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?
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).
I came across your post regarding visualizing PICRUSt2 output data in R and I wanted to recommend ggpicrust2, a comprehensive R package that offers a seamless and intuitive solution for analyzing and interpreting the results of PICRUSt2 functional prediction.
ggpicrust2 integrates pathway name/description annotations, ten of the most advanced differential abundance (DA) methods, and visualization of DA results. It can help researchers, data scientists, and bioinformaticians better understand the underlying biological processes and mechanisms in their PICRUSt2 output data. ggpicrust2 offers various visualization options, including heat maps and bar plots, making it an excellent tool for visualizing your PICRUSt2 data in R.