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.