Datasets:
Formats:
parquet
Languages:
English
Size:
10M - 100M
Tags:
biology
chemistry
drug-discovery
clinical-trials
protein-protein-interaction
gene-essentiality
License:
File size: 21,627 Bytes
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This script is safe to run while ETL is in progress because it only reads:
- Downloaded PubChem files
- SID lookup SQLite
Output:
- JSON report in exports/
- Markdown summary in exports/
"""
from __future__ import annotations
import argparse
import json
import sqlite3
from datetime import datetime, timezone
from pathlib import Path
import pandas as pd
from negbiodb.db import _PROJECT_ROOT
from negbiodb.download import load_config
from negbiodb.etl_pubchem import (
_find_col,
_normalize_accession,
_normalize_columns,
)
_HUMAN_TAXID = 9606
_ASSAY_TYPE_CANDIDATES = [
"assay_type",
"outcome_type",
"bioassay_type",
"bioassay_types",
"aid_type",
"activity_type",
"screening_type",
]
_ACCESSION_COLS = {
"protein_accession",
"protein_accessions",
"proteinaccession",
"uniprot",
"uniprot_accession",
"uniprots_id",
"uniprots_ids",
}
_TAXID_COLS = {
"target_taxid",
"target_taxids",
"taxonomy_id",
"taxonomy_ids",
}
_TARGET_COLS = _ACCESSION_COLS | {
"gene_id",
"gene_ids",
"target_id",
} | _TAXID_COLS
def _pct(n: int, d: int) -> float | None:
if d == 0:
return None
return round((100.0 * n) / d, 4)
def _file_meta(path: Path) -> dict:
st = path.stat()
return {
"path": str(path),
"bytes": int(st.st_size),
"size_mb": round(st.st_size / (1024 * 1024), 2),
"mtime_utc": datetime.fromtimestamp(st.st_mtime, tz=timezone.utc).isoformat(),
}
def _lookup_sid_rows(
lookup_conn: sqlite3.Connection,
sids: list[int],
) -> dict[int, tuple[int | None, str | None]]:
if not sids:
return {}
unique_sids = list(dict.fromkeys(int(sid) for sid in sids))
out: dict[int, tuple[int | None, str | None]] = {}
batch_size = 500
for i in range(0, len(unique_sids), batch_size):
batch = unique_sids[i : i + batch_size]
placeholders = ",".join("?" * len(batch))
rows = lookup_conn.execute(
f"SELECT sid, cid, smiles FROM sid_cid_map WHERE sid IN ({placeholders})",
batch,
).fetchall()
for sid, cid, smiles in rows:
out[int(sid)] = (None if cid is None else int(cid), smiles)
return out
def scan_bioassays(path: Path, chunksize: int) -> tuple[dict, set[int]]:
stats = {
"rows_total": 0,
"rows_aid_valid": 0,
"rows_confirmatory": 0,
"rows_confirmatory_with_target_annotation": 0,
"rows_confirmatory_human_taxid": 0,
"unique_confirmatory_aids": 0,
}
confirmatory_aids: set[int] = set()
aid_col = None
assay_type_candidates: list[str] = []
target_cols: list[str] = []
taxid_cols: list[str] = []
reader = pd.read_csv(
path,
sep="\t",
compression="gzip" if path.suffix == ".gz" else None,
chunksize=chunksize,
low_memory=False,
)
for chunk in reader:
chunk = _normalize_columns(chunk)
cols = list(chunk.columns)
stats["rows_total"] += len(chunk)
if aid_col is None:
aid_col = _find_col(cols, ["aid"])
if aid_col is None:
raise ValueError(f"Could not find AID column in {path}")
assay_type_candidates = [c for c in _ASSAY_TYPE_CANDIDATES if c in cols]
target_cols = [c for c in cols if c in _TARGET_COLS]
taxid_cols = [c for c in cols if c in _TAXID_COLS]
out = chunk[pd.to_numeric(chunk[aid_col], errors="coerce").notna()].copy()
if out.empty:
continue
out[aid_col] = out[aid_col].astype(int)
stats["rows_aid_valid"] += len(out)
assay_type_col = None
for c in assay_type_candidates:
if out[c].astype(str).str.contains("confirm", case=False, na=False).any():
assay_type_col = c
