Omics methods are widely used in basic biology and translational medicine research. More and more omics data are collected to explain the impact of certain risk factors on clinical outcomes. To explain the mechanism of the risk factors, a core question is how to find the genes/proteins/metabolites that mediate their effects on the clinical outcome. Mediation analysis is a modeling framework to study the relationship between risk factors and pathological outcomes, via mediator variables. However, high-dimensional omics data are far more challenging than traditional data: (1) From tens of thousands of genes, can we overcome the curse of dimensionality to reliably select a set of mediators? (2) How do we ensure that the selected mediators are functionally consistent? (3) Many biological mechanisms contain nonlinear effects. How do we include nonlinear effects in the high-dimensional mediation analysis? (4) How do we consider multiple risk factors at the same time? To meet these challenges, we propose a new exploratory mediation analysis framework, medNet, which focuses on finding mediators through predictive modeling. We propose new definitions for predictive exposure, predictive mediator, and predictive network mediator, using a statistical hypothesis testing framework to identify predictive exposures and mediators. Additionally, two heuristic search algorithms are proposed to identify network mediators, essentially subnetworks in the genome-scale biological network that mediate the effects of single or multiple exposures. We applied medNet on a breast cancer data set and a metabolomics data set combined with food intake questionnaire data. It identified functionally consistent network mediators for the exposures’ impact on the outcome, facilitating data interpretation.

中文翻译：

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组学数据中探索性网络中介分析的新框架


组学方法广泛应用于基础生物学和转化医学研究。收集越来越多的组学数据来解释某些风险因素对临床结果的影响。为了解释危险因素的机制，一个核心问题是如何找到介导其对临床结果影响的基因/蛋白质/代谢物。中介分析是一种通过中介变量研究风险因素与病理结果之间关系的建模框架。然而，高维组学数据比传统数据更具挑战性：（1）我们能否从数以万计的基因中克服维数诅咒，可靠地选择一组中介体？ （2）如何确保所选中介体功能一致？ (3)许多生物机制都含有非线性效应。我们如何在高维中介分析中包含非线性效应？ （4）如何同时考虑多种风险因素？为了应对这些挑战，我们提出了一个新的探索性中介分析框架 medNet，其重点是通过预测建模寻找中介。我们提出了预测暴露、预测中介和预测网络中介的新定义，使用统计假设检验框架来识别预测暴露和中介。此外，还提出了两种启发式搜索算法来识别网络中介，本质上是基因组规模生物网络中调节单次或多次暴露影响的子网络。我们将 medNet 应用于乳腺癌数据集和结合食物摄入问卷数据的代谢组学数据集。它确定了功能一致的网络中介因素，以反映暴露对结果的影响，从而促进数据解释。