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correlation_without_bs_seq.py

+import pandas as pd
+import numpy as np
+from scipy.stats import spearmanr
+import seaborn as sns
+import matplotlib.pyplot as plt
+
+# Charger les fichiers de données dans des DataFrames pandas
+df1 = pd.read_csv("C:\\Users\\SCD UM\\Desktop\\methylation_frequency\\Intersection\\Correlation_without_BS_seq\\MET1_DSP_commun_without_bs_seq.bed", sep='\t', names=["chromosome", "start", "frequency"])
+df2 = pd.read_csv("C:\\Users\\SCD UM\\Desktop\\methylation_frequency\\Intersection\\Correlation_without_BS_seq\\MET1_Megalodon_commun_without_bs_seq.bed", sep='\t',  names=["chromosome", "start", "frequency"])
+df3 = pd.read_csv("C:\\Users\\SCD UM\\Desktop\\methylation_frequency\\Intersection\\Correlation_without_BS_seq\\MET1_Tombo_commun_without_bs_seq.bed", sep='\t', names=["chromosome", "start","frequency"])
+
+data1 = df1["frequency"]
+data2 = df2["frequency"]
+data3 = df3["frequency"]
+
+# Calculer la corrélation entre les fichiers
+corr_matrix = pd.concat([data1, data2, data3], axis=1, keys=["DeepSignalPlant", "Megalodon", "Tombo"]).corr()
+
+# Créer une figure
+plt.figure(figsize=(8, 6))
+
+# Générer la heatmap de la matrice de corrélation
+sns.heatmap(corr_matrix, annot=True, cmap="coolwarm")
+
+# Définir le titre du graphique
+plt.title("Matrice de corrélation")
+
+# Afficher le graphique
+plt.show()
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