Supplemental Figure 2.  Predicting functional categories with a multiple species network built from a smaller number of microarrays.  A-D.  We built a multiple species network from 979 microarrays instead of the full 3182.  We compared its performance predicting KEGG functional categories to the performance of networks built from data from single species.  We constructed a co-expression network from each species by selecting a Pearson correlation cutoff of k and linked every pair of genes with a correlation of k or higher. We re-iterated this procedure at various settings of k to generate expression maps with differing degrees of coverage and predictive power.  We also constructed co-expression networks from multiple species as described above, using not only the cutoff of P < .05 used in the network discussed above but for varying P-values.   For each functional category, we combined the neighbors in the map of all genes from the category, and plotted the percent of genes from the category that were included (x-axis; coverage) versus the percent of interactions that were between two genes in that category (y-axis; accuracy).   We varied the Pearson threshold for constructing the map in each case to obtain different maps that result in different coverage and accuracy.