Canonical Correspondence Analysis

What is Canonical Correspondence Analysis?

Canonical correspondence analysis (CCA) is the canonical form of correspondence analysis (CA). As a form of direct gradient analysis, wherein a matrix of explanatory variables intervenes in the calculation of the CA solution, only correspondence that can be 'explained' by the matrix of explanatory variables is represented in the final results. As with CA, this technique is suitable for response variables showing unimodal distributions and preserves χ2 (chi-squared) distances (click here for more information about distances) between objects. In fact, it can be computed from a matrix of χ2 distances that is passed on to a form of redundancy analysis (RDA) which uses object marginal sums (row totals) as a weighting parameter. The result of this weighted RDA is that only those response variables that are maximally related to linear combinations of the explanatory variables provided are ordinated in a Euclidean space. These are then canonical variables. The correlation of the explanatory variables to the final ordination determines their 'importance'.

Overall solutions

Venn Diagram Service

Selection principle of RDA or model

  • We use species-sample data (97% similar sample OTU table) to do DCA analysis, see the size of the first axis of Lengths of gradient in the analysis result, if it is greater than 4.0, CCA should be selected If it is between 3.0-4.0, both RDA and CCA can be selected. If it is less than 3.0, the result of RDA is better than CCA.

Results and interpretation

  • Many implementations of CCA will report the total inertia of the solution alongside the inertia that was successfully constrained by the explanatory variables. The quotient of the constrained inertia over the total inertia indicates how good the overall 'fit' was. Further, each CCA axis is associated with an eigenvalue.
  • For constrained axes (i.e. those that are linear combinations of the explanatory variables), the eigenvalues are a fraction of the total constrained inertia. Thus, they express the amount of the constrained inertia expressed by each constrained axis.
  • The correlation of the canonical axes with the explanatory matrix is reported as well as the significance of each correlation determined by permutation. Significance can be tested for the overall solution or for individual ordination axes (and their eigenvalues) derived from the response data.

Software and algorithm

PC-ORD or CANOCO software for drawing.

Why choose us?

  • CD ComputaBio has a mature statistical analysis technology platform.
  • CD ComputaBio has a first-class expert technical team, our team is professional and experienced.
  • CD ComputaBio has established a professional after-sales service team to provide customers with efficient, fast and practical solutions.

Our services

Project name Canonical correspondence analysis
Our services CD ComputaBio offers canonical correspondence analysis service to meet the specific needs of different customers.
Sample requirements
  • The variables in the explanatory matrix should be chosen with care. If explanatory variables are included too liberally, there is an increased risk of distorting the resulting CCA results.
  • Only examine the significance and effects of individual axes if the overall CCA solution is found to be significant.
Screening cycle Decide according to your needs.
Deliverables We provide you with raw data and analysis service.
Price Inquiry

CD ComputaBio' canonical correspondence analysis service can significantly reduce the cost and labor of the subsequent experiments. Canonical correspondence analysis service is a personalized and customized innovative scientific research service. Each project needs to be evaluated before the corresponding analysis plan and price can be determined. If you want to know more about service prices or technical details, please feel free to contact us.


  1. Sheik CS, Mitchell TW, Rizvi FZ, Rehman Y, Faisal M, et al. Exposure of Soil Microbial Communities to Chromium and Arsenic Alters Their Diversity and Structure. PLoS ONE. 2012:7(6).
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