debian/changelog

-q2-feature-table (2019.7.0+dfsg-1) UNRELEASED; urgency=medium
+q2-feature-table (2019.10.0+dfsg-1) UNRELEASED; urgency=medium
 
   * New upstream version
   * debhelper-compat 12
-  * Standards-Version: 4.4.0
   * Respect DEB_BUILD_OPTIONS in override_dh_auto_test target
-  * Specify the required version of qiime2 and plugins
 
- -- Liubov Chuprikova <chuprikovalv@gmail.com>  Sat, 07 Sep 2019 14:19:37 +0200
+ -- Liubov Chuprikova <chuprikovalv@gmail.com>  Sun, 29 Dec 2019 19:10:59 +0300
 
 q2-feature-table (2019.4.0+dfsg-1) unstable; urgency=medium
 

q2_feature_table/_heatmap/_visualizer.py

@@ -65,7 +65,7 @@ heatmap_choices = {
                      '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'}
+                     'winter', 'winter_r', 'cividis', 'cividis_r'}
 }
 
 
@@ -75,7 +75,7 @@ _clustering_map = {'both': {'col_cluster': True, 'row_cluster': True},
                    'none': {'col_cluster': False, 'row_cluster': False}}
 
 
-def _munge_metadata(metadata, table, cluster):
+def _munge_sample_metadata(metadata, table, cluster):
     metadata = metadata.filter_ids(table.index)
     column_name = metadata.name
 
@@ -97,16 +97,32 @@ def _munge_metadata(metadata, table, cluster):
     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,
-            metadata: qiime2.CategoricalMetadataColumn = None,
+            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 metadata is not None:
-        table = _munge_metadata(metadata, table, cluster)
+    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:

q2_feature_table/_version.py

@@ -23,9 +23,9 @@ def get_keywords():
     # setup.py/versioneer.py will grep for the variable names, so they must
     # each be defined on a line of their own. _version.py will just call
     # get_keywords().
-    git_refnames = " (tag: 2019.7.0)"
-    git_full = "7e18386b0948bb436e76daf0d72691ba0dc636b9"
-    git_date = "2019-07-30 18:15:52 +0000"
+    git_refnames = " (tag: 2019.10.0)"
+    git_full = "6cc22b63649fb3fc24855f1bdb8e4864c9e6081b"
+    git_date = "2019-11-01 01:04:23 +0000"
     keywords = {"refnames": git_refnames, "full": git_full, "date": git_date}
     return keywords
 

q2_feature_table/plugin_setup.py

@@ -440,7 +440,8 @@ plugin.visualizers.register_function(
         'table': FeatureTable[Frequency]
     },
     parameters={
-        'metadata': MetadataColumn[Categorical],
+        'sample_metadata': MetadataColumn[Categorical],
+        'feature_metadata': MetadataColumn[Categorical],
         'normalize': Bool,
         'title': Str,
         'metric': Str % Choices(q2_feature_table.heatmap_choices['metric']),
@@ -459,9 +460,14 @@ plugin.visualizers.register_function(
         'table': 'The feature table to visualize.'
     },
     parameter_descriptions={
-        'metadata': 'Annotate the sample IDs with these metadata values. '
-                    'When metadata is present and `cluster`=\'feature\', '
-                    'samples will be sorted by the metadata values.',
+        'sample_metadata': 'Annotate the sample IDs with these sample '
+                           'metadata values. When metadata is present and '
+                           '`cluster`=\'feature\', samples will be sorted by '
+                           'the metadata values.',
+        'feature_metadata': 'Annotate the feature IDs with these feature '
+                            'metadata values. When metadata is present and '
+                            '`cluster`=\'sample\', features will be sorted by '
+                            'the metadata values.',
         'normalize': 'Normalize the feature table by adding a psuedocount '
                      'of 1 and then taking the log10 of the table.',
         'title': 'Optional custom plot title.',

q2_feature_table/tests/test_heatmap.py

@@ -16,7 +16,8 @@ from pandas.testing import assert_frame_equal
 import qiime2
 
 from q2_feature_table import heatmap
-from q2_feature_table._heatmap._visualizer import _munge_metadata
+from q2_feature_table._heatmap._visualizer import (
+    _munge_sample_metadata, _munge_feature_metadata)
 
