Evaluating flow cytometer performance with weighted quadratic least squares analysis of LED and multi‐level bead data

• Published in: Cytometry. Part A Vol. 91; no. 3; pp. 232 - 249
• Main Authors: Parks, David R., El Khettabi, Faysal, Chase, Eric, Hoffman, Robert A., Perfetto, Stephen P., Spidlen, Josef, Wood, James C.S., Moore, Wayne A., Brinkman, Ryan R.
• Format: Journal Article
• Language: English
• Published: United States Wiley Subscription Services, Inc 01.03.2017
• Subjects: Automation; Balances (scales); bioinformatics; Calibration; Channels; clustering algorithms; Cytometry; data analysis; Data points; Data processing; Dyes; flow cytometry; Flow Cytometry - methods; Flow Cytometry - statistics & numerical data; Fluorescence; Formulations; Least-Squares Analysis; LED; Linearity; Mathematical models; microspheres; Models, Theoretical; Multilevel; Optical Imaging - methods; Parameter uncertainty; photoelectron scale; regression analysis; Reliability analysis; sensitivity; Statistical analysis; Statistical methods; statistics; photoelectron scale; data analysis; clustering algorithms; microspheres; bioinformatics; regression analysis; LED; flow cytometry; sensitivity; statistics
• ISSN: 1552-4922; 1552-4930
• DOI: 10.1002/cyto.a.23052

We developed a fully automated procedure for analyzing data from LED pulses and multilevel bead sets to evaluate backgrounds and photoelectron scales of cytometer fluorescence channels. The method improves on previous formulations by fitting a full quadratic model with appropriate weighting and by providing standard errors and peak residuals as well as the fitted parameters themselves. Here we describe the details of the methods and procedures involved and present a set of illustrations and test cases that demonstrate the consistency and reliability of the results. The automated analysis and fitting procedure is generally quite successful in providing good estimates of the Spe (statistical photoelectron) scales and backgrounds for all the fluorescence channels on instruments with good linearity. The precision of the results obtained from LED data is almost always better than that from multilevel bead data, but the bead procedure is easy to carry out and provides results good enough for most purposes. Including standard errors on the fitted parameters is important for understanding the uncertainty in the values of interest. The weighted residuals give information about how well the data fits the model, and particularly high residuals indicate bad data points. Known photoelectron scales and measurement channel backgrounds make it possible to estimate the precision of measurements at different signal levels and the effects of compensated spectral overlap on measurement quality. Combining this information with measurements of standard samples carrying dyes of biological interest, we can make accurate comparisons of dye sensitivity among different instruments. Our method is freely available through the R/Bioconductor package flowQB. © 2017 International Society for Advancement of Cytometry In this study, Parks et al. report a fully automated procedure for analyzing flow cytometry data from LED pulse and multilevel bead sets. The figure shows the distribution of the LED c1/Bead c1 (= Bead QI/LED QI = Bead Spe/LED Spe) ratio in relation to the Bead c1 relative standard error for 7 measurement channels of 10 instruments that should have good linearity and for which usable LED, Sph8, and TF6 data and fits are available. The authors consider points in the highlighted region with LEDc1/Beadc1 between 0.8 and 1.2 and bead fit c1 relative standard errors no more than 10% to represent good results for the bead fitting.