The University of Southampton
University of Southampton Institutional Repository

Single-cell transcriptional analysis to uncover regulatory circuits driving cell fate decisions in early mouse development

Chen, H., Guo, J., Mishra, S.K., Robson, P., Niranjan, M. and Zheng, J. (2015) Single-cell transcriptional analysis to uncover regulatory circuits driving cell fate decisions in early mouse development Bioinformatics, 31, (7), pp. 1060-1066. (doi:10.1093/bioinformatics/btu777).

Record type: Article

Abstract

Motivation: Transcriptional regulatory networks controlling cell fate decisions in mammalian embryonic development remain elusive despite a long time of research. The recent emergence of single-cell RNA profiling technology raises hope for new discovery. Although experimental works have obtained intriguing insights into the mouse early development, a holistic and systematic view is still missing. Mathematical models of cell fates tend to be concept-based, not designed to learn from real data. To elucidate the regulatory mechanisms behind cell fate decisions, it is highly desirable to synthesize the data-driven and knowledge-driven modeling approaches.

Results: We propose a novel method that integrates the structure of a cell lineage tree with transcriptional patterns from single-cell data. This method adopts probabilistic Boolean network (PBN) for network modeling, and genetic algorithm as search strategy. Guided by the ‘directionality’ of cell development along branches of the cell lineage tree, our method is able to accurately infer the regulatory circuits from single-cell gene expression data, in a holistic way. Applied on the single-cell transcriptional data of mouse preimplantation development, our algorithm outperforms conventional methods of network inference. Given the network topology, our method can also identify the operational interactions in the gene regulatory network (GRN), corresponding to specific cell fate determination. This is one of the first attempts to infer GRNs from single-cell transcriptional data, incorporating dynamics of cell development along a cell lineage tree.

Availability and implementation: Implementation of our algorithm is available from the authors upon request.

Full text not available from this repository.

More information

Accepted/In Press date: 17 November 2014
e-pub ahead of print date: 20 November 2014
Published date: 2015
Organisations: Vision, Learning and Control

Identifiers

Local EPrints ID: 396613
URI: http://eprints.soton.ac.uk/id/eprint/396613
ISSN: 1367-4803
PURE UUID: 824cc9ef-c09e-417b-be62-0f6648316264

Catalogue record

Date deposited: 13 Jun 2016 11:28
Last modified: 17 Jul 2017 18:47

Export record

Altmetrics

Contributors

Author: H. Chen
Author: J. Guo
Author: S.K. Mishra
Author: P. Robson
Author: M. Niranjan
Author: J. Zheng

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×