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SpaDiff denoises sequence-based spatial transcriptomics via diffusion process
Jiazhang Cai1, Yongkai Chen2, Luyang Fang1
1Department of Statistics, University of Georgia, Athens, GA, USA.
Cell Reports Methods
|July 17, 2026
Summary
SpaDiff corrects spot-swapping artifacts in spatial transcriptomics, improving gene expression accuracy. This mass-conserving framework enhances downstream analysis for more reliable biological insights.
Area of Science:
- Genomics
- Computational Biology
- Bioinformatics
Background:
- Sequence-based spatial transcriptomics enables gene expression mapping within tissues.
- Spot-swapping is an artifact that redistributes RNA, distorting spatial data.
- This distortion biases downstream analyses like clustering and domain identification.
Purpose of the Study:
- To introduce SpaDiff, a novel denoising framework to correct spot-swapping artifacts.
- To enhance the accuracy of spatial gene expression data.
- To improve the reliability of downstream spatial transcriptomics analyses.
Main Methods:
- SpaDiff utilizes a mass-conserving denoising framework.
- The method employs score-guided reverse diffusion for artifact correction.
- Validation performed on simulated and real spatial transcriptomics datasets.
Main Results:
- SpaDiff effectively corrects RNA redistribution caused by spot-swapping.
- The framework demonstrates robust restoration of spatial gene expression patterns.
- Improved spatial domain recovery and preservation of tumor heterogeneity observed in real tissues.
Conclusions:
- SpaDiff significantly enhances the spatial accuracy of gene expression data.
- The framework supports more reliable downstream analyses, including clustering and spatial domain identification.
- SpaDiff is a valuable tool for accurate spatial transcriptomics research.
