Gene Set Enrichment Analysis
The original post for this tutorial could be found on my GitHub. Please refer to the very end of the page for the references list that I used to make this thread available.
Assume we performed an RNA-seq (or microarray gene expression) experiment and now want to know what pathway/biological process shows enrichment for our [differentially expressed] genes. There are terminologies someone may need to be familiar with before diving into the topic thoroughly:
A gene set is an unordered collection of genes that are functionally related.
A pathway can be interpreted as a gene set by ignoring functional relationships among genes.
Gene Ontology (GO)
GO describe gene function. A gene role/function could be attributed to the three main classes:
• Molecular Function (MF) : which defines molecular activities of gene products.
• Cellular Component (CC) : This describes where gene products are active/localized.
• Biological Process (BP) : which describes pathways and processes that the gene product is active in.
Kyoto Encyclopedia of Genes and Genomes (KEGG)
KEGG is a collection of manually curated pathway maps representing molecular interaction and reaction networks.
There are different methods widely used for functional enrichment analysis:
1- Over Representation Analysis (ORA): This is the simplest version of enrichment analysis and at the same time the most widely used approach. The concept in this approach is based on a Fisher exact test p-value in a contingency table. There is a relatively large number of web-tools R package for ORA. Personally I am a fan of DAVID web tools however its last update was in 2016 (DAVID 6.8 Oct. 2016).
2- Gene Set Enrichment Analysis (GSEA):
It was developed by Broad Institute. This is the preferred method when genes are coming from an expression experiment like microarray and RNA-seq. However, the original methodology was designed to work on microarray but later modification made it suitable for RNA-seq also. In this approach, you need to rank your genes based on a statistic (like what DESeq2 provide), and then perform enrichment analysis against different pathways (= gene set). You have to download the gene set files into your local system. The point is that here the algorithm will use all genes in the ranked list for enrichment analysis. [in contrast to ORA where only genes passed a specific threshold (like DE ones) would be used for enrichment analysis]. You can find more details about the methodology on the original PNAS paper.. To download these gene sets in a folder go to the MSigDB website, register, and download the data.
Steps in this tutorial:
(1)differential expression analysis,
(2) doing GSEA
and finally (3) Visualization.
#_______________Loading packages______________________________# library(DESeq2) library(org.Hs.eg.db) library(tibble) library(dplyr) library(tidyr) library(fgsea) library(ggplot2) library(reshape2) library(ComplexHeatmap) library(circlize) #________________diffrential expression analysis______________# # reading expression data matrix and getting rid of what we dont like # my data is raw count data comming from RNA-seq on 476 bladder cancer sample rna <- read.table("Uromol1_CountData.v1.csv", header = T, sep = ",") dim(rna) #  60488 477 head(rna, 5) # setting row name for expression matrix rownamefordata <- substr(rna$genes,1,15) rownames(rna) <- rownamefordata rm(rownamefordata) # removing column with gene name rna <- rna[, -1] # reading clinical data # read uromol clinical uromol_clin <- read.table("uromol_clinic.csv", sep = ",", header = T) names(uromol_clin) #Setting row name for clinical data ids <- uromol_clin$UniqueID rownames(uromol_clin) <- ids rm(ids) # I want to compare samples with Ta stage against those in higher stage (non_Ta) # making column for comparison table(uromol_clin$Stage) # CIS T1 T2-4 Ta # 3 112 16 