示例数据
library(clusterProfiler) data(gcSample) #载入 str(gcSample) #数据集格式 lapply(gcSample, head) #查看数据集
> str(gcSample) List of 8 $ X1: chr [1:216] "4597" "7111" "5266" "2175" ... $ X2: chr [1:805] "23450" "5160" "7126" "26118" ... $ X3: chr [1:392] "894" "7057" "22906" "3339" ... $ X4: chr [1:838] "5573" "7453" "5245" "23450" ... $ X5: chr [1:929] "5982" "7318" "6352" "2101" ... $ X6: chr [1:585] "5337" "9295" "4035" "811" ... $ X7: chr [1:582] "2621" "2665" "5690" "3608" ... $ X8: chr [1:237] "2665" "4735" "1327" "3192" ... > lapply(gcSample, head) $X1 [1] "4597" "7111" "5266" "2175" "755" "23046" $X2 [1] "23450" "5160" "7126" "26118" "8452" "3675" $X3 [1] "894" "7057" "22906" "3339" "10449" "6566" $X4 [1] "5573" "7453" "5245" "23450" "6500" "4926" $X5 [1] "5982" "7318" "6352" "2101" "8882" "7803" $X6 [1] "5337" "9295" "4035" "811" "23365" "4629" $X7 [1] "2621" "2665" "5690" "3608" "3550" "533" $X8 [1] "2665" "4735" "1327" "3192" "5573" "9528"
富集分析
多个基因集的富集分析
xx <- compareCluster(gcSample, fun="enrichKEGG", organism="hsa", pvalueCutoff=0.05) table(xx@compareClusterResult$Cluster) #每个基因集富集个数 head(as.data.frame(xx)) #查看完整结果
> table(xx@compareClusterResult$Cluster) X1 X2 X3 X4 X5 X6 X7 X8 0 3 3 18 10 1 15 19 > head(as.data.frame(xx)) Cluster ID Description GeneRatio 1 X2 hsa04110 Cell cycle 18/384 2 X2 hsa05169 Epstein-Barr virus infection 23/384 3 X2 hsa05340 Primary immunodeficiency 8/384 4 X3 hsa04512 ECM-receptor interaction 9/187 5 X3 hsa04060 Cytokine-cytokine receptor interaction 17/187 6 X3 hsa04151 PI3K-Akt signaling pathway 19/187 BgRatio pvalue p.adjust qvalue 1 126/8105 2.441211e-05 0.007470105 0.006989572 2 202/8105 7.911793e-05 0.012105043 0.011326356 3 38/8105 3.297441e-04 0.033633898 0.031470314 4 88/8105 1.815667e-04 0.045098637 0.042158192 5 295/8105 4.490651e-04 0.045098637 0.042158192 6 354/8105 5.048355e-04 0.045098637 0.042158192 geneID 1 991/1869/890/1871/701/990/10926/9088/8317/9700/9134/1029/2810/699/11200/23594/8555/4173 2 4067/3383/7128/1869/890/1871/578/864/637/9641/6891/355/9134/5971/916/956/6850/7187/3551/919/4734/958/6772 3 100/6891/3932/973/916/925/958/64421 4 7057/3339/1299/3695/1101/3679/3910/3696/3693 5 2919/4982/3977/6375/8200/608/8792/3568/2057/1438/8718/655/652/10220/50615/51561/7042 6 894/7057/6794/2247/1299/3695/2252/2066/1101/8817/1021/5105/3679/3082/2057/3910/3551/3696/3693 Count 1 18 2 23 3 8 4 9 5 17 6 19
dotplot(xx) #气泡图
多个分组的富集分析
示例数据
data(geneList, package="DOSE") #载入DOSE包中的数据 head(geneList) #查看数据 包含基因名及其foldchange mydf <- data.frame(Entrez=names(geneList), FC=geneList) # 以下内容目的是构建分组 也可以用别的分组 # 将FC大于1的标注为上调 反之为下调 mydf <- mydf[abs(mydf$FC) > 1,] mydf$group <- "upregulated" mydf$group[mydf$FC < 0] <- "downregulated" # 将FC绝对值大于2 的标注为B 反之为A mydf$othergroup <- "A" mydf$othergroup[abs(mydf$FC) > 2] <- "B" head(mydf) # 查看示例数据(两个分组)
> head(mydf) Entrez FC group othergroup 4312 4312 4.572613 upregulated B 8318 8318 4.514594 upregulated B 10874 10874 4.418218 upregulated B 55143 55143 4.144075 upregulated B 55388 55388 3.876258 upregulated B 991 991 3.677857 upregulated B
分析及其可视化
# 可以进行单组或多组分析,在 + 号后添加即可 formula_res <- compareCluster(Entrez~group+othergroup, data=mydf, fun='enrichKEGG') dotplot(formula_res)
# 同样可以进行分面操作 dotplot(formula_res, x=~group) + ggplot2::facet_grid(~othergroup) #对于只用一次该包的功能可以这么写
基本用法
最后附上用法参数。
compareCluster(geneClusters, fun = "enrichGO", data = "", ...)
参数
References
Chapter 11 Biological theme comparison | clusterProfiler: universal enrichment tool for functional and comparative study (hiplot.com.cn)
