Gene expression data analysis using spatiotemporal blind source separation

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.