多基因网络和不同基因组数据的贝叶斯联合建模以识别转录因子的靶基因
Bayesian joint modeling of multiple gene networks and diverse genomic data to identify target genes of a transcription factor
摘要：
我们考虑多个基因网络和不同的基因组数据，包括蛋白质-DNA结合，基因表达和DNA序列数据的综合建模，以准确地识别转录因子（TF）的调控靶基因。而不是对待所有的基因平等和独立的先验在现有的联合建模方法，我们将生物先验知识，基因网络上的相邻基因往往是（或不）调节一起由TF。我们工作的一个关键贡献是，为了最大限度地利用所有现有的生物学知识，我们允许将多个基因网络的基因组数据的联合建模，通过引入基于使用多个马尔可夫随机场（MRF）的混合模型。我们的工作的另一个重要贡献是允许不同的基因组数据相关，并检查现有方法中采用的独立性假设的有效性和效果。由于完全贝叶斯方法，可以基于MCMC样本进行关于模型参数的推断。应用于E.大肠杆菌数据集，连同模拟研究，证明了效用和统计效率的增益与建议的联合模型。
英文摘要：
We consider integrative modeling of multiple gene networks and diverse genomic data, including protein-DNA binding, gene expression and DNA sequence data, to accurately identify the regulatory target genes of a transcription factor (TF). Rather than treating all the genes equally and independently a priori in existing joint modeling approaches, we incorporate the biological prior knowledge that neighboring genes on a gene network tend to be (or not to be) regulated together by a TF. A key contribution of our work is that, to maximize the use of all existing biological knowledge, we allow incorporation of multiple gene networks into joint modeling of genomic data by introducing a mixture model based on the use of multiple Markov random fields (MRFs). Another important contribution of our work is to allow different genomic data to be correlated and to examine the validity and effect of the independence assumption as adopted in existing methods. Due to a fully Bayesian approach, inference about model parameters can be carried out based on MCMC samples. Application to an E. coli data set, together with simulation studies, demonstrates the utility and statistical efficiency gains with the proposed joint model.
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