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Summary: http://www.inma.ucl.ac.be/~absil/Publi/stICA.htm 28Nov2008
Gene expression data analysis using
spatiotemporal blind source separation
Matthieu Sainlez1
, P.-A. Absil2
, and Andrew E. Teschendorff3
1- CRISIA, Haute Ecole Robert Schuman,
Chemin de Weyler 2, B-6700 Arlon, Belgium (matthieu.sainlez@hers.be)
2- Department of Mathematical Engineering, Universit´e catholique de Louvain,
B-1348 Louvain-la-Neuve, Belgium (http://www.inma.ucl.ac.be/~absil/)
3- Medical Genomics Group, Paul O'Gorman Building, UCL Cancer Institute,
University College London, London WC1 6BT, UK
Abstract. We propose a "time-biased" and a "space-biased" method for
spatiotemporal independent component analysis (ICA). The methods rely
on computing an orthogonal approximate joint diagonalizer of a collection
of covariance-like matrices. In the time-biased version, the time signatures
of the ICA modes are imposed to be white, whereas the space-biased ver-
sion imposes the same condition on the space signatures. We apply the
two methods to the analysis of gene expression data, where the genes play
the role of the space and the cell samples stand for the time. This study
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