Example gene set analysis: The case of SARS-CoV2 infection
Mark Ziemann & Kaumadi Wijesooriya
2021-12-07
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 SRP253951 and we are comparing A549 cells with and without infection with SARS-CoV-2.
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 difference in transcriptome caused by SARS-CoV-2 infection.
name="SRP253951"
mdat<-getDEE2Metadata("hsapiens")
samplesheet <- mdat[grep("SRP253951",mdat$SRP_accession),]
samplesheet <- mdat[which(mdat$SRX_accession %in% c("SRX8089264","SRX8089265","SRX8089266","SRX8089267","SRX8089268","SRX8089269")),]
samplesheet$trt <- factor(c(0,0,0,1,1,1))
s1 <- samplesheet
s1 %>% kbl(caption = "sample sheet") %>% kable_paper("hover", full_width = F)| SRR_accession | QC_summary | SRX_accession | SRS_accession | SRP_accession | Sample_name | GEO_series | Library_name | trt | |
|---|---|---|---|---|---|---|---|---|---|
| 229503 | SRR11517674 | PASS | SRX8089264 | SRS6456133 | SRP253951 | GSM4462336 | GSE147507 | 0 | |
| 229504 | SRR11517675 | PASS | SRX8089265 | SRS6456134 | SRP253951 | GSM4462337 | GSE147507 | 0 | |
| 229505 | SRR11517676 | PASS | SRX8089266 | SRS6456135 | SRP253951 | GSM4462338 | GSE147507 | 0 | |
| 229506 | SRR11517677 | PASS | SRX8089267 | SRS6456136 | SRP253951 | GSM4462339 | GSE147507 | 1 | |
| 229507 | SRR11517678 | PASS | SRX8089268 | SRS6456137 | SRP253951 | GSM4462340 | GSE147507 | 1 | |
| 229508 | SRR11517679 | PASS | SRX8089269 | SRS6456139 | SRP253951 | GSM4462341 | GSE147507 | 1 |
w<-getDEE2("hsapiens",samplesheet$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] 15182Now 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 | |
|---|---|---|---|---|---|---|
| ENSG00000113739 | 3523.734 | 4.004593 | 0.1012568 | 39.54888 | 0 | 0 |
| ENSG00000176153 | 8851.309 | -2.785312 | 0.0820808 | -33.93378 | 0 | 0 |
| ENSG00000058085 | 5767.111 | 3.699041 | 0.1148334 | 32.21223 | 0 | 0 |
| ENSG00000169710 | 6917.854 | -2.259158 | 0.0703230 | -32.12547 | 0 | 0 |
| ENSG00000170421 | 41561.919 | -2.233482 | 0.0742019 | -30.10007 | 0 | 0 |
| ENSG00000100297 | 2476.082 | -2.364383 | 0.0802583 | -29.45965 | 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 = 15182## Note: no. genes in output = 14186## Note: estimated proportion of input genes in output = 0.934mres <- 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: 98message(paste("Number of down-regulated pathways:",length(m_dn) ))## Number of down-regulated pathways: 322head(mres$enrichment_result,10) %>% kbl() %>% kable_paper("hover", full_width = F)| set | setSize | pANOVA | s.dist | p.adjustANOVA | |
|---|---|---|---|---|---|
| 146 | Cell Cycle | 597 | 0 | -0.3095063 | 0 |
| 148 | Cell Cycle, Mitotic | 481 | 0 | -0.3219944 | 0 |
| 234 | DNA Replication | 128 | 0 | -0.5250080 | 0 |
| 1174 | Synthesis of DNA | 117 | 0 | -0.5238061 | 0 |
| 827 | Processing of Capped Intron-Containing Pre-mRNA | 236 | 0 | -0.3528510 | 0 |
| 147 | Cell Cycle Checkpoints | 250 | 0 | -0.3357336 | 0 |
| 1346 | mRNA Splicing - Major Pathway | 178 | 0 | -0.3903556 | 0 |
| 1345 | mRNA Splicing | 186 | 0 | -0.3816160 | 0 |
| 595 | M Phase | 342 | 0 | -0.2803765 | 0 |
| 409 | G2/M Checkpoints | 131 | 0 | -0.4457228 | 0 |
ORA with clusterprofiler
Clusterprofiler uses a hypergeometric test. Firstly I will conduct the analysis separately for up and down regulated genes and with the correct backgound (as intended by the developers).
genesets2 <- read.gmt("ReactomePathways.gmt")
de_up <- rownames(subset(de,log2FoldChange>0,padj<0.05))
de_up <- unique(gt[which(rownames(gt) %in% de_up),1])
de_dn <- rownames(subset(de,log2FoldChange<0,padj<0.05))
de_dn <- unique(gt[which(rownames(gt) %in% de_dn),1])
de_bg <- rownames(de)
de_bg <- unique(gt[which(rownames(gt) %in% de_bg),1])
c_up <- as.data.frame(enricher(gene = de_up, universe = de_bg, maxGSSize = 5000, TERM2GENE = genesets2))
c_up <- rownames(subset(c_up, p.adjust < 0.05))
c_dn <- as.data.frame(enricher(gene = de_dn, universe = de_bg, maxGSSize = 5000, TERM2GENE = genesets2))
c_dn <- rownames(subset(c_dn, p.adjust < 0.05))Now performing ORA with clusterprofiler combining up and down.
