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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)
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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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Approximate Bayesian Computation ; Multivariate Test Statistics ; Parameter Estimation ; Single-cell Time-series
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Konferenztitel Computational Methods in Systems Biology : 13th International Conference, CMSB 2015
Konferzenzdatum 16-18 September 2015
Konferenzort Nantes, France
Zeitschrift Lecture Notes in Computer Science
Quellenangaben Band: 9308, Heft: , Seiten: 52-63 Artikelnummer: , Supplement: ,
Verlag Springer
Verlagsort Berlin [u.a.]
Begutachtungsstatus nicht peer-reviewed