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Integrative Cross-sample Alignment And Spatially Differential Gene Analysis For Spatial Transcriptomics.

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Integrative Cross-sample Alignment And Spatially Differential Gene Analysis For Spatial Transcriptomics.

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Related Experiment Video

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

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 Tan^1,2,3, Zezhou Wang^1,4, Ai Wang^5,6

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

Nature Communications

|June 25, 2026

View abstract on PubMed

Summary

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.

Related Experiment Videos

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

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

Published on: October 31, 2025

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.
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.