mmvec: strange biplot and heat map results

Dear :qiime2: community,

I am trying to estimate microbe-metabolite interactions with mmvec (in qiime-2020.6 since tensorflow won't install in newer qiime2 versions) and my biplot and heat map look really strange! :boom:

The metabolite data is normalised for an internal octanol standard and each sample measured twice so I used the mean thereof and filtered with frequency >1.
My microbiome data is just a filtered FeatureTable of Fungi.

In the Emperor plot, all taxa align on a single error and Axis 1 has >93%!

In the Heatmap there seems to be a block of negative correlation - but I don't think I should remove any outliers or normalise the input data.

Stranger even, the model diagnostics look pretty good and I get a Pseudo Q-squared of 0.63. :face_with_monocle:

I would appreciate any ideas or input on why my outputs look this way and what I can do to get "better" results. :raised_hands:

Thanks for putting together this great plugin!
Lena

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I am attaching the files and code I am using:

qiime mmvec paired-omics
--i-microbes zero_filtered_table.qza
--i-metabolites metabolites_filtered.qza
--p-summary-interval 1
--output-dir model_summary

qiime emperor biplot
--i-biplot model_summary/conditional_biplot.qza
--m-sample-metadata-file Volatiles_categories.txt
--m-feature-metadata-file taxonomy.tsv
--p-ignore-missing-samples
--o-visualization emperor.qzv

qiime mmvec heatmap
--i-ranks model_summary/conditionals.qza
--m-microbe-metadata-file taxonomy.tsv
--m-microbe-metadata-column Taxon
--m-metabolite-metadata-file Volatiles_categories.txt
--m-metabolite-metadata-column Category
--p-level 2
--o-visualization mmvec-heatmap.qzv

metabolites_filtered.qza (456.6 KB)
zero_filtered_table.qza (191.9 KB)
Volatiles_categories.txt (7.6 KB)
Uploading: taxonomy.tsv...

1 Like

It might not be a problem -- I would be curious to see what your alpha / beta diversity for both your microbiome / metabolome looks like. If you have an acute stressor (i.e. antibiotics, or a pathogenic take over), you could see a dramatic shift in occurring in your community (which is what is hinted by your very skewed MMvec PC axes).

A Pseudo-Q2 = 0.63 is very good, I haven't seen many studies that have achieved that level of cross-validation accuracy. So you may have something biologically interesting :slight_smile:

4 Likes