Error Plugin error from gneiss:array must not contain infs or NaNs

Hello,

I am trying to run QIIME gneiss gradient-clustering but I am getting the following Error (Please see below). Anybody have an idea of why I am getting this error?

Thank you so much!!

qiime gneiss gradient-clustering \
  --i-table grouped_by_taxa.qza \
  --m-gradient-file metadata.tsv \
  --m-gradient-column Numbering\
  --p-ignore-missing-samples True\
  --o-clustering gradient-hierarchy.qza --verbose 

Traceback (most recent call last):
  File "/cluster/biocompute/software/qiime2/qiime2-2019-10/envs/qiime2-2019.10/lib/python3.6/site-packages/q2cli/commands.py", line 328, in __call__
    results = action(**arguments)
  File "</cluster/biocompute/software/qiime2/qiime2-2019-10/envs/qiime2-2019.10/lib/python3.6/site-packages/decorator.py:decorator-gen-295>", line 2, in gradient_clustering
  File "/cluster/biocompute/software/qiime2/qiime2-2019-10/envs/qiime2-2019.10/lib/python3.6/site-packages/qiime2/sdk/action.py", line 240, in bound_callable
    output_types, provenance)
  File "/cluster/biocompute/software/qiime2/qiime2-2019-10/envs/qiime2-2019.10/lib/python3.6/site-packages/qiime2/sdk/action.py", line 383, in _callable_executor_
    output_views = self._callable(**view_args)
  File "/cluster/biocompute/software/qiime2/qiime2-2019-10/envs/qiime2-2019.10/lib/python3.6/site-packages/q2_gneiss/cluster/_cluster.py", line 106, in gradient_clustering
    t = gradient_linkage(table, c, method='average')
  File "/cluster/biocompute/software/qiime2/qiime2-2019-10/envs/qiime2-2019.10/lib/python3.6/site-packages/gneiss/cluster/_pba.py", line 203, in gradient_linkage
    t = rank_linkage(mean_X)
  File "/cluster/biocompute/software/qiime2/qiime2-2019-10/envs/qiime2-2019.10/lib/python3.6/site-packages/gneiss/cluster/_pba.py", line 125, in rank_linkage
    dm = DistanceMatrix.from_iterable(r, euclidean)
  File "/cluster/biocompute/software/qiime2/qiime2-2019-10/envs/qiime2-2019.10/lib/python3.6/site-packages/skbio/stats/distance/_base.py", line 778, in from_iterable
    key, keys)
  File "/cluster/biocompute/software/qiime2/qiime2-2019-10/envs/qiime2-2019.10/lib/python3.6/site-packages/skbio/stats/distance/_base.py", line 159, in from_iterable
    dm[i, j] = metric(a, b)
  File "/cluster/biocompute/software/qiime2/qiime2-2019-10/envs/qiime2-2019.10/lib/python3.6/site-packages/scipy/spatial/distance.py", line 620, in euclidean
    return minkowski(u, v, p=2, w=w)
  File "/cluster/biocompute/software/qiime2/qiime2-2019-10/envs/qiime2-2019.10/lib/python3.6/site-packages/scipy/spatial/distance.py", line 523, in minkowski
    dist = norm(u_v, ord=p)
  File "/cluster/biocompute/software/qiime2/qiime2-2019-10/envs/qiime2-2019.10/lib/python3.6/site-packages/scipy/linalg/misc.py", line 137, in norm
    a = np.asarray_chkfinite(a)
  File "/cluster/biocompute/software/qiime2/qiime2-2019-10/envs/qiime2-2019.10/lib/python3.6/site-packages/numpy/lib/function_base.py", line 496, in asarray_chkfinite
    "array must not contain infs or NaNs")
ValueError: array must not contain infs or NaNs

Can you provide all of the commands that you ran? One possibility is that you have zeros in your data. You may need to add pseudocounts.

Hi @mortonjt,

Thank you very much for your prompt response!

I do have zeros in my data. The codes that I posted was actually the beginning of my command. I was following the tutorial for GNEISS (https://docs.qiime2.org/2019.10/tutorials/gneiss/) and I didn’t see anything about running pseudocounts. Should the pseudocounts be the first step before running gradient-clustering?

I do remember seeing an step for adding pseudocounts in older versions of QIIME.

Thank you!!

@mortonjt

I was reading the tutorial again, and in the QIIME2 version that I am using (2019.10) both methods (correlation-clustering and gradient-clustering) include a default pseudocount parameter.

Thanks, Joao

@mortonjt,

I have a follow-up question. I saw on another post (Gneiss ols regression model fitting) that you recommended filtering the data by:

  1. Features that have few reads (i.e. less than 10 reads across all samples).
  2. Features that are rarely observed (i.e. present in less than 5 samples in a study).
  3. Features that have very low variance (i.e. less than 10e-4)

Could you please provide the command in QIIME2 to do that filtering?

I am assuming the filtering would have to be done in the first step. Before running any clustering analysis (that might have default pseudocount transformation) or before running something like “composition add-pseudocount”.

Thank you, Joao

We’d need a little more information to provide feedback. For instance, what does the distribution of your counts look like? Are there samples with zero reads? Do you have taxa with zero counts? Is there a scenario where the taxa across all of the samples have the same counts? How many samples / taxa do you have?

Regarding how to filter, the first two filtering commands can be done using the qiime feature-table commands (see tutorial). Filtering by variance is more tricky - there is no out-of-the-box command for that. Doing this would look something like the following python script

import qiime2
from biom import Table
from biom.utils import biom_open
import numpy as np
table = qiime2.Artifact.load('<your table>').view(Table)
filter_func = lambda v, i, m: np.var(v) > 10e-4
table.filter(filter_func, axis='observation')
with biom_open('<your filtered table>', 'wb') as out:
    table.to_hdf5(out, 'filtered')
2 Likes

@mortonjt,

I’ve now done the first two filterings using the tutorial you suggested and that worked well. Through that, I generated the ‘feature-frequency-grouped_by_taxa.qza’ file which I am now trying to import into my python script for filtering by variance (please see script below).

#!/usr/bin/env python3

import qiime2
from biom import Table
from biom.utils import biom_open
import numpy as np

table = qiime2.Artifact.load('feature-frequency-grouped_by_taxa.qza').view(Table)
filter_func = lambda v, i, m: np.var(v) > 10e-4
table.filter(filter_func, axis='#OTU ID')
with biom_open('variance-filtered-feature-frequency-grouped_by_taxa.qza', 'wb') as out:
    table.to_hdf5(out, 'variance-filtered')

My first question to you is regarding the axis. Do I need to replace ‘observation’ for something else? Please note that in my script, I’ve replaced the ‘observation’ by ‘#OTU ID’, which is the name of my column in the feature-table.biom file that contains the taxa information.

Also, when trying to run my script, I am getting the following error when calling biom.utils. Do you know what am I missing? It looks like some of the biom utilities are missing, even though biom convert works normal for me.

Traceback (most recent call last):
  File "./1Step_Data_Filtering", line 5, in <module>
    from biom.utils import biom_open
ModuleNotFoundError: No module named 'biom.utils'

Thank you, Joao

@JoaoGabrielMoraes you should be able to keep the axis as observation – the biom-format API usually defines a table’s axes as “sample” and “observation”, with the understanding that “observation” can refer to whatever sort of feature you have (OTUs, ASVs, genes, …) If you’re interested, I recommend checking out here for the biom-format docs on the .filter() function. (So, long story short, I suspect replacing observation with #OTU ID will cause problems.)

I get the same error when I try to import from biom.utils, so I’m guessing that particular submodule might be outdated or something. (So you should probably remove the from biom.utils import biom_open line from the script, since it’s causing the script to fail.)

You should be able to save the BIOM Table object to a new QZA file just using the QIIME 2 Artifact API as follows:

# This would go right below the table.filter() call in your script
# (It replaces the "with biom_open()" block)
filtered_table_artifact = qiime2.Artifact.import_data("FeatureTable[Frequency]", table)
filtered_table_artifact.save("variance-filtered-table.qza")

Hope this helps!

1 Like

@fedarko, that did work. Thank you so much!!

I have another question =)

I am trying to use qiime feature-table summarize to evaluate my data before and after filtering (after removing features with low reads, features rarely observed, and features with very low variance).

