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

A core property of solid tissue is the spatial arrangement of cell types into stereotypical spatial patterns. These cells can be investigated with spatial omics technologies to reveal both their omics features (transcriptomes, proteomes, etc), and their spatial coordinates. Because a cell’s state can be influenced by interactions with other cells, it is informative to cluster cells using their omics signatures as well as their spatial relationships. We present BANKSY (Singhal et al., Nat. Genetics, 2024), an algorithm with R and Python implementations that identifies both cell types and tissue domains from spatially-resolved -omics data. It does so by embedding cells in a product space of their own and neighbourhood omics features. BANKSY revealed 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 correction. Critically, it is substantially faster and more scalable than existing methods, enabling the processing of datasets with millions of cells. BANKSY comes in both Python and R implementations, and works with major single cell frameworks like SingleCellExperiment, Seurat, and Scanpy.

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