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It is largely taken for granted that differential abundance analysis is, by default, the best first step when analyzing genomic data.
Diversity and Evenness: A Unifying Notation and Its Consequences
M. O. Hill · 1973
Earlier work this paper cites.
Logistic disease incidence models and case-control studies
R. L. PRENTICE and R. PYKE · 1979
Earlier work this paper cites.
Log contrast models for experiments with mixtures
J. Aitchison and J. Bacon-Shone · 1984
Earlier work this paper cites.
The Statistical Analysis of Compositional Data
J Aitchison · 1986
Earlier work this paper cites.
Gene Ontology: tool for the unification of biology
Michael Ashburner, Catherine A. Ball, Judith A. Blake, David Botstein, Heather Butler, J. Michael Cherry, Allan P. Davis, Kara Dolinski, Selina S. Dwight, Janan T. Eppig, Midori A. Harris, David P. Hill, Laurie Issel-Tarver, Andrew Kasarskis, Suzanna Lewis, John C. Matese, Joel E. Richardson, Martin Ringwald, Gerald M. Rubin, and Gavin Sherlock · 2000
Earlier work this paper cites.
KEGG: kyoto encyclopedia of genes and genomes
M. Kanehisa and S. Goto · 2000
Earlier work this paper cites.
Biological stoichiometry from genes to ecosystems
J. J. Elser, R. W. Sterner, E. Gorokhova, W. F. Fagan, T. A. Markow, J. B. Cotner, J. F. Harrison, S. E. Hobbie, G. M. Odell, and L. W. Weider · 2000
Earlier work this paper cites.
Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author)
Leo Breiman · 2001
Earlier work this paper cites.
Biological Stoichiometry: An Ecological Perspective on Tumor Dynamics
James J. Elser, John D. Nagy, and Yang Kuang · 2003
Earlier work this paper cites.
Isometric Logratio Transformations for Compositional Data Analysis
J. J. Egozcue, V. Pawlowsky-Glahn, G. Mateu-Figueras, and C. Barceló-Vidal · 2003
Earlier work this paper cites.
Groups of Parts and Their Balances in Compositional Data Analysis
J. J. Egozcue and V. Pawlowsky-Glahn · 2005
Earlier work this paper cites.
Mapping and quantifying mammalian transcriptomes by RNA-Seq
Ali Mortazavi, Brian A. Williams, Kenneth McCue, Lorian Schaeffer, and Barbara Wold · 2008
Earlier work this paper cites.
WGCNA: an R package for weighted correlation network analysis
Peter Langfelder and Steve Horvath · 2008
Earlier work this paper cites.
A sparse PLS for variable selection when integrating omics data
Kim-Anh Lê Cao, Debra Rossouw, Christèle Robert-Granié, and Philippe Besse · 2008
Earlier work this paper cites.
Differential expression analysis for sequence count data
Simon Anders and Wolfgang Huber · 2010
Earlier work this paper cites.
edgeR: a Bioconductor package for differential expression analysis of digital gene expression data
Mark D. Robinson, Davis J. McCarthy, and Gordon K. Smyth · 2010
Earlier work this paper cites.
A scaling normalization method for differential expression analysis of RNA-seq data
Mark D. Robinson and Alicia Oshlack · 2010
Earlier work this paper cites.
The gene balance hypothesis: implications for gene regulation, quantitative traits and evolution
James A. Birchler and Reiner A. Veitia · 2010
Earlier work this paper cites.
Principal balances
Vera Pawlowsky-Glahn, Juan José Egozcue, and Raimon Tolosana Delgado · 2011
Earlier work this paper cites.
Sparse PLS discriminant analysis: biologically relevant feature selection and graphical displays for multiclass problems
Kim-Anh Lê Cao, Simon Boitard, and Philippe Besse · 2011
Earlier work this paper cites.
Quantitative analysis of fission yeast transcriptomes and proteomes in proliferating and quiescent cells
Samuel Marguerat, Alexander Schmidt, Sandra Codlin, Wei Chen, Ruedi Aebersold, and Jürg Bähler · 2012
Earlier work this paper cites.
