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ClussCluster  

Simultaneous Detection of Clusters and Cluster-Specific Genes in High-Throughput Transcriptome Data
View on CRAN: Click here


Download and install ClussCluster package within the R console
Install from CRAN:
install.packages("ClussCluster")

Install from Github:
library("remotes")
install_github("cran/ClussCluster")

Install by package version:
library("remotes")
install_version("ClussCluster", "0.1.0")



Attach the package and use:
library("ClussCluster")
Maintained by
Li Jun
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2019-07-02
Latest Update: 2019-07-02
Description:
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and Jun Li, "Simultaneous Detection of Clusters and Cluster-Specific Genes in High-throughput Transcriptome Data" (Unpublished).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.
How to cite:
Li Jun (2019). ClussCluster: Simultaneous Detection of Clusters and Cluster-Specific Genes in High-Throughput Transcriptome Data. R package version 0.1.0, https://cran.r-project.org/web/packages/ClussCluster. Accessed 18 Feb. 2025.
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Complete documentation for ClussCluster
Functions, R codes and Examples using the ClussCluster R package
Some associated functions: ClussCluster . Hou_sim . filter_gene . plot_ClussCluster . plot_ClussCluster_Gap . print_ClussCluster . print_ClussCluster_Gap . sim_dat . 
Some associated R codes: ClussCluster.R . Hou_sim.dat.R . dat.R . internal_functions.R . test.R .  Full ClussCluster package functions and examples
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