Assisted estimation of gene expression graphical models.
Assisted estimation of gene expression graphical models.
复制标题
基因表达图形模型的辅助估计。
DOI：
10.1002/gepi.22377
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发表时间：
2021-06
影响因子：
2.1
通讯作者：
Ma S
中科院分区：
文献类型：
--
作者：
Yi H;Zhang Q;Sun Y;Ma S
在基因表达数据的研究中，网络分析发挥了独特的重要作用。在基因表达高斯图模型的构建中，为了适应高维数和低样本量的特点，并产生可解释的结果，通常进行正则化估计。在这里，我们使用GeO-GGM来表示仅基因表达的GGM。基因表达受调控因子调控。GeR-GGM（基因表达调节剂GGM），其调节基因表达以及它们的调节剂，已经相应地构建。在实际资料分析中，由于模型参数多、样本量小、信号弱等原因造成“信息缺乏”，GeO-GGM和GeR-GGM的构造往往不能令人满意。在这篇文章中，我们认识到，与基因表达和调控之间的调节，GeO-GGM和它的GeR-GGM对应物的稀疏结构可以满足一个层次。因此，我们提出了一个联合估计，加强了层次结构，并使用一个GeO-GGM的建设，以协助其GeR-GGM对应，反之亦然。严格建立了一致性性质，并开发了一种有效的计算算法。仿真结果表明，辅助构造优于GeO-GGM和GeR-GGM的分离构造。两个TCGA数据集进行了分析，导致与直接竞争对手不同的发现。
In the study of gene expression data, network analysis has played a uniquely important role. To accommodate the high dimensionality and low sample size and generate interpretable results, regularized estimation is usually conducted in the construction of gene expression Gaussian Graphical Models. Here we use GeO-GGM to represent gene-expression-only GGM. Gene expressions are regulated by regulators. GeR-GGMs (gene-expression-regulator GGMs), which accommodate gene expressions as well as their regulators, have been constructed accordingly. In practical data analysis, with a “lack of information” caused by the large number of model parameters, limited sample size, and weak signals, the construction of both GeO-GGMs and GeR-GGMs is often unsatisfactory. In this article, we recognize that with the regulation between gene expressions and regulators, the sparsity structures of a GeO-GGM and its GeR-GGM counterpart can satisfy a hierarchy. Accordingly, we propose a joint estimation which reinforces the hierarchical structure and use the construction of a GeO-GGM to assist that of its GeR-GGM counterpart and vice versa. Consistency properties are rigorously established, and an effective computational algorithm is developed. In simulation, the assisted construction outperforms the separation construction of GeO-GGM and GeR-GGM. Two TCGA datasets are analyzed, leading to findings different from the direct competitors.
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