Pattern recognition of gene expression data on biochemical networks with simple wavelet transforms

Biological networks show a rather complex, scale-free topology consisting of few highly connected (hubs) and many low connected (peripheric and concatenating) nodes. Furthermore, they contain regions of rather high connectivity, as in e.g. metabolic pathways. To analyse data for an entire network consisting of several thousands of nodes and vertices is not manageable. This inspired us to divide the network into functionally coherent sub-graphs and analysing the data that correspond to each of these sub-graphs individually. We separated the network in a two-fold way: 1. clustering approach: sub-graphs were defined by higher connected regions using a clustering procedure on the network; and 2. connected edge approach: paths of concatenated edges connecting striking combinations of the data were selected and taken as sub-graphs for further analysis. As experimental data we used gene expression data of the bacterium Escherichia coli which was exposed to two distinctive environments: oxygen rich and oxygen deprived. We mapped the data onto the corresponding biochemical network and extracted disciminating features using Haar wavelet transforms for both strategies. In comparison to standard methods, our approaches yielded a much more consistent image of the changed regulation in the cells. In general, our concept may be transferred to network analyses on any interaction data, when data for two comparable states of the associated nodes are made available.

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