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. (2014) 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).


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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.

Item Type: Article
Digital Object Identifier (DOI): doi:10.1093/bioinformatics/btu777
ISSNs: 1367-4803 (print)
Organisations: Vision, Learning and Control
ePrint ID: 396613
Date :
Date Event
17 November 2014Accepted/In Press
20 November 2014e-pub ahead of print
Date Deposited: 13 Jun 2016 11:28
Last Modified: 17 Apr 2017 02:47
Further Information:Google Scholar

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