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Although co-expression between two gene profiles implies that they are under the same transcriptional control and functionally correlated, the resulting interactions are often indirect.
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The Pearson correlation coefficient was used to measure similarity between two genes' profiles.
Therefore, when inferring the relationship between two gene expression profiles, the other expression profiles can be taken into account to discriminate between direct (Figure 2(a)) and indirect (Figure 2(b) and (c)) interactions.
Similarity between two gene expression profiles was measured by Pearson correlation coefficient.
The log2-transformed expression intensities of 54,675 probe sets with RMA summarized data from 24 microarrays were used to calculate the correlation coefficients between two gene expression profiles and construct the heatmap.
One can use any of these normalizations before calculating the Pearson's or Euclidean distance between two gene expression profiles.
The nodes of these networks represent genes, and the edges are weighted by Pearson's correlation between two gene expression profiles.
For this, we modeled our expression network as an undirected graph, where a node represents a gene and an edge is drawn between two genes if their expression profiles are correlated beyond a Pearson correlation coefficient threshold.
This confirms the view that correlated interaction profiles between two genes suggest shared function [8], [14], [11], [12] –in this case reflected in the presence/absence of a genetic interaction between such two genes.
The genes were screened by analyzing the difference in expression profiles between two genes.
The correlation coefficient (R) values correspond to the similarity of the expression profile between two genes.
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