Abstract
Highly specific direct genome-scale expression discovery from two biological samples facilitates functional discovery of molecular systems. Here, expression data from cDNA arrays are ranked and curve-fitted. The algorithm uses filters based on the derivatives (slopes) of the curve fits. The rules are set to (i) filter the largest number of artifactual ratios from same-to-same datasets and (ii) maximize discovery from direct comparisons of different samples. The unsupervised discovery is optimized without lowering specificity. The false discovery rates are significantly lower than other methods. The discovered states of genetic expression facilitate functional discovery and are validated by real-time RT-PCR. Better quality improves sensitivity.