Modelling transcriptional regulation with a mixture of factor analyzers and variational Bayesian expectation maximization.

Kuang Lin, Dirk Husmeier
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Abstract

Understanding the mechanisms of gene transcriptional regulation through analysis of high-throughput postgenomic data is one of the central problems of computational systems biology. Various approaches have been proposed, but most of them fail to address at least one of the following objectives: (1) allow for the fact that transcription factors are potentially subject to posttranscriptional regulation; (2) allow for the fact that transcription factors cooperate as a functional complex in regulating gene expression, and (3) provide a model and a learning algorithm with manageable computational complexity. The objective of the present study is to propose and test a method that addresses these three issues. The model we employ is a mixture of factor analyzers, in which the latent variables correspond to different transcription factors, grouped into complexes or modules. We pursue inference in a Bayesian framework, using the Variational Bayesian Expectation Maximization (VBEM) algorithm for approximate inference of the posterior distributions of the model parameters, and estimation of a lower bound on the marginal likelihood for model selection. We have evaluated the performance of the proposed method on three criteria: activity profile reconstruction, gene clustering, and network inference.

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模拟转录调节与因子分析和变分贝叶斯期望最大化的混合物。
通过分析高通量基因组后数据来理解基因转录调控的机制是计算系统生物学的核心问题之一。已经提出了各种方法,但大多数方法都未能解决以下目标中的至少一个:(1)考虑到转录因子可能受到转录后调控的事实;(2)考虑到转录因子作为一个功能复合体在调节基因表达方面的合作,以及(3)提供一个具有可控计算复杂性的模型和学习算法。本研究的目的是提出并测试一种解决这三个问题的方法。我们采用的模型是因子分析的混合物,其中潜在变量对应于不同的转录因子,分组成复合体或模块。我们在贝叶斯框架中进行推理,使用变分贝叶斯期望最大化(VBEM)算法对模型参数的后验分布进行近似推理,并估计模型选择的边际似然的下界。我们在三个标准上评估了所提出方法的性能:活动谱重建、基因聚类和网络推断。
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