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Colomé-Tatché, M. ; Theis, F.J.

Statistical single cell multi-omics integration.

Curr. Opin. Syst. Biol. 7, 54-59 (2018)
Verlagsversion Postprint DOI
Open Access Hybrid
Creative Commons Lizenzvertrag
Single cell high throughput genomic measurements are revolutionizing the fields of biology and medicine, providing a means to tackle biological problems that have thus far been inaccessible, such as the systematic discovery of new cell types, the identification of cellular heterogeneity in health and disease, or the cell-fate decisions taking place during differentiation and reprogramming. Recently implemented multi–omics measurements of genomes, transcriptomes, epigenomes, proteomes and chromatin organization are opening up new avenues to begin to disentangle the causal relationship between -omics layers and how these co-determine higher-order cellular phenotypes. This technological revolution is not restricted to basic science but promises major breakthroughs in medical diagnostics and treatments. In this paper we review existing computational methods for the analysis and integration of different -omics layers and discuss what new approaches are needed to leverage the full potential of single cell multi-omics data.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Review
Sprache englisch
Veröffentlichungsjahr 2018
HGF-Berichtsjahr 2018
ISSN (print) / ISBN 2452-3100
e-ISSN 2452-3100
Zeitschrift Current Opinion in Systems Biology
Quellenangaben Band: 7, Heft: , Seiten: 54-59 Artikelnummer: , Supplement: ,
Verlag Elsevier
Verlagsort Amsterdam
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
G-554200-001
DOI 10.1016/j.coisb.2018.01.003
Scopus ID 85045325679
Erfassungsdatum 2018-07-16
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