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and Jun Li, "Simultaneous Detection of Clusters and Cluster-Specific Genes in High-throughput Transcriptome Data" (Unpublished).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

Downloads:

Reference manual: ClussCluster.pdf
Vignettes: ClussCluster
Package source: ClussCluster_0.1.0.tar.gz
Windows binaries: r-devel: ClussCluster_0.1.0.zip, r-devel-gcc8: ClussCluster_0.1.0.zip, r-release: ClussCluster_0.1.0.zip, r-oldrel: ClussCluster_0.1.0.zip
OS X binaries: r-release: ClussCluster_0.1.0.tgz, r-oldrel: ClussCluster_0.1.0.tgz

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