# 安装包
if (!requireNamespace("data.table", quietly = TRUE)) {
install.packages("data.table")
}if (!requireNamespace("jsonlite", quietly = TRUE)) {
install.packages("jsonlite")
}if (!requireNamespace("clusterProfiler", quietly = TRUE)) {
install_github("YuLab-SMU/clusterProfiler")
}
# 加载包
library(data.table)
library(jsonlite)
library(clusterProfiler)
自定义基因富集分析
自定义基因集。
环境配置
系统: Cross-platform (Linux/MacOS/Windows)
编程语言: R
依赖包:
data.table
;jsonlite
;clusterProfiler
数据准备
# 加载数据
<- data.table::fread(jsonlite::read_json("https://hiplot.cn/ui/basic/diy-gsea/data.json")$exampleData$textarea[[1]])
data1 <- as.data.frame(data1)
data1 <- data.table::fread(jsonlite::read_json("https://hiplot.cn/ui/basic/diy-gsea/data.json")$exampleData$textarea[[2]])
data2 <- as.data.frame(data2)
data2
# 整理数据格式
2] <- as.numeric(data1[,2])
data1[,<- data1[,2]
geneList names(geneList) <- data1[,1]
<- sort(geneList, decreasing = TRUE)
geneList <- data.frame(term=data2[,1], gene=data2[,2])
term
# 查看数据
head(term)
term gene
1 GO_ADAPTIVE_IMMUNE_RESPONSE ADAM17
2 GO_ADAPTIVE_IMMUNE_RESPONSE AICDA
3 GO_ADAPTIVE_IMMUNE_RESPONSE ALCAM
4 GO_ADAPTIVE_IMMUNE_RESPONSE ANXA1
5 GO_ADAPTIVE_IMMUNE_RESPONSE BATF
6 GO_ADAPTIVE_IMMUNE_RESPONSE BCL10
可视化
# 自定义基因富集分析
<- clusterProfiler::GSEA(geneList, TERM2GENE = term, pvalueCutoff = 1)
y <- gseaplot(
p
y,@result$Description[1],
ycolor = "#000000",
by = "runningScore",
color.line = "#4CAF50",
color.vline= "#FA5860",
title = "DIY GSEA Plot",
)
p
