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准备以上的东西，接下来一行代码解决。 #以KEGG Pathway示例 KEGG <- gseKEGG(gene_fc, organism = "hsa") #具体参数在下面 > KEGG <- gseKEGG(gene_fc, organism = "hsa") Reading KEGG annotation online: Reading KEGG annotation online: preparing geneSet collections... GSEA analysis... leading edge analysis... done... Warning messages: 1: In preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (0.13% of the list). The order of those tied genes will be arbitrary, which may produce unexpected results. 2: In serialize(data, node$con) : 载入时'package:stats'可能无用 3: In serialize(data, node$con) : 载入时'package:stats'可能无用 4: In serialize(data, node$con) : 载入时'package:stats'可能无用 5: In serialize(data, node$con) : 载入时'package:stats'可能无用 6: In serialize(data, node$con) : 载入时'package:stats'可能无用 7: In serialize(data, node$con) : 载入时'package:stats'可能无用 8: In fgseaMultilevel(...) : For some pathways, in reality P-values are less than 1e-10. You can set the `eps` argument to zero for better estimation. 如果要做GO富集呢？ #GO富集 GO <- gseGO( gene_fc, #gene_fc ont = "BP",# "BP"、"MF"和"CC"或"ALL" OrgDb = org.Hs.eg.db,#人类注释基因 keyType = "ENTREZID", pvalueCutoff = 0.05, pAdjustMethod = "BH",#p值校正方法 ) #KEGG富集 gseKEGG( geneList, organism = "hsa", keyType = "kegg", exponent = 1, minGSSize = 10, maxGSSize = 500, eps = 1e-10, pvalueCutoff = 0.05, pAdjustMethod = "BH", verbose = TRUE, use_internal_data = FALSE, seed = FALSE, by = "fgsea", ... ) head(KEGG)#看一下这个文件 > head(KEGG) ID Description setSize enrichmentScore NES hsa03010 hsa03010 Ribosome 99 -0.8707285 -2.370839 hsa05152 hsa05152 Tuberculosis 87 0.8678558 1.786981 hsa05171 hsa05171 Coronavirus disease - COVID-19 142 -0.5976011 -1.704522 hsa04512 hsa04512 ECM-receptor interaction 19 -0.8866402 -1.913989 pvalue p.adjust qvalues rank leading_edge hsa03010 0.0000000001 0.0000000257 2.431579e-08 289 tags=65%, list=6%, signal=62% hsa05152 0.0002124294 0.0272971804 2.582695e-02 279 tags=30%, list=6%, signal=29% hsa05171 0.0004376904 0.0290749106 2.750893e-02 289 tags=46%, list=6%, signal=45% hsa04512 0.0004525278 0.0290749106 2.750893e-02 250 tags=58%, list=5%, signal=55% core_enrichment hsa03010 6231/6193/4736/6235/2197/6218/6166/6167/6157/3921/6129/140801/6152/6125/6169/6124/9349/6141/6138/6187/6228/6144/6135/6202/6155/6154/6132/6160/6159/6147/6156/6210/6230/6175/6122/6128/11224/23521/9045/25873/6161/6201/6208/6189/6181/6188/6133/6165/6194/6139/6168/6224/6143/6142/6222/6164/6176/6232/6206/6223/6171/6233/6134/6137 hsa05152 |
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