Principal coordinate analysis (PCoA) is a non-constrained data dimension reduction analysis method. It is also a visualization method to study the similarity or difference of data. After sorting through a series of eigenvalues and eigenvectors, selecting the eigenvalues that are mainly ranked in the top, PCoA can find the most important coordinates in the distance matrix. The result is a rotation of the data matrix, which does not change the mutual position relationship between the sample points, but only changes the coordinate system. Differences between individuals or groups can be observed through PCoA.
PCoA is similar to PCA. The main difference is that PCA is based on Euclidean distance, but PCoA is based on distances other than Euclidean distance to find out the potential principal components affecting the difference of sample community composition by dimension reduction. In the PCoA plot, each point represents a sample, and the points of the same color come from the same group. The closer the distance between the two points, the smaller the difference in community composition between the two.
Fig.1 Principal coordinate analysis (PCoA) plot of the bacterial microbiome inhabiting rhizosphere soil and pseudostem of two different banana breeding lines. (Xie Y, et al. 2016)
Our services in terms of principal coordinate analysis include,
Analytical tools used in our principal coordinate analysis service include the R language tool, and other related tools. R language tool can be used for both analysis and plotting.
In general, our turnaround time is 2-4 weeks depending on the size of your project.
Creative Biogene is an expert in the field of the principal coordinate analysis. We use advanced analysis platforms to provide you with results of good reproducibility. The service we offer is excellent in speed, accuracy and sensitivity. More discounts, more surprises, we are look forward to your selection!
If you are interested in our services, please contact us for more details.