箱线图（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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箱线图通过中位数、四分位数和离群值概括数值型变量的分布。在生物医学研究中，箱线图是比较不同基因型、批次或处理组之间生物标志物水平的标准紧凑展示方式。CairoMakie 提供箱线图与抖动散点叠加，支持出版界推荐的「展示原始数据」风格。

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

我们模拟三种基因型（WT、HET、KO）的血清生物标志物检测数据，每组 60 个重复，并在 KO 组注入若干极端值用于演示离群值展示。

Random.seed!(7)

genotypes = ["WT", "HET", "KO"]
n_per_genotype = 60
values = vcat(
    exp.(1.2 .+ 0.45 .* randn(n_per_genotype)),
    exp.(1.55 .+ 0.45 .* randn(n_per_genotype)),
    exp.(1.95 .+ 0.55 .* randn(n_per_genotype)),
)
values[end-2:end] .*= 3.5            # KO 组的极端值
df = DataFrame(Genotype = repeat(genotypes, inner = n_per_genotype),
               Biomarker = values)
180×2 DataFrame
155 rows omitted
Row Genotype Biomarker
String Float64
1 WT 1.87811
2 WT 4.25669
3 WT 3.4972
4 WT 3.62601
5 WT 1.9446
6 WT 4.89356
7 WT 9.61532
8 WT 2.78055
9 WT 1.60657
10 WT 1.60722
11 WT 3.65945
12 WT 1.78551
13 WT 2.97571
⋮ ⋮ ⋮
169 KO 6.93621
170 KO 5.51431
171 KO 7.59819
172 KO 3.71913
173 KO 7.91828
174 KO 8.12042
175 KO 7.1711
176 KO 24.6837
177 KO 7.21306
178 KO 14.0128
179 KO 15.2286
180 KO 18.0943

可视化

基础箱线图

箱体跨越四分位距（IQR），中线为中位数，须线延伸至 1.5 × IQR；超出该范围的点以离群值绘制。

fig = Figure(size = (440, 340))
ax = Axis(fig[1, 1], xlabel = "Genotype", ylabel = "Biomarker level (ng/mL)")
boxplot!(ax, fill(1, n_per_genotype), df.Biomarker[df.Genotype .== "WT"]; width = 0.5, color = :slateblue)
boxplot!(ax, fill(2, n_per_genotype), df.Biomarker[df.Genotype .== "HET"]; width = 0.5, color = :slateblue)
boxplot!(ax, fill(3, n_per_genotype), df.Biomarker[df.Genotype .== "KO"]; width = 0.5, color = :slateblue)
ax.xticks = (1:3, genotypes)
hidespines!(ax, :t, :r)
fig
图 1: 不同基因型血清生物标志物的箱线图

箱线图叠加散点

由于箱体会隐藏原始数据，叠加单个观测值是出版界推荐的风格：浅色箱体配以随机 x 抖动的 scatter! 散点层。

fig = Figure(size = (440, 340))
ax = Axis(fig[1, 1], xlabel = "Genotype", ylabel = "Biomarker level (ng/mL)")
for (i, g) in enumerate(genotypes)
    v = df.Biomarker[df.Genotype .== g]
    boxplot!(ax, fill(i, length(v)), v; width = 0.4,
             color = (:gray85, 0.8), show_outliers = false)
    jitter = i .+ 0.28 .* (rand(length(v)) .- 0.5)
    scatter!(ax, jitter, v; color = (:black, 0.45), markersize = 6)
end
ax.xticks = (1:3, genotypes)
hidespines!(ax, :t, :r)
fig
图 2: 箱线图叠加原始数据点

横向箱线图

类别标签较长时，横向布局（orientation = :horizontal）更易读。

fig = Figure(size = (500, 300))
ax = Axis(fig[1, 1], xlabel = "Biomarker level (ng/mL)", ylabel = "Genotype")
for (i, g) in enumerate(genotypes)
    v = df.Biomarker[df.Genotype .== g]
    boxplot!(ax, fill(i, length(v)), v; orientation = :horizontal,
             width = 0.5, color = :olivedrab)
end
ax.yticks = (1:3, genotypes)
hidespines!(ax, :t, :r)
fig
图 3: 长类别标签的横向箱线图

参考文献

  1. Makie development team. Makie.jl documentation — boxplot. https://docs.makie.org/stable/reference/plots/boxplot
  2. Danisch, S., & Krumbiegel, J. (2021). Makie.jl: Flexible high-performance data visualization for Julia. Journal of Open Source Software, 6(65), 3349.
  3. Spitzer, M., Wildenhain, J., Rappsilber, J., & Tyers, M. (2014). BoxPlotR: a web tool for generation of box plots. Nature Methods, 11(2), 121-122.