Research article

Methods for evaluating gene expression from Affymetrix microarray datasets

Ning Jiang^{* 1} , Lindsey J Leach^{* 1} , Xiaohua Hu³ , Elena Potokina¹ , Tianye Jia¹ , Arnis Druka² , Robbie Waugh² , Michael J Kearsey¹ and Zewei W Luo^1,3

¹School of Biosciences, The University of Birmingham, Edgbaston Birmingham B15 2TT, England, UK

²Scottish Crop Research Institute, Invergowrie, Dundee DD2 5DA, Scotland, UK

³Institute of Biostatistics, Fudan University, Shanghai 200433, PR China

author email corresponding author email* Contributed equally

BMC Bioinformatics 2008, 9:284doi:10.1186/1471-2105-9-284

Published:	17 June 2008

Abstract

Background

Affymetrix high density oligonucleotide expression arrays are widely used across all fields of biological research for measuring genome-wide gene expression. An important step in processing oligonucleotide microarray data is to produce a single value for the gene expression level of an RNA transcript using one of a growing number of statistical methods. The challenge for the researcher is to decide on the most appropriate method to use to address a specific biological question with a given dataset. Although several research efforts have focused on assessing performance of a few methods in evaluating gene expression from RNA hybridization experiments with different datasets, the relative merits of the methods currently available in the literature for evaluating genome-wide gene expression from Affymetrix microarray data collected from real biological experiments remain actively debated.

Results

The present study reports a comprehensive survey of the performance of all seven commonly used methods in evaluating genome-wide gene expression from a well-designed experiment using Affymetrix microarrays. The experiment profiled eight genetically divergent barley cultivars each with three biological replicates. The dataset so obtained confers a balanced and idealized structure for the present analysis. The methods were evaluated on their sensitivity for detecting differentially expressed genes, reproducibility of expression values across replicates, and consistency in calling differentially expressed genes. The number of genes detected as differentially expressed among methods differed by a factor of two or more at a given false discovery rate (FDR) level. Moreover, we propose the use of genes containing single feature polymorphisms (SFPs) as an empirical test for comparison among methods for the ability to detect true differential gene expression on the basis that SFPs largely correspond to cis-acting expression regulators. The PDNN method demonstrated superiority over all other methods in every comparison, whilst the default Affymetrix MAS5.0 method was clearly inferior.

Conclusion

A comprehensive assessment of seven commonly used data extraction methods based on an extensive barley Affymetrix gene expression dataset has shown that the PDNN method has superior performance for the detection of differentially expressed genes.