Approximate geodesic distances reveal biologically relevant structures in microarray data

Nilsson, JensLund University,Lunds universitet,Matematik LTH,Matematikcentrum,Institutioner vid LTH,Lunds Tekniska Högskola,Mathematics (Faculty of Engineering),Centre for Mathematical Sciences,Departments at LTH,Faculty of Engineering, LTH (författare)

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.

NATURVETENSKAP Biologi Bioinformatik och systembiologi hsv//swe
NATURAL SCIENCES Biological Sciences Bioinformatics and Systems Biology hsv//eng
Lymphoma
Microarray
Gene expression
Manifold learning
Lung cancer
Isomap
Nonlinear dimensionality reduction

Fioretos, ThoasLund University,Lunds universitet,Avdelningen för klinisk genetik,Institutionen för laboratoriemedicin,Medicinska fakulteten,Division of Clinical Genetics,Department of Laboratory Medicine,Faculty of Medicine(Swepub:lu)kgen-tfi (författare)
Höglund, MattiasLund University,Lunds universitet,Avdelningen för klinisk genetik,Institutionen för laboratoriemedicin,Medicinska fakulteten,Division of Clinical Genetics,Department of Laboratory Medicine,Faculty of Medicine(Swepub:lu)kgen-mho (författare)
Fontes, MagnusLund University,Lunds universitet,Matematik LTH,Matematikcentrum,Institutioner vid LTH,Lunds Tekniska Högskola,Mathematics (Faculty of Engineering),Centre for Mathematical Sciences,Departments at LTH,Faculty of Engineering, LTH(Swepub:lu)math-mfo (författare)
Matematik LTHMatematikcentrum (creator_code:org_t)

Ingår i:Bioinformatics: Oxford University Press (OUP)20:6, s. 874-8801367-48031367-4811

Av författaren/redakt...: Nilsson, Jens; Fioretos, Thoas; Höglund, Mattias; Fontes, Magnus

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