Mito_Upsetplot.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "electrical-sport",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from upsetplot import plot\n",
    "from matplotlib import pyplot as plt\n",
    "import sys\n",
    "import os\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "egyptian-permit",
   "metadata": {},
   "source": [
    "<h1> UpSetPlot: Mitochondrial Proteins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "brutal-patent",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\fhansen\\AppData\\Local\\Temp\\ipykernel_4568\\232418499.py:41: MatplotlibDeprecationWarning: \n",
      "The change_geometry function was deprecated in Matplotlib 3.4 and will be removed two minor releases later. Use set_subplotspec instead.\n",
      "  current_figure.axes[2].change_geometry(3,6,9)\n",
      "C:\\Users\\fhansen\\AppData\\Local\\Temp\\ipykernel_4568\\232418499.py:41: MatplotlibDeprecationWarning: \n",
      "The update_params function was deprecated in Matplotlib 3.4 and will be removed two minor releases later.\n",
      "  current_figure.axes[2].change_geometry(3,6,9)\n",
      "C:\\Users\\fhansen\\AppData\\Local\\Temp\\ipykernel_4568\\232418499.py:41: MatplotlibDeprecationWarning: \n",
      "The figbox attribute was deprecated in Matplotlib 3.4 and will be removed two minor releases later. Use get_position() instead.\n",
      "  current_figure.axes[2].change_geometry(3,6,9)\n",
      "C:\\Users\\fhansen\\AppData\\Local\\Temp\\ipykernel_4568\\232418499.py:42: MatplotlibDeprecationWarning: \n",
      "The change_geometry function was deprecated in Matplotlib 3.4 and will be removed two minor releases later. Use set_subplotspec instead.\n",
      "  current_figure.axes[3].change_geometry(3,2,2)\n",
      "C:\\Users\\fhansen\\AppData\\Local\\Temp\\ipykernel_4568\\232418499.py:42: MatplotlibDeprecationWarning: \n",
      "The update_params function was deprecated in Matplotlib 3.4 and will be removed two minor releases later.\n",
      "  current_figure.axes[3].change_geometry(3,2,2)\n",
      "C:\\Users\\fhansen\\AppData\\Local\\Temp\\ipykernel_4568\\232418499.py:42: MatplotlibDeprecationWarning: \n",
      "The figbox attribute was deprecated in Matplotlib 3.4 and will be removed two minor releases later. Use get_position() instead.\n",
      "  current_figure.axes[3].change_geometry(3,2,2)\n",
      "C:\\Users\\fhansen\\AppData\\Local\\Temp\\ipykernel_4568\\232418499.py:43: MatplotlibDeprecationWarning: \n",
      "The change_geometry function was deprecated in Matplotlib 3.4 and will be removed two minor releases later. Use set_subplotspec instead.\n",
      "  current_figure.axes[1].change_geometry(3,2,4)\n",
      "C:\\Users\\fhansen\\AppData\\Local\\Temp\\ipykernel_4568\\232418499.py:43: MatplotlibDeprecationWarning: \n",
      "The update_params function was deprecated in Matplotlib 3.4 and will be removed two minor releases later.\n",
      "  current_figure.axes[1].change_geometry(3,2,4)\n",
      "C:\\Users\\fhansen\\AppData\\Local\\Temp\\ipykernel_4568\\232418499.py:43: MatplotlibDeprecationWarning: \n",
      "The figbox attribute was deprecated in Matplotlib 3.4 and will be removed two minor releases later. Use get_position() instead.\n",
      "  current_figure.axes[1].change_geometry(3,2,4)\n",
      "C:\\Users\\fhansen\\AppData\\Local\\Temp\\ipykernel_4568\\232418499.py:44: MatplotlibDeprecationWarning: \n",
      "The change_geometry function was deprecated in Matplotlib 3.4 and will be removed two minor releases later. Use set_subplotspec instead.\n",
      "  current_figure.axes[0].change_geometry(3,2,4)\n",
      "C:\\Users\\fhansen\\AppData\\Local\\Temp\\ipykernel_4568\\232418499.py:44: MatplotlibDeprecationWarning: \n",
      "The update_params function was deprecated in Matplotlib 3.4 and will be removed two minor releases later.\n",
      "  current_figure.axes[0].change_geometry(3,2,4)\n",
      "C:\\Users\\fhansen\\AppData\\Local\\Temp\\ipykernel_4568\\232418499.py:44: MatplotlibDeprecationWarning: \n",
