plot_pairwise_diff_rituximab.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import itertools\n",
    "from scipy.stats import spearmanr\n",
    "\n",
    "from pathlib import Path\n",
    "\n",
    "import os\n",
    "import sys  \n",
    "\n",
    "# set path for pixelator directory \n",
    "sys.path.insert(0, \"/Users/janrkauert/pixelator/\")\n",
    "\n",
    "# load functions from pixelator\n",
    "from pixelator.pixeldataset import PixelDataset\n",
    "from pixelator.graph import Graph\n",
    "\n",
    "# load standard workflow single cell functions\n",
    "from colocalization_measures.plot import plot_mutliple_markers\n",
    "from colocalization_measures.higher_order_similarity import calculate_higher_order_similarity\n",
    "from colocalization_measures.adjusted_local_assortativity import calculate_adjusted_local_assorativity\n",
    "from colocalization_measures.colocalization_scores import create_colocalization_scores\n",
    "\n",
    "# get directory with graph eval files\n",
    "path_name = \"C:/Users/janrh/Downloads/Pixelator_data\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# all possible markers from the MPX method\n",
    "# all MPX markers\n",
    "marker_names = ['CD9', 'CD62P', 'CD27', 'CD36', 'mIgG2b', 'CD337', 'CD3E', 'CD274', 'CD55', 'CD161', 'CD14', 'CD48', 'CD82', 'CD64', \n",
    "                'CD11c', 'CD54', 'CD11b', 'CD44', 'CD154', 'B2M', 'CD268', 'CD18', 'CD37', 'CD4', 'CD29', 'CD11a', 'CD47', 'CD7', \n",
    "                'CD2', 'CD35', 'CD45', 'CD8', 'CD314', 'CD22', 'CD19', 'CD127', 'CD53', 'CD52', 'CD229', 'CD72', 'CD59', 'CD163', 'CD38', \n",
    "                'CD25', 'CD41', 'CD150', 'CD278', 'ACTB', 'CD152', 'mIgG1',  'CD5', 'CD26', 'CD197', 'CD50', 'CD328', 'CD279', 'CD200',\n",
    "                'CD71', 'CD102', 'CD244', 'CD45RB', 'CD40', 'CD45RA', 'CD84', 'CD49D', 'CD162', 'CD1d', 'CD137', 'CD32', 'CD69', 'CD20', 'CD33', \n",
    "                'CD158', 'HLA-ABC', 'mIgG2a', 'CD86', 'CD43', 'CD16', 'TCRb', 'HLA-DR']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# non zero pairwise markers for the Rituximab experiment\n",
    "marker_names = [\"CD27\", \"CD55\", \"CD48\", \"CD82\", \"CD54\", \"CD44\", \"B2M\", \"CD18\", \"CD37\", \"CD29\", \"CD11a\", \"CD47\", \"CD45\", \"CD22\", \n",
