Archive 4 checkpoints (1-4)

paper-releases/20260928/diffusion/data/source_provenance/immicro/offset_sample_size_20260914/prepare_features.py

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+"""Prepare paired ImMicro condition features; CPU only, no model loading.
+
+Pseudobulk is the expectation of a K-cell sample of the donor's complete
+QC-review-pass population: all indexed captures are summed, then multiplied
+by K / observed_QC_cells. This is NOT a realized K-cell subsample. Invalid
+features remain explicit masks; zeros used at the model-input boundary do
+not imply measured biological zeros. No gene-length correction is applied.
+"""
+from __future__ import annotations
+
+import argparse
+import csv
+import hashlib
+import json
+import re
+from collections import Counter
+from datetime import datetime, timezone
+from pathlib import Path
+
+import h5py
+import numpy as np
+
+ROOT = Path('/home/sj_server_1')
+REPO = ROOT / 'PBISC/immun-pbmc-fm'
+DEFAULT_RUN = ROOT / 'gblee/datalake/paired-pbmc-bulk-sc/processing/strict_qc_v1_20260913'
+DEFAULT_CACHE = REPO / 'model_7_no_annotation_sh_hwang/runs/E2_full_no_annotation_4epoch_seed20260627/conditioner_cache.pt'
+DEFAULT_TIERS = REPO / 'model_8_sparse_data_diffusion/artifacts/panel_ladder_train256x128_seed20260627_v3/tiers'
+CONDITIONER_SOURCE = REPO / 'model_7_no_annotation_sh_hwang/src/model7_no_annotation/data.py'
+SCALARS = ('bulk_total_log1p', 'bulk_per_cell_log1p', 'bulk_detected_fraction',
+           'bulk_log_mean', 'bulk_log_std', 'bulk_top1pct_mass_fraction')
+
+
+def sha256(path: Path) -> str:
+    digest = hashlib.sha256()
+    with path.open('rb') as handle:
+        for block in iter(lambda: handle.read(1024 * 1024), b''):
+            digest.update(block)
+    return digest.hexdigest()
+
+
+def text_array(values: np.ndarray) -> np.ndarray:
+    return np.asarray([v.decode() if isinstance(v, bytes) else str(v) for v in values])
+
+
+def require(condition: bool, message: str) -> None:
+    if not condition:
+        raise ValueError(message)
+
+
+def feature_parts(raw: np.ndarray, k: int, eps: float) -> tuple[np.ndarray, np.ndarray]:
+    """Float64 equivalent of current AnnotationFreeConditioner._feature_parts."""
+    raw = np.asarray(raw, dtype=np.float64)
+    require(raw.ndim == 1 and raw.size > 0, 'counts must be a nonempty vector')
+    require(np.isfinite(raw).all() and (raw >= 0).all(), 'invalid counts')
+    require(k > 0 and int(k) == k, 'K must be a positive integer')
+    bulk = np.log1p(raw)
+    # Preserve the actual conditioner log1p/expm1 route, including rounding.
+    decoded = np.maximum(np.expm1(bulk), 0.0)
+    total = max(float(decoded.sum()), eps)
+    genes = np.log1p(decoded / max(total / float(k), eps))
+    top_n = max(1, int(np.ceil(0.01 * decoded.size)))
+    scalars = np.asarray([
+        np.log1p(total), np.log1p(total / float(k)), (decoded > 0).mean(),
+        bulk.mean(), bulk.std(), np.sort(decoded)[-top_n:].sum() / total,
+    ], dtype=np.float64)
+    return genes, scalars
+
+
+def condition(raw: np.ndarray, k: int, state: dict) -> tuple[np.ndarray, np.ndarray]:
+    require(raw.shape == (int(state['n_genes']),), 'conditioner gene-axis size mismatch')
+    eps = float(state['eps'])
+    genes, scalars = feature_parts(raw, k, eps)
+    z = np.concatenate([
+        (genes - np.asarray(state['gene_mean'], dtype=np.float64)) /
+        np.maximum(np.asarray(state['gene_std'], dtype=np.float64), eps),
+        (scalars - np.asarray(state['scalar_mean'], dtype=np.float64)) /
+        np.maximum(np.asarray(state['scalar_std'], dtype=np.float64), eps),
+    ])
+    require(np.isfinite(z).all(), 'nonfinite standardized condition')
+    # Original transform finally casts to float32; retain float64 for analysis.
