# 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.