自定义基因富集分析

作者

[编辑] 郑虎;

[审核] .

修改于

2026-01-27

注记

Hiplot 网站

本页面为 Hiplot DIY GSEA 插件的源码版本教程，您也可以使用 Hiplot 网站实现无代码绘图，更多信息请查看以下链接:

https://hiplot.cn/basic/diy-gsea?lang=zh_cn

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

# 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)) {
  BiocManager::install("clusterProfiler")
}

# 加载包
library(data.table)
library(jsonlite)
library(clusterProfiler)
sessioninfo::session_info("attached")
─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.5.2 (2025-10-31)
 os       Ubuntu 24.04.3 LTS
 system   x86_64, linux-gnu
 ui       X11
 language (EN)
 collate  C.UTF-8
 ctype    C.UTF-8
 tz       UTC
 date     2026-01-28
 pandoc   3.1.3 @ /usr/bin/ (via rmarkdown)
 quarto   1.8.27 @ /usr/local/bin/quarto

─ Packages ───────────────────────────────────────────────────────────────────
 package         * version date (UTC) lib source
 clusterProfiler * 4.18.4  2025-12-15 [1] Bioconduc~
 data.table      * 1.18.0  2025-12-24 [1] RSPM
 jsonlite        * 2.0.0   2025-03-27 [1] RSPM

 [1] /home/runner/work/_temp/Library
 [2] /opt/R/4.5.2/lib/R/site-library
 [3] /opt/R/4.5.2/lib/R/library
 * ── Packages attached to the search path.

──────────────────────────────────────────────────────────────────────────────

数据准备

# 加载数据
data1 <- data.table::fread(jsonlite::read_json("https://hiplot.cn/ui/basic/diy-gsea/data.json")$exampleData$textarea[[1]])
data1 <- as.data.frame(data1)
data2 <- data.table::fread(jsonlite::read_json("https://hiplot.cn/ui/basic/diy-gsea/data.json")$exampleData$textarea[[2]])
data2 <- as.data.frame(data2)

# 整理数据格式
data1[,2] <- as.numeric(data1[,2])
geneList <- data1[,2]
names(geneList) <- data1[,1]
geneList <- sort(geneList, decreasing = TRUE)
term <- data.frame(term=data2[,1], gene=data2[,2])

# 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
y <- clusterProfiler::GSEA(geneList, TERM2GENE = term, pvalueCutoff = 1)
p <- gseaplot(
  y,
  y@result$Description[1],
  color = "#000000",
  by = "runningScore",
  color.line = "#4CAF50",
  color.vline= "#FA5860",
  title = "DIY GSEA Plot",
  )

p
图 1: 自定义基因富集分析