In this section
@article{Kiselev:2017:10.1038/nmeth.4236,
author = {Kiselev, V and Kirschner, K and Schaub, MT and Andrews, T and Yiu, A and Chandra, T and Natarajan, KN and Reik, W and Barahona, M and Green, AR and Hemberg, M},
doi = {10.1038/nmeth.4236},
journal = {Nature Methods},
pages = {483--486},
title = {SC3: consensus clustering of single-cell RNA-seq data},
url = {http://dx.doi.org/10.1038/nmeth.4236},
volume = {14},
year = {2017}
}
TY - JOUR
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.
AU - Kiselev,V
AU - Kirschner,K
AU - Schaub,MT
AU - Andrews,T
AU - Yiu,A
AU - Chandra,T
AU - Natarajan,KN
AU - Reik,W
AU - Barahona,M
AU - Green,AR
AU - Hemberg,M
DO - 10.1038/nmeth.4236
EP - 486
PY - 2017///
SN - 1548-7105
SP - 483
TI - SC3: consensus clustering of single-cell RNA-seq data
T2 - Nature Methods
UR - http://dx.doi.org/10.1038/nmeth.4236
UR - http://hdl.handle.net/10044/1/45444
VL - 14
ER -