SpatialSort: A Bayesian Model for Clustering and Cell Population Annotation of Spatial Proteomics Data

Abstract

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.

ESQmodel: biologically informed evaluation of 2-D cell segmentation quality in multiplexed tissue images

“…Specifically, the prior expression matrix is a cell type by marker matrix which represents the prior belief of the marker intensity for each cell type. A method to construct prior beliefs is described previously [3]. For each measured marker, we define a precision τ j which follows a Gamma prior distribution.…”

“…Emerging high-throughput platforms, such as imaging mass cytometry (IMC) [1], are capable of extracting signals in the form of images from around fifty protein markers from intact specimens. This enables spatially aware phenotyping of single cells as well as the visualization of protein distribution to analyze spatial organization within tissues [2,3].…”