Haifen Chen1, Jing Guo1, Shital K Mishra1, Paul Robson1, Mahesan Niranjan1, Jie Zheng2. 1. School of Computer Engineering, Nanyang Technological University, Singapore 639798, Singapore, Genome Institute of Singapore, Biopolis, Singapore 138672, Singapore and School of Electronics and Computer Science, University of Southampton, Highfield, Southampton SO17 1BJ, UK. 2. School of Computer Engineering, Nanyang Technological University, Singapore 639798, Singapore, Genome Institute of Singapore, Biopolis, Singapore 138672, Singapore and School of Electronics and Computer Science, University of Southampton, Highfield, Southampton SO17 1BJ, UK School of Computer Engineering, Nanyang Technological University, Singapore 639798, Singapore, Genome Institute of Singapore, Biopolis, Singapore 138672, Singapore and School of Electronics and Computer Science, University of Southampton, Highfield, Southampton SO17 1BJ, UK.
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. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Fiona K Hamey; Sonia Nestorowa; Sarah J Kinston; David G Kent; Nicola K Wilson; Berthold Göttgens Journal: Proc Natl Acad Sci U S A Date: 2017-06-06 Impact factor: 11.205