The Library
A stochastic transcriptional switch model for single cell imaging data
Tools
Tools
Tools
Hey, Kirsty L., Momiji, Hiroshi, Featherstone, Karen, Davis, Julian R.E., White, Michael R.H., Rand, D. A. (David A.) and Finkenstädt, Bärbel (2015) A stochastic transcriptional switch model for single cell imaging data. Biostatistics, 16 (4). pp. 655-669. doi:10.1093/biostatistics/kxv010
|
PDF
WRAP_kxv010.pdf - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (615Kb) | Preview |
Official URL: http://dx.doi.org/10.1093/biostatistics/kxv010
Abstract
Gene expression is made up of inherently stochastic processes within single cells and can be modeled through stochastic reaction networks (SRNs). In particular, SRNs capture the features of intrinsic variability arising from intracellular biochemical processes. We extend current models for gene expression to allow the transcriptional process within an SRN to follow a random step or switch function which may be estimated using reversible jump Markov chain Monte Carlo (MCMC). This stochastic switch model provides a generic framework to capture many different dynamic features observed in single cell gene expression. Inference for such SRNs is challenging due to the intractability of the transition densities. We derive a model-specific birth-death approximation and study its use for inference in comparison with the linear noise approximation where both approximations are considered within the unifying framework of state-space models. The methodology is applied to synthetic as well as experimental single cell imaging data measuring expression of the human prolactin gene in pituitary cells.
| Item Type: | Journal Article | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| Subjects: | Q Science > QH Natural history > QH426 Genetics | ||||||||
| Divisions: | Faculty of Science > Statistics Faculty of Science > Centre for Systems Biology |
||||||||
| Library of Congress Subject Headings (LCSH): | Biometry, Biometry -- Statistical methods, Gene expression, Bayesian statistical theory | ||||||||
| Journal or Publication Title: | Biostatistics | ||||||||
| Publisher: | Oxford University Press | ||||||||
| ISSN: | 1465-4644 | ||||||||
| Official Date: | October 2015 | ||||||||
| Dates: |
|
||||||||
| Volume: | 16 | ||||||||
| Number: | 4 | ||||||||
| Page Range: | pp. 655-669 | ||||||||
| DOI: | 10.1093/biostatistics/kxv010 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Access rights to Published version: | Open Access | ||||||||
| Funder: | Engineering and Physical Sciences Research Council (EPSRC), Wellcome Trust (London, England) | ||||||||
| Grant number: | RSMAA.3020.SRA, 67252 |
Request changes or add full text files to a record
Repository staff actions (login required)
 |
View Item |
Downloads
Downloads per month over past year
