curve.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1822322/3319552035.py:58: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.boxplot(x='category', y='value', data=data, palette=palette)\n",
      "/tmp/ipykernel_1822322/3319552035.py:58: UserWarning: The palette list has more values (10) than needed (8), which may not be intended.\n",
      "  sns.boxplot(x='category', y='value', data=data, palette=palette)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array([-0.2,  0. ,  0.2,  0.4,  0.6,  0.8,  1. ]),\n",
       " [Text(0, -0.2, '−0.2'),\n",
       "  Text(0, 0.0, '0.0'),\n",
       "  Text(0, 0.2, '0.2'),\n",
       "  Text(0, 0.4000000000000001, '0.4'),\n",
       "  Text(0, 0.6000000000000001, '0.6'),\n",
       "  Text(0, 0.8, '0.8'),\n",
       "  Text(0, 1.0000000000000002, '1.0')])"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve, auc, precision_recall_curve\n",
    "import matplotlib.pyplot as plt\n",
    "import scanpy as sc \n",
    "import numpy as np\n",
    "\n",
    "import pandas as pd \n",
    "import seaborn as sns\n",
    "\n",
    "from scipy.stats import mannwhitneyu\n",
    "\n",
    "train_adata = sc.read_h5ad('/home/cuiyan/mms/CODEX_hnc_s.h5ad')\n",
    "\n",
    "batch_obs = 'slice'\n",
    "ct_obs = 'cell_type'\n",
    "\n",
    "\n",
    "plt.rcParams['font.family'] = 'Arial'\n",
    "plt.rcParams['axes.edgecolor'] = 'black'\n",
    "plt.rcParams['axes.linewidth'] = 1.5\n",
    "plt.rcParams['axes.labelweight'] = 'bold'\n",
    "plt.rcParams['axes.titleweight'] = 'bold'\n",
    "plt.rcParams['axes.titlesize'] = 20  # Adjust title size\n",
    "plt.rcParams['axes.labelsize'] = 15\n",
    "\n",
    "\n",
    "x = []\n",
    "y  = []\n",
    "\n",
    "for s in train_adata[train_adata.obs['condition'] == 1].obs[batch_obs].unique():\n",
    "    adata_ = train_adata[train_adata.obs[batch_obs] == s].copy()\n",
    "    X = adata_.obs['new_labels'].astype(int).sum() / adata_.shape[0] \n",
    "    x.append(X)\n",
    "    y.append(adata_.obs['pTR_max'].values[0])\n",
    "\n",
    "\n",
    "label = ['80-90%', '10-20%', '20%', '40%', '100%', '20-30%', '>90%', '30-40%']\n",
    "rank =  [5,0,1,4,7,2,6,3]\n",
    "draw_label = ['10-20%', '20%', '20-30%', '30-40%', '40%', '80-90%', '>90%', '100%']\n",
    "y_test= [rank[label.index(i)] for i in y]\n",
    "\n",
    "y = y_test\n",
    "\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "data = pd.DataFrame({'value' : x,\n",
    "                     'category': y,\n",
    "                     })\n",
    "\n",
    "import palettable\n",
    "from matplotlib.colors import  ListedColormap\n",
    "palette =  palettable.cmocean.sequential.Dense_10.mpl_colors\n",
    "\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "\n",
    "sns.boxplot(x='category', y='value', data=data, palette=palette)\n",
    "sns.stripplot(x='category', y='value', data=data, color='darkblue', jitter=True, alpha=0.5)\n",
    "\n",
    "# Calculating mean values for each category\n",
    "mean_values = data.groupby('category')['value'].mean().reset_index()\n",
    "\n",
    "# Lineplot for mean values\n",
    "sns.lineplot(x='category', y='value', data=mean_values, marker='o', color='#FF71CD', sort=False)\n",
    "\n",
    "plt.title('Area proportion score for diiferent pTR situation')\n",
    "plt.ylabel('Area proportion')\n",
    "plt.xlabel('')\n",
    "draw_label = ['10-20%', '20%', '20-30%', '30-40%', '40%', '80-90%', '>90%', '100%']\n",
    "plt.xticks(range(len(label)), draw_label, rotation=45, fontsize=15)\n",
