only check for size column if model_cell_size is True

starling/starling.py

@@ -30,7 +30,8 @@ class ST(pl.LightningModule):
     :param dist_option: The distribution to use, one of 'T' for Student-T (df=2) or 'N' for Normal (Gaussian), defaults to T
     :param singlet_prop: The proportion of anticipated segmentation error free cells
     :param model_cell_size: Whether STARLING should incoporate cell size in the model
-    :param cell_size_col_name: The column name in ``AnnData`` (anndata.obs)
+    :param cell_size_col_name: The column name in ``AnnData`` (anndata.obs). Required only if ``model_cell_size`` is ``True``,
+        otherwise ignored.
     :param model_zplane_overlap: If cell size is modelled, should STARLING model z-plane overlap
     :param model_regularizer: Regularizer term impose on synethic doublet loss (BCE)
     :param learning_rate: Learning rate of ADAM optimizer for STARLING

starling/utility.py

@@ -201,7 +201,7 @@ def validate_starling_arguments(
             f"Argument `model_cell_size` must be boolean, received {type(model_cell_size)}"
         )
 
-    if cell_size_col_name not in adata.obs:
+    if model_cell_size and cell_size_col_name not in adata.obs:
         raise ValueError(
             f"Argument `cell_size_col_name` must be a valid column in `adata.obs`"
         )

tests/test_utility.py

@@ -1,6 +1,6 @@
 from anndata import AnnData
 
-from starling.utility import init_clustering
+from starling.utility import init_clustering, validate_starling_arguments
 
 
 def assert_annotated(adata: AnnData, k):
@@ -30,3 +30,16 @@ def test_init_clustering_pg(simple_adata):
     k = 2
     initialized = init_clustering("PG", simple_adata, k)
     assert_annotated(initialized, k)
+
+
+def test_validation_passes_with_no_size(simple_adata):
+    validate_starling_arguments(
+        adata=simple_adata,
+        cell_size_col_name="nonexistent",
+        dist_option="T",
+        singlet_prop=0.5,
+        model_cell_size=False,
+        model_zplane_overlap=False,
+        model_regularizer=0.1,
+        learning_rate=1e-3,
+    )