break
if assay_type_col is None and assay_type_candidates:
assay_type_col = assay_type_candidates[0]
if assay_type_col is not None:
conf = out[out[assay_type_col].astype(str).str.contains("confirm", case=False, na=False)]
else:
conf = out
if conf.empty:
continue
stats["rows_confirmatory"] += len(conf)
confirmatory_aids.update(int(v) for v in conf[aid_col].tolist())
if target_cols:
target_mask = pd.Series(False, index=conf.index)
for c in target_cols:
col = conf[c]
if c in _ACCESSION_COLS:
target_mask = target_mask | col.map(_normalize_accession).notna()
else:
target_mask = target_mask | col.notna()
stats["rows_confirmatory_with_target_annotation"] += int(target_mask.sum())
if taxid_cols:
human_mask = pd.Series(False, index=conf.index)
for c in taxid_cols:
human_mask = human_mask | (pd.to_numeric(conf[c], errors="coerce") == _HUMAN_TAXID)
stats["rows_confirmatory_human_taxid"] += int(human_mask.sum())
stats["unique_confirmatory_aids"] = len(confirmatory_aids)
stats["confirmatory_with_target_pct"] = _pct(
stats["rows_confirmatory_with_target_annotation"],
stats["rows_confirmatory"],
)
stats["confirmatory_human_taxid_pct"] = _pct(
stats["rows_confirmatory_human_taxid"],
stats["rows_confirmatory"],
)
return stats, confirmatory_aids
def scan_aid_map(path: Path, chunksize: int) -> tuple[dict, dict[int, str]]:
stats = {
"rows_total": 0,
"rows_aid_valid": 0,
"rows_accession_valid": 0,
"unique_aids_with_accession": 0,
"duplicate_aid_rows": 0,
"conflicting_duplicate_aid_rows": 0,
}
aid_to_uniprot: dict[int, str] = {}
aid_col = None
uniprot_col = None
reader = pd.read_csv(
path,
sep="\t",
compression="gzip" if path.suffix == ".gz" else None,
chunksize=chunksize,
low_memory=False,
)
for chunk in reader:
chunk = _normalize_columns(chunk)
cols = list(chunk.columns)
stats["rows_total"] += len(chunk)
if aid_col is None:
aid_col = _find_col(cols, ["aid"])
if aid_col is None:
raise ValueError(f"Could not find AID column in {path}")
uniprot_col = _find_col(
cols,
[
"uniprot",
"uniprotkb_ac_id",
"uniprotkb_ac",
"uniprotkb_id",
"uniprot_accession",
"protein_accession",
"proteinaccession",
"accession",
],
)
if uniprot_col is None:
raise ValueError(f"Could not find UniProt/accession column in {path}")
out = chunk[[aid_col, uniprot_col]].copy()
out = out[pd.to_numeric(out[aid_col], errors="coerce").notna()]
if out.empty:
continue
out[aid_col] = out[aid_col].astype(int)
stats["rows_aid_valid"] += len(out)
out[uniprot_col] = out[uniprot_col].map(_normalize_accession)
out = out[out[uniprot_col].notna()]
if out.empty:
continue
stats["rows_accession_valid"] += len(out)
for r in out.itertuples(index=False):
aid = int(getattr(r, aid_col))
acc = str(getattr(r, uniprot_col))
if aid in aid_to_uniprot:
stats["duplicate_aid_rows"] += 1
if aid_to_uniprot[aid] != acc:
stats["conflicting_duplicate_aid_rows"] += 1
continue
aid_to_uniprot[aid] = acc
stats["unique_aids_with_accession"] = len(aid_to_uniprot)
stats["aid_valid_pct"] = _pct(stats["rows_aid_valid"], stats["rows_total"])
stats["accession_valid_pct"] = _pct(stats["rows_accession_valid"], stats["rows_total"])
return stats, aid_to_uniprot
def summarize_sid_lookup(path: Path) -> dict:
with sqlite3.connect(str(path)) as conn:
total = conn.execute("SELECT COUNT(*) FROM sid_cid_map").fetchone()[0]
with_cid = conn.execute("SELECT COUNT(*) FROM sid_cid_map WHERE cid IS NOT NULL").fetchone()[0]
with_smiles = conn.execute(
"SELECT COUNT(*) FROM sid_cid_map WHERE smiles IS NOT NULL AND TRIM(smiles) <> ''"
).fetchone()[0]
