RT Journal Article
SR Electronic
T1 SC3 – consensus clustering of single-cell RNA-Seq data
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 036558
DO 10.1101/036558
A1 Vladimir Yu. Kiselev
A1 Kristina Kirschner
A1 Michael T. Schaub
A1 Tallulah Andrews
A1 Tamir Chandra
A1 Kedar N Natarajan
A1 Wolf Reik
A1 Mauricio Barahona
A1 Anthony R Green
A1 Martin Hemberg
YR 2016
UL http://biorxiv.org/content/early/2016/01/13/036558.abstract
AB Using single-cell RNA-seq (scRNA-seq), the full transcriptome of individual cells can be acquired, enabling a quantitative characterisation of cell-type based on expression profiles. Due to the large variability in gene expression, assigning cells into groups based on the transcriptome remains challenging. We present Single-Cell Consensus Clustering (SC3), a tool for unsupervised clustering of scRNA-seq data. SC3 integrates many different clustering solutions through a consensus approach, thereby increasing its accuracy and robustness against noise. Tests on nine published datasets show that SC3 outperforms existing methods, yet SC3 remains scalable for large datasets, as shown by the analysis of a dataset containing ~45,000 cells. To enhance the accessibility to users with limited bioinformatics expertise, SC3 features an interactive graphical implementation, which aids the biological interpretation by identifying marker genes, differentially expressed genes and outlier cells. Finally, we apply SC3 to identify different subclones of neoplastic cells in data collected from patients.