book.bib

@Book{xie2015,
  title = {Dynamic Documents with {R} and knitr},
  author = {Yihui Xie},
  publisher = {Chapman and Hall/CRC},
  address = {Boca Raton, Florida},
  year = {2015},
  edition = {2nd},
  note = {ISBN 978-1498716963},
  url = {http://yihui.name/knitr/},
}

@article{McKnight2019a,
author = {McKnight, Donald T. and Huerlimann, Roger and Bower, Deborah S. and Schwarzkopf, Lin and Alford, Ross A. and Zenger, Kyall R.},
doi = {10.1111/2041-210X.13115},
file = {:Users/christiankrohn/Documents/Mendeley Desktop/McKnight et al/Methods in Ecology and Evolution/McKnight et al. - 2019 - Methods for normalizing microbiome data An ecological perspective.pdf:pdf},
issn = {2041210X},
journal = {Methods in Ecology and Evolution},
keywords = {Bray–Curtis,community comparisons,diversity,evenness,ordination,principal coordinates analysis,simulation},
mendeley-groups = {Methods/Methods{\_}normalisation},
title = {{Methods for normalizing microbiome data: An ecological perspective}},
year = {2019}
}

@article{Cameron2021,
author = {Cameron, Ellen S and Schmidt, Philip J and Tremblay, Benjamin J-M and Emelko, Monica B and M{\"{u}}ller, Kirsten M},
issn = {2045-2322},
journal = {Scientific reports},
mendeley-groups = {Methods/Methods{\_}normalisation},
number = {1},
pages = {1--13},
publisher = {Nature Publishing Group},
title = {{Enhancing diversity analysis by repeatedly rarefying next generation sequencing data describing microbial communities}},
volume = {11},
year = {2021}
}

@article{Gloor2017,
author = {Gloor, Gregory B. and Macklaim, Jean M. and Pawlowsky-Glahn, Vera and Egozcue, Juan J.},
doi = {10.3389/fmicb.2017.02224},
issn = {1664302X},
journal = {Frontiers in Microbiology},
keywords = {Bayesian estimation,Compositional data,Correlation,Count normalization,High-throughput sequencing,Microbiota,Relative abundance},
mendeley-groups = {Methods/Methods{\_}normalisation},
number = {NOV},
title = {{Microbiome datasets are compositional: And this is not optional}},
volume = {8},
year = {2017}
}

@article{McMurdie2014,
author = {McMurdie, Paul J and Holmes, Susan},
file = {:Users/christiankrohn/Documents/Mendeley Desktop/McMurdie, Holmes/PLoS computational biology/McMurdie, Holmes - 2014 - Waste not, want not why rarefying microbiome data is inadmissible.PDF:PDF},
isbn = {1553-7358},
journal = {PLoS computational biology},
mendeley-groups = {Methods/Methods{\_}normalisation},
number = {4},
pages = {e1003531},
title = {{Waste not, want not: why rarefying microbiome data is inadmissible}},
volume = {10},
year = {2014}
}

@misc{Hugerth2017a,
author = {Hugerth, Luisa W. and Andersson, Anders F.},
booktitle = {Frontiers in Microbiology},
doi = {10.3389/fmicb.2017.01561},
file = {:Users/christiankrohn/Documents/Mendeley Desktop/Hugerth, Andersson/Frontiers in Microbiology/Hugerth, Andersson - 2017 - Analysing microbial community composition through amplicon sequencing From sampling to hypothesis testing.pdf:pdf},
issn = {1664302X},
keywords = {16S rRNA,Amplicon sequencing,Bioinformatics,Biostatistics,Microbial ecology,Microbiome,NGS},
mendeley-groups = {Methods/Methods{\_}normalisation},
number = {SEP},
title = {{Analysing microbial community composition through amplicon sequencing: From sampling to hypothesis testing}},
volume = {8},
year = {2017}
}

@article{Bharti2021,
author = {Bharti, Richa and Grimm, Dominik G},
file = {:Users/christiankrohn/Documents/Mendeley Desktop/Bharti, Grimm/Briefings in bioinformatics/Bharti, Grimm - 2021 - Current challenges and best-practice protocols for microbiome analysis.pdf:pdf},
issn = {1477-4054},
journal = {Briefings in bioinformatics},
mendeley-groups = {Methods/Methods{\_}AmpliconSequencing},
number = {1},
pages = {178--193},
publisher = {Oxford University Press},
title = {{Current challenges and best-practice protocols for microbiome analysis}},
volume = {22},
year = {2021}
}