with_both = conn.execute(
"SELECT COUNT(*) FROM sid_cid_map WHERE cid IS NOT NULL AND smiles IS NOT NULL AND TRIM(smiles) <> ''"
).fetchone()[0]
return {
"rows_total": int(total),
"rows_with_cid": int(with_cid),
"rows_with_smiles": int(with_smiles),
"rows_with_cid_and_smiles": int(with_both),
"with_cid_pct": _pct(int(with_cid), int(total)),
"with_smiles_pct": _pct(int(with_smiles), int(total)),
"with_cid_and_smiles_pct": _pct(int(with_both), int(total)),
}
def sample_bioactivities(
bioactivities_path: Path,
sid_lookup_path: Path,
confirmatory_aids: set[int],
aid_to_uniprot: dict[int, str],
chunksize: int,
sample_rows: int,
) -> dict:
stats = {
"rows_sampled": 0,
"rows_aid_sid_valid": 0,
"rows_inactive": 0,
"rows_inactive_confirmatory": 0,
"rows_inactive_confirmatory_with_uniprot": 0,
"rows_inactive_confirmatory_with_cid_smiles": 0,
"rows_inactive_confirmatory_ready_all": 0,
"rows_inactive_confirmatory_human_taxid": 0,
}
aid_col = None
sid_col = None
cid_col = None
outcome_col = None
accession_col = None
taxid_col = None
with sqlite3.connect(str(sid_lookup_path)) as sid_conn:
reader = pd.read_csv(
bioactivities_path,
sep="\t",
compression="gzip" if bioactivities_path.suffix == ".gz" else None,
chunksize=chunksize,
low_memory=False,
)
remaining = sample_rows
for chunk in reader:
if remaining <= 0:
break
if len(chunk) > remaining:
chunk = chunk.iloc[:remaining].copy()
remaining -= len(chunk)
chunk = _normalize_columns(chunk)
cols = list(chunk.columns)
if aid_col is None:
aid_col = _find_col(cols, ["aid"])
sid_col = _find_col(cols, ["sid"])
cid_col = _find_col(cols, ["cid"])
outcome_col = _find_col(cols, ["activity_outcome", "activityoutcome"])
accession_col = _find_col(
cols,
["protein_accession", "proteinaccession", "uniprot_accession", "uniprot"],
)
taxid_col = _find_col(cols, ["target_taxid", "target_tax_id", "taxid", "taxonomy_id"])
if aid_col is None or sid_col is None or outcome_col is None:
raise ValueError(
"Missing required bioactivities columns: need AID, SID, Activity Outcome"
)
stats["rows_sampled"] += len(chunk)
out = pd.DataFrame()
out["aid"] = pd.to_numeric(chunk[aid_col], errors="coerce")
out["sid"] = pd.to_numeric(chunk[sid_col], errors="coerce")
out["cid"] = pd.to_numeric(chunk[cid_col], errors="coerce") if cid_col else pd.NA
out["activity_outcome"] = chunk[outcome_col].astype(str)
out["uniprot_direct"] = (
chunk[accession_col].map(_normalize_accession) if accession_col else None
)
out["target_taxid"] = (
pd.to_numeric(chunk[taxid_col], errors="coerce") if taxid_col else pd.NA
)
out = out[out["aid"].notna() & out["sid"].notna()].copy()
if out.empty:
continue
out["aid"] = out["aid"].astype(int)
out["sid"] = out["sid"].astype(int)
stats["rows_aid_sid_valid"] += len(out)
inactive = out[out["activity_outcome"].str.contains("inactive", case=False, na=False)].copy()
if inactive.empty:
continue
stats["rows_inactive"] += len(inactive)
inactive = inactive[inactive["aid"].isin(confirmatory_aids)].copy()
if inactive.empty:
continue
stats["rows_inactive_confirmatory"] += len(inactive)
aid_uniprot = inactive["aid"].map(aid_to_uniprot).map(_normalize_accession)
inactive["resolved_uniprot"] = inactive["uniprot_direct"].where(
inactive["uniprot_direct"].notna(),
aid_uniprot,
)
stats["rows_inactive_confirmatory_with_uniprot"] += int(
inactive["resolved_uniprot"].notna().sum()
)
sid_lookup = _lookup_sid_rows(sid_conn, inactive["sid"].tolist())
resolved_cids: list[int | None] = []
resolved_smiles: list[str | None] = []
for r in inactive.itertuples(index=False):
