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Nature methods
May, 2017;14 (5): 483-486.

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

Vladimir Yu Kiselev, Kristina Kirschner, Michael T Schaub, Tallulah Andrews, Andrew Yiu, Tamir Chandra, Kedar N Natarajan, Wolf Reik, Mauricio Barahona, Anthony R Green, Martin Hemberg
Author Information
  1. Vladimir Yu Kiselev: Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK. ORCID
  2. Kristina Kirschner: Cambridge Institute for Medical Research, Wellcome Trust/MRC Stem Cell Institute and Department of Haematology, University of Cambridge, Hills Road, Cambridge, UK.
  3. Michael T Schaub: Department of Mathematics and naXys, University of Namur, Namur, Belgium.
  4. Tallulah Andrews: Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK. ORCID
  5. Andrew Yiu: Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK.
  6. Tamir Chandra: Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK.
  7. Kedar N Natarajan: Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK.
  8. Wolf Reik: Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK.
  9. Mauricio Barahona: Department of Mathematics, Imperial College London, London, UK. ORCID
  10. Anthony R Green: Cambridge Institute for Medical Research, Wellcome Trust/MRC Stem Cell Institute and Department of Haematology, University of Cambridge, Hills Road, Cambridge, UK.
  11. Martin Hemberg: Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK.
PMID: 28346451 DOI: 10.1038/nmeth.4236

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.

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Grants

  1. 098051/Wellcome Trust
  2. 21762/Cancer Research UK
  3. MC_PC_12009/Medical Research Council

MeSH Term

Cluster Analysis
Datasets as Topic
Gene Expression Profiling
Hematopoietic Stem Cells
High-Throughput Nucleotide Sequencing
Humans
Sequence Analysis, RNA
Single-Cell Analysis
Support Vector Machine
Journal Article

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