Recent advances in cDNA and oligonucleotide DNA arrays have made it possible to measure the abundance of mRNA transcripts for many genes simultaneously. The analysis of such experiments is non-trivial because of large data size and many levels of variation introduced at different stages such as sample preparation, amplification, labeling, hybridization and scanning. The analysis is further complicated by the fact that large differences may exist among different probes used to interrogate the same gene. However, an attractive feature of high-density oligonucleotide arrays such as those produced by photolitography and inkjet technology is the standardization of chip manufacturing and hybridization process. As a result, probe-specific biases, although significant, are highly reproducible and predictable, and their adverse effect can be reduced by proper modeling and analysis methods. Here, we propose a model-based meta-analysis approach to handle estimation uncertainty due to probe-level effects. We present methods for computing standard errors for the expression indexes and confidence intervals for fold changes. By using this model, information from past experiments can be effectively utilized in the analysis of new experimental data from arrays of the same type.