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

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 varianceτ2. To estimateτ2, 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.转录因子（transcription factor）的扰动理应影响其直接靶基因（direct targets）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：其中零假设组（null group）对应非直接靶基因，但这类基因可存在微小的非零效应。我们通过方差未知的高斯分布（Gaussian distribution）对零假设组内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，并在温和条件下为其优良性能提供了理论保障。我们通过仿真实验将该新型建模方法与现有方法进行对比，结果显示新型方法在不同场景下均表现出更稳定、更优异的性能。此外，我们将该方法应用于一项基因敲低（gene knock-down）实验的真实数据集，相较于原有的针对非零效应检验的两组模型，获得了更优的结果。