Volcano Plot in Matplotlib — Complete Guide

You will learn how to create a volcano plot for differential expression analysis using Matplotlib.

The Problem

Vizualizarea genelor diferential exprimate necesita un grafic care combina magnitudinea schimbarii (fold change) cu semnificatia statistica (p-value). Un volcano plot incorect construit mascheaza genele relevante.

The Wrong Way

Trasarea punctelor fara a marca genele semnificative si fara praguri clare:

import matplotlib.pyplot as plt
import numpy as np
log2fc = np.random.normal(0, 1.5, 5000)
pval = -np.log10(np.random.uniform(0, 1, 5000))
plt.scatter(log2fc, pval, s=5)
plt.show()

Ce se intampla: Nu se disting genele up-regulate de cele down-regulate, iar pragurile de semnificatie lipsesc.

The Right Way

Adauga praguri de fold change si p-value, si coloreaza genele semnificative:

log2fc = np.random.normal(0, 1.5, 5000)
pval = -np.log10(np.random.uniform(0, 1, 5000))

colors = ['grey'] * len(log2fc)
for i in range(len(log2fc)):
    if abs(log2fc[i]) > 1 and pval[i] > -np.log10(0.05):
        colors[i] = 'red' if log2fc[i] > 0 else 'blue'

plt.figure(figsize=(8, 6))
plt.scatter(log2fc, pval, c=colors, s=8, alpha=0.6)
plt.axvline(-1, color='grey', linestyle='--', linewidth=0.8)
plt.axvline(1, color='grey', linestyle='--', linewidth=0.8)
plt.axhline(-np.log10(0.05), color='grey', linestyle='--', linewidth=0.8)
plt.xlabel('log2 Fold Change'), plt.ylabel('-log10(p-value)')
plt.title('Volcano Plot - Differential Expression')
plt.tight_layout()
plt.show()

Rezultat asteptat: Genele up-regulate (rosu) si down-regulate (albastru) sunt clar evidentiate, cu praguri vizibile.

Step-by-Step Fix

1. Calculeaza fold change si p-value ajustat

Asigura-te ca folosesti p-value ajustat (FDR/Benjamini-Hochberg), nu raw p-value.

2. Defineste criteriile de semnificatie

fc_threshold = 1  # |log2FC| > 1
pval_threshold = 0.05  # p-adjusted < 0.05

3. Coloreaza si adnota genele

for i, (fc, p) in enumerate(zip(log2fc, neg_log_pval)):
    if abs(fc) > fc_threshold and p > -np.log10(pval_threshold):
        if fc > 0:
            plt.annotate(gene_names[i], (fc, p),
                        fontsize=6, alpha=0.7)

Prevention Tips

• Foloseste p-value ajustat (FDR) nu raw p-value pentru semnificatie
• Defineste clar pragurile de fold change si p-value in legenda
• Adnotaza top gene (cele mai semnificative) pentru a oferi context biologic
• Foloseste culori distincte pentru up/down-regulate si gene nesemnificative
• Adauga liniile de prag cu stil intrerupt pentru claritate

Common Mistakes

1. Foloseste raw p-value in loc de p-value ajustat -- multe fals-pozitive
2. Nu distinge intre up si down -- diferenta biologica se pierde
3. Adnoteaza prea multe gene -- graficul devine ilizibil
4. Praguri arbitrare fara justificare biologica
5. Nu raporteaza numarul de gene semnificative in fiecare directie

Practice Exercise

Analizeaza un set de date RNA-seq, identifica genele diferential exprimate (|log2FC| > 1, p-adj < 0.05) si creaza volcano plot-ul corespunzator.

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