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

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 cel...

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Bibliographic Details
Published inGenome Biology Vol. 23; no. 1; pp. 168 - 35
Main Authors Li, Zheng, Zhou, Xiang
Format Journal Article
LanguageEnglish
Published England BioMed Central 04.08.2022
BMC
Subjects
Bayes Theorem
Bayesian analysis
Cell type
Cluster Analysis
Clustering analysis
Computer applications
Datasets
Gene expression
Hypothalamus
Localization
Method
Multi-sample analysis
Multi-scale analysis
Performance evaluation
Simulation
Spatial discrimination
Spatial domain
Spatial transcriptomics
Transcriptome
Transcriptomics
BASS
Bayesian hierarchical model
Multi-scale analysis
Spatial domain
Spatial transcriptomics
Clustering analysis
Tissue section
Cell type
Multi-sample analysis
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Summary: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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content type line 23
ISSN:1474-760X
1474-7596
1474-760X
DOI:10.1186/s13059-022-02734-7