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

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Pages (from-to)483-486
Number of pages4
JournalNature Methods
Volume14
DOIs
Publication statusPublished - 27 Mar 2017
Externally publishedYes

Keywords

  • Gene expression
  • Machine learning
  • RNA sequencing
  • Software

Fingerprint

Dive into the research topics of 'SC3: consensus clustering of single-cell RNA-seq data'. Together they form a unique fingerprint.

Cite this