Adversarial generation of gene expression data.

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 criticized because they fail to emulate key properties of gene expression data. In this article, we develop a method based on a conditional generative adversarial network to generate realistic transcriptomics data for Escherichia 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 preserve tissue- and cancer-specific propertie

SUBMITTER: Vinas R 

PROVIDER: S-EPMC8756177 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature