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Approximate inference of gene regulatorynetwork models from RNA-Seq time series data

Thorne, Tom (2018) Approximate inference of gene regulatorynetwork models from RNA-Seq time series data BMC Bioinformatics, 19 (127).

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Abstract

Background: Inference of gene regulatory network structures from RNA-Seq data is challenging due to the natureof the data, as measurements take the form of counts of reads mapped to a given gene. Here we present a model forRNA-Seq time series data that applies a negative binomial distribution for the observations, and uses sparse regressionwith a horseshoe prior to learn a dynamic Bayesian network of interactions between genes. We use a variationalinference 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 worldRNA-Seq data. We compare our method to other sparse regression approaches and find improved performance inlearning directed networks. We demonstrate an application of our method to a publicly available human neuronalstem cell differentiation RNA-Seq time series data set to infer the underlying network structure. Conclusions: Our method is able to improve performance on synthetic data by explicitly modelling the statisticaldistribution of the data when learning networks from RNA-Seq time series. Applying approximate inferencetechniques we can learn network structures quickly with only moderate computing resources.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Computer Science
Authors :
NameEmailORCID
Thorne, Tomtom.thorne@surrey.ac.uk
Date : 11 April 2018
DOI : 10.1186/s12859-018-2125-2
Copyright Disclaimer : © The Author(s) 2018. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Depositing User : James Marshall
Date Deposited : 17 Jun 2020 09:18
Last Modified : 17 Jun 2020 09:18
URI: http://epubs.surrey.ac.uk/id/eprint/858006

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