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BMCBI
2010

Comparative study of unsupervised dimension reduction techniques for the visualization of microarray gene expression data

10 years 1 months ago
Comparative study of unsupervised dimension reduction techniques for the visualization of microarray gene expression data
Background: Visualization of DNA microarray data in two or three dimensional spaces is an important exploratory analysis step in order to detect quality issues or to generate new hypotheses. Principal Component Analysis (PCA) is a widely used linear method to define the mapping between the high-dimensional data and its low-dimensional representation. During the last decade, many new nonlinear methods for dimension reduction have been proposed, but it is still unclear how well these methods capture the underlying structure of microarray gene expression data. In this study, we assessed the performance of the PCA approach and of six nonlinear dimension reduction methods, namely Kernel PCA, Locally Linear Embedding, Isomap, Diffusion Maps, Laplacian Eigenmaps and Maximum Variance Unfolding, in terms of visualization of microarray data. Results: A systematic benchmark, consisting of Support Vector Machine classification, cluster validation and noise evaluations was applied to ten microarra...
Christoph Bartenhagen, Hans-Ulrich Klein, Christia
Added 09 Dec 2010
Updated 09 Dec 2010
Type Journal
Year 2010
Where BMCBI
Authors Christoph Bartenhagen, Hans-Ulrich Klein, Christian Ruckert, Xiaoyi Jiang, Martin Dugas
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