{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/timkempchen/mambaforge/envs/spacec/lib/python3.10/site-packages/anndata/utils.py:429: FutureWarning: Importing read_csv from `anndata` is deprecated. Import anndata.io.read_csv instead.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/timkempchen/mambaforge/envs/spacec/lib/python3.10/site-packages/anndata/utils.py:429: FutureWarning: Importing read_text from `anndata` is deprecated. Import anndata.io.read_text instead.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/timkempchen/mambaforge/envs/spacec/lib/python3.10/site-packages/anndata/utils.py:429: FutureWarning: Importing read_excel from `anndata` is deprecated. Import anndata.io.read_excel instead.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/timkempchen/mambaforge/envs/spacec/lib/python3.10/site-packages/anndata/utils.py:429: FutureWarning: Importing read_mtx from `anndata` is deprecated. Import anndata.io.read_mtx instead.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/timkempchen/mambaforge/envs/spacec/lib/python3.10/site-packages/anndata/utils.py:429: FutureWarning: Importing read_loom from `anndata` is deprecated. Import anndata.io.read_loom instead.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/timkempchen/mambaforge/envs/spacec/lib/python3.10/site-packages/anndata/utils.py:429: FutureWarning: Importing read_hdf from `anndata` is deprecated. Import anndata.io.read_hdf instead.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/timkempchen/mambaforge/envs/spacec/lib/python3.10/site-packages/anndata/utils.py:429: FutureWarning: Importing read_csv from `anndata` is deprecated. Import anndata.io.read_csv instead.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/timkempchen/mambaforge/envs/spacec/lib/python3.10/site-packages/anndata/utils.py:429: FutureWarning: Importing read_excel from `anndata` is deprecated. Import anndata.io.read_excel instead.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/timkempchen/mambaforge/envs/spacec/lib/python3.10/site-packages/anndata/utils.py:429: FutureWarning: Importing read_hdf from `anndata` is deprecated. Import anndata.io.read_hdf instead.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/timkempchen/mambaforge/envs/spacec/lib/python3.10/site-packages/anndata/utils.py:429: FutureWarning: Importing read_loom from `anndata` is deprecated. Import anndata.io.read_loom instead.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/timkempchen/mambaforge/envs/spacec/lib/python3.10/site-packages/anndata/utils.py:429: FutureWarning: Importing read_mtx from `anndata` is deprecated. Import anndata.io.read_mtx instead.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/timkempchen/mambaforge/envs/spacec/lib/python3.10/site-packages/anndata/utils.py:429: FutureWarning: Importing read_text from `anndata` is deprecated. Import anndata.io.read_text instead.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/timkempchen/mambaforge/envs/spacec/lib/python3.10/site-packages/anndata/utils.py:429: FutureWarning: Importing read_umi_tools from `anndata` is deprecated. Import anndata.io.read_umi_tools instead.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "2024-11-17 21:37:06.807506: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  SSE4.1 SSE4.2\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "INFO:root: * TissUUmaps version: 3.1.1.6\n"
     ]
    }
   ],
   "source": [
    "import panel as pn\n",
    "import scanpy as sc\n",
    "import spacec as sp\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import warnings\n",
    "from pyFlowSOM import map_data_to_nodes, som\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def launch_interactive_clustering():\n",
    "    warnings.filterwarnings('ignore')\n",
    "    pn.extension('deckgl', design='bootstrap', theme='default', template='bootstrap')\n",
    "    pn.state.template.config.raw_css.append(\"\"\"\n",
    "    #main {\n",
    "    padding: 0;\n",
    "    }\"\"\")\n",
    "\n",
    "    # Define the app\n",
    "    def create_clustering_app():\n",
    "        \n",
    "        # Define the clustering function\n",
    "        def clustering(\n",
    "            adata,\n",
    "            clustering=\"leiden\",\n",
    "            marker_list=None,\n",
