  Optical pooled screening (OPS) combines automated microscopy and genetic
perturbations to systematically study gene function in a scalable and
cost-effective way. Leveraging the resulting data requires extracting
biologically informative representations of cellular perturbation phenotypes
from images. We employ a style-transfer approach to learn gene-level feature
representations from images of genetically perturbed cells obtained via OPS.
Our method outperforms widely used engineered features in clustering gene
representations according to gene function, demonstrating its utility for
uncovering latent biological relationships. This approach offers a promising
alternative to investigate the role of genes in health and disease.