      "The figbox attribute was deprecated in Matplotlib 3.4 and will be removed two minor releases later. Use get_position() instead.\n",
      "  current_figure.axes[0].change_geometry(3,2,4)\n"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 1080x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "path = os.path.dirname(os.path.abspath('__file__'))+'\\\\Prepared_tables\\\\'\n",
    "path_saving = os.path.dirname(os.path.abspath('__file__'))+'\\\\raw_figures\\\\UpSet\\\\'\n",
    "\n",
    "df_prot = pd.read_csv((path+ 'Prep_Protein Groups_mito.csv'), low_memory= False)\n",
    "\n",
    "lower_lim = 0\n",
    "group_valid_value = 0.01\n",
    "\n",
    "\n",
    "B = (df_prot[\"Valid values_B_\"] >= group_valid_value)\n",
    "BAT = (df_prot[\"Valid values_BAT_\"] >= group_valid_value)\n",
    "H = (df_prot[\"Valid values_H_\"] >= group_valid_value)\n",
    "K = (df_prot[\"Valid values_K_\"] >= group_valid_value)\n",
    "L = (df_prot[\"Valid values_L_\"] >= group_valid_value)\n",
    "S = (df_prot[\"Valid values_S_\"] >= group_valid_value)\n",
    "SKM = (df_prot[\"Valid values_SKM_\"] >= group_valid_value)\n",
    "\n",
    "new_lst = [list(x) for x in zip(B, BAT, H,K, L,S, SKM)]\n",
    "lists = [list(item) for item in set(tuple(row) for row in new_lst)]\n",
    "value= []\n",
    "for i in range(len(lists)):\n",
    "    value.append(new_lst.count(lists[i]))\n",
    "\n",
    "lists_index_filter = [i for i, e in enumerate(value) if e > lower_lim]\n",
    "value_filter = [ i for i in value if i > lower_lim ]\n",
    "\n",
    "index = pd.MultiIndex.from_tuples(lists, names=['Brain', 'BAT','Heart', 'Kidney'  , 'Liver','Spleen', 'SKM'])\n",
    "\n",
    "s = pd.Series(value_filter, index=index[lists_index_filter])\n",
    "\n",
    "\n",
    "plot(s, show_counts= True, sort_by= 'cardinality', totals_plot_elements= 5,intersection_plot_elements=7)\n",
    "current_figure = plt.gcf()\n",
    "current_figure.axes[3].set_ylabel('Intersection size',size='xx-large', family='Arial')\n",
    "current_figure.axes[1].set_yticklabels(current_figure.axes[1].get_yticklabels(),size='x-large', family='Arial')\n",
    "current_figure.axes[3].set_xbound(-0.5,9.5)\n",
    "#current_figure.axes[3].texts= current_figure.axes[3].texts[:10]\n",
    "current_figure.set_size_inches(15,10)\n",
    "\n",
    "\n",
    "current_figure.axes[2].change_geometry(3,6,9)\n",
    "current_figure.axes[3].change_geometry(3,2,2)\n",
    "current_figure.axes[1].change_geometry(3,2,4)\n",
    "current_figure.axes[0].change_geometry(3,2,4)\n",
    "\n",
    "#current_figure.savefig((path_saving + r'Upsetplot_prot_1Valid.pdf'))\n",
    "\n",
    "exclusive_prot = len([i for i in range(len(new_lst)) if sum(new_lst[i]) == 1])\n",
    "two_tissue_prot = len([i for i in range(len(new_lst)) if sum(new_lst[i]) == 2])\n",
    "common_prot = len([i for i in range(len(new_lst)) if sum(new_lst[i]) == 7])\n",
    "all_prot = len(new_lst)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "later-hotel",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "89"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exclusive_prot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "earlier-museum",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_prot['new_lst'] = new_lst\n",
    "df_prot['occ'] = [sum(df_prot['new_lst'][i]) for i in range(len(df_prot))]\n",
    "#df_prot_exclusive = df_prot[df_prot['occ'] == 1].reset_index(drop=True)\n",
    "\n",
    "path_saving = os.path.dirname(os.path.abspath('__file__'))+'\\\\Prepared_tables\\\\'\n",
    "#df_prot.to_csv((path_saving+ 'test.csv'), index= False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "elect-seminar",
   "metadata": {},
   "source": [
    "<h1> UpSetPlot: Mitochondrial STYsites"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "competent-animation",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\fhansen\\AppData\\Local\\Temp\\ipykernel_4568\\437227964.py:45: MatplotlibDeprecationWarning: \n",
