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annotate CSP2/bin/chooseRefs.py @ 39:93393808f415

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"planemo upload"
author rliterman
date Thu, 12 Dec 2024 13:53:15 -0500
parents 893a6993efe3
children
rev   line source
rliterman@0 1 #!/usr/bin/env python3
rliterman@0 2
rliterman@0 3 import numpy as np
rliterman@0 4 import os
rliterman@0 5 import pandas as pd
rliterman@0 6 import sys
rliterman@0 7 from sklearn.cluster import KMeans
rliterman@0 8 from sklearn.metrics import silhouette_score
rliterman@0 9 import scipy.stats
rliterman@0 10 from itertools import combinations
rliterman@0 11 from Bio import SeqIO
rliterman@0 12 import argparse
rliterman@0 13
rliterman@0 14 def getOptimalK(data, ref_count):
rliterman@0 15
rliterman@0 16 silhouette_scores = []
rliterman@0 17
rliterman@0 18 kmeans_1 = KMeans(n_clusters=1, random_state=0, n_init='auto').fit(data)
rliterman@0 19 kmeans_2 = KMeans(n_clusters=2, random_state=0, n_init='auto').fit(data)
rliterman@0 20
rliterman@0 21 # Compare 1 vs. 2
rliterman@0 22 inertia_1 = kmeans_1.inertia_
rliterman@0 23 inertia_2 = kmeans_2.inertia_
rliterman@0 24 if inertia_1 > inertia_2:
rliterman@0 25 negative_movements = 1
rliterman@0 26 else:
rliterman@0 27 negative_movements = 0
rliterman@0 28
rliterman@0 29 # Add k2 data
rliterman@0 30 labels = kmeans_2.labels_
rliterman@0 31 score = silhouette_score(data, labels)
rliterman@0 32 silhouette_scores.append(score)
rliterman@0 33 prev_score = score
rliterman@0 34
rliterman@0 35 for k in range(3, ref_count + 3):
rliterman@0 36 kmeans = KMeans(n_clusters=k, random_state=0, n_init='auto').fit(data)
rliterman@0 37 labels = kmeans.labels_
rliterman@0 38 score = silhouette_score(data, labels)
rliterman@0 39
rliterman@0 40 if score < prev_score:
rliterman@0 41 negative_movements += 1
rliterman@0 42 else:
rliterman@0 43 negative_movements = 0
rliterman@0 44
rliterman@0 45 silhouette_scores.append(score)
rliterman@0 46
rliterman@0 47 # Stop if two consecutive negative movements occur
rliterman@0 48 if negative_movements == 2:
rliterman@0 49 break
rliterman@0 50
rliterman@0 51 prev_score = score
rliterman@0 52
rliterman@0 53 if (inertia_1 < inertia_2) & (silhouette_scores[0] > silhouette_scores[1]):
rliterman@0 54 optimal_k = 1
rliterman@0 55 else:
rliterman@0 56 optimal_k = np.argmax(silhouette_scores) + 2
rliterman@0 57
rliterman@0 58 return optimal_k
rliterman@0 59
rliterman@0 60 def fasta_info(file_path):
rliterman@0 61 records = list(SeqIO.parse(file_path, 'fasta'))
rliterman@0 62 contig_count = int(len(records))
rliterman@0 63 lengths = sorted([len(record) for record in records], reverse=True)
rliterman@0 64 assembly_bases = sum(lengths)
rliterman@0 65
rliterman@0 66 cumulative_length = 0
rliterman@0 67 n50 = None
rliterman@0 68 n90 = None
rliterman@0 69 l50 = None
rliterman@0 70 l90 = None
rliterman@0 71
rliterman@0 72 for i, length in enumerate(lengths, start=1):
rliterman@0 73 cumulative_length += length
rliterman@0 74 if cumulative_length >= assembly_bases * 0.5 and n50 is None:
rliterman@0 75 n50 = length
rliterman@0 76 l50 = i
rliterman@0 77 if cumulative_length >= assembly_bases * 0.9 and n90 is None:
rliterman@0 78 n90 = length
rliterman@0 79 l90 = i
rliterman@0 80 if n50 is not None and n90 is not None:
rliterman@0 81 break
rliterman@0 82
rliterman@0 83 return [file_path,contig_count,assembly_bases,n50,n90,l50,l90]
rliterman@0 84
rliterman@0 85 parser = argparse.ArgumentParser(description='Choose reference isolates based on FASTA metrics and mean distances.')
