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 RNA sequencing. 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.

中文翻译：

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高尖峰数据的自适应局部错误发现率程序及其应用酵母SET4缺失突变体的RNA测序数据


染色质动力学对于基因表达和基因组稳定性的调节至关重要。为了加深对调节染色质动态的因素的理解，编码这些因素的基因被删除，并使用 RNA 测序等方法确定差异基因表达谱。在这里，我们分析了一个基因表达数据集，旨在揭示模型系统酿酒酵母（芽殖酵母）中相对未表征的染色质调节因子 Set4 的功能。本文的主题是鉴定Set4缺失细胞中的高差异表达基因（简称Set4 突变体数据集）与野生型酵母细胞相比。套装4 突变数据在其表达的对数倍变化上产生尖峰分布，并且可以合理地假设没有高度差异表达的基因来自两种正态分布的混合。我们提出了一种自适应局部错误发现率（FDR）程序，它根据经验估计对数倍数变化的零分布。我们通过数值表明，与现有方法不同，我们提出的方法将 FDR 控制在目标水平 (0.05)，并且在寻找差异表达基因方面也具有竞争力。最后，我们应用我们的程序来分析 Set4 突变体数据集。