Accurate inference of genome-wide spatial expression with iSpatial

Abstract

Spatially resolved transcriptomic analyses can reveal molecular insights underlying tissue structure and context-dependent cell-cell or cell-environment interaction. Because of the current technical limitation, obtaining genome-wide spatial transcriptome at single-cell resolution is challenging. Here, we developed a new algorithm named iSpatial to derive the spatial pattern of the entire transcriptome by integrating spatial transcriptomic and single-cell RNA-seq datasets. Compared to other existing methods, iSpatial has higher accuracy in predicting gene expression and spatial distribution. Furthermore, it reduces false-positive and false-negative signals in the original datasets. By testing iSpatial with multiple spatial transcriptomic datasets, we demonstrate its wide applicability to datasets from different tissues and by different techniques. Thus, we provide a computational approach to reveal spatial organization of the entire transcriptome at single-cell resolution. With numerous high-quality datasets available in the public domain, iSpatial provides a unique way to understand the structure and function of complex tissues and disease processes.