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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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BISON: bi-clustering of spatial omics data with feature selection.

Bencong Zhu1,2, Alberto Cassese3, Marina Vannucci4

  • 1Department of Statistics, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong.

Bioinformatics (Oxford, England)
|September 9, 2025
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Summary

This study introduces a unified Bayesian model to simultaneously identify spatial domains and their specific genes in spatially resolved transcriptomics data. This approach overcomes limitations of existing two-stage methods, improving gene discovery for spatial biology.

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Area of Science:

  • Genomics
  • Computational Biology
  • Spatial Transcriptomics

Background:

  • Spatially resolved transcriptomics (SRT) enables gene expression profiling with spatial context.
  • Understanding spatial gene function is key for biological mechanisms like cancer-immune interactions.
  • Current methods for identifying spatial domain-specific genes risk "double-dipping" due to a two-stage approach.

Purpose of the Study:

  • To develop a unified statistical framework for simultaneous spatial domain identification and discriminating gene (DG) detection.
  • To overcome the "double-dipping" issue inherent in existing two-stage methods for SRT data analysis.
  • To provide a robust method for uncovering genes that define distinct spatial domains within tissues.

Main Methods:

  • A unified Bayesian latent block model is proposed.
  • The model simultaneously clusters spatial locations and identifies discriminating genes (DGs).
  • The method integrates spatial domain detection and DG identification into a single statistical framework.

Main Results:

  • The proposed Bayesian model effectively identifies spatial domains and associated DGs.
  • Simulation experiments validate the model's efficacy and robustness.
  • Application to benchmark SRT datasets demonstrates successful DG identification for spatial domains.

Conclusions:

  • The unified Bayesian approach provides a powerful tool for analyzing SRT data.
  • This method enhances the understanding of spatial gene function and tissue architecture.
  • The BISON R/C++ implementation is publicly available for researchers.