    "            resolution=1,\n",
    "            n_neighbors=10,\n",
    "            reclustering=False,\n",
    "            key_added=None,\n",
    "            key_filter=None,\n",
    "            subset_cluster=None,\n",
    "            seed=42,\n",
    "            fs_xdim=10,\n",
    "            fs_ydim=10,\n",
    "            fs_rlen=10,  # FlowSOM parameters\n",
    "            **cluster_kwargs,\n",
    "        ):\n",
    "            \"\"\"\n",
    "            Perform clustering on the given annotated data matrix.\n",
    "\n",
    "            Parameters\n",
    "            ----------\n",
    "            adata : AnnData\n",
    "                The annotated data matrix of shape n_obs x n_vars.\n",
    "            clustering : str, optional\n",
    "                The clustering algorithm to use. Defaults to \"leiden\".\n",
    "            marker_list : list, optional\n",
    "                A list of markers for clustering. Defaults to None.\n",
    "            resolution : int, optional\n",
    "                The resolution for the clustering algorithm. Defaults to 1.\n",
    "            n_neighbors : int, optional\n",
    "                The number of neighbors to use for the neighbors graph. Defaults to 10.\n",
    "            reclustering : bool, optional\n",
    "                Whether to recluster the data. Defaults to False.\n",
    "            key_added : str, optional\n",
    "                The key name to add to the adata object. Defaults to None.\n",
    "            seed : int, optional\n",
    "                Seed for random state. Default is 42.\n",
    "            fs_xdim : int, optional\n",
    "                X dimension for FlowSOM. Default is 10.\n",
    "            fs_ydim : int, optional\n",
    "                Y dimension for FlowSOM. Default is 10.\n",
    "            fs_rlen : int, optional\n",
    "                Rlen for FlowSOM. Default is 10.\n",
    "\n",
    "            Returns\n",
    "            -------\n",
    "            AnnData\n",
    "                The annotated data matrix with the clustering results added.\n",
    "            \"\"\"\n",
    "            if clustering not in [\"leiden\", \"louvain\", \"leiden_gpu\", \"flowSOM\"]:\n",
    "                print(\n",
    "                    \"Invalid clustering options. Please select from leiden, louvain, leiden_gpu, or flowSOM!\"\n",
    "                )\n",
    "                sys.exit()\n",
    "\n",
    "            if key_added is None:\n",
    "                key_added = clustering + \"_\" + str(resolution)\n",
    "\n",
    "            if marker_list is not None:\n",
    "                if len(list(set(marker_list) - set(adata.var_names))) > 0:\n",
    "                    print(\"Marker list not all in adata var_names! Using intersection instead!\")\n",
    "                    marker_list = list(set(marker_list) & set(adata.var_names))\n",
    "                    print(\"New marker_list: \" + \" \".join(marker_list))\n",
    "                adata = adata[:, marker_list]\n",
    "\n",
    "            if not reclustering and clustering != \"flowSOM\":\n",
    "                sc.pp.neighbors(adata, n_neighbors=n_neighbors)\n",
    "                sc.tl.umap(adata)\n",
    "\n",
    "            if clustering == \"leiden\":\n",
    "                sc.tl.leiden(\n",
    "                    adata,\n",
    "                    resolution=resolution,\n",
    "                    key_added=key_added,\n",
    "                    random_state=seed,\n",
    "                    **cluster_kwargs,\n",
    "                )\n",
    "            elif clustering == \"louvain\":\n",
    "                sc.tl.louvain(\n",
    "                    adata,\n",
    "                    resolution=resolution,\n",
    "                    key_added=key_added,\n",
    "                    random_state=seed,\n",
    "                    **cluster_kwargs,\n",
    "                )\n",
    "            elif clustering == \"flowSOM\":\n",
    "                # Implement FlowSOM clustering\n",
    "                adata_df = pd.DataFrame(\n",
    "                    adata.X, index=adata.obs.index, columns=adata.var.index\n",
    "                )\n",
