums(mx)),horiz=TRUE,las=1,main="number of reads per sample in SRP038101")

Now make a MDS plot.

mds <- cmdscale(dist(t(mx)))

# expand plot area
XMIN=min(mds[,1])*1.3
XMAX=max(mds[,1])*1.3
YMIN=min(mds[,2])*1.3
YMAX=max(mds[,2])*1.3

cols <- as.character(grepl("Treated",ss$Experiment_title))
cols <- gsub("FALSE","lightblue",cols)
cols <- gsub("TRUE","pink",cols)
plot(mds, xlab="Coordinate 1", ylab="Coordinate 2",
  xlim=c(XMIN,XMAX),ylim=c(YMIN,YMAX),
  pch=19,cex=2,col=cols, main="MDS plot")
text(cmdscale(dist(t(mx))), labels=colnames(mx) )

Differential expression analysis

I will be using DESeq2 for DE analysis, the count matrix is prefiltered using a detection threshold of 10 reads per sample across all samples. All genes that meet the detection threshold will comprise the background list. The first 6 rows of the count matrix are shows.

mxf <- mx[which(rowMeans(mx)>=10),]
dim(mxf)
## [1] 13168     6
head(mxf,6) %>%
  kbl(caption="Count matrix format") %>%
  kable_paper("hover", full_width = F)
Count matrix format
SRR1171523 SRR1171524 SRR1171525 SRR1171526 SRR1171527 SRR1171528
ENSG00000225630 MTND2P28 494 396 340 333 415 418
ENSG00000237973 MTCO1P12 52 39 40 30 37 29
ENSG00000248527 MTATP6P1 853 544 537 582 702 716
ENSG00000228327 AL669831.1 17 13 21 21 22 12
ENSG00000228794 LINC01128 42 27 30 32 40 23
ENSG00000230699 AL645608.3 20 11 13 10 15 22 
dds <- DESeqDataSetFromMatrix(countData = mxf , colData = ss, design = ~ trt )
res <- DESeq(dds)
## estimating size factors
## estimating dispersions
## gene-wise dispersion estimates
## mean-dispersion relationship
## final dispersion estimates
## fitting model and testing
z <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <-cbind(as.data.frame(z),mxf)
def <-as.data.frame(zz[order(zz$pvalue),])

head(def,10) %>%
  kbl(caption="Top DE genes for Aza treatment") %>%
  kable_paper("hover", full_width = F)
Top DE genes for Aza treatment
baseMean log2FoldChange lfcSE stat pvalue padj SRR1171523 SRR1171524 SRR1171525 SRR1171526 SRR1171527 SRR1171528
ENSG00000165949 IFI27 1960.1970 -3.384492 0.0938869 -36.04861 0 0 4689 3583 2758 309 384 334
ENSG00000090382 LYZ 7596.0299 -1.650342 0.0561143 -29.41036 0 0 14212 10237 10789 3476 3764 3738
ENSG00000115461 IGFBP5 531.2217 -5.071157 0.1795239 -28.24781 0 0 1320 823 1025 23 26 43
ENSG00000157601 MX1 827.1511 -2.877795 0.1047823 -27.46450 0 0 1732 1501 1206 184 223 186
ENSG00000111331 OAS3 2127.2010 -2.661214 0.0972124 -27.37525 0 0 4204 3977 2972 562 614 560
ENSG00000070915 SLC12A3 424.5509 -3.374852 0.1298671 -25.98697 0 0 1012 721 653 63 85 76
ENSG00000234745 HLA-B 3197.0159 -1.431566 0.0604169 -23.69479 0 0 6085 4256 4023 1590 1872 1719
ENSG00000137965 IFI44 409.0957 -2.978581 0.1319352 -22.57608 0 0 829 740 635 76 111 89
ENSG00000204525 HLA-C 1631.6421 -1.461550 0.0660214 -22.13750 0 0 3112 2150 2106 791 923 891
ENSG00000110042 DTX4 524.1318 -2.470219 0.1173182 -21.05572 0 0 1166 883 688 166 168 145

Make a smear plot.

sigf <- subset(def,padj<=0.05)

