Work

MichiGAN: sampling from disentangled representations of single-cell data using generative adversarial networks

Year: 2021

Type: article

Abstract: Deep generative models such as variational autoencoders (VAEs) and generative adversarial networks (GANs) generate and manipulate high-dimensional images. We systematically assess the complementary strengths and weaknesses of these models on single-cell gene expression data. We also develop MichiGAN, a novel neural network that combines the strengths of VAEs and GANs to sample from disentangled representations without sacrificing data generation quality. We learn disentangled representations of three large single-cell RNA-seq datasets and use MichiGAN to sample from these representations. MichiGAN allows us to manipulate semantically distinct aspects of cellular identity and predict single-cell gene expression response to drug treatment.

Source: Genome biology

DOI: 10.1186/s13059-021-02373-4

Authors: Hengshi Yu, Joshua D. Welch

Institution: University of Michigan

Language: English

Topic: Single-cell and spatial transcriptomics

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: gold

Estimated APC paid: $4,890

Funders: National Institutes of Health, National Human Genome Research Institute