MOTIVATION: The false discovery rate (FDR) provides a key statistical assessment for microarray studies. Its value depends on the proportion pi(0) of non-differentially expressed (non-DE) genes. In most microarray studies, many genes have small effects not easily separable from non-DE genes. As a result, current methods often overestimate pi(0) and FDR, leading to unnecessary loss of power in the overall analysis. METHODS: For the common two-sample comparison we derive a natural mixture model of the test statistic and an explicit bias formula in the standard estimation of pi(0). We suggest an improved estimation of pi(0) based on the mixture model and describe a practical likelihood-based procedure for this purpose. RESULTS: The analysis shows that a large bias occurs when pi(0) is far from 1 and when the non-centrality parameters of the distribution of the test statistic are near zero. The theoretical result also explains substantial discrepancies between non-parametric and model-based estimates of pi(0). Simulation studies indicate mixture-model estimates are less biased than standard estimates. The method is applied to breast cancer and lymphoma data examples. AVAILABILITY: An R-package OCplus containing functions to compute pi(0) based on the mixture model, the resulting FDR and other operating characteristics of microarray data, is freely available at http://www.meb.ki.se/~yudpaw CONTACT: yudi.pawitan@meb.ki.se and alexander.ploner@meb.ki.se.
MOTIVATION: The false discovery rate (FDR) provides a key statistical assessment for microarray studies. Its value depends on the proportion pi(0) of non-differentially expressed (non-DE) genes. In most microarray studies, many genes have small effects not easily separable from non-DE genes. As a result, current methods often overestimate pi(0) and FDR, leading to unnecessary loss of power in the overall analysis. METHODS: For the common two-sample comparison we derive a natural mixture model of the test statistic and an explicit bias formula in the standard estimation of pi(0). We suggest an improved estimation of pi(0) based on the mixture model and describe a practical likelihood-based procedure for this purpose. RESULTS: The analysis shows that a large bias occurs when pi(0) is far from 1 and when the non-centrality parameters of the distribution of the test statistic are near zero. The theoretical result also explains substantial discrepancies between non-parametric and model-based estimates of pi(0). Simulation studies indicate mixture-model estimates are less biased than standard estimates. The method is applied to breast cancer and lymphoma data examples. AVAILABILITY: An R-package OCplus containing functions to compute pi(0) based on the mixture model, the resulting FDR and other operating characteristics of microarray data, is freely available at http://www.meb.ki.se/~yudpaw CONTACT: yudi.pawitan@meb.ki.se and alexander.ploner@meb.ki.se.
Authors: E R Salazar; H G Richter; C Spichiger; N Mendez; D Halabi; K Vergara; I P Alonso; F A Corvalán; C Azpeleta; M Seron-Ferre; C Torres-Farfan Journal: J Physiol Date: 2018-09-17 Impact factor: 5.182
Authors: Adolfo Sequeira; Firoza Mamdani; Carl Ernst; Marquis P Vawter; William E Bunney; Veronique Lebel; Sonia Rehal; Tim Klempan; Alain Gratton; Chawki Benkelfat; Guy A Rouleau; Naguib Mechawar; Gustavo Turecki Journal: PLoS One Date: 2009-08-11 Impact factor: 3.240
Authors: Monique L Den Boer; Marjon van Slegtenhorst; Renée X De Menezes; Meyling H Cheok; Jessica G C A M Buijs-Gladdines; Susan T C J M Peters; Laura J C M Van Zutven; H Berna Beverloo; Peter J Van der Spek; Gaby Escherich; Martin A Horstmann; Gritta E Janka-Schaub; Willem A Kamps; William E Evans; Rob Pieters Journal: Lancet Oncol Date: 2009-01-08 Impact factor: 41.316