@inproceedings{c9cff1000ad94d28a66d2c406d1b63d4,
title = "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.",
keywords = "Approximate Bayesian computation, Multivariate test statistics, Parameter estimation, Single-cell time-series",
author = "Carolin Loos and Carsten Marr and Theis, {Fabian J.} and Jan Hasenauer",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 13th International Conference on Computational Methods in Systems Biology, CMSB 2015 ; Conference date: 16-09-2015 Through 18-09-2015",
year = "2015",
doi = "10.1007/978-3-319-23401-4_6",
language = "English",
isbn = "9783319234007",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "52--63",
editor = "Olivier Roux and J{\'e}r{\'e}mie Bourdon",
booktitle = "Computational Methods in Systems Biology - 13th International Conference, CMSB 2015, Proceedings",
}