Gradient directed regularization for sparse Gaussian concentration graphs, with applications to inference of genetic networks
Gradient directed regularization for sparse Gaussian concentration graphs, with applications to inference of genetic networks
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DOI：
10.1093/biostatistics/kxj008
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发表时间：
2006-04-01
期刊：
影响因子：
2.1
通讯作者：
Gui, J
中科院分区：
文献类型：
--
作者：
Li, HZ;Gui, J
大规模微阵列基因表达数据提供了构建遗传网络或生物学途径的可能性。高斯图模型是构建遗传网络的一种有效方法。然而，大多数可用的构建高斯图的方法都没有考虑到网络的稀疏性，并且在计算上要求更高或不可行，特别是在高维和低样本量的设置中。本文介绍了一种阈值梯度下降（TGD）正则化方法，用于估计高斯图模型下的稀疏精度矩阵，并演示了其在遗传网络识别中的应用。这种方法在计算上是可行的，并且可以很容易地结合有关网络结构的先验生物学知识。仿真结果表明，该方法比不考虑图稀疏性的方法能更好地估计精度矩阵。我们还介绍了拟南芥类异戊二烯生物合成基因网络的推断结果。这些结果表明，所提出的程序确实可以识别基于微阵列基因表达数据的生物学上有意义的遗传网络。
Large-scale microarray gene expression data provide the possibility of constructing genetic networks or biological pathways. Gaussian graphical models have been suggested to provide an effective method for constructing such genetic networks. However, most of the available methods for constructing Gaussian graphs do not account for the sparsity of the networks and are computationally more demanding or infeasible, especially in the settings of high dimension and low sample size. We introduce a threshold gradient descent (TGD) regularization procedure for estimating the sparse precision matrix in the setting of Gaussian graphical models and demonstrate its application to identifying genetic networks. Such a procedure is computationally feasible and can easily incorporate prior biological knowledge about the network structure. Simulation results indicate that the proposed method yields a better estimate of the precision matrix than the procedures that fail to account for the sparsity of the graphs. We also present the results on inference of a gene network for isoprenoid biosynthesis in Arabidopsis thaliana. These results demonstrate that the proposed procedure can indeed identify biologically meaningful genetic networks based on microarray gene expression data.