Adaptive Reproducible High-Dimensional Nonlinear Inference for Big Biological Data

大数据现在无处不在现代科学研究的各个领域。许多当代应用， 例如最近的国家微生物组计划（NMI），极大地要求高度灵活的统计机器 学习可以产生可解释和可重复结果的方法。因此， 重要的是，要确定造成大量 可用的协变量，可以在统计学上用公式表示为 一般的高维非线性模型。尽管霰弹枪宏基因组学有着巨大的应用 尽管有许多研究，但大多数现有的研究集中在细菌有机体的研究上。然而，病毒 病毒与宿主的相互作用在控制微生物群落的功能方面发挥着重要作用。在 此外，病毒已被证明与复杂疾病有关。然而，对 病毒在人类疾病中的作用还远远没有得到充分的研究。这项建议的目的是 开发数学上严格和计算效率高的方法来处理高度复杂的大规模 数据和这些方法的应用，以解决基本和重要的生物和 生物医学问题。有四个相互关联的目标。在目标1中，我们将从理论上研究 最近提出的无模型仿制品（MFK）程序，理论上已经证明， 控制任意模型和任意尺寸的FDR。我们还将从理论上证明 MFK关于协变量分布的误指定。这些研究将奠定基础 我们在其他方面的发展。在目标2中，我们将开发深度学习方法来预测病毒 将新算法与MFK算法相结合，实现了对病毒模体的FDR控制 发现，并调查我们的新程序的能力和鲁棒性。在目标3中，我们将 考虑到病毒-宿主基序相互作用，并调整我们在Aim 2中的算法和理论， 病毒-宿主感染相互作用状态。在目标4中，我们将应用前三个开发的方法 旨在分析ExperimentHub中的鸟枪宏基因组学数据集，以识别病毒和病毒宿主 在某些目标FDR水平上与几种疾病相关的相互作用。算法和结果 将通过网络传播。这项研究的结果将对宏基因组学非常重要 在各种环境下进行研究。

Big data is now ubiquitous in every field of modern scientific research. Many contemporary applications, such as the recent national microbiome initiative (NMI), greatly demand highly flexible statistical machine learning methods that can produce both interpretable and reproducible results. Thus, it is of paramount importance to identify crucial causal factors that are responsible for the response from a large number of available covariates, which can be statistically formulated as the false discovery rate (FDR) control in general high-dimensional nonlinear models. Despite the enormous applications of shotgun metagenomic studies, most existing investigations concentrate on the study of bacterial organisms. However, viruses and virus-host interactions play important roles in controlling the functions of the microbial communities. In addition, viruses have been shown to be associated with complex diseases. Yet, investigations into the roles of viruses in human diseases are significantly underdeveloped. The objective of this proposal is to develop mathematically rigorous and computationally efficient approaches to deal with highly complex big data and the applications of these approaches to solve fundamental and important biological and biomedical problems. There are four interrelated aims. In Aim 1, we will theoretically investigate the power of the recently proposed model-free knockoffs (MFK) procedure, which has been theoretically justified to control FDR in arbitrary models and arbitrary dimensions. We will also theoretically justify the robustness of MFK with respect to the misspecification of covariate distribution. These studies will lay the foundations for our developments in other aims. In Aim 2, we will develop deep learning approaches to predict viral contigs with higher accuracy, integrate our new algorithm with MFK to achieve FDR control for virus motif discovery, and investigate the power and robustness of our new procedure. In Aim 3, we will take into account the virus-host motif interactions and adapt our algorithms and theories in Aim 2 for predicting virus-host infectious interaction status. In Aim 4, we will apply the developed methods from the first three aims to analyze the shotgun metagenomics data sets in ExperimentHub to identify viruses and virus-host interactions associated with several diseases at some target FDR level. Both the algorithms and results will be disseminated through the web. The results from this study will be important for metagenomics studies under a variety of environments.