Bayesian inference on stochastic gene transcription from flow cytometry data.

ABSTRACT:

Motivation

Transcription in single cells is an inherently stochastic process as mRNA levels vary greatly between cells, even for genetically identical cells under the same experimental and environmental conditions. We present a stochastic two-state switch model for the population of mRNA molecules in single cells where genes stochastically alternate between a more active ON state and a less active OFF state. We prove that the stationary solution of such a model can be written as a mixture of a Poisson and a Poisson-beta probability distribution. This finding facilitates inference for single cell expression data, observed at a single time point, from flow cytometry experiments such as FACS or fluorescence in situ hybridization (FISH) as it allows one to sample directly from the equi

SUBMITTER: Tiberi S 

PROVIDER: S-EPMC6129284 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature