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

• Published in: BMC bioinformatics Vol. 17; no. 1; p. 355
• Main Authors: Lim, Chee Yee, Wang, Huange, Woodhouse, Steven, Piterman, Nir, Wernisch, Lorenz, Fisher, Jasmin, Göttgens, Berthold
• Format: Journal Article
• Language: English
• Published: London BioMed Central 06.09.2016; BioMed Central Ltd; Springer Nature B.V
• Subjects: Algorithms; Animals; Bayes Theorem; Bioinformatics; Biomedical and Life Sciences; Cells - chemistry; Cells - cytology; Cells - metabolism; Computational Biology - methods; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Gene expression; Gene Expression Profiling; Gene Regulatory Networks; Genetic algorithms; Humans; Life Sciences; Methodology; Methodology Article; Microarrays; Models, Genetic; Networks analysis; Physiological aspects; Single-Cell Analysis; Technological change; Training; United Kingdom; Executable model; BOOLEAN scoring function; Single-cell gene expression; Network reconstruction; Model learning; Asynchronous Boolean model
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-016-1235-y

Background Rapid 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. Results Here 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. Conclusions BTR 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.