qiime sample-classifier heatmap (version 2019.4)

--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]:

--m-metadata-column: COLUMN MetadataColumn[Categorical] Metadata file to convert to feature table. [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 metadata. [default: False]:

--p-no-normalize: Normalize the feature table by adding a psuedocount of 1 and then taking the log10 of the table. [default: False]:

--p-metric: :

--p-method: :

--p-cluster: :

--p-color-scheme: :

--m-metadata-file METADATA:

Generate heatmap of important features.

Generate a heatmap of important features. Features are filtered based on importance scores; samples are optionally grouped by 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.
metadata : MetadataColumn[Categorical], optional: Metadata file to convert to feature table.
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 metadata.
normalize : Bool, optional: Normalize the feature table by adding a psuedocount of 1 and then taking the log10 of the table.
metric : Str % Choices('sokalmichener', 'yule', 'russellrao', 'rogerstanimoto', 'minkowski', 'correlation', 'jaccard', 'cosine', 'chebyshev', 'euclidean', 'sqeuclidean', 'cityblock', 'matching', 'dice', 'mahalanobis', 'kulsinski', 'seuclidean', 'sokalsneath', 'canberra', 'hamming', 'braycurtis'), optional: Metrics exposed by seaborn (see http://seaborn.pydata.org/generated/sea born.clustermap.html#seaborn.clustermap for more detail).
method : Str % Choices('average', 'complete', 'median', 'centroid', '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('features', 'samples', 'none', 'both'), optional: Specify which axes to cluster.
color_scheme : Str % Choices('rocket', 'RdYlBu_r', 'winter', 'viridis', 'BrBG', 'magma_r', 'YlOrBr', 'seismic', 'Set1', 'gist_yarg_r', 'gist_rainbow', 'autumn_r', 'Wistia_r', 'cool_r', 'hsv_r', 'flag', 'Vega20b_r', 'mako_r', 'coolwarm', 'Vega20_r', 'jet', 'cubehelix_r', 'bwr_r', 'brg', 'RdPu', 'gist_ncar', 'RdPu_r', 'cool', 'gnuplot_r', 'Set3', 'ocean_r', 'tab20b', 'tab20b_r', 'OrRd_r', 'Spectral_r', 'gray', 'OrRd', 'prism_r', 'Wistia', 'hot', 'icefire', 'plasma', 'Purples_r', 'viridis_r', 'gnuplot2_r', 'CMRmap', 'YlGnBu_r', 'Set3_r', 'PuRd_r', 'Reds_r', 'gray_r', 'Dark2', 'Vega20b', 'Paired', 'gist_yarg', 'RdYlGn_r', 'Greens', 'YlGnBu', 'tab20', 'gnuplot', 'PuBuGn_r', 'spectral_r', 'tab20c_r', 'gist_rainbow_r', 'hot_r', 'spring_r', 'gist_gray', 'Blues_r', 'Spectral', 'magma', 'GnBu_r', 'inferno_r', 'tab10', 'gist_gray_r', 'seismic_r', 'spring', 'BuPu_r', 'spectral', 'BrBG_r', 'PiYG', 'inferno', 'YlOrBr_r', 'Set1_r', 'YlOrRd_r', 'summer', 'vlag', 'Oranges_r', 'Dark2_r', 'binary_r', 'icefire_r', 'RdYlGn', 'hsv', 'gist_heat', 'PRGn_r', 'pink_r', 'Pastel2_r', 'copper_r', 'BuGn', 'gist_stern_r', 'PuRd', 'gist_earth_r', 'YlOrRd', 'Reds', 'gist_earth', 'Greens_r', 'Vega10', 'ocean', 'brg_r', 'rainbow_r', 'tab20_r', 'Purples', 'Blues', 'Accent', 'afmhot_r', 'Set2_r', 'RdBu', 'Vega20c', 'Pastel1', 'RdGy_r', 'PuBuGn', 'copper', 'PuOr', 'jet_r', 'YlGn_r', 'gist_heat_r', 'PuOr_r', 'bone', 'BuGn_r', 'terrain_r', 'mako', 'Vega20c_r', 'Paired_r', 'PuBu_r', 'tab20c', 'Pastel1_r', 'flag_r', 'plasma_r', 'Oranges', 'autumn', 'bone_r', 'GnBu', 'bwr', 'Greys', 'RdBu_r', 'RdGy', 'winter_r', 'gnuplot2', 'coolwarm_r', 'pink', 'nipy_spectral', 'Greys_r', 'summer_r', 'Vega20', 'BuPu', 'afmhot', 'gist_stern', 'binary', 'PiYG_r', 'YlGn', 'rocket_r', 'cubehelix', 'rainbow', 'Accent_r', 'CMRmap_r', 'PRGn', 'PuBu', 'tab10_r', 'prism', 'vlag_r', 'Set2', 'gist_ncar_r', 'nipy_spectral_r', 'Pastel2', 'terrain', 'RdYlBu', 'Vega10_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.