Hierarchical Modeling for Rare Event Detection and Cell Subset Alignment across Flow Cytometry Samples

PLoS Computational Biology

Volumn 9, Issue 7, 2013, Pages

  Cron, Andrew ^a   Gouttefangeas, Cécile ^b   Frelinger, Jacob ^c   Lin, Lin ^d   Singh, Satwinder K ^e   Britten, Cedrik M ^f   Welters, Marij J P ^e   van der Burg, Sjoerd H ^e   West, Mike ^a,c   Chan, Cliburn ^c,g  

^a Duke University   (United States)

^b UNIVERSITY OF TÜBINGEN   (Germany)

^c DUKE UNIVERSITY   (United States)

^d Fred Hutchinson Cancer Research Center   (United States)

^e LEIDEN UNIVERSITY MEDICAL CENTER   (Netherlands)

^f UNIVERSITY MEDICAL CENTER MAINZ   (Germany)

^g DUKE UNIVERSITY SCHOOL OF MEDICINE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CYTOLOGY; FLOW CYTOMETRY; GAUSSIAN DISTRIBUTION; OPEN SOURCE SOFTWARE; OPEN SYSTEMS; SET THEORY; T-CELLS;

CELL-BE; CELL/B.E; EVENTS DETECTION; EXTREMELY LOW FREQUENCIES; GAUSSIAN MIXTURE MODEL; HIERARCHICAL MODEL; MULTIPARAMETERS; MULTIPLE SAMPLES; PERIPHERAL BLOOD MONONUCLEAR CELLS; SINGLE CELLS ANALYSIS;

ANTIGENS;

HLA ANTIGEN; RETROVIRUS VECTOR; T LYMPHOCYTE RECEPTOR;

ACCURACY; ANTIGEN SPECIFICITY; ARTICLE; CELL SUBPOPULATION; CELLULAR DISTRIBUTION; COMPUTER PROGRAM; FLOW CYTOMETRY; GENETIC TRANSDUCTION; IMMUNE RESPONSE; INFORMATION PROCESSING; KERNEL METHOD; MATHEMATICAL ANALYSIS; PERIPHERAL BLOOD MONONUCLEAR CELL; PROBABILITY; PROTEIN BINDING; QUANTITATIVE ANALYSIS; REPRODUCIBILITY; SENSITIVITY ANALYSIS; STATISTICAL MODEL; T LYMPHOCYTE;

EID: 84880849822     PISSN: 1553734X     EISSN: 15537358     Source Type: Journal    
DOI: 10.1371/journal.pcbi.1003130     Document Type: Article

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