Article ID Journal Published Year Pages File Type
8340564 Methods 2015 14 Pages PDF
Abstract
Single-cell experimental techniques provide informative data to help uncover dynamical processes inside a cell. Making full use of such data requires dedicated computational methods to estimate biophysical process parameters and states in a model-based manner. In particular, the treatment of heterogeneity or cell-to-cell variability deserves special attention. The present article provides an introduction to one particular class of algorithms which employ marginalization in order to take heterogeneity into account. An overview of alternative approaches is provided for comparison. We treat two frequently encountered scenarios in single-cell experiments, namely, single-cell trajectory data and single-cell distribution data.
Related Topics
Life Sciences Biochemistry, Genetics and Molecular Biology Biochemistry
Authors
, , ,