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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.

Options:
  --i-table ARTIFACT PATH FeatureTable[Frequency]
                                  The feature table containing the samples
                                  over which diversity metrics should be
                                  computed.  [required]
  --i-phylogeny ARTIFACT PATH 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.  [required]
  --p-sampling-depth INTEGER RANGE
                                  The total frequency that each sample should
                                  be rarefied to prior to computing diversity
                                  metrics.  [required]
  --m-metadata-file MULTIPLE FILE
                                  Metadata file or artifact viewable as
                                  metadata. This option may be supplied
                                  multiple times to merge metadata. The sample
                                  metadata to use in the emperor plots.
                                  [required]
  --p-n-jobs INTEGER RANGE        [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)
                                  [default: 1]
  --o-rarefied-table ARTIFACT PATH FeatureTable[Frequency]
                                  The resulting rarefied feature table.
                                  [required if not passing --output-dir]
  --o-faith-pd-vector ARTIFACT PATH SampleData[AlphaDiversity]
                                  Vector of Faith PD values by sample.
                                  [required if not passing --output-dir]
  --o-observed-otus-vector ARTIFACT PATH SampleData[AlphaDiversity]
                                  Vector of Observed OTUs values by sample.
                                  [required if not passing --output-dir]
  --o-shannon-vector ARTIFACT PATH SampleData[AlphaDiversity]
                                  Vector of Shannon diversity values by
                                  sample.  [required if not passing --output-
                                  dir]
  --o-evenness-vector ARTIFACT PATH SampleData[AlphaDiversity]
                                  Vector of Pielou's evenness values by
                                  sample.  [required if not passing --output-
                                  dir]
  --o-unweighted-unifrac-distance-matrix ARTIFACT PATH DistanceMatrix
                                  Matrix of unweighted UniFrac distances
                                  between pairs of samples.  [required if not
                                  passing --output-dir]
  --o-weighted-unifrac-distance-matrix ARTIFACT PATH DistanceMatrix
                                  Matrix of weighted UniFrac distances between
                                  pairs of samples.  [required if not passing
                                  --output-dir]
  --o-jaccard-distance-matrix ARTIFACT PATH DistanceMatrix
                                  Matrix of Jaccard distances between pairs of
                                  samples.  [required if not passing --output-
                                  dir]
  --o-bray-curtis-distance-matrix ARTIFACT PATH DistanceMatrix
                                  Matrix of Bray-Curtis distances between
                                  pairs of samples.  [required if not passing
                                  --output-dir]
  --o-unweighted-unifrac-pcoa-results ARTIFACT PATH PCoAResults
                                  PCoA matrix computed from unweighted UniFrac
                                  distances between samples.  [required if not
                                  passing --output-dir]
  --o-weighted-unifrac-pcoa-results ARTIFACT PATH PCoAResults
                                  PCoA matrix computed from weighted UniFrac
                                  distances between samples.  [required if not
                                  passing --output-dir]
  --o-jaccard-pcoa-results ARTIFACT PATH PCoAResults
                                  PCoA matrix computed from Jaccard distances
                                  between samples.  [required if not passing
                                  --output-dir]
  --o-bray-curtis-pcoa-results ARTIFACT PATH PCoAResults
                                  PCoA matrix computed from Bray-Curtis
                                  distances between samples.  [required if not
                                  passing --output-dir]
  --o-unweighted-unifrac-emperor VISUALIZATION PATH
                                  Emperor plot of the PCoA matrix computed
                                  from unweighted UniFrac.  [required if not
                                  passing --output-dir]
  --o-weighted-unifrac-emperor VISUALIZATION PATH
                                  Emperor plot of the PCoA matrix computed
                                  from weighted UniFrac.  [required if not
                                  passing --output-dir]
  --o-jaccard-emperor VISUALIZATION PATH
                                  Emperor plot of the PCoA matrix computed
                                  from Jaccard.  [required if not passing
                                  --output-dir]
  --o-bray-curtis-emperor VISUALIZATION PATH
                                  Emperor plot of the PCoA matrix computed
                                  from Bray-Curtis.  [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.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.