For scRNA-seq data, it selects features and clusters the cells simultaneously for single-cell UMI data. It has a novel feature selection method using the zero inflation instead of gene variance, and computationally faster than other existing methods since it only relies on PCA+Kmeans rather than graph-clustering or consensus clustering.
|Bioconductor views||Clustering DifferentialExpression GeneExpression Sequencing SingleCell|
|Package repository||View on GitHub|
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