{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,11]],"date-time":"2025-07-11T10:51:43Z","timestamp":1752231103735,"version":"3.37.3"},"reference-count":22,"publisher":"Oxford University Press (OUP)","issue":"15","license":[{"start":{"date-parts":[[2018,3,1]],"date-time":"2018-03-01T00:00:00Z","timestamp":1519862400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/about_us\/legal\/notices"}],"funder":[{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","award":["R01GM124061","R37AI051231","U19AI057266"],"award-info":[{"award-number":["R01GM124061","R37AI051231","U19AI057266"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["NSFC41476120","NSFC41676119"],"award-info":[{"award-number":["NSFC41476120","NSFC41676119"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,8,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>It is well known that batch effects exist in RNA-seq data and other profiling data. Although some methods do a good job adjusting for batch effects by modifying the data matrices, it is still difficult to remove the batch effects entirely. The remaining batch effect can cause artifacts in the detection of patterns in the data.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>In this study, we consider the batch effect issue in the pattern detection among the samples, such as clustering, dimension reduction and construction of networks between subjects. Instead of adjusting the original data matrices, we design an adaptive method to directly adjust the dissimilarity matrix between samples. In simulation studies, the method achieved better results recovering true underlying clusters, compared to the leading batch effect adjustment method ComBat. In real data analysis, the method effectively corrected distance matrices and improved the performance of clustering algorithms.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The R package is available at: https:\/\/github.com\/tengfei-emory\/QuantNorm.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/bty117","type":"journal-article","created":{"date-parts":[[2018,2,27]],"date-time":"2018-02-27T20:13:18Z","timestamp":1519762398000},"page":"2634-2641","source":"Crossref","is-referenced-by-count":19,"title":["Mitigating the adverse impact of batch effects in sample pattern detection"],"prefix":"10.1093","volume":"34","author":[{"given":"Teng","family":"Fei","sequence":"first","affiliation":[{"name":"Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, USA"}]},{"given":"Tengjiao","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Life Sciences and Technology, Tongji University, Shanghai, China"}]},{"given":"Weiyang","family":"Shi","sequence":"additional","affiliation":[{"name":"Ministry of Education Key Laboratory of Marine Genetics and Breeding, College of Marine Life Sciences, Ocean University of China, Qingdao, China"}]},{"given":"Tianwei","family":"Yu","sequence":"additional","affiliation":[{"name":"Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, USA"}]}],"member":"286","published-online":{"date-parts":[[2018,3,1]]},"reference":[{"year":"2017","author":"Adler","key":"2023012713053218800_bty117-B1"},{"key":"2023012713053218800_bty117-B2","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1093\/bioinformatics\/btg385","article-title":"Adjustment of systematic microarray data biases","volume":"20","author":"Benito","year":"2004","journal-title":"Bioinformatics"},{"first-page":"1","year":"2015","author":"Butts","key":"2023012713053218800_bty117-B4"},{"key":"2023012713053218800_bty117-B5","doi-asserted-by":"crossref","first-page":"e17238","DOI":"10.1371\/journal.pone.0017238","article-title":"Removing batch effects in analysis of expression microarray data: an evaluation of six batch adjustment methods","volume":"6","author":"Chen","year":"2011","journal-title":"PLoS One"},{"key":"2023012713053218800_bty117-B6","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1093\/biostatistics\/kxr034","article-title":"Using control genes to correct for unwanted variation in microarray data","volume":"13","author":"Gagnon-Bartsch","year":"2012","journal-title":"Biostatistics"},{"key":"2023012713053218800_bty117-B7","doi-asserted-by":"crossref","first-page":"121","DOI":"10.12688\/f1000research.6536.1","article-title":"A reanalysis of mouse ENCODE comparative gene expression data","volume":"4","author":"Gilad","year":"2015","journal-title":"F1000Res"},{"key":"2023012713053218800_bty117-B8","doi-asserted-by":"crossref","first-page":"61","DOI":"10.2144\/000114133","article-title":"Library construction for next-generation sequencing: overviews and challenges","volume":"56","author":"Head","year":"2014","journal-title":"Biotechniques"},{"key":"2023012713053218800_bty117-B10","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1007\/BF01908075","article-title":"Comparing partitions","volume":"2","author":"Hubert","year":"1985","journal-title":"J. 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