Sparse dictionary learning recovers pleiotropy from human cell fitness screens

TL;DR

This paper introduces 'Webster', a sparse modeling approach that uncovers pleiotropic gene functions and complex genetic architectures from high-dimensional fitness screens, revealing multiple functional effects of gene perturbations.

Contribution

The novel 'Webster' method models pleiotropy in fitness screens by representing gene perturbations as sums of independent functions, improving interpretation of complex genetic data.

Findings

Recovered pleiotropic functions for DNA damage proteins

Untangled signaling pathways upstream of shared effectors

Learned cellular hierarchy in an unsupervised manner

Abstract

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.