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Analysis of Single-Cell Data: ODE Constrained Mixture Modeling and Approximate Bayesian Computation

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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Detail Information
Series Title
BestMasters
Call Number
515.6 LOO a
Publisher
Wiesbaden : Springer Spektrum Wiesbaden.,
Collation
XXI, 92
Language
English
ISBN/ISSN
978-3-658-13234-7
Classification
515.6
Content Type
text
Media Type
computer
Carrier Type
online resource
Edition
Ed. 1
Subject(s)
Computational Mathematics and Numerical Analysis
Mathematical and Computational Biology
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Cataloger
umi
Source
https://link.springer.com/content/pdf/10.1007/978-3-658-13234-7.pdf?pdf=button
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