DET=nrow(mxf)
NSIG=nrow(sigf)
NUP=nrow(subset(sigf,log2FoldChange>0))
NDN=nrow(subset(sigf,log2FoldChange<0))

HEADER=paste(DET,"detected genes;",NSIG,"w/FDR<0.05;",NUP,"up;",NDN,"down")

plot(log10(def$baseMean) ,def$log2FoldChange,
  cex=0.6,pch=19,col="darkgray",
  main="5-azacitidine treatment in AML3 cells",
  xlab="log10(basemean)",ylab="log2(fold change)")

points(log10(sigf$baseMean) ,sigf$log2FoldChange,
  cex=0.6,pch=19,col="red")

mtext(HEADER)

In the next sections I will run enrichment analysis with over-representation analysis (ORA) test and compare it to functional class scoring. I will also investigate some strange behviour of the ORA tool clusterprofiler.

ORA with Clusterprofiler custom background with otherwise default analysis

I’ve compiled a reporting checklist:

Reporting criteria Method/resource used
Origin of gene sets BioCarta (2023-06-21)
Tool used ClusterProfiler (check version at foot of report)
Statistical test used hypergeometric test
P-value correction for multiple comparisons FDR method
Background list Genes with >=10 reads per sample on average across all samples
Gene Selection Criteria DESeq2 FDR<0.05
ORA directionality Separate tests for up- and down-regulation
Data availability via DEE2 at accession SRP038101 (human)
Other parameters Min gene set size of 10

Here I provide a custom background gene list.

Get the gene sets loaded in R. These are Biocarta gene sets

# from https://www.gsea-msigdb.org/gsea/msigdb/human/collections.jsp 16th June 2023
genesets2 <- read.gmt("c2.cp.biocarta.v2023.1.Hs.symbols.gmt")
gsets <- gmtPathways("c2.cp.biocarta.v2023.1.Hs.symbols.gmt")

message(paste("number of genes described in the annotation set:",length(unique(genesets2$gene))))
## number of genes described in the annotation set: 1509

Now filter the gene names into three lists, up-regulated, down-regulated and background. Background is simply all genes that were detected.

defup <- rownames(subset(def,padj<=0.05 & log2FoldChange>0))
defup <- unique(sapply(strsplit(defup," "),"[[",2))

defdn <- rownames(subset(def,padj<=0.05 & log2FoldChange<0))
defdn <- unique(sapply(strsplit(defdn," "),"[[",2))

bg <- rownames(def)
bg <- unique(sapply(strsplit(bg," "),"[[",2))

message(paste("number of genes in background:",length(bg)))
## number of genes in background: 13164

Enrichment firstly with upregulated genes

ora_up <- as.data.frame(enricher(gene = defup ,
  universe = bg,  maxGSSize = 5000, TERM2GENE = genesets2,
  pAdjustMethod="fdr",  pvalueCutoff = 1, qvalueCutoff = 1  ))

ora_up$geneID <- NULL
ora_up <- subset(ora_up,p.adjust<0.05 & Count >=10)
ora_ups <- rownames(ora_up)

gr <- as.numeric(sapply(strsplit(ora_up$GeneRatio,"/"),"[[",1)) /
  as.numeric(sapply(strsplit(ora_up$GeneRatio,"/"),"[[",2))

br <- as.numeric(sapply(strsplit(ora_up$BgRatio,"/"),"[[",1)) /
  as.numeric(sapply(strsplit(ora_up$BgRatio,"/"),"[[",2))

ora_up$es <- gr/br
ora_up <- ora_up[order(-ora_up$es),]
ora_up$Description=NULL

head(ora_up) %>%
  kbl(row.names = FALSE, caption="Top upregulated pathways in Aza treatment") %>%
  kable_paper("hover", full_width = F)
Top upregulated pathways in Aza treatment
ID GeneRatio BgRatio pvalue p.adjust qvalue Count es
BIOCARTA_CELLCYCLE_PATHWAY 11/163 22/1056 0.0001296 0.0072607 0.0067107 11 3.239264
BIOCARTA_VDR_PATHWAY 10/163 24/1056 0.0016212 0.0398815 0.0368610 10 2.699387 
topup2 <- rev(head(ora_up$es,10))
names(topup2) <- rev(head(ora_up$ID,10))

The reported background size does not match the dataset.

