Construction, Visualisation, and Clustering of Transcription Networks from Microarray Expression Data
Figure 2
Network Connectivity and Clustering
The relationships are shown (A–B) between the number of connected components in the GNF mouse tissue expression network and Pearson correlation coefficient threshold used. As the threshold increases, the tendency is for the network to fragment into smaller unconnected graphs. However, it can be seen from the difference between graphs (A–B) that many of these unconnected components comprise relatively few nodes.
(C) Log–log frequency plot of node degree (i.e., total number of edges for each node) for the 0.9 Pearson threshold graph. These networks show an unusual topography relative to other networks derived from biological data. Here, a relatively large number of nodes show high-degree connectivity. These nodes represent genes forming core structures within the network being highly connected to neighbouring nodes.
(D) MCL cluster counts (with inflation threshold set at 2.2) for networks derived at varying Pearson thresholds. Small clusters (≤4) account for a high proportion of the overall number of clusters (E). The red dotted lines show these relationships for MAS5 scaled data; the black dotted lines show gcRMA normalised data.