SpaceTrooper: a quality control framework for imaging-basedspatial omics data

Abstract

Quality control (QC) is a critical step in the analysis of imaging-based single-cell spatial omics data, yet standardized metrics tailored to these technologies are still lacking. Most existing QC approaches are adapted from single-cell sequencing workflows and rely on fixed thresholds, limiting their ability to capture complex artifacts arising from image processing, tissue morphology, and platform-specific effects. Here, we present SpaceTrooper, a data-driven QC framework that computes an integrated per-cell quality score by combining expression-derived and morphological features. Without relying on fixed thresholds, SpaceTrooper systematically identifies low-quality cells caused by segmentation errors, signal loss, elevated background, spatial distortions, and tissue-derived artifacts. Across diverse tissues, technologies, and modalities, SpaceTrooper robustly detects technical failures and substantially improves clustering and cell-type coherence relative to conventional QC strategies.