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S3R: Modeling spatially varying associations with Spatially Smooth Sparse Regression.

Xinyu Zhou1,2, Pengtao Dang3, Haixu Tang1

  • 1Department of Computer Science, Indiana University, Bloomington, IN, 46202, USA.

Biorxiv : the Preprint Server for Biology
|September 18, 2025
PubMed
Summary
This summary is machine-generated.

Spatially Smooth Sparse Regression (S3R) is a new statistical framework for spatial transcriptomics data. It reveals how molecular associations change across tissues, improving biological insights from complex gene expression patterns.

Keywords:
cross–cell type co-variationsparse regressionspatial interactionsspatial transcriptomicsspatially variable relationships

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Area of Science:

  • Genomics
  • Computational Biology
  • Statistical Modeling

Background:

  • Spatial transcriptomics (ST) data presents challenges like noise, cell mixing, and high dimensionality.
  • Existing models struggle to capture dynamic molecular associations across tissue locations.

Purpose of the Study:

  • To introduce Spatially Smooth Sparse Regression (S3R), a novel statistical framework for analyzing ST data.
  • To develop a method that estimates location-specific coefficients for linking molecular features across tissue space.
  • To provide a scalable and interpretable regression framework for diverse biological questions in ST.

Main Methods:

  • S3R integrates structured sparsity with a minimum-spanning-tree-guided smoothness penalty.
  • The framework estimates location-specific coefficients for high-dimensional spatial predictors.
  • An efficient implementation is provided to handle large ST datasets.

Main Results:

  • S3R accurately recovers spatially varying effects and selects relevant predictors in synthetic data.
  • It recapitulates layer-specific gene associations in human brain ST data.
  • S3R demixes cell type-attributed expression fields in infection and cancer data, revealing spatial gradients and cell-cell interactions.
  • Analysis of breast cancer data delineates gene expression contributions at multiple contextual levels.

Conclusions:

  • S3R offers a robust and flexible approach to dissecting complex spatial relationships in ST data.
  • The method enhances the biological interpretability of gene expression patterns and cell-cell crosstalk.
  • S3R provides a scalable and unified framework for addressing various ST analysis challenges.