Hi again everyone,

May I ask a few more questions please?

Below I have 3 plots generated using Bray-Curtis, Unweighted Unifrac, and Weighted Unifrac metrics. The points were colored by the subjects whom the samples were taken from.

I'm trying to understand why Bray-Curtis seems to produce the clearest separation between groups (subjects), followed by Unweighted Unifrac, and lastly Weighted Unifrac.

Actually, is the above conclusion correct?

According to this page by Buttigieg and Ramette:

A successful PCoA will generate a few (2-3) axes with relatively large eigenvalues, capturing above 50% of the variation in the input data, with all other axes having small eigenvalues.

Because the 3 axes produced by Unweighted Unifrac metric here only captured 50.17% of the variation in my data, can it be considered "barely successful" (therefore, not a good choice for this data set)?

Is it because there are differences in OTU abundance between samples from different subjects that were ignored by Unweighted Unifrac (that's what I understood the metric does - please kindly correct me if I'm wrong)?

If the above is true, is it then safe to assume that for samples with some differences in abundance between groups, Bray-Curtis would be the best choice? But before I generated these plots, I thought Weighted Unifrac would be a better choice.

Here are the results of **qiime diversity core-metrics-phylogenetic** on my data:

Bray-Curtis:

Unweighted Unifrac:

Weighted Unifrac:

Thank you very much! I'm so grateful that this forum exists and is frequented by kind, helpful people!