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

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.

Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data

“…Next, we considered how to simulate these networks to create in silico single-cell gene expression datasets. Several recent studies on GRN inference from such data [7,9,10,18,27] have used GeneNetWeaver [23], a method originally developed for generating time courses of bulk-RNA datasets from a given GRN. Accordingly, we simulated the six synthetic networks using GeneNetWeaver via the procedure outlined in Chan et al [7] (see Supplementary Section S1 for details).…”

“…We start this section by giving an overview of GeneNetWeaver [23], a popular method for simulating bulk gene expression datasets. It is being used increasingly for simulating single cell transcriptional data as well [7,9,10,18,27]. Next, we describe the BoolODE framework that we have developed and highlight its differences with GeneNetWeaver.…”

“…Synthetic networks or dense sub-networks of large-scale gene regulatory networks are primarily used to generate simulated datasets of single-cell gene expression [7,9,10,18,27]. However, these networks may not capture the complex regulation in any specific developmental process.…”

“…Some of these methods were originally developed for bulk transcriptional data but have since been applied to single-cell data [3,4,13,16]. Other publications presented approaches to reverse engineer Boolean networks from single cell transcriptomic data [11,[17][18][19]. An experimentalist seeking to analyze a new dataset faces a daunting task in selecting an appropriate inference method.…”

Using probabilistic graphical models to reconstruct biological networks and linkage maps

“…Notably, this grouping was consistent with the one shown by the data providers, who assigned indicators Chr. 6,Chr.3,Chr.5,Chr.2,Chr.1,Chr.4 and Chr.7 to the seven linkage groups, respectively. Table 4.5 offers, for each linkage group, a summary of the total number of markers, the number of unique markers, average map length across five mapping runs, and the highest value of N.N.Stress.…”