  In high-throughput functional genomic screens, each gene product is commonly
assumed to exhibit a singular biological function within a defined protein
complex or pathway. In practice, a single gene perturbation may induce multiple
cascading functional outcomes, a genetic principle known as pleiotropy. Here,
we model pleiotropy in fitness screen collections by representing each gene
perturbation as the sum of multiple perturbations of biological functions, each
harboring independent fitness effects inferred empirically from the data. Our
approach ('Webster') recovered pleiotropic functions for DNA damage proteins
from genotoxic fitness screens, untangled distinct signaling pathways upstream
of shared effector proteins from cancer cell fitness screens, and learned
aspects of the cellular hierarchy in an unsupervised manner. Modeling compound
sensitivity profiles in terms of genetically defined functions recovered
compound mechanisms of action. Our approach establishes a sparse approximation
mechanism for unraveling complex genetic architectures underlying
high-dimensional gene perturbation readouts.
