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bioRxiv · 10.1101/2020.06.09.141689

Gene Expression Imputation with Generative Adversarial Imputation Nets

Vinas Torne, R.·Azevedo, T.·Gamazon, E.·Lio, P.
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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-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.

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Vinas Torne, R., Azevedo, T., Gamazon, E., Lio, P.. 2020-06-10. Gene Expression Imputation with Generative Adversarial Imputation Nets. https://doi.org/10.1101/2020.06.09.141689

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