+    return z, np.clip(z, -float(state['clip_z']), float(state['clip_z']))
+
+
+def aggregate_captures(row: dict, gene_ids: np.ndarray) -> tuple[np.ndarray, np.ndarray, int, list[dict]]:
+    entries = json.loads(row['sc_capture_sample_positions'])
+    paths = json.loads(row['sc_pseudobulkpaths'])
+    h5adpaths = json.loads(row['sc_h5adpaths'])
+    require(len(entries) == int(row['sc_capture_count']), 'capture count mismatch')
+    require(len(entries) == len(paths) == len(set(paths)), 'missing or duplicate captures')
+    require({v['path'] for v in entries} == set(paths), 'indexed capture paths differ')
+    require({v['canonical_h5adpath'] for v in entries} == set(h5adpaths), 'H5AD capture paths differ')
+    sums = np.zeros(len(gene_ids), dtype=np.float64)
+    valid = np.ones(len(gene_ids), dtype=bool)
+    n_cells = 0
+    capture_meta = []
+    for entry in entries:
+        require(entry['sample_id'] == row['sc_sample_id'], 'capture sample identity mismatch')
+        path = Path(entry['path'])
+        pos = int(entry['sample_index'])
+        with h5py.File(path, 'r') as handle:
+            require(np.array_equal(text_array(handle['gene_id'][:]), gene_ids), 'capture gene order mismatch')
+            ids = text_array(handle['sample_id'][:])
+            require(0 <= pos < len(ids) and ids[pos] == entry['sample_id'], 'capture position mismatch')
+            require(np.count_nonzero(ids == entry['sample_id']) == 1, 'duplicate sample identity')
+            require(handle.attrs.get('units') == 'UMI_counts', 'unverified capture count units')
+            values = np.asarray(handle['qc_review_pass_cells_sum'][pos], dtype=np.float64)
+            mask = np.asarray(handle['harmonization_valid'][:], dtype=bool)
+            count = int(handle['n_cells'][1, pos])
+            require(count >= 0, 'negative QC cell count')
+            require(np.isfinite(values).all() and (values >= 0).all(), 'invalid capture aggregate')
+            require(np.array_equal(values, np.rint(values)), 'noninteger source UMI aggregate')
+            require(np.all(values[~mask] == 0), 'invalid SC genes contain aggregate mass')
+            sums += values
+            valid &= mask
+            n_cells += count
+            capture_meta.append({'path': str(path), 'sample_index': pos,
+                                 'sample_id': entry['sample_id'], 'qc_cells': count,
+                                 'canonical_count_sum': float(values.sum()),
+                                 'source_count_sum': float(handle['source_total_mass'][1, pos])})
+    require(n_cells == int(row['sc_n_soft_QC_review_pass_cells']), 'sum of capture QC cells differs from index')
+    return sums, valid, n_cells, capture_meta
+
+
+def pool_from_row(row: dict) -> str:
+    metadata = json.loads(row['candidate_metadata_json'])
+    captures = metadata['scrna_orig_ident'].split(';')
+    pools = set()
+    for capture in captures:
+        match = re.fullmatch(r'XHLT2-POOL-(DB\d+)-SCG\d+', capture)
+        require(match is not None, f'unknown published pool encoding: {capture}')
+        pools.add(match.group(1))
+    require(len(pools) == 1, 'donor spans different pools')
+    require(len(captures) == int(row['sc_capture_count']), 'published capture count mismatch')
+    return next(iter(pools))
+
+
+def load_cache(cache_path: Path) -> tuple[dict, dict]:
+    # A 734-KB local fitted-statistics file; no model checkpoint or CUDA calls.
+    import torch
+    payload = torch.load(cache_path, map_location='cpu', weights_only=False)
+    state = payload['conditioner_state']
+    require(state['schema_version'] == 'model7.annotation_free_conditioner.v1', 'unsupported conditioner')
+    require(state['condition_mode'] == 'bio_bulk_depth', 'unsupported condition mode')
+    n_genes = int(state['n_genes'])
+    require(state['feature_names'] == [f'bulk_gene::{i}' for i in range(n_genes)] + list(SCALARS),
+            'condition feature layout mismatch')
+    for key, size in [('gene_mean', n_genes), ('gene_std', n_genes), ('scalar_mean', 6), ('scalar_std', 6)]:
+        state[key] = np.asarray(state[key], dtype=np.float64)
+        require(state[key].shape == (size,) and np.isfinite(state[key]).all(), f'invalid {key}')
+    require((state['gene_std'] > 0).all() and (state['scalar_std'] > 0).all(), 'invalid cache scale')
+    return state, {key: payload[key] for key in ('schema_version', 'data_signature', 'corpus_fingerprint', 'created_at')}
+
+
+def verify_panel_manifest(tiers_dir: Path, tier: str, cache_fingerprint: str,
+                          position_path: Path, selected_path: Path, n_genes: int) -> set[Path]:
+    """Verify authoritative v2 tier records; only finite tiers have legacy files."""
