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Setting objective thresholds for rare event detection in flow cytometry.

dc.contributor.author Chan, C
dc.contributor.author Denny, Thomas Norton
dc.contributor.author Enzor, Jennifer
dc.contributor.author Frelinger, J
dc.contributor.author McKinnon, K
dc.contributor.author Richards, AJ
dc.contributor.author Staats, Janet
dc.contributor.author Weinhold, Kent James
dc.coverage.spatial Netherlands
dc.date.accessioned 2017-06-01T19:24:13Z
dc.date.available 2017-06-01T19:24:13Z
dc.date.issued 2014-07
dc.identifier https://www.ncbi.nlm.nih.gov/pubmed/24727143
dc.identifier S0022-1759(14)00118-5
dc.identifier.uri https://hdl.handle.net/10161/14686
dc.description.abstract 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.
dc.language eng
dc.relation.ispartof J Immunol Methods
dc.relation.isversionof 10.1016/j.jim.2014.04.002
dc.subject Automated analysis
dc.subject ICS
dc.subject Positivity
dc.subject Rare events
dc.subject Reproducibility
dc.subject Standardization
dc.subject Algorithms
dc.subject Automation, Laboratory
dc.subject Biomarkers
dc.subject Cytokines
dc.subject Flow Cytometry
dc.subject Guideline Adherence
dc.subject Humans
dc.subject Laboratories
dc.subject Laboratory Proficiency Testing
dc.subject Leukocytes, Mononuclear
dc.subject Monitoring, Immunologic
dc.subject Observer Variation
dc.subject Practice Guidelines as Topic
dc.subject Predictive Value of Tests
dc.subject Program Development
dc.subject Quality Control
dc.subject Quality Indicators, Health Care
dc.subject Reproducibility of Results
dc.subject Specimen Handling
dc.title Setting objective thresholds for rare event detection in flow cytometry.
dc.type Journal article
pubs.author-url https://www.ncbi.nlm.nih.gov/pubmed/24727143
pubs.begin-page 54
pubs.end-page 61
pubs.organisational-group Basic Science Departments
pubs.organisational-group Biostatistics & Bioinformatics
pubs.organisational-group Clinical Science Departments
pubs.organisational-group Duke
pubs.organisational-group Duke Cancer Institute
pubs.organisational-group Duke Human Vaccine Institute
pubs.organisational-group Immunology
pubs.organisational-group Institutes and Centers
pubs.organisational-group Medicine
pubs.organisational-group Medicine, Duke Human Vaccine Institute
pubs.organisational-group Pathology
pubs.organisational-group School of Medicine
pubs.organisational-group Statistical Science
pubs.organisational-group Surgery
pubs.organisational-group Surgery, Surgical Sciences
pubs.organisational-group Trinity College of Arts & Sciences
pubs.publication-status Published
pubs.volume 409
dc.identifier.eissn 1872-7905


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