Mercurial > repos > bgruening > flexynesis

annotate flexynesis_utils.py @ 6:33816f44fc7d draft default tip

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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/flexynesis commit 6b520305ec30e6dc37eba92c67a5368cea0fc5ad
author bgruening
date Wed, 23 Jul 2025 07:49:41 +0000
parents 466b593fd87e
children
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525c661a7fdc planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/flexynesis commit b2463fb68d0ae54864d87718ee72f5e063aa4587
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1 #!/usr/bin/env python
525c661a7fdc planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/flexynesis commit b2463fb68d0ae54864d87718ee72f5e063aa4587
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3 import argparse
525c661a7fdc planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/flexynesis commit b2463fb68d0ae54864d87718ee72f5e063aa4587
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4 import os
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5 import sys
525c661a7fdc planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/flexynesis commit b2463fb68d0ae54864d87718ee72f5e063aa4587
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6 from pathlib import Path
525c661a7fdc planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/flexynesis commit b2463fb68d0ae54864d87718ee72f5e063aa4587
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7
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8 import pandas as pd
525c661a7fdc planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/flexynesis commit b2463fb68d0ae54864d87718ee72f5e063aa4587
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11 def read_data(data_input, index=False):
525c661a7fdc planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/flexynesis commit b2463fb68d0ae54864d87718ee72f5e063aa4587
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12 """Load CSV or TSV data file."""
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13 try:
525c661a7fdc planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/flexynesis commit b2463fb68d0ae54864d87718ee72f5e063aa4587
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14 file_ext = Path(data_input).suffix.lower()
525c661a7fdc planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/flexynesis commit b2463fb68d0ae54864d87718ee72f5e063aa4587
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15 sep = ',' if file_ext == '.csv' else '\t'
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16 index_col = 0 if index else None
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18 if file_ext in ['.csv', '.tsv', '.txt', '.tab', '.tabular']:
525c661a7fdc planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/flexynesis commit b2463fb68d0ae54864d87718ee72f5e063aa4587
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19 return pd.read_csv(data_input, sep=sep, index_col=index_col)
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20 else:
525c661a7fdc planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/flexynesis commit b2463fb68d0ae54864d87718ee72f5e063aa4587
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21 raise ValueError(f"Unsupported file extension: {file_ext}")
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22 except Exception as e:
525c661a7fdc planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/flexynesis commit b2463fb68d0ae54864d87718ee72f5e063aa4587
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23 raise ValueError(f"Error loading data from {data_input}: {e}") from e
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26 def binarize_mutations(df, gene_idx=1, sample_idx=2):
525c661a7fdc planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/flexynesis commit b2463fb68d0ae54864d87718ee72f5e063aa4587
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27 """
525c661a7fdc planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/flexynesis commit b2463fb68d0ae54864d87718ee72f5e063aa4587
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28 Binarize mutation data by creating a matrix of gene x sample with 1/0 values.
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29 """
525c661a7fdc planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/flexynesis commit b2463fb68d0ae54864d87718ee72f5e063aa4587
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30 # galaxy index is 1-based, convert to zero-based
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31 gene_idx -= 1
525c661a7fdc planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/flexynesis commit b2463fb68d0ae54864d87718ee72f5e063aa4587
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32 sample_idx -= 1
525c661a7fdc planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/flexynesis commit b2463fb68d0ae54864d87718ee72f5e063aa4587
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33 # check idx
525c661a7fdc planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/flexynesis commit b2463fb68d0ae54864d87718ee72f5e063aa4587
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34 if gene_idx >= len(df.columns) or sample_idx >= len(df.columns):
525c661a7fdc planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/flexynesis commit b2463fb68d0ae54864d87718ee72f5e063aa4587
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35 raise ValueError(f"Column indices out of bounds. DataFrame has {len(df.columns)} columns, "
525c661a7fdc planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/flexynesis commit b2463fb68d0ae54864d87718ee72f5e063aa4587
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36 f"but requested indices are {gene_idx} and {sample_idx}")
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37 if gene_idx == sample_idx:
525c661a7fdc planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/flexynesis commit b2463fb68d0ae54864d87718ee72f5e063aa4587
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38 raise ValueError("Gene and sample column indices must be different")
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39
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40 # Get column names by index
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41 gene_col = df.columns[gene_idx]
525c661a7fdc planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/flexynesis commit b2463fb68d0ae54864d87718ee72f5e063aa4587
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42 print(f"Using gene column: {gene_col} (index {gene_idx})")
525c661a7fdc planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/flexynesis commit b2463fb68d0ae54864d87718ee72f5e063aa4587
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43 sample_col = df.columns[sample_idx]
525c661a7fdc planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/flexynesis commit b2463fb68d0ae54864d87718ee72f5e063aa4587
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44 print(f"Using sample column: {sample_col} (index {sample_idx})")
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45
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46 # Check if columns contain data
525c661a7fdc planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/flexynesis commit b2463fb68d0ae54864d87718ee72f5e063aa4587
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47 if df[gene_col].isna().all():
525c661a7fdc planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/flexynesis commit b2463fb68d0ae54864d87718ee72f5e063aa4587
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48 raise ValueError(f"Gene column (index {gene_idx}) contains only NaN values.")