Now repeat with the downregulated genes

n_pw=10

ora_dn <- as.data.frame(enricher(gene = defdn ,
  universe = bg,  maxGSSize = 5000, TERM2GENE = genesets2,
  pAdjustMethod="fdr",  pvalueCutoff = 1, qvalueCutoff = 1  ))

ora_dn$geneID <- NULL
ora_dn <- subset(ora_dn,p.adjust<0.05 & Count >=10)
ora_dns <- rownames(ora_dn)

gr <- as.numeric(sapply(strsplit(ora_dn$GeneRatio,"/"),"[[",1)) /
  as.numeric(sapply(strsplit(ora_dn$GeneRatio,"/"),"[[",2))

br <- as.numeric(sapply(strsplit(ora_dn$BgRatio,"/"),"[[",1)) /
  as.numeric(sapply(strsplit(ora_dn$BgRatio,"/"),"[[",2))

ora_dn$es <- gr/br
ora_dn <- ora_dn[order(-ora_dn$es),]
ora_dn$Description=NULL

head(ora_dn,n_pw) %>%
  kbl(row.names = FALSE, caption="Top downregulated pathways in Aza treatment") %>%
  kable_paper("hover", full_width = F)
Top downregulated pathways in Aza treatment
ID GeneRatio BgRatio pvalue p.adjust qvalue Count es
BIOCARTA_EICOSANOID_PATHWAY 11/255 17/1056 0.000404 0.0321179 0.0299809 11 2.679585 
topdn2 <- head(ora_dn$es,n_pw)
names(topdn2) <- head(ora_dn$ID,n_pw)

Make a barplot

par(mar=c(5,20,5,1))

cols <- c(rep("blue",n_pw),rep("red",n_pw))

barplot(c(topdn2,topup2),
  horiz=TRUE,las=1,cex.names=0.65,col=cols,
  main="top DE Biocarta",
  xlab="ES",
  cex.main=0.9)

mtext("ORA test")

ORA with Clusterprofiler with custom background and special gene set list to fix the potential bug

I’ve compiled a reporting checklist:

Reporting criteria Method/resource used
Origin of gene sets Biocarta (2023-06-21)
Tool used ClusterProfiler (check version at foot of report)
Statistical test used hypergeometric test
P-value correction for multiple comparisons FDR method
Background list Genes with >=10 reads per sample on average across all samples
Gene Selection Criteria DESeq2 FDR<0.05
ORA directionality Separate tests for up- and down-regulation
Data availability via DEE2 at accession SRP038101 (human)
Other parameters Min gene set size of 10

Here I provide a background gene list for cluterprofiler to use, like a user should.

I have also done some modification to the Biocarta gene sets, to en

Now filter the gene names into three lists, up-regulated, down-regulated and background. Background is simply all genes that were detected.

defup <- rownames(subset(def,padj<=0.05 & log2FoldChange>0))
defup <- unique(sapply(strsplit(defup," "),"[[",2))

defdn <- rownames(subset(def,padj<=0.05 & log2FoldChange<0))
defdn <- unique(sapply(strsplit(defdn," "),"[[",2))

bg <- rownames(def)
bg <- unique(sapply(strsplit(bg," "),"[[",2))
message(paste("number of genes in background:",length(bg)))
## number of genes in background: 13164

Adding all detected genes to the background appears to improve results!

bgdf <- data.frame("background",bg)
colnames(bgdf) <- c("term","gene")
genesets2 <- rbind(genesets2,bgdf)

