相关矩阵图（Julia）

作者

[编辑] 王诗翔

[贡献] Claude Code —— AI 生成，独立 AI 复核；

修改于

2026-10-19

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相关矩阵图把特征面板的两两相关系数编码为彩色矩阵单元格，一眼读出共表达模块与冗余标志物。在生物医学研究中，它是标志物面板的常规 QC。Julia 的 Statistics.cor 计算矩阵，CairoMakie.heatmap! 渲染，并通过 text! 逐格标注数值。

示例

环境配置

  • 系统要求：跨平台（Linux/MacOS/Windows）
  • 编程语言：Julia
  • 依赖包：CairoMakie、DataFrames、Statistics 
using CairoMakie
using DataFrames
using Statistics
using Random

数据准备

我们模拟一个含两个相关模块（T 细胞标志物与髓系标志物）加一个弱相关增殖评分的 8 标志物免疫面板，并计算 Pearson 矩阵。

Random.seed!(101)

n = 250
latent_t = randn(n)
latent_m = randn(n)
panel = DataFrame(
    CD3   = latent_t .+ 0.4 .* randn(n),
    CD4   = latent_t .+ 0.5 .* randn(n),
    CD8   = latent_t .+ 0.5 .* randn(n),
    IL7R  = latent_t .+ 0.6 .* randn(n),
    CD14  = latent_m .+ 0.4 .* randn(n),
    CD68  = latent_m .+ 0.5 .* randn(n),
    LYZ   = latent_m .+ 0.5 .* randn(n),
    MKI67 = randn(n))
markers = names(panel)
corr = Matrix(cor(Matrix(panel)))
8×8 Matrix{Float64}:
  1.0         0.847918    0.851106   …  -0.0763281  -0.121005   -0.0607499
  0.847918    1.0         0.830831      -0.0535264  -0.0561228  -0.0197228
  0.851106    0.830831    1.0           -0.0657583  -0.0852999  -0.0260574
  0.817423    0.795928    0.77068       -0.0428039  -0.0724778  -0.129194
 -0.0678254  -0.0418892  -0.0612808      0.835868    0.832141   -0.00942036
 -0.0763281  -0.0535264  -0.0657583  …   1.0         0.803521    0.0111169
 -0.121005   -0.0561228  -0.0852999      0.803521    1.0         0.0349363
 -0.0607499  -0.0197228  -0.0260574      0.0111169   0.0349363   1.0

可视化

带标注的相关热图

使用反转的发散色图（红=正相关）、固定 ±1 的 colorrange 保证跨研究可比，并用 text! 逐格标注数值。

fig = Figure(size = (540, 440))
ax = Axis(fig[1, 1]; xticks = (1:8, markers), yticks = (1:8, markers),
          xticklabelrotation = 0.6)
hm = heatmap!(ax, corr; colormap = Reverse(:RdBu), colorrange = (-1, 1))
for i in 1:8, j in 1:8
    text!(ax, j, i; text = string(round(corr[i, j]; digits = 2)),
          align = (:center, :center), fontsize = 9,
          color = abs(corr[i, j]) > 0.55 ? :white : :black)
end
Colorbar(fig[1, 2], hm; label = "Pearson r")
fig
图 1: 标志物面板相关矩阵图（标注系数）

下三角相关矩阵图

用 NaN 掩掉镜像上三角去除冗余；nan_color 让空格保持不可见。

corr_masked = [i < j ? NaN : corr[i, j] for i in 1:8, j in 1:8]

fig = Figure(size = (460, 420))
ax = Axis(fig[1, 1]; xticks = (1:8, markers), yticks = (1:8, markers),
          xticklabelrotation = 0.6)
heatmap!(ax, corr_masked; colormap = Reverse(:RdBu), colorrange = (-1, 1),
         nan_color = :white)
for i in 1:8, j in 1:i
    text!(ax, j, i; text = string(round(corr[i, j]; digits = 2)),
          align = (:center, :center), fontsize = 9,
          color = abs(corr[i, j]) > 0.55 ? :white : :black)
end
hidespines!(ax)
fig
图 2: 下三角相关矩阵图（上三角掩膜）

参考文献

  1. Makie development team. Makie.jl documentation — heatmap. https://docs.makie.org/stable/reference/plots/heatmap
  2. Danisch, S., & Krumbiegel, J. (2021). Makie.jl: Flexible high-performance data visualization for Julia. Journal of Open Source Software, 6(65), 3349.