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SparseDC (version 0.1.17)

Implementation of SparseDC Algorithm

Description

Implements the algorithm described in Barron, M., Zhang, S. and Li, J. 2017, "A sparse differential clustering algorithm for tracing cell type changes via single-cell RNA-sequencing data", Nucleic Acids Research, gkx1113, . This algorithm clusters samples from two different populations, links the clusters across the conditions and identifies marker genes for these changes. The package was designed for scRNA-Seq data but is also applicable to many other data types, just replace cells with samples and genes with variables. The package also contains functions for estimating the parameters for SparseDC as outlined in the paper. We recommend that users further select their marker genes using the magnitude of the cluster centers.

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Version

Install

install.packages('SparseDC')

Monthly Downloads

259

Version

0.1.17

License

GPL-3

Maintainer

Jun Li

Last Published

January 4th, 2018

Functions in SparseDC (0.1.17)

lambda2_calculator

Lambda 2 Calculator.
pre_proc_data

Pre-process Data
update_mu

Update the Center Values
sim_data

Data Simulator
sparsedc_cluster

Sparse Differential Clustering
sparsedc_gap

Gap Statistic Calculator
update_c

Update Clusters
condition_biase

Biase Data Conditions
cell_type_biase

Biase Data Cell Type
S_func

The soft thresholding operator
data_biase

Biase Data
generate_uni_dat

Uniform data generator For use with the gap statistic. Generates datasets drawn from the reference distribution where each reference feature is generated uniformly over the range of observed values for that feature.
lambda1_calculator

Lambda 1 Calculator.