Enrichment firstly with upregulated genes

orafix_up <- as.data.frame(enricher(gene = defup ,
  universe = bg,  maxGSSize = 5000, TERM2GENE = genesets2,
  pAdjustMethod="fdr",  pvalueCutoff = 1, qvalueCutoff = 1  ))

orafix_up$geneID <- NULL
orafix_up <- subset(orafix_up,p.adjust<0.05 & Count >=10)
orafix_ups <- rownames(orafix_up)

gr <- as.numeric(sapply(strsplit(orafix_up$GeneRatio,"/"),"[[",1)) /
  as.numeric(sapply(strsplit(orafix_up$GeneRatio,"/"),"[[",2))

br <- as.numeric(sapply(strsplit(orafix_up$BgRatio,"/"),"[[",1)) /
  as.numeric(sapply(strsplit(orafix_up$BgRatio,"/"),"[[",2))

orafix_up$es <- gr/br
orafix_up <- orafix_up[order(-orafix_up$es),]
orafix_up$Description=NULL

head(orafix_up) %>%
  kbl(row.names = FALSE, caption="Top upregulated pathways in Aza treatment") %>%
  kable_paper("hover", full_width = F)
Top upregulated pathways in Aza treatment
ID GeneRatio BgRatio pvalue p.adjust qvalue Count es
BIOCARTA_CELLCYCLE_PATHWAY 11/1672 22/13164 0.0000247 0.0015214 0.0013671 11 3.936603
BIOCARTA_VDR_PATHWAY 10/1672 24/13164 0.0003842 0.0094512 0.0084928 10 3.280502
BIOCARTA_G1_PATHWAY 10/1672 27/13164 0.0011538 0.0198599 0.0178459 10 2.916002 
topup2 <- rev(head(orafix_up$es,10))
names(topup2) <- rev(head(orafix_up$ID,10))

Now with the downregulated genes

n_pw=10

orafix_dn <- as.data.frame(enricher(gene = defdn ,
  universe = bg,  maxGSSize = 5000, TERM2GENE = genesets2,
  pAdjustMethod="fdr",  pvalueCutoff = 1, qvalueCutoff = 1  ))

orafix_dn$geneID <- NULL
orafix_dn <- subset(orafix_dn,p.adjust<0.05 & Count >=10)
orafix_dns <- rownames(orafix_dn)

gr <- as.numeric(sapply(strsplit(orafix_dn$GeneRatio,"/"),"[[",1)) /
  as.numeric(sapply(strsplit(orafix_dn$GeneRatio,"/"),"[[",2))

br <- as.numeric(sapply(strsplit(orafix_dn$BgRatio,"/"),"[[",1)) /
  as.numeric(sapply(strsplit(orafix_dn$BgRatio,"/"),"[[",2))

orafix_dn$es <- gr/br
orafix_dn <- orafix_dn[order(-orafix_dn$es),]
orafix_dn$Description=NULL

head(orafix_dn,n_pw) %>%
  kbl(row.names = FALSE, caption="Top downregulated pathways in Aza treatment") %>%
  kable_paper("hover", full_width = F)
Top downregulated pathways in Aza treatment
ID GeneRatio BgRatio pvalue p.adjust qvalue Count es
BIOCARTA_EICOSANOID_PATHWAY 11/1926 17/13164 0.0000034 0.0005359 0.0003796 11 4.422576
BIOCARTA_IL6_PATHWAY 11/1926 20/13164 0.0000299 0.0009551 0.0006766 11 3.759190
BIOCARTA_BIOPEPTIDES_PATHWAY 13/1926 26/13164 0.0000216 0.0009551 0.0006766 13 3.417445
BIOCARTA_TPO_PATHWAY 11/1926 22/13164 0.0000945 0.0025042 0.0017739 11 3.417445
BIOCARTA_GH_PATHWAY 10/1926 24/13164 0.0012046 0.0119705 0.0084796 10 2.847871
BIOCARTA_PDGF_PATHWAY 11/1926 27/13164 0.0008632 0.0108388 0.0076779 11 2.784585
BIOCARTA_EGF_PATHWAY 10/1926 25/13164 0.0017470 0.0154317 0.0109315 10 2.733956
BIOCARTA_PYK2_PATHWAY 10/1926 27/13164 0.0034166 0.0254998 0.0180634 10 2.531441
BIOCARTA_BCR_PATHWAY 10/1926 29/13164 0.0061535 0.0407666 0.0288780 10 2.356859
BIOCARTA_FMLP_PATHWAY 10/1926 29/13164 0.0061535 0.0407666 0.0288780 10 2.356859 
topdn2 <- head(orafix_dn$es,n_pw)
names(topdn2) <- head(orafix_dn$ID,n_pw)

