Weighted sliced inverse regression for scalable supervised dimensionality reduction of spatial transcriptomics data

With the rise of spatially resolved transcriptomics biotechnologies, performing spatially-aware analysis of the resulting data is crucial to maximise advances in biological understanding. Dimensionality reduction is a first step in most analyses of spatial transcriptomics data, and common approaches that preserve total variation of gene expression features do not usually preserve biologically meaningful spatial variation. To this end, we develop weighted sliced inverse regression (wSIR), a sufficient dimension reduction technique that aims to retain the predictive power of the spatial coordinates. wSIR is applicable to multiple distinct spatial transcriptomic datasets and is highly scalable, with over 100, 000 cells processed in under two minutes on a standard laptop. The feature loadings are interpretable, and new non-spatial data can be projected into wSIR's low-dimensional space for downstream analyses. We examine wSIR's performance and demonstrate its biological relevance through two case studies in human breast cancer and mouse early embryonic development.