Docstring:
Usage: qiime sample-classifier heatmap [OPTIONS]
Generate a heatmap of important features. Features are filtered based on
importance scores; samples are optionally grouped by sample metadata; and a
heatmap is generated that displays (normalized) feature abundances per
sample.
Inputs:
--i-table ARTIFACT FeatureTable[Frequency]
Feature table containing all features that should be
used for target prediction. [required]
--i-importance ARTIFACT FeatureData[Importance]
Feature importances. [required]
Parameters:
--m-sample-metadata-file METADATA
--m-sample-metadata-column COLUMN MetadataColumn[Categorical]
Sample metadata column to use for sample labeling or
grouping. [optional]
--m-feature-metadata-file METADATA
--m-feature-metadata-column COLUMN MetadataColumn[Categorical]
Feature metadata (e.g., taxonomy) to use for
labeling features in the heatmap. [optional]
--p-feature-count INTEGER
Range(0, None) Filter feature table to include top N most important
features. Set to zero to include all features.
[default: 50]
--p-importance-threshold NUMBER
Range(0, None) Filter feature table to exclude any features with an
importance score less than this threshold. Set to
zero to include all features. [default: 0]
--p-group-samples / --p-no-group-samples
Group samples by sample metadata. [default: False]
--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-missing-samples TEXT Choices('error', 'ignore')
How to handle missing samples in metadata. "error"
will fail if missing samples are detected. "ignore"
will cause the feature table and metadata to be
filtered, so that only samples found in both files
are retained. [default: 'ignore']
--p-metric TEXT Choices('braycurtis', 'canberra', 'chebyshev',
'cityblock', 'correlation', 'cosine', 'dice', 'euclidean', 'hamming',
'jaccard', 'kulsinski', 'mahalanobis', 'matching', 'minkowski',
'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener',
'sokalsneath', 'sqeuclidean', 'yule')
Metrics exposed by seaborn (see
http://seaborn.pydata.org/generated/seaborn.clusterma
p.html#seaborn.clustermap for more detail).
[default: 'braycurtis']
--p-method TEXT Choices('average', 'centroid', 'complete', 'median',
'single', 'ward', 'weighted')
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('both', 'features', 'none', 'samples')
Specify which axes to cluster. [default: 'features']
--p-color-scheme TEXT Choices('Accent', 'Accent_r', 'Blues', 'Blues_r',
'BrBG', 'BrBG_r', 'BuGn', 'BuGn_r', 'BuPu', 'BuPu_r', 'CMRmap',
'CMRmap_r', 'Dark2', 'Dark2_r', 'GnBu', 'GnBu_r', 'Greens', 'Greens_r',
'Greys', 'Greys_r', 'OrRd', 'OrRd_r', 'Oranges', 'Oranges_r', 'PRGn',
'PRGn_r', 'Paired', 'Paired_r', 'Pastel1', 'Pastel1_r', 'Pastel2',
'Pastel2_r', 'PiYG', 'PiYG_r', 'PuBu', 'PuBuGn', 'PuBuGn_r', 'PuBu_r',
'PuOr', 'PuOr_r', 'PuRd', 'PuRd_r', 'Purples', 'Purples_r', 'RdBu',
'RdBu_r', 'RdGy', 'RdGy_r', 'RdPu', 'RdPu_r', 'RdYlBu', 'RdYlBu_r',
'RdYlGn', 'RdYlGn_r', 'Reds', 'Reds_r', 'Set1', 'Set1_r', 'Set2',
'Set2_r', 'Set3', 'Set3_r', 'Spectral', 'Spectral_r', 'Vega10',
'Vega10_r', 'Vega20', 'Vega20_r', 'Vega20b', 'Vega20b_r', 'Vega20c',
'Vega20c_r', 'Wistia', 'Wistia_r', 'YlGn', 'YlGnBu', 'YlGnBu_r', 'YlGn_r',
'YlOrBr', 'YlOrBr_r', 'YlOrRd', 'YlOrRd_r', 'afmhot', 'afmhot_r',
'autumn', 'autumn_r', 'binary', 'binary_r', 'bone', 'bone_r', 'brg',
