| Reference : Gene expression data analysis using spatiotemporal blind source separation |
| Scientific congresses, symposiums and conference proceedings : Paper published in a book | |||
| Engineering, computing & technology : Multidisciplinary, general & others | |||
| http://hdl.handle.net/10993/16299 | |||
| Gene expression data analysis using spatiotemporal blind source separation | |
| English | |
Sainlez, Matthieu  [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >] | |
| Absil, Pierre-Antoine [] | |
| Teschendorff, Andrew E. [] | |
| 2009 | |
| ESANN'2009 proceedings, European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning. | |
| Verleysen, Michel | |
| d-side | |
| 159-163 | |
| Yes | |
| No | |
| International | |
| 2-930307-09-9 | |
| Evere | |
| Belgium | |
| ESANN'2009 , European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning. | |
| du 22 avril 2009 au 24 avril 2009 | |
| Université Catholique de Louvain-la-Neuve -UCL | |
| Katholiek Universiteit Leuven - KUL | |
| Bruges | |
| Belgium | |
| [en] independent component analysis ; gene expression ; spatiotemporal ICA | |
| [en] 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. | |
| http://hdl.handle.net/10993/16299 |
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