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还是个花里胡哨的可视化，是关于GSEA的。首先进行致谢：https://github.com/YuLab-SMU/enrichplot，复现Nature图表：GSEA分析及可视化包装函数。富集分析（clusterProfiler）和部分可视化函数参考Y叔的包。也注意，我们这个函数的可视化针对的就是clusterProfiler包的GSEA分析结果，其他不适用！一部分可视化思路使用的是之前我们号“TS的美梦”发布的函数。完整版函数已发布微信VIP，请查收！可视化结果如下：
为什么有这样的可视化，之前群里有老师发的，目前没有参考文献！我们对其进行了优化，直接包为函数，方便使用，展示上做了选择，可以直接展示值，可以展示气泡图。首先看看函数参数：
res## Gene Set Enrichment Analysis##...@organism hsa#...@setType KEGG#...@keytype ENTREZID#...@geneList Named num [1:15681] 9.58 8.87 8.07 7.94 7.67 ...- attr(*, "names")= chr [1:15681] "11314" "84634" "123787" "4884" ...#...nPerm#...pvalues adjusted by 'BH' with cutoff <0.05#...37 enriched terms found'data.frame': 37 obs. of 11 variables:$ ID : chr "hsa05168" "hsa03010" "hsa04080" "hsa04020" ...$ Description : chr "Herpes simplex virus 1 infection" "Ribosome" "Neuroactive ligand-receptor interaction" "Calcium signaling pathway" ...$ setSize : int 454 130 167 191 183 52 62 88 119 199 ...$ enrichmentScore: num -0.385 0.601 0.528 0.497 0.478 ...$ NES : num -1.97 2.17 1.95 1.85 1.78 ...$ pvalue : num 4.85e-15 7.92e-10 2.36e-07 1.85e-06 1.26e-05 ...$ p.adjust : num 1.62e-12 1.33e-07 2.63e-05 1.55e-04 8.45e-04 ...$ qvalue : num 1.26e-12 1.03e-07 2.04e-05 1.20e-04 6.56e-04 ...$ rank : num 4133 3497 1750 2180 3295 ...$ leading_edge : chr "tags=43%, list=26%, signal=33%" "tags=65%, list=22%, signal=51%" "tags=32%, list=11%, signal=28%" "tags=31%, list=14%, signal=27%" ...$ core_enrichment: chr "ZNF234/ZNF155/ZNF559/TBK1/ZNF790/ZNF3/ZNF761/ZNF565/ZNF763/ZNF79/ZNF180/ZFP37/SRSF7/ZSCAN32/ZNF286A/ZNF746/TNFR"| __truncated__ "MRPS6/RPL22L1/RPS27/RPS28/RPS9/RPL28/RPL12/RPL36/FAU/RPLP2/RPL10/RPL36AL/MRPL1/MRPL17/RPL10A/RPS21/RPL21/RPL37A"| __truncated__ "KISS1R/NMU/LEP/APLN/ADRB3/OPRK1/PTH1R/GABRB2/NMB/CHRM2/ADRA2B/ADRB1/GRIK4/GRIN2A/BDKRB1/PRSS3/EDN2/HRH1/CHRM4/A"| __truncated__ "CXCR4/ADRB3/FGF4/FGF22/CHRM2/NOS3/PDE1B/ADRB1/PRKCB/CACNA1I/CACNA1F/GRIN2A/BDKRB1/PDGFRA/HRH1/CACNA1E/VEGFA/SLC"| __truncated__ ...#...CitationT Wu, E Hu, S Xu, M Chen, P Guo, Z Dai, T Feng, L Zhou, W Tang, L Zhan, X Fu, S Liu, X Bo, and G Yu.clusterProfiler 4.0: A universal enrichment tool for interpreting omics data.The Innovation. 2021, 2(3):100141
setwd("D:\\KS项目\\公众号文章\\GSEA可视化new")df <- read.csv('df_Genes.csv', header = T)df$gene <- rownames(df)write.csv(df, file = 'df.csv')res <- KS_GSEA(gene = df$gene_name,LogFC = df$log2FoldChange,analysis = "KEGG",package = 'clusterProfiler',OrgDb = 'org.Hs.eg.db')save(res, file = "res.RData")# load("D:/KS项目/公众号文章/GSEA可视化new/res.RData")ks_plot_gsea(data = res,term = res@result$Description[1:10],rankColor="#90191B",labels_size=T)
ks_plot_gsea(data = res,term = res@result$Description[1:10],rankColor="#90191B",labels_size=F)
ks_plot_gsea(data = res,term = c("immunoglobulin production","detection of light stimulus","B cell receptor signaling pathway","respiratory system development","regulation of viral entry into host cell","transmission of nerve impulse","macrophage migration","T cell cytokine production","regulation of guanylate cyclase activity","regulation of dendrite extension"),rankColor="#90191B",labels_size=T)ks_plot_gsea(data = res,term = c("immunoglobulin production","detection of light stimulus","B cell receptor signaling pathway","respiratory system development","regulation of viral entry into host cell","transmission of nerve impulse","macrophage migration","T cell cytokine production","regulation of guanylate cyclase activity","regulation of dendrite extension"),rankColor="#90191B",labels_size=F)
