SpotSweeper: spatially aware quality control for spatial transcriptomics

Quality control (QC) is a crucial step to ensure the reliability of data obtained from RNA sequencing experiments, including spatially resolved transcriptomics (SRT). Existing QC approaches for SRT that have been adopted from single-cell or single-nucleus RNA sequencing methods are confounded by spatial biology and are inappropriate for SRT data. In addition, no methods currently exist for identifying histological tissue artifacts that are unique to SRT. Here, we introduce SpotSweeper, a spatially aware QC method that leverages local neighborhoods to correct for spatial confounding in order to identify both local outliers and regional artifacts in SRT. Using SpotSweeper on publicly available data, we identify a consistent set of Visium barcoded spots as systematically low quality and demonstrate that SpotSweeper accurately identifies two distinct types of regional artifacts. SpotSweeper represents a substantial advancement in spatially resolved transcriptomics QC for SRT, providing a robust, generalizable framework to ensure data reliability across diverse experimental conditions and technologies.