OWVolcanoPlot: general improvements, use of GeneScoring component

Author: JakaKokosar

orangecontrib/bioinformatics/tests/widgets/ow_components/__init__.py

@@ -0,0 +1,73 @@
+import unittest
+
+from AnyQt.QtTest import QSignalSpy
+
+from Orange.data import Table
+from Orange.widgets.widget import OWWidget
+from Orange.widgets.settings import SettingProvider
+from Orange.widgets.tests.base import WidgetTest
+from Orange.widgets.tests.utils import simulate
+
+from orangecontrib.bioinformatics.utils.statistics import score_hypergeometric_test
+from orangecontrib.bioinformatics.widgets.ow_components import GeneScoringComponent
+
+
+class MockWidget(OWWidget):
+    name = "Mock"
+    scoring_component = SettingProvider(GeneScoringComponent)
+
+    def __init__(self):
+        self.scoring_component = GeneScoringComponent(self, self.mainArea)
+
+
+class TestGeneScoringComponent(WidgetTest):
+    def setUp(self):
+        self.widget = MockWidget()
+        self.component = self.widget.scoring_component
+
+    def test_scoring_methods_combobox(self):
+        combo_box_values = [
+            self.component.score_method_combo.itemText(i) for i in range(self.component.score_method_combo.count())
+        ]
+        self.assertTrue(len(combo_box_values) > 0)
+        self.assertEqual([name for name, _ in self.component.score_methods], combo_box_values)
+
+        signals_cb_emits = QSignalSpy(self.component.score_method_changed)
+        simulate.combobox_run_through_all(self.component.score_method_combo)
+
+        self.assertEqual(self.component.score_method_combo.currentIndex(), self.component.current_method_index)
+        self.assertEqual(self.component.current_method_index, len(combo_box_values) - 1)
+
+        # number of signals combobox emits should be equal to the length of available scoring methods
+        self.assertEqual(len(combo_box_values), len(signals_cb_emits))
+
+    def test_expression_threshold_spinbox(self):
+        # find index of item in combobox for hypergeometric test
+        method_index, *_ = [
+            index
+            for index, (name, method) in enumerate(self.component.score_methods)
+            if method == score_hypergeometric_test
+        ]
+
+        # check if spinbox appears after hypergeometric test is selected
+        self.assertTrue(self.component.expression_threshold_box.isHidden())
+        simulate.combobox_activate_index(self.component.score_method_combo, method_index)
+        self.assertFalse(self.component.expression_threshold_box.isHidden())
+
+    def test_group_values(self):
+        self.assertIsNone(self.component.data)
+        self.component.initialize(Table('iris'))
+        self.assertIsNotNone(self.component.data)
+
+        # we expect only one value 'iris class attribute'
+        combo_box_value, *_ = [
+            self.component.group_combo.itemText(i) for i in range(self.component.group_combo.count())
+        ]
+        self.assertEqual(combo_box_value, 'iris')
+
+        group_values = [self.component.list_widget.item(i).text() for i in range(self.component.list_widget.count())]
+        self.assertEqual(group_values, ['Iris-setosa', 'Iris-versicolor', 'Iris-virginica'])
+
+
+if __name__ == "__main__":
+    unittest.main()

orangecontrib/bioinformatics/tests/widgets/ow_components/test_gene_scoring.py

@@ -12,8 +12,9 @@
 ALTERNATIVES = [ALT_GREATER, ALT_TWO, ALT_LESS]
 
 
-def score_t_test(a, b, axis=0, alternative=ALT_TWO):
-    # type: (np.array, np.array, int, str) -> Tuple[Union[float, np.array], Union[float, np.array]]
+def score_t_test(
+    a: np.array, b: np.array, axis: int = 0, **kwargs
+) -> Tuple[Union[float, np.array], Union[float, np.array]]:
     """ Run t-test. Enable setting different alternative hypothesis.
     Probabilities are exact due to symmetry of the test.
 
@@ -24,7 +25,7 @@ def score_t_test(a, b, axis=0, alternative=ALT_TWO):
     scipy.stats.ttest_ind
 
     """
-    # alt = kwargs.get("alternative", ALT_TWO)
+    alternative = kwargs.get("alternative", ALT_TWO)
     assert alternative in ALTERNATIVES
     scores, pvalues = scipy.stats.ttest_ind(a, b, axis=axis)
 
@@ -41,7 +42,7 @@ def score_t_test(a, b, axis=0, alternative=ALT_TWO):
         return scores, 1.0 - pvalues
 
 
-def score_mann_whitney(a, b, **kwargs):
+def score_mann_whitney(a: np.array, b: np.array, **kwargs) -> Tuple[np.array, np.array]:
     axis = kwargs.get('axis', 0)
     a, b = np.asarray(a, dtype=float), np.asarray(b, dtype=float)
 
@@ -71,12 +72,15 @@ def score_mann_whitney(a, b, **kwargs):
     return np.array(statistics), np.array(p_values)
 
