Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

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

View ORCID ProfileMohamed Marouf, View ORCID ProfilePierre Machart, View ORCID ProfileVikas Bansal, Christoph Kilian, View ORCID ProfileDaniel S. Magruder, Christian F. Krebs, View ORCID ProfileStefan Bonn
doi: https://doi.org/10.1101/390153
Mohamed Marouf
Institute of Medical Systems Biology, University Medical Center Hamburg-Eppendorf, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Mohamed Marouf
Pierre Machart
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Pierre Machart
Vikas Bansal
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Vikas Bansal
Christoph Kilian
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Daniel S. Magruder
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Daniel S. Magruder
Christian F. Krebs
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Stefan Bonn
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Stefan Bonn
  • For correspondence: sbonn@uke.de
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

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

  • 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.
    Back to top
    PreviousNext
    Posted October 24, 2018.
    Download PDF
    Email

    Thank you for your interest in spreading the word about bioRxiv.

    NOTE: Your email address is requested solely to identify you as the sender of this article.

    Enter multiple addresses on separate lines or separate them with commas.
    Realistic in silico generation and augmentation of single cell RNA-seq data using Generative Adversarial Neural Networks
    (Your Name) has forwarded a page to you from bioRxiv
    (Your Name) thought you would like to see this page from the bioRxiv website.
    CAPTCHA
    This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
    Share
    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
    Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
    Citation Tools
    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

    Citation Manager Formats

    • BibTeX
    • Bookends
    • EasyBib
    • EndNote (tagged)
    • EndNote 8 (xml)
    • Medlars
    • Mendeley
    • Papers
    • RefWorks Tagged
    • Ref Manager
    • RIS
    • Zotero
    • Tweet Widget
    • Facebook Like
    • Google Plus One

    Subject Area

    • Bioinformatics
    Subject Areas
    All Articles
    • Animal Behavior and Cognition (4381)
    • Biochemistry (9581)
    • Bioengineering (7087)
    • Bioinformatics (24845)
    • Biophysics (12598)
    • Cancer Biology (9952)
    • Cell Biology (14347)
    • Clinical Trials (138)
    • Developmental Biology (7945)
    • Ecology (12103)
    • Epidemiology (2067)
    • Evolutionary Biology (15985)
    • Genetics (10921)
    • Genomics (14735)
    • Immunology (9869)
    • Microbiology (23647)
    • Molecular Biology (9477)
    • Neuroscience (50839)
    • Paleontology (369)
    • Pathology (1539)
    • Pharmacology and Toxicology (2681)
    • Physiology (4013)
    • Plant Biology (8655)
    • Scientific Communication and Education (1508)
    • Synthetic Biology (2391)
    • Systems Biology (6427)
    • Zoology (1346)