BANKSY unifies cell typing and tissue domain segmentation for scalable spatial omics data analysis

Spatial omics data are clustered to define both cell types and tissue domains. We present Building Aggregates with a Neighborhood Kernel and Spatial Yardstick (BANKSY), an algorithm that unifies these two spatial clustering problems by embedding cells in a product space of their own and the local ne...

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Bibliographic Details
Published in:Nature genetics Vol. 56; no. 3; pp. 431 - 441
Main Authors: Singhal, Vipul, Chou, Nigel, Lee, Joseph, Yue, Yifei, Liu, Jinyue, Chock, Wan Kee, Lin, Li, Chang, Yun-Ching, Teo, Erica Mei Ling, Aow, Jonathan, Lee, Hwee Kuan, Chen, Kok Hao, Prabhakar, Shyam
Format: Journal Article
Language:English
Published: New York Nature Publishing Group US 01.03.2024
Nature Publishing Group
Subjects:
631/114/1314
631/114/794
631/1647/48
631/1647/794
631/208/199
Agriculture
Algorithms
Animal Genetics and Genomics
Animals
Benchmarking
Benchmarks
Biomedical and Life Sciences
Biomedicine
Cancer Research
Cells
Clustering
Data Analysis
Datasets
Embedding
Gene Expression Profiling
Gene Function
Gene sequencing
Human Genetics
Medical imaging
Mice
Microenvironments
Neighborhoods
Neuroimaging
Quality control
RNA
Segmentation
Spatial analysis
Spatial data
Transcriptome
Transcriptomes
Transcriptomics
Typing
ISSN:1061-4036, 1546-1718, 1546-1718
Online Access:Get full text
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Description
Summary:Spatial omics data are clustered to define both cell types and tissue domains. We present Building Aggregates with a Neighborhood Kernel and Spatial Yardstick (BANKSY), an algorithm that unifies these two spatial clustering problems by embedding cells in a product space of their own and the local neighborhood transcriptome, representing cell state and microenvironment, respectively. BANKSY’s spatial feature augmentation strategy improved performance on both tasks when tested on diverse RNA (imaging, sequencing) and protein (imaging) datasets. BANKSY revealed unexpected niche-dependent cell states in the mouse brain and outperformed competing methods on domain segmentation and cell typing benchmarks. BANKSY can also be used for quality control of spatial transcriptomics data and for spatially aware batch effect correction. Importantly, it is substantially faster and more scalable than existing methods, enabling the processing of millions of cell datasets. In summary, BANKSY provides an accurate, biologically motivated, scalable and versatile framework for analyzing spatially resolved omics data. BANKSY is an algorithm with R and Python implementations that identifies both cell types and tissue domains from spatially resolved omics data by incorporating spatial kernels capturing microenvironmental information. It is applicable to a range of technologies and is scalable to millions of cells.
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ISSN:1061-4036
1546-1718
1546-1718
DOI:10.1038/s41588-024-01664-3