added unit test for enrich

Author: davidsebfischer

diffxpy/testing/det.py

@@ -517,7 +517,7 @@ def summary(
             "pval": self.pval,
             "qval": self.qval,
             "log2fc": self.log2_fold_change(),
-            "mean": self.mean
+            "mean": self.mean,
             "zero_mean": self.mean == 0
         })
 
@@ -2488,7 +2488,7 @@ def plot_heatmap(
         plt.close(fig)
 
         if return_axs:
-            return axs
+            return ax
         else:
             return
 

diffxpy/unit_test/test_enrich.py

@@ -0,0 +1,49 @@
+import unittest
+import logging
+import numpy as np
+import pandas as pd
+import scipy.stats as stats
+
+from batchglm.api.models.glm_nb import Simulator
+import diffxpy.api as de
+
+
+class TestEnrich(unittest.TestCase):
+
+    def test_for_fatal(self):
+        """
+        """
+        logging.getLogger("tensorflow").setLevel(logging.ERROR)
+        logging.getLogger("batchglm").setLevel(logging.WARNING)
+        logging.getLogger("diffxpy").setLevel(logging.WARNING)
+
+        sim = Simulator(num_observations=50, num_features=10)
+        sim.generate_sample_description(num_batches=0, num_conditions=2)
+        sim.generate()
+
+        test = de.test.wald(
+            data=sim.X,
+            factor_loc_totest="condition",
+            formula_loc="~ 1 + condition",
+            sample_description=sim.sample_description,
+            gene_names=[str(x) for x in range(sim.X.shape[1])],
+            training_strategy="DEFAULT",
+            dtype="float64"
+        )
+        print(test.gene_ids)
+
+        # Set up reference gene sets.
+        rs = de.enrich.RefSets()
+        rs.add(id="set1", source="manual", gene_ids=["1", "3"])
+        rs.add(id="set2", source="manual", gene_ids=["5", "6"])
+
+        enrich_test = de.enrich.test(
+            ref=rs,
+            det=test,
+            de_threshold=0.05
+        )
+        return True
+
+
+if __name__ == '__main__':
+    unittest.main()

diffxpy/unit_test/test_single.py

@@ -10,7 +10,7 @@
 
 class TestSingleNull(unittest.TestCase):
 
-    def test_null_distribution_wald(self, n_cells: int = 2000, n_genes: int = 100):
+    def test_null_distribution_wald(self, n_cells: int = 2000, n_genes: int = 200):
         """
         Test if de.wald() generates a uniform p-value distribution
         if it is given data simulated based on the null model. Returns the p-value
@@ -52,7 +52,7 @@ def test_null_distribution_wald(self, n_cells: int = 2000, n_genes: int = 100):
 
         return True
 
-    def test_null_distribution_wald_multi(self, n_cells: int = 2000, n_genes: int = 100):
+    def test_null_distribution_wald_multi(self, n_cells: int = 2000, n_genes: int = 200):
         """
         Test if de.wald() (multivariate mode) generates a uniform p-value distribution
         if it is given data simulated based on the null model. Returns the p-value
@@ -92,7 +92,7 @@ def test_null_distribution_wald_multi(self, n_cells: int = 2000, n_genes: int =
 
         return True
 
-    def test_null_distribution_lrt(self, n_cells: int = 2000, n_genes: int = 100):
+    def test_null_distribution_lrt(self, n_cells: int = 2000, n_genes: int = 200):
         """
         Test if de.lrt() generates a uniform p-value distribution
         if it is given data simulated based on the null model. Returns the p-value
@@ -134,7 +134,7 @@ def test_null_distribution_lrt(self, n_cells: int = 2000, n_genes: int = 100):
 
         return True
 
-    def test_null_distribution_ttest(self, n_cells: int = 2000, n_genes: int = 100):
+    def test_null_distribution_ttest(self, n_cells: int = 2000, n_genes: int = 200):
         """
         Test if de.t_test() generates a uniform p-value distribution
         if it is given data simulated based on the null model. Returns the p-value
@@ -173,7 +173,7 @@ def test_null_distribution_ttest(self, n_cells: int = 2000, n_genes: int = 100):
 
         return True
 
-    def test_null_distribution_wilcoxon(self, n_cells: int = 2000, n_genes: int = 100):
+    def test_null_distribution_wilcoxon(self, n_cells: int = 2000, n_genes: int = 200):
         """
         Test if de.wilcoxon() generates a uniform p-value distribution
         if it is given data simulated based on the null model. Returns the p-value