SC3: consensus clustering of single-cell RNA-seq data. 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.
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References in zbMATH (referenced in 4 articles )
Showing results 1 to 4 of 4.
- Lin, Zhixiang; Zamanighomi, Mahdi; Daley, Timothy; Ma, Shining; Wong, Wing Hung: Model-based approach to the joint analysis of single-cell data on chromatin accessibility and gene expression (2020)
- Suner, Aslı: Clustering methods for single-cell RNA-sequencing expression data: performance evaluation with varying sample sizes and cell compositions (2019)
- Sassi Hidri, Minyar; Zoghlami, Mohamed Ali; Ben Ayed, Rahma: Speeding up the large-scale consensus fuzzy clustering for handling big data (2018)
- Lin, Lin; Li, Jia: Clustering with hidden Markov model on variable blocks (2017)