A Mixture Model-Based Approach to the Clustering of Microarray Expression Data

Provides unsupervised selection and clustering of microarray data using mixture models. Following the methods described in McLachlan, Bean and Peel (2002) a subset of genes are selected based one the likelihood ratio statistic for the test of one versus two components when fitting mixtures of t-distributions to the expression data for each gene. The dimensionality of this gene subset is further reduced through the use of mixtures of factor analyzers, allowing the tissue samples to be clustered by fitting mixtures of normal distributions.


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Provides unsupervised selection and clustering of microarray data using mixture models. Following the methods described in McLachlan, Bean and Peel (2002) https://doi.org/10.1093/bioinformatics/18.3.413 a subset of genes are selected based one the likelihood ratio statistic for the test of one versus two components when fitting mixtures of t-distributions to the expression data for each gene. The dimensionality of this gene subset is further reduced through the use of mixtures of factor analyzers, allowing the tissue samples to be clustered by fitting mixtures of normal distributions.

Installation

You can install EMMIXgene from github with:

# install.packages("devtools")
devtools::install_github("andrewthomasjones/EMMIXgene_no_f")

Example

News

EMMIXgene 0.1.1

  • Fixed ambiguous typing in C++ code.

EMMIXgene 0.1.0

  • EMMIXgene - software previously available as a standalone program is now available as an R package.

Reference manual

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install.packages("EMMIXgene")

0.1.1 by Andrew Thomas Jones, 8 months ago


Browse source code at https://github.com/cran/EMMIXgene


Authors: Andrew Thomas Jones


Documentation:   PDF Manual  


GPL (>= 3) license


Imports Rcpp, stats, mclust, reshape, ggplot2, scales, tools

Suggests R.rsp

Linking to Rcpp, RcppArmadillo, BH

System requirements: C++11


See at CRAN