@article{stoll2007bacterial,
  title={Bacterial microbiota associated with ants of the genus Tetraponera},
  author={<NAME> and <NAME> Gross, ROY and <NAME>},
  journal={Biological Journal of the Linnean Society},
  volume={90},
  number={3},
  pages={399--412},
  year={2007},
  publisher={Oxford University Press}
}

@article{berlec2012novel,
  title={Novel techniques and findings in the study of plant microbiota: search for plant probiotics},
  author={<NAME>{\v{s}}},
  journal={Plant science},
  volume={193},
  pages={96--102},
  year={2012},
  publisher={Elsevier}
}

@article{hyde2013living,
  title={The living dead: bacterial community structure of a cadaver at the onset and end of the bloat stage of decomposition},
  author={<NAME> and Haarmann, <NAME> and Lynne, <NAME> and Bucheli, <NAME> and Petrosino, <NAME>},
  journal={PloS one},
  volume={8},
  number={10},
  year={2013},
  publisher={Public Library of Science}
}

@article{lederberg2001ome,
  title={Ome SweetOmics--A genealogical treasury of words},
  author={<NAME> and McCray, <NAME>},
  journal={The Scientist},
  volume={15},
  number={7},
  pages={8--8},
  year={2001},
  publisher={Scientist Inc.}
}

@Article{Lundberg2012,
author={Lundberg, <NAME>.
and Lebeis, <NAME>.
and <NAME>
and Yourstone, Scott
and <NAME>
and <NAME>
and <NAME>
and Engelbrektson, Anna
and Kunin, Victor
and Rio, <NAME>
and Edgar, <NAME>.
and Eickhorst, Thilo
and Ley, <NAME>.
and Hugenholtz, Philip
and Tringe, <NAME>
and Dangl, <NAME>.},
title={Defining the core Arabidopsis thaliana root microbiome},
journal={Nature},
year={2012},
volume={488},
number={7409},
pages={86-90},
abstract={Sequencing of the Arabidopsis thaliana root microbiome shows that its composition is strongly influenced by location, inside or outside the root, and by soil type.},
issn={1476-4687},
doi={10.1038/nature11237},
url={https://doi.org/10.1038/nature11237}
}


 
 @article{van14abstract,
  title={An abstract of a letter from antonie van leeuwenhoek, Sep. 12, 1683. about animals in the scrurf of the teeth},
  author={<NAME>},
  journal={Philos. Trans. R. Soc. Lond},
  volume={14},
  pages={568--574}
}

@article{peterson2009nih,
  title={The NIH human microbiome project},
  author={<NAME> Garges, <NAME> Giovanni, Maria and McInnes, Pamela and Wang, Lu and Schloss, <NAME> and Bonazzi, Vivien and McEwen, <NAME> and Wetterstrand, <NAME> and Deal, Carolyn and others},
  journal={Genome research},
  volume={19},
  number={12},
  pages={2317--2323},
  year={2009},
  publisher={Cold Spring Harbor Lab}
}

@article{NIHGroup01122009,
author = {The NIH HMP Working Group and <NAME>, <NAME>, Maria and McInnes, Pamela and Wang, Lu and Schloss, <NAME>. and Bonazzi, Vivien and McEwen, <NAME>. and Wetterstrand, <NAME>. and Deal, Carolyn and Baker, <NAME>. and <NAME>, Valentina and Howcroft, <NAME> and Karp, <NAME>. and Lunsford, <NAME> and Wellington, <NAME>. and <NAME> and Wright, Michael and <NAME> and <NAME> and Mills, Melody and Salomon, Rachelle and Mullins, Christopher and Akolkar, <NAME>, <NAME>, <NAME>, <NAME>, Michael and Khalsa, <NAME>, Hannah and Pontzer, Carol and Portnoy, <NAME>, <NAME>. and Starke-Reed, <NAME>, <NAME>, <NAME>, <NAME>, Mark}, 
title = {The NIH Human Microbiome Project},
volume = {19}, 
number = {12}, 
pages = {2317-2323}, 
year = {2009}, 
doi = {10.1101/gr.096651.109}, 
abstract ={The Human Microbiome Project (HMP), funded as an initiative of the NIH Roadmap for Biomedical Research (http://nihroadmap.nih.gov), is a multi-component community resource. The goals of the HMP are: (1) to take advantage of new, high-throughput technologies to characterize the human microbiome more fully by studying samples from multiple body sites from each of at least 250 “normal” volunteers; (2) to determine whether there are associations between changes in the microbiome and health/disease by studying several different medical conditions; and (3) to provide both a standardized data resource and new technological approaches to enable such studies to be undertaken broadly in the scientific community. The ethical, legal, and social implications of such research are being systematically studied as well. The ultimate objective of the HMP is to demonstrate that there are opportunities to improve human health through monitoring or manipulation of the human microbiome. The history and implementation of this new program are described here.}, 
URL = {http://genome.cshlp.org/content/19/12/2317.abstract}, 
eprint = {http://genome.cshlp.org/content/19/12/2317.full.pdf+html}, 
journal = {Genome Research} 
}

