heatmap: Generate a heatmap representation of a feature table¶

Docstring:

Usage: qiime feature-table heatmap [OPTIONS]

  Generate a heatmap representation of a feature table with optional
  clustering on both the sample and feature axes.

  Tip: To generate a heatmap containing taxonomic annotations, use `qiime
  taxa collapse` to collapse the feature table at the desired taxonomic
  level.

Inputs:
  --i-table ARTIFACT FeatureTable[Frequency]
                         The feature table to visualize.            [required]
Parameters:
  --m-sample-metadata-file METADATA
  --m-sample-metadata-column COLUMN  MetadataColumn[Categorical]
                         Annotate the sample IDs with these sample metadata
                         values. When metadata is present and
                         `cluster`='feature', samples will be sorted by the
                         metadata values.                           [optional]
  --m-feature-metadata-file METADATA
  --m-feature-metadata-column COLUMN  MetadataColumn[Categorical]
                         Annotate the feature IDs with these feature metadata
                         values. When metadata is present and
                         `cluster`='sample', features will be sorted by the
                         metadata values.                           [optional]
  --p-normalize / --p-no-normalize
                         Normalize the feature table by adding a psuedocount
                         of 1 and then taking the log10 of the table.
                                                               [default: True]
  --p-title TEXT         Optional custom plot title.                [optional]
  --p-metric TEXT Choices('cosine', 'matching', 'canberra', 'mahalanobis',
    'yule', 'russellrao', 'euclidean', 'hamming', 'rogerstanimoto',
    'chebyshev', 'kulsinski', 'minkowski', 'jaccard', 'braycurtis',
    'seuclidean', 'correlation', 'sqeuclidean', 'sokalmichener', 'dice',
    'sokalsneath', 'cityblock')
                         Metrics exposed by seaborn (see
                         http://seaborn.pydata.org/generated/seaborn.clusterma
                         p.html#seaborn.clustermap for more detail).
                                                        [default: 'euclidean']
  --p-method TEXT Choices('single', 'weighted', 'complete', 'average',
    'median', 'ward', 'centroid')
                         Clustering methods exposed by seaborn (see
                         http://seaborn.pydata.org/generated/seaborn.clusterma
                         p.html#seaborn.clustermap for more detail).
                                                          [default: 'average']
  --p-cluster TEXT Choices('features', 'samples', 'both', 'none')
                         Specify which axes to cluster.      [default: 'both']
  --p-color-scheme TEXT Choices('Wistia_r', 'pink', 'tab10', 'hot', 'brg',
    'gist_gray', 'gist_earth', 'Blues_r', 'PiYG', 'Accent_r', 'Reds_r',
    'Vega20c_r', 'gist_yarg', 'Greys', 'GnBu', 'rainbow', 'Set1_r', 'PuRd_r',
    'PuBu_r', 'Vega20_r', 'Pastel2_r', 'copper_r', 'gist_earth_r', 'viridis',
    'Set3', 'Blues', 'PuBu', 'tab10_r', 'prism_r', 'GnBu_r', 'hot_r',
    'Vega10', 'YlOrBr_r', 'viridis_r', 'BuGn_r', 'seismic', 'Spectral',
    'PuBuGn', 'binary', 'autumn', 'hsv_r', 'gist_stern_r', 'Set2',
    'rainbow_r', 'BrBG', 'RdGy_r', 'YlOrRd', 'winter', 'PuOr', 'YlOrBr',
    'rocket_r', 'terrain', 'mako', 'RdBu', 'bone', 'hsv', 'vlag_r', 'PiYG_r',
    'cool_r', 'gist_rainbow', 'Pastel1', 'gist_rainbow_r', 'RdBu_r', 'RdPu_r',
    'icefire', 'gist_ncar_r', 'gnuplot', 'PRGn', 'gist_yarg_r', 'YlGn',
    'Pastel1_r', 'tab20_r', 'plasma_r', 'Accent', 'winter_r', 'icefire_r',
    'prism', 'cividis', 'Oranges_r', 'gist_ncar', 'Vega20', 'cubehelix_r',
    'Pastel2', 'afmhot', 'terrain_r', 'flag', 'PuOr_r', 'YlGnBu', 'seismic_r',
    'YlOrRd_r', 'afmhot_r', 'gray_r', 'spectral', 'jet_r', 'PuBuGn_r',
    'spring_r', 'RdYlGn', 'bone_r', 'plasma', 'Vega20c', 'magma_r',
    'tab20b_r', 'Wistia', 'cubehelix', 'Set3_r', 'spring', 'gist_gray_r',
    'rocket', 'Paired_r', 'BuPu_r', 'autumn_r', 'cividis_r', 'Vega20b_r',
    'magma', 'cool', 'Dark2_r', 'PuRd', 'inferno_r', 'Greens', 'Dark2',
    'Spectral_r', 'gist_stern', 'ocean_r', 'RdYlGn_r', 'coolwarm_r', 'tab20',
    'BuGn', 'BuPu', 'gray', 'Greys_r', 'Purples_r', 'Set1', 'Oranges',
    'Paired', 'RdYlBu_r', 'bwr', 'spectral_r', 'OrRd_r', 'Vega20b',
    'Greens_r', 'gnuplot_r', 'BrBG_r', 'Purples', 'jet', 'gist_heat_r',
    'RdYlBu', 'RdPu', 'YlGnBu_r', 'tab20c_r', 'OrRd', 'gist_heat', 'tab20c',
    'Set2_r', 'RdGy', 'mako_r', 'brg_r', 'vlag', 'binary_r', 'summer',
    'ocean', 'summer_r', 'CMRmap', 'gnuplot2', 'nipy_spectral', 'tab20b',
    'Reds', 'gnuplot2_r', 'coolwarm', 'flag_r', 'bwr_r', 'pink_r', 'PRGn_r',
    'Vega10_r', 'copper', 'YlGn_r', 'CMRmap_r', 'inferno', 'nipy_spectral_r')
                         The matplotlib colorscheme to generate the heatmap
                         with.                             [default: 'rocket']
Outputs:
  --o-visualization VISUALIZATION
                                                                    [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).
  --example-data PATH    Write example data and exit.
  --citations            Show citations and exit.
  --help                 Show this message and exit.