Merge pull request #492 from plotly/clustergram-row-labels

Fix Clustergram when passing labels.

CHANGELOG.md

@@ -1,9 +1,13 @@
 # Changelog
 
-## [Unreleased]
+## [0.4.8] - 2020-03-16
 ### Changed
 * [#489](https://github.com/plotly/dash-bio/pull/489) Renamed async modules with hyphen `-` instead of tilde `~`
-- [#494](https://github.com/plotly/dash-bio/pull/494) Update from React 16.8.6 to 16.13.0
+* [#494](https://github.com/plotly/dash-bio/pull/494) Update from React 16.8.6 to 16.13.0
+
+### Added
+* [#492](https://github.com/plotly/dash-bio/pull/492) Added working support of
+  labels in Clustergram.
 
 ## [0.4.7] - 2020-02-21
 ### Added

dash_bio/component_factory/_clustergram.py

@@ -66,8 +66,8 @@ def Clustergram(
    (observation labels).
 - column_labels (list; optional): List of column category labels
    (observation labels).
-- hidden_labels (list; optional): List of labels not to display on the
-    final plot.
+- hidden_labels (list; optional): List containing strings 'row' and/or 'col'
+    if row and/or column labels should be hidden on the final plot.
 - standardize (string; default 'none'): The dimension for standardizing
     values, so that the mean is 0 and the standard deviation is 1,
     along the specified dimension: 'row', 'column', or 'none'.
@@ -162,8 +162,6 @@ def Clustergram(
 - width (number; default 500): The width of the graph, in px.
 
     """
-    if hidden_labels is None:
-        hidden_labels = []
     if color_threshold is None:
         color_threshold = dict(row=0, col=0)
 
@@ -243,16 +241,16 @@ def __init__(
             hidden_labels = []
         if color_threshold is None:
             color_threshold = dict(row=0, col=0)
-        if row_labels is None:
-            row_labels = [str(i) for i in range(data.shape[0])]
-            hidden_labels.append("row")
-        if column_labels is None:
-            column_labels = [str(i) for i in range(data.shape[1])]
-            hidden_labels.append("col")
+        # Always keep unique identifiers for rows
+        row_ids = list(range(data.shape[0]))
+        # Always keep unique identifiers for columns
+        column_ids = list(range(data.shape[1]))
 
         self._data = data
         self._row_labels = row_labels
+        self._row_ids = row_ids
         self._column_labels = column_labels
+        self._column_ids = column_ids
         self._cluster = cluster
         self._row_dist = row_dist
         self._col_dist = col_dist
@@ -359,16 +357,23 @@ def figure(self, computed_traces=None):
             (
                 dt,
                 self._data,
-                self._row_labels,
-                self._column_labels,
+                self._row_ids,
+                self._column_ids,
             ) = self._compute_clustered_data()
         else:
             # use, if available, the precomputed dendrogram and heatmap
             # traces (as well as the row and column labels)
             dt = computed_traces["dendro_traces"]
             heatmap = computed_traces["heatmap"]
-            self._row_labels = computed_traces["row_labels"]
-            self._column_labels = computed_traces["column_labels"]
+            self._row_ids = computed_traces["row_ids"]
+            self._column_ids = computed_traces["column_ids"]
+
+        # Match reordered rows and columns with their respective labels
+        if self._row_labels:
+            self._row_labels = [self._row_labels[r] for r in self._row_ids]
+        if self._column_labels:
+            self._column_labels = [self._column_labels[r]
+                                   for r in self._column_ids]
 
         # this dictionary relates curve numbers (accessible from the
         # hoverData/clickData props) to cluster numbers
@@ -501,7 +506,7 @@ def figure(self, computed_traces=None):
         xaxis2.update(scaleanchor="x5")
 
         if len(tickvals_col) == 0:
-            tickvals_col = [10 * i + 5 for i in range(len(self._column_labels))]
+            tickvals_col = [10 * i + 5 for i in range(len(self._column_ids))]
 
         # add in all of the labels
         fig["layout"]["xaxis5"].update(  # pylint: disable=invalid-sequence-index
@@ -518,7 +523,7 @@ def figure(self, computed_traces=None):
         )
 
         if len(tickvals_row) == 0:
-            tickvals_row = [10 * i + 5 for i in range(len(self._row_labels))]
+            tickvals_row = [10 * i + 5 for i in range(len(self._row_ids))]
 
         fig["layout"]["yaxis5"].update(  # pylint: disable=invalid-sequence-index
             tickmode="array",
@@ -676,8 +681,8 @@ def figure(self, computed_traces=None):
         computed_traces = {
             "dendro_traces": dt,
             "heatmap": heatmap,
-            "row_labels": self._row_labels,
-            "column_labels": self._column_labels,
+            "row_ids": self._row_ids,
+            "column_ids": self._column_ids,
         }
 
         return (fig, computed_traces, cluster_curve_numbers)
@@ -746,8 +751,8 @@ def _compute_clustered_data(self):
         # first, compute the clusters
         (Zcol, Zrow) = self._get_clusters()
 
-        clustered_column_labels = self._column_labels
-        clustered_row_labels = self._row_labels
+        clustered_column_ids = self._column_ids
+        clustered_row_ids = self._row_ids
 
