Classifying antibodies using flow cytometry data: class prediction and class discovery

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dc.contributor.author Salganik, MP
dc.contributor.author Milford, EL
dc.contributor.author Hardie, DL
dc.contributor.author Shaw, S
dc.contributor.author Wand, M
dc.date.accessioned 2011-02-07T06:17:50Z
dc.date.issued 2005-01
dc.identifier.citation Biometrical Journal, 2005, 47 (5), pp. 740 - 754
dc.identifier.issn 0323-3847
dc.identifier.other C1UNSUBMIT en_US
dc.identifier.uri http://hdl.handle.net/10453/12989
dc.description.abstract Classifying monoclonal antibodies, based on the similarity of their binding to the proteins (antigens) on the surface of blood cells, is essential for progress in immunology, hematology and clinical medicine. The collaborative efforts of researchers from many countries have led to the classification of thousands of antibodies into 247 clusters of differentiation (CD). Classification is based on flow cytometry and biochemical data. In preliminary classifications of antibodies based on flow cytometry data, the object requiring classification (an antibody) is described by a set of random samples from unknown densities of fluorescence intensity. An individual sample is collected in the experiment, where a population of cells of a certain type is stained by the identical fluorescently marked replicates of the antibody of interest. Samples are collected for multiple cell types. The classification problems of interest include identifying new CDs (class discovery or unsupervised learning) and assigning new antibodies to the known CD clusters (class prediction or supervised learning). These problems have attracted limited attention from statisticians. We recommend a novel approach to the classification process in which a computer algorithm suggests to the analyst the subset of the most appropriate classifications of an antibody in class prediction problems or the most similar pairs/groups of antibodies in class discovery problems. The suggested algorithm speeds up the analysis of a flow cytometry data by a factor 1020. This allows the analyst to focus on the interpretation of the automatically suggested preliminary classification solutions and on planning the subsequent biochemical experiments
dc.publisher Wiley - VCH Verlag GmbH & Co. KGaA
dc.relation.isbasedon 10.1002/bimj.200310142
dc.title Classifying antibodies using flow cytometry data: class prediction and class discovery
dc.type Journal Article
dc.parent Biometrical Journal
dc.journal.volume 5
dc.journal.volume 47
dc.journal.number 5 en_US
dc.publocation Germany en_US
dc.identifier.startpage 740 en_US
dc.identifier.endpage 754 en_US
dc.cauo.name SCI.Mathematical Sciences en_US
dc.conference Verified OK en_US
dc.for 0104 Statistics
dc.personcode 110509
dc.percentage 100 en_US
dc.classification.name Statistics en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom en_US
dc.date.activity en_US
dc.location.activity en_US
dc.description.keywords * Classification
dc.description.keywords * Monoclonal antibodies
dc.description.keywords * Flow cytometry
dc.description.keywords * Dissimilarity measure
dc.description.keywords * Kernel smoothing
dc.description.keywords * SiZer
dc.description.keywords * Class discovery
dc.description.keywords * Class prediction
dc.description.keywords * Unsupervised learning
dc.description.keywords * Supervised learning
pubs.embargo.period Not known
pubs.organisational-group /University of Technology Sydney
pubs.organisational-group /University of Technology Sydney/Faculty of Science
utslib.copyright.status Closed Access
utslib.copyright.date 2015-04-15 12:17:09.805752+10
pubs.consider-herdc false
utslib.collection.history School of Mathematical Sciences (ID: 340)
utslib.collection.history Closed (ID: 3)
utslib.collection.history School of Mathematical Sciences (ID: 340)


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