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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Vydáno v:Genome Biology Ročník 23; číslo 1; s. 168
Hlavní autoři: Li, Zheng, Zhou, Xiang
Médium: Journal Article
Jazyk:angličtina
Vydáno: London BioMed Central 04.08.2022
Springer Nature B.V
BMC
Témata:
Animal Genetics and Genomics
Bayes Theorem
Bayesian analysis
Bayesian theory
Bioinformatics
Biomedical and Life Sciences
Cell type
Cluster Analysis
Clustering analysis
Computer applications
cortex
data collection
Datasets
domain
Evolutionary Biology
Gene expression
genome
Human Genetics
Hypothalamus
landscapes
Life Sciences
Localization
Method
Microbial Genetics and Genomics
Multi-sample analysis
Multi-scale analysis
Performance evaluation
Plant Genetics and Genomics
Simulation
Spatial discrimination
Spatial domain
Spatial transcriptomics
Transcriptome
Transcriptomics
Bayesian hierarchical model
BASS
Multi-scale analysis
Spatial domain
Spatial transcriptomics
Clustering analysis
Tissue section
Cell type
Multi-sample analysis
ISSN:1474-760X, 1474-7596, 1474-760X
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Abstract 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.
AbstractList 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.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.
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.
Abstract 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.
ArticleNumber 168
Author Li, Zheng
Zhou, Xiang
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  orcidid: 0000-0002-4331-7599
  surname: Zhou
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  email: xzhousph@umich.edu
  organization: Department of Biostatistics, University of Michigan, Center for Statistical Genetics, University of Michigan
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Issue 1
Keywords Bayesian hierarchical model
BASS
Multi-scale analysis
Spatial domain
Spatial transcriptomics
Clustering analysis
Tissue section
Cell type
Multi-sample analysis
Language English
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Snippet Spatial transcriptomic studies are reaching single-cell spatial resolution, with data often collected from multiple tissue sections. Here, we present a...
Abstract Spatial transcriptomic studies are reaching single-cell spatial resolution, with data often collected from multiple tissue sections. Here, we present...
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StartPage 168
SubjectTerms Animal Genetics and Genomics
Bayes Theorem
Bayesian analysis
Bayesian theory
Bioinformatics
Biomedical and Life Sciences
Cell type
Cluster Analysis
Clustering analysis
Computer applications
cortex
data collection
Datasets
domain
Evolutionary Biology
Gene expression
genome
Human Genetics
Hypothalamus
landscapes
Life Sciences
Localization
Method
Microbial Genetics and Genomics
Multi-sample analysis
Multi-scale analysis
Performance evaluation
Plant Genetics and Genomics
Simulation
Spatial discrimination
Spatial domain
Spatial transcriptomics
Transcriptome
Transcriptomics
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Title BASS: multi-scale and multi-sample analysis enables accurate cell type clustering and spatial domain detection in spatial transcriptomic studies
URI https://link.springer.com/article/10.1186/s13059-022-02734-7
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