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pairwise-differences: Paired difference testing and boxplotsΒΆ

Docstring:

Usage: qiime longitudinal pairwise-differences [OPTIONS]

  Performs paired difference testing between samples from each subject.
  Sample pairs may represent a typical intervention study (e.g., samples
  collected pre- and post-treatment), paired samples from two different
  timepoints (e.g., in a longitudinal study design), or identical samples
  receiving different treatments. This action tests whether the change in a
  numeric metadata value "metric" differs from zero and differs between
  groups (e.g., groups of subjects receiving different treatments), and
  produces boxplots of paired difference distributions for each group. Note
  that "metric" can be derived from a feature table or metadata.

Inputs:
  --i-table ARTIFACT FeatureTable[RelativeFrequency]
                         Feature table to optionally use for paired
                         comparisons.                               [optional]
Parameters:
  --m-metadata-file METADATA...
    (multiple            Sample metadata file containing
     arguments will be   individual-id-column.
     merged)                                                        [required]
  --p-metric TEXT        Numerical metadata or artifact column to test.
                                                                    [required]
  --p-state-column TEXT  Metadata column containing state (e.g., Time) across
                         which samples are paired.                  [required]
  --p-state-1 TEXT       Baseline state column value.               [required]
  --p-state-2 TEXT       State column value to pair with baseline.  [required]
  --p-individual-id-column TEXT
                         Metadata column containing subject IDs to use for
                         pairing samples. WARNING: if replicates exist for an
                         individual ID at either state-1 or state-2, that
                         subject will be dropped and reported in standard
                         output by default. Set replicate_handling="random" to
                         instead randomly select one member.        [required]
  --p-group-column TEXT  Metadata column on which to separate groups for
                         comparison                                 [optional]
  --p-parametric / --p-no-parametric
                         Perform parametric (ANOVA and t-tests) or
                         non-parametric (Kruskal-Wallis, Wilcoxon, and
                         Mann-Whitney U tests) statistical tests.
                                                              [default: False]
  --p-palette TEXT Choices('Set1', 'Set2', 'Set3', 'Pastel1', 'Pastel2',
    'Paired', 'Accent', 'Dark2', 'tab10', 'tab20', 'tab20b', 'tab20c',
    'viridis', 'plasma', 'inferno', 'magma', 'terrain', 'rainbow', 'cividis')
                         Color palette to use for generating boxplots.
                                                             [default: 'Set1']
  --p-replicate-handling TEXT Choices('error', 'random', 'drop')
                         Choose how replicate samples are handled. If
                         replicates are detected, "error" causes method to
                         fail; "drop" will discard all replicated samples;
                         "random" chooses one representative at random from
                         among replicates.                  [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 pairwise_differences

Docstring:

Paired difference testing and boxplots

Performs paired difference testing between samples from each subject.
Sample pairs may represent a typical intervention study (e.g., samples
collected pre- and post-treatment), paired samples from two different
timepoints (e.g., in a longitudinal study design), or identical samples
receiving different treatments. This action tests whether the change in a
numeric metadata value "metric" differs from zero and differs between
groups (e.g., groups of subjects receiving different treatments), and
produces boxplots of paired difference distributions for each group. Note
that "metric" can be derived from a feature table or metadata.

Parameters
----------
metadata : Metadata
    Sample metadata file containing individual_id_column.
metric : Str
    Numerical metadata or artifact column to test.
state_column : Str
    Metadata column containing state (e.g., Time) across which samples are
    paired.
state_1 : Str
    Baseline state column value.
state_2 : Str
    State column value to pair with baseline.
individual_id_column : Str
    Metadata column containing subject IDs to use for pairing samples.
    WARNING: if replicates exist for an individual ID at either state_1 or
    state_2, that subject will be dropped and reported in standard output
    by default. Set replicate_handling="random" to instead randomly select
    one member.
group_column : Str, optional
    Metadata column on which to separate groups for comparison
parametric : Bool, optional
    Perform parametric (ANOVA and t-tests) or non-parametric (Kruskal-
    Wallis, Wilcoxon, and Mann-Whitney U tests) statistical tests.
palette : Str % Choices('Set1', 'Set2', 'Set3', 'Pastel1', 'Pastel2', 'Paired', 'Accent', 'Dark2', 'tab10', 'tab20', 'tab20b', 'tab20c', 'viridis', 'plasma', 'inferno', 'magma', 'terrain', 'rainbow', 'cividis'), optional
    Color palette to use for generating boxplots.
replicate_handling : Str % Choices('error', 'random', 'drop'), optional
    Choose how replicate samples are handled. If replicates are detected,
    "error" causes method to fail; "drop" will discard all replicated
    samples; "random" chooses one representative at random from among
    replicates.
table : FeatureTable[RelativeFrequency], optional
    Feature table to optionally use for paired comparisons.

Returns
-------
visualization : Visualization