{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T02:43:40Z","timestamp":1773283420251,"version":"3.50.1"},"reference-count":15,"publisher":"Oxford University Press (OUP)","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2005,2,15]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Motivation: Identifying differentially regulated genes in experiments comparing two experimental conditions is often a key step in the microarray data analysis process. Many different approaches and methodological developments have been put forward, yet the question remains open.<\/jats:p><jats:p>Results: Varmixt is a powerful and efficient novel methodology for this task. It is based on a flexible and realistic variance modelling strategy. It compares favourably with other popular techniques (standard t-test, SAM and Cyber-T). The relevance of the approach is demonstrated with real-world and simulated datasets. The analysis strategy was successfully applied to both a \u2018two-colour\u2019 cDNA microarray and an Affymetrix Genechip. Strong control of false positive and false negative rates is proven in large simulation studies.<\/jats:p><jats:p>Availability: The R package is freely available at http:\/\/www.inapg.inra.fr\/ens_rech\/mathinfo\/recherche\/mathematique\/outil.html<\/jats:p><jats:p>Contact: \u00a0delmar@inapg.inra.fr<\/jats:p><jats:p>Supplementary information: \u00a0http:\/\/www.inapg.inra.fr\/ens_rech\/mathinfo\/recherche\/mathematique\/outil.html<\/jats:p>","DOI":"10.1093\/bioinformatics\/bti023","type":"journal-article","created":{"date-parts":[[2004,9,17]],"date-time":"2004-09-17T00:13:37Z","timestamp":1095380017000},"page":"502-508","source":"Crossref","is-referenced-by-count":73,"title":["VarMixt: efficient variance modelling for the differential analysis of replicated gene expression data"],"prefix":"10.1093","volume":"21","author":[{"given":"Paul","family":"Delmar","sequence":"first","affiliation":[]},{"given":"St\u00e9phane","family":"Robin","sequence":"additional","affiliation":[]},{"given":"Jean Jacques","family":"Daudin","sequence":"additional","affiliation":[]}],"member":"286","published-online":{"date-parts":[[2004,9,16]]},"reference":[{"key":"2023013107234563200_B1","doi-asserted-by":"crossref","unstructured":"Baldi, P. and Long, A. 2001A bayesian framework for the analysis of microarray expression data: regularized t-test and statistical inferences of gene changes. Bioinformatics17509\u2013519","DOI":"10.1093\/bioinformatics\/17.6.509"},{"key":"2023013107234563200_B2","doi-asserted-by":"crossref","unstructured":"Cole, S.W., Galic, Z., Zack, J.A. 2003Controlling false-negative errors in microarray differential expression analysis: a prim approach. Bioinformatics191808\u20131816","DOI":"10.1093\/bioinformatics\/btg242"},{"key":"2023013107234563200_B3","unstructured":"Delmar, P., Robin, S., Tronik-Leroux, D., Daudin, J. 2005Mixture model on the variance for the differential analysis of gene expression data. J. R. Stat. Soc., Ser. C5431\u201350"},{"key":"2023013107234563200_B4","doi-asserted-by":"crossref","unstructured":"Draghici, S., Kulaeva, O., Hoff, B., Petrov, A., Shams, S., Tainsky, M.A. 2003Noise sampling method: an ANOVA approach allowing robust selection of differentially regulated genes measured by DNA microarrays. Bioinformatics191348\u20131359","DOI":"10.1093\/bioinformatics\/btg165"},{"key":"2023013107234563200_B5","unstructured":"Gentleman, R. and Carey, V. 2002Bioconductor. R News211\u201316"},{"key":"2023013107234563200_B6","unstructured":"Hughes, T., Marton, M., Jones, A., Roberts, C., Stoughton, R., Armour, C., Bennett, H., Coffey, E., Dai, H., He, Y. 2000Functional discovery via a compendium of expression profiles. Cell102109\u2013126"},{"key":"2023013107234563200_B7","doi-asserted-by":"crossref","unstructured":"Irizarry, R.A., Bolstad, B.M., Collin, F., Cope, L.M., Hobbs, B., Speed, T.P. 2003Summaries of affymetrix genechip probe level data. Nucleic Acids Res.31e15","DOI":"10.1093\/nar\/gng015"},{"key":"2023013107234563200_B8","unstructured":"Kerr, M., Afshari, C., Bennett, L., Bushel, P., Martinez, J., Walker, N., Churchill, G. 2002Statistical analysis of a gene expression microarray experiment with replication. Stat. Sinica12203\u2013218"},{"key":"2023013107234563200_B9","unstructured":"L\u00f6nnstedt, I. and Speed, T. 2002Replicated microarray data. Stat. Sinica1231\u201346"},{"key":"2023013107234563200_B10","doi-asserted-by":"crossref","unstructured":"Mary-Huard, T., Daudin, J.-J., Robin, S., Bitton, F., Cabannes, E., Hilson, P. 2004Spotting effect in microarray experiments. BMC Bioinformatics563","DOI":"10.1186\/1471-2105-5-63"},{"key":"2023013107234563200_B11","doi-asserted-by":"crossref","unstructured":"Rocke, D.M. and Durbin, B. 2003Approximate variance-stabilizing transformations for gene-expression microarray data. Bioinformatics19966\u2013972","DOI":"10.1093\/bioinformatics\/btg107"},{"key":"2023013107234563200_B12","unstructured":"Schuchhardt, J., Beule, D., Malik, A., Wolski, E., Eickhoff, H., Lehrach, H., Herzel, H. 2000Normalization strategies for cdna microarrays. Nucleic Acids Res.28e41"},{"key":"2023013107234563200_B13","doi-asserted-by":"crossref","unstructured":"Tusher, V., Tibshirani, R., Chu, G. 2001Significance analysis of microarrays applied to ionizing radiation response. Proc. Nat Acad. Sci. USA985116\u20135121","DOI":"10.1073\/pnas.091062498"},{"key":"2023013107234563200_B14","doi-asserted-by":"crossref","unstructured":"Wang, S. and Ethier, S. 2004A generalized likelihood ratio test to identify differentially expressed genes from microarray data. Bioinformatics20100\u2013104","DOI":"10.1093\/bioinformatics\/btg384"},{"key":"2023013107234563200_B15","doi-asserted-by":"crossref","unstructured":"Wolfinger, R.D., Gibson, G., Wolfinger, E.D., Bennett, L., Hamadeh, H., Bushel, P., Afshari, C., Paules, R.S. 2001Assessing gene significance from cdna microarray expression data via mixed models. J. Comput. Biol.8625\u2013637","DOI":"10.1089\/106652701753307520"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/21\/4\/502\/48965080\/bioinformatics_21_4_502.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/21\/4\/502\/48965080\/bioinformatics_21_4_502.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,18]],"date-time":"2024-12-18T16:56:22Z","timestamp":1734540982000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/21\/4\/502\/203155"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2004,9,16]]},"references-count":15,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2005,2,15]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/bti023","relation":{},"ISSN":["1367-4811","1367-4803"],"issn-type":[{"value":"1367-4811","type":"electronic"},{"value":"1367-4803","type":"print"}],"subject":[],"published-other":{"date-parts":[[2005,2,15]]},"published":{"date-parts":[[2004,9,16]]}}}