In the gene co-expression network, each gene corresponds to a node. And if the pairwise expression similarity score of a pair of nodes is higher than a certain threshold, a pair of nodes will be connected by an undirected edge. Constructing a co-expression network from gene expression data sets has become a widely used alternative to conventional analysis methods. Large-scale gene co-expression networks have been applied, for example, to prove that functionally related genes are usually co-expressed in various data sets and different organisms. By constructing co-expression networks for different conditions (such as normal and cancerous states), it is possible to study changes in disease-mediated network connections.
Figure 1. Gene co-expression network.
The input data for constructing a gene co-expression network is often represented as a matrix. If we have the gene expression values of m genes for n samples (conditions), the input data would be an m×n matrix, termed the expression matrix. For instance, in a microarray experiment the expression values of thousands of genes are measured for several samples.
In the first step, a similarity score (co-expression measure) is calculated between each pair of rows in expression matrix. The resulting matrix is an m×m matrix called the similarity matrix. Each element in this matrix shows how similarly the expression levels of two genes change together.
In the second step, the elements in the similarity matrix which are above a certain threshold (i.e. indicate significant co-expression) are replaced by 1 and the remaining elements are replaced by 0.
Figure 2. The two general steps for constructing a gene co-expression network: calculating co-expression score.
|Project name||Gene co-expression network analysis|
|Samples requirements||Our network analysis services in biology require you to provide specific requirements.|
|Detection cycle||Decide according to your needs.|
|Service including||We provide you with raw data and modeling results.|
ComputaBio provides corresponding gene co-expression network analysis. Our gene co-expression network analysis have proven to be very useful for understanding the biochemical basis of physiological events at different stages of drug development (even in different fields such as materials science). ComputaBio team has been working in this field for more than ten years and has published his findings in top scientific journals. If you have a need for network analysis services, please feel free to contact us.