The biological relationship between two genes, represented as nodes, is shown as a line.
An edge between two genes represented by nodes was defined for correlation coefficients beyond a chosen threshold, resulting in a discretized matrix with 1, if the correlation coefficient was greater than that threshold and 0 otherwise.
In the first case, the profile information is transformed into a gene similarity network, where the strength of a link between two genes represents the global similarity of their profiles (Pei et al., 2005; Segal et al., 2003; Ulitsky and Shamir, 2007).
These nine genes represent 22 transcripts.
The distance between two genes is represented by the thickness of the line connecting the two corresponding nodes in the tree, and the colour of the line represents the GO category (Molecular Function, Biological Process, Cellular Compartment) represented.
We formalize the wiring between genes by conditional correlation graphs 28 where the genes of interest define the nodes and a direct influence between two genes is represented by an edge.
In other words, the mere presence of an edge between two genes does not necessarily represent a transcriptional regulatory relationship between them.
These algorithms output a functional linkage graph [3], [15] in which an edge between two nodes (genes) represents functional similarity with a reliability score (probability) assigned to each edge.
In our context, each node in V represents a gene, each edge in E represents an interaction between two genes and P associates to each edge the probability of the existence of the interaction it represents.
Pearson correlation coefficient (PCC) can be used to measure the linear dependence between two genes, with a value of '1' representing a perfect, positive correlation.
