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.
Package details |
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Bioconductor views | Clustering DifferentialExpression GeneExpression Sequencing SingleCell |
Maintainer | |
License | GPL (>=2) |
Version | 1.0.0 |
URL | https://github.com/tk382/HIPPO |
Package repository | View on GitHub |
Installation |
Install the latest version of this package by entering the following in R:
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