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# ----------------------------------------------------------------------------
# Copyright (c) 2016-2022, QIIME 2 development team.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file LICENSE, distributed with this software.
# ----------------------------------------------------------------------------
import os.path
import pkg_resources
import q2templates
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import qiime2
TEMPLATES = pkg_resources.resource_filename('q2_feature_table._heatmap',
'assets')
heatmap_choices = {
'metric': {'braycurtis', 'canberra', 'chebyshev', 'cityblock',
'correlation', 'cosine', 'dice', 'euclidean', 'hamming',
'jaccard', 'kulsinski', 'mahalanobis', 'matching', 'minkowski',
'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener',
'sokalsneath', 'sqeuclidean', 'yule'},
'method': {'single', 'complete', 'average', 'weighted', 'centroid',
'median', 'ward'},
'cluster': {'samples', 'features', 'both', 'none'},
'color_scheme': {'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', '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', 'cividis', 'cividis_r'}
}
_clustering_map = {'both': {'col_cluster': True, 'row_cluster': True},
'samples': {'col_cluster': False, 'row_cluster': True},
'features': {'col_cluster': True, 'row_cluster': False},
'none': {'col_cluster': False, 'row_cluster': False}}
def _munge_sample_metadata(metadata, table, cluster):
metadata = metadata.filter_ids(table.index)
column_name = metadata.name
metadata_df = metadata.to_dataframe()
metadata_df = metadata_df.fillna('[No Value]')
metadata_df['merged-id'] = metadata_df[column_name].str.cat(
metadata_df.index, sep=' | ')
# Inner join here because we have already validated that all sample IDs in
# the table are present in the metadata, and the metadata has been filtered
# to only include the table's IDs.
table = table.join(metadata_df, how='inner')
# It doesn't make sense to sort the samples if clustering is enabled on
# the sample axis (e.g. `both` or `samples`).
if cluster == 'features':
table.sort_values(column_name, axis=0, ascending=True, inplace=True)
table.set_index('merged-id', inplace=True)
table.index.name = '%s | %s' % (column_name, metadata_df.index.name)
table.drop([column_name], axis=1, inplace=True)
return table
def _munge_feature_metadata(metadata, table, cluster):
metadata = metadata.filter_ids(table.columns)
column_name = metadata.name
metadata_df = metadata.to_dataframe()
# replace feature IDs with feature metadata annotations
table.columns = metadata_df.reindex(table.columns)[column_name].values
if cluster == 'samples':
table = table.sort_index(axis=1)
return table
def heatmap(output_dir, table: pd.DataFrame,
sample_metadata: qiime2.CategoricalMetadataColumn = None,
feature_metadata: qiime2.CategoricalMetadataColumn = None,
normalize: bool = True, title: str = None,
metric: str = 'euclidean', method: str = 'average',
cluster: str = 'both', color_scheme: str = 'rocket') -> None:
if table.empty:
raise ValueError('Cannot visualize an empty table.')
if sample_metadata is not None:
table = _munge_sample_metadata(sample_metadata, table, cluster)
# relabel feature table feature IDs with feature metadata column values
if feature_metadata is not None:
table = _munge_feature_metadata(feature_metadata, table, cluster)
cbar_label = 'frequency'
if normalize:
table = table.apply(lambda x: np.log10(x + 1))
cbar_label = 'log10 frequency'
# Hard-coded values for reasonable plots
scaletron, labelsize, dpi = 50, 8, 100
sns.set(rc={'xtick.labelsize': labelsize, 'ytick.labelsize': labelsize,
'figure.dpi': dpi})
width, height = table.shape[1] / scaletron, table.shape[0] / scaletron
heatmap_plot = sns.clustermap(table, method=method, metric=metric,
**_clustering_map[cluster],
cmap=color_scheme,
xticklabels=True, yticklabels=True,
cbar_kws={'label': cbar_label})
if title is not None:
heatmap_plot.fig.suptitle(title)
hm = heatmap_plot.ax_heatmap.get_position()
cbar = heatmap_plot.cax.get_position()
row = heatmap_plot.ax_row_dendrogram.get_position()
col = heatmap_plot.ax_col_dendrogram.get_position()
# Resize the plot to set cell aspect-ratio to square
heatmap_plot.ax_heatmap.set_position([hm.x0, hm.y0, width, height])
heatmap_plot.cax.set_position([cbar.x0, hm.y0 + height, cbar.width,
cbar.height])
heatmap_plot.ax_row_dendrogram.set_position([row.x0, row.y0, row.width,
height])
heatmap_plot.ax_col_dendrogram.set_position([col.x0, hm.y0 + height, width,
col.height])
# https://stackoverflow.com/a/34697479/3776794
plt.setp(heatmap_plot.ax_heatmap.xaxis.get_majorticklabels(), rotation=90)
plt.setp(heatmap_plot.ax_heatmap.yaxis.get_majorticklabels(), rotation=0)
for ext in ['png', 'svg']:
img_fp = os.path.join(output_dir, 'feature-table-heatmap.%s' % ext)
heatmap_plot.savefig(img_fp)
index_fp = os.path.join(TEMPLATES, 'index.html')
q2templates.render(index_fp, output_dir, context={'normalize': normalize})
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