Title
Analysis of Single-Cell Data [electronic resource] : ODE Constrained Mixture Modeling and Approximate Bayesian Computation / by Carolin Loos.
Publication
Wiesbaden : Springer Fachmedien Wiesbaden : Imprint: Springer Spektrum, 2016.
Physical Description
XXI, 92 p. 26 illus : online resource.
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Summary
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. Contents Modeling and Parameter Estimation for Single-Cell Data ODE Constrained Mixture Modeling for Multivariate Data Approximate Bayesian Computation Using Multivariate Statistics Target Groups Researchers and students in the fields of (bio-)mathematics, statistics, bioinformatics System biologists, biostatisticians, bioinformaticians The Author 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“.
Variant and related titles
Springer ebooks.
Other formats
Printed edition:
Added to Catalog
April 06, 2016
Contents
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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