Cancer progression is dynamic, and tracking dynamic modules is promising for cancer diagnosis and therapy. Accumulated genomic data provide us an opportunity to investigate the underlying mechanisms of cancers. However, as far as we know, no algorithm has been designed for dynamic modules by integrating heterogeneous omics data. To address this issue, we propose an integrative framework for dynamic module detection based on regularized nonnegative matrix factorization method (DrNMF) by integrating the gene expression and protein interaction network. To remove the heterogeneity of genomic data, we divide the samples of expression profiles into groups to construct gene co-expression networks. To characterize the dynamics of modules, the temporal smoothness framework is adopted, in which the gene co-expression network at the previous stage and protein interaction network are incorporated into the objective function of DrNMF via regularization. The experimental results demonstrate that DrNMF is superior to state-of-the-art methods in terms of accuracy. For breast cancer data, the obtained dynamic modules are more enriched by the known pathways, and can be used to predict the stages of cancers and survival time of patients. The proposed model and algorithm provide an effective integrative analysis of heterogeneous genomic data for cancer progression.

中文翻译：

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基于矩阵分解的癌症动态模块异质基因组数据集成框架

癌症进展是动态的，跟踪动态模块有望用于癌症诊断和治疗。积累的基因组数据为我们提供了研究癌症潜在机制的机会。然而，据我们所知，目前还没有通过集成异构组学数据为动态模块设计算法。为了解决这个问题，我们通过整合基因表达和蛋白质相互作用网络，提出了一个基于正则化非负矩阵分解方法（DrNMF）的动态模块检测集成框架。为了消除基因组数据的异质性，我们将表达谱样本分组以构建基因共表达网络。为了表征模块的动态，采用时间平滑框架，其中，前一阶段的基因共表达网络和蛋白质相互作用网络通过正则化并入 DrNMF 的目标函数中。实验结果表明，DrNMF 在准确性方面优于最先进的方法。对于乳腺癌数据，获得的动态模块更丰富了已知途径，可用于预测癌症的分期和患者的生存时间。所提出的模型和算法为癌症进展的异质基因组数据提供了有效的综合分析。获得的动态模块通过已知途径更加丰富，可用于预测癌症的阶段和患者的生存时间。所提出的模型和算法为癌症进展的异质基因组数据提供了有效的综合分析。获得的动态模块通过已知途径更加丰富，可用于预测癌症的阶段和患者的生存时间。所提出的模型和算法为癌症进展的异质基因组数据提供了有效的综合分析。