Merge pull request #119 from saezlab/dev

v1.2.1
saezlab · Jun 11, 2024 · 84b2b22 · 84b2b22
2 parents b8e7fcc + cc7308f
commit 84b2b22
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diff --git a/.bumpversion.cfg b/.bumpversion.cfg
@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 1.2.0
+current_version = 1.2.1
 commit = True
 tag = True
 files = pyproject.toml liana/__init__.py

diff --git a/docs/source/notebooks/bivariate.ipynb b/docs/source/notebooks/bivariate.ipynb
diff --git a/docs/source/notebooks/misty.ipynb b/docs/source/notebooks/misty.ipynb
diff --git a/docs/source/notebooks/sma.ipynb b/docs/source/notebooks/sma.ipynb
diff --git a/docs/source/notebooks/targeted.ipynb b/docs/source/notebooks/targeted.ipynb
diff --git a/docs/source/release_notes.rst b/docs/source/release_notes.rst
@@ -1,6 +1,16 @@
 Release notes
 =============
 
+1.2.1 (11.06.2024)
+-------------------------------------------------
+- Added +1 to the max_neighbours to account for the spot itself in the spatial connectivities.
+
+- Replaced Squidpy's neighbourhood graph with liana's radial basis kernel, but with a fixed number of neighbours for each spot. This does not account for edges, but differences are minimal does not require squidpy as a dependency. One can easily replace it on demand. (# https://github.com/saezlab/liana-py/issues/112)
+
+- Fixed Python version range between 3.8 and 3.12 (Merged #112)
+
+- Improved the Differential Expression Vignette be more explicit about the causal subnetwork search results (related to #66)
+
 1.2.0 (24.05.2024)
 -------------------------------------------------
 

diff --git a/liana/__init__.py b/liana/__init__.py
@@ -1,4 +1,4 @@
-__version__ = '1.2.0'
+__version__ = '1.2.1'
 
 from liana import method as mt, plotting as pl, resource as rs, multi as mu, utils as ut, testing
 

diff --git a/liana/method/sp/_misty/_Misty.py b/liana/method/sp/_misty/_Misty.py
@@ -23,7 +23,8 @@ def __init__(self,
                  obs:(pd.DataFrame | None)=None,
                  spatial_key:str=K.spatial_key,
                  enforce_obs:bool=True,
-                 **kwargs):
+                 **kwargs
+                 ):
         """
         Construct a MistyData object from a dictionary of views (anndatas).
 

diff --git a/liana/method/sp/_misty/_misty_constructs.py b/liana/method/sp/_misty/_misty_constructs.py
@@ -13,7 +13,6 @@
 from liana.resource import select_resource
 from liana.method._pipe_utils import prep_check_adata
 from liana.method.sp._utils import _add_complexes_to_var
-from liana._logging import _check_if_installed
 
 def _make_view(adata, nz_threshold=0.1, add_obs=False, use_raw=False,
                layer=None, connecitivity=None, spatial_key=None, verbose=False):
@@ -57,8 +56,7 @@ def genericMistyData(intra,
                      cutoff = 0.1,
                      add_juxta=True,
                      n_neighs = 6,
-                     verbose=False,
-                     **kwargs,
+                     verbose=False
                      ):
 
     """
@@ -96,14 +94,12 @@ def genericMistyData(intra,
     cutoff : `float`, optional (default: 0.1)
         The cutoff for the connectivity matrix.
     add_juxta : `bool`, optional (default: True)
-        Whether to add the juxtaview. The juxtaview is constructed using `squidpy.gr.spatial_neighbors`,
-        and should represent the direct spatial neighbors of each cell/spot.
+        Whether to add the juxtaview. The juxtaview is constructed using only the nearest neighbors.
+        A bandwidth of 5 times the bandwidth of the paraview is used to ensure that the nearest neighbors within the radius.
     n_neighs : `int`, optional (default: 6)
         The number of neighbors to consider when constructing the juxtaview.
     verbose : `bool`, optional (default: False)
         Whether to print progress.
-    **kwargs : `dict`, optional
-        Additional arguments to pass to `squidpy.gr.spatial_neighbors`.
 
