Trying to visualise q2-picrust2 data on R

Hi all,

I successfully ran my data through q2-picrust2 and received the appropriate output. However, this out means nothing without a way to process the data. I am wondering if anyone has found a way to visualise their Pircust2 data through R (specifically a heat map or bar plot). Could this be accomplished using phyloseq or similar packages? How would I read the Picrust2 data into phyloseq? Lastly, (and independently of Picrust2) how would you go about creating a Venn diagram of shared sequences between all samples?

An example could be found in this paper: Biodiversity and Activity of the Gut Microbiota across the Life History of the Insect Herbivore Spodoptera littoralis | Scientific Reports / Insect-based diet, a promising nutritional source, modulates gut microbiota composition and SCFAs production in laying hens | Scientific Reports

Thank you for all the help and I hope everyone had a great festive season!

Kind regards,

Johann

Hi @Johanndb,
I suspect that you have a FeatureTable[Frequency] as output that you're interested in working with. There are a lot of tools in QIIME 2 for downstream analysis of these - e.g., core-metrics. Most actions that can take a QIIME 2 FeatureTable[Frequency] that was generated for example with q2-dada2 should work with the result of q2-picrust2 (the exception would be actions that require another input that you might not have, like a phylogenetic tree).

If you specifically want to use R for your next steps, I recommend looking into using qiime2R - that's a third party tool that is very popular for working with QIIME 2 data in R.

As for Venn diagrams, we don't have a direct way to generate those in QIIME 2, but this post provides a suggestion.

In general, I would recommend doing a test such as beta-group-significance, which will tell you if your groups are significantly dissimilar from each other in their composition as that will have a clearer interpretation. You could also pair that with a Venn diagram to aid in interpretation of the diagram (Jaccard distance might be a particularly relevant metric there, as it's effectively the sum of features that fall outside the intersection of two samples in a Venn diagram of their features).

Hope this helps!

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