  We introduce a novel method to unite deep learning with biology by which
generative adversarial networks (GANs) generate transcriptome perturbations and
reveal condition-defining gene expression patterns. We find that a generator
conditioned to perturb any input gene expression profile simulates a realistic
transition between source and target RNA expression states. The perturbed
samples follow a similar distribution to original samples from the dataset,
also suggesting these are biologically meaningful perturbations. Finally, we
show that it is possible to identify the genes most positively and negatively
perturbed by the generator and that the enriched biological function of the
perturbed genes are realistic. We call the framework the Transcriptome State
Perturbation Generator (TSPG), which is open source software available at
https://github.com/ctargon/TSPG.