+    manifest_path = tiers_dir.parent / 'manifest.json'
+    manifest = json.loads(manifest_path.read_text())
+    require(manifest['schema_version'] == 'pbisc_sdd.panel_ladder.v2', 'unsupported panel ladder schema')
+    require(manifest['corpus_fingerprint'] == cache_fingerprint, 'panel/cache training corpus mismatch')
+    require(manifest['selection_scope'] == 'train_split_only', 'panel selection is not train-only')
+    entry = manifest['tiers'][tier]
+    require(entry['n_genes'] == n_genes, 'panel manifest gene count mismatch')
+    require(Path(entry['gene_positions_path']).resolve() == position_path.resolve(), 'panel manifest position path mismatch')
+    require(Path(entry['selected_genes_path']).resolve() == selected_path.resolve(), 'panel manifest table path mismatch')
+    require(entry['gene_positions_sha256'] == sha256(position_path), 'panel position hash mismatch')
+    require(entry['selected_genes_sha256'] == sha256(selected_path), 'panel table hash mismatch')
+    provenance_paths = {manifest_path}
+    if tier in manifest['finite_tiers']:
+        legacy_path = Path(entry['legacy_selection_manifest_path'])
+        require(entry['legacy_selection_manifest_sha256'] == sha256(legacy_path), 'legacy panel manifest hash mismatch')
+        legacy = json.loads(legacy_path.read_text())
+        require(legacy['corpus_fingerprint'] == cache_fingerprint, 'legacy panel/cache training corpus mismatch')
+        require(legacy['positions_sha256'] == entry['gene_positions_sha256'], 'legacy panel position hash mismatch')
+        require(legacy['selected_genes_sha256'] == entry['selected_genes_sha256'], 'legacy panel table hash mismatch')
+        provenance_paths.add(legacy_path)
+    else:
+        require(tier == 'broad' and entry['policy'] == 'all_eligible_genes', 'unsupported non-finite panel policy')
+        require(n_genes == manifest['n_eligible_genes'], 'broad panel eligible gene count mismatch')
+    return provenance_paths
+
+
+def prepare(run_dir: Path, cache_path: Path, tiers_dir: Path, out: Path, k: int = 1000) -> dict:
+    require(k == 1000, 'primary analysis is predefined at K=1000')
+    require(not out.exists() and not out.with_suffix('.json').exists(), 'refusing to overwrite existing feature artifact')
+    index_path = run_dir / 'reports/PAIR_EXPORT_INDEX.csv'
+    all_rows = list(csv.DictReader(index_path.open()))
+    strict = [r for r in all_rows if r['study_id'] == 'H02_ImMicro' and r['strict_pair_eligible'] == 'True']
+    require(len(strict) == 108, 'expected exactly 108 strict ImMicro donors')
+    excluded = [r for r in strict if int(r['sc_n_soft_QC_review_pass_cells']) < k]
+    require([r['donor_id'] for r in excluded] == ['HS33'], 'unexpected low-cell donor exclusion')
+    rows = [r for r in strict if int(r['sc_n_soft_QC_review_pass_cells']) >= k]
+    require(len(rows) == 107 and len({r['donor_id'] for r in rows}) == 107, 'expected 107 unique retained donors')
+    require(all(int(r['sc_capture_count']) == 4 for r in rows), 'each retained donor must have four indexed captures')
+    canonical_path = run_dir / 'reference/canonical_genes.tsv'
+    canonical = list(csv.DictReader(canonical_path.open(), delimiter='\t'))
+    gene_ids = np.asarray([r['gene_id'] for r in canonical])
+    require(len(gene_ids) == 20097 and len(set(gene_ids)) == 20097, 'canonical axis mismatch')
+    require([int(r['target_index']) for r in canonical] == list(range(len(gene_ids))), 'canonical target order mismatch')
+    state, cache_meta = load_cache(cache_path)
+    require(int(state['n_genes']) == len(gene_ids), 'fitted-statistics axis mismatch')
+    training_per_cell_depth = float(np.expm1(state['scalar_mean'][1]))
+    require(training_per_cell_depth > 0, 'invalid training depth anchor')
+    arrays: dict[str, np.ndarray] = {'gene_ids': gene_ids, 'donor_ids': np.asarray([r['donor_id'] for r in rows]),
+                                   'pools': np.asarray([pool_from_row(r) for r in rows]),