345 # selecting only Ta samples uromol_clin$isTa <- as.factor(ifelse(uromol_clin$Stage == "Ta", "Ta", "non_Ta")) levels(uromol_clin$isTa) #  "non-Ta" "Ta" # making sure dataset are compatible regrding to sample order rna <- rna[, row.names(uromol_clin)] all(rownames(uromol_clin) %in% colnames(rna)) #_______Making_Expression_Object__________# #Making DESeqDataSet object which stores all experiment data dds <- DESeqDataSetFromMatrix(countData = rna, colData = uromol_clin, design = ~ isTa) # prefilteration: it is not necessary but recommended to filter out low expressed genes keep <- rowSums(counts(dds)) >= 10 dds <- dds[keep,] rm(keep) # data tranfromation vsd <- vst(dds, blind=FALSE) #________________DE_analysis_____________# dds <- DESeq(dds) #This would take some time res <- results(dds, alpha=0.05) summary(res) #_________________GSEA___________________# # Steps toward doing gene set enrichment analysis (GSEA): # 1- obtaining a stats for ranking genes in your experiment, # 2- creating a named vector out of the DESeq2 result # 3- Obtaining a gene set from mysigbd # 4- doing analysis # already we performed DESeq2 analysis and have statistics for working on it res$row <- rownames(res) # important notice: if you have not such stats in your result (say comming from edgeR), # you may need to create a rank metric for your genes. To do this: # metric = -log10(pvalue)/sign(log2FC) # Map Ensembl gene IDs to symbol. First create a mapping table. ens2symbol <- AnnotationDbi::select(org.Hs.eg.db, key=res$row, columns="SYMBOL", keytype="ENSEMBL") names(ens2symbol) <- "row" ens2symbol <- as_tibble(ens2symbol) ens2symbol # joining res <- merge(data.frame(res), ens2symbol, by=c("row")) # remove the NAs, averaging statitics for a multi-hit symbol res2 <- res %>% select(SYMBOL, stat) %>% na.omit() %>% distinct() %>% group_by(SYMBOL) %>% summarize(stat=mean(stat)) #res2 # creating a named vector [ranked genes] ranks <- res2$stat names(ranks) <- res2$SYMBOL # Load the pathway (gene set) into a named list # downloaded mysigdb were located in my "~" directory: pathways.hallmark <- gmtPathways("~/mysigdb/h.all.v7.2.symbols.gmt") # show a few lines from the pathways file head(pathways.hallmark) #Running fgsea algorithm: fgseaRes <- fgseaMultilevel(pathways=pathways.hallmark, stats=ranks) # Tidy the results: fgseaResTidy <- fgseaRes %>% as_tibble() %>% arrange(desc(NES)) # order by normalized enrichment score (NES) # To see what genes are in each of these pathways: gene.in.pathway <- pathways.hallmark %>% enframe("pathway", "SYMBOL") %>% unnest(cols = c(SYMBOL)) %>% inner_join(res, by="SYMBOL") #______________________VISUALIZATION______________________________# #__________bar plot _______________# # Plot the normalized enrichment scores. #Color the bar indicating whether or not the pathway was significant: fgseaResTidy$adjPvalue <- ifelse(fgseaResTidy$padj <= 0.05, "significant", "non-significant") cols <- c("non-significant" = "grey", "significant" = "red") ggplot(fgseaResTidy, aes(reorder(pathway, NES), NES, fill = adjPvalue)) + geom_col() + scale_fill_manual(values = cols) + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) + coord_flip() + labs(x="Pathway", y="Normalized Enrichment Score", title="Hallmark pathways Enrichment Score from GSEA")
#__________Enrichment Plot_______# # Enrichment plot for E2F target gene set plotEnrichment(pathway = pathways.hallmark[["HALLMARK_E2F_TARGETS"]], ranks)
plotGseaTable(pathways.hallmark[fgseaRes$pathway[fgseaRes$padj < 0.05]], ranks, fgseaRes, gseaParam=0.5)
