Modelling cell-cell interactions from spatial molecular data with spatial variance component analysis

Abstract

Technological advances allow for assaying multiplexed spatially resolved RNA and protein expression profiling of individual cells, thereby capturing physiological tissue contexts of single cell variation. While methods for the high-throughput generation of spatial expression profiles are increasingly accessible, computational methods for studying the relevance of the spatial organization of tissues on cell-cell heterogeneity are only beginning to emerge. Here, we present spatial variance component analysis (SVCA), a computational framework for the analysis of spatial molecular data. SVCA enables quantifying the effect of cell-cell interactions, as well as environmental and intrinsic cell features on the expression levels of individual genes or proteins. In application to a breast cancer Imaging Mass Cytometry dataset, our model allows for robustly estimating spatial variance signatures, identifying cell-cell interactions as a major driver of expression heterogeneity. Finally, we apply SVCA to high-dimensional imaging-derived RNA data, where we identify molecular pathways that are linked to cell-cell interactions.