{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["22(4)"],"submitter":["Wolski WE"],"funding":["Universit?t Z?rich"],"pubmed_abstract":["Mass spectrometry is widely used for quantitative proteomics studies, relative protein quantification, and differential expression analysis of proteins. There is a large variety of quantification software and analysis tools. Nevertheless, there is a need for a modular, easy-to-use application programming interface in <i>R</i> that transparently supports a variety of well principled statistical procedures to make applying them to proteomics data, comparing and understanding their differences easy. The <i>prolfqua</i> package integrates essential steps of the mass spectrometry-based differential expression analysis workflow: quality control, data normalization, protein aggregation, statistical modeling, hypothesis testing, and sample size estimation. The package makes integrating new data fo"],"journal":["Journal of proteome research"],"pagination":["1092-1104"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10088014"],"repository":["biostudies-literature"],"pubmed_title":["<i>prolfqua</i>: A Comprehensive <i>R</i>-Package for Proteomics Differential Expression Analysis."],"pmcid":["PMC10088014"],"pubmed_authors":["d'Errico M","Schlapbach R","Wolski WE","Nanni P","Grossmann J","Panse C"],"additional_accession":[]},"is_claimable":false,"name":"<i>prolfqua</i>: A Comprehensive <i>R</i>-Package for Proteomics Differential Expression Analysis.","description":"Mass spectrometry is widely used for quantitative proteomics studies, relative protein quantification, and differential expression analysis of proteins. There is a large variety of quantification software and analysis tools. Nevertheless, there is a need for a modular, easy-to-use application programming interface in <i>R</i> that transparently supports a variety of well principled statistical procedures to make applying them to proteomics data, comparing and understanding their differences easy. The <i>prolfqua</i> package integrates essential steps of the mass spectrometry-based differential expression analysis workflow: quality control, data normalization, protein aggregation, statistical modeling, hypothesis testing, and sample size estimation. The package makes integrating new data fo","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023 Apr","modification":"2026-05-29T05:27:33.485Z","creation":"2026-05-16T03:07:30.315Z"},"accession":"S-EPMC10088014","cross_references":{"pubmed":["36939687"],"doi":["10.1021/acs.jproteome.2c00441"]}}