Imputes missing values in large-scale high-dimensional phenome data. phenomeImpute contains four variations of K-nearest-neighbor (KNN) methods and was compared with two existing methods, multivariate imputation by chained equations and missForest. The four variations are imputation by variables (KNN-V), by subjects (KNN-S), their weighted hybrid (KNN-H) and an adaptively weighted hybrid (KNN-A). The results show that Imputation of missing values with low imputability measures increased imputation errors greatly and could potentially deteriorate downstream analyses.

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phenomeImpute specifications

Software type:
Package
Restrictions to use:
None
Programming languages:
R
Stability:
Stable
Interface:
Command line interface
Operating system:
Unix/Linux, Mac OS, Windows
Computer skills:
Advanced

Publications

  • (Liao et al., 2014) Missing value imputation in high-dimensional phenomic data: imputable or not, and how?. BMC Bioinformatics.
    PMID: 25371041

Credits

Institution(s)

Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, USA; Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA; Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA, USA; Pulmonary, Critical Care and Sleep Medicine, Yale School of Medicine, New Haven, CT, USA; Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA

Funding source(s)

This study is supported by NIH R21MH094862, U01HL108642, U01HL112707 and RC2HL101715.

Link to literature

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