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Bayesian hidden mark interaction model for detecting spatially variable genes in imaging-based spatially resolved

Jie Yang1, Xi Jiang2, Kevin Wang Jin3

  • 1Department of Mathematical Sciences, The University of Texas at Dallas, Richardson, TX, United States.

Frontiers in Genetics
|May 10, 2024
PubMed
Summary
This summary is machine-generated.

We developed a new Bayesian framework to identify spatially variable genes in imaging-based spatially resolved transcriptomics (SRT) data. Our method accurately captures complex spatial patterns, even with irregular cell distributions, advancing gene expression analysis.

Keywords:
bayesian mark interaction modeldouble metropolis-hastings algorithmenergy functionspatial transcriptomicszero-inflated negative binomial mixture model

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Area of Science:

  • Genomics
  • Computational Biology
  • Bioinformatics

Background:

  • Spatially resolved transcriptomics (SRT) enables cell molecular characterization with spatial context.
  • Identifying spatially variable genes is crucial for understanding tissue organization and function.
  • Existing methods have limitations with irregular spatial data and specific modeling assumptions.

Purpose of the Study:

  • To develop a generalized energy-based framework for analyzing gene expression in imaging-based SRT data.
  • To accommodate irregular spatial distributions of cells in SRT datasets.
  • To accurately identify spatially variable genes by modeling their expression patterns.

Main Methods:

  • Developed a Bayesian model using a zero-inflated negative binomial mixture model for noise reduction.
  • Incorporated a geostatistical mark interaction model with a generalized energy function.
  • Employed auxiliary variable Markov chain Monte Carlo (MCMC) algorithms for posterior distribution sampling.

Main Results:

  • The proposed method accurately captured various spatial patterns in simulation studies.
  • Analysis of seqFISH and STARmap datasets identified novel and strong spatial gene expression patterns.
  • The framework effectively handles the irregular spatial distribution of cells in imaging-based SRT.

Conclusions:

  • The generalized energy-based framework offers a robust approach for identifying spatially variable genes from imaging-based SRT data.
  • This method overcomes limitations of existing approaches, particularly for non-lattice data.
  • The findings advance the analysis of spatial transcriptomics, revealing new insights into cellular organization.