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49 if df[sample_col].isna().all():
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50 raise ValueError(f"Sample column (index {sample_idx}) contains only NaN values.")
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51
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52 # Group by gene and sample, count mutations
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53 mutation_counts = df.groupby([gene_col, sample_col]).size().reset_index(name='count')
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54
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55 # Create pivot table
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56 mutation_matrix = mutation_counts.pivot(index=gene_col, columns=sample_col, values='count').fillna(0)
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57
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58 # Binarize: convert any count > 0 to 1
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59 mutation_matrix[mutation_matrix > 0] = 1
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60
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61 return mutation_matrix
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63
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64 def make_data_dict(clin_path, omics_paths):
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65 """Read clinical and omics data files into a dictionary."""
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66 data = {}
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67
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68 # Read clinical data
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69 print(f"Reading clinical data from {clin_path}")
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70 try:
525c661a7fdc planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/flexynesis commit b2463fb68d0ae54864d87718ee72f5e063aa4587
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71 clin = read_data(clin_path, index=True)
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72
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73 if clin.empty:
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74 raise ValueError(f"Clinical file {clin_path} is empty")
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75 data['clin'] = clin
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76 print(f"Loaded clinical data: {clin.shape[0]} samples, {clin.shape[1]} features")
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77 except Exception as e:
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78 raise ValueError(f"Error reading clinical file {clin_path}: {e}")
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79
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80 # Read omics data
525c661a7fdc planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/flexynesis commit b2463fb68d0ae54864d87718ee72f5e063aa4587
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81 print(f"Reading omics data from {', '.join(omics_paths)}")
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82 for path in omics_paths:
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83 try:
525c661a7fdc planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/flexynesis commit b2463fb68d0ae54864d87718ee72f5e063aa4587
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84 name = os.path.splitext(os.path.basename(path))[0]
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85 df = read_data(path, index=True)
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86 if df.empty:
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87 print(f"Warning: Omics file {path} is empty, skipping")
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88 continue
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89 data[name] = df
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90 print(f"Loaded {name}: {df.shape[0]} features, {df.shape[1]} samples")
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91 except Exception as e:
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92 print(f"Warning: Error reading omics file {path}: {e}")
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93 continue
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94
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95 if len(data) == 1: # Only clinical data loaded
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96 raise ValueError("No omics data was successfully loaded")
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97
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98 return data
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99
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100
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101 def validate_data_consistency(data):
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102 """Validate that clinical and omics data have consistent samples."""
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103 clin_samples = set(data['clin'].index)
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104
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105 for name, df in data.items():
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106 if name == 'clin':
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107 continue
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108
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109 omics_samples = set(df.columns)
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110
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111 # Check for sample overlap
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112 common_samples = clin_samples.intersection(omics_samples)
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113 if len(common_samples) == 0:
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114 raise ValueError(f"No common samples between clinical data and {name}")
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115
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116 missing_in_omics = clin_samples - omics_samples
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117 missing_in_clin = omics_samples - clin_samples
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118
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119 if missing_in_omics:
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120 print(f"Warning: {len(missing_in_omics)} clinical samples not found in {name}")
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121 if missing_in_clin:
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122 print(f"Warning: {len(missing_in_clin)} samples in {name} not found in clinical data")
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123
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124 return True
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125
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126
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127 def split_and_save_data(data, ratio=0.7, output_dir='.'):
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128 """Split data into train/test sets and save to files."""