    "                som_input_arr = adata_df.to_numpy()\n",
    "                node_output = som(\n",
    "                    som_input_arr,\n",
    "                    xdim=fs_xdim,\n",
    "                    ydim=fs_ydim,\n",
    "                    rlen=fs_rlen,\n",
    "                    seed=seed,\n",
    "                )\n",
    "                clusters, dists = map_data_to_nodes(node_output, som_input_arr)\n",
    "                clusters = pd.Categorical(clusters)\n",
    "                adata.obs[key_added] = clusters\n",
    "            else:\n",
    "                print(\"Clustering method not implemented in this example.\")\n",
    "\n",
    "            return adata\n",
    "        \n",
    "        # Callback to load data\n",
    "        def load_data(event):\n",
    "            if not input_path.value or not os.path.isfile(input_path.value):\n",
    "                output_area.object = \"**Please enter a valid AnnData file path.**\"\n",
    "                return\n",
    "            adata = sc.read_h5ad(input_path.value)\n",
    "            adata_container['adata'] = adata\n",
    "            marker_list_input.options = list(adata.var_names)\n",
    "            output_area.object = \"**AnnData file loaded successfully.**\"\n",
    "\n",
    "        # Callback to run clustering\n",
    "        def run_clustering(event):\n",
    "            adata = adata_container.get('adata', None)\n",
    "            if adata is None:\n",
    "                output_area.object = \"**Please load an AnnData file first.**\"\n",
    "                return\n",
    "            marker_list = list(marker_list_input.value) if marker_list_input.value else None\n",
    "            key_added = key_added_input.value if key_added_input.value else clustering_method.value + '_' + str(resolution.value)\n",
    "            # Start loading indicator\n",
    "            loading_indicator.active = True\n",
    "            output_area.object = \"**Clustering in progress...**\"\n",
    "            # Run clustering\n",
    "            try:\n",
    "                if clustering_method.value == 'flowSOM':\n",
    "                    adata = clustering(\n",
    "                        adata,\n",
    "                        clustering=clustering_method.value,\n",
    "                        marker_list=marker_list,\n",
    "                        reclustering=reclustering.value,\n",
    "                        seed=seed.value,\n",
    "                        key_added=key_added,\n",
    "                        fs_xdim=fs_xdim.value,\n",
    "                        fs_ydim=fs_ydim.value,\n",
    "                        fs_rlen=fs_rlen.value\n",
    "                    )\n",
    "                else:\n",
    "                    adata = clustering(\n",
    "                        adata,\n",
    "                        clustering=clustering_method.value,\n",
    "                        marker_list=marker_list,\n",
    "                        resolution=resolution.value,\n",
    "                        n_neighbors=n_neighbors.value,\n",
    "                        reclustering=reclustering.value,\n",
    "                        seed=seed.value,\n",
    "                        key_added=key_added\n",
    "                    )\n",
    "                adata_container['adata'] = adata\n",
    "                output_area.object = \"**Clustering completed.**\"\n",
    "                # Automatically generate visualization\n",
    "                key_to_visualize = key_added\n",
    "                tabs = []\n",
    "                sc.pl.umap(adata, color=[key_to_visualize], show=False)\n",
    "                umap_fig = plt.gcf()\n",
    "                plt.close()\n",
    "                tabs.append(('UMAP', pn.pane.Matplotlib(umap_fig, dpi=100)))\n",
    "                if marker_list:\n",
    "                    sc.pl.dotplot(adata, marker_list, groupby=key_to_visualize, dendrogram=True, show=False)\n",
    "                    dotplot_fig = plt.gcf()\n",
    "                    plt.close()\n",
    "                    tabs.append(('Dotplot', pn.pane.Matplotlib(dotplot_fig, dpi=100)))\n",
    "                # Generate histogram plot\n",
