A benchmark for statistical microarray data analysis that preserves actual biological and technical variance

BMC Bioinformatics

Methodology Article

Fabrice Berger¹  Eric Bareke¹  Benoît De Hertogh¹  Anthoula Gaigneaux¹  Eric Depiereux¹  Michael Pierre¹  Bertrand De Meulder¹ 

[1] Unité de Recherche en Biologie Moléculaire, Facultés Universitaires Notre-Dame de la Paix (F.U.N.D.P.), Rue de Bruxelles, 61, B-5000, Namur, Belgium;

关键词: Real Dataset;    Benchmark Dataset;    Average Fold Change;    Positive Predictive Power;    Residual Mean Square;   

DOI  :  10.1186/1471-2105-11-17

 received in 2009-05-29, accepted in 2010-01-11,  发布年份 2010

来源: Springer

【 摘 要 】

BackgroundRecent reanalysis of spike-in datasets underscored the need for new and more accurate benchmark datasets for statistical microarray analysis. We present here a fresh method using biologically-relevant data to evaluate the performance of statistical methods.ResultsOur novel method ranks the probesets from a dataset composed of publicly-available biological microarray data and extracts subset matrices with precise information/noise ratios. Our method can be used to determine the capability of different methods to better estimate variance for a given number of replicates. The mean-variance and mean-fold change relationships of the matrices revealed a closer approximation of biological reality.ConclusionsPerformance analysis refined the results from benchmarks published previously.We show that the Shrinkage t test (close to Limma) was the best of the methods tested, except when two replicates were examined, where the Regularized t test and the Window t test performed slightly better.AvailabilityThe R scripts used for the analysis are available at http://urbm-cluster.urbm.fundp.ac.be/~bdemeulder/.

【 授权许可】

Unknown   
© De Hertogh et al; licensee BioMed Central Ltd. 2010. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.