Approximate inference of gene regulatory network models from RNA-Seq time series data.

ABSTRACT:

Background

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.

Results

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 find improved performance in learning directed network

SUBMITTER: Thorne T 

PROVIDER: S-EPMC5896118 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature