Datta Anisha, Sikdar Sinjini, Gill Ryan
Louisville Collegiate School and Department of Mathematics, University of Louisville.
Department of Bioinformatics and Biostatistics, University of Louisville.
Bioinformation. 2014 Oct 30;10(10):647-51. doi: 10.6026/97320630010647. eCollection 2014.
Various methods to determine the connectivity scores between groups of proteins associated with lung adenocarcinoma are examined. Proteins act together to perform a wide range of functions within biological processes. Hence, identification of key proteins and their interactions within protein networks can provide invaluable information on disease mechanisms. Differential network analysis provides a means of identifying differences in the interactions among proteins between two networks. We use connectivity scores based on the method of partial least squares to quantify the strength of the interactions between each pair of proteins. These scores are then used to perform permutation-based statistical tests. This examines if there are significant differences between the network connectivity scores for individual proteins or classes of proteins. The expression data from a study on lung adenocarcinoma is used in this study. Connectivity scores are computed for a group of 109 subjects who were in the complete remission and as well as for a group of 51 subjects whose cancer had progressed. The distributions of the connectivity scores are similar for the two networks yet subtle but statistically significant differences have been identified and their impact discussed.
研究了多种确定与肺腺癌相关的蛋白质组之间连接性得分的方法。蛋白质共同作用以在生物过程中执行广泛的功能。因此，识别关键蛋白质及其在蛋白质网络中的相互作用可以提供有关疾病机制的宝贵信息。差异网络分析提供了一种识别两个网络之间蛋白质相互作用差异的方法。我们使用基于偏最小二乘法的连接性得分来量化每对蛋白质之间相互作用的强度。然后使用这些得分进行基于排列的统计检验。这检查了单个蛋白质或蛋白质类别在网络连接性得分之间是否存在显著差异。本研究使用了一项关于肺腺癌的研究中的表达数据。为一组处于完全缓解状态的109名受试者以及一组癌症已进展的51名受试者计算连接性得分。两个网络的连接性得分分布相似，但已识别出细微但具有统计学意义的差异并讨论了其影响。
