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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.

Options:
  --m-metadata-file MULTIPLE FILE
                                  Metadata file or artifact viewable as
                                  metadata. This option may be supplied
                                  multiple times to merge metadata. Sample
                                  metadata file containing
                                  individual_id_column.  [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 [Pastel1|Set3|Set1|tab20|Pastel2|magma|rainbow|Paired|inferno|Accent|tab10|plasma|Dark2|Set2|tab20b|tab20c|terrain|viridis]
                                  Color palette to use for generating
                                  boxplots.  [default: Set1]
  --p-replicate-handling [drop|random|error]
                                  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]
  --i-table ARTIFACT PATH FeatureTable[RelativeFrequency]
                                  Feature table to optionally use for paired
                                  comparisons.  [optional]
  --o-visualization VISUALIZATION PATH
                                  [required if not passing --output-dir]
  --output-dir DIRECTORY          Output unspecified results to a directory
  --cmd-config FILE               Use config file for command options
  --verbose                       Display verbose output to stdout and/or
                                  stderr during execution of this action.
                                  [default: False]
  --quiet                         Silence output if execution is successful
                                  (silence is golden).  [default: False]
  --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({'Accent', 'Dark2', 'Paired', 'Pastel1', 'Pastel2', 'Set1', 'Set2', 'Set3', 'inferno', 'magma', 'plasma', 'rainbow', 'tab10', 'tab20', 'tab20b', 'tab20c', 'terrain', 'viridis'}), optional
    Color palette to use for generating boxplots.
replicate_handling : Str % Choices({'drop', 'error', 'random'}), 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