Inferring the time-invariant topology of a nonlinear sparse gene regulatory network using fully Bayesian spline autoregression
Inferring the time-invariant topology of a nonlinear sparse gene regulatory network using fully Bayesian spline autoregression
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DOI：
10.1093/biostatistics/kxr009
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发表时间：
2011-10-01
期刊：
影响因子：
2.1
通讯作者：
Burroughs, Nigel J.
中科院分区：
文献类型：
--
作者：
Morrissey, Edward R.;Juarez, Miguel A.;Burroughs, Nigel J.
我们提出了一个基于惩罚样条法的半参数贝叶斯模型，用于从纵向数据中恢复因果相互作用网络的时不变拓扑。我们的动机是从低分辨率微阵列时间序列推断基因调控网络，其中非线性相互作用的存在是众所周知的。亲子关系的映射是通过用亲属关系指标来增强模型，并为这些指标提供一个整体或基因层次结构。适当的先验规范对于控制样条线的灵活性至关重要，特别是在数据稀缺的情况下；因此，我们提供了一个信息丰富的、适当的先验。使用来自常微分方程模型的合成数据和来自拟南芥昼夜节律的实验数据集的基因表达，证明了网络推理相对于线性模型的实质性改进。
We propose a semiparametric Bayesian model, based on penalized splines, for the recovery of the time-invariant topology of a causal interaction network from longitudinal data. Our motivation is inference of gene regulatory networks from low-resolution microarray time series, where existence of nonlinear interactions is well known. Parenthood relations are mapped by augmenting the model with kinship indicators and providing these with either an overall or gene-wise hierarchical structure. Appropriate specification of the prior is crucial to control the flexibility of the splines, especially under circumstances of scarce data; thus, we provide an informative, proper prior. Substantive improvement in network inference over a linear model is demonstrated using synthetic data drawn from ordinary differential equation models and gene expression from an experimental data set of the Arabidopsis thaliana circadian rhythm.