Bioinfo Matplotlib Genomics

You will learn how to create standard bioinformatics plots: Manhattan, volcano, and box plots.

The Problem

The bioinfo matplotlib genomics pattern is frequently misapplied by data scientists and Python developers, leading to runtime errors, incorrect results, or inefficient code. This quick-fix guide shows the correct implementation and common pitfalls to avoid when working with BIOINFO in Python.

The Wrong Way

The most common mistake is using the wrong method signature, incorrect parameters, or misunderstanding the underlying data structure. Here is what typically goes wrong:

import matplotlib.pyplot as plt
import numpy as np
# Manhattan plot (GWAS)
chromosomes = np.repeat(range(1, 23), 100)
positions = np.tile(range(100), 22)
p_values = -np.log10(np.random.uniform(0, 1, 2200))
colors = ['navy' if c % 2 else 'orange' for c in chromosomes]
plt.figure(figsize=(12, 4))
plt.scatter(range(2200), p_values, c=colors, s=8)
plt.axhline(-np.log10(5e-8), color='red', linestyle='--', label='Genome-wide significance')
plt.xlabel('Chromosome'), plt.ylabel('-log10(p-value)')
plt.title('Manhattan Plot')
plt.legend()
plt.show()

What happens: Manhattan plot with alternating chromosome colors and significance threshold line.

This approach fails because the API contract is violated -- parameters are passed in the wrong order, the input shape doesn't match expectations, or the method is called on an incompatible object type.

The Right Way

The correct approach uses the proper API with the right parameters. Here is the fixed version:

plt.figure(figsize=(8, 6))
plt.boxplot([np.random.normal(10, 2, 50), np.random.normal(12, 2, 50)], labels=['Control', 'Treatment'])
plt.ylabel('Expression Level')
plt.title('Gene Expression: Control vs Treatment')
plt.show()

Expected output:

Box plot comparing expression distributions.

Step-by-Step Fix

1. Understand the data types and shapes

Before applying any operation, verify the data types and shapes of your inputs. In Python Data Science, most errors come from type or shape mismatches.

# Always inspect your data first
print(type(data))
print(data.shape if hasattr(data, 'shape') else 'No shape')
print(data.dtype if hasattr(data, 'dtype') else 'No dtype')

2. Apply the correct method with proper arguments

Use the corrected code shown above. Pay special attention to keyword arguments that control behavior like axis, inplace, or how.

3. Verify the result

Always validate that the output matches expectations before proceeding:

# Verification pattern
result = perform_operation(data)
assert some_condition(result), "Operation failed unexpectedly"
print(f"Success: {result.shape if hasattr(result, 'shape') else result}")

Prevention Tips

• Use Manhattan plots for GWAS results across chromosomes: Use Manhattan plots for GWAS results across chromosomes
• Use volcano plots (log2FC vs -log10 p-value) for differential expression: Use volcano plots (log2FC vs -log10 p-value) for differential expression
• Use box plots for comparing expression distributions between groups: Use box plots for comparing expression distributions between groups
• Use heatmaps for clustered expression matrices: Use heatmaps for clustered expression matrices
• Use genome matplotlib or circos for circular genome plots: Use genome matplotlib or circos for circular genome plots

Common Mistakes

1. Using scatter plot colors that don't distinguish chromosomes in Manhattan plots - Using scatter plot colors that don't distinguish chromosomes in Manhattan plots
2. Not showing significance thresholds on Manhattan/volcano plots - Not showing significance thresholds on Manhattan/volcano plots

These mistakes appear frequently in real-world bioinfo code. DodaTech's contributors have identified these patterns through analysis of open-source projects, production systems, and community forums like Stack Overflow.

Practice Exercise

Create a volcano plot showing log2 fold change vs significance for differential expression analysis results.

This exercise reinforces the concepts covered in this guide. Try implementing it before checking online solutions. This hands-on approach ensures you retain the knowledge and can apply it independently.

FAQ

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