Gene Expression Imputation with Generative Adversarial Imputation Nets

• Published in: bioRxiv
• Main Authors: Ramon Vinas Torne, Azevedo, Tiago, Gamazon, Eric, Lio, Pietro
• Format: Paper
• Language: English
• Published: Cold Spring Harbor Cold Spring Harbor Laboratory Press 10.06.2020
• Subjects: Gene expression; Ribonucleic acid; RNA
• DOI: 10.1101/2020.06.09.141689

A question of fundamental biological significance is to what extent the expression of a subset of genes can be used to recover the full transcriptome, with important implications for biological discovery and clinical application. To address this challenge, we present GAIN-GTEx, a method for gene expression imputation based on Generative Adversarial Imputation Networks. In order to increase the applicability of our approach, we leverage data from GTEx v8, a reference resource that has generated a comprehensive collection of transcriptomes from a diverse set of human tissues. We compare our model to several standard and state-of-the-art imputation methods and show that GAIN-GTEx is significantly superior in terms of predictive performance and runtime. Furthermore, our results indicate strong generalisation on RNA-Seq data from 3 cancer types across varying levels of missingness. Our work can facilitate a cost-effective integration of large-scale RNA biorepositories into genomic studies of disease, with high applicability across diverse tissue types. Competing Interest Statement The authors have declared no competing interest. Footnotes * https://gtexportal.org/