## GO分析 ### 应用场景 功能富集就是GO分析常规的应用  > 所用数据和KEGG一样 ```R load('./FC_P.Rdata')#rt_FC_P # 准备KEGG需要的数据 library(clusterProfiler) library(pathview) eg = bitr(row.names(rt_FC_P), fromType="SYMBOL", toType="ENTREZID", OrgDb="org.Hs.eg.db") gene_list <- rt_FC_P$logFC[match(eg$SYMBOL, row.names(rt_FC_P), nomatch = 0)] names(gene_list) <- eg$ENTREZID gene_list <- gene_list[order(gene_list, decreasing = TRUE)] gene <- names(gene_list)[abs(gene_list) > 1] ``` ```R # 计算gene和gene list所富集的生物学功能 # GO analysis ggo <- groupGO(gene = gene, OrgDb = org.Hs.eg.db, ont = "CC", level = 3, readable = TRUE) ego <- enrichGO(gene = gene, universe = names(gene_list), OrgDb = org.Hs.eg.db, ont = "CC", pAdjustMethod = "BH", pvalueCutoff = 0.1, qvalueCutoff = 0.05, readable = TRUE) # barplot展示功能富集的基因数量和pvalue barplot(ego, showCategory=8) # dotplot展示功能富集的基因数量和pvalue dotplot(ego) # 以网络展示富集生物学功能之间的关联 emapplot(ego) # 以网络展示主要的生物学功能基因，以及该基因的表达变化 cnetplot(ego, categorySize = "pvalue", foldChange = gene_list) ```