    "                \"CD19\", \"CD72\", \"CD59\", \"CD38\", \"CD26\", \"CD197\", \"CD71\", \"CD40\", \"CD45RA\", \"CD84\", \"CD49D\", \"CD32\", \"HLA-ABC\", \n",
    "                \"CD86\", \"HLA-DR\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get all combinations with CD20\n",
    "combinations = list(itertools.product([\"CD20\"], marker_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "path_data = \"C:/Users/janrh/Documents/GitHub/TDA-local-assortativity-paper/Rituximab/Control\"\n",
    "\n",
    "# calculate pairwise colocalization scores\n",
    "similarity_measure_df_S0 = create_colocalization_scores(marker_names, path_data, combinations)\n",
    "similarity_measure_df_S0 = similarity_measure_df_S0.apply(lambda x: np.nan_to_num(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "combinations = list(itertools.product([\"Rituximab\"], marker_names))\n",
    "path_data = \"C:/Users/janrh/Documents/GitHub/TDA-local-assortativity-paper/Rituximab/Treated\"\n",
    "\n",
    "# calculate pairwise colocalization scores\n",
    "similarity_measure_df_S3 = create_colocalization_scores(marker_names, path_data, combinations)\n",
    "similarity_measure_df_S3 = similarity_measure_df_S3.apply(lambda x: np.nan_to_num(x))\n",
    "\n",
    "similarity_measure_df_S3[\"Marker_combinations\"] = [\"CD20\" + label[9:] for label in similarity_measure_df_S3.index.to_list()]\n",
    "similarity_measure_df_S3 = similarity_measure_df_S3.set_index(\"Marker_combinations\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# combine the two colocalization dataframes\n",
    "df_original = pd.concat([similarity_measure_df_S3.T, similarity_measure_df_S0.T], \n",
    "                        keys=['Treated', 'Control']).reset_index().drop(labels=[\"level_1\"], axis=1).set_index(\"level_0\")\n",
    "\n",
    "if \"CD20_CD20\" in df_original.index.to_list():\n",
    "    df_original = df_original.drop(labels=[\"CD20_CD20\"], axis=1)\n",
    "df = df_original.melt(ignore_index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_pol_mean = df_original.groupby(\"level_0\").get_group(\"Treated\").apply(lambda x: np.mean(x))\n",
    "least_pol_mean = df_original.groupby(\"level_0\").get_group(\"Control\").apply(lambda x: np.median(x))\n",
    "\n",
    "differential_2 = top_pol_mean - least_pol_mean\n",
    "differential_2 = differential_2.apply(lambda x: np.round(x, 5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import wilcoxon\n",
    "\n",
    "def statical_test(frist_sample, second_sample):\n",
    "\n",
    "    statistical_significance = []\n",
    "    for x_pos, marker_combination in enumerate(df_original.columns):\n",
    "        scores_marker_1 = df_original.groupby(\"level_0\").get_group(frist_sample)[marker_combination].values[:600]\n",
    "        scores_marker_2 = df_original.groupby(\"level_0\").get_group(second_sample)[marker_combination].values[:600]\n",
    "\n",
    "        if max(scores_marker_1) == 0 and min(scores_marker_1) == 0 and max(scores_marker_2) == 0 and min(scores_marker_2) == 0:\n",
    "            statistical_significance.append(np.nan)\n",
    "        else:\n",
    "            test = wilcoxon(x=scores_marker_1, y=scores_marker_2)\n",
    "            \n",
    "            p_value = test[1]\n",
    "            if 0.01 < p_value:\n",
    "                statistical_significance.append(np.nan)\n",
    "\n",
    "            elif 0.001 < p_value and p_value < 0.01:\n",
    "                statistical_significance.append(x_pos)\n",
    "\n",
    "            elif 0.0001 < p_value and p_value < 0.001:\n",
    "                statistical_significance.append(x_pos - 0.1)\n",
    "                statistical_significance.append(x_pos + 0.1)\n",
    "\n",
    "            else:\n",
    "                statistical_significance.append(x_pos - 0.2)\n",
    "                statistical_significance.append(x_pos)\n",
    "                statistical_significance.append(x_pos + 0.2)\n",
    "\n",
    "    return statistical_significance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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n448/TtSqVStlzqmnnppYu3Zt2th+MG7cuMQ333xT6vdWr16deP/99zNa5wdDhgxJieXqq6/e7JyOHTsmx3fs2DHt2GHDhqWsP2zYsKziK8vs2bMTrVu3zjj20aNHp82pkl577bVEnTp1kmvvs88+m53Tv3//lHhmz56d8fESiU2f1ddff32ZY4uLixO77rpryvjdd989sWzZsjLnrF27NnHIIYekzGnbtm1i9erVZc4p+TyLiESdOnUSL774YtpzefTRR1PmtGrVKrF+/frNXQIAAAAAACiTHdEAAAAAAIC0li5dmtLebrvtUna02pp69eoV9957b8ouOSVtt912cd1116X0bW63mU6dOkXHjh0zjqNu3brxyCOPRJMmTZJ9w4YNy3j+D0488cQYMmRI2jE777xz/OpXv0rpS3c+Tz/9dMyfPz/Z7tKlSzzwwAORn1/2fwu1aNEinnnmmZTdczIxZMiQ5C5A+fn5MWLEiIx2yzv99NPjiiuuSLYnT54c//73v7M6dmWrrHttSwwePDilfdZZZ8Vf/vKXtK/pxg499NBS+7/55pt48MEHU/oeeOCB2HPPPdOu94c//CGOPvroZLu4uDj+/Oc/ZxTL5syYMSNefPHFZDs/Pz+efvrptPmZn58fDz74YOy6667JvtWrV8ddd91V5pwbb7wxNmzYkGz36dMn/vGPf0SdOnUyinOvvfZKyf+N1atXL/bbb7+M1vnBddddFwcccECyPXz48Kzm58KyZcvi6KOPjq+//jrZd8YZZ5S6Q9oPDjjggLQ5VdLhhx8eV111VbL94YcfxieffLJlAVeC119/PT7++ONku379+jFy5Mho2rRpmXPq1KkTzz77bLRp0ybZN3/+/Hj88cezOvZNN90UxxxzTNoxP//5z2PfffdNtn/YERMAAAAAALaUQjQAAAAAACCtkoVo6T5gX9mGDBmSUbHUaaedltKeMGFChcfSvHnzlGKc9957L+s1brzxxozGnX766SntdOfz8MMPp7QHDx4ctWvX3uwxunbtGgMGDMgonoiIadOmxauvvpoSY+/evTOe/5vf/CZq1qyZbD/zzDMZz90aqsq99uGHH8b777+fbDdq1CjuvPPOCln78ccfj9WrVyfb+++/f/Tr1y+jubfffntK+6mnnorly5eXO6YHHnggEolEsn3mmWfGPvvss9l5NWvWjFtvvTWl77777ktZ6wfz58+Pp556Ktn+oZAt08K+yvLzn/88+fXChQtj1qxZOYwmvXXr1sVJJ50U06dPT/YdcMABMXz48AovVD777LNT2lvyrK0s999/f0r78ssvjw4dOmx2XpMmTWLQoEEpfffee2/Gx91+++3j0ksvzWhsNu8fAAAAAACwOQrRAAAAAACAtFauXJnSbtCgQU7iaNKkSRx55JEZjW3evHlKMcDcuXMrJaZOnTolv543b14sWrQo47m77rprdO/ePaOxPXr0SCnaKut8iouLU4qWGjRoECeeeGLGMZUs+Ein5M5f55xzTsZzI77fhW2vvfZKtt9+++2s5lemqnSvvfbaayntc889t8KKQd96662U9nnnnZfx3K5du6YUHq5fvz4++OCDnMZ0+OGHxw477JBsf/311/Hpp59uMu6NN96IoqKiZPvoo4+OnXbaaQuirVgbP08iIiZOnJijSNJLJBIxYMCAGD16dLKva9euMWLEiIx3lMtGVb4uG1+DiIhf/OIXGc8988wzo169esn2+PHjUwpD0znllFMy3sFyjz32SGlX1vshAAAAAADbBoVoAAAAAABAWo0aNUppr1q1Kidx9OzZM6sdi1q1apX8Opudmr755pt44IEH4he/+EX07Nkz2rZtGw0aNIi8vLxN/vzxj39Mmbt48eKMj9OrV6+Mx9aqVSul+Kis85kxY0ZK4WDPnj2jbt26GR9nn332SSl4S6dk4Vg25/ODjQu4pk+fXuruVbmwte61TJS8zsccc0yFrT1mzJiU9iGHHJLV/EMPPTSlXd5CtHXr1sVHH32UbNeqVSv69OmT8fz8/Pw4+OCDNxtTZV7TkjZs2BD/+te/4le/+lUceOCBscMOO0Tjxo0jPz9/k+dJyeLHbJ4nW9Nvf/vbePLJJ5Pt1q1bx8svvxzNmjXLap0PP/wwrr322jjmmGNixx13jObNm0etWrU2uS4li9uqynUpLCyMr7/+Otnu2LFj/OQnP8l4fuPGjVOem0VFRTF27NiM5mbzvN34+RRR8c8oAAAAAAC2LZn9TyIAAAAAALDNat68eUo7Vx9iL/lh+s3ZeOe2NWvWbHb8qlWrYvDgwfH//t//i3Xr1mUdX8T3RWyZ2pLz+aEAo6zzmTdvXkq7W7duWR2jbt26UVBQELNmzdrs2GnTpqW0sz2fkoqKimLFihXRpEmTcq1TESr7XsvGZ599ltLekoK/0iQSiZSdkRo3bhwFBQVZrbH77runtL/44otyxbRgwYJYv359st21a9eoXbt21jE98sgjaWOqrGu6sUQiEQ8++GBce+21sXDhwi1aI5vnydby97//PW655ZZku0GDBvHCCy9kde+8/fbbMXDgwJg8efIWxVBVrsucOXNS2rvttlvWa+y+++4phZGZ5lA2z6iSu5hW9DMKAAAAAIBti0I0AAAAAAAgrZKFaIsWLcpJHNns7JWtxYsXxyGHHBIff/xxudbJpoCtPOdT1s5hJQs0tqSoa+Od19JZsmRJ1mtvzvLly6tEIVpl3mvZWrp0afLrvLy8aNmyZYWsu3z58iguLk62W7RokfUaJWNZtmxZuWIqOX9LzjWTmDa+phHlL6Isqbi4OPr37x+PPvpoudbZ0oLYyvKvf/0rLrvssmS7Ro0a8eSTT2ZVyHfPPffExRdfXK7dD6vKddla92tpKuP9AwAAAAAAMqEQDQAAAAAASKtr164p7ZUrV8asWbOic+fOOYqo4v3sZz/bpAitffv2cfDBB0f37t1jhx12iIYNG0a9evUiPz8/Oebhhx9O2X0p10oWaGS7m1RERJ06dTIaVxm7Em1cGMX3Vq5cmfy6fv36KfdfeXz77bcp7ZK7JmWi5JyNY63KMZXsa9iwYdbHSWfo0KGbFKE1btw4+vbtG3vttVe0b98+mjZtGnXr1o1atWolx0yaNCmuvPLKCo2loowbNy7OOOOMKCoqSvb99a9/jeOOOy7jNd58881NitBq1qwZffr0iX333Tc6duwYrVq1irp1627yHDr88MPLfxIVrCrmEAAAAAAAVDaFaAAAAAAAQFr77bdf1KxZM7777rtk37hx4340hWgjR46MUaNGJduNGjWKu+66K84888zNFv288cYblRxddkruJlayUCITK1asyGhc/fr1U8a+/PLLUbNm+f7rqU2bNuWa/2PUqFGj5C5Jq1evjuLi4gopRitZfLVq1aqs1yg5p1GjRtUippJ93377bYXtNPf111/HzTffnNJ3zTXXxLXXXrvZgreNi7yqktmzZ8dxxx0Xq1evTvZdffXVcfHFF2e1zhVXXJFShHbsscfG3XffHTvssEPaeVVlB7SSqmIOAQAAAABAZVOIBgAAAAAApNWgQYPo2bNnfPjhh8m+kSNHxhlnnJHDqCrOk08+mdK+55574swzz8xo7tKlSysjpC3WvHnzlPZXX32V9Rrz58/PaFzLli1TCtF69uwZrVq1yvp4pNe8efNkIVoikYjFixdXyHVu0qRJ5OfnJ3ehW7JkSdZrLF68OKXdrFmzcsVUcn5lxVQyTxYuXBgFBQVZH6s0I0aMiDVr1iTbv/zlL+PGG2/MaG5Ve55ERCxbtiyOOeaY+Prrr5N9Z5xxRtx0001ZrfPpp5/GxIkTk+0ePXrEs88+m9GujVXxukRsvfsVAAAAAACqkvL/ukQAAAAAAOBH7+STT05pP/vss1v0ofuq6IMPPkh+3aJFizjttNMynjt16tTKCGmLdevWLfLy8pLtjQs/MvHll19uUhhRlk6dOqW0Z82aldWxyMxOO+2U0h43blyFrJuXlxft27dPtlesWBGFhYVZrTFp0qSUdseOHcsVU9u2bVMKk6ZPnx7r16+v8Jgq65pGpD5PIiIuueSSjOdWtefJunXr4sQTT4zp06cn+w488MAYPnx4ynMmEyWvywUXXJBREVpE1bsuPyh5b5W89zJR0TkEAAAAAACVTSEaAAAAAACwWRdeeGE0bNgw2V63bl3cfPPNOYyo4my800/nzp2jRo0aGc1bsWJFjB8/vrLC2iLNmjWLzp07J9uzZs2KGTNmZDx/5MiRGY89+OCDU9r/+c9/Mp5L5g444ICU9ksvvVRha++3334p7Wxfw5LjS66Xrdq1a8eee+6ZbK9fvz7eeeedjOcnEokYNWrUZmOqzGu68fMkIqJLly4Zz61KOZRIJKJ///7x9ttvJ/u6desWzz//fNSpUyfr9bbmdcnPT/0v8EQikdX8TBUUFETr1q2T7Tlz5sTnn3+e8fyVK1emFEHWrFkzevXqVaExAgAAAABARVOIBgAAAAAAbFazZs3i/PPPT+m7/fbbY8yYMeVeuyKLQLbExkUK2ey+9OCDD8batWsrI6RyOeGEE1Lad955Z0bzioqK4u677874OEcddVRK+957740NGzZkPJ/MlLzOjzzySHzzzTcVsvZBBx2U0h4+fHjGc2fMmBHvvvtusl2nTp3Yd999cxrT66+/HnPnzk2227ZtGzvvvPMm4w499NCoWbNmsv3yyy/HzJkzsw+2FCWLnjJ9pnz00Ufx/vvvV0gMFeE3v/lN/OMf/0i2W7duHS+99FI0a9Zsi9bb0uuybt26ePDBB7M6VoMGDVLaq1evzmp+Nspzvz7xxBOxZs2aZLtXr15Rv379igoNAAAAAAAqhUI0AAAAAAAgI4MGDYqCgoJku6ioKE466aT4+OOPt2i9DRs2xJVXXhmXXnppBUW4Zdq0aZP8eurUqRkV+cybNy8GDx5ciVFtuf/6r/9Kad91113xwQcfbHberbfemtVruddee6XsijZ37tz43//938wDJSM9e/aMPn36JNsrVqyosJw588wzU4p23n777Xj++eczmnvFFVektE877bRo0qRJuWM6//zzIy8vL9l+7LHHMtp5sKioKK6++uqUvgsuuKDUsa1bt44zzjgj2S4uLo7zzz8/iouLtzDq/7Px8yQiMtrRraioKC655JJyH7ui/P3vf49bb7012W7QoEG8+OKLKc//bG3JdYmIuO666zbZTW1zmjdvntKePXt2VvOzUfIeu/322+PLL7/c7LwVK1bEoEGDUvpKPrsBAAAAAKAqUogGAAAAAABkpGnTpvGPf/wjatWqlexbsGBBHHTQQfHEE09ssuNNOm+99Vb06tUr/vSnP2U1rzL07t07+fX69evjmmuuSTt+0aJFcdxxx1XYrlQVrUuXLnH22Wcn2999910ce+yx8Z///KfU8cXFxfGnP/0prr322oiIlCKgzRk6dGjk5//ffzfdcsstMWTIkKxe0y+//DKuuuqqGDt2bMZztjXXX399yuvy+OOPx69//euMr/Mbb7xRan/Tpk3jvPPOS+k777zzYvLkyZuN58UXX0y28/Pz4/LLL88ols3Zeeed47jjjku2i4uLo1+/fmmLexKJRFxwwQUxadKkZF+DBg3ioosuKnPONddcE3Xq1Em233777TjjjDNi3bp1GcU5bty4WL58+Sb9Gz9PIr4vpNp416uSioqK4rzzzqsyu6H961//issuuyzZrlGjRvzjH/+Ivfbaq1zrlrwud999d8yaNSvtnHvuuSduu+22rI+1yy67pLSffvrprNfI1GGHHRa77bZbsr1q1ao48cQTS703frB+/fr42c9+FvPnz0/2tW3bNs4666xKixMAAAAAACqKQjQAAAAAACBj++yzT9x3330pxUfLli2Ls846K/bdd9+455574osvvih17syZM+P222+P3r17R9++fTdb7LK1nHvuuSntu+++O/r37x9z5sxJ6V+5cmU88MADsdtuu8VHH30UERHdunXbWmFm5Y477ojWrVsn20uXLo1DDz00jjrqqLjzzjvjX//6VzzzzDNxww03xO677x5XXnllJBKJ2HXXXeOnP/1pxsfZf//944Ybbkjpu/7662PvvfeOJ598MpYtW7bJnKKiopg2bVrce++9ceSRR0anTp3itttui1WrVm35Cf/IHXbYYfE///M/KX1/+ctfYq+99opnn3221Gs3a9as+Mtf/hK9evWKww47rMy1b7jhhpSdrpYtWxa9e/eOP/3pT5u8flOmTIl+/frFkCFDUvqvuuqq2HPPPbfgzEr397//PZo2bZpsz5kzJ/bcc8948MEHNznXDz74IPr27RvDhw9P6b/tttuiXbt2ZR6je/fumxQ5/fOf/4xddtklHnrooVLv3S+//DLuv//+OPjgg2PvvfcudczJJ58cjRo1SrYnTJgQBx98cIwZMyZl3HfffRevvvpq7LvvvvHwww9HRNV4npxxxhlRVFSUbJ922mlRp06d+Pe//53Vn5LXpnPnzinPlpUrV8aBBx4Y//znP+O7775LGTtp0qQ4/fTT46KLLopEIpH1denbt29KwfRDDz0Up5xySjz44IPx8ssvp8T57rvvZrV2SXl5eTFs2LCU402YMCH22GOPeOaZZ2L9+vXJ/uLi4nj99dejV69e8dprr6Ws8+CDD0bdunXLFQsAAAAAAGwNNXMdAAAAAAAAUL30798/mjRpEv37948VK1Yk+8eOHZvc1ap58+ax3XbbRZMmTWLJkiUxf/78WL16danrtW3bdqvEXZYjjjgijjnmmHjppZeSfQ8//HA8/PDDseOOO8Z2220X33zzTcyePTulqOCss86KnXbaKQYPHpyLsNNq0aJFvPrqq3HIIYfE0qVLk/2vvvpqvPrqq6XOadmyZTz99NPxy1/+MtlXo0aNzR7rt7/9bSxcuDD+/Oc/J/vGjx8fZ555ZuTn50eHDh2iRYsWERHxzTffpL0XKNtNN90Uc+fOjaeeeirZN3HixOjXr1/Url07OnbsGM2bN49Vq1bFl19+mfGOfY0aNYp//vOfccQRRySLh1atWhVXXnllXHPNNdGpU6do3LhxzJ8/P+bNm7fJ/KOOOmqTwrTy2mGHHeLRRx+Nfv36JXcoW7x4cZx//vkxcODA6NSpU9SrVy/mzp0bCxcu3GT+eeedl3Y3tB8MHDgwPv/885R797PPPosBAwZEjRo1omPHjtGiRYtYt25dzJ8/PxYtWrTZNVu0aBHXXXddXH311cm+MWPGxH777RetW7eODh06xNq1a2POnDkpz8+uXbvGTTfdFCeeeOJmj1GZSubmE088EU888UTW67z55pvRt2/flL7bbrst+vbtGxs2bIiIiPnz58dpp50WDRs2jJ122iny8/Pjyy+/jK+//jo5p0GDBvHYY49Fz549Mz52q1at4pxzzokHH3ww2ffcc8/Fc889t8nYjh07RmFhYXYnV0LPnj3jzjvvjIsuuiiKi4sjIqKwsDBOPfXUaNSoURQUFESNGjVizpw5pRYvDh48OI466qhyxQAAAAAAAFuLHdEAAAAAAICsnXTSSTFp0qQyiyaWLl0aM2bMiA8//DA+++yzUguPWrRoEX/+859j9OjRlR3uZj322GOxzz77bNL/+eefx5gxY2LGjBkpRWhnnHFGDBs2bGuGmLXdd9893n333ejdu3dGY995553Yeeed49tvv032N2nSJKNj3X777fHQQw+l7GIV8f0OQIWFhTF+/PgYP358mfdCo0aNNplLqpo1a8aTTz4Z//u//5uy+1JExPr162PmzJkxZsyYmDJlSsZFaD/o1atXjB49OnbccceU/g0bNsSnn34a48aNK7UIbcCAATFy5MioXbt21uezOccee2y8+uqr0apVq5T+NWvWxCeffBLjx4/fpAitRo0acc0118QDDzyQ8XFuv/32uPPOO6N+/fop/UVFRfH555/H2LFjY/LkyRkVof3gqquuigsvvHCT/q+//jrGjh0bH3/8cUoR2q677hqvv/56NG7cOONjVEe9e/eO++67b5P799tvv42JEyfG+PHjU4rQmjVrFi+88MIW7bZ3xx13xOGHH17umDP1X//1X/HEE0+k7IYX8f3Obx9//HF89NFHmxSh1alTJ/72t7/F73//+60WJwAAAAAAlJdCNAAAAAAAYIsUFBTE888/HxMnToxLLrkk2rRps9k5DRo0iKOPPjqefPLJmDdvXvz617/epCghF5o2bRqjR4+O6667Lm3x1S677BKPP/54PPHEE5VSfFPRunbtGu+8806MGDEizjrrrOjcuXM0aNAgateuHR06dIhTTjklnnzyyRg/fnx06dIlIiJlB7VMC9EiIs4999woLCyMoUOHxs4777zZ8c2aNYtTTz01Hn744ViwYEHsscceWZ/ftiYvLy+GDh0a06ZNiwEDBmy2eK9169ZxwQUXxIcffrjZtXv06BGffPJJ3HrrrZsUpG2sZs2aceihh8bbb78dw4YNq9T8Peigg2LWrFlx7bXXRrt27cocV7du3TjppJNi4sSJceONN2Z9nEsuuSQ+++yzuOyyy6J169ZpxzZt2jTOPPPMeOONN6KgoKDMcXfffXdyV8WytGrVKoYOHRpjx46NHXbYIeu4q6P+/fvH6NGj48ADDyxzTN26deO8886LqVOnbrKrWqYaNWoUr776arzyyitx3nnnxR577BHNmzev1Pv1tNNOi1mzZsWll14azZs3TxvbueeeG9OnT49LL7200uIBAAAAAIDKkJdIJBK5DgIAAAAAAPhx+Oyzz2LKlCkxd+7cWLlyZSQSiWjatGk0b948unfvHrvsskvUqFEj12GmtXbt2nj//fdj2rRpsWzZsqhdu3a0a9cu9t5774wKrKqzFStWRLNmzaK4uDgiIvr27RtvvvnmFq01b968GDt2bCxcuDCWLFkS+fn50bhx49h+++2jW7du8ZOf/CTy8/3OxPIoKipK7jq4aNGiWL16dTRs2DC233772GWXXaJr166Rl5e3RWtPnz49Jk6cGAsXLozVq1dHixYtYvvtt48+ffpkVaBYkT766KOYOnVqLFy4MNatWxfbbbddtG/fPvr06bPJjmZbKpFIxEcffRTTpk2LRYsWxcqVK6NBgwbRpk2b6N69e/To0SOrZ9gP640fPz4WL14ciUQiWrVqFT169IhevXpV+edhZSosLIx333035s+fH+vWrYumTZtGly5donfv3hX2eubKD7k5c+bMWLhwYRQXF8d2220XO+64Y/Tu3btKFGADAAAAAMCWUIgGAAAAAABARESMGDEiTjrppGT7yiuvjFtvvTV3AQEAAAAAAABVhl8zCQAAAAAAQERE/PWvf01p77///jmKBAAAAAAAAKhq7IgGAAAAAADwI5RIJCIvLy/j8ffcc09cdNFFyXbr1q1j7ty5UatWrcoIDwAAAAAAAKhm7IgGAAAAAADwI3T55ZfH73//+/jqq6/Sjlu7dm0MHTo0LrnkkpT+Sy+9VBEaAAAAAAAAkGRHNAAAAAAAgB+hAQMGxEMPPRT5+fnRp0+f2H///aN79+7RvHnz+O6772LRokXx4YcfxogRI+Lrr79OmbvnnnvGmDFjFKIBAAAAAAAASTVzHQAAAAAAAACVp7i4OEaPHh2jR4/OaPwuu+wSzz//vCI0AAAAAAAAIEV+rgMAAAAAAACg4rVr1y7y8vIyHl+rVq248MIL4913340OHTpUYmQAAAAAAABAdZSXSCQSuQ4CAAAAAACAijd37tx45ZVX4r333otp06bFnDlzYvny5bF+/fpo3LhxtGjRIrp37x59+/aNfv36KUADAAAAAAAAyqQQDQAAAAAAAAAAAAAAAIC08nMdAAAAAAAAAAAAAAAAAABVm0I0AAAAAAAAAAAAAAAAANJSiAYAAAAAAAAAAAAAAABAWgrRAAAAAAAAAAAAAAAAAEhLIRoAAAAAAAAAAAA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      "text/plain": [
       "<Figure size 3500x1200 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(40, 10)})\n",
    "sns.set_theme(context=\"paper\", style=\"whitegrid\", font_scale=2.5)\n",
    "\n",
    "fig = plt.figure(figsize=(35, 12))\n",
    "#fig.suptitle('Differential colocalization', fontsize=22)\n",
    "\n",
    "ax = fig.add_subplot(2, 1, 1)\n",
    "sns.boxplot(data=df, x=\"Marker_combinations\", y=\"value\", hue=df.index.values, showfliers=False, \n",
    "            width=0.8, palette=[\"#BDCCDF\", \"#E3C29F\", \"#A2C6C2\"], linewidth=1.4)\n",
    "\n",
    "# adjust axis\n",
    "plt.xticks(rotation=90)\n",
    "plt.ylabel(\"Colocalization scores\")\n",
    "\n",
    "sns.reset_orig()\n",
    "ax = fig.add_subplot(2, 1, 2)\n",
    "ax.set_title(\"Change in colocalization\", fontsize=30, x=0.5, y=-2.6)\n",
    "\n",
    "# get max values of the difference to create color bar values\n",
    "max_values = max(np.abs(differential_2.values))\n",
    "\n",
    "# test sigificane of mean shift for Treated and control sample, more dots = more sigificant\n",
    "statists_S_C = statical_test(\"Treated\", \"Control\")\n",
    "sns.scatterplot(x=statists_S_C, y=[0] * len(statists_S_C), color=\"black\", sizes=5)\n",
    "\n",
    "# plot colorbar indicading the shift of colocalization\n",
    "im = plt.imshow([differential_2.values], interpolation='none', aspect='equal', \n",
    "                cmap=sns.diverging_palette(220, 20, as_cmap=True), vmin=-max_values, vmax=max_values)\n",
    "\n",
    "# add colorbar\n",
    "cb = plt.colorbar(im, ax=ax, orientation=\"horizontal\", pad=0.2)\n",
    "cb.ax.tick_params(labelsize=26)\n",
    "\n",
    "# remove labels from the axis\n",
    "ax.set_xticklabels([])\n",
    "ax.set_yticklabels([\"T - C \"], fontsize=26)\n",
    "ax.set_xticks([])\n",
    "ax.set_yticks([0])\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "#plt.savefig(\"Pairwise_diff_plot_retuximab.jpg\")\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.11.4"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}