 
-def score_hypergeometric_test(a, b, threshold=1, **kwargs):
+def score_hypergeometric_test(a: np.array, b: np.array, threshold: float = 1.0, **kwargs) -> Tuple[np.array, np.array]:
     """
     Run a hypergeometric test. The probability in a two-sided test is approximated
     with the symmetric distribution with more extreme of the tails.
     """
-    # type: (np.ndarray, np.ndarray, float) -> np.ndarray
+    axis = kwargs.get('axis', 0)
+
+    if axis == 1:
+        a, b = a.T, b.T
 
     # Binary expression matrices
     _a = (a >= threshold).astype(int)

orangecontrib/bioinformatics/utils/statistics.py

@@ -1,13 +1,16 @@
+from typing import Optional
+
 import numpy as np
 
+from Orange.data import Table
 from Orange.widgets import gui, settings
 from Orange.widgets.widget import Msg
-from Orange.widgets.settings import SettingProvider
+from Orange.widgets.settings import SettingProvider, DomainContextHandler
 from Orange.widgets.visualize.owscatterplot import OWScatterPlotBase, OWDataProjectionWidget
 
-from orangecontrib.bioinformatics.utils.statistics import score_t_test, score_fold_change
-from orangecontrib.bioinformatics.widgets.utils.gui import label_selection
-from orangecontrib.bioinformatics.widgets.utils.data import GENE_ID_COLUMN, GENE_AS_ATTRIBUTE_NAME
+from orangecontrib.bioinformatics.utils.statistics import score_fold_change
+from orangecontrib.bioinformatics.widgets.utils.data import TableAnnotation
+from orangecontrib.bioinformatics.widgets.ow_components import GeneScoringComponent
 
 
 class VolcanoGraph(OWScatterPlotBase):
@@ -28,117 +31,127 @@ class Warning(OWDataProjectionWidget.Warning):
             'Insufficient data to compute statistics.' 'More than one measurement per class should be provided '
         )
 
-        gene_enrichment = Msg('{}, {}.')
-        no_selected_gene_sets = Msg('No gene set selected, select them from Gene Sets box.')
-
     class Error(OWDataProjectionWidget.Error):
         exclude_error = Msg('Target labels most exclude/include at least one value.')
         negative_values = Msg('Negative values in the input. The inputs cannot be in ratio scale.')
-        data_not_annotated = Msg('The input date is not annotated as expexted. Please refer to documentation.')
+        data_not_annotated = Msg('The input date is not annotated as expected. Please refer to documentation.')
         gene_column_id_missing = Msg('Can not identify genes column. Please refer to documentation.')
 
-    GRAPH_CLASS = VolcanoGraph
+    settingsHandler = DomainContextHandler()
     graph = SettingProvider(VolcanoGraph)
-    embedding_variables_names = ('log2 (ratio)', '-log10 (P_value)')
+    scoring_component = SettingProvider(GeneScoringComponent)
 
-    stored_selections = settings.ContextSetting([])
-    current_group_index = settings.ContextSetting(0)
+    GRAPH_CLASS = VolcanoGraph
+    embedding_variables_names = ('log2 (ratio)', '-log10 (P_value)')
 
     def __init__(self):
         super().__init__()
+        self._data: Optional[Table] = None
+        self.genes_in_columns: Optional[str] = None
+        self.gene_id_column: Optional[str] = None
+        self.gene_id_attribute: Optional[str] = None
+
+        self.fold: Optional[np.array] = None
+        self.log_p_values: Optional[np.array] = None
+        self.valid_data: Optional[np.array] = None
 
     def _add_controls(self):
-        box = gui.vBox(self.controlArea, "Target Labels")
-        self.group_selection_widget = label_selection.LabelSelectionWidget()
-        self.group_selection_widget.groupChanged.connect(self.on_target_values_changed)
-        self.group_selection_widget.groupSelectionChanged.connect(self.on_target_values_changed)
-        box.layout().addWidget(self.group_selection_widget)
+        box = gui.vBox(self.controlArea, True, margin=0)
+        self.scoring_component = GeneScoringComponent(self, box)
+        self.scoring_component.group_changed.connect(self.setup_plot)
+        self.scoring_component.selection_changed.connect(self.setup_plot)
+        self.scoring_component.score_method_changed.connect(self.setup_plot)
+        self.scoring_component.expression_threshold_changed.connect(self.setup_plot)
 
         super()._add_controls()
         self.gui.add_widgets([self.gui.ShowGridLines], self._plot_box)
 
-    def get_embedding(self):
+    def _compute(self):
         self.Error.exclude_error.clear()
 
-        group, target_indices = self.group_selection_widget.selected_split()
-
-        if self.data and group is not None and target_indices:
-            X = self.data.X
-            I1 = label_selection.group_selection_mask(self.data, group, target_indices)
-            I2 = ~I1
-
-            # print(group)
-            if isinstance(group, label_selection.RowGroup):
-                X = X.T
-
-            N1, N2 = np.count_nonzero(I1), np.count_nonzero(I2)
+        if self.data:
+            x = self.data.X
+            score_method = self.scoring_component.get_score_method()
+            i1 = self.scoring_component.get_selection_mask()
+            i2 = ~i1
 
