Thoas Fioretos
Research team manager
Approximate geodesic distances reveal biologically relevant structures in microarray data
Author
Summary, in English
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.
Department/s
- Mathematics (Faculty of Engineering)
- Division of Clinical Genetics
Publishing year
2004
Language
English
Pages
874-880
Publication/Series
Bioinformatics
Volume
20
Issue
6
Document type
Journal article
Publisher
Oxford University Press
Topic
- Bioinformatics and Systems Biology
Keywords
- Lymphoma
- Microarray
- Gene expression
- Manifold learning
- Lung cancer
- Isomap
- Nonlinear dimensionality reduction
Status
Published
ISBN/ISSN/Other
- ISSN: 1367-4803