## Analysis of Single-Cell Data: ODE Constrained Mixture Modeling and Approximate Bayesian ComputationCarolin Loos introduces two novel approaches for the analysis of single-cell data. Both approaches can be used to study cellular heterogeneity and therefore advance a holistic understanding of biological processes. The first method, ODE constrained mixture modeling, enables the identification of subpopulation structures and sources of variability in single-cell snapshot data. The second method estimates parameters of single-cell time-lapse data using approximate Bayesian computation and is able to exploit the temporal cross-correlation of the data as well as lineage information. |

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### Contents

1 Introduction | 2 |

2 Background | 5 |

3 ODE Constrained Mixture Modeling for Multivariate Data Using Moment Equations | 15 |

4 Approximate Bayesian Computation
for SingleCell TimeLapse Data Using Multivariate Statistics | 57 |

5 Summary and Discussion | 85 |

Bibliography | 87 |

### Other editions - View all

Analysis of Single-Cell Data: ODE Constrained Mixture Modeling and ... Carolin Loos No preview available - 2016 |

### Common terms and phrases

ABC SMC algorithm Analysis of Single-Cell Approximate Bayesian Computation bivariate normal distribution calculation cell-to-cell variability chemical kinetics constrained mixture modeling conversion process corresponding covariance cross-match test CTMCs data of NGF-induced data set dose response equations experimental data extrinsic noise Figure Hasenauer heterogeneity Hypothesis Testing kinetic parameters likelihood function log-normal distribution log-normal median MAP estimates Markov chain measurand measurement noise method mixture distribution mixture probabilities model selection multivariate measurements multivariate normal distribution multivariate test statistics NGF stimulation NGF-induced Erk phosphorylation NGF-induced Erk signaling number of mol number of molecules number of simulations obtained ODE constrained mixture ODE-MMs with RREs optimal model parameter estimation particles pErk level UI perturbation kernel population Posterior approximations posterior distribution reaction samples Scenario single-cell data single-cell snapshot data single-cell time-lapse data species subpopulation total Erk levels trajectories TrkA true posterior univariate