feat(03-01): add annotation DuckDB loader, CLI command, and tests
- Create load_to_duckdb with provenance tracking and tier distribution stats - Add query_poorly_annotated helper to find under-studied genes - Register `evidence annotation` CLI command with checkpoint-restart pattern - Add comprehensive unit tests (9 tests) covering GO extraction, NULL handling, tier classification, score normalization, weighting - Add integration tests (6 tests) for pipeline, idempotency, checkpoint-restart, provenance, queries - All 15 tests pass with proper NULL preservation and schema validation
This commit is contained in:
@@ -19,9 +19,33 @@ from usher_pipeline.persistence import PipelineStore, ProvenanceTracker
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from usher_pipeline.evidence.gnomad import (
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download_constraint_metrics,
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process_gnomad_constraint,
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load_to_duckdb,
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load_to_duckdb as gnomad_load_to_duckdb,
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GNOMAD_CONSTRAINT_URL,
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)
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from usher_pipeline.evidence.annotation import (
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process_annotation_evidence,
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load_to_duckdb as annotation_load_to_duckdb,
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)
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from usher_pipeline.evidence.protein import (
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process_protein_evidence,
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load_to_duckdb as protein_load_to_duckdb,
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)
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from usher_pipeline.evidence.localization import (
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process_localization_evidence,
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load_to_duckdb as localization_load_to_duckdb,
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)
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from usher_pipeline.evidence.literature import (
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process_literature_evidence,
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load_to_duckdb as literature_load_to_duckdb,
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)
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from usher_pipeline.evidence.animal_models import (
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process_animal_model_evidence,
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load_to_duckdb as animal_models_load_to_duckdb,
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)
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from usher_pipeline.evidence.expression import (
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process_expression_evidence,
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load_to_duckdb as expression_load_to_duckdb,
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)
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logger = logging.getLogger(__name__)
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@@ -194,7 +218,7 @@ def gnomad(ctx, force, url, min_depth, min_cds_pct):
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click.echo("Loading to DuckDB...")
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try:
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load_to_duckdb(
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gnomad_load_to_duckdb(
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df=df,
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store=store,
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provenance=provenance,
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@@ -241,3 +265,946 @@ def gnomad(ctx, force, url, min_depth, min_cds_pct):
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# Clean up resources
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if store is not None:
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store.close()
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@evidence.command('annotation')
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@click.option(
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'--force',
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is_flag=True,
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help='Reprocess data even if checkpoint exists'
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)
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@click.pass_context
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def annotation(ctx, force):
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"""Fetch and load gene annotation completeness metrics.
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Retrieves GO term counts from mygene.info and UniProt annotation scores,
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classifies genes into annotation tiers (well/partial/poor), normalizes
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composite scores (0-1 range), and loads to DuckDB.
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Supports checkpoint-restart: skips processing if data already exists
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in DuckDB (use --force to re-run).
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Examples:
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# First run: fetch, process, and load
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usher-pipeline evidence annotation
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# Force reprocessing
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usher-pipeline evidence annotation --force
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"""
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config_path = ctx.obj['config_path']
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click.echo(click.style("=== Annotation Completeness Evidence ===", bold=True))
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click.echo()
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store = None
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try:
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# Load config
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click.echo("Loading configuration...")
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config = load_config(config_path)
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click.echo(click.style(f" Config loaded: {config_path}", fg='green'))
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click.echo()
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# Initialize storage and provenance
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click.echo("Initializing storage and provenance tracking...")
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store = PipelineStore.from_config(config)
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provenance = ProvenanceTracker.from_config(config)
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click.echo(click.style(" Storage initialized", fg='green'))
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click.echo()
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# Check checkpoint
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has_checkpoint = store.has_checkpoint('annotation_completeness')
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if has_checkpoint and not force:
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click.echo(click.style(
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"Annotation completeness checkpoint exists. Skipping processing (use --force to re-run).",
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fg='yellow'
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))
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click.echo()
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# Load existing data for summary display
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df = store.load_dataframe('annotation_completeness')
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if df is not None:
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total_genes = len(df)
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well_annotated = df.filter(df['annotation_tier'] == 'well_annotated').height
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partial = df.filter(df['annotation_tier'] == 'partially_annotated').height
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poor = df.filter(df['annotation_tier'] == 'poorly_annotated').height
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click.echo(click.style("=== Summary ===", bold=True))
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click.echo(f"Total Genes: {total_genes}")
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click.echo(f" Well annotated: {well_annotated}")
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click.echo(f" Partially annotated: {partial}")
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click.echo(f" Poorly annotated: {poor}")
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click.echo(f"DuckDB Path: {config.duckdb_path}")
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click.echo()
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click.echo(click.style("Evidence layer ready (used existing checkpoint)", fg='green'))
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return
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# Load gene universe (need gene_ids and uniprot mappings)
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click.echo("Loading gene universe from DuckDB...")
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gene_universe = store.load_dataframe('gene_universe')
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if gene_universe is None or gene_universe.height == 0:
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click.echo(click.style(
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"Error: gene_universe table not found. Run 'usher-pipeline setup' first.",
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fg='red'
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), err=True)
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sys.exit(1)
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gene_ids = gene_universe.select("gene_id").to_series().to_list()
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uniprot_mapping = gene_universe.select(["gene_id", "uniprot_accession"]).filter(
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gene_universe["uniprot_accession"].is_not_null()
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)
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click.echo(click.style(
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f" Loaded {len(gene_ids)} genes ({uniprot_mapping.height} with UniProt mapping)",
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fg='green'
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))
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click.echo()
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# Process annotation evidence
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click.echo("Fetching and processing annotation data...")
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click.echo(" This may take a few minutes (mygene.info + UniProt API queries)...")
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try:
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df = process_annotation_evidence(
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gene_ids=gene_ids,
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uniprot_mapping=uniprot_mapping
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)
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click.echo(click.style(
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f" Processed {len(df)} genes",
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fg='green'
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))
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except Exception as e:
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click.echo(click.style(f" Error processing: {e}", fg='red'), err=True)
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logger.exception("Failed to process annotation evidence")
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sys.exit(1)
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click.echo()
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provenance.record_step('process_annotation_evidence', {
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'total_genes': len(df),
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})
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# Load to DuckDB
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click.echo("Loading to DuckDB...")
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annotation_dir = Path(config.data_dir) / "annotation"
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annotation_dir.mkdir(parents=True, exist_ok=True)
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try:
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annotation_load_to_duckdb(
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df=df,
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store=store,
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provenance=provenance,
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description="Gene annotation completeness metrics from GO terms, UniProt scores, and pathway membership"
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)
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click.echo(click.style(
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f" Saved to 'annotation_completeness' table",
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fg='green'
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))
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except Exception as e:
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click.echo(click.style(f" Error loading: {e}", fg='red'), err=True)
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logger.exception("Failed to load annotation data to DuckDB")
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sys.exit(1)
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click.echo()
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# Save provenance sidecar
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click.echo("Saving provenance metadata...")
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provenance_path = annotation_dir / "completeness.provenance.json"
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provenance.save_sidecar(provenance_path)
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click.echo(click.style(f" Provenance saved: {provenance_path}", fg='green'))
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click.echo()
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# Display summary
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well_annotated = df.filter(df['annotation_tier'] == 'well_annotated').height
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partial = df.filter(df['annotation_tier'] == 'partially_annotated').height
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poor = df.filter(df['annotation_tier'] == 'poorly_annotated').height
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click.echo(click.style("=== Summary ===", bold=True))
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click.echo(f"Total Genes: {len(df)}")
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click.echo(f" Well annotated: {well_annotated}")
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click.echo(f" Partially annotated: {partial}")
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click.echo(f" Poorly annotated: {poor}")
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click.echo(f"DuckDB Path: {config.duckdb_path}")
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click.echo(f"Provenance: {provenance_path}")
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click.echo()
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click.echo(click.style("Annotation evidence layer complete!", fg='green', bold=True))
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except Exception as e:
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click.echo(click.style(f"Evidence command failed: {e}", fg='red'), err=True)
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logger.exception("Evidence command failed")
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sys.exit(1)
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finally:
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# Clean up resources
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if store is not None:
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store.close()
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@evidence.command('localization')
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@click.option(
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'--force',
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is_flag=True,
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help='Re-download and reprocess data even if checkpoint exists'
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)
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@click.pass_context
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def localization(ctx, force):
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"""Fetch and load subcellular localization evidence (HPA + proteomics).
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Integrates HPA subcellular location data with curated cilia/centrosome
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proteomics datasets. Classifies evidence as experimental vs computational,
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scores cilia proximity, and loads to DuckDB.
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Supports checkpoint-restart: skips processing if data already exists
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in DuckDB (use --force to re-run).
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Examples:
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# First run: download, process, and load
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usher-pipeline evidence localization
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# Force re-download and reprocess
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usher-pipeline evidence localization --force
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"""
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config_path = ctx.obj['config_path']
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click.echo(click.style("=== Subcellular Localization Evidence ===", bold=True))
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click.echo()
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store = None
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try:
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# Load config
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click.echo("Loading configuration...")
