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Titolo:
Pattern recognition in flow cytometry
Autore:
Boddy, L; Wilkins, MF; Morris, CW;
Indirizzi:
Cardiff Univ, Cardiff Sch Biosci, Cardiff CF10 3TL, S Glam, Wales Cardiff Univ Cardiff S Glam Wales CF10 3TL ardiff CF10 3TL, S Glam, Wales Univ Glamorgan, Sch Comp, Pontypridd CF37 1DL, M Glam, Wales Univ Glamorgan Pontypridd M Glam Wales CF37 1DL d CF37 1DL, M Glam, Wales
Titolo Testata:
CYTOMETRY
fascicolo: 3, volume: 44, anno: 2001,
pagine: 195 - 209
SICI:
0196-4763(20010701)44:3<195:PRIFC>2.0.ZU;2-9
Fonte:
ISI
Lingua:
ENG
Soggetto:
NEURAL-NETWORK ANALYSIS; RADIAL BASIS FUNCTION; LIST MODE DATA; MARINE-PHYTOPLANKTON; CLUSTER-ANALYSIS; MISSING VALUES; IDENTIFICATION; CLASSIFICATION; MULTIVARIATE; CELL;
Keywords:
phytoplankton; multivariate statistics; artificial neural networks; clustering;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Life Sciences
Citazioni:
85
Recensione:
Indirizzi per estratti:
Indirizzo: Boddy, L Cardiff Univ, Cardiff Sch Biosci, POB 915,Pk Pl, Cardiff CF10 3TL, S Glam,Wales Cardiff Univ POB 915,Pk Pl Cardiff S Glam Wales CF10 3TL am,Wales
Citazione:
L. Boddy et al., "Pattern recognition in flow cytometry", CYTOMETRY, 44(3), 2001, pp. 195-209

Abstract

Background: Analytical flow cytometry (AFC), by quantifying sometimes morethan 10 optical parameters on cells at rates of approximately 10(3) cells/s, rapidly generates vast quantities of multidimensional data, which provides a considerable challenge for data analysis. We review the application ofmultivariate data analysis and pattern recognition techniques to flow cytometry. Methods: Approaches were divided into two broad types depending on whetherthe aim was identification or clustering. Multivariate statistical approaches, supervised artificial neural networks (ANNs), problems of overlapping character distributions, unbounded data sets, missing parameters, scaling up, and estimating proportions of different types of cells comprised the first category. Classic clustering methods, fuzzy clustering, and unsupervisedANNs comprised the second category. We demonstrate the state of the art byusing AFC data on marine phytoplankton populations. Results and Conclusions: information held within the large quantities of data generated by AFC was tractable using ANNs, but for field studies the problem of obtaining suitable training data needs to be resolved, and coping with an almost infinite number of cell categories needs further research. (C) 2001 Wiley-Liss, Inc.

ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 21/01/20 alle ore 06:52:28