 
 class TestHeatmap(unittest.TestCase):
@@ -59,7 +60,27 @@ class TestHeatmap(unittest.TestCase):
         md = qiime2.CategoricalMetadataColumn(
             pd.Series(['milo', 'summer', 'russ'], name='pet',
                       index=pd.Index(['S1', 'S2', 'S3'], name='id')))
-        heatmap(self.output_dir, self.table, metadata=md)
+        heatmap(self.output_dir, self.table, sample_metadata=md)
+
+        self.assertBasicVizValidity(self.output_dir)
+
+    def test_with_feature_metadata(self):
+        feature_md = qiime2.CategoricalMetadataColumn(
+            pd.Series(['peanut', 'dog'], name='species',
+                      index=pd.Index(['O1', 'O2'], name='id')))
+        heatmap(self.output_dir, self.table, feature_metadata=feature_md)
+
+        self.assertBasicVizValidity(self.output_dir)
+
+    def test_with_sample_and_feature_metadata(self):
+        md = qiime2.CategoricalMetadataColumn(
+            pd.Series(['milo', 'summer', 'russ'], name='pet',
+                      index=pd.Index(['S1', 'S2', 'S3'], name='id')))
+        feature_md = qiime2.CategoricalMetadataColumn(
+            pd.Series(['peanut', 'dog'], name='species',
+                      index=pd.Index(['O1', 'O2'], name='id')))
+        heatmap(self.output_dir, self.table, sample_metadata=md,
+                feature_metadata=feature_md)
 
         self.assertBasicVizValidity(self.output_dir)
 
@@ -75,14 +96,14 @@ class TestHeatmap(unittest.TestCase):
                       index=pd.Index(['S1', 'S3'], name='id')))
 
         with self.assertRaisesRegex(ValueError, 'not present.*S2'):
-            heatmap(self.output_dir, self.table, metadata=md)
+            heatmap(self.output_dir, self.table, sample_metadata=md)
 
     def test_extra_metadata_ids(self):
         md = qiime2.CategoricalMetadataColumn(
             pd.Series(['milo', 'summer', 'russ', 'peanut'], name='pet',
                       index=pd.Index(['S1', 'S2', 'S3', 'S4'], name='id')))
 
-        heatmap(self.output_dir, self.table, metadata=md)
+        heatmap(self.output_dir, self.table, sample_metadata=md)
 
         self.assertBasicVizValidity(self.output_dir)
 
@@ -96,7 +117,8 @@ class TestHeatmap(unittest.TestCase):
             pd.Series(['milo', 'summer', 'russ'], name='pet',
                       index=pd.Index(['S1', 'S2', 'S3'], name='id')))
 
-        heatmap(self.output_dir, self.table, metadata=md, cluster='features')
+        heatmap(self.output_dir, self.table, sample_metadata=md,
+                cluster='features')
 
         self.assertBasicVizValidity(self.output_dir)
 
@@ -105,7 +127,8 @@ class TestHeatmap(unittest.TestCase):
             pd.Series(['milo', 'summer', 'russ'], name='pet',
                       index=pd.Index(['S1', 'S2', 'S3'], name='id')))
 
-        heatmap(self.output_dir, self.table, metadata=md, cluster='none')
+        heatmap(self.output_dir, self.table, sample_metadata=md,
+                cluster='none')
 
         self.assertBasicVizValidity(self.output_dir)
 
@@ -116,11 +139,11 @@ class TestPrivateHelpers(unittest.TestCase):
                                   columns=['O1', 'O2'],
                                   index=['S1', 'S2', 'S3'])
 
-    def test_munge_metadata_simple(self):
+    def test_munge_sample_metadata_simple(self):
         md = qiime2.CategoricalMetadataColumn(
             pd.Series(['milo', 'russ', 'russ'], name='pet',
                       index=pd.Index(['S1', 'S2', 'S3'], name='id')))
-        obs = _munge_metadata(md, self.table, 'both')
+        obs = _munge_sample_metadata(md, self.table, 'both')
 
         exp_idx = pd.Index(['milo | S1', 'russ | S2', 'russ | S3'],
                            name='pet | id')
@@ -128,11 +151,11 @@ class TestPrivateHelpers(unittest.TestCase):
                            index=exp_idx)
         assert_frame_equal(exp, obs)
 
-    def test_munge_metadata_ids_different_order(self):
+    def test_munge_sample_metadata_ids_different_order(self):
         md = qiime2.CategoricalMetadataColumn(
             pd.Series(['russ', 'milo', 'russ'], name='pet',
                       index=pd.Index(['S2', 'S1', 'S3'], name='id')))
-        obs = _munge_metadata(md, self.table, 'both')
+        obs = _munge_sample_metadata(md, self.table, 'both')
 
         exp_idx = pd.Index(['milo | S1', 'russ | S2', 'russ | S3'],
                            name='pet | id')
@@ -140,18 +163,18 @@ class TestPrivateHelpers(unittest.TestCase):
                            index=exp_idx)
         assert_frame_equal(exp, obs)
 
-    def test_munge_metadata_missing_samples(self):
+    def test_munge_sample_metadata_missing_samples(self):
         md = qiime2.CategoricalMetadataColumn(
             pd.Series(['milo', 'russ'], name='pet',
                       index=pd.Index(['S1', 'S3'], name='id')))
         with self.assertRaisesRegex(ValueError, 'not present.*S2'):
-            _munge_metadata(md, self.table, 'both')
+            _munge_sample_metadata(md, self.table, 'both')
 
