PhenoQC: fast quality checks for clinical phenotype tables in genomic research

What is it about?

Phenotypic tables power genotype–phenotype studies. Errors, missing values, and inconsistent terms slow analysis and bias results. PhenoQC is a configuration-driven toolkit that brings three steps into one workflow: schema validation, ontology mapping, and missing-data imputation. It checks structure and types against a JSON schema, aligns phenotype text to standard ontologies (HPO, DO, MPO) with exact, synonym, and fuzzy matching, and fills gaps using baselines or KNN, MICE, and low-rank SVD. It audits imputation effects with standardized mean difference, variance ratio, Kolmogorov–Smirnov, population stability index, and Cramér’s V. It scales with chunk-based parallelism and runs via CLI or a web GUI. In tests, PhenoQC processed up to 100k records with near-linear scaling, reached ≈97–99% ontology-mapping accuracy under text noise, and on two UCI clinical datasets (CKD and Heart Disease) imputed all missing numeric cells and produced clean reports. The output is analysis-ready and reproducible.

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Photo by John on Unsplash

Photo by John on Unsplash

Why is it important?

Most tools cover only one slice of quality control. PhenoQC unifies schema enforcement, semantic harmonization, and principled imputation in a single, auditable pipeline. This reduces ad-hoc tool-chaining, cuts manual curation, and improves cross-study comparability. The design scales to large cohorts and preserves provenance for reruns, which strengthens downstream genomic findings. Table 1 in the paper states this gap and contribution explicitly.

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