Optimizing the design of spatial genomics studies


Spatially-resolved genomic technologies have shown promise for studying the relationship between the structural arrangement of cells and their functional behavior. While numerous sequencing and imaging platforms exist for performing spatial transcriptomics and spatial proteomics profiling, these experiments remain expensive and labor-intensive. Thus, when performing spatial genomics experiments using multiple tissue slices, there is a need to select the tissue cross sections that will be maximally informative for the purposes of the experiment. In this work, we formalize the problem of experimental design for spatial genomics experiments, which we generalize into a problem class that we call structured batch experimental design. We propose approaches for optimizing these designs in two types of spatial genomics studies: one in which the goal is to construct a spatially-resolved genomic atlas of a tissue, and another in which the goal is to localize a region of interest in a tissue, such as a tumor. We demonstrate the utility of these optimal designs, where each slice is a two-dimensional plane, on several spatial genomics datasets.