For example, assume two sample networks (corresponding to two groups of samples) and two corresponding measures of cor K, C).
Specifically, we used PGNet to compare genome-wide correlated expressions with a seed gene and the differential expressions between two sample groups and yielded a regulatory network of genes that are mutually associated.
The extremely low coincidence of edges between pm and (mm or pp) implies that the networks do contain very specific information; this is reinforced by the same comparison between two sample subsets (Table 5).
One example is to construct a network of samples from microarray data, where the nodes are samples and the similarity between two samples can be measured by the Pearson correlation coefficient between their gene expression profiles.
The gene-pairs that passed this test are more highly conserved between the two sample subsets with the overlap between the mm pp intersections being around 73%, about 10% higher than for the single mm or pp networks (not shown).
This algorithm superimposes gene-expression values with corresponding network proteins, begins from every protein (seed) in the PPI network, and greedily appends interactions to identify the subnetwork starting from each seed whose mean expression for each sample best discriminates between the two sample types (In our case, the sample types are T-ALL and nonleukemia/healthy conditions).
Since the WTP values differ between the two samples with those for the physician network as the only exception, one may draw.
In pm networks, also about 60% of the gene-pairs exist in both up-down senses (pm:mp); here again a slightly higher level, about 70%, were found between these reciprocal-pair networks from the two sample subsets, so the up-down pairs, which are found in both senses, are found more reproducibly (data not shown).
Between-group voxel-wise comparisons were performed within binary mask of the predefined DMN network using two sample t-test with SPM8.
