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Metadata selection is followed by a small-column sparse extraction; +no full expression matrix or corpus-wide expression search is constructed. +""" +from __future__ import annotations +import argparse +from collections import defaultdict +import hashlib +import json +from pathlib import Path +import time + +import numpy as np +import pandas as pd +import pyarrow.parquet as pq +import zarr + +ROOT = Path(__file__).resolve().parent +M7 = Path('/home/sj_server_1/PBISC/immun-pbmc-fm/model_7_no_annotation_sh_hwang/data') +E2 = M7 / 'e2_full_source' +BLOCK = Path('/home/sj_server_1/PBISC/PBISC-data/single-cell-data-block-blood') +KEY = ['dataset_id', 'donor_id', 'sample_id'] +LABELS = {'C1Q+ CD16 monocyte', 'CD16+_Monocyte_C1QAhi', 'flagged_intermediate_monocyte_C1QAhi'} +LABEL_COLS = ['label_original', 'label_original_raw_rebuilt'] + [f'source_celltype_l{i}' for i in range(1, 5)] +GENES = ['C1QA', 'CD163', 'CD68', 'WARS1', 'LGALS3'] +IDS = ['ENSG00000173372', 'ENSG00000177575', 'ENSG00000129226', 'ENSG00000140105', 'ENSG00000131981'] +COLS = np.array([291, 11665, 15192, 13485, 13194]) +zarr.config.set({'async.concurrency': 2, 'threading.max_workers': 2}) + +def sha(p): + h = hashlib.sha256() + with Path(p).open('rb') as f: + for b in iter(lambda: f.read(1 << 20), b''): h.update(b) + return h.hexdigest() + +def receipt(p): + p = Path(p) + return {'path': str(p), 'bytes': p.stat().st_size, 'sha256': sha(p)} + +def write_json(p, x): + Path(p).write_text(json.dumps(x, indent=2) + '\n') + +def prepare(): + records, selected, shards = [], [], [] + sp = M7 / 'e2_full_index/metadata/sample_index.parquet' + si = pd.read_parquet(sp) + records.append(receipt(sp)) + for k in KEY: si[k] = si[k].astype(str) + assert not si.duplicated(KEY).any() + files = [p for p in sorted((E2 / 'metadata/obs_sc.parquet').glob('*.parquet')) + if p.name[:4] in ['006_', '009_', '065_', '200_'] and not p.name.endswith('__strict_overlay.parquet')] + assert len(files) == 89 + total_rows = 0 + for i, ep in enumerate(files): + bp = BLOCK / 'metadata/obs_sc.parquet' / ep.name + eq, bq = pq.ParquetFile(ep), pq.ParquetFile(bp) + assert eq.metadata.num_rows == bq.metadata.num_rows + cols = [c for c in LABEL_COLS if c in bq.schema.names] + bi = iter(bq.iter_batches(batch_size=32768, columns=['source_row_index'] + cols, use_threads=False)) + offset = 0 + local = [] + for eb in eq.iter_batches(batch_size=32768, columns=KEY + ['source_row_index'], use_threads=False): + e, b = eb.to_pandas(), next(bi).to_pandas() + assert len(e) == len(b) and np.array_equal(e.source_row_index, b.source_row_index) + hits = b[cols].isin(LABELS) + keep = hits.any(axis=1).to_numpy() + if keep.any(): + v = e.loc[keep].copy() + for k in KEY: v[k] = v[k].astype(str) + v['shard_row'] = np.flatnonzero(keep) + offset + v['source_label'] = b.loc[keep, cols].where(hits.loc[keep]).bfill(axis=1).iloc[:, 0].to_numpy() + v = v.merge(si[KEY + ['split', 'sample_key']], on=KEY, how='left', validate='many_to_one') + assert v.split.notna().all() + v = v[v.split == 'train'].copy() + v['shard_id'] = ep.stem + v['flagged'] = v.source_label.str.startswith('flagged_') + local.append(v) + offset += len(e) + assert next(bi, None) is None and offset == eq.metadata.num_rows + total_rows += offset + if local: selected.extend(local) + records.extend([receipt(ep), receipt(bp)]) + shards.append({'shard_id': ep.stem, 'rows': offset}) + print(f'metadata {i+1}/{len(files)}', flush=True) + v = pd.concat(selected, ignore_index=True) + assert len(v) == 34122 and int(v.flagged.sum()) == 903 + assert not v.duplicated(['shard_id', 'shard_row']).any() + assert total_rows == 