BTR: training asynchronous Boolean models using single-cell expression data

BMC Bioinformatics

Methodology Article

Lorenz Wernisch¹  Nir Piterman²  Berthold Göttgens³  Huange Wang³  Steven Woodhouse³  Chee Yee Lim³  Jasmin Fisher⁴ 

[1] Biostatistics Unit, Medical Research Council, Cambridge, UK;Department of Computer Science, University of Leicester, Leicester, UK;Department of Haematology, Wellcome Trust and MRC Cambridge Stem Cell Institute, Cambridge Institute for Medical Research, University of Cambridge, Hills Road, CB2 0XY, Cambridge, UK;Microsoft Research Cambridge, Cambridge, UK;Department of Biochemistry, University of Cambridge, Cambridge, UK;

关键词: Asynchronous Boolean model;    Single-cell gene expression;    Model learning;    Network reconstruction;    BOOLEAN scoring function;    Executable model;   

DOI  :  10.1186/s12859-016-1235-y

 received in 2016-04-21, accepted in 2016-09-01,  发布年份 2016

来源: Springer

【 摘 要 】

BackgroundRapid technological innovation for the generation of single-cell genomics data presents new challenges and opportunities for bioinformatics analysis. One such area lies in the development of new ways to train gene regulatory networks. The use of single-cell expression profiling technique allows the profiling of the expression states of hundreds of cells, but these expression states are typically noisier due to the presence of technical artefacts such as drop-outs. While many algorithms exist to infer a gene regulatory network, very few of them are able to harness the extra expression states present in single-cell expression data without getting adversely affected by the substantial technical noise present.ResultsHere we introduce BTR, an algorithm for training asynchronous Boolean models with single-cell expression data using a novel Boolean state space scoring function. BTR is capable of refining existing Boolean models and reconstructing new Boolean models by improving the match between model prediction and expression data. We demonstrate that the Boolean scoring function performed favourably against the BIC scoring function for Bayesian networks. In addition, we show that BTR outperforms many other network inference algorithms in both bulk and single-cell synthetic expression data. Lastly, we introduce two case studies, in which we use BTR to improve published Boolean models in order to generate potentially new biological insights.ConclusionsBTR provides a novel way to refine or reconstruct Boolean models using single-cell expression data. Boolean model is particularly useful for network reconstruction using single-cell data because it is more robust to the effect of drop-outs. In addition, BTR does not assume any relationship in the expression states among cells, it is useful for reconstructing a gene regulatory network with as few assumptions as possible. Given the simplicity of Boolean models and the rapid adoption of single-cell genomics by biologists, BTR has the potential to make an impact across many fields of biomedical research.

【 授权许可】

CC BY   
© The Author(s). 2016

【 图 表 】

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