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SparseMDC (version 0.99.5)

Implementation of SparseMDC Algorithm

Description

Implements the algorithm described in Barron, M., and Li, J. (Not yet published). This algorithm clusters samples from multiple ordered 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 SparseMDC 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('SparseMDC')

Monthly Downloads

139

Version

0.99.5

License

GPL-3

Maintainer

Jun Li

Last Published

August 2nd, 2018

Functions in SparseMDC (0.99.5)

pen_calculator

Penalty calculator
pre_proc_data

Pre-process data
sparsemdc_gap

Gap Statistic Calculator
update_c

update_c
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 Calcualtor
score_calc

Score calculator
S_func

The soft thresholding operator
cell_type_biase

Biase Data Cell Type
condition_biase

Biase Data Conditions
data_biase

Biase Data
update_mu

update_mu
mu_solver

Mu Solver
mu_calc

mu Calculator
lambda2_calculator

Lambda 2 Calculator
sdc_mpar

SparseDC Multi Parallel
sparse_mdc

SparseDC Multi