Statistical Approaches to Decreasing the Discrepancy of Non-Detects in QPCR Data

• Activity Number: 87 - Invited ePoster Session: a Statistical Smörgåsbord
• Type: Invited
• Date/Time: Sunday, July 29, 2018 : 8:30 PM to 10:30 PM
• Sponsor: ENAR
• Author(s): Valeriia Sherina and Love Tanzy* and Matthew N. McCall
• Companies: University of Rochester Medical Center and University of Rochester Medical Center
• Keywords: qPCR; gene expression; missing data; direct estimation; multiple imputation; non-detects

Abstract

Quantitative real-time PCR (qPCR) is one of the most widely used methods to measure gene expression. One of the problems of qPCR data is the presence of non-detects - those reactions failing to produce a minimum amount of signal. Recently developed single imputation procedures, while better than previously used methods, underestimate residual variance, which can lead to anti-conservative inference. We propose to treat non-detects as non-random missing data, model the missing data mechanism, and use this model to impute missing values or obtain direct estimates of relevant model parameters. To account for the uncertainty inherent in the imputation, we propose a multiple imputation procedure, which provides a set of plausible values for each non-detect. In the proposed modeling framework, there are three sources of uncertainty: parameter estimation, the missing data mechanism, and measurement error. The proposed methods result in unbiased estimates of the model parameters; therefore, these approaches may be beneficial when estimating both absolute and differential gene expression. The developed methods are implemented in the R/Bioconductor package "nondetects".