基于隐时空马尔可夫随机场模型的基因表达时程数据网络分析
A hidden spatial-temporal Markov random field model for network-based analysis of time course gene expression data
摘要：
微阵列时程（MTC）基因表达数据通常被收集来研究生物过程的动态性质。一个重要的问题是确定随着时间的推移表现出不同表达谱的基因和在给定的生物过程中受到干扰的途径。虽然有方法可用于鉴定随时间具有差异表达水平的基因，但缺乏可以将途径信息并入鉴定在生物过程期间被修饰/激活的途径的方法。在本文中，我们开发了一个隐藏的时空马尔可夫随机场（hstMRF）为基础的方法，用于识别基因和子网络，与生物过程相关的，在网络上的基因的差异表达模式的依赖关系建模随着时间的推移和网络的途径。仿真研究表明，该方法是非常有效的，在识别基因和修改的子网络，并具有更高的灵敏度比常用的程序，不使用的路径结构或时间依赖性的信息，具有类似的错误发现率。应用于人类全身性炎症的微阵列基因表达研究，确定了KEGG通路上的一组核心基因，这些基因随时间推移显示出明显的差异表达模式。此外，该方法证实了TOLL样信号通路在对内毒素的免疫应答中起重要作用。
英文摘要：
Microarray time course (MTC) gene expression data are commonly collected to study the dynamic nature of biological processes. One important problem is to identify genes that show different expression profiles over time and pathways that are perturbed during a given biological process. While methods are available to identify the genes with differential expression levels over time, there is a lack of methods that can incorporate the pathway information in identifying the pathways being modified/activated during a biological process. In this paper we develop a hidden spatial-temporal Markov random field (hstMRF)-based method for identifying genes and subnetworks that are related to biological processes, where the dependency of the differential expression patterns of genes on the networks are modeled over time and over the network of pathways. Simulation studies indicated that the method is quite effective in identifying genes and modified subnetworks and has higher sensitivity than the commonly used procedures that do not use the pathway structure or time dependency information, with similar false discovery rates. Application to a microarray gene expression study of systemic inflammation in humans identified a core set of genes on the KEGG pathways that show clear differential expression patterns over time. In addition, the method confirmed that the TOLL-like signaling pathway plays an important role in immune response to endotoxins.
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