20609391 + expected = {'006': 2726, '009': 3058, '065': 25268, '200': 2167} + assert v[~v.flagged].groupby(v.dataset_id.str[:3]).size().to_dict() == expected + v.to_parquet(ROOT / 'selected_training_candidates.parquet', index=False) + plans = [] + for sid, d in v.groupby('shard_id', sort=True): + zp = BLOCK / 'counts/sc_counts' / (sid + '.zarr') + g = zarr.open_group(str(zp), mode='r') + for f in sorted(zp.glob('**/zarr.json')): records.append(receipt(f)) + attrs = dict(g.attrs) + assert attrs['shape'][0] == next(s['rows'] for s in shards if s['shard_id'] == sid) + mask = np.asarray(g['measurement_mask'][:])[COLS] + assert mask.shape == (5,) and mask.sum() == 5 + ptr = np.asarray(g['indptr'][:], dtype=np.int64) + rows = d.shard_row.to_numpy() + chunk = g['indices'].chunks[0] + assert chunk == g['data'].chunks[0] == 1000000 + chunks = sorted(set(c for r in rows for c in range(int(ptr[r]) // chunk, (int(ptr[r+1]) - 1) // chunk + 1))) + paths = [zp / a / 'c' / str(c) for c in chunks for a in ['indices', 'data']] + comp = sum(p.stat().st_size for p in paths if p.exists()) + plans.append({'shard_id': sid, 'candidate_cells': len(d), 'chunks': chunks, + 'compressed_bytes_to_read': comp, 'decompressed_bytes_upper_bound': len(chunks) * chunk * 12, + 'candidate_nnz': int((ptr[rows+1] - ptr[rows]).sum()), + 'measured': mask.tolist(), 'count_source': attrs['source_count_matrix']}) + write_json(ROOT / 'extraction_plan.json', {'candidate_cells': len(v), 'unflagged': int((~v.flagged).sum()), + 'flagged': int(v.flagged.sum()), 'source_rows_metadata_only': total_rows, + 'compressed_bytes_to_read': sum(p['compressed_bytes_to_read'] for p in plans), + 'decompressed_bytes_upper_bound': sum(p['decompressed_bytes_upper_bound'] for p in plans), + 'plans': plans, 'metadata_sources': records}) + print(json.dumps({'candidate_cells': len(v), 'compressed_GiB': sum(p['compressed_bytes_to_read'] for p in plans)/2**30, + 'decoded_GiB': sum(p['decompressed_bytes_upper_bound'] for p in plans)/2**30}), flush=True) + +def extract(): + plan = json.loads((ROOT / 'extraction_plan.json').read_text()) + v = pd.read_parquet(ROOT / 'selected_training_candidates.parquet') + out = np.zeros((len(v), 5), dtype=np.int64) + row_sum = np.zeros(len(v), dtype=np.int64) + chunk_receipts, checks = [], [] + for i, pp in enumerate(plan['plans']): + sid = pp['shard_id'] + d = v[v.shard_id == sid].sort_values('shard_row') + zp = BLOCK / 'counts/sc_counts' / (sid + '.zarr') + g = zarr.open_group(str(zp), mode='r') + assert np.array_equal(np.asarray(g['measurement_mask'][:])[COLS], np.ones(5)) + ptr = np.asarray(g['indptr'][:], dtype=np.int64) + segs = defaultdict(list) + size = 1000000 + for oi, r in zip(d.index, d.shard_row): + start, end = int(ptr[r]), int(ptr[r+1]) + while start < end: + c = start // size + stop = min(end, (c+1)*size) + segs[c].append((int(oi), start-c*size, stop-c*size)) + start = stop + assert sorted(segs) == pp['chunks'] + source_nnz = 0 + for c, sections in sorted(segs.items()): + start, end = c*size, min((c+1)*size, g['indices'].shape[0]) + ids = np.asarray(g['indices'][start:end], dtype=np.int32) + values = np.asarray(g['data'][start:end], dtype=np.int64) + assert len(ids) == len(values) and (values >= 0).all() + for oi, a, b in sections: + ix, vals = ids[a:b], values[a:b] + source_nnz += b-a + row_sum[oi] += vals.sum() + for j, col in enumerate(COLS): out[oi, j] += vals[ix == col].sum() + for arr in ['indices', 'data']: + fp = zp / arr / 'c' / str(c) + if fp.exists(): chunk_receipts.append(receipt(fp)) + assert source_nnz == pp['candidate_nnz'] + checks.append({'shard_id': sid, 'candidate_cells': len(d), 'selected_nnz': source_nnz, + 'unique_chunks': len(segs), 