Analysis of Single-Cell Data

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.