{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T06:17:41Z","timestamp":1772173061857,"version":"3.50.1"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1010091","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2022,5,31]],"date-time":"2022-05-31T00:00:00Z","timestamp":1653955200000}}],"reference-count":70,"publisher":"Public Library of Science (PLoS)","issue":"5","license":[{"start":{"date-parts":[[2022,5,18]],"date-time":"2022-05-18T00:00:00Z","timestamp":1652832000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R21CA253408"],"award-info":[{"award-number":["R21CA253408"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["P20GM130454"],"award-info":[{"award-number":["P20GM130454"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["P30CA023108"],"award-info":[{"award-number":["P30CA023108"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01LM012723"],"award-info":[{"award-number":["R01LM012723"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>Research in human-associated microbiomes often involves the analysis of taxonomic count tables generated via high-throughput sequencing. It is difficult to apply statistical tools as the data is high-dimensional, sparse, and compositional. An approachable way to alleviate high-dimensionality and sparsity is to aggregate variables into pre-defined sets. Set-based analysis is ubiquitous in the genomics literature and has demonstrable impact on improving interpretability and power of downstream analysis. Unfortunately, there is a lack of sophisticated set-based analysis methods specific to microbiome taxonomic data, where current practice often employs abundance summation as a technique for aggregation. This approach prevents comparison across sets of different sizes, does not preserve inter-sample distances, and amplifies protocol bias. Here, we attempt to fill this gap with a new single-sample taxon enrichment method that uses a novel log-ratio formulation based on the competitive null hypothesis commonly used in the enrichment analysis literature. Our approach, titled competitive balances for taxonomic enrichment analysis (CBEA), generates sample-specific enrichment scores as the scaled log-ratio of the subcomposition defined by taxa within a set and the subcomposition defined by its complement. We provide sample-level significance testing by estimating an empirical null distribution of our test statistic with valid p-values. Herein, we demonstrate, using both real data applications and simulations, that CBEA controls for type I error, even under high sparsity and high inter-taxa correlation scenarios. Additionally, CBEA provides informative scores that can be inputs to downstream analyses such as prediction tasks.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1010091","type":"journal-article","created":{"date-parts":[[2022,5,18]],"date-time":"2022-05-18T13:38:56Z","timestamp":1652881136000},"page":"e1010091","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":5,"title":["CBEA: Competitive balances for taxonomic enrichment analysis"],"prefix":"10.1371","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2072-3279","authenticated-orcid":true,"given":"Quang P.","family":"Nguyen","sequence":"first","affiliation":[]},{"given":"Anne G.","family":"Hoen","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6794-9945","authenticated-orcid":true,"given":"H. Robert","family":"Frost","sequence":"additional","affiliation":[]}],"member":"340","published-online":{"date-parts":[[2022,5,18]]},"reference":[{"issue":"7758","key":"pcbi.1010091.ref001","doi-asserted-by":"crossref","first-page":"641","DOI":"10.1038\/s41586-019-1238-8","article-title":"The Integrative Human Microbiome Project","volume":"569","author":"LM Proctor","year":"2019","journal-title":"Nature"},{"key":"pcbi.1010091.ref002","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.jnutbio.2018.10.003","article-title":"Gut Microbiome and Type 2 Diabetes: Where We Are and Where to Go?","volume":"63","author":"S Sharma","year":"2019","journal-title":"The Journal