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Fuetterer, C.* ; Augustin, T.* ; Fuchs, C.

Adapted single-cell consensus clustering (adaSC3).

Adv. Data Anal. Classif. 14, 885-896 (2020)
Verlagsversion Forschungsdaten DOI
Open Access Hybrid
Creative Commons Lizenzvertrag
The analysis of single-cell RNA sequencing data is of great importance in health research. It challenges data scientists, but has enormous potential in the context of personalized medicine. The clustering of single cells aims to detect different subgroups of cell populations within a patient in a data-driven manner. Some comparison studies denote single-cell consensus clustering (SC3), proposed by Kiselev et al. (Nat Methods 14(5):483–486, 2017), as the best method for classifying single-cell RNA sequencing data. SC3 includes Laplacian eigenmaps and a principal component analysis (PCA). Our proposal of unsupervised adapted single-cell consensus clustering (adaSC3) suggests to replace the linear PCA by diffusion maps, a non-linear method that takes the transition of single cells into account. We investigate the performance of adaSC3 in terms of accuracy on the data sets of the original source of SC3 as well as in a simulation study. A comparison of adaSC3 with SC3 as well as with related algorithms based on further alternative dimension reduction techniques shows a quite convincing behavior of adaSC3.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Diffusion Maps ; Non-linear Embedding ; Simulation Data ; Single-cell Consensus Clustering ; Single-cell Rna Sequencing Data; Gene-expression; Diffusion Maps; Embryos; Fate
Sprache englisch
Veröffentlichungsjahr 2020
HGF-Berichtsjahr 2020
ISSN (print) / ISBN 1862-5347
e-ISSN 1862-5355
Zeitschrift Advances in Data Analysis and Classification
Quellenangaben Band: 14, Heft: 4, Seiten: 885-896 Artikelnummer: , Supplement: ,
Verlag Springer
Verlagsort Tiergartenstrasse 17, D-69121 Heidelberg, Germany
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
Förderungen Projekt DEAL
DOI 10.1007/s11634-020-00428-1
WOS ID WOS:000599108200001
Scopus ID 85097545975
Erfassungsdatum 2020-12-21
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