Gene balance hypothesis: Connecting issues of dosage sensitivity across biological disciplines
James A. Birchler and Reiner A. Veitia · 2012
Earlier work this paper cites.
ANOVA-Like Differential Expression (ALDEx) Analysis for Mixed Population RNA-Seq
Andrew D. Fernandes, Jean M. Macklaim, Thomas G. Linn, Gregor Reid, and Gregory B. Gloor · 2013
Earlier work this paper cites.
Differential abundance analysis for microbial marker-gene surveys
Joseph N. Paulson, O. Colin Stine, Héctor Corrada Bravo, and Mihai Pop · 2013
Earlier work this paper cites.
A comparison of methods for differential expression analysis of RNA-seq data
Charlotte Soneson and Mauro Delorenzi · 2013
Earlier work this paper cites.
A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis
Marie-Agnès Dillies, Andrea Rau, Julie Aubert, Christelle Hennequet-Antier, Marine Jeanmougin, Nicolas Servant, Céline Keime, Guillemette Marot, David Castel, Jordi Estelle, Gregory Guernec, Bernd Jagla, Luc Jouneau, Denis Laloë, Caroline Le Gall, Brigitte Schaëffer, Stéphane Le Crom, Mickaël Guedj, Florence Jaffrézic, and French StatOmique Consortium · 2013
Earlier work this paper cites.
Fundamental Concepts of Compositional Data Analysis
K. Gerald van den Boogaart and Raimon Tolosana-Delgado · 2013
Earlier work this paper cites.
voom: precision weights unlock linear model analysis tools for RNA-seq read counts
Charity W. Law, Yunshun Chen, Wei Shi, and Gordon K. Smyth · 2014
Earlier work this paper cites.
Waste Not, Want Not: Why Rarefying Microbiome Data Is Inadmissible
Paul J. McMurdie and Susan Holmes · 2014
Earlier work this paper cites.
Analysis of composition of microbiomes: a novel method for studying microbial composition
Siddhartha Mandal, Will Van Treuren, Richard A. White, Merete Eggesbø, Rob Knight, and Shyamal D. Peddada · 2015
Earlier work this paper cites.
Single mammalian cells compensate for differences in cellular volume and DNA copy number through independent global transcriptional mechanisms
Olivia Padovan-Merhar, Gautham P. Nair, Andrew G. Biaesch, Andreas Mayer, Steven Scarfone, Shawn W. Foley, Angela R. Wu, L. Stirling Churchman, Abhyudai Singh, and Arjun Raj · 2015
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Large-scale benchmarking reveals false discoveries and count transformation sensitivity in 16S rRNA gene amplicon data analysis methods used in microbiome studies
Jonathan Thorsen, Asker Brejnrod, Martin Mortensen, Morten A. Rasmussen, Jakob Stokholm, Waleed Abu Al-Soud, Søren Sørensen, Hans Bisgaard, and Johannes Waage · 2016
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Representing genetic variation with synthetic DNA standards
Ira W. Deveson, Wendy Y. Chen, Ted Wong, Simon A. Hardwick, Stacey B. Andersen, Lars K. Nielsen, John S. Mattick, and Tim R. Mercer · 2016
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Spliced synthetic genes as internal controls in RNA sequencing experiments
Simon A. Hardwick, Wendy Y. Chen, Ted Wong, Ira W. Deveson, James Blackburn, Stacey B. Andersen, Lars K. Nielsen, John S. Mattick, and Tim R. Mercer · 2016
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A field guide for the compositional analysis of any-omics data
Thomas P. Quinn, Ionas Erb, Greg Gloor, Cedric Notredame, Mark F. Richardson, and Tamsyn M. Crowley · 2019
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Co-existence of Network Architectures Supporting the Human Gut Microbiome
Caitlin V. Hall, Anton Lord, Richard Betzel, Martha Zakrzewski, Lisa A. Simms, Andrew Zalesky, Graham Radford-Smith, and Luca Cocchi · 2019
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A Generic Multivariate Framework for the Integration of Microbiome Longitudinal Studies With Other Data Types
Antoine Bodein, Olivier Chapleur, Arnaud Droit, and Kim-Anh Lê Cao · 2019