Make a barplot

par(mar=c(5,20,5,1))

cols <- c(rep("blue",n_pw),rep("red",n_pw))

barplot(c(topdn2,topup2),
  horiz=TRUE,las=1,cex.names=0.65,col=cols,
  main="top DE Biocarta",
  xlab="ES",
  cex.main=0.9)

mtext("ORA fix test")

Enrichment analysis with functional class scoring

Reporting criteria Method/resource used
Origin of gene sets Biocarta (2023-06-21)
Tool used FGSEA (check version at foot of report)
Statistical test used Kolmogorov-Smirnov test
P-value correction for multiple comparisons FDR method
Background list Genes with >=10 reads per sample on average across all samples
Gene Selection Criteria DESeq2 FDR<0.05
ORA directionality Separate tests for up- and down-regulation
Data availability via DEE2 at accession SRP038101 (human)
Other parameters Min gene set size of 10 
def2 <- def
def2$genename <- sapply(strsplit(rownames(def2)," "),"[[",2)
def2 <- def2[,c("stat","genename")]
def2 <- aggregate(. ~ genename, def2, sum)
stat <- def2$stat
names(stat) <- def2$genename
fgseaRes <- fgsea(pathways=gsets, stats=stat, minSize=10, nPermSimple = 10000)
fgseaRes <- fgseaRes[order(fgseaRes$pval),]
fgsea_up <- subset(fgseaRes,padj<0.05 & ES>0)
fgsea_ups <- fgsea_up$pathway
fgsea_dn <- subset(fgseaRes,padj<0.05 & ES<0)
fgsea_dns <- fgsea_dn$pathway

fgsea_up <-  data.frame(fgsea_up[order(-fgsea_up$ES),])

fgsea_dn <-  data.frame(fgsea_dn[order(fgsea_dn$ES),])

