{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T11:20:17Z","timestamp":1775474417289,"version":"3.50.1"},"reference-count":68,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2023,1,19]],"date-time":"2023-01-19T00:00:00Z","timestamp":1674086400000},"content-version":"vor","delay-in-days":1,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"China Scholarship Council - University of Melbourne","award":["201707510003"],"award-info":[{"award-number":["201707510003"]}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2022TQ0370"],"award-info":[{"award-number":["2022TQ0370"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100020771","name":"Young Scientists Fund of the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["32200077"],"award-info":[{"award-number":["32200077"]}],"id":[{"id":"10.13039\/501100020771","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,3,19]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Microbial communities are highly dynamic and sensitive to changes in the environment. Thus, microbiome data are highly susceptible to batch effects, defined as sources of unwanted variation that are not related to and obscure any factors of interest. Existing batch effect correction methods have been primarily developed for gene expression data. As such, they do not consider the inherent characteristics of microbiome data, including zero inflation, overdispersion and correlation between variables. We introduce new multivariate and non-parametric batch effect correction methods based on Partial Least Squares Discriminant Analysis (PLSDA). PLSDA-batch first estimates treatment and batch variation with latent components, then subtracts batch-associated components from the data. The resulting batch-effect-corrected data can then be input in any downstream statistical analysis. Two variants are proposed to handle unbalanced batch x treatment designs and to avoid overfitting when estimating the components via variable selection. We compare our approaches with popular methods managing batch effects, namely, removeBatchEffect, ComBat and Surrogate Variable Analysis, in simulated and three case studies using various visual and numerical assessments. We show that our three methods lead to competitive performance in removing batch variation while preserving treatment variation, especially for unbalanced batch $\\times $ treatment designs. Our downstream analyses show selections of biologically relevant taxa. This work demonstrates that batch effect correction methods can improve microbiome research outputs. Reproducible code and vignettes are available on GitHub.<\/jats:p>","DOI":"10.1093\/bib\/bbac622","type":"journal-article","created":{"date-parts":[[2023,1,19]],"date-time":"2023-01-19T04:54:03Z","timestamp":1674104043000},"source":"Crossref","is-referenced-by-count":55,"title":["PLSDA-batch: a multivariate framework to correct for batch effects in microbiome data"],"prefix":"10.1093","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7067-9093","authenticated-orcid":false,"given":"Yiwen","family":"Wang","sequence":"first","affiliation":[{"name":"Shenzhen Branch , Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, , 97 Buxin Rd, Shenzhen, 518000, Guangdong , China"},{"name":"Chinese Academy of Agricultural Sciences , Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, , 97 Buxin Rd, Shenzhen, 518000, Guangdong , China"},{"name":"Melbourne Integrative Genomics, School of Mathematics and Statistics, The University of Melbourne , 30 Royal Parade, Melbourne, 3052, VIC , Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3923-1116","authenticated-orcid":false,"given":"Kim-Anh","family":"L\u00ea Cao","sequence":"additional","affiliation":[{"name":"Melbourne Integrative Genomics, School of Mathematics and Statistics, The University of Melbourne , 30 Royal Parade, Melbourne, 3052, VIC , Australia"}]}],"member":"286","published-online":{"date-parts":[[2023,1,18]]},"reference":[{"key":"2023032004262174400_","doi-asserted-by":"crossref","DOI":"10.3389\/fmicb.2018.02247","article-title":"The gut microbiota in the pathogenesis and therapeutics of inflammatory bowel disease","volume":"9","author":"Zuo","year":"2018","journal-title":"Front 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