Gene Expression Imputation with Generative Adversarial Imputation Nets

Abstract

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-theart 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.

DeCompress: tissue compartment deconvolution of targeted mRNA expression panels using compressed sensing

“…Another imputation method by Viñas et al. ( 51 ) used recent machine learning methodology ( 52 ) to provide efficient and accurate transcriptomic reconstruction in healthy, unperturbed tissue from the Genotype-Tissue Expression (GTEx) Project ( 53 , 54 ). These ideas provide a promising avenue to expand the feature space of targeted panels, rendering them more applicable for reference-free deconvolution methods.…”