2018-09-07

Jasmit Shah, PhD, Department of Bioinformatics and Biostatistics, University of Louisville

"Why are Missing Values Important? Novel Approach for Imputing Missing Values in High Dimensional Data"

With the rise of metabolomics, the development of methods to address analytical challenges in the analysis of metabolomics data is of great importance. Missing values (MVs) are pervasive, and often ignored, yet the treatment of MVs can have a substantial impact on downstream statistical analyses. The MVs problem in metabolomics is quite challenging, and can arise because the metabolite is not biologically present in the sample, or is present in the sample but at a concentration below the lower limit of detection (LOD), or is present in the sample but undetected due to technical issues related to sample pre-processing steps.

In this study we propose a Bayesian modeling approach to feature a cohesive and robust modeling structure for MVs in high dimensional metabolomics data. Our model accounts for MVs due to the truncation threshold, as well as other sources of missingness unrelated to true metabolite abundance. Statistical inference and data imputation are performed simultaneously using an MCMC algorithm and a hypothesis testing framework for differential abundance of metabolites between treatment group is considered.

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