High-Dimensional Regression for Data Integration

数据集成的高维回归

• 批准号: 10707448
• 负责人: Juan Pablo Lewinger
• 金额: $ 27.53万
• 依托单位: UNIVERSITY OF SOUTHERN CALIFORNIA
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-01 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10707448
• 关键词: Address; Assessment tool; Biological; Chromosome Mapping; Colorectal Cancer; Complex; DNA; DNA Integration; Data; Data Analytics; Data Correlations; Data Set; Diagnostic; Disease; Disease Outcome; Elements; Enhancers; Evaluation; Event; Gene Expression; Gene Expression Profiling; Genes; Genetic Transcription; Genomics; Goals; Joints; Knowledge; Link; Malignant Neoplasms; Malignant neoplasm of ovary; Malignant neoplasm of prostate; Mediating; Methods; Modeling; Molecular; Outcome; Pathway interactions; Performance; Play; Population; Regulatory Element; Risk Factors; Role; Scanning; Statistical Methods; Time; Transcriptional Regulation; Untranslated RNA; Variant; Work; computerized tools; data integration; diagnostic signature; diagnostic tool; epigenomics; feature selection; flexibility; genome wide association study; high dimensionality; improved; insight; novel; predictive modeling; prognostic; prognostic signature; prognostic tool; tool; trait; transcriptomics

Project 1: High-Dimensional Regression for Data Integration Abstract Associated with high-dimensional omic (e.g. genomic, transcriptomic, epigenomic) features there is a rich set of functional and regulatory annotations, pathway information, and disease-specific knowledge from previous studies that is routinely used to interpret analyses of omic data. In this project, we propose to develop integrative regression methods capable of incorporating this array of external information a priori, rather than post hoc, to improve prediction performance, selection of predictive and associated features, and to gain insight into potential biological mechanisms in studies with highdimensional omic data. In our first Specific Aim we propose a general high-dimensional mixed modelling framework for integrating meta-features (e.g. functional annotations, pathways) into omic studies, with the flexibility to handle quantitative, categorical, and time-to-event outcomes, as well as the ability to accommodate correlated data through the inclusion of random effects. Our proposed approach brings together mixed modeling, high-dimensional regularized regression, and an empirical Bayes strategy that makes the direct estimation of tuning penalty parameters from the data analytically and computationally tractable. The proposed integrative models can be deployed in ‘predictive mode’ to develop diagnostic and prognostic signatures, or in ‘discovery mode’ to identify omic features associated with disease outcomes. We also propose an accompanying set of tools for inference and model interpretation. Our second Specific Aim focuses on integrative high-dimensional regression models for transcription-wide association studies (TWAS). We propose to leverage recent advances linking enhancers and other DNA regulatory elements and their proximal target genes to improve the prediction of genetically regulated gene expression with the goal of boosting the power and localization ability of TWAS. In our third Specific Aim, we focus on applications of the methods in Aims 1 and 2 to several cancer datasets.

项目1：数据集成的高维回归 抽象的 与高维组学（例如基因组、转录组、表观基因组）特征相关的是 丰富的功能和调控注释、通路信息和疾病特定知识 根据以前的研究，通常用于解释组学数据的分析。 建议开发能够整合这一系列外部因素的综合回归方法 先验信息，而不是事后信息，以提高预测性能，选择预测 和相关特征，并深入了解高维组学数据研究中的潜在生物学机制。在我们的第一个特定目标中，我们提出了一种通用的高维混合数据。 用于将元特征（例如功能注释、路径）集成到组学中的建模框架 研究，能够灵活地处理定量、分类和事件发生时间结果，以及 通过包含随机效应来容纳相关数据的能力。 该方法结合了混合建模、高维正则回归和经验 贝叶斯策略，通过分析数据直接估计调整惩罚参数 所提出的综合模型可以在“预测模式”下部署。 开发诊断和预后特征，或在“发现模式”下识别组学特征 我们还提出了一套用于推断和相关的工具。 我们的第二个具体目标侧重于积分高维回归。 我们建议利用最新进展进行全转录关联研究（TWAS）模型。 连接增强子和其他 DNA 调控元件及其近端靶基因，以改善 预测基因调控的基因表达，目的是增强功效和定位 在我们的第三个具体目标中，我们重点关注目标 1 和 2 中方法的应用。 几个癌症数据集。