Gene Co-Expression Network Analysis

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.

Gene co-expression network.Figure 1. Gene co-expression network.

Constructing gene co-expression networks

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.

The two general steps for constructing a gene co-expression network: calculating co-expression score.Figure 2. The two general steps for constructing a gene co-expression network: calculating co-expression score.

Selection of sample data

  • The first category is to select as many sample data as possible under different conditions to calculate the correlation. This kind of analysis method, which can be regarded as condition-independent, shows the most basic co-expression relationship between genes.
  • The second type, using functional expression data under specific conditions, is called condition-dependent.
  • In the third category, the user can freely choose which type of expression data to combine.
  • The fourth category is for users to upload data for calculation.

Gene Co-Expression Network Analysis 3

Our Gene co-expression network analysis

Project name Gene co-expression network analysis
Samples requirement Our network analysis services in biology require you to provide specific requirements.
Timeline Decide according to your needs.
Deliverables We provide you with raw data and modeling results.
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Applications for Gene co-expression network analysis

Gene Co-Expression Network Analysis 4

  • Finding clusters or modules of highly relevant genes.
  • Summarizing such modules using an intramodular hub gene or the module eigengene.
  • Relating modules to one another and to external sample traits with eigengene network methodology.
  • Calculating the measures of module membership.
  • Gene Network Reverse Engineering.
  • Plant Biology - Co-expression analyses have been extensively used to search for novel genes involved in specific plant pathways.

CD 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). CD 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.

We provide a variety of modeling services, but not limited to:

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