{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# CHAOS 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 numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "import sys\n", "sys.path.append('../Mouse_Spleen/')\n", "sys.path.append('../../../')\n", "from SpatialCOC.utils import calculate_chaos" ] }, { "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)\n", " adata_results[slice_name] = {\n", " 'adata': result,\n", " 'chaos_scores': {}\n", " }" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Calculating CHAOS" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "for slice_name, data in adata_results.items():\n", " adata = data['adata'] # Assume data is an AnnData object\n", " spatial_coords = adata.obsm['spatial'] # Retrieve spatial coordinates\n", " for method in adata.obs.columns: # Iterate over each method\n", " labels = adata.obs[method].values # Retrieve clustering labels\n", " chaos_values = calculate_chaos(spatial_coords, labels) # Calculate CHAOS scores\n", " data['chaos_scores'][method] = chaos_values # Store CHAOS scores" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Sheet '1' created with 10 methods\n", "Sheet '2' created with 10 methods\n", "\n", "Results saved to: chaos_results.xlsx\n" ] } ], "source": [ "import pandas as pd\n", "import numpy as np\n", "\n", "# 创建一个字典来存储所有结果\n", "all_results = {}\n", "\n", "# 遍历每个切片\n", "for slice_name, data in adata_results.items():\n", " adata = data['adata']\n", " spatial_coords = adata.obsm['spatial']\n", " \n", " slice_chaos_results = {}\n", " \n", " for method in adata.obs.columns:\n", " labels = adata.obs[method].values\n", " chaos_values = calculate_chaos(spatial_coords, labels)\n", " data['chaos_scores'][method] = chaos_values\n", " \n", " # 确保转换为Python float列表,而不是numpy数组\n", " if isinstance(chaos_values, (list, np.ndarray)):\n", " slice_chaos_results[method] = [float(v) for v in chaos_values]\n", " else:\n", " slice_chaos_results[method] = [float(chaos_values)]\n", " \n", " all_results[slice_name] = slice_chaos_results\n", "\n", "# 保存到Excel\n", "output_path = 'chaos_results.xlsx'\n", "with pd.ExcelWriter(output_path, engine='openpyxl') as writer:\n", " \n", " for slice_name, chaos_results in all_results.items():\n", " \n", " # 转换为DataFrame:方法为行,每个CHAOS值为列\n", " df = pd.DataFrame.from_dict(chaos_results, orient='index')\n", " \n", " # 重命名列为 CHAOS_1, CHAOS_2, ...\n", " df.columns = [f'CHAOS_{i+1}' for i in df.columns]\n", " df.index.name = 'Method'\n", " df = df.reset_index()\n", " \n", " sheet_name = str(slice_name)[:31]\n", " df.to_excel(writer, sheet_name=sheet_name, index=False)\n", " \n", " print(f\"Sheet '{sheet_name}' created with {len(df)} methods\")\n", "\n", "print(f\"\\nResults saved to: {output_path}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Ploting" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Drawing plot for 1 - CHAOS Score\n" ] }, { "data": { 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Drawing plot for 2 - CHAOS Score\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Define methods and corresponding colors\n", "methods = ['SpatialCOC', 'COSMOS','SpatialGlue', 'Seurat', 'MultiVI', 'MultiMAP', 'STAGATE', 'SpaGCN']\n", "colors = ['#D0836F', '#df871b', '#E4BE64', '#94aebd', '#87AC9A', '#9B8169', '#8D8E99', '#F5ECBA']\n", "\n", "# Set global font size and font\n", "fontsize = 20\n", "plt.rcParams['font.sans-serif'] = ['Arial']\n", "plt.rcParams['font.size'] = fontsize\n", "\n", "\n", "# Iterate through each slice\n", "for slice_name in slices:\n", " # Get the CHAOS score data for the current slice\n", " chaos_scores = adata_results[slice_name]['chaos_scores']\n", " \n", " # Prepare data: CHAOS scores for each method\n", " data = [chaos_scores[method] for method in methods]\n", " \n", " # Calculate the medians of Modality1 and Modality2 separately\n", " median_modality1 = np.median(data[0]) # Median of Modality1\n", " median_modality2 = np.median(data[1]) # Median of Modality2\n", " \n", " # Determine the best performance between the two medians\n", " max_median = min(median_modality1, median_modality2)\n", " \n", " # Automatically calculate the y-axis range\n", " all_scores = np.concatenate(data) # Combine all data into one array\n", " y_min = np.min(all_scores) - 0.05 * (np.max(all_scores) - np.min(all_scores)) # Slightly less than the minimum value\n", " y_max = np.max(all_scores) + 0.05 * (np.max(all_scores) - np.min(all_scores)) # Slightly more than the maximum value\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):\n", " patch.set_facecolor(color)\n", " \n", " # Remove x-axis tick labels\n", " plt.xticks([]) # Remove x-axis tick labels\n", " \n", " # Set the unified y-axis range\n", " y_max = 2.6 if slice_name == '1' else 3\n", " plt.ylim(y_min, y_max)\n", " \n", " # Add y-axis label\n", " plt.ylabel('CHAOS Score', fontsize=fontsize)\n", " \n", " # Thicken the plot'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(methods) + 1):\n", " plt.axvline(x=x, color='gray', linestyle='--', alpha=1, zorder=-10, linewidth=1.5)\n", " \n", " # Draw a horizontal line to represent the best median performance\n", " plt.axhline(y=max_median, color='red', linestyle='--', linewidth=2)\n", " \n", " # Print plotting information\n", " print(f\"Drawing plot for {slice_name} - CHAOS Score\")\n", "\n", " plt.tight_layout(pad=0.1, rect=[0.03, 0, 1, 0.98])\n", " \n", " # Save the image\n", " save_path = f\"./replicate{slice_name}/\"\n", " plt.savefig(save_path + f\"CHAOS_Score.png\", dpi=500)\n", " plt.savefig(save_path + f\"CHAOS_Score.eps\")\n", " \n", " plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Appeal", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.5" } }, "nbformat": 4, "nbformat_minor": 2 }