
@article{huang2021,
  title =	 {{\it TreeSummarizedExperiment}: a S4 class for data with
                  hierarchical structure [version 2]},
  author =	 {Ruizhu Huang and Charlotte Soneson and Felix
                  G.M. Ernst and Kevin C. Rue-Albrecht and Guangchuang
                  Yu and Stephanie C. Hicks and Mark D. Robinson},
  year =	 2021,
  journal =	 {F1000Research},
  volume =	 9,
  pages =	 1246,
  doi =		 {10.12688/f1000research.26669.2}
}




@article{lahti2020,
  title =	 {Upgrading the R/Bioconductor ecosystem for
                  microbiome research [version 1]},
  author =	 {Lahti, L and Ernst, FGM and Shetty, SA and Borman, T
                  and Huang, T and Braccia, DJ and Bravo, HC},
  year =	 2020,
  journal =	 {F1000Research},
  volume =	 9,
  pages =	 1464,
  note =	 {Presentation at the European Bioconductor Meeting},
  doi =		 {10.7490/f1000research.1118447.1},
  URL =		 {microbiome.github.io}
}


@article{silverman2017,
    title = {A phylogenetic transform enhances analysis of compositional microbiota data},
    author = {Justin D Silverman and Alex D Washburne and Sayan Mukherjee and Lawrence A David},
    year = {2017},
    journal = {eLife},
    volume = {6},
    doi = {10.7554/eLife.21887},
    url = {https://elifesciences.org/content/6/e21887},
  }

@article{mcmurdie2013,
	Abstract = {BACKGROUND: the analysis of microbial communities through dna sequencing brings many challenges: the integration of different types of data with methods from ecology, genetics, phylogenetics, multivariate statistics, visualization and testing. With the increased breadth of experimental designs now being pursued, project-specific statistical analyses are often needed, and these analyses are often difficult (or impossible) for peer researchers to independently reproduce. The vast majority of the requisite tools for performing these analyses reproducibly are already implemented in R and its extensions (packages), but with limited support for high throughput microbiome census data. RESULTS: Here we describe a software project, phyloseq, dedicated to the object-oriented representation and analysis of microbiome census data in R. It supports importing data from a variety of common formats, as well as many analysis techniques. These include calibration, filtering, subsetting, agglomeration, multi-table comparisons, diversity analysis, parallelized Fast UniFrac, ordination methods, and production of publication-quality graphics; all in a manner that is easy to document, share, and modify. We show how to apply functions from other R packages to phyloseq-represented data, illustrating the availability of a large number of open source analysis techniques. We discuss the use of phyloseq with tools for reproducible research, a practice common in other fields but still rare in the analysis of highly parallel microbiome census data. We have made available all of the materials necessary to completely reproduce the analysis and figures included in this article, an example of best practices for reproducible research. CONCLUSIONS: The phyloseq project for R is a new open-source software package, freely available on the web from both GitHub and Bioconductor.},
	Author = {McMurdie, P. J. and Holmes, S.},
	Doi = {10.1371/journal.pone.0061217},
	Issn = {1932-6203 (Electronic) 1932-6203 (Linking)},
	Journal = {PLoS One},
	Keywords = {Data Interpretation, Statistical Humans *Metagenome Multivariate Analysis Phylogeny Principal Component Analysis Sequence Analysis, DNA *Software},
	Number = {4},
	Pages = {e61217},
	Title = {phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data},
	Type = {Journal Article},
	Url = {http://www.ncbi.nlm.nih.gov/pubmed/23630581},
	Volume = {8},
	Year = {2013},
	Bdsk-Url-1 = {http://www.ncbi.nlm.nih.gov/pubmed/23630581},
	Bdsk-Url-2 = {http://dx.doi.org/10.1371/journal.pone.0061217}}


@article{caporaso2011,
	Author = {Caporaso, J Gregory and Lauber, Christian L and Walters, William A and Berg-Lyons, Donna and Lozupone, Catherine A and Turnbaugh, Peter J and Fierer, Noah and Knight, Rob},
	Issn = {0027-8424},
	Journal = {Proceedings of the National Academy of Sciences},
	Pages = {4516-4522},
	Title = {Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample},
	Type = {Journal Article},
	Volume = {108},
	Year = {2011}}


@article{egozcue2003,
	Author = {Egozcue, J. J. and Pawlowsky-Glahn, V. and Mateu-Figueras, G. and Barcel{\'o}-Vidal, C.},
	Doi = {10.1023/a:1023818214614},
	Issn = {08828121},
	Journal = {Mathematical Geology},
	Keywords = {Aitchison distance Aitchison geometry Geodesic Orthogonal subcompositions Ternary diagram},
	Number = {3},
	Pages = {279-300},
	Title = {Isometric Logratio Transformations for Compositional Data Analysis},
	Type = {Journal Article},
	Volume = {35},
	Year = {2003}}

