## KEGG分析 ### 应用场景 通路富集就是KEGG分析常规的应用  >rt_FC_P数据结构如上图 ```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富集通路 kk <- enrichKEGG(gene = gene, organism = 'hsa', pvalueCutoff = 0.05) kk2 <- gseKEGG(geneList = gene_list, organism = 'hsa', pvalueCutoff = 0.05, verbose = FALSE) # barplot展示通路富集基因数量和pvalue barplot(kk, showCategory = 8) # dotplot展示通路富集基因数量和pvalue dotplot(kk2) # 看具体的通路上基因表达变化情况 library("pathview") hsa04610 <- pathview(gene.data = gene_list, pathway.id = "hsa04610", species = "hsa", limit = list(gene=max(abs(gene_list)), cpd=1)) ```