To do so, I’ve created the following script:

#!/usr/bin/bash

#Creating Summary tables:

#Before filtering
qiime feature-table summarize\
    --i-table grouped_by_taxa.qza\  
    --o-visualization 1st-summary.table.qzv

#After Filtering Features (1st Step Script)
qiime feature-table summarize\
    --i-table feature-frequency-grouped_by_taxa.qza\ 
    --o-visualization 2nd-after-filter-features-summary.table.qzv

#After Variance Filtering
qiime feature-table summarize \
    --i-table variance-filtered-table.qza\ 
    --o-visualization 3rd-after-variance-filtering-summary.table.qzv

However, I am getting the following errors:

 (1/2) Invalid value for "--i-table": 'grouped_by_taxa.qza ' is not a valid
  filepath
 (2/2) Missing option "--o-visualization".  ("--output-dir" may also be used)

 (1/2) Invalid value for "--i-table": 'feature-frequency-grouped_by_taxa.qza '
  is not a valid filepath
 (2/2) Missing option "--o-visualization".  ("--output-dir" may also be used)
 
(1/2) Invalid value for "--i-table": 'variance-filtered-table.qza ' is not a
  valid filepath
 (2/2) Missing option "--o-visualization".  ("--output-dir" may also be used)

I don’t understand why Qiime2 doesn’t like my artfact feature tables when I use qiime feature-table summarize.

Any thoughts?

Cool, glad that worked!

For this different problem, there are a few things worth trying.

One thing that stands out to me is that your error messages all go something like 'grouped_by_taxa.qza ' is not a valid filepath – note the extra space after the QZA. (Also note that they all complain about the --o-visualization option not being specified, when it clearly has been.) I think this is caused by you having the backslash immediately after the input table filenames in your script – could you try replacing lines like --i-table grouped_by_taxa.qza\ with --i-table grouped_by_taxa.qza \ , so that there’s now a space between the .qza and the backslash?

(What I think is happening is that the backslash after the .qza is causing your input filepaths to be interpreted as something like "grouped_by_taxa.qza ", and this is somehow messing up the rest of each qiime feature-table summarize command.)

If that doesn’t solve your problem, then the next thing I’d suggest trying is making sure that your script is being run from the directory you expect it to be. Try adding in a line before any of your qiime feature-table summarize commands that just contains the command pwd, and check to make sure that this directory contains the grouped_by_taxa.qza, etc. files.

Hopefully this works, but if not let us know and we’ll see what we can do :slight_smile:

2 Likes

@fedarko, it was in fact a space issue. I used set list in vim to see spaces in my script and there were some extra spaces that were generating the errors.

Thanks again for your help!

Best, Joao

2 Likes

I have one more question.

I was able to evaluate my data before and after trimming, and now I am having trouble running the last step of GNEISS on my filtered data.

I am using the following command:

qiime gneiss balance-taxonomy \
  --i-table variance-filtered-table.qza\
  --i-tree hierarchy.qza \
  --i-taxonomy Classifier_output.qza\
  --p-taxa-level 2 \ 
  --p-balance-name 'y0' \
  --m-metadata-file mapping.txt \
  --m-metadata-column Site \
  --o-visualization y0_taxa_summary.qzv --verbose

And I am getting the following error:

Plugin warning from gneiss:

