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Analysis of single-cell data using ODE constrained mixture modeling and approximate Bayesian computation.

München, Technische Universität, Fakultät für Mathematik, Master-Thesis, 2015, 88 S.
Publ. Version/Full Text DOI
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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Publication type Other: Thesis
Thesis type Master thesis
e-ISSN 978-3-658-13234-7
ISBN 978-3-658-13233-0
Quellenangaben Volume: , Issue: , Pages: 88 S. Article Number: , Supplement: ,
Publisher Springer Spektrum
University Technische Universität
University place München
Faculty Fakultät für Mathematik