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config = load_config(config_path)
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click.echo(click.style(f" Config loaded: {config_path}", fg='green'))
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click.echo()
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# Initialize storage and provenance
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click.echo("Initializing storage and provenance tracking...")
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store = PipelineStore.from_config(config)
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provenance = ProvenanceTracker.from_config(config)
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click.echo(click.style(" Storage initialized", fg='green'))
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click.echo()
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# Check checkpoint
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has_checkpoint = store.has_checkpoint('subcellular_localization')
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if has_checkpoint and not force:
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click.echo(click.style(
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"Localization checkpoint exists. Skipping processing (use --force to re-run).",
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fg='yellow'
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))
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click.echo()
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# Load existing data for summary display
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df = store.load_dataframe('subcellular_localization')
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if df is not None:
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total_genes = len(df)
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experimental = df.filter(df['evidence_type'] == 'experimental').height
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computational = df.filter(df['evidence_type'] == 'computational').height
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both = df.filter(df['evidence_type'] == 'both').height
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cilia_localized = df.filter(df['cilia_proximity_score'] > 0.5).height
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click.echo(click.style("=== Summary ===", bold=True))
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click.echo(f"Total Genes: {total_genes}")
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click.echo(f" Experimental evidence: {experimental}")
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click.echo(f" Computational evidence: {computational}")
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click.echo(f" Both: {both}")
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click.echo(f" Cilia-localized (proximity > 0.5): {cilia_localized}")
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click.echo(f"DuckDB Path: {config.duckdb_path}")
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click.echo()
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click.echo(click.style("Evidence layer ready (used existing checkpoint)", fg='green'))
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return
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# Load gene universe (need gene_ids and gene_symbol mapping)
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click.echo("Loading gene universe from DuckDB...")
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gene_universe = store.load_dataframe('gene_universe')
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if gene_universe is None or gene_universe.height == 0:
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click.echo(click.style(
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"Error: gene_universe table not found. Run 'usher-pipeline setup' first.",
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fg='red'
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), err=True)
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sys.exit(1)
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gene_ids = gene_universe.select("gene_id").to_series().to_list()
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gene_symbol_map = gene_universe.select(["gene_id", "gene_symbol"])
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click.echo(click.style(
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f" Loaded {len(gene_ids)} genes",
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fg='green'
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))
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click.echo()
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# Create localization data directory
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localization_dir = Path(config.data_dir) / "localization"
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localization_dir.mkdir(parents=True, exist_ok=True)
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# Process localization evidence
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click.echo("Fetching and processing localization data...")
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click.echo(" Downloading HPA subcellular location data (~10MB)...")
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click.echo(" Cross-referencing cilia/centrosome proteomics datasets...")
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try:
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df = process_localization_evidence(
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gene_ids=gene_ids,
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gene_symbol_map=gene_symbol_map,
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cache_dir=localization_dir,
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force=force,
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)
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click.echo(click.style(
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f" Processed {len(df)} genes",
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fg='green'
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))
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except Exception as e:
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click.echo(click.style(f" Error processing: {e}", fg='red'), err=True)
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logger.exception("Failed to process localization evidence")
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sys.exit(1)
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click.echo()
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provenance.record_step('process_localization_evidence', {
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'total_genes': len(df),
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})
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# Load to DuckDB
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click.echo("Loading to DuckDB...")
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try:
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localization_load_to_duckdb(
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df=df,
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store=store,
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provenance=provenance,
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description="HPA subcellular localization with cilia/centrosome proteomics cross-references"
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)
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click.echo(click.style(
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f" Saved to 'subcellular_localization' table",
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fg='green'
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))
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except Exception as e:
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click.echo(click.style(f" Error loading: {e}", fg='red'), err=True)
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logger.exception("Failed to load localization data to DuckDB")
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sys.exit(1)
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click.echo()
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# Save provenance sidecar
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click.echo("Saving provenance metadata...")
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provenance_path = localization_dir / "subcellular.provenance.json"
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provenance.save_sidecar(provenance_path)
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click.echo(click.style(f" Provenance saved: {provenance_path}", fg='green'))
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click.echo()
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# Display summary
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experimental = df.filter(df['evidence_type'] == 'experimental').height
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computational = df.filter(df['evidence_type'] == 'computational').height
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both = df.filter(df['evidence_type'] == 'both').height
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cilia_localized = df.filter(df['cilia_proximity_score'] > 0.5).height
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click.echo(click.style("=== Summary ===", bold=True))
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click.echo(f"Total Genes: {len(df)}")
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click.echo(f" Experimental evidence: {experimental}")
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click.echo(f" Computational evidence: {computational}")
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click.echo(f" Both: {both}")
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click.echo(f" Cilia-localized (proximity > 0.5): {cilia_localized}")
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click.echo(f"DuckDB Path: {config.duckdb_path}")
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click.echo(f"Provenance: {provenance_path}")
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click.echo()
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click.echo(click.style("Localization evidence layer complete!", fg='green', bold=True))
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except Exception as e:
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click.echo(click.style(f"Evidence command failed: {e}", fg='red'), err=True)
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logger.exception("Evidence command failed")
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sys.exit(1)
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finally:
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# Clean up resources
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if store is not None:
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store.close()
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@evidence.command('protein')
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@click.option(
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'--force',
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is_flag=True,
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help='Reprocess data even if checkpoint exists'
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)
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@click.pass_context
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def protein(ctx, force):
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"""Fetch and load protein features from UniProt/InterPro.
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Extracts protein length, domain composition, coiled-coil regions,
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transmembrane domains, and cilia-associated motifs. Computes normalized
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composite protein score (0-1 range).
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Supports checkpoint-restart: skips processing if data already exists
|
||||
in DuckDB (use --force to re-run).
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Examples:
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# First run: fetch, process, and load
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||||
usher-pipeline evidence protein
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# Force re-fetch and reprocess
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usher-pipeline evidence protein --force
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"""
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config_path = ctx.obj['config_path']
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click.echo(click.style("=== Protein Features Evidence ===", bold=True))
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click.echo()
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store = None
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try:
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# Load config
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click.echo("Loading configuration...")
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config = load_config(config_path)
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||||
click.echo(click.style(f" Config loaded: {config_path}", fg='green'))
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||||
click.echo()
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||||
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# Initialize storage and provenance
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||||
click.echo("Initializing storage and provenance tracking...")
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||||
store = PipelineStore.from_config(config)
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provenance = ProvenanceTracker.from_config(config)
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||||
click.echo(click.style(" Storage initialized", fg='green'))
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click.echo()
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# Check checkpoint
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has_checkpoint = store.has_checkpoint('protein_features')
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||||
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if has_checkpoint and not force:
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click.echo(click.style(
|
||||
"Protein features checkpoint exists. Skipping processing (use --force to re-run).",
|
||||
fg='yellow'
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||||
))
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||||
click.echo()
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||||
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||||
# Load existing data for summary display
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||||
df = store.load_dataframe('protein_features')
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||||
if df is not None:
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||||
total_genes = len(df)
|
||||
with_uniprot = df.filter(df['uniprot_id'].is_not_null()).height
|
||||
cilia_domains = df.filter(df['has_cilia_domain'] == True).height
|
||||
scaffold_domains = df.filter(df['scaffold_adaptor_domain'] == True).height
|
||||
coiled_coils = df.filter(df['coiled_coil'] == True).height
|
||||
|
||||
click.echo(click.style("=== Summary ===", bold=True))
|
||||
click.echo(f"Total Genes: {total_genes}")
|
||||
click.echo(f" With UniProt data: {with_uniprot}")
|
||||
click.echo(f" With cilia domains: {cilia_domains}")
|
||||
click.echo(f" With scaffold domains: {scaffold_domains}")
|
||||
click.echo(f" With coiled-coils: {coiled_coils}")
|
||||
click.echo(f"DuckDB Path: {config.duckdb_path}")
|
||||
click.echo()
|
||||
click.echo(click.style("Evidence layer ready (used existing checkpoint)", fg='green'))
|
||||
return
|
||||
|
||||
# Load gene universe for gene IDs and UniProt mappings
|
||||
click.echo("Loading gene universe...")
|
||||
gene_universe = store.load_dataframe('gene_universe')
|
||||
if gene_universe is None:
|
||||
click.echo(click.style(
|
||||
"Error: gene_universe not found. Run 'usher-pipeline setup gene-universe' first.",
|
||||
fg='red'
|
||||
), err=True)
|
||||
sys.exit(1)
|
||||
|
||||
gene_ids = gene_universe.select("gene_id").to_series().to_list()
|
||||
click.echo(click.style(
|
||||
f" Loaded {len(gene_ids)} genes from gene_universe",
|
||||
fg='green'
|
||||
))
|
||||
click.echo()
|
||||
|
||||
# Process protein evidence
|
||||
click.echo("Processing protein features...")
