Example gene set analysis: The case of Set7 knockdown
Mark Ziemann & Kaumadi Wijesooriya
2022-01-18
Source: https://github.com/markziemann/SurveyEnrichmentMethods
Intro
Here we are performing an analysis of some gene expression data to demonstrate the difference between ORA and FCS methods and to highlight the differences caused by improper background gene set use.
The dataset being used is SRP038101 and we are comparing the cells expressing a set7kd shRNA construct (case) compared to the scrambled construct (control).
Data are obtained from http://dee2.io/
suppressPackageStartupMessages({
library("getDEE2")
library("DESeq2")
library("clusterProfiler")
library("mitch")
library("kableExtra")
library("eulerr")
})Get expression data
I’m using some RNA-seq data looking at the effect of Set7 knockdown on HMEC cells.
name="SRP096177"
mdat<-getDEE2Metadata("hsapiens")
samplesheet <- mdat[grep("SRP096177",mdat$SRP_accession),]
samplesheet<-samplesheet[order(samplesheet$SRR_accession),]
samplesheet$trt<-as.factor(c(1,1,1,0,0,0))
s1 <- samplesheet
s1 %>% kbl(caption = "sample sheet") %>% kable_paper("hover", full_width = F)| SRR_accession | QC_summary | SRX_accession | SRS_accession | SRP_accession | Sample_name | GEO_series | Library_name | trt | |
|---|---|---|---|---|---|---|---|---|---|
| 379112 | SRR5150592 | PASS | SRX2468682 | SRS1901000 | SRP096177 | GSM2448982 | GSE93236 | 1 | |
| 379113 | SRR5150593 | PASS | SRX2468683 | SRS1901005 | SRP096177 | GSM2448983 | GSE93236 | 1 | |
| 379114 | SRR5150594 | PASS | SRX2468684 | SRS1901001 | SRP096177 | GSM2448984 | GSE93236 | 1 | |
| 379115 | SRR5150595 | PASS | SRX2468685 | SRS1901002 | SRP096177 | GSM2448985 | GSE93236 | 0 | |
| 379116 | SRR5150596 | PASS | SRX2468686 | SRS1901003 | SRP096177 | GSM2448986 | GSE93236 | 0 | |
| 379117 | SRR5150597 | PASS | SRX2468687 | SRS1901004 | SRP096177 | GSM2448987 | GSE93236 | 0 |
w<-getDEE2("hsapiens",samplesheet$SRR_accession,metadata=mdat,legacy = TRUE)## For more information about DEE2 QC metrics, visit
## https://github.com/markziemann/dee2/blob/master/qc/qc_metrics.mdx<-Tx2Gene(w)
x<-x$Tx2Gene
# save the genetable for later
gt<-w$GeneInfo[,1,drop=FALSE]
gt$accession<-rownames(gt)
# counts
x1<-x[,which(colnames(x) %in% samplesheet$SRR_accession)]Here show the number of genes in the annotation set, and those detected above the detection threshold.
# filter out lowly expressed genes
x1<-x1[which(rowSums(x1)/ncol(x1)>=(10)),]
nrow(x)## [1] 39297nrow(x1)## [1] 15607Now multidimensional scaling (MDS) plot to show the correlation between the datasets. If the control and case datasets are clustered separately, then it is likely that there will be many differentially expressed genes with FDR<0.05.
plot(cmdscale(dist(t(x1))), xlab="Coordinate 1", ylab="Coordinate 2", pch=19, col=s1$trt, main="MDS")
Differential expression
Now run DESeq2 for control vs case.
