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

合作研究：基于网络的组学数据综合分析统计方法

• 批准号: 1545277
• 负责人: George Michailidis
• 金额: $ 31.55万
• 依托单位: University of Florida
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-01-01 至 2017-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1545277&HistoricalAwards=false
• 关键词: Collaborative; Research; Statistical; Methodology; Network

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.

该项目的总体目标是描绘基于基因、转录物、蛋白质和代谢物的协调活性的途径，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，我们解决的问题，集成和分析的多个来源的高维生物数据的网络结构。这是一个有据可查的事实，即不同分子区室之间的相关性相对较低，而来自单个区室的信息往往是高度嘈杂的，甚至是不完整的。因此，有必要开发先进的模型和技术，用于整合来自不同组学平台的多个数据集，同时明确考虑到车厢内和车厢之间的相互作用的可用信息。特别强调的是路径分析和富集，由于其在复杂疾病的发病和进展中的作用。将遵循以下方向：（1）开发基于网络的方法，整合来自多个组学平台的数据，用于途径分析和富集。(2)发展快速计算演算法以评估大规模网路整合模式。调查相关的推理问题和研究拟议的估计连同其鲁棒性的网络信息的噪声水平的属性。(3)介绍了新的超图模型，用于评估利用不同程度的信息的基础网络的结构和准确性的通路的差异活动。(4)开发一种基于扰动P值的新方案，用于检测有助于生物标志物发现的途径的活性成员。(5)将所提出的方法学应用到一个易于使用的软件工具中。所提出的研究计划将产生三方面的影响：方法学、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通路的差异活性，这些模型整合了来自多个组学平台的数据，并利用了有关基础网络结构和准确性的不同程度的信息，（B）对大规模计算问题的系统理解（广义）混合线性模型;和（c）一个新的计划的基础上扰动的P-值，用于识别活性成员的途径，成为治疗药物的潜在目标。科学认识的提高将在应用层面产生切实影响。许多提出的方法已经被用于分析高维基因组，蛋白质组和代谢组学数据，重点是识别不同疾病（主要是癌症）状态下的活性途径（子网）。此外，一些新的实验正在设计阶段，将利用该项目中提出的一些先进模型和技术。这项建议的另一个关键方面是开发一个易于使用的从业人员开放源码软件，建立在一个域独立的工作流程管理系统。这使得用户可以通过以简单和透明的方式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互动与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监督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。