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Enrichment analysis



Load the following packages:


If the FindMarkers or FindAllMarkers functions were used, we obtained a table listing only the significant genes, but we don’t have any information of fold change for the non-significant genes. Therefore, we can use the over-representation analysis which is a threshold-based method. Using our list of significant genes, we can test if any gene set is over-represented among significant genes or not using a test similar to a Fisher test to compare differences in proportions.

The clusterProfiler package provides functions for over-representation analysis of Gene Ontology gene sets (among other functions, including functions for actual GSEA) or KEGG gene sets.

Genes can be labeled using different types of labels, eg symbol, Ensembl ID, Entrez ID. To list the allowed label types use:

BiocManager::install("", update = FALSE)

Let’s select a set of genes that are downregulated in the tumor cells compared to normal:

tum_down <- subset(tum_vs_norm,
                   tum_vs_norm$avg_log2FC < -1 &
                     tum_vs_norm$p_val_adj < 0.05)
tum_down_genes <- rownames(tum_down)

We can do a Gene Ontology term over-representation analysis based on this set of genes. Make sure you check out the help of this function to understand its arguments:

tum_vs_norm_go <- clusterProfiler::enrichGO(tum_down_genes,
                                            keyType = "SYMBOL",
                                            ont = "BP",
                                            minGSSize = 50)

The results are stored in the @result slot:


Some GO terms seem redundant because they contain many of the same genes, which is a characteristic of Gene Ontology gene sets. We can simplify this list by removing redundant gene sets:

enr_go <- clusterProfiler::simplify(tum_vs_norm_go)

We can quite easily generate a plot called an enrichment map with the enrichplot package:

                     showCategory = 30, cex_label_category = 0.5)

Instead of testing for Gene Ontology terms, we can also test for other gene set collections. For example the Hallmark collection from MSigDB:

gmt <- msigdbr::msigdbr(species = "human", category = "H")

We can use the function enricher to test for over-representation of any set of genes of the Hallmark collection. We have to include the “universe”, i.e. the full list of background, non significant genes, against which to test for differences in proportions:

tum_vs_norm_enrich <- clusterProfiler::enricher(gene = tum_down_genes,
                                                universe = rownames(proB),
                                                pAdjustMethod = "BH",
                                                pvalueCutoff  = 0.05,
                                                qvalueCutoff  = 0.05,
                                                TERM2GENE = gmt[,c("gs_name", "gene_symbol")])

When using the genes down-regulated in tumor, among the over-represented Hallmark gene sets, we have HALLMARK_G2M_CHECKPOINT, which includes genes involved in the G2/M checkpoint in the progression through the cell division cycle.


Clear environment

Clear your environment:

rm(list = ls())