rliterman@27 86 parser.add_argument('--ref_count', type=int, default=1, help='Number of reference isolates to select')
rliterman@0 87 parser.add_argument('--mash_triangle_file', type=str, help='Path to the mash triangle file')
rliterman@28 88 parser.add_argument('--trim_name', nargs='?', const="", default="", type=str, help='Trim name')
rliterman@0 89 args = parser.parse_args()
rliterman@0 90
rliterman@0 91 ref_count = args.ref_count
rliterman@0 92 mash_triangle_file = os.path.abspath(args.mash_triangle_file)
rliterman@0 93 trim_name = args.trim_name
rliterman@0 94 ref_file = os.path.join(os.path.dirname(mash_triangle_file), 'CSP2_Ref_Selection.tsv')
rliterman@0 95
rliterman@0 96 # Get Sample IDs
rliterman@0 97 sample_df = pd.read_csv(mash_triangle_file, sep='\t', usecols=[0], skip_blank_lines=True).dropna()
rliterman@0 98 sample_df = sample_df[sample_df[sample_df.columns[0]].str.strip() != '']
rliterman@0 99 sample_df.columns = ['Path']
rliterman@0 100 sample_df['Isolate_ID'] = [os.path.splitext(os.path.basename(file))[0].replace(trim_name, '') for file in sample_df[sample_df.columns[0]].tolist()]
rliterman@0 101 assembly_names = [os.path.splitext(os.path.basename(file))[0].replace(trim_name, '') for file in sample_df[sample_df.columns[0]].tolist()]
rliterman@0 102 num_isolates = sample_df.shape[0]
rliterman@0 103
rliterman@0 104 # Get FASTA metrics
rliterman@0 105 metrics_df = pd.DataFrame(sample_df['Path'].apply(fasta_info).tolist(), columns=['Path', 'Contigs', 'Length', 'N50','N90','L50','L90'])
rliterman@0 106 metrics_df['Assembly_Bases_Zscore'] = metrics_df['Length'].transform(scipy.stats.zscore).astype('float').round(3).fillna(0)
rliterman@0 107 metrics_df['Contig_Count_Zscore'] = metrics_df['Contigs'].transform(scipy.stats.zscore).astype('float').round(3).fillna(0)
rliterman@0 108 metrics_df['N50_Zscore'] = metrics_df['N50'].transform(scipy.stats.zscore).astype('float').round(3).fillna(0)
rliterman@0 109
rliterman@0 110 # Find outliers
rliterman@0 111 inlier_df = metrics_df.loc[(metrics_df['N50_Zscore'] > -3) &
rliterman@0 112 (metrics_df['Assembly_Bases_Zscore'] < 3) &
rliterman@0 113 (metrics_df['Assembly_Bases_Zscore'] > -3) &
rliterman@0 114 (metrics_df['Contig_Count_Zscore'] < 3)]
rliterman@0 115
rliterman@0 116 inlier_count = inlier_df.shape[0]
rliterman@0 117 inlier_isolates = [os.path.splitext(os.path.basename(file))[0].replace(trim_name, '') for file in inlier_df[inlier_df.columns[0]].tolist()]
rliterman@0 118
rliterman@0 119 # If not enough or just enough inliers, script is done
rliterman@0 120 if ref_count > inlier_count:
rliterman@0 121 sys.exit("Error: Fewer inliers than requested references?")
rliterman@0 122 elif ref_count == inlier_count:
rliterman@0 123 print(",".join(inlier_df['Path'].tolist()))
rliterman@0 124 sys.exit(0)
rliterman@0 125
rliterman@0 126 # Left join metrics_df and inlier_df
rliterman@0 127 sample_df = inlier_df.merge(sample_df, on = "Path", how='left')[['Isolate_ID','Path','Contigs','Length','N50','N90','L50','L90','N50_Zscore']]
rliterman@0 128
rliterman@0 129 # Create distance matrix
rliterman@0 130 with open(mash_triangle_file) as mash_triangle:
rliterman@0 131 a = np.zeros((num_isolates, num_isolates))
rliterman@0 132 mash_triangle.readline()
rliterman@0 133 mash_triangle.readline()
rliterman@0 134 idx = 1
rliterman@0 135 for line in mash_triangle:
rliterman@0 136 tokens = line.split()
rliterman@0 137 distances = [float(token) for token in tokens[1:]]
rliterman@0 138 a[idx, 0: len(distances)] = distances
rliterman@0 139 a[0: len(distances), idx] = distances
rliterman@0 140 idx += 1