      "The change_geometry function was deprecated in Matplotlib 3.4 and will be removed two minor releases later. Use set_subplotspec instead.\n",
      "  current_figure.axes[2].change_geometry(3,6,9)\n",
      "C:\\Users\\fhansen\\AppData\\Local\\Temp\\ipykernel_4568\\437227964.py:45: MatplotlibDeprecationWarning: \n",
      "The update_params function was deprecated in Matplotlib 3.4 and will be removed two minor releases later.\n",
      "  current_figure.axes[2].change_geometry(3,6,9)\n",
      "C:\\Users\\fhansen\\AppData\\Local\\Temp\\ipykernel_4568\\437227964.py:45: MatplotlibDeprecationWarning: \n",
      "The figbox attribute was deprecated in Matplotlib 3.4 and will be removed two minor releases later. Use get_position() instead.\n",
      "  current_figure.axes[2].change_geometry(3,6,9)\n",
      "C:\\Users\\fhansen\\AppData\\Local\\Temp\\ipykernel_4568\\437227964.py:46: MatplotlibDeprecationWarning: \n",
      "The change_geometry function was deprecated in Matplotlib 3.4 and will be removed two minor releases later. Use set_subplotspec instead.\n",
      "  current_figure.axes[3].change_geometry(3,2,2)\n",
      "C:\\Users\\fhansen\\AppData\\Local\\Temp\\ipykernel_4568\\437227964.py:46: MatplotlibDeprecationWarning: \n",
      "The update_params function was deprecated in Matplotlib 3.4 and will be removed two minor releases later.\n",
      "  current_figure.axes[3].change_geometry(3,2,2)\n",
      "C:\\Users\\fhansen\\AppData\\Local\\Temp\\ipykernel_4568\\437227964.py:46: MatplotlibDeprecationWarning: \n",
      "The figbox attribute was deprecated in Matplotlib 3.4 and will be removed two minor releases later. Use get_position() instead.\n",
      "  current_figure.axes[3].change_geometry(3,2,2)\n",
      "C:\\Users\\fhansen\\AppData\\Local\\Temp\\ipykernel_4568\\437227964.py:47: MatplotlibDeprecationWarning: \n",
      "The change_geometry function was deprecated in Matplotlib 3.4 and will be removed two minor releases later. Use set_subplotspec instead.\n",
      "  current_figure.axes[1].change_geometry(3,2,4)\n",
      "C:\\Users\\fhansen\\AppData\\Local\\Temp\\ipykernel_4568\\437227964.py:47: MatplotlibDeprecationWarning: \n",
      "The update_params function was deprecated in Matplotlib 3.4 and will be removed two minor releases later.\n",
      "  current_figure.axes[1].change_geometry(3,2,4)\n",
      "C:\\Users\\fhansen\\AppData\\Local\\Temp\\ipykernel_4568\\437227964.py:47: MatplotlibDeprecationWarning: \n",
      "The figbox attribute was deprecated in Matplotlib 3.4 and will be removed two minor releases later. Use get_position() instead.\n",
      "  current_figure.axes[1].change_geometry(3,2,4)\n",
      "C:\\Users\\fhansen\\AppData\\Local\\Temp\\ipykernel_4568\\437227964.py:48: MatplotlibDeprecationWarning: \n",
      "The change_geometry function was deprecated in Matplotlib 3.4 and will be removed two minor releases later. Use set_subplotspec instead.\n",
      "  current_figure.axes[0].change_geometry(3,2,4)\n",
      "C:\\Users\\fhansen\\AppData\\Local\\Temp\\ipykernel_4568\\437227964.py:48: MatplotlibDeprecationWarning: \n",
      "The update_params function was deprecated in Matplotlib 3.4 and will be removed two minor releases later.\n",
      "  current_figure.axes[0].change_geometry(3,2,4)\n",
      "C:\\Users\\fhansen\\AppData\\Local\\Temp\\ipykernel_4568\\437227964.py:48: MatplotlibDeprecationWarning: \n",
      "The figbox attribute was deprecated in Matplotlib 3.4 and will be removed two minor releases later. Use get_position() instead.\n",
      "  current_figure.axes[0].change_geometry(3,2,4)\n"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 1080x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "path = os.path.dirname(os.path.abspath('__file__'))+'\\\\Prepared_tables\\\\'\n",
    "path_saving = os.path.dirname(os.path.abspath('__file__'))+'\\\\raw_figures\\\\UpSet\\\\'\n",
    "\n",
    "df_prot = pd.read_csv((path+ 'Prep_STYsites_mito.csv'), low_memory= False)\n",
    