y <- DESeqDataSetFromMatrix(countData = round(x1), colData = s1, design = ~ trt)## converting counts to integer modey <- DESeq(y)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testingde <- results(y)
de<-as.data.frame(de[order(de$pvalue),])
rownames(de)<-sapply(strsplit(rownames(de),"\\."),"[[",1)
head(de) %>% kbl() %>% kable_paper("hover", full_width = F)| baseMean | log2FoldChange | lfcSE | stat | pvalue | padj | |
|---|---|---|---|---|---|---|
| ENSG00000168542 | 1259.1001 | 2.7325696 | 0.0952226 | 28.69666 | 0 | 0 |
| ENSG00000164692 | 11379.8760 | 2.2961488 | 0.0887089 | 25.88407 | 0 | 0 |
| ENSG00000172531 | 2130.9201 | -1.7175060 | 0.0712043 | -24.12082 | 0 | 0 |
| ENSG00000106484 | 4549.7360 | 1.2253274 | 0.0581660 | 21.06603 | 0 | 0 |
| ENSG00000163017 | 300.4833 | 4.8785541 | 0.2741929 | 17.79242 | 0 | 0 |
| ENSG00000130508 | 10277.9876 | -0.9442904 | 0.0530794 | -17.79015 | 0 | 0 |
Now let’s have a look at some of the charts showing differential expression. In particular, an MA plot and volcano plot.
maplot <- function(de,contrast_name) {
sig <-subset(de, padj < 0.05 )
up <-rownames(subset(de, padj < 0.05 & log2FoldChange > 0))
dn <-rownames(subset(de, padj < 0.05 & log2FoldChange < 0))
GENESUP <- length(up)
GENESDN <- length(dn)
DET=nrow(de)
SUBHEADER = paste(GENESUP, "up, ", GENESDN, "down", DET, "detected")
ns <-subset(de, padj > 0.05 )
plot(log2(de$baseMean),de$log2FoldChange,
xlab="log2 basemean", ylab="log2 foldchange",
pch=19, cex=0.5, col="dark gray",
main=contrast_name, cex.main=0.7)
points(log2(sig$baseMean),sig$log2FoldChange,
pch=19, cex=0.5, col="red")
mtext(SUBHEADER,cex = 0.7)
}
make_volcano <- function(de,name) {
sig <- subset(de,padj<0.05)
N_SIG=nrow(sig)
N_UP=nrow(subset(sig,log2FoldChange>0))
N_DN=nrow(subset(sig,log2FoldChange<0))
DET=nrow(de)
HEADER=paste(N_SIG,"@5%FDR,", N_UP, "up", N_DN, "dn", DET, "detected")
plot(de$log2FoldChange,-log10(de$padj),cex=0.5,pch=19,col="darkgray",
main=name, xlab="log2 FC", ylab="-log10 pval", xlim=c(-6,6))
mtext(HEADER)
grid()
points(sig$log2FoldChange,-log10(sig$padj),cex=0.5,pch=19,col="red")
}
maplot(de,name)
make_volcano(de,name)
Gene sets from Reactome
In order to perform gene set analysis, we need some gene sets.
if (! file.exists("ReactomePathways.gmt")) {
download.file("https://reactome.org/download/current/ReactomePathways.gmt.zip",
destfile="ReactomePathways.gmt.zip")
unzip("ReactomePathways.gmt.zip")
}
genesets<-gmt_import("ReactomePathways.gmt")FCS with Mitch
Mitch uses rank-ANOVA statistics for enrichment detection.
