Steady-state RMA and saline vs ethanol S-score expression datasets were analyzed using a graph theoretical algorithm [32] to identify gene co-expression networks. We first calculated all pairwise Pearson correlations across probe-sets, where each probe-set is represented as a vector of BXD expression values, and used this data to construct an unweighted graph in which vertices represent probe-sets and edges were present whenever the absolute value of the correlation between two probe-sets was ≥0.7. The choice of threshold when converting a weighted graph to an unweighted graph is analogous to the choice of p-value when determining significance; it is chosen to produce a reasonable tradeoff between false positives and false negatives. A correlation threshold of |0.7| across 27 strains yields a correlation p-value of 4.8e-05 (calculated using Student's t-distribution). Such low p-values are indicative of the rigor of graph theoretical techniques.
