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Adversarial generation of gene expression data

Viñas, R; Andrés-Terré, H; Liò, P; Bryson, K; (2022) Adversarial generation of gene expression data. Bioinformatics , 38 (3) pp. 730-737. 10.1093/bioinformatics/btab035. Green open access

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Abstract

MOTIVATION: High-throughput gene expression can be used to address a wide range of fundamental biological problems, but datasets of an appropriate size are often unavailable. Moreover, existing transcriptomics simulators have been criticised because they fail to emulate key properties of gene expression data. In this paper, we develop a method based on a conditional generative adversarial network to generate realistic transcriptomics data for E. coli and humans. We assess the performance of our approach across several tissues and cancer types. RESULTS: We show that our model preserves several gene expression properties significantly better than widely used simulators such as SynTReN or GeneNetWeaver. The synthetic data preserves tissue and cancer-specific properties of transcriptomics data. Moreover, it exhibits real gene clusters and ontologies both at local and global scales, suggesting that the model learns to approximate the gene expression manifold in a biologically meaningful way. AVAILABILITY: Code is available at: https://github.com/rvinas/adversarial-gene-expression. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Type: Article
Title: Adversarial generation of gene expression data
Location: England
Open access status: An open access version is available from UCL Discovery
DOI: 10.1093/bioinformatics/btab035
Publisher version: https://doi.org/10.1093/bioinformatics/btab035
Language: English
Additional information: Copyright © The Author(s) 2021. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10121566
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