Approximate bayesian computation for stochastic single-cell time-lapse data using multivariate test statistics

Abstract

Stochastic dynamics of individual cells are mostly modeled with continuous time Markov chains (CTMCs). The parameters of CTMCs can be inferred using likelihood-based and likelihood-free methods. In this paper, we introduce a likelihood-free approximate Bayesian computation (ABC) approach for single-cell time-lapse data. This method uses multivariate statistics on the distribution of single-cell trajectories. We evaluated our method for samples of a bivariate normal distribution as well as for artificial equilibrium and non-equilibrium single-cell time-series of a one-stage model of gene expression. In addition, we assessed our method for parameter variability and for the case of tree-structured time-series data. A comparison with an existing method using univariate statistics revealed an improved parameter identifiability using multivariate test statistics.