Paper ID sheet
- TITLE: Gene expression data analysis using spatiotemporal blind source separation
- AUTHORS: Matthieu Sainlez, P.-A. Absil, and Andrew E. Teschendorff
- 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 version 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 is a step towards
addressing a question first raised by Liebermeister, on whether ICA
methods for gene expression analysis should impose independence
across genes or across cell samples. Our preliminary experiment
indicates that both approaches have value, and that exploring the
continuum between these two extremes can provide useful information
about the interactions between genes and their impact on the
phenotype.
- KEY WORDS: independent component analysis (ICA), blind source separation (BSS), joint diagonalization, gene expression analysis
- STATUS: Proceedings of the 17th European Symposium on Artificial Neural Networks (ESANN), pp. 159-164, 2009.
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