Empirical Null Estimation Using Zero Inflated Discrete Mixture Distributions and Its Application to Protein Domain Data

• Activity Number: 465 - Biometrics and High-Dimensional Data
• Type: Contributed
• Date/Time: Wednesday, August 2, 2017 : 8:30 AM to 10:20 AM
• Sponsor: Biometrics Section
• Author(s): Iris Ivy Gauran* and Junyong Park and DoHwan Park and Maricel Kann and Thomas Peterson and John Zylstra and John Spouge and Johan Lim
• Companies: and University of Maryland at Baltimore County and University of Maryland, Baltimore County and University of Maryland, Baltimore County and University of Maryland, Baltimore County and University of Maryland, Baltimore County and NCBI,NLM,NIH and Seoul National University
• Keywords: EM Algorithm ; Local False Discovery Rate ; Protein Domain ; Zero-Inflated Generalized Poisson

Abstract

In recent mutation studies, analyses based on protein domain positions are gaining popularity over gene-centric approaches since the latter have limitations in considering the functional context that the position of the mutation provides. This presents a large-scale simultaneous inference problem, with hundreds of hypothesis tests to consider at the same time. This paper aims to select significant mutation counts while controlling a given level of Type I error via False Discovery Rate (FDR) procedures. One main assumption is that mutation counts follow a zero-inflated model in order to account for the true zeros in the count model and the excess zeros. Also, we assumed that there exists a cut-off value such that smaller counts than this value are generated from the null distribution. We present several data-dependent methods to determine the cut-off value. We also consider a two-stage procedure based on screening process so that the number of mutations exceeding a certain value should be considered as significant mutations. Simulated and protein domain data sets are used to illustrate this procedure in estimation of the empirical null using a mixture of discrete distributions.