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bioRxiv · 10.1101/2025.09.24.678276

CytoScan: Automated detection of technical anomalies for cytometry quality control

Mocking, T. R.·Zwolle, F.·Park, Y.·Kelder, A.·Saeys, Y.·Cloos, J.·van Gassen, S.·Bachas, C.
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Abstract

Studies evaluating cellular phenotypes by cytometry techniques are increasingly facing analytical challenges due to the multitudes of samples and parameters that are evaluated concurrently. Spurious technical effects resulting from a lack of standardization can affect marker distributions and further complicate multi-sample analyses. User-friendly tools for exploratory data analysis to identify such technical effects in large datasets are lacking. To fill this gap, we present a novel R package, CytoScan, that evaluates inter-measurement variation in cytometry datasets and allows for detecting anomalous measurements after data acquisition. CytoScan can detect two types of anomalies: files with limited similarity to others within a dataset (outliers) and files with limited similarity to previously acquired high-quality reference data (novelties). Using simulations of skewed marker distributions and real-life technical effects we demonstrate that CytoScan can accurately detect such anomalies. CytoScan can be applied to large cytometry datasets on consumer-grade hardware with informative visualizations, providing accessible quality-control for more reliable analyses.

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Mocking, T. R., Zwolle, F., Park, Y., Kelder, A., Saeys, Y., Cloos, J., van Gassen, S., Bachas, C.. 2025-09-26. CytoScan: Automated detection of technical anomalies for cytometry quality control. https://doi.org/10.1101/2025.09.24.678276

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