balance-taxonomy is deprecated and will be removed in a future version of this plugin.
/cluster/biocompute/software/qiime2/qiime2-2019-10/envs/qiime2-2019.10/lib/python3.6/site-packages/qiime2/sdk/action.py:236: FutureWarning: This Visualizer is deprecated and will be removed in a future version of this plugin.
Traceback (most recent call last):
  File "/cluster/biocompute/software/qiime2/qiime2-2019-10/envs/qiime2-2019.10/lib/python3.6/site-packages/q2cli/commands.py", line 328, in __call__
    results = action(**arguments)
  File "</cluster/biocompute/software/qiime2/qiime2-2019-10/envs/qiime2-2019.10/lib/python3.6/site-packages/decorator.py:decorator-gen-286>", line 2, in balance_taxonomy
  File "/cluster/biocompute/software/qiime2/qiime2-2019-10/envs/qiime2-2019.10/lib/python3.6/site-packages/qiime2/sdk/action.py", line 240, in bound_callable
    output_types, provenance)
  File "/cluster/biocompute/software/qiime2/qiime2-2019-10/envs/qiime2-2019.10/lib/python3.6/site-packages/qiime2/sdk/action.py", line 445, in _callable_executor_
    ret_val = self._callable(output_dir=temp_dir, **view_args)
  File "/cluster/biocompute/software/qiime2/qiime2-2019-10/envs/qiime2-2019.10/lib/python3.6/site-packages/q2_gneiss/plot/_plot.py", line 70, in balance_taxonomy
    num_features = taxa_df.loc[num_clade.subset()]
  File "/cluster/biocompute/software/qiime2/qiime2-2019-10/envs/qiime2-2019.10/lib/python3.6/site-packages/pandas/core/indexing.py", line 1424, in __getitem__
    return self._getitem_axis(maybe_callable, axis=axis)
  File "/cluster/biocompute/software/qiime2/qiime2-2019-10/envs/qiime2-2019.10/lib/python3.6/site-packages/pandas/core/indexing.py", line 1839, in _getitem_axis
    return self._getitem_iterable(key, axis=axis)
  File "/cluster/biocompute/software/qiime2/qiime2-2019-10/envs/qiime2-2019.10/lib/python3.6/site-packages/pandas/core/indexing.py", line 1133, in _getitem_iterable
    keyarr, indexer = self._get_listlike_indexer(key, axis, raise_missing=False)
  File "/cluster/biocompute/software/qiime2/qiime2-2019-10/envs/qiime2-2019.10/lib/python3.6/site-packages/pandas/core/indexing.py", line 1092, in _get_listlike_indexer
    keyarr, indexer, o._get_axis_number(axis), raise_missing=raise_missing
  File "/cluster/biocompute/software/qiime2/qiime2-2019-10/envs/qiime2-2019.10/lib/python3.6/site-packages/pandas/core/indexing.py", line 1177, in _validate_read_indexer
    key=key, axis=self.obj._get_axis_name(axis)
KeyError: "None of [Index(['D_0__Bacteria;D_1__Actinobacteria;D_2__Actinobacteria;D_3__Propionibacteriales;D_4__Nocardioidaceae;D_5__Nocardioides',\n       'D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__Clostridiales;D_4__Lachnospiraceae;D_5__Lachnoclostridium 10;D_6__uncultured bacterium',\n       'D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__Clostridiales;D_4__Lachnospiraceae;D_5__Lachnospiraceae UCG-006;D_6__uncultured bacterium',\n       'D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__Clostridiales;D_4__Lachnospiraceae;D_5__Lachnoclostridium',\n       'D_0__Bacteria;D_1__Bacteroidetes;D_2__Bacteroidia;D_3__Bacteroidales;D_4__Porphyromonadaceae;D_5__Porphyromonas',\n       'D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__Clostridiales;D_4__Lachnospiraceae;D_5__uncultured;Ambiguous_taxa',\n       'D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__Clostridiales;D_4__Family XI;D_5__Parvimonas',\n       'D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__Clostridiales;D_4__Ruminococcaceae;D_5__Ruminococcaceae UCG-007',\n       'D_0__Bacteria;D_1__Bacteroidetes;D_2__Bacteroidia;D_3__Bacteroidales;D_4__Paludibacteraceae;D_5__uncultured;D_6__uncultured Paludibacter sp.',\n       'D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__Clostridiales;D_4__Ruminococcaceae;D_5__Ruminiclostridium 9;Ambiguous_taxa',\n       ...\n       'D_0__Bacteria;D_1__Proteobacteria;D_2__Alphaproteobacteria;D_3__Rhizobiales;D_4__Rhizobiaceae;D_5__Allorhizobium-Neorhizobium-Pararhizobium-Rhizobium;D_6__uncultured Rhizobium sp.',\n       'D_0__Bacteria;D_1__Proteobacteria;D_2__Gammaproteobacteria;D_3__Betaproteobacteriales;D_4__Hydrogenophilaceae;D_5__Tepidiphilus',\n       'D_0__Bacteria;D_1__Lentisphaerae;D_2__Lentisphaeria;D_3__Victivallales;D_4__vadinBE97;D_5__uncultured rumen bacterium;D_6__uncultured rumen bacterium',\n       'D_0__Bacteria;D_1__Firmicutes;D_2__Bacilli;D_3__Bacillales;D_4__Bacillaceae;D_5__Bacillus;D_6__Bacillus thermoamylovorans',\n       'D_0__Bacteria;D_1__Proteobacteria;D_2__Gammaproteobacteria;D_3__Betaproteobacteriales;D_4__Neisseriaceae;D_5__Neisseria',\n       'D_0__Archaea;D_1__Euryarchaeota;D_2__Methanobacteria;D_3__Methanobacteriales;D_4__Methanobacteriaceae;D_5__Methanobrevibacter',\n       'D_0__Bacteria;D_1__Actinobacteria;D_2__Thermoleophilia;D_3__Solirubrobacterales;D_4__Solirubrobacteraceae;D_5__uncultured',\n       'D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__Clostridiales;D_4__Lachnospiraceae;D_5__Anaerostipes',\n       'D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__Clostridiales;D_4__Ruminococcaceae;D_5__Oscillibacter;D_6__uncultured bacterium',\n       'D_0__Bacteria;D_1__Patescibacteria;D_2__Saccharimonadia;D_3__Saccharimonadales;D_4__Saccharimonadaceae'],\n      dtype='object', name='Feature ID', length=672)] are in the [index]"

