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

Lecture Notes Comp. Sci. 9308, 52-63 (2015)
Open Access Green as soon as Postprint is submitted to ZB.
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.
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Publication type Article: Journal article
Document type Scientific Article
Keywords Approximate Bayesian Computation ; Multivariate Test Statistics ; Parameter Estimation ; Single-cell Time-series
Reviewing status