Dissection of Regulatory Networks that Are Altered in Disease via Differential Co-expression
Figure 1
Overview of the class specific differential correlation (DC) analysis.
The input (left) is a set of expression profiles from different classes of samples. In one analysis (top center), T-scores are computed for the class of interest and are normalized using the T-scores calculated on random data sets, created by shuffling the sample labels. The normalized scores are then used to find gene clusters that manifest DC in the tested class compared to all other classes (top right, up/down-correlated modules; blue edges indicate class-specific DC). A second similarity analysis (bottom center) is performed to detect gene pairs that are co-expressed in all classes. In each class, an EM algorithm is used to divide the correlations to high (‘denoted “mates,” red distribution) and low (denoted “non-mates,” green distribution), and consistent similarities are defined as cases in which gene pairs are mates in all classes. The two scores are used to find pairs of gene modules in which each module is a group of consistently correlated genes (red edges), whereas the correlation between the modules is differential (blue edges). These module pairs are denoted as meta-modules (center right). As a by-product, individual modules are recorded (bottom right).