基因差异表达排序图

作者

[编辑] 郑虎;

[审核] .

修改于

2026-01-27

注记

Hiplot 网站

本页面为 Hiplot Gene Ranking Dotplot 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/gene-rank?lang=zh_cn

基因表达排序可视化。

环境配置

  • 系统: Cross-platform (Linux/MacOS/Windows)

  • 编程语言: R

  • 依赖包: data.table; jsonlite; ggrepel; ggplot2; RColorBrewer

# 安装包
if (!requireNamespace("data.table", quietly = TRUE)) {
  install.packages("data.table")
}
if (!requireNamespace("jsonlite", quietly = TRUE)) {
  install.packages("jsonlite")
}
if (!requireNamespace("ggrepel", quietly = TRUE)) {
  install.packages("ggrepel")
}
if (!requireNamespace("ggplot2", quietly = TRUE)) {
  install.packages("ggplot2")
}
if (!requireNamespace("RColorBrewer", quietly = TRUE)) {
  install.packages("RColorBrewer")
}

# 加载包
library(data.table)
library(jsonlite)
library(ggrepel)
library(ggplot2)
library(RColorBrewer)
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
 data.table   * 1.18.0  2025-12-24 [1] RSPM
 ggplot2      * 4.0.1   2025-11-14 [1] RSPM
 ggrepel      * 0.9.6   2024-09-07 [1] RSPM
 jsonlite     * 2.0.0   2025-03-27 [1] RSPM
 RColorBrewer * 1.1-3   2022-04-03 [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
data <- data.table::fread(jsonlite::read_json("https://hiplot.cn/ui/basic/gene-rank/data.json")$exampleData$textarea[[1]])
data <- as.data.frame(data)

# 整理数据格式
## 按 log2FoldChange 和 pvalue 排序
data <- data[order(-data$log2FC, data$pvalue), ]
## 添加 rank 列
data$rank <- 1:nrow(data)
## 获取 top n 上调和下调基因进行标记
top_n <- 5
top_n_up <- rownames(head(data, top_n))
top_n_down <- rownames(tail(data, top_n))
genes_to_label <- c(top_n_up, top_n_down)
data2 <- data[genes_to_label, ]


# 查看数据
head(data)
        gene   log2FC       pvalue rank
514 LOC91370 3.889820 8.478310e-03    1
293   LRRC25 3.492413 5.000000e-05    2
470   BEGAIN 3.312996 3.347634e-03    3
159   RDM1P5 3.125070 1.538852e-02    4
194    CLDN1 3.096516 2.622163e-02    5
626    KCNS1 2.949729 2.770000e-09    6

可视化

# 基因差异表达排序图
p <- 
  ggplot(data, aes(rank, log2FC, color = pvalue, size = abs(log2FC))) + 
  geom_point() + 
  scale_color_gradientn(colours = colorRampPalette(brewer.pal(11,'RdYlBu'))(100)) +
  geom_hline(yintercept = c(-1, 1), linetype = 2, size = 0.3) +
  geom_hline(yintercept = 0, linetype = 1, size = 0.5) +
  geom_vline(xintercept = median(data$rank), linetype = 2, size = 0.3) + 
  geom_text_repel(data = data2, aes(rank, log2FC, label = gene),
                  size = 3, color = "red") +
  xlab("") + ylab("") + 
  ylim(c(-max(abs(data$log2FC)), max(abs(data$log2FC)))) +
  labs(color = "Pvalue", size = "Log2FoldChange") +
  theme_bw(base_size = 12) +
  theme(text = element_text(family = "Arial"),
        plot.title = element_text(size = 12,hjust = 0.5),
        axis.title = element_text(size = 12),
        axis.text = element_text(size = 10),
        axis.text.x = element_text(angle = 0, hjust = 0.5,vjust = 1),
        legend.position = "right",
        legend.direction = "vertical",
        legend.title = element_text(size = 10),
        legend.text = element_text(size = 10))

p
图 1: 基因差异表达排序图