Extensions of Sparse Canonical Correlation Analysis with Applications to Genomic Data
Extensions of Sparse Canonical Correlation Analysis with Applications to Genomic Data
复制标题
DOI：
10.2202/1544-6115.1470
复制
发表时间：
2009-01-01
影响因子：
0.9
通讯作者：
Tibshirani, Robert J.
中科院分区：
文献类型：
--
作者：
Witten, Daniela M.;Tibshirani, Robert J.
在最近的工作中，一些作者介绍了稀疏典型相关分析（sparse canonical correlation analysis，简称稀疏CCA）的方法。假设在同一组观测数据上有两组测量结果。稀疏CCA是一种识别两组相互高度相关的变量的稀疏线性组合的方法。它已被证明在分析高维基因组数据时是有用的，当两组分析可用于同一组样本时。在本文中，我们提出了稀疏CCA方法的两个扩展。(1)稀疏CCA是一种无监督方法；也就是说，它没有利用每次观察可能可用的结果测量（例如，生存时间或癌症亚型）。我们提出了对稀疏CCA的扩展，我们称之为稀疏监督CCA，它可以识别相互关联并与结果相关联的两组变量的线性组合。(2)研究人员在同一组样品上收集两种以上的分析数据变得越来越普遍；例如，SNP、基因表达和DNA拷贝数测量都是可用的。我们发展了稀疏多重CCA，以将稀疏CCA方法扩展到两个以上数据集的情况。我们在模拟数据和最近发表的公开可用的弥漫性大b细胞淋巴瘤数据集上展示了这些新方法。
In recent work, several authors have introduced methods for sparse canonical correlation analysis (sparse CCA). Suppose that two sets of measurements are available on the same set of observations. Sparse CCA is a method for identifying sparse linear combinations of the two sets of variables that are highly correlated with each other. It has been shown to be useful in the analysis of high-dimensional genomic data, when two sets of assays are available on the same set of samples. In this paper, we propose two extensions to the sparse CCA methodology. (1) Sparse CCA is an unsupervised method; that is, it does not make use of outcome measurements that may be available for each observation (e.g., survival time or cancer subtype). We propose an extension to sparse CCA, which we call sparse supervised CCA, which results in the identification of linear combinations of the two sets of variables that are correlated with each other and associated with the outcome. (2) It is becoming increasingly common for researchers to collect data on more than two assays on the same set of samples; for instance, SNP, gene expression, and DNA copy number measurements may all be available. We develop sparse multiple CCA in order to extend the sparse CCA methodology to the case of more than two data sets. We demonstrate these new methods on simulated data and on a recently published and publicly available diffuse large B-cell lymphoma data set.