Spatially-aware dimension reduction of transcriptomics data

Lauren Okamoto, Andrew Jones, Archit Verma, Barbara E. Engelhardt

Keywords: spatial genomics, dimension reduction, transcriptomics, Gaussian processes

TL;DR: We present a Bayesian statistical model that performs dimension reduction for genomics data in a spatially-aware manner.

Abstract: Spatial sequencing technologies have allowed for studying the relationship between the physical organization of cells and their functional behavior. However, interpreting these data and deriving insights from them remains difficult. Here, we present a Bayesian statistical model that performs dimension reduction for these data in a spatially-aware manner. In particular, our proposed model captures the low-dimensional structure of gene expression while accounting for the spatial variability of expression. Our model also allows us to project dissociated scRNA-seq data onto a spatial grid, as well as use scRNA-seq impute and smooth the expression of spatial sequencing data. Through simulations and applications to spatial sequencing data, we show that our model captures joint structure of spatially-resolved and dissociated sequencing data.