| import anndata as ad |
| import pandas as pd |
| import os |
| import glob |
|
|
| h5ad_dir = "C:/Users/Swamy/OneDrive - Zafrens/Desktop/Z-Screen Deepdive/Shared_Source_Data_June2026/external_reference/scperturb_downloads" |
| files = glob.glob(os.path.join(h5ad_dir, "*.h5ad")) |
|
|
| metadata_records = [] |
|
|
| for f in files: |
| try: |
| adata = ad.read_h5ad(f, backed='r') |
| dataset_name = os.path.basename(f) |
| |
| |
| cell_lines = adata.obs['cell_line'].unique().tolist() if 'cell_line' in adata.obs else [] |
| perturbations = adata.obs['perturbation'].unique().tolist() if 'perturbation' in adata.obs else [] |
| n_cells = adata.n_obs |
| n_vars = adata.n_vars |
| |
| metadata_records.append({ |
| "dataset_id": dataset_name.replace(".h5ad", ""), |
| "file": dataset_name, |
| "cell_lines": ", ".join([str(x) for x in cell_lines]), |
| "num_perturbations": len(perturbations), |
| "num_cells": n_cells, |
| "num_genes": n_vars |
| }) |
| print(f"Processed {dataset_name}") |
| except Exception as e: |
| print(f"Error reading {f}: {e}") |
|
|
| if metadata_records: |
| df = pd.DataFrame(metadata_records) |
| out_csv = os.path.join(h5ad_dir, "scperturb_summary_metadata.csv") |
| df.to_csv(out_csv, index=False) |
| print(f"\nMetadata successfully extracted to {out_csv}") |
| print(df) |
| else: |
| print("No complete metadata could be extracted yet.") |
|
|
