100 10 1 1 10 k0 100 BMC Genomics 2005, 6:131 http://www.biomedcentral.com/1471-2164/6/131 EFfifgeuctreof4combining data on network topology Effect of combining data on network topology. Average value of k1 as a function of k0 displayed on a log.-log. scale for the network composed of essential yeast genes. In each case, where the dataset includes information derived from protein com- plex analysis pair-wise interactions are determined using the spoke model. Green: network resulting from the incorporation of all protein interactions not classified as being obtained in "small-scale" studies. Brown: network resulting from the protein com- plex study performed by Ho and co-workers [7]. Purple: network resulting from Y2H interactions identified by Ito and co- workers [8]. Note that the relatively strong negative correlation present within the two networks generated using data from individual high-throughput studies (brown and purple on the graph) is significantly reduced when all available high throughput data are combined (green). have distinct topological properties (Figure 1), by using the same data and employing another (apparently equally valid [19]) model, we show that the previously observed negative correlation reverts to a positive correlation for both the global and essential networks, and the topolo- gies (in terms of both the correlation between log(k0) and log (<k1>) and the slope of the resulting graphs) of both also appear similar (Figure 3). The difference between the topologies of the spoke and matrix model networks is probably a consequence of the models themselves. In the spoke model, bait proteins tend to have a higher degree than prey proteins; thus typical interactions within the network are between high-degree and low-degree mem- bers. Conversely, in the matrix model, all proteins found within a complex are assigned connections with all others; thus typical interactions tend to be between proteins with similar connectivities. The ease with which the overall topology of the networks is flipped, and the disparity between results given above, further highlights the degree of caution that should be exercised when attempting to draw biologically meaningful conclusions from studies of this type. In addition to the observations made about the correla- tions between neighbouring nodes, we have also shown the importance of using the correct control when selecting nodes for randomization studies involving network con- nectivity. We found that, by simply matching the degree distribution of the nodes within the essential network in that of the randomly selected sample (composed entirely of non-essential genes), we were able to achieve very sim- ilar levels of network connectivity. This result suggests that Page 6 of 8 (page number not for citation purposes)