IEEE/ACM Trans Comput Biol Bioinform. 2022 Jan-Feb;19(1):305-316. doi: 10.1109/TCBB.2020.3004808. Epub 2022 Feb 3.
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.
癌症进展是动态的，追踪动态模块对于癌症的诊断和治疗具有很大的应用前景。积累的基因组数据为我们研究癌症的潜在机制提供了机会。然而，据我们所知，目前还没有算法能够通过整合异质组学数据来设计动态模块。为了解决这个问题，我们提出了一种基于正则化非负矩阵分解方法（DrNMF）的动态模块检测的综合框架，该方法通过整合基因表达和蛋白质相互作用网络来实现。为了消除基因组数据的异质性，我们将表达谱的样本分成若干组来构建基因共表达网络。为了描述模块的动态特性，采用了时间平滑框架，其中将上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纳入 DrNMF 的目标函数。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，所获得的动态模块通过已知的途径得到了更丰富的富集，并且可以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一种有效的综合分析方法。
