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Bayesian inference on stochastic gene transcription from flow cytometry data.

Simone Tiberi1,2,3, Mark Walsh4, Massimo Cavallaro3,4

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We developed a stochastic model for gene transcription in single cells, revealing its mRNA distribution as a mix of Poisson and Poisson-beta. This enables robust Bayesian inference from flow cytometry data, separating molecular noise from measurement errors.

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Area of Science:

  • Molecular Biology
  • Computational Biology
  • Biostatistics

Background:

  • Gene transcription in single cells is inherently stochastic, leading to significant mRNA level variations.
  • Existing methods struggle to disentangle intrinsic molecular noise from measurement errors in single-cell expression data.

Purpose of the Study:

  • To present a stochastic two-state switch model for mRNA population dynamics in single cells.
  • To develop a Bayesian inferential framework for analyzing single-cell expression data from flow cytometry.
  • To effectively separate molecular stochasticity from measurement noise.

Main Methods:

  • Developed a stochastic two-state switch model for gene transcription.
  • Derived the stationary solution as a mixture of Poisson and Poisson-beta distributions.
  • Proposed a Bayesian inference methodology using a pseudo-marginal approach for unobserved states.

Main Results:

  • The model's stationary solution provides a direct link to equilibrium mRNA distributions.
  • The Bayesian framework successfully separates intrinsic stochasticity from measurement noise.
  • The methodology was validated through simulation studies and applied to FISH flow cytometry data.

Conclusions:

  • The proposed stochastic model and Bayesian inference framework offer a powerful tool for analyzing single-cell gene transcription.
  • This approach facilitates more accurate interpretation of expression data from flow cytometry experiments.
  • The method is broadly applicable to various single-cell transcriptomics studies.