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Related Experiment Video

Updated: Jan 17, 2026

Mining Spatial Transcriptomics Datasets using DeepSpaceDB
10:16

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

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SPACE: Spatially variable gene clustering adjusting for cell type effect for improved spatial domain detection.

Sikta Das Adhikari1, Nina G Steele2,3,4, Brian Theisen5

  • 1Department of Statistics and Probability, Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, MI, 48824, United States.

Nucleic Acids Research
|September 23, 2025
PubMed
Summary

We developed SPACE, a new framework for spatial transcriptomics (ST) analysis. It classifies spatially variable genes (SVGs) to improve the detection of spatial domains and understand tissue architecture.

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Area of Science:

  • Genomics
  • Computational Biology
  • Bioinformatics

Background:

  • Spatial transcriptomics (ST) advances biological understanding by revealing gene expression in tissue context.
  • Identifying spatially variable genes (SVGs) is key for spatial domain detection, reflecting tissue architecture and biological processes.
  • Current methods embedding top SVGs can mix signals and obscure biological structures.

Purpose of the Study:

  • To propose SPACE, a novel framework for classifying SVGs based on spatial patterns.
  • To enhance spatial domain detection by grouping SVGs and reducing signal confounding.
  • To provide a method that does not require prior knowledge of gene cluster numbers or cell types.

Main Methods:

  • SPACE classifies SVGs by analyzing their spatial expression patterns.
  • The framework adjusts for shared cell-type confounding effects.
  • It groups SVGs to preserve distinct biological patterns and reduce signal mixing.

Main Results:

  • SPACE effectively classifies SVGs without needing prior information on gene clusters or cell types.
  • The method improves the detection of diverse spatial structures in ST data.
  • Analyses using simulations and real data confirm SPACE's efficiency.

Conclusions:

  • SPACE offers an efficient and promising approach for spatial transcriptomics analysis.
  • The framework enhances the biological insights derived from SVGs by accurate classification.
  • SPACE improves spatial domain detection and understanding of tissue organization.