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 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.
Bioinformatics. 2015 Apr 1;31(7):1060-6. doi: 10.1093/bioinformatics/btu777. Epub 2014 Nov 20.
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.
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.
Implementation of our algorithm is available from the authors upon request.
Supplementary data are available at Bioinformatics online.
尽管已经进行了很长时间的研究，但控制哺乳动物胚胎发育中细胞命运决定的转录调控网络仍然难以捉摸。单细胞 RNA 分析技术的最新出现带来了新发现的希望。尽管实验工作已经深入了解了小鼠早期发育，但仍缺乏整体和系统的观点。细胞命运的数学模型往往基于概念，而不是为了从实际数据中学习而设计的。为了阐明细胞命运决定背后的调控机制，非常需要综合数据驱动和知识驱动的建模方法。
我们提出了一种新的方法，该方法将细胞谱系树的结构与单细胞数据的转录模式相结合。该方法采用概率布尔网络（PBN）进行网络建模，并采用遗传算法作为搜索策略。在细胞谱系树分支上沿着细胞发育的“方向性”的指导下，我们的方法能够全面准确地从单细胞基因表达数据中推断出调控回路。将我们的算法应用于小鼠植入前发育的单细胞转录数据，该算法优于网络推断的常规方法。给定网络拓扑结构，我们的方法还可以识别基因调控网络（GRN）中的操作相互作用，这些相互作用对应于特定的细胞命运决定。这是首次尝试从单细胞转录数据中推断 GRN，同时结合细胞谱系树上细胞发育的动态。
我们的算法实现可应要求提供给作者。
补充数据可在生物信息学在线获得。
