Synthetic whole-slide image tile generation with gene expression profile-infused deep generative models Carrillo Pérez, Francisco Herrera Maldonado, Luis Javier In this work, we propose an approach to generate whole-slide image (WSI) tiles by using deep generative models infused with matched gene expression profiles. First, we train a variational autoencoder (VAE) that learns a latent, lower-dimensional representation of multi-tissue gene expression profiles. Then, we use this representation to infuse generative adversarial networks (GANs) that generate lung and brain cortex tissue tiles, resulting in a new model that we call RNA-GAN. Tiles generated by RNA-GAN were preferred by expert pathologists compared with tiles generated using traditional GANs, and in addition, RNA-GAN needs fewer training epochs to generate high-quality tiles. Finally, RNA-GAN was able to generalize to gene expression profiles outside of the training set, showing imputation capabilities. A web-based quiz is available for users to play a game distinguishing real and synthetic tiles: https://rna-gan.stanford.edu/, and the code for RNA-GAN is available here: https://github.com/gevaertlab/RNA-GAN. 2023-10-24T11:00:58Z 2023-10-24T11:00:58Z 2023-08-28 journal article Carrillo-Perez et al., 2023, Cell Reports Methods 3, 100534 August 28, 2023 ª 2023 The Author(s). [https://doi.org/10.1016/j.crmeth.2023.100534] https://hdl.handle.net/10481/85217 10.1016/j.crmeth.2023.100534 eng http://creativecommons.org/licenses/by-nc-nd/4.0/ open access Attribution-NonCommercial-NoDerivatives 4.0 Internacional Elsevier