Department of Information Systems and Analytics, College of Business, Bryant University, Smithfield, 02917, RI, USA.
Center for Health and Behavioral Sciences, Bryant University, Smithfield, 02917, RI, USA.
Brief Bioinform. 2023 Mar 19;24(2). doi: 10.1093/bib/bbad084.
Large-scale multiple perturbation experiments have the potential to reveal a more detailed understanding of the molecular pathways that respond to genetic and environmental changes. A key question in these studies is which gene expression changes are important for the response to the perturbation. This problem is challenging because (i) the functional form of the nonlinear relationship between gene expression and the perturbation is unknown and (ii) identification of the most important genes is a high-dimensional variable selection problem. To deal with these challenges, we present here a method based on the model-X knockoffs framework and Deep Neural Networks to identify significant gene expression changes in multiple perturbation experiments. This approach makes no assumptions on the functional form of the dependence between the responses and the perturbations and it enjoys finite sample false discovery rate control for the selected set of important gene expression responses. We apply this approach to the Library of Integrated Network-Based Cellular Signature data sets which is a National Institutes of Health Common Fund program that catalogs how human cells globally respond to chemical, genetic and disease perturbations. We identified important genes whose expression is directly modulated in response to perturbation with anthracycline, vorinostat, trichostatin-a, geldanamycin and sirolimus. We compare the set of important genes that respond to these small molecules to identify co-responsive pathways. Identification of which genes respond to specific perturbation stressors can provide better understanding of the underlying mechanisms of disease and advance the identification of new drug targets.
大规模的多重扰动实验有可能揭示出对遗传和环境变化做出反应的分子途径的更详细的理解。在这些研究中，一个关键问题是哪些基因表达变化对于响应扰动是重要的。这个问题具有挑战性，原因在于：(i) 基因表达与扰动之间的非线性关系的函数形式未知，以及 (ii) 识别最重要的基因是一个高维变量选择问题。为了应对这些挑战，我们在这里提出了一种基于模型-X 置换框架和深度神经网络的方法，用于识别多重扰动实验中的显著基因表达变化。这种方法对响应和扰动之间的依赖关系的函数形式没有任何假设，并且对于所选的重要基因表达响应集，它具有有限样本的错误发现率控制。我们将这种方法应用于整合网络细胞特征库数据集（Library of Integrated Network-Based Cellular Signature datasets），这是美国国立卫生研究院共同基金计划，该计划记录了人类细胞如何对化学、遗传和疾病扰动做出全球响应。我们确定了直接受蒽环类抗生素、伏立诺他、曲古抑菌素 A、格尔德霉素和西罗莫司扰动调节的重要基因。我们比较了对这些小分子有反应的重要基因集，以识别共同反应的途径。识别哪些基因对特定的扰动胁迫有反应，可以更好地理解疾病的潜在机制，并推进新的药物靶点的识别。
