Cargando…
Bayesian inference on stochastic gene transcription from flow cytometry data
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...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6129284/ https://www.ncbi.nlm.nih.gov/pubmed/30423089 http://dx.doi.org/10.1093/bioinformatics/bty568 |
_version_ | 1783353773837516800 |
---|---|
author | Tiberi, Simone Walsh, Mark Cavallaro, Massimo Hebenstreit, Daniel Finkenstädt, Bärbel |
author_facet | Tiberi, Simone Walsh, Mark Cavallaro, Massimo Hebenstreit, Daniel Finkenstädt, Bärbel |
author_sort | Tiberi, Simone |
collection | PubMed |
description | 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 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 expression data from FISH flow cytometry experiments. AVAILABILITY AND IMPLEMENTATION: All analyses were implemented in R. Source code and the experimental data are available at https://github.com/SimoneTiberi/Bayesian-inference-on-stochastic-gene-transcription-from-flow-cytometry-data. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. |
format | Online Article Text |
id | pubmed-6129284 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-61292842018-09-12 Bayesian inference on stochastic gene transcription from flow cytometry data Tiberi, Simone Walsh, Mark Cavallaro, Massimo Hebenstreit, Daniel Finkenstädt, Bärbel Bioinformatics Eccb 2018: European Conference on Computational Biology Proceedings 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 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 expression data from FISH flow cytometry experiments. AVAILABILITY AND IMPLEMENTATION: All analyses were implemented in R. Source code and the experimental data are available at https://github.com/SimoneTiberi/Bayesian-inference-on-stochastic-gene-transcription-from-flow-cytometry-data. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2018-09-01 2018-09-08 /pmc/articles/PMC6129284/ /pubmed/30423089 http://dx.doi.org/10.1093/bioinformatics/bty568 Text en © The Author(s) 2018. Published by Oxford University Press. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Eccb 2018: European Conference on Computational Biology Proceedings Tiberi, Simone Walsh, Mark Cavallaro, Massimo Hebenstreit, Daniel Finkenstädt, Bärbel Bayesian inference on stochastic gene transcription from flow cytometry data |
title | Bayesian inference on stochastic gene transcription from flow cytometry data |
title_full | Bayesian inference on stochastic gene transcription from flow cytometry data |
title_fullStr | Bayesian inference on stochastic gene transcription from flow cytometry data |
title_full_unstemmed | Bayesian inference on stochastic gene transcription from flow cytometry data |
title_short | Bayesian inference on stochastic gene transcription from flow cytometry data |
title_sort | bayesian inference on stochastic gene transcription from flow cytometry data |
topic | Eccb 2018: European Conference on Computational Biology Proceedings |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6129284/ https://www.ncbi.nlm.nih.gov/pubmed/30423089 http://dx.doi.org/10.1093/bioinformatics/bty568 |
work_keys_str_mv | AT tiberisimone bayesianinferenceonstochasticgenetranscriptionfromflowcytometrydata AT walshmark bayesianinferenceonstochasticgenetranscriptionfromflowcytometrydata AT cavallaromassimo bayesianinferenceonstochasticgenetranscriptionfromflowcytometrydata AT hebenstreitdaniel bayesianinferenceonstochasticgenetranscriptionfromflowcytometrydata AT finkenstadtbarbel bayesianinferenceonstochasticgenetranscriptionfromflowcytometrydata |