PertSpectra: Interpretable Matrix Factorization for Predicting Functional Impact of Genetic Perturbation Experiments

In drug discovery, measuring the effects of genetic perturbations is a powerful tool for studying unknown disease mechanisms, but biological interpretation of these effects, especially with the advent of screens involving combinatorial perturbations, remains challenging. To address limitations in current methodology we introduce PertSpectra, a guided triple matrix factorization that incorporates perturbation information and regularizes the model using a known gene-gene interaction graph prior to generate sparse, biologically relevant latent factors that capture perturbational effects. We evaluate PertSpectra on three single-cell RNAseq datasets with both single and combinatorial genetic perturbations, measuring latent space interpretability, predictive ability on unseen combinations of observed perturbations, and stratification of functionally similar perturbations. We show that PertSpectra provides an integrated modeling approach to understanding combinatorial perturbation data in the context of drug discovery.