ClustMIC: Unsupervised clustering on (GPL) intensities and associated...

In ManonMartin/MBXUCL: R Routines for metabolomic data

Description

Unsupervised clustering on (GPL) intensities based on Ward and K-Means algorithms. Calculation of MIC statistical criteria of clustering quality: Dunn, Davies-Bouldin, Rand and adjusted-Rand indexes.

Usage

ClustMIC(Intensities, nClust, Trcl, Dendr = TRUE)

Arguments

Intensities	A numeric matrix of intensities (can be a pTreatGPL object).
nClust	The number of groups to retrieve (donors, mixtures, ...).
Trcl	The real groups' memberships of the samples, true class.
Dendr	Logical argument (TRUE/FALSE) to obtain graphical dendrogram based on the Ward algorithm.

Value

A list of MIC quality indexes (Dunn, Davies-Bouldin, Rand and adjusted-Rand):

DunnW

Dunn index for Ward clustering

DunnKM

Dunn index for Kmeans clustering

DBW

Davies-Bouldin index for Ward clustering

DBKM

Davies-Bouldin index for Kmeans clustering

RandW

Rand index for Ward clustering

RandKM

Rand index for Kmeans clustering

AdjRandW

Adjusted Rand index for Ward clustering

AdjRandKM

Adjusted Rand index for Kmeans clustering

Author(s)

Baptiste Feraud, Manon Martin

Examples

data('HumanSerum')
ClustMIC(Intensities = HumanSerumSpectra, nClust = 4, Trcl = ClassHS, Dendr = TRUE)

ManonMartin/MBXUCL documentation built on Nov. 26, 2021, 8:45 p.m.