We review some powerful new algorithms that build on the intuitive biological interpretation techniques for statistical analysis of functional genomics experiments. Although they were originally designed for transcriptomics, we argue that these algorithms are applicable to any type of -omics study (transcriptomics, proteomics, metabolomics). Rank Products (RP), a strictly non-parametric test statistic to detect differentially regulated elements (genes, proteins, metabolites) in genome-wide screens. RP is particularly powerful for noisy data and low numbers of replicates and makes full use of the availability of a large number of parallel measurements that is typical of modern large-scale experiments. Iterative Group Analysis (iGA), a statistical method that makes the transition from regulated single elements to significant classes of elements, and thus provides an automatic functional annotation of an experiment. Graph-based iGA (GiGA), an extension of iGA that combines experimental data with a broad variety of biological annotations to highlight physiologically relevant regions in a given "evidence graph" (e.g., metabolic networks, signaling pathway diagrams, protein interaction maps). The sequential application of these techniques yields an increasingly abstract interpretation of experimental data that is at the same time quantitative, statistically rigorous, and biologically significant. The results can be used either as helpful tools to guide data visualization and exploration, or as the input for downstream computational applications in a systems biology framework. ?? Mary Ann Liebert, Inc.