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Aliee, H. ; Theis, F.J.

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

Cell Syst. 12, 706-715.e4 (2021)
Publ. Version/Full Text Research data DOI PMC
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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).
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Publication type Article: Journal article
Document type Scientific Article
Keywords Bulk Deconvolution ; Bulk Rna-seq ; Feature Selection, Marker Genes ; Multi-objective Optimization ; Single-cell Rna-seq; Cell; Signatures
Language english
Publication Year 2021
HGF-reported in Year 2021
ISSN (print) / ISBN 2405-4712
e-ISSN 2405-4720
Journal Cell Systems
Quellenangaben Volume: 12, Issue: 7, Pages: 706-715.e4 Article Number: , Supplement: ,
Publisher Elsevier
Publishing Place Maryland Heights, MO
Institute(s) Institute of Computational Biology (ICB)
POF-Topic(s) 30205 - Bioengineering and Digital Health
Research field(s) Enabling and Novel Technologies
PSP Element(s) G-503800-001
Grants 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
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