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Realistic in silico generation and augmentation of single cell RNA-seq data using Generative Adversarial Neural Networks

Mohamed Marouf, Pierre Machart, Vikas Bansal, Christoph Kilian, Daniel S. Magruder, Christian F. Krebs, View ORCID ProfileStefan Bonn
doi: https://doi.org/10.1101/390153
Mohamed Marouf
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Pierre Machart
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Vikas Bansal
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Christoph Kilian
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Daniel S. Magruder
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Christian F. Krebs
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Stefan Bonn
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  • ORCID record for Stefan Bonn
  • For correspondence: sbonn@uke.de
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Abstract

A fundamental problem in biomedical research is the low number of observations available, mostly due to a lack of available biosamples, prohibitive costs, or ethical reasons. Augmenting few real observations with generated in silico samples could lead to more robust analysis results and a higher reproducibility rate. Here we propose the use of conditional single cell Generative Adversarial Neural Networks (cscGANs) for the realistic generation of single cell RNA-seq data. cscGANs learn non-linear gene-gene dependencies from complex, multi cell type samples and use this information to generate realistic cells of defined types. Augmenting sparse cell populations with cscGAN generated cells improves downstream analyses such as the detection of marker genes, the robustness and reliability of classifiers, the assessment of novel analysis algorithms, and might reduce the number of animal experiments and costs in consequence. cscGANs outperform existing methods for single cell RNA-seq data generation in quality and hold great promise for the realistic generation and augmentation of other biomedical data types.

  • List of abbreviations

    scGAN
    single-cell Generative Adversarial Network
    cscGAN
    conditional single-cell Generative Adversarial Network
    RNA-seq
    RiboNucleic Acid sequencing
    scRNA-seq
    single-cell RiboNucleic Acid sequencing
    GAN
    Generative Adversarial Network
    PBMC
    Peripheral Blood MonoCytes
    t-SNE
    t-distributed Stochastic Neighbor Embedding
    RF
    Random Forest
    ROC
    Receiver Operating Characteristic
    AUC
    Area Under the (ROC) Curve
    GPU
    Graphics Processing Unit
    LSN
    Library Size Normalization
    UMI
    Unique Molecular Identifiers
    PCA
    Principal Component Analysis
    PCs
    Principal Components
    CNN
    Convolutional Neural Network
    FC
    Fully-Connected
    ReLU
    Rectified Linear Unit
    ACGAN
    Auxiliary Classifier Generative Adversarial Network
  • Copyright 
    The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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    Posted August 13, 2018.
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    Realistic in silico generation and augmentation of single cell RNA-seq data using Generative Adversarial Neural Networks
    Mohamed Marouf, Pierre Machart, Vikas Bansal, Christoph Kilian, Daniel S. Magruder, Christian F. Krebs, Stefan Bonn
    bioRxiv 390153; doi: https://doi.org/10.1101/390153
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    Realistic in silico generation and augmentation of single cell RNA-seq data using Generative Adversarial Neural Networks
    Mohamed Marouf, Pierre Machart, Vikas Bansal, Christoph Kilian, Daniel S. Magruder, Christian F. Krebs, Stefan Bonn
    bioRxiv 390153; doi: https://doi.org/10.1101/390153

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