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Related Experiment Video

Updated: Oct 30, 2025

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data
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Non-parametric modelling of temporal and spatial counts data from RNA-seq experiments.

Nuha BinTayyash1, Sokratia Georgaka2, S T John3,4

  • 1School of Computer Science, University of Manchester, Manchester M13 9PL, UK.

Bioinformatics (Oxford, England)
|July 2, 2021
PubMed
Summary
This summary is machine-generated.

GPcounts offers efficient Gaussian process regression for RNA sequencing count data. This method accurately models temporal and spatial gene expression changes, outperforming existing models for over-dispersed data.

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Area of Science:

  • Computational biology
  • Statistical genetics
  • Bioinformatics

Background:

  • Negative binomial distribution is suitable for RNA sequencing count data.
  • Gaussian process (GP) regression models temporal/spatial gene expression but struggles with large datasets.
  • Existing GP methods with negative binomial likelihoods do not scale to modern transcriptomics data.

Purpose of the Study:

  • Introduce GPcounts, a scalable GP regression package for RNA sequencing count data.
  • Implement efficient GP regression using variational Bayesian inference and negative binomial likelihood.
  • Enable accurate modeling of temporal and spatial gene expression changes in large-scale transcriptomics.

Main Methods:

  • Utilize variational Bayesian inference for computational efficiency.
  • Employ a negative binomial likelihood with a logarithmic link function for mean changes.
  • Fit dispersion parameters using maximum likelihood estimation.
  • Optionally model dropout with a zero-inflated negative binomial distribution.

Main Results:

  • GPcounts demonstrates superior performance in identifying changes in over-dispersed count data compared to Gaussian or Poisson models.
  • Successfully applied GPcounts to single-cell RNA-seq data for temporal inference (pseudotime and branching).
  • Identified spatially variable genes in mouse olfactory bulb data, outperforming existing GP methods.
  • Showed GPcounts effectively models temporal and spatial count data where simpler models fail.

Conclusions:

  • GPcounts provides a scalable and effective solution for GP regression on RNA sequencing count data.
  • The package accurately models complex temporal and spatial patterns in gene expression.
  • GPcounts is a valuable tool for analyzing large-scale single-cell and spatial transcriptomics datasets.