@Article{Turnbaugh2007,
author={Turnbaugh, <NAME>.
and Ley, <NAME>.
and Hamady, Micah
and Fraser-Liggett, <NAME>.
and Knight, Rob
and Gordon, <NAME>.},
title={The Human Microbiome Project},
journal={Nature},
year={2007},
volume={449},
number={7164},
pages={804-810},
abstract={A strategy to understand the microbial components of the human genetic and metabolic landscape and how they contribute to normal physiology and predisposition to disease.},
issn={1476-4687},
doi={10.1038/nature06244},
url={https://doi.org/10.1038/nature06244}
}



@Article{LloydPrice2017,
author={Lloyd-Price, Jason
and <NAME>
and Rahnavard, Gholamali
and Crabtree, Jonathan
and Orvis, Joshua
and Hall, <NAME>
and Brady, Arthur
and Creasy, <NAME>.
and McCracken, Carrie
and Giglio, <NAME>.
and McDonald, Daniel
and Franzosa, <NAME>.
and Knight, Rob
and White, Owen
and Huttenhower, Curtis},
title={Strains, functions and dynamics in the expanded Human Microbiome Project},
journal={Nature},
year={2017},
volume={550},
number={7674},
pages={61-66},
abstract={The characterization of baseline microbial and functional diversity in the human microbiome has enabled studies of microbiome-related disease, diversity, biogeography, and molecular function. The National Institutes of Health Human Microbiome Project has provided one of the broadest such characterizations so far. Here we introduce a second wave of data from the study, comprising 1,631 new metagenomes (2,355 total) targeting diverse body sites with multiple time points in 265 individuals. We applied updated profiling and assembly methods to provide new characterizations of microbiome personalization. Strain identification revealed subspecies clades specific to body sites; it also quantified species with phylogenetic diversity under-represented in isolate genomes. Body-wide functional profiling classified pathways into universal, human-enriched, and body site-enriched subsets. Finally, temporal analysis decomposed microbial variation into rapidly variable, moderately variable, and stable subsets. This study furthers our knowledge of baseline human microbial diversity and enables an understanding of personalized microbiome function and dynamics.},
issn={1476-4687},
doi={10.1038/nature23889},
url={https://doi.org/10.1038/nature23889}
}


 
 @Manual{graves2019mult,
    title = {multcompView: Visualizations of Paired Comparisons},
    author = {<NAME> and <NAME> and <NAME> with help from <NAME>},
    year = {2019},
    note = {R package version 0.1-8},
    url = {https://CRAN.R-project.org/package=multcompView},
  }

@Manual{clarke2017ggbee,
    title = {ggbeeswarm: Categorical Scatter (Violin Point) Plots},
    author = {<NAME> and <NAME>},
    year = {2017},
    note = {R package version 0.6.0},
    url = {https://CRAN.R-project.org/package=ggbeeswarm},
  }