    "                cluster_counts = adata.obs[key_to_visualize].value_counts()\n",
    "                cluster_counts.sort_index(inplace=True)\n",
    "                cluster_counts.plot(kind='bar')\n",
    "                plt.xlabel('Cluster')\n",
    "                plt.ylabel('Number of Cells')\n",
    "                plt.title(f'Cluster Counts for {key_to_visualize}')\n",
    "                hist_fig = plt.gcf()\n",
    "                plt.close()\n",
    "                tabs.append(('Histogram', pn.pane.Matplotlib(hist_fig, dpi=100)))\n",
    "                # Add new tabs to visualization area\n",
    "                for name, pane in tabs:\n",
    "                    visualization_area.append((name, pane))\n",
    "                # Update cluster annotations\n",
    "                clusters = adata.obs[key_to_visualize].unique().astype(str)\n",
    "                annotations_df = pd.DataFrame({'Cluster': clusters, 'Annotation': ['']*len(clusters)})\n",
    "                cluster_annotation.value = annotations_df\n",
    "            except Exception as e:\n",
    "                output_area.object = f\"**Error during clustering: {e}**\"\n",
    "            finally:\n",
    "                # Stop loading indicator\n",
    "                loading_indicator.active = False\n",
    "\n",
    "        # Callback to run subclustering\n",
    "        def run_subclustering(event):\n",
    "            adata = adata_container.get('adata', None)\n",
    "            if adata is None:\n",
    "                output_area.object = \"**Please run clustering first.**\"\n",
    "                return\n",
    "            if not subcluster_key.value or not subcluster_values.value:\n",
    "                output_area.object = \"**Please provide subcluster key and values.**\"\n",
    "                return\n",
    "            clusters = [c.strip() for c in subcluster_values.value.split(',')]\n",
    "            key_added = subcluster_key.value + '_subcluster'\n",
    "            # Start loading indicator for subclustering\n",
    "            loading_indicator_subcluster.active = True\n",
    "            output_area.object = \"**Subclustering in progress...**\"\n",
    "            try:\n",
    "                sc.tl.leiden(\n",
    "                    adata,\n",
    "                    seed=seed.value,\n",
    "                    restrict_to=(subcluster_key.value, clusters),\n",
    "                    resolution=subcluster_resolution.value,\n",
    "                    key_added=key_added\n",
    "                )\n",
    "                adata_container['adata'] = adata\n",
    "                output_area.object = \"**Subclustering completed.**\"\n",
    "                # Update visualization\n",
    "                tabs = []\n",
    "                sc.pl.umap(adata, color=[key_added], show=False)\n",
    "                umap_fig = plt.gcf()\n",
    "                plt.close()\n",
    "                tabs.append(('UMAP_Sub', pn.pane.Matplotlib(umap_fig, dpi=100)))\n",
    "                marker_list = list(marker_list_input.value) if marker_list_input.value else None\n",
    "                if marker_list:\n",
    "                    sc.pl.dotplot(adata, marker_list, groupby=key_added, dendrogram=True, show=False)\n",
    "                    dotplot_fig = plt.gcf()\n",
    "                    plt.close()\n",
    "                    tabs.append(('Dotplot_Sub', pn.pane.Matplotlib(dotplot_fig, dpi=100)))\n",
    "                # Generate histogram plot\n",
    "                cluster_counts = adata.obs[key_added].value_counts()\n",
    "                cluster_counts.sort_index(inplace=True)\n",
    "                cluster_counts.plot(kind='bar')\n",
    "                plt.xlabel('Subcluster')\n",
    "                plt.ylabel('Number of Cells')\n",
    "                plt.title(f'Subcluster Counts for {key_added}')\n",
    "                hist_fig = plt.gcf()\n",
    "                plt.close()\n",
    "                tabs.append(('Histogram_Sub', pn.pane.Matplotlib(hist_fig, dpi=100)))\n",
    "                # Add new tabs to visualization area\n",
    "                for name, pane in tabs:\n",
    "                    visualization_area.append((name, pane))\n",
