Variable selection and multivariate methods for the identification of microorganisms by flow cytometry
1999; Wiley; Volume: 35; Issue: 2 Linguagem: Inglês
10.1002/(sici)1097-0320(19990201)35
ISSN1097-0320
AutoresHazel M. Davey, A. R. Jones, Adrian D. Shaw, Douglas B. Kell,
Tópico(s)Advanced Chemical Sensor Technologies
ResumoCytometryVolume 35, Issue 2 p. 162-168 Original ArticleFree Access Variable selection and multivariate methods for the identification of microorganisms by flow cytometry Hazel M. Davey, Corresponding Author Hazel M. Davey [email protected] Institute of Biological Sciences, University of Wales, Aberystwyth, Ceredigion, Wales, United KingdomInstitute of Biological Sciences, University of Wales, Aberystwyth, Ceredigion SY23 3DD, Wales, UK.Search for more papers by this authorAlun Jones, Alun Jones Institute of Biological Sciences, University of Wales, Aberystwyth, Ceredigion, Wales, United KingdomSearch for more papers by this authorAdrian D. Shaw, Adrian D. Shaw Institute of Biological Sciences, University of Wales, Aberystwyth, Ceredigion, Wales, United KingdomSearch for more papers by this authorDouglas B. Kell, Douglas B. Kell Institute of Biological Sciences, University of Wales, Aberystwyth, Ceredigion, Wales, United KingdomSearch for more papers by this author Hazel M. Davey, Corresponding Author Hazel M. Davey [email protected] Institute of Biological Sciences, University of Wales, Aberystwyth, Ceredigion, Wales, United KingdomInstitute of Biological Sciences, University of Wales, Aberystwyth, Ceredigion SY23 3DD, Wales, UK.Search for more papers by this authorAlun Jones, Alun Jones Institute of Biological Sciences, University of Wales, Aberystwyth, Ceredigion, Wales, United KingdomSearch for more papers by this authorAdrian D. Shaw, Adrian D. Shaw Institute of Biological Sciences, University of Wales, Aberystwyth, Ceredigion, Wales, United KingdomSearch for more papers by this authorDouglas B. Kell, Douglas B. Kell Institute of Biological Sciences, University of Wales, Aberystwyth, Ceredigion, Wales, United KingdomSearch for more papers by this author First published: 14 January 1999 https://doi.org/10.1002/(SICI)1097-0320(19990201)35:2 3.0.CO;2-UCitations: 42AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Abstract Background: When exploited fully, flow cytometry can be used to provide multiparametric data for each cell in the sample of interest. While this makes flow cytometry a powerful technique for discriminating between different cell types, the data can be difficult to interpret. Traditionally, dual-parameter plots are used to visualize flow cytometric data, and for a data set consisting of seven parameters, one should examine 21 of these plots. A more efficient method is to reduce the dimensionality of the data (e.g., using unsupervised methods such as principal components analysis) so that fewer graphs need to be examined, or to use supervised multivariate data analysis methods to give a prediction of the identity of the analyzed particles. Materials and Methods: We collected multiparametric data sets for microbiological samples stained with six cocktails of fluorescent stains. Multivariate data analysis methods were explored as a means of microbial detection and identification. Results: We show that while all cocktails and all methods gave good accuracy of predictions (>94%), careful selection of both the stains and the analysis method could improve this figure (to >99% accuracy), even in a data set that was not used in the formation of the supervised multivariate calibration model. Conclusions: Flow cytometry provides a rapid method of obtaining multiparametric data for distinguishing between microorganisms. Multivariate data analysis methods have an important role to play in extracting the information from the data obtained. Artificial neural networks proved to be the most suitable method of data analysis. Cytometry 35:162–168, 1999. © 1999 Wiley-Liss, Inc. LITERATURE CITED 1 Shapiro HM. Practical flow cytometry, 3rd ed. New York: Alan R. Liss, Inc.; 1995. 2 Davey HM, Davey CL, Kell DB. On the determination of the size of microbial cells using flow cytometry. In: D Lloyd, editor. Flow cytometry in microbiology. 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