@article{herve2010singular,
author = {<NAME> Williams, <NAME>.},
title = {Principal component analysis},
journal = {WIREs Computational Statistics },
volume = {2},
number = {4},
pages = {433-459},
keywords = {singular and eigen value decomposition, bilinear decomposition, factor scores and loadings, RESS PRESS, multiple factor analysis},
doi = {10.1002/wics.101},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/wics.101},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/wics.101},
abstract = {Abstract Principal component analysis (PCA) is a multivariate technique that analyzes a data table in which observations are described by several inter-correlated quantitative dependent variables. Its goal is to extract the important information from the table, to represent it as a set of new orthogonal variables called principal components, and to display the pattern of similarity of the observations and of the variables as points in maps. The quality of the PCA model can be evaluated using cross-validation techniques such as the bootstrap and the jackknife. PCA can be generalized as correspondence analysis (CA) in order to handle qualitative variables and as multiple factor analysis (MFA) in order to handle heterogeneous sets of variables. Mathematically, PCA depends upon the eigen-decomposition of positive semi-definite matrices and upon the singular value decomposition (SVD) of rectangular matrices. Copyright © 2010 <NAME> \& Sons, Inc. This article is categorized under: Statistical and Graphical Methods of Data Analysis > Multivariate Analysis Statistical and Graphical Methods of Data Analysis > Dimension Reduction},
year = {2010}
}


@article{huerta2017fast,
    author = {Huerta-Cepas, <NAME>, <NAME>, <NAME> and Szklarczyk, <NAME>, <NAME> and <NAME>, Christian and <NAME>},
    title = "{Fast Genome-Wide Functional Annotation through Orthology Assignment by eggNOG-Mapper}",
    journal = {Molecular Biology and Evolution},
    volume = {34},
    number = {8},
    pages = {2115-2122},
    year = {2017},
    month = {04},
    abstract = "{Orthology assignment is ideally suited for functional inference. However, because predicting orthology is computationally intensive at large scale, and most pipelines are relatively inaccessible (e.g., new assignments only available through database updates), less precise homology-based functional transfer is still the default for (meta-)genome annotation. We, therefore, developed eggNOG-mapper, a tool for functional annotation of large sets of sequences based on fast orthology assignments using precomputed clusters and phylogenies from the eggNOG database. To validate our method, we benchmarked Gene Ontology (GO) predictions against two widely used homology-based approaches: BLAST and InterProScan. Orthology filters applied to BLAST results reduced the rate of false positive assignments by 11\\%, and increased the ratio of experimentally validated terms recovered over all terms assigned per protein by 15\\%. Compared with InterProScan, eggNOG-mapper achieved similar proteome coverage and precision while predicting, on average, 41 more terms per protein and increasing the rate of experimentally validated terms recovered over total term assignments per protein by 35\\%. EggNOG-mapper predictions scored within the top-5 methods in the three GO categories using the CAFA2 NK-partial benchmark. Finally, we evaluated eggNOG-mapper for functional annotation of metagenomics data, yielding better performance than interProScan. eggNOG-mapper runs ∼15× faster than BLAST and at least 2.5× faster than InterProScan. The tool is available standalone and as an online service at http://eggnog-mapper.embl.de.}",
    issn = {0737-4038},
    doi = {10.1093/molbev/msx148},
    eprint = {https://academic.oup.com/mbe/article-pdf/34/8/2115/24367816/msx148.pdf},
}

@article{aitchison1983principal,
    author = {<NAME>.},
    title = "{Principal component analysis of compositional data}",
    journal = {Biometrika},
    volume = {70},
    number = {1},
    pages = {57-65},
    year = {1983},
    month = {04},
    abstract = "{Compositional data, consisting of vectors of proportions, have proved difficult to handle statistically because of the awkward constraint that the components of each vector must sum to unity. Moreover such data sets frequently display marked curvature so that linear techniques such as standard principal component analysis are likely to prove inadequate. From a critical reexamination of previous approaches we evolve, through adaptation of recently introduced transformation techniques for compositional data analysis, a log linear contrast form of principal component analysis and illustrate its advantages in applications.}",
    issn = {0006-3444},
    doi = {10.1093/biomet/70.1.57},
    url = {https://doi.org/10.1093/biomet/70.1.57},
    eprint = {https://academic.oup.com/biomet/article-pdf/70/1/57/663053/70-1-57.pdf},
}

