大样本相关性热图

作者

[编辑] 郑虎;

[审核] .

修改于

2026-01-27

注记

Hiplot 网站

本页面为 Hiplot Corrplot Big Data 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/big-corrplot?lang=zh_cn

相关性热图是一种分析多个变量，两两之间相关性的图形。

环境配置

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

  • 编程语言: R

  • 依赖包: data.table; jsonlite; ComplexHeatmap

# 安装包
if (!requireNamespace("data.table", quietly = TRUE)) {
  install.packages("data.table")
}
if (!requireNamespace("jsonlite", quietly = TRUE)) {
  install.packages("jsonlite")
}
if (!requireNamespace("ComplexHeatmap", quietly = TRUE)) {
  BiocManager::install("ComplexHeatmap")
}

# 加载包
library(data.table)
library(jsonlite)
library(ComplexHeatmap)
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
 ComplexHeatmap * 2.26.0  2025-10-29 [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

载入数据为基因名称及每个样本的表达量。

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

# 整理数据格式
data <- data[!is.na(data[, 1]), ]
idx <- duplicated(data[, 1])
data[idx, 1] <- paste0(data[idx, 1], "--dup-", cumsum(idx)[idx])
rownames(data) <- data[, 1]
data <- data[, -1]
str2num_df <- function(x) {
  x[] <- lapply(x, function(l) as.numeric(l))
  x
}
tmp <- t(str2num_df(data))
corr <- round(cor(tmp, use = "na.or.complete", method = "pearson"), 3)

# 查看数据
head(corr)
         RGL4   MPP7   UGCG CYSTM1  ANXA2 ENDOD1 ARHGAP24   CST7 HIST1H2BM
RGL4    1.000  0.914  0.929  0.936 -0.592 -0.908    0.888  0.949    -0.603
MPP7    0.914  1.000  0.852  0.907 -0.543 -0.862    0.762  0.899    -0.656
UGCG    0.929  0.852  1.000  0.956 -0.440 -0.791    0.854  0.840    -0.694
CYSTM1  0.936  0.907  0.956  1.000 -0.358 -0.762    0.812  0.852    -0.632
ANXA2  -0.592 -0.543 -0.440 -0.358  1.000  0.826   -0.660 -0.723     0.541
ENDOD1 -0.908 -0.862 -0.791 -0.762  0.826  1.000   -0.907 -0.961     0.709
         EREG   EMP1  NFAM1 SLC40A1   CD52 HIST1H2BH PFKFB3 SNORD116-20  STX11
RGL4   -0.021 -0.495  0.859   0.506 -0.704    -0.680  0.889       0.188  0.953
MPP7   -0.196 -0.447  0.898   0.648 -0.734    -0.770  0.842       0.048  0.915
UGCG    0.153 -0.358  0.858   0.361 -0.671    -0.711  0.943       0.202  0.951
CYSTM1  0.074 -0.272  0.866   0.339 -0.612    -0.683  0.933       0.225  0.985
ANXA2   0.222  0.902 -0.662  -0.668  0.775     0.626 -0.463       0.375 -0.374
ENDOD1  0.191  0.713 -0.872  -0.611  0.854     0.791 -0.814       0.141 -0.787
        SYNE2   TCN1
RGL4    0.780  0.889
MPP7    0.795  0.888
UGCG    0.922  0.927
CYSTM1  0.908  0.973
ANXA2  -0.327 -0.249
ENDOD1 -0.657 -0.708

可视化

# 大样本相关性热图
p <- ComplexHeatmap::Heatmap(
  corr, col = colorRampPalette(c("#4477AA","#FFFFFF","#BB4444"))(50),
  clustering_distance_rows = "euclidean",
  clustering_method_rows = "ward.D2",
  clustering_distance_columns = "euclidean",
  clustering_method_columns = "ward.D2",
  show_column_dend = FALSE, show_row_dend = FALSE,
  column_names_gp = gpar(fontsize = 8),
  row_names_gp = gpar(fontsize = 8)
)

p
图 1: 大样本相关性热图

红色系表示两个基因之间呈正相关，蓝色系表示两个基因之间呈负相关，每一格中的数字表示相关系数。