Literature DB >> 28160404

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

David R Parks1, Faysal El Khettabi2, Eric Chase3, Robert A Hoffman4, Stephen P Perfetto5, Josef Spidlen6, James C S Wood7, Wayne A Moore1, Ryan R Brinkman8.   

Abstract

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. © 2017 International Society for Advancement of Cytometry.

Entities:  

Keywords:  LED; bioinformatics; clustering algorithms; data analysis; flow cytometry; microspheres; photoelectron scale; regression analysis; sensitivity; statistics

Mesh:

Year:  2017        PMID: 28160404      PMCID: PMC5483398          DOI: 10.1002/cyto.a.23052

Source DB:  PubMed          Journal:  Cytometry A        ISSN: 1552-4922            Impact factor:   4.355


  8 in total

1.  Noise, sensitivity, and resolution of flow cytometers.

Authors:  H B Steen
Journal:  Cytometry       Date:  1992

2.  A new "Logicle" display method avoids deceptive effects of logarithmic scaling for low signals and compensated data.

Authors:  David R Parks; Mario Roederer; Wayne A Moore
Journal:  Cytometry A       Date:  2006-06       Impact factor: 4.355

3.  Using flowViz to visualize flow cytometry data.

Authors:  D Sarkar; N Le Meur; R Gentleman
Journal:  Bioinformatics       Date:  2008-02-01       Impact factor: 6.937

4.  Resolution of dimly fluorescent particles: a practical measure of fluorescence sensitivity.

Authors:  E S Chase; R A Hoffman
Journal:  Cytometry       Date:  1998-10-01

5.  Q and B values are critical measurements required for inter-instrument standardization and development of multicolor flow cytometry staining panels.

Authors:  Stephen P Perfetto; Pratip K Chattopadhyay; James Wood; Richard Nguyen; David Ambrozak; Juliane P Hill; Mario Roederer
Journal:  Cytometry A       Date:  2014-10-23       Impact factor: 4.355

6.  Update for the logicle data scale including operational code implementations.

Authors:  Wayne A Moore; David R Parks
Journal:  Cytometry A       Date:  2012-03-12       Impact factor: 4.355

7.  Quantifying spillover spreading for comparing instrument performance and aiding in multicolor panel design.

Authors:  Richard Nguyen; Stephen Perfetto; Yolanda D Mahnke; Pratip Chattopadhyay; Mario Roederer
Journal:  Cytometry A       Date:  2013-02-06       Impact factor: 4.355

8.  flowCore: a Bioconductor package for high throughput flow cytometry.

Authors:  Florian Hahne; Nolwenn LeMeur; Ryan R Brinkman; Byron Ellis; Perry Haaland; Deepayan Sarkar; Josef Spidlen; Errol Strain; Robert Gentleman
Journal:  BMC Bioinformatics       Date:  2009-04-09       Impact factor: 3.169

  8 in total
  2 in total

1.  Unlocking autofluorescence in the era of full spectrum analysis: Implications for immunophenotype discovery projects.

Authors:  Vanta J Jameson; Tina Luke; Yuting Yan; Angela Hind; Maximilien Evrard; Kevin Man; Laura K Mackay; Axel Kallies; Jose A Villadangos; Hamish E G McWilliam; Alexis Perez-Gonzalez
Journal:  Cytometry A       Date:  2022-03-29       Impact factor: 4.714

2.  Methodology for evaluating and comparing flow cytometers: A multisite study of 23 instruments.

Authors:  David R Parks; Wayne A Moore; Ryan R Brinkman; Yong Chen; Danilo Condello; Faysal El Khettabi; John P Nolan; Stephen P Perfetto; Doug Redelman; Josef Spidlen; Jonathan Van Dyke; Lili Wang; James C S Wood
Journal:  Cytometry A       Date:  2018-09-23       Impact factor: 4.355

  2 in total

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