'brg_r', 'bwr', 'bwr_r', 'cividis', 'cividis_r', 'cool', 'cool_r',
'coolwarm', 'coolwarm_r', 'copper', 'copper_r', 'cubehelix',
'cubehelix_r', 'flag', 'flag_r', 'gist_earth', 'gist_earth_r',
'gist_gray', 'gist_gray_r', 'gist_heat', 'gist_heat_r', 'gist_ncar',
'gist_ncar_r', 'gist_rainbow', 'gist_rainbow_r', 'gist_stern',
'gist_stern_r', 'gist_yarg', 'gist_yarg_r', 'gnuplot', 'gnuplot2',
'gnuplot2_r', 'gnuplot_r', 'gray', 'gray_r', 'hot', 'hot_r', 'hsv',
'hsv_r', 'icefire', 'icefire_r', 'inferno', 'inferno_r', 'jet', 'jet_r',
'magma', 'magma_r', 'mako', 'mako_r', 'nipy_spectral', 'nipy_spectral_r',
'ocean', 'ocean_r', 'pink', 'pink_r', 'plasma', 'plasma_r', 'prism',
'prism_r', 'rainbow', 'rainbow_r', 'rocket', 'rocket_r', 'seismic',
'seismic_r', 'spectral', 'spectral_r', 'spring', 'spring_r', 'summer',
'summer_r', 'tab10', 'tab10_r', 'tab20', 'tab20_r', 'tab20b', 'tab20b_r',
'tab20c', 'tab20c_r', 'terrain', 'terrain_r', 'viridis', 'viridis_r',
'vlag', 'vlag_r', 'winter', 'winter_r')
Color scheme for heatmap. [default: 'rocket']
Outputs:
--o-heatmap VISUALIZATION
Heatmap of important features. [required]
--o-filtered-table ARTIFACT FeatureTable[Frequency]
Filtered feature table containing data displayed in
heatmap. [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.
Import:
from qiime2.plugins.sample_classifier.pipelines import heatmap
Docstring:
Generate heatmap of important features.
Generate a heatmap of important features. Features are filtered based on
importance scores; samples are optionally grouped by sample metadata; and a
heatmap is generated that displays (normalized) feature abundances per
sample.
Parameters
----------
table : FeatureTable[Frequency]
Feature table containing all features that should be used for target
prediction.
importance : FeatureData[Importance]
Feature importances.
sample_metadata : MetadataColumn[Categorical], optional
Sample metadata column to use for sample labeling or grouping.
feature_metadata : MetadataColumn[Categorical], optional
Feature metadata (e.g., taxonomy) to use for labeling features in the
heatmap.
feature_count : Int % Range(0, None), optional
Filter feature table to include top N most important features. Set to
zero to include all features.
importance_threshold : Float % Range(0, None), optional
Filter feature table to exclude any features with an importance score
less than this threshold. Set to zero to include all features.
group_samples : Bool, optional
Group samples by sample metadata.
normalize : Bool, optional
Normalize the feature table by adding a psuedocount of 1 and then
taking the log10 of the table.
missing_samples : Str % Choices('error', 'ignore'), optional
How to handle missing samples in metadata. "error" will fail if missing
samples are detected. "ignore" will cause the feature table and
metadata to be filtered, so that only samples found in both files are
retained.
metric : Str % Choices('braycurtis', 'canberra', 'chebyshev', 'cityblock', 'correlation', 'cosine', 'dice', 'euclidean', 'hamming', 'jaccard', 'kulsinski', 'mahalanobis', 'matching', 'minkowski', 'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'yule'), optional
Metrics exposed by seaborn (see http://seaborn.pydata.org/generated/sea
born.clustermap.html#seaborn.clustermap for more detail).
