The accumulated omic data provides an opportunity to exploit the mechanisms of cancers and poses a challenge for their integrative analysis. Although extensive efforts have been devoted to address this issue, the current algorithms result in undesirable performance because of the complexity of patterns and heterogeneity of data. In this study, the ultimate goal is to propose an effective and efficient algorithm (called NMF-DEC) to identify clusters by integrating the interactome and transcriptome data. By treating the expression profiles of genes as attributes of vertices in the gene interaction networks, we transform the integrative analysis of omic data into clustering of attributed networks. To circumvent the heterogeneity, we construct a similarity network for the attributes of genes and cast it into the common module detection problem in multi-layer networks. The NMF-DEC explores the relation between attributes and topological structure of networks by jointly factorizing the similarity and interaction networks with the same basis. In this optimization, the interaction network is dynamically updated and the information of attributes is dynamically incorporated, providing a better strategy to characterize the structure of modules in attributed networks. Extensive experiments indicate that compared with state-of-the-art baselines, NMF-DEC is more accurate on social network, and show better performance on cancer attributed networks, implying the superiority of the proposed methods for the integrative analysis of omic data.

中文翻译：

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通过动态联合分解多层图对癌症归因网络进行聚类


积累的组学数据为探索癌症机制提供了机会，并对其综合分析提出了挑战。尽管已经付出了大量努力来解决这个问题，但由于模式的复杂性和数据的异构性，当前的算法导致性能不佳。本研究的最终目标是提出一种有效且高效的算法（称为 NMF-DEC），通过整合相互作用组和转录组数据来识别簇。通过将基因的表达谱视为基因相互作用网络中顶点的属性，我们将组学数据的综合分析转变为属性网络的聚类。为了规避异质性，我们构建了基因属性的相似网络，并将其转化为多层网络中的公共模块检测问题。 NMF-DEC通过共同分解具有相同基础的相似性和交互网络来探索网络属性和拓扑结构之间的关系。在这种优化中，交互网络动态更新，属性信息动态合并，为表征属性网络中模块的结构提供了更好的策略。大量实验表明，与最先进的基线相比，NMF-DEC 在社交网络上更加准确，并且在癌症归因网络上表现出更好的性能，这意味着所提出的组学数据综合分析方法的优越性。