of Nutritional Biochemistry"},{"issue":"2","key":"pcbi.1010091.ref003","doi-asserted-by":"crossref","first-page":"113","DOI":"10.3746\/pnf.2020.25.2.113","article-title":"The Influence of the Gut Microbiome on Obesity in Adults and the Role of Probiotics, Prebiotics, and Synbiotics for Weight Loss","volume":"25","author":"A Aoun","year":"2020","journal-title":"Preventive Nutrition and Food Science"},{"issue":"4","key":"pcbi.1010091.ref004","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1038\/nrg3182","article-title":"The Human Microbiome: At the Interface of Health and Disease","volume":"13","author":"I Cho","year":"2012","journal-title":"Nature Reviews Genetics"},{"issue":"7","key":"pcbi.1010091.ref005","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1038\/nmeth.3869","article-title":"DADA2: High-resolution Sample Inference from Illumina Amplicon Data","volume":"13","author":"BJ Callahan","year":"2016","journal-title":"Nature Methods"},{"issue":"10","key":"pcbi.1010091.ref006","doi-asserted-by":"crossref","first-page":"902","DOI":"10.1038\/nmeth.3589","article-title":"MetaPhlAn2 for Enhanced Metagenomic Taxonomic Profiling","volume":"12","author":"DT Truong","year":"2015","journal-title":"Nature Methods"},{"key":"pcbi.1010091.ref007","doi-asserted-by":"crossref","first-page":"977","DOI":"10.1002\/9781119487845.ch35","volume-title":"Handbook of Statistical Genomics","author":"H Li","year":"2019"},{"issue":"1","key":"pcbi.1010091.ref008","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1146\/annurev-statistics-010814-020351","article-title":"Microbiome, Metagenomics, and High-Dimensional Compositional Data Analysis","volume":"2","author":"H Li","year":"2015","journal-title":"Annual Review of Statistics and Its Application"},{"key":"pcbi.1010091.ref009","doi-asserted-by":"crossref","DOI":"10.3389\/fmicb.2017.02224","article-title":"Microbiome Datasets Are Compositional: And This Is Not Optional","volume":"8","author":"GB Gloor","year":"2017","journal-title":"Frontiers in Microbiology"},{"issue":"2","key":"pcbi.1010091.ref010","doi-asserted-by":"crossref","first-page":"e1002375","DOI":"10.1371\/journal.pcbi.1002375","article-title":"Ten Years of Pathway Analysis: Current Approaches and Outstanding Challenges","volume":"8","author":"P Khatri","year":"2012","journal-title":"PLOS Computational Biology"},{"issue":"8","key":"pcbi.1010091.ref011","doi-asserted-by":"crossref","first-page":"980","DOI":"10.1093\/bioinformatics\/btm051","article-title":"Analyzing Gene Expression Data in Terms of Gene Sets: Methodological Issues","volume":"23","author":"JJ Goeman","year":"2007","journal-title":"Bioinformatics"},{"issue":"43","key":"pcbi.1010091.ref012","doi-asserted-by":"crossref","first-page":"15545","DOI":"10.1073\/pnas.0506580102","article-title":"Gene Set Enrichment Analysis: A Knowledge-Based Approach for Interpreting Genome-Wide Expression Profiles","volume":"102","author":"A Subramanian","year":"2005","journal-title":"Proceedings of the National Academy of Sciences"},{"issue":"1","key":"pcbi.1010091.ref013","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1038\/75556","article-title":"Gene Ontology: Tool for the Unification of Biology","volume":"25","author":"M Ashburner","year":"2000","journal-title":"Nature genetics"},{"issue":"6","key":"pcbi.1010091.ref014","doi-asserted-by":"crossref","first-page":"565","DOI":"10.1177\/0962280209351908","article-title":"Gene Set Enrichment Analysis Made Simple","volume":"18","author":"RA Irizarry","year":"2009","journal-title":"Statistical methods in medical research"},{"issue":"1","key":"pcbi.1010091.ref015","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1186\/1471-2105-14-7","article-title":"GSVA: Gene Set Variation Analysis for Microarray and RNA-Seq Data","volume":"14","author":"S H\u00e4nzelmann","year":"2013","journal-title":"BMC Bioinformatics"},{"issue":"16","key":"pcbi.1010091.ref016","doi-asserted-by":"crossref","first-page":"e94","DOI":"10.1093\/nar\/gkaa582","article-title":"Variance-Adjusted Mahalanobis (VAM): A Fast