Trustworthiness and metrics in visualizing similarity of gene expression.
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.
Kaski
Samuel
S
Neural Networks Research Centre, Helsinki University of Technology P,O, Box 9800, FIN-02015 HUT, Finland. samuel.kaski@hut.fi
Nikkilä
Janne
J
Oja
Merja
M
Venna
Jarkko
J
Törönen
Petri
P
Castrén
Eero
E
D000818:Animals; D016000:Cluster Analysis; D003196:Computer Graphics / Q000592:standards* / Q000639:trends; D020869:Gene Expression Profiling / Q000379:methods / Q000592:standards* / Q000706:statistics & numerical data; D005786:Gene Expression Regulation / Q000235:genetics; D015966:Gene Expression Regulation, Fungal / Q000235:genetics; D006801:Humans; D051379:Mice; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods / Q000592:standards* / Q000706:statistics & numerical data; D012689:Sequence Homology, Nucleic Acid