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

By Carolin Loos

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.

• Format: Paperback
• Language: English
• ISBN: 3658132337
• ISBN13: 9783658132330
• Release Date: March 2016
• Publisher: Springer Spektrum
• Length: 92 Pages
• Weight: 0.35 lbs.
• Dimensions: 0.3" x 5.8" x 8.3"