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. CONCLUSION: The method presented is a powerful tool for extracting information on differential expression from GeneChip expression studies with limited or no replicates

BGX: a Bioconductor package for the Bayesian integrated analysis of Affymetrix GeneChips

Abstract Background Affymetrix 3' GeneChip microarrays are widely used to profile the expression of thousands of genes simultaneously. They differ from many other microarray types in that GeneChips are hybridised using a single labelled extract and because they contain multiple 'match' and 'mismatch' sequences for each transcript. Most algorithms extract the signal from GeneChip experiments in a sequence of separate steps, including background correction and normalisation, which inhibits the simultaneous use of all available information. They principally provide a point estimate of gene expression and, in contrast to BGX, do not fully integrate the uncertainty arising from potentially heterogeneous responses of the probes. Results BGX is a new Bioconductor R package that implements an integrated Bayesian approach to the analysis of 3' GeneChip data. The software takes into account additive and multiplicative error, non-specific hybridisation and replicate summarisation in the spirit of the model outlined in 1. It also provides a posterior distribution for the expression of each gene. Moreover, BGX can take into account probe affinity effects from probe sequence information where available. The package employs a novel adaptive Markov chain Monte Carlo (MCMC) algorithm that raises considerably the efficiency with which the posterior distributions are sampled from. Finally, BGX incorporates various ways to analyse the results, such as ranking genes by expression level as well as statistically based methods for estimating the amount of up and down regulated genes between two conditions. Conclusion BGX performs well relative to other widely used methods at estimating expression levels and fold changes. It has the advantage that it provides a statistically sound measure of uncertainty for its estimates. BGX includes various analysis functions to visualise and exploit the rich output that is produced by the Bayesian model.</p

BGX: a Bioconductor package for the Bayesian integrated analysis of Affymetrix GeneChips-2

<p><b>Copyright information:</b></p><p>Taken from "BGX: a Bioconductor package for the Bayesian integrated analysis of Affymetrix GeneChips"</p><p>http://www.biomedcentral.com/1471-2105/8/439</p><p>BMC Bioinformatics 2007;8():439-439.</p><p>Published online 12 Nov 2007</p><p>PMCID:PMC2216047.</p><p></p>As probe affinity categories increase, the distributions shift from left to right. The black density line is the distribution from the original BGX model and illustrates the discriminatory power of the probe affinity extension

BGX: a Bioconductor package for the Bayesian integrated analysis of Affymetrix GeneChips-6

<p><b>Copyright information:</b></p><p>Taken from "BGX: a Bioconductor package for the Bayesian integrated analysis of Affymetrix GeneChips"</p><p>http://www.biomedcentral.com/1471-2105/8/439</p><p>BMC Bioinformatics 2007;8():439-439.</p><p>Published online 12 Nov 2007</p><p>PMCID:PMC2216047.</p><p></p> (left). plots the density of the difference in the posterior distributions of a given gene between two conditions (right)

BGX: a Bioconductor package for the Bayesian integrated analysis of Affymetrix GeneChips-5

<p><b>Copyright information:</b></p><p>Taken from "BGX: a Bioconductor package for the Bayesian integrated analysis of Affymetrix GeneChips"</p><p>http://www.biomedcentral.com/1471-2105/8/439</p><p>BMC Bioinformatics 2007;8():439-439.</p><p>Published online 12 Nov 2007</p><p>PMCID:PMC2216047.</p><p></p> on the parameter of expressed genes (left & centre). A similar improvement was observed for the IACT of the parameter, for all genes (right)

BGX: a Bioconductor package for the Bayesian integrated analysis of Affymetrix GeneChips-1

<p><b>Copyright information:</b></p><p>Taken from "BGX: a Bioconductor package for the Bayesian integrated analysis of Affymetrix GeneChips"</p><p>http://www.biomedcentral.com/1471-2105/8/439</p><p>BMC Bioinformatics 2007;8():439-439.</p><p>Published online 12 Nov 2007</p><p>PMCID:PMC2216047.</p><p></p>ted genes between two conditions using a routine incorporated in the package