Cellular State Transformations using Generative Adversarial Networks

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 r...

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Bibliographic Details
Published inarXiv.org
Main Authors Targonski, Colin, Shealy, Benjamin T, Smith, Melissa C, Feltus, F Alex
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 28.06.2019
Subjects
Cellular communication
Conditioning
Gene expression
Generative adversarial networks
Genes
Machine learning
Open source software
Perturbation
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Summary: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.
ISSN:2331-8422