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129 # Validate data consistency first
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130 validate_data_consistency(data)
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131
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132 samples = data['clin'].index.tolist()
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133
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134 train_samples = list(pd.Series(samples).sample(frac=ratio, random_state=42))
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135 test_samples = list(set(samples) - set(train_samples))
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136
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137 train_data = {}
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138 test_data = {}
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139
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140 for key, df in data.items():
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141 try:
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142 if key == 'clin':
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143 train_data[key] = df.loc[df.index.intersection(train_samples)]
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144 test_data[key] = df.loc[df.index.intersection(test_samples)]
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145 else:
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146 train_data[key] = df.loc[:, df.columns.intersection(train_samples)]
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147 test_data[key] = df.loc[:, df.columns.intersection(test_samples)]
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148 except Exception as e:
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149 print(f"Error splitting data {key}: {e}")
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150 continue
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151
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152 # Create output directories
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153 os.makedirs(os.path.join(output_dir, 'train'), exist_ok=True)
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154 os.makedirs(os.path.join(output_dir, 'test'), exist_ok=True)
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155
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156 # Save train and test data
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157 for key in data.keys():
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158 try:
5
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159 train_data[key].to_csv(os.path.join(output_dir, 'train', f'{key}.tabular'), sep='\t')
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160 test_data[key].to_csv(os.path.join(output_dir, 'test', f'{key}.tabular'), sep='\t')
3
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161 except Exception as e:
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162 print(f"Error saving {key}: {e}")
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163 continue
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164
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165
6
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166 def validate_numeric_column(df, column_names, require_integer=False):
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167 """ Validate that a column(s) in the DataFrame contains numeric values. """
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168 if isinstance(column_names, str):
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169 # Handle comma-separated string: "col1,col2,col3"
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170 if ',' in column_names:
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171 column_names = [col.strip() for col in column_names.split(',')]
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172 else:
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diff changeset
173 # Single column name
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174 column_names = [column_names]
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175
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176 # Validate each column
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177 for column_name in column_names:
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178 if column_name not in df.columns:
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179 raise ValueError(f"Column '{column_name}' not found in DataFrame.")
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180
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diff changeset
181 try:
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182 numeric_col = pd.to_numeric(df[column_name], errors='raise')
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diff changeset
183 except Exception as e:
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184 raise ValueError(f"Non-numeric values found in column '{column_name}': {e}")
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185
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186 if require_integer:
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187 # Check if all non-null values are equivalent to integers
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188 non_null_values = numeric_col.dropna()
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189 if not (non_null_values == non_null_values.round()).all():
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190 raise ValueError(f"Column '{column_name}' contains non-integer numeric values.")
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191 print(f"Column '{column_name}': All values are integers or integer-equivalent floats.")
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192 else:
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193 print(f"Column '{column_name}': All values are numeric (integers and floats accepted).")
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194
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195
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196 def validate_survival(df, column_names):
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197 """Validate survival column(s) (integer)."""
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198 validate_numeric_column(df, column_names, require_integer=True)
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199
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200
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201 def validate_covariate(df, column_names):
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202 """Validate covariate column(s) (numeric)."""
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203 validate_numeric_column(df, column_names, require_integer=False)
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204
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205
3
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206 def main():
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207 parser = argparse.ArgumentParser(description='Flexynesis extra utilities')
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208
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209 parser.add_argument("--util", type=str, required=True,
6
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210 choices=['split', 'binarize', 'validate_survival', 'validate_covariate'],
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211 help="Utility function: 'split' for spiting data to train and test, 'binarize' for creating a binarized matrix from a mutation data, 'validate_survival' for validating survival data.")
3
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212
6
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213 # Arguments for split (clin also for validate_survival and validate_covariate)
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214 parser.add_argument('--clin', required=False,
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215 help='Path to clinical data CSV file (samples in rows)')
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216 parser.add_argument('--omics', required=False,
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217 help='Comma-separated list of omics CSV files (samples in columns)')
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218 parser.add_argument('--split', type=float, default=0.7,
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219 help='Train split ratio (default: 0.7)')
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220
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221 # Arguments for binarize
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222 parser.add_argument('--mutations', type=str, required=False,
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223 help='Path to mutation data CSV file (samples in rows, genes in columns)')
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224 parser.add_argument('--gene_idx', type=int, default=0,
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225 help='Column index for genes in mutation data (default: 0)')
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226 parser.add_argument('--sample_idx', type=int, default=1,
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227 help='Column index for samples in mutation data (default: 1)')
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228
6
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229 # Arguments for validate_survival and validate_covariate
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230 parser.add_argument('--clin_variable', type=str, required=False,
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231 help='Column name for clinical variable (e.g., death, SEX, ...)')
