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Reducing Spreading: Removing the Impact of Irrelevant Dyes Improves Unmixed Flow Cytometry Data
1De Novo Research, Inc., Pasadena, California, USA.
Summary
A new flow cytometry unmixing method, TRU-OLS, uses biological knowledge to improve data accuracy. This approach reduces variability in complex staining panels by unmixing dyes only present on individual events.
Area of Science:
- Immunology
- Biotechnology
- Computational Biology
Background:
- Flow cytometry staining panels have grown in complexity, often involving 40-50 dyes on spectral cytometers.
- Current unmixing methods include all dyes in the mixing matrix, increasing data variance even for dyes not present on individual events.
Purpose of the Study:
- Introduce a novel unmixing method, TRU-OLS (True Residuals Unmixing by Ordinary Least Squares).
- To decrease the variance of unmixed abundance distributions in flow cytometry data.
Main Methods:
- TRU-OLS leverages a priori biological knowledge, specifically unstained controls.
- It unmixes each event using only the dyes confirmed to be present on that specific event.
Main Results:
- TRU-OLS statistically and visually reduces the variance of unmixed abundances.
- This improvement is demonstrated in both simple (4-6 color) and complex (40 color) staining panels.
Conclusions:
- TRU-OLS offers a more accurate and precise method for unmixing complex flow cytometry data.
- The method enhances data reliability by accounting for the actual dye subset on individual cellular events.
