A powerful method for detecting differentially expressed genes from GeneChip arrays that does not require replicates.
BACKGROUND
Studies of differential expression that use Affymetrix GeneChip arrays are often carried out with a limited number of replicates. Reasons for this include financial considerations and limits on the available amount of RNA for sample preparation. In addition, failed hybridizations are not uncommon leading to a further reduction in the number of replicates available for analysis. Most existing methods for studying differential expression rely on the availability of replicates and the demand for alternative methods that require few or no replicates is high.


RESULTS
We describe a statistical procedure for performing differential expression analysis without replicates. The procedure relies on a Bayesian integrated approach (BGX) to the analysis of Affymetrix GeneChips. The BGX method estimates a posterior distribution of expression for each gene and condition, from a simultaneous consideration of the available probe intensities representing the gene in a condition. Importantly, posterior distributions of expression are obtained regardless of the number of replicates available. We exploit these posterior distributions to create ranked gene lists that take into account the estimated expression difference as well as its associated uncertainty. We estimate the proportion of non-differentially expressed genes empirically, allowing an informed choice of cut-off for the ranked gene list, adapting an approach proposed by Efron. We assess the performance of the method, and compare it to those of other methods, on publicly available spike-in data sets, as well as in a proper biological setting.


CONCLUSIONS
The method presented is a powerful tool for extracting information on differential expression from GeneChip expression studies with limited or no replicates.
Hein
Anne-Mette K
AM
Dept. of Epidemiology and Public Health, Imperial College London, Norfolk Place, London, UK. a.hein@imperial.ac.uk
Richardson
Sylvia
S
D000465:Algorithms; D000818:Animals; D001499:Bayes Theorem; D020869:Gene Expression Profiling; D005786:Gene Expression Regulation; D005821:Genetic Techniques; D006801:Humans; D051379:Mice; D015233:Models, Statistical; D009693:Nucleic Acid Hybridization; D020411:Oligonucleotide Array Sequence Analysis; D012313:RNA / Q000737:chemistry; D012372:ROC Curve
D012313:RNA