{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T18:08:51Z","timestamp":1774116531571,"version":"3.50.1"},"reference-count":17,"publisher":"Oxford University Press (OUP)","issue":"5","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2006,3,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Motivation: The false discovery rate (fdr) is a key tool for statistical assessment of differential expression (DE) in microarray studies. Overall control of the fdr alone, however, is not sufficient to address the problem of genes with small variance, which generally suffer from a disproportionally high rate of false positives. It is desirable to have an fdr-controlling procedure that automatically accounts for gene variability.<\/jats:p><jats:p>Methods: We generalize the local fdr as a function of multiple statistics, combining a common test statistic for assessing DE with its standard error information. We use a non-parametric mixture model for DE and non-DE genes to describe the observed multi-dimensional statistics, and estimate the distribution for non-DE genes via the permutation method. We demonstrate this fdr2d approach for simulated and real microarray data.<\/jats:p><jats:p>Results: The fdr2d allows objective assessment of DE as a function of gene variability. We also show that the fdr2d performs better than commonly used modified test statistics.<\/jats:p><jats:p>Availability: An R-package OCplus containing functions for computing fdr2d() and other operating characteristics of microarray data is available at<\/jats:p><jats:p>Contact: \u00a0alexander.ploner@meb.ki.se<\/jats:p>","DOI":"10.1093\/bioinformatics\/btk013","type":"journal-article","created":{"date-parts":[[2005,12,21]],"date-time":"2005-12-21T02:18:10Z","timestamp":1135131490000},"page":"556-565","source":"Crossref","is-referenced-by-count":66,"title":["Multidimensional local false discovery rate for microarray studies"],"prefix":"10.1093","volume":"22","author":[{"given":"Alexander","family":"Ploner","sequence":"first","affiliation":[{"name":"Department of Medical Epidemiology and Biostatistics, Karolinska Institutet 1 \u00a0 1 \u00a0 \u00a0 17177 Stockholm, Sweden"}]},{"given":"Stefano","family":"Calza","sequence":"additional","affiliation":[{"name":"Dipartimento di Scienze Biomediche e Biotecnologie, Universit\u00e0 degli Studi di Brescia 2 \u00a0 2 \u00a0 \u00a0 11 25123 Brescia, Italy"}]},{"given":"Arief","family":"Gusnanto","sequence":"additional","affiliation":[{"name":"MRC Biostatistics Unit, Institute of Public Health 3 \u00a0 3 \u00a0 \u00a0 Cambridge CB2 2SR, UK"}]},{"given":"Yudi","family":"Pawitan","sequence":"additional","affiliation":[{"name":"Department of Medical Epidemiology and Biostatistics, Karolinska Institutet 1 \u00a0 1 \u00a0 \u00a0 17177 Stockholm, Sweden"}]}],"member":"286","published-online":{"date-parts":[[2005,12,20]]},"reference":[{"key":"2023012408530438800_b1","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1111\/j.2517-6161.1995.tb02031.x","article-title":"Controling the false discovery rate: a practical and powerful approach to multiple testing","volume":"57","author":"Benjamini","year":"1995","journal-title":"J. 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