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0a476ff | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 | import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
# Updated Volcano Plot Function
def plot_volcano(data):
# Ensure required columns exist
required_columns = ["logFC", "P.Value", "Target"]
if not all(col in data.columns for col in required_columns):
raise ValueError(f"Data is missing required columns: {required_columns}")
# Calculate -log10(p-value)
data['-log10_pvalue'] = -np.log10(data['P.Value'])
# Define thresholds
fold_change_threshold = 0.6 # Adjust as needed
pvalue_threshold = 0.05 # Adjust as needed
data['Significant'] = (np.abs(data['logFC']) > fold_change_threshold) & (data['P.Value'] < pvalue_threshold)
# Create the volcano plot
plt.figure(figsize=(10, 8))
sns.scatterplot(
data=data,
x='logFC',
y='-log10_pvalue',
# hue='Significant',
palette={True: 'red', False: 'grey'},
legend=False
)
# Add vertical and horizontal threshold lines
plt.axvline(x=-fold_change_threshold, linestyle='--', color='blue', linewidth=1)
plt.axvline(x=fold_change_threshold, linestyle='--', color='blue', linewidth=1)
plt.axhline(y=-np.log10(pvalue_threshold), linestyle='--', color='green', linewidth=1)
# Label the plot
plt.title("Volcano Plot", fontsize=16)
plt.xlabel("Log2 Fold Change", fontsize=14)
plt.ylabel("-Log10 P-value", fontsize=14)
# Annotate significant points with protein names
significant_points = data[data['Significant']]
for _, row in significant_points.iterrows():
plt.text(row['logFC'], row['-log10_pvalue'], row['Target'], fontsize=9)
plt.tight_layout()
return plt
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