updating decoupler dependency

Author: vckraemer

.gitignore

@@ -397,6 +397,7 @@ MigrationBackup/
 FodyWeavers.xsd
 
 # VS Code files for those working on multiple tools
+.vscode
 .vscode/*
 !.vscode/settings.json
 !.vscode/tasks.json

src/intratalkerpy/tf/get_condition_significant.py

@@ -61,11 +61,45 @@ def condition_comparison_significant(tf_activities, out_path, celltype, conditio
                     g = g.cat.set_categories([vs1, vs2])
 
                     #finding markers for the condition comparison
-                    res_tmp = dc.rank_sources_groups(comparison_sub, groupby= condition, reference="rest", method="t-test")
+                    res_tmp_new_format = dc.tl.rankby_group(comparison_sub, groupby= condition, reference="rest", method="t-test")
+                    res_tmp = (
+                        res_tmp_new_format.rename(
+                            columns={
+                                "name": "names",
+                                "stat": "statistic",
+                                "pval": "pvals",
+                                "padj": "pvals_adj",
+                            }
+                        )
+                        .astype(
+                            {
+                                "group": str,
+                                "reference": str,
+                                "names": str,
+                            }
+                        )
+                    )
                     res_tmp.rename(columns={"group": "condition", "statistic" : "scores"}, inplace=True)
 
                     #calculating wilcoxon scores to use for r value calculation (used in heatmaps)
-                    res_heatmap = dc.rank_sources_groups(comparison_sub, groupby= condition, reference="rest", method="wilcoxon")
+                    res_heatmap_new_format = dc.tl.rankby_group(comparison_sub, groupby= condition, reference="rest", method="wilcoxon")
+                    res_heatmap = (
+                        res_heatmap_new_format.rename(
+                            columns={
+                                "name": "names",
+                                "stat": "statistic",
+                                "pval": "pvals",
+                                "padj": "pvals_adj",
+                            }
+                        )
+                        .astype(
+                            {
+                                "group": str,
+                                "reference": str,
+                                "names": str,
+                            }
+                        )
+                    )
                     res_heatmap.rename(columns={"group": "condition", "statistic" : "scores"}, inplace=True)
 
                     #subsetting into both conditions to calculate r value

src/intratalkerpy/tf/get_significant_TFs.py

@@ -56,7 +56,24 @@ def get_significant_tfs(tf_activities_sub, condition, out_path, tf_condition_sig
     number_of_clusters = (len(tf_activities_sub.obs[celltype].unique()))
 
     #get marker TFs for single condition 
-    anndataobject_markers = dc.rank_sources_groups(tf_activities_sub, groupby= celltype, reference="rest", method="t-test")
+    anndataobject_markers_new_format = dc.tl.rankby_group(tf_activities_sub, groupby= celltype, reference="rest", method="t-test")
+    anndataobject_markers = (
+        anndataobject_markers_new_format.rename(
+            columns={
+                "name": "names",
+                "stat": "statistic",
+                "pval": "pvals",
+                "padj": "pvals_adj",
+            }
+        )
+        .astype(
+            {
+                "group": str,
+                "reference": str,
+                "names": str,
+            }
+        )
+    )
     anndataobject_markers.rename(columns={"names" : "gene", "group": "cluster", "statistic" : "scores"}, inplace=True)
 
     anndataobject_markers["tag"] = None

src/intratalkerpy/tf/utils.py

@@ -73,7 +73,7 @@ def map_t_value(tf_scores_df, anndataobject_markers):
     '''
     Description:
     -------------
-    Merges the significant gene output from the dc.rank_sources_groups function with the t value/tf score from the filtered and summarized decoupler tf activity dataframe.
+    Merges the significant gene output from the dc.tl.rankby_group function with the t value/tf score from the filtered and summarized decoupler tf activity dataframe.
 
     Parameters:
     -----------

tests/test_package_smoke.py

@@ -14,6 +14,14 @@ def test_public_modules_import_and_expose_expected_api():
     assert hasattr(perturbation, "pl")
 
 
+def test_external_dependency_functions_are_callable():
+    import decoupler as dc
+    from coldelta import colDeltaCorpartial
+
+    assert callable(dc.tl.rankby_group)
+    assert callable(colDeltaCorpartial)
+
+
 def test_transcription_factor_reference_data_is_packaged():
     data_files = [
         "rtf_db_human.csv",