@Article{weiss2017norm,
author={<NAME>
and Xu, <NAME>
and <NAME>
and <NAME>
and <NAME>
and <NAME>
and Lozupone, Catherine
and Zaneveld, <NAME>.
and V{\'a}zquez-Baeza, Yoshiki
and Birmingham, Amanda
and Hyde, <NAME>.
and Knight, Rob},
title={Normalization and microbial differential abundance strategies depend upon data characteristics},
journal={Microbiome},
year={2017},
volume={5},
number={1},
pages={27},
abstract={Data from 16S ribosomal RNA (rRNA) amplicon sequencing present challenges to ecological and statistical interpretation. In particular, library sizes often vary over several ranges of magnitude, and the data contains many zeros. Although we are typically interested in comparing relative abundance of taxa in the ecosystem of two or more groups, we can only measure the taxon relative abundance in specimens obtained from the ecosystems. Because the comparison of taxon relative abundance in the specimen is not equivalent to the comparison of taxon relative abundance in the ecosystems, this presents a special challenge. Second, because the relative abundance of taxa in the specimen (as well as in the ecosystem) sum to 1, these are compositional data. Because the compositional data are constrained by the simplex (sum to 1) and are not unconstrained in the Euclidean space, many standard methods of analysis are not applicable. Here, we evaluate how these challenges impact the performance of existing normalization methods and differential abundance analyses.},
issn={2049-2618},
doi={10.1186/s40168-017-0237-y},
url={https://doi.org/10.1186/s40168-017-0237-y}
}

@book{scholkopf2001learn,
author = {<NAME>, <NAME>.},
title = {Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond},
year = {2001},
isbn = {0262194759},
publisher = {MIT Press},
address = {Cambridge, MA, USA}
}

@Article{karatzoglou2004kern,
    title = {kernlab -- An {S4} Package for Kernel Methods in {R}},
    author = {<NAME> and <NAME> and <NAME> and <NAME>},
    journal = {Journal of Statistical Software},
    year = {2004},
    volume = {11},
    number = {9},
    pages = {1--20},
    url = {http://www.jstatsoft.org/v11/i09/},
  }
  
@Manual{greenwell2019gbm,
    title = {gbm: Generalized Boosted Regression Models},
    author = {<NAME> and <NAME> and <NAME> and GBM Developers},
    year = {2019},
    note = {R package version 2.1.5},
    url = {https://CRAN.R-project.org/package=gbm},
  }

@article{lovell2015prop,
    author = {<NAME> AND Pawlowsky-Glahn, Vera AND Egozcue, <NAME> AND Marguerat, Samuel AND <NAME>},
    journal = {PLOS Computational Biology},
    publisher = {Public Library of Science},
    title = {Proportionality: A Valid Alternative to Correlation for Relative Data},
    year = {2015},
    month = {03},
    volume = {11},
    url = {https://doi.org/10.1371/journal.pcbi.1004075},
    pages = {1-12},
    abstract = {Author Summary Relative abundance data is common in the life sciences, but appreciation that it needs special analysis and interpretation is scarce. Correlation is popular as a statistical measure of pairwise association but should not be used on data that carry only relative information. Using timecourse yeast gene expression data, we show how correlation of relative abundances can lead to conclusions opposite to those drawn from absolute abundances, and that its value changes when different components are included in the analysis. Once all absolute information has been removed, only a subset of those associations will reliably endure in the remaining relative data, specifically, associations where pairs of values behave proportionally across observations. We propose a new statistic ϕ to describe the strength of proportionality between two variables and demonstrate how it can be straightforwardly used instead of correlation as the basis of familiar analyses and visualization methods.},
    number = {3},
    doi = {10.1371/journal.pcbi.1004075}
}

