Understanding beta-group-significance permanova pairwise statistics


I have looked through the forum to the best of my ability to find answers to my questions, but have come up short on a few questions. I realize they are probably simple answers, but Qiime2 is new to me, and so is the statistics involved.

To quickly summarize my work, I have a collection of serum samples from aerosol challenged rhesus macaques that I tested with a serodiagnostic assay to look for immune responses. The serum samples can be categorized by collection date, such as pre-challenge (-7), day 7, day 14, and day 28. I used the qiime diversity beta/diversity PcoA (Thanks to Matt Dillon’s help!) to create a principle coordinate analysis of my samples in hopes that the samples would stratify by collection date due to the immune responses produced… They did! Commands I ran are below:

biom convert -i table.txt -o table.biom --to-hdf5
qiime tools import --type ‘FeatureTable[Frequency]’ --input-path table.biom --output-path table.qza
qiime diversity beta --i-table table.qza --p-metric braycurtis --o-distance-matrix dm_bray_curtis.qza
qiime diversity pcoa --i-distance-matrix dm_bray_curtis.qza --o-pcoa pcoa_bray_curtis.qza
qiime emperor plot --i-pcoa pcoa_bray_curtis.qza --m-metadata-file metadata.tsv --o-visualization emperor_bray_curtis.qzv

Open up emperor_bray_curtis.qzv at https://view.qiime2.org

I took this a step further and explored the qiime diversity beta-group-significance plugin to see if the difference between my “collection day” groups were significant. I chose a PERMANOVA method, pairwise comparison, and 999 permuations (no particular reason why). Commands I ran below:
qiime diversity beta-group-significance --i-distance-matrix --m-metadata-file --m-metadata-column --p-method --p-pairwise --p-permutations --o-visualization

Thanks to previous forums, the graphs produced make sense to me. Here is an example where I have distances 1-7 (these are early terminal draws, essentially another group)


I’d like to ask how to interpret the statistics that are associated with this analysis. I understand that the pseudo-F value is difficult to interpret and due to many variables. If there is more information I can learn about how to interpret the pseudo-F, I’d be delighted to know! I’d like to know more about the p-value, if it is safe to say a p-value less than 0.05 is deemed significant, and also how to understand the q-value.


If you need more information, please let me know!

igg_bc_1-7 image

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You have probably read through these, but the following are the best resources for info on interpreting PERMANOVA:

and of course the original publication (linked from those links)

Yes (or rather the q value if you are doing multiple group comparisons)

The q-value is the p-value corrected for multiple tests. See here:

Since you are doing a longitudinal analysis you may also want to check out q2-longitudinal, which would allow you to do paired tests and various longitudinal analyses that are appropriate for your data. See here for a tutorial:

Good luck!

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Thank-you! This is great!


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