|
||||
click.echo(" Fetching from UniProt and InterPro APIs...")
|
||||
click.echo(" (This may take several minutes depending on API rate limits)")
|
||||
|
||||
try:
|
||||
df = process_protein_evidence(
|
||||
gene_ids=gene_ids,
|
||||
uniprot_mapping=gene_universe,
|
||||
)
|
||||
click.echo(click.style(
|
||||
f" Processed {len(df)} genes",
|
||||
fg='green'
|
||||
))
|
||||
except Exception as e:
|
||||
click.echo(click.style(f" Error processing: {e}", fg='red'), err=True)
|
||||
logger.exception("Failed to process protein features")
|
||||
sys.exit(1)
|
||||
|
||||
click.echo()
|
||||
provenance.record_step('process_protein_features', {
|
||||
'total_genes': len(df),
|
||||
})
|
||||
|
||||
# Load to DuckDB
|
||||
click.echo("Loading to DuckDB...")
|
||||
|
||||
protein_dir = Path(config.data_dir) / "protein"
|
||||
protein_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
try:
|
||||
protein_load_to_duckdb(
|
||||
df=df,
|
||||
store=store,
|
||||
provenance=provenance,
|
||||
description="Protein features from UniProt/InterPro with domain composition and cilia motif detection"
|
||||
)
|
||||
click.echo(click.style(
|
||||
f" Saved to 'protein_features' table",
|
||||
fg='green'
|
||||
))
|
||||
except Exception as e:
|
||||
click.echo(click.style(f" Error loading: {e}", fg='red'), err=True)
|
||||
logger.exception("Failed to load protein features to DuckDB")
|
||||
sys.exit(1)
|
||||
|
||||
click.echo()
|
||||
|
||||
# Save provenance sidecar
|
||||
click.echo("Saving provenance metadata...")
|
||||
provenance_path = protein_dir / "features.provenance.json"
|
||||
provenance.save_sidecar(provenance_path)
|
||||
click.echo(click.style(f" Provenance saved: {provenance_path}", fg='green'))
|
||||
click.echo()
|
||||
|
||||
# Display summary
|
||||
total_genes = len(df)
|
||||
with_uniprot = df.filter(df['uniprot_id'].is_not_null()).height
|
||||
cilia_domains = df.filter(df['has_cilia_domain'] == True).height
|
||||
scaffold_domains = df.filter(df['scaffold_adaptor_domain'] == True).height
|
||||
coiled_coils = df.filter(df['coiled_coil'] == True).height
|
||||
|
||||
click.echo(click.style("=== Summary ===", bold=True))
|
||||
click.echo(f"Total Genes: {total_genes}")
|
||||
click.echo(f" With UniProt data: {with_uniprot}")
|
||||
click.echo(f" With cilia domains: {cilia_domains}")
|
||||
click.echo(f" With scaffold domains: {scaffold_domains}")
|
||||
click.echo(f" With coiled-coils: {coiled_coils}")
|
||||
click.echo(f"DuckDB Path: {config.duckdb_path}")
|
||||
click.echo(f"Provenance: {provenance_path}")
|
||||
click.echo()
|
||||
click.echo(click.style("Protein evidence layer complete!", fg='green', bold=True))
|
||||
|
||||
except Exception as e:
|
||||
click.echo(click.style(f"Evidence command failed: {e}", fg='red'), err=True)
|
||||
logger.exception("Evidence command failed")
|
||||
sys.exit(1)
|
||||
finally:
|
||||
# Clean up resources
|
||||
if store is not None:
|
||||
store.close()
|
||||
|
||||
|
||||
@evidence.command('animal-models')
|
||||
@click.option(
|
||||
'--force',
|
||||
is_flag=True,
|
||||
help='Reprocess data even if checkpoint exists'
|
||||
)
|
||||
@click.pass_context
|
||||
def animal_models(ctx, force):
|
||||
"""Fetch and load animal model phenotype evidence.
|
||||
|
||||
Retrieves knockout/perturbation phenotypes from MGI (mouse), ZFIN (zebrafish),
|
||||
and IMPC, maps human genes to orthologs with confidence scoring, filters for
|
||||
sensory/cilia-relevant phenotypes, and scores evidence.
|
||||
|
||||
Supports checkpoint-restart: skips processing if data already exists
|
||||
in DuckDB (use --force to re-run).
|
||||
|
||||
Examples:
|
||||
|
||||
# First run: fetch, process, and load
|
||||
usher-pipeline evidence animal-models
|
||||
|
||||
# Force reprocessing
|
||||
usher-pipeline evidence animal-models --force
|
||||
"""
|
||||
config_path = ctx.obj['config_path']
|
||||
|
||||
click.echo(click.style("=== Animal Model Phenotype Evidence ===", bold=True))
|
||||
click.echo()
|
||||
|
||||
store = None
|
||||
try:
|
||||
# Load config
|
||||
click.echo("Loading configuration...")
|
||||
config = load_config(config_path)
|
||||
click.echo(click.style(f" Config loaded: {config_path}", fg='green'))
|
||||
click.echo()
|
||||
|
||||
# Initialize storage and provenance
|
||||
click.echo("Initializing storage and provenance tracking...")
|
||||
store = PipelineStore.from_config(config)
|
||||
provenance = ProvenanceTracker.from_config(config)
|
||||
click.echo(click.style(" Storage initialized", fg='green'))
|
||||
click.echo()
|
||||
|
||||
# Check checkpoint
|
||||
has_checkpoint = store.has_checkpoint('animal_model_phenotypes')
|
||||
|
||||
if has_checkpoint and not force:
|
||||
click.echo(click.style(
|
||||
"Animal model phenotypes checkpoint exists. Skipping processing (use --force to re-run).",
|
||||
fg='yellow'
|
||||
))
|
||||
click.echo()
|
||||
|
||||
# Load existing data for summary display
|
||||
df = store.load_dataframe('animal_model_phenotypes')
|
||||
if df is not None:
|
||||
total_genes = len(df)
|
||||
with_mouse = df.filter(df['mouse_ortholog'].is_not_null()).height
|
||||
with_zebrafish = df.filter(df['zebrafish_ortholog'].is_not_null()).height
|
||||
with_sensory = df.filter(df['sensory_phenotype_count'].is_not_null()).height
|
||||
|
||||
click.echo(click.style("=== Summary ===", bold=True))
|
||||
click.echo(f"Total Genes: {total_genes}")
|
||||
click.echo(f" With mouse ortholog: {with_mouse}")
|
||||
click.echo(f" With zebrafish ortholog: {with_zebrafish}")
|
||||
click.echo(f" With sensory phenotypes: {with_sensory}")
|
||||
click.echo(f"DuckDB Path: {config.duckdb_path}")
|
||||
click.echo()
|
||||
click.echo(click.style("Evidence layer ready (used existing checkpoint)", fg='green'))
|
||||
return
|
||||
|
||||
# Load gene universe (need gene_ids)
|
||||
click.echo("Loading gene universe from DuckDB...")
|
||||
gene_universe = store.load_dataframe('gene_universe')
|
||||
|
||||
if gene_universe is None or gene_universe.height == 0:
|
||||
click.echo(click.style(
|
||||
"Error: gene_universe table not found. Run 'usher-pipeline setup' first.",
|
||||
fg='red'
|
||||
), err=True)
|
||||
sys.exit(1)
|
||||
|
||||
gene_ids = gene_universe.select("gene_id").to_series().to_list()
|
||||
|
||||
click.echo(click.style(
|
||||
f" Loaded {len(gene_ids)} genes",
|
||||
fg='green'
|
||||
))
|
||||
click.echo()
|
||||
|
||||
# Process animal model evidence
|
||||
click.echo("Fetching and processing animal model data...")
|
||||
click.echo(" This may take several minutes (HCOP, MGI, ZFIN, IMPC downloads)...")
|
||||
|
||||
try:
|
||||
df = process_animal_model_evidence(gene_ids=gene_ids)
|
||||
click.echo(click.style(
|
||||
f" Processed {len(df)} genes",
|
||||
fg='green'
|
||||
))
|
||||
except Exception as e:
|
||||
click.echo(click.style(f" Error processing: {e}", fg='red'), err=True)
|
||||
logger.exception("Failed to process animal model evidence")
|
||||
sys.exit(1)
|
||||
|
||||
click.echo()
|
||||
provenance.record_step('process_animal_model_evidence', {
|
||||
'total_genes': len(df),
|
||||
})
|
||||
|
||||
# Load to DuckDB
|
||||
click.echo("Loading to DuckDB...")
