Abstract
Spatial transcriptomics technology has developed rapidly in recent years, with various sequencing-based platforms such as 10× Visium, Slide-seq, and Stereo-seq becoming widely used by researchers. Each platform brings its own set of protocols and customized data analysis pipelines, which presents challenges when the goal is to obtain uniformly preprocessed data that is conveniently formatted for downstream analysis. To address the need for simpler, open-source solutions that deal with sequencing-based spatial transcriptomics (sST) data from different platforms, we present stPipe, a comprehensive and modular preprocessing pipeline for all current mainstream sST platforms. stPipe is implemented as an R/Bioconductor package that handles various analysis steps, including (i) data processing from raw FASTQ files to create a spatially resolved gene count matrix; (ii) the collation of relevant quality control metrics to ensure unwanted artifacts can be filtered; and (iii) the adoption of standardized data storage containers to allow results to be easily passed on to a wide range of downstream analysis packages. A key use case for stPipe is in methods benchmarking, and we demonstrate how the uniform processing of sST data collected on reference tissue samples from the cadasSTre and SpatialBenchVisium projects is made easier, allowing comparisons between different technology platforms and downstream analysis tools.