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]
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']
--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).
--example-data PATH Write example data and exit.
--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 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.
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