m <- mitch_import(de,DEtype = "DEseq2", geneTable = gt)## The input is a single dataframe; one contrast only. Converting
## it to a list for you.## Note: Mean no. genes in input = 15607## Note: no. genes in output = 14564## Note: estimated proportion of input genes in output = 0.933mres <- mitch_calc(m,genesets = genesets)## Note: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.m_up <- subset(mres$enrichment_result,p.adjustANOVA<0.05 & s.dist > 0)[,1]
m_dn <- subset(mres$enrichment_result,p.adjustANOVA<0.05 & s.dist < 0)[,1]
message(paste("Number of up-regulated pathways:",length(m_up) ))## Number of up-regulated pathways: 308message(paste("Number of down-regulated pathways:",length(m_dn) ))## Number of down-regulated pathways: 44head(mres$enrichment_result,10) %>% kbl() %>% kable_paper("hover", full_width = F)| set | setSize | pANOVA | s.dist | p.adjustANOVA | |
|---|---|---|---|---|---|
| 346 | Eukaryotic Translation Elongation | 93 | 0 | -0.5843493 | 0 |
| 796 | Peptide chain elongation | 88 | 0 | -0.5846275 | 0 |
| 1066 | Selenocysteine synthesis | 91 | 0 | -0.5615284 | 0 |
| 1015 | Response of EIF2AK4 (GCN2) to amino acid deficiency | 100 | 0 | -0.5355088 | 0 |
| 629 | Metabolism of RNA | 661 | 0 | 0.2112927 | 0 |
| 1335 | Viral mRNA Translation | 88 | 0 | -0.5452426 | 0 |
| 348 | Eukaryotic Translation Termination | 92 | 0 | -0.5319553 | 0 |
| 668 | Mitotic Metaphase and Anaphase | 222 | 0 | 0.3387003 | 0 |
| 665 | Mitotic Anaphase | 221 | 0 | 0.3384478 | 0 |
| 387 | Formation of a pool of free 40S subunits | 100 | 0 | -0.4948438 | 0 |
m_up_nom <- subset(mres$enrichment_result,pANOVA<0.05 & s.dist > 0)[,1]
m_dn_nom <- subset(mres$enrichment_result,pANOVA<0.05 & s.dist < 0)[,1]ORA with clusterprofiler
Clusterprofiler uses a hypergeometric test. Firstly I will conduct the analysis separately for up and down regulated genes and with the correct backgound (as intended by the developers).
genesets2 <- read.gmt("ReactomePathways.gmt")
de_up <- rownames(subset(de,log2FoldChange>0,padj<0.05))
de_up <- unique(gt[which(rownames(gt) %in% de_up),1])
de_dn <- rownames(subset(de,log2FoldChange<0,padj<0.05))
de_dn <- unique(gt[which(rownames(gt) %in% de_dn),1])
de_bg <- rownames(de)
de_bg <- unique(gt[which(rownames(gt) %in% de_bg),1])
c_up <- as.data.frame(enricher(gene = de_up, universe = de_bg, maxGSSize = 5000, TERM2GENE = genesets2, pAdjustMethod="fdr"))
c_up <- rownames(subset(c_up, p.adjust < 0.05))
c_dn <- as.data.frame(enricher(gene = de_dn, universe = de_bg, maxGSSize = 5000, TERM2GENE = genesets2, pAdjustMethod="fdr"))
c_dn <- rownames(subset(c_dn, p.adjust < 0.05))
c_up_nom <- as.data.frame(enricher(gene = de_up, universe = de_bg, maxGSSize = 5000, TERM2GENE = genesets2, pAdjustMethod="none" ))
c_up_nom <- rownames(subset(c_up_nom, pvalue < 0.05))
