Validation of differential gene expression algorithms: Application comparing fold-change estimation to hypothesis testing

Background: Sustained research on the problem of determining which genes are differentially expressed on the basis of microarray data has yielded a plethora of statistical algorithms, each justified by theory, simulation, or ad hoc validation and yet differing in practical results from equally justified algorithms. Recently, a concordance method that measures agreement among gene lists have been introduced to assess various aspects of differential gene expression detection. This method has the advantage of basing its assessment solely on the results of real data analyses, but as it requires examining gene lists of given sizes, it may be unstable. Results: Two methodologies for assessing predictive error are described: a cross-validation method and a posterior predictive method. As a nonparametric method of estimating prediction error from observed expression levels, cross validation provides an empirical approach to assessing algorithms for detecting differential gene expression that ...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Corey M. Yanofsky, David R. Bickel
• Keywords: Algorithms, BMCBI 2010, Gene Lists, Posterior Predictive Method