feature-table filter-features 'No filtering was requested'

Hello!
I get the following error when I use the feature-table filter-samples command:
Plugin error from feature-table:
No filtering was requested.
Debug info has been saved to /tmp/qiime2-q2cli-err-4al5fat7.log

The file contains the following:
Traceback (most recent call last):
File “/home/borysao/miniconda3/envs/qiime2-2020.8/lib/python3.6/site-packages/q2cli/commands.py”, line 329, in call
results = action(**arguments)
File “”, line 2, in filter_features
File “/home/borysao/miniconda3/envs/qiime2-2020.8/lib/python3.6/site-packages/qiime2/sdk/action.py”, line 245, in bound_callable
output_types, provenance)
File “/home/borysao/miniconda3/envs/qiime2-2020.8/lib/python3.6/site-packages/qiime2/sdk/action.py”, line 390, in callable_executor
output_views = self._callable(**view_args)
File “/home/borysao/miniconda3/envs/qiime2-2020.8/lib/python3.6/site-packages/q2_feature_table/_filter.py”, line 87, in filter_features
where=where, axis=‘observation’, exclude_ids=exclude_ids)
File “/home/borysao/miniconda3/envs/qiime2-2020.8/lib/python3.6/site-packages/q2_feature_table/_filter.py”, line 38, in _filter_table
raise ValueError(“No filtering was requested.”)
ValueError: No filtering was requested.

I run the following code:
qiime feature-table filter-features
–i-table table-dn-90.qza
–o-filtered-table table-filtered-90.qza

I don’t know why that error appears

I am using the latest version of qiime2:
qiime2-2020.8 in miniconda 3 (Python 3.8)
Oracle VM VirtualVox
Linux with Ubuntu 20.04.1 LTS

I’m not sure why an error necessarily appears @Borysao, but it’s my understanding from filter-features that all default parameters are effectively null unless you invoke a change. In other words, running filter-features without any additional arguments should just reproduce the same file you input.

That’s why I’m not sure if there should be an error, insofar as a warning - you haven’t asked the program to filter a particular thing. You could filter by requiring a certain read abundance (min or max), or a certain number of samples (min or max), or a variety of other parameters defined by some metadata file (for example, maybe your samples were collected from dogs, cats, and unicorns, and you want to filter out those pesky unicorns…). See the documentation for more details on the particular parameters of filter-features as well as this tutorial that gives more context on filtering QIIME formatted data more broadly.

Good luck! :unicorn:

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