     Returns
     -------
@@ -121,14 +117,12 @@ def genericMistyData(intra,
         extra = intra
 
     if add_juxta:
-        sq = _check_if_installed('squidpy')
-        neighbors, _ = sq.gr.spatial_neighbors(adata=extra,
-                                               copy=True,
-                                               spatial_key=spatial_key,
-                                               set_diag=set_diag,
-                                               n_neighs=n_neighs,
-                                               **kwargs
-                                               )
+        neighbors = spatial_neighbors(extra,
+                                      bandwidth=bandwidth*5,
+                                      spatial_key=spatial_key,
+                                      max_neighbours=n_neighs,
+                                      set_diag=set_diag,
+                                      inplace=False)
 
         views['juxta'] = _make_view(adata=extra, nz_threshold=nz_threshold,
                                     use_raw=extra_use_raw, layer=extra_layer,

diff --git a/liana/tests/test_bivar.py b/liana/tests/test_bivar.py
@@ -225,7 +225,7 @@ def test_large_adata():
               )
     lrdata = adata.obsm['local_scores']
     np.testing.assert_almost_equal(lrdata.X.mean(), 0.00048977, decimal=4)
-    np.testing.assert_almost_equal(lrdata.var['morans'].mean(), 0.00030397394, decimal=4)
+    np.testing.assert_almost_equal(lrdata.var['morans'].mean(), 0.00012773558, decimal=4)
 
 
 def test_wrong_interactions():

diff --git a/liana/tests/test_misty.py b/liana/tests/test_misty.py
@@ -19,7 +19,6 @@ def test_misty_para():
                              cutoff=0,
                              add_juxta=False,
                              set_diag=False,
-                             seed=133
                              )
     misty(model=RandomForestModel, bypass_intra=False, seed=42, n_estimators=11)
     assert np.isin(list(misty.uns.keys()), ['target_metrics', 'interactions']).all()
@@ -37,9 +36,7 @@ def test_misty_bypass():
                              bandwidth=10,
                              add_juxta=True,
                              set_diag=True,
-                             cutoff=0,
-                             coord_type="generic",
-                             delaunay=True)
+                             cutoff=0)
     misty(model=RandomForestModel, alphas=1, bypass_intra=True, seed=42, n_estimators=11)
     assert np.isin(['juxta', 'para'], misty.uns['target_metrics'].columns).all()
     assert ~np.isin(['intra'], misty.uns['target_metrics'].columns).all()
@@ -51,7 +48,7 @@ def test_misty_bypass():
     assert interactions['importances'].sum().round(10) == 22.0
     np.testing.assert_almost_equal(interactions[(interactions['target']=='ligC') &
                                                (interactions['predictor']=='ligA')]['importances'].values,
-                                   np.array([0.0444664, 0.0551506]), decimal=3)
+                                   np.array([0.095, 0.07]), decimal=3)
 
 
 def test_misty_groups():
@@ -60,8 +57,6 @@ def test_misty_groups():
                              add_juxta=True,
                              set_diag=False,
                              cutoff=0,
-                             coord_type="generic",
-                             delaunay=True
                              )
     misty(model=RandomForestModel,
           alphas=1,
@@ -82,7 +77,7 @@ def test_misty_groups():
     # assert that there are self interactions = var_n * var_n
     interactions = misty.uns['interactions']
     self_interactions = interactions[(interactions['target']==interactions['predictor'])]
-    # 11 vars * 4 envs * 3 views = 132; NOTE: However, I drop NAs -> to be refactored...
+    # 11 vars * 4 envs * 3 views = 132; NOTE: However, I drop NAs
     assert self_interactions.shape == (44, 5)
     assert self_interactions[self_interactions['view']=='intra']['importances'].isna().all()
 
