RT Journal Article
SR Electronic
T1 Approximate inference of gene regulatory network models from RNA-Seq time series data
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 149674
DO 10.1101/149674
A1 Thomas Thorne
YR 2017
UL http://biorxiv.org/content/early/2017/06/14/149674.abstract
AB Inference of gene regulatory network structures from RNA-Seq data is challenging due to the nature of the data, as measurements take the form of counts of reads mapped to a given gene. Here we present a model for RNA-Seq time series data that applies a negative binomial distribution for the observations, and uses sparse regression with a horseshoe prior to learn a dynamic Bayesian network of interactions between genes. We use a variational inference scheme to learn approximate posterior distributions for the model parameters. The methodology is benchmarked on synthetic data designed to replicate the distribution of real world RNA-Seq data. We compare our method to other sparse regression approaches and information theoretic methods. We demonstrate an application of our method to a publicly available human neuronal stem cell differentiation RNA-Seq time series.