         # calculate dendrogram from clusters; sch.dendrogram returns sets
         # of four coordinates that make up the 'u' shapes in the dendrogram
@@ -756,18 +761,18 @@ def _compute_clustered_data(self):
                 Zcol,
                 orientation="top",
                 color_threshold=self._color_threshold["col"],
-                labels=self._column_labels,
+                labels=self._column_ids,
                 no_plot=True,
             )
-            clustered_column_labels = Pcol["ivl"]
+            clustered_column_ids = Pcol["ivl"]
             trace_list["col"] = self._color_dendro_clusters(Pcol, "col")
 
         if Zrow is not None:
             Prow = sch.dendrogram(
                 Zrow,
                 orientation="left",
                 color_threshold=self._color_threshold["row"],
-                labels=self._row_labels,
+                labels=self._row_ids,
                 no_plot=True,
             )
             # need to flip the coordinates for the row dendrogram
@@ -776,21 +781,21 @@ def _compute_clustered_data(self):
                 "dcoord": Prow["icoord"],
                 "color_list": Prow["color_list"],
             }
-            clustered_row_labels = Prow["ivl"]
+            clustered_row_ids = Prow["ivl"]
             trace_list["row"] = self._color_dendro_clusters(Prow_tmp, "row")
 
         # now, we need to rearrange the data array to fit the labels
 
         # first get reordered indices
-        rl_indices = [self._row_labels.index(r) for r in clustered_row_labels]
-        cl_indices = [self._column_labels.index(c) for c in clustered_column_labels]
+        rl_indices = [self._row_ids.index(r) for r in clustered_row_ids]
+        cl_indices = [self._column_ids.index(c) for c in clustered_column_ids]
 
         # modify the data here; first shuffle rows,
         # then transpose and shuffle columns,
         # then transpose again
         clustered_data = self._data[rl_indices].T[cl_indices].T
 
-        return trace_list, clustered_data, clustered_row_labels, clustered_column_labels
+        return trace_list, clustered_data, clustered_row_ids, clustered_column_ids
 
     def _color_dendro_clusters(self, P, dim):
         """Color each cluster below the color threshold separately.

dash_bio/package-info.json

@@ -1 +1 @@
-{"name": "dash_bio", "version": "0.4.7", "author": "The Plotly Team <dashbio@plot.ly>"}
+{"name": "dash_bio", "version": "0.4.8", "author": "The Plotly Team <dashbio@plot.ly>"}

package.json

@@ -1,6 +1,6 @@
 {
   "name": "dash-bio",
-  "version": "0.4.7",
+  "version": "0.4.8",
   "description": "Dash components for bioinformatics",
   "repository": {
     "type": "git",

tests/integration/test_clustergram.py

@@ -212,3 +212,64 @@ def test_dbcl006_df_input_row_cluster(dash_duo):
 
     assert len(dash_duo.find_elements('g.subplot.x2y2')) == 0
     assert len(dash_duo.find_elements('g.subplot.x4y4')) == 1
+
+
+def test_dbcl007_hidden_labels(dash_duo):
+
+    app = dash.Dash(__name__)
+
+    data = _mtcars_data
+    row_labels = list(_mtcars_data.index)
+    col_labels = list(_mtcars_data.columns)
+
+    app.layout = html.Div(nested_component_layout(
+        dash_bio.Clustergram(
+            data=data,
+            row_labels=row_labels,
+            column_labels=col_labels
+        )
+    ))
+
+    nested_component_app_callback(
+        app,
+        dash_duo,
+        component=dash_bio.Clustergram,
+        component_data=data,
+        test_prop_name='hidden_labels',
+        test_prop_value='row',
+        prop_value_type='string'
+    )
+
+    # ensure that row labels are hidden
+    assert len(dash_duo.find_elements('g.yaxislayer-above g.y5tick')) == 0
+    # ensure that column labels are displayed
+    assert len(dash_duo.find_elements('g.xaxislayer-above g.x5tick')) == \
+        len(col_labels)
+
+    # create a new instance of the app to test hiding of column labels
+
+    app = dash.Dash(__name__)
+
+    app.layout = html.Div(nested_component_layout(
+        dash_bio.Clustergram(
+            data=data,
+            row_labels=row_labels,
+            column_labels=col_labels
+        )
+    ))
+
+    nested_component_app_callback(
+        app,
+        dash_duo,
+        component=dash_bio.Clustergram,
+        component_data=data,
+        test_prop_name='hidden_labels',
+        test_prop_value='col',
+        prop_value_type='string'
+    )
+
+    # ensure that column labels are hidden
+    assert len(dash_duo.find_elements('g.xaxislayer-above g.x5tick')) == 0
+    # ensure that row labels are displayed
+    assert len(dash_duo.find_elements('g.yaxislayer-above g.y5tick')) == \
+        len(row_labels)

tests/unit/test_clustergram.py

@@ -49,3 +49,37 @@ def test_read_dataframe():
     clustered_data = CLUSTERED_DATA
 
     assert np.array_equal(curves_dict['heatmap']['z'], clustered_data)
+
+
+def test_row_labels():
+    """Test that specifying row labels preserves clustering."""
+
+    data = DATA
+    row_labels = ['a', 'b', 'b', 'b', 'b', 'b']
+    _, _, curves_dict = Clustergram(
+        data,
+        generate_curves_dict=True,
+        return_computed_traces=True,
+        row_labels=row_labels,
+        center_values=False
+    )
+    clustered_data = CLUSTERED_DATA
+
+    assert np.array_equal(curves_dict['heatmap']['z'], clustered_data)
+
+
+def test_column_labels():
+    """Test that specifying column labels preserves clustering."""
+
+    data = DATA.T
+    column_labels = ['a', 'b', 'b', 'b', 'b', 'b']
+    _, _, curves_dict = Clustergram(
+        data,
+        generate_curves_dict=True,
+        return_computed_traces=True,
+        column_labels=column_labels,
+        center_values=False
+    )
+    clustered_data = CLUSTERED_DATA.T
+
+    assert np.array_equal(curves_dict['heatmap']['z'], clustered_data)