Analysis of Single-Cell Data: ODE Constrained Mixture Modeling and Approximate Bayesian Computation

Analysis of Single-Cell Data: ODE Constrained Mixture Modeling and Approximate Bayesian Computation

English | March 18, 2016 | ISBN: 3658132337 | 116 Pages | PDF | 4.46 MB

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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