and Accurate Method for Cell-Specific Gene Set Scoring","volume":"48","author":"HR Frost","year":"2020","journal-title":"Nucleic Acids Research"},{"issue":"3","key":"pcbi.1010091.ref017","doi-asserted-by":"crossref","first-page":"799","DOI":"10.1038\/s41596-019-0264-1","article-title":"Using MicrobiomeAnalyst for Comprehensive Statistical, Functional, and Meta-Analysis of Microbiome Data","volume":"15","author":"J Chong","year":"2020","journal-title":"Nature Protocols"},{"issue":"giz107","key":"pcbi.1010091.ref018","article-title":"A Field Guide for the Compositional Analysis of Any-Omics Data","volume":"8","author":"TP Quinn","year":"2019","journal-title":"GigaScience"},{"issue":"16","key":"pcbi.1010091.ref019","doi-asserted-by":"crossref","first-page":"2870","DOI":"10.1093\/bioinformatics\/bty175","article-title":"Understanding Sequencing Data as Compositions: An Outlook and Review","volume":"34","author":"TP Quinn","year":"2018","journal-title":"Bioinformatics"},{"issue":"1","key":"pcbi.1010091.ref020","doi-asserted-by":"crossref","DOI":"10.1038\/s41467-019-10656-5","article-title":"Establishing Microbial Composition Measurement Standards with Reference Frames","volume":"10","author":"JT Morton","year":"2019","journal-title":"Nature Communications"},{"issue":"12","key":"pcbi.1010091.ref021","doi-asserted-by":"crossref","first-page":"550","DOI":"10.1186\/s13059-014-0550-8","article-title":"Moderated Estimation of Fold Change and Dispersion for RNA-seq Data with DESeq2","volume":"15","author":"MI Love","year":"2014","journal-title":"Genome Biology"},{"issue":"1","key":"pcbi.1010091.ref022","doi-asserted-by":"crossref","DOI":"10.1186\/s40168-017-0237-y","article-title":"Normalization and Microbial Differential Abundance Strategies Depend upon Data Characteristics","volume":"5","author":"S Weiss","year":"2017","journal-title":"Microbiome"},{"issue":"3","key":"pcbi.1010091.ref023","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1111\/2041-210X.13115","article-title":"Methods for Normalizing Microbiome Data: An Ecological Perspective","volume":"10","author":"DT McKnight","year":"2019","journal-title":"Methods in Ecology and Evolution"},{"key":"pcbi.1010091.ref024","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1214\/lnms\/1215463786","article-title":"Principles of Compositional Data Analysis","author":"J Aitchison","year":"1994","journal-title":"Lecture Notes-Monograph Series"},{"key":"pcbi.1010091.ref025","doi-asserted-by":"crossref","first-page":"e46923","DOI":"10.7554\/eLife.46923","article-title":"Consistent and Correctable Bias in Metagenomic Sequencing Experiments","volume":"8","author":"MR McLaren","year":"2019","journal-title":"eLife"},{"issue":"7","key":"pcbi.1010091.ref026","doi-asserted-by":"crossref","first-page":"795","DOI":"10.1007\/s11004-005-7381-9","article-title":"Groups of Parts and Their Balances in Compositional Data Analysis","volume":"37","author":"JJ Egozcue","year":"2005","journal-title":"Mathematical Geology"},{"issue":"38","key":"pcbi.1010091.ref027","doi-asserted-by":"crossref","first-page":"13544","DOI":"10.1073\/pnas.0506577102","article-title":"Discovering Statistically Significant Pathways in Expression Profiling Studies","volume":"102","author":"L Tian","year":"2005","journal-title":"Proceedings of the National Academy of Sciences"},{"issue":"4","key":"pcbi.1010091.ref028","doi-asserted-by":"crossref","first-page":"e00053","DOI":"10.1128\/mSystems.00053-18","article-title":"Balances: A New Perspective for Microbiome Analysis","volume":"3","author":"J Rivera-Pinto","year":"2018","journal-title":"mSystems"},{"issue":"17","key":"pcbi.1010091.ref029","doi-asserted-by":"crossref","first-page":"e133","DOI":"10.1093\/nar\/gks461","article-title":"Camera: A Competitive Gene Set Test Accounting for Inter-Gene Correlation","volume":"40","author":"D Wu","year":"2012","journal-title":"Nucleic Acids Research"},{"issue":"3","key":"pcbi.1010091.ref030","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1023\/A:1023818214614","article-title":"Isometric Logratio Transformations for Compositional Data Analysis","volume":"35","author":"JJ Egozcue","year":"2003","journal-title":"Mathematical Geology"},{"issue":"D1","key":"pcbi.1010091.ref031","doi-asserted-by":"crossref","first-page":"D590","DOI":"10.1093\/nar\/gks1219","article-title":"The SILVA Ribosomal RNA Gene Database Project: Improved Data Processing and Web-Based Tools","volume":"41","author":"C Quast","year":"2013","journal-title":"Nucleic Acids Research"},{"issue":"4","key":"pcbi.1010091.ref032","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v064.i04","article-title":"Fitdistrplus: An R Package for Fitting Distributions","volume":"64","author":"ML Delignette-Muller","year":"2015","journal-title":"Journal of Statistical Software"},{"issue":"6","key":"pcbi.1010091.ref033","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v032.i06","article-title":"Mixtools: An R Package for Analyzing Finite Mixture Models","volume":"32","author":"T Benaglia","year":"2009","journal-title":"Journal of Statistical Software"},{"key":"pcbi.1010091.ref034","first-page":"26","article-title":"Phylogenetic Factorization of Compositional Data Yields Lineage-Level Associations in Microbiome Datasets","author":"AD Washburne","year":"2017","journal-title":"PeerJ"},{"key":"pcbi.1010091.ref035","doi-asserted-by":"crossref","first-page":"e21887","DOI":"10.7554\/eLife.21887","article-title":"A Phylogenetic Transform Enhances Analysis of Compositional Microbiota Data","volume":"6","author":"JD Silverman","year":"2017","journal-title":"eLife"},{"issue":"1","key":"pcbi.1010091.ref036","doi-asserted-by":"crossref","first-page":"e00162","DOI":"10.1128\/mSystems.00162-16","article-title":"Balance Trees Reveal Microbial Niche Differentiation","volume":"2","author":"JT Morton","year":"2017","journal-title":"mSystems"},{"issue":"2","key":"pcbi.1010091.ref037","doi-asserted-by":"crossref","first-page":"261","DOI":"10.2307\/2335470","article-title":"Logistic-Normal Distributions:Some Properties and Uses","volume":"67","author":"J Aitchison","year":"1980","journal-title":"Biometrika"},{"issue":"465","key":"pcbi.1010091.ref038","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1198\/016214504000000089","article-title":"Large-Scale Simultaneous Hypothesis Testing","volume":"99","author":"B Efron","year":"2004","journal-title":"Journal of the American Statistical Association"},{"issue":"5","key":"pcbi.1010091.ref039","doi-asserted-by":"crossref","first-page":"e1004226","DOI":"10.1371\/journal.pcbi.1004226","article-title":"Sparse and Compositionally Robust Inference of Microbial Ecological Networks","volume":"11","author":"ZD Kurtz","year":"2015","journal-title":"PLOS Computational Biology"},{"issue":"1","key":"pcbi.1010091.ref040","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1471-2105-10-47","article-title":"A General Modular Framework for Gene Set Enrichment Analysis","volume":"10","author":"M Ackermann","year":"2009","journal-title":"BMC bioinformatics"},{"issue":"1","key":"pcbi.1010091.ref041","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1093\/bib\/bbz158","article-title":"Toward a Gold Standard for Benchmarking Gene Set Enrichment Analysis","volume":"22","author":"L Geistlinger","year":"2021","journal-title":"Briefings in bioinformatics"},{"issue":"11","key":"pcbi.1010091.ref042","doi-asserted-by":"crossref","first-page":"1023","DOI":"10.1038\/nmeth.4468","article-title":"Accessible, Curated Metagenomic Data through ExperimentHub","volume":"14","author":"E Pasolli","year":"2017","journal-title":"Nature Methods"},{"key":"pcbi.1010091.ref043","article-title":"HMP16SData: Efficient Access to the Human Microbiome Project through Bioconductor","author":"L Schiffer","year":"2019","journal-title":"American Journal of Epidemiology"},{"issue":"10","key":"pcbi.1010091.ref044","doi-asserted-by":"crossref","first-page":"796","DOI":"10.1038\/s41592-018-0141-9","article-title":"Qiita: Rapid, Web-Enabled Microbiome Meta-Analysis","volume":"15","author":"A Gonzalez","year":"2018","journal-title":"Nature