📊 Visualize Your Data – Day 1

Box Plot vs Violin Plot in Bioinformatics

After successfully completing my Whole Genome Sequencing analysis series, I’m starting a new daily blog series focused on data visualization commonly used in bioinformatics and molecular genomics papers.

Many learners can generate results but struggle with:

● Choosing the right plot

● Interpreting distributions correctly

● Producing publication-ready figures

This new series — “Visualize Your Data” — focuses on common plots seen in bioinformatics papers, using reproducible toy data and practical tips from real manuscript preparation.

🎯 Today’s focus

Box Plot vs Violin Plot

Both plots visualize data distributions, but they emphasize different aspects of the data.

🔹 When should you use a box plot?

A box plot is ideal when you want to:

● Compare medians across groups

● Highlight quartiles (Q1–Q3) and outliers

● Keep figures simple and reviewer-friendly

📌 Common in:

● Gene expression comparisons

● MAG abundance across conditions

● DNA:RNA ratio analyses

🔹 When should you use a violin plot?

A violin plot is useful when:

You have larger datasets

Distribution shape matters (e.g., multimodal data)

You want to visualize density, not just summary statistics

📌 Common in:

● Transcript abundance distributions

● Functional gene abundance

● Single-cell or large-sample datasets

🧪 Toy dataset (example)

We’ll use a simple toy dataset representing gene expression across two conditions.

Create toy data R version

 set.seed(123)

toy_data <- data.frame(
  Condition = rep(c("Control", "Treatment"), each = 30),
  Expression = c(
    rnorm(30, mean = 10, sd = 2),
    rnorm(30, mean = 15, sd = 3)
  )
)

📦 R visualization (ggplot2) *Box plot (R)

library(ggplot2)

theme_pub <- theme(
  panel.grid.major = element_blank(),
  panel.grid.minor = element_blank(),
  panel.background = element_blank(),
  panel.border = element_rect(color = "black", fill = NA, size = 0.8),
  axis.line = element_blank(),
  axis.text = element_text(color = "black", size = 12),
  axis.title = element_text(color = "black", size = 12),
  plot.title = element_text(color = "black", size = 12, face = "bold"),
  legend.text = element_text(color = "black", size = 12),
  legend.title = element_text(color = "black", size = 12)
)

ggplot(toy_data, aes(x = Condition, y = Expression, fill = Condition)) +
  geom_boxplot(outlier.shape = 16, alpha = 0.7) +
  labs(
    title = "Box Plot of Gene Expression",
    y = "Expression level",
    x = ""
  ) +
  theme_pub +
  theme(legend.position = "none")

*Violin plot (R)

ggplot(toy_data, aes(x = Condition, y = Expression, fill = Condition)) +
  geom_violin(trim = FALSE, alpha = 0.7) +
  geom_boxplot(width = 0.15, fill = "white", outlier.shape = NA) +
  labs(
    title = "Violin Plot of Gene Expression",
    y = "Expression level",
    x = ""
  ) +
  theme_pub +
  theme(legend.position = "none")

🐍 Python equivalents (matplotlib + seaborn)

Many bioinformatics workflows rely on Python, so below are equivalent plots using Python.

Create toy data (Python)

import numpy as np
import pandas as pd

np.random.seed(123)

toy_data = pd.DataFrame({
    "Condition": ["Control"] * 30 + ["Treatment"] * 30,
    "Expression": np.concatenate([
        np.random.normal(10, 2, 30),
        np.random.normal(15, 3, 30)
    ])
})

*Box plot (Python)

palette = {
    "Control": "#4C72B0",     # blue
    "Treatment": "#DD8452"    # orange
}

sns.boxplot(
    data=toy_data,
    x="Condition",
    y="Expression",
    palette=palette
)

plt.title("Box Plot of Gene Expression")
plt.ylabel("Expression level")
plt.xlabel("")
plt.show()

*Violin plot (Python)

import seaborn as sns
import matplotlib.pyplot as plt

sns.violinplot(
    data=toy_data,
    x="Condition",
    y="Expression",
    palette=palette,
    inner="box",
    cut=0
)

plt.title("Violin Plot of Gene Expression")
plt.ylabel("Expression level")
plt.xlabel("")
plt.show()

These codes and themes are customized based on my preferences and are routinely used in my manuscripts, posters, and other presentations.

⚠️ Common mistakes to avoid

❌ Using violin plots for very small datasets ❌ Showing density without summary statistics ❌ Overusing colors or inconsistent palettes ❌ Forgetting to label axes clearly

🎨 Note on editing figures in Adobe Illustrator

Even with powerful tools like R and Python, they don’t always give exactly what journals or reviewers expect.

In real-world manuscript preparation:

● Fonts may need adjustment

● Panel alignment may require manual tweaking

● Figure spacing, labels, or symbols may need fine control

● you may need to combine two different figure to 1 single figure

📌 Best practice

● Export plots as vector formats (.pdf or .svg)

● Perform final polishing in Adobe Illustrator (or Inkscape)

● Examples of common Illustrator edits:

● Aligning multi-panel figures

● Standardizing font sizes across panels

● Adjusting legend placement

● Adding panel labels (A, B, C)

This hybrid workflow (R/Python → Illustrator) is very common in published bioinformatics papers.

📝 Box plot vs Violin plot — quick summary

📌 Take-home message

● Box plots are ideal for clean, simple comparisons

● Violin plots reveal distribution structure

● Combining both often provides the clearest interpretation

● Final figure polishing is often done outside R/Python

🔜 Coming next in the series

● Heatmaps (scaling & clustering)

● PCA vs UMAP

● Volcano plots

● Bubble plots

● Presence–absence plots

If you’re learning bioinformatics or preparing figures for a manuscript, I hope this series helps you visualize your data with confidence.