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Bayesian inference on stochastic gene transcription from flow cytometry data
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Tiberi, Simone, Walsh, Mark David, Cavallaro, Massimo, Hebenstreit, Daniel and Finkenstädt, Bärbel (2018) Bayesian inference on stochastic gene transcription from flow cytometry data. Bioinformatics, 34 (17). i647-i655. doi:10.1093/bioinformatics/bty568 ISSN 1367-4811.
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Official URL: https://doi.org/10.1093/bioinformatics/bty568
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 derive the stationary solution of such a model and prove that it can be written as a mixture of a Poisson and a Poisson-beta probability distribution. This finding facilitates inference for single cell data, observed at a single time point, from flow cytometry experiments such as FACS or FISH as it allows one to sample directly from the equilibrium distribution of the mRNA population. We hence propose a Bayesian inferential methodology using a pseudo-marginal approach and a recent approximation to integrate over unobserved states associated with measurement error.
Results
We provide a general inferential framework which can be widely used to study transcription in single cells from the kind of data arising in flow cytometry experiments. The approach allows us to separate between the intrinsic stochasticity of the molecular dynamics and the measurement noise. The methodology is tested in simulation studies and results are obtained for experimental multiple single cell data from in situ hybridization (FISH) flow cytometry experiments
Item Type: | Journal Article | |||||||||||||||
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Subjects: | Q Science > QA Mathematics Q Science > QH Natural history > QH426 Genetics |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Life Sciences (2010- ) | |||||||||||||||
Library of Congress Subject Headings (LCSH): | Genetic transcription -- Mathematical models, Messenger RNA, Stochastic processes, Poisson distribution | |||||||||||||||
Journal or Publication Title: | Bioinformatics | |||||||||||||||
Publisher: | Oxford University Press | |||||||||||||||
ISSN: | 1367-4811 | |||||||||||||||
Official Date: | 1 September 2018 | |||||||||||||||
Dates: |
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Volume: | 34 | |||||||||||||||
Number: | 17 | |||||||||||||||
Page Range: | i647-i655 | |||||||||||||||
DOI: | 10.1093/bioinformatics/bty568 | |||||||||||||||
Status: | Peer Reviewed | |||||||||||||||
Publication Status: | Published | |||||||||||||||
Access rights to Published version: | Open Access (Creative Commons) | |||||||||||||||
Date of first compliant deposit: | 8 June 2018 | |||||||||||||||
Date of first compliant Open Access: | 17 September 2018 | |||||||||||||||
RIOXX Funder/Project Grant: |
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