Protocol to recover single-cell gene expression profiles from spatial transcriptomics data using cluster computing.

Abstract

Many widely used spatial transcriptomics technologies, such as Visium, capture data at multicellular resolution, precluding single-cell analysis. Here, we present scResolve, a computational protocol to recover single-cell gene expression profiles from low-resolution spatial transcriptomics data. We describe steps for computational environment setup and preparing data and formatting. We then detail procedures for running super-resolution inference and cell segmentation modules. scResolve runs in a cluster environment, leveraging parallel computing to accelerate data processing and deliver faster results. For complete details on the use and execution of this protocol, please refer to Chen et al.¹.