{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T11:11:35Z","timestamp":1788347495207,"version":"build-2803163510"},"reference-count":8,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2021,11,12]],"date-time":"2021-11-12T00:00:00Z","timestamp":1636675200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002347","name":"Bundesministerium f\u00fcr Bildung und Forschung","doi-asserted-by":"publisher","award":["161L0212E"],"award-info":[{"award-number":["161L0212E"]}],"id":[{"id":"10.13039\/501100002347","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,1,27]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>First-line data quality assessment and exploratory data analysis are integral parts of any data analysis workflow. In high-throughput quantitative omics experiments (e.g. transcriptomics, proteomics and metabolomics), after initial processing, the data are typically presented as a matrix of numbers (feature IDs \u00d7 samples). Efficient and standardized data quality metrics calculation and visualization are key to track the within-experiment quality of these rectangular data types and to guarantee for high-quality datasets and subsequent biological question-driven inference.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We present MatrixQCvis, which provides interactive visualization of data quality metrics at the per-sample and per-feature level using R\u2019s shiny framework. It provides efficient and standardized ways to analyze data quality of quantitative omics data types that come in a matrix-like format (features IDs \u00d7 samples). MatrixQCvis builds upon the Bioconductor SummarizedExperiment S4 class and thus facilitates the integration into existing workflows.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>MatrixQCVis is implemented in R. It is available via Bioconductor and released under the GPL v3.0 license.<\/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\/btab748","type":"journal-article","created":{"date-parts":[[2021,11,5]],"date-time":"2021-11-05T08:23:59Z","timestamp":1636100639000},"page":"1181-1182","source":"Crossref","is-referenced-by-count":11,"title":["MatrixQCvis: shiny-based interactive data quality exploration for omics data"],"prefix":"10.1093","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7917-5580","authenticated-orcid":false,"given":"Thomas","family":"Naake","sequence":"first","affiliation":[{"name":"Genome Biology Unit, European Molecular Biology Laboratory , Heidelberg 69117, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wolfgang","family":"Huber","sequence":"additional","affiliation":[{"name":"Genome Biology Unit, European Molecular Biology Laboratory , Heidelberg 69117, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2021,11,12]]},"reference":[{"key":"2023020108515784000_btab748-B1","author":"Ahlmann-Eltze","year":"2019"},{"key":"2023020108515784000_btab748-B2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1200\/PO.17.00135","article-title":"Clinical value of RNA sequencing-based classifiers for prediction of the five conventional breast cancer biomarkers: a report from the population-based multicenter Sweden cancerome analysis network-breast initiative","volume":"2","author":"Brueffer","year":"2018","journal-title":"JCO Precis. Oncol"},{"key":"2023020108515784000_btab748-B3","author":"Chang","year":"2021"},{"key":"2023020108515784000_btab748-B4","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1038\/s41586-019-0987-8","article-title":"Proteomics identifies new therapeutic targets of early-stage hepatocellular carcinoma","volume":"567","author":"Jiang","year":"2019","journal-title":"Nature"},{"key":"2023020108515784000_btab748-B5","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1093\/bioinformatics\/btn647","article-title":"arrayQualityMetrics\u2014a bioconductor package for quality assessment of microarray data","volume":"25","author":"Kauffmann","year":"2009","journal-title":"Bioinformatics"},{"key":"2023020108515784000_btab748-B6","doi-asserted-by":"crossref","first-page":"10241","DOI":"10.1021\/acs.analchem.0c00136","article-title":"Concepts and software package for efficient quality control in targeted metabolomics studies: meTaQuaC","volume":"92","author":"Kuhring","year":"2020","journal-title":"Anal. 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