    "plt.yticks(fontsize=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'batch_obs' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[1], line 27\u001b[0m\n\u001b[1;32m     25\u001b[0m lens \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m     26\u001b[0m events \u001b[38;5;241m=\u001b[39m []\n\u001b[0;32m---> 27\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m s \u001b[38;5;129;01min\u001b[39;00m train_adata[train_adata\u001b[38;5;241m.\u001b[39mobs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcondition\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m]\u001b[38;5;241m.\u001b[39mobs[\u001b[43mbatch_obs\u001b[49m]\u001b[38;5;241m.\u001b[39munique():\n\u001b[1;32m     28\u001b[0m     adata_ \u001b[38;5;241m=\u001b[39m train_adata[train_adata\u001b[38;5;241m.\u001b[39mobs[batch_obs] \u001b[38;5;241m==\u001b[39m s]\n\u001b[1;32m     29\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m np\u001b[38;5;241m.\u001b[39misnan(adata_\u001b[38;5;241m.\u001b[39mobs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrecurred\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mvalues)\u001b[38;5;241m.\u001b[39msum() \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n",
      "\u001b[0;31mNameError\u001b[0m: name 'batch_obs' is not defined"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve, auc, precision_recall_curve\n",
    "import matplotlib.pyplot as plt\n",
    "import scanpy as  sc \n",
    "\n",
    "import pandas as pd \n",
    "import seaborn as sns\n",
    "\n",
    "from scipy.stats import mannwhitneyu\n",
    "import numpy as np\n",
    "\n",
    "plt.rcParams['font.family'] = 'Arial'\n",
    "plt.rcParams['axes.edgecolor'] = 'black'\n",
    "plt.rcParams['axes.linewidth'] = 1.5\n",
    "plt.rcParams['axes.labelweight'] = 'bold'\n",
    "plt.rcParams['axes.titleweight'] = 'bold'\n",
    "plt.rcParams['axes.titlesize'] = 20  # Adjust title size\n",
    "plt.rcParams['axes.labelsize'] = 15\n",
    "\n",
    "\n",
    "train_adata = sc.read_h5ad('/home/cuiyan/mms/CODEX_hnc_l.h5ad')\n",
    "\n",
    "\n",
    "x = []\n",
    "y = []\n",
    "lens = []\n",
    "events = []\n",
    "for s in train_adata[train_adata.obs['condition'] == 1].obs[batch_obs].unique():\n",
    "    adata_ = train_adata[train_adata.obs[batch_obs] == s]\n",
    "    if np.isnan(adata_.obs['recurred'].values).sum() == 0:\n",
    "        X = adata_.obs['new_labels'].astype(int).sum() / adata_.shape[0]\n",
    "        x.append(X)\n",
    "        lens.append(adata_.obs['survival_day'].values[0])\n",
    "        y.append(adata_.obs['recurred'].values[0])\n",
    "        events.append(adata_.obs['hpvstatus'].values[0])\n",
    "\n",
    "\n",
    "\n",
    "y_true = [i == 1  for i in events]\n",
    "\n",
    "\n",
    "x_0 = x\n",
    "\n",
    "\n",
    "from sklearn.metrics import roc_curve, auc, precision_recall_curve\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Colors for each ROC curve\n",
    "colors = ['#FF5DA2']\n",
    "labels = ['Taichi area fraction score']\n",
    "\n",
    "# Create the figure once\n",
    "plt.figure(figsize=(8, 6))\n",
    "\n",
    "# Loop through x_0, x_1, x_2 and plot ROC curves\n",
    "for i, y_scores in enumerate([x_0]):\n",
    "\n",
    "    # Calculate ROC Curve and ROC AUC\n",
    "    fpr, tpr, thresholds = roc_curve(y_true, y_scores)\n",
    "    roc_auc = auc(fpr, tpr)\n",
    "\n",
    "    # Plot ROC curve for each set of scores\n",
    "    plt.plot(fpr, tpr, color=colors[i], lw=2, label=f'{labels[i]} ROC curve (area = {roc_auc:.2f})', alpha=0.7)\n",
    "\n",
    "# Plot diagonal line for reference\n",
    "plt.plot([0, 1], [0, 1], color='gray', lw=2, linestyle='--')\n",
    "\n",
    "# Set limits, labels, and title\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC for HPV infection situation')\n",
    "\n",
    "# Display the legend showing AUC for each curve\n",
    "plt.legend(loc=\"lower right\")\n",
    "\n",
    "# Format ticks and display the plot\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12)\n",