@book{egozcue2015_book,
	Author = {Pawlowsky-Glahn, Vera and Egozcue, Juan J. and Tolosana-Delgado, Raimon},
	Isbn = {9781119003144 1119003148 9781119003113 1119003113},
	Keywords = {Multivariate analysis. Mathematical statistics. Geometric analysis. Electronic books.},
	Publisher = {John Wiley \& Sons, Ltd},
	Series = {Statistics in practice},
	Title = {Modeling and analysis of compositional data},
	Type = {Book},
	Year = {2015}}

@article{egozcue2016,
	Author = {Egozcue, J. J. and Pawlowsky-Glahn, V.},
	Journal = {Austrian Journal of Statistics},
	Title = {Changing the Reference Measure in the Simlex and its Weightings Effects},
	Type = {Journal Article},
	Volume={45},
	Number={4},
	Pages={25-44},
	Year = {2016}}

@article{egozcue_2005_ilr,
	Author = {Egozcue, J. J. and Pawlowsky-Glahn, V.},
	Issn = {0882-8121},
	Journal = {Mathematical Geology},
	Number = {7},
	Pages = {795-828},
	Title = {Groups of parts and their balances in compositional data analysis},
	Type = {Journal Article},
	Volume = {37},
	Year = {2005}}

@article{friedman2012,
	Author = {Friedman, J. and Alm, E. J.},
	Doi = {10.1371/journal.pcbi.1002687},
	Issn = {1553-7358 (Electronic) 1553-734X (Linking)},
	Journal = {PLoS Comput Biol},
	Number = 9,
	Pages = {e1002687},
	Title = {Inferring correlation networks from genomic survey data},
	Type = {Journal Article},
	Url = {http://www.ncbi.nlm.nih.gov/pubmed/23028285},
	Volume = 8,
	Year = 2012,
	Bdsk-Url-1 = {http://www.ncbi.nlm.nih.gov/pubmed/23028285},
	Bdsk-Url-2 = {http://dx.doi.org/10.1371/journal.pcbi.1002687}
}

@article{gloor2016,
	Abstract = {PURPOSE: The ability to properly analyze and interpret large microbiome data sets has lagged behind our ability to acquire such data sets from environmental or clinical samples. Sequencing instruments impose a structure on these data: the natural sample space of a 16S rRNA gene sequencing data set is a simplex, which is a part of real space that is restricted to nonnegative values with a constant sum. Such data are compositional and should be analyzed using compositionally appropriate tools and approaches. However, most of the tools for 16S rRNA gene sequencing analysis assume these data are unrestricted. METHODS: We show that existing tools for compositional data (CoDa) analysis can be readily adapted to analyze high-throughput sequencing data sets. RESULTS: The Human Microbiome Project tongue versus buccal mucosa data set shows how the CoDa approach can address the major elements of microbiome analysis. Reanalysis of a publicly available autism microbiome data set shows that the CoDa approach in concert with multiple hypothesis test corrections prevent false positive identifications. CONCLUSIONS: The CoDa approach is readily scalable to microbiome-sized analyses. We provide example code and make recommendations to improve the analysis and reporting of microbiome data sets.},
	Author = {Gloor, G. B. and Wu, J. R. and Pawlowsky-Glahn, V. and Egozcue, J. J.},
	Doi = {10.1016/j.annepidem.2016.03.003},
	Issn = {1873-2585 (Electronic) 1047-2797 (Linking)},
	Journal = {Annals Epidemiology},
	Keywords = {16S rRNA gene sequencing Compositional data Microbiome Multivariate analysis},
	Number = {5},
	Pages = {322-9},
	Title = {It's all relative: analyzing microbiome data as compositions},
	Type = {Journal Article},
	Url = {http://www.ncbi.nlm.nih.gov/pubmed/27143475},
	Volume = {26},
	Year = {2016},
	Bdsk-Url-1 = {http://www.ncbi.nlm.nih.gov/pubmed/27143475},
	Bdsk-Url-2 = {http://dx.doi.org/10.1016/j.annepidem.2016.03.003}}

@inbook{lovell2011,
	Author = {Lovell, David and M{\"u}ller, Warren and Taylor, Jen and Zwart, Alec and Helliwell, Chris and Pawlowsky-Glahn, Vera and Buccianti, Antonella},
	Booktitle = {Compositional Data Analysis: Theory and Applications},
	Pages = {193-207},
	Publisher = {John Wiley \& Sons, Ltd.},
	Title = {Proportions, percentages, ppm: do the molecular biosciences treat compositional data right},
	Type = {Book Section},
	Year = {2011}}

@article{yu2016,
  Author = {Yu, Guangchuang and Smith, David K and Zhu, Huachen and Guan, Yi and Lam, Tommy Tsan‐Yuk},
  Title = {ggtree{:} an R package for visualization and annotation of phylogenetic trees with their covariates and other associated data},
  Journal = {Methods in Ecology and Evolution},
  Year = {2016},
  Type = {Journal Article},
  Doi={10.1111/2041-210X.12628}}

@Article{schliep2016,
  title = {phangorn: phylogenetic analysis in R},
  author = {K.P. Schliep},
  journal = {Bioinformatics},
  year = {2011},
  volume = {27},
  number = {4},
  pages = {592--593}}



