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BASIN: Bayesian mAtrix variate normal model with Spatial and sparsIty priors in Non-negative deconvolution.

Jiasen Zhang1, Xi Qiao2, Liangliang Zhang2

  • 1Department of Mathematics, Applied Mathematics and Statistics, Case Western Reserve University, Cleveland, OH.

Arxiv
|November 24, 2025
PubMed
Summary

BASIN enhances spatial transcriptomics by performing cell type deconvolution using Bayesian non-negative matrix factorization. This method accurately infers cellular composition in tissues, offering robust uncertainty quantification.

Keywords:
Bayesian NMFdeconvolutionmatrix normal distributionspatial transcriptomics

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Area of Science:

  • Computational Biology
  • Genomics
  • Bioinformatics

Background:

  • Spatial transcriptomics provides gene expression data with spatial resolution.
  • Current methods often lack cellular resolution, requiring cell type deconvolution.
  • Inferring cellular composition is crucial for understanding tissue microenvironments.

Purpose of the Study:

  • To introduce BASIN, a novel method for cell type deconvolution in spatial transcriptomics.
  • To address limitations in cellular resolution of existing spatial transcriptomics data.
  • To provide robust and accurate inference of cell type proportions.

Main Methods:

  • Modeled deconvolution as a non-negative matrix factorization (NMF) problem with a graph Laplacian prior.
  • Developed a matrix variate Bayesian NMF method incorporating nonnegativity and sparsity priors.
  • Employed a Gibbs sampler to approximate posterior distributions and quantify uncertainty.

Main Results:

  • BASIN outperforms existing deconvolution methods in accuracy and efficiency on diverse spatial transcriptomics datasets.
  • The incorporated priors significantly influence the deconvolution results.
  • The method provides a distribution of possible solutions, enhancing robustness.

Conclusions:

  • BASIN offers a powerful and accurate approach for cell type deconvolution in spatial transcriptomics.
  • The Bayesian framework provides inherent uncertainty quantification, crucial for biological interpretation.
  • This method advances the analysis of spatially resolved gene expression data.