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Statistical Analysis of Metagenomics Data
M. Luz Calle · 2019
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The isometric logratio transformation in compositional data analysis: a practical evaluation
Michael Greenacre and Eric Grunsky · 2019
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Ionas Erb and Cedric Notredame · 2016
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It’s all relative: analyzing microbiome data as compositions
Gregory B. Gloor, Jia Rong Wu, Vera Pawlowsky-Glahn, and Juan José Egozcue · 2016
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Dimension reduction techniques for the integrative analysis of multi-omics data
Chen Meng, Oana A. Zeleznik, Gerhard G. Thallinger, Bernhard Kuster, Amin M. Gholami, and Aedín C. Culhane · 2016
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Application of multivariate statistical techniques in microbial ecology
O. Paliy and V. Shankar · 2016
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A broken promise: microbiome differential abundance methods do not control the false discovery rate
Stijn Hawinkel, Federico Mattiello, Luc Bijnens, and Olivier Thas · 2017
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Normalization and microbial differential abundance strategies depend upon data characteristics
Sophie Weiss, Zhenjiang Zech Xu, Shyamal Peddada, Amnon Amir, Kyle Bittinger, Antonio Gonzalez, Catherine Lozupone, Jesse R. Zaneveld, Yoshiki Vázquez-Baeza, Amanda Birmingham, Embriette R. Hyde, and Rob Knight · 2017
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Finding the centre: corrections for asymmetry in high-throughput sequencing datasets
Jia R. Wu, Jean M. Macklaim, Briana L. Genge, and Gregory B. Gloor · 2017
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Microbiome Datasets Are Compositional: And This Is Not Optional
Gregory B. Gloor, Jean M. Macklaim, Vera Pawlowsky-Glahn, and Juan J. Egozcue · 2017
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Multi-Omic Analysis of the Microbiome and Metabolome in Healthy Subjects Reveals Microbiome-Dependent Relationships Between Diet and Metabolites
Zheng-Zheng Tang, Guanhua Chen, Qilin Hong, Shi Huang, Holly M. Smith, Rachana D. Shah, Matthew Scholz, and Jane F. Ferguson · 2019
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Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases
Jason Lloyd-Price, Cesar Arze, Ashwin N. Ananthakrishnan, Melanie Schirmer, Julian Avila-Pacheco, Tiffany W. Poon, Elizabeth Andrews, Nadim J. Ajami, Kevin S. Bonham, Colin J. Brislawn, David Casero, Holly Courtney, Antonio Gonzalez, Thomas G. Graeber, A. Brantley Hall, Kathleen Lake, Carol J. Landers, Himel Mallick, Damian R. Plichta, Mahadev Prasad, Gholamali Rahnavard, Jenny Sauk, Dmitry Shungin, Yoshiki Vázquez-Baeza, Richard A. White, Jonathan Braun, Lee A. Denson, Janet K. Jansson, Rob Knight, Subra Kugathasan, Dermot P. B. McGovern, Joseph F. Petrosino, Thaddeus S. Stappenbeck, Harland S. Winter, Clary B. Clish, Eric A. Franzosa, Hera Vlamakis, Ramnik J. Xavier, and Curtis Huttenhower · 2019
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Metagenomic and metabolomic analyses reveal distinct stage-specific phenotypes of the gut microbiota in colorectal cancer
Shinichi Yachida, Sayaka Mizutani, Hirotsugu Shiroma, Satoshi Shiba, Takeshi Nakajima, Taku Sakamoto, Hikaru Watanabe, Keigo Masuda, Yuichiro Nishimoto, Masaru Kubo, Fumie Hosoda, Hirofumi Rokutan, Minori Matsumoto, Hiroyuki Takamaru, Masayoshi Yamada, Takahisa Matsuda, Motoki Iwasaki, Taiki Yamaji, Tatsuo Yachida, Tomoyoshi Soga, Ken Kurokawa, Atsushi Toyoda, Yoshitoshi Ogura, Tetsuya Hayashi, Masanori Hatakeyama, Hitoshi Nakagama, Yutaka Saito, Shinji Fukuda, Tatsuhiro Shibata, and Takuji Yamada · 2019
