ResolVI: addressing noise and bias in spatial transcriptomics.

Technologies for estimating RNA expression at high throughput, in intact tissue slices and with high spatial resolution (spatial transcriptomics) shed new light on how cells communicate and tissues function. A fundamental step in analyzing data generated by subcellular resolution spatial transcriptomics technologies is quantification, namely, segmenting the plane into regions, each approximating a cell, and then collating the molecules inside each region to estimate the cellular expression profile. Despite many advances in this area, a persistent problem is that of the incorrect assignment of molecules to cells, which limits many current applications to the level of a priori-defined cell subsets and complicates the discovery of novel cell states. Here we develop resolVI, a model that operates downstream of any segmentation algorithm to generate a probabilistic representation, correcting for the misassignment of molecules, as well as for batch effects and other nuisance factors. We demonstrate that resolVI improves our ability to distinguish between cell states, identify subtle expression changes in space and perform integrated analysis across datasets. ResolVI is available as open source software within scvi-tools.