Nested effects models for high-dimensional phenotyping screens

Motivation: In high-dimensional phenotyping screens, a large number of cellular features is observed after perturbing genes by knockouts or RNA interference. Comprehensive analysis of perturbation effects is one of the most powerful techniques for attributing functions to genes, but not much work has been done so far to adapt statistical and computational methodology to the specific needs of large-scale and high-dimensional phenotyping screens. Results: We introduce and compare probabilistic methods to efficiently infer a genetic hierarchy from the nested structure of observed perturbation effects. These hierarchies elucidate the structures of signaling pathways and regulatory networks. Our methods achieve two goals: ( 1) they reveal clusters of genes with highly similar phenotypic profiles, and ( 2) they order ( clusters of) genes according to subset relationships between phenotypes. We evaluate our algorithms in the controlled setting of simulation studies and show their practical use in two experimental scenarios: ( 1) a data set investigating the response to microbial challenge in Drosophila melanogaster, and ( 2) a compendium of expression profiles of Saccharomyces cerevisiae knockout strains. We show that our methods identify biologically justified genetic hierarchies of perturbation effects.