Clustering of Promoter Types Based on Motif Frequency and Distribution

To implement the clustering of promoter types based on motif frequency and distribution using Python, you can follow these steps:

1. Import the required libraries:

   import pandas as pd
   import numpy as np
   from sklearn.cluster import KMeans
   

2. Prepare your data:

   • Read the dataset containing motif frequency and distribution information for each promoter region into a Pandas DataFrame.
   • Make sure your dataset has columns for promoter regions, motif frequencies, and motif distributions on the + and - strands.

3. Perform clustering:

   • Select the features (motif frequencies and distributions) that you want to use for clustering.
   • Normalize the selected features using Min-Max scaling or another appropriate method.
   • Choose the number of clusters (k) you want to create.
   • Apply the K-means clustering algorithm to cluster the data based on the selected features.

     # Select features for clustering
     features = ['motif_frequency', 'positive_strand_distribution', 'negative_strand_distribution']
     
     # Normalize the features
     normalized_data = (data[features] - data[features].min()) / (data[features].max() - data[features].min())
     
     # Apply K-means clustering
     kmeans = KMeans(n_clusters=k)
     clusters = kmeans.fit_predict(normalized_data)
     

4. Analyze the clustering results:

   • Assign the cluster labels to the original dataset.

     data['cluster'] = clusters
     

   • Analyze the characteristics of each cluster, such as the average motif frequency and distribution, by grouping the data by cluster labels and calculating the mean values.

     cluster_means = data.groupby('cluster')[features].mean()
     

5. Visualize the clustering results:

   • Create visualizations, such as scatter plots or bar plots, to show the distribution of motifs in different clusters.
   • Plot the average motif frequency and distribution for each cluster.

     cluster_means.plot(kind='bar')
     

Remember to adjust the implementation based on your specific dataset and requirements. You may need to preprocess the data or use different clustering algorithms depending on your needs.

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