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Scalable Methods for Classification of Heterogeneous High-Dimensional Data

异构高维数据分类的可扩展方法

基本信息

批准号：
1712943
负责人：
Irina Gaynanova
金额：
$ 16.25万
依托单位：
Texas A&M University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-07-01 至 2020-06-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1712943&HistoricalAwards=false
关键词：
Scalable Methods Classification Heterogeneous Dimensional

项目摘要

Recent technological advances have enabled routine collection of large-scale high-dimensional data in the biomedical fields. For example, in cancer research it is common to use multiple high-throughput technology platforms to measure genotype, gene expression levels, and methylation levels. One of the main challenges in the analysis of such data is the identification of key biological measurements that can be used to classify the subject into a known cancer subtype. While significant progress has been made in the development of computationally efficient classification methods to address this challenge, existing methods do not adequately take into account the heterogeneity across the cancer subtypes and the mixed types of measurements (binary/count/continuous) across technology platforms. As such, existing methods may fail to identify relevant biological patterns. The goal of this project is to develop new classification methods that explicitly take into account the type and heterogeneity of measurements. While the primary focus is on methodology, high priority will be given to computational considerations and software development to encourage dissemination and ensure ease of use for domain scientists. Regularized linear discriminant methods are commonly used for simultaneous classification and variable selection due to their interpretability and computational efficiency. These methods, however, rely on unrealistic assumptions of equality of group-covariance matrices and normality of measurements. This project aims to address the limitations present in current discriminant approaches, and has three objectives: (1) to develop computationally efficient quadratic classification rules that perform variable selection; (2) to generalize the discriminant analysis framework to non-normal measurements; (3) to develop a classification framework for mixed type data coming from multiple technology platforms collected on the same set of subjects. The key methodological innovation is the combination of sparse low-rank singular value decomposition, which enables computational efficiency, with geometric interpretation of linear discriminant analysis, which allows for the construction of nonlinear classification rules by redefining the space for discrimination.

最近的技术进步使生物医学领域的大规模高维数据的常规收集成为可能。例如，在癌症研究中，通常使用多个高通量技术平台来测量基因型、基因表达水平和甲基化水平。分析此类数据的主要挑战之一是确定可用于将受试者分类为已知癌症亚型的关键生物学测量值。虽然在开发计算效率高的分类方法以应对这一挑战方面取得了重大进展，但现有方法没有充分考虑到癌症亚型之间的异质性以及跨技术平台的混合测量类型（二进制/计数/连续）。因此，现有的方法可能无法识别相关的生物模式。该项目的目标是开发新的分类方法，明确考虑到测量的类型和异质性。虽然主要的焦点是方法论，但将高度优先考虑计算方面的考虑和软件开发，以鼓励传播并确保领域科学家易于使用。正则化线性判别方法由于其可解释性和计算效率高，被广泛用于同时分类和变量选择。然而，这些方法依赖于群协方差矩阵相等和测量正态性的不切实际的假设。该项目旨在解决当前判别方法存在的局限性，并有三个目标：(1)开发执行变量选择的计算效率高的二次分类规则；(2)将判别分析框架推广到非正态测量；(3)针对同一主题集采集的来自多个技术平台的混合型数据开发分类框架。关键的方法创新是将稀疏低秩奇异值分解与线性判别分析的几何解释相结合，前者提高了计算效率，后者允许通过重新定义判别空间来构建非线性分类规则。