Learning to Encode Cellular Responses to Systematic Perturbations with Deep Generative Models

Components of cellular signaling systems are organized as hierarchical networks, and perturbing different components of the system often leads to transcriptomic profiles that exhibit compositional statistical patterns. Mining such patterns to investigate how cellular signals are encoded is an important problem in systems biology. Here, we investigated the capability of deep generative models (DGMs) for modeling signaling systems and learning representations for transcriptomic profiles derived from cells under diverse perturbations. Specifically, we show that the variational autoencoder and the supervised vector-quantized variational autoencoder can accurately regenerate gene expression data. Both models can learn representations that reveal the relationships between different classes of perturbagens and enable mappings between drugs and their target genes. In summary, DGMs can adequately depict how cellular signals are encoded. The resulting representations have broad applications in systems biology, such as studying the mechanism-of-action of drugs.