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

Banzi, B.; Righelli, D.; Marchionni, M.; Romano, O.; Forcato, M.; Risso, D.; Bicciato, S.

2025-12-26 bioinformatics
10.64898/2025.12.24.696336 bioRxiv
Show 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.

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"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.