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qiime longitudinal pairwise-distances (version 2019.4)
--m-metadata-files
--m-metadata-file 0

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('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.

Returns

visualization : Visualization