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Activity Number: 340 - SPEED: SPAAC SESSION III
Type: Topic-Contributed
Date/Time: Thursday, August 12, 2021 : 10:00 AM to 11:50 AM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #317748
Title: Adaptive Local False Discovery Rate Procedures for Highly Spiky Data and Their Application to RNA Sequencing Data of Yeast SET4 Deletion Mutants
Author(s): Mark Louie Ramos* and DoHwan Park and Junyong Park and Johan Lim and Erin Green and Eric Joshua Garcia and Khoa Tran
Companies: University of Maryland Baltimore County and University of Maryland Baltimore County and Seoul National University and Seoul National University and University of Maryland Baltimore County and University of Maryland Baltimore County and University of Maryland Baltimore County
Keywords: Empirical Approximation;; False Discovery Rate; Mixture of Normal; Multiple Testing
Abstract:

It is of interest to examine a gene expression dataset aimed at uncovering the function of the relatively uncharacterized chromatin regulator, Set4, in the model system Saccharomyces cerevisiae (budding yeast). This study is focused on identifying the highly differentially-expressed genes in cells deleted for Set4 (referred to as Set4 delta mutant dataset) compared to the wild type yeast cells. The Set4 delta mutant data produce a spiky distribution on the log fold changes of their expressions, and it is reasonably assumed that genes which are not highly differentially-expressed come from a mixture of two normal distributions. We propose an adaptive local false discovery rate (FDR) procedure, which estimates the null distribution of the log fold changes empirically. We numerically show that, unlike existing approaches, our proposed method controls FDR at the aimed level (0.05) and also has competitive power in finding differentially expressed genes. Finally, we apply our procedure to analyzing the Set4 delta mutant dataset.


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