Statistical mixture modeling for cell subtype identification in flow cytometry

Cytometry Part A

Volumn 73, Issue 8, 2008, Pages 693-701

  Chan, Cliburn ^a,b   Feng, Feng ^a   Ottinger, Janet ^b   Foster, David ^c   West, Mike ^d   Kepler, Thomas B ^a,d  

^a DUKE UNIVERSITY SCHOOL OF MEDICINE   (United States)

^b DUKE UNIVERSITY MEDICAL CENTER   (United States)

^c UNIVERSITY OF CALGARY   (Canada)

^d Duke University   (United States)

Author keywords

Automation; Bayesian analysis; Gating; Identification; Markov chain Monte Carlo; Mixture models; Statistics

Indexed keywords

ANTIBODY; CD19 ANTIBODY; CD3 ANTIBODY; CD4 ANTIBODY; CD8 ANTIBODY; CELL MARKER; FLUORESCEIN ISOTHIOCYANATE;

ANIMAL CELL; ARTICLE; AUTOANALYSIS; B LYMPHOCYTE; BAYES THEOREM; BLOOD CELL; BLOOD SAMPLING; CD4 LYMPHOCYTE COUNT; CD8+ T LYMPHOCYTE; CELL LABELING; CELL TYPE; CONJUGATION; FLOW CYTOMETRY; FLUORESCENCE ACTIVATED CELL SORTING; GRANULOCYTE; HUMAN; HUMAN CELL; MATHEMATICAL ANALYSIS; MATHEMATICAL MODEL; MONONUCLEAR CELL; MONTE CARLO METHOD; MOUSE; MULTIVARIATE ANALYSIS; NATURAL KILLER CELL; NONHUMAN; NORMAL DISTRIBUTION; NORMAL HUMAN; PREDICTIVE VALIDITY; PRIORITY JOURNAL; PROBABILITY; STAINING; STATISTICAL MODEL; VALIDATION PROCESS;

EID: 48849105886     PISSN: 15524922     EISSN: 15524930     Source Type: Journal    
DOI: 10.1002/cyto.a.20583     Document Type: Article

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