A non-negative matrix factorization method for detecting modules in heterogeneous omics multi-modal data
A non-negative matrix factorization method for detecting modules in heterogeneous omics multi-modal data
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DOI：
10.1093/bioinformatics/btv544
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发表时间：
2016-01-01
期刊：
影响因子：
5.8
通讯作者：
Michailidis, George
中科院分区：
文献类型：
--
作者：
Yang, Zi;Michailidis, George
动机：高通量组学技术的最新进展使生物医学研究人员能够收集大规模的基因组数据。因此，人们越来越有兴趣开发方法来整合这些数据，以获得关于潜在生物系统的更深层次的见解。综合研究的一个关键挑战是不同组学数据源的异构性，这使得从特定于源的噪声或外部影响中区分感兴趣的协调信号变得困难。结果：我们提出了一种基于非负矩阵因式分解的多模式数据分析方法。我们提供了一种用于联合分解所涉及的数据矩阵的算法，该算法还包括用于高维设置的稀疏选项。在合成数据和从癌症基因组图谱获得的卵巢癌样本的真实DNA甲基化、基因表达和miRNA表达数据上对所提方法的性能进行了评估。结果显示，患者样本中存在与癌症相关途径以及先前建立的卵巢癌亚型相关的共同模块。
Motivation: Recent advances in high-throughput omics technologies have enabled biomedical researchers to collect large-scale genomic data. As a consequence, there has been growing interest in developing methods to integrate such data to obtain deeper insights regarding the underlying biological system. A key challenge for integrative studies is the heterogeneity present in the different omics data sources, which makes it difficult to discern the coordinated signal of interest from source-specific noise or extraneous effects.Results: We introduce a novel method of multi-modal data analysis that is designed for heterogeneous data based on non-negative matrix factorization. We provide an algorithm for jointly decomposing the data matrices involved that also includes a sparsity option for high-dimensional settings. The performance of the proposed method is evaluated on synthetic data and on real DNA methylation, gene expression and miRNA expression data from ovarian cancer samples obtained from The Cancer Genome Atlas. The results show the presence of common modules across patient samples linked to cancer-related pathways, as well as previously established ovarian cancer subtypes.