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.

技术进步导致了医学中高通量“ OMICS”数据的快速扩散，这些数据是临床有效的个性化医学的关键。为了实现这一目标，迫切需要迫切需要开采这些数据并发现生物标志物，药物靶标，疾病网络和疾病子类型的统计和计算工具。但是，有两个主要因素使这种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，其中包括连续数据（微阵列，甲基化阵列），计数数据（RNA序列）和​​二进制/分类数据（SNP，CNP，CNV）。这些多样化的数据集并不总是满足标准高维统计模型施加的典型分布假设。其次，为了使科学家利用其所有数据并了解疾病的完整分子基础，这些多样的OMIC数据集需要合并为一个多元统计模型。 该提案旨在通过一个新的统计框架解决这两个问题，以综合分析在同一受试者组上测量的多组高维数据。关键的统计方法使用指数式的家庭分布理论来概括两个基础高维统计框架，主要成分分析（PCA）和图形模型，以共同分析转录，Epi-genomics和功能基因组学数据。 这项研究将应用于高通量的癌症基因组学数据，并导致（a）发现分子癌亚型及其基因组特征，以及（b）建立整体疾病网络模型。 通过利用所有可用类型的基因组生物标志物的信息，提出的方法将有可能使科学发现对个性化医学至关重要。 所提出的工作还将广泛地用于整合多组“ OMICS”数据，包括基因组学，蛋白质组学，代谢组学和成像。 除了医学外，理论框架和统计方法还将在指数级家庭，统计学习和综合分析的新兴领域中取得重大进步，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。