+                                   'requested_k': np.asarray(k, dtype=np.int64), 'scalar_names': np.asarray(SCALARS)}
+    require(len(set(arrays['pools'])) == 7, 'expected seven published pools')
+    bulk_list, pseudo_list, bulk_masks, sc_masks, common_masks = [], [], [], [], []
+    counts, capture_counts, donor_meta = [], [], []
+    source_paths = {index_path, canonical_path, cache_path, CONDITIONER_SOURCE,
+                    run_dir / 'reference/REFERENCE_MANIFEST.json'}
+    for row in rows:
+        bulk_path = Path(row['bulk_h5adpath'])
+        with h5py.File(bulk_path, 'r') as handle:
+            pos = int(row['bulk_obs_index'])
+            require(text_array(handle['obs/sample_id'][:])[pos] == row['bulk_obs_key'], 'bulk observation index mismatch')
+            require(text_array(handle['obs/source_GSM'][:])[pos] == row['bulk_GSM'], 'bulk GSM mismatch')
+            require(np.array_equal(text_array(handle['var/gene_id'][:]), gene_ids), 'bulk gene order mismatch')
+            bulk = np.asarray(handle['X'][pos], dtype=np.float64)
+            bv = np.asarray(handle['layers/harmonization_valid'][pos], dtype=bool)
+            require(np.isfinite(bulk[bv]).all() and (bulk[bv] >= 0).all(), 'invalid valid-gene bulk values')
+            require(np.array_equal(bulk[bv], np.rint(bulk[bv])), 'noninteger STAR counts')
+        pseudo, sv, n_cells, captures = aggregate_captures(row, gene_ids)
+        with np.load(row['common_valid_gene_mask_path'], allow_pickle=False) as maskfile:
+            require(np.array_equal(maskfile['gene_id'], gene_ids), 'pair-mask gene order mismatch')
+            require(np.array_equal(maskfile['bulk_valid'], bv), 'bulk mask differs from index')
+            require(np.array_equal(maskfile['sc_all_captures_valid'], sv), 'SC mask differs from index')
+            common = bv & sv
+            require(np.array_equal(maskfile['common_valid'], common), 'pair common mask differs')
+            require(int(common.sum()) == int(row['common_valid_gene_count']), 'pair common coverage differs')
+        bulk_list.append(bulk)
+        pseudo_list.append(pseudo)
+        bulk_masks.append(bv)
+        sc_masks.append(sv)
+        common_masks.append(common)
+        counts.append(n_cells)
+        capture_counts.append([v['qc_cells'] for v in captures])
+        donor_meta.append({'donor_id': row['donor_id'], 'pool': pool_from_row(row),
+                           'bulk_GSM': row['bulk_GSM'], 'bulk_obs_index': int(row['bulk_obs_index']),
+                           'bulk_obs_key': row['bulk_obs_key'], 'qc_cells': n_cells, 'captures': captures})
+        source_paths.add(bulk_path)
+        source_paths.add(Path(row['common_valid_gene_mask_path']))
+        source_paths.update(Path(v['path']) for v in captures)
+    arrays.update(bulk_raw=np.vstack(bulk_list), pseudo_raw_sum=np.vstack(pseudo_list),
+                  bulk_valid=np.vstack(bulk_masks), sc_valid=np.vstack(sc_masks), pair_valid=np.vstack(common_masks),
+                  qc_cell_counts=np.asarray(counts, dtype=np.int64), capture_qc_cells=np.asarray(capture_counts, dtype=np.int64))
+    arrays['pseudo_expected_k'] = arrays['pseudo_raw_sum'] * (k / arrays['qc_cell_counts'][:, None])
+    arrays['common_all_donors'] = np.all(arrays['pair_valid'], axis=0)
+    native_bulk = np.where(arrays['bulk_valid'], arrays['bulk_raw'], 0.0)
+    native_pseudo = np.where(arrays['sc_valid'], arrays['pseudo_expected_k'], 0.0)
+    common_bulk = np.where(arrays['pair_valid'], arrays['bulk_raw'], 0.0)
+    common_pseudo = np.where(arrays['pair_valid'], arrays['pseudo_expected_k'], 0.0)
+    def depth_match(matrix: np.ndarray) -> np.ndarray:
+        require(np.all(matrix.sum(axis=1) > 0), 'empty count profile')
+        return matrix * (k * training_per_cell_depth / matrix.sum(axis=1))[:, None]
+    scenarios = {'bulk_native': native_bulk, 'pseudo_native': native_pseudo,
+                 'bulk_depthmatched': depth_match(native_bulk), 'pseudo_depthmatched': depth_match(native_pseudo),
