基因密度图

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 Density 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-density?lang=zh_cn

染色体数据展示。

环境配置

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

  • 编程语言: R

  • 依赖包: data.table; jsonlite; circlize; ComplexHeatmap; gtrellis; tidyverse; ggplotify; RColorBrewer

# 安装包
if (!requireNamespace("data.table", quietly = TRUE)) {
  install.packages("data.table")
}
if (!requireNamespace("jsonlite", quietly = TRUE)) {
  install.packages("jsonlite")
}
if (!requireNamespace("circlize", quietly = TRUE)) {
  install.packages("circlize")
}
if (!requireNamespace("ComplexHeatmap", quietly = TRUE)) {
  BiocManager::install("ComplexHeatmap")
}
if (!requireNamespace("gtrellis", quietly = TRUE)) {
  remotes::install_github("jokergoo/gtrellis")
}
if (!requireNamespace("tidyverse", quietly = TRUE)) {
  install.packages("tidyverse")
}
if (!requireNamespace("ggplotify", quietly = TRUE)) {
  install.packages("ggplotify")
}
if (!requireNamespace("RColorBrewer", quietly = TRUE)) {
  install.packages("RColorBrewer")
}

# 加载包
library(data.table)
library(jsonlite)
library(circlize)
library(ComplexHeatmap)
library(gtrellis)
library(tidyverse)
library(ggplotify)
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
 BiocGenerics   * 0.56.0  2025-10-29 [1] Bioconduc~
 circlize       * 0.4.17  2025-12-08 [1] RSPM
 ComplexHeatmap * 2.26.0  2025-10-29 [1] Bioconduc~
 data.table     * 1.18.0  2025-12-24 [1] RSPM
 dplyr          * 1.1.4   2023-11-17 [1] RSPM
 forcats        * 1.0.1   2025-09-25 [1] RSPM
 generics       * 0.1.4   2025-05-09 [1] RSPM
 GenomicRanges  * 1.62.1  2025-12-08 [1] Bioconduc~
 ggplot2        * 4.0.1   2025-11-14 [1] RSPM
 ggplotify      * 0.1.3   2025-09-20 [1] RSPM
 gtrellis       * 1.35.1  2025-11-02 [1] Github (jokergoo/gtrellis@86749f0)
 IRanges        * 2.44.0  2025-10-29 [1] Bioconduc~
 jsonlite       * 2.0.0   2025-03-27 [1] RSPM
 lubridate      * 1.9.4   2024-12-08 [1] RSPM
 purrr          * 1.2.1   2026-01-09 [1] RSPM
 RColorBrewer   * 1.1-3   2022-04-03 [1] RSPM
 readr          * 2.1.6   2025-11-14 [1] RSPM
 S4Vectors      * 0.48.0  2025-10-29 [1] Bioconduc~
 Seqinfo        * 1.0.0   2025-10-29 [1] Bioconduc~
 stringr        * 1.6.0   2025-11-04 [1] RSPM
 tibble         * 3.3.1   2026-01-11 [1] RSPM
 tidyr          * 1.3.2   2025-12-19 [1] RSPM
 tidyverse      * 2.0.0   2023-02-22 [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/gene-density/data.json")$exampleData$textarea[[1]])
data1 <- as.data.frame(data1)
data2 <- data.table::fread(jsonlite::read_json("https://hiplot.cn/ui/basic/gene-density/data.json")$exampleData$textarea[[2]])
data2 <- as.data.frame(data2)

# 整理数据格式
chrNum <- str_replace(unique(data1$chr), "Chr|chr", "")
data1$chr <- factor(data1$chr, levels = paste0("Chr", chrNum))
data2$chr <- factor(data2$chr, levels = paste0("Chr", chrNum))
# 设置窗口计算基因密度
windows <- 100 * 1000 # 默认:100kb
gene_density <- genomicDensity(data2, window.size = windows)
gene_density$chr <- factor(gene_density$chr,
  levels =  paste0("Chr", chrNum)
)

# 查看数据
head(data1)
    chr start      end
1  Chr5     0 29958434
2  Chr8     0 28443022
3  Chr9     0 23012720
4 Chr10     0 23207287
5 Chr12     0 27531856
head(data2)
    chr start   end
1 Chr10 38648 40060
2 Chr10 45941 58338
3 Chr10 67119 72971
4 Chr10 75410 76305
5 Chr10 80964 82250
6 Chr10 94798 97746

可视化

# 设置画板颜色
palettes <- c("#B2182B","#EF8A62","#FDDBC7","#D1E5F0","#67A9CF","#2166AC")
col_fun <- colorRamp2(
  seq(0, max(gene_density[[4]]), length = 6), rev(palettes)
  )
cm <- ColorMapping(col_fun = col_fun)
# 设置图例
lgd <- color_mapping_legend(
  cm, plot = F, title = "density", color_bar = "continuous"
  )
# 绘制基因密度分布热图
p <- as.ggplot(function() {
  gtrellis_layout(
    data1, n_track = 2, ncol = 1, byrow = FALSE,
    track_axis = FALSE, add_name_track = FALSE,
    xpadding = c(0.1, 0), gap = unit(1, "mm"),
    track_height = unit.c(unit(1, "null"), unit(4, "mm")),
    track_ylim = c(0, max(gene_density[[4]]), 0, 1),
    border = FALSE, asist_ticks = FALSE,
    legend = lgd
    )
  # 添加基因面积图 track
  add_lines_track(gene_density, gene_density[[4]],
                  area = TRUE, gp = gpar(fill = "pink"))
  # 添加基因密度热图 track
  add_heatmap_track(gene_density, gene_density[[4]], fill = col_fun)
  add_track(track = 2, clip = FALSE, panel_fun = function(gr) {
    chr <- get_cell_meta_data("name")
    if (chr == paste("Chr", length(chrNum), sep = "")) {
      grid.lines(get_cell_meta_data("xlim"), unit(c(0, 0), "npc"),
                 default.units = "native")
      }
    grid.text(chr, x = 0.01, y = 0.38, just = c("left", "bottom"))
    })
  circos.clear()
  })

p
图 1: 基因密度图