qiime diversity beta-rarefaction (version 2019.4)

--i-table: ARTIFACT FeatureTable[Frequency] Feature table upon which to perform beta diversity rarefaction analyses. [required]:

--p-metric: :

--p-clustering-method: :

--p-sampling-depth: INTEGER Range(1, None) The total frequency that each sample should be rarefied to prior to computing the diversity metric. [required]:

--i-phylogeny: ARTIFACT Phylogenetic tree containing tip identifiers that Phylogeny[Rooted] correspond to the feature identifiers in the table. This tree can contain tip ids that are not present in the table, but all feature ids in the table must be present in this tree. [required for phylogenetic metrics] [optional]:

--p-iterations: INTEGER Number of times to rarefy the feature table at a Range(2, None) given sampling depth. [default: 10]:

--p-correlation-method: :

--p-color-scheme: :

--m-metadata-file [required]s

--m-metadata-file [required] 0

Beta diversity rarefaction

Repeatedly rarefy a feature table to compare beta diversity results within a given rarefaction depth. For a given beta diversity metric, this visualizer will provide: an Emperor jackknifed PCoA plot, samples clustered by UPGMA or neighbor joining with support calculation, and a heatmap showing the correlation between rarefaction trials of that beta diversity metric.

Parameters

table : FeatureTable[Frequency]: Feature table upon which to perform beta diversity rarefaction analyses.
metric : Str % Choices('generalized_unifrac', 'sokalmichener', 'matching', 'yule', 'weighted_normalized_unifrac', 'dice', 'russellrao', 'rogerstanimoto', 'mahalanobis', 'wminkowski', 'kulsinski', 'aitchison', 'seuclidean', 'correlation', 'sokalsneath', 'hamming', 'jaccard', 'unweighted_unifrac', 'weighted_unifrac', 'cosine', 'canberra', 'euclidean', 'sqeuclidean', 'chebyshev', 'cityblock', 'canberra_adkins', 'braycurtis'): The beta diversity metric to be computed.
clustering_method : Str % Choices('upgma', 'nj'): Samples can be clustered with neighbor joining or UPGMA. An arbitrary rarefaction trial will be used for the tree, and the remaining trials are used to calculate the support of the internal nodes of that tree.
metadata : Metadata: The sample metadata used for the Emperor jackknifed PCoA plot.
sampling_depth : Int % Range(1, None): The total frequency that each sample should be rarefied to prior to computing the diversity metric.
iterations : Int % Range(2, None), optional: Number of times to rarefy the feature table at a given sampling depth.
phylogeny : Phylogeny[Rooted], optional: Phylogenetic tree containing tip identifiers that correspond to the feature identifiers in the table. This tree can contain tip ids that are not present in the table, but all feature ids in the table must be present in this tree. [required for phylogenetic metrics]
correlation_method : Str % Choices('pearson', 'spearman'), optional: The Mantel correlation test to be applied when computing correlation between beta diversity distance matrices.
color_scheme : Str % Choices('BrBG', 'BrBG_r', 'PRGn', 'PRGn_r', 'PiYG', 'PiYG_r', 'PuOr', 'PuOr_r', 'RdBu', 'RdBu_r', 'RdGy', 'RdGy_r', 'RdYlBu', 'RdYlBu_r', 'RdYlGn', 'RdYlGn_r'), optional: The matplotlib color scheme to generate the heatmap with.

Returns

visualization : Visualization