Analysis of high-dimensional biomarker studies typically treats all the biomarkers assessed as one large set from which biomarkers associated with clinical outcome or state are identified, controlling the false discovery rate (FDR). As an alternative, Efron showed how biomarker sets can be divided into classes and a separate FDR analysis conducted in each class using information from the whole biomarker set to preserve the asymptotics and efficiencies of false discovery rate calculations. We apply this separate class approach to true discovery rate degree of association (TDRDA) set analysis, which is used to identify sets of biomarkers having strong association with clinical or state while controlling the FDR. Careful choice of classes with considerations of biology, genomics, or prior related studies should increase the proportion of truly associated biomarkers or the degree of association in the associated biomarkers in the selected classes, thus increasing the identification power of the separate class analysis relative to the overall analysis.