Deicode RPCA biplot - Are the vectors distorting the plot?


I am using DEICODE on my 16S samples and I believe something weird is happening, as you can see in this plot - aitch-biplot-rarefied.qzv (1.7 MB). It seems that the vectors are somehow influencing the first axis of the PCOA and the variation I used to see is shifted to the second and third axes.

I re-did the PCOA biplot following the instructions in this other qiime2 post, and this was my resulting biplot- emperor-vis-biplot-pcoa-aitch.qzv (1.7 MB). For this last plot, I used aitchison as the metric for the qiime diversity beta step (to produce the distance matrix).

That being said, I have three questions:

  1. Why are the vectors of the DEICODE biplot interfering with the results of the ordination?
  2. What exactly is the aitchison metric doing when I select it through the qiime diversity beta method? I don't really know what where to look for that info- as well as for the rest of the metrics here.
  3. Are the plots generated from the two approaches supposed to be similar? Or should I expect some differences on the resulting ordinations?


  1. I’m not exactly sure what you mean that this …
  2. The Aitchison metric is computed using pseudocounts (since you can’t take logs of zeros). This is expected to give you an arbitrarily bad bias. DEICODE has a more robust way to deal with missing values, so we recommend to use that one instead.
  3. No.
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Hi @mortonjt,

Thanks for your quick reply.

Regarding the first question, what I mean is that all the vectors are going in the same direction, and as a result, my samples are clumped on the left side of the plot. This is not seen on the plot that I produced with the other method, or any other ordination that I have produced before. I just want to know if perhaps this plot is supposed to look that way, or if my data have problems.

As for the second question, thanks for clarifying that. I know what decoide is supposed to be doing, but I didn't have enough information to understand what the aitchison metric implemented in the qiime diversity beta method is supposed to be doing. Again, if anyone knows a source where the metrics implemented in qiime diversity beta are explained, then we could do a more informed selection of the metrics. Anyhow, do you know if the aitchison metric does a ilr or a clr transformation? I just want to compare both methods, although I am more inclined to use deicode.



That I’m not sure about - that probably has to do properly centering the data. There may just be bias issues. @yoshiki may have more insights on this.

It doesn’t matter – the Euclidean distance between ilr / clr are equivalent (which is also known as the Aitchison distance). More can be found in Chapter 3 and 4 in the Modelling and Analysis of Compositional Data textbook (link here).

Hi @yakshi.ortiz,

You can find a brief summary of sorts here. To read a more detailed explanation of each you’ll have to research those on your own however as those concepts go deep into ecology and are a bit beyond the scope of a bioinformatics-focused environment.

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Hi @Mehrbod_Estaki,

Thank you a lot for the link. That works perfectly!


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I don’t believe this has to do with the centering. But I can double-check this, if you can send me the artifact you used to create that plot.

If you’re in need a more in depth look at various ecological metrics, I would recommend the book Measuring Biological Diversity (Magurran 2004). It’s not perfect, but it does discuss most of the options currently available in QIIME 2 and their pros and cons on different types of data. I’ve found it incredibly helpful.


@yoshiki, thanks for offering to help. I just sent you a message with the artifact.

@prayle1, thank you too for the suggestion. I will definitely look into that.

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@yakshi.ortiz, see the post here: QIIME 2 2019.4 is now available!

It is likely that the beta diversity results posted here are wrong due to a bug

@mortonjt, Thanks for letting me know about the update/bug. I will try with the newest version of QIIME 2 and keep you posted.


Hi @yakshi.ortiz,

I think this may have been a centering issue with the biplot that was recently fixed in DEICODE v0.2.3. Could you try again after updating to the newest version? I think your problem should be fixed.