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

TLDR

Spatial omics data are clustered to define both cell types and tissue domains. The authors introduce BANKSY, an algorithm that unifies cell type and tissue domain clustering by embedding cells in a product space of their transcriptome and the local neighborhood transcriptome. BANKSY embeds cells in a product space of their own transcriptome and the local neighborhood transcriptome, using a neighborhood kernel and spatial yardstick to augment spatial features, and can also perform quality control and spatially aware batch correction. Across diverse RNA and protein spatial datasets, BANKSY outperformed competing methods in cell typing and domain segmentation, revealed niche‑dependent cell states, and demonstrated superior speed and scalability for analyzing millions of cells.

Abstract

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.