Markov random field model for network-based analysis of genomic data
Markov random field model for network-based analysis of genomic data
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DOI：
10.1093/bioinformatics/btm129
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发表时间：
2007-06-15
期刊：
影响因子：
5.8
通讯作者：
Li, Hongzhe
中科院分区：
文献类型：
--
作者：
Wei, Zhi;Li, Hongzhe
动机：基因组研究的一个中心问题是识别与疾病和其他生物过程有关的基因和途径。通过基因集富集分析，识别的基因或单变量检验统计量通常与已知的生物学途径相关联，以识别所涉及的途径。然而，大多数用于鉴定差异表达（DE）基因的程序在鉴定这些基因的阶段不利用已知的途径信息。在这篇文章中，我们开发了一种基于马尔可夫随机场（MRF）的方法来识别与疾病相关的基因和子网络。结果：仿真研究表明，该方法能有效地识别与疾病相关的基因和子网络，并且比不使用通路结构信息的常用方法具有更高的灵敏度和更低的错误发现率。两个乳腺癌微阵列基因表达数据集的应用程序确定了几个KEGG转录途径，与乳腺癌复发或生存由于breastcancer.Conclusions：建议的MRF为基础的模型有效地利用了已知的通路结构，在确定DE基因和可能与表型相关的子网络。随着越来越多的生物网络被识别并记录在数据库中，所提出的方法应该在识别与疾病和其他生物过程相关的子网络中找到更多的应用。
Motivation: A central problem in genomic research is the identification of genes and pathways involved in diseases and other biological processes. The genes identified or the univariate test statistics are often linked to known biological pathways through gene set enrichment analysis in order to identify the pathways involved. However, most of the procedures for identifying differentially expressed (DE) genes do not utilize the known pathway information in the phase of identifying such genes. In this article, we develop a Markov random field (MRF)-based method for identifying genes and subnetworks that are related to diseases. Such a procedure models the dependency of the DE patterns of genes on the networks using a local discrete MRF model.Results: Simulation studies indicated that the method is quite effective in identifying genes and subnetworks that are related to disease and has higher sensitivity and lower false discovery rates than the commonly used procedures that do not use the pathway structure information. Applications to two breast cancer microarray gene expression datasets identified several subnetworks on several of the KEGG transcriptional pathways that are related to breast cancer recurrence or survival due to breast cancer.Conclusions: The proposed MRF-based model efficiently utilizes the known pathway structures in identifying the DE genes and the subnetworks that might be related to phenotype. As more biological networks are identified and documented in databases, the proposed method should find more applications in identifying the subnetworks that are related to diseases and other biological processes.