Otus responsable for clustering in PCoA

Dear all,
I have a question.
I performed a microbiota analysis of my samples with QIIME2 2. After I had calculate PCoA.
I want you know if exist a way to extract how and which many taxa are responsible for the clustering of my samples? I need to have a list of taxa.
thanks in advance for the response

Hi @rparadiso,

You may want to look into DECIODE, which is a PCA-based plug in.

If you’re using other metrics, there isn’t a one-to-one correspondence between the PCoA coordinates and the exact taxonomic composition. You could use something like a bi-plot to tell you about the taxonomy, but that (usually) makes the assumption that the most abundant taxa are the ones driving your clustering.

You could also use differential abundance testing like ANCOM, Gneiss, etc. These may not tell you exactly which features are driving clustering but they will help you identify differentially abundant features.



Hi @jwdebelius,
first of all thanks for your answer.

I have two question.
1- Why in my plot there is the otus code at the end of the arrow and not the name of the corresponding taxa?
2- It is possible do this plot using weighted and unweighted UniFrac distance matrix obtained from beta analysis in Qiime2 (core analysis) ?


Hi @rparadiso,

Yes, those are the ASV identifiers for each of the important features.

Yes, but not in the same way. You can use the biplot-pcoa function in q2-diversity to generate the coordinates and then there’s a biplot function in Emperor. Im not sure if there are tutorials, you could search the forum or just read the function documentation. (I tend to use the python API so Im not the best person to ask about the command line).

For that, you may want to use a collapsed table, or think about selecting differentially abundant organisms.


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