Cellular State Transformations using Generative Adversarial Networks

TL;DR

This paper presents TSPG, a GAN-based framework that generates biologically meaningful transcriptome perturbations, enabling the simulation of gene expression state transitions and identification of key perturbed genes.

Contribution

The authors introduce TSPG, a novel GAN-based method for simulating transcriptome state changes and identifying condition-specific gene perturbations.

Findings

Perturbed samples mimic real gene expression distributions.

The generator can simulate realistic transitions between gene expression states.

Identified perturbed genes are enriched in relevant biological functions.

Abstract

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.