Kemal Yaylali
e76ae847a1
A review of the ranking's arithmetic found four things wrong, all of which made the score look better informed than it was. Measurements below are from this repo, not estimates. **Components now abstain instead of inventing a number.** A run without a VEP cache returns no allele frequencies, and rarity_score(None) read that as "absent from gnomAD, therefore maximally rare" and awarded every variant a free 0.25. jobs.has_frequencies / has_effect_scores record what the run actually produced, absent components are dropped from the weighted mean, and the remaining weights are renormalised so the score keeps its meaning. The UI shows "not looked up" rather than a bar, and the funnel stops calling a step "rare" when nothing was filtered. **Allele frequency is no longer a model feature.** It dominated: the same missense variant scored 0.887 at AF 0 and 0.0003 at AF 0.01. That double- counted, because the ranking already scores frequency explicitly, putting ~45% of every rank on one measurement; and it was circular, because ACMG assigns ClinVar's benign labels using frequency (BA1/BS1). Retraining without it moves missense AUROC from 0.872 to 0.500 — exactly random. The old figure was allele frequency, not variant-effect knowledge. The model therefore abstains unless CADD or AlphaMissense is present, since otherwise it only restates the consequence class. **Phenotype matching is weighted by information content** and HPO annotations are propagated up the ontology. Counting terms alike let "global developmental delay" (IC 0.93) count as much as "dilated left subclavian artery" (IC 7.88). **A real bug in the propagation, found by checking it.** The ancestor walk read a pre-order DFS backwards, which on a DAG lets a term resolve before one of its parents and inherit that parent alone instead of its lineage. It dropped 399 terms out of the phenotype branch, Camptodactyly and Chiari malformation among them. Now a true post-order, tested against a reference transitive closure. The ontology arithmetic moved to rarelens_ml.hpo so it is covered by tests, and rarelens_ml.benchmark measures the whole thing: across 10,178 published cases the causal gene ranks first 45.9-81.0% of the time against 5,269 genes, versus 0.02% for chance. docs/data.md reports that with its contamination (HPO's annotations come from these same case reports), and includes the measurement showing information-content weighting earns its place while propagation does not - kept anyway, for a reason the docs argue rather than assume.
88 lines
3.4 KiB
Python
Executable File
88 lines
3.4 KiB
Python
Executable File
#!/usr/bin/env python3
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"""Load HPO into the gene_phenotypes and hpo_terms tables: the reference data the ranking uses.
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Two source files:
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https://purl.obolibrary.org/obo/hp/hpoa/genes_to_phenotype.txt (gene -> term, ~20 MB)
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https://purl.obolibrary.org/obo/hp.obo (the ontology itself, ~10 MB)
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The ontology matters because HPO's gene annotations are direct, and a patient may be described one
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level away from whichever term the curator chose. `rarelens_ml.hpo` holds the propagation and
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information-content arithmetic, with the tests, since it is what the phenotype half of the ranking
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rests on. Run through the ml environment, which is where that package lives: `make hpo`.
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Cite the Human Phenotype Ontology when showing results; see docs/data.md.
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"""
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import argparse
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import csv
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import io
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import os
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import sys
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import urllib.request
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from rarelens_ml.hpo import ancestors_of, information_content, parse_obo, propagate
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from sqlalchemy import create_engine, text
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from sqlalchemy.engine import make_url
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GENES_URL = "https://purl.obolibrary.org/obo/hp/hpoa/genes_to_phenotype.txt"
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OBO_URL = "https://purl.obolibrary.org/obo/hp.obo"
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def fetch(url: str) -> io.TextIOBase:
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print(f"downloading {url}", file=sys.stderr)
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return io.TextIOWrapper(urllib.request.urlopen(url), encoding="utf-8")
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def annotations(handle: io.TextIOBase) -> set[tuple[str, str]]:
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"""Unique (gene, term) pairs; the file repeats them once per associated disease."""
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return {
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(row["gene_symbol"][:60], row["hpo_id"][:20])
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for row in csv.DictReader(handle, delimiter="\t")
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if row.get("gene_symbol") and row.get("hpo_id")
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}
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def main() -> None:
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p = argparse.ArgumentParser()
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p.add_argument("--url", default=GENES_URL)
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p.add_argument("--obo", default=OBO_URL)
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p.add_argument("--file", help="use a local genes_to_phenotype.txt instead of downloading")
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p.add_argument("--obo-file", help="use a local hp.obo instead of downloading")
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a = p.parse_args()
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url = os.environ.get("DATABASE_URL")
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if not url:
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sys.exit("DATABASE_URL is not set")
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with (open(a.obo_file) if a.obo_file else fetch(a.obo)) as handle:
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parents, names = parse_obo(handle)
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ancestors = ancestors_of(parents)
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with (open(a.file) if a.file else fetch(a.url)) as handle:
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direct = annotations(handle)
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genes = propagate(direct, ancestors)
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ic = information_content(genes)
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rows = [(gene, term, names.get(term, term)) for gene, terms in genes.items() for term in terms]
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print(
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f"{len(direct)} direct annotations over {len(genes)} genes -> {len(rows)} after "
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f"propagation; {len(ic)} terms with information content",
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file=sys.stderr,
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)
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engine = create_engine(make_url(url).set(drivername="postgresql+psycopg"))
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with engine.begin() as conn:
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conn.execute(text("TRUNCATE gene_phenotypes RESTART IDENTITY"))
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conn.execute(text("TRUNCATE hpo_terms"))
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cursor = conn.connection.cursor()
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with cursor.copy("COPY gene_phenotypes (gene_symbol, hpo_id, hpo_name) FROM STDIN") as copy:
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for row in rows:
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copy.write_row(row)
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with cursor.copy("COPY hpo_terms (hpo_id, name, ic) FROM STDIN") as copy:
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for term, value in ic.items():
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copy.write_row((term, names.get(term, term), value))
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print(f"loaded {len(rows)} annotations and {len(ic)} terms", file=sys.stderr)
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if __name__ == "__main__":
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main()
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