BioXpedia is proud to offer data analysis using network analysis.
Network analysis is used for investigating the correlation or association among biological molecules in a complex biological system.
The data analysis includes the following components:
- Detailed PDF report.
- Data handling.
- Employment of correlation analysis.
- Visualization of the biological network.
Read below for more information on network analysis:
Network analysis is used to look at relationships in the dataset. The most common use is for gene/protein correlation or co-expression analysis. Correlation measures association between variables. This association can be positive or negative and the correlation coefficient will likewise be positive or negative.
In a gene co-expression analysis, the network identifies genes that are co-expressed, which means that they have similar expression patterns. This is very useful for identifying genes that are regulated in a similar way to a gene of interest or to identify multiple genes related to some effect. Co-expression analysis can also be used between samples to find out if the same genes have the same expression profile in each sample (Lu et al., 2019).
Correlation can be measured in different ways the most common of which is Pearson r correlation. Pearson r correlation can be used to test if there is a significant linear relationship between two normally distributed variables. Sometimes, however, variables may not be normally distributed and the relationship between them may be non-linear. In these cases, the non-parametric alternatives Spearman or Kendall rank correlation can be used. These correlation measures have very few assumptions about the distribution and relationship, but are also less powerful, which means that the Pearson r correlation is preferred, when its assumptions are met.
When a correlation analysis has been carried out, a common way of visualizing it is using a heatmap. All the investigated genes/proteins are on both axis and the color intensity of cells show the degree of association. On the diagonal of such a figure are cells that show correlation between a gene and itself and thus the cells of the diagonal will have high intensity colors to show a perfect correlation or be set to a grey color to indicate that these values of perfect correlation are non-informative (Zhai et al., 2017).
- Zhai, Xiaofeng et al. “Colon cancer recurrence‑associated genes revealed by WGCNA co‑expression network analysis.” Molecular medicine reports 16,5 (2017): 6499-6505.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5865817/
- Lu C, Pu Y, Liu Y, et al. Comparative transcriptomics and weighted gene co-expression correlation network analysis (WGCNA) reveal potential regulation mechanism of carotenoid accumulation in Chrysanthemum × morifolium. Plant Physiol Biochem. 2019;142:415‐428.
https://www.sciencedirect.com/science/article/abs/pii/S098194281930302X?via%3Dihub