scAEGAN: Unification of Single-Cell Genomics Data by Adversarial Learning of Latent Space Correspondences

Khan, Sumeer Ahmad; Lehman, Robert S.; Martinez-de-Morentin, Xabier; Ruiz, Alberto Maillo; Lagani, Vincenzo; Kiani, Narsis A.; Tegnér, Jesper

doi:10.1101/2022.04.19.488745

Cited by 5 publications

(7 citation statements)

References 27 publications

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“…As we have shown here, researchers do not need to start from scratch and can repurpose existing networks for evolutionary applications. Unsurprisingly, such repurposing has led to several creative purposes well outside of the developer's initial intention, such as applying cycleGANs [72] to normalize single cell RNA-seq data across datasets [73] and using natural language processing models to predict the effect of nucleotide variants [74]. At a time when unprecedented resources are being used to develop deep-learning technologies outside of biology, biologists from all fields should consider the myriad ways in which these technologies can be leveraged to solve problems and answer longstanding questions.…”

Section: Discussionmentioning

confidence: 99%

This population does not exist: learning the distribution of evolutionary histories with generative adversarial networks

Booker

Ray

Schrider

2022

Preprint

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Numerous studies over the last decade have demonstrated the utility of machine learning methods when applied to population genetic tasks. More recent studies show the potential of deep learning methods in particular, which allow researchers to approach problems without making prior assumptions about how the data should be summarized or manipulated, instead learning their own internal representation of the data in an attempt to maximize inferential accuracy. One type of deep neural network, called Generative Adversarial Networks (GANs), can even be used to generate new data, and this approach has been used to create individual artificial human genomes free from privacy concerns. In this study, we further explore the application of GANs in population genetics by designing and training a network to learn the statistical distribution of population genetic alignments (i.e. data sets consisting of sequences from an entire population sample) under several diverse evolutionary histories—the first GAN capable of performing this task. After testing multiple different neural network architectures, we report the results of a fully differentiable Deep-Convolutional Wasserstein GAN with gradient penalty that is capable of generating artificial examples of population genetic alignments that successfully mimic key aspects of the training data, including the site frequency spectrum, differentiation between populations, and patterns of linkage disequilibrium. We demonstrate consistent training success across various evolutionary models, including models of panmictic and subdivided populations, populations at equilibrium and experiencing changes in size, and populations experiencing either no selection or positive selection of various strengths, all without the need for extensive hyperparameter tuning. Overall, our findings highlight the ability of GANs to learn and mimic population genetic data, and suggest future areas where this work can be applied in population genetics research that we discuss herein.AUTHOR SUMMARYThe application of deep-learning to biological problems has expanded greatly over the last decade. One type of deep neural network, called a Generative Adversarial Network (GAN), attempts to generate artificial examples of a given type of data by learning to fool a discriminator that is simultaneously learning to discriminate between real and artificial examples. In this study, we design a GAN whose purpose is to generate artificial examples of genetic alignments from biological populations of varying evolutionary histories—essentially learning the statistical distribution of those evolutionary histories. We show that our GAN is able to mimic key aspects of the genetic alignments relevant to population genetics, and that the GAN does not require extensive tuning of the network parameters. Ultimately, this work demonstrates the ability of these networks to learn and mimic population genetic data, and highlights future areas where this work can be applied and expanded.

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Section: Discussionmentioning

confidence: 99%

This population does not exist: learning the distribution of evolutionary histories with generative adversarial networks

Booker

Ray

Schrider

2022

Preprint

View full text Add to dashboard Cite

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“…4 and Figure 1 E. More generally, this concept is based on a cycle GAN [71] and is also present in, e.g., Khan et al [26], Wang et al [61], Xu et al [65], Zhao et al [70] and Zuo et al [73].…”

Section: Approaches For Paired Datamentioning

confidence: 91%

“…Similar to the sciCAN model presented by Xu et al [60], scAEGAN [24] also embraces the concept of cycle consistency, integrating the adversarial training mechanism of a cycle GAN [66] into an autoencoder framework. Specifically, for each modality, a discriminator and a generator are defined.…”

Section: Literature Reviewmentioning

confidence: 99%

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The performance of deep generative models for learning joint embeddings of single-cell multi-omics data

Brombacher

Hackenberg

Kreutz

et al. 2022

Preprint

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Recent extensions of single-cell studies to multiple data modalities raise new questions regarding experimental design. For example, the challenge of sparsity in single-omics data might be partly resolved by compensating for missing information across modalities. In particular, deep learning approaches, such as deep generative models (DGMs), can potentially uncover complex patterns via a joint embedding. Yet, this also raises the question of sample size requirements for identifying such patterns from single-cell multi-omics data. Here, we empirically examine the quality of DGM-based integrations for varying sample sizes. We first review the literature and give a short overview of deep learning methods for multi-omics integration. Next, we consider six popular tools in more detail and examine their robustness to different cell numbers, covering two of the most common multi-omics types currently favored. Specifically, we use data featuring simultaneous gene expression measurements at the RNA level and protein abundance measurements for cell surface proteins (CITE-seq), as well as data where chromatin accessibility and RNA expression are measured in thousands of cells (10x Multiome). We examine the ability of the methods to learn joint embeddings based on biological and technical metrics. Finally, we provide recommendations for the design of multi-omics experiments and discuss potential future developments.

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“…This corresponds to the idea of cyclical adversarial training as described in Section 2.4 and Figure 1E . More generally, this concept is based on a cycle GAN ( Zhu et al, 2017 ) and is also present in, e.g., Xu et al (2021a) ; Zhao et al (2022) ; Khan et al (2022) ; Wang et al (2022) and Zuo et al (2021) .…”

Section: Literature Reviewmentioning

confidence: 99%

The performance of deep generative models for learning joint embeddings of single-cell multi-omics data

Brombacher

Hackenberg

Kreutz

et al. 2022

Front. Mol. Biosci.

View full text Add to dashboard Cite

Recent extensions of single-cell studies to multiple data modalities raise new questions regarding experimental design. For example, the challenge of sparsity in single-omics data might be partly resolved by compensating for missing information across modalities. In particular, deep learning approaches, such as deep generative models (DGMs), can potentially uncover complex patterns via a joint embedding. Yet, this also raises the question of sample size requirements for identifying such patterns from single-cell multi-omics data. Here, we empirically examine the quality of DGM-based integrations for varying sample sizes. We first review the existing literature and give a short overview of deep learning methods for multi-omics integration. Next, we consider eight popular tools in more detail and examine their robustness to different cell numbers, covering two of the most common multi-omics types currently favored. Specifically, we use data featuring simultaneous gene expression measurements at the RNA level and protein abundance measurements for cell surface proteins (CITE-seq), as well as data where chromatin accessibility and RNA expression are measured in thousands of cells (10x Multiome). We examine the ability of the methods to learn joint embeddings based on biological and technical metrics. Finally, we provide recommendations for the design of multi-omics experiments and discuss potential future developments.

show abstract

scAEGAN: Unification of Single-Cell Genomics Data by Adversarial Learning of Latent Space Correspondences

Cited by 5 publications

References 27 publications

This population does not exist: learning the distribution of evolutionary histories with generative adversarial networks

This population does not exist: learning the distribution of evolutionary histories with generative adversarial networks

The performance of deep generative models for learning joint embeddings of single-cell multi-omics data

The performance of deep generative models for learning joint embeddings of single-cell multi-omics data

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