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core-metrics-phylogenetic: Core diversity metrics (phylogenetic and non-phylogenetic)ΒΆ

Docstring:

Usage: qiime diversity core-metrics-phylogenetic [OPTIONS]

  Applies a collection of diversity metrics (both phylogenetic and non-
  phylogenetic) to a feature table.

Inputs:
  --i-table ARTIFACT FeatureTable[Frequency]
                          The feature table containing the samples over which
                          diversity metrics should be computed.     [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]
Parameters:
  --p-sampling-depth INTEGER
    Range(1, None)        The total frequency that each sample should be
                          rarefied to prior to computing diversity metrics.
                                                                    [required]
  --m-metadata-file METADATA...
    (multiple arguments   The sample metadata to use in the emperor plots.
     will be merged)                                                [required]
  --p-n-jobs INTEGER      [beta/beta-phylogenetic methods only, excluding
    Range(0, None)        weighted_unifrac] - The number of jobs to use for
                          the computation. This works by breaking down the
                          pairwise matrix into n-jobs even slices and
                          computing them in parallel. If -1 all CPUs are used.
                          If 1 is given, no parallel computing code is used at
                          all, which is useful for debugging. For n-jobs below
                          -1, (n_cpus + 1 + n-jobs) are used. Thus for n-jobs
                          = -2, all CPUs but one are used. (Description from
                          sklearn.metrics.pairwise_distances)     [default: 1]
Outputs:
  --o-rarefied-table ARTIFACT FeatureTable[Frequency]
                          The resulting rarefied feature table.     [required]
  --o-faith-pd-vector ARTIFACT SampleData[AlphaDiversity]
                          Vector of Faith PD values by sample.      [required]
  --o-observed-otus-vector ARTIFACT SampleData[AlphaDiversity]
                          Vector of Observed OTUs values by sample. [required]
  --o-shannon-vector ARTIFACT SampleData[AlphaDiversity]
                          Vector of Shannon diversity values by sample.
                                                                    [required]
  --o-evenness-vector ARTIFACT SampleData[AlphaDiversity]
                          Vector of Pielou's evenness values by sample.
                                                                    [required]
  --o-unweighted-unifrac-distance-matrix ARTIFACT
    DistanceMatrix        Matrix of unweighted UniFrac distances between
                          pairs of samples.                         [required]
  --o-weighted-unifrac-distance-matrix ARTIFACT
    DistanceMatrix        Matrix of weighted UniFrac distances between pairs
                          of samples.                               [required]
  --o-jaccard-distance-matrix ARTIFACT
    DistanceMatrix        Matrix of Jaccard distances between pairs of
                          samples.                                  [required]
  --o-bray-curtis-distance-matrix ARTIFACT
    DistanceMatrix        Matrix of Bray-Curtis distances between pairs of
                          samples.                                  [required]
  --o-unweighted-unifrac-pcoa-results ARTIFACT
    PCoAResults           PCoA matrix computed from unweighted UniFrac
                          distances between samples.                [required]
  --o-weighted-unifrac-pcoa-results ARTIFACT
    PCoAResults           PCoA matrix computed from weighted UniFrac
                          distances between samples.                [required]
  --o-jaccard-pcoa-results ARTIFACT
    PCoAResults           PCoA matrix computed from Jaccard distances between
                          samples.                                  [required]
  --o-bray-curtis-pcoa-results ARTIFACT
    PCoAResults           PCoA matrix computed from Bray-Curtis distances
                          between samples.                          [required]
  --o-unweighted-unifrac-emperor VISUALIZATION
                          Emperor plot of the PCoA matrix computed from
                          unweighted UniFrac.                       [required]
  --o-weighted-unifrac-emperor VISUALIZATION
                          Emperor plot of the PCoA matrix computed from
                          weighted UniFrac.                         [required]
  --o-jaccard-emperor VISUALIZATION
                          Emperor plot of the PCoA matrix computed from
                          Jaccard.                                  [required]
  --o-bray-curtis-emperor VISUALIZATION
                          Emperor plot of the PCoA matrix computed from
                          Bray-Curtis.                              [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.diversity.pipelines import core_metrics_phylogenetic

Docstring:

Core diversity metrics (phylogenetic and non-phylogenetic)

Applies a collection of diversity metrics (both phylogenetic and non-
phylogenetic) to a feature table.

Parameters
----------
table : FeatureTable[Frequency]
    The feature table containing the samples over which diversity metrics
    should be computed.
phylogeny : Phylogeny[Rooted]
    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.
sampling_depth : Int % Range(1, None)
    The total frequency that each sample should be rarefied to prior to
    computing diversity metrics.
metadata : Metadata
    The sample metadata to use in the emperor plots.
n_jobs : Int % Range(0, None), optional
    [beta/beta-phylogenetic methods only, excluding weighted_unifrac] - The
    number of jobs to use for the computation. This works by breaking down
    the pairwise matrix into n_jobs even slices and computing them in
    parallel. If -1 all CPUs are used. If 1 is given, no parallel computing
    code is used at all, which is useful for debugging. For n_jobs below
    -1, (n_cpus + 1 + n_jobs) are used. Thus for n_jobs = -2, all CPUs but
    one are used. (Description from sklearn.metrics.pairwise_distances)

Returns
-------
rarefied_table : FeatureTable[Frequency]
    The resulting rarefied feature table.
faith_pd_vector : SampleData[AlphaDiversity]
    Vector of Faith PD values by sample.
observed_otus_vector : SampleData[AlphaDiversity]
    Vector of Observed OTUs values by sample.
shannon_vector : SampleData[AlphaDiversity]
    Vector of Shannon diversity values by sample.
evenness_vector : SampleData[AlphaDiversity]
    Vector of Pielou's evenness values by sample.
unweighted_unifrac_distance_matrix : DistanceMatrix
    Matrix of unweighted UniFrac distances between pairs of samples.
weighted_unifrac_distance_matrix : DistanceMatrix
    Matrix of weighted UniFrac distances between pairs of samples.
jaccard_distance_matrix : DistanceMatrix
    Matrix of Jaccard distances between pairs of samples.
bray_curtis_distance_matrix : DistanceMatrix
    Matrix of Bray-Curtis distances between pairs of samples.
unweighted_unifrac_pcoa_results : PCoAResults
    PCoA matrix computed from unweighted UniFrac distances between samples.
weighted_unifrac_pcoa_results : PCoAResults
    PCoA matrix computed from weighted UniFrac distances between samples.
jaccard_pcoa_results : PCoAResults
    PCoA matrix computed from Jaccard distances between samples.
bray_curtis_pcoa_results : PCoAResults
    PCoA matrix computed from Bray-Curtis distances between samples.
unweighted_unifrac_emperor : Visualization
    Emperor plot of the PCoA matrix computed from unweighted UniFrac.
weighted_unifrac_emperor : Visualization
    Emperor plot of the PCoA matrix computed from weighted UniFrac.
jaccard_emperor : Visualization
    Emperor plot of the PCoA matrix computed from Jaccard.
bray_curtis_emperor : Visualization
    Emperor plot of the PCoA matrix computed from Bray-Curtis.