@article{Washburne2017,
   abstract = {Marker gene sequencing of microbial communities has generated big datasets of microbial relative abundances varying across environmental conditions, sample sites and treatments. These data often come with putative phylogenies, providing unique opportunities to investigate how shared evolutionary history affects microbial abundance patterns. Here, we present a method to identify the phylogenetic factors driving patterns in microbial community composition. We use the method, “phylofactorization,” to re-analyze datasets from the human body and soil microbial communities, demonstrating how phylofactorization is a dimensionality-reducing tool, an ordination-visualization tool, and an inferential tool for identifying edges in the phylogeny along which putative functional ecological traits may have arisen.},
   author = {Alex D. Washburne and Justin D. Silverman and Jonathan W. Leff and Dominic J. Bennett and John L. Darcy and Sayan Mukherjee and Noah Fierer and Lawrence A. David},
   doi = {10.7717/peerj.2969},
   issn = {21678359},
   journal = {PeerJ},
   keywords = {Community phylogenetics,Compositional data,Factor analysis,Microbial biogeography,Microbiome,Phylofactorization,Sequence-count data},
   pages = {2969},
   title = {Phylogenetic factorization of compositional data yields lineage-level associations in microbiome datasets},
   volume = {5},
   year = {2017},
}

@article{Washburne2019a,
   abstract = {The problem of pattern and scale is a central challenge in ecology. In community ecology, an important scale is that at which we aggregate species to define our units of study, such as aggregation of “nitrogen fixing trees” to understand patterns in carbon sequestration. With the emergence of massive community ecological data sets, there is a need to objectively identify the scales for aggregating species to capture well-defined patterns in community ecological data. The phylogeny is a scaffold for identifying scales of species-aggregation associated with macroscopic patterns. Phylofactorization was developed to identify phylogenetic scales underlying patterns in relative abundance data, but many ecological data, such as presence-absences and counts, are not relative abundances yet may still have phylogenetic scales capturing patterns of interest. Here, we broaden phylofactorization to a graph-partitioning algorithm identifying phylogenetic scales in community ecological data. As a graph-partitioning algorithm, phylofactorization connects many tools from data analysis to phylogenetically informed analyses of community ecological data. Two-sample tests identify five phylogenetic factors of mammalian body mass which arose during the K-Pg extinction event, consistent with other analyses of mammalian body mass evolution. Projection of data onto coordinates connecting the phylogeny and graph-partitioning algorithm yield a phylogenetic principal components analysis which refines our understanding of the major sources of variation in the human gut microbiome. These same coordinates allow generalized additive modeling of microbes in Central Park soils, confirming that a large clade of Acidobacteria thrive in neutral soils. The graph-partitioning algorithm extends to generalized linear and additive modeling of exponential family random variables by phylogenetically constrained reduced-rank regression or stepwise factor contrasts. All of these tools can be implemented with the R package phylofactor.},
   author = {Alex D. Washburne and Justin D. Silverman and James T. Morton and Daniel J. Becker and Daniel Crowley and Sayan Mukherjee and Lawrence A. David and Raina K. Plowright},
   doi = {10.1002/ecm.1353},
   issn = {15577015},
   issue = {2},
   journal = {Ecological Monographs},
   keywords = {community ecology,dimensionality reduction,graph partitioning,microbiome,phylofactorization,phylogeny},
   pages = {e01353},
   title = {Phylofactorization: a graph partitioning algorithm to identify phylogenetic scales of ecological data},
   volume = {89},
   year = {2019},
}

@article{Yu2017,
author = {Yu, Guangchuang and Smith, David K and Zhu, Huachen and Guan, Yi and Lam, Tommy Tsan‐Yuk},
issn = {2041-210X},
journal = {Methods in Ecology and Evolution},
number = {1},
pages = {28--36},
publisher = {Wiley Online Library},
title = {{ggtree: an R package for visualization and annotation of phylogenetic trees with their covariates and other associated data}},
volume = {8},
year = {2017}
}

@article{Wickham2016a,
author = {Wickham, Hadley and Chang, Winston and Wickham, Maintainer Hadley},
journal = {Create elegant data visualisations using the grammar of graphics. Version},
number = {1},
pages = {1--189},
title = {{Package ‘ggplot2'}},
volume = {2},
year = {2016}
}