Stochastic Modeling Approaches for Single-Cell Analyses

Thomas, Philipp

doi:10.1016/b978-0-12-801238-3.11539-9

Cited by 2 publications

(4 citation statements)

References 102 publications

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“…Even if expression counts are drawn from a Beta-Poisson distribution, the counts may equally well be fitted by other distributions depending on the underlying parameters. For example, the Beta-Poisson distribution reduces to a negative binomial distribution for large

; and to a Poisson distribution when the effective synthesis rate, and consequently the mean, is very small ( Shahrezaei and Swain 2008a , Thomas 2020 , Ham et al 2021 ). These alternative distributions have fewer parameters than the Beta-Poisson distribution.…”

Section: Resultsmentioning

confidence: 99%

“…The telegraph model is theoretically extensively analysed, and it is known that it admits a Beta-Poisson distribution at steady-state ( Peccoud and Ycart 1995 , Kepler and Elston 2001 , Raj et al 2006 , Shahrezaei and Swain 2008a , Kim and Marioni 2013 ). At the bursty limit of transcription, the solution of the telegraph model can be approximated as a negative-binomial distribution characterized by the burst size and burst frequency ( Raj et al 2006 , Shahrezaei and Swain 2008a , Kumar et al 2015 , Amrhein et al 2019 , Thomas 2020 ). Moreover, the negative binomial (NB) distribution is a versatile over-dispersed distribution that is commonly used in bulk and scRNA-seq studies to model gene expression capturing both biological and technical dispersion ( Anders and Huber 2012 , Love et al 2014 , Tang et al 2020 , Svensson 2020 ).…”

Section: Introductionmentioning

confidence: 99%

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Modelling capture efficiency of single-cell RNA-sequencing data improves inference of transcriptome-wide burst kinetics

et al. 2023

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Motivation Gene expression is characterised by stochastic bursts of transcription that occur at brief and random periods of promoter activity. The kinetics of gene expression burstiness differs across the genome and is dependent on the promoter sequence, among other factors. Single-cell RNA sequencing (scRNA-seq) has made it possible to quantify the cell-to-cell variability in transcription at a global genome-wide level. However, scRNA-seq data is prone to technical variability, including low and variable capture efficiency of transcripts from individual cells. Results Here, we propose a novel mathematical theory for the observed variability in scRNA-seq data. Our method captures burst kinetics and variability in both the cell size and capture efficiency, which allows us to propose several likelihood-based and simulation-based methods for the inference of burst kinetics from scRNA-seq data. Using both synthetic and real data, we show that the simulation-based methods provide an accurate, robust and flexible tool for inferring burst kinetics from scRNA-seq data. In particular, in a supervised manner, a simulation-based inference method based on neural networks proves to be accurate and useful when applied to both allele and non-allele-specific scRNA-seq data. Availability The code for Neural Network and Approximate Bayesian Computation inference is available at https://github.com/WT215/nnRNA and https://github.com/WT215/Julia_ABC respectively. Supplementary information Supplementary data are available at Bioinformatics online.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modelling capture efficiency of single-cell RNA-sequencing data improves inference of transcriptome-wide burst kinetics

et al. 2023

View full text Add to dashboard Cite

show abstract

“…The telegraph model is analysed theoretically extensively and it is known that it admits Beta-Poisson distribution at steady-state [22, 23, 7, 24, 25]. At the bursty limit of transcription the solution of the telegraph model can be approximated as a negative-binomial distribution characterised by burst size and burst frequency [7, 24, 26, 27, 28]. Moreover, the negative binomial (NB) distribution is a versatile over-dispersed distribution that is commonly used in bulk and scRNA-seq studies to model gene expression capturing both biological and technical dispersion [29, 30, 31, 32].…”

Section: Introductionmentioning

confidence: 99%

“…At the bursty limit of transcription the solution of the telegraph model can be approximated as a negative-binomial distribution characterised by burst size and burst frequency [7,24,26,27,28].…”

Section: Introductionmentioning

confidence: 99%

Modelling capture efficiency of single cell RNA-sequencing data improves inference of transcriptome-wide burst kinetics

Tang

Jørgensen

Marguerat

et al. 2023

Preprint

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Gene expression is characterised by stochastic bursts of transcription that occur at brief and random periods of promoter activity. The kinetics of gene expression burstiness differs across the genome and is dependent on the promoter sequence, among other factors. Single-cell RNA sequencing (scRNA-seq) has made it possible to quantify the cell-to-cell variability in transcription at a global genome-wide level. However, scRNA-seq data is prone to technical variability, including low and variable capture efficiency of transcripts from individual cells. Here, we propose a novel mathematical theory for the observed variability in scRNA-seq data. Our method captures burst kinetics and variability in both cell size and capture efficiency, which allows us to propose several likelihood-based and simulation-based methods for the inference of burst kinetics from scRNA-seq data. Using both synthetic and real data, we show that the simulation-based methods provide an accurate, robust and flexible tool for inferring burst kinetics from scRNA-seq data. In particular, in supervised manner, a simulation-based inference method based on neural networks proves to be accurate and useful in application to both allele and non-allele specific scRNA-seq data.

show abstract

Stochastic Modeling Approaches for Single-Cell Analyses

Cited by 2 publications

References 102 publications

Modelling capture efficiency of single-cell RNA-sequencing data improves inference of transcriptome-wide burst kinetics

Modelling capture efficiency of single-cell RNA-sequencing data improves inference of transcriptome-wide burst kinetics

Modelling capture efficiency of single cell RNA-sequencing data improves inference of transcriptome-wide burst kinetics

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