return nan instead of crashing when every emd stratum is too small

* Guard the pd.concat() calls in both the horizontal and vertical path
* Warn as soon as one cell type is missing from a split, not two
* Fix the newline in that warning

Author: rcannood

src/metrics/emd/helper.py

@@ -175,6 +175,18 @@ def get_vert_emd_for_integrated_adata(i_adata: ad.AnnData, markers_to_assess: li
 
             emd_vals.append(emd_df)
 
+    if len(emd_vals) == 0:
+        # every sample combination and cell type fell below the 50 cell floor,
+        # so there is nothing to concatenate.
+
+        print(
+            f"{i_adata.uns['dataset_id']} from {i_adata.uns['method_id']} does not have"
+            f" at least 50 cells for any sample combination and cell type."
+            f" Skipping EMD vertical calculation."
+        )
+
+        return np.nan
+
     # concatenate EMD values
     emd_vals = pd.concat(emd_vals)
     # remove unparsable characters like "/"
@@ -224,11 +236,11 @@ def calculate_horizontal_emd(
             i_split1_donor.obs["cell_type"].unique(),
             i_split2_donor.obs["cell_type"].unique(),
         )
-        if len(cell_type_not_in_both) > 1:
+        if len(cell_type_not_in_both) > 0:
             print(
                 f"In donor {donor}: some cell types are in split 1"
                 f" but not in split 2.\n"
-                f"Cell types missing: {''.join(cell_type_not_in_both)}]n"
+                f"Cell types missing: {', '.join(cell_type_not_in_both)}\n"
                 f"Computing cell type EMD using just cell types common in both."
             )
 
@@ -265,6 +277,21 @@ def calculate_horizontal_emd(
 
             emd_per_donor_per_ct.append(emd_df)
 
+    if len(emd_per_donor_per_ct) == 0:
+        # every donor and cell type fell below the 50 cell floor,
+        # so there is nothing to concatenate.
+
+        print(
+            f"{i_split1_adata.uns['dataset_id']} from {i_split1_adata.uns['method_id']}"
+            f" does not have at least 50 cells for any donor and cell type."
+            f" Skipping EMD horizontal calculation."
+        )
+
+        return {
+            KEY_MEAN_EMD_CT: np.nan,
+            KEY_EMD_HORZ_PER_DONOR: np.nan,
+        }
+
     emd_per_donor_per_ct = pd.concat(emd_per_donor_per_ct)
 
     # compute the mean and max per ct and for global.