To provide a fine-grained regional classification, we turned to a Self Organizing Map (SOM) to cluster genome segmentation regions based on their assay signal characteristics (Figure 7). The segmentation regions were initially randomly assigned to a 1,350-state map in a two-dimensional toroidal space (Figure 7A). This map can be visualised as a two dimensional rectangular plane onto which the various signal distributions can be plotted. For instance, the rectangle at the bottom left of Figure 7A shows the distribution of the genome in the initial randomised map. The SOM was then trained using the 12 different ChIP-seq and DNase-seq assays in the six cell types previously analyzed in the large-scale segmentations (i.e. over 72-dimensional space). After training, the SOM clustering was again visualised in two dimensions, now showing the organized distribution of genome segments (lower right hand, Figure 7A). Individual data sets associated with the genome segments in each SOM map unit (hexagonal cells) can then be visualised in the same framework to learn how each additional kind of data is distributed on the chromatin state map. Figure 7B shows
