SC3: consensus clustering of single-cell RNA-seq data

Journal article

Single-cell RNA-seq enables the quantitative characterization of cell types 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 (http://bioconductor.org/packages/SC3). We demonstrate that SC3 is capable of identifying subclones from the transcriptomes of neoplastic cells collected from patients.

Authors: Kiselev, V; Kirschner, K; Schaub, M; Andrews, T; Yiu, A

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Springer Nature
• Journal: Nature Methods
• Volume: 14
• Issue: 5
• Pages: 483-486
• Publication date: 2017-03-27
• Acceptance date: 2017-03-01
• DOI: 10.1038/nmeth.4236
• EISSN: 1548-7105
• ISSN: 1548-7091
• Pmid: 28346451
• Language: English
• Keywords: Datasets as Topic; Gene Expression Profiling; Humans; Cluster Analysis; Hematopoietic Stem Cells; Single-Cell Analysis; Support Vector Machine; Sequence Analysis, RNA; High-Throughput Nucleotide Sequencing