rliterman@0 141
rliterman@0 142 dist_df = pd.DataFrame(a, index=assembly_names, columns=assembly_names).loc[inlier_isolates,inlier_isolates]
rliterman@0 143 # Get mean distances after masking diagonal
rliterman@0 144 mask = ~np.eye(dist_df.shape[0], dtype=bool)
rliterman@0 145 mean_distances = dist_df.where(mask).mean().reset_index()
rliterman@0 146 mean_distances.columns = ['Isolate_ID', 'Mean_Distance']
rliterman@0 147
rliterman@0 148 sample_df = sample_df.merge(mean_distances, on='Isolate_ID', how='left')
rliterman@0 149 sample_df['Mean_Distance_Zscore'] = sample_df['Mean_Distance'].transform(scipy.stats.zscore).astype('float').round(3)
rliterman@0 150 sample_df['Base_Score'] = sample_df['N50_Zscore'] - sample_df['Mean_Distance_Zscore'].fillna(0)
rliterman@0 151
rliterman@0 152 if ref_count == 1:
rliterman@0 153 print(",".join(sample_df.nlargest(1, 'Base_Score')['Path'].tolist()))
rliterman@0 154 sys.exit(0)
rliterman@0 155
rliterman@0 156 optimal_k = getOptimalK(dist_df, ref_count)
rliterman@0 157
rliterman@0 158 if optimal_k == 1:
rliterman@0 159 print(",".join(sample_df.nlargest(ref_count, 'Base_Score')['Path'].tolist()))
rliterman@0 160 sys.exit(0)
rliterman@0 161
rliterman@0 162 kmeans = KMeans(n_clusters=optimal_k, random_state=0,n_init='auto').fit(dist_df)
rliterman@0 163 clusters = kmeans.labels_
rliterman@0 164
rliterman@0 165 cluster_df = pd.DataFrame({'Isolate_ID': dist_df.index, 'Cluster': clusters}).merge(sample_df, on='Isolate_ID',how='left')
rliterman@0 166 cluster_counts = cluster_df['Cluster'].value_counts().reset_index()
rliterman@0 167 cluster_counts.columns = ['Cluster', 'count']
rliterman@0 168 cluster_counts['Prop'] = cluster_counts['count'] / cluster_counts['count'].sum()
rliterman@0 169 cluster_df = cluster_df.merge(cluster_counts[['Cluster', 'Prop']], on='Cluster')
rliterman@0 170
rliterman@0 171 # Grab top ref
rliterman@0 172 final_ref_df = cluster_df.nlargest(1, 'Base_Score')
rliterman@0 173 refs_chosen = final_ref_df['Isolate_ID'].tolist()
rliterman@0 174
rliterman@0 175 possible_refs = cluster_df.loc[~cluster_df['Isolate_ID'].isin(refs_chosen)].copy()
rliterman@0 176
rliterman@0 177 while len(refs_chosen) < ref_count:
rliterman@0 178 possible_refs['Mean_Ref_Distance'] = possible_refs['Isolate_ID'].apply(lambda isolate_id: np.mean(dist_df.loc[isolate_id, refs_chosen].values))
rliterman@0 179 possible_refs['Mean_Ref_Distance_Zscore'] = possible_refs['Mean_Ref_Distance'].transform(scipy.stats.zscore).astype('float').round(3)
rliterman@0 180 possible_refs['Sort_Score'] = possible_refs.apply(lambda row: (row['Base_Score'] + row['Mean_Ref_Distance_Zscore']) if row['Mean_Ref_Distance_Zscore'] <= 0 else (row['Base_Score'] + (row['Mean_Ref_Distance_Zscore']*row['Prop'])), axis=1)
rliterman@0 181
rliterman@0 182 final_ref_df = pd.concat([final_ref_df, possible_refs.nlargest(1, 'Sort_Score').drop(['Sort_Score','Mean_Ref_Distance','Mean_Ref_Distance_Zscore'],axis=1)])
rliterman@0 183 refs_chosen = final_ref_df['Isolate_ID'].tolist()
rliterman@0 184 possible_refs = possible_refs.loc[~possible_refs['Isolate_ID'].isin(refs_chosen)].copy()
rliterman@0 185
rliterman@0 186 non_ref_df = cluster_df.loc[~cluster_df['Isolate_ID'].isin(refs_chosen)].sort_values('Base_Score', ascending=False)
rliterman@0 187 non_ref_df['Is_Ref'] = False
rliterman@0 188 final_ref_df['Is_Ref'] = True
rliterman@39 189
rliterman@39 190 with open(ref_file, 'w') as f:
rliterman@39 191 pd.concat([final_ref_df, non_ref_df]).reset_index(drop=True).to_csv(f, index=False, sep="\t")
rliterman@0 192
rliterman@0 193 print(",".join(final_ref_df['Path'].tolist()))