scholarly journals AutoSpill is a principled framework that simplifies the analysis of multichromatic flow cytometry data

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Carlos P. Roca ◽  
Oliver T. Burton ◽  
Václav Gergelits ◽  
Teresa Prezzemolo ◽  
Carly E. Whyte ◽  
...  

AbstractCompensating in flow cytometry is an unavoidable challenge in the data analysis of fluorescence-based flow cytometry. Even the advent of spectral cytometry cannot circumvent the spillover problem, with spectral unmixing an intrinsic part of such systems. The calculation of spillover coefficients from single-color controls has remained essentially unchanged since its inception, and is increasingly limited in its ability to deal with high-parameter flow cytometry. Here, we present AutoSpill, an alternative method for calculating spillover coefficients. The approach combines automated gating of cells, calculation of an initial spillover matrix based on robust linear regression, and iterative refinement to reduce error. Moreover, autofluorescence can be compensated out, by processing it as an endogenous dye in an unstained control. AutoSpill uses single-color controls and is compatible with common flow cytometry software. AutoSpill allows simpler and more robust workflows, while reducing the magnitude of compensation errors in high-parameter flow cytometry.

2020 ◽  
Author(s):  
Carlos P. Roca ◽  
Oliver T. Burton ◽  
Teresa Prezzemolo ◽  
Carly E. Whyte ◽  
Richard Halpert ◽  
...  

AbstractCompensating in classical flow cytometry or unmixing in spectral systems is an unavoidable challenge in the data analysis of fluorescence-based flow cytometry. In both cases, spillover coefficients are estimated for each fluorophore using single-color controls. This approach has remained essentially unchanged since its inception, and is increasingly limited in its ability to deal with high-parameter flow cytometry. Here, we present AutoSpill, a novel approach for calculating spillover coefficients or spectral signatures of fluorophores. The approach combines automated gating of cells, calculation of an initial spillover matrix based on robust linear regression, and iterative refinement to reduce error. Moreover, autofluorescence can be compensated out, by processing it as an endogenous dye in an unstained control. AutoSpill uses single-color controls and is compatible with common flow cytometry software, but it differs in two key aspects from current methods: (1) it is much less demanding in the preparation of controls, as it does not require the presence of well-defined positive and negative populations, and (2) it does not require manual tuning of the spillover matrix, as the algorithm iteratively computes the tuning, producing an optimal compensation matrix. Another algorithm, AutoSpread, complements this approach, providing a robust estimate of the Spillover Spreading Matrix (SSM), while avoiding the need for well-defined positive and negative populations. Together, AutoSpill and AutoSpread provide a superior solution to the problem of fluorophore spillover, allowing simpler and more robust workflows in high-parameter flow cytometry.


2014 ◽  
Vol 10 (8) ◽  
pp. e1003806 ◽  
Author(s):  
Greg Finak ◽  
Jacob Frelinger ◽  
Wenxin Jiang ◽  
Evan W. Newell ◽  
John Ramey ◽  
...  

Methods ◽  
2017 ◽  
Vol 112 ◽  
pp. 201-210 ◽  
Author(s):  
Holger Hennig ◽  
Paul Rees ◽  
Thomas Blasi ◽  
Lee Kamentsky ◽  
Jane Hung ◽  
...  

2009 ◽  
Vol 2009 ◽  
pp. 1-19 ◽  
Author(s):  
Ali Bashashati ◽  
Ryan R. Brinkman

Flow cytometry (FCM) is widely used in health research and in treatment for a variety of tasks, such as in the diagnosis and monitoring of leukemia and lymphoma patients, providing the counts of helper-T lymphocytes needed to monitor the course and treatment of HIV infection, the evaluation of peripheral blood hematopoietic stem cell grafts, and many other diseases. In practice, FCM data analysis is performed manually, a process that requires an inordinate amount of time and is error-prone, nonreproducible, nonstandardized, and not open for re-evaluation, making it the most limiting aspect of this technology. This paper reviews state-of-the-art FCM data analysis approaches using a framework introduced to report each of the components in a data analysis pipeline. Current challenges and possible future directions in developing fully automated FCM data analysis tools are also outlined.


2013 ◽  
Vol 10 (3) ◽  
pp. 228-238 ◽  
Author(s):  
Nima Aghaeepour ◽  
◽  
Greg Finak ◽  
Holger Hoos ◽  
Tim R Mosmann ◽  
...  

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