High resolution model & distance function prior [draft]

cell2location/cell_comm/around_target.py

@@ -118,7 +118,7 @@ def compute_weighted_average_around_target(
             source_cell_type_data,
             index=adata.obs_names,
             columns=source_names,
-        )
+        ).astype("float32")
         # get normalising quantile values
         source_normalisation_quantile = source_cell_type_data.quantile(normalisation_quantile, axis=0)
         # compute average abundance above this quantile
@@ -151,7 +151,7 @@ def compute_weighted_average_around_target(
             # to account for locations with no neighbours within a bin (sum == 0)
             data_[np.isnan(data_)] = 0
             # complete the average for a given sample
-            source_cell_type_data.loc[sample_ind, :] = data_
+            source_cell_type_data.loc[sample_ind, :] = data_.astype("float32")
     # normalise data by normalising quantile (global value across distance bins)
     source_cell_type_data = source_cell_type_data / source_normalisation_quantile
     # account for cases of undetected signal
@@ -183,8 +183,9 @@ def compute_weighted_average_around_target(
 
         weighted_avg_ = pd.Series(weighted_avg_, name=ct, index=source_names)
 
-        # hack to make self interactions less apparent
-        weighted_avg_[ct] = weighted_avg_[~weighted_avg_.index.isin([ct])].max() + 0.02
+        if genes_to_use_as_source is None:
+            # hack to make self interactions less apparent
+            weighted_avg_[ct] = (weighted_avg_[~weighted_avg_.index.isin([ct])].max() + 0.02).astype("float32")
         # complete the results dataframe
         weighted_avg.loc[f"target {ct}", :] = weighted_avg_
 

cell2location/cluster_averages/cluster_averages.py

@@ -3,7 +3,13 @@
 from scipy.sparse import csr_matrix
 
 
-def compute_cluster_averages(adata, labels, use_raw=True, layer=None):
+def compute_cluster_averages(
+    adata,
+    labels,
+    use_raw=True,
+    layer=None,
+    use_dask=False,
+):
     """
     Compute average expression of each gene in each cluster
 
@@ -44,7 +50,17 @@ def compute_cluster_averages(adata, labels, use_raw=True, layer=None):
     averages_mat = np.zeros((1, x.shape[1]))
 
     for c in all_clusters:
-        sparse_subset = csr_matrix(x[np.isin(adata.obs[labels], c), :])
+        if use_dask:
+            from dask import config
+            from dask.array.core import Array
+
+            with config.set(**{"array.slicing.split_large_chunks": False}):
+                cur_data = x[np.isin(adata.obs[labels], c), :]
+                if isinstance(cur_data, Array):
+                    cur_data = cur_data.compute()
+        else:
+            cur_data = x[np.isin(adata.obs[labels], c), :]
+        sparse_subset = csr_matrix(cur_data)
         aver = sparse_subset.mean(0)
         averages_mat = np.concatenate((averages_mat, aver))
     averages_mat = averages_mat[1:, :].T