clusters.detection {MetChem}

Detection of clusters.

Description

This function calculates the structural similarity between different metabolites and perform hierarchical clustering using the KODAMA algorithm and detect the optimal number of clusters. The procedure is repeated to ensure the robustness of the detection.

Usage

clusters.detection  (smiles,
                     repetition=10,
                     k=50,
                     seed=12345,
                     max_nc = 30,
                     dissimilarity.parameters=list(),
                     kodama.matrix.parameters=list(),
                     kodama.visualization.parameters=list(),
                     hclust.parameters=list(method="ward.D"),
                     verbose = TRUE)

Arguments

smiles

A list of smile notations for the study metabolites dataset.

repetition

The number of time the KODAMA analysis is repeated.

k

A number of components of multidimensional scaling.

seed

Seed for the generation of random numbers.

max_nc

Maximum number of clusters.

dissimilarity.parameters

Optional parameters for chemical.dissimilarity function.

kodama.matrix.parameters

Optional parameters for KODAMA.matrix function.

kodama.visualization.parameters

Optional parameters for KODAMA.visualization function.

hclust.parameters

Optional parameters for hclust function.

verbose

If verbose is TRUE, it displays the progress for each iteration.

Value

A list contains all results of KODAMA chemical similarity analysis and hierarchical clustering.

See Also

KODAMA.matrix

Examples

data(Metabolites)

res=clusters.detection(Metabolites$SMILES)