mlpMatrix

The ClusterSignificance package provides tools to assess
if clusters have a separation different from random or permuted
data. ClusterSignificance investigates clusters of two or more
groups by first, projecting all points onto a one dimensional
line. Cluster separations are then scored and the probability
of the seen separation being due to chance is evaluated using a
permutation method.

Jason T Serviss

ClusterSignificance

Investigates Significance of Clusters by Reducing the Data to
One Dimension to be Able to Easy Set a Score for the
Separation, and a p-Value is then Calculated from Permutations
of the Original Data

mlpMatrix function

Matrix
<dl>
<dt>rownames</dt><dd>Groups</dd> <dt>colnames</dt><dd>dimension number</dd> </dl>

mlpMatrix: Simulated data used to demonstrate the Mlp method.

Description

Usage

Arguments

Value

Format

Examples