The geneList dataset, which contains fold change of gene expression levels between breast tumor and normal samples and is provided by the DOSE package, was used in this example. The DEGs were identified by a criterion of fold change >2. As demonstrated in Figure 1A, the top 30 enriched terms are highly connected, and it seems that the DEGs are associated with a single functional module. Visualizing top enriched terms is a common approach to present and interpret the enrichment result. However, the top results are dominated by a large number of highly similar terms. After removing redundant terms, the result reveals a more global view with several different functional modules (Figure 1B). This feature simplifies the enrichment results, assists in interpretation, and avoids the annotation/interpretation bias.18library(clusterProfiler)data(geneList, package="DOSE")## fold change > 2 as DE genesde <- names(geneList)[abs(geneList) > 2]ego <- enrichGO(de, OrgDb = "org.Hs.eg.db", ont="BP", readable=TRUE)## use simplify to remove redundant termsego2 <- simplify(ego, cutoff=0.7, by="p.adjust", select_fun=min)
