PuSH - Publikationsserver des Helmholtz Zentrums München

Export: TXT Text RIS Endnote (RIS) BIB BibTeX
Aliee, H. ; Theis, F.J.

AutoGeneS: Automatic gene selection using multi-objective optimization for RNA-seq deconvolution.

Cell Syst. 12, 706-715.e4 (2021)
Verlagsversion Forschungsdaten DOI PMC
Open Access Hybrid
Creative Commons Lizenzvertrag
Knowing cell-type proportions in a tissue is very important to identify which cells or cell types are targeted by a disease or perturbation. Hence, several deconvolution methods have been proposed to infer cell-type proportions from bulk RNA samples. Their performance with noisy reference profiles and closely correlated cell types highly depends on the set of genes undergoing deconvolution. In this work, we introduce AutoGeneS, a platform that automatically extracts discriminative genes and reveals the cellular heterogeneity of bulk RNA samples. AutoGeneS requires no prior knowledge about marker genes and selects genes by simultaneously optimizing multiple criteria: minimizing the correlation and maximizing the distance between cell types. AutoGeneS can be applied to reference profiles from various sources like single-cell experiments or sorted cell populations. Ground truth cell proportions analyzed by flow cytometry confirmed the accuracy of AutoGeneS in identifying cell-type proportions. AutoGeneS is available for use via a standalone Python package (https://github.com/theislab/AutoGeneS).
Impact Factor
Scopus SNIP
Web of Science
Times Cited
Scopus
Cited By
Altmetric
10.304
2.222
2
12
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
Schlagwörter Bulk Deconvolution ; Bulk Rna-seq ; Feature Selection, Marker Genes ; Multi-objective Optimization ; Single-cell Rna-seq; Cell; Signatures
Sprache englisch
Veröffentlichungsjahr 2021
HGF-Berichtsjahr 2021
ISSN (print) / ISBN 2405-4712
e-ISSN 2405-4720
Zeitschrift Cell Systems
Quellenangaben Band: 12, Heft: 7, Seiten: 706-715.e4 Artikelnummer: , Supplement: ,
Verlag Elsevier
Verlagsort Maryland Heights, MO
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
Förderungen sparse2big
Helmholtz Association's Initiative and Networking Fund through Helmholtz AI
DOI 10.1016/j.cels.2021.05.006
WOS ID WOS:000675884900004
Scopus ID 85110119657
PubMed ID 34293324
Erfassungsdatum 2021-08-02
[➜Korrektur melden]