Analysis Of Single-cell Data: Ode Constrained Mixture Modeling And Approximate Bayesian Computation (bestmasters)
by Carolin Loos /
2016 / English / PDF
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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.
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.
