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

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
Pages (from-to)	483-486
Number of pages	4
Journal	Nature Methods
Volume	14
DOIs	https://doi.org/10.1038/nmeth.4236
Publication status	Published - 27 Mar 2017
Externally published	Yes

Keywords

Gene expression
Machine learning
RNA sequencing
Software

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10.1038/nmeth.4236

SC3 - consensus clustering of single-cell RNA-Seq dataAccepted author manuscript, 44.3 KB

Tamir Chandra
- Deanery of Molecular, Genetic and Population Health Sciences - Chancellor's Fellow
- MRC Human Genetics Unit
Person: Academic: Research Active

Cite this

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title = "SC3: consensus clustering of single-cell RNA-seq data",

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.",

keywords = "Gene expression, Machine learning, RNA sequencing, Software",

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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month = mar,

day = "27",

doi = "10.1038/nmeth.4236",

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journal = "Nature Methods",

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SC3 : consensus clustering of single-cell RNA-seq data. / Kiselev, Vladimir Yu; Kirschner, Kristina; Schaub, Michael T; Andrews, Tallulah; Yiu, Andrew; Chandra, Tamir; Natarajan, Kedar N; Reik, Wolf; Barahona, Mauricio; Green, Anthony R; Hemberg, Martin.

In: Nature Methods, Vol. 14, 27.03.2017, p. 483-486.

Research output: Contribution to journal › Article › peer-review

TY - JOUR

T1 - SC3

T2 - consensus clustering of single-cell RNA-seq data

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 - 2017/3/27

Y1 - 2017/3/27

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.

KW - Gene expression

KW - Machine learning

KW - RNA sequencing

KW - Software

U2 - 10.1038/nmeth.4236

DO - 10.1038/nmeth.4236

M3 - Article

C2 - 28346451

VL - 14

SP - 483

EP - 486

JO - Nature Methods

JF - Nature Methods

SN - 1548-7091

ER -

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

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Keywords

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Tamir Chandra

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