update some feature and fix some bugs.

stereo/algorithm/community_detection.py

@@ -72,7 +72,7 @@ def params_init(
             elif 'spatial_stereoseq' in slice._ann_data.obsm:
                 slice._ann_data.obsm['spatial'] = np.array(slice._ann_data.obsm['spatial_stereoseq'].copy())
             # annotation data must be of string type
-            slice._ann_data.obs[annotation] = slice._ann_data.obs[annotation].astype('str')
+            # slice._ann_data.obs[annotation] = slice._ann_data.obs[annotation].astype('str')
             # create a set of existing cell types in all slices
             self.cell_types = self.cell_types.union(set(slice._ann_data.obs[annotation].unique()))
             # if any of the samples lacks the cell type palette, set the flag
@@ -232,6 +232,7 @@ def _main(self, slices, annotation="annotation", **kwargs):
             self.slices[slice_id]._ann_data.obs.loc[
                 algo.adata.obs[f'tissue_{algo.method_key}'].index, 'cell_communities'] = algo.adata.obs[
                 f'tissue_{algo.method_key}']
+            self.slices[slice_id]._ann_data.obs['cell_communities'].fillna('unknown', inplace=True)
 
             # save anndata objects for further use
             if self.params['save_adata']:
@@ -306,7 +307,7 @@ def cluster(self, merged_tissue):  # TODO, merged_tissue da bude AnnBasedStereoE
                                     resolution=self.params['resolution'])
             merged_tissue._ann_data.obs['leiden'] = merged_tissue._ann_data.obs['leiden'].astype('int')
             merged_tissue._ann_data.obs['leiden'] -= 1
-            merged_tissue._ann_data.obs['leiden'] = merged_tissue._ann_data.obs['leiden'].astype('str')
+            merged_tissue._ann_data.obs['leiden'] = merged_tissue._ann_data.obs['leiden'].astype('U')
             merged_tissue._ann_data.obs['leiden'] = merged_tissue._ann_data.obs['leiden'].astype('category')
         elif self.params['cluster_algo'] == 'spectral':
             merged_tissue._ann_data.obsm['X_pca_dummy'] = merged_tissue._ann_data.X

stereo/algorithm/get_niche.py

@@ -53,6 +53,9 @@ def main(
         # adata_result = adata_full[(list(result_target_sender.index) + list(result_target_sender.columns)), :]
         cell_list = list(result_target_sender.index) + list(result_target_sender.columns)
         data_result = filter_cells(data_full, cell_list=cell_list, inplace=inplace)
+        if not inplace:
+            data_result.tl.result.set_item_callback = None
+
         for res_key in data_result.tl.key_record['pca']:
             data_result.tl.result[res_key] = data_result.tl.result[res_key][
                 np.isin(data_full.cell_names, cell_list)].copy()
@@ -68,5 +71,8 @@ def main(
                 index=data_result.cell_names,
                 dtype='category'
             )
-
+
+        if not inplace:
+            data_result.tl.result.contain_method = None
+            data_result.tl.result.get_item_method = None
         return data_result

stereo/core/ms_data.py

@@ -552,8 +552,8 @@ def to_integrate(
                 res = stereo_exp_data.cells._obs[item[idx]]
                 sample_idx = self._names.index(scope_names[idx])
                 new_index = res.index.astype('str') + f'-{sample_idx}'
-                res.index = new_index
-                self.merged_data.cells._obs.loc[new_index, res_key] = res
+                # res.index = new_index
+                self.merged_data.cells._obs.loc[new_index, res_key] = res.to_numpy()
             elif type == 'var':
                 raise NotImplementedError
             else:

stereo/core/stereo_exp_data.py

@@ -95,6 +95,7 @@ def __init__(
         self._position = position
         self._position_z = position_z
         self._position_offset = None
+        self._position_min = None
         self._bin_type = bin_type
         self._bin_size = bin_size
         self._tl = None
@@ -414,6 +415,14 @@ def position_offset(self):
     @position_offset.setter
     def position_offset(self, position_offset):
         self._position_offset = position_offset
+
+    @property
+    def position_min(self):
+        return self._position_min
+
+    @position_min.setter
+    def position_min(self, position_min):
+        self._position_min = position_min 
 
     @property
     def offset_x(self):
@@ -589,7 +598,9 @@ def reset_position(self):
             for bno in batches:
                 idx = np.where(self.cells.batch == bno)[0]
                 self.position[idx] -= self.position_offset[bno]
+                self.position[idx] += self.position_min[bno]
         self.position_offset = None
+        self.position_min = None
 
 
 class AnnBasedStereoExpData(StereoExpData):
@@ -600,6 +611,7 @@ def __init__(
             based_ann_data: anndata.AnnData = None,
             bin_type: str = None,
             bin_size: int = None,
+            spatial_key: Union[str, list, np.ndarray] = 'spatial',
             *args,
             **kwargs
     ):
@@ -637,6 +649,8 @@ def __init__(
 
         if self._ann_data.raw:
             self._tl._raw = AnnBasedStereoExpData(based_ann_data=self._ann_data.raw.to_adata())
+
+        self._spatial_key = spatial_key
 
     def __str__(self):
         return str(self._ann_data)

stereo/plots/plot_collection.py

@@ -844,8 +844,8 @@ def cluster_scatter(
         group_list = res['group'].to_numpy()
         n = np.unique(group_list).size
         palette = stereo_conf.get_colors(colors, n=n)
-        x = self.data.position[:, 0].astype(int)
-        y = self.data.position[:, 1].astype(int)
+        x = self.data.position[:, 0]
+        y = self.data.position[:, 1]
         x_min, x_max = x.min(), x.max()
         y_min, y_max = y.min(), y.max()
         boundary = [x_min, x_max, y_min, y_max]

stereo/plots/vt3d_browser/stereopy_3D_browser.py

@@ -445,7 +445,7 @@ def get_anno(self):
         """
         xyz = np.concatenate([self._data.position, self._data.position_z], axis=1)
         df = pd.DataFrame(data=xyz, columns=['x', 'y', 'z'])
-        df = df.astype(int)  # force convert to int to save space
+        # df = df.astype(int)  # force convert to int to save space
         if self._cluster_label in self._data.cells._obs.columns:
             df['anno'] = self._data.cells._obs[self._cluster_label].to_numpy()
         else: