I’ve recently been using Qiime2 Studio and wanted to do some calculations myself over in R to familiarise myself with the data before pipelining the study analysis.
I have a file “Feature Table 1.qza” that I ran through the feature-table > Summarize table menu. In the “Interactive Sample Detail” tab, I found that there were five samples with zero counts.
S7 0
S78 0
S51 0
S36 0
S9 0
Using the qiime2R package in R, I read the “Feature Table 1.qza” file and performed colSums on these zero IDs above:
Would you mind sharing “Feature Table 1.qza” so that we could take a look? You can send it in a private message to me (click on my icon and hit the “message” button) if you don’t want to post the data publicly.
This behaviour is interesting as at last check there was an underlying issue with read_biom (the function used by qiime2R) importing tables that had samples with 0 counts. Sharing your artifact would be really helpful to see what is going on!
Thanks both, apologies but the second half of my message seems to have been hacked off! I’ll send the QZA along to Nicholas, really appreciate the help!
So this looks like it is probably an issue with read_biom, as @jbisanz mentioned.
If you have any familiarity with python you could use pandas to explore your data in R-like dataframes. Otherwise, you could bypass the buggy read_biom and instead:
export your feature table to a biom
use biom convert --to-tsv to convert to a TSV file
The counts for the zero-samples are being put into only 2 features which would hopefully tip off most users that there is an issue as these samples would be extreme outliers in every analysis. I was able to reproduce this behavior in a second dataset.