ClussCluster: Simultaneous Detection of Clusters and Cluster-Specific Genes in High-Throughput Transcriptome Data

Implements a new method 'ClussCluster' descried in Ge Jiang and Jun Li, "Simultaneous Detection of Clusters and Cluster-Specific Genes in High-throughput Transcriptome Data" (Unpublished). Simultaneously perform clustering analysis and signature gene selection on high-dimensional transcriptome data sets. To do so, 'ClussCluster' incorporates a Lasso-type regularization penalty term to the objective function of K- means so that cell-type-specific signature genes can be identified while clustering the cells.

Version:	0.1.0
Depends:	R (≥ 2.10.0)
Imports:	stats (≥ 3.5.0), utils (≥ 3.5.0), VennDiagram, scales (≥ 1.0.0), reshape2 (≥ 1.4.3), ggplot2 (≥ 3.1.0), rlang (≥ 0.3.4)
Suggests:	knitr, rmarkdown (≥ 1.13)
Published:	2019-07-02
Author:	Li Jun [cre], Jiang Ge [aut], Wang Chuanqi [ctb]
Maintainer:	Li Jun <jun.li at nd.edu>
License:	GPL-3
NeedsCompilation:	no
Materials:	README
CRAN checks:	ClussCluster results

Documentation:

Reference manual:	ClussCluster.pdf
Vignettes:	ClussCluster

Downloads:

Package source:	ClussCluster_0.1.0.tar.gz
Windows binaries:	r-devel: ClussCluster_0.1.0.zip, r-release: ClussCluster_0.1.0.zip, r-oldrel: ClussCluster_0.1.0.zip
macOS binaries:	r-release (arm64): ClussCluster_0.1.0.tgz, r-oldrel (arm64): ClussCluster_0.1.0.tgz, r-release (x86_64): ClussCluster_0.1.0.tgz, r-oldrel (x86_64): ClussCluster_0.1.0.tgz

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