深圳网站建设公司大全,云南哪有网站建设报价的,有没有什么做热力图的图表网站,上海人才市场档案存放中心之前我们写过GO、KEGG的富集分析#xff0c;参见#xff1a;补充更新#xff1a;GO、KEGG#xff08;批量分组#xff09;分析及可视化。演示了差异基因KEGG或者GO的分析流程。其实差异基因的富集分析输入的文件只需要一组基因就可以了。所以我们发挥了专治懒病的优良传统…之前我们写过GO、KEGG的富集分析参见补充更新GO、KEGG批量分组分析及可视化。演示了差异基因KEGG或者GO的分析流程。其实差异基因的富集分析输入的文件只需要一组基因就可以了。所以我们发挥了专治懒病的优良传统将KEGG、GOBP、CC、MF的分析封装为一个函数您只需要提供gene选择物种即可只有human和mouse。而且一次性完成KEGG和GO分析结果免去了分析两次的麻烦。这样应该也不会出错了吧。
函数内容如下其中相关参数可按照自己的需求修改
Enrichment_KEGGgo_analusis - function(genes, speciesc(human,mouse)){ library(org.Hs.eg.db) library(clusterProfiler) if(species human){ genes_df - bitr(genes, fromTypeSYMBOL, toTypeENTREZID, OrgDborg.Hs.eg.db, drop TRUE) organism hsa OrgDb org.Hs.eg.db } if(species mouse){ genes_df - bitr(genes, fromTypeSYMBOL, toTypeENTREZID, OrgDborg.Mm.eg.db, drop TRUE) organism mmu OrgDb org.Mm.eg.db } colnames(genes_df) - c(gene,EntrzID) # KEGG kegg.re - enrichKEGG(gene genes_df$EntrzID, organism organism, keyType kegg, pAdjustMethod fdr, pvalueCutoff 0.05, qvalueCutoff 0.05, minGSSize 10, maxGSSize 500) if (is.null(kegg.re)) {} else {kegg.re - setReadable(kegg.re, OrgDb OrgDb, keyTypeENTREZID)} print(kegg Done) # GO go.re1 - enrichGO(gene genes_df$EntrzID, keyType ENTREZID, OrgDb OrgDb, ontBP, pAdjustMethod fdr, pvalueCutoff 0.05, qvalueCutoff 0.05, minGSSize 10, maxGSSize 500, readable TRUE); print(GOBP Done) go.re2 - enrichGO(gene genes_df$EntrzID, keyType ENTREZID, OrgDb OrgDb, ontCC, pAdjustMethod fdr, pvalueCutoff 0.05, qvalueCutoff 0.05, minGSSize 10, maxGSSize 500, readable TRUE); print(GOCC Done) go.re3 - enrichGO(gene genes_df$EntrzID, keyType ENTREZID, OrgDb OrgDb, ontMF, pAdjustMethod fdr, pvalueCutoff 0.05, qvalueCutoff 0.05, minGSSize 10, maxGSSize 500, readable TRUE); print(GOMF Done) enrich_list - list(kegg.re, go.re1, go.re2, go.re3) names(enrich_list) - c(KEGG,GO_BP,GO_CC,GO_MF) return(enrich_list)}
我们演示一下。这里我们直接用向量提供了基因。如果您的文件是差异基因很好弄只需要$符号传入gene symbol那一列即可。
genes - c(c(MAST4,IL4R,SYT1,PRDM1,AUTS2,KNL1, CD79A, PLXDC2,NKG7,NELL2,BACH2,DIAPH3, SYN3, NTNG1, ADAM23,SOX5,TMPO, ARHGAP6,FCRL1,CD19))results - Enrichment_KEGGgo_analusis(genes genes, species human) #运行日志载入需要的程辑包AnnotationDbi
clusterProfiler v4.6.2 For help: https://yulab-smu.top/biomedical-knowledge-mining-book/
If you use clusterProfiler in published research, please cite:T 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
载入程辑包‘clusterProfiler’
The following object is masked from ‘package:AnnotationDbi’:select
The following object is masked from ‘package:IRanges’:slice
The following object is masked from ‘package:S4Vectors’:rename
The following objects are masked from ‘package:plyr’:arrange, mutate, rename, summarise
The following object is masked from ‘package:stats’:filter
select() returned 1:1 mapping between keys and columnsReading KEGG annotation online: https://rest.kegg.jp/link/hsa/pathway...Reading KEGG annotation online: https://rest.kegg.jp/list/pathway/hsa...[1] kegg Done[1] GOBP Done[1] GOCC Done[1] GOMF DoneWarning messages:1: 程辑包‘AnnotationDbi’是用R版本4.2.2 来建造的 2: In utils::download.file(url, quiet TRUE, method method, ...) : the wininet method is deprecated for http:// and https:// URLs3: In utils::download.file(url, quiet TRUE, method method, ...) : the wininet method is deprecated for http:// and https:// URLs
结果分别储存在list中这样很方便了吧 有需要的可以试一下总之是为了省时省力那些在线的分析工具的底层原理也就是这样。觉得分享有用的点个赞、分享下再走呗
