Analysis of Single-Cell Data
ODE Constrained Mixture Modeling and Approximate Bayesian Computation| By: | Carolin Loos |
| Publisher: | Springer Nature |
| Print ISBN: | 9783658132330 |
| eText ISBN: | 9783658132347 |
| Edition: | 0 |
| Copyright: | 2016 |
| Format: | Page Fidelity |
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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.