Bioinformatics Advance Access originally published online on January 29, 2004
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Bioinformatics 20(6) © Oxford University Press 2004; all rights reserved.
Approximate geodesic distances reveal biologically relevant structures in microarray data
1 Centre for Mathematical Sciences, Lund University, Box 118, SE-221 00 Lund, Sweden and 2 Department of Clinical Genetics, Lund University Hospital, SE-221 85 Lund, Sweden
Received on February 19, 2003
; revised on September 12, 2003
; accepted on September 15, 2003
Advance Access Publication January 29, 2004
Motivation: Genome-wide gene expression measurements, as currently determined by the microarray technology, can be represented mathematically as points in a high-dimensional gene expression space. Genes interact with each other in regulatory networks, restricting the cellular gene expression profiles to a certain manifold, or surface, in gene expression space. To obtain knowledge about this manifold, various dimensionality reduction methods and distance metrics are used. For data points distributed on curved manifolds, a sensible distance measure would be the geodesic distance along the manifold. In this work, we examine whether an approximate geodesic distance measure captures biological similarities better than the traditionally used Euclidean distance.
Results: We computed approximate geodesic distances, determined by the Isomap algorithm, for one set of lymphoma and one set of lung cancer microarray samples. Compared with the ordinary Euclidean distance metric, this distance measure produced more instructive, biologically relevant, visualizations when applying multidimensional scaling. This suggests the Isomap algorithm as a promising tool for the interpretation of microarray data. Furthermore, the results demonstrate the benefit and importance of taking nonlinearities in gene expression data into account.
Contact: jensn{at}maths.lth.se