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Nonlinear dimensionality reduction of gene expression data

Nilsson, Jens LU (2006)
Abstract
Using microarray measurements techniques, it is possible to measure the activity of genes simultaneously across the whole genome. Since genes influence each others activity levels through complex regulatory networks, such gene expression measurements are state samples of a dynamical system. Gene expression data has proven useful for diagnosis and definition of disease subgroups, for inference of the functional role of a given gene or for the deciphering of complex disease mechanisms. However, the extraction of meaning from data sets of such size and complexity needs to be aided by computational methods. Dimensionality reduction methods represent high-dimensional data as point configurations in lower-dimensional space in a way that... (More)
Using microarray measurements techniques, it is possible to measure the activity of genes simultaneously across the whole genome. Since genes influence each others activity levels through complex regulatory networks, such gene expression measurements are state samples of a dynamical system. Gene expression data has proven useful for diagnosis and definition of disease subgroups, for inference of the functional role of a given gene or for the deciphering of complex disease mechanisms. However, the extraction of meaning from data sets of such size and complexity needs to be aided by computational methods. Dimensionality reduction methods represent high-dimensional data as point configurations in lower-dimensional space in a way that optimally preserves geometrical or statistical properties. Nonlinear dimensionality reduction takes into account that data may be sampled from a general Riemannian manifold and attempts to uncover its intrinsic geometry.



This thesis deals with the application of spectral methods of nonlinear dimensionality reduction to gene expression data. It is demonstrated that nonlinear dimensionality reduction often yields more biologically relevant lower-dimensional representations compared with linear methods. A method for robust estimation of geodesic distances is further proposed. (Less)
Please use this url to cite or link to this publication:
author
supervisor
organization
publishing date
type
Thesis
publication status
published
subject
keywords
Gene expression, Manifold learning, Nonlinear dimensionality reduction, Microarray
pages
108 pages
ISBN
91-631-8376-5
language
English
LU publication?
yes
id
21a96803-f63c-4839-87c9-7b23cff8bd3a (old id 631165)
date added to LUP
2007-12-11 10:12:57
date last changed
2016-09-19 08:45:17
@misc{21a96803-f63c-4839-87c9-7b23cff8bd3a,
  abstract     = {Using microarray measurements techniques, it is possible to measure the activity of genes simultaneously across the whole genome. Since genes influence each others activity levels through complex regulatory networks, such gene expression measurements are state samples of a dynamical system. Gene expression data has proven useful for diagnosis and definition of disease subgroups, for inference of the functional role of a given gene or for the deciphering of complex disease mechanisms. However, the extraction of meaning from data sets of such size and complexity needs to be aided by computational methods. Dimensionality reduction methods represent high-dimensional data as point configurations in lower-dimensional space in a way that optimally preserves geometrical or statistical properties. Nonlinear dimensionality reduction takes into account that data may be sampled from a general Riemannian manifold and attempts to uncover its intrinsic geometry. <br/><br>
<br/><br>
This thesis deals with the application of spectral methods of nonlinear dimensionality reduction to gene expression data. It is demonstrated that nonlinear dimensionality reduction often yields more biologically relevant lower-dimensional representations compared with linear methods. A method for robust estimation of geodesic distances is further proposed.},
  author       = {Nilsson, Jens},
  isbn         = {91-631-8376-5},
  keyword      = {Gene expression,Manifold learning,Nonlinear dimensionality reduction,Microarray},
  language     = {eng},
  pages        = {108},
  title        = {Nonlinear dimensionality reduction of gene expression data},
  year         = {2006},
}