{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Moran Visualization of the Mouse Spleen Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import scanpy as sc\n",
    "import squidpy as sq\n",
    "import uuid\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import sys\n",
    "sys.path.append('../Mouse_Spleen/')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "slices = ['1', '2']  ## 1, 2\n",
    "# List of methods\n",
    "methods = ['SpatialCOC', 'COSMOS', 'SpatialGlue', 'Seurat', 'MultiVI', 'MultiMAP', 'STAGATE', 'SpaGCN', 'Modality1', 'Modality2']\n",
    "# Create a dictionary to store data for each slice\n",
    "adata_results = {}\n",
    "\n",
    "# Load data and store it\n",
    "for slice_name in slices:\n",
    "    path = f'../../Mouse_Spleen_Replicate{slice_name}.h5ad'\n",
    "    result = sc.read_h5ad(path)  # Assuming 'sc' is the Scanpy library\n",
    "    adata_results[slice_name] = result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'1': AnnData object with n_obs × n_vars = 2568 × 0\n",
       "     obs: 'SpatialCOC', 'SpatialGlue', 'STAGATE', 'SpaGCN', 'Modality1', 'Modality2', 'MultiMAP', 'MultiVI', 'Seurat', 'COSMOS'\n",
       "     uns: 'COSMOS_colors', 'MultiMAP_colors', 'Seurat_colors'\n",
       "     obsm: 'COSMOS', 'Modality1', 'Modality2', 'MultiMAP', 'MultiVI', 'STAGATE', 'Seurat', 'SpatialCOC', 'SpatialGlue', 'spatial',\n",
       " '2': AnnData object with n_obs × n_vars = 2768 × 0\n",
       "     obs: 'SpatialCOC', 'SpatialGlue', 'STAGATE', 'SpaGCN', 'Modality1', 'Modality2', 'MultiMAP', 'MultiVI', 'Seurat', 'COSMOS'\n",
       "     uns: 'COSMOS_colors', 'Seurat_colors', 'SpaKnit_colors'\n",
       "     obsm: 'COSMOS', 'Modality1', 'Modality2', 'MultiMAP', 'MultiVI', 'STAGATE', 'Seurat', 'SpatialCOC', 'SpatialGlue', 'spatial'}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata_results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculating Moran's I Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "1\n"
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   "source": [
    "# Dictionary to store Moran's I scores for each slice\n",
    "moran_scores = {}\n",
    "\n",
    "# Iterate over each slice and its corresponding data\n",
    "for slice_name, adata in adata_results.items():\n",
    "    print(slice_name)\n",
    "    # Construct the spatial graph (adjust parameters based on the data type)\n",
    "    sq.gr.spatial_neighbors(adata, coord_type='grid')  # or 'generic'\n",
    "    moran_scores[slice_name] = {}\n",
    "    \n",
    "    # Iterate over each method\n",
    "    for method in methods:\n",
    "        # Skip if the method is not present in the observation columns\n",
    "        if method not in adata.obs.columns:\n",
    "            continue\n",
    "        \n",
    "        # Get the cluster information for the current method\n",
    "        cluster = adata.obs[method]\n",
    "        # Get unique categories, ignoring NaN values\n",
    "        categories = cluster.dropna().unique()\n",
    "        moran_scores[slice_name][method] = {}\n",
    "        \n",
    "        # Iterate over each category\n",
    "        for category in categories:\n",
    "            # Create a mask for the current category\n",
    "            mask = cluster == category\n",
    "            # Generate a unique temporary column name\n",
    "            temp_col = f\"temp_{uuid.uuid4().hex}\"\n",
    "            \n",
    "            try:\n",
    "                # Add the mask as a new column in the observation DataFrame\n",
    "                adata.obs[temp_col] = mask.astype(int)\n",
    "                # Calculate Moran's I spatial autocorrelation\n",
    "                sq.gr.spatial_autocorr(\n",
    "                    adata,\n",
    "                    mode=\"moran\",\n",
    "                    n_perms=100,  # Number of permutations for significance testing\n",
    "                    attr=\"obs\",  # Attribute type (observations)\n",
    "                    genes=[temp_col],  # Use the temporary column name\n",
    "                    n_jobs=4,  # Number of jobs to run in parallel\n",
    "                    seed=2024  # Random seed for reproducibility\n",
    "                )\n",
    "                # Retrieve the Moran's I score from the results\n",
    "                score = adata.uns['moranI'].loc[temp_col, 'I']\n",
