Microarray experiments are characterized by the presence of many sources of experimental bias and a remarkably large technical variability. The assessment of differential expression for genes transcribed into a small number of mRNA copies heavily depends on the proper quantification of background fluorescence within spot. The rough model `observed = hybridization plus background' fluorescence is at first reformulated at spot level, then it is embedded into a Bayesian hierarchical model suited for fitting control spots. The novelties of the approach include the background correction performed on the latent mean of replicated spots, and an explicit model for outlying observations at low fluorescence values in which the probability of occurrence and their magnitude depend on the background fluorescence intensity. The analysis of unpublished data from a maize ear tissues experiment confirms the feasibility of MCMC inferences as regard the computational burden.



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