Methods"},{"issue":"43","key":"pcbi.1010091.ref045","doi-asserted-by":"crossref","first-page":"1686","DOI":"10.21105\/joss.01686","article-title":"Welcome to the Tidyverse","volume":"4","author":"H Wickham","year":"2019","journal-title":"Journal of Open Source Software"},{"key":"pcbi.1010091.ref046","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1186\/1471-2105-12-77","article-title":"pROC: An Open-Source Package for R and S+ to Analyze and Compare ROC Curves","author":"X Robin","year":"2011","journal-title":"BMC Bioinformatics"},{"issue":"4","key":"pcbi.1010091.ref047","doi-asserted-by":"crossref","first-page":"e1003531","DOI":"10.1371\/journal.pcbi.1003531","article-title":"Waste Not, Want Not: Why Rarefying Microbiome Data Is Inadmissible","volume":"10","author":"PJ McMurdie","year":"2014","journal-title":"PLOS Computational Biology"},{"key":"pcbi.1010091.ref048","unstructured":"Ernst FGM, Shetty SA, Borman T, Lahti L. Mia: Microbiome Analysis; 2021."},{"issue":"57","key":"pcbi.1010091.ref049","doi-asserted-by":"crossref","first-page":"2959","DOI":"10.21105\/joss.02959","article-title":"The Targets R Package: A Dynamic Make-like Function-Oriented Pipeline Toolkit for Reproducibility and High-Performance Computing","volume":"6","author":"WM Landau","year":"2021","journal-title":"Journal of Open Source Software"},{"issue":"7402","key":"pcbi.1010091.ref050","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1038\/nature11234","article-title":"Structure, Function and Diversity of the Healthy Human Microbiome","volume":"486","author":"THMP Consortium","year":"2012","journal-title":"Nature"},{"issue":"2","key":"pcbi.1010091.ref051","first-page":"119","article-title":"Approximate Is Better than \u201cExact\u201d for Interval Estimation of Binomial Proportions","volume":"52","author":"A Agresti","year":"1998","journal-title":"The American Statistician"},{"issue":"2","key":"pcbi.1010091.ref052","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1111\/omi.12108","article-title":"Microbial Dynamics during Conversion from Supragingival to Subgingival Biofilms in an in Vitro Model","volume":"31","author":"T Thurnheer","year":"2016","journal-title":"Molecular Oral Microbiology"},{"key":"pcbi.1010091.ref053","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.annepidem.2019.03.005","article-title":"Tobacco Exposure Associated with Oral Microbiota Oxygen Utilization in the New York City Health and Nutrition Examination Study","volume":"34","author":"F Beghini","year":"2019","journal-title":"Annals of Epidemiology"},{"key":"pcbi.1010091.ref054","unstructured":"Calgaro M. Mcalgaro93\/Sc2meta: Paper Release; 2020. Zenodo."},{"issue":"3","key":"pcbi.1010091.ref055","doi-asserted-by":"crossref","first-page":"837","DOI":"10.2307\/2531595","article-title":"Comparing the Areas under Two or More Correlated Receiver Operating Characteristic Curves: A Nonparametric Approach","volume":"44","author":"ER DeLong","year":"1988","journal-title":"Biometrics"},{"issue":"8","key":"pcbi.1010091.ref056","doi-asserted-by":"crossref","first-page":"822","DOI":"10.1038\/nbt.2939","article-title":"Identification and Assembly of Genomes and Genetic Elements in Complex Metagenomic Samples without Using Reference Genomes","volume":"32","author":"HB Nielsen","year":"2014","journal-title":"Nature Biotechnology"},{"issue":"3","key":"pcbi.1010091.ref057","doi-asserted-by":"crossref","first-page":"382","DOI":"10.1016\/j.chom.2014.02.005","article-title":"The Treatment-Naive Microbiome in New-Onset Crohn\u2019s Disease","volume":"15","author":"D Gevers","year":"2014","journal-title":"Cell Host & Microbe"},{"issue":"1","key":"pcbi.1010091.ref058","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"L Breiman","year":"2001","journal-title":"Machine Learning"},{"key":"pcbi.1010091.ref059","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: Synthetic Minority Over-sampling Technique","volume":"16","author":"NV Chawla","year":"2002","journal-title":"Journal of Artificial Intelligence Research"},{"key":"pcbi.1010091.ref060","unstructured":"Kuhn M, Wickham H. Tidymodels: A Collection of Packages for Modeling and Machine Learning Using Tidyverse Principles.; 2020."