A copula method for modeling directional dependence of genes
A copula method for modeling directional dependence of genes
复制标题
DOI：
10.1186/1471-2105-9-225
复制
发表时间：
2008-05-01
影响因子：
3
通讯作者：
Sohn, Insuk
中科院分区：
文献类型：
--
作者：
Kim, Jong-Min;Jung, Yoon-Sung;Sohn, Insuk
背景：基因作为生命的基本构件相互作用，形成一个复杂的网络。具有不同功能的基因组之间的关系可以用基因网络来表示。随着巨大的微阵列数据集在公共领域的沉积，基因网络的研究现在成为可能。近年来，从基因表达数据中重建基因网络的研究越来越受到人们的关注。最近的工作包括线性模型、布尔网络模型和贝叶斯网络。其中，贝叶斯网络似乎是构建基因网络最有效的方法。贝叶斯网络方法的一个主要问题是计算时间过长。这个问题是由于该方法需要很大的搜索空间的交互特性造成的。由于通过使用Copulas来拟合模型不需要迭代、先验的推导和复杂的后验分布计算，因此可以消除参考广泛的搜索空间的需要，从而产生可管理的计算能力。贝叶斯网络方法产生条件概率的离散表达式。在Copula方法中，不需要特征的离散性，它涉及到使用连续随机变量的统一表示。该方法克服了贝叶斯网络方法用于基因-基因相互作用的局限性，即由于二进制转换而导致信息丢失。结果：采用基于Copula函数的方向依赖度量方法分析了两个基因数据集(一组为8个组蛋白基因，另一组为19个基因，包括DNA聚合酶、DNA解旋酶、B型细胞周期蛋白基因、DNA引物、辐射敏感基因、修复相关基因、复制蛋白A编码基因、DNA复制起始因子、Securin基因、核小体组装因子和粘附素复合体的一个亚基)的基因交互作用。我们已经将我们的结果与文献中其他方法的结果进行了比较。虽然微阵列结果显示转录共调控模式，并不意味着基因产物是物理上相互作用的，但这种紧密的遗传联系可能表明每个基因产物在其他基因产物之间有直接或间接的联系。事实上，最近对蛋白质相互作用图的综合分析表明，这些组蛋白基因在物理上是相互联系的，支持我们的方法所获得的结果。结论：结果表明，我们的方法可以替代贝叶斯网络来建模基因相互作用。我们方法的一个优点是，基因之间的依赖关系不是假设为线性的。另一个优点是，我们的方法可以检测方向依赖。我们希望我们的研究可能有助于设计人工药物候选，它可以阻断或激活具有生物意义的途径。此外，我们的Copula方法可以扩展到研究局部环境对蛋白质-蛋白质相互作用的影响。Copula互信息方法将有助于提出一种新的ARACNE算法：一种用于基因调控网络重建的算法。
Background: Genes interact with each other as basic building blocks of life, forming a complicated network. The relationship between groups of genes with different functions can be represented as gene networks. With the deposition of huge microarray data sets in public domains, study on gene networking is now possible. In recent years, there has been an increasing interest in the reconstruction of gene networks from gene expression data. Recent work includes linear models, Boolean network models, and Bayesian networks. Among them, Bayesian networks seem to be the most effective in constructing gene networks. A major problem with the Bayesian network approach is the excessive computational time. This problem is due to the interactive feature of the method that requires large search space. Since fitting a model by using the copulas does not require iterations, elicitation of the priors, and complicated calculations of posterior distributions, the need for reference to extensive search spaces can be eliminated leading to manageable computational affords. Bayesian network approach produces a discretely expression of conditional probabilities. Discreteness of the characteristics is not required in the copula approach which involves use of uniform representation of the continuous random variables. Our method is able to overcome the limitation of Bayesian network method for gene-gene interaction, i.e. information loss due to binary transformation.Results: We analyzed the gene interactions for two gene data sets ( one group is eight histone genes and the other group is 19 genes which include DNA polymerases, DNA helicase, type B cyclin genes, DNA primases, radiation sensitive genes, repaire related genes, replication protein A encoding gene, DNA replication initiation factor, securin gene, nucleosome assembly factor, and a subunit of the cohesin complex) by adopting a measure of directional dependence based on a copula function. We have compared our results with those from other methods in the literature. Although microarray results show a transcriptional co-regulation pattern and do not imply that the gene products are physically interactive, this tight genetic connection may suggest that each gene product has either direct or indirect connections between the other gene products. Indeed, recent comprehensive analysis of a protein interaction map revealed that those histone genes are physically connected with each other, supporting the results obtained by our method.Conclusion: The results illustrate that our method can be an alternative to Bayesian networks in modeling gene interactions. One advantage of our approach is that dependence between genes is not assumed to be linear. Another advantage is that our approach can detect directional dependence. We expect that our study may help to design artificial drug candidates, which can block or activate biologically meaningful pathways. Moreover, our copula approach can be extended to investigate the effects of local environments on protein-protein interactions. The copula mutual information approach will help to propose the new variant of ARACNE ( Algorithm for the Reconstruction of Accurate Cellular Networks): an algorithm for the reconstruction of gene regulatory networks.