Model-based analysis of oligonucleotide arrays: Expression index computation and outlier detection

Recent advances in cDNA and oligonucleotide DNA arrays have made it possibl e to measure the abundance of mRNA transcripts for many genes simultaneousl y. The analysis of such experiments is nontrivial because of large data siz e and many levels of variation introduced at different stages of the experi ments. The analysis is further complicated by the large differences that ma y exist among different probes used to interrogate the same gene. However, an attractive feature of high-density oligonucleotide arrays such as those produced by photolithography and inkjet technology is the standardization o f 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 method s. Here, we propose a statistical model for the probe-level data, and devel op model-based estimates for gene expression indexes. We also present model -based methods for identifying and handling cross-hybridizing probes and co ntaminating array regions. Applications of these results will be presented elsewhere.