{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T06:36:27Z","timestamp":1784010987456,"version":"3.55.0"},"reference-count":28,"publisher":"Oxford University Press (OUP)","issue":"17","license":[{"start":{"date-parts":[[2021,3,8]],"date-time":"2021-03-08T00:00:00Z","timestamp":1615161600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/100000057","name":"National Institute of General Medical Sciences","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000057","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institute of Health","doi-asserted-by":"publisher","award":["R01GM126558"],"award-info":[{"award-number":["R01GM126558"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100008091","name":"University of Rochester","doi-asserted-by":"publisher","award":["UL1 TR002001"],"award-info":[{"award-number":["UL1 TR002001"]}],"id":[{"id":"10.13039\/100008091","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100006108","name":"National Center for Advancing Translational Sciences","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100006108","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,9,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>We developed super-delta2, a differential gene expression analysis pipeline designed for multi-group comparisons for RNA-seq data. It includes a customized one-way ANOVA F-test and a post-hoc test for pairwise group comparisons; both are designed to work with a multivariate normalization procedure to reduce technical noise. It also includes a trimming procedure with bias-correction to obtain robust and approximately unbiased summary statistics used in these tests. We demonstrated the asymptotic applicability of super-delta2 to log-transformed read counts in RNA-seq data by large sample theory based on Negative Binomial Poisson (NBP) distribution.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We compared super-delta2 with three commonly used RNA-seq data analysis methods: limma\/voom, edgeR and DESeq2 using both simulated and real datasets. In all three simulation settings, super-delta2 not only achieved the best overall statistical power, but also was the only method that controlled type I error at the nominal level. When applied to a breast cancer dataset to identify differential expression pattern associated with multiple pathologic stages, super-delta2 selected more enriched pathways than other methods, which are directly linked to the underlying biological condition (breast cancer).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>In conclusion, by incorporating trimming and bias-correction in the normalization step, super-delta2 was able to achieve tight control of type I error. Because the hypothesis tests are based on asymptotic normal approximation of the NBP distribution, super-delta2 does not require computationally expensive iterative optimization procedures used by methods such as edgeR and DESeq2, which occasionally have convergence issues.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>Our method is implemented in a R-package, \u2018superdelta2\u2019, freely available at: https:\/\/github.com\/fhlsjs\/superdelta2.<\/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\/btab155","type":"journal-article","created":{"date-parts":[[2021,3,4]],"date-time":"2021-03-04T07:13:12Z","timestamp":1614841992000},"page":"2627-2636","source":"Crossref","is-referenced-by-count":8,"title":["Super-delta2: an enhanced differential expression analysis procedure for multi-group comparisons of RNA-seq data"],"prefix":"10.1093","volume":"37","author":[{"given":"Zihan","family":"Cui","sequence":"first","affiliation":[{"name":"Department of Statistics, Florida State University , Tallahassee, FL, 32304,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuhang","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Statistics, Florida State University , Tallahassee, FL, 32304,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7429-7615","authenticated-orcid":false,"given":"Jinfeng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Statistics, Florida State University , Tallahassee, FL, 32304,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2330-3544","authenticated-orcid":false,"given":"Xing","family":"Qiu","sequence":"additional","affiliation":[{"name":"Department of Biostatistics and Computational Biology, University of Rochester , Rochester, NY, 14624,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2021,3,8]]},"reference":[{"key":"2025102001570186200_btab155-B1","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 Biol"},{"key":"2025102001570186200_btab155-B2","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1093\/bioinformatics\/19.2.185","article-title":"A comparison of normalization methods for high density oligonucleotide array data based on variance and bias","volume":"19","author":"Bolstad","year":"2003","journal-title":"Bioinformatics"},{"key":"2025102001570186200_btab155-B3","doi-asserted-by":"crossref","first-page":"9546","DOI":"10.1073\/pnas.0914005107","article-title":"Independent filtering increases detection power for high-throughput experiments","volume":"107","author":"Bourgon","year":"2010","journal-title":"Proc. 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