Plugin error from gneiss:

  "None of [Index(['D_0__Bacteria;D_1__Actinobacteria;D_2__Actinobacteria;D_3__Propionibacteriales;D_4__Nocardioidaceae;D_5__Nocardioides',\n       'D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__Clostridiales;D_4__Lachnospiraceae;D_5__Lachnoclostridium 10;D_6__uncultured bacterium',\n       'D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__Clostridiales;D_4__Lachnospiraceae;D_5__Lachnospiraceae UCG-006;D_6__uncultured bacterium',\n       'D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__Clostridiales;D_4__Lachnospiraceae;D_5__Lachnoclostridium',\n       'D_0__Bacteria;D_1__Bacteroidetes;D_2__Bacteroidia;D_3__Bacteroidales;D_4__Porphyromonadaceae;D_5__Porphyromonas',\n       'D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__Clostridiales;D_4__Lachnospiraceae;D_5__uncultured;Ambiguous_taxa',\n       'D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__Clostridiales;D_4__Family XI;D_5__Parvimonas',\n       'D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__Clostridiales;D_4__Ruminococcaceae;D_5__Ruminococcaceae UCG-007',\n       'D_0__Bacteria;D_1__Bacteroidetes;D_2__Bacteroidia;D_3__Bacteroidales;D_4__Paludibacteraceae;D_5__uncultured;D_6__uncultured Paludibacter sp.',\n       'D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__Clostridiales;D_4__Ruminococcaceae;D_5__Ruminiclostridium 9;Ambiguous_taxa',\n       ...\n       'D_0__Bacteria;D_1__Proteobacteria;D_2__Alphaproteobacteria;D_3__Rhizobiales;D_4__Rhizobiaceae;D_5__Allorhizobium-Neorhizobium-Pararhizobium-Rhizobium;D_6__uncultured Rhizobium sp.',\n       'D_0__Bacteria;D_1__Proteobacteria;D_2__Gammaproteobacteria;D_3__Betaproteobacteriales;D_4__Hydrogenophilaceae;D_5__Tepidiphilus',\n       'D_0__Bacteria;D_1__Lentisphaerae;D_2__Lentisphaeria;D_3__Victivallales;D_4__vadinBE97;D_5__uncultured rumen bacterium;D_6__uncultured rumen bacterium',\n       'D_0__Bacteria;D_1__Firmicutes;D_2__Bacilli;D_3__Bacillales;D_4__Bacillaceae;D_5__Bacillus;D_6__Bacillus thermoamylovorans',\n       'D_0__Bacteria;D_1__Proteobacteria;D_2__Gammaproteobacteria;D_3__Betaproteobacteriales;D_4__Neisseriaceae;D_5__Neisseria',\n       'D_0__Archaea;D_1__Euryarchaeota;D_2__Methanobacteria;D_3__Methanobacteriales;D_4__Methanobacteriaceae;D_5__Methanobrevibacter',\n       'D_0__Bacteria;D_1__Actinobacteria;D_2__Thermoleophilia;D_3__Solirubrobacterales;D_4__Solirubrobacteraceae;D_5__uncultured',\n       'D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__Clostridiales;D_4__Lachnospiraceae;D_5__Anaerostipes',\n       'D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__Clostridiales;D_4__Ruminococcaceae;D_5__Oscillibacter;D_6__uncultured bacterium',\n       'D_0__Bacteria;D_1__Patescibacteria;D_2__Saccharimonadia;D_3__Saccharimonadales;D_4__Saccharimonadaceae'],\n      dtype='object', name='Feature ID', length=672)] are in the [index]"

See above for debug info.

