Assessment of self-organizing maps to analyze sole-carbon source utilization profiles

• Published in: Journal of microbiological methods 2005-07, Vol.62 (1), p.89-102
• Main Authors: Leflaive, Joséphine, Céréghino, Régis, Danger, Michaël, Lacroix, Gérard, Ten-Hage, Loïc
• Format: Article
• Language: English
• Subjects: Bacteria - growth & development; Bacteria - metabolism; Biodiversity and Ecology; Biolog; Biological and medical sciences; Biotechnology; Carbon - metabolism; Chlorophyta - growth & development; Chlorophyta - metabolism; Cluster Analysis; Community-level physiological profiles; Ecosystem; Environmental Sciences; Fundamental and applied biological sciences. Psychology; Microbial ecology; Mission oriented research; Multivariate analyses; Neural Networks (Computer); Principal Component Analysis; Prospectives; Self-organizing maps
• ISSN: 0167-7012; 1872-8359
• DOI: 10.1016/j.mimet.2005.02.002

The use of community-level physiological profiles obtained with Biolog microplates is widely employed to consider the functional diversity of bacterial communities. Biolog produces a great amount of data which analysis has been the subject of many studies. In most cases, after some transformations, these data were investigated with classical multivariate analyses. Here we provided an alternative to this method, that is the use of an artificial intelligence technique, the Self-Organizing Maps (SOM, unsupervised neural network). We used data from a microcosm study of algae-associated bacterial communities placed in various nutritive conditions. Analyses were carried out on the net absorbances at two incubation times for each substrates and on the chemical guild categorization of the total bacterial activity. Compared to Principal Components Analysis and cluster analysis, SOM appeared as a valuable tool for community classification, and to establish clear relationships between clusters of bacterial communities and sole-carbon sources utilization. Specifically, SOM offered a clear bidimensional projection of a relatively large volume of data and were easier to interpret than plots commonly obtained with multivariate analyses. They would be recommended to pattern the temporal evolution of communities' functional diversity.