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Related Experiment Video

Updated: Jun 27, 2026

Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq
10:22

Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq

Published on: October 31, 2025

Integrative cross-sample alignment and spatially differential gene analysis for spatial transcriptomics.

Yecheng Tan1,2,3, Zezhou Wang1,4, Ai Wang5,6

  • 1Research Institute of Intelligent Complex Systems, Fudan University, Shanghai, China.

Nature Communications
|June 25, 2026
PubMed
Summary

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This summary is machine-generated.

CODA aligns and compares multiple spatial transcriptomics slices, even from different platforms. This framework enhances the analysis of gene expression patterns across samples for biological discovery.

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Spatial transcriptomics (ST) technologies provide gene expression data with spatial context.
  • Comparing multiple ST slices is difficult due to nonlinear distortions and limited overlap.
  • Existing methods struggle with cross-platform and cross-sample comparisons.

Purpose of the Study:

  • To introduce CODA, an integrative framework for cross-sample alignment and spatially differential gene analysis.
  • To enable robust comparison and extraction of spatial gene expression patterns across diverse ST data.
  • To overcome challenges in aligning and comparing ST slices from various platforms.

Main Methods:

  • CODA learns a shared low-dimensional latent feature space across samples.

Related Experiment Videos

Last Updated: Jun 27, 2026

Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq
10:22

Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq

Published on: October 31, 2025

  • It performs global affine alignment and transformer-based feature matching for domain identification.
  • Local nonlinear refinements use large deformation diffeomorphic metric mapping for accurate alignment.

    • Main Results:

    • CODA demonstrates strong performance in alignment accuracy, computational efficiency, and memory usage across ST platforms.
    • The framework successfully identifies common spatial domains.
    • CODA uncovers spatially informative genes related to normal and disease conditions via immunofluorescence and enrichment analysis.

    Conclusions:

    • CODA provides a robust and effective framework for spatial transcriptomics analysis.
    • It significantly improves cross-sample alignment and comparison capabilities.
    • The framework has broad applicability for uncovering spatially resolved biological insights.