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

Chee Yee Lim,Lorenz Wernisch,Huange Wang,Berthold Göttgens,Nir Piterman,Steven Woodhouse,Jasmin Fisher

doi:10.1186/s12859-016-1235-y

Abstract

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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1235-y) contains supplementary material, which is available to authorized users.

Highlights

Rapid technological innovation for the generation of single-cell genomics data presents new challenges and opportunities for bioinformatics analysis
An asynchronous Boolean model uses the asynchronous update scheme, which specifies that at most one gene is updated between two consecutive states
The key component in BTR is a novel Boolean state space (BSS) scoring function, which BTR uses to infer a Boolean model through an optimisation process

Summary

Introduction

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. Because single cell analysis commonly reports expression states for hundreds of individual cells, this unique information offers new opportunities for the development of algorithms that can reconstruct gene regulatory networks. Changes in network dynamics can be described by using dynamic models, which possess different levels of granularity and precision ranging from the simpler Boolean models to more complex differential equation-based models. More complex models such as differential equation-based models offer high precision predictions, and have been used to describe gene regulatory networks [14,15,16,17]. Boolean models were first used to study gene regulatory networks by Kauffman in the 1970s, and since have been used extensively to study different biological systems [20,21,22,23]

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