    "plt.tight_layout()\n",
    "\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_curve, auc, precision_recall_curve\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import pandas as pd \n",
    "import seaborn as sns\n",
    "\n",
    "from scipy.stats import mannwhitneyu\n",
    "\n",
    "\n",
    "#plt.rcParams['font.family'] = 'Arial'\n",
    "plt.rcParams['axes.edgecolor'] = 'black'\n",
    "plt.rcParams['axes.linewidth'] = 1.5\n",
    "plt.rcParams['axes.labelweight'] = 'bold'\n",
    "plt.rcParams['axes.titleweight'] = 'bold'\n",
    "plt.rcParams['axes.titlesize'] = 20  # Adjust title size\n",
    "plt.rcParams['axes.labelsize'] = 15\n",
    "\n",
    "\n",
    "train_adata = sc.read_h5ad('/home/cuiyan/mms/CODEX_dead.h5ad')\n",
    "\n",
    "\n",
    "x = []\n",
    "y = []\n",
    "y_1 = []\n",
    "lens = []\n",
    "events = []\n",
    "for s in train_adata[train_adata.obs['condition'] == 1].obs[batch_obs].unique():\n",
    "    adata_ = train_adata[train_adata.obs[batch_obs] == s]\n",
    "    X = adata_.obs['new_labels'].astype(int).sum() \n",
    "    x.append(X)\n",
    "    lens.append(adata_.obs['length_of_disease_free_survival'].values[0])\n",
    "    y.append(adata_.obs['recurrence'].values[0])\n",
    "    y_1.append(adata_.obs['primary_outcome'].values[0])\n",
    "    events.append(adata_.obs['alive_or_deceased'].values[0])\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Log-Rank Test p-value: 0.042995675527470426\n",
      "4.095572147245649\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np \n",
    "\n",
    "duration = np.array(lens)\n",
    "index = ~np.isnan(duration)\n",
    "duration = duration[index]\n",
    "values = np.array(x)[index]\n",
    "#groups = values\n",
    "groups = values > np.mean(values)\n",
    "event = np.array(events)[index]\n",
    "\n",
    "# 示例数据\n",
    "data = {\n",
    "    'group': groups.astype(int),\n",
    "    'duration': duration,\n",
    "    'event':(event == 'Dead').astype(int)\n",
    "}\n",
    "\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "\n",
    "from lifelines import KaplanMeierFitter\n",
    "from lifelines.statistics import logrank_test\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 初始化KaplanMeierFitter对象\n",
    "kmf = KaplanMeierFitter()\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 6))  # Change figure size as needed\n",
    "\n",
    "# Loop through each group\n",
    "colors = {0: '#711DB0', 1: '#EF4040'}  # Define colors for each group\n",
    "for name, grouped_df in df.groupby('group'):\n",
    "    # Fit data\n",
    "    kmf.fit(grouped_df['duration'], grouped_df['event'], label=name)\n",
    "    # Plot survival curve with specified color\n",
    "    kmf.plot_survival_function(ax=ax, color=colors[name])\n",
    "\n",
    "plt.title('Kaplan-Meier Survival Curve split based on Area Proportion')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Survival Probability')\n",
    "#plt.savefig(f'/home/cy/mms/eps_figure/fig_5_codex_survival_curve.eps', bbox_inches='tight', pad_inches=0, format='eps')\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12)\n",
    "plt.show()\n",
    "\n",
    "# Separating the two groups for Log-Rank test\n",
    "group_A = df[df['group'] == 1]\n",
    "group_B = df[df['group'] == 0]\n",
    "\n",
    "results = logrank_test(group_A['duration'], group_B['duration'], event_observed_A=group_A['event'], event_observed_B=group_B['event'])\n",
    "print('Log-Rank Test p-value:', results.p_value)\n",
    "print(results.test_statistic)\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "taichi-env",
   "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": "3.11.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}