Last updated: 2023-07-03

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Here, we present a comprehensive comparative study evaluating the KOMPUTE imputation algorithm with data from the International Mouse Phenotyping Consortium (IMPC). Our analysis explores the IMPC data version 16 across three distinct phenotyping domains: Body Composition, Clinical Chemistry and Open Field.

In our initial phase (Analysis 1), we assessed the performance of the KOMPUTE method by conducting extensive simulation studies. We then compare its performance to that of a Singular Value Decomposition (SVD) matrix completion method (Kurucz, et al., 2007).

To further evaluate the effectiveness of KOMPUTE in more realistic scenarios, we conducted additional experiments (Analyses 2-4) employing actual measured association Z-scores from three phenotype domains: Body Composition (8 phenotypes), Clinical Chemistry (19 phenotypes), and Open Field (14 phenotypes). For each domain, we started by estimating a phenotype correlation matrix utilizing control mice phenotypes and adjusted for non-biological factors via PVCA and ComBat. Subsequently, we randomly masked 1000 gene-phenotype association Z-scores from each domain. Using the remaining Z-scores alongside the estimated phenotype correlation matrix, we imputed the missing values employing the KOMPUTE method.

IMPC Data - Version 16