PMID- 30423089
OWN - NLM
STAT- MEDLINE
DCOM- 20190923
LR  - 20220129
IS  - 1367-4811 (Electronic)
IS  - 1367-4803 (Print)
IS  - 1367-4803 (Linking)
VI  - 34
IP  - 17
DP  - 2018 Sep 1
TI  - Bayesian inference on stochastic gene transcription from flow cytometry data.
PG  - i647-i655
LID - 10.1093/bioinformatics/bty568 [doi]
AB  - 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.
FAU - Tiberi, Simone
AU  - Tiberi S
AD  - Institute of Molecular Life Sciences, University of Zurich, Zurich, Switzerland.
AD  - Swiss Institue of Bioinformatics, University of Zurich, Zurich, Switzerland.
AD  - Department of Statistics, University of Warwick, Coventry, UK.
FAU - Walsh, Mark
AU  - Walsh M
AD  - School of Life Sciences, University of Warwick, Coventry, UK.
FAU - Cavallaro, Massimo
AU  - Cavallaro M
AD  - Department of Statistics, University of Warwick, Coventry, UK.
AD  - School of Life Sciences, University of Warwick, Coventry, UK.
FAU - Hebenstreit, Daniel
AU  - Hebenstreit D
AD  - School of Life Sciences, University of Warwick, Coventry, UK.
FAU - Finkenstadt, Barbel
AU  - Finkenstadt B
AD  - Department of Statistics, University of Warwick, Coventry, UK.
LA  - eng
GR  - BB/M017982/1/BB_/Biotechnology and Biological Sciences Research Council/United 
      Kingdom
GR  - BB/L006340/1/BB_/Biotechnology and Biological Sciences Research Council/United 
      Kingdom
GR  - MR/M013170/1/MRC_/Medical Research Council/United Kingdom
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
PL  - England
TA  - Bioinformatics
JT  - Bioinformatics (Oxford, England)
JID - 9808944
SB  - IM
MH  - *Bayes Theorem
MH  - Flow Cytometry
MH  - In Situ Hybridization, Fluorescence
MH  - Software
MH  - Stochastic Processes
MH  - *Transcription, Genetic
PMC - PMC6129284
EDAT- 2018/11/14 06:00
MHDA- 2019/09/24 06:00
PMCR- 2018/09/08
CRDT- 2018/11/14 06:00
PHST- 2018/11/14 06:00 [entrez]
PHST- 2018/11/14 06:00 [pubmed]
PHST- 2019/09/24 06:00 [medline]
PHST- 2018/09/08 00:00 [pmc-release]
AID - 5093237 [pii]
AID - bty568 [pii]
AID - 10.1093/bioinformatics/bty568 [doi]
PST - ppublish
SO  - Bioinformatics. 2018 Sep 1;34(17):i647-i655. doi: 10.1093/bioinformatics/bty568.