My first question relates to the Parkinson’s mouse tutorial. How do you read an emperor plot and what are the different coordinates/axis? And how do you know what actually does have more beta diversity?
Hello Bethanie,
Great questions! Welcome to the forum! 
The Parkinson's Mouse Tutorial is one of the more advanced tutorials, and I think a good introduction to these concepts is in the Moving Pictures Tutorial: Alpha and Beta diversity analysis. This tutorial not only explains the concepts, but it also offers 'discussion questions' that show you what you could learn from a given graph.
Got to check out Moving Pictures! 
If you are interested in an article, try this one:
Figure 7 has Emperor plots and shows exactly how to interpret them.
(This paper uses an older version of Qiime, so the Qiime-specific stuff is going to be different but the biology is going to be the same.)
Let us know if you have more questions!
Colin
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Hi Colin,
I’ve checked out the moving pictures tutorial, and the fmt one 
but am finding the Parkinson’s mouse the most helpful. And thanks for the article!!
So I understand that I’m looking for clustering in the emperor plot, by different metadata columns, to see if there is a difference in diversity. But is there a way of quantifying which group has more diversity? Is one of the axis beta diversity? I know you can edit the axis to show time or other variables, but what are the original axes?
Hope this makes sense,
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It sounds like maybe you are thinking more in terms of alpha diversity here, which is not really related to the emperor visualizations... I recommend reading up more on alpha vs. beta diversity and the differences between these.
Alpha diversity is going to tell you how many species/phylotypes etc are in each sample, giving you a sense of "which group is more diverse"
Beta diversity is going to tell you how different those samples/groups are from one another. So higher beta diversity would be something like larger intra-group distance, which does not sound like what you are after. But maybe it is.
So quantify alpha diversity with qiime diversity alpha-group-significance and beta diversity with qiime diversity beta-group-significance
Here is a nice primer:
https://mb3is.megx.net/gustame/dissimilarity-based-methods/principal-coordinates-analysis
I hope that helps!
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Thank you so much!! I’m new to microbial ecology/statistics but the Gusta Me blog really helped!!
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