Enhancing flow cytometry discrimination with geometric transformation
USRE46559E1 · kind E1 · reissue
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| Filing date | Feb 21, 2017 |
| Grant date | Sep 26, 2017 |
| Priority date | — |
| Expiry date | Feb 21, 2037 |
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- Technology area (CPC —)General
Abstract
In flow cytometry, particles (2) can be distinguished between populations (8) by combining n-dimensional parameter data, which may be derived from signal data from a particle, to mathematically achieve numerical results representative of an alteration (48). An alteration may include a rotational alteration, a scaled alteration, or perhaps even a translational alteration. Alterations may enhance separation of data points which may provide real-time classification (49) of signal data corresponding to individual particles into one of at least two populations.
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