Scatter Plot (Julia)

Author

Modified

2026-04-04

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A scatter plot displays values for two continuous variables as a collection of points. Julia’s CairoMakie package (part of the Makie.jl ecosystem) provides high-performance, GPU-accelerated plotting capabilities ideal for large biomedical datasets. Makie offers publication-quality rendering with a composable, declarative API.

Example

Setup

System Requirements: Cross-platform (Linux/MacOS/Windows)
Programming Language: Julia
Dependencies: CairoMakie, DataFrames, Statistics

using CairoMakie
using DataFrames
using Statistics
using Random

Data Preparation

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

Visualization

Basic Scatter Plot

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

Scatter Plot with Trend Lines

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

Figure 2: Scatter Plot with Linear Trend Lines

Density Scatter Plot

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

References

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

📋 AI Skill: Scatter Plot (Julia)