},{"issue":"1","key":"pcbi.1010091.ref061","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1214\/19-AOAS1283","article-title":"Modeling Microbial Abundances and Dysbiosis with Beta-Binomial Regression","volume":"14","author":"BD Martin","year":"2020","journal-title":"The Annals of Applied Statistics"},{"issue":"7269","key":"pcbi.1010091.ref062","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1038\/nature08460","article-title":"Systematic RNA Interference Reveals That Oncogenic KRAS-Driven Cancers Require TBK1","volume":"462","author":"DA Barbie","year":"2009","journal-title":"Nature"},{"issue":"1","key":"pcbi.1010091.ref063","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1186\/s13059-020-02104-1","article-title":"Assessment of Statistical Methods from Single Cell, Bulk RNA-seq, and Metagenomics Applied to Microbiome Data","volume":"21","author":"M Calgaro","year":"2020","journal-title":"Genome Biology"},{"issue":"1","key":"pcbi.1010091.ref064","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1186\/s13073-016-0302-3","article-title":"An Adaptive Association Test for Microbiome Data","volume":"8","author":"C Wu","year":"2016","journal-title":"Genome Medicine"},{"key":"pcbi.1010091.ref065","article-title":"Scalable Estimation of Microbial Co-Occurrence Networks with Variational Autoencoders","author":"JT Morton","year":"2021","journal-title":"Bioinformatics"},{"issue":"9","key":"pcbi.1010091.ref066","doi-asserted-by":"crossref","first-page":"e1008913","DOI":"10.1371\/journal.pcbi.1008913","article-title":"A Statistical Model for Describing and Simulating Microbial Community Profiles","volume":"17","author":"S Ma","year":"2021","journal-title":"PLOS Computational Biology"},{"key":"pcbi.1010091.ref067","unstructured":"Naim I, Gildea D. Convergence of the EM Algorithm for Gaussian Mixtures with Unbalanced Mixing Coefficients. Proceedings of the 29th International Coference on International Conference on Machine Learning. 2012; p. 8."},{"issue":"9","key":"pcbi.1010091.ref068","doi-asserted-by":"crossref","first-page":"2688","DOI":"10.1016\/j.csda.2012.02.012","article-title":"Model-Based Replacement of Rounded Zeros in Compositional Data: Classical and Robust Approaches","volume":"56","author":"JA Mart\u00edn-Fern\u00e1ndez","year":"2012","journal-title":"Computational Statistics & Data Analysis"},{"issue":"3","key":"pcbi.1010091.ref069","first-page":"422","article-title":"Structural Zeros in High-Dimensional Data with Applications to Microbiome Studies","volume":"18","author":"A Kaul","year":"2017","journal-title":"Biostatistics"},{"issue":"1","key":"pcbi.1010091.ref070","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1186\/s12859-021-04216-2","article-title":"Exploring the Functional Composition of the Human Microbiome Using a Hand-Curated Microbial Trait Database","volume":"22","author":"JL Weissman","year":"2021","journal-title":"BMC Bioinformatics"}],"updated-by":[{"DOI":"10.1371\/journal.pcbi.1010091","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2022,5,31]],"date-time":"2022-05-31T00:00:00Z","timestamp":1653955200000}}],"container-title":["PLOS Computational Biology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pcbi.1010091","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,31]],"date-time":"2022-05-31T13:44:30Z","timestamp":1654004670000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pcbi.1010091"}},"subtitle":[],"editor":[{"given":"Nicola","family":"Segata","sequence":"first","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2022,5,18]]},"references-count":70,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2022,5,18]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pcbi.1010091","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2021.09.07.459294","asserted-by":"object"}]},"ISSN":["1553-7358"],"issn-type":[{"value":"1553-7358","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,18]]}}}