    "                # Update cluster annotations\n",
    "                clusters = adata.obs[key_added].unique().astype(str)\n",
    "                annotations_df = pd.DataFrame({'Cluster': clusters, 'Annotation': ['']*len(clusters)})\n",
    "                cluster_annotation.value = annotations_df\n",
    "            except Exception as e:\n",
    "                output_area.object = f\"**Error during subclustering: {e}**\"\n",
    "            finally:\n",
    "                # Stop loading indicator for subclustering\n",
    "                loading_indicator_subcluster.active = False\n",
    "\n",
    "        # Callback to save annotations\n",
    "        def save_annotations(event):\n",
    "            adata = adata_container.get('adata', None)\n",
    "            if adata is None:\n",
    "                output_area.object = \"**No AnnData object to annotate.**\"\n",
    "                return\n",
    "            annotation_dict = dict(zip(cluster_annotation.value['Cluster'], cluster_annotation.value['Annotation']))\n",
    "            key_to_annotate = key_added_input.value if key_added_input.value else clustering_method.value + '_' + str(resolution.value)\n",
    "            adata.obs['cell_type'] = adata.obs[key_to_annotate].astype(str).map(annotation_dict).astype('category')\n",
    "            output_area.object = \"**Annotations saved to AnnData object.**\"\n",
    "\n",
    "        def save_adata(event):\n",
    "            adata = adata_container.get('adata', None)\n",
    "            if adata is None:\n",
    "                output_area.object = \"**No AnnData object to save.**\"\n",
    "                return\n",
    "            if not output_dir.value:\n",
    "                output_area.object = \"**Please specify an output directory.**\"\n",
    "                return\n",
    "            os.makedirs(output_dir.value, exist_ok=True)\n",
    "            output_filepath = os.path.join(output_dir.value, 'adata_annotated.h5ad')\n",
    "            adata.write(output_filepath)\n",
    "            output_area.object = f\"**AnnData saved to {output_filepath}.**\"\n",
    "\n",
    "        # Callback to run spatial visualization\n",
    "        def run_spatial_visualization(event):\n",
    "            adata = adata_container.get('adata', None)\n",
    "            if adata is None:\n",
    "                output_area.object = \"**Please load an AnnData file first.**\"\n",
    "                return\n",
    "            try:\n",
    "                sp.pl.catplot(\n",
    "                    adata, \n",
    "                    color=spatial_color.value, \n",
    "                    unique_region=spatial_unique_region.value, \n",
    "                    X=spatial_x.value, \n",
    "                    Y=spatial_y.value, \n",
    "                    n_columns=spatial_n_columns.value, \n",
    "                    palette=spatial_palette.value, \n",
    "                    savefig=spatial_savefig.value, \n",
    "                    output_fname=spatial_output_fname.value, \n",
    "                    output_dir=output_dir.value, \n",
    "                    figsize=spatial_figsize.value, \n",
    "                    size=spatial_size.value\n",
    "                )\n",
    "                spatial_fig = plt.gcf()\n",
    "                plt.close()\n",
    "                # Add new tab to visualization area\n",
    "                visualization_area.append(('Spatial Visualization', pn.pane.Matplotlib(spatial_fig, dpi=100)))\n",
    "                output_area.object = \"**Spatial visualization completed.**\"\n",
    "            except Exception as e:\n",
    "                output_area.object = f\"**Error during spatial visualization: {e}**\"\n",
    "\n",
    "        # File paths\n",
    "        input_path = pn.widgets.TextInput(name='AnnData File Path', placeholder='Enter path to .h5ad file')\n",
    "        output_dir = pn.widgets.TextInput(name='Output Directory', placeholder='Enter output directory path')\n",
    "        load_data_button = pn.widgets.Button(name='Load Data', button_type='primary')\n",
    "\n",
    "        # Clustering parameters\n",
