Identifying sources of variation from emperor plot


Can anyone explain how to identify the sources of variation represented in the axes of an emperor plot?

Thank you!

Hi @danielee,

I’m not totally sure I understand your question, but I’ll take a stab? The number represents the amount of variation explained. But, because of hte way PCoA works, there’s not factor loading ro something similar for most distance metrics. You could try a biplot, which will show the location of organsims in PCoA space. However, there’s variability utility there: often the most abundant taxa (which are frequently visualized) are not the factors which are driving seperation. You could look into DECOIDE, which is a plug in based on PCA, rather than PCoA and can provide a better sense of factor loading.



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