Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA, USA.
BMC Bioinformatics. 2010 Mar 30;11:162. doi: 10.1186/1471-2105-11-162.
In cancer, gene networks and pathways often exhibit dynamic behavior, particularly during the process of carcinogenesis. Thus, it is important to prioritize those genes that are strongly associated with the functionality of a network. Traditional statistical methods are often inept to identify biologically relevant member genes, motivating researchers to incorporate biological knowledge into gene ranking methods. However, current integration strategies are often heuristic and fail to incorporate fully the true interplay between biological knowledge and gene expression data.
To improve knowledge-guided gene ranking, we propose a novel method called coordinative component analysis (COCA) in this paper. COCA explicitly captures those genes within a specific biological context that are likely to be expressed in a coordinative manner. Formulated as an optimization problem to maximize the coordinative effort, COCA is designed to first extract the coordinative components based on a partial guidance from knowledge genes and then rank the genes according to their participation strengths. An embedded bootstrapping procedure is implemented to improve statistical robustness of the solutions. COCA was initially tested on simulation data and then on published gene expression microarray data to demonstrate its improved performance as compared to traditional statistical methods. Finally, the COCA approach has been applied to stem cell data to identify biologically relevant genes in signaling pathways. As a result, the COCA approach uncovers novel pathway members that may shed light into the pathway deregulation in cancers.
We have developed a new integrative strategy to combine biological knowledge and microarray data for gene ranking. The method utilizes knowledge genes for a guidance to first extract coordinative components, and then rank the genes according to their contribution related to a network or pathway. The experimental results show that such a knowledge-guided strategy can provide context-specific gene ranking with an improved performance in pathway member identification.
在癌症中，基因网络和途径通常表现出动态行为，特别是在癌变过程中。因此，重要的是要优先考虑那些与网络功能密切相关的基因。传统的统计方法通常无法识别具有生物学意义的成员基因，这促使研究人员将生物学知识纳入基因排名方法中。然而，当前的整合策略往往是启发式的，未能充分纳入生物学知识与基因表达数据之间的真实相互作用。
为了改进知识引导的基因排名，我们在本文中提出了一种名为协同成分分析（COCA）的新方法。COCA 明确捕获了特定生物学背景下可能以协调方式表达的那些基因。作为最大化协同作用的优化问题，COCA 旨在首先根据知识基因的部分指导提取协同成分，然后根据它们的参与强度对基因进行排名。实施了一个嵌入式自举程序来提高解决方案的统计稳健性。COCA 最初在模拟数据上进行了测试，然后在已发表的基因表达微阵列数据上进行了测试，以证明与传统统计方法相比其性能得到了提高。最后，COCA 方法已应用于干细胞数据，以鉴定信号通路中的生物学相关基因。结果，COCA 方法揭示了可能揭示癌症中途径失调的新途径成员。
我们开发了一种新的整合策略，将生物学知识和微阵列数据结合起来进行基因排名。该方法利用知识基因进行指导，首先提取协同成分，然后根据它们与网络或途径的贡献对基因进行排名。实验结果表明，这种知识引导的策略可以提供特定于上下文的基因排名，并在途径成员识别方面提高性能。
