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

• Main Authors: Gaigneaux Anthoula, Bareke Eric, Pierre Michael, Berger Fabrice, De Meulder Bertrand, De Hertogh Benoît, Depiereux Eric
• Format: Article
• Language: English
• Published: BMC 2010-01-01
• Series: BMC Bioinformatics
• Online Access: http://www.biomedcentral.com/1471-2105/11/17

<p>Abstract</p> <p>Background</p> <p>Recent 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.</p> <p>Results</p> <p>Our 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.</p> <p>Conclusions</p> <p>Performance analysis refined the results from benchmarks published previously.</p> <p>We show that the Shrinkage <it>t </it>test (close to Limma) was the best of the methods tested, except when two replicates were examined, where the Regularized <it>t </it>test and the Window <it>t </it>test performed slightly better.</p> <p>Availability</p> <p>The R scripts used for the analysis are available at <url>http://urbm-cluster.urbm.fundp.ac.be/~bdemeulder/</url>.</p>