Analysis of Single-Cell Data

ODE Constrained Mixture Modeling and Approximate Bayesian Computation
1. Auflage 2016
ISBN: 978-3-658-13233-0
Verlag: Springer

Buch, Englisch, 92 Seiten, Format (B × H): 148 mm x 210 mm, Gewicht: 1607 g

Reihe: BestMasters

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. 

Zielgruppe

Research

Autoren/Hrsg.

Loos, Carolin

Fachgebiete

• Mathematik | Informatik EDV | Informatik Angewandte Informatik Bioinformatik
• Naturwissenschaften Biowissenschaften Angewandte Biologie Bioinformatik
• Naturwissenschaften Biowissenschaften Angewandte Biologie Biomathematik
• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen

Inhaltsverzeichnis

Modeling and Parameter Estimation for Single-Cell Data.- ODE Constrained Mixture Modeling for Multivariate Data.- Approximate Bayesian Computation Using Multivariate Statistics.

Über Autor(innen)

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“.