--- a
+++ b/openomics/visualization/umap.py
@@ -0,0 +1,46 @@
+import numpy as np
+import plotly.express as px
+import umap
+
+
+def d3_umap(X, y_km, heat=None):
+    """
+    Args:
+        X:
+        y_km:
+        heat:
+    """
+    reducer = umap.UMAP(random_state=1234, n_components=3)
+    X_embedded = reducer.fit_transform(X)
+    node_colors = get_node_colormap(y_km)
+    x, y, z = X_embedded[:, 0], X_embedded[:, 1], X_embedded[:, 2]
+
+    fig = px.scatter_3d(x=x, y=y, z=z, color=node_colors)
+    fig.show()
+    return reducer
+
+
+def get_node_colormap(node_label):
+    """
+    Args:
+        node_label:
+    """
+    if type(node_label) == list:
+        node_labels = node_label
+        sorted_node_labels = sorted(set(node_labels), reverse=True)
+        colors = np.linspace(0, 1, len(sorted_node_labels))
+        node_colormap = {f: colors[sorted_node_labels.index(f)] for f in set(node_labels)}
+        node_colors = [node_colormap[n] if n in node_colormap.keys() else None for n in node_labels]
+
+    elif node_label.dtype == "object":
+        node_labels = node_label.str.split("|", expand=True)[0]
+        sorted_node_labels = sorted(node_labels.unique(), reverse=True)
+        colors = np.linspace(0, 1, len(sorted_node_labels))
+        node_colormap = {f: colors[sorted_node_labels.index(f)] for f in node_labels.unique()}
+        node_colors = [node_colormap[n] if n in node_colormap.keys() else None for n in node_labels]
+
+    elif node_label.dtype == "float":
+        node_labels = node_label.values
+        node_colormap = None
+        node_colors = [n / node_labels.max() for n in node_labels]
+    return node_colors