+                 'bulk_common': common_bulk, 'pseudo_common': common_pseudo}
+    full_conditions = {}
+    for name, matrix in scenarios.items():
+        transformed = [condition(v, k, state) for v in matrix]
+        full_conditions[name + '_unclipped'] = np.vstack([v[0] for v in transformed])
+        full_conditions[name + '_clipped'] = np.vstack([v[1] for v in transformed])
+    panels = {}
+    for tier in ('2k', '6k', 'broad'):
+        position_path = tiers_dir / tier / 'gene_positions.npy'
+        selected_path = tiers_dir / tier / 'selected_genes.csv'
+        positions = np.load(position_path, allow_pickle=False)
+        require(positions.dtype.kind in 'iu' and positions.ndim == 1 and positions.size > 0, 'invalid panel positions')
+        require(len(set(positions)) == len(positions) and positions.min() >= 0 and positions.max() < len(gene_ids), 'invalid panel bounds')
+        selected_genes = list(csv.DictReader(selected_path.open()))
+        require(np.array_equal(np.asarray([r['gene_id'] for r in selected_genes]), gene_ids[positions]),
+                'panel named genes disagree with canonical positions')
+        require(np.array_equal(np.asarray([int(r['panel_position']) for r in selected_genes]), positions),
+                'panel table positions disagree with NPY positions')
+        source_paths.update(verify_panel_manifest(tiers_dir, tier, cache_meta['corpus_fingerprint'],
+                                                  position_path, selected_path, len(positions)))
+        selected = np.concatenate([positions, np.arange(len(gene_ids), len(gene_ids) + 6)])
+        arrays[f'{tier}_positions'] = positions
+        arrays[f'{tier}_common_mask'] = arrays['common_all_donors'][positions]
+        arrays[f'{tier}_pair_valid'] = arrays['pair_valid'][:, positions]
+        for name, matrix in full_conditions.items():
+            arrays[f'{tier}_{name}'] = matrix[:, selected]
+        source_paths.add(position_path)
+        source_paths.add(selected_path)
+        panels[tier] = {'gene_count': int(len(positions)), 'condition_dimensions': int(len(positions) + 6),
+                        'common_gene_count': int(arrays[f'{tier}_common_mask'].sum())}
+    metadata = {'schema': 'immicro_offset_features_v1', 'created_at_utc': datetime.now(timezone.utc).isoformat(),
+                'n_donors': len(rows), 'excluded_donors': [{'donor': r['donor_id'], 'qc_cells': int(r['sc_n_soft_QC_review_pass_cells'])} for r in excluded],
+                'pool_counts': dict(Counter(arrays['pools'].tolist())), 'k': k,
+                'pseudobulk_semantics': 'all_four_captures_QC_sum_times_K_divided_by_donor_total_QC_cells;expectation_not_realized_subsample',
+                'bulk_units': 'STAR_forward_raw_gene_counts;no_gene_length_correction',
+                'pseudo_source_units': 'UMI_counts', 'feature_dtype': 'float64',
+                'conditioner_final_cast_difference': 'float64 retained; deployment transform casts clipped output to float32',
+                'scalars_computed_on': 'full_20097_axis_before_panel_subsetting',
+                'native_missing_feature_policy': 'explicit_invalid_mask;model_input_zero_fill_only;not_measured_zero',
+                'common_sensitivity': 'both_inputs_zero_outside_pair_valid_then_recompute_full_axis_features;changes_input_support',
+                'depthmatched_semantics': 'both_bulk_and_pseudo_scaled_to_fixed_K_times_expm1_training_scalar_mean_1',
+                'training_depth_anchor_per_cell': training_per_cell_depth, 'cache': cache_meta,
+                'panels': panels, 'donors': donor_meta,
+                'input_provenance': [{'path': str(p), 'bytes': p.stat().st_size, 'sha256': sha256(p)} for p in sorted(source_paths)],
+                'script_sha256': sha256(Path(__file__)), 'array_shapes': {name: list(v.shape) for name, v in arrays.items()}}
+    out.parent.mkdir(parents=True, exist_ok=True)