c_dn_nom <- as.data.frame(enricher(gene = de_dn, universe = de_bg, maxGSSize = 5000, TERM2GENE = genesets2, pAdjustMethod="none"))
c_dn_nom <- rownames(subset(c_dn_nom, pvalue < 0.05))Now performing ORA with clusterprofiler with whole genome background list
wg_bg <- w$GeneInfo$GeneSymbol
f_up <- as.data.frame(enricher(gene = de_up, universe = wg_bg, maxGSSize = 5000, TERM2GENE = genesets2, pAdjustMethod="fdr"))
f_up <- rownames(subset(f_up, p.adjust < 0.05))
f_dn <- as.data.frame(enricher(gene = de_dn, universe = wg_bg, maxGSSize = 5000, TERM2GENE = genesets2, pAdjustMethod="fdr"))
f_dn <- rownames(subset(f_dn, p.adjust < 0.05))
f_up_nom <- as.data.frame(enricher(gene = de_up, universe = wg_bg, maxGSSize = 5000, TERM2GENE = genesets2, pAdjustMethod="none"))
f_up_nom <- rownames(subset(f_up_nom, pvalue < 0.05))
f_dn_nom <- as.data.frame(enricher(gene = de_dn, universe = wg_bg, maxGSSize = 5000, TERM2GENE = genesets2, pAdjustMethod="none"))
f_dn_nom <- rownames(subset(f_dn_nom, pvalue < 0.05))
f_de_nom <- union(f_up_nom,f_dn_nom)Bar chart of significance
Here the idea is to classify the pathways as 1 not significant, 2 nominally significant, or 3 FDR significant
c_up_df <- as.data.frame(enricher(gene = de_up, universe = de_bg, maxGSSize = 5000, TERM2GENE = genesets2,
pAdjustMethod="fdr" ,qvalueCutoff=1,pvalueCutoff=1))
n_c_up_ns <- nrow( subset(c_up_df,pvalue>0.05) )
n_c_up_nom <- nrow( subset(c_up_df,pvalue<0.05 ) )
n_c_up_fdr <- nrow( subset(c_up_df,p.adjust<0.05) )
c_dn_df <- as.data.frame(enricher(gene = de_dn, universe = de_bg, maxGSSize = 5000, TERM2GENE = genesets2,
pAdjustMethod="fdr" ,qvalueCutoff=1,pvalueCutoff=1))
n_c_dn_ns <- nrow( subset(c_dn_df,pvalue>0.05) )
n_c_dn_nom <- nrow( subset(c_dn_df,pvalue<0.05 ) )
n_c_dn_fdr <- nrow( subset(c_dn_df,p.adjust<0.05) )
n_m_up <- length(m_up)
n_m_dn <- length(m_dn)
n_m_up_nom <- length(m_up_nom)
n_m_dn_nom <- length(m_dn_nom)
par(mar=c(5,10,5,2))
ngenes <- c("ORA up fdr"=n_c_up_fdr,"ORA up nom"=n_c_up_nom,
"ORA dn fdr"=n_c_dn_fdr,"ORA dn nom"=n_c_dn_nom,
"FCS up fdr"=n_m_up,"FCS up nom"=n_m_up_nom,
"FCS dn fdr"=n_m_dn,"FCS dn nom"=n_m_dn_nom )
barplot(ngenes, horiz=TRUE,las=1)
c_up <- subset(c_up_df,p.adjust<0.05)$ID
c_dn <- subset(c_dn_df,p.adjust<0.05)$ID
c_up_nom <- subset(c_up_df,pvalue<0.05)$ID
c_dn_nom <- subset(c_dn_df,pvalue<0.05)$ID
n_c_fdr <- length(union(c_dn,c_up))
n_c_nom <- length(union(c_dn_nom,c_up_nom))
n_m_fdr <- length(subset(mres$enrichment_result,p.adjustANOVA<0.05 )[,1])
n_m_nom <- length(subset(mres$enrichment_result,pANOVA<0.05 )[,1])
par(mar=c(5,5,5,2))
ngenes <- c("ORA FDR<0.05"=n_c_fdr,"ORA p<0.05"=n_c_nom,"FCS FDR<0.05"=n_m_fdr,"FCS p<0.05"=n_m_nom)
barplot(ngenes,ylab="no. gene sets")
text((0:3*1.2)+0.7,ngenes-50,labels=ngenes,cex=1.1)
Venn diagram comparison
The Venn (or Euler to be more correct) diagram is useful to visualise the overlaps between sets.
