Nicholas M. Sampas

Estimation of the confidence limits of oligonucleotide array-based measurements of differential expression

Microarrays can be used to simultaneously measure the differential expression states of many mRNAs in two samples. Such measurements are limited by systematic and random errors. Systematic errors include labeling bias, imperfect feature morphologies, mismatched sample concentrations, and cross-hybridization. Random errors arise from chemical and scanning noise, particularly for low signals. We have used a combination of fluor-exchanged two- color labeling and improved normalization methods to minimize systematic errors from labeling bias, imperfect features, and mismatched sample concentrations. On-array specificity control proves and experimentally proven probe design algorithms were used to correct for cross- hybridization. Random errors were reduced via automated non-uniform feature flagging and an advanced scanner design. We have scored feature significance, using established statistical tests. We have then estimated the intrinsic random measurement error as a function of average probe signal via sample self-comparison experiments (human K-562 cell mRNA). Finally, we have combined all of these tools in the analysis of differential expression measurements between K-562 cells and HeLa cells. The results establish the importance of the elimination of systematic errors and the objective assessment of the effects of random errors in producing reliable estimates of differential expression.