    "        clustering_method = pn.widgets.Select(name='Clustering Method', options=[\"leiden\", \"louvain\", \"flowSOM\"])\n",
    "        resolution = pn.widgets.FloatInput(name='Resolution', value=1.0)\n",
    "        n_neighbors = pn.widgets.IntInput(name='Number of Neighbors', value=10)\n",
    "        reclustering = pn.widgets.Checkbox(name='Reclustering', value=False)\n",
    "        seed = pn.widgets.IntInput(name='Random Seed', value=42)\n",
    "        key_added_input = pn.widgets.TextInput(name='Key Added', placeholder='Enter key to add to AnnData.obs', value='')\n",
    "        marker_list_input = pn.widgets.MultiChoice(name='Marker List', options=[], width=950)\n",
    "\n",
    "        # Subclustering parameters\n",
    "        subcluster_key = pn.widgets.TextInput(name='Subcluster Key', placeholder='Enter key to filter on (e.g., \"leiden_1\")')\n",
    "        subcluster_values = pn.widgets.TextInput(name='Subcluster Values', placeholder='Enter clusters to subset (comma-separated)')\n",
    "        subcluster_resolution = pn.widgets.FloatInput(name='Subcluster Resolution', value=0.3)\n",
    "        subcluster_button = pn.widgets.Button(name='Run Subclustering', button_type='primary')\n",
    "\n",
    "        # Cluster annotation\n",
    "        cluster_annotation = pn.widgets.DataFrame(pd.DataFrame(columns=['Cluster', 'Annotation']), name='Cluster Annotations', autosize_mode='fit_columns')\n",
    "        save_annotations_button = pn.widgets.Button(name='Save Annotations', button_type='success')\n",
    "\n",
    "        fs_xdim = pn.widgets.IntInput(name='FlowSOM xdim', value=10)\n",
    "        fs_ydim = pn.widgets.IntInput(name='FlowSOM ydim', value=10)\n",
    "        fs_rlen = pn.widgets.IntInput(name='FlowSOM rlen', value=10)\n",
    "\n",
    "        # Buttons\n",
    "        run_clustering_button = pn.widgets.Button(name='Run Clustering', button_type='primary')\n",
    "        save_adata_button = pn.widgets.Button(name='Save AnnData', button_type='success')\n",
    "\n",
    "        # Loading indicators\n",
    "        loading_indicator = pn.widgets.Progress(name='Clustering Progress', active=False, bar_color='primary')\n",
    "        loading_indicator_subcluster = pn.widgets.Progress(name='Subclustering Progress', active=False, bar_color='primary')\n",
    "\n",
    "        # Output areas\n",
    "        output_area = pn.pane.Markdown()\n",
    "        visualization_area = pn.Tabs()  # Changed to pn.Tabs to hold multiple plots\n",
    "\n",
    "        # Global variable to hold the AnnData object\n",
    "        adata_container = {}\n",
    "        \n",
    "        # Spatial visualization parameters\n",
    "        spatial_color = pn.widgets.TextInput(name='Color By Column', placeholder='Enter group column name (e.g., cell_type_coarse)')\n",
    "        spatial_unique_region = pn.widgets.TextInput(name='Unique Region Column', value='unique_region')\n",
    "        spatial_x = pn.widgets.TextInput(name='X Coordinate Column', value='x')\n",
    "        spatial_y = pn.widgets.TextInput(name='Y Coordinate Column', value='y')\n",
    "        spatial_n_columns = pn.widgets.IntInput(name='Number of Columns', value=2)\n",
    "        spatial_palette = pn.widgets.TextInput(name='Color Palette', value='tab20')\n",
    "        spatial_figsize = pn.widgets.FloatInput(name='Figure Size', value=17)\n",
    "        spatial_size = pn.widgets.FloatInput(name='Point Size', value=20)\n",
    "        spatial_savefig = pn.widgets.Checkbox(name='Save Figure', value=False)\n",
    "        spatial_output_fname = pn.widgets.TextInput(name='Output Filename', placeholder='Enter output filename')\n",
    "        run_spatial_visualization_button = pn.widgets.Button(name='Run Spatial Visualization', button_type='primary')\n",
    "\n",
    "        # Link callbacks\n",
    "        load_data_button.on_click(load_data)\n",
    "        run_clustering_button.on_click(run_clustering)\n",
    "        subcluster_button.on_click(run_subclustering)\n",
    "        save_annotations_button.on_click(save_annotations)\n",
    "        save_adata_button.on_click(save_adata)\n",
    "        run_spatial_visualization_button.on_click(run_spatial_visualization)\n",
    "\n",
    "        # Clustering Tab Layout\n",
