{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,13]],"date-time":"2026-06-13T16:52:30Z","timestamp":1781369550700,"version":"3.54.1"},"reference-count":29,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2022,12,10]],"date-time":"2022-12-10T00:00:00Z","timestamp":1670630400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Agencia Andaluza del Conocimiento, Regional Government of Andaluc\u00eda, in Spain"},{"DOI":"10.13039\/501100008530","name":"European Regional Development Fund","doi-asserted-by":"publisher","award":["B-TIC-136-UGR20"],"award-info":[{"award-number":["B-TIC-136-UGR20"]}],"id":[{"id":"10.13039\/501100008530","id-type":"DOI","asserted-by":"publisher"}]},{"name":"State Research Agency"},{"name":"Spain and the European Social Fund","award":["RYC2020-030536-I"],"award-info":[{"award-number":["RYC2020-030536-I"]}]},{"DOI":"10.13039\/100005910","name":"AEI","doi-asserted-by":"publisher","award":["PID2020-118139RB-I00"],"award-info":[{"award-number":["PID2020-118139RB-I00"]}],"id":[{"id":"10.13039\/100005910","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,1,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>ANOVA Simultaneous Component Analysis (ASCA) is a popular method for the analysis of multivariate data yielded by designed experiments. Meaningful associations between factors\/interactions of the experimental design and measured variables in the dataset are typically identified via significance testing, with permutation tests being the standard go-to choice. However, in settings with large numbers of variables, like omics (genomics, transcriptomics, proteomics and metabolomics) experiments, the \u2018holistic\u2019 testing approach of ASCA (all variables considered) often overlooks statistically significant effects encoded by only a few variables (biomarkers).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We hereby propose Variable-selection ASCA (VASCA), a method that generalizes ASCA through variable selection, augmenting its statistical power without inflating the Type-I error risk. The method is evaluated with simulations and with a real dataset from a multi-omic clinical experiment. We show that VASCA is more powerful than both ASCA and the widely adopted false discovery rate controlling procedure; the latter is used as a benchmark for variable selection based on multiple significance testing. We further illustrate the usefulness of VASCA for exploratory data analysis in comparison to the popular partial least squares discriminant analysis method and its sparse counterpart.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>The code for VASCA is available in the MEDA Toolbox at https:\/\/github.com\/josecamachop\/MEDA-Toolbox (release v1.3). The simulation results and motivating example can be reproduced using the repository at https:\/\/github.com\/josecamachop\/VASCA\/tree\/v1.0.0 (DOI 10.5281\/zenodo.7410623).<\/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\/btac795","type":"journal-article","created":{"date-parts":[[2022,12,10]],"date-time":"2022-12-10T07:24:23Z","timestamp":1670657063000},"source":"Crossref","is-referenced-by-count":16,"title":["Variable-selection ANOVA Simultaneous Component Analysis (VASCA)"],"prefix":"10.1093","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9804-8122","authenticated-orcid":false,"given":"Jos\u00e9","family":"Camacho","sequence":"first","affiliation":[{"name":"Signal Theory, Networking and Communications Department, University of Granada , Granada 18014, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Raffaele","family":"Vitale","sequence":"additional","affiliation":[{"name":"University of Lille, CNRS, LASIRE (UMR 8516), Laboratoire Avanc\u00e9 de Spectroscopie pour les Interactions, la R\u00e9activit\u00e9 et l\u2019Environnement , Lille F-59000, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2141-7185","authenticated-orcid":false,"given":"David","family":"Morales-Jim\u00e9nez","sequence":"additional","affiliation":[{"name":"Signal Theory, Networking and Communications Department, University of Granada , Granada 18014, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carolina","family":"G\u00f3mez-Llorente","sequence":"additional","affiliation":[{"name":"Department of Biochemistry and Molecular Biology II, School of Pharmacy, Institute of Nutrition and Food Technology \u201cJos\u00e9 Mataix\u201d, Biomedical Research Center, University of Granada , Granada 18160, Spain"},{"name":"Instituto de Investigaci\u00f3n Biosanitaria, ibs.GRANADA , Granada, Spain"},{"name":"CIBEROBN (Physiopathology of Obesity and Nutrition CB12\/03\/30038), Instituto de Salud Carlos III , Madrid 28029, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2022,12,10]]},"reference":[{"key":"2023010723555106000_btac795-B1","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1080\/00949650215733","article-title":"Permutation tests for multi-factorial analysis of variance","volume":"73","author":"Anderson","year":"2003","journal-title":"J. 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