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Wolf, F.A. ; Hamey, F.K.* ; Plass, M.* ; Solana, J.* ; Dahlin, J.S.* ; Göttgens, B.* ; Rajewsky, N.* ; Simon, L. ; Theis, F.J.

PAGA: Graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells.

Genome Biol. 20:59 (2019)
Verlagsversion DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
Single-cell RNA-seq quantifies biological heterogeneity across both discrete cell types and continuous cell transitions. Partition-based graph abstraction (PAGA) provides an interpretable graph-like map of the arising data manifold, based on estimating connectivity of manifold partitions (https://github.com/theislab/paga). PAGA maps preserve the global topology of data, allow analyzing data at different resolutions, and result in much higher computational efficiency of the typical exploratory data analysis workflow. We demonstrate the method by inferring structure-rich cell maps with consistent topology across four hematopoietic datasets, adult planaria and the zebrafish embryo and benchmark computational performance on one million neurons.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
Schlagwörter Identity; Stem
ISSN (print) / ISBN 1474-760X
e-ISSN 1465-6906
Zeitschrift Genome Biology
Quellenangaben Band: 20, Heft: 1, Seiten: , Artikelnummer: 59 Supplement: ,
Verlag BioMed Central
Verlagsort Campus, 4 Crinan St, London N1 9xw, England
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)