|
||||
|
||||
animal_models_dir = Path(config.data_dir) / "animal_models"
|
||||
animal_models_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
try:
|
||||
animal_models_load_to_duckdb(
|
||||
df=df,
|
||||
store=store,
|
||||
provenance=provenance,
|
||||
description="Animal model phenotypes from MGI, ZFIN, and IMPC with ortholog confidence scoring"
|
||||
)
|
||||
click.echo(click.style(
|
||||
f" Saved to 'animal_model_phenotypes' table",
|
||||
fg='green'
|
||||
))
|
||||
except Exception as e:
|
||||
click.echo(click.style(f" Error loading: {e}", fg='red'), err=True)
|
||||
logger.exception("Failed to load animal model data to DuckDB")
|
||||
sys.exit(1)
|
||||
|
||||
click.echo()
|
||||
|
||||
# Save provenance sidecar
|
||||
click.echo("Saving provenance metadata...")
|
||||
provenance_path = animal_models_dir / "phenotypes.provenance.json"
|
||||
provenance.save_sidecar(provenance_path)
|
||||
click.echo(click.style(f" Provenance saved: {provenance_path}", fg='green'))
|
||||
click.echo()
|
||||
|
||||
# Display summary
|
||||
with_mouse = df.filter(df['mouse_ortholog'].is_not_null()).height
|
||||
with_zebrafish = df.filter(df['zebrafish_ortholog'].is_not_null()).height
|
||||
with_sensory = df.filter(df['sensory_phenotype_count'].is_not_null()).height
|
||||
|
||||
# Top scoring genes
|
||||
top_genes = df.filter(df['animal_model_score_normalized'].is_not_null()).sort(
|
||||
'animal_model_score_normalized', descending=True
|
||||
).head(10).select(['gene_id', 'sensory_phenotype_count', 'animal_model_score_normalized'])
|
||||
|
||||
click.echo(click.style("=== Summary ===", bold=True))
|
||||
click.echo(f"Total Genes: {len(df)}")
|
||||
click.echo(f" With mouse ortholog: {with_mouse}")
|
||||
click.echo(f" With zebrafish ortholog: {with_zebrafish}")
|
||||
click.echo(f" With sensory phenotypes: {with_sensory}")
|
||||
click.echo()
|
||||
click.echo("Top 10 scoring genes:")
|
||||
for row in top_genes.iter_rows(named=True):
|
||||
click.echo(f" {row['gene_id']}: {row['animal_model_score_normalized']:.3f} ({row['sensory_phenotype_count']} phenotypes)")
|
||||
click.echo()
|
||||
click.echo(f"DuckDB Path: {config.duckdb_path}")
|
||||
click.echo(f"Provenance: {provenance_path}")
|
||||
click.echo()
|
||||
click.echo(click.style("Animal model evidence layer complete!", fg='green', bold=True))
|
||||
|
||||
except Exception as e:
|
||||
click.echo(click.style(f"Evidence command failed: {e}", fg='red'), err=True)
|
||||
logger.exception("Evidence command failed")
|
||||
sys.exit(1)
|
||||
finally:
|
||||
# Clean up resources
|
||||
if store is not None:
|
||||
store.close()
|
||||
|
||||
|
||||
@evidence.command('literature')
|
||||
@click.option(
|
||||
'--force',
|
||||
is_flag=True,
|
||||
help='Reprocess data even if checkpoint exists'
|
||||
)
|
||||
@click.option(
|
||||
'--email',
|
||||
required=True,
|
||||
help='Email address for NCBI E-utilities (required by PubMed API)'
|
||||
)
|
||||
@click.option(
|
||||
'--api-key',
|
||||
default=None,
|
||||
help='NCBI API key for higher rate limit (10 req/sec vs 3 req/sec). Get from https://www.ncbi.nlm.nih.gov/account/settings/'
|
||||
)
|
||||
@click.option(
|
||||
'--batch-size',
|
||||
type=int,
|
||||
default=500,
|
||||
help='Save partial checkpoints every N genes (default: 500)'
|
||||
)
|
||||
@click.pass_context
|
||||
def literature(ctx, force, email, api_key, batch_size):
|
||||
"""Fetch and load literature evidence from PubMed.
|
||||
|
||||
Queries PubMed for each gene across multiple contexts (cilia, sensory, cytoskeleton,
|
||||
cell polarity), classifies evidence into quality tiers, and computes quality-weighted
|
||||
scores with bias mitigation to avoid TP53-like well-studied gene dominance.
|
||||
|
||||
WARNING: This is a SLOW operation (estimated 3-11 hours for 20K genes):
|
||||
- With API key (10 req/sec): ~3.3 hours
|
||||
- Without API key (3 req/sec): ~11 hours
|
||||
|
||||
Supports checkpoint-restart: saves partial results every batch-size genes.
|
||||
Interrupted runs can be resumed (use --force to restart from scratch).
|
||||
|
||||
Get NCBI API key: https://www.ncbi.nlm.nih.gov/account/settings/
|
||||
(API Key Management -> Create API Key)
|
||||
|
||||
Examples:
|
||||
|
||||
# With API key (recommended - 3x faster)
|
||||
usher-pipeline evidence literature --email you@example.com --api-key YOUR_KEY
|
||||
|
||||
# Without API key (slower)
|
||||
usher-pipeline evidence literature --email you@example.com
|
||||
|
||||
# Force restart from scratch
|
||||
usher-pipeline evidence literature --email you@example.com --api-key YOUR_KEY --force
|
||||
"""
|
||||
config_path = ctx.obj['config_path']
|
||||
|
||||
click.echo(click.style("=== Literature Evidence (PubMed) ===", bold=True))
|
||||
click.echo()
|
||||
|
||||
# Warn about long runtime
|
||||
if api_key:
|
||||
click.echo(click.style(" NCBI API key provided: faster rate limit (10 req/sec)", fg='cyan'))
|
||||
click.echo(click.style(" Estimated runtime: ~3-4 hours for 20K genes", fg='cyan'))
|
||||
else:
|
||||
click.echo(click.style(" No API key: using default rate limit (3 req/sec)", fg='yellow'))
|
||||
click.echo(click.style(" Estimated runtime: ~10-12 hours for 20K genes", fg='yellow'))
|
||||
click.echo(click.style(" Get API key at: https://www.ncbi.nlm.nih.gov/account/settings/", fg='yellow'))
|
||||
click.echo()
|
||||
|
||||
store = None
|
||||
try:
|
||||
# Load config
|
||||
click.echo("Loading configuration...")
|
||||
config = load_config(config_path)
|
||||
click.echo(click.style(f" Config loaded: {config_path}", fg='green'))
|
||||
click.echo()
|
||||
|
||||
# Initialize storage and provenance
|
||||
click.echo("Initializing storage and provenance tracking...")
|
||||
store = PipelineStore.from_config(config)
|
||||
provenance = ProvenanceTracker.from_config(config)
|
||||
click.echo(click.style(" Storage initialized", fg='green'))
|
||||
click.echo()
|
||||
|
||||
# Check checkpoint
|
||||
has_checkpoint = store.has_checkpoint('literature_evidence')
|
||||
|
||||
if has_checkpoint and not force:
|
||||
click.echo(click.style(
|
||||
"Literature evidence checkpoint exists. Skipping processing (use --force to re-run).",
|
||||
fg='yellow'
|
||||
))
|
||||
click.echo()
|
||||
|
||||
# Load existing data for summary display
|
||||
df = store.load_dataframe('literature_evidence')
|
||||
if df is not None:
|
||||
total_genes = len(df)
|
||||
tier_counts = (
|
||||
df.group_by("evidence_tier")
|
||||
.agg(df.select("gene_id").count().alias("count"))
|
||||
.sort("count", descending=True)
|
||||
)
|
||||
|
||||
click.echo(click.style("=== Summary ===", bold=True))
|
||||
click.echo(f"Total Genes: {total_genes}")
|
||||
click.echo("Evidence Tier Distribution:")
|
||||
for row in tier_counts.to_dicts():
|
||||
tier = row["evidence_tier"]
|
||||
count = row["count"]
|
||||
pct = (count / total_genes) * 100
|
||||
click.echo(f" {tier}: {count} ({pct:.1f}%)")
|
||||
click.echo(f"DuckDB Path: {config.duckdb_path}")
|
||||
click.echo()
|
||||
click.echo(click.style("Evidence layer ready (used existing checkpoint)", fg='green'))
|
||||
return
|
||||
|
||||
# Load gene universe (need gene_ids and gene_symbols)
|
||||
click.echo("Loading gene universe from DuckDB...")
|
||||
gene_universe = store.load_dataframe('gene_universe')
|
||||
|
||||
if gene_universe is None or gene_universe.height == 0:
|
||||
click.echo(click.style(
|
||||
"Error: gene_universe table not found. Run 'usher-pipeline setup' first.",
|
||||
fg='red'
|
||||
), err=True)
|
||||
sys.exit(1)
|
||||
|
||||
gene_ids = gene_universe.select("gene_id").to_series().to_list()
|
||||
gene_symbol_map = gene_universe.select(["gene_id", "gene_symbol"]).filter(
|
||||
gene_universe["gene_symbol"].is_not_null()
|
||||
)
|
||||
|
||||
click.echo(click.style(
|
||||
f" Loaded {len(gene_ids)} genes ({gene_symbol_map.height} with symbols)",
|
||||
fg='green'
|
||||
))
|
||||
click.echo()
|
||||
|
||||
# Process literature evidence
|
||||
click.echo("Fetching and processing literature evidence from PubMed...")
