Multivariate analyses in microbial ecology

By A. Ramette. FEMS Microbiology Ecology, 2007

1,861 citations recorded by the source corpus.

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.

Subfields: Ecological Modeling, Ecology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/ac6608f76ba438cb23a94215c8e123b288d3fcc0

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Citations in the corpus

Total and active microbial communities and phoD as affected by phosphate depletion and pH in soil (2016, methodology): “Relationships between the microbial and phoD-harboring community structures and soil and plant properties standardized by the z-score method (Ramette 2007) were analyzed using RDA on T-RFLP data for the archaeal, bacterial and fungal community and on 454- sequencing data for the phoD-harboring…”
Effect of land management and Prosopis juliflora (Sw.) DC trees on soil microbial community and enzymatic activities in intensive silvopastoral systems of Colombia (2012, background): “In the statistic outut, the global R takes a value of 0 if there is no separation of the oil microbial community structure due to the factor analyzed, and if total separation takes place (Ramette, 2007).”
Dirichlet Multinomial Mixtures: Generative Models for Microbial Metagenomics (2012, methodology): “. Sub-sampling can be used to reduce all samples to the same size but that inevitably throws away large amounts of meaningful data. The majority of studies have used exploratory statistics to search for natural patterns in the data, unsupervised learning again. A common strategy is to use multivaria”
Characterization of the Gut Microbiome Using 16S or Shotgun Metagenomics (2016, background): “In most cases, both techniques will lead to similar conclusions and it is a matter of debate which method is more appropriate (Ramette, 2007; Zur et al., 2007).”
454‐sequencing reveals stochastic local reassembly and high disturbance tolerance within arbuscular mycorrhizal fungal communities (2012, methodology): “0, suggesting a linear treatment of the data (Ramette 2007).”
Impact of natural oil and higher hydrocarbons on microbial diversity, distribution, and activity in Gulf of Mexico cold-seep sediments (2010, methodology): “Distance-based redundancy analyses (db-RDA) (Legendre and Legendre, 1998; Ramette, 2007; Ramette and Tiedje, 2007) were applied to relate genetic variation to environmental variation.”
MetaboAnalystR 3.0: Toward an Optimized Workflow for Global Metabolomics (2020, background): “The majority of these pathways are biologically meaningful according to previous studies including bile acid [28,29], vitamin E [30], vitamin D3 [31,32], galactose [33], glycerophospholipid [33], fatty acid [29,34], and hyaluronan [35] metabolism pathways.”
Plant–soil feedbacks contribute to an intransitive competitive network that promotes both genetic and species diversity (2011, methodology): “We analyzed microbial community patterns separately for the three microbial taxonomic groups using constrained correspondence analysis (CCA) with the three plant community types as constraints (Ramette 2007).”