A Bayesian framework for the inference of gene regulatory networks from time and pseudo-time series data
A Bayesian framework for the inference of gene regulatory networks from time and pseudo-time series data
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DOI：
10.1093/bioinformatics/btx605
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发表时间：
2018-03-15
期刊：
影响因子：
5.8
通讯作者：
Huang, Yufei
中科院分区：
文献类型：
--
作者：
Sanchez-Castillo, M.;Blanco, D.;Huang, Yufei
动机：分子谱分析技术已经发展到单细胞测定，其中同时筛选群体中每个细胞的密集分子谱。来自异质细胞群的高通量单细胞实验可以通过实验和计算分选为样品序列伪时间排序的样品。这些数据集，包括大量的样本的分析，有可能发现的动态的基础监管programmes.Results：我们提出了一种新的方法，用于建模和推断基因调控网络的高通量时间序列和伪时间排序的单细胞数据。我们的方法是基于一阶自回归移动平均模型，它推断的基因调控网络的变分贝叶斯框架内。我们用合成数据验证了我们的方法，并将其应用于斑马鱼中小鼠胚胎细胞和造血细胞的单细胞qPCR和RNA-Seq数据。
Motivation: Molecular profiling techniques have evolved to single-cell assays, where dense molecular profiles are screened simultaneously for each cell in a population. High-throughput single-cell experiments from a heterogeneous population of cells can be experimentally and computationally sorted as a sequence of samples pseudo-temporally ordered samples. The analysis of these datasets, comprising a large number of samples, has the potential to uncover the dynamics of the underlying regulatory programmes.Results: We present a novel approach for modelling and inferring gene regulatory networks from high-throughput time series and pseudo-temporally sorted single-cell data. Our method is based on a first-order autoregressive moving-average model and it infers the gene regulatory network within a variational Bayesian framework. We validate our method with synthetic data and we apply it to single cell qPCR and RNA-Seq data for mouse embryonic cells and hematopoietic cells in zebra fish.