Motivation Recent advances in spatial proteomics technologies have enabled the profiling of dozens of proteins in thousands of single cells in situ. This has created the opportunity to move beyond quantifying the composition of cell types in tissue, and instead probe the spatial relationships between cells. However, most current methods for clustering data from these assays only consider the expression values of cells and ignore the spatial context. Furthermore, existing approaches do not account for prior information about the expected cell populations in a sample. Results To address these shortcomings, we developed SpatialSort, a spatially aware Bayesian clustering approach that allows for the incorporation of prior biological knowledge. Our method is able to account for the affinities of cells of different types to neighbour in space, and by incorporating prior information about expected cell populations, it is able to simultaneously improve clustering accuracy and perform automated annotation of clusters. Using synthetic and real data, we show that by using spatial and prior information SpatialSort improves clustering accuracy. We also demonstrate how SpatialSort can perform label transfer between spatial and nonspatial modalities through the analysis of a real world diffuse large B-cell lymphoma dataset. Availability and implementation Source code is available on Github at: https://github.com/Roth-Lab/SpatialSort.

Recently developed spatial proteomics technologies can profile the expression of dozens of proteins in thousands of single cells in-situ. This has created the opportunity to move beyond quantifying the composition of cell types in tissue, and instead begin probing the spatial relationships between cells. A prerequisite to such analysis is to first cluster the data and annotate the populations represented by the clusters. However, current methods for clustering data from these assays only consider the expression values of cells and ignore the spatial context. Furthermore, existing approaches do not account for prior information about the expected cell populations in a sample. To address these shortcomings, we developed SpatialSort, a spatially aware Bayesian clustering approach that allows for the incorporation of prior biological knowledge. A novel feature of our method is the ability to account for the affinities of cells of different types to neighbour in space. In addition, by incorporating prior information about expected cell populations, SpatialSort is able to simultaneously improve clustering accuracy and perform automated annotation of clusters. Using synthetic and real data we show that SpatialSort outperforms state of the art methods in terms of clustering accuracy. We further demonstrate how SpatialSort can perform label transfer between spatial and non-spatial modalities through the analysis of a real world diffuse large B-cell lymphoma dataset.

Motivation: Single cell segmentation is critical in the processing of spatial omics data to accurately perform cell type identification and analyze spatial expression patterns. Segmentation methods often rely on semi-supervised annotation or labeled training data which are highly dependent on user expertise. To ensure the quality of segmentation, current evaluation strategies quantify accuracy by assessing cellular masks or through iterative inspection by pathologists. While these strategies each address either the statistical or biological aspects of segmentation, there lacks an unified approach to evaluating segmentation accuracy. Results: In this paper, we present ESQmodel, a Bayesian probabilistic method to evaluate single cell segmentation using expression data. By using the extracted cellular data from segmentation and a prior belief of cellular composition as input, ESQmodel computes per cell entropy to assess segmentation quality by how consistent cellular expression profiles match with cell type expectations.