diff --git a/src/spac/visualization.py b/src/spac/visualization.py
index 9003c163..f49dce6a 100644
--- a/src/spac/visualization.py
+++ b/src/spac/visualization.py
@@ -403,9 +403,11 @@ def tsne_plot(adata, color_column=None, ax=None, **kwargs):
 
     return fig, ax
 
+
 def histogram(adata, feature=None, annotation=None, layer=None,
               group_by=None, together=False, ax=None,
-              x_log_scale=False, y_log_scale=False, **kwargs):
+              x_log_scale=False, y_log_scale=False,
+              defined_color_map=None, **kwargs):
     """
     Plot the histogram of cells based on a specific feature from adata.X
     or annotation from adata.obs.
@@ -447,6 +449,10 @@ def histogram(adata, feature=None, annotation=None, layer=None,
     y_log_scale : bool, default False
         If True, the y-axis will be set to log scale.
 
+    defined_color_map : str, optional, default=None
+        Key in adata.uns used to retrieve a color mapping dictionary to
+        color code the histogram.
+
     **kwargs
         Additional keyword arguments passed to seaborn histplot function.
         Key arguments include:
@@ -495,7 +501,6 @@ def histogram(adata, feature=None, annotation=None, layer=None,
             DataFrame containing the data used for plotting the histogram.
 
     """
-
     # If no feature or annotation is specified, apply default behavior
     if feature is None and annotation is None:
         # Default to the first feature in adata.var_names
@@ -530,6 +535,10 @@ def histogram(adata, feature=None, annotation=None, layer=None,
 
     df = pd.concat([df, adata.obs], axis=1)
 
+    if defined_color_map:
+        color_dict = get_defined_color_map(adata, defined_color_map)
+        kwargs.setdefault("palette", color_dict)
+
     if feature and annotation:
         raise ValueError("Cannot pass both feature and annotation,"
                          " choose one.")
@@ -567,7 +576,7 @@ def cal_bin_num(
     ):
         bins = max(int(2*(num_rows ** (1/3))), 1)
         print(f'Automatically calculated number of bins is: {bins}')
-        return(bins)
+        return (bins)
 
     num_rows = plot_data.shape[0]
 
@@ -657,8 +666,7 @@ def calculate_histogram(data, bins, bin_edges=None):
             # Set default values if not provided in kwargs
             kwargs.setdefault("multiple", "stack")
             kwargs.setdefault("element", "bars")
-
-
+            
             sns.histplot(data=hist_data, x='bin_center', weights='count',
                          hue=group_by, ax=ax, **kwargs)
             # If plotting feature specify which layer
@@ -682,8 +690,14 @@ def calculate_histogram(data, bins, bin_edges=None):
                              groups[i]][data_column]
                 hist_data = calculate_histogram(group_data, kwargs['bins'])
 
+                # If defined_color_map provided, retrieves color map
+                group_color = None
+                if defined_color_map:
+                    group_color = color_dict.get(group, None)
+
                 sns.histplot(data=hist_data, x="bin_center", ax=ax_i,
-                    weights='count', **kwargs)
+                             weights='count', color=group_color, **kwargs)
+
                 # If plotting feature specify which layer
                 if feature:
                     ax_i.set_title(f'{groups[i]} with Layer: {layer}')
@@ -720,7 +734,13 @@ def calculate_histogram(data, bins, bin_edges=None):
         hist_data = calculate_histogram(plot_data[data_column], kwargs['bins'])
         if pd.api.types.is_numeric_dtype(plot_data[data_column]):
             ax.set_xlim(hist_data['bin_left'].min(),
-            hist_data['bin_right'].max())
+                        hist_data['bin_right'].max())
+
+        # Set default color from custom color map if available
+        if defined_color_map:
+            color_dict = get_defined_color_map(adata,defined_color_map)
+            default_color = list(color_dict.values())[0]
+            kwargs['color'] = default_color
 
         sns.histplot(
             data=hist_data,