minor changes

panpipes/python_scripts/batch_correct_scvi.py

@@ -149,14 +149,17 @@
 )
 
 scvi_model_args =  {k: v for k, v in params['rna']['scvi']['model_args'].items() if v is not None}
+print(scvi_model_args)
 scvi_training_args =  {k: v for k, v in params['rna']['scvi']['training_args'].items() if v is not None}
+print(scvi_training_args)
 scvi_training_plan =  {k: v for k, v in params['rna']['scvi']['training_plan'].items() if v is not None}
+print(scvi_training_plan)
 
 L.info("Defining model")
 vae = scvi.model.SCVI(rna, **scvi_model_args) 
 L.info("Running scVI")
 vae.train(**scvi_training_args, plan_kwargs=scvi_training_plan) 
-
+L.info("Finished Training now saving model")
 vae.save(os.path.join("batch_correction", "scvi_model"), 
                   anndata=False)
 

panpipes/python_scripts/batch_correct_totalvi.py

@@ -219,6 +219,10 @@
 else:
     totalvi_training_plan =  {k: v for k, v in params['multimodal']['totalvi']['training_plan'].items() if v is not None}
 
+print(totalvi_model_args)
+print(totalvi_training_args)
+print(totalvi_training_plan)
+
 L.info("Defining model")
 vae = scvi.model.TOTALVI(rna, **totalvi_model_args)
 L.info("Running totalVI")
@@ -252,6 +256,7 @@
 mdata.obsm["X_totalVI"] = vae.get_latent_representation()
 
 if batch_categories is not None:
+    L.debug(batch_categories)
     if type(batch_categories) is not list:
         batch_categories = [batch_categories]
     normX, protein = vae.get_normalized_expression(

panpipes/python_scripts/batch_correct_wnn.py

@@ -91,6 +91,7 @@
     else: 
         dict_graph[x]["obsm"] = None
 
+L.info(dict_graph)
 
 if dict_graph["rna"]["obsm"] == "X_scvi":
     dict_graph["rna"]["obsm"] = "X_scVI"

panpipes/python_scripts/make_mudataspatial_from_csv.py

@@ -132,7 +132,7 @@ def check_dir_transform(infile_path, transform_file):
 
 L.info("Resulting AnnData is:")
 L.info(adata)
-L.info("Creating MuData")
+L.info("Creating MuData with .mod['spatial']")
 
 mdata = MuData({"spatial": adata})
 

panpipes/python_scripts/plot_custom_markers_umap.py

@@ -101,6 +101,7 @@ def main(adata, mod, layer_choice, df, basis):
 else:
     # we have multimodal object
     for mod in modalities:
+        print(mod)
         df_sub = df[df['mod'] == mod]
         mdata.update_obs()
         try:
@@ -113,6 +114,7 @@ def main(adata, mod, layer_choice, df, basis):
             bb = []
         if len(bb) > 0 :
             for basis, layer in product(bb, ll):
+                print(basis,layer)
                 main(adata=mdata[mod], 
                     mod=mod,
                     layer_choice = layer,

panpipes/python_scripts/refmap_scvitools.py

@@ -144,6 +144,7 @@
     max_epochs = 200
     train_kwargs = {'weight_decay': 0.0}
 
+print(train_kwargs)
 
 if reference_architecture=="scvi":
     L.info("Running scVI")

panpipes/python_scripts/run_filter_spatial.py

@@ -101,6 +101,7 @@ def test_matching_df_ignore_cat(new_df, old_df):
     # this will go through the modalities one at a time,
     # then the categories max, min and bool
     for mod in mdata.mod.keys():
+        L.info(mod)
         if mod in filter_dict.keys():
             for marg in filter_dict[mod].keys():
                 if marg == "obs":

panpipes/python_scripts/run_scanpyQC_spatial.py

@@ -150,7 +150,7 @@
         sys.exit("The path of the  cell cycle genes tsv file '%s' could not be found" % args.ccgenes)
 
 
-# Aug 2023: we now need to update the mdata object to pick the calc proportion outputs made on 
+#TODO: we now need to update the mdata object to pick the calc proportion outputs made on 
 # spatial = mdata['spatial']
 
 mdata.update()