Gill Ryan, Datta Somnath, Datta Susmita
Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, KY, 40202, USA.
Curr Pharm Des. 2014;20(1):4-10. doi: 10.2174/138161282001140113122316.
A complex disease like cancer is hardly caused by one gene or one protein singly. It is usually caused by the perturbation of the network formed by several genes or proteins. In the last decade several research teams have attempted to construct interaction maps of genes and proteins either experimentally or reverse engineer interaction maps using computational techniques. These networks were usually created under a certain condition such as an environmental condition, a particular disease, or a specific tissue type. Lately, however, there has been greater emphasis on finding the differential structure of the existing network topology under a novel condition or disease status to elucidate the perturbation in a biological system. In this review/tutorial article we briefly mention some of the research done in this area; we mainly illustrate the computational/statistical methods developed by our team in recent years for differential network analysis using publicly available gene expression data collected from a well known cancer study. This data includes a group of patients with acute lymphoblastic leukemia and a group with acute myeloid leukemia. In particular, we describe the statistical tests to detect the change in the network topology based on connectivity scores which measure the association or interaction between pairs of genes. The tests under various scores are applied to this data set to perform a differential network analysis on gene expression for human leukemia. We believe that, in the future, differential network analysis will be a standard way to view the changes in gene expression and protein expression data globally and these types of tests could be useful in analyzing the complex differential signatures.
像癌症这样的复杂疾病很少是由单个基因或单个蛋白质单独引起的。它通常是由几个基因或蛋白质形成的网络紊乱所导致。在过去十年中，几个研究团队试图通过实验构建基因和蛋白质的相互作用图谱，或者使用计算技术反向构建相互作用图谱。这些网络通常是在特定条件下创建的，比如环境条件、特定疾病或特定组织类型。然而，最近人们更加注重在新的条件或疾病状态下寻找现有网络拓扑结构的差异，以阐明生物系统中的紊乱情况。在这篇综述/教程文章中，我们简要提及了该领域的一些研究；我们主要阐述了我们团队近年来开发的用于差异网络分析的计算/统计方法，这些方法使用了从一项著名癌症研究中收集的公开可用基因表达数据。这些数据包括一组急性淋巴细胞白血病患者和一组急性髓系白血病患者。特别是，我们描述了基于连接性得分检测网络拓扑结构变化的统计检验，连接性得分用于衡量基因对之间的关联或相互作用。在各种得分下的检验被应用于该数据集，以对人类白血病的基因表达进行差异网络分析。我们相信，未来差异网络分析将成为一种全局查看基因表达和蛋白质表达数据变化的标准方法，并且这类检验在分析复杂的差异特征时可能会很有用。
