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pairwise-distances: Paired pairwise distance testing and boxplotsΒΆ

Docstring:

Usage: qiime longitudinal pairwise-distances [OPTIONS]

  Performs pairwise distance testing between sample pairs 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 pairwise distance between each subject pair differs between groups
  (e.g., groups of subjects receiving different treatments) and produces
  boxplots of paired distance distributions for each group.

Options:
  --i-distance-matrix ARTIFACT PATH DistanceMatrix
                                  Matrix of distances between pairs of
                                  samples.  [required]
  --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-group-column TEXT           Metadata column on which to separate groups
                                  for comparison  [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-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]
  --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_distances

Docstring:

Paired pairwise distance testing and boxplots

Performs pairwise distance testing between sample pairs 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
pairwise distance between each subject pair differs between groups (e.g.,
groups of subjects receiving different treatments) and produces boxplots of
paired distance distributions for each group.

Parameters
----------
distance_matrix : DistanceMatrix
    Matrix of distances between pairs of samples.
metadata : Metadata
    Sample metadata file containing individual_id_column.
group_column : Str
    Metadata column on which to separate groups for comparison
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.
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.

Returns
-------
visualization : Visualization