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

doi:10.1038/nmeth.4236

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

Quantitative Health Sciences

Research output: Contribution to journal › Article › peer-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 language	English (US)
Pages (from-to)	483-486
Number of pages	4
Journal	Nature Methods
Volume	14
Issue number	5
DOIs	https://doi.org/10.1038/nmeth.4236
State	Published - Apr 27 2017

ASJC Scopus subject areas

Biotechnology
Biochemistry
Molecular Biology
Cell Biology

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10.1038/nmeth.4236

Cite this

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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.",

author = "Kiselev, \{Vladimir Yu\} and Kristina Kirschner and Schaub, \{Michael T.\} and Tallulah Andrews and Andrew Yiu and Tamir Chandra and Natarajan, \{Kedar N.\} and Wolf Reik and Mauricio Barahona and Green, \{Anthony R.\} and Martin Hemberg",

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AU - Kiselev, Vladimir Yu

AU - Kirschner, Kristina

AU - Schaub, Michael T.

AU - Andrews, Tallulah

AU - Yiu, Andrew

AU - Chandra, Tamir

AU - Natarajan, Kedar N.

AU - Reik, Wolf

AU - Barahona, Mauricio

AU - Green, Anthony R.

AU - Hemberg, Martin

PY - 2017/4/27

Y1 - 2017/4/27

N2 - 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.

AB - 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.

UR - https://www.scopus.com/pages/publications/85016121177

UR - https://www.scopus.com/pages/publications/85016121177#tab=citedBy

U2 - 10.1038/nmeth.4236

DO - 10.1038/nmeth.4236

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SP - 483

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JO - Nature Methods

JF - Nature Methods

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SC3: Consensus clustering of single-cell RNA-seq data

Abstract

ASJC Scopus subject areas

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