de_de <- rownames(subset(de,padj<0.05))
de_de <- unique(gt[which(rownames(gt) %in% de_de),1])
d_de <- as.data.frame(enricher(gene = de_de, universe = de_bg, maxGSSize = 5000, TERM2GENE = genesets2))
d_de <- rownames(subset(d_de, p.adjust < 0.05))Now performing ORA with clusterprofiler with whole genome background list
de_bg <- w$GeneInfo$GeneSymbol
f_up <- as.data.frame(enricher(gene = de_up, universe = de_bg, maxGSSize = 5000, TERM2GENE = genesets2))
f_up <- rownames(subset(f_up, p.adjust < 0.05))
f_dn <- as.data.frame(enricher(gene = de_dn, universe = de_bg, maxGSSize = 5000, TERM2GENE = genesets2))
f_dn <- rownames(subset(f_dn, p.adjust < 0.05))Now performing ORA (combining up and down gene lists) with clusterprofiler with whole genome background list
e_de <- as.data.frame(enricher(gene = de_de, universe = de_bg, maxGSSize = 5000, TERM2GENE = genesets2))
e_de <- rownames(subset(e_de, p.adjust < 0.05))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))
v0 <- list("ORA up"=c_up,"ORA dn"=c_dn,
"ORA comb" = d_de)
plot(euler(v0),quantities = TRUE, edges = "gray", main="effect of combining up and down regulated genes")
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")
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)")
vx <- list("ORA up"=c_up,"ORA dn"=c_dn,
"ORA comb" = d_de, "ORA* comb" = e_de)
plot(euler(vx),quantities = TRUE, edges = "gray", main="combining up and down genes and whole genome bg*")
v3 <- list("ORA up"=c_up,"ORA dn"=c_dn,
"ORA* up"=f_up,"ORA* dn"=f_dn ,
"FCS up"=m_up, "FCS dn"=m_dn)
png("images/fcs_ora6.png")
plot(euler(v1),quantities = TRUE, edges = "gray", main="FCS vs ORA")
dev.off()## png
## 2png("images/orabg6.png")
plot(euler(v2),quantities = TRUE, edges = "gray", main="Effect of inappropriate background* (whole genome)")
dev.off()## png
## 2png("images/oracomb6.png")
plot(euler(vx),quantities = TRUE, main="combining up and down genes and whole genome bg*")
dev.off()## png
## 2pdf("images/fcs_ora6.pdf",width=4,height=4)
plot(euler(v1),quantities = TRUE, edges = "gray", main="FCS vs ORA")
dev.off()## png
## 2pdf("images/orabg6.pdf",width=4,height=4)
plot(euler(v2),quantities = TRUE, edges = "gray", main="Effect of inappropriate background* (whole genome)")
dev.off()## png
## 2pdf("images/oracomb6.pdf",width=4,height=4)
plot(euler(vx),quantities = TRUE, edges = "gray", main="combining up and down genes and whole genome bg*")
dev.off()## png
## 2Jaccard calculation
# ORA vs ORA combined
dc <- length(intersect(d_de, c(c_up,c_dn))) / length(union(d_de, c(c_up,c_dn)))
# ORA vs ORA* combined
ec <- length(intersect(e_de, c(c_up,c_dn))) / length(union(e_de, c(c_up,c_dn)))
# 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)))
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)
# ORA vs ORA*
cf <- length(intersect(c_de, f_de )) / length(union(c_de, f_de))
# FCS vs ORA*
mf <- length(intersect(m_de, f_de )) / length(union(m_de, f_de))
dat <- c("FCS vs ORA"=cm,"ORA vs ORA*"=cf,"FCS vs ORA*"=mf, "ORA vs ORA comb"=dc, "ORA vs ORA* comb"=ec)
dat## FCS vs ORA ORA vs ORA* FCS vs ORA* ORA vs ORA comb
## 0.57109005 0.40033501 0.56568779 0.06884058
## ORA vs ORA* comb
## 0.33873582barplot(dat,ylab="jaccard metric")
saveRDS(dat,file = "ex6dat.rds")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/openblas-pthread/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/liblapack.so.3
