Recovering reproducible and local signal in genomic data

恢复基因组数据中的可重复和局部信号

• 批准号: 10892772
• 负责人: Roberta de Vito
• 金额: $ 16.37万
• 依托单位: BROWN UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-06-01 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10892772
• 关键词: Biological; Biological Factors; Biological Sciences; Biology; Brain; Breathing; Centers of Research Excellence; Communities; Complex; Computational Biology; Data; Data Science; Dimensions; Experimental Designs; Factor Analysis; Foundations; Genes; Genomics; Goals; Learning; Logistic Regressions; Machine Learning; Malignant Neoplasms; Measurement; Measures; Meta-Analysis; Methodology; Methods; Morphologic artifacts; Population; Positioning Attribute; Regression Analysis; Reproducibility; Research; Signal Transduction; Software Tools; Source; Statistical Methods; Statistical Models; Structure; Techniques; Time; Tissues; Variant; Work; cancer type; data reduction; design; experimental study; genomic data; high dimensionality; human disease; multidimensional data; novel; software development; technology development; tool

Challenge. One of the most important challenge in biological science today is to elucidate the extent to which complex experiments, which measure hundreds of thousands of variables, can be analyzed to generate consistent and global signal when repeated, to identify local signal related to tissues, cancer types or population structure. Importantly, we must include the intrinsic diversity of variation across different studies and control for technical confounders as part of this task. Most measurements from high-dimensional biological experiments display variation arising both from biological sources, such as genes belonging to a different tissue or different positions in the brains. While some components reappear across multiple tissues, global biological signal is more likely than spurious signal to be reproducibly present in multiple tissues. Our challenge is to systematically and reliably identify the global biological factors, and estimate the signal specific to each study. Aims. In order to meet this challenge, we propose a novel concept that combines ideas from meta-analysis and statistical modeling dimension reduction. We posit that one can develop high-dimensional data reduction techniques that at the same time function as multi-study tools to extract consistent signal and local specific signal.This proposal develops statistical methods for identifying shared and study-specific signal across multiple cancer studies. In this work, it is crucial to understand the shared signal - here, gene co-expression shared across different cancer types - and the signal specific to each study. This proposal will pilot this concept by building novel classes of multi-logistic regression and factor analysis methods. The key is to decompose data from each study into latent dimensions, some of which are global while some are not and only specific to a local signal. This will simultaneously achieve two goals: learning reproducible biological features shared among studies, and identifying the variation specific of each study. Specific aims include methodology design, software development and applications. Impact. The concepts, approaches, and software tools generated by this research will have a direct impact on the ability of the biomedical community to reproducibly identify stable signals across multiple high-throughput biology studies and to capture local signals. Our tools will also enable a more reliable identification of artifacts and thus facilitate more efficient experimental designs and guide technological development. We also hope to impact data sciences beyond genomics. Our study will be the first opportunity to evaluate the novel concept of sharing latent factors as well as estimating local latent structures. The proposed work could subsequently provide the inspiration, as well and the practical foundation, for expanding this concept to a variety of another dimension reduction and machine learning techniques.

挑战.当今生物科学中最重要的挑战之一是阐明复杂性在多大程度上 测量数十万个变量的实验可以进行分析，以生成一致的全局信号， 重复，以识别与组织、癌症类型或群体结构相关的局部信号。 重要的是，我们必须包括不同研究中变异的内在多样性，并控制技术混杂因素， 这项任务的一部分。 来自高维生物实验的大多数测量显示来自生物来源的变化，例如 因为基因属于大脑中不同的组织或不同的位置。虽然某些组件会在多个 在组织中，全局生物信号比伪信号更可能可再现地存在于多个组织中。我们的挑战是 系统可靠地识别全局生物学因素，并估计每个研究的特定信号。 目标。为了应对这一挑战，我们提出了一个新的概念，结合了元分析和统计的想法， 建模降维我们认为，可以开发高维数据减少技术，在同一时间 时间函数作为多学习工具，提取一致性信号和局部特定信号，发展了统计方法 用于识别多项癌症研究中的共享和研究特异性信号。在这项工作中，理解共享的 信号-在这里，不同癌症类型之间共享的基因共表达-以及每个研究的特定信号。这项建议会 通过建立新型的多元逻辑回归和因子分析方法来引导这一概念。关键是分解 将每个研究的数据转换为潜在维度，其中一些是全球性的，而一些不是并且仅特定于局部信号。 这将同时实现两个目标：学习研究中共享的可重复的生物学特征，并确定 每项研究的具体变化。具体目标包括方法设计、软件开发和应用。 冲击本研究所产生的概念、方法和软件工具将直接影响 生物医学界可重复地识别多个高通量生物学研究中的稳定信号， 本地信号我们的工具还将能够更可靠地识别工件，从而促进更有效的实验。 设计和指导技术发展。我们还希望影响基因组学以外的数据科学。我们的研究将是 第一次有机会评估共享潜在因素以及估计局部潜在结构的新概念。的 拟议的工作可以随后提供灵感，以及和实际基础，扩大这一概念， 各种其他降维和机器学习技术。