This evaluation shows how unmixing algorithms affect population identification and resolution in flow cytometry, suggesting improvements in data interpretation.
The expansion of full spectral flow cytometry enabled us to run ultra-high-dimensional panels of up to 50 fluorochromes, offering unprecedented in-depth immunophenotyping. However, this advancement introduces significant analytical challenges, particularly in unmixing accuracy, population spread, and panel design. This study evaluates the impact of various unmixing algorithms on biological interpretation using different OMIP datasets. We demonstrate that algorithmic discrepancies can lead to loss of resolution, population misidentification, and incorrect interpretation of the biological information. Through comparative analysis and the use of measures like the Median Mismatch Index (MMI), Spillover Spread Matrix (SSM) and robust Standard Deviation (rSD), we highlight the limitations of current tools and propose strategies for optimized use of single stain, predicting the unmixing accuracy for a set of fluorochromes, and cares that need to be taken for correct data interpretation in high-parameter cytometry. We also showed the present version of SSM may not be suitable to predict the spillover spread for ultra-large panels.
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Bhowmick et al. (2025) studied this question.
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