**tl;dr: ** Is there a possibility to export the data which creates the Boxplots during balance-taxonomy?
I’m using gneiss for some community analyses at the moment.
Therefore i want to correlate more than two factors (e.g. different tree species against control and themselves).
As fas as i know, gneiss is only capable of plotting boxplots (balance-taxonomy) of two factors.
Is there a possibility to export the data which creates the Boxplots during balance-taxonomy?
I tried to use an “–output-dir” instead of the visualisation and to export the qzv in generel. Both doesn’t work for now.
I saw that sometimes there is the possibility to download a.csv-file directly from the qiime2viewer.
Would be really nice this feature could also be added sometimes in the future.
According to the documentation for balance-taxonomy, I’m pretty sure
--output-dir should do exactly this…
What version of qiime are you using?
Have you tried exporting or extracting the
Let me know what you find,
thanks for your reply.
I’m using QIIME 2 version: 2017.12.1 (q2cli version: 2017.12.0) at the moment.
i tried the
--output-dir variable and it just generated a folder with the .qzv-file.
I also tried to
qiime tools extract and this worked temporary. I’ll take a further look at the extracted files and if they provide the information i need.
Thanks for your input, i only tried
export and this doesn’t worked.
These are just standard q2 archives – I think @ebolyen and @thermokarst could elaborate more on that.
Note that a q2 archive is really just a zip file. A sure fire way to open a zip file is just
tar -zxvf <your-file-name.qzv>
@mortonjt, I think @Tjelve is more interested in getting the summaries that define each of these boxplots, or maybe the original distributions themselves. I don’t think this data is readily available in a .qza and it doesn’t seem like the .qzv provides it as a csv/tsv download either.
How would we go about getting the pre-plot info from balance-taxonomy? Are there some functions in
gneiss we could call separately?
I don’t think that information is accessible. Underneath the hood, we are just using seaborn (see function here).
However, seaborn is a little customizable, so it is possible to use the Python function to do more custom visualization (i.e. nested box plots).
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