  A new method to sort gene expression patterns into functional groups is
presented. The method is based on a sorting algorithm using a non-local
similarity score, which takes all other patterns in the dataset into account.
The method is therefore very robust with respect to noise. Using the expression
data for yeast, we extract information about functional groups. Without prior
knowledge of parameters the cell cycle regulated genes in yeast can be
identified. Furthermore a second, independent cell clock is identified. The
capability of the algorithm to extract information about signal flow in the
regulatory network underlying the expression patterns is demonstrated.
