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BatchSVG: identifying batch-biased genes in the application of spatially variable gene detection
Kinnary Shah1, Christine Hou1,2, Jacqueline R Thompson1
1Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, United States.
Bioinformatics (Oxford, England)
|July 16, 2026
Summary
BatchSVG identifies and removes spatially variable genes (SVGs) biased by technical artifacts in spatial transcriptomics data. This improves downstream analyses by filtering out slide-associated genes.
Area of Science:
- Genomics
- Computational Biology
- Bioinformatics
Background:
- Identifying spatially variable genes (SVGs) is crucial for analyzing spatially resolved transcriptomics (SRT) data.
- Current methods often analyze tissue sections individually, limiting large-scale atlas analysis.
- Technical biases, like slide effects, can confound SVG identification and impact downstream analyses.
Purpose of the Study:
- To introduce BatchSVG, a novel computational tool for identifying and removing batch-biased SVGs in SRT data.
- To enhance the accuracy of downstream analyses by mitigating technical artifacts.
Main Methods:
- BatchSVG compares gene deviance ranks from binomial models with and without batch covariates.
- Genes with significant rank changes are inferred to be associated with batch effects.
- The approach is validated on two SRT datasets.
Main Results:
- BatchSVG effectively identifies genes influenced by technical artifacts such as slide batches.
- Removal of batch-biased genes leads to improved performance in downstream tasks like spatial domain detection.
- The tool facilitates more robust analysis of large-scale spatial atlases.
Conclusions:
- BatchSVG provides a robust method for correcting batch-biased SVGs in SRT.
- Accurate SVG identification is essential for reliable large-scale spatial atlases.
- The tool enhances the utility of SRT data for biological discovery.
