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BGP: identifying gene-specific branching dynamics from single-cell data with a branching Gaussian process.

Alexis Boukouvalas1, James Hensman2, Magnus Rattray3

  • 1Division of Informatics, Imaging and Data Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Oxford Road, Manchester, UK. alexis.boukouvalas@manchester.ac.uk.

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|May 31, 2018
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Summary

We developed a new method, the branching Gaussian process (BGP), to identify gene branching dynamics and estimate branching times in single-cell differentiation. This approach accurately models complex cell population trajectories from gene expression data.

Keywords:
Branching dynamicsGaussian processSingle cell RNA-seq

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Area of Science:

  • Computational Biology
  • Genomics
  • Developmental Biology

Background:

  • Single-cell gene expression analysis reveals cell population dynamics.
  • Pseudotime methods infer developmental trajectories from transcriptomic data.
  • Identifying branching events is crucial for understanding cell differentiation.

Purpose of the Study:

  • To develop a novel non-parametric model for identifying gene-specific branching dynamics.
  • To estimate branching times with associated credible regions in cell differentiation.
  • To assess the model's performance on diverse single-cell datasets.

Main Methods:

  • Development of the branching Gaussian process (BGP) model.
  • Application of BGP to simulated data for validation.
  • Analysis of real-world single-cell RNA sequencing data (haematopoiesis and stem cells).

Main Results:

  • The BGP model successfully identifies branching dynamics in individual genes.
  • Accurate estimation of branching times with credible intervals was achieved.
  • The method demonstrated robustness against technical noise and data dropout.

Conclusions:

  • The branching Gaussian process (BGP) is an effective tool for analyzing cell differentiation dynamics.
  • BGP provides valuable insights into gene-specific branching events and timing.
  • The model's robustness makes it suitable for various single-cell transcriptomic studies.