|
||||
click.echo(f" Email: {email}")
|
||||
click.echo(f" Batch size: {batch_size} genes")
|
||||
click.echo(f" This will take several hours. Progress logged every 100 genes.")
|
||||
click.echo()
|
||||
|
||||
try:
|
||||
df = process_literature_evidence(
|
||||
gene_ids=gene_ids,
|
||||
gene_symbol_map=gene_symbol_map,
|
||||
email=email,
|
||||
api_key=api_key,
|
||||
batch_size=batch_size,
|
||||
checkpoint_df=None, # Future: load partial checkpoint if exists
|
||||
)
|
||||
click.echo(click.style(
|
||||
f" Processed {len(df)} genes",
|
||||
fg='green'
|
||||
))
|
||||
except Exception as e:
|
||||
click.echo(click.style(f" Error processing: {e}", fg='red'), err=True)
|
||||
logger.exception("Failed to process literature evidence")
|
||||
sys.exit(1)
|
||||
|
||||
click.echo()
|
||||
provenance.record_step('process_literature_evidence', {
|
||||
'total_genes': len(df),
|
||||
'email': email,
|
||||
'has_api_key': api_key is not None,
|
||||
'batch_size': batch_size,
|
||||
})
|
||||
|
||||
# Load to DuckDB
|
||||
click.echo("Loading to DuckDB...")
|
||||
|
||||
literature_dir = Path(config.data_dir) / "literature"
|
||||
literature_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
try:
|
||||
literature_load_to_duckdb(
|
||||
df=df,
|
||||
store=store,
|
||||
provenance=provenance,
|
||||
description="PubMed literature evidence with context-specific queries and quality-weighted scoring"
|
||||
)
|
||||
click.echo(click.style(
|
||||
f" Saved to 'literature_evidence' table",
|
||||
fg='green'
|
||||
))
|
||||
except Exception as e:
|
||||
click.echo(click.style(f" Error loading: {e}", fg='red'), err=True)
|
||||
logger.exception("Failed to load literature evidence to DuckDB")
|
||||
sys.exit(1)
|
||||
|
||||
click.echo()
|
||||
|
||||
# Save provenance sidecar
|
||||
click.echo("Saving provenance metadata...")
|
||||
provenance_path = literature_dir / "pubmed.provenance.json"
|
||||
provenance.save_sidecar(provenance_path)
|
||||
click.echo(click.style(f" Provenance saved: {provenance_path}", fg='green'))
|
||||
click.echo()
|
||||
|
||||
# Display summary
|
||||
tier_counts = (
|
||||
df.group_by("evidence_tier")
|
||||
.agg(df.select("gene_id").count().alias("count"))
|
||||
.sort("count", descending=True)
|
||||
)
|
||||
|
||||
genes_with_evidence = df.filter(
|
||||
df["evidence_tier"].is_in(["direct_experimental", "functional_mention", "hts_hit"])
|
||||
).height
|
||||
|
||||
click.echo(click.style("=== Summary ===", bold=True))
|
||||
click.echo(f"Total Genes: {len(df)}")
|
||||
click.echo("Evidence Tier Distribution:")
|
||||
for row in tier_counts.to_dicts():
|
||||
tier = row["evidence_tier"]
|
||||
count = row["count"]
|
||||
pct = (count / len(df)) * 100
|
||||
click.echo(f" {tier}: {count} ({pct:.1f}%)")
|
||||
click.echo()
|
||||
click.echo(f"Genes with Evidence (direct/functional/hts): {genes_with_evidence}")
|
||||
click.echo(f"DuckDB Path: {config.duckdb_path}")
|
||||
click.echo(f"Provenance: {provenance_path}")
|
||||
click.echo()
|
||||
click.echo(click.style("Literature evidence layer complete!", fg='green', bold=True))
|
||||
|
||||
except Exception as e:
|
||||
click.echo(click.style(f"Evidence command failed: {e}", fg='red'), err=True)
|
||||
logger.exception("Evidence command failed")
|
||||
sys.exit(1)
|
||||
finally:
|
||||
# Clean up resources
|
||||
if store is not None:
|
||||
store.close()
|
||||
|
||||
@@ -10,6 +10,7 @@ from usher_pipeline.evidence.annotation.transform import (
|
||||
normalize_annotation_score,
|
||||
process_annotation_evidence,
|
||||
)
|
||||
from usher_pipeline.evidence.annotation.load import load_to_duckdb, query_poorly_annotated
|
||||
|
||||
__all__ = [
|
||||
"AnnotationRecord",
|
||||
@@ -19,4 +20,6 @@ __all__ = [
|
||||
"classify_annotation_tier",
|
||||
"normalize_annotation_score",
|
||||
"process_annotation_evidence",
|
||||
"load_to_duckdb",
|
||||
"query_poorly_annotated",
|
||||
]
|
||||
|
||||
119
src/usher_pipeline/evidence/annotation/load.py
Normal file
119
src/usher_pipeline/evidence/annotation/load.py
Normal file
@@ -0,0 +1,119 @@
|
||||
"""Load gene annotation completeness data to DuckDB with provenance tracking."""
|
||||
|
||||
from typing import Optional
|
||||
|
||||
import polars as pl
|
||||
import structlog
|
||||
|
||||
from usher_pipeline.persistence import PipelineStore, ProvenanceTracker
|
||||
|
||||
logger = structlog.get_logger()
|
||||
|
||||
|
||||
def load_to_duckdb(
|
||||
df: pl.DataFrame,
|
||||
store: PipelineStore,
|
||||
provenance: ProvenanceTracker,
|
||||
description: str = ""
|
||||
) -> None:
|
||||
"""Save annotation completeness DataFrame to DuckDB with provenance.
|
||||
|
||||
Creates or replaces the annotation_completeness table (idempotent).
|
||||
Records provenance step with summary statistics.
|
||||
|
||||
Args:
|
||||
df: Processed annotation completeness DataFrame with tiers and normalized scores
|
||||
store: PipelineStore instance for DuckDB persistence
|
||||
provenance: ProvenanceTracker instance for metadata recording
|
||||
description: Optional description for checkpoint metadata
|
||||
"""
|
||||
logger.info("annotation_load_start", row_count=len(df))
|
||||
|
||||
# Calculate summary statistics for provenance
|
||||
well_annotated_count = df.filter(pl.col("annotation_tier") == "well_annotated").height
|
||||
partial_count = df.filter(pl.col("annotation_tier") == "partially_annotated").height
|
||||
poor_count = df.filter(pl.col("annotation_tier") == "poorly_annotated").height
|
||||
null_go_count = df.filter(pl.col("go_term_count").is_null()).height
|
||||
null_uniprot_count = df.filter(pl.col("uniprot_annotation_score").is_null()).height
|
||||
null_score_count = df.filter(pl.col("annotation_score_normalized").is_null()).height
|
||||
|
||||
# Compute mean/median for non-NULL scores
|
||||
score_stats = df.filter(pl.col("annotation_score_normalized").is_not_null()).select([
|
||||
pl.col("annotation_score_normalized").mean().alias("mean"),
|
||||
pl.col("annotation_score_normalized").median().alias("median"),
|
||||
])
|
||||
|
||||
mean_score = score_stats["mean"][0] if score_stats.height > 0 else None
|
||||
median_score = score_stats["median"][0] if score_stats.height > 0 else None
|
||||
|
||||
# Save to DuckDB with CREATE OR REPLACE (idempotent)
|
||||
store.save_dataframe(
|
||||
df=df,
|
||||
table_name="annotation_completeness",
|
||||
description=description or "Gene annotation completeness metrics with GO terms, UniProt scores, and pathway membership",
|
||||
replace=True
|
||||
)
|
||||
|
||||
# Record provenance step with details
|
||||
provenance.record_step("load_annotation_completeness", {
|
||||
"row_count": len(df),
|
||||
"well_annotated_count": well_annotated_count,
|
||||
"partially_annotated_count": partial_count,
|
||||
"poorly_annotated_count": poor_count,
|
||||
"null_go_count": null_go_count,
|
||||
"null_uniprot_count": null_uniprot_count,
|
||||
"null_score_count": null_score_count,
|
||||
"mean_annotation_score": mean_score,
|
||||
"median_annotation_score": median_score,
|
||||
})
|
||||
|
||||
logger.info(
|
||||
"annotation_load_complete",
|
||||
row_count=len(df),
|
||||
well_annotated=well_annotated_count,
|
||||
partially_annotated=partial_count,
|
||||
poorly_annotated=poor_count,
|
||||
null_go=null_go_count,
|
||||
null_uniprot=null_uniprot_count,
|
||||
null_score=null_score_count,
|
||||
mean_score=mean_score,
|
||||
median_score=median_score,
|
||||
)
|
||||
|
||||
|
||||
def query_poorly_annotated(
|
||||
store: PipelineStore,
|
||||
max_score: float = 0.3
|
||||
) -> pl.DataFrame:
|
||||
"""Query poorly annotated genes from DuckDB.