head(fgsea_up,n_pw) %>%
  kbl(row.names = FALSE, caption="FGSEA:Top upregulated pathways in Aza treatment") %>%
  kable_paper("hover", full_width = F)
FGSEA:Top upregulated pathways in Aza treatment
pathway pval padj log2err ES NES size leadingEdge
BIOCARTA_NPC_PATHWAY 0.0000364 0.0060825 0.5573322 0.8313478 2.112773 11 KPNA2 , KPNB1 , NUP153 , RCC1 , RANBP2 , NUP214 , RAN , NUP62 , RANGAP1
BIOCARTA_RANMS_PATHWAY 0.0002150 0.0097604 0.5188481 0.8248418 2.033163 10 KPNA2 , KPNB1 , TPX2 , KIF15 , RCC1 , RANBP1 , AURKA , RAN , RANGAP1
BIOCARTA_PTC1_PATHWAY 0.0030284 0.0293845 0.4317077 0.7476978 1.843010 10 CDC25A, CDK1 , CCNB1 , CCNH , XPO1
BIOCARTA_RB_PATHWAY 0.0029019 0.0293845 0.4317077 0.7106077 1.850390 12 CDC25A, CDK1 , YWHAH , TP53 , CDK4 , CDK2 , CHEK1 , WEE1
BIOCARTA_SM_PATHWAY 0.0031504 0.0293845 0.4317077 0.6774617 1.851676 14 SRSF2 , SNRPD3, SNRPF , SNRPA , SNRPD1, SNRPB2, SNRPG , SNRPE
BIOCARTA_STATHMIN_PATHWAY 0.0047464 0.0381532 0.3350686 0.6697015 1.830465 14 CDK1 , CCNB1 , PRKAR1B, STMN1 , PRKAR2A, PRKACB
BIOCARTA_MCM_PATHWAY 0.0050262 0.0381532 0.4070179 0.6469155 1.860795 17 MCM6 , CDC6 , ORC6 , MCM4 , CCNE1, ORC1 , CDK2 , ORC2
BIOCARTA_ATRBRCA_PATHWAY 0.0009461 0.0143636 0.4772708 0.6185903 1.889635 21 FANCD2, RAD1 , BRCA1 , FANCA , BRCA2 , TP53 , FANCG , RAD17 , RAD51 , MRE11 , CHEK1 , ATR 
head(fgsea_dn,n_pw) %>%
  kbl(row.names = FALSE, caption="FGSEA:Top downregulated pathways in Aza treatment") %>%
  kable_paper("hover", full_width = F)
FGSEA:Top downregulated pathways in Aza treatment
pathway pval padj log2err ES NES size leadingEdge
BIOCARTA_IL10_PATHWAY 0.0005483 0.0120415 0.4772708 -0.8584672 -1.924127 10 STAT1 , STAT5A, HMOX1 , IL10RA, BLVRA , TNF , IL10RB, STAT3
BIOCARTA_NKCELLS_PATHWAY 0.0002338 0.0097604 0.5188481 -0.7950834 -1.996747 16 B2M , HLA-A , MAPK3 , PTK2B , MAP2K1, PTPN6 , VAV1 , IL18 , PAK1 , SYK
BIOCARTA_GSK3_PATHWAY 0.0001443 0.0097604 0.5188481 -0.7867519 -2.007905 17 GJA1 , EIF2AK2, MYD88 , CCND1 , FZD1
BIOCARTA_IL12_PATHWAY 0.0049803 0.0381532 0.2698423 -0.7633513 -1.754980 11 JUN , ETV5 , JAK2 , TYK2 , IL18 , STAT4 , MAPK14
BIOCARTA_EICOSANOID_PATHWAY 0.0007018 0.0130216 0.4772708 -0.7558723 -1.929096 17 PTGER4 , ALOX5 , PTGS2 , ALOX5AP, PTGER3 , PTGFR , PTGER2 , TBXAS1 , PLCB1 , PLA2G4A, EPHX2
BIOCARTA_CCR5_PATHWAY 0.0031672 0.0293845 0.3350686 -0.7409241 -1.801996 14 JUN , CXCR4 , PTK2B , CALM3 , FOS , CCL2 , PRKCB , MAPK14, CALM1 , GNAQ , CALM2
BIOCARTA_TPO_PATHWAY 0.0003061 0.0102247 0.4984931 -0.7365451 -1.989923 22 STAT1 , JUN , MAPK3 , STAT5A, JAK2 , MAP2K1, FOS , STAT3 , SHC1 , STAT5B, PRKCB
BIOCARTA_IL6_PATHWAY 0.0031462 0.0293845 0.4317077 -0.7022186 -1.860851 20 JUN , MAPK3 , IL6R , JAK3 , JAK2 , MAP2K1, FOS , CEBPB , SRF , STAT3 , SHC1
BIOCARTA_EPO_PATHWAY 0.0033915 0.0298093 0.3182797 -0.6994297 -1.810258 18 JUN , MAPK3 , STAT5A, JAK2 , MAP2K1, FOS , PTPN6 , SHC1 , STAT5B
BIOCARTA_RHO_PATHWAY 0.0072583 0.0470977 0.2182598 -0.6946647 -1.744559 16 GSN , ARHGAP5 , DIAPH1 , ARHGEF11, PIP5K1A , ARHGAP4 , ARHGAP1 , BAIAP2 , TLN1 , ARHGEF1 
fgsea_up <- head(fgsea_up,n_pw)
fgsea_up_vec <- fgsea_up$ES
names(fgsea_up_vec) <- fgsea_up$pathway
fgsea_up_vec <- rev(fgsea_up_vec)

fgsea_dn <- head(fgsea_dn,n_pw)
fgsea_dn_vec <- fgsea_dn$ES * -1
names(fgsea_dn_vec) <- fgsea_dn$pathway

Make a barplot

par(mar=c(5,20,5,1))

cols <- c(rep("blue",n_pw),rep("red",n_pw))

barplot(c(fgsea_dn_vec,fgsea_up_vec),
  horiz=TRUE,las=1,cex.names=0.65,col=cols,
  main="top DE Biocarta",
  xlab="ES",
  cex.main=0.9)

mtext("FCS test")

