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)
sample sheet
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$SRR_accession,metadata=mdat,legacy = TRUE)
## For more information about DEE2 QC metrics, visit
##     https://github.com/markziemann/dee2/blob/master/qc/qc_metrics.md
x<-Tx2Gene(w)
x<-x$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] 39297
nrow(x1)
## [1] 15607

Now 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 mode
y <- DESeq(y)
## estimating size factors
## estimating dispersions
## gene-wise dispersion estimates
## mean-dispersion relationship
## final dispersion estimates
## fitting model and testing
de <- 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 0.0952126 28.69967 0 0
ENSG00000164692 11379.8760 2.2961488 0.0887385 25.87546 0 0
ENSG00000172531 2130.9201 -1.7175061 0.0711786 -24.12951 0 0
ENSG00000106484 4549.7360 1.2253275 0.0581383 21.07606 0 0
ENSG00000130508 10277.9876 -0.9442904 0.0530537 -17.79877 0 0
ENSG00000163017 300.4833 4.8785546 0.2742716 17.78731 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 = 0.933
mres <- 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: 308
message(paste("Number of down-regulated pathways:",length(m_dn) ))
## Number of down-regulated pathways: 44
head(mres$enrichment_result,10)  %>% kbl() %>% kable_paper("hover", full_width = F)
set setSize pANOVA s.dist p.adjustANOVA
346 Eukaryotic Translation Elongation 93 0 -0.5843716 0
796 Peptide chain elongation 88 0 -0.5846542 0
1066 Selenocysteine synthesis 91 0 -0.5615496 0
1015 Response of EIF2AK4 (GCN2) to amino acid deficiency 100 0 -0.5355268 0
629 Metabolism of RNA 661 0 0.2112968 0
1335 Viral mRNA Translation 88 0 -0.5452661 0
348 Eukaryotic Translation Termination 92 0 -0.5319808 0
668 Mitotic Metaphase and Anaphase 222 0 0.3387173 0
665 Mitotic Anaphase 221 0 0.3384649 0
387 Formation of a pool of free 40S subunits 100 0 -0.4948645 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_ora4.png")
plot(euler(v1),quantities = TRUE, edges = "gray", main="FCS vs ORA")
dev.off()
## png 
##   2
png("images/orabg4.png")
plot(euler(v2),quantities = TRUE, edges = "gray", main="Effect of inappropriate background* (whole genome)")
dev.off()
## png 
##   2
png("images/oracomb4.png")
plot(euler(vx),quantities = TRUE, main="combining up and down genes and whole genome bg*")
dev.off()
## png 
##   2
pdf("images/fcs_ora4.pdf",width=4,height=4)
plot(euler(v1),quantities = TRUE, edges = "gray", main="FCS vs ORA")
dev.off()
## png 
##   2
pdf("images/orabg4.pdf",width=4,height=4)
plot(euler(v2),quantities = TRUE, edges = "gray", main="Effect of inappropriate background* (whole genome)")
dev.off()
## png 
##   2
pdf("images/oracomb4.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 
##   2

Jaccard 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 
##        0.6292135        0.4209524        0.5563506        0.4201389 
## ORA vs ORA* comb 
##        0.4634703
barplot(dat,ylab="jaccard metric")

saveRDS(dat,file = "ex4dat.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