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plot-feature-volatility: Plot longitudinal feature volatility and importancesΒΆ

Docstring:

Usage: qiime longitudinal plot-feature-volatility [OPTIONS]

  Plots an interactive control chart of feature abundances (y-axis) in each
  sample across time (or state; x-axis). Feature importance scores and
  descriptive statistics for each feature are plotted in interactive bar
  charts below the control chart, facilitating exploration of longitudinal
  feature data. This visualization is intended for use with the feature-
  volatility pipeline; use that pipeline to access this visualization.

Inputs:
  --i-table ARTIFACT FeatureTable[RelativeFrequency]
                         Feature table containing features found in
                         importances.                               [required]
  --i-importances ARTIFACT FeatureData[Importance]
                         Feature importance scores.                 [required]
Parameters:
  --m-metadata-file METADATA...
    (multiple            Sample metadata file containing
     arguments will be   individual-id-column.
     merged)                                                        [required]
  --p-state-column TEXT  Metadata column containing state (time) variable
                         information.                               [required]
  --p-individual-id-column TEXT
                         Metadata column containing IDs for individual
                         subjects.                                  [optional]
  --p-default-group-column TEXT
                         The default metadata column on which to separate
                         groups for comparison (all categorical metadata
                         columns will be available in the visualization).
                                                                    [optional]
  --p-yscale TEXT Choices('linear', 'pow', 'sqrt', 'log')
                         y-axis scaling strategy to apply. [default: 'linear']
  --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]
  --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']
Outputs:
  --o-visualization VISUALIZATION
                                                                    [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).
  --examples             Show usage examples and exit.
  --citations            Show citations and exit.
  --help                 Show this message and exit.

Import:

from qiime2.plugins.longitudinal.visualizers import plot_feature_volatility

Docstring:

Plot longitudinal feature volatility and importances

Plots an interactive control chart of feature abundances (y-axis) in each
sample across time (or state; x-axis). Feature importance scores and
descriptive statistics for each feature are plotted in interactive bar
charts below the control chart, facilitating exploration of longitudinal
feature data. This visualization is intended for use with the feature-
volatility pipeline; use that pipeline to access this visualization.

Parameters
----------
table : FeatureTable[RelativeFrequency]
    Feature table containing features found in importances.
importances : FeatureData[Importance]
    Feature importance scores.
metadata : Metadata
    Sample metadata file containing individual_id_column.
state_column : Str
    Metadata column containing state (time) variable information.
individual_id_column : Str, optional
    Metadata column containing IDs for individual subjects.
default_group_column : Str, optional
    The default metadata column on which to separate groups for comparison
    (all categorical metadata columns will be available in the
    visualization).
yscale : Str % Choices('linear', 'pow', 'sqrt', 'log'), optional
    y-axis scaling strategy to apply.
importance_threshold : Float % Range(0, None, inclusive_start=False) | Str % Choices('q1', 'q2', 'q3'), optional
    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.
feature_count : Int % Range(1, None) | Str % Choices('all'), optional
    Filter feature table to include top N most important features. Set to
    "all" to include all features.
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.

Returns
-------
visualization : Visualization