Use target cell_key when mapping (#122)

* Use target cell_key when mapping

* Fix test

scarf/datastore/mapping_datastore.py

@@ -33,6 +33,7 @@ def run_mapping(
         target_assay: Assay,
         target_name: str,
         target_feat_key: str,
+        target_cell_key: str = "I",
         from_assay: Optional[str] = None,
         cell_key: str = "I",
         feat_key: Optional[str] = None,
@@ -57,6 +58,7 @@ def run_mapping(
             target_name: Name of target data. This used to keep track of projections in the Zarr hierarchy
             target_feat_key: This will be used to name wherein the normalized target data will be saved in its own
                              zarr hierarchy.
+            target_cell_key: Cell key for the target data. (Default value: 'I')
             from_assay: Name of assay to be used. If no value is provided then the default assay will be used.
             cell_key: Cell key. Should be same as the one that was used in the desired graph. (Default value: 'I')
             feat_key:  Feature key. Should be same as the one that was used in the desired graph. By default, the latest
@@ -119,6 +121,7 @@ def run_mapping(
             cell_key,
             feat_key,
             target_feat_key,
+            target_cell_key,
             filter_null,
             exclude_missing,
             self.nthreads,
@@ -151,15 +154,23 @@ def run_mapping(
             logger.warning(f"`save_k` was decreased to {ann_obj.k}")
             save_k = ann_obj.k
         target_data = daskarr.from_zarr(
-            target_assay.z[f"normed__I__{target_feat_key}/data"], inline_array=True
+            target_assay.z[f"normed__{target_cell_key}__{target_feat_key}/data"],
+            inline_array=True,
         )
         if run_coral is True:
             # Reversing coral here to correct target data
             coral(
-                target_data, ann_obj.data, target_assay, target_feat_key, self.nthreads
+                target_data,
+                ann_obj.data,
+                target_assay,
+                target_feat_key,
+                target_cell_key,
+                self.nthreads,
             )
             target_data = daskarr.from_zarr(
-                target_assay.z[f"normed__I__{target_feat_key}/data_coral"],
+                target_assay.z[
+                    f"normed__{target_cell_key}__{target_feat_key}/data_coral"
+                ],
                 inline_array=True,
             )
         if ann_obj.method == "pca" and run_coral is False:
@@ -328,6 +339,7 @@ def get_target_classes(
         store = self.zw[store_loc]
         indices = store["indices"][:]
         dists = store["distances"][:]
+
         preds = []
         weights = 1 - (dists / dists.max(axis=1).reshape(-1, 1))
         for n in range(indices.shape[0]):

scarf/mapping_utils.py

@@ -38,14 +38,15 @@ def _correlation_alignment(s: daskarr, t: daskarr, nthreads: int) -> daskarr:
     return daskarr.dot(s, a_coral)
 
 
-def coral(source_data, target_data, assay, feat_key: str, nthreads: int):
+def coral(source_data, target_data, assay, feat_key: str, cell_key: str, nthreads: int):
     """Applies CORAL error correction to the input data.
 
     Args:
         source_data ():
         target_data ():
         assay ():
         feat_key ():
+        cell_key ():
         nthreads ():
     """
     from .writers import dask_to_zarr
@@ -87,7 +88,7 @@ def coral(source_data, target_data, assay, feat_key: str, nthreads: int):
     dask_to_zarr(
         data,
         assay.z["/"],
-        f"{assay.z.name}/normed__I__{feat_key}/data_coral",
+        f"{assay.z.name}/normed__{cell_key}__{feat_key}/data_coral",
         1000,
         nthreads,
         msg="Writing out coral corrected data",
@@ -149,6 +150,7 @@ def align_features(
     source_cell_key: str,
     source_feat_key: str,
     target_feat_key: str,
+    target_cell_key: str,
     filter_null: bool,
     exclude_missing: bool,
     nthreads: int,
@@ -185,11 +187,10 @@ def align_features(
     norm_params = source_assay.z[normed_loc].attrs["subset_params"]
     sorted_t_idx = np.array(sorted(t_idx[t_idx != -1]))
 
-    # TODO: add target cell key
     normed_data = target_assay.normed(
-        target_assay.cells.active_index("I"), sorted_t_idx, **norm_params
+        target_assay.cells.active_index(target_cell_key), sorted_t_idx, **norm_params
     )
-    loc = f"{target_assay.z.name}/normed__I__{target_feat_key}/data"
+    loc = f"{target_assay.z.name}/normed__{target_cell_key}__{target_feat_key}/data"
 
     og = create_zarr_dataset(
         target_assay.z["/"], loc, (1000,), "float64", (normed_data.shape[0], len(t_idx))