An alternative approach, hierarchical clustering, used centered and scaled log ratios of DE microRNAs from all time points per brain region with the package "clValid" (version 0.606) [38]. This R package was used to compare multiple algorithms simultaneously to identify the best clustering approach and the optimal number of clusters. Clustered microRNAs were plotted with R package "MmPalateMiRNA" [39] with scaled log-ratios vs time. Clustering was visualized using GraphPad Prism version 6.07 for Windows, GraphPad Software, La Jolla California USA, www.graphpad.com. The number of unique, non-overlapping expression patterns indicates the number of assigned clusters for each brain region. For each identified microRNA cluster, IPA was used to construct a network relating the microRNAs with their DE gene targets reported in [26] using the same statistical significance thresholds as described above. First, microRNAs in each cluster and their 120h DE targets were added to a new pathway in IPA, and then related and connected using the Ingenuity Knowledge Base. The "grow" tool was used sequentially to add the ten 0h DE targets, and then the ten 8h DE targets, with the highest connectivity to the network, connecting all molecules after each addition. Default settings were used for all IPA pathway functions.
