feature-volatility: Feature volatility analysis¶

Docstring:

Usage: qiime longitudinal feature-volatility [OPTIONS]

  Identify features that are predictive of a numeric metadata column,
  state_column (e.g., time), and plot their relative frequencies across states
  using interactive feature volatility plots. A supervised learning regressor
  is used to identify important features and assess their ability to predict
  sample states. state_column will typically be a measure of time, but any
  numeric metadata column can be used.

Inputs:
  --i-table ARTIFACT FeatureTable[Frequency]
                          Feature table containing all features that should
                          be used for target prediction.            [required]
Parameters:
  --m-metadata-file METADATA...
    (multiple arguments   Sample metadata file containing
     will be merged)      individual-id-column.                     [required]
  --p-state-column TEXT   Metadata containing collection time (state) values
                          for each sample. Must contain exclusively numeric
                          values.                                   [required]
  --p-individual-id-column TEXT
                          Metadata column containing IDs for individual
                          subjects.                                 [optional]
  --p-cv INTEGER          Number of k-fold cross-validations to perform.
    Range(1, None)                                                [default: 5]
  --p-random-state INTEGER
                          Seed used by random number generator.     [optional]
  --p-n-jobs NTHREADS     Number of jobs to run in parallel.      [default: 1]
  --p-n-estimators INTEGER
    Range(1, None)        Number of trees to grow for estimation. More trees
                          will improve predictive accuracy up to a threshold
                          level, but will also increase time and memory
                          requirements. This parameter only affects ensemble
                          estimators, such as Random Forest, AdaBoost,
                          ExtraTrees, and GradientBoosting.     [default: 100]
  --p-estimator TEXT Choices('RandomForestRegressor',
    'ExtraTreesRegressor', 'GradientBoostingRegressor', 'AdaBoostRegressor',
    'ElasticNet', 'Ridge', 'Lasso', 'KNeighborsRegressor', 'LinearSVR', 'SVR')
                          Estimator method to use for sample prediction.
                                            [default: 'RandomForestRegressor']
  --p-parameter-tuning / --p-no-parameter-tuning
                          Automatically tune hyperparameters using random
                          grid search.                        [default: False]
  --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: 'error']
  --p-importance-threshold VALUE Float % Range(0, None,
    inclusive_start=False) | Str % Choices('q1', 'q2', 'q3')
                          Filter feature table to exclude any features with
                          an importance score less than this threshold. Set to
                          "q1", "q2", or "q3" to select the first, second, or
                          third quartile of values. Set to "None" to disable
                          this filter.                              [optional]
  --p-feature-count VALUE Int % Range(1, None) | Str % Choices('all')
                          Filter feature table to include top N most
                          important features. Set to "all" to include all
                          features.                             [default: 100]
Outputs:
  --o-filtered-table ARTIFACT FeatureTable[RelativeFrequency]
                          Feature table containing only important features.
                                                                    [required]
  --o-feature-importance ARTIFACT FeatureData[Importance]
                          Importance of each input feature to model accuracy.
                                                                    [required]
  --o-volatility-plot VISUALIZATION
                          Interactive volatility plot visualization.
                                                                    [required]
  --o-accuracy-results VISUALIZATION
                          Accuracy results visualization.           [required]
  --o-sample-estimator ARTIFACT SampleEstimator[Regressor]
                          Trained sample regressor.                 [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.