# 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.