A model-based expression index from oligonucleotide expression arrays.
Wing Hung Wong
University of California, Los Angeles.
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.