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Applying ICA is motivated by the idea that the variation in molecular data
is generated by divers factors s.
These may include internal biological factors as well as external
environmental or technical factors. Each observed variable x (e.g., gene)
can therefore be seen as derived from a specific combination of these factors.
The illustrated factors may represent an increase of
temperature (s1), an internal circadian
rhythm (s2), and different
ecotypes (s3).
With the assumption that the factors are independent of each other,
ICA can be applied to a data set X in order to identify the original factors s
and the dependencies given by the
matrix A.
[M. Scholz, Ph.D. thesis | Bioinformatics publication (pdf) | matlab code]