diff --git a/src/pages/1-samples_selection.py b/src/pages/1-samples_selection.py
index cfefd420c65a33def7fd327ff3878eec22368411..b38cec1e3698bc705c76cd4ec84e7e51edea9dd2 100644
--- a/src/pages/1-samples_selection.py
+++ b/src/pages/1-samples_selection.py
@@ -562,17 +562,36 @@ if not spectra.empty:
 st.subheader('III - Selected Samples for Reference Analysis', divider='blue')
 if selected:
     c16, c17 = st.columns([3, 1])
-    c16.write("Tabular identifiers of selected samples for reference analysis:")
+    with c16:
+        st.write("Tabular identifiers of selected samples for reference analysis:")
 
-    if 'labels' in globals():
-        labels['cluster'] = labels.index
-        labels.index = labels['names']
-        result = DataFrame({'names': selected,
-                            'cluster': np.array(labels)[selected]}, index=selected)
+        if 'labels' in globals():
+            labels['cluster'] = labels.index
+            labels.index = labels['names']
+            result = DataFrame({'names': selected,
+                                'cluster': np.array(labels)[selected]}, index=selected)
 
-    else:
-
-        st.write(meta_data)
+        else:
+            if 'name' in meta_data.columns:
+                subset = meta_data.drop('name', axis = 1).loc[selected]
+            else:
+                subset = meta_data.loc[selected]
+            st.write(subset)
+
+        with c17:
+            st.info(f'Information !\n - The total number of samples: {n_specs}.\n- The number of samples selected for reference analysis: {len(selected)}.\n - The proportion of samples selected for reference analysis: {round(len(selected)/n_specs*100)}%.')
+            
+            selected_col = st.selectbox('Color by:  ', options=filter, format_func=lambda x: x if x else "<Select>",
+                                     disabled=True if len(filter) == 1 else False)
+            if selected_col:
+                cmap2 = dict(
+                    zip(set(md_df_st_.loc[selected][selected_col]), colorslist[:len(set(md_df_st_.loc[selected][selected_col]))]))
+                st.write('The distribution of selected samples across categories')
+                
+                barhsel = barhplot(md_df_st_.loc[selected][[specs_col]], cmap=cmap2)
+                st.pyplot(barhsel)
+
+            
 
 
 #         if meta_data.empty:
@@ -587,8 +606,6 @@ if selected:
 #             sam1.insert(loc=0, column='index', value=selected_samples_idx)
 #             sam1.insert(loc=1, column='cluster', value=np.array(labels)[selected_samples_idx])
 #         sam1.index = np.arange(len(selected_samples_idx))+1
-#         with c17:
-#             st.info(f'Information !\n - The total number of samples: {n_specs}.\n- The number of samples selected for reference analysis: {sam1.shape[0]}.\n - The proportion of samples selected for reference analysis: {round(sam1.shape[0]/n_specs*100)}%.')
 #         sam = sam1
 
 #         if clus_method =='HDBSCAN':