Identification of 72 phytoplankton species by radial basis function neural network analysis of flow cytometric data

Abstract

MEPS Marine Ecology Progress Series Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsTheme Sections MEPS 195:47-59 (2000) - doi:10.3354/meps195047 Identification of 72 phytoplankton species by radial basis function neural network analysis of flow cytometric data Lynne Boddy1,*, C. W. Morris2, M. F. Wilkins1, Luan Al-Haddad2, G. A. Tarran3, R. R. Jonker4, P. H. Burkill3 1Cardiff School of Biosciences, University of Cardiff, Cardiff CF1 3TL, United Kingdom 2School of Computing, University of Glamorgan, Pontypridd, United Kingdom 3Centre for Coastal and Marine Sciences, Plymouth Marine Laboratory, Prospect Place, West Hoe, Plymouth PL1 3DH, United Kingdom 4AquaSense Lab, Kruislaan 411, 1090 HC Amsterdam, The Netherlands *E-mail: boddyl@cardiff.ac.uk ABSTRACT: Radial basis function artificial neural networks (ANNs) were trained to discriminate between phytoplankton species based on 7 flow cytometric parameters measured on axenic cultures. Comparison was made between the performance of networks restricted to using radially-symmetric basis functions and networks using more general arbitrarily oriented ellipsoidal basis functions, with the latter proving significantly superior in performance. ANNs trained on 62, 54 and 72 taxa identified them with respectively 77, 73 and 70% overall success. As well as high success in identification, high confidence of correct identification was also achieved. Misidentifications resulted from overlap of character distributions. Improved overall identification success can be achieved by grouping together species with similar character distributions. This can be done within genera or based on groupings indicated in dendrograms constructed for the data on all species. When an ANN trained on 1 data set was tested with data on cells grown under different light conditions, overall successful identification was low (<20%), but when an ANN was trained on a combined data set identification success was high (>70%). Clearly it is essential to include data on cells covering the whole spectrum of biological variation. Ways of obtaining data for training ANNs to identify phytoplankton from field samples are discussed. KEY WORDS: Radial basis functions · Neural networks · Principal component analysis · Dinoflagellates · Prymnesiomonads · Flagellates · Cryptomonads · Diatoms Full text in pdf format PreviousNextExport citation RSS - Facebook - Tweet - linkedIn Cited by Published in MEPS Vol. 195. Publication date: March 31, 2000 Print ISSN:0171-8630; Online ISSN:1616-1599 Copyright © 2000 Inter-Research.