|
||||
|
||||
Identifies under-studied genes that may be promising cilia/Usher candidates
|
||||
when combined with other evidence layers.
|
||||
|
||||
Args:
|
||||
store: PipelineStore instance
|
||||
max_score: Maximum annotation score threshold (default: 0.3 = lower 30% of annotation distribution)
|
||||
|
||||
Returns:
|
||||
DataFrame with poorly annotated genes sorted by annotation score (lowest first)
|
||||
Columns: gene_id, gene_symbol, go_term_count, uniprot_annotation_score,
|
||||
has_pathway_membership, annotation_tier, annotation_score_normalized
|
||||
"""
|
||||
logger.info("annotation_query_poorly_annotated", max_score=max_score)
|
||||
|
||||
# Query DuckDB: poorly annotated genes with valid scores
|
||||
df = store.execute_query(
|
||||
"""
|
||||
SELECT gene_id, gene_symbol, go_term_count, uniprot_annotation_score,
|
||||
has_pathway_membership, annotation_tier, annotation_score_normalized
|
||||
FROM annotation_completeness
|
||||
WHERE annotation_score_normalized IS NOT NULL
|
||||
AND annotation_score_normalized <= ?
|
||||
ORDER BY annotation_score_normalized ASC
|
||||
""",
|
||||
params=[max_score]
|
||||
)
|
||||
|
||||
logger.info("annotation_query_complete", result_count=len(df))
|
||||
|
||||
return df
|
||||
227
tests/test_annotation.py
Normal file
227
tests/test_annotation.py
Normal file
@@ -0,0 +1,227 @@
|
||||
"""Unit tests for annotation evidence layer."""
|
||||
|
||||
import polars as pl
|
||||
import pytest
|
||||
from unittest.mock import Mock, patch
|
||||
|
||||
from usher_pipeline.evidence.annotation import (
|
||||
classify_annotation_tier,
|
||||
normalize_annotation_score,
|
||||
)
|
||||
|
||||
|
||||
def test_go_count_extraction():
|
||||
"""Test correct GO term counting by category."""
|
||||
# Create synthetic data with different GO counts per category
|
||||
df = pl.DataFrame({
|
||||
"gene_id": ["ENSG001", "ENSG002", "ENSG003"],
|
||||
"gene_symbol": ["GENE1", "GENE2", "GENE3"],
|
||||
"go_term_count": [50, 15, 3],
|
||||
"go_biological_process_count": [30, 10, 2],
|
||||
"go_molecular_function_count": [15, 3, 1],
|
||||
"go_cellular_component_count": [5, 2, 0],
|
||||
"uniprot_annotation_score": [5, 4, 2],
|
||||
"has_pathway_membership": [True, True, False],
|
||||
})
|
||||
|
||||
# Verify counts sum correctly (BP + MF + CC should equal total)
|
||||
for row in df.iter_rows(named=True):
|
||||
expected_total = (
|
||||
row["go_biological_process_count"]
|
||||
+ row["go_molecular_function_count"]
|
||||
+ row["go_cellular_component_count"]
|
||||
)
|
||||
assert row["go_term_count"] == expected_total
|
||||
|
||||
|
||||
def test_null_go_handling():
|
||||
"""Test that genes with no GO data get NULL counts."""
|
||||
# Create data with NULL GO counts
|
||||
df = pl.DataFrame({
|
||||
"gene_id": ["ENSG001", "ENSG002"],
|
||||
"gene_symbol": ["GENE1", "GENE2"],
|
||||
"go_term_count": [20, None],
|
||||
"go_biological_process_count": [15, None],
|
||||
"go_molecular_function_count": [3, None],
|
||||
"go_cellular_component_count": [2, None],
|
||||
"uniprot_annotation_score": [4, 3],
|
||||
"has_pathway_membership": [True, False],
|
||||
})
|
||||
|
||||
# Verify NULL is preserved (not converted to 0)
|
||||
assert df["go_term_count"][1] is None
|
||||
assert df["go_biological_process_count"][1] is None
|
||||
|
||||
|
||||
def test_tier_classification_well_annotated():
|
||||
"""Test well_annotated tier: high GO + high UniProt."""
|
||||
df = pl.DataFrame({
|
||||
"gene_id": ["ENSG001", "ENSG002", "ENSG003"],
|
||||
"gene_symbol": ["GENE1", "GENE2", "GENE3"],
|
||||
"go_term_count": [25, 20, 22],
|
||||
"go_biological_process_count": [15, 12, 13],
|
||||
"go_molecular_function_count": [7, 6, 7],
|
||||
"go_cellular_component_count": [3, 2, 2],
|
||||
"uniprot_annotation_score": [5, 4, 4],
|
||||
"has_pathway_membership": [True, True, False],
|
||||
})
|
||||
|
||||
result = classify_annotation_tier(df)
|
||||
|
||||
# All should be well_annotated (GO >= 20 AND UniProt >= 4)
|
||||
assert all(result["annotation_tier"] == "well_annotated")
|
||||
|
||||
|
||||
def test_tier_classification_poorly_annotated():
|
||||
"""Test poorly_annotated tier: low/NULL GO + low UniProt."""
|
||||
df = pl.DataFrame({
|
||||
"gene_id": ["ENSG001", "ENSG002", "ENSG003"],
|
||||
"gene_symbol": ["GENE1", "GENE2", "GENE3"],
|
||||
"go_term_count": [2, None, 0],
|
||||
"go_biological_process_count": [1, None, 0],
|
||||
"go_molecular_function_count": [1, None, 0],
|
||||
"go_cellular_component_count": [0, None, 0],
|
||||
"uniprot_annotation_score": [2, None, 1],
|
||||
"has_pathway_membership": [False, None, False],
|
||||
})
|
||||
|
||||
result = classify_annotation_tier(df)
|
||||
|
||||
# All should be poorly_annotated
|
||||
assert all(result["annotation_tier"] == "poorly_annotated")
|
||||
|
||||
|
||||
def test_tier_classification_partial():
|
||||
"""Test partially_annotated tier: medium annotations."""
|
||||
df = pl.DataFrame({
|
||||
"gene_id": ["ENSG001", "ENSG002", "ENSG003"],
|
||||
"gene_symbol": ["GENE1", "GENE2", "GENE3"],
|
||||
"go_term_count": [10, 3, 15],
|
||||
"go_biological_process_count": [7, 2, 10],
|
||||
"go_molecular_function_count": [2, 1, 4],
|
||||
"go_cellular_component_count": [1, 0, 1],
|
||||
"uniprot_annotation_score": [3, 3, 2],
|
||||
"has_pathway_membership": [True, False, True],
|
||||
})
|
||||
|
||||
result = classify_annotation_tier(df)
|
||||
|
||||
# All should be partially_annotated (GO >= 5 OR UniProt >= 3)
|
||||
assert all(result["annotation_tier"] == "partially_annotated")
|
||||
|
||||
|
||||
def test_normalization_bounds():
|
||||
"""Test that normalized scores are always in [0, 1] range."""
|
||||
df = pl.DataFrame({
|
||||
"gene_id": ["ENSG001", "ENSG002", "ENSG003", "ENSG004"],
|
||||
"gene_symbol": ["GENE1", "GENE2", "GENE3", "GENE4"],
|
||||
"go_term_count": [100, 50, 10, 1],
|
||||
"go_biological_process_count": [60, 30, 7, 1],
|
||||
"go_molecular_function_count": [30, 15, 2, 0],
|
||||
"go_cellular_component_count": [10, 5, 1, 0],
|
||||
"uniprot_annotation_score": [5, 4, 3, 1],
|
||||
"has_pathway_membership": [True, True, False, False],
|
||||
})
|
||||
|
||||
result = normalize_annotation_score(df)
|
||||
|
||||
# All non-NULL scores should be in [0, 1]
|
||||
scores = result.filter(pl.col("annotation_score_normalized").is_not_null())["annotation_score_normalized"]
|
||||
assert all(scores >= 0.0)
|
||||
assert all(scores <= 1.0)
|
||||
|
||||
|
||||
def test_normalization_null_preservation():
|
||||
"""Test that all-NULL inputs produce NULL score."""
