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Hierarchical Probabilistic Interaction Modeling for Multiple Gene Expression Replicates

机译:多个基因表达重复的分层概率交互建模

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摘要

Microarray technology allows for the collection of multiple replicates of gene expression time course data for hundreds of genes at a handful of time points. Developing hypotheses about a gene transcriptional network, based on time course gene expression data is an important and very challenging problem. In many situations there are similarities which suggest a hierarchical structure between the replicates. This paper develops posterior probabilities for network features based on multiple hierarchical replications. Through Bayesian inference, in conjunction with the Metropolis-Hastings algorithm and model averaging, a hierarchical multiple replicate algorithm is applied to seven sets of simulated data and to a set of Arabidopsis thaliana gene expression data. The models of the simulated data suggest high posterior probabilities for pairs of genes which have at least moderate signal partial correlation. For the Arabidopsis model, many of the highest posterior probability edges agree with the literature.
机译:微阵列技术允许在少数几个时间点收集数百个基因的基因表达时程数据的多个重复。基于时程基因表达数据发展有关基因转录网络的假设是一个重要且具有挑战性的问题。在许多情况下,存在相似之处,表明重复之间存在分层结构。本文研究了基于多层分层复制的网络功能的后验概率。通过贝叶斯推断,结合Metropolis-Hastings算法和模型平均,将分层多重复制算法应用于七组模拟数据和一组拟南芥基因表达数据。模拟数据的模型表明,具有至少中等信号偏相关的基因对具有较高的后验概率。对于拟南芥模型,许多最高的后验概率边缘与文献一致。

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