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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 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 | RelativeFrequency |
    PresenceAbsence | Composition]
                          Feature table containing all features that should
                          be used for target prediction.            [optional]
Parameters:
  --m-metadata-file METADATA...
    (multiple arguments   Metadata file to convert to feature table.
     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']
  --p-drop-all-unique / --p-no-drop-all-unique
                          If True, columns that contain a unique value for
                          every ID will be dropped.           [default: False]
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).
  --recycle-pool TEXT     Use a cache pool for pipeline resumption. QIIME 2
                          will cache your results in this pool for reuse by
                          future invocations. These pool are retained until
                          deleted by the user. If not provided, QIIME 2 will
                          create a pool which is automatically reused by
                          invocations of the same action and removed if the
                          action is successful. Note: these pools are local to
                          the cache you are using.
  --no-recycle            Do not recycle results from a previous failed
                          pipeline run or save the results from this run for
                          future recycling.
  --parallel              Execute your action in parallel. This flag will use
                          your default parallel config.
  --parallel-config FILE  Execute your action in parallel using a config at
                          the indicated path.
  --example-data PATH     Write example data and exit.
  --citations             Show citations and exit.
  --use-cache DIRECTORY   Specify the cache to be used for the intermediate
                          work of this action. If not provided, the default
                          cache under $TMP/qiime2/ will be used.
                          IMPORTANT FOR HPC USERS: If you are on an HPC system
                          and are using parallel execution it is important to
                          set this to a location that is globally accessible
                          to all nodes in the cluster.
  --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 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 | RelativeFrequency | PresenceAbsence | Composition], 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.
drop_all_unique : Bool, optional
    If True, columns that contain a unique value for every ID will be
    dropped.

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