High-dimensional unsupervised learning, with applications to genomics

高维无监督学习及其在基因组学中的应用

• 批准号: 8212779
• 负责人: Daniela Witten
• 金额: $ 37.71万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-20 至 2016-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8212779
• 关键词: Address; Biological; Biological Assay; Cancer Patient; Categories; Classification; Computer software; Copy Number Polymorphism; DNA Sequence; DNA copy number; Data; Data Element; Data Set; Databases; Development; Disease; Drug Formulations; Elements; Gene Expression; Gene Expression Profile; Generations; Genes; Genomics; Glioblastoma; Goals; Graph; Individual; Joints; Lead; Learning; Left; Malignant Neoplasms; Malignant neoplasm of lung; Measurement; Medicine; Methodology; Methods; Methylation; Mining; Modeling; Network-based; Noise; Normal Statistical Distribution; Normal tissue morphology; Other Genetics; Outcome; Pathway interactions; Patients; Pattern; Performance; Principal Component Analysis; RNA Sequences; Research; Sampling; Signal Transduction; Single Nucleotide Polymorphism; Statistical Methods; Statistical Models; Stem cells; Structure; Techniques; Technology; Testing; Time; Tumor Subtype; Validation; Validity of Results; base; cancer therapy; cancer type; clinically relevant; disorder subtype; flexibility; gene discovery; leiomyosarcoma; malignant breast neoplasm; novel strategies; outcome forecast; response; stem cell biology; stem cell population; tool; tumor; Address; Biological; Biological Assay; Cancer Patient; Categories; Classification; Computer software; Copy Number Polymorphism; DNA Sequence; DNA copy number; Data; Data Element; Data Set; Databases; Development; Disease; Drug Formulations; Elements; Gene Expression; Gene Expression Profile; Generations; Genes; Genomics; Glioblastoma; Goals; Graph; Individual; Joints; Lead; Learning; Left; Malignant Neoplasms; Malignant neoplasm of lung; Measurement; Medicine; Methodology; Methods; Methylation; Mining; Modeling; Network-based; Noise; Normal Statistical Distribution; Normal tissue morphology; Other Genetics; Outcome; Pathway interactions; Patients; Pattern; Performance; Principal Component Analysis; RNA Sequences; Research; Sampling; Signal Transduction; Single Nucleotide Polymorphism; Statistical Methods; Statistical Models; Stem cells; Structure; Techniques; Technology; Testing; Time; Tumor Subtype; Validation; Validity of Results; base; cancer therapy; cancer type; clinically relevant; disorder subtype; flexibility; gene discovery; leiomyosarcoma; malignant breast neoplasm; novel strategies; outcome forecast; response; stem cell biology; stem cell population; tool; tumor

