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BASS: multi-scale and multi-sample analysis enables accurate cell type clustering and spatial domain detection in
Zheng Li1,2, Xiang Zhou3,4
1Department of Biostatistics, University of Michigan, Ann Arbor, MI, 48109, USA.
Genome Biology
|August 4, 2022
Summary
We developed BASS, a computational method for analyzing spatial transcriptomics data at single-cell resolution across multiple samples and scales. This tool enhances the accuracy of transcriptomic and cellular landscape analysis in tissues.
Area of Science:
- Computational biology
- Genomics
- Bioinformatics
Background:
- Spatial transcriptomics is advancing to single-cell resolution.
- Data is often generated from multiple tissue sections, posing analytical challenges.
- Existing methods may not effectively integrate multi-scale and multi-sample analyses.
Purpose of the Study:
- To introduce BASS, a novel computational method for multi-scale and multi-sample analysis of single-cell resolution spatial transcriptomics data.
- To enable simultaneous cell type clustering and spatial domain detection.
- To improve the accuracy and power of spatial transcriptomic data analysis.
Main Methods:
- Bayesian hierarchical modeling framework.
- Simultaneous cell type clustering at the single-cell scale.
- Spatial domain detection at the tissue regional scale.
- Application to multiple tissue datasets.
Main Results:
- BASS enables simultaneous multi-scale and multi-sample analysis.
- Demonstrated substantial power gain in simulations and real data.
- Accurate identification of transcriptomic and cellular landscapes.
- Successful application to cortex and hypothalamus datasets.
Conclusions:
- BASS provides a powerful framework for analyzing complex spatial transcriptomics data.
- The method facilitates deeper insights into tissue architecture and cellular composition.
- BASS advances the field of single-cell spatial transcriptomics analysis.