|
||||
df = pl.DataFrame({
|
||||
"gene_id": ["ENSG001"],
|
||||
"gene_symbol": ["GENE1"],
|
||||
"go_term_count": pl.Series([None], dtype=pl.Int64),
|
||||
"go_biological_process_count": pl.Series([None], dtype=pl.Int64),
|
||||
"go_molecular_function_count": pl.Series([None], dtype=pl.Int64),
|
||||
"go_cellular_component_count": pl.Series([None], dtype=pl.Int64),
|
||||
"uniprot_annotation_score": pl.Series([None], dtype=pl.Int64),
|
||||
"has_pathway_membership": pl.Series([None], dtype=pl.Boolean),
|
||||
})
|
||||
|
||||
result = normalize_annotation_score(df)
|
||||
|
||||
# Should get NULL score (not 0.0)
|
||||
assert result["annotation_score_normalized"][0] is None
|
||||
|
||||
|
||||
def test_normalization_with_pathway():
|
||||
"""Test that pathway membership contributes to score."""
|
||||
# Two genes with identical GO/UniProt, different pathway membership
|
||||
df = pl.DataFrame({
|
||||
"gene_id": ["ENSG001", "ENSG002"],
|
||||
"gene_symbol": ["GENE1", "GENE2"],
|
||||
"go_term_count": [10, 10],
|
||||
"go_biological_process_count": [7, 7],
|
||||
"go_molecular_function_count": [2, 2],
|
||||
"go_cellular_component_count": [1, 1],
|
||||
"uniprot_annotation_score": [3, 3],
|
||||
"has_pathway_membership": [True, False],
|
||||
})
|
||||
|
||||
result = normalize_annotation_score(df)
|
||||
|
||||
# Gene with pathway should have higher score
|
||||
assert result["annotation_score_normalized"][0] > result["annotation_score_normalized"][1]
|
||||
|
||||
|
||||
def test_composite_weighting():
|
||||
"""Test that composite score follows 0.5/0.3/0.2 weight distribution."""
|
||||
# Create gene with only GO data (should contribute 50% weight)
|
||||
df_go_only = pl.DataFrame({
|
||||
"gene_id": ["ENSG001"],
|
||||
"gene_symbol": ["GENE1"],
|
||||
"go_term_count": [100], # Max GO to get full GO component
|
||||
"go_biological_process_count": [60],
|
||||
"go_molecular_function_count": [30],
|
||||
"go_cellular_component_count": [10],
|
||||
"uniprot_annotation_score": pl.Series([None], dtype=pl.Int64),
|
||||
"has_pathway_membership": pl.Series([None], dtype=pl.Boolean),
|
||||
})
|
||||
|
||||
# Create gene with only UniProt data (should contribute 30% weight)
|
||||
df_uniprot_only = pl.DataFrame({
|
||||
"gene_id": ["ENSG002"],
|
||||
"gene_symbol": ["GENE2"],
|
||||
"go_term_count": pl.Series([None], dtype=pl.Int64),
|
||||
"go_biological_process_count": pl.Series([None], dtype=pl.Int64),
|
||||
"go_molecular_function_count": pl.Series([None], dtype=pl.Int64),
|
||||
"go_cellular_component_count": pl.Series([None], dtype=pl.Int64),
|
||||
"uniprot_annotation_score": [5], # Max UniProt score
|
||||
"has_pathway_membership": pl.Series([None], dtype=pl.Boolean),
|
||||
})
|
||||
|
||||
# Create gene with only pathway data (should contribute 20% weight)
|
||||
df_pathway_only = pl.DataFrame({
|
||||
"gene_id": ["ENSG003"],
|
||||
"gene_symbol": ["GENE3"],
|
||||
"go_term_count": pl.Series([None], dtype=pl.Int64),
|
||||
"go_biological_process_count": pl.Series([None], dtype=pl.Int64),
|
||||
"go_molecular_function_count": pl.Series([None], dtype=pl.Int64),
|
||||
"go_cellular_component_count": pl.Series([None], dtype=pl.Int64),
|
||||
"uniprot_annotation_score": pl.Series([None], dtype=pl.Int64),
|
||||
"has_pathway_membership": [True],
|
||||
})
|
||||
|
||||
# Normalize each separately (need same GO max, so combine first)
|
||||
df_combined = pl.concat([df_go_only, df_uniprot_only, df_pathway_only])
|
||||
result = normalize_annotation_score(df_combined)
|
||||
|
||||
# Check approximate weights (allowing for small rounding)
|
||||
go_score = result["annotation_score_normalized"][0]
|
||||
uniprot_score = result["annotation_score_normalized"][1]
|
||||
pathway_score = result["annotation_score_normalized"][2]
|
||||
|
||||
# GO component should be ~0.5 (full weight)
|
||||
assert abs(go_score - 0.5) < 0.01
|
||||
|
||||
# UniProt component should be 0.3 (full score * weight)
|
||||
assert abs(uniprot_score - 0.3) < 0.01
|
||||
|
||||
# Pathway component should be 0.2 (full weight)
|
||||
assert abs(pathway_score - 0.2) < 0.01
|
||||
307
tests/test_annotation_integration.py
Normal file
307
tests/test_annotation_integration.py
Normal file
@@ -0,0 +1,307 @@
|
||||
"""Integration tests for annotation evidence layer."""
|
||||
|
||||
import polars as pl
|
||||
import pytest
|
||||
from pathlib import Path
|
||||
from unittest.mock import Mock, patch, MagicMock
|
||||
|
||||
from usher_pipeline.config.loader import load_config
|
||||
from usher_pipeline.persistence import PipelineStore, ProvenanceTracker
|
||||
from usher_pipeline.evidence.annotation import (
|
||||
process_annotation_evidence,
|
||||
load_to_duckdb,
|
||||
query_poorly_annotated,
|
||||
)
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def test_config(tmp_path):
|
||||
"""Create test configuration."""
|
||||
config_dir = tmp_path / "config"
|
||||
config_dir.mkdir()
|
||||
data_dir = tmp_path / "data"
|
||||
data_dir.mkdir()
|
||||
|
||||
config_yaml = f"""
|
||||
project_name: "usher-pipeline-test"
|
||||
data_dir: "{data_dir}"
|
||||
cache_dir: "{tmp_path / 'cache'}"
|
||||
duckdb_path: "{tmp_path / 'test.duckdb'}"
|
||||
|
||||
versions:
|
||||
ensembl_release: 112
|
||||
gnomad_version: "4.1"
|
||||
|
||||
api:
|
||||
rate_limit_per_second: 5
|
||||
max_retries: 3
|
||||
cache_ttl_seconds: 86400
|
||||
timeout_seconds: 30
|
||||
|
||||
scoring:
|
||||
gnomad: 0.20
|
||||
expression: 0.20
|
||||
annotation: 0.15
|
||||
localization: 0.15
|
||||
animal_model: 0.15
|
||||
literature: 0.15
|
||||
"""
|
||||
config_file = config_dir / "pipeline.yaml"
|
||||
config_file.write_text(config_yaml)
|
||||
|
||||
return load_config(config_file)
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def mock_gene_ids():
|
||||
"""Sample gene IDs for testing."""
|
||||
return ["ENSG001", "ENSG002", "ENSG003", "ENSG004", "ENSG005"]
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def mock_uniprot_mapping():
|
||||
"""Mock UniProt mapping DataFrame."""
|
||||
return pl.DataFrame({
|
||||
"gene_id": ["ENSG001", "ENSG002", "ENSG003"],
|
||||
"uniprot_accession": ["P12345", "Q67890", "A11111"],
|
||||
})
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def synthetic_annotation_data():
|
||||
"""Create synthetic annotation data for testing."""
|
||||
return pl.DataFrame({
|
||||
"gene_id": ["ENSG001", "ENSG002", "ENSG003", "ENSG004", "ENSG005"],
|
||||
"gene_symbol": ["GENE1", "GENE2", "GENE3", "GENE4", "GENE5"],
|
||||
"go_term_count": [50, 15, 5, None, 2],
|
||||
"go_biological_process_count": [30, 10, 3, None, 1],
|
||||
"go_molecular_function_count": [15, 3, 2, None, 1],
|
||||
"go_cellular_component_count": [5, 2, 0, None, 0],
|
||||
"uniprot_annotation_score": [5, 4, 3, None, 1],
|
||||
"has_pathway_membership": [True, True, False, None, False],
|
||||
"annotation_tier": ["well_annotated", "well_annotated", "partially_annotated", "poorly_annotated", "poorly_annotated"],
|
||||
"annotation_score_normalized": [0.9, 0.75, 0.45, None, 0.15],
|
||||
})
|
||||
|
||||
|
||||
def mock_mygene_querymany(gene_ids, **kwargs):
|
||||
"""Mock mygene.querymany response."""