#________ Heatmap Plot_____________# # pathways with significant enrichment score sig.path <- fgseaResTidy$pathway[fgseaResTidy$adjPvalue == "significant"] sig.gen <- unique(na.omit(gene.in.pathway$SYMBOL[gene.in.pathway$pathway %in% sig.path])) ### create a new data-frame that has '1' for when a gene is part of a term, and '0' when not h.dat <- dcast(gene.in.pathway[, c(1,2)], SYMBOL~pathway) rownames(h.dat) <- h.dat$SYMBOL h.dat <- h.dat[, -1] h.dat <- h.dat[rownames(h.dat) %in% sig.gen, ] h.dat <- h.dat[, colnames(h.dat) %in% sig.path] # keep those genes with 3 or more occurnes table(data.frame(rowSums(h.dat))) # 1 2 3 4 5 6 # 1604 282 65 11 1 1 h.dat <- h.dat[data.frame(rowSums(h.dat)) >= 3, ] # topTable <- res[res$SYMBOL %in% rownames(h.dat), ] rownames(topTable) <- topTable$SYMBOL # match the order of rownames in toptable with that of h.dat topTableAligned <- topTable[which(rownames(topTable) %in% rownames(h.dat)),] topTableAligned <- topTableAligned[match(rownames(h.dat), rownames(topTableAligned)),] all(rownames(topTableAligned) == rownames(h.dat)) # colour bar for -log10(adjusted p-value) for sig.genes dfMinusLog10FDRGenes <- data.frame(-log10( topTableAligned[which(rownames(topTableAligned) %in% rownames(h.dat)), 'padj'])) dfMinusLog10FDRGenes[dfMinusLog10FDRGenes == 'Inf'] <- 0 # colour bar for fold changes for sigGenes dfFoldChangeGenes <- data.frame( topTableAligned[which(rownames(topTableAligned) %in% rownames(h.dat)), 'log2FoldChange']) # merge both dfGeneAnno <- data.frame(dfMinusLog10FDRGenes, dfFoldChangeGenes) colnames(dfGeneAnno) <- c('Gene score', 'Log2FC') dfGeneAnno[,2] <- ifelse(dfGeneAnno$Log2FC > 0, 'Up-regulated', ifelse(dfGeneAnno$Log2FC < 0, 'Down-regulated', 'Unchanged')) colours <- list( 'Log2FC' = c('Up-regulated' = 'royalblue', 'Down-regulated' = 'yellow')) haGenes <- rowAnnotation( df = dfGeneAnno, col = colours, width = unit(1,'cm'), annotation_name_side = 'top') # Now a separate color bar for the GSEA enrichment padj. This will # also contain the enriched term names via annot_text() # colour bar for enrichment score from fgsea results dfEnrichment <- fgseaRes[, c("pathway", "NES")] dfEnrichment <- dfEnrichment[dfEnrichment$pathway %in% colnames(h.dat)] dd <- dfEnrichment$pathway dfEnrichment <- dfEnrichment[, -1] rownames(dfEnrichment) <- dd colnames(dfEnrichment) <- 'Normalized\n Enrichment score' haTerms <- HeatmapAnnotation( df = dfEnrichment, Term = anno_text( colnames(h.dat), rot = 45, just = 'right', gp = gpar(fontsize = 12)), annotation_height = unit.c(unit(1, 'cm'), unit(8, 'cm')), annotation_name_side = 'left') # now generate the heatmap hmapGSEA <- Heatmap(h.dat, name = 'GSEA hallmark pathways enrichment', split = dfGeneAnno[,2], col = c('0' = 'white', '1' = 'forestgreen'), rect_gp = gpar(col = 'grey85'), cluster_rows = TRUE, show_row_dend = TRUE, row_title = 'Top Genes', row_title_side = 'left', row_title_gp = gpar(fontsize = 11, fontface = 'bold'), row_title_rot = 90, show_row_names = TRUE, row_names_gp = gpar(fontsize = 11, fontface = 'bold'), row_names_side = 'left', row_dend_width = unit(35, 'mm'), cluster_columns = TRUE, show_column_dend = TRUE, column_title = 'Enriched terms', column_title_side = 'top', column_title_gp = gpar(fontsize = 12, fontface = 'bold'), column_title_rot = 0, show_column_names = FALSE, show_heatmap_legend = FALSE, clustering_distance_columns = 'euclidean', clustering_method_columns = 'ward.D2', clustering_distance_rows = 'euclidean', clustering_method_rows = 'ward.D2', bottom_annotation = haTerms) tiff("GSEA_enrichment_2.tiff", units="in", width=13, height=22, res=400) draw(hmapGSEA + haGenes, heatmap_legend_side = 'right', annotation_legend_side = 'right') dev.off()
1- clusterProfiler: universal enrichment tool for functional and comparative study
2- Fast Gene Set Enrichment Analysis
3- Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles
4- DAVID Bioinformatics Resources 6.8
5- DESeq results in pathways in 60 Seconds with the fgsea package
6- Rank-rank hypergeometric overlap: identification of statistically significant overlap between gene-expression signatures
7- Clustering of DAVID gene enrichment results from gene expression studies by Kevin Blighe.