SC3: Consensus Clustering of Single-Cell RNA-seq Data

Nat Methods. 2017 May;14(5):483-486. doi: 10.1038/nmeth.4236. Epub 2017 Mar 27.

Abstract

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.

MeSH terms

  • Cluster Analysis
  • Datasets as Topic
  • Gene Expression Profiling / methods*
  • Hematopoietic Stem Cells / cytology
  • High-Throughput Nucleotide Sequencing / methods*
  • Humans
  • Sequence Analysis, RNA / methods*
  • Single-Cell Analysis / methods*
  • Support Vector Machine