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metatable: Convert (and merge) positive numeric metadata (in)to feature table.ΒΆ

Docstring:

Usage: qiime sample-classifier metatable [OPTIONS]

  Convert numeric sample metadata from TSV file into a feature table.
  Optionally merge with an existing feature table. Only numeric metadata
  will be converted; categorical columns will be silently dropped. By
  default, if a table is used as input only samples found in both the table
  and metadata (intersection) are merged, and others are silently dropped.
  Set missing_samples="error" to raise an error if samples found in the
  table are missing from the metadata file. The metadata file can always
  contain a superset of samples. Note that columns will be dropped if they
  are non-numeric, contain only unique values, contain no unique values
  (zero variance), contain only empty cells, or contain negative values.
  This method currently only converts postive numeric metadata into feature
  data. Tip: convert categorical columns to dummy variables to include them
  in the output feature table.

Inputs:
  --i-table ARTIFACT FeatureTable[Frequency]
                       Feature table containing all features that should be
                       used for target prediction.                  [optional]
Parameters:
  --m-metadata-file METADATA...
    (multiple          Metadata file to convert to feature table.
     arguments will
     be merged)                                                     [required]
  --p-missing-samples TEXT Choices('error', 'ignore')
                       How to handle missing samples in metadata. "error"
                       will fail if missing samples are detected. "ignore"
                       will cause the feature table and metadata to be
                       filtered, so that only samples found in both files are
                       retained.                           [default: 'ignore']
  --p-missing-values TEXT Choices('drop_samples', 'drop_features', 'error',
    'fill')            How to handle missing values (nans) in metadata.
                       Either "drop_samples" with missing values,
                       "drop_features" with missing values, "fill" missing
                       values with zeros, or "error" if any missing values are
                       found.                               [default: 'error']
Outputs:
  --o-converted-table ARTIFACT FeatureTable[Frequency]
                       Converted feature table                      [required]
Miscellaneous:
  --output-dir PATH    Output unspecified results to a directory
  --verbose / --quiet  Display verbose output to stdout and/or stderr during
                       execution of this action. Or silence output if
                       execution is successful (silence is golden).
  --citations          Show citations and exit.
  --help               Show this message and exit.

Import:

from qiime2.plugins.sample_classifier.pipelines import metatable

Docstring:

Convert (and merge) positive numeric metadata (in)to feature table.

Convert numeric sample metadata from TSV file into a feature table.
Optionally merge with an existing feature table. Only numeric metadata will
be converted; categorical columns will be silently dropped. By default, if
a table is used as input only samples found in both the table and metadata
(intersection) are merged, and others are silently dropped. Set
missing_samples="error" to raise an error if samples found in the table are
missing from the metadata file. The metadata file can always contain a
superset of samples. Note that columns will be dropped if they are non-
numeric, contain only unique values, contain no unique values (zero
variance), contain only empty cells, or contain negative values. This
method currently only converts postive numeric metadata into feature data.
Tip: convert categorical columns to dummy variables to include them in the
output feature table.

Parameters
----------
metadata : Metadata
    Metadata file to convert to feature table.
table : FeatureTable[Frequency], optional
    Feature table containing all features that should be used for target
    prediction.
missing_samples : Str % Choices('error', 'ignore'), optional
    How to handle missing samples in metadata. "error" will fail if missing
    samples are detected. "ignore" will cause the feature table and
    metadata to be filtered, so that only samples found in both files are
    retained.
missing_values : Str % Choices('drop_samples', 'drop_features', 'error', 'fill'), optional
    How to handle missing values (nans) in metadata. Either "drop_samples"
    with missing values, "drop_features" with missing values, "fill"
    missing values with zeros, or "error" if any missing values are found.

Returns
-------
converted_table : FeatureTable[Frequency]
    Converted feature table