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Gut microbiome structure and metabolic activity in inflammatory bowel disease
Eric A. Franzosa, Alexandra Sirota-Madi, Julian Avila-Pacheco, Nadine Fornelos, Henry J. Haiser, Stefan Reinker, Tommi Vatanen, A. Brantley Hall, Himel Mallick, Lauren J. McIver, Jenny S. Sauk, Robin G. Wilson, Betsy W. Stevens, Justin M. Scott, Kerry Pierce, Amy A. Deik, Kevin Bullock, Floris Imhann, Jeffrey A. Porter, Alexandra Zhernakova, Jingyuan Fu, Rinse K. Weersma, Cisca Wijmenga, Clary B. Clish, Hera Vlamakis, Curtis Huttenhower, and Ramnik J. Xavier · 2019
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Learning representations of microbe–metabolite interactions
James T. Morton, Alexander A. Aksenov, Louis Felix Nothias, James R. Foulds, Robert A. Quinn, Michelle H. Badri, Tami L. Swenson, Marc W. Van Goethem, Trent R. Northen, Yoshiki Vazquez-Baeza, Mingxun Wang, Nicholas A. Bokulich, Aaron Watters, Se Jin Song, Richard Bonneau, Pieter C. Dorrestein, and Rob Knight · 2019
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Counts: an outstanding challenge for log-ratio analysis of compositional data in the molecular biosciences
David R Lovell, Xin-Yi Chua, and Annette McGrath · 2020
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Plant–microbiome interactions: from community assembly to plant health
Pankaj Trivedi, Jan E. Leach, Susannah G. Tringe, Tongmin Sa, and Brajesh K. Singh · 2020
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Application of ecological and evolutionary theory to microbiome community dynamics across systems
James E. McDonald, Julian R. Marchesi, and Britt Koskella · 2020
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Profile of the gut microbiota of adults with obesity: a systematic review
Louise Crovesy, Daniele Masterson, and Eliane Lopes Rosado · 2020
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The Firmicutes/Bacteroidetes Ratio: A Relevant Marker of Gut Dysbiosis in Obese Patients?
Fabien Magne, Martin Gotteland, Lea Gauthier, Alejandra Zazueta, Susana Pesoa, Paola Navarrete, and Ramadass Balamurugan · 2020
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DeepTRIAGE: interpretable and individualised biomarker scores using attention mechanism for the classification of breast cancer sub-types
Adham Beykikhoshk, Thomas P. Quinn, Samuel C. Lee, Truyen Tran, and Svetha Venkatesh · 2020
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Interpretable Log Contrasts for the Classification of Health Biomarkers: a New Approach to Balance Selection
Thomas P. Quinn and Ionas Erb · 2020
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Variable selection in microbiome compositional data analysis
Antoni Susin, Yiwen Wang, Kim-Anh Lê Cao, and M. Luz Calle · 2020
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Amalgamations are valid in compositional data analysis, can be used in agglomerative clustering, and their logratios have an inverse transformation
Michael Greenacre · 2020
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Amalgams: data-driven amalgamation for the dimensionality reduction of compositional data
Thomas P Quinn and Ionas Erb · 2020
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Deep in the Bowel: Highly Interpretable Neural Encoder-Decoder Networks Predict Gut Metabolites from Gut Microbiome
Vuong Le, Thomas P. Quinn, Truyen Tran, and Svetha Venkatesh · 2020
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Rank normalization empowers a t-test for microbiome differential abundance analysis while controlling for false discoveries
Matthew L Davis, Yuan Huang, and Kai Wang · 2021
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Equivolumetric Protocol Generates Library Sizes Proportional to Total Microbial Load in 16S Amplicon Sequencing
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