Intrinsically Bayesian robust classifier for single-cell gene expression trajectories in gene regulatory networks

Abstract

BackgroundExpression-based phenotype classification using either microarray or RNA-Seq measurements suffers from a lack of specificity because pathway timing is not revealed and expressions are averaged across groups of cells. This paper studies expression-based classification under the assumption that single-cell measurements are sampled at a sufficient rate to detect regulatory timing. Thus, observations are expression trajectories. In effect, classification is performed on data generated by an underlying gene regulatory network.ResultsNetwork regulation is modeled via a Boolean network with perturbation, regulation not fully determined owing to inherent biological randomness. The binary assumption is not critical because the resulting Markov chain characterizes expression trajectories. We assume a partially known Gaussian observation model belonging to an uncertainty class of models. We derive the intrinsically Bayesian robust classifier to discriminate between wild-type and mutated networks based on expression trajectories. The classifier minimizes the expected error across the uncertainty class relative to the prior distribution. We test it using a mammalian cell-cycle model, discriminating between the normal network and one in which gene p27 is mutated, thereby producing a cancerous phenotype. Tests examine all model aspects, including trajectory length, perturbation probability, and the hyperparameters governing the prior distribution over the uncertainty class.ConclusionsSimulations show the rates at which the expected error is diminished by smaller perturbation probability, longer trajectories, and hyperparameters that tighten the prior distribution relative to the unknown true network. For average-expression measurement, methods have been proposed to obtain prior distributions. These should be extended to the more mathematically difficult, but more informative, expression trajectories.Electronic supplementary materialThe online version of this article (10.1186/s12918-018-0549-y) contains supplementary material, which is available to authorized users.

Autonomous efficient experiment design for materials discovery with Bayesian model averaging

“…The so-called Bayesian Optimization (BO) [12] in the literature corresponds to these cases, where the prior model is sequentially updated after each experiment. Bayesian parametric and nonparametric models are widely used in other fields such as bioinformatics [13][14][15][16][17][18]. When prior knowledge about the form of the objective function exists and/or many observations of the objective values at different parts of the input space are available, one can use a parametric model as a surrogate model.…”

Scalable Optimal Bayesian Classification of Single-Cell Trajectories under Regulatory Model Uncertainty

“…To more comprehensively evaluate the proposed method, we have compared it with two other classification methods, i.e. IRB [12] and Plug-In [11]. The performance comparisons under four different scenarios are provided in Table 2.…”

“…The drawback of this method is its inability to use prior knowledge in deriving the classifier. In [12], the intrinsically Bayesian robust (IBR) classifier for the trajectories is developed. This IBR classifier is optimal relative to the prior distribution of unknown parameters.…”

Selected research articles from the 2017 International Workshop on Computational Network Biology: Modeling, Analysis, and Control (CNB-MAC)

“…After the workshop, 12 papers [1][2][3][4][5][6][7][8][9][10][11][12] have been accepted for publication in the CNB-MAC 2017 partner journals after an additional round of review and revision. The following journals have partnered with CNB-MAC 2017: BMC Bioinformatics, BMC Genomics, BMC Systems Biology, and IET Systems Biology.…”

“…Should single-cell expression trajectories be available such that the measurements are made at a sufficiently high rate to capture the regulatory timing in gene regulatory networks, the classification accuracy may be significantly improved. Karbalayghareh et al [8] investigates the performance of intrinsically Bayesian robust classifiers for discriminating between wild-type and mutated gene regulatory networks based on single-cell gene expression trajectories, where it is assumed that the network model is only partially known. The study reveals how the length of the trajectories, the amount of uncertainty in the underlying model, as well as other parameters affect the classification error.…”