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Adapted single-cell consensus clustering (adaSC3).
Adv. Data Anal. Classif. 14, 885-896 (2020)
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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Publication type
Article: Journal article
Document type
Scientific Article
Keywords
Diffusion Maps ; Non-linear Embedding ; Simulation Data ; Single-cell Consensus Clustering ; Single-cell Rna Sequencing Data; Gene-expression; Diffusion Maps; Embryos; Fate
Language
english
Publication Year
2020
HGF-reported in Year
2020
ISSN (print) / ISBN
1862-5347
e-ISSN
1862-5355
Quellenangaben
Volume: 14,
Issue: 4,
Pages: 885-896
Publisher
Springer
Publishing Place
Tiergartenstrasse 17, D-69121 Heidelberg, Germany
Reviewing status
Peer reviewed
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
Projekt DEAL
WOS ID
WOS:000599108200001
Scopus ID
85097545975
Erfassungsdatum
2020-12-21