Any ideas on how to fix this issue? The new file that I haven’t used previously is the one specified under the --i-taxonomy input command. So this error might has something to do with using the wrong input taxonomy file.

Thanks again, Joao

The error suggests that there is something weird going on with the taxonomy input. The taxonomy strings shouldn’t be found in the first column. I’d double check that file post the first 5 lines to this chat to confirm this.

1 Like

Thank you @mortonjt!! Please see below what the first 5 lines of my taxonomy file look like.

The first column is actually Feature ID. Looks like I am using the wrong --i--taxonomy input file. Do you know of a command that I could use to generate the correct file?

Thank you, Joao

|Feature ID|Taxon|Confidence|
|---|---|---|
|73a47b387f87f35792c3d2f5d5930f7a|D_0__Bacteria;D_1__Tenericutes;D_2__Mollicutes;D_3__Mycoplasmatales;D_4__Mycoplasmataceae;D_5__Mycoplasma;D_6__Mycoplasma wenyonii|0.999975333675401|
|5a7b179b1b45f0fe2282f260bf073f60|D_0__Bacteria;D_1__Actinobacteria;D_2__Actinobacteria;D_3__Propionibacteriales;D_4__Propionibacteriaceae;D_5__Cutibacterium|0.9999247794855793|
|65d43491988bfe557da4d86a5ba25dae|D_0__Bacteria;D_1__Firmicutes;D_2__Bacilli;D_3__Bacillales;D_4__Staphylococcaceae;D_5__Staphylococcus|0.9999987355990309|
|912f795ef3617aeb65ff9ad69b9567a8|D_0__Bacteria;D_1__Proteobacteria;D_2__Gammaproteobacteria;D_3__Xanthomonadales;D_4__Xanthomonadaceae;D_5__Stenotrophomonas|0.9999919783317762|

Hi @mortonjt,

Also, please see the first 5 lines of my feature-table used in the --i-table. There is no Feature ID column in my feature-table.

