Simplified collectri function

decoupler/omnip.py

@@ -14,7 +14,6 @@
 ]
 
 import os
-import sys
 import builtins
 from types import ModuleType
 from typing import Iterable
@@ -110,8 +109,7 @@ def _static_fallback(
         query: str,
         resource: str,
         organism: int | str,
-        **kwargs
-    ) -> pd.DataFrame:
+        **kwargs) -> pd.DataFrame:
     """
     Fallback for static tables.
     """
@@ -177,7 +175,7 @@ def get_progeny(
 
     try:
         p = op.requests.Annotations.get(resources='PROGENy', **kwargs)
-    except:
+    except Exception:
         p = _static_fallback(
             query='annotations',
             resource='PROGENy',
@@ -280,7 +278,7 @@ def get_resource(
             entity_type='protein',
             **kwargs
         )
-    except:
+    except Exception:
         df = _static_fallback(
             query='annotations',
             resource=name,
@@ -389,7 +387,7 @@ def get_dorothea(
             genesymbols=True,
             organism=_organism,
         )
-    except:
+    except Exception:
         do = _static_fallback(
             query='interactions',
             resource='DoRothEA',
@@ -467,6 +465,18 @@ def get_dorothea(
     return do.reset_index(drop=True)
 
 
+def merge_genes_to_complexes(ct_cmplx):
+    cmpl_gsym = []
+    for s in ct_cmplx['source_genesymbol']:
+        if s.startswith('JUN') or s.startswith('FOS'):
+            cmpl_gsym.append('AP1')
+        elif s.startswith('REL') or s.startswith('NFKB'):
+            cmpl_gsym.append('NFKB')
+        else:
+            cmpl_gsym.append(s)
+    ct_cmplx.loc[:, 'source_genesymbol'] = cmpl_gsym
+
+
 def get_collectri(
         organism: str | int = 'human',
         split_complexes=False,
@@ -516,7 +526,7 @@ def get_collectri(
             loops=True,
             **kwargs
         )
-    except:
+    except Exception:
         ct = _static_fallback(
             query='interactions',
             resource='CollecTRI',
@@ -531,7 +541,7 @@ def get_collectri(
                 strict_evidences=True,
             )
             ct = pd.concat([ct, mirna], ignore_index=True)
-        except:
+        except Exception:
             _warn_failure('TF-miRNA interaction', static_fallback=False)
 
     # Separate gene_pairs from normal interactions
@@ -542,15 +552,7 @@ def get_collectri(
 
     # Merge gene_pairs into complexes
     if not split_complexes:
-        cmpl_gsym = []
-        for s in ct_cmplx['source_genesymbol']:
-            if s.startswith('JUN') or s.startswith('FOS'):
-                cmpl_gsym.append('AP1')
-            elif s.startswith('REL') or s.startswith('NFKB'):
-                cmpl_gsym.append('NFKB')
-            else:
-                cmpl_gsym.append(s)
-        ct_cmplx.loc[:, 'source_genesymbol'] = cmpl_gsym
+        merge_genes_to_complexes(ct_cmplx)
 
     # Merge
     ct = pd.concat([ct_inter, ct_cmplx])
@@ -559,15 +561,7 @@ def get_collectri(
     ct = ct.drop_duplicates(['source_genesymbol', 'target_genesymbol'])
 
     # Add weight
-    weights = []
-    for is_stimulation, is_inhibition in zip(ct['is_stimulation'], ct['is_inhibition']):
-        if is_stimulation:
-            weights.append(1)
-        elif is_inhibition:
-            weights.append(-1)
-        else:
-            weights.append(1)
-    ct['weight'] = weights
+    ct['weight'] = np.where(ct['is_inhibition'], -1, 1)
 
     # Select and rename columns
     ct = ct.rename(columns={'source_genesymbol': 'source', 'target_genesymbol': 'target', 'references_stripped': 'PMID'})
@@ -766,7 +760,7 @@ def get_ksn_omnipath(
     ksn = ksn.drop_duplicates(['source', 'target', 'weight'])
 
     # If duplicates remain, keep dephosphorylation
-    ksn = ksn.groupby(['source', 'target']).min().reset_index()
+    ksn = ksn.groupby(['source', 'target'], observed=True).min().reset_index()
 
     return ksn
 

decoupler/plotting.py

@@ -678,7 +678,7 @@ def plot_metrics_scatter(df, x='auroc', y='auprc', groupby=None, show_text=True,
         # Reformat df
         sub = (
             df[msk]
-            .groupby(['method', 'metric'])
+            .groupby(['method', 'metric'], observed=True)
             .mean(numeric_only=True).reset_index()
             .pivot(index='method', columns='metric', values='score').reset_index()
         )
@@ -861,10 +861,10 @@ def plot_metrics_boxplot(df, metric, groupby=None, figsize=(5, 5), dpi=100, ax=N
         # Compute order
         order = (
             df
-            .groupby(['method', groupby])
+            .groupby(['method', groupby], observed=True)
             .mean(numeric_only=True)
             .reset_index()
-            .groupby('method')
+            .groupby('method', observed=True)
             .max()
             .sort_values('score')
             .index
@@ -1709,7 +1709,8 @@ def get_source_idxs(n_sources, act, by_abs):
         else:
             s_idx = np.argsort(-act.values[0])[:n_sources]
     else:
-        raise ValueError('n_sources needs to be a list of source names or an integer number, {0} was passed.'.format(type(n_sources)))
+        raise ValueError('n_sources needs to be a list of source names or an \
+        integer number, {0} was passed.'.format(type(n_sources)))
     return s_idx
 
 
@@ -1725,13 +1726,14 @@ def get_target_idxs(n_targets, obs, net, by_abs):
         t_idx = (
             net
             .sort_values(['source', 'prod'], ascending=[True, False])
-            .groupby(['source'])
+            .groupby(['source'], observed=True)
             .head(n_targets)
             .index
             .values
         )
     else:
-        raise ValueError('n_targets needs to be a list of target names or an integer number, {0} was passed.'.format(type(n_targets)))
+        raise ValueError('n_targets needs to be a list of target names or an \
+        integer number, {0} was passed.'.format(type(n_targets)))
     return t_idx
 
 
@@ -1766,8 +1768,8 @@ def get_obs_act_net(act, obs, net, n_sources, n_targets, by_abs):
 def add_colors(g, act, obs, s_norm, t_norm, s_cmap, t_cmap):
 
     mpl = check_if_matplotlib(return_mpl=True)
-    s_cmap = mpl.cm.get_cmap(s_cmap)
-    t_cmap = mpl.cm.get_cmap(t_cmap)
+    s_cmap = mpl.colormaps.get_cmap(s_cmap)
+    t_cmap = mpl.colormaps.get_cmap(t_cmap)
 
     color = []
     for i, k in enumerate(g.vs['label']):

decoupler/tests/test_omnip.py

@@ -6,6 +6,7 @@
     get_resource,
     show_resources,
     get_dorothea,
+    merge_genes_to_complexes,
     get_collectri,
     get_ksn_omnipath
 )
@@ -36,6 +37,13 @@ def test_get_dorothea():
     get_dorothea(organism='mouse')
 
 
+def test_():
+    df = pd.DataFrame()
+    df['source_genesymbol'] = ['JUN1', 'JUN2', 'RELA', 'NFKB3', 'STAT1']
+    merge_genes_to_complexes(df)
+    assert df['source_genesymbol'].unique().size == 3
+
+
 def test_get_collectri():
     df = get_collectri(organism='human', split_complexes=False)
     assert type(df) is pd.DataFrame