Gene Hierarchies from High-Dimensional Phenotyping

Florian Markowetz

Lewis-Sigler Institute, Princeton University

Functional genomics has a long tradition of inferring the inner working of a cell by breaking it. Observing cellular features after knocking out or silencing a gene  reveals  genes essential for the organism or a particular pathway.  In high-dimensional phenotyping screens a large number of cellular features is observed. I will introduce a probabilistic method to infer a genetic hierarchy from the nested structure of observed perturbation effects. The method not only reveals clusters of genes with similar phenotypic profiles, but additionally orders the clusters according to subset relationships between the sets of phenotypes.