##
## 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] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] rmdformats_1.0.3 beeswarm_0.4.0
## [3] eulerr_6.1.1 mitch_1.5.1
## [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_0.30.0
## [15] BiocGenerics_0.38.0 getDEE2_1.2.0
## [17] anytime_0.3.9 kableExtra_1.3.4
## [19] XML_3.99-0.8 reutils_0.2.3
## [21] vioplot_0.3.7 zoo_1.8-9
## [23] sm_2.2-5.7 wordcloud_2.6
## [25] RColorBrewer_1.1-2 rsvg_2.1.2
## [27] DiagrammeRsvg_0.1 DiagrammeR_1.0.6.1
## [29] forcats_0.5.1 stringr_1.4.0
## [31] dplyr_1.0.7 purrr_0.3.4
## [33] readr_2.0.2 tidyr_1.1.4
## [35] tibble_3.1.5 ggplot2_3.3.5
## [37] tidyverse_1.3.1
##
## loaded via a namespace (and not attached):
## [1] utf8_1.2.2 tidyselect_1.1.1 RSQLite_2.2.8
## [4] AnnotationDbi_1.54.1 htmlwidgets_1.5.4 grid_4.1.2
## [7] BiocParallel_1.26.2 scatterpie_0.1.7 munsell_0.5.0
## [10] withr_2.4.2 colorspace_2.0-2 GOSemSim_2.18.1
## [13] highr_0.9 knitr_1.36 rstudioapi_0.13
## [16] DOSE_3.18.3 GenomeInfoDbData_1.2.6 polyclip_1.10-0
## [19] bit64_4.0.5 farver_2.1.0 downloader_0.4
## [22] vctrs_0.3.8 treeio_1.16.2 generics_0.1.0
## [25] xfun_0.26 R6_2.5.1 graphlayouts_0.7.2
## [28] locfit_1.5-9.4 bitops_1.0-7 cachem_1.0.6
## [31] reshape_0.8.8 fgsea_1.18.0 gridGraphics_0.5-1
## [34] DelayedArray_0.18.0 assertthat_0.2.1 promises_1.2.0.1
## [37] scales_1.1.1 ggraph_2.0.5 enrichplot_1.12.3
## [40] gtable_0.3.0 tidygraph_1.2.0 rlang_0.4.11
## [43] genefilter_1.74.0 systemfonts_1.0.2 splines_4.1.2
## [46] lazyeval_0.2.2 htm2txt_2.1.1 broom_0.7.9
## [49] yaml_2.2.1 reshape2_1.4.4 modelr_0.1.8
## [52] backports_1.2.1 httpuv_1.6.3 qvalue_2.24.0
## [55] tools_4.1.2 bookdown_0.24 ggplotify_0.1.0
## [58] gplots_3.1.1 ellipsis_0.3.2 jquerylib_0.1.4
## [61] Rcpp_1.0.7 plyr_1.8.6 visNetwork_2.1.0
## [64] zlibbioc_1.38.0 RCurl_1.98-1.5 viridis_0.6.1
## [67] cowplot_1.1.1 haven_2.4.3 ggrepel_0.9.1
## [70] fs_1.5.0 magrittr_2.0.1 data.table_1.14.2
## [73] DO.db_2.9 reprex_2.0.1 hms_1.1.1
## [76] patchwork_1.1.1 mime_0.12 evaluate_0.14
## [79] xtable_1.8-4 readxl_1.3.1 gridExtra_2.3
## [82] compiler_4.1.2 KernSmooth_2.23-20 V8_3.6.0
## [85] crayon_1.4.1 shadowtext_0.0.9 htmltools_0.5.2
## [88] ggfun_0.0.4 later_1.3.0 tzdb_0.1.2
## [91] geneplotter_1.70.0 aplot_0.1.1 lubridate_1.8.0
## [94] DBI_1.1.1 tweenr_1.0.2 dbplyr_2.1.1
## [97] MASS_7.3-54 Matrix_1.3-4 cli_3.0.1
## [100] igraph_1.2.6 pkgconfig_2.0.3 xml2_1.3.2
## [103] ggtree_3.0.4 svglite_2.0.0 annotate_1.70.0
## [106] bslib_0.3.1 webshot_0.5.2 XVector_0.32.0
## [109] rvest_1.0.1 yulab.utils_0.0.4 digest_0.6.28
## [112] Biostrings_2.60.2 polylabelr_0.2.0 rmarkdown_2.11
## [115] cellranger_1.1.0 fastmatch_1.1-3 tidytree_0.3.6
## [118] curl_4.3.2 gtools_3.9.2 shiny_1.7.1
## [121] lifecycle_1.0.1 nlme_3.1-153 jsonlite_1.7.2
## [124] echarts4r_0.4.2 viridisLite_0.4.0 fansi_0.5.0
## [127] pillar_1.6.3 lattice_0.20-45 GGally_2.1.2
## [130] KEGGREST_1.32.0 fastmap_1.1.0 httr_1.4.2
## [133] survival_3.2-13 GO.db_3.13.0 glue_1.4.2
## [136] png_0.1-7 bit_4.0.4 ggforce_0.3.3
## [139] stringi_1.7.5 sass_0.4.0 blob_1.2.2
## [142] caTools_1.18.2 memoise_2.0.0 ape_5.5