Physics – Data Analysis – Statistics and Probability
Scientific paper
2011-10-20
PLoS ONE 7 (2012) e31929
Physics
Data Analysis, Statistics and Probability
33 Pages, 18 Figures, 5 Tables
Scientific paper
10.1371/journal.pone.0031929
We introduce a graph-theoretic approach to extract clusters and hierarchies in complex data-sets in an unsupervised and deterministic manner, without the use of any prior information. This is achieved by building topologically embedded networks containing the subset of most significant links and analyzing the network structure. For a planar embedding, this method provides both the intra-cluster hierarchy, which describes the way clusters are composed, and the inter-cluster hierarchy which describes how clusters gather together. We discuss performance, robustness and reliability of this method by first investigating several artificial data-sets, finding that it can outperform significantly other established approaches. Then we show that our method can successfully differentiate meaningful clusters and hierarchies in a variety of real data-sets. In particular, we find that the application to gene expression patterns of lymphoma samples uncovers biologically significant groups of genes which play key-roles in diagnosis, prognosis and treatment of some of the most relevant human lymphoid malignancies.
Aste Tomaso
Di Matteo Tiziana
Song Won-Min
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