Hope you can help. I´ve tried to run jaccard and bray-curtis distance metrics on a small data set using the command line found here: https://docs.qiime2.org/2019.10/plugins/available/diversity/beta/
I´ve used an OTU-table made in excel and then converted to biom using command line
(biom convert -i table.txt -o table.from_txt_hdf5.biom --table-type=“OTU table” --to-hdf5)
and subsequently qza “Feature-table”-file using command line
(qiime tools import
When I do the analysis, I get a result file named […].qza, but cant figure out how to view the results of the analysis. When uploading to qiime view all I get is description of file format etc:
Guess there is some thing obvious I dont understand? I also wonder if it is possible to create a vizualization file (emperor) of bray-curtis and jaccard distance metrics.
Good morning Ruben,
You are close! That
.qza file only includes the distance matrix, so now you need to make a
.qzv file with the visualization.
In the Moving Pictures tutorial, the recommendation is to use one of the qiime diversity core-metrics-* commands, so that you get the output
.qza and also the
Does that approach work well for you?
EDIT: Justine has some great advice below about building Emperor plots.
So, I have a slightly different view on the issue from @colinbrislawn. (Diverisity is often a good thing in microbiomes!)
I like the control you get from running individual commands. So, to get to where you want to go here, you’re missing two steps. You currently have a distance matrix (awesome!)
- You need to project that distance matrix into PCoA space using
qiime diversity pcoa. For example, you might do
qiime diversity pcoa \
--i-distance-matrix Braycurtis_Blank_1_extraction_.qza \
- Now, you need to project the pcoa into a visualization, which happens in the emperor plug in:
qiime emperor plot \
--i-pcoa Braycurtis_Blank_1_extraction_pcoa.qza \
--m-metadata-file map.txt \
The reason I like knowing teh seperate steps is because it gives me more flexibility with where I enter the process. So, sometimes, I want to filter my data. Rather than having to re-run core diversity metrics (which is a good place to start!) I can just take my distance matrix from core-diversity, filter it, and then do the visualization with a different set of samples. (PCoA depends on the set of samples you’re visualizing and so it needs to be regenerated whenever you want ot look at a different subset of your data. But, the underlying distances don’t change unless you like, re-rarify your data or filter the data.)
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