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SpaDiff: Denoising for Sequence-based Spatial Transcriptomics via Diffusion Process
Jiazhang Cai1, Yongkai Chen2, Luyang Fang1
1Department of Statistics, University of Georgia, 310 Herty Drive, Athens, 30602, GA, United States.
Biorxiv : the Preprint Server for Biology
|November 24, 2025
Summary
We developed SpaDiff, a new method to fix spot-swapping in spatial transcriptomics. This diffusion-based approach improves gene expression data accuracy and spatial analysis for better biological insights.
Area of Science:
- Molecular Biology
- Genomics
- Bioinformatics
Background:
- Spatial transcriptomics offers gene expression analysis with spatial resolution.
- Spot-swapping, or RNA molecule drift, introduces noise and reduces spatial specificity in this data.
- Accurate spatial information is crucial for understanding tissue architecture and cellular interactions.
Purpose of the Study:
- To introduce SpaDiff, a novel denoising method for spatial transcriptomics data.
- To address the challenge of spot-swapping and its impact on data integrity.
- To improve the accuracy of downstream spatial analysis.
Main Methods:
- SpaDiff models spot-swapping as a diffusion process.
- The method simulates RNA molecule displacement and reverses it to restore original positions.
- Molecular counts are preserved throughout the denoising process.
Main Results:
- SpaDiff effectively enhances the spatial specificity of gene expression.
- The method improves overall data integrity by reducing noise from spot-swapping.
- Evaluations on simulated and real data confirm SpaDiff's efficacy.
Conclusions:
- SpaDiff is a powerful tool for correcting spot-swapping in spatial transcriptomics.
- The enhanced data quality supports more accurate downstream analyses, including clustering and spatial domain identification.
- This method advances the reliable application of spatial transcriptomics in biological research.
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