Comparison of seven methods for producing Affymetrix expression scores based on False Discovery Rates in disease profiling data

Background: A critical step in processing oligonucleotide microarray data is combining the information in multiple probes to produce a single number that best captures the expression level of a RNA transcript. Several systematic studies comparing multiple methods for array processing have used tightly controlled calibration data sets as the basis for comparison. Here we compare performances for seven processing methods using two data sets originally collected for disease profiling studies. An emphasis is placed on understanding sensitivity for detecting differentially expressed genes in terms of two key statistical determinants: test statistic variability for nondifferentially expressed genes, and test statistic size for truly differentially expressed genes. Results: In the two data sets considered here, up to seven-fold variation across the processing methods was found in the number of genes detected at a given false discovery rate (FDR). The best performing methods called up to 90% ...

• Type: Journal
• Year: 2005
• Where: BMCBI
• Authors: Kerby Shedden, Wei Chen, Rork Kuick, Debashis Ghosh, James W. MacDonald, Kathleen R. Cho, Thomas J. Giordano, Stephen B. Gruber, Eric R. Fearon, Jeremy M. G. Taylor, Samir Hanash
• Keywords: BMCBI 2005, Expressed Genes, Processing Methods, Test Statistics