    "                # Store the score in the dictionary\n",
    "                moran_scores[slice_name][method][category] = score\n",
    "            finally:\n",
    "                # Clean up: Remove the temporary column\n",
    "                if temp_col in adata.obs:\n",
    "                    del adata.obs[temp_col]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Storing the Results as an Excel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to convert category keys to integers and sort them in ascending order\n",
    "def convert_and_sort_categories(moran_scores):\n",
    "    for slice_name, methods in moran_scores.items():\n",
    "        for method, categories in methods.items():\n",
    "            # Convert category keys to integers and sort them\n",
    "            sorted_categories = {\n",
    "                int(k): v for k, v in sorted(categories.items(), key=lambda x: int(x[0]))\n",
    "            }\n",
    "            moran_scores[slice_name][method] = sorted_categories\n",
    "    return moran_scores\n",
    "\n",
    "# Convert category keys to integers and sort them\n",
    "moran_scores = convert_and_sort_categories(moran_scores)\n",
    "\n",
    "# Create an Excel file\n",
    "with pd.ExcelWriter(\"moran_scores_output.xlsx\") as writer:\n",
    "    # Dictionary to store the average values for all slices\n",
    "    all_averages = {}\n",
    "    \n",
    "    for slice_name, methods in moran_scores.items():\n",
    "        # Convert to DataFrame (each row represents a method, column names are categories)\n",
    "        df = pd.DataFrame.from_dict(methods, orient=\"index\")\n",
    "        \n",
    "        # Calculate the mean value for each row\n",
    "        averages = df.mean(axis=1)\n",
    "        all_averages[slice_name] = averages\n",
    "        \n",
    "        # Clean the sheet name (Excel limit: max 31 characters, no special symbols)\n",
    "        sheet_name = str(slice_name)\\\n",
    "            .replace(\":\", \"_\").replace(\"/\", \"_\")[:31]\n",
    "        \n",
    "        # Write to Excel (missing values are represented as empty strings)\n",
    "        df.to_excel(\n",
    "            writer,\n",
    "            sheet_name=sheet_name,\n",
    "            na_rep=\"\",\n",
    "            index_label=\"Method\"\n",
    "        )\n",
    "    \n",
    "    # Store the average values of all slices in a new sheet\n",
    "    averages_df = pd.DataFrame.from_dict(all_averages, orient=\"index\")\n",
    "    \n",
    "    # Transpose the averages DataFrame so that each row corresponds to a slice and each column corresponds to a method's average value\n",
    "    averages_df = averages_df.transpose()\n",
    "    \n",
    "    # Write the averages sheet\n",
    "    averages_df.to_excel(\n",
    "        writer,\n",
    "        sheet_name=\"Averages\",\n",
    "        index_label=\"Method\"\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Drawing plot for replicate 1 - Moran Score\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Drawing plot for replicate 2 - Moran Score\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Drawing plot for replicate Averages - Moran Score\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define methods\n",
    "methods = ['SpatialCOC', 'COSMOS', 'SpatialGlue', 'Seurat', 'MultiVI', 'MultiMAP', 'STAGATE', 'SpaGCN']\n",
    "# Define colors corresponding to each method\n",
    "colors = ['#D0836F', '#df871b', '#E4BE64', '#94aebd', '#87AC9A', '#9B8169', '#8D8E99', '#F5ECBA']\n",
    "\n",
    "# Set y-axis range (adjust according to actual data)\n",
    "y_min = -0.02\n",
    "y_max = 1.02\n",
    "\n",
    "# Set global font size and font\n",
    "fontsize = 24\n",
    "plt.rcParams['font.sans-serif'] = ['Arial']\n",
    "plt.rcParams['font.size'] = fontsize\n",
    "\n",
    "# Read the Excel file\n",
    "file_path = 'moran_scores_output.xlsx'  # Replace with your Excel file path\n",
    "xls = pd.ExcelFile(file_path)\n",
    "\n",
    "# Get all sheet names (each sheet represents a slice)\n",
    "slices = xls.sheet_names\n",
    "\n",
    "# Iterate through each slice\n",
    "for slice_name in slices:\n",
    "    # Read data from the current sheet\n",
    "    df = pd.read_excel(xls, sheet_name=slice_name)\n",
    "    \n",
    "    # Prepare data: Moran score for each method\n",
