New Exploratory Metabonomic Tools: Visual Cluster Analysis
Clustering is a well–studied problem, where the goal is to divide data into groups of similar objects using an unsupervised learning method. We introduce a clustering method on three-way arrays making use of an exploratory visualization approach.
We first apply a three-way factor model, e.g., a PARAFAC or Tucker3 model, to model three-way data. We then employ hierarchical clustering techniques based on standard similarity measures on the loadings of the component matrix corresponding to the mode of interest. Rather than scatter plots, we exploit dendrograms to represent the cluster structure. Furthermore, we enable the graphical display of differences and similarities in the variable modes among clusters by the use of visualization tools.
For example, when we apply the clustering scheme on a metabonomic dataset containing HPLC measurements of commercial extracts of St. John’s Wort, the extracts are clustered as shown in the dendrogram. When clusters corresponding to preparations 2 and 3 are selected from the dendrogram, elution profiles and spectral profiles of these preparations can be further explored using the visualization tools and the compounds accounting for the differences can be identified successfully.
New Exploratory Metabonomic Tools
Evrim Acar, Rasmus Bro, Bonnie Schmidt
Journal of Chemometrics, 2008, 22, 91-100
- Clustering (version 1, 05212007)
Note: To run the program, you need PLS_Toolbox as well as Statistics Toolbox.