Setting objective thresholds for rare event detection in flow cytometry.
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The accurate identification of rare antigen-specific cytokine positive cells from peripheral blood mononuclear cells (PBMC) after antigenic stimulation in an intracellular staining (ICS) flow cytometry assay is challenging, as cytokine positive events may be fairly diffusely distributed and lack an obvious separation from the negative population. Traditionally, the approach by flow operators has been to manually set a positivity threshold to partition events into cytokine-positive and cytokine-negative. This approach suffers from subjectivity and inconsistency across different flow operators. The use of statistical clustering methods does not remove the need to find an objective threshold between between positive and negative events since consistent identification of rare event subsets is highly challenging for automated algorithms, especially when there is distributional overlap between the positive and negative events ("smear"). We present a new approach, based on the Fβ measure, that is similar to manual thresholding in providing a hard cutoff, but has the advantage of being determined objectively. The performance of this algorithm is compared with results obtained by expert visual gating. Several ICS data sets from the External Quality Assurance Program Oversight Laboratory (EQAPOL) proficiency program were used to make the comparisons. We first show that visually determined thresholds are difficult to reproduce and pose a problem when comparing results across operators or laboratories, as well as problems that occur with the use of commonly employed clustering algorithms. In contrast, a single parameterization for the Fβ method performs consistently across different centers, samples, and instruments because it optimizes the precision/recall tradeoff by using both negative and positive controls.
Laboratory Proficiency Testing
Practice Guidelines as Topic
Predictive Value of Tests
Quality Indicators, Health Care
Reproducibility of Results
Published Version (Please cite this version)10.1016/j.jim.2014.04.002
Publication InfoRichards, Adam J; Staats, Janet; Enzor, Jennifer; McKinnon, Katherine; Frelinger, Jacob; Denny, Thomas N; ... Chan, Cliburn (2014). Setting objective thresholds for rare event detection in flow cytometry. J Immunol Methods, 409. pp. 54-61. 10.1016/j.jim.2014.04.002. Retrieved from https://hdl.handle.net/10161/14686.
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Associate Professor of Biostatistics and Bioinformatics
Computational immunology (stochastic and spatial models and simulations, T cell signaling, immune regulation) Statistical methodology for immunological laboratory techniques (flow cytometry, CFSE analysis, receptor-ligand binding and signaling kinetics) Informatics of the immune system (reference and application ontologies, meta-programming, text mining and machine learning)
Professor in Medicine
Thomas N. Denny, MSc, M.Phil, is the Chief Operating Officer of the Duke Human Vaccine Institute (DHVI) and the Center for HIV/AIDS Vaccine Immunology (CHAVI), and a Professor of Medicine in the Department of Medicine at Duke University Medical Center. He is also an Affiliate Member of the Duke Global Health Institute. He has recently been appointed to the Duke University Fuqua School of Business Health Sector Advisory Council. Previously, he was an Associate Professor of Pathology, Laboratory M
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