Thanks, Joao

#OTU ID	JM001-A05	JM002-A06	JM003-A07	JM004-A08	JM005-A09	JM006-A10	JM007-A11	JM008-A12	JM009-B01	JM010-B02	JM011-B03	JM012-B04	JM013-B05	JM014-B06	JM015-B07	JM016-B08	JM017-B09	JM018-B10	JM019-B11	JM020-B12	JM021-C01	JM022-C02	JM023-C03	JM024-C04	JM025-C05	JM026-C06	JM027-C07	JM028-C08	JM029-C09	JM030-C10	JM031-C11	JM032-C12	JM033-D01	JM034-D02	JM035-D03	JM036-D04	JM037-D05	JM038-D06	JM039-D07	JM040-D08	JM041-D09	JM042-D10	JM043-D11	JM044-D12	JM045-E01	JM046-E02	JM047-E03	JM048-E04	JM049-E05	JM050-E06	JM051-E07	JM052-E08	JM053-E09	JM054-E10	JM055-E11	JM056-E12	JM057-F01	JM058-F02	JM059-F03	JM060-F04	JM061-F05	JM062-F06	JM063-F07	JM064-F08	JM065-F09	JM066-F10	JM067-F11	JM068-F12	JM069-G01	JM070-G02	JM071-G03	JM072-G04	JM073-G05	JM074-G06	JM075-G07	JM076-G08	JM077-G09	JM078-G10	JM079-G11	JM080-G12	JM081-A01	JM082-A02	JM083-A03	JM084-A04	JM085-A05	JM086-A06	JM087-A07	JM088-A08	JM089-A09	JM090-A10	JM091-A11	JM092-A12	JM093-B01	JM094-B02	JM095-B03	JM096-B04	JM097-B05	JM098-B06	JM099-B07	JM100-B08	JM101-B09	JM102-B10	JM103-B11	JM104-B12	JM105-C01	JM106-C02	JM107-C03	JM108-C04	JM109-C05	JM110-C06	JM111-C07	JM112-C08	JM113-C09	JM114-C10	JM115-C11	JM116-C12	JM117-D01	JM118-D02	JM119-D03	JM120-D04	JM121-D05	JM122-D06	JM123-D07	JM124-D08	JM125-D09	JM126-D10	JM127-D11	JM128-D12	ML001-A01	ML002-A02	ML003-A03	ML004-A04	ML005-A05	ML006-A06	ML007-A07	ML008-A08	ML009-A09	ML010-A10	ML011-A11	ML012-A12	ML013-B01	ML014-B02	ML015-B03	ML016-B04	ML017-B05	ML018-B06	ML019-B07	ML020-B08	ML021-B09	ML022-B10	ML023-B11	ML024-B12	ML025-C01	ML026-C02	ML027-C03	ML028-C04	ML029-C05	ML030-C06	ML031-C07	ML032-C08	ML033-C09	ML034-C10	ML035-C11	ML036-C12	ML037-D01	ML038-D02	ML039-D03	ML040-D04	ML041-D05	ML042-D06	ML043-D07	ML044-D08	ML045-D09	ML046-D10	ML047-D11	ML048-D12	ML049-E01	ML050-E02	ML051-E03	ML052-E04	ML053-E05	ML054-E06	ML055-E07	ML056-E08	ML057-E09	ML058-E10	ML059-E11	ML060-E12	ML061-F01	ML062-F02	ML063-F03	ML064-F04	ML065-F05	ML066-F06	ML067-F07	ML068-F08	ML069-F09	ML070-F10	ML071-F11	ML072-F12	ML073-G01	ML074-G02	ML075-G03	ML076-G04	ML077-G05	ML078-G06	ML079-G07	ML080-G08	ML081-G09	ML082-G10	ML083-G11	ML084-G12	ML085-H01	ML086-H02	ML087-H03	ML088-H04	ML089-H05	ML090-H06	ML091-H07	ML092-H08	ML093-H09	ML094-H10	ML095-H11	ML096-H12	ML097-A01	ML098-A02	ML099-A03	ML100-A04
D_0__Bacteria;D_1__Tenericutes;D_2__Mollicutes;D_3__Mycoplasmatales;D_4__Mycoplasmataceae;D_5__Mycoplasma;D_6__Mycoplasma wenyonii	7.0	0.0	5.0	0.0	7.0	17.0	8.0	0.0	45.0	0.0	0.0	0.0	0.0	37.0	0.0	2.0	3.0	0.0	2.0	0.0	25.0	0.0	0.0	338.0	0.0	0.0	0.0	30.0	0.0	4.0	0.0	0.0	7.0	0.0	0.0	0.0	5.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	11.0	0.0	63.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	8.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	10.0	0.0	0.0	0.0	0.0	174.0	39683.0	60.0	2577.0	302.0	0.0	1526.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	14.0	0.0	98.0	0.0	0.0	12.0	0.0	0.0	0.0	8.0	25.0	0.0	10.0	2.0	0.0	0.0	0.0	11.0	0.0	0.0	6.0	0.0	0.0	0.0	0.0	0.0	0.0	3.0	0.0	12.0	5.0	14.0	0.0	0.0	0.0	0.0	0.0	0.0	35.0	51.0	0.0	2.0	0.0	0.0	0.0	3.0	0.0	5.0	0.0	0.0	0.0	8.0	0.0	2.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	22.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	6.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	3.0	0.0	0.0	0.0	0.0	0.0	0.0	25.0	197.0	0.0	0.0	17.0	46.0	29.0	0.0	0.0	2.0	194.0	67.0	0.0	14.0	21.0	24.0	9.0	0.0	0.0	0.0
