Abstract / Description of output
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 |
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Pages (from-to) | 483-486 |
Number of pages | 4 |
Journal | Nature Methods |
Volume | 14 |
DOIs | |
Publication status | Published - 27 Mar 2017 |
Externally published | Yes |
Keywords / Materials (for Non-textual outputs)
- Gene expression
- Machine learning
- RNA sequencing
- Software