Joint conditional Gaussian graphical models with multiple sources of genomic data.
Joint conditional Gaussian graphical models with multiple sources of genomic data.
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DOI：
10.3389/fgene.2013.00294
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发表时间：
2013
影响因子：
3.7
通讯作者：
Zhao H
中科院分区：
文献类型：
--
作者：
Chun H;Chen M;Li B;Zhao H
确定有意义的基因网络是具有挑战性的，因为生物相互作用往往是特定条件的，并与外部因素混淆。为了便于网络推理，有必要集成多个基因组数据源。例如，在所谓的遗传基因组研究中，人们可以利用分子标记数据对从多个组织测量的表达数据集进行联合建模。在本文中，我们提出了一个联合条件高斯图形模型(JCGGM)，旨在基于多个数据源对生物过程进行建模。该方法采用条件模型和联合稀疏正则化相结合的方法，能够集成多个信息源。我们将我们的方法应用于一个真实的数据集，该数据集测量了来自重组近交系大鼠的四个组织(肾脏、肝脏、心脏和脂肪)的基因表达。我们的方法显示，在参与胰岛素反应促进糖转运体介导的葡萄糖转运途径的基因中，肝脏组织的组织特异性基因调控水平最高，其次是心脏和脂肪组织，这一发现只有通过我们的JCGGM方法才能获得。
It is challenging to identify meaningful gene networks because biological interactions are often condition-specific and confounded with external factors. It is necessary to integrate multiple sources of genomic data to facilitate network inference. For example, one can jointly model expression datasets measured from multiple tissues with molecular marker data in so-called genetical genomic studies. In this paper, we propose a joint conditional Gaussian graphical model (JCGGM) that aims for modeling biological processes based on multiple sources of data. This approach is able to integrate multiple sources of information by adopting conditional models combined with joint sparsity regularization. We apply our approach to a real dataset measuring gene expression in four tissues (kidney, liver, heart, and fat) from recombinant inbred rats. Our approach reveals that the liver tissue has the highest level of tissue-specific gene regulations among genes involved in insulin responsive facilitative sugar transporter mediated glucose transport pathway, followed by heart and fat tissues, and this finding can only be attained from our JCGGM approach.