making a taxa-bar-plots.qzv after removing contaminants

Hello everyone,

I am new to QIIME2 so I apologize up front for a rudimentary question.

I would like to make a taxa-bar-plots.qzv from my dataset, AFTER all contaminant samples are removed. I removed all taxa from my contaminant sample (Quiagenkit) from my experimental samples in my ASV_taxo.tsv file; and then I removed the Quiagenkit sample completely from my map.

Here is the code I ran to produce the taxa-bar-plots.qzv:
--i-table DOC-table.qza
--i-taxonomy DOC-taxonomy.qza
--m-metadata-file DOC_map_contaminants_removed.tsv
--o-visualization taxa-bar-plots.qzv

Here is the error message it produced:
Plugin error from taxa:
Sample IDs found in the table are missing in the metadata: {'Qiagenkit'}.
Debug info has been saved to /var/folders/bg/x61stg5n6qqf7n16x12q0jjr0000gn/T/qiime2-q2cli-err-hbdvl6xt.log

When I repeated the code with my original map (including contaminants) it worked:
--i-table DOC-table.qza
--i-taxonomy DOC-taxonomy.qza
--m-metadata-file DOC_map.tsv
--o-visualization taxa-bar-plots.qzv

But again, I would like the bar plot to exclude my contaminants. How might I go about doing that?

Thanks so much,

DOC_map_contaminants_removed.tsv (2.0 KB)

DOC_map.tsv (3.7 KB)

DOC_ASV_taxo_contaminants_removed.tsv (181.4 KB)

Hey Aurora,

This makes sense, because

and now you also need to remove it from your DOC-table.qza. Once the sample is removed from both your mapping file and your feature table, this should work fine. Try qiime feature-table filter-samples, and let us know how you...

Oh no! Wait! :triangular_flag_on_post:

Uh... you may have read about this in other posts, but this might cause big problems :scream_cat:

Don't let me tell you how to filter your table! I just wanted to mention it because I know reviewer three is going to have opinions about filtering negative controls :upside_down_face:

wait what?

The Illumina platform doesn't always assign the right barcode to the right read, and the most abundant amplicons are the most likely get a mismatched barcode and end up in other samples. This means that a perfectly empty negative control would still get some reads assigned to it during sequencing that were never in the original sample. If all the ASVs in this negative control were removed, you would lose the most common, real ASVs in the whole data set.


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