sid_info = sid_lookup.get(int(r.sid))
cid = int(r.cid) if pd.notna(r.cid) else None
smiles = None
if sid_info is not None:
lookup_cid, lookup_smiles = sid_info
if cid is None:
cid = lookup_cid
smiles = lookup_smiles
resolved_cids.append(cid)
resolved_smiles.append(smiles)
inactive["resolved_cid"] = resolved_cids
inactive["resolved_smiles"] = resolved_smiles
cid_smiles_ok = inactive["resolved_cid"].notna() & inactive["resolved_smiles"].notna()
stats["rows_inactive_confirmatory_with_cid_smiles"] += int(cid_smiles_ok.sum())
ready_all = cid_smiles_ok & inactive["resolved_uniprot"].notna()
stats["rows_inactive_confirmatory_ready_all"] += int(ready_all.sum())
if taxid_col is not None:
stats["rows_inactive_confirmatory_human_taxid"] += int(
(inactive["target_taxid"] == _HUMAN_TAXID).sum()
)
stats["inactive_pct"] = _pct(stats["rows_inactive"], stats["rows_sampled"])
stats["inactive_confirmatory_pct"] = _pct(
stats["rows_inactive_confirmatory"],
stats["rows_sampled"],
)
stats["inactive_confirmatory_with_uniprot_pct"] = _pct(
stats["rows_inactive_confirmatory_with_uniprot"],
stats["rows_inactive_confirmatory"],
)
stats["inactive_confirmatory_with_cid_smiles_pct"] = _pct(
stats["rows_inactive_confirmatory_with_cid_smiles"],
stats["rows_inactive_confirmatory"],
)
stats["inactive_confirmatory_ready_all_pct"] = _pct(
stats["rows_inactive_confirmatory_ready_all"],
stats["rows_inactive_confirmatory"],
)
stats["inactive_confirmatory_human_taxid_pct"] = _pct(
stats["rows_inactive_confirmatory_human_taxid"],
stats["rows_inactive_confirmatory"],
)
return stats
def render_markdown(report: dict) -> str:
files = report["files"]
bioassays = report["bioassays"]
aid_map = report["aid_map"]
sid_lookup = report["sid_lookup"]
sample = report["bioactivities_sample"]
lines = [
"# PubChem QC Report",
"",
f"- generated_at_utc: {report['generated_at_utc']}",
f"- sample_rows_target: {sample['sample_rows_target']}",
f"- sample_rows_actual: {sample['rows_sampled']}",
"",
"## Files",
"",
"| file | size_mb | mtime_utc |",
"|---|---:|---|",
]
for key in [
"bioactivities",
"bioassays",
"aid_uniprot",
"sid_cid_smiles",
"sid_lookup_db",
]:
meta = files[key]
lines.append(f"| {key} | {meta['size_mb']} | {meta['mtime_utc']} |")
lines += [
"",
"## Bioassays",
"",
f"- rows_total: {bioassays['rows_total']}",
f"- rows_confirmatory: {bioassays['rows_confirmatory']}",
f"- unique_confirmatory_aids: {bioassays['unique_confirmatory_aids']}",
f"- rows_confirmatory_with_target_annotation: {bioassays['rows_confirmatory_with_target_annotation']}",
f"- confirmatory_with_target_pct: {bioassays['confirmatory_with_target_pct']}",
f"- rows_confirmatory_human_taxid: {bioassays['rows_confirmatory_human_taxid']}",
f"- confirmatory_human_taxid_pct: {bioassays['confirmatory_human_taxid_pct']}",
"",
"## AID Map",
"",
f"- rows_total: {aid_map['rows_total']}",
f"- rows_accession_valid: {aid_map['rows_accession_valid']}",
f"- unique_aids_with_accession: {aid_map['unique_aids_with_accession']}",
f"- duplicate_aid_rows: {aid_map['duplicate_aid_rows']}",
f"- conflicting_duplicate_aid_rows: {aid_map['conflicting_duplicate_aid_rows']}",
"",
"## SID Lookup",
"",
f"- rows_total: {sid_lookup['rows_total']}",
f"- rows_with_cid: {sid_lookup['rows_with_cid']}",
f"- rows_with_smiles: {sid_lookup['rows_with_smiles']}",
f"- rows_with_cid_and_smiles: {sid_lookup['rows_with_cid_and_smiles']}",
"",
"## Bioactivities Sample",
"",
f"- rows_sampled: {sample['rows_sampled']}",
f"- rows_inactive: {sample['rows_inactive']}",
