Adjusted HVG with batch selection

besca/st/_wrapper_funcs.py

@@ -308,7 +308,7 @@ def clr_normalize(adata, results_folder):
     return adata
 
 
-def highly_variable_genes(adata, batch_key=None, n_shared=2):
+def highly_variable_genes(adata, batch_key=None, n_shared=2, only_shared=False):
     """Calculate highly variable genes and return filtered adata containing only the HVGs.
 
     Parameters
@@ -320,7 +320,8 @@ def highly_variable_genes(adata, batch_key=None, n_shared=2):
     n_shared: `int` | default = 2
         requirement for selection of HVGs - HVGs shared in nr_samples/n_shared will be included.
         A higher value will result in a less stringent selection, e.g. with 2 HVGs need to be present
-        in at least 50% of the samples.
+        in at least 50% of the samples, in addition to the default scanpy behavior.
+    only_shared: `bool` | default = False
 
     Returns
     -------
@@ -342,6 +343,9 @@ def highly_variable_genes(adata, batch_key=None, n_shared=2):
     )
     if batch_key is not None:
         hvglist = adata.var["highly_variable"].copy()
+        ## Set to False so that default scanpy selection ignored
+        if only_shared:
+            hvglist[hvglist==True]=False 
         hvglist.loc[
             adata.var["highly_variable_nbatches"]
             >= len(set(adata.obs[batch_key])) / n_shared,