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

Generative modeling and latent space arithmetics predict single-cell perturbation response across cell types, studies and species

M. Lotfollahi, F. Alexander Wolf, Fabian J. Theis
doi: https://doi.org/10.1101/478503
M. Lotfollahi
1Helmholtz Center Munich – German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg, Munich, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
F. Alexander Wolf
1Helmholtz Center Munich – German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg, Munich, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Fabian J. Theis
1Helmholtz Center Munich – German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg, Munich, Germany
2Department of Mathematics, Technische Universität München, Munich, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

Abstract

Accurately modeling cellular response to perturbations is a central goal of computational biology. While such modeling has been proposed based on statistical, mechanistic and machine learning models in specific settings, no generalization of predictions to phenomena absent from training data i.e. ‘out-of-sample’ have yet been demonstrated. Here, we present scGen, a model combining variational autoencoders and latent space vector arithmetics for high-dimensional single-cell gene expression data. In benchmarks across a broad range of examples, we show that scGen accurately models dose and infection response of cells across cell types, studies and species. In particular, we demonstrate that scGen learns cell type and species specific response implying that it captures features that distinguish responding from non-responding genes and cells. With the upcoming availability of large-scale atlases of organs in healthy state, we envision scGen to become a tool for experimental design through in silico screening of perturbation response in the context of disease and drug treatment.

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-ND 4.0 International license.
Back to top
PreviousNext
Posted November 29, 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.
Generative modeling and latent space arithmetics predict single-cell perturbation response across cell types, studies and species
(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
Generative modeling and latent space arithmetics predict single-cell perturbation response across cell types, studies and species
M. Lotfollahi, F. Alexander Wolf, Fabian J. Theis
bioRxiv 478503; doi: https://doi.org/10.1101/478503
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
Generative modeling and latent space arithmetics predict single-cell perturbation response across cell types, studies and species
M. Lotfollahi, F. Alexander Wolf, Fabian J. Theis
bioRxiv 478503; doi: https://doi.org/10.1101/478503

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 (3505)
  • Biochemistry (7348)
  • Bioengineering (5324)
  • Bioinformatics (20266)
  • Biophysics (10019)
  • Cancer Biology (7744)
  • Cell Biology (11305)
  • Clinical Trials (138)
  • Developmental Biology (6437)
  • Ecology (9953)
  • Epidemiology (2065)
  • Evolutionary Biology (13325)
  • Genetics (9361)
  • Genomics (12586)
  • Immunology (7702)
  • Microbiology (19024)
  • Molecular Biology (7443)
  • Neuroscience (41041)
  • Paleontology (300)
  • Pathology (1229)
  • Pharmacology and Toxicology (2138)
  • Physiology (3161)
  • Plant Biology (6861)
  • Scientific Communication and Education (1273)
  • Synthetic Biology (1896)
  • Systems Biology (5313)
  • Zoology (1089)