f"- rows_inactive_confirmatory: {sample['rows_inactive_confirmatory']}",
f"- rows_inactive_confirmatory_with_uniprot: {sample['rows_inactive_confirmatory_with_uniprot']}",
f"- rows_inactive_confirmatory_with_cid_smiles: {sample['rows_inactive_confirmatory_with_cid_smiles']}",
f"- rows_inactive_confirmatory_ready_all: {sample['rows_inactive_confirmatory_ready_all']}",
f"- inactive_confirmatory_ready_all_pct: {sample['inactive_confirmatory_ready_all_pct']}",
"",
]
return "\n".join(lines) + "\n"
def main() -> None:
parser = argparse.ArgumentParser(description="Generate PubChem raw-data QC report")
parser.add_argument(
"--sample-rows",
type=int,
default=2_000_000,
help="Rows to sample from bioactivities.tsv.gz (set 0 to disable sample)",
)
parser.add_argument(
"--chunksize",
type=int,
default=200_000,
help="Chunk size for streamed TSV reads",
)
parser.add_argument(
"--output-prefix",
type=str,
default="pubchem_qc",
help="Output filename prefix under exports/",
)
args = parser.parse_args()
cfg = load_config()
pubchem_cfg = cfg["downloads"]["pubchem"]
bioactivities_path = _PROJECT_ROOT / pubchem_cfg["dest"]
bioassays_path = _PROJECT_ROOT / pubchem_cfg["bioassays_dest"]
aid_uniprot_path = _PROJECT_ROOT / pubchem_cfg["aid_uniprot_dest"]
sid_cid_smiles_path = _PROJECT_ROOT / pubchem_cfg["sid_cid_smiles_dest"]
sid_lookup_path = _PROJECT_ROOT / pubchem_cfg["sid_lookup_db"]
for p in [bioactivities_path, bioassays_path, aid_uniprot_path, sid_cid_smiles_path, sid_lookup_path]:
if not p.exists():
raise FileNotFoundError(f"Required file missing: {p}")
bioassays_stats, confirmatory_aids = scan_bioassays(bioassays_path, chunksize=args.chunksize)
aid_map_stats, aid_to_uniprot = scan_aid_map(aid_uniprot_path, chunksize=args.chunksize)
sid_lookup_stats = summarize_sid_lookup(sid_lookup_path)
sample_stats = {
"sample_rows_target": int(args.sample_rows),
}
if args.sample_rows > 0:
sample_stats.update(
sample_bioactivities(
bioactivities_path=bioactivities_path,
sid_lookup_path=sid_lookup_path,
confirmatory_aids=confirmatory_aids,
aid_to_uniprot=aid_to_uniprot,
chunksize=args.chunksize,
sample_rows=args.sample_rows,
)
)
else:
sample_stats["rows_sampled"] = 0
report = {
"generated_at_utc": datetime.now(timezone.utc).isoformat(),
"files": {
"bioactivities": _file_meta(bioactivities_path),
"bioassays": _file_meta(bioassays_path),
"aid_uniprot": _file_meta(aid_uniprot_path),
"sid_cid_smiles": _file_meta(sid_cid_smiles_path),
"sid_lookup_db": _file_meta(sid_lookup_path),
},
"bioassays": bioassays_stats,
"aid_map": aid_map_stats,
"sid_lookup": sid_lookup_stats,
"bioactivities_sample": sample_stats,
}
exports_dir = _PROJECT_ROOT / cfg["paths"]["exports_dir"]
exports_dir.mkdir(parents=True, exist_ok=True)
ts = datetime.now(timezone.utc).strftime("%Y%m%dT%H%M%SZ")
json_path = exports_dir / f"{args.output_prefix}_{ts}.json"
md_path = exports_dir / f"{args.output_prefix}_{ts}.md"
json_path.write_text(json.dumps(report, indent=2), encoding="utf-8")
md_path.write_text(render_markdown(report), encoding="utf-8")
print(f"QC JSON: {json_path}")
print(f"QC Markdown: {md_path}")
print(f"Sample rows: {sample_stats.get('rows_sampled', 0)}")
if sample_stats.get("rows_inactive_confirmatory_ready_all") is not None:
print(
"Ready-all rows (sample): "
f"{sample_stats.get('rows_inactive_confirmatory_ready_all')} "
f"({sample_stats.get('inactive_confirmatory_ready_all_pct')}%)"
)
if __name__ == "__main__":
main()
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