Fully Bayesian Imputation Model for MNAR Data in QPCR

• Activity Number: 310 - SPEED:Statistical Methods for GWAs, Genetics, Genomics, and Other Omics Studies, Part 2
• Type: Contributed
• Date/Time: Tuesday, July 30, 2019 : 9:25 AM to 10:10 AM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Valeriia Sherina* and Matthew N McCall and Tanzy M.T. Love
• Companies: and University of Rochester Medical Center and University of Rochester Medical Center
• Keywords: Bayesian model; Missing not at random; Quantitative real-time PCR

Abstract

We propose a new statistical approach to obtain differential gene expression of non-detects in quantitative real-time PCR experiments through Bayesian hierarchical modeling. We propose to treat non-detects as non-random missing data, model the missing data mechanism, and use this model to impute Ct values or obtain direct estimates of relevant model parameters. A typical laboratory does not have the resources to perform experiments with a large number of replicates; therefore, we propose an approach that does not rely on large sample theory. We aim to demonstrate the possibilities that exist for analyzing qPCR data in the presence of non-random missingness through the use of Bayesian estimation. Bayesian analysis typically allows for smaller data sets to be analyzed without losing power while retaining precision. In this work we introduce and describe our hierarchical model and chosen prior distributions, assess the model sensitivity to the choice of prior, perform convergence diagnostics for the Markov Chain Monte Carlo, and present the results of a real data application.