Am I interpreting weighted vs. unweighted distance matrix unifrac boxplots correctly?

Thanks @cherman2!

I am trying to correlate translocation of bacteria from feces (B) and intestines (C) to tumors (A). I am trying to identify trends and any information I can get to find out what's happening in this phenomenon. What I am understanding from your comment is that there is no particular trend in terms of this translocation and potentially that there is similarity between fecal bacteria (B) to tumor microbiome (A).

...abundance seems to be a driver in the differences between these microbiomes and not the "who is there

Sorry, just to clarify so I understand correctly, are you saying this is happening in this case i.e. abundance seems to be driving these differences or is this something about Unifrac in general?

Thanks you and appreciate your help!

Hi @macrobiome,
Sorry for the incredibly long answer! I hope it is helpful :qiime2:

This is so interesting!
Are there samples in tumor, intestines and feces groups that are sampled from one individual?
If they are samples from the same subject that would probably violate permanova's assumption of independence. I say "probably" because it is debated whether 2 separate microbiomes located on the same person are independent, but if you expect for there to be a transfer of microbe between the microbiome than it is definitely dependent and permanova is not the correct tool for this data.

There might be a trend but I think that there is another factor that isn't explained by these microbiome sites that is causing similarities between your microbiome sites. If one subject was sampled for all of your groups, the tumor microbiome of that subject might be more similar to the same subjects fecal microbiome than the tumor microbiome is to other tumor microbiomes. This could explain what we are seeing from your plots where fecal seems to be more similar to tumor in some cases than tumor is to itself.

I think this possibly supports your hypothesis of transferring microbes because if there are microbes that are transferring between these microbiomes sites that would increase the microbiome's similarity to each other and you would expect that to happen within a subject. This reminds me of a paper about PDAC that found transfer from the gut microbiome to the tumor microbiome and is really interesting: Tumor Microbiome Diversity and Composition Influence Pancreatic Cancer Outcomes - PubMed. You might already be familiar with this paper but I thought I would share anyway!

Also a little shameless plug, I develop a qiime2 plugin called q2-FMT, which is currently in alpha release, that helps assess engraftment of microbes in the recipient after a fecal microbiota transplant. I think with some minor tweaks this could work to track translocation of bacteria between different sites.

Basically, you would have to assign one or more of the microbiomes sites as your "donor" and the others as the recipient. This could help you track what microbes are being transferred between your sites, and how similar those sites are to each other.

I go into alittle bit more detail here if that is of interest: Colonization from inoculum - #4 by gregcaporaso and I am more than happy to help more if this sounds of interest to you!

By looking at the differences between the Unweighted Unifrac and Weighted Unifrac results we can see that abundance is a driver in finding significant differences. Because Unweighted Unifrac is not significant and Weighted Unifrac is significant and because Weighted Unifrac factors in abundances while Unweighted Unifrac doesn't, we can likely point to abundance as a main factor as to why weighted is significant. If you want to understand this more I would look into differences between Weighted and Unweighted Unifrac, there are a lot of sources out there that can explain the differences more thoroughly.


Thank you @cherman2 for your incredibly insightful response!

This particular dataset takes samples from 8 different mice, so we essentially had 8 tumors, 8 fecal samples, and 8 intestinal samples. Can we apply permanova in this case? Of course, we were under the assumption that fecal microbiome across mice would be pretty similar, but the study was conducted in two phases (n=4 mice per phase) more than a year apart. Is it possible that there may be intra-sample similarities i.e. mouse 1's tumor-feces-intestines may have good correlation but when we incorporate different mice, the analyses shows up as not similar?

That sounds exactly like what I have been looking for. I will try running q2-FMT and get back to you on how it goes.

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Hi @macrobiome,

Since your data is matched by a subject. I.e. one mouse was sampled for all three of these groups permanova can not be used. A good question to ask when thinking about dependence is: Is there a mouse that is in more than one of the groups I am comparing? If the answer is yes your samples are not dependent and permanova is not a good option. For your study, it seems like One mouse was sampled for all 3 of the groups you are comparing and therefore your data is not independent.

It is possible that the 2 different sequencing runs or a year between sampling may also be a confounding variable

I am not sure I understand this question. It sounds like you are wondering if a mouse has good correlations between its microbiomes but this is not really the question that permanova asks. Permanova tests if the microbiomes are different not if they are the same (thats a lot harder to prove).

YAY! :qiime2: Let me know how it goes! I am here to help!

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I understand. Thank you

Is there an alternative way to go about this apart from permanova?

Hi @macrobiome

It's is a little difficult with your study design to investigate if these microbiome are significantly different. You could try a Mantel test and test if there are correlations between your microbiomes? mantel: Apply the Mantel test to two distance matrices — QIIME 2 2023.5.1 documentation

Hi @cherman2
Thanks for your help so far. I tried running q2-fmt but ran into some issues

I ran the following command

qiime fmt engraftment --i-diversity-measure core-metrics-transloc/faith_pd_vector.qza
--m-metadata-file transloc-tumor.tsv 
--p-compare baseline 
--p-time-column week 
--p-reference-column SubjectID 
--p-where '[SampleType]'="Tumor"' 
--p-against-group 0 
--p-p-val-approx asymptotic 
--o-stats stats.qza 
--o-raincloud-plot raincloud-plot.qzv

It ran for a while (1 hour) and nothing happened. I tried running in --verbose again but don't see anything. My metadata file is below

transloc-tumor.tsv (2.2 KB)
Please let me know your thoughts. Thank you!

Hi @macrobiome,
Sorry for my late reply!
I am not sure what is happening here! This really shouldn't take that long. This is silly but make sure that you are hitting enter!

The first issue I see is that your donor samples do not seem to be in your metadata file? Could you add the donor samples and try running this again?


Hi @cherman2! I definitely hit enter haha

The intestines samples in the metadata file are supposed to be the donor while the tumor samples are supposed to be the recipient. Is there a problem with how the metadata file columns are set up? I put the SampleID of the donors (Intestinal samples) in the InitialDonorSampleID column next to their respective recipients. That is why the InitialDonorSampleID next to the intestine samples is empty.

Hi @macrobiome,

Good! I always have to check :sweat_smile:

I misunderstood how your metadata was set up. There is no issue with your donor metadata column.
However you will want to add a "fake" time column. I would put a column that is called week in your metadata. Give all the tumor values 1 and leave all the intestines blank. (This is part of the tweaking this method to fix your study)

Lastly remove the filtering parameter, you will need all your samples in order to run this anaysis.

--p-where '[SampleType]'="Tumor"' 

Can you try those suggestions and let me know how it goes. If this doesn't work for you would you share your faith_pd_vector with me so that I can try it? (You can DM me your data if your want)
Hope that helps!

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