'all_markers_measured': True}) + print(f'expression {i+1}/{len(plan["plans"])} {sid[:3]} n={len(d)}', flush=True) + assert (out >= 0).all() and (row_sum > 0).all() + for i, gene in enumerate(GENES): v[gene] = out[:, i] + v['total_counts_20097'] = row_sum + v['markers_detected'] = (out > 0).sum(axis=1) + v['all_five_detected'] = (out > 0).all(axis=1) + v['c1qa_plus_at_least_three_others'] = (out[:, 0] > 0) & ((out[:, 1:] > 0).sum(axis=1) >= 3) + v.to_parquet(ROOT / 'candidate_marker_counts.parquet', index=False) + summarize(v, plan, checks, chunk_receipts) + +def summarize(v, plan, checks, chunk_receipts): + groups = [] + for flag, fs in v.groupby('flagged'): + for name, d in [('ALL', fs)] + list(fs.groupby(fs.dataset_id.str[:3], sort=True)): + base = {'dataset': name, 'flagged': bool(flag), 'cells': len(d), + 'samples': d.sample_key.nunique(), 'dataset_scoped_donors': len(d[KEY[:2]].drop_duplicates())} + for gene in GENES: + base[gene + '_detected'] = int((d[gene] > 0).sum()) + base[gene + '_fraction'] = float((d[gene] > 0).mean()) + base[gene + '_mean_raw_counts'] = float(d[gene].mean()) + for k in range(6): base[f'exactly_{k}_detected'] = int((d.markers_detected == k).sum()) + for name2 in ['all_five_detected', 'c1qa_plus_at_least_three_others']: + q = d[d[name2]] + base[name2] = len(q) + base[name2 + '_samples'] = q.sample_key.nunique() + base[name2 + '_dataset_scoped_donors'] = len(q[KEY[:2]].drop_duplicates()) + groups.append(base) + pd.DataFrame(groups).to_csv(ROOT / 'summary.csv', index=False) + sample = v.groupby(KEY + ['sample_key', 'flagged'], dropna=False).agg( + candidate_cells=('shard_row', 'size'), all_five=('all_five_detected', 'sum'), + c1qa_plus_three_others=('c1qa_plus_at_least_three_others', 'sum')).reset_index() + sample.to_csv(ROOT / 'sample_support.csv', index=False) + labels = v.groupby(['dataset_id', 'source_label']).agg(cells=('shard_row', 'size'), + all_five=('all_five_detected', 'sum'), c1qa_plus_three_others=('c1qa_plus_at_least_three_others', 'sum')).reset_index() + labels.to_csv(ROOT / 'source_label_summary.csv', index=False) + write_json(ROOT / 'expression_source_receipts.json', chunk_receipts) + result = {'status': 'verified', 'selection_scope': 'source-annotated C1Q-high candidates in exact registered train split; not all training cells', + 'detection_rule': 'raw count > 0; descriptive co-detection, not macrophage identity or high-expression gate', + 'genes': dict(zip(GENES, IDS)), 'WARS1_stored_symbol': 'WARS', + 'all_markers_measured_in_all_candidate_shards': True, 'counts': groups, + 'shard_checks': checks, 'input_metadata': plan['metadata_sources'], + 'input_expression_chunk_receipt': receipt(ROOT / 'expression_source_receipts.json'), + 'limitations': ['Candidate selection is conditioned on existing C1Q source labels.', + 'This does not estimate whole-training-corpus macrophage or marker-positive prevalence.', + 'Zero counts are finite-sampling non-detection despite measured gene availability.', + 'Co-detection does not establish macrophage rather than monocyte identity.', + 'Donor IDs are dataset scoped; cross-dataset person duplication is not resolved.', + 'AIDA flagged source annotations remain separate.'], + 'outputs': [receipt(ROOT / n) for n in ['selected_training_candidates.parquet', 'candidate_marker_counts.parquet', + 'summary.csv', 'sample_support.csv', 'source_label_summary.csv']]} + write_json(ROOT / 'RESULT.json', result) + print(pd.DataFrame(groups)[['dataset', 'flagged', 'cells', 'all_five_detected', 'c1qa_plus_at_least_three_others']].to_string(index=False), flush=True) + +if __name__ == '__main__': + parser = 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