Detecting Strong Signals in Gene Perturbation Experiments: An Adaptive Approach With Power Guarantee and FDR Control

<b>The perturbation of a transcription factor should affect the expression levels of its direct targets. However, not all genes showing changes in expression are direct targets. To increase the chance of detecting direct targets, we propose a modified two-group model where the null group corresponds to genes which are not direct targets, but can have small nonzero effects. We model the behavior of genes from the null set by a Gaussian distribution with unknown variance</b>τ2<b>. To estimate</b>τ2<b>, we focus on a simple estimation approach, the iterated empirical Bayes estimation. We conduct a detailed analysis of the properties of the iterated EB estimate and provide theoretical guarantee of its good performance under mild conditions. We provide simulations comparing the new modeling approach with existing methods, and the new approach shows more stable and better performance under different situations. We also apply it to a real dataset from gene knock-down experiments and obtained better results compared with the original two-group model testing for nonzero effects.</b>

转录因子（transcription factor）的扰动应当会影响其直接靶基因的表达水平。然而，并非所有表达发生变化的基因均为直接靶基因。为提升检测直接靶基因的概率，我们提出一种改进的两组模型：其中零组对应并非直接靶标、但可存在微小非零效应的基因。我们采用方差未知的高斯分布（Gaussian distribution）对零集中基因的行为进行建模。为估计该方差τ²，我们采用一种简单的估计方法——迭代经验贝叶斯估计（iterated empirical Bayes estimation）。我们对迭代EB估计量的性质展开了详细分析，并给出了其在温和条件下具备优良性能的理论保证。我们通过模拟实验将该新型建模方法与现有方法进行对比，结果显示新方法在不同场景下均表现更为稳定且优异。此外，我们将其应用于一项来自基因敲低（gene knock-down）实验的真实数据集，相较用于检测非零效应的原始两组模型，获得了更优的结果。