联合和个体变异解释（JIVE），用于多种数据类型的综合分析
Joint and individual variation explained (JIVE) for integrated analysis of multiple data types
摘要：
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人的变异解释（JIVE），一般分解的变化，这样的数据集的综合分析。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，降低了数据的维度，并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，比流行的两块方法，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。数据和软件可在https://genome.unc.edu/jive/上获得
英文摘要：
Research in several fields now requires the analysis of data sets 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 data sets. 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 structures. 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. Data and software are available at https://genome.unc.edu/jive/
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
