Evaluating methods for ranking differentially expressed genes applied to MicroArray Quality Control data

Background: Statistical methods for ranking differentially expressed genes (DEGs) from gene expression data should be evaluated with regard to high sensitivity, specificity, and reproducibility. In our previous studies, we evaluated eight gene ranking methods applied to only Affymetrix GeneChip data. A more general evaluation that also includes other microarray platforms, such as the Agilent or Illumina systems, is desirable for determining which methods are suitable for each platform and which method has better inter-platform reproducibility. Results: We compared the eight gene ranking methods using the MicroArray Quality Control (MAQC) datasets produced by five manufacturers: Affymetrix, Applied Biosystems, Agilent, GE Healthcare, and Illumina. The area under the curve (AUC) was used as a measure for both sensitivity and specificity. Although the highest AUC values can vary with the definition of “true” DEGs, the best methods were, in most cases, either the weighted average diff...

• Type: Journal
• Year: 2011
• Where: BMCBI
• Authors: Koji Kadota, Kentaro Shimizu
• Keywords: Biometrics, BMCBI 2011, Gene Expression Analysis, Gene Expression Data, Microarray Quality Control