Zhang Fan, Miecznikowski Jeffrey C, Tritchler David L
Department of Biostatistics, SUNY University at Buffalo, Buffalo NY14214,USA.
Department of Biostatistics, SUNY University at Buffalo, Buffalo NY, USA.
Stat Appl Genet Mol Biol. 2020 Feb 29;19(1):/j/sagmb.2020.19.issue-1/sagmb-2018-0026/sagmb-2018-0026.xml. doi: 10.1515/sagmb-2018-0026.
Functional pathways involve a series of biological alterations that may result in the occurrence of many diseases including cancer. With the availability of various "omics" technologies it becomes feasible to integrate information from a hierarchy of biological layers to provide a more comprehensive understanding to the disease. In many diseases, it is believed that only a small number of networks, each relatively small in size, drive the disease. Our goal in this study is to develop methods to discover these functional networks across biological layers correlated with the phenotype. We derive a novel Network Summary Matrix (NSM) that highlights potential pathways conforming to least squares regression relationships. An algorithm called Decomposition of Network Summary Matrix via Instability (DNSMI) involving decomposition of NSM using instability regularization is proposed. Simulations and real data analysis from The Cancer Genome Atlas (TCGA) program will be shown to demonstrate the performance of the algorithm.
功能通路涉及一系列生物学改变，这些改变可能导致包括癌症在内的多种疾病的发生。随着各种“组学”技术的出现，整合来自生物层次结构各层的信息以更全面地了解疾病变得可行。在许多疾病中，人们认为只有少数网络（每个网络规模相对较小）驱动疾病的发生。我们在本研究中的目标是开发方法来发现跨生物层与表型相关的这些功能网络。我们推导了一种新颖的网络汇总矩阵（NSM），它突出了符合最小二乘回归关系的潜在通路。提出了一种名为通过不稳定性分解网络汇总矩阵（DNSMI）的算法，该算法涉及使用不稳定性正则化对NSM进行分解。来自癌症基因组图谱（TCGA）项目的模拟和真实数据分析将展示该算法的性能。
