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plot-feature-volatility: Plot longitudinal feature volatility and importances¶
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).
--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.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