PT  - JOURNAL ARTICLE
AU  - Vladimir Yu. Kiselev
AU  - Kristina Kirschner
AU  - Michael T. Schaub
AU  - Tallulah Andrews
AU  - Tamir Chandra
AU  - Kedar N Natarajan
AU  - Wolf Reik
AU  - Mauricio Barahona
AU  - Anthony R Green
AU  - Martin Hemberg
TI  - SC3 - consensus clustering of single-cell RNA-Seq data
AID  - 10.1101/036558
DP  - 2016 Jan 01
TA  - bioRxiv
PG  - 036558
4099  - http://biorxiv.org/content/early/2016/04/14/036558.short
4100  - http://biorxiv.org/content/early/2016/04/14/036558.full
AB  - Using single-cell RNA-seq (scRNA-seq), the full transcriptome of individual cells can be acquired, enabling a quantitative cell-type characterisation 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 achieves high accuracy and robustness by consistently integrating different clustering solutions through a consensus approach. Tests on nine published datasets show that SC3 outperforms 4 existing methods, while remaining scalable for large datasets, as shown by the analysis of a dataset containing 44,808 cells. Moreover, an interactive graphical implementation makes SC3 accessible to a wide audience of users, and SC3 also aids biological interpretation by identifying marker genes, differentially expressed genes and outlier cells. We illustrate the capabilities of SC3 by characterising newly obtained transcriptomes from subclones of neoplastic cells collected from patients.