SC3: consensus clustering of single-cell RNA-seq data.
Vladimir Yu KiselevKristina KirschnerMichael T SchaubTallulah S AndrewsAndrew YiuTamir ChandraKedar N NatarajanWolf ReikMauricio BarahonaAnthony R GreenMartin HembergPublished in: Nature methods (2017)
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.
Keyphrases
- single cell
- rna seq
- high throughput
- end stage renal disease
- chronic kidney disease
- clinical practice
- ejection fraction
- induced apoptosis
- machine learning
- peritoneal dialysis
- prognostic factors
- high resolution
- stem cells
- electronic health record
- big data
- oxidative stress
- mass spectrometry
- endoplasmic reticulum stress
- bone marrow
- patient reported
- deep learning
- cell therapy