+    temp = out.with_name(out.name + '.tmp')
+    with temp.open('wb') as handle:
+        np.savez_compressed(handle, **arrays)
+    temp.replace(out)
+    metadata['feature_npz_sha256'] = sha256(out)
+    metadata['feature_npz_path'] = str(out.resolve())
+    out.with_suffix('.json').write_text(json.dumps(metadata, indent=2) + '\n')
+    return metadata
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument('--out', type=Path, required=True)
+    parser.add_argument('--run-dir', type=Path, default=DEFAULT_RUN)
+    parser.add_argument('--cache', type=Path, default=DEFAULT_CACHE)
+    parser.add_argument('--tiers-dir', type=Path, default=DEFAULT_TIERS)
+    parser.add_argument('--k', type=int, default=1000)
+    args = parser.parse_args()
+    metadata = prepare(args.run_dir, args.cache, args.tiers_dir, args.out, args.k)
+    print(json.dumps({k: metadata[k] for k in ('n_donors', 'pool_counts', 'panels', 'feature_npz_path')}, indent=2))
+
+
+if __name__ == '__main__':
+    main()

paper-releases/20260928/diffusion/data/source_provenance/immicro/strict_qc/ASSAY_QC_SUMMARY.csv

@@ -0,0 +1,166 @@
+modality,accession,assay_id,status,units,samples,source_genes,canonical_coverage,raw_integer_count_eligible,nonfinite_values,fractional_values,duplicate_source_rows_quarantined,export_path,caveats,cells,qc_review_pass_cells,duplicate_representation,cell_metadata_linkage,error
+bulk,GSE152418,GSE152418_counts,organized_with_explicit_masks,raw_gene_counts,34,60683,0.9905458526148181,True,0,0,0,/home/sj_server_1/gblee/datalake/paired-pbmc-bulk-sc/processing/strict_qc_v1_20260913/bulk/GSE152418_counts/harmonized.h5ad,[],,,,,
+bulk,GSE198533,GSE198533_count,organized_with_explicit_masks,raw_gene_counts,19,58735,0.9873115390356769,True,0,0,0,/home/sj_server_1/gblee/datalake/paired-pbmc-bulk-sc/processing/strict_qc_v1_20260913/bulk/GSE198533_count/harmonized.h5ad,"[""Condition/age/sex similarities do not establish donor pairing.""]",,,,,
+bulk,GSE198533,GSE198533_fpkm,organized_with_explicit_masks,FPKM,19,58735,0.9873115390356769,False,0,549415,0,/home/sj_server_1/gblee/datalake/paired-pbmc-bulk-sc/processing/strict_qc_v1_20260913/bulk/GSE198533_fpkm/harmonized.h5ad,"[""Condition/age/sex similarities do not establish donor pairing.""]",,,,,
+bulk,GSE314922,GSE314922_STAR_forward_counts,organized_with_explicit_masks,STAR_forward_raw_gene_counts,108,58735,0.9873115390356769,True,0,0,0,/home/sj_server_1/gblee/datalake/paired-pbmc-bulk-sc/processing/strict_qc_v1_20260913/bulk/GSE314922_STAR_forward_counts/harmonized.h5ad,"[""STAR column 3 (one based; first-read strand aligned with RNA) selected; unstranded and reverse columns are never summed."", ""Tecan Universal Plus mRNA-Seq with NuQuant protocol is recorded in GEO. Per-sample forward fractions below 0.8 trigger review flags, not data correction."", ""Original genotype VCF files remain outside expression matrices.""]",,,,,
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+      "sha256": "3351f2b7511407020a0e63a4d979f45957fa5f598b2408673dada55d964a42e9"
+    },
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+      "bytes": 542,
+      "sha256": "bd6c47a5ff8f9adbf3763aa41f3478406268e31ee0beed903170e31a1665663a"
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+      "bytes": 902,
+      "sha256": "ebdf4a58a97ea64d87dd7330d54aa2b865f9843e3bfe92e24ba6a824c4825b24"
+    },
+    "postprocess_bundle_v3/MANIFEST.json": {
+      "bytes": 789,
+      "sha256": "2909d25428329652126994bff7fe595a85a58cf4746f184e5e3b370f047c2b2e"
+    },
+    "postprocess_bundle_v2/MANIFEST.json": {
+      "bytes": 859,
+      "sha256": "c2ce06a668436b61c41d1efc6ec08110ece7e9715bd78d6651d2c93119e96e6c"
+    }
+  },
+  "schema_note": "Matrices, masks and file/identity metadata only; no biological inference",
+  "cached_export_history": "Valid v2/v3/v4 outputs retained; later bundles repair linkage or reader cache without altering source files. Superseded directories are excluded."
