{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T05:56:21Z","timestamp":1781157381776,"version":"3.54.1"},"reference-count":28,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T00:00:00Z","timestamp":1774656000000},"content-version":"vor","delay-in-days":1,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"publisher","award":["318346496\u2014SFB1292\/2 TP19N"],"award-info":[{"award-number":["318346496\u2014SFB1292\/2 TP19N"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,4,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Summary<\/jats:title>\n                    <jats:p>Modern omics experiments now involve multiple conditions and complex designs, producing an increasingly large set of differential expression and functional enrichment analysis results. However, no standardized data structure exists to store and contextualize these results together with their metadata, leaving researchers with an unmanageable and potentially non-reproducible collection of results that are difficult to navigate and\/or share. Here we introduce DeeDeeExperiment, a new S4 class for managing and storing omics data analysis results, implemented within the Bioconductor ecosystem, which promotes interoperability, reproducibility and good documentation. This class extends the widely used SingleCellExperiment object by introducing dedicated slots for Differential Expression (DEA) and Functional Enrichment Analysis (FEA) results, allowing users to organize, store, and retrieve information on multiple contrasts and associated metadata within a single data object, ultimately streamlining the management and interpretation of many omics datasets.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>DeeDeeExperiment is available on Bioconductor under the MIT license (https:\/\/bioconductor.org\/packages\/DeeDeeExperiment), with its development version also available on Github (https:\/\/github.com\/imbeimainz\/DeeDeeExperiment).<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btag157","type":"journal-article","created":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T12:48:45Z","timestamp":1774529325000},"source":"Crossref","is-referenced-by-count":0,"title":["DeeDeeExperiment: building an infrastructure for integrating and managing omics data analysis results in R\/Bioconductor"],"prefix":"10.1093","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8357-0938","authenticated-orcid":false,"given":"Najla","family":"Abassi","sequence":"first","affiliation":[{"name":"Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center Mainz , Mainz, 55131,","place":["Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-1290-1553","authenticated-orcid":false,"given":"Lea","family":"Schwarz","sequence":"additional","affiliation":[{"name":"Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center Mainz , Mainz, 55131,","place":["Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-9858-0137","authenticated-orcid":false,"given":"Edoardo","family":"Filippi","sequence":"additional","affiliation":[{"name":"Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center Mainz , Mainz, 55131,","place":["Germany"]},{"name":"Department of Nephrology, Rheumatology and Kidney Transplantation, University Medical Center Mainz , Mainz, 55131,","place":["Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3252-7758","authenticated-orcid":false,"given":"Federico","family":"Marini","sequence":"additional","affiliation":[{"name":"Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center Mainz , Mainz, 55131,","place":["Germany"]},{"name":"Research Center for Immunotherapy (FZI) Mainz , Mainz, 55131,","place":["Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,3,27]]},"reference":[{"key":"2026061100564440900_btag157-B1","doi-asserted-by":"publisher","first-page":"1600","DOI":"10.1093\/bioinformatics\/btl140","article-title":"Improved scoring of functional groups from gene expression data by decorrelating go graph structure","volume":"22","author":"Alexa","year":"2006","journal-title":"Bioinformatics"},{"key":"2026061100564440900_btag157-B2","doi-asserted-by":"publisher","first-page":"242","DOI":"10.1038\/s41592-019-0700-8","article-title":"Publisher correction: orchestrating single-cell analysis with bioconductor","volume":"17","author":"Amezquita","year":"2020","journal-title":"Nat Methods"},{"key":"2026061100564440900_btag157-B3","doi-asserted-by":"publisher","first-page":"e315","DOI":"10.1002\/mco2.315","article-title":"Applications of multi-omics analysis in human diseases","volume":"4","author":"Chen","year":"2023","journal-title":"MedComm (2020)"},{"key":"2026061100564440900_btag157-B4","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1186\/1471-2105-14-128","article-title":"Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool","volume":"14","author":"Chen","year":"2013","journal-title":"BMC Bioinformatics"},{"key":"2026061100564440900_btag157-B5","doi-asserted-by":"publisher","first-page":"6077","DOI":"10.1038\/s41467-020-19894-4","article-title":"muscat detects subpopulation-specific state transitions from multi-sample multi-condition single-cell transcriptomics data","volume":"11","author":"Crowell","year":"2020","journal-title":"Nat Commun"},{"key":"2026061100564440900_btag157-B6","doi-asserted-by":"publisher","first-page":"117225","DOI":"10.1016\/j.trac.2023.117225","article-title":"Scaling-up metabolomics: current state and perspectives","volume":"167","author":"Hajjar","year":"2023","journal-title":"TrAC Trends Anal Chem"},{"key":"2026061100564440900_btag157-B7","doi-asserted-by":"publisher","first-page":"1481","DOI":"10.1038\/s41592-024-02305-7","article-title":"Large-scale foundation model on single-cell transcriptomics","volume":"21","author":"Hao","year":"2024","journal-title":"Nat Methods"},{"key":"2026061100564440900_btag157-B8","doi-asserted-by":"publisher","first-page":"1246","DOI":"10.12688\/f1000research.26669.2","article-title":"TreeSummarizedExperiment: a S4 class for data with hierarchical structure","volume":"9","author":"Huang","year":"2020","journal-title":"F1000Res"},{"key":"2026061100564440900_btag157-B9","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1038\/nmeth.3252","article-title":"Orchestrating