PuSH - Publikationsserver des Helmholtz Zentrums München

Export: TXT Text RIS Endnote (RIS) BIB BibTeX
Lotfollahi, M. ; Wolf, F.A. ; Theis, F.J.

scGen predicts single-cell perturbation responses.

Nat. Methods 16, 715-721 (2019)
DOI PMC
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
Accurately modeling cellular response to perturbations is a central goal of computational biology. While such modeling has been based on statistical, mechanistic and machine learning models in specific settings, no generalization of predictions to phenomena absent from training data (out-of-sample) has yet been demonstrated. Here, we present scGen (https://github.com/theislab/scgen), a model combining variational autoencoders and latent space vector arithmetics for high-dimensional single-cell gene expression data. We show that scGen accurately models perturbation and infection response of cells across cell types, studies and species. In particular, we demonstrate that scGen learns cell-type and species-specific responses 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 a 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.
Impact Factor
Scopus SNIP
Web of Science
Times Cited
Scopus
Cited By
Altmetric
28.467
5.343
44
84
Tags
Anmerkungen
Besondere Publikation
Auf Hompepage verbergern

Zusatzinfos bearbeiten
Eigene Tags bearbeiten
Privat
Eigene Anmerkung bearbeiten
Privat
Auf Publikationslisten für
Homepage nicht anzeigen
Als besondere Publikation
markieren
Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Sprache englisch
Veröffentlichungsjahr 2019
HGF-Berichtsjahr 2019
ISSN (print) / ISBN 1548-7091
e-ISSN 1548-7105
Zeitschrift Nature Methods
Quellenangaben Band: 16, Heft: 8, Seiten: 715-721 Artikelnummer: , Supplement: ,
Verlag Nature Publishing Group
Verlagsort New York, NY
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-503800-001
DOI 10.1038/s41592-019-0494-8
WOS ID WOS:000477857700027
Scopus ID 85069972336
PubMed ID 31363220
Erfassungsdatum 2019-08-13
[➜Korrektur melden]