Collaborative Research: Statistical Methodology for Network based Integrative Analysis of Omics Data

该项目的总体目标是描述基于基因、转录物、蛋白质和代谢物的协调活动的途径，这些途径可能作为治疗靶点，并创建基于组学的生物标志物面板，用于疾病的早期检测和预后。具体而言，我们解决了具有网络结构的多来源高维生物数据的集成和分析问题。这是一个充分证明的事实，不同的分子室之间的相关性是相对较低的，而从一个单独的室中得到的信息往往是高度嘈杂的，甚至是不完整的。因此，需要开发先进的模型和技术来集成来自不同组学平台的多个数据集，同时明确考虑隔间内部和隔间之间相互作用的可用信息。由于它们在复杂疾病的发生和进展中的作用，特别强调通路分析和富集。研究方向如下：(1)开发基于网络的方法，整合来自多个组学平台的数据，进行通路分析和富集。(2)基于大规模网络综合模型的快速计算算法的发展。研究相关的推理问题，研究所提出的估计器的性质及其对所使用的网络信息的噪声水平的鲁棒性。(3)引入新的超图模型，用于评估利用不同程度的关于底层网络结构和准确性的信息的通路的差异活动。(4)开发了一种基于扰动p值的新方案，用于检测有助于发现生物标志物的通路的活性成员。(5)将建议的方法实现为易于使用的软件工具。拟议的研究计划将有三个方面的影响：方法、科学和教育。在方法学方面，基于该项目的研究将导致(a)开发一个综合框架，用于评估基于不同模型的途径差异活动，这些模型集成了来自多个组学平台的数据，并利用了有关底层网络的结构和准确性的不同程度的信息；(b)对大规模（广义）混合线性模型中涉及的计算问题的系统理解；(c)一种基于扰动p值的新方案，用于识别成为治疗药物潜在靶点的通路的活性成员。增强的科学理解将在应用层面产生切实的影响。许多提出的方法已经用于高维基因组学、蛋白质组学和代谢组学数据的分析，重点是确定不同疾病（主要是癌症）状态下的活性途径（子网络）。此外，一些新的实验正在设计阶段，这些实验将利用本项目中提出的一些先进模型和技术。该建议的另一个关键方面是开发一个易于从业者使用的开源软件，构建在一个独立于领域的工作流管理系统中。这允许用户以简单和透明的方式通过添加自己的功能和计算工具来增强软件。这一研究议程产生的新方法程序将通过跨学科的相互作用和合作以及通过在会议和专门讲习班上的介绍，传播给有关科学界。最后，在教育方面，该项目的材料将为在pi指导下工作的博士生提供研究课题；因此，它将在培养未来的定量科学家方面发挥重要作用。

The overarching goal of this project is to delineate pathways-based on coordinated activity of genes, transcripts, proteins and metabolites, that could potentially serve as therapeutic targets, as well as create Omics based biomarker panels for early detection and prognosis of disease. Specifically, we address the problem of integration and analysis of multiple sources of high dimensional biological data with network structure. It is a well documented fact that correlation between different molecular compartments is relatively low, while the information derived from a single compartment is often highly noisy or even incomplete. Hence, there is a need to develop advanced models and techniques for integrating multiple data sets from diverse Omics platforms, while taking explicitly into consideration the available information of interactions within and between compartments. Particular emphasis is placed on pathway analysis and enrichment due to their role in complex diseases onset and progression. The following directions will be pursued: (1) Development of network based methods that integrate data from multiple Omics platforms for pathway analysis and enrichment. (2) Development of fast computational algorithms for estimating large scale network based integrative models. Investigation of associated inference problems and study of properties of proposed estimators together with their robustness to the noise levels of the network information employed. (3) Introduction of novel hypergraph models for assessing differential activity of pathways that utilize different degrees of information about the structure and accuracy of the underlying network. (4) Development of a novel scheme based on perturbed P-values for detecting active members of pathways that would aid in biomarker discovery. (5) Implementation of the propose methodology into an easy to use software tool.The proposed research program will have a three-pronged impact: methodological, scientific and educational. On the methodology front, the research based on this project will lead (a) to a developing a comprehensive framework for assessing differential activity of pathways based on different models that integrate data from multiple Omics platforms and utilize different degrees of information about the structure and accuracy of the underlying network, (b) a systematicunderstanding of the computational issues involved in large scale (generalized) mixed linear models;and (c) a novel scheme based on perturbed P-values for identifying active members of pathways that become potential targets for therapeutic drugs. The enhanced scientific understanding will provide tangible impact at the level of applications. A number of the proposed methods have already been used in the analysis of high dimensional genomic, proteomic and metabolomic data with emphasis on identifying active pathways (subnetworks) in different disease (primarily cancer) states. Further, a number of new experiments are in the design stage that would utilize some of the advanced models and techniques proposed in this project. Another key aspect of this proposal is the development of an easy to use by practitioners open source software, built within a domain independent workflow management system. This allows users to enhance the software by adding their own functionality and computational tools in an easy and transparent manner. The novel methodological procedures ensuing from this research agenda will be disseminated to the relevant scientific communities, both via inter-disciplinary interaction and collaboration and through presentations at conferences and specialized workshops. Finally, on the educational front, the material from the project will provide research topics for doctoral students working under the supervision of the PIs; it will therefore play an important role in the training of future quantitative scientists.