@@ -110,7 +105,7 @@ def test_linear_misty():
 
     assert misty.uns['interactions'].shape == (330, 4)
     actual = misty.uns['interactions']['importances'].values.mean()
-    np.testing.assert_almost_equal(actual, 0.4941761900911731, decimal=3)
+    np.testing.assert_almost_equal(actual, 0.5135328101662447, decimal=3)
 
 
 def test_misty_mask():
@@ -126,7 +121,7 @@ def test_misty_mask():
     np.testing.assert_almost_equal(misty.uns['target_metrics']['intra_R2'].mean(), 0.4248588250759459, decimal=3)
 
     assert misty.uns['interactions'].shape == (330, 4)
-    np.testing.assert_almost_equal(misty.uns['interactions']['importances'].sum(), 141.05332654128952, decimal=0)
+    np.testing.assert_almost_equal(misty.uns['interactions']['importances'].sum(), 149.30560405771703, decimal=0)
 
 
 def test_misty_custom():

diff --git a/liana/utils/query_bandwidth.py b/liana/utils/query_bandwidth.py
@@ -1,12 +1,12 @@
 import numpy as np
 from sklearn.neighbors import BallTree
-from plotnine import ggplot, aes, geom_line, geom_point, theme_bw, xlab, ylab, scale_y_continuous
+from plotnine import ggplot, aes, geom_line, geom_point, theme_bw, xlab, ylab
 from pandas import DataFrame
 
 def query_bandwidth(coordinates: np.ndarray,
                     start: int = 0,
                     end: int = 500,
-                    interval_n:int = 50,
+                    interval_n: int = 50,
                     reference: np.ndarray = None
                     ):
     """
@@ -49,13 +49,12 @@ def query_bandwidth(coordinates: np.ndarray,
         num_neighbors = tree.query_radius(_reference, r=max_distance, count_only=True)
 
         # calculate the average number of neighbors
-        avg_nn = np.mean(num_neighbors)
-        df.loc[n, 'neighbours'] = avg_nn
+        avg_nn = np.ceil(np.median(num_neighbors))
+        df.loc[n, 'neighbours'] = avg_nn - 1
 
     p = (ggplot(df, aes(x='bandwith', y='neighbours')) +
          geom_line() +
          geom_point() +
-         scale_y_continuous(breaks=range(start, end, interval_n)) +
          theme_bw(base_size=16) +
          xlab("Bandwidth") +
          ylab("Number of Neighbors")

diff --git a/liana/utils/spatial_neighbors.py b/liana/utils/spatial_neighbors.py
@@ -23,7 +23,7 @@ def _linear(distance_mtx, bandwidth):
 @d.dedent
 def spatial_neighbors(adata: AnnData,
                       bandwidth=None,
-                      cutoff=None,
+                      cutoff=0.1,
                       max_neighbours=None,
                       kernel='gaussian',
                       set_diag=False,
@@ -103,9 +103,9 @@ def spatial_neighbors(adata: AnnData,
     if max_neighbours is None:
         max_neighbours = int(adata.shape[0] / 10)
 
-    tree = NearestNeighbors(n_neighbors=max_neighbours,
-                        algorithm='ball_tree',
-                        metric='euclidean').fit(_reference)
+    tree = NearestNeighbors(n_neighbors=max_neighbours + 1, # +1 to exclude self
+                            algorithm='ball_tree',
+                            metric='euclidean').fit(_reference)
     dist = tree.kneighbors_graph(coordinates, mode='distance')
 
     # prevent float overflow
@@ -114,7 +114,7 @@ def spatial_neighbors(adata: AnnData,
     # define zone of indifference
     dist.data[dist.data < zoi] = np.inf
 
-    # NOTE: dist gets converted to a connectivity matrix
+    # NOTE: dist gets converted to a connectivity (proximity) matrix
     if kernel == 'gaussian':
         dist.data = _gaussian(dist.data, bandwidth)
     elif kernel == 'misty_rbf':