NCMIS, CEMS, RCSDS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China.
School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China.
Nucleic Acids Res. 2018 Jul 6;46(12):5967-5976. doi: 10.1093/nar/gky440.
With the rapid development of biotechnology, multi-dimensional genomic data are available for us to study the regulatory associations among multiple levels. Thus, it is essential to develop a tool to identify not only the modular patterns from multiple levels, but also the relationships among these modules. In this study, we adopt a novel non-negative matrix factorization framework (NetNMF) to integrate pairwise genomic data in a network manner. NetNMF could reveal the modules of each dimension and the connections within and between both types of modules. We first demonstrated the effectiveness of NetNMF using a set of simulated data and compared it with two typical NMF methods. Further, we applied it to two different types of pairwise genomic datasets including microRNA (miRNA) and gene expression data from The Cancer Genome Atlas and gene expression and pharmacological data from the Cancer Genome Project. We respectively identified a two-level miRNA-gene module network and a two-level gene-drug module network. Not only have the majority of identified modules significantly functional implications, but also the three types of module pairs have closely biological associations. This module discovery tool provides us comprehensive insights into the mechanisms of how the two levels of molecules cooperate with each other.
随着生物技术的快速发展，我们可以获得多维基因组数据来研究多个层次之间的调控关联。因此，开发一种工具来识别不仅是多层次的模块模式，而且是这些模块之间的关系至关重要。在这项研究中，我们采用了一种新颖的非负矩阵分解框架（NetNMF），以网络方式整合成对的基因组数据。NetNMF 可以揭示每个维度的模块以及两种类型的模块内部和之间的连接。我们首先使用一组模拟数据验证了 NetNMF 的有效性，并将其与两种典型的 NMF 方法进行了比较。此外，我们将其应用于两种不同类型的成对基因组数据集，包括来自癌症基因组图谱的 microRNA (miRNA) 和基因表达数据以及来自癌症基因组项目的基因表达和药理学数据。我们分别识别出了一个两层 miRNA-基因模块网络和一个两层基因-药物模块网络。不仅大多数识别出的模块具有显著的功能意义，而且三种类型的模块对之间也具有密切的生物学关联。这个模块发现工具使我们全面了解两个层次的分子如何相互合作的机制。