-    def test_munge_metadata_empty_values(self):
+    def test_munge_sample_metadata_empty_values(self):
         md = qiime2.CategoricalMetadataColumn(
             pd.Series([None, 'russ', np.nan], name='pet',
                       index=pd.Index(['S1', 'S2', 'S3'], name='id')))
-        obs = _munge_metadata(md, self.table, 'both')
+        obs = _munge_sample_metadata(md, self.table, 'both')
 
         exp_idx = pd.Index(['[No Value] | S1', 'russ | S2', '[No Value] | S3'],
                            name='pet | id')
@@ -159,11 +182,11 @@ class TestPrivateHelpers(unittest.TestCase):
                            index=exp_idx)
         assert_frame_equal(exp, obs)
 
-    def test_munge_metadata_sort_samples(self):
+    def test_munge_sample_metadata_sort_samples(self):
         md = qiime2.CategoricalMetadataColumn(
             pd.Series(['peanut', 'milo', 'russ'], name='pet',
                       index=pd.Index(['S1', 'S2', 'S3'], name='id')))
-        obs = _munge_metadata(md, self.table, 'features')
+        obs = _munge_sample_metadata(md, self.table, 'features')
 
         exp_idx = pd.Index(['milo | S2', 'peanut | S1', 'russ | S3'],
                            name='pet | id')
@@ -171,6 +194,71 @@ class TestPrivateHelpers(unittest.TestCase):
                            index=exp_idx)
         assert_frame_equal(exp, obs)
 
+    def test_munge_feature_metadata_simple(self):
+        feature_md = qiime2.CategoricalMetadataColumn(
+            pd.Series(['peanut', 'dog'], name='species',
+                      index=pd.Index(['O1', 'O2'], name='id')))
+        obs = _munge_feature_metadata(feature_md, self.table, 'both')
+
+        exp = pd.DataFrame(
+            [[0, 10], [10, 12], [10, 11]], columns=['peanut', 'dog'],
+            index=pd.Index(['S1', 'S2', 'S3']))
+        assert_frame_equal(exp, obs)
+
+    def test_munge_feature_metadata_ids_different_order(self):
+        feature_md = qiime2.CategoricalMetadataColumn(
+            pd.Series(['dog', 'peanut'], name='species',
+                      index=pd.Index(['O2', 'O1'], name='id')))
+        obs = _munge_feature_metadata(feature_md, self.table, 'both')
+
+        exp = pd.DataFrame(
+            [[0, 10], [10, 12], [10, 11]], columns=['peanut', 'dog'],
+            index=pd.Index(['S1', 'S2', 'S3']))
+        assert_frame_equal(exp, obs)
+
+    def test_munge_feature_metadata_missing_features(self):
+        feature_md = qiime2.CategoricalMetadataColumn(
+            pd.Series(
+                ['dog'], name='species', index=pd.Index(['O2'], name='id')))
+        with self.assertRaisesRegex(ValueError, 'not present.*O1'):
+            _munge_feature_metadata(feature_md, self.table, 'both')
+
+    def test_munge_feature_metadata_is_superset(self):
+        feature_md = qiime2.CategoricalMetadataColumn(
+            pd.Series(['peanut', 'dog', 'cujo'], name='species',
+                      index=pd.Index(['O1', 'O2', 'O3'], name='id')))
+        obs = _munge_feature_metadata(feature_md, self.table, 'both')
+
+        exp = pd.DataFrame(
+            [[0, 10], [10, 12], [10, 11]], columns=['peanut', 'dog'],
+            index=pd.Index(['S1', 'S2', 'S3']))
+        assert_frame_equal(exp, obs)
+
+    def test_munge_feature_metadata_sort_samples(self):
+        feature_md = qiime2.CategoricalMetadataColumn(
+            pd.Series(['peanut', 'dog'], name='species',
+                      index=pd.Index(['O1', 'O2'], name='id')))
+        obs = _munge_feature_metadata(feature_md, self.table, 'samples')
+
+        exp = pd.DataFrame(
+            [[10, 0], [12, 10], [11, 10]], columns=['dog', 'peanut'],
+            index=pd.Index(['S1', 'S2', 'S3']))
+        assert_frame_equal(exp, obs)
+
+    def test_munge_feature_metadata_sort_duplicate_feature_names(self):
+        new_tab = self.table.copy()
+        new_tab['O3'] = [1, 2, 3]
+        feature_md = qiime2.CategoricalMetadataColumn(
+            pd.Series(['peanut', 'dog', 'peanut'], name='species',
+                      index=pd.Index(['O1', 'O2', 'O3'], name='id')))
+        obs = _munge_feature_metadata(feature_md, new_tab, 'samples')
+
+        exp = pd.DataFrame(
+            [[10, 0, 1], [12, 10, 2], [11, 10, 3]],
+            columns=['dog', 'peanut', 'peanut'],
+            index=pd.Index(['S1', 'S2', 'S3']))
+        assert_frame_equal(exp, obs)
+
 
 if __name__ == "__main__":
     unittest.main()