SC3 - consensus clustering of single-cell RNA-Seq data

By Vladimir Yu. Kiselev, K. Kirschner, Michael T. Schaub, Tallulah S. Andrews, A. Yiu, Tamir Chandra, K. Natarajan, Wolf Reik, Mauricio Barahona, A. Green and 1 more. Nature Methods, 2016

1,337 citations recorded by the source corpus.

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.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/cc9a507c459bcc36b2053cbfd5b74db923e5da0b

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Citations in the corpus

Generalizable and Scalable Visualization of Single-Cell Data Using Neural Networks (2018, methodology): “The level of agreement was quantified by the adjusted Rand index (Rand, 1971), following previous work (Kiselev et al., 2017).”
The Human Cell Atlas (2017, background): “, 2016b) and that individual sub-clones can be readily identified in one patient (Kiselev et al., 2017).”
Orchestrating single-cell analysis with Bioconductor (2019, background): “Finally, the SC3 61 and clusterExperiment 62 packages calculate consensus clusters derived from multiple parameterizations.”
Splatter: simulation of single-cell RNA sequencing data (2017, methodology): “This approach is taken by tools such as SC3 [5], CIDR [6], and Seurat [7] and is appropriate for a sample of mature cells where it is reasonable to expect cells to have a particular type.”
SCENIC: Single-cell regulatory network inference and clustering (2017, methodology): “To compare the accuracy of cell clustering based on SCENIC GRNs with standard clustering methods, we used the recent benchmark assay from the SC3 publication 14 , based on the adjusted Rand index (ARI) 36 .”
Spatial transcriptomics at subspot resolution with BayesSpace (2021, methodology): “25 ); two recently published spatial clustering algorithms, HMRF (as implemented in the Giotto package) 18 and stLearn 19 ; and the clustering partitions originally reported by Maynard et al. 4 in the spatialLIBD package, which involve Walktrap clustering of spatial coordinates and principal…”
Ensemble learning: A survey (2018, methodology): “Consensus clustering has been shown to be very effective in discovering biological meaningful clusters in gene expression data (Kiselev et al., 2017; Monti et al., 2003; Verhaak et al., 2010), video shot segmentation (Chang, Lee, Hong, & Archibald, 2008; Zheng, Zhang, & Li, 2012), online event…”
DrImpute: imputing dropout events in single cell RNA sequencing data (2017, methodology): “We grouped these datasets into three levels (gold, silver and bronze) based on the supporting evidence of the reported cell labels [26].”