method : Str % Choices('average', 'centroid', 'complete', 'median', 'single', 'ward', 'weighted'), optional
Clustering methods exposed by seaborn (see http://seaborn.pydata.org/ge
nerated/seaborn.clustermap.html#seaborn.clustermap for more detail).
cluster : Str % Choices('both', 'features', 'none', 'samples'), optional
Specify which axes to cluster.
color_scheme : Str % Choices('Accent', 'Accent_r', 'Blues', 'Blues_r', 'BrBG', 'BrBG_r', 'BuGn', 'BuGn_r', 'BuPu', 'BuPu_r', 'CMRmap', 'CMRmap_r', 'Dark2', 'Dark2_r', 'GnBu', 'GnBu_r', 'Greens', 'Greens_r', 'Greys', 'Greys_r', 'OrRd', 'OrRd_r', 'Oranges', 'Oranges_r', 'PRGn', 'PRGn_r', 'Paired', 'Paired_r', 'Pastel1', 'Pastel1_r', 'Pastel2', 'Pastel2_r', 'PiYG', 'PiYG_r', 'PuBu', 'PuBuGn', 'PuBuGn_r', 'PuBu_r', 'PuOr', 'PuOr_r', 'PuRd', 'PuRd_r', 'Purples', 'Purples_r', 'RdBu', 'RdBu_r', 'RdGy', 'RdGy_r', 'RdPu', 'RdPu_r', 'RdYlBu', 'RdYlBu_r', 'RdYlGn', 'RdYlGn_r', 'Reds', 'Reds_r', 'Set1', 'Set1_r', 'Set2', 'Set2_r', 'Set3', 'Set3_r', 'Spectral', 'Spectral_r', 'Vega10', 'Vega10_r', 'Vega20', 'Vega20_r', 'Vega20b', 'Vega20b_r', 'Vega20c', 'Vega20c_r', 'Wistia', 'Wistia_r', 'YlGn', 'YlGnBu', 'YlGnBu_r', 'YlGn_r', 'YlOrBr', 'YlOrBr_r', 'YlOrRd', 'YlOrRd_r', 'afmhot', 'afmhot_r', 'autumn', 'autumn_r', 'binary', 'binary_r', 'bone', 'bone_r', 'brg', 'brg_r', 'bwr', 'bwr_r', 'cividis', 'cividis_r', 'cool', 'cool_r', 'coolwarm', 'coolwarm_r', 'copper', 'copper_r', 'cubehelix', 'cubehelix_r', 'flag', 'flag_r', 'gist_earth', 'gist_earth_r', 'gist_gray', 'gist_gray_r', 'gist_heat', 'gist_heat_r', 'gist_ncar', 'gist_ncar_r', 'gist_rainbow', 'gist_rainbow_r', 'gist_stern', 'gist_stern_r', 'gist_yarg', 'gist_yarg_r', 'gnuplot', 'gnuplot2', 'gnuplot2_r', 'gnuplot_r', 'gray', 'gray_r', 'hot', 'hot_r', 'hsv', 'hsv_r', 'icefire', 'icefire_r', 'inferno', 'inferno_r', 'jet', 'jet_r', 'magma', 'magma_r', 'mako', 'mako_r', 'nipy_spectral', 'nipy_spectral_r', 'ocean', 'ocean_r', 'pink', 'pink_r', 'plasma', 'plasma_r', 'prism', 'prism_r', 'rainbow', 'rainbow_r', 'rocket', 'rocket_r', 'seismic', 'seismic_r', 'spectral', 'spectral_r', 'spring', 'spring_r', 'summer', 'summer_r', 'tab10', 'tab10_r', 'tab20', 'tab20_r', 'tab20b', 'tab20b_r', 'tab20c', 'tab20c_r', 'terrain', 'terrain_r', 'viridis', 'viridis_r', 'vlag', 'vlag_r', 'winter', 'winter_r'), optional
Color scheme for heatmap.
Returns
-------
heatmap : Visualization
Heatmap of important features.
filtered_table : FeatureTable[Frequency]
Filtered feature table containing data displayed in heatmap.