+}

paper-releases/20260928/diffusion/data/source_provenance/immicro/strict_qc/FINAL_REPORT_KO.md

@@ -0,0 +1,66 @@
+# 파일 QC·유전자 ID 표준화 완료 보고서
+
+**파일 정리 작업을 완료했다.** 원본 112개 파일의 SHA256을 다시 검증하고 중첩 tar/zip과 H5AD gzip을 안전하게 해제했다. 원본 데이터와 이전 단계 기록은 보존했다. 작업 범위는 파일·행렬·ID·공개 메타데이터 정합성이며 새로운 생물학적 해석은 수행하지 않았다.
+
+| 검증 항목 | 결과 |
+|---|---:|
+| 원본 파일 재검증 | 112/112 |
+| 표준 bulk 발현 테이블 | 14 |
+| 표준 single-cell 기본 library | 138 |
+| 표준 H5AD 전체 재검증 | **152/152 통과, 실패 0** |
+| 원본/생성 기록 대비 수치 총량 검증 | 152/152 |
+| H5/MTX 중복 표현의 모든 행렬 값 일치 | **12/12** |
+| 실제 파일의 AnnData 공개 reader 검사 | 6/6 |
+| 코드 회귀·경계 조건 테스트 | **131/131** |
+| 모든 표준 파일의 고정 유전자 축 | **20,097개, ID·순서 검증** |
+
+독립 검증기는 예상 파일 목록과 실제 결과를 대조하고, sparse pointer/index의 형식·범위·순서·중복, 모든 저장 수치의 유효성, 결측·격리 마스크, 단위와 출처 보존을 확인했다. 빈 결과 목록이나 누락된 export가 성공으로 통과하지 않도록 검사했다. H5/MTX 12쌍은 행·열 합계뿐 아니라 모든 sparse entry가 정확히 동일함을 확인했다.
+
+**사용할 파일**
+
+- `../bulk/<assay>/harmonized.h5ad`: sample×20,097 표준 bulk 데이터.
+- `../singlecell/<accession>/<library>/canonical.h5ad`: barcode×20,097 표준 single-cell 데이터.
+- `PAIR_EXPORT_INDEX.csv`: 원본 donor·visit 증거를 실제 bulk row와 sc capture/aggregate 파일에 연결한 인덱스.
+- `pair_gene_masks/*.npz`: 각 연결 항목의 gene ID와 공통 유효 유전자 마스크.
+- `ASSAY_QC_SUMMARY.csv`, `STUDY_READINESS.csv`, `SAMPLE_QC_SUMMARY.csv`: 파일·표본 수준 정리 결과.
+- `INDEPENDENT_VALIDATION.json`: 152개 표준 파일의 독립 검증 및 SHA256.
+
+`superseded_*`는 수정 전 산출물을 보존한 기록이다. 사용본은 위의 `bulk/`, `singlecell/` 및 현재 인덱스가 가리키는 파일이다.
+
+**값과 결측의 처리**
+
+Count, kallisto estimated count, ScaledTPM, TPM, FPKM, log2CPM, microarray 단위를 각각 보존했다. 소수 값을 반올림하거나 log 값을 역변환해 raw count로 만들지 않았다. 베체트 파일에 함께 들어 있던 19개 count와 19개 FPKM 열은 별도 assay로 분리했다. ImMicro STAR는 문서에 맞는 forward 열을 사용하고 다른 strand 합계와 검토 지표를 함께 남겼다.
