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

被引：0

作者：

Kiselev V.Y. ^{[1
]}

Kirschner K. ^{[2
]}

Schaub M.T. ^{[3
,4
]}

Andrews T. ^{[1
]}

Yiu A. ^{[1
]}

Chandra T. ^{[1
,5
]}

Natarajan K.N. ^{[1
,6
]}

Reik W. ^{[1
,5
,7
]}

Barahona M. ^{[8
]}

Green A.R. ^{[2
]}

Hemberg M. ^{[1
]}

机构：

[1] Wellcome Trust Sanger Institute, Hinxton, Cambridge

[2] Cambridge Institute for Medical Research, Wellcome Trust/MRC Stem Cell Institute, Department of Haematology, University of Cambridge, Hills Road, Cambridge

[3] Department of Mathematics and NaXys, University of Namur, Namur

[4] ICTEAM, Université Catholique de Louvain, Louvain-la-Neuve

[5] Epigenetics Programme, Babraham Institute, Babraham, Cambridge

[6] EMBL-European Bioinformatics Institute, Hinxton, Cambridge

[7] Centre for Trophoblast Research, University of Cambridge, Cambridge

[8] Department of Mathematics, Imperial College London, London

来源：

Nature Methods | 2017年 / 14卷 / 5期

基金：

英国医学研究理事会; 英国工程与自然科学研究理事会; 英国生物技术与生命科学研究理事会;

关键词：

D O I：

10.1038/nmeth.4236

中图分类号：

学科分类号：

摘要：

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. © 2017 Nature America, Inc. part of Springer Nature. All rights reserved.

引用

页码：483 / 486

页数：3

共 50 条

[1] 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
[J]. NATURE METHODS, 2017, 14 (05) : 483 - +
[2] Consensus clustering of single-cell RNA-seq data by enhancing network affinity
Cui, Yaxuan
Zhang, Shaoqiang
Liang, Ying
Wang, Xiangyun
Ferraro, Thomas N.
Chen, Yong
[J]. BRIEFINGS IN BIOINFORMATICS, 2021, 22 (06)
[3] Analysis of Single-Cell RNA-seq Data by Clustering Approaches
Zhu, Xiaoshu
Li, Hong-Dong
Guo, Lilu
Wu, Fang-Xiang
Wang, Jianxin
[J]. CURRENT BIOINFORMATICS, 2019, 14 (04) : 314 - 322
[4] Deep Learning for Clustering Single-cell RNA-seq Data
Zhu, Yuan
Bai, Litai
Ning, Zilin
Fu, Wenfei
Liu, Jie
Jiang, Linfeng
Fei, Shihuang
Gong, Shiyun
Lu, Lulu
Deng, Minghua
Yi, Ming
[J]. CURRENT BIOINFORMATICS, 2024, 19 (03) : 193 - 210
[5] HNC: a hybrid neighbourhood-consensus clustering algorithm for single-cell RNA-seq data
Das, Priyojit
Saha, Sujay
[J]. INTERNATIONAL JOURNAL OF DATA MINING AND BIOINFORMATICS, 2021, 25 (3-4) : 161 - 180
[6] An active learning approach for clustering single-cell RNA-seq data
Lin, Xiang
Liu, Haoran
Wei, Zhi
Roy, Senjuti Basu
Gao, Nan
[J]. LABORATORY INVESTIGATION, 2022, 102 (03) : 227 - 235
[7] Single-cell RNA-seq data clustering by deep information fusion
Ren, Liangrui
Wang, Jun
Li, Wei
Guo, Maozu
Yu, Guoxian
[J]. BRIEFINGS IN FUNCTIONAL GENOMICS, 2024, 23 (02) : 128 - 137
[8] A Global Similarity Learning for Clustering of Single-Cell RNA-Seq Data
Zhu, Xiaoshu
Guo, Lilu
Xu, Yunpei
Li, Hong-Dong
Liao, Xingyu
Wu, Fang-Xiang
Peng, Xiaoqing
[J]. 2019 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (BIBM), 2019, : 261 - 266
[9] Impact of similarity metrics on single-cell RNA-seq data clustering
Kim, Taiyun
Chen, Irene Rui
Lin, Yingxin
Wang, Andy Yi-Yang
Yang, Jean Yee Hwa
Yang, Pengyi
[J]. BRIEFINGS IN BIOINFORMATICS, 2019, 20 (06) : 2316 - 2326
[10] Identification of cancer subtypes from single-cell RNA-seq data using a consensus clustering method
Yanglan Gan
Ning Li
Guobing Zou
Yongchang Xin
Jihong Guan
[J]. BMC Medical Genomics, 11

← 1 2 3 4 5 →