top_genes_cluster_tissues

Provides unsupervised selection and clustering of microarray data
using mixture models. Following the methods described in McLachlan, Bean and
Peel (2002) <doi: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.

Andrew Jones

EMMIXgene

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

Andrew Thomas Jones

top_genes_cluster_tissues function

<dl><dt>gen</dt>
<dd>An EMMIXgene object produced by select_genes().</dd>
<dt>n_top</dt>
<dd>number of top genes (as ranked by likelihood) to be selected</dd>
<dt>method</dt>
<dd>Method for separating tissue classes. Can be either 't' for a 
univariate mixture of t-distributions on gene cluster means, 
or 'mfa' for a mixture of factor analysers.</dd>
<dt>q</dt>
<dd>number of factors if using mfa</dd>
<dt>g</dt>
<dd>number of components if using mfa</dd></dl>

Arguments

Cluster tissues — top_genes_cluster_tissues

<dl>

<dt>gen</dt>
<dd>An EMMIXgene object produced by select_genes().</dd>


<dt>n_top</dt>
<dd>number of top genes (as ranked by likelihood) to be selected</dd>


<dt>method</dt>
<dd>Method for separating tissue classes. Can be either 't' for a 
univariate mixture of t-distributions on gene cluster means, 
or 'mfa' for a mixture of factor analysers.</dd>


<dt>q</dt>
<dd>number of factors if using mfa</dd>


<dt>g</dt>
<dd>number of components if using mfa</dd>

</dl>

top_genes_cluster_tissues: Cluster tissues

Description

Usage

Value

Arguments

Examples