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arXiv · 1608.07204

Empirical Null Estimation using Discrete Mixture Distributions and its Application to Protein Domain Data

Iris Ivy Gauran·Junyong Park·Johan Lim·DoHwan Park·John Zylstra·Thomas Peterson·Maricel Kann·John Spouge
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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 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.

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Iris Ivy Gauran, Junyong Park, Johan Lim, DoHwan Park, John Zylstra, Thomas Peterson, Maricel Kann, John Spouge. 2016-08-25. Empirical Null Estimation using Discrete Mixture Distributions and its Application to Protein Domain Data. https://arxiv.org/abs/1608.07204

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