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ClussCluster

This package implements a new method ClussCluster to 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.

Installing

To install this package and load it into R, do the following:

devtools::install_github("gabriellajg/ClussCluster")
library(ClussCluster)

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Version

Install

install.packages('ClussCluster')

Monthly Downloads

181

Version

0.1.0

License

GPL-3

Maintainer

Jun Li

Last Published

July 2nd, 2019

Functions in ClussCluster (0.1.0)

plot_ClussCluster

Plots the results of ClussCluster
sim_dat

A simulated expression data set.
print_ClussCluster

Prints out the results of ClussCluster
Hou_sim

A truncated subset of the scRNA-seq expression data set from Hou et.al (2016)
filter_gene

Gene Filter
plot_ClussCluster_Gap

Plots the results of ClussCluster_Gap
print_ClussCluster_Gap

Prints out the results of ClussCluster_Gap Prints the gap statistics and number of genes selected for each candidate tuning parameter.
ClussCluster

Performs simultaneous detection of cell types and cell-type-specific signature genes