Research article

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

Kerby Shedden¹ , Wei Chen² , Rork Kuick³ , Debashis Ghosh² , James Macdonald⁴ , Kathleen R Cho⁵ , Thomas J Giordano⁵ , Stephen B Gruber⁶ , Eric R Fearon⁶ , Jeremy MG Taylor² and Samir Hanash³

¹Department of Statistics, University of Michigan, Ann Arbor, Michigan, USA

²Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA

³Department of Pediatrics, University of Michigan, Ann Arbor, Michigan, USA

⁴Comprehensive Cancer Center, University of Michigan, Ann Arbor, Michigan, USA

⁵Department of Pathology, University of Michigan, Ann Arbor, Michigan, USA

⁶Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA

author email corresponding author email

BMC Bioinformatics 2005, 6:26doi:10.1186/1471-2105-6-26

Published:	10 February 2005

Abstract

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 non-differentially 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% of the same genes differentially expressed, had less variable test statistics under randomization, and had a greater number of large test statistics in the experimental data. Poor performance of one method was directly tied to a tendency to produce highly variable test statistic values under randomization. Based on an overall measure of performance, two of the seven methods (Dchip and a trimmed mean approach) are superior in the two data sets considered here. Two other methods (MAS5 and GCRMA-EB) are inferior, while results for the other three methods are mixed.

Conclusions

Choice of processing method has a major impact on differential expression analysis of microarray data. Previously reported performance analyses using tightly controlled calibration data sets are not highly consistent with results reported here using data from human tissue samples. Performance of array processing methods in disease profiling and other realistic biological studies should be given greater consideration when comparing Affymetrix processing methods.