OpenLB
Open Library of Bioscience
Advanced Search
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.

References

  1. Genome Biol. 2016 Jul 01;17(1):144 [PMID: 27368803]
  2. Nat Protoc. 2014 Jan;9(1):171-81 [PMID: 24385147]
  3. Cell. 2015 May 21;161(5):1202-1214 [PMID: 26000488]
  4. Nature. 2015 Sep 10;525(7568):251-5 [PMID: 26287467]
  5. Nucleic Acids Res. 2015 Apr 20;43(7):e47 [PMID: 25605792]
  6. N Engl J Med. 2015 Feb 12;372(7):601-612 [PMID: 25671252]
  7. Bioinformatics. 2015 Jun 15;31(12):1974-80 [PMID: 25805722]
  8. Cell Rep. 2014 May 22;7(4):1130-42 [PMID: 24813893]
  9. Dev Cell. 2010 Apr 20;18(4):675-85 [PMID: 20412781]
  10. Science. 2014 Feb 14;343(6172):776-9 [PMID: 24531970]
  11. Nucleic Acids Res. 2016 Jul 8;44(W1):W83-9 [PMID: 27098042]
  12. PLoS Comput Biol. 2015 Nov 24;11(11):e1004575 [PMID: 26600239]
  13. Nat Biotechnol. 2014 Sep;32(9):896-902 [PMID: 25150836]
  14. Science. 2014 Jan 10;343(6167):193-6 [PMID: 24408435]
  15. Bioinformatics. 2014 Aug 1;30(15):2114-20 [PMID: 24695404]
  16. Genome Biol. 2014;15(12):550 [PMID: 25516281]
  17. J Exp Med. 1996 Jun 1;183(6):2551-8 [PMID: 8676076]
  18. Dev Cell. 2015 Nov 9;35(3):366-82 [PMID: 26555056]
  19. Genome Biol. 2004;5(10):R80 [PMID: 15461798]
  20. Bioinformatics. 2009 May 1;25(9):1105-11 [PMID: 19289445]
  21. BMC Bioinformatics. 2016 Mar 22;17:140 [PMID: 27005807]
  22. N Engl J Med. 2013 Dec 19;369(25):2391-2405 [PMID: 24325359]
  23. Immunity. 2012 Apr 20;36(4):529-41 [PMID: 22520846]
  24. Bioinformatics. 2017 Apr 15;33(8):1179-1186 [PMID: 28088763]
  25. PLoS Genet. 2006 Dec;2(12):e190 [PMID: 17194218]

Grants

  1. /Wellcome Trust
  2. 098051/Wellcome Trust
  3. 21762/Cancer Research UK
  4. 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

Links to CNCB-NGDC Resources

GEN: GEND000353 (SC3-consensus clustering of single cell RNA-Seq data)

Word Cloud

Similar Articles

Cited By