    "    data = []\n",
    "    for method in methods:\n",
    "        # Get the data row for the current method and ignore missing values\n",
    "        method_data = df.loc[df['Method'] == method].drop(columns=['Method']).values.flatten()\n",
    "        method_data = method_data[~np.isnan(method_data)]  # Ignore NaN values\n",
    "        data.append(method_data)\n",
    "    \n",
    "    # If all data for a method is missing, skip that method\n",
    "    data = [d for d in data if len(d) > 0]\n",
    "    \n",
    "    # If there is no valid data for any method, skip the current slice\n",
    "    if not data:\n",
    "        print(f\"No valid data for {slice_name}, skipping...\")\n",
    "        continue\n",
    "    \n",
    "    # Calculate the medians of Modality1 and Modality2 separately\n",
    "    modality1_data = df.loc[df['Method'] == 'Modality1'].drop(columns=['Method']).values.flatten()\n",
    "    modality1_data = modality1_data[~np.isnan(modality1_data)]  # Ignore NaN values\n",
    "    median_modality1 = np.median(modality1_data) if len(modality1_data) > 0 else None\n",
    "\n",
    "    modality2_data = df.loc[df['Method'] == 'Modality2'].drop(columns=['Method']).values.flatten()\n",
    "    modality2_data = modality2_data[~np.isnan(modality2_data)]  # Ignore NaN values\n",
    "    median_modality2 = np.median(modality2_data) if len(modality2_data) > 0 else None\n",
    "\n",
    "    # Determine the best performance between the two medians\n",
    "    if median_modality1 is not None and median_modality2 is not None:\n",
    "        best_median = max(median_modality1, median_modality2)  # Choose the smaller median as the best performance\n",
    "    else:\n",
    "        best_median = None\n",
    "    \n",
    "    # Create a new figure\n",
    "    plt.figure(figsize=(5, 5))\n",
    "    \n",
    "    # Draw the boxplot\n",
    "    bp = plt.boxplot(data, patch_artist=True,\n",
    "                     boxprops=dict(linestyle='-', linewidth=1, edgecolor='black'),  # Box edges\n",
    "                     medianprops=dict(linestyle='-', linewidth=1, color='black'),  # Median line\n",
    "                     whiskerprops=dict(linestyle='-', linewidth=1, color='black'),  # Whiskers\n",
    "                     capprops=dict(linestyle='-', linewidth=1, color='black'),  # Caps\n",
    "                     widths=0.85,  # Box width\n",
    "                     showfliers=False)  # Do not show outliers\n",
    "    \n",
    "    # Color the boxes\n",
    "    for patch, color in zip(bp['boxes'], colors[:len(data)]):  # Color only the boxes with actual data\n",
    "        patch.set_facecolor(color)\n",
    "    \n",
    "    # Remove x-axis tick labels\n",
    "    plt.xticks([])  # Remove x-axis tick labels\n",
    "    \n",
    "    # Set a unified y-axis range\n",
    "    plt.ylim(y_min, y_max)\n",
    "    \n",
    "    # Add y-axis label\n",
    "    plt.ylabel('Moran\\'s I Score', fontsize=fontsize)\n",
    "    \n",
    "    # Thicken the figure's border\n",
    "    for spine in plt.gca().spines.values():\n",
    "        spine.set_linewidth(2)\n",
    "    \n",
    "    # Add grid lines\n",
    "    plt.grid(axis='y', color='gray', linestyle='--', alpha=1, zorder=-10, linewidth=1.5)\n",
    "    \n",
    "    # Add vertical dashed lines\n",
    "    for x in range(1, len(data) + 1):\n",
    "        plt.axvline(x=x, color='gray', linestyle='--', alpha=1, zorder=-10, linewidth=1.5)\n",
    "    \n",
    "    # Add a red dashed line for the median of single modality values\n",
    "    if best_median is not None:\n",
    "        plt.axhline(y=best_median, color='#FF0000', linestyle='--', linewidth=2.5, zorder=10)\n",
    "    \n",
    "    # Print plot information\n",
    "    print(f\"Drawing plot for replicate {slice_name} - Moran Score\")\n",
    "    \n",
    "    # Adjust layout\n",
    "    plt.tight_layout(pad=0.1, rect=[0, 0, 1, 1])\n",
    "    \n",
    "    # Save the image\n",
    "    if slice_name == \"Averages\":\n",
    "        save_path = \"./\"\n",
    "    else:\n",
    "        save_path = f\"./replicate{slice_name}/\"\n",
    "    plt.savefig(save_path + f\"Moran_Score.png\", dpi=500)\n",
    "    plt.savefig(save_path + f\"Moran_Score.eps\")\n",
    "    \n",
    "    plt.show()"
   ]
  }
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