par(cex.main=0.5)
par(mar=c(2,2,2,2))
v1 <- list("FCS up"=m_up, "FCS dn"=m_dn,
"ORA up"=c_up,"ORA dn"=c_dn)
plot(euler(v1),quantities = TRUE, edges = "gray", main="FCS compared to ORA")
v0 <- list("FDR up"=m_up, "FDR dn"=m_dn,
"Nom up"=m_up_nom,"Nom dn"=m_dn_nom)
plot(euler(v0),quantities = TRUE, edges = "gray", main="Effect of FDR correction on FCS results")
v0 <- list("FDR up"=c_up, "FDR dn"=c_dn,
"Nom up"=c_up_nom,"Nom dn"=c_dn_nom)
plot(euler(v0),quantities = TRUE, edges = "gray", main="Effect of FDR correction on ORA results")
ora_nom <- union(c_up_nom,c_dn_nom)
ora_fdr <- union(c_up,c_dn)
fcs_nom <- union(m_up_nom,m_dn_nom)
fcs_fdr <- union(m_up,m_dn)
v3 <- list("ORA nom"=ora_nom, "ORA FDR"=ora_fdr,
"FCS nom"=fcs_nom,"FCS FDR"=fcs_fdr)
plot(euler(v3),quantities = TRUE, edges = "gray", main="Effect of FDR correction")
v2 <- list("ORA up"=c_up,"ORA dn"=c_dn,
"ORA* up"=f_up,"ORA* dn"=f_dn )
plot(euler(v2),quantities = TRUE, edges = "gray", main="Effect of inappropriate background* (whole genome)")
Jaccard calculation
# FCS vs ORA
cm <- length(intersect(c(c_up,c_dn), c(m_up,m_dn))) / length(union(c(c_up,c_dn), c(m_up,m_dn)))
#FCS fdr vs nom
fcs_fdr_nom <- length(intersect(c(fcs_nom), c(fcs_fdr))) / length(union(c(fcs_nom), c(fcs_fdr)))
#ORA fdr vs nom
ora_fdr_nom <- length(intersect(c(ora_nom), c(ora_fdr))) / length(union(c(ora_nom), c(ora_fdr)))
m_up <- gsub("^","up ",m_up)
m_dn <- gsub("^","dn ",m_dn)
m_de <- union(m_up,m_dn)
c_up <- gsub("^","up ",c_up)
c_dn <- gsub("^","dn ",c_dn)
c_de <- union(c_up,c_dn)
f_up <- gsub("^","up ",f_up)
f_dn <- gsub("^","dn ",f_dn)
f_de <- union(f_up,f_dn)
f_up_nom <- gsub("^","up ",f_up_nom)
f_dn_nom <- gsub("^","dn ",f_dn_nom)
f_de_nom <- union(f_up,f_dn_nom)
# ORA vs ORA*
cf <- length(intersect(c_de, f_de )) / length(union(c_de, f_de))
# ORA vs ORA*nom
cfn <- length(intersect(c_de, f_de_nom )) / length(union(c_de, f_de_nom))
dat <- c( "FCS vs ORA"=cm,
"FCS: FDR vs nominal"=fcs_fdr_nom,
"ORA: FDR vs nominal"= ora_fdr_nom,
"ORA vs ORA*"=cf,
"ORA vs ORA*nom"=cfn)
dat## FCS vs ORA FCS: FDR vs nominal ORA: FDR vs nominal ORA vs ORA*
## 0.6292135 0.7652174 0.5547445 0.4209524
## ORA vs ORA*nom
## 0.3745763saveRDS(dat,file = "ex4dat.rds")
par(mar=c(5,10,3,1))
barplot(rev(dat),xlab="Jaccard index",horiz = TRUE, las =1, xlim=c(0,.8) , main=name)
text( x=rev(dat)-0.05 , y= 1:length(rev(dat))*1.2-0.5, labels = signif(rev(dat),2))
Session information
sessionInfo()## R version 4.1.2 (2021-11-01)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.3 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_AU.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_AU.UTF-8 LC_COLLATE=en_AU.UTF-8
