Sampling from Disentangled Representations of Single-Cell Data Using Generative Adversarial Networks

Mapping Intimacies ◽

10.1101/2021.01.15.426872 ◽

2021 ◽

Author(s):

Hengshi Yu ◽

Joshua D. Welch

Keyword(s):

Gene Expression ◽

Single Cell ◽

Gene Expression Data ◽

Generative Models ◽

Generative Adversarial Networks ◽

Expression Data ◽

Gene Expression Response ◽

Adversarial Networks ◽

Cell Gene Expression ◽

Cell Gene

AbstractDeep generative models, including variational autoencoders (VAEs) and generative adversarial networks (GANs), have achieved remarkable successes in generating and manipulating highdimensional images. VAEs excel at learning disentangled image representations, while GANs excel at generating realistic images. Here, we systematically assess disentanglement and generation performance on single-cell gene expression data and find that these strengths and weaknesses of VAEs and GANs apply to single-cell gene expression data in a similar way. We also develop MichiGAN1, 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 two large singlecell RNA-seq datasets [13, 68] 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.

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

Genome Biology ◽

10.1186/s13059-021-02373-4 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Hengshi Yu ◽

Joshua D. Welch

Keyword(s):

Gene Expression ◽

Single Cell ◽

Generative Models ◽

Generative Adversarial Networks ◽

Data Generation ◽

Gene Expression Response ◽

Adversarial Networks ◽

Cell Gene Expression ◽

Expression Response ◽

Cell Gene

AbstractDeep 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.

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Interpretable generative deep learning: an illustration with single cell gene expression data

Human Genetics ◽

10.1007/s00439-021-02417-6 ◽

2022 ◽

Author(s):

Martin Treppner ◽

Harald Binder ◽

Moritz Hess

Keyword(s):

Gene Expression ◽

Single Cell ◽

Gene Expression Data ◽

Latent Variables ◽

Generative Models ◽

Omics Data ◽

Expression Data ◽

Cell Gene Expression ◽

The Relationship ◽

Cell Gene

AbstractDeep generative models can learn the underlying structure, such as pathways or gene programs, from omics data. We provide an introduction as well as an overview of such techniques, specifically illustrating their use with single-cell gene expression data. For example, the low dimensional latent representations offered by various approaches, such as variational auto-encoders, are useful to get a better understanding of the relations between observed gene expressions and experimental factors or phenotypes. Furthermore, by providing a generative model for the latent and observed variables, deep generative models can generate synthetic observations, which allow us to assess the uncertainty in the learned representations. While deep generative models are useful to learn the structure of high-dimensional omics data by efficiently capturing non-linear dependencies between genes, they are sometimes difficult to interpret due to their neural network building blocks. More precisely, to understand the relationship between learned latent variables and observed variables, e.g., gene transcript abundances and external phenotypes, is difficult. Therefore, we also illustrate current approaches that allow us to infer the relationship between learned latent variables and observed variables as well as external phenotypes. Thereby, we render deep learning approaches more interpretable. In an application with single-cell gene expression data, we demonstrate the utility of the discussed methods.

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