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Inferring extrinsic noise from single-cell gene expression data using approximate Bayesian computation

Overview of attention for article published in BMC Systems Biology, August 2016
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (74th percentile)
  • High Attention Score compared to outputs of the same age and source (88th percentile)

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11 tweeters

Citations

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18 Dimensions

Readers on

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42 Mendeley
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Title
Inferring extrinsic noise from single-cell gene expression data using approximate Bayesian computation
Published in
BMC Systems Biology, August 2016
DOI 10.1186/s12918-016-0324-x
Pubmed ID
Authors

Oleg Lenive, Paul D. W. Kirk, Michael P. H. Stumpf

Abstract

Gene expression is known to be an intrinsically stochastic process which can involve single-digit numbers of mRNA molecules in a cell at any given time. The modelling of such processes calls for the use of exact stochastic simulation methods, most notably the Gillespie algorithm. However, this stochasticity, also termed "intrinsic noise", does not account for all the variability between genetically identical cells growing in a homogeneous environment. Despite substantial experimental efforts, determining appropriate model parameters continues to be a challenge. Methods based on approximate Bayesian computation can be used to obtain posterior parameter distributions given the observed data. However, such inference procedures require large numbers of simulations of the model and exact stochastic simulation is computationally costly. In this work we focus on the specific case of trying to infer model parameters describing reaction rates and extrinsic noise on the basis of measurements of molecule numbers in individual cells at a given time point. To make the problem computationally tractable we develop an exact, model-specific, stochastic simulation algorithm for the commonly used two-state model of gene expression. This algorithm relies on certain assumptions and favourable properties of the model to forgo the simulation of the whole temporal trajectory of protein numbers in the system, instead returning only the number of protein and mRNA molecules present in the system at a specified time point. The computational gain is proportional to the number of protein molecules created in the system and becomes significant for systems involving hundreds or thousands of protein molecules. We employ this simulation algorithm with approximate Bayesian computation to jointly infer the model's rate and noise parameters from published gene expression data. Our analysis indicates that for most genes the extrinsic contributions to noise will be small to moderate but certainly are non-negligible.

Twitter Demographics

The data shown below were collected from the profiles of 11 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 42 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 2%
United States 1 2%
Germany 1 2%
Unknown 39 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 20 48%
Researcher 11 26%
Student > Master 5 12%
Professor 2 5%
Student > Doctoral Student 1 2%
Other 2 5%
Unknown 1 2%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 18 43%
Mathematics 6 14%
Agricultural and Biological Sciences 5 12%
Physics and Astronomy 5 12%
Computer Science 3 7%
Other 1 2%
Unknown 4 10%

Attention Score in Context

This research output has an Altmetric Attention Score of 6. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 13 September 2018.
All research outputs
#4,134,654
of 17,499,602 outputs
Outputs from BMC Systems Biology
#159
of 1,116 outputs
Outputs of similar age
#68,147
of 271,516 outputs
Outputs of similar age from BMC Systems Biology
#3
of 18 outputs
Altmetric has tracked 17,499,602 research outputs across all sources so far. Compared to these this one has done well and is in the 76th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,116 research outputs from this source. They receive a mean Attention Score of 3.5. This one has done well, scoring higher than 85% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 271,516 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 74% of its contemporaries.
We're also able to compare this research output to 18 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 88% of its contemporaries.