+
+Bulk는 NaN과 `source_feature_present`, `harmonization_valid` layer를 사용한다. Single-cell sparse 저장의 0은 `.var`의 두 마스크와 함께 해석해야 한다. 원본 feature에 없거나 ID가 모호해 제외된 유전자를 관측된 0으로 해석해서는 안 된다. 표준 reader의 `.uns`에도 단위·출처·마스크 의미·reference hash가 유지된다.
+
+**20,097개로 정렬됐다는 사실은 20,097개가 모두 관측됐다는 뜻이 아니다.** 연결 가능한 항목의 공통 유효 유전자 수는 **16,131–19,656개**이며, 전부 20,097개가 유효한 항목은 없다. DREAM count 원본은 9,649개 유전자만 제공해 canonical 연결 범위가 약 47.8%이다. 원본에 없는 범위를 복원하지 않았다.
+
+**샘플 연결 결과**
+
+파일 연결 가능한 인덱스 행은 **139개**다. 이 수에는 assay/protocol 구분이 포함되며 환자 139명이라는 뜻이 아니다.
+
+| 묶음 | 파일 연결 항목 | 제한 |
+|---|---:|---|
+| ImMicro | **108** | 같은 sample·PBMC·count 조건 확인. HS33은 34개 barcode 행뿐이며, 1,000개 이상 review-pass 행을 요구하면 **107개** |
+| Arunachalam | 12 | 7개는 donor·시점, HC 5개는 donor만 확인. DC 농축·재혼합 제한 유지 |
+| Ding | 14 | PBMC1/PBMC2 × protocol 구분. 14명의 독립 donor가 아니며 bulk는 TPM |
+| CLL | 5 | 명시적 sample alias와 GSM crosswalk. 분리 CD19 bulk 및 log2CPM 제한 |
+
+동일 sample/visit·donor·PBMC끼리의 시료 범위·raw-count 단위를 모두 요구하는 인덱스 조건을 충족한 것은 **ImMicro 108개**다. 이는 세포 수 또는 전체 유전자 관측을 보장하는 조건이 아니므로 107개 실용 후보와 각 common-gene mask를 함께 사용한다.
+
+**연결을 확정하지 않은 항목**
+
+- **Behçet·Asan:** 파일 정리는 완료했으나 정확한 bulk↔sc donor crosswalk는 공개 자료에서 확인하지 못했다.
+- **Randolph:** bulk 89명×2조건=178개 열의 GSM 대응은 확인했다. 현재 파일에 cell barcode→donor/condition map이 없어 per-donor 연결은 보류했다.
+- **Hannover:** 24개 donor–시점 metadata pair와 HTO feature→sample 표를 확인했다. HTO 원 수치는 보존했으며 cell별 HTO assignment를 추정하지 않았다.
+- **DREAM:** 75개 GEO sample pair가 존재하지만 H5AD 내부 library label→환자 대응은 미확정이다. 관련 TCR library label과 교집합이 0이어서 연결하지 않았다.
+- **SoupLadle:** 다운로드된 bulk 측 파일은 VCF이며 발현행렬이 아니다. 표현형 파일로 잘못 읽지 않았다.
+- **Bernardes·Kotliarov:** 현재 취득 범위는 bulk 측만 있으므로 없는 counterpart를 만들지 않았다.
+
+미해결 사유와 출처는 `../pairing_audit/README.md`, 원본 기준 대응표, `PAIR_EXPORT_INDEX.csv`에 보존했다. 메타데이터상의 donor·시점 증거, cell-level 연결, 시료 제한, 단위 제한을 서로 독립적으로 표시한다.
+
+**실행과 재현성**
+
+처리는 CPU 전용 systemd 사용자 서비스에서 수행했고 메모리 상한·swap 금지·양의 OOM score를 적용했다. CUDA 작업을 실행하지 않았다. 대형 HDF5 chunk를 반복 해제하던 병목은 제한된 캐시와 dataset handle 재사용으로 해결했으며 값의 동일성을 회귀 테스트로 확인했다.
+
+현재 산출물·검증 보고서·reference·코드 버전의 해시는 `FINAL_MANIFEST.json`에 있다. 원본 acquisition 검증과 이번 processing 검증은 서로 다른 단계의 기록이다.