Analysis of Single-Cell Data: ODE Constrained Mixture Modeling and Approximate Bayesian Computation
Carolin 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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3 ODE Constrained Mixture Modeling for Multivariate Data Using Moment Equations
4 Approximate Bayesian Computation for SingleCell TimeLapse Data Using Multivariate Statistics
5 Summary and Discussion