Collaborative Research: Statistical Methods for Integrated Analysis of High-Throughput Biomedical Data

合作研究：高通量生物医学数据综合分析的统计方法

• 批准号: 1661802
• 负责人: Pradeep Ravikumar
• 金额: $ 17.35万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1661802&HistoricalAwards=false
• 关键词: Collaborative; Research; Statistical; Methods; Integrated

Technological advances have led to a rapid proliferation of high-throughput "omics" data in medicine that hold the key to clinically effective personalized medicine. To realize this goal, statistical and computational tools to mine this data and discover biomarkers, drug targets, disrupted disease networks, and disease sub-types are urgently needed. There are, however, two primary factors which make the development of such statistical tools challenging. First, many high-throughput genomic technologies produce varied heterogeneous data, which include continuous data (microarrays, methylation arrays), count data (RNA-sequencing), and binary/categorical data (SNPs, CNV). These varied data sets do not always satisfy typical distributional assumptions imposed by standard high-dimensional statistical models. Second, in order for scientists to leverage all of their data and understand the complete molecular basis of disease, these varied omics data sets need to be combined into a single multivariate statistical model. This proposal seeks to address these two issues with a new statistical framework for integrated analysis of multiple sets of high-dimensional data measured on the same group of subjects. The key statistical approach uses the theory of exponential family distributions to generalize two foundational high-dimensional statistical frameworks, principal components analysis (PCA) and graphical models, so as to jointly analyze transcriptional, epi-genomics and functional genomics data. This research will be applied to high-throughput cancer genomics data and lead to new methods to (a) discover molecular cancer sub-types along with their genomic signatures and (b) build a holistic network model of disease. By leveraging information across all the different types available of genomic biomarkers, the proposed methods will have the potential to make scientific discoveries critical for personalized medicine. The proposed work will also be broadly applicable to integrating multiple sets of "omics" data, including genomics, proteomics, metabolomics, and imaging. Beyond medicine, the theoretical framework and statistical methods will make significant advances in the theory of exponential families, statistical learning, and the emerging field of integrative analysis as well as have broad applicability in other disciplines such as engineering and security. All results will be disseminated through publications, conferences, and open-source software; this research will also provide training and educational opportunities for doctoral and postdoctoral scholars.

技术进步导致医学中高通量“组学”数据的迅速扩散，这些数据是临床有效的个性化医疗的关键。为了实现这一目标，迫切需要统计和计算工具来挖掘这些数据并发现生物标志物、药物靶点、破坏的疾病网络和疾病亚型。然而，有两个主要因素使得此类统计工具的开发具有挑战性。首先，许多高通量基因组技术产生各种异质数据，包括连续数据（微阵列、甲基化阵列）、计数数据（RNA测序）和二进制/分类数据（SNP、CNV）。这些不同的数据集并不总是满足标准高维统计模型所施加的典型分布假设。其次，为了让科学家利用所有数据并了解疾病的完整分子基础，需要将这些不同的组学数据集组合成一个多元统计模型。 该提案旨在通过一个新的统计框架来解决这两个问题，该框架用于对同一组受试者测量的多组高维数据进行综合分析。关键的统计方法利用指数族分布理论概括了主成分分析（PCA）和图模型这两个基础高维统计框架，从而联合分析转录、表观基因组学和功能基因组学数据。 这项研究将应用于高通量癌症基因组学数据，并带来新方法：(a) 发现分子癌症亚型及其基因组特征；(b) 建立疾病的整体网络模型。 通过利用所有不同类型的基因组生物标志物的信息，所提出的方法将有可能使科学发现对个性化医疗至关重要。 拟议的工作还将广泛适用于整合多组“组学”数据，包括基因组学、蛋白质组学、代谢组学和成像。 除了医学之外，理论框架和统计方法还将在指数族理论、统计学习和新兴的综合分析领域取得重大进展，并在工程和安全等其他学科中具有广泛的适用性。 所有结果将通过出版物、会议和开源软件传播；这项研究还将为博士和博士后学者提供培训和教育机会。