@article {John864,
	author = {<NAME>, <NAME>. and Harms, <NAME>. and Yavitt, <NAME>. and Stallard, <NAME>. and Mirabello, <NAME>, <NAME>. and <NAME> and <NAME>, <NAME>, <NAME>.},
	title = {Soil nutrients influence spatial distributions of tropical tree species},
	volume = {104},
	number = {3},
	pages = {864--869},
	year = {2007},
	doi = {10.1073/pnas.0604666104},
	publisher = {National Academy of Sciences},
	abstract = {The importance of niche vs. neutral assembly mechanisms in structuring tropical tree communities remains an important unsettled question in community ecology [Bell G (2005) Ecology 86:1757{\textendash}1770]. There is ample evidence that species distributions are determined by soils and habitat factors at landscape (\&lt;104 km2) and regional scales. At local scales (\&lt;1 km2), however, habitat factors and species distributions show comparable spatial aggregation, making it difficult to disentangle the importance of niche and dispersal processes. In this article, we test soil resource-based niche assembly at a local scale, using species and soil nutrient distributions obtained at high spatial resolution in three diverse neotropical forest plots in Colombia (La Planada), Ecuador (Yasuni), and Panama (Barro Colorado Island). Using spatial distribution maps of \&gt;0.5 million individual trees of 1,400 species and 10 essential plant nutrients, we used Monte Carlo simulations of species distributions to test plant{\textendash}soil associations against null expectations based on dispersal assembly. We found that the spatial distributions of 36{\textendash}51\% of tree species at these sites show strong associations to soil nutrient distributions. Neutral dispersal assembly cannot account for these plant{\textendash}soil associations or the observed niche breadths of these species. These results indicate that belowground resource availability plays an important role in the assembly of tropical tree communities at local scales and provide the basis for future investigations on the mechanisms of resource competition among tropical tree species.BCI,Barro Colorado Island;PCM,Poisson cluster model;NMDS,nonmetric multidimensional scaling;Nmin,nitrogen mineralization;PC,principal component},
	issn = {0027-8424},
	URL = {https://www.pnas.org/content/104/3/864},
	eprint = {https://www.pnas.org/content/104/3/864.full.pdf},
	journal = {Proceedings of the National Academy of Sciences}
}


@Article{ijms18081654,
AUTHOR = {<NAME> Mengoni, Alessio and Fiscarelli, Ersilia and Segata, Nicola and Taccetti, Giovanni and Dolce, Daniela and Paganin, Patrizia and Morelli, Patrizia and <NAME> and <NAME>, Alessandra and Lucidi, Vincenzina and Bevivino, Annamaria},
TITLE = {A Different Microbiome Gene Repertoire in the Airways of Cystic Fibrosis Patients with Severe Lung Disease},
JOURNAL = {International Journal of Molecular Sciences},
VOLUME = {18},
YEAR = {2017},
NUMBER = {8},
ARTICLE-NUMBER = {1654},
URL = {https://www.mdpi.com/1422-0067/18/8/1654},
PubMedID = {28758937},
ISSN = {1422-0067},
ABSTRACT = {In recent years, next-generation sequencing (NGS) was employed to decipher the structure and composition of the microbiota of the airways in cystic fibrosis (CF) patients. However, little is still known about the overall gene functions harbored by the resident microbial populations and which specific genes are associated with various stages of CF lung disease. In the present study, we aimed to identify the microbial gene repertoire of CF microbiota in twelve patients with severe and normal/mild lung disease by performing sputum shotgun metagenome sequencing. The abundance of metabolic pathways encoded by microbes inhabiting CF airways was reconstructed from the metagenome. We identified a set of metabolic pathways differently distributed in patients with different pulmonary function; namely, pathways related to bacterial chemotaxis and flagellar assembly, as well as genes encoding efflux-mediated antibiotic resistance mechanisms and virulence-related genes. The results indicated that the microbiome of CF patients with low pulmonary function is enriched in virulence-related genes and in genes encoding efflux-mediated antibiotic resistance mechanisms. Overall, the microbiome of severely affected adults with CF seems to encode different mechanisms for the facilitation of microbial colonization and persistence in the lung, consistent with the characteristics of multidrug-resistant microbial communities that are commonly observed in patients with severe lung disease.},
DOI = {10.3390/ijms18081654}
}

