SC3 - consensus clustering of single-cell RNA-Seq data

Single-cell RNA-seq (scRNA-seq) enables a quantitative cell-type characterisation based on global transcriptome profiles. We present Single-Cell Consensus Clustering (SC3), a user-friendly tool for unsupervised clustering which achieves high accuracy and robustness by combining multiple clustering s...

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Detalles Bibliográficos
Autores principales: Kiselev, Vladimir Yu., Kirschner, Kristina, Schaub, Michael T., Andrews, Tallulah, Yiu, Andrew, Chandra, Tamir, Natarajan, Kedar N, Reik, Wolf, Barahona, Mauricio, Green, Anthony R, Hemberg, Martin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2017
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5410170/
https://www.ncbi.nlm.nih.gov/pubmed/28346451
http://dx.doi.org/10.1038/nmeth.4236
Descripción
Sumario:Single-cell RNA-seq (scRNA-seq) enables a quantitative cell-type characterisation based on global transcriptome profiles. We present Single-Cell Consensus Clustering (SC3), a user-friendly tool for unsupervised clustering which achieves high accuracy and robustness by combining multiple clustering solutions through a consensus approach. We demonstrate that SC3 is capable of identifying subclones based on the transcriptomes from neoplastic cells collected from patients.

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