clusters.detection: Detection of clusters.

In MetChem: Chemical Structural Similarity Analysis

Detection of clusters.

Description

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

Usage

clusters.detection  (smiles,
                     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.
k	The 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, KODAMA.visualization

Examples

data(Metabolites)

res=clusters.detection(Metabolites$SMILES) 

MetChem documentation built on June 8, 2025, 10:50 a.m.