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232
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233 # common arguments (binarize and split)
3
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234 parser.add_argument('--out', default='.',
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235 help='Output directory (default: current directory)')
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236
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237 args = parser.parse_args()
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238
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239 try:
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240 # validate utility function
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241 if not args.util:
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242 raise ValueError("Utility function must be specified")
6
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243 if args.util not in ['split', 'binarize', 'validate_survival', 'validate_covariate']:
3
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244 raise ValueError(f"Invalid utility function: {args.util}")
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245
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246 if args.util == 'split':
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247 # Validate inputs
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248 if not args.clin:
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249 raise ValueError("Clinical data file must be provided")
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250 if not args.omics:
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251 raise ValueError("At least one omics file must be provided")
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252 if not os.path.isfile(args.clin):
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253 raise FileNotFoundError(f"Clinical file not found: {args.clin}")
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254 # Validate split ratio
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255 if not 0 < args.split < 1:
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256 raise ValueError(f"Split ratio must be between 0 and 1, got {args.split}")
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257
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258 elif args.util == 'binarize':
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259 # Validate mutation data file
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260 if not args.mutations:
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261 raise ValueError("Mutation data file must be provided")
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262 if not os.path.isfile(args.mutations):
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263 raise FileNotFoundError(f"Mutation data file not found: {args.mutations}")
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264 # Validate gene and sample indices
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265 if args.gene_idx < 0 or args.sample_idx < 0:
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266 raise ValueError("Gene and sample indices must be non-negative integers")
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267
6
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268 elif args.util == 'validate_survival' or args.util == 'validate_covariate':
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269 # Validate clinical data file
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270 if not args.clin:
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271 raise ValueError("Clinical data file must be provided")
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272 if not os.path.isfile(args.clin):
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273 raise FileNotFoundError(f"Clinical file not found: {args.clin}")
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274 # Validate survival event variable
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275 if not args.clin_variable:
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276 raise ValueError("Survival event variable must be specified")
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277
3
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278 # Create output directory if it doesn't exist
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279 if not os.path.exists(args.out):
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280 os.makedirs(args.out)
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281
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282 if args.util == 'split':
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283 # Parse omics files
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284 omics_files = [f.strip() for f in args.omics.split(',') if f.strip()]
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285 if not omics_files:
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286 raise ValueError("At least one omics file must be provided")
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287 # Check omics files exist
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288 for f in omics_files:
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289 if not os.path.isfile(f):
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290 raise FileNotFoundError(f"Omics file not found: {f}")
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291 data = make_data_dict(args.clin, omics_files)
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292 split_and_save_data(data, ratio=args.split, output_dir=args.out)
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293
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294 elif args.util == 'binarize':
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295 mutations_df = read_data(args.mutations, index=False)
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296 if mutations_df.empty:
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297 raise ValueError("Mutation data file is empty")
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298
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299 binarized_matrix = binarize_mutations(mutations_df, gene_idx=args.gene_idx, sample_idx=args.sample_idx)
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300 # Save binarized matrix
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301 output_file = os.path.join(args.out, 'binarized_mutations.tabular')
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302 binarized_matrix.to_csv(output_file, sep='\t')
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303 print(f"Binarized mutation matrix saved to {output_file}")
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304
6
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305 elif args.util == 'validate_survival':
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306 clin_df = read_data(args.clin, index=False)
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307 if clin_df.empty:
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308 raise ValueError("Clinical data file is empty")
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309
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310 # Validate survival event variable
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311 validate_survival(clin_df, args.clin_variable)
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312
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313 elif args.util == 'validate_covariate':
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314 clin_df = read_data(args.clin, index=False)
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315 if clin_df.empty:
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316 raise ValueError("Clinical data file is empty")
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317
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318 # Validate clinical variable
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319 validate_covariate(clin_df, args.clin_variable)
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320
3
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321 except Exception as e:
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322 print(f"Error: {e}", file=sys.stderr)
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323 sys.exit(1)
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324
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325
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326 if __name__ == "__main__":
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327 main()