Compare pathway results

v1 <- list("ORA up"=ora_ups, "ORA fix up"=orafix_ups, "FCS up"=fgsea_ups)
v2 <- list("ORA dn"=ora_dns, "ORA fix dn"=orafix_dns, "FCS dn"=fgsea_dns)
v3 <- list("ORA"=union(ora_ups,ora_dns),
  "ORA fix"=union(orafix_ups,orafix_dns),
  "FCS"=union(fgsea_ups,fgsea_dns))

par(mar=c(10,10,10,10))
par(mfrow=c(2,1))
plot(euler(v1),quantities = list(cex = 2), labels = list(cex = 2),main="upregulated genes")

plot(euler(v2),quantities = list(cex = 2), labels = list(cex = 2),main="downregulated genes")

plot(euler(v3),quantities = list(cex = 2), labels = list(cex = 2),main="up- and down-regulated genes")

Jaccard index comparing ORA and FCS.

jaccard <- function(a, b) {
    intersection = length(intersect(a, b))
    union = length(a) + length(b) - intersection
    return (intersection/union)
}

ora <- c(ora_ups,ora_dns)
fcs <- c(fgsea_ups,fgsea_dns)

jaccard(ora,fcs)
## [1] 0.03448276

Jaccard index comparing ORA and ORA fix.

ora <- c(ora_ups,ora_dns)
orafix <- c(orafix_ups,orafix_dns)

jaccard(ora,orafix)
## [1] 0.2

Jaccard index comparing ORA fix and FCS.

orafix <- c(orafix_ups,orafix_dns)
fcs <- c(fgsea_ups,fgsea_dns)

jaccard(orafix,fcs)
## [1] 0.2

Drill in to a specific pathway

In this case the top upregulated and downregulated pathways

upname <- head(fgsea_up,1)$pathway
plotEnrichment(gsets[[upname]], stat, gseaParam = 1, ticksSize = 0.2)

dnname <- head(fgsea_dn,1)$pathway
plotEnrichment(gsets[[dnname]], stat, gseaParam = 1, ticksSize = 0.2)

Now a heatmap of these gene sets.

# reads per million normalisation
rpm <- apply(mxf,2, function(x) { x/sum(x) * 1000000} )
colnames(rpm) <- c("UNTR1","UNTR2","UNTR3","TRT1","TRT2","TRT3")
gnames_up <- gsets[[which(names(gsets) == upname)]]
gnames_dn <- gsets[[which(names(gsets) == dnname)]]
gene_ids <- rownames(mxf)
gene_names <- sapply(strsplit(gene_ids," "),"[[",2)
rpm_up <- rpm[which(gene_names %in% gnames_up),]
rownames(rpm_up) <- sapply(strsplit(rownames(rpm_up)," "),"[[",2)
rpm_dn <- rpm[which(gene_names %in% gnames_dn),]
rownames(rpm_dn) <- sapply(strsplit(rownames(rpm_dn)," "),"[[",2)
colsidecols <- c("blue","blue","blue","red","red","red")
heatmap.2(rpm_up,scale="row",trace="none",margins=c(10,15),main=upname,ColSideColors=colsidecols)

heatmap.2(rpm_dn,scale="row",trace="none",margins=c(10,15),main=dnname,ColSideColors=colsidecols)

