富集分析的弦图搞起

0.需求解读

左边是几个GO term，右边是基因。每个term一个颜色，基因的颜色按照logFC渐变。基因与term之间有连线，就是他们之间有从属关系。

1.数据和R包准备

library(clusterProfiler)
library(org.Hs.eg.db)
library(GOplot)
library(stringr)

load("step4output.Rdata")
head(deg)

##       logFC   AveExpr         t      P.Value    adj.P.Val        B probe_id
## 1  5.780170  7.370282  82.94833 3.495205e-12 1.163798e-07 16.32898  8133876
## 2 -4.212683  9.106625 -68.40113 1.437468e-11 2.393169e-07 15.71739  7965335
## 3  5.633027  8.763220  57.61985 5.053466e-11 4.431880e-07 15.04752  7972259
## 4 -3.801663  9.726468 -57.21112 5.324059e-11 4.431880e-07 15.01709  7972217
## 5  3.263063 10.171635  50.51733 1.324638e-10 8.821294e-07 14.45166  8129573
## 6 -3.843247  9.667077 -45.87910 2.681063e-10 1.487856e-06 13.97123  8015806
##   symbol change ENTREZID
## 1   CD36     up      948
## 2  DUSP6   down     1848
## 3    DCT     up     1638
## 4  SPRY2   down    10253
## 5  MOXD1     up    26002
## 6   ETV4   down     2118

这个数据是简单的芯片差异分析得到的表格，在生信星球公众号回复“富集输入”即可获得。我只取了其中的100个差异基因来做富集分析。

deg = deg[deg$change!="stable",]
deg = deg[1:100,]
gene_diff = deg$symbol
ego_BP <- enrichGO(gene = gene_diff,
                   keyType = "SYMBOL",
                   OrgDb= org.Hs.eg.db,
                   ont = "BP",
                   pAdjustMethod = "BH",
                   minGSSize = 1,
                   pvalueCutoff = 0.05,
                   qvalueCutoff = 0.05)
class(ego_BP)

## [1] "enrichResult"
## attr(,"package")
## [1] "DOSE"

clusterprofiler富集分析得到的结果是对象，先把他变成弦图的输入数据：

ego <- data.frame(ego_BP) 
colnames(ego)

## [1] "ID"          "Description" "GeneRatio"   "BgRatio"     "pvalue"     
## [6] "p.adjust"    "qvalue"      "geneID"      "Count"

ego <- ego[1:10,c(1,2,8,6)] 

ego$geneID <- str_replace_all(ego$geneID,"/",",") 
names(ego)=c("ID","Term","Genes","adj_pval")
ego$Category = "BP"
head(ego)

##                    ID                                            Term
## GO:0050730 GO:0050730 regulation of peptidyl-tyrosine phosphorylation
## GO:0018108 GO:0018108               peptidyl-tyrosine phosphorylation
## GO:0018212 GO:0018212                  peptidyl-tyrosine modification
## GO:0048732 GO:0048732                               gland development
## GO:0014033 GO:0014033               neural crest cell differentiation
## GO:0050673 GO:0050673                   epithelial cell proliferation
##                                                                             Genes
## GO:0050730          CD36,AREG,TGFA,CD24,SFRP1,ITGB3,MIR221,SH2B3,CNTN1,INPP5F,MVP
## GO:0018108    CD36,AREG,TGFA,CD24,SFRP1,ITGB3,MIR221,EPHA5,SH2B3,CNTN1,INPP5F,MVP
## GO:0018212    CD36,AREG,TGFA,CD24,SFRP1,ITGB3,MIR221,EPHA5,SH2B3,CNTN1,INPP5F,MVP
## GO:0048732  ETV5,CCND1,AREG,SERPINF1,SFRP1,IGFBP5,JUN,SEMA3C,SOX2,SNAI2,PBX1,E2F7
## GO:0014033                                    SFRP1,SEMA3C,SNAI2,ZEB2,MEF2C,FOLR1
## GO:0050673 NUPR1,CCND1,AREG,SERPINF1,TGFA,SFRP1,IGFBP5,JUN,ITGB3,SOX2,SNAI2,MEF2C
##                adj_pval Category
## GO:0050730 0.0001118840       BP
## GO:0018108 0.0001609363       BP
## GO:0018212 0.0001609363       BP
## GO:0048732 0.0006502891       BP
## GO:0014033 0.0006502891       BP
## GO:0050673 0.0006502891       BP

还需要提供一个由基因和logFC组成的数据框

genes = data.frame(ID=deg$symbol,
                   logFC=deg$logFC)
head(genes)

##      ID     logFC
## 1  CD36  5.780170
## 2 DUSP6 -4.212683
## 3   DCT  5.633027
## 4 SPRY2 -3.801663
## 5 MOXD1  3.263063
## 6  ETV4 -3.843247

数据备齐了，画图就很简单，几句代码就搞定：

circ <- circle_dat(ego,genes)
chord <- chord_dat(data=circ, genes=genes,process = ego$Term) # 

GOChord(chord, space = 0.02, gene.order = 'logFC', gene.space = 0.25, gene.size = 5)

其实clusterprofiler里也有类似的图

cnetplot(ego_BP, categorySize="pvalue",colorEdge = TRUE,circular = TRUE)

参考：https://cran.r-project.org/web/packages/GOplot/vignettes/GOplot_vignette.html
https://www.jianshu.com/p/48ac98098760