Merge pull request #83 from klarman-cell-observatory/boli

Updated for spatial analysis

pegasusio/multimodal_data.py

@@ -407,7 +407,7 @@ def filter_data(self,
         unselected = []
         mito_dict = DictWithDefault(mito_prefix)
         for key, unidata in self.data.items():
-            if (key in focus_set) and (unidata.get_modality() == "rna"):
+            if (key in focus_set) and (unidata.get_modality() in {"rna", "visium"}):
                 if ("passed_qc" not in unidata.obs) or (not cache_passqc):
                     calc_qc_filters(unidata,
                         select_singlets = select_singlets,

pegasusio/qc_utils.py

@@ -81,7 +81,7 @@ def calc_qc_filters(
     --------
     >>> calc_qc_filters(unidata, min_umis = 500, select_singlets = True)
     """
-    assert unidata.uns["modality"] == "rna"
+    assert unidata.uns["modality"] in {"rna", "visium"}
 
     filters = []
 
@@ -129,13 +129,14 @@ def calc_qc_filters(
     if max_umis is not None:
         filters.append(unidata.obs["n_counts"] < max_umis)
 
-    if (mito_prefix is not None) and (percent_mito is not None):
+    if mito_prefix is not None:
         mito_genes = unidata.var_names.map(lambda x: x.startswith(mito_prefix)).values.nonzero()[0]
         unidata.obs["percent_mito"] = (
             unidata.X[:, mito_genes].sum(axis=1).A1
             / np.maximum(unidata.obs["n_counts"].values, 1.0)
         ) * 100
-        filters.append(unidata.obs["percent_mito"] < percent_mito)
+        if percent_mito is not None:
+            filters.append(unidata.obs["percent_mito"] < percent_mito)
 
     if len(filters) > 0:
         selected = np.logical_and.reduce(filters)

pegasusio/spatial_data.py

@@ -28,7 +28,7 @@ def __init__(
         feature_multiarrays: Optional[Dict[str, np.ndarray]] = None,
         barcode_multigraphs: Optional[Dict[str, csr_matrix]] = None,
         feature_multigraphs: Optional[Dict[str, csr_matrix]] = None,
-        cur_matrix: str = "raw.data",
+        cur_matrix: str = "X",
         img: Optional[pd.DataFrame] = None,
     ) -> None:
         assert metadata["modality"] == "visium"

pegasusio/spatial_utils.py

@@ -47,7 +47,7 @@ def load_visium_folder(input_path) -> MultimodalData:
     barcode_metadata = pd.concat([rna_data.obs, spatial_metadata], axis=1)
     feature_metadata = rna_data.var
 
-    matrices = {"raw.data": rna_data.X}
+    matrices = {"X": rna_data.X}
     metadata = {"genome": rna_data.get_genome(), "modality": "visium"}
 
     #  Store “pxl_col_in_fullres” and ”pxl_row_in_fullres” as a 2D array,