diff --git a/drcme/bin/run_existing_spca_on_new_data.py b/drcme/bin/run_existing_spca_on_new_data.py
index 644f079..6446d48 100755
--- a/drcme/bin/run_existing_spca_on_new_data.py
+++ b/drcme/bin/run_existing_spca_on_new_data.py
@@ -98,7 +98,7 @@ def main(orig_transform_file, orig_datasets, new_datasets, params_file,
                                             params_file=params_file)
         orig_data_objects.append(data_for_spca)
         orig_specimen_ids_list.append(specimen_ids)
-    orig_data_for_spca = []
+    orig_data_for_spca = {}
     for i, do in enumerate(orig_data_objects):
         for k in do:
             if k not in orig_data_for_spca:
@@ -128,7 +128,7 @@ def main(orig_transform_file, orig_datasets, new_datasets, params_file,
                                             params_file=params_file)
         new_data_objects.append(data_for_spca)
         new_specimen_ids_list.append(specimen_ids)
-    data_for_spca = []
+    data_for_spca = {}
     for i, do in enumerate(new_data_objects):
         for k in do:
             if k not in data_for_spca:
diff --git a/drcme/spca.py b/drcme/spca.py
index eeb1216..7f1517e 100755
--- a/drcme/spca.py
+++ b/drcme/spca.py
@@ -195,16 +195,13 @@ def orig_mean_and_std_for_zscore(spca_results, orig_data, spca_params,
         Standard deviations of sPCs
     """
     Z_list = []
-    for ds in orig_data:
-        data = ds["data"]
-        for k in ds["part_keys"]:
-            _, _, _, indices = spca_params[k]
-            d = data[:, indices]
-            above_thresh = spca_results[k]["pev"] >= pev_threshold
-            Z = d.dot(spca_results[k]["loadings"][:, above_thresh])
-            if np.any(np.isnan(Z)):
-                print("NaNs found", k)
-            Z_list.append(Z)
+    subset_data = select_data_subset(orig_data, spca_params)
+    for k, d in subset_data.items():
+        above_thresh = spca_results[k]["pev"] >= pev_threshold
+        Z = d.dot(spca_results[k]["loadings"][:, above_thresh])
+        if np.any(np.isnan(Z)):
+            print("NaNs found", k)
+        Z_list.append(Z)
 
     combo_orig = np.hstack(Z_list)
     return combo_orig.mean(axis=0), combo_orig.std(axis=0)
@@ -238,16 +235,13 @@ def spca_transform_new_data(spca_results, new_data, spca_zht_params, orig_mean,
         Transformed and z-scored sPC values
     """
     Z_list = []
-    for ds in new_data:
-        data = ds["data"]
-        for k in ds["part_keys"]:
-            _, _, _, indices = spca_zht_params[k]
-            d = data[:, indices]
-            above_thresh = spca_results[k]["pev"] >= pev_threshold
-            Z = d.dot(spca_results[k]["loadings"][:, above_thresh])
-            if np.any(np.isnan(Z)):
-                print("NaNs found", k)
-            Z_list.append(Z)
+    subset_data = select_data_subset(new_data, spca_zht_params)
+    for k, d in subset_data.items():
+        above_thresh = spca_results[k]["pev"] >= pev_threshold
+        Z = d.dot(spca_results[k]["loadings"][:, above_thresh])
+        if np.any(np.isnan(Z)):
+            print("NaNs found", k)
+        Z_list.append(Z)
 
     combo_new = np.hstack(Z_list)
     combo = (combo_new - orig_mean) / orig_std