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

Docstring:

Usage: qiime diversity core-metrics [OPTIONS]

  Applies a collection of diversity metrics (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]
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          The sample metadata to use in the emperor plots.
     arguments will
     be merged)                                                     [required]
  --p-n-jobs INTEGER   [beta methods only] - The number of jobs to use for
    Range(0, None)     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-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-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-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-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

Docstring:

Core diversity metrics (non-phylogenetic)

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

Parameters
----------
table : FeatureTable[Frequency]
    The feature table containing the samples over which diversity metrics
    should be computed.
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 methods only] - 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.
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.
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.
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.
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.