Fix set_obs throwing ValueError when Series aligns to MuData.

CHANGELOG.md

@@ -12,6 +12,8 @@ and this project adheres to [Semantic Versioning][].
 
 -   Fix default value for `n_jobs` in `ir.tl.ir_query` that could lead to an error ([#498](https://github.com/scverse/scirpy/pull/498)).
 -   Update description of D50 diversity metric in documentation ([#499](https://github.com/scverse/scirpy/pull/498)).
+-   Fix `clonotype_modularity` not being able to store result in MuData in some cases ([#504](https://github.com/scverse/scirpy/pull/504)).
+-   Fix issue with creating sparse matrices from generators with the latest scipy version ([#504](https://github.com/scverse/scirpy/pull/504))
 
 ## v0.16.0
 

src/scirpy/tests/test_util.py

@@ -69,16 +69,20 @@ def test_data_handler_no_airr():
         DataHandler(adata, "airr", "airr")
 
 
-def test_data_handler_get_obs():
+@pytest.fixture
+def mdata_with_smaller_adata():
     adata_gex = AnnData(obs=pd.DataFrame(index=["c1", "c2", "c3"]).assign(both=[11, 12, 13]))
     adata_airr = AnnData(obs=pd.DataFrame(index=["c3", "c4", "c5"]).assign(both=[14, 15, 16]))
     mdata = MuData({"gex": adata_gex, "airr": adata_airr})
     mdata["airr"].obs["airr_only"] = [3, 4, 5]
 
     mdata.obs["mudata_only"] = [1, 2, 3, 4, 5]
     mdata.obs["both"] = [np.nan, np.nan, 114, 115, 116]
+    return mdata
+
 
-    params = DataHandler(mdata, "airr")
+def test_data_handler_get_obs(mdata_with_smaller_adata):
+    params = DataHandler(mdata_with_smaller_adata, "airr")
     # can retrieve value from mudata
     npt.assert_equal(params.get_obs("mudata_only").values, np.array([1, 2, 3, 4, 5]))
     # Mudata takes precedence
@@ -109,6 +113,27 @@ def test_data_handler_get_obs():
     )
 
 
+@pytest.mark.parametrize(
+    "value,exception",
+    [
+        [pd.Series([1, 2, 3], index=["c1", "c3", "c4"]), None],
+        [pd.Series([1, 2, 3], index=["c1", "c3", "c8"]), None],
+        [pd.Series([1, 2, 3, 4, 5], index=["c1", "c2", "c3", "c4", "c5"]), None],
+        [[1, 2, 3], None],
+        [[1, 2, 3, 4], ValueError],
+        [[1, 2, 3, 4, 5], None],
+        [[1, 2, 3, 4, 5, 6], ValueError],
+    ],
+)
+def test_data_handler_set_obs(mdata_with_smaller_adata, value, exception):
+    params = DataHandler(mdata_with_smaller_adata, "airr")
+    if exception is not None:
+        with pytest.raises(exception):
+            params.set_obs("test", value)
+    else:
+        params.set_obs("test", value)
+
+
 def test_data_handler_initalize_from_object(adata_tra):
     dh = DataHandler(adata_tra, "airr", "airr")
     dh2 = DataHandler(dh)

src/scirpy/util/__init__.py

@@ -166,11 +166,20 @@ def _get_obs_col(self, column: str) -> pd.Series:
     def set_obs(self, key: str, value: Union[pd.Series, Sequence[Any], np.ndarray]) -> None:
         """Store results in .obs of AnnData and MuData.
 
+        If `value` is not a Series, if the length is equal to the params.mdata, we assume it aligns to the
+        MuData object. Otherwise, if the length is equal to the params.adata, we assume it aligns to the
+        AnnData object. Otherwise, a ValueError is thrown.
+
         The result will be written to `mdata.obs["{airr_mod}:{key}"]` and to `adata.obs[key]`.
         """
         # index series with AnnData (in case MuData has different dimensions)
         if not isinstance(value, pd.Series):
-            value = pd.Series(value, index=self.adata.obs_names)
+            if len(value) == self.data.shape[0]:
+                value = pd.Series(value, index=self.data.obs_names)
+            elif len(value) == self.adata.shape[0]:
+                value = pd.Series(value, index=self.adata.obs_names)
+            else:
+                raise ValueError("Provided values without index and can't align with either MuData or AnnData.")
         if isinstance(self.data, MuData):
             # write to both AnnData and MuData
             if self._airr_mod is None:

src/scirpy/util/graph/__init__.py

@@ -144,7 +144,7 @@ def _get_sparse_from_igraph(graph, *, simplified, weight_attr=None):
     shape = graph.vcount()
     shape = (shape, shape)
     if len(edges) > 0:
-        adj_mat = csr_matrix((weights, zip(*edges)), shape=shape)
+        adj_mat = csr_matrix((weights, list(zip(*edges))), shape=shape)
         if simplified:
             # make symmetrical and add diagonal
             adj_mat = adj_mat + adj_mat.T - sparse.diags(adj_mat.diagonal()) + sparse.diags(np.ones(adj_mat.shape[0]))