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Related Experiment Video

Updated: Jul 12, 2025

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
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SMASH: Scalable Method for Analyzing Spatial Heterogeneity of genes in spatial transcriptomics data.

Souvik Seal1, Benjamin G Bitler2, Debashis Ghosh3

  • 1Department of Public Health Sciences, School of Medicine, Medical University of South Carolina, Charleston, South Carolina, United States of America.

Plos Genetics
|October 20, 2023
PubMed
Summary

We introduce SMASH, a novel non-parametric method for identifying spatially variable genes (SVGs) in spatial transcriptomics (ST) data. SMASH balances computational efficiency and statistical power, outperforming existing methods in simulations and real-world applications.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • High-throughput spatial transcriptomics (ST) enables gene expression analysis within tissue context.
  • Identifying spatially variable genes (SVGs) is crucial for understanding tissue structure and function.
  • Current SVG detection methods face challenges with computational cost or statistical power.

Purpose of the Study:

  • To develop a computationally efficient and statistically powerful method for detecting SVGs in ST data.
  • To introduce the SMASH method as a balanced solution for SVG identification.
  • To demonstrate the utility of SMASH across diverse ST datasets and platforms.

Main Methods:

  • Proposed SMASH, a non-parametric statistical approach for SVG detection.
  • Conducted comparative analyses against existing SVG detection methods using simulated datasets.
  • Applied SMASH to four distinct ST datasets from various technological platforms.

Main Results:

  • SMASH demonstrated superior statistical power and robustness compared to existing methods in simulations.
  • The method effectively identified biologically relevant SVGs across different ST datasets.
  • SMASH offers a practical balance between computational demand and analytical performance.

Conclusions:

  • SMASH provides a valuable new tool for the analysis of spatial transcriptomics data.
  • The method enhances the ability to uncover biological insights from tissue-level gene expression patterns.
  • SMASH contributes to advancing the field of spatial genomics and tissue biology.