Adaptive local false discovery rate procedures for highly spiky data and their application RNA sequencing data of yeast SET4 deletion mutants

Abstract

Chromatin dynamics are central to the regulation of gene expression and genome stability. In order to improve understanding of the factors regulating chromatin dynamics, the genes encoding these factors are deleted and the differential gene expression profiles are determined using approaches such as RNAsequencing. Here, we analyzed a gene expression dataset aimed at uncovering the function of the relatively uncharacterized chromatin regulator, Set4, in the model system Saccharomyces cerevisiae (budding yeast). The main theme of this paper focuses on identifying the highly differentially-expressed genes in cells deleted for Set4 (referred to as Set4∆ mutant dataset) compared to the wild type yeast cells. The Set4∆ 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∆ mutant dataset.