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CompensAID: An Automated Detection Tool for Reference Errors
Rosan Olsman1, Sarah Bonte2,3, Mattias Hofmans4,5
1Laboratory Medical Immunology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands.
Summary
CompensAID automatically detects reference errors in flow cytometry data, improving quality control. This R-based tool flags marker combinations with potential inaccuracies, reducing manual inspection burdens.
Area of Science:
- Immunology
- Computational Biology
- Biotechnology
Background:
- Flow cytometry data requires mathematical unmixing using reference controls.
- Inaccurate controls (reference errors) distort fluorochrome abundance estimates and population distributions.
- Manual inspection of marker combinations for errors is impractical for complex panels and large datasets.
Purpose of the Study:
- To develop CompensAID, an open-source R-based tool for automatically identifying potential reference errors in flow cytometry.
- To support and enhance quality control workflows in flow cytometry data analysis.
Main Methods:
- CompensAID uses density-based cutoff detection to gate negative and positive populations.
- The Secondary Stain Index (SSI) is computed on segmented positive populations.
- Marker combinations are flagged if the last segment's SSI is below -1.
Main Results:
- CompensAID achieved a sensitivity of 0.96 in conventional flow cytometry, identifying 23 out of 24 suspected marker combinations.
- In spectral flow cytometry, sensitivity was 0.74, flagging 21 out of 28 suspected combinations.
- False positives were observed, often due to suboptimal gating or low event counts.
Conclusions:
- CompensAID provides a robust method for detecting potential reference errors in flow cytometry.
- The tool significantly reduces the need for manual inspection, enhancing data reliability.
- Integration of CompensAID into quality control pipelines is recommended for improved flow cytometry data analysis.
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