Sparse graphical models for exploring gene expression data
Sparse graphical models for exploring gene expression data
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DOI：
10.1016/j.jmva.2004.02.009
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发表时间：
2004-07-01
影响因子：
1.6
通讯作者：
West, M
中科院分区：
文献类型：
--
作者：
Dobra, A;Hans, C;West, M
我们讨论了大规模图形模型的理论结构和构造方法，其动机是它们在评估和辅助探索基因表达数据中的关联模式方面的潜力。理论讨论涵盖了高斯图模型、依赖网络和特定类型的有向无环图之间的基本概念和联系，我们称之为组成网络。我们描述了一种建设性的方法来为非常高维的分布生成有趣的图形模型，该方法建立在这些不同的风格化图形表示之间的关系上。跨维度的模型和先验的一致性问题是关键。由此产生的方法在评估大规模基因表达数据中的关联模式方面具有价值，以期产生对与已知分子途径或一组特定基因相关的基因的生物学见解。最初的一些例子涉及乳腺癌中的雌激素受体途径，以及Rb-E2F细胞增殖控制途径。(C)2004 Elsevier Inc.保留所有权利。
We discuss the theoretical structure and constructive methodology for large-scale graphical models, motivated by their potential in evaluating and aiding the exploration of patterns of association in gene expression data. The theoretical discussion covers basic ideas and connections between Gaussian graphical models, dependency networks and specific classes of directed acyclic graphs we refer to as compositional networks. We describe a constructive approach to generating interesting graphical models for very high-dimensional distributions that builds on the relationships between these various stylized graphical representations. Issues of consistency of models and priors across dimension are key. The resulting methods are of value in evaluating patterns of association in large-scale gene expression data with a view to generating biological insights about genes related to a known molecular pathway or set of specified genes. Some initial examples relate to the estrogen receptor pathway in breast cancer, and the Rb-E2F cell proliferation control pathway. (C) 2004 Elsevier Inc. All rights reserved.