@Article{robin2011proc,
author={<NAME>
and <NAME>
and Hainard, Alexandre
and Tiberti, Natalia
and Lisacek, Fr{\'e}d{\'e}rique
and <NAME>
and M{\"u}ller, Markus},
title={pROC: an open-source package for R and S+ to analyze and compare ROC curves},
journal={BMC Bioinformatics},
year={2011},
volume={12},
number={1},
pages={77},
abstract={Receiver operating characteristic (ROC) curves are useful tools to evaluate classifiers in biomedical and bioinformatics applications. However, conclusions are often reached through inconsistent use or insufficient statistical analysis. To support researchers in their ROC curves analysis we developed pROC, a package for R and S+ that contains a set of tools displaying, analyzing, smoothing and comparing ROC curves in a user-friendly, object-oriented and flexible interface.},
issn={1471-2105},
doi={10.1186/1471-2105-12-77},
url={https://doi.org/10.1186/1471-2105-12-77}
}

@Book{wickham2016ggplot2,
    author = {<NAME>},
    title = {ggplot2: Elegant Graphics for Data Analysis},
    publisher = {Springer-Verlag New York},
    year = {2016},
    isbn = {978-3-319-24277-4},
    url = {https://ggplot2.tidyverse.org},
  }
  
  @Inbook{wilkinson2012grammar,
author="<NAME>",
editor="<NAME>.
and H{\"a}rdle, <NAME>
and <NAME>",
title="The Grammar of Graphics",
bookTitle="Handbook of Computational Statistics: Concepts and Methods",
year="2012",
publisher="Springer Berlin Heidelberg",
address="Berlin, Heidelberg",
pages="375--414",
abstract="The Grammar of Graphics, or GOG, denotes a system with seven orthogonal components. By orthogonal, we mean there are seven graphical component sets whose elements are aspects of the general system and that every combination of aspects in the product of all these sets is meaningful. This sense of the word orthogonality, a term used by computer designers to describe a combinatoric system of components or building blocks, is in some sense similar to the orthogonal factorial analysis of variance (ANOVA), where factors have levels and all possible combinations of levels exist in the ANOVA design. If we interpret each combination of features in a GOG system as a point in a network, then the world described by GOG is represented in a seven-dimensional rectangular lattice.",
isbn="978-3-642-21551-3",
doi="10.1007/978-3-642-21551-3_13",
url="https://doi.org/10.1007/978-3-642-21551-3_13"
}

@article {bacci2020tax,
	author = {<NAME> <NAME>, <NAME>, <NAME> <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, Annamaria},
	title = {Taxonomic variability over functional stability in the microbiome of Cystic Fibrosis patients chronically infected by Pseudomonas aeruginosa},
	elocation-id = {609057},
	year = {2020},
	doi = {10.1101/609057},
	publisher = {Cold Spring Harbor Laboratory},
	abstract = {Although the cystic fibrosis (CF) lung microbiome has been characterized in several studies, little is still known about the functions harboured by those bacteria, and how they change with disease status and antibiotic treatment. The aim of this study was to investigate the taxonomic and functional temporal dynamics of airways microbiome in a cohort of CF patients. Multiple sputum samples were collected over 15 months from 22 patients with chronic P. aeruginosa infection, for a total of 79 samples. DNA extracted from samples was subjected to shotgun metagenomic sequencing allowing either strain-level taxonomic profiling and assessment of the functional metagenomic repertoire. High inter-patient taxonomic heterogeneity was found with short-term compositional changes during exacerbations and following antibiotic treatment. Each patient exhibited distinct sputum microbial communities at the taxonomic level, and strain-specific colonization of traditional CF pathogens, including P. aeruginosa, and emerging pathogens. Sputum microbiome was found to be extraordinarily resilient following antibiotic treatment, with rapid recovery of taxa and metagenome-associated gene functions. In particular, a large core set of genes, including antibiotic resistance genes, were shared across patients despite observed differences in clinical status or antibiotic treatment, and constantly detected in the lung microbiome of all subjects independently from known antibiotic exposure, suggesting an overall microbiome-associated functions stability despite taxonomic fluctuations of the communities.IMPORTANCE While the dynamics of CF sputum microbial composition were highly patient-specific, the overall sputum metagenome composition was stable, showing a high resilience along time and antibiotic exposure. The high degree of redundancy in the CF lung microbiome could testifies ecological aspects connected to the disease that were never considered so far, as the large core-set of genes shared between patients despite observed differences in clinical status or antibiotic treatment. Investigations on the actual functionality (e.g. by metatranscriptomics) of the identified core-set of genes could provide clues on genetic function of the microbiome to be targeted in future therapeutic treatments.},
	URL = {https://www.biorxiv.org/content/early/2020/01/27/609057},
	eprint = {https://www.biorxiv.org/content/early/2020/01/27/609057.full.pdf},
	journal = {bioRxiv}
}