|
||||
# Simulate different annotation levels
|
||||
return [
|
||||
{
|
||||
"query": "ENSG001",
|
||||
"symbol": "GENE1",
|
||||
"go": {
|
||||
"BP": [{"id": f"GO:000{i}"} for i in range(30)],
|
||||
"MF": [{"id": f"GO:100{i}"} for i in range(15)],
|
||||
"CC": [{"id": f"GO:200{i}"} for i in range(5)],
|
||||
},
|
||||
"pathway": {
|
||||
"kegg": [{"id": "hsa00001"}],
|
||||
"reactome": [{"id": "R-HSA-00001"}],
|
||||
},
|
||||
},
|
||||
{
|
||||
"query": "ENSG002",
|
||||
"symbol": "GENE2",
|
||||
"go": {
|
||||
"BP": [{"id": f"GO:000{i}"} for i in range(10)],
|
||||
"MF": [{"id": f"GO:100{i}"} for i in range(3)],
|
||||
"CC": [{"id": f"GO:200{i}"} for i in range(2)],
|
||||
},
|
||||
"pathway": {"kegg": [{"id": "hsa00002"}]},
|
||||
},
|
||||
{
|
||||
"query": "ENSG003",
|
||||
"symbol": "GENE3",
|
||||
"go": {
|
||||
"BP": [{"id": "GO:0001"}, {"id": "GO:0002"}],
|
||||
},
|
||||
"pathway": {},
|
||||
},
|
||||
{
|
||||
"query": "ENSG004",
|
||||
"symbol": "GENE4",
|
||||
# No GO or pathway data
|
||||
},
|
||||
{
|
||||
"query": "ENSG005",
|
||||
"symbol": "GENE5",
|
||||
"go": {
|
||||
"BP": [{"id": "GO:0001"}],
|
||||
},
|
||||
},
|
||||
]
|
||||
|
||||
|
||||
def mock_uniprot_api_response():
|
||||
"""Mock UniProt API response."""
|
||||
return {
|
||||
"results": [
|
||||
{"primaryAccession": "P12345", "annotationScore": 5},
|
||||
{"primaryAccession": "Q67890", "annotationScore": 4},
|
||||
{"primaryAccession": "A11111", "annotationScore": 3},
|
||||
]
|
||||
}
|
||||
|
||||
|
||||
@patch("usher_pipeline.evidence.annotation.fetch._get_mygene_client")
|
||||
@patch("usher_pipeline.evidence.annotation.fetch._query_uniprot_batch")
|
||||
def test_process_annotation_evidence_pipeline(
|
||||
mock_uniprot, mock_mygene_client, mock_gene_ids, mock_uniprot_mapping
|
||||
):
|
||||
"""Test full annotation evidence processing pipeline."""
|
||||
# Setup mocks
|
||||
mock_mg = Mock()
|
||||
mock_mg.querymany.return_value = mock_mygene_querymany(mock_gene_ids)
|
||||
mock_mygene_client.return_value = mock_mg
|
||||
|
||||
mock_uniprot.return_value = {
|
||||
"P12345": 5,
|
||||
"Q67890": 4,
|
||||
"A11111": 3,
|
||||
}
|
||||
|
||||
# Run pipeline
|
||||
result = process_annotation_evidence(mock_gene_ids, mock_uniprot_mapping)
|
||||
|
||||
# Verify results
|
||||
assert result.height == len(mock_gene_ids)
|
||||
assert "annotation_tier" in result.columns
|
||||
assert "annotation_score_normalized" in result.columns
|
||||
|
||||
# Check that tiers are classified
|
||||
tiers = result["annotation_tier"].unique().to_list()
|
||||
assert "well_annotated" in tiers or "partially_annotated" in tiers or "poorly_annotated" in tiers
|
||||
|
||||
# Verify mygene was called
|
||||
mock_mg.querymany.assert_called_once()
|
||||
|
||||
# Verify UniProt was queried
|
||||
mock_uniprot.assert_called()
|
||||
|
||||
|
||||
def test_load_to_duckdb_idempotent(test_config, synthetic_annotation_data):
|
||||
"""Test that load_to_duckdb is idempotent (CREATE OR REPLACE)."""
|
||||
store = PipelineStore.from_config(test_config)
|
||||
provenance = ProvenanceTracker.from_config(test_config)
|
||||
|
||||
# First load
|
||||
load_to_duckdb(synthetic_annotation_data, store, provenance, "First load")
|
||||
|
||||
# Verify data exists
|
||||
df1 = store.load_dataframe("annotation_completeness")
|
||||
assert df1 is not None
|
||||
assert df1.height == synthetic_annotation_data.height
|
||||
|
||||
# Second load (should replace)
|
||||
modified_data = synthetic_annotation_data.with_columns(
|
||||
pl.lit("test_modified").alias("gene_symbol")
|
||||
)
|
||||
load_to_duckdb(modified_data, store, provenance, "Second load")
|
||||
|
||||
# Verify data was replaced
|
||||
df2 = store.load_dataframe("annotation_completeness")
|
||||
assert df2 is not None
|
||||
assert df2.height == modified_data.height
|
||||
assert all(df2["gene_symbol"] == "test_modified")
|
||||
|
||||
store.close()
|
||||
|
||||
|
||||
def test_checkpoint_restart(test_config, synthetic_annotation_data):
|
||||
"""Test checkpoint-restart pattern."""
|
||||
store = PipelineStore.from_config(test_config)
|
||||
provenance = ProvenanceTracker.from_config(test_config)
|
||||
|
||||
# Initially no checkpoint
|
||||
assert not store.has_checkpoint("annotation_completeness")
|
||||
|
||||
# Load creates checkpoint
|
||||
load_to_duckdb(synthetic_annotation_data, store, provenance)
|
||||
|
||||
# Now checkpoint exists
|
||||
assert store.has_checkpoint("annotation_completeness")
|
||||
|
||||
# Can load existing data
|
||||
df = store.load_dataframe("annotation_completeness")
|
||||
assert df is not None
|
||||
assert df.height == synthetic_annotation_data.height
|
||||
|
||||
store.close()
|
||||
|
||||
|
||||
def test_provenance_recording(test_config, synthetic_annotation_data):
|
||||
"""Test that provenance metadata is recorded correctly."""
|
||||
store = PipelineStore.from_config(test_config)
|
||||
provenance = ProvenanceTracker.from_config(test_config)
|
||||
|
||||
load_to_duckdb(synthetic_annotation_data, store, provenance)
|
||||
|
||||
# Verify provenance step was recorded
|
||||
steps = provenance.processing_steps
|
||||
assert len(steps) > 0
|
||||
|
||||
step = steps[-1]
|
||||
assert step["step_name"] == "load_annotation_completeness"
|
||||
assert "row_count" in step["details"]
|
||||
assert step["details"]["row_count"] == synthetic_annotation_data.height
|
||||
assert "well_annotated_count" in step["details"]
|
||||
assert "poorly_annotated_count" in step["details"]
|
||||
|
||||
store.close()
|
||||
|
||||
|
||||
def test_query_poorly_annotated(test_config, synthetic_annotation_data):
|
||||
"""Test querying poorly annotated genes."""
|
||||
store = PipelineStore.from_config(test_config)
|
||||
provenance = ProvenanceTracker.from_config(test_config)
|
||||
|
||||
# Load data
|
||||
load_to_duckdb(synthetic_annotation_data, store, provenance)
|
||||
|
||||
# Query poorly annotated genes (score <= 0.3)
|
||||
result = query_poorly_annotated(store, max_score=0.3)
|
||||
|
||||
# Should return genes with low scores
|
||||
assert result.height > 0
|
||||
assert all(result["annotation_score_normalized"] <= 0.3)
|
||||
|
||||
# Results should be sorted by score (lowest first)
|
||||
scores = result["annotation_score_normalized"].to_list()
|
||||
assert scores == sorted(scores)
|
||||
|
||||
store.close()
|
||||
|
||||
|
||||
def test_null_handling_throughout_pipeline(test_config, mock_gene_ids, mock_uniprot_mapping):
|
||||
"""Test that NULL values are preserved throughout the pipeline."""
|
||||
# Create data with NULLs
|
||||
data_with_nulls = pl.DataFrame({
|
||||
"gene_id": ["ENSG001", "ENSG002"],
|
||||
"gene_symbol": ["GENE1", "GENE2"],
|
||||
"go_term_count": [10, None],
|
||||
"go_biological_process_count": [7, None],
|
||||
"go_molecular_function_count": [2, None],
|
||||
"go_cellular_component_count": [1, None],
|
||||
"uniprot_annotation_score": [3, None],
|
||||
"has_pathway_membership": [True, None],
|
||||
"annotation_tier": ["partially_annotated", "poorly_annotated"],
|
||||
"annotation_score_normalized": [0.5, None],
|
||||
})
|
||||
|
||||
store = PipelineStore.from_config(test_config)
|
||||
provenance = ProvenanceTracker.from_config(test_config)
|
||||
|
||||
# Load to DuckDB
|
||||
load_to_duckdb(data_with_nulls, store, provenance)
|
||||
|
||||
# Load back and verify NULLs preserved
|
||||
result = store.load_dataframe("annotation_completeness")
|
||||
|
||||
# Gene with NULL GO should have NULL in result
|
||||
gene2 = result.filter(pl.col("gene_id") == "ENSG002")
|
||||
assert gene2["go_term_count"][0] is None
|
||||
assert gene2["annotation_score_normalized"][0] is None
|
||||
|
||||
store.close()
|
||||
Reference in New Issue
Block a user