D_0__Bacteria;D_1__Actinobacteria;D_2__Actinobacteria;D_3__Propionibacteriales;D_4__Propionibacteriaceae;D_5__Cutibacterium	44.0	28.0	62.0	27.0	18.0	10.0	11.0	32.0	28.0	7.0	30.0	38.0	40.0	120.0	17.0	31.0	10.0	25.0	19.0	1288.0	136.0	38.0	156.0	17.0	38.0	192.0	110.0	356.0	30.0	0.0	104.0	45.0	26.0	26.0	19.0	43.0	53.0	0.0	17.0	32.0	3.0	0.0	2.0	0.0	3.0	0.0	19.0	9.0	12.0	38.0	25.0	4.0	5.0	242.0	67.0	11.0	14.0	0.0	0.0	672.0	6.0	324.0	10.0	11.0	25.0	0.0	0.0	0.0	16.0	25.0	75.0	122.0	12.0	31.0	25.0	5.0	20.0	145.0	126.0	75.0	266.0	1360.0	226.0	105.0113.0	703.0	1565.0	118.0	292.0	2753.0	86.0	70.0	151.0	14.0	856.0	36.0	5444.0	6790.0	121.0	189.0	33.0	26.0	1535.0	26.0	1697.0	421.0	16.0	303.0	48.0	55.0	34.0	14.0	1731.0	531.0	66.0	17.0	52.0	222.0	1455.0	288.0	570.0	313.0	92.0	154.085.0	22.0	152.0	29.0	3.0	17.0	39.0	165.0	32.0	33.0	0.0	20.0	3.0	3.0	18.0	11.0	14.0	39.0	8.0	0.0	23.0	56.0	20.0	78.0	8.0	117.0	24.0	105.0	39.0	123.0	12.0	39.0	26.0	39.0	18.0	22.0	21.0	15.0	7.0	6.0	10.0	4.0	7.0	27.0	10.0	4.0	6.0	9.0	2.0	11.0	23.0	59.0	17.0	0.0	18.0	22.0	36.0	54.0	23.0	4.0	23.0	29.0	41.0	28.0	58.0	26.0	15.0	0.0	7.0	37.0	3.0	20.0	0.0	0.0	13.0	29.0	7.0	25.0	23.0	4.0	33.0	18.0	22.0	0.0	15.0	41.0	44.0	18.0	529.0	183.0	154.0	85.0	289.0	621.0	152.0	140.0	110.0	105.0	25.0	105.022.0	23.0	300.0	104.0
D_0__Bacteria;D_1__Firmicutes;D_2__Bacilli;D_3__Bacillales;D_4__Staphylococcaceae;D_5__Staphylococcus	38.0	17.0	96.0	54.0	0.0	44.0	10.0	8.0	57.0	15.0	7.0	44.0	17.0	79.0	0.0	9.0	29.0	26.0	49.0	491.0	27.0	80.0	32.0	45.0	80.0	107.0	83.0	174.0	44.0	10.0	25.0	0.0	28.0	0.0	35.0	0.0	3.0	16.0	9.0	18.0	4.0	0.0	4.0	17.0	11.0	5.0	0.0	11.0	0.0	50.0	18.0	38.0	16.0	166.0	50.0	14.0	5.0	0.0	0.0	16.0	14.0	63.0	11.0	10.0	0.0	36.0	0.0	17.0	5.0	0.0	228.0	7.0	26.0	54.0	16.0	4.0	9.0	28.0	39.0	0.0	27.0	171.0	140.0	47.0	31.0	83.0	131.037.0	34.0	288.0	0.0	5.0	34.0	15.0	65.0	18.0	570.0	1183.0	19.0	38.0	18.0	0.0	155.0	36.0	193.0	51.0	8.0	25.0	52.0	6.0	0.0	31.0	103.0	11.0	37.0	0.0	17.0	15.0	51.0	34.0	27.0	32.0	13.0	39.0	0.0	0.0	29.0	11.0	0.0	18.0	32.0	21.0	14.0	16.0	15.0	29.0	23.0	6.0	0.0	14.0	18.0	33.0	11.0	18.0	0.0	33.0	21.0	48.0	0.0	39.0	6.0	56.0	43.0	22.0	11.0	9.0	4.0	0.0	10.0	7.0	15.0	31.0	5.0	8.0	6.0	4.0	2.0	0.0	5.0	7.0	13.0	5.0	0.0	11.0	16.0	20007.0	0.0	0.0	0.0	15.0	11.0	74.0	14.0	15.0	75.0	0.0	13.0	13.0	14.0	42.0	14.0	5.0	15.0	24.0	38.0	9.0	6.0	15.0	0.0	14.0	6.0	5.0	29.0	21.0	7.0	14.0	29.0	5.0	23.0	3.0	15.0	40.0	523.0	105.0	13.0	57.0	97.0	58.0	64.0	33.0	43.0	61.0	20.0	13.0	10.0	9.0	69.0	25.0

@JoaoGabrielMoraes, I think you are on the right track, there is something weird going on with both files.

I’d double check to make sure that your table has ASVs rather than taxonomies. Also, I’m not sure why you have | delimiters in your taxonomy file. Referring back to the moving pictures tutorial maybe useful for this: https://docs.qiime2.org/2020.2/tutorials/moving-pictures/