Rau Andrea, Jaffrézic Florence, Foulley Jean-Louis, Doerge Rebecca W
Purdue University, INRA AgroParisTech, USA.
Stat Appl Genet Mol Biol. 2010;9:Article 9. doi: 10.2202/1544-6115.1513. Epub 2010 Jan 15.
Gene regulatory networks refer to the interactions that occur among genes and other cellular products. The topology of these networks can be inferred from measurements of changes in gene expression over time. However, because the measurement device (i.e., microarrays) typically yields information on thousands of genes over few biological replicates, these systems are quite difficult to elucidate. An approach with proven effectiveness for inferring networks is the Dynamic Bayesian Network. We have developed an iterative empirical Bayesian procedure with a Kalman filter that estimates the posterior distributions of network parameters. We compare our method to similar existing methods on simulated data and real microarray time series data. We find that the proposed method performs comparably on both model-based and data-based simulations in considerably less computational time. The R and C code used to implement the proposed method are publicly available in the R package ebdbNet.
基因调控网络指的是基因与其他细胞产物之间发生的相互作用。这些网络的拓扑结构可以从基因表达随时间变化的测量结果中推断出来。然而，由于测量设备（即微阵列）通常在很少的生物重复样本上产生数千个基因的信息，这些系统很难阐明。一种经证实对推断网络有效的方法是动态贝叶斯网络。我们开发了一种带有卡尔曼滤波器的迭代经验贝叶斯程序，用于估计网络参数的后验分布。我们将我们的方法与模拟数据和真实微阵列时间序列数据上的类似现有方法进行了比较。我们发现，所提出的方法在基于模型和基于数据的模拟中表现相当，且计算时间大大减少。用于实现所提出方法的R和C代码在R包ebdbNet中公开可用。
