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.
--use-cache DIRECTORY Specify the cache to be used for the intermediate
work of this pipeline. 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.
--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 | 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