-            if not N1 or not N2:
+            n1, n2 = np.count_nonzero(i1), np.count_nonzero(i2)
+            if not n1 or not n2:
                 self.Error.exclude_error()
                 return
 
-            if N1 < 2 and N2 < 2:
+            if n1 < 2 and n2 < 2:
                 self.Warning.insufficient_data()
 
-            X1, X2 = X[:, I1], X[:, I2]
-
-            if np.any(X1 < 0.0) or np.any(X2 < 0):
+            x1, x2 = x[:, i1], x[:, i2]
+            if np.any(x1 < 0.0) or np.any(x2 < 0):
                 self.Error.negative_values()
-                X1 = np.full_like(X1, np.nan)
-                X2 = np.full_like(X2, np.nan)
+                x1 = np.full_like(x1, np.nan)
+                x2 = np.full_like(x2, np.nan)
+
+            with np.errstate(divide='ignore', invalid='ignore'):
+                self.fold = score_fold_change(x1, x2, axis=1, log=True)
+                _, p_values = score_method(x1, x2, axis=1, threshold=self.scoring_component.get_expression_threshold())
+                self.log_p_values = np.log10(p_values)
+
+    def get_embedding(self):
+        if self.data is None:
+            return None
+
+        if self.fold is None or self.log_p_values is None:
+            return
+
+        self.valid_data = np.isfinite(self.fold) & np.isfinite(self.log_p_values)
+        return np.array([self.fold, -self.log_p_values]).T
+
+    def send_data(self):
+        group_sel, data, graph = None, self._get_projection_data(), self.graph
+        if graph.selection is not None:
+            group_sel = np.zeros(len(data), dtype=int)
+            group_sel[self.valid_data] = graph.selection
 
-            with np.errstate(divide="ignore", invalid="ignore"):
-                fold = score_fold_change(X1, X2, axis=1, log=True)
-                _, p_values = score_t_test(X1, X2, axis=1)
-                log_p_values = np.log10(p_values)
+        selected_data = self._get_selected_data(data, graph.get_selection(), group_sel)
 
-            self.valid_data = np.isfinite(fold) & np.isfinite(p_values)
-            return np.array([fold, -log_p_values]).T
+        if self.genes_in_columns and selected_data:
+            selected_data = Table.transpose(selected_data, feature_names_column='Feature name')
+
+        self.Outputs.selected_data.send(selected_data)
 
     def setup_plot(self):
+        self._compute()
         super().setup_plot()
         for axis, var in (("bottom", 'log<sub>2</sub> (ratio)'), ("left", '-log<sub>10</sub> (P_value)')):
             self.graph.set_axis_title(axis, var)
 
-    def on_target_values_changed(self, index):
-        # Save the current selection to persistent settings
-        self.current_group_index = index
-        selected_indices = [ind.row() for ind in self.group_selection_widget.currentGroupSelection().indexes()]
-
-        if self.current_group_index != -1 and selected_indices:
-            self.stored_selections[self.current_group_index] = selected_indices
-
-        self.setup_plot()
-
     def set_data(self, data):
         self.Warning.clear()
         self.Error.clear()
-        super().set_data(data)
-        self.group_selection_widget.set_data(self, self.data)
 
-        if self.data:
-            if not self.stored_selections:
-                self.stored_selections = [[0] for _ in self.group_selection_widget.targets]
-            self.group_selection_widget.set_selection()
+        if data:
+            self.genes_in_columns = data.attributes.get(TableAnnotation.gene_as_attr_name, None)
+            self.gene_id_column = data.attributes.get(TableAnnotation.gene_id_column, None)
+            self.gene_id_attribute = data.attributes.get(TableAnnotation.gene_id_attribute, None)
+
+            if self.genes_in_columns:
+                self._data = data
+                # override default meta_attr_name value to avoid unexpected changes.
+                data = Table.transpose(data, meta_attr_name='Feature name')
+
+            super().set_data(data)
+            self.scoring_component.initialize(self.data)
 
     def check_data(self):
         self.clear_messages()
-        use_attr_names = self.data.attributes.get(GENE_AS_ATTRIBUTE_NAME, None)
-        gene_id_column = self.data.attributes.get(GENE_ID_COLUMN, None)
-
         if self.data is not None and (len(self.data) == 0 or len(self.data.domain) == 0):
             self.data = None
 
-        if use_attr_names is None:
+        if self.genes_in_columns is None:
             # Note: input data is not annotated properly.
             self.Error.data_not_annotated()
             self.data = None
 
-        if gene_id_column is None:
-            # Note: Can not identify genes column.
-            self.Error.gene_column_id_missing()
-            self.data = None
-
 
 if __name__ == "__main__":
-    pass
+    from Orange.widgets.utils.widgetpreview import WidgetPreview
+
+    WidgetPreview(OWVolcanoPlot).run()

orangecontrib/bioinformatics/widgets/OWVolcanoPlot.py

@@ -0,0 +1,3 @@
+from .gene_scoring import GeneScoringComponent
+
+__all__ = ('GeneScoringComponent',)