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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 two 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. A feature table artifact is required input, though whether
  "metric" is derived from the feature table or metadata is optional.

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')
                         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).
  --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 two 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. A
feature table artifact is required input, though whether "metric" is
derived from the feature table or metadata is optional.

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'), 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