{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,22]],"date-time":"2025-02-22T00:45:53Z","timestamp":1740185153487,"version":"3.37.3"},"reference-count":36,"publisher":"Oxford University Press (OUP)","issue":"16","license":[{"start":{"date-parts":[[2020,5,25]],"date-time":"2020-05-25T00:00:00Z","timestamp":1590364800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"Iowa Agriculture and Home Economics Experiment Station","award":["IOW03617"],"award-info":[{"award-number":["IOW03617"]}]},{"DOI":"10.13039\/100005825","name":"USDA\/NIFA","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100005825","id-type":"DOI","asserted-by":"crossref"}]},{"name":"State of Iowa"},{"name":"Agriculture and Food Research Initiative Competitive","award":["2011-68004-30336"],"award-info":[{"award-number":["2011-68004-30336"]}]},{"name":"United States Department of Agriculture (USDA) National Institute of Food and Agriculture"},{"name":"National Institute of General Medical Sciences (NIGMS) of the National Institutes of Health"},{"name":"National Science Foundation (NSF)\/NIGMS Mathematical Biology Program","award":["R01GM109458"],"award-info":[{"award-number":["R01GM109458"]}]},{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,8,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>With the reduction in price of next-generation sequencing technologies, gene expression profiling using RNA-seq has increased the scope of sequencing experiments to include more complex designs, such as designs involving repeated measures. In such designs, RNA samples are extracted from each experimental unit at multiple time points. The read counts that result from RNA sequencing of the samples extracted from the same experimental unit tend to be temporally correlated. Although there are many methods for RNA-seq differential expression analysis, existing methods do not properly account for within-unit correlations that arise in repeated-measures designs.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We address this shortcoming by using normalized log-transformed counts and associated precision weights in a general linear model pipeline with continuous autoregressive structure to account for the correlation among observations within each experimental unit. We then utilize parametric bootstrap to conduct differential expression inference. Simulation studies show the advantages of our method over alternatives that do not account for the correlation among observations within experimental units.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>We provide an R package rmRNAseq implementing our proposed method (function TC_CAR1) at https:\/\/cran.r-project.org\/web\/packages\/rmRNAseq\/index.html. Reproducible R codes for data analysis and simulation are available at https:\/\/github.com\/ntyet\/rmRNAseq\/tree\/master\/simulation.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btaa525","type":"journal-article","created":{"date-parts":[[2020,5,19]],"date-time":"2020-05-19T20:57:59Z","timestamp":1589921879000},"page":"4432-4439","source":"Crossref","is-referenced-by-count":10,"title":["rmRNAseq: differential expression analysis for repeated-measures RNA-seq data"],"prefix":"10.1093","volume":"36","author":[{"given":"Yet","family":"Nguyen","sequence":"first","affiliation":[{"name":"Department of Mathematics and Statistics, Old Dominion University , Norfolk, VA 23529, USA"}]},{"given":"Dan","family":"Nettleton","sequence":"additional","affiliation":[{"name":"Department of Statistics, Iowa State University , Ames, IA 50011, USA"}]}],"member":"286","published-online":{"date-parts":[[2020,5,25]]},"reference":[{"key":"2023062213542498100_btaa525-B1","doi-asserted-by":"crossref","first-page":"589","DOI":"10.1093\/biostatistics\/kxx005","article-title":"Variance component score test for time-course gene set analysis of longitudinal RNA-seq data","volume":"18","author":"Agniel","year":"2017","journal-title":"Biostatistics"},{"key":"2023062213542498100_btaa525-B2","doi-asserted-by":"crossref","first-page":"i113","DOI":"10.1093\/bioinformatics\/btu274","article-title":"Methods for time series analysis of RNA-seq data with application to human Th17 cell differentiation","volume":"30","author":"\u00c4ij\u00f6","year":"2014","journal-title":"Bioinformatics"},{"key":"2023062213542498100_btaa525-B3","doi-asserted-by":"crossref","first-page":"R106","DOI":"10.1186\/gb-2010-11-10-r106","article-title":"Differential expression analysis for sequence count data","volume":"11","author":"Anders","year":"2010","journal-title":"Genome Biology"},{"key":"2023062213542498100_btaa525-B4","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1186\/1471-2105-11-94","article-title":"Evaluation of statistical methods for normalization and differential expression in mRNA-seq experiments","volume":"11","author":"Bullard","year":"2010","journal-title":"BMC Bioinformatics"},{"key":"2023062213542498100_btaa525-B5","doi-asserted-by":"crossref","first-page":"829","DOI":"10.1080\/01621459.1979.10481038","article-title":"Robust locally weighted regression and smoothing scatterplots","volume":"74","author":"Cleveland","year":"1979","journal-title":"J. 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