A comparison of statistical methods for analysis of high density oligonucleotide array data.

Gene expression profiling has become an invaluable tool in functional genomics. A wide variety of statistical methods have been employed to analyze the data generated in experiments using Affymetrix GeneChip microarrays. It is important to understand the relative performance of these methods in terms of accuracy in detecting and quantifying relative gene expression levels and changes in gene expression. Three different analysis approaches have been compared in this work: non-parametric statistical methods implemented in Affymetrix Microarray Analysis Suite v5.0 (MAS5); an error-modeling based approach implemented in Rosetta Resolver v3.1; and an intensity-modeling approach implemented in dChip v1.1. A Latin Square data set generated and made available by Affymetrix was used in the comparison. All three methods-Resolver, MAS5 and the version of dChip based on the difference between perfect match and mismatch intensities-perform well in quantifying gene expression. Presence calls made by MAS5 and Resolver perform well at high concentrations, but they cannot be relied upon at low concentrations. The performance of Resolver and MAS5 in detecting 2-fold changes in transcript concentration is superior to that of dChip. At a comparable false positive rate, Resolver and MAS5 are able to detect many more true changes in transcript concentration. Estimated fold changes calculated by all the methods are biased below the true values.