{"doi":"10.1021/acs.jproteome.4c00461","title":"Calculating and Reporting Coefficients of Variation for DIA-Based Proteomics","abstract":null,"journal":"Journal of Proteome Research","year":2024,"id":637523,"datarank":0.48283137373023016,"base_score":3.2188758248682006,"endowment":3.2188758248682006,"self_citation_contribution":0.48283137373023016,"citation_network_contribution":0.0,"self_endowment_contribution":0.48283137373023016,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":24,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1328834,"name":"Alejandro J. Brenes","orcid":"0000-0001-8298-2463","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Calculating and Reporting Coefficients of Variation for DIA-Based Proteomics","abstract":"The coefficient of variation (CV) is a measure that is frequently used to assess data dispersion for mass spectrometry-based proteomics. In the current era of burgeoning technical developments, there has been an increased focus on using CVs to measure the quantitative precision of new methods. Thus, it has also become important to define a set of guidelines on how to calculate and report the CVs. This perspective shows the effects that the CV equation, data normalization as well as software parameters, can have on data dispersion and CVs, highlighting the importance of reporting all these variables within the methods section. It also proposes a set of recommendations to calculate and report CVs for technical studies, where the main objective is to benchmark technical developments with a focus on precision. To assist in this process, a novel R package to calculate CVs (proteomicsCV) is also included.","is_dataset_classified":null,"base_score":3.1780538303479458,"endowment":3.1780538303479458,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"39573822","pmcid":"PMC11629372","openalex_id":"https://openalex.org/W4404627873","authors":[],"funders":[],"total_grants":0,"fwci":3.8467,"citation_percentile":0.9505123,"influential_citations":0,"citation_trend":[{"year":2025,"count":16},{"year":2026,"count":7}],"oa_status":"hybrid","license":"cc-by","oa_locations":[{"url":"https://pubs.acs.org/doi/pdf/10.1021/acs.jproteome.4c00461?ref=article_openPDF","host_type":"journal"},{"url":"https://pubs.acs.org/doi/pdf/10.1021/acs.jproteome.4c00461?ref=article_openPDF","host_type":"publisher"},{"url":"https://pubs.acs.org/doi/pdf/10.1021/acs.jproteome.4c00461","host_type":"publisher"},{"url":"https://doi.org/10.1021/acs.jproteome.4c00461","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/39573822","host_type":"repository"},{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/11629372","host_type":"repository"},{"url":"https://www.research.ed.ac.uk/files/482085023/brenes-2024-calculating-and-reporting-coefficients-of-variation-for-dia-based-proteomics.pdf","host_type":"repository"},{"url":"https://www.research.ed.ac.uk/en/publications/04889afb-b549-4e51-a2bf-3d70e8e292ec","host_type":"repository"},{"url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC11629372/pdf/pr4c00461.pdf","host_type":"repository"},{"url":"https://europepmc.org/articles/PMC11629372","host_type":"Europe_PMC"},{"url":"https://europepmc.org/articles/PMC11629372?pdf=render","host_type":"Europe_PMC"}],"fields_of_study":["Advanced Proteomics Techniques and Applications","Metabolomics and Mass Spectrometry Studies","Mass Spectrometry Techniques and Applications","Proteomics","Software","Mass Spectrometry","Humans","Reproducibility of Results"],"mesh_terms":["Humans","Software","Mass Spectrometry","Reproducibility of Results","Proteomics"],"keywords":["Normalization (sociology)","Data set","Computer science","Benchmark (surveying)","Data mining","Set (abstract data type)","Artificial intelligence","Proteomics","Precision","Quantification","coefficient of variation","Cv"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Zero hunger"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[{"name":"pxd"}],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-06T19:06:14.676098Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}