I am writing since I have used the qiime2 picrust plugin for a pilot analysis.
Now I would like to do something descriptive with graphics, such as clustering or maybe even differential analysis.
I have found this R package ggpicrust to be used with the original picrust software (not the qiime2 plugin)
I was wondering if there is a tutorial to direclty process qiime2 picrust plugin results to obtain grouping of KEGG Orthologies to better visualise the results.
Thanks a lot,
Here is the tutorial for q2-picrust. You can also export these results for use in other tools or use the native picrust 2 environment. This page also links to other tutorials and discussion about how to analyze picrust 2 output.
thanks, however I have already data obtained following the above-mentioned tutorial on my data. My question refers to visualization and post analysis (clustering based on the hierachical structures of the Kegg Ontologies). I have provided an example of such a package however I am not fully satisfied with that solution since it requires instead of q2picrust formatted outputs teh ones produced by "normal" picrust algorithm. Copuld you please comment on this line? I am in search for an R package directly parsin q2picrust results. Hoping this is clear. I am waiting for you kind reply,
You can continue to use the the output you generated via q2-picrust2 by simply exporting the data within the artifacts. That is, once the the data are exported they should be in the form required for further downstream use via standard picrust2, ggpicrust, etc...
You may need to convert the exported tables from biom to tsv, using biom conversion tools, which is built into QIIME 2.
I, thanks a lot for practical answer. I do have only a minimal trouble due to the header line (#) present in the q2picrust exported artifacts while I imagine that the picrust output does not hav this header structure, however thanks to your suggestion I will just go and simplify the header comparing with the picrus output wiyhout searching for additional parsers. Thanks a lot, Michela