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

Deep-learning-based cell composition analysis from tissue expression profiles

View ORCID ProfileKevin Menden, View ORCID ProfileMohamed Marouf, View ORCID ProfileSergio Oller, Anupriya Dalmia, Karin Kloiber, View ORCID ProfilePeter Heutink, View ORCID ProfileStefan Bonn
doi: https://doi.org/10.1101/659227
Kevin Menden
1German Center for Neurodegenerative Diseases Tuebingen, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Kevin Menden
  • For correspondence: sbonn@uke.de Kevin.Menden@dzne.de
Mohamed Marouf
2Institute 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
Sergio Oller
2Institute 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 Sergio Oller
Anupriya Dalmia
1German Center for Neurodegenerative Diseases Tuebingen, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Karin Kloiber
2Institute 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
Peter Heutink
1German Center for Neurodegenerative Diseases Tuebingen, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Peter Heutink
Stefan Bonn
1German Center for Neurodegenerative Diseases Tuebingen, Germany
2Institute 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 Stefan Bonn
  • For correspondence: sbonn@uke.de Kevin.Menden@dzne.de
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

Abstract

We present Scaden, a deep neural network for cell deconvolution that uses gene expression information to infer the cellular composition of tissues. Scaden is trained on single cell RNA-seq data to engineer discriminative features that confer robustness to bias and noise, making complex data preprocessing and feature selection unnecessary. We demonstrate that Scaden outperforms existing deconvolution algorithms in both precision and robustness. A single trained network reliably deconvolves bulk RNA-seq and microarray, human and mouse tissue expression data and leverages the combined information of multiple data sets. Due to this stability and flexibility, we surmise that deep learning will become an algorithmic mainstay for cell deconvolution of various data types. Scaden’s comprehensive software package is easy to use on novel as well as diverse existing expression datasets available in public resources, deepening the molecular and cellular understanding of developmental and disease processes.

Footnotes

  • Many novel datasets used for deconvolution. Novel cell type-specific deconvolution analysis with some striking results. Many figures and results overhauled.

  • List of abbreviations

    RNA-seq
    Next Generation RNA Sequencing
    GEP
    gene expression profile matrix
    SVR
    Support Vector Regression
    DNN
    Deep Neural Network
    scRNA-seq
    single cell RNA-seq
    simulated tissue
    training data generated by mixing proportions of scRNA-seq data
    PBMC
    peripheral blood mononuclear cells
    CCC
    concordance correlation coefficient
    r
    Pearson’s correlation coefficient
    CS
    CIBERSORT
    CSx
    CIBERSORTx
    CPM
    Cell Population Mapping
  • 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 November 18, 2019.
    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.
    Deep-learning-based cell composition analysis from tissue expression profiles
    (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
    Deep-learning-based cell composition analysis from tissue expression profiles
    Kevin Menden, Mohamed Marouf, Sergio Oller, Anupriya Dalmia, Karin Kloiber, Peter Heutink, Stefan Bonn
    bioRxiv 659227; doi: https://doi.org/10.1101/659227
    Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
    Citation Tools
    Deep-learning-based cell composition analysis from tissue expression profiles
    Kevin Menden, Mohamed Marouf, Sergio Oller, Anupriya Dalmia, Karin Kloiber, Peter Heutink, Stefan Bonn
    bioRxiv 659227; doi: https://doi.org/10.1101/659227

    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 (4658)
    • Biochemistry (10313)
    • Bioengineering (7636)
    • Bioinformatics (26241)
    • Biophysics (13481)
    • Cancer Biology (10650)
    • Cell Biology (15363)
    • Clinical Trials (138)
    • Developmental Biology (8467)
    • Ecology (12776)
    • Epidemiology (2067)
    • Evolutionary Biology (16794)
    • Genetics (11373)
    • Genomics (15431)
    • Immunology (10580)
    • Microbiology (25087)
    • Molecular Biology (10172)
    • Neuroscience (54234)
    • Paleontology (398)
    • Pathology (1660)
    • Pharmacology and Toxicology (2884)
    • Physiology (4326)
    • Plant Biology (9213)
    • Scientific Communication and Education (1582)
    • Synthetic Biology (2545)
    • Systems Biology (6761)
    • Zoology (1459)