Identifying Shared Regions to Measure Change in Location of Spatial Transcriptomics
Abstract
Spatial transcriptomics is a new technology that enables measurement of the whole transcriptome of a tissue slide at high resolution. One challenge with this technology is that tissue slices from different biological conditions rarely align perfectly, thus it is difficult to identify when a gene or cell-type changes its spatial distribution between different samples. To address this challenge, we have adapted methods designed to remove batch effects from single-cell RNA-seq data to identify conserved spatial regions which can be used to measure relative locations and positional changes between different samples of spatial transcriptomics. This was accomplished by developing our Graph-based Integration and Analysis of Spatial Transcriptomics (GIAST) tool. We used synthetic data with a known ground truth and published datasets with sequential slices of spatial transcriptomics and manually annotated conserved structures to compare our tools performance to existing single-cell RNA-seq integration tools. We demonstrated our tools ability to consistently perform well compared to other tools on these datasets while at the fraction of the run time as well as determining change in location.