DESCRIPTION (provided by applicant): This project involves the development of statistical methodology for the analysis of large- scale genomic data, such as gene expression, DNA copy number, and DNA sequencing data. In genomic studies, the goal is often to identify signal in the data in an unsupervised way. For instance, given the gene expression measurements for a set of patients with lung cancer, one might wish to discover previously unknown lung cancer subtypes that are characterized by distinct gene expression signatures and that might differ with respect to prognosis or response to therapy. However, the search for signal in genomic data is made difficult by the fact that the number of variables (e.g. genes) is generally orders of magnitude greater than the number of observations (e.g. lung cancer patients). As a result, principled methods must be developed to discover signal without overfitting. Furthermore, there is a need for objective ways to assess the validity of results obtained. This proposal has four specific aims, each of which involves the development of a new statistical method for solving a problem that arises in the analysis of genomic data. Aim 1: A method to learn multiple related genomic networks at once. For instance, one might expect that the gene expression networks for cancer and normal tissues will look similar to each other, with certain specific differences. The current proposal will provide a way to learn both networks simultaneously, in order to identify gene pathways that are perturbed in cancer. The proposed approach involves applying shrinkage penalties to the Gaussian graphical model formulation for network estimation. Aim 2: A principled approach for simultaneously clustering the rows and columns of a data matrix (e.g. patients and genes). The standard approach for discovering signal in genomic data involves clustering rows and columns independently, but the proposed approach will have increased power to discover biologically relevant clusters. The proposed approach involves applying shrinkage penalties to the matrix-variate normal distribution. Aim 3: A tool for the integrative analysis of multiple genomic data types collected on a single set of patient samples. For instance, if gene expression data, copy number data, and methylation data are collected for a single set of samples, then this will allow for the discovery of subsets of patients that are characterized by particular signatures of gene expression, copy number variation, and methylation. This could lead to the discovery of clinically relevant subtypes of cancer and other diseases. The proposed approach is an extension of the approach described in Aim 2. Aim 4: A flexible framework for the validation of clusters discovered in structured genomic data, such as DNA copy number and single nucleotide polymorphism data, in order to determine whether clusters discovered reflect signal or simply noise. The proposed approach is related to cross-validation, and will be extended to develop a method for the validation of other unsupervised statistical tools, such as those described in Aims 1-3 above. The statistical tools that result from the proposed research will be implemented in freely available software. PUBLIC HEALTH RELEVANCE: A major goal of research in genomics is the development of personalized medicine - treatments for cancer and other diseases that are tailored to an individual based on his or her DNA sequence or other genetic information. Though some advances towards this goal have been made, overall progress has been disappointingly slow due to the difficulty in mining through extremely large genomic data sets in order to discover disease-related information. This project addresses this difficulty via the development of new statistical methods for making sense of genomic data.

描述（由申请人提供）：该项目涉及开发用于分析大规模基因组数据的统计方法，例如基因表达，DNA拷贝数和DNA测序数据。在基因组研究中，该目标通常是以无监督的方式识别数据中的信号。例如，鉴于一组肺癌患者的基因表达测量值，人们可能希望发现以前未知的肺癌亚型，这些亚型的特征是不同的基因表达特征，并且在预后或对治疗的反应方面可能有所不同。但是，由于变量（例如基因）的数量通常大于观察次数（例如肺癌患者），因此很难在基因组数据中寻找信号。结果，必须开发有原则的方法以发现信号而不过度拟合。此外，需要客观的方法来评估所获得的结果的有效性。该提案具有四个具体的目标，每个目标涉及开发一种新的统计方法，用于解决基因组数据分析中出现的问题。目标1：一次学习多个相关基因组网络的方法。例如，人们可能会期望癌症和正常组织的基因表达网络彼此相似，并且存在某些特定差异。当前的建议将提供一种同时学习两个网络的方法，以识别癌症受到干扰的基因途径。所提出的方法涉及将收缩惩罚应用于高斯图形模型公式以进行网络估计。目标2：一种同时聚集数据矩阵的行和列的原则方法（例如，患者和基因）。在基因组数据中发现信号的标准方法涉及独立的聚类行和列，但是所提出的方法将具有提高发现生物学相关簇的功率。提出的方法涉及将收缩惩罚应用于基质变化的正态分布。 AIM 3：用于对一组患者样本收集的多种基因组数据类型进行整合分析的工具。例如，如果为一组样品收集基因表达数据，拷贝数数据和甲基化数据，则可以发现以基因表达，拷贝数变化和甲基化为特征的患者的子集。这可能导致发现癌症和其他疾病的临床相关亚型。所提出的方法是AIM 2中描述的方法的扩展。AIM4：一个灵活的框架，用于验证在结构化基因组数据中发现的簇，例如DNA拷贝数和单核苷酸多态性数据，以确定发现簇是否发现了簇或简单地反映信号。所提出的方法与交叉验证有关，并将扩展以开发一种验证其他无监督统计工具的方法，例如上面的AIMS 1-3中所述的方法。拟议研究产生的统计工具将在免费提供的软件中实施。 公共卫生相关性：基因组学研究的主要目标是开发个性化医学 - 癌症治疗和其他根据其DNA序列或其他遗传信息量身定制的疾病。尽管已经取得了一些进步，但由于难以通过极大的基因组数据集开采以发现与疾病相关的信息，因此总体进步令人失望。该项目通过开发新的统计方法来解决这种困难，以理解基因组数据。