Penalized principal component analysis of microarray data

Nikulin, Vladimir and McLachlan, Geoffrey J. (2010). Penalized principal component analysis of microarray data. In: F. Masulli, L. Peterson and R. Tagliaferri, 6th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics. 6th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2009, Genoa, Italy, (82-96). 15-17 October, 2009. doi:10.1007/978-3-642-14571-1_7


Author Nikulin, Vladimir
McLachlan, Geoffrey J.
Title of paper Penalized principal component analysis of microarray data
Conference name 6th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2009
Conference location Genoa, Italy
Conference dates 15-17 October, 2009
Proceedings title 6th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics   Check publisher's open access policy
Journal name Lecture Notes in Computer Science   Check publisher's open access policy
Place of Publication Germany
Publisher Springer
Publication Year 2010
Sub-type Fully published paper
DOI 10.1007/978-3-642-14571-1_7
ISBN 9783642145704
ISSN 0302-9743
Editor F. Masulli
L. Peterson
R. Tagliaferri
Volume 6160
Start page 82
End page 96
Total pages 15
Collection year 2011
Language eng
Abstract/Summary The high dimensionality of microarray data, the expressions of thousands of genes in a much smaller number of samples, presents challenges that affect the validity of the analytical results. Hence attention has to be given to some form of dimension reduction to represent the data in terms of a smaller number of variables. The latter are often chosen to be a linear combinations of the original variables (genes) called metagenes. One commonly used approach is principal component analysis (PCA), which can be implemented via a singular value decomposition (SVD). However, in the case of a high-dimensional matrix, SVD may be very expensive in terms of computational time. We propose to reduce the SVD task to the ordinary maximisation problem with an Euclidean norm which may be solved easily using gradient-based optimisation. We demonstrate the effectiveness of this approach to the supervised classification of gene expression data. © 2010 Springer-Verlag.
Keyword Cross-validation
Gene expression data
Gradient-based optimisation
K-means clustering
Singular value decomposition
Q-Index Code E1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Monographic series

Document type: Conference Paper
Collections: School of Mathematics and Physics
Official 2011 Collection
 
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Created: Wed, 23 Feb 2011, 02:35:10 EST by Kay Mackie on behalf of School of Mathematics & Physics