Adaptive double self-organizing maps for clustering gene expression profiles

Authors:
H. Ressom;D. Wang;P. Natarajan
Affiliations:
Department of Electrical and Computer Engineering, University of Maine, 201 Barrows Hall, Orono, ME;Department of Electrical and Computer Engineering, University of Maine, 201 Barrows Hall, Orono, ME;Department of Electrical and Computer Engineering, University of Maine, 201 Barrows Hall, Orono, ME
Venue:
Neural Networks - 2003 Special issue: Advances in neural networks research — IJCNN'03
Year:
2003

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Abstract

This paper introduces a new model of self-organizing map (SOM) known as adaptive double self-organizing map (ADSOM). ADSOM has a flexible topology and performs data partitioning and cluster visualization simultaneously without requiring a priori knowledge about the number of clusters. It combines features of the popular SOM with two-dimensional position vectors, which serve as a visualization tool to detect the number of clusters present in the data. ADSOM updates its free parameters and allows convergence of its position vectors to a fairly consistent number of clusters provided its initial number of nodes is greater than the expected number of clusters. A novel index is introduced based on hierarchical clustering of the final locations of position vectors. The index allows automated detection of the number of clusters, thereby reducing human error that could be incurred from counting clusters visually. To test ADSOM's consistency in data partitioning, we examine the number of common profiles found in the clusters that were obtained by varying the initial number of nodes. This provides a confidence measure for the clusters formed by ADSOM and illustrates the effect of different initial number of nodes on data partitioning. The reliance of ADSOM in identifying number of clusters is demonstrated by applying it to publicly available yeast gene expression data.