high-throughput genomic analysis with bioconductor","volume":"12","author":"Huber","year":"2015","journal-title":"Nat Methods"},{"key":"2026061100564440900_btag157-B10","doi-asserted-by":"publisher","first-page":"20150202","DOI":"10.1098\/rsta.2015.0202","article-title":"Principal component analysis: a review and recent developments","volume":"374","author":"Jolliffe","year":"2016","journal-title":"Philos Trans A Math Phys Eng Sci"},{"key":"2026061100564440900_btag157-B11","doi-asserted-by":"publisher","author":"Korotkevich","year":"2021","DOI":"10.1101\/060012"},{"key":"2026061100564440900_btag157-B12","doi-asserted-by":"publisher","first-page":"5551","DOI":"10.1093\/bioinformatics\/btaa1031","article-title":"struct: an R\/bioconductor-based framework for standardized metabolomics data analysis and beyond","volume":"36","author":"Lloyd","year":"2020","journal-title":"Bioinformatics"},{"key":"2026061100564440900_btag157-B13","doi-asserted-by":"publisher","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":"Love","year":"2014","journal-title":"Genome Biol"},{"key":"2026061100564440900_btag157-B14","doi-asserted-by":"publisher","first-page":"e411","DOI":"10.1002\/cpz1.411","article-title":"Interactive and reproducible workflows for exploring and modeling RNA-seq data with pcaexplorer, ideal, and genetonic","volume":"2","author":"Ludt","year":"2022","journal-title":"Curr Protoc"},{"key":"2026061100564440900_btag157-B15","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Maaten","year":"2008","journal-title":"J Mach Learn Res"},{"key":"2026061100564440900_btag157-B16","doi-asserted-by":"publisher","first-page":"610","DOI":"10.1186\/s12859-021-04461-5","article-title":"Genetonic: an R\/bioconductor package for streamlining the interpretation of RNA-seq data","volume":"22","author":"Marini","year":"2021","journal-title":"BMC Bioinformatics"},{"key":"2026061100564440900_btag157-B17","doi-asserted-by":"publisher","DOI":"10.18129\/B9.bioc.SummarizedExperiment"},{"key":"2026061100564440900_btag157-B18","doi-asserted-by":"publisher","first-page":"1179","DOI":"10.1093\/bioinformatics\/btw777","article-title":"Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R","volume":"33","author":"McCarthy","year":"2017","journal-title":"Bioinformatics"},{"key":"2026061100564440900_btag157-B19","doi-asserted-by":"publisher","author":"McInnes","year":"2018","DOI":"10.48550\/arXiv.1802.03426"},{"key":"2026061100564440900_btag157-B20","doi-asserted-by":"publisher","first-page":"3922","DOI":"10.1038\/s41467-024-47899-w","article-title":"Optimizing differential expression analysis for proteomics data via high-performing rules and ensemble inference","volume":"15","author":"Peng","year":"2024","journal-title":"Nat Commun"},{"key":"2026061100564440900_btag157-B21","doi-asserted-by":"publisher","first-page":"e39","DOI":"10.1158\/0008-5472.can-17-0344","article-title":"Software for the integration of multiomics experiments in bioconductor","volume":"77","author":"Ramos","year":"2017","journal-title":"Cancer Res"},{"key":"2026061100564440900_btag157-B22","doi-asserted-by":"publisher","first-page":"3128","DOI":"10.1093\/bioinformatics\/btac299","article-title":"Spatialexperiment: infrastructure for spatially-resolved transcriptomics data in R using bioconductor","volume":"38","author":"Righelli","year":"2022","journal-title":"Bioinformatics"},{"key":"2026061100564440900_btag157-B23","doi-asserted-by":"publisher","first-page":"e47","DOI":"10.1093\/nar\/gkv007","article-title":"limma powers differential expression analyses for RNA-sequencing and microarray studies","volume":"43","author":"Ritchie","year":"2015","journal-title":"Nucleic Acids Res"},{"key":"2026061100564440900_btag157-B24","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1093\/bioinformatics\/btp616","article-title":"edgeR: a bioconductor package for differential expression analysis of digital gene expression data","volume":"26","author":"Robinson","year":"2010","journal-title":"Bioinformatics"},{"key":"2026061100564440900_btag157-B25","doi-asserted-by":"publisher","first-page":"741","DOI":"10.12688\/f1000research.14966.1","article-title":"iSEE: interactive summarized experiment explorer","volume":"7","author":"Rue-Albrecht","year":"2018","journal-title":"F1000Res"},{"key":"2026061100564440900_btag157-B26","doi-asserted-by":"publisher","first-page":"824","DOI":"10.1002\/jimd.12663","article-title":"Integrative omics approaches to advance rare disease diagnostics","volume":"46","author":"Smirnov","year":"2023","journal-title":"J Inherit Metab Dis"},{"key":"2026061100564440900_btag157-B27","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1146\/annurev-biodatasci-072018-021255","article-title":"RNA sequencing data: Hitchhiker\u2019s guide to expression analysis","volume":"2","author":"Van den Berge","year":"2019","journal-title":"Annu Rev Biomed Data Sci"},{"key":"2026061100564440900_btag157-B28","doi-asserted-by":"publisher","first-page":"284","DOI":"10.1089\/omi.2011.0118","article-title":"clusterProfiler: an R package for comparing biological themes among gene clusters","volume":"16","author":"Yu","year":"2012","journal-title":"OMICS"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/advance-article-pdf\/doi\/10.1093\/bioinformatics\/btag157\/67605185\/btag157.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/42\/4\/btag157\/67605185\/btag157.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/42\/4\/btag157\/67605185\/btag157.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T04:56:51Z","timestamp":1781153811000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/doi\/10.1093\/bioinformatics\/btag157\/8551566"}},"subtitle":[],"editor":[{"given":"Russell","family":"Schwartz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2026,3,27]]},"references-count":28,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2026,4,7]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btag157","relation":{},"ISSN":["1367-4803","1367-4811"],"issn-type":[{"value":"1367-4803","type":"print"},{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2026,4]]},"published":{"date-parts":[[2026,3,27]]},"article-number":"btag157"}}