Biologically informed variational autoencoders allow predictive modeling of genetic and drug induced perturbations

Abstract

Variational Autoencoders (VAE) have rapidly increased in popularity in biological applications and have already successfully been used on many omic datasets. Their latent space provides a low dimensional representation of input data, and VAEs have been applied for example for clustering of single-cell transcriptomic data. However, due to their non-linear nature, the patterns that VAEs learn in the latent space remain obscure. To shed light on the inner workings of VAE and enable direct interpretability of the model through its structure, we designed a novel VAE, OntoVAE (Ontology guided VAE) that can incorporate any ontology in its latent space and decoder part and, thus, provide pathway or phenotype activities for the ontology terms. In this work, we demonstrate that OntoVAE can be applied in the context of predictive modeling, and show its ability to predict the effects of genetic or drug induced perturbations using different ontologies and both, bulk and single-cell transcriptomic datasets. Finally, we provide a flexible framework which can be easily adapted to any ontology and dataset.