Carolin Loos introduces two novel approaches for theanalysis of single-cell data. Both approaches can be used to study cellularheterogeneity and therefore advance a holistic understanding of biologicalprocesses. The first method, ODE constrained mixture modeling, enables theidentification of subpopulation structures and sources of variability in single-cellsnapshot data. The second method estimates parameters of single-cell time-lapsedata using approximate Bayesian computation and is able to exploit the temporalcross-correlation of the data as well as lineage information.
Carolin Loos introduces two novel approaches for theanalysis of single-cell data. Both approaches can be used to study cellularheterogeneity and therefore advance a holistic understanding of biologicalprocesses. The first method, ODE constrained mixture modeling, enables theidentification of subpopulation structures and sources of variability in single-cellsnapshot data. The second method estimates parameters of single-cell time-lapsedata using approximate Bayesian computation and is able to exploit the temporalcross-correlation of the data as well as lineage information.
Carolin Loos is currently doing her PhD at the Institute of Computational Biology at the Helmholtz Zentrum München. She is member of the junior research group "Data-driven Computational Modeling".
Inhaltsangabe
Modeling and Parameter Estimation for Single-CellData.- ODE Constrained Mixture Modeling for Multivariate Data.- ApproximateBayesian Computation Using Multivariate Statistics.
Modeling and Parameter Estimation for Single-Cell Data.- ODE Constrained Mixture Modeling for Multivariate Data.- Approximate Bayesian Computation Using Multivariate Statistics.
Modeling and Parameter Estimation for Single-CellData.- ODE Constrained Mixture Modeling for Multivariate Data.- ApproximateBayesian Computation Using Multivariate Statistics.
Modeling and Parameter Estimation for Single-Cell Data.- ODE Constrained Mixture Modeling for Multivariate Data.- Approximate Bayesian Computation Using Multivariate Statistics.
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