JOINT AND INDIVIDUAL VARIATION EXPLAINED (JIVE) FOR INTEGRATED ANALYSIS OF MULTIPLE DATA TYPES.
JOINT AND INDIVIDUAL VARIATION EXPLAINED (JIVE) FOR INTEGRATED ANALYSIS OF MULTIPLE DATA TYPES.
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DOI：
10.1214/12-aoas597
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发表时间：
2013-03-01
期刊：
影响因子：
--
通讯作者：
Nobel AB
中科院分区：
文献类型：
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作者：
Lock EF;Hoadley KA;Marron JS;Nobel AB
现在，多个领域的研究需要分析数据集，其中多个高维类型的数据可用于一组公共对象。特别是，癌症基因组图谱（TCGA）包括来自相同癌性肿瘤样本的几种不同基因组技术的数据。在本文中，我们介绍了联合和个体变异解释（JIVE），这是一种用于此类数据集综合分析的变异通用分解。分解由三个方面组成：低秩近似捕获跨数据类型的联合变化，低秩近似每个数据类型的结构化变化个体，和残留噪声。JIVE量化了数据类型之间的联合变化量，降低了数据的维数，为联合和个体结构的可视化探索提供了新的方向。所提出的方法是主成分分析的扩展，并具有明显的优势，比流行的两个块的方法，如典型相关分析和偏最小二乘法。多形性胶质母细胞瘤肿瘤样本的基因表达和miRNA数据的JIVE分析揭示了基因-miRNA关联，并提供了肿瘤类型的更好表征。
Research in several fields now requires the analysis of datasets in which multiple high-dimensional types of data are available for a common set of objects. In particular, The Cancer Genome Atlas (TCGA) includes data from several diverse genomic technologies on the same cancerous tumor samples. In this paper we introduce Joint and Individual Variation Explained (JIVE), a general decomposition of variation for the integrated analysis of such datasets. The decomposition consists of three terms: a low-rank approximation capturing joint variation across data types, low-rank approximations for structured variation individual to each data type, and residual noise. JIVE quantifies the amount of joint variation between data types, reduces the dimensionality of the data, and provides new directions for the visual exploration of joint and individual structure. The proposed method represents an extension of Principal Component Analysis and has clear advantages over popular two-block methods such as Canonical Correlation Analysis and Partial Least Squares. A JIVE analysis of gene expression and miRNA data on Glioblastoma Multiforme tumor samples reveals gene-miRNA associations and provides better characterization of tumor types.