## [5] LC_MONETARY=en_AU.UTF-8 LC_MESSAGES=en_AU.UTF-8
## [7] LC_PAPER=en_AU.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_AU.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] beeswarm_0.4.0 eulerr_6.1.1
## [3] kableExtra_1.3.4 mitch_1.4.1
## [5] clusterProfiler_4.0.5 DESeq2_1.32.0
## [7] SummarizedExperiment_1.22.0 Biobase_2.52.0
## [9] MatrixGenerics_1.4.3 matrixStats_0.61.0
## [11] GenomicRanges_1.44.0 GenomeInfoDb_1.28.4
## [13] IRanges_2.26.0 S4Vectors_0.30.2
## [15] BiocGenerics_0.38.0 getDEE2_1.2.0
##
## loaded via a namespace (and not attached):
## [1] shadowtext_0.1.1 fastmatch_1.1-3 systemfonts_1.0.3
## [4] plyr_1.8.6 igraph_1.2.11 lazyeval_0.2.2
## [7] polylabelr_0.2.0 splines_4.1.2 BiocParallel_1.26.2
## [10] ggplot2_3.3.5 digest_0.6.29 yulab.utils_0.0.4
## [13] htmltools_0.5.2 GOSemSim_2.18.1 viridis_0.6.2
## [16] GO.db_3.13.0 fansi_1.0.0 magrittr_2.0.1
## [19] memoise_2.0.1 Biostrings_2.60.2 annotate_1.70.0
## [22] graphlayouts_0.8.0 svglite_2.0.0 enrichplot_1.12.3
## [25] colorspace_2.0-2 rvest_1.0.2 blob_1.2.2
## [28] ggrepel_0.9.1 xfun_0.29 dplyr_1.0.7
## [31] crayon_1.4.2 RCurl_1.98-1.5 jsonlite_1.7.2
## [34] scatterpie_0.1.7 genefilter_1.74.1 survival_3.2-13
## [37] ape_5.6-1 glue_1.6.0 polyclip_1.10-0
## [40] gtable_0.3.0 zlibbioc_1.38.0 XVector_0.32.0
## [43] webshot_0.5.2 htm2txt_2.1.1 DelayedArray_0.18.0
## [46] scales_1.1.1 DOSE_3.18.3 DBI_1.1.2
## [49] GGally_2.1.2 Rcpp_1.0.7 viridisLite_0.4.0
## [52] xtable_1.8-4 gridGraphics_0.5-1 tidytree_0.3.7
## [55] bit_4.0.4 htmlwidgets_1.5.4 httr_1.4.2
## [58] fgsea_1.18.0 gplots_3.1.1 RColorBrewer_1.1-2
## [61] ellipsis_0.3.2 pkgconfig_2.0.3 reshape_0.8.8
## [64] XML_3.99-0.8 farver_2.1.0 sass_0.4.0
## [67] locfit_1.5-9.4 utf8_1.2.2 ggplotify_0.1.0
## [70] tidyselect_1.1.1 rlang_0.4.12 reshape2_1.4.4
## [73] later_1.3.0 AnnotationDbi_1.54.1 munsell_0.5.0
## [76] tools_4.1.2 cachem_1.0.6 downloader_0.4
## [79] generics_0.1.1 RSQLite_2.2.9 evaluate_0.14
## [82] stringr_1.4.0 fastmap_1.1.0 yaml_2.2.1
## [85] ggtree_3.0.4 knitr_1.37 bit64_4.0.5
## [88] tidygraph_1.2.0 caTools_1.18.2 purrr_0.3.4
## [91] KEGGREST_1.32.0 ggraph_2.0.5 nlme_3.1-153
## [94] mime_0.12 aplot_0.1.2 xml2_1.3.3
## [97] DO.db_2.9 rstudioapi_0.13 compiler_4.1.2
## [100] png_0.1-7 treeio_1.16.2 tibble_3.1.6
## [103] tweenr_1.0.2 geneplotter_1.70.0 bslib_0.3.1
## [106] stringi_1.7.6 highr_0.9 lattice_0.20-45
## [109] Matrix_1.4-0 vctrs_0.3.8 pillar_1.6.4
## [112] lifecycle_1.0.1 jquerylib_0.1.4 data.table_1.14.2
## [115] cowplot_1.1.1 bitops_1.0-7 httpuv_1.6.5
## [118] patchwork_1.1.1 qvalue_2.24.0 R6_2.5.1
## [121] promises_1.2.0.1 KernSmooth_2.23-20 echarts4r_0.4.3
## [124] gridExtra_2.3 gtools_3.9.2 MASS_7.3-54
## [127] assertthat_0.2.1 GenomeInfoDbData_1.2.6 grid_4.1.2
## [130] ggfun_0.0.4 tidyr_1.1.4 rmarkdown_2.11
## [133] ggforce_0.3.3 shiny_1.7.1