feat: Add feature dispersion script

bin/Feature_Dispersion.py

@@ -0,0 +1,221 @@
+#!/usr/bin/env python
+
+import argparse
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+from ete3 import Tree
+
+def round_to_sig_figs(value, sig_figs):
+    if value == 0:
+        return 0
+    return round(value, sig_figs - int(np.floor(np.log10(abs(value)))) - 1)
+
+def calculate_phylogenetic_diversity(tree):
+    sum_branch_lengths = 0.0
+    for node in tree.traverse():
+        sum_branch_lengths += node.dist
+    return sum_branch_lengths
+
+def calculate_feature_counts(feature_file):
+    feature_df = pd.read_csv(feature_file, sep='\t', index_col=0)
+    feature_df.reset_index(inplace=True)
+    feature_df.rename(columns={'index': 'genome_id'}, inplace=True)
+
+    # Initialize lists to store data for the new dataframe
+    features = []
+    total_counts = []
+    genomes_list = []
+
+    # Iterate over each feature column
+    for feature_col in feature_df.columns[1:]:  # Exclude 'genome_id' column
+        # Collect the genomes where this feature is present into a comma-separated list
+        genomes_with_feature = feature_df[feature_df[feature_col] == 1]['genome_id'].tolist()
+        genomes_str = ','.join(genomes_with_feature)
+
+        # Calculate the total count of this feature across all genomes
+        total_count_feature = feature_df[feature_col].sum()
+
+        # Append data to lists
+        features.append(feature_col)
+        total_counts.append(total_count_feature)
+        genomes_list.append(genomes_str)
+
+    # Create a new dataframe from the collected data
+    features_df = pd.DataFrame({
+        'feature': features,
+        'total_count': total_counts,
+        'genomes_list': genomes_list
+    })
+
+    return features_df
+
+def verify_genome_ids(tree, feature_file, samplesheet_file=None):
+    feature_df = pd.read_csv(feature_file, sep='\t', index_col=0)
+    feature_genomes = set(feature_df.index)
+
+    if samplesheet_file:
+        samplesheet_df = pd.read_csv(samplesheet_file, usecols=[0], header=0, names=['genome_id'], skiprows=1)
+        samplesheet_genomes = set(samplesheet_df['genome_id'])
+    else:
+        samplesheet_genomes = set()
+
+    tree_genomes = set(tree.get_leaf_names())
+
+    missing_in_tree = feature_genomes.union(samplesheet_genomes) - tree_genomes
+
+    if missing_in_tree:
+        print(f"Error: The following genome IDs are missing in the phylogenetic tree: {', '.join(missing_in_tree)}")
+        exit(1)
+
+def generate_heatmap(output_df, output_heatmap):
+    bins = np.arange(0, 1.1, 0.1)
+    max_genome_count = output_df['Genome Count'].max()
+    genome_bins = np.linspace(0, max_genome_count, 11)  # Generate 11 edges to create 10 bins
+    genome_bins_labels = [f'{int(genome_bins[i]) + 1}-{int(genome_bins[i + 1])}' for i in range(len(genome_bins) - 1)]
+
+    heatmap_data = pd.DataFrame(0, index=genome_bins_labels, columns=bins)
+
+    for index, row in output_df.iterrows():
+        pd_ratio = row['PD Ratio']
+        genome_count = row['Genome Count']
+        bin_idx = np.digitize(pd_ratio, bins) - 1
+        genome_bin_idx = np.digitize(genome_count, genome_bins) - 1
+
+        # Ensure the indices are within the valid range
+        bin_idx = min(bin_idx, len(bins) - 1)
+        genome_bin_idx = min(genome_bin_idx, len(genome_bins_labels) - 1)
+
+        heatmap_data.iloc[genome_bin_idx, bin_idx] += 1
+
+    plt.figure(figsize=(12, 8))
+    sns.heatmap(np.log1p(heatmap_data), cmap="Reds", cbar_kws={'label': 'Number of Features (log scale)'}, annot=heatmap_data, fmt='g', linewidths=.5)
+    plt.xlabel('PD Ratio Bins')
+    plt.ylabel('Genome Count Bins')
+    plt.title('Heatmap of Features by PD Ratio and Genome Count')
+    plt.xticks(ticks=np.arange(0.5, len(bins), 1), labels=np.round(bins, 1))
+    plt.yticks(ticks=np.arange(0.5, len(genome_bins_labels), 1), labels=genome_bins_labels)
+    plt.gca().invert_yaxis()
+    plt.savefig(output_heatmap)
+    plt.close()
+
+
+
+
+
+def main(tree_file, feature_file, output_base, samplesheet_file=None, samplesheet_columns=None):
+    ref_tree = Tree(tree_file)
+
+    # Verify genome IDs
+    verify_genome_ids(ref_tree, feature_file, samplesheet_file)
+
+    # Calculate phylogenetic diversity
+    total_diversity = calculate_phylogenetic_diversity(ref_tree)
+
+    # Calculate feature counts
+    feature_distr = calculate_feature_counts(feature_file)
+
+    # Read samplesheet if provided
+    if samplesheet_file:
+        samplesheet_df = pd.read_csv(samplesheet_file, header=0)
+        available_columns = set(samplesheet_df.columns)
+        if 'genome_id' not in available_columns:
+            print("Error: 'genome_id' column is missing in the samplesheet.")
+            exit(1)
+
+        samplesheet_data = {}
+        for column in samplesheet_columns:
+            if column in available_columns:
+                samplesheet_data[column] = samplesheet_df[['genome_id', column]].set_index('genome_id')[column].to_dict()
+            else:
+                print(f"Warning: Column '{column}' not found in the samplesheet. Skipping.")
+    else:
+        samplesheet_data = {}
+
+    # Create an empty DataFrame to store the output
+    output_columns = [
+        'Feature Name',
+        'Total PD', 'Projected PD', 'PD Ratio',
+        'Genome Count','PD Ratio / Genome Count'
+    ]
+
+    # Add columns for each requested samplesheet column
+    for column in samplesheet_columns:
+        output_columns.append(f'{column} Distinct Values')
+        output_columns.append(f'PD Ratio / {column} Values')
+
+    output_df = pd.DataFrame(columns=output_columns)
+
+    # Iterate over each feature in feature_distr
+    for index, row in feature_distr.iterrows():
+        feature_name = row['feature']
+        genomes_list = row['genomes_list'].split(',')
+        genome_count = row['total_count']
+
+        # Only proceed if the feature is present in more than one genome
+        if len(genomes_list) > 1:
+            # Generate a list of genomes to keep (those that have the feature)
+            genomes_to_keep = [genome for genome in genomes_list if genome in ref_tree]
+
+            # Create a copy of the original tree with only the relevant genomes
+            projected_tree = ref_tree.copy()
+            projected_tree.prune(genomes_to_keep)
+
+            # Calculate phylogenetic diversity of the projected tree
+            projected_diversity = calculate_phylogenetic_diversity(projected_tree)
+
+            # Calculate the ratio of projected diversity to total diversity
+            ratio_diversity = projected_diversity / total_diversity
+
+            # Calculate the ratio of projected phylogenetic diversity to total count of genomes
+            genome_ratio_phylogenetic_diversity = ratio_diversity / genome_count
+
+            # Prepare the row for output
+            output_row = [
+                feature_name,
+                round_to_sig_figs(total_diversity, 4),
+                round_to_sig_figs(projected_diversity, 4),
+                round_to_sig_figs(ratio_diversity, 4),
+                round_to_sig_figs(genome_count, 4),
+                round_to_sig_figs(genome_ratio_phylogenetic_diversity, 4)
+            ]
+
+            # Add values for each requested samplesheet column
+            for column in samplesheet_columns:
+                if column in samplesheet_data:
+                    # Identify distinct values for the column
+                    distinct_values = set(samplesheet_data[column].get(genome, None) for genome in genomes_list if genome in samplesheet_data[column])
+                    distinct_values.discard(None)
+                    V = len(distinct_values)
+                    PD_ratio_per_V = ratio_diversity / V if V > 0 else 0
+                    output_row.extend([V, round_to_sig_figs(PD_ratio_per_V, 4)])
+                else:
+                    output_row.extend([None, None])
+
+            # Add the row to the output DataFrame
+            output_df.loc[index] = output_row
+
+    output_df_sorted = output_df.sort_values(by='PD Ratio / Genome Count')
+
+    # Save the output dataframe to a TSV file
+    output_df_sorted.to_csv(f"{output_base}.tsv", sep='\t', index=False)
+
+    # Generate the heatmap
+    generate_heatmap(output_df_sorted, f"{output_base}.png")
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description='Calculate feature statistics based on phylogenetic tree and genus information.')
+    parser.add_argument('--tree_file', type=str, required=True, help='Path to the Newick tree file')
+    parser.add_argument('--feature_file', type=str, required=True, help='Path to the feature presence/absence file')
+    parser.add_argument('--output_base', type=str, required=True, help='Base name for the output files (without extension)')
+    parser.add_argument('--samplesheet_file', type=str, help='Path to the file mapping genome IDs to other properties')
+    parser.add_argument('--samplesheet_columns', type=str, help='Comma-separated list of columns to process from the samplesheet')
+    args = parser.parse_args()
+
+    if args.samplesheet_columns:
+        samplesheet_columns = args.samplesheet_columns.split(',')
+    else:
+        samplesheet_columns = []
+
+    main(args.tree_file, args.feature_file, args.output_base, args.samplesheet_file, samplesheet_columns)

conf/modules.config

@@ -458,6 +458,14 @@ process {
             saveAs: { filename -> filename.equals('versions.yml') ? null : filename }
         ]
     }
+
+    withName: FEATURE_DISPERSION {
+        publishDir = [
+            path: { "${params.outdir}/phylogenomics/feature_dispersion" },
+            mode: params.publish_dir_mode,
+        ]
+    }
+
     // Recombination
     withName: VERTICALL_PAIRWISE {
         ext.prefix = { "cluster_${cluster}" }