散点图（Julia）

作者

[编辑] Bizard 团队。

修改于

2026-04-04

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散点图以点的集合展示两个连续变量的值。Julia 的 CairoMakie 包（Makie.jl 生态系统的一部分）提供了高性能的绘图功能，非常适合大型生物医学数据集。Makie 通过可组合的声明式 API 提供出版级别的渲染质量。

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

Random.seed!(42)
n = 150
species = repeat(["setosa", "versicolor", "virginica"], inner=50)
sepal_length = [randn(50) .* 0.35 .+ 5.0;
                randn(50) .* 0.52 .+ 5.9;
                randn(50) .* 0.64 .+ 6.6]
sepal_width = [randn(50) .* 0.38 .+ 3.4;
               randn(50) .* 0.31 .+ 2.8;
               randn(50) .* 0.32 .+ 3.0]
iris_df = DataFrame(species=species, sepal_length=sepal_length, sepal_width=sepal_width)

150×3 DataFrame

125 rows omitted

Row	species	sepal_length	sepal_width
	String	Float64	Float64
1	setosa	4.87282	3.44153
2	setosa	5.08811	3.19805
3	setosa	4.88975	3.43855
4	setosa	4.89106	4.06589
5	setosa	5.28571	3.21915
6	setosa	5.16686	3.35007
7	setosa	4.69916	3.59429
8	setosa	4.48575	3.45141
9	setosa	4.25998	3.00572
10	setosa	5.01532	3.23638
11	setosa	4.71113	3.18741
12	setosa	5.2941	3.395
13	setosa	5.15186	3.6258
⋮	⋮	⋮	⋮
139	virginica	6.93975	2.62209
140	virginica	6.97755	3.70858
141	virginica	6.94162	3.20095
142	virginica	6.95693	2.73005
143	virginica	7.86145	3.01312
144	virginica	5.12471	2.88178
145	virginica	6.22286	3.06058
146	virginica	8.263	3.38505
147	virginica	6.10909	2.68394
148	virginica	5.49659	3.09755
149	virginica	7.40266	3.07188
150	virginica	6.43618	2.74329

可视化

基础散点图

fig = Figure(size=(700, 500))
ax = Axis(fig[1,1], xlabel="Sepal Length (cm)", ylabel="Sepal Width (cm)",
          title="Iris Scatter Plot")
colors_map = Dict("setosa" => :steelblue, "versicolor" => :coral, "virginica" => :green)
for sp in unique(iris_df.species)
    mask = iris_df.species .== sp
    scatter!(ax, iris_df.sepal_length[mask], iris_df.sepal_width[mask],
             color=colors_map[sp], markersize=10, alpha=0.7, label=sp)
end
axislegend(ax, position=:rt)
fig

带趋势线的散点图

Random.seed!(42)
n2 = 100
gene_a_tumor = randn(n2) .* 2 .+ 7
gene_b_tumor = 0.6 .* gene_a_tumor .+ randn(n2) .* 1.0 .+ 1.5
gene_a_normal = randn(n2) .* 2 .+ 5
gene_b_normal = 0.4 .* gene_a_normal .+ randn(n2) .* 1.2 .+ 2.0

fig2 = Figure(size=(700, 500))
ax2 = Axis(fig2[1,1], xlabel="Gene A Expression", ylabel="Gene B Expression",
           title="Gene Expression Correlation by Group")
scatter!(ax2, gene_a_tumor, gene_b_tumor, color=(:red, 0.5), markersize=8, label="Tumor")
scatter!(ax2, gene_a_normal, gene_b_normal, color=(:steelblue, 0.5), markersize=8, label="Normal")

for (x, y, col) in [(gene_a_tumor, gene_b_tumor, :red),
                     (gene_a_normal, gene_b_normal, :steelblue)]
    x_sorted = sort(x)
    m = (length(x) * sum(x .* y) - sum(x) * sum(y)) /
        (length(x) * sum(x.^2) - sum(x)^2)
    b = mean(y) - m * mean(x)
    lines!(ax2, x_sorted, m .* x_sorted .+ b, color=col, linewidth=2, linestyle=:dash)
end
axislegend(ax2, position=:lt)
fig2

密度散点图

Random.seed!(123)
n3 = 2000
x_dense = randn(n3) .* 3
y_dense = x_dense .* 0.5 .+ randn(n3) .* 2

fig3 = Figure(size=(700, 550))
ax3 = Axis(fig3[1,1], xlabel="Log2 Fold Change", ylabel="-Log10(P-value)",
           title="Density Scatter")
hm = hexbin!(ax3, x_dense, y_dense, cellsize=0.4, colormap=:viridis)
Colorbar(fig3[1,2], hm, label="Count")
fig3

参考文献

Danisch, S., & Krumbiegel, J. (2021). Makie.jl: Flexible high-performance data visualization for Julia. JOSS, 6(65), 3349.

📋 AI Skill: 散点图（Julia）

示例