BASS: multi-scale and multi-sample analysis enables accurate cell type clustering and spatial domain detection in spatial transcriptomic studies.

> 商业许可源文 · EUROPE_PMC · [CC-BY](https://creativecommons.org/licenses/by/)

书目信息

引用：Li Z, Zhou X. (2022). BASS: multi-scale and multi-sample analysis enables accurate cell type clustering and spatial domain detection in spatial transcriptomic studies. Genome biology. PMID 35927760 · PMC9351148 · DOI 10.1186/s13059-022-02734-7

证据类型：PRIMARY_RESEARCH

主题：rna-seq、single-cell、spatial-omics

被引次数（采集时）：122

原始记录：[Europe PMC](https://europepmc.org/article/MED/35927760)

来源许可：[CC-BY](https://creativecommons.org/licenses/by/)

作者摘要（按来源许可复用）

Spatial transcriptomic studies are reaching single-cell spatial resolution, with data often collected from multiple tissue sections. Here, we present a computational method, BASS, that enables multi-scale and multi-sample analysis for single-cell resolution spatial transcriptomics. BASS performs cell type clustering at the single-cell scale and spatial domain detection at the tissue regional scale, with the two tasks carried out simultaneously within a Bayesian hierarchical modeling framework. We illustrate the benefits of BASS through comprehensive simulations and applications to three datasets. The substantial power gain brought by BASS allows us to reveal accurate transcriptomic and cellular landscape in both cortex and hypothalamus.

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