Session information

For reproducibility


Click HERE to show session info 

sessionInfo()
## R version 4.3.0 (2023-04-21)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.2 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0 
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
## 
## 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       
## 
## time zone: Australia/Melbourne
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] gplots_3.1.3                eulerr_7.0.0               
##  [3] fgsea_1.22.0                clusterProfiler_4.4.4      
##  [5] kableExtra_1.3.4            DESeq2_1.36.0              
##  [7] SummarizedExperiment_1.26.1 Biobase_2.56.0             
##  [9] MatrixGenerics_1.8.1        matrixStats_0.63.0         
## [11] GenomicRanges_1.48.0        GenomeInfoDb_1.32.2        
## [13] IRanges_2.30.0              S4Vectors_0.34.0           
## [15] BiocGenerics_0.42.0         getDEE2_1.6.0              
## 
## loaded via a namespace (and not attached):
##   [1] RColorBrewer_1.1-3     rstudioapi_0.14        jsonlite_1.8.4        
##   [4] magrittr_2.0.3         farver_2.1.1           rmarkdown_2.21        
##   [7] zlibbioc_1.42.0        vctrs_0.6.2            memoise_2.0.1         
##  [10] RCurl_1.98-1.12        ggtree_3.4.1           webshot_0.5.4         
##  [13] htmltools_0.5.5        gridGraphics_0.5-1     sass_0.4.5            
##  [16] KernSmooth_2.23-20     bslib_0.4.2            plyr_1.8.8            
##  [19] cachem_1.0.7           igraph_1.4.2           lifecycle_1.0.3       
##  [22] pkgconfig_2.0.3        Matrix_1.5-4           R6_2.5.1              
##  [25] fastmap_1.1.1          GenomeInfoDbData_1.2.8 digest_0.6.31         
##  [28] aplot_0.1.10           enrichplot_1.16.1      colorspace_2.1-0      
##  [31] patchwork_1.1.2        AnnotationDbi_1.58.0   geneplotter_1.74.0    
##  [34] RSQLite_2.3.1          labeling_0.4.2         fansi_1.0.4           
##  [37] httr_1.4.5             polyclip_1.10-4        compiler_4.3.0        
##  [40] bit64_4.0.5            withr_2.5.0            downloader_0.4        
##  [43] BiocParallel_1.30.3    viridis_0.6.2          DBI_1.1.3             
##  [46] highr_0.10             ggforce_0.4.1          MASS_7.3-59           
##  [49] DelayedArray_0.22.0    caTools_1.18.2         gtools_3.9.4          
##  [52] tools_4.3.0            DO.db_2.9              scatterpie_0.1.9      
##  [55] ape_5.7-1              glue_1.6.2             nlme_3.1-162          
##  [58] GOSemSim_2.22.0        polylabelr_0.2.0       shadowtext_0.1.2      
##  [61] grid_4.3.0             reshape2_1.4.4         generics_0.1.3        
##  [64] gtable_0.3.3           tidyr_1.3.0            data.table_1.14.8     
##  [67] tidygraph_1.2.3        xml2_1.3.4             utf8_1.2.3            
##  [70] XVector_0.36.0         ggrepel_0.9.3          pillar_1.9.0          
##  [73] stringr_1.5.0          yulab.utils_0.0.6      genefilter_1.78.0     
##  [76] splines_4.3.0          dplyr_1.1.2            tweenr_2.0.2          
##  [79] treeio_1.20.1          lattice_0.21-8         survival_3.5-5        
##  [82] bit_4.0.5              annotate_1.74.0        tidyselect_1.2.0      
##  [85] GO.db_3.15.0           locfit_1.5-9.7         Biostrings_2.64.0     
##  [88] knitr_1.42             gridExtra_2.3          svglite_2.1.1         
##  [91] xfun_0.39              graphlayouts_0.8.4     stringi_1.7.12        
##  [94] lazyeval_0.2.2         ggfun_0.0.9            yaml_2.3.7            
##  [97] evaluate_0.20          codetools_0.2-19       ggraph_2.1.0          
## [100] tibble_3.2.1           qvalue_2.28.0          ggplotify_0.1.0       
## [103] cli_3.6.1              xtable_1.8-4           systemfonts_1.0.4     
## [106] munsell_0.5.0          jquerylib_0.1.4        Rcpp_1.0.10           
## [109] png_0.1-8              XML_3.99-0.14          parallel_4.3.0        
## [112] ggplot2_3.4.2          blob_1.2.4             DOSE_3.22.0           
## [115] htm2txt_2.2.2          bitops_1.0-7           tidytree_0.4.2        
## [118] viridisLite_0.4.1      scales_1.2.1           purrr_1.0.1           
## [121] crayon_1.5.2           rlang_1.1.1            fastmatch_1.1-3       
## [124] KEGGREST_1.36.3        rvest_1.0.3