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      Multivariate analyses in microbial ecology

      review-article
      Fems Microbiology Ecology
      Blackwell Publishing Ltd
      ordination, multivariate, modeling, statistics, gradient

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          Abstract

          Environmental microbiology is undergoing a dramatic revolution due to the increasing accumulation of biological information and contextual environmental parameters. This will not only enable a better identification of diversity patterns, but will also shed more light on the associated environmental conditions, spatial locations, and seasonal fluctuations, which could explain such patterns. Complex ecological questions may now be addressed using multivariate statistical analyses, which represent a vast potential of techniques that are still underexploited. Here, well-established exploratory and hypothesis-driven approaches are reviewed, so as to foster their addition to the microbial ecologist toolbox. Because such tools aim at reducing data set complexity, at identifying major patterns and putative causal factors, they will certainly find many applications in microbial ecology.

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          Most cited references67

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          Cluster analysis and display of genome-wide expression patterns.

          A system of cluster analysis for genome-wide expression data from DNA microarray hybridization is described that uses standard statistical algorithms to arrange genes according to similarity in pattern of gene expression. The output is displayed graphically, conveying the clustering and the underlying expression data simultaneously in a form intuitive for biologists. We have found in the budding yeast Saccharomyces cerevisiae that clustering gene expression data groups together efficiently genes of known similar function, and we find a similar tendency in human data. Thus patterns seen in genome-wide expression experiments can be interpreted as indications of the status of cellular processes. Also, coexpression of genes of known function with poorly characterized or novel genes may provide a simple means of gaining leads to the functions of many genes for which information is not available currently.
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            Microbial biogeography: putting microorganisms on the map.

            We review the biogeography of microorganisms in light of the biogeography of macroorganisms. A large body of research supports the idea that free-living microbial taxa exhibit biogeographic patterns. Current evidence confirms that, as proposed by the Baas-Becking hypothesis, 'the environment selects' and is, in part, responsible for spatial variation in microbial diversity. However, recent studies also dispute the idea that 'everything is everywhere'. We also consider how the processes that generate and maintain biogeographic patterns in macroorganisms could operate in the microbial world.
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              Numerical ecology

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                Author and article information

                Journal
                FEMS Microbiol Ecol
                fem
                Fems Microbiology Ecology
                Blackwell Publishing Ltd
                0168-6496
                1574-6941
                November 2007
                25 September 2007
                : 62
                : 2
                : 142-160
                Affiliations
                Microbial habitat group, Max Planck Institute for Marine Microbiology Bremen, Germany
                Author notes
                Correspondence: Alban Ramette, Microbial habitat group, Max Planck Institute for Marine Microbiology, Celsiusstrasse 1, 28359 Bremen, Germany. Tel.: +49 421 2028 863; fax: +49 421 2028 690; e-mail: aramette@ 123456mpi-bremen.de
                Article
                10.1111/j.1574-6941.2007.00375.x
                2121141
                17892477
                10974325-6e95-4184-b844-d68f1853c6be
                © 2007 Max Planck Society Journal compilation © 2007 Federation of European Microbiological Societies
                History
                : 17 January 2007
                : 18 July 2007
                : 20 July 2007
                Categories
                MiniReviews

                Microbiology & Virology
                ordination,multivariate,modeling,statistics,gradient
                Microbiology & Virology
                ordination, multivariate, modeling, statistics, gradient

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