{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T08:43:14Z","timestamp":1771490594101,"version":"3.50.1"},"reference-count":9,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2004,9,6]],"date-time":"2004-09-06T00:00:00Z","timestamp":1094428800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/2.0"},{"start":{"date-parts":[[2004,9,6]],"date-time":"2004-09-06T00:00:00Z","timestamp":1094428800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/2.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                        <jats:title>Background<\/jats:title>\n                        <jats:p>Thousands of genes in a genomewide data set are tested against some null hypothesis, for detecting differentially expressed genes in microarray experiments. The expected proportion of false positive genes in a set of genes, called the False Discovery Rate (FDR), has been proposed to measure the statistical significance of this set. Various procedures exist for controlling the FDR. However the threshold (generally 5%) is arbitrary and a specific measure associated with each gene would be worthwhile.<\/jats:p>\n                     <\/jats:sec><jats:sec>\n                        <jats:title>Results<\/jats:title>\n                        <jats:p>Using process intensity estimation methods, we define and give estimates of the local FDR, which may be considered as the probability for a gene to be a false positive. After a global assessment rule controlling the false positive error, the local FDR is a valuable guideline for deciding wether a gene is differentially expressed. The interest of the method is illustrated on three well known data sets. A R routine for computing local FDR estimates from <jats:italic>p<\/jats:italic>-values is available at <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"http:\/\/www.inapg.fr\/ens_rech\/mathinfo\/recherche\/mathematique\/outil.html\">http:\/\/www.inapg.fr\/ens_rech\/mathinfo\/recherche\/mathematique\/outil.html<\/jats:ext-link>.<\/jats:p>\n                     <\/jats:sec><jats:sec>\n                        <jats:title>Conclusions<\/jats:title>\n                        <jats:p>The local FDR associated with each gene measures the probability that it is a false positive. It gives the opportunity to compute the FDR of any given group of clones (of the same gene) or genes pertaining to the same regulation network or the same chromosomic region.<\/jats:p>\n                     <\/jats:sec>","DOI":"10.1186\/1471-2105-5-125","type":"journal-article","created":{"date-parts":[[2004,9,15]],"date-time":"2004-09-15T08:32:40Z","timestamp":1095237160000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":91,"title":["Determination of the differentially expressed genes in microarray experiments using local FDR"],"prefix":"10.1186","volume":"5","author":[{"given":"J","family":"Aubert","sequence":"first","affiliation":[]},{"given":"A","family":"Bar-Hen","sequence":"additional","affiliation":[]},{"given":"J-J","family":"Daudin","sequence":"additional","affiliation":[]},{"given":"S","family":"Robin","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2004,9,6]]},"reference":[{"key":"241_CR1","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1111\/j.2517-6161.1995.tb02031.x","volume":"57,1","author":"Y Benjamini","year":"1995","unstructured":"Benjamini Y, Hochberg Y: Controlling the false discovery rate: a practical and powerful approach to multiple testing.\n                           JRSSB 1995, 57,1: 289\u2013300.","journal-title":"JRSSB"},{"key":"241_CR2","doi-asserted-by":"publisher","first-page":"9440","DOI":"10.1073\/pnas.1530509100","volume":"100,16","author":"JD Storey","year":"2003","unstructured":"Storey JD, Tibshirani R: Statistical 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TP: Statistical methods for identifying differentially expressed genes in replicated cdna microarray experiments.\n                           Statistica Sinica 2002, 12: 1.","journal-title":"Statistica Sinica"},{"key":"241_CR8","doi-asserted-by":"crossref","DOI":"10.1002\/9781119115151","volume-title":"Statistics for Spatial Data","author":"N Cressie","year":"1993","unstructured":"Cressie N: Statistics for Spatial Data New York: Wiley 1993."},{"key":"241_CR9","volume-title":"Continuous Univariate Distributions","author":"NL Johnson","year":"1995","unstructured":"Johnson NL, Kotz S, Balakrishnan N: Continuous Univariate Distributions New York: Wiley 1995."}],"updated-by":[{"DOI":"10.1186\/1471-2105-6-42","type":"erratum","label":"Erratum","source":"publisher","updated":{"date-parts":[[2005,3,3]],"date-time":"2005-03-03T00:00:00Z","timestamp":1109808000000}}],"container-title":["BMC 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