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Background  Functional analysis of data from genome-scale experiments, such as microarrays, requires an extensive selection of differentially expressed genes. Under many conditions, the proportion of differentially expressed genes is considerable, making the selection criteria a balance between the inclusion of false positives and the exclusion of false negatives.    Results  We developed an analytical method to determine a  p -value threshold from a microarray experiment that is dependent on the quality and design of the data set. To this aim, populations of  p -values are modeled as mathematical functions in which the parameters to describe these functions are estimated in an unsupervised manner. The strength of the method is exemplified by its application to a published gene expression data set of sporadic and familial breast tumors with  BRCA1  or  BRCA2  mutations.    Conclusion  We present an objective and unsupervised way to set thresholds adapted to the quality and design of the experiment. The resulting mathematical description of the data sets of genome-scale experiments enables a probabilistic approach in systems biology.

Dissecting systems-wide data using mixture models: application to identify affected cellular processes.