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