diff --git a/src/spac/visualization.py b/src/spac/visualization.py
index 0ab0ee11..50842cc9 100644
--- a/src/spac/visualization.py
+++ b/src/spac/visualization.py
@@ -35,7 +35,7 @@ def visualize_2D_scatter(
     x, y, labels=None, point_size=None, theme=None,
     ax=None, annotate_centers=False,
     x_axis_title='Component 1', y_axis_title='Component 2', plot_title=None,
-    color_representation=None, **kwargs
+    color_representation=None, color_map=None, **kwargs
 ):
     """
     Visualize 2D data using plt.scatter.
@@ -65,6 +65,8 @@ def visualize_2D_scatter(
         Title for the plot.
     color_representation : str, optional
         Description of what the colors represent.
+    color_map : dictionary, optional
+        Dictionary containing colors for label annotations.
     **kwargs
         Additional keyword arguments passed to plt.scatter.
 
@@ -83,6 +85,8 @@ def visualize_2D_scatter(
         raise ValueError("x and y must have the same length.")
     if labels is not None and len(labels) != len(x):
         raise ValueError("Labels length should match x and y length.")
+    if color_map is not None and not isinstance(color_map, dict):
+        raise ValueError("`color_map` must be a dict mapping label→color.")
 
     # Define color themes
     themes = {
@@ -136,20 +140,21 @@ def visualize_2D_scatter(
                     "Categorical."
                 )
 
-            # Combine colors from multiple colormaps
             cmap1 = plt.get_cmap('tab20')
             cmap2 = plt.get_cmap('tab20b')
             cmap3 = plt.get_cmap('tab20c')
             colors = cmap1.colors + cmap2.colors + cmap3.colors
-
-            # Use the number of unique clusters to set the colormap length
-            cmap = ListedColormap(colors[:len(unique_clusters)])
+            cluster_to_color = color_map if color_map is not None else {
+                str(cluster): colors[i % len(colors)]
+                for i, cluster in enumerate(unique_clusters)
+            }
 
             for idx, cluster in enumerate(unique_clusters):
                 mask = np.array(labels) == cluster
+                color = cluster_to_color.get(str(cluster), 'gray')
                 ax.scatter(
                     x[mask], y[mask],
-                    color=cmap(idx),
+                    color=color,
                     label=cluster,
                     s=point_size
                 )