File size: 5,758 Bytes
ef78b20 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 |
#!/usr/bin/env python3
"""
Consolidated script to diagnose and fix h5ad files for transcriptformer.
This script performs a series of checks to validate an AnnData object and
automatically applies fixes for common issues, preparing the data for
inference with transcriptformer.
Usage:
python preprocess_adata.py <input_h5ad_file> <output_h5ad_file>
"""
import sys
import os
import numpy as np
import anndata as ad
import scanpy as sc
from pathlib import Path
def preprocess_adata(input_path, output_path):
"""
Diagnose and fix an h5ad file for transcriptformer compatibility.
"""
print(f"🚀 Starting preprocessing for: {input_path}")
print("=" * 70)
# 1. Load Data
print("📖 1. Loading AnnData object...")
if not os.path.exists(input_path):
print(f"❌ ERROR: Input file not found: {input_path}")
return False
try:
adata = ad.read_h5ad(input_path)
print(f"✅ Loaded: {adata.shape[0]} cells × {adata.shape[1]} genes")
except Exception as e:
print(f"❌ ERROR: Could not load AnnData file. Reason: {e}")
return False
original_shape = adata.shape
# 2. Run Diagnostics
print("\n🔬 2. Running Diagnostics...")
issues_found = []
# Check for NaN/Inf values
has_nan = np.isnan(adata.X.data).any() if hasattr(adata.X, 'data') else np.isnan(adata.X).any()
has_inf = np.isinf(adata.X.data).any() if hasattr(adata.X, 'data') else np.isinf(adata.X).any()
if has_nan: issues_found.append("NaN values found in data matrix.")
if has_inf: issues_found.append("Infinite values found in data matrix.")
print(f" - NaN/Inf values: {'❌ Found' if has_nan or has_inf else '✅ None'}")
# Check for unique gene indices
if adata.var.index.nunique() < len(adata.var.index):
issues_found.append("Duplicate gene indices (var_names) found.")
print(" - Duplicate gene indices: ❌ Found")
else:
print(" - Duplicate gene indices: ✅ Unique")
# Check for ensembl_id column
if 'ensembl_id' not in adata.var.columns:
issues_found.append("'ensembl_id' column missing in var.")
print(" - 'ensembl_id' column: ❌ Missing")
else:
print(" - 'ensembl_id' column: ✅ Present")
# Check for zero-expression genes
genes_before_filter = adata.n_vars
sc.pp.filter_genes(adata, min_cells=1)
if adata.n_vars < genes_before_filter:
num_removed = genes_before_filter - adata.n_vars
issues_found.append(f"{num_removed} genes with zero expression found.")
print(f" - Zero-expression genes: ❌ Found ({num_removed} genes)")
else:
print(" - Zero-expression genes: ✅ None")
# Restore original object for fixing step
adata = ad.read_h5ad(input_path)
# 3. Apply Fixes
print("\n🔧 3. Applying Fixes...")
fixes_applied = []
# Fix: Ensure var_names are unique
if adata.var.index.nunique() < len(adata.var.index):
adata.var_names_make_unique()
fixes_applied.append("Made var_names unique using .var_names_make_unique()")
print(" - ✅ Made gene indices (var_names) unique.")
else:
print(" - ✅ Gene indices are already unique.")
# Fix: Add ensembl_id column if it's missing
if 'ensembl_id' not in adata.var.columns:
print(" - Adding 'ensembl_id' column from var.index.")
adata.var['ensembl_id'] = adata.var.index
fixes_applied.append("Added 'ensembl_id' column from var.index.")
else:
print(" - ✅ 'ensembl_id' column already exists.")
# Fix: Filter out genes with zero expression
genes_before_filter = adata.n_vars
sc.pp.filter_genes(adata, min_cells=1)
if adata.n_vars < genes_before_filter:
num_removed = genes_before_filter - adata.n_vars
fixes_applied.append(f"Removed {num_removed} genes with no expression.")
print(f" - ✅ Removed {num_removed} zero-expression genes.")
else:
print(" - ✅ No zero-expression genes to remove.")
# 4. Save Processed File
print("\n💾 4. Saving Processed File...")
try:
adata.write(output_path)
print(f" - ✅ Successfully saved to: {output_path}")
except Exception as e:
print(f"❌ ERROR: Could not save file. Reason: {e}")
return False
# 5. Final Summary
print("\n📋 5. Summary")
print("-" * 70)
print(f" - Original shape: {original_shape[0]} cells × {original_shape[1]} genes")
print(f" - Final shape: {adata.shape[0]} cells × {adata.shape[1]} genes")
print("\n - Issues Found:")
if issues_found:
for issue in issues_found:
print(f" - {issue}")
else:
print(" - None")
print("\n - Fixes Applied:")
if fixes_applied:
for fix in fixes_applied:
print(f" - {fix}")
else:
print(" - None")
print("\n🎉 Preprocessing complete!")
return True
def main():
if len(sys.argv) != 3:
print("Usage: python preprocess_adata.py <input_h5ad_file> <output_h5ad_file>")
sys.exit(1)
input_path = sys.argv[1]
output_path = sys.argv[2]
if os.path.abspath(input_path) == os.path.abspath(output_path):
print("❌ ERROR: Input and output paths cannot be the same.")
sys.exit(1)
if os.path.exists(output_path):
response = input(f"⚠️ Output file already exists: {output_path}\nOverwrite? (y/N): ")
if response.lower() != 'y':
print("Operation cancelled.")
sys.exit(1)
success = preprocess_adata(input_path, output_path)
if not success:
sys.exit(1)
if __name__ == "__main__":
main() |