Figure 1

Time - Point ProbeSet Data Analysis. A. Unsupervised machine learning grouping of animals by expression. To visualize temporal patterns as well as inter-animal variability, unsupervised machine learning was employed followed by a divisive hierarchical clustering algorithm (DIANA) to cluster differentially expressed ProbeSets in any pair-wise contrast (see Methods). Finally, standard agglomerative hierarchical clustering was used to group animals. The result is visualized using the Heatplus package of BioConductor. Heatmap (columns: samples; rows: genes, in red and blue coloring, depicting up- and down-regulation respectively). B. Principal Component Analysis of Individual Time Point Transcripts. Using Partek GS version 6.5, we performed principal component analysis (PCA) of the 33042 transcripts on the 230 2.0 GeneChip array for all animals at each time point to assess variability of the data across individual animals and time points. There are inter-individual differences but eclipses show that there are no outliers in our experiment. Additionally, the eclipses of Day 7, 14 and 56 cross each other, which indicate some level of commonality between these time points, as was evidenced and shown in the tree view of the heat map. C. Volcano plots of fold change values of all 33042 ProbeSets vs. transformed (− log₁₀) ANOVA t test p-values. Individual time point data were plotted for comparison. ANOVA t test p-values for pair-wise contrasts between each time point data relative to sham were calculated and transformed to - log₁₀ values and plotted against fold change values. D. Percentage of ProbeSets with ANOVA t test p-values higher than 0.05. The percentage of ProbeSets with p-values higher than 0.05 was calculated at all time-points and plotted at various fold change values.