Research article

Trustworthiness and metrics in visualizing similarity of gene expression

Samuel Kaski¹ , Janne Nikkilä¹ , Merja Oja¹ , Jarkko Venna¹ , Petri Törönen² and Eero Castrén^2,3

¹  Neural Networks Research Centre, Helsinki University of Technology P.O. Box 9800, FIN-02015 HUT, Finland

²  A.I. Virtanen-Institute, University of Kuopio P.O. Box 1627, FIN-70211 Kuopio, Finland

³  Neuroscience Center, University of Helsinki, P.O. Box 56, 00014 Helsinki, Finland

author email corresponding author email

BMC Bioinformatics 2003, 4:48doi:10.1186/1471-2105-4-48

Published:	13 October 2003

Abstract

Background

Conventionally, the first step in analyzing the large and high-dimensional data sets measured by microarrays is visual exploration. Dendrograms of hierarchical clustering, self-organizing maps (SOMs), and multidimensional scaling have been used to visualize similarity relationships of data samples. We address two central properties of the methods: (i) Are the visualizations trustworthy, i.e., if two samples are visualized to be similar, are they really similar? (ii) The metric. The measure of similarity determines the result; we propose using a new learning metrics principle to derive a metric from interrelationships among data sets.

Results

The trustworthiness of hierarchical clustering, multidimensional scaling, and the self-organizing map were compared in visualizing similarity relationships among gene expression profiles. The self-organizing map was the best except that hierarchical clustering was the most trustworthy for the most similar profiles. Trustworthiness can be further increased by treating separately those genes for which the visualization is least trustworthy. We then proceed to improve the metric. The distance measure between the expression profiles is adjusted to measure differences relevant to functional classes of the genes. The genes for which the new metric is the most different from the usual correlation metric are listed and visualized with one of the visualization methods, the self-organizing map, computed in the new metric.

Conclusions

The conjecture from the methodological results is that the self-organizing map can be recommended to complement the usual hierarchical clustering for visualizing and exploring gene expression data. Discarding the least trustworthy samples and improving the metric still improves it.