DIY GSEA

Authors

[Editor] Hu Zheng;

[Contributors]

Make your geneset.

Setup

System Requirements: Cross-platform (Linux/MacOS/Windows)
Programming language: R
Dependent packages: data.table; jsonlite; clusterProfiler

# Install packages
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")
}

# Load packages
library(data.table)
library(jsonlite)
library(clusterProfiler)

Data Preparation

# Load data
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)

# convert data structure
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])

# View data
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

Visualization

# DIY GSEA
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