US 12,306,087 B2
Method for optimal scaling of cytometry data for machine learning analysis and systems for same
Nikolay Samusik, Monterey, CA (US); and Joseph T. Trotter, La Jolla, CA (US)
Assigned to BECTON, DICKINSON AND COMPANY, Franklin Lakes, NJ (US)
Filed by Becton, Dickinson and Company, Franklin Lakes, NJ (US)
Filed on Sep. 9, 2021, as Appl. No. 17/470,461.
Claims priority of provisional application 63/115,994, filed on Nov. 19, 2020.
Prior Publication US 2022/0155209 A1, May 19, 2022
Int. Cl. G01N 15/1434 (2024.01); G01N 15/10 (2024.01); G01N 15/149 (2024.01)
CPC G01N 15/1434 (2013.01) [G01N 2015/1006 (2013.01); G01N 15/149 (2024.01)] 14 Claims
OG exemplary drawing
 
1. A computer-implemented method of displaying clustered cytometric data, the method comprising:
obtaining cytometric data for a sample, wherein the cytometric data comprises measurements of a plurality of parameters from particles irradiated in the sample flowing in a flow stream;
clustering the cytometric data;
displaying the clustered cytometric data;
receiving a selection of a parameter of interest of a plurality of parameters of the cytometric data;
displaying the parameter of interest;
receiving a positive measurement interval and a negative measurement interval of the displayed parameter of interest;
transforming the parameter of interest of the cytometric data based at least in part on the positive and negative measurement intervals, wherein transforming the parameter of interest comprises rescaling the positive and negative measurement intervals for the parameter of interest;
clustering the transformed cytometric data; and
automatically updating the display of the clustered cytometric data to reflect the transformed cytometric data.