@article{vandenboogaart2008comp,
title = "“compositions”: A unified R package to analyze compositional data",
journal = "Computers & Geosciences",
volume = "34",
number = "4",
pages = "320 - 338",
year = "2008",
issn = "0098-3004",
doi = "10.1016/j.cageo.2006.11.017",
author = "<NAME> and <NAME>",
keywords = "Aitchison geometry, Compositional data, Euclidean space, Multivariate data analysis, Software",
abstract = "This contribution presents a new R package, called “compositions”. It provides tools to analyze amount or compositional data sets in four different geometries, each one associated with an R class: rplus (for amounts, or open compositions, in a real, classical geometry), aplus (for amounts in a logarithmic geometry), rcomp (for closed compositions in a real geometry) and acomp (for closed compositions in a logistic geometry, following a log-ratio approach). The package allows to compare results obtained with these four approaches, since an analogous analysis can be performed according to each geometry, with minimal and straightforward modifications of the instructions. Beside these grounding classes, the package also includes: the most-basic features such as data transformations (e.g.logarithm, or additive logistic transform), basic statistics (both the classical ones, and those developed in the log-ratio framework of compositional analysis), high-level graphics (like ternary diagram matrix and scatter-plots) and high-level analysis (e.g.principal components or cluster analysis). Results of these functions and analysis are also provided in a consistent way among the four geometries, to ease their comparison."
}

@Manual{rcore2019,
    title = {R: A Language and Environment for Statistical Computing},
    author = {{<NAME>}},
    organization = {R Foundation for Statistical Computing},
    address = {Vienna, Austria},
    year = {2019},
    url = {https://www.R-project.org/},
  }


@article{bacci2019taxonomic,
  title={Taxonomic and functional dynamics of lung microbiome in cystic fibrosis patients chronically infected with Pseudomonas aeruginosa},
  author={<NAME> and <NAME> and <NAME> and <NAME> and <NAME> and <NAME>, Francesca and <NAME> and <NAME> and <NAME> and <NAME> and others},
  journal={bioRxiv},
  pages={609057},
  year={2019},
  publisher={Cold Spring Harbor Laboratory}
}

@article{callahan2016dada2,
  title={DADA2: high-resolution sample inference from Illumina amplicon data},
  author={Callahan, <NAME> and McMurdie, <NAME> and Rosen, <NAME> and Han, <NAME> and Johnson, <NAME> and <NAME>},
  journal={Nature methods},
  volume={13},
  number={7},
  pages={581},
  year={2016},
  publisher={Nature Publishing Group}
}

@article{martin2011cutadapt,
  title={Cutadapt removes adapter sequences from high-throughput sequencing reads},
  author={<NAME>},
  journal={EMBnet. journal},
  volume={17},
  number={1},
  pages={10--12},
  year={2011}
}

@article{wright2016using,
  title={Using DECIPHER v2. 0 to analyze big biological sequence data in R.},
  author={<NAME>},
  journal={R Journal},
  volume={8},
  number={1},
  year={2016}
}

@article{quast2012silva,
  title={The SILVA ribosomal RNA gene database project: improved data processing and web-based tools},
  author={<NAME> Pruesse, Elmar and <NAME> and <NAME> and <NAME> and <NAME> and <NAME>{\"o}rg and Gl{\"o}ckner, <NAME>},
  journal={Nucleic acids research},
  volume={41},
  number={D1},
  pages={D590--D596},
  year={2012},
  publisher={Oxford University Press}
}

@Manual{oksanen2019vegan,
    title = {vegan: Community Ecology Package},
    author = {<NAME> and <NAME> and <NAME> and <NAME> and <NAME> and <NAME> and <NAME> and <NAME> and <NAME> and <NAME> and <NAME> and <NAME> and <NAME>},
    year = {2019},
    note = {R package version 2.5-6},
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