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fix(03-05): handle NULL columns and deprecated polars API in animal models

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- Add NULL/empty column checks in fetch_ortholog_mapping
- Fix NULL handling in filter_sensory_phenotypes with is_not_null guard
- Replace deprecated str.concat with str.join
- Add explicit schema to empty DataFrames for consistency
This commit is contained in:
gbanyan
2026-02-11 20:38:36 +08:00
parent a52724aff4
commit ed21f18a98
2 changed files with 50 additions and 29 deletions
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51
src/usher_pipeline/evidence/animal_models/fetch.py
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@@ -168,27 +168,36 @@ def fetch_ortholog_mapping(gene_ids: list[str]) -> pl.DataFrame:
logger.info("hcop_zebrafish_columns", columns=zebrafish_df.columns)
# Parse zebrafish ortholog data
zebrafish_orthologs = (
zebrafish_df
.filter(pl.col("human_ensembl_gene").is_in(gene_ids))
.select([
pl.col("human_ensembl_gene").alias("gene_id"),
pl.col("zebrafish_symbol").alias("zebrafish_ortholog"),
pl.col("support").str.split(",").list.len().alias("support_count"),
])
.with_columns([
pl.when(pl.col("support_count") >= 8)
.then(pl.lit("HIGH"))
.when(pl.col("support_count") >= 4)
.then(pl.lit("MEDIUM"))
.otherwise(pl.lit("LOW"))
.alias("zebrafish_ortholog_confidence")
])
.sort(["gene_id", "support_count"], descending=[False, True])
.group_by("gene_id")
.first()
.select(["gene_id", "zebrafish_ortholog", "zebrafish_ortholog_confidence"])
)
# Handle case where zebrafish_df might be empty or missing expected columns
if "zebrafish_symbol" in zebrafish_df.columns and len(zebrafish_df) > 0:
zebrafish_orthologs = (
zebrafish_df
.filter(pl.col("human_ensembl_gene").is_in(gene_ids))
.select([
pl.col("human_ensembl_gene").alias("gene_id"),
pl.col("zebrafish_symbol").alias("zebrafish_ortholog"),
pl.col("support").str.split(",").list.len().alias("support_count"),
])
.with_columns([
pl.when(pl.col("support_count") >= 8)
.then(pl.lit("HIGH"))
.when(pl.col("support_count") >= 4)
.then(pl.lit("MEDIUM"))
.otherwise(pl.lit("LOW"))
.alias("zebrafish_ortholog_confidence")
])
.sort(["gene_id", "support_count"], descending=[False, True])
.group_by("gene_id")
.first()
.select(["gene_id", "zebrafish_ortholog", "zebrafish_ortholog_confidence"])
)
else:
# Return empty DataFrame with correct schema
zebrafish_orthologs = pl.DataFrame({
"gene_id": [],
"zebrafish_ortholog": [],
"zebrafish_ortholog_confidence": [],
}, schema={"gene_id": pl.String, "zebrafish_ortholog": pl.String, "zebrafish_ortholog_confidence": pl.String})
logger.info("zebrafish_orthologs_mapped", count=len(zebrafish_orthologs))