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
- Vladimir Yu Kiselev: Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK. ORCID
- Kristina Kirschner: Cambridge Institute for Medical Research, Wellcome Trust/MRC Stem Cell Institute and Department of Haematology, University of Cambridge, Hills Road, Cambridge, UK.
- Michael T Schaub: Department of Mathematics and naXys, University of Namur, Namur, Belgium.
- Tallulah Andrews: Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK. ORCID
- Andrew Yiu: Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK.
- Tamir Chandra: Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK.
- Kedar N Natarajan: Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK.
- Wolf Reik: Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK.
- Mauricio Barahona: Department of Mathematics, Imperial College London, London, UK. ORCID
- 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.
- Martin Hemberg: Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK.
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.
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
