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Hierarchical classification of microorganisms based on high‐dimensional phenotypic data
Author(s) -
Tafintseva Valeria,
Vigneau Evelyne,
Shapaval Volha,
Cariou Véronique,
Qannari El Mostafa,
Kohler Achim
Publication year - 2018
Publication title -
journal of biophotonics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.877
H-Index - 66
eISSN - 1864-0648
pISSN - 1864-063X
DOI - 10.1002/jbio.201700047
Subject(s) - linear discriminant analysis , phylogenetic tree , hierarchical clustering , random forest , artificial intelligence , data set , computer science , pattern recognition (psychology) , partial least squares regression , tree (set theory) , tree structure , data mining , biology , cluster analysis , machine learning , mathematics , data structure , genetics , mathematical analysis , gene , programming language
The classification of microorganisms by high‐dimensional phenotyping methods such as FTIR spectroscopy is often a complicated process due to the complexity of microbial phylogenetic taxonomy. A hierarchical structure developed for such data can often facilitate the classification analysis. The hierarchical tree structure can either be imposed to a given set of phenotypic data by integrating the phylogenetic taxonomic structure or set up by revealing the inherent clusters in the phenotypic data. In this study, we wanted to compare different approaches to hierarchical classification of microorganisms based on high‐dimensional phenotypic data. A set of 19 different species of molds (filamentous fungi) obtained from the mycological strain collection of the Norwegian Veterinary Institute (Oslo, Norway) is used for the study. Hierarchical cluster analysis is performed for setting up the classification trees. Classification algorithms such as artificial neural networks (ANN), partial least‐squared discriminant analysis and random forest (RF) are used and compared. The 2 methods ANN and RF outperformed all the other approaches even though they did not utilize predefined hierarchical structure. To our knowledge, the RF approach is used here for the first time to classify microorganisms by FTIR spectroscopy.