The concept of canonical correlation analysis (CCA) was first proposed by the statistician Harold Hotelling in 1936. CCA is an understanding of the cross-covariance matrix, which is a multivariate statistical analysis method to reflect the overall correlation between two groups of indicators by using the correlation between the pairs of comprehensive variables. CCA reduces high-dimensional data to one dimension through dimensionality reduction, and then uses correlation coefficients for correlation analysis.
CCA helps to clarify the complex and diverse interrelationships between the two sets of variables at a deeper level, and provided that the two sets of variables are continuous variables and their data must obey a multivariate normal distribution. CCA has a wide range of applications. Just like the RDA, in biology, CCA is applied to study the relationship between a group of variables and the biological environment.
Fig.1 Canonical correlation analysis (CCA). Ordination diagram of the fungal community data together with soil variables: moisture and temperature (Temp), electrical conductivity (EC) and clay content. (Vargas L, et al. 2015)
Our services in terms of canonical correlation analysis include,
Under the OUT of 97% similarity, CANOCO is used to make the CCA plot.
In general, our turnaround time is 2-4 weeks depending on the size of your project.
Creative Biogene is a forward-looking institution that offers canonical correlation analysis service to help you with bioinformatics analysis. Our advanced analysis platforms are established to provide you with results of good reproducibility. Combining rich project experience, strict data quality control and professional analysis process, we will ensure that the project is carried out accurately and quickly. We look forward to working with you for your cooperation.
If you are interested in our services, please contact us for more details.