    "        clustering_tab = pn.Column(\n",
    "            pn.pane.Markdown(\"### Load Data\"),\n",
    "            pn.Row(input_path, output_dir, load_data_button),\n",
    "            pn.layout.Divider(),\n",
    "            pn.pane.Markdown(\"### Clustering Parameters\"),\n",
    "            pn.Row(clustering_method, resolution, n_neighbors),\n",
    "            pn.Row(seed, reclustering),\n",
    "            pn.Row(fs_xdim, fs_ydim, fs_rlen),\n",
    "            key_added_input,\n",
    "            marker_list_input,\n",
    "            pn.layout.Divider(),\n",
    "            pn.Row(run_clustering_button, loading_indicator),\n",
    "            output_area\n",
    "        )\n",
    "\n",
    "        # Subclustering Tab Layout\n",
    "        subclustering_tab = pn.Column(\n",
    "            pn.pane.Markdown(\"### Subclustering Parameters\"),\n",
    "            pn.Row(subcluster_key, subcluster_values, subcluster_resolution),\n",
    "            pn.layout.Divider(),\n",
    "            pn.Row(subcluster_button, loading_indicator_subcluster),\n",
    "            output_area\n",
    "        )\n",
    "\n",
    "        # Annotation Tab Layout\n",
    "        annotation_tab = pn.Column(\n",
    "            pn.pane.Markdown(\"### Cluster Annotation\"),\n",
    "            cluster_annotation,\n",
    "            pn.layout.Divider(),\n",
    "            save_annotations_button,\n",
    "            output_area\n",
    "        )\n",
    "\n",
    "        # Save Tab Layout\n",
    "        save_tab = pn.Column(\n",
    "            pn.pane.Markdown(\"### Save Data\"),\n",
    "            save_adata_button,\n",
    "            output_area\n",
    "        )\n",
    "\n",
    "        # Spatial Visualization Tab Layout\n",
    "        spatial_visualization_tab = pn.Column(\n",
    "            pn.pane.Markdown(\"### Spatial Visualization Parameters\"),\n",
    "            pn.Row(spatial_color, spatial_palette),\n",
    "            pn.Row(spatial_unique_region, spatial_n_columns),\n",
    "            pn.Row(spatial_x, spatial_y),\n",
    "            pn.Row(spatial_figsize, spatial_size),\n",
    "            pn.layout.Divider(),\n",
    "            pn.Row(spatial_savefig, spatial_output_fname),\n",
    "            pn.layout.Divider(),\n",
    "            pn.Row(run_spatial_visualization_button),\n",
    "            output_area\n",
    "        )\n",
    "\n",
    "        # Assemble Tabs\n",
    "        tabs = pn.Tabs(\n",
    "            (\"Clustering\", clustering_tab),\n",
    "            (\"Subclustering\", subclustering_tab),\n",
    "            (\"Annotation\", annotation_tab),\n",
    "            (\"Spatial Visualization\", spatial_visualization_tab),\n",
    "            (\"Save\", save_tab)\n",
    "        )\n",
    "\n",
    "        # Main Layout with Visualization Area\n",
    "        main_layout = pn.Row(\n",
    "            tabs,\n",
    "            visualization_area,\n",
    "            sizing_mode='stretch_both'\n",
    "        )\n",
    "\n",
    "        return main_layout\n",
    "\n",
    "    # Run the app\n",
    "    main_layout = create_clustering_app()\n",
    "\n",
    "    main_layout.servable(title='SPACEc Clustering App')\n",
    "    \n",
    "    return main_layout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  font-size: var(--jp-ui-font-size1);\n",
       "  color: var(--vscode-editor-foreground, var(--jp-ui-font-color1));\n",
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       "\n",
       "/* Override VSCode background color */\n",
       ".cell-output-ipywidget-background:has(\n",
       "    > .cell-output-ipywidget-background > .lm-Widget > *[data-root-id]\n",
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       "  var render_items = [{\"docid\":\"76c6d844-0e87-40bd-87cb-9fa7a0bfc6c2\",\"roots\":{\"18e4a484-b471-4917-b147-bf807e791079\":\"f717307a-f859-4e24-9e56-b871e63e6056\"},\"root_ids\":[\"18e4a484-b471-4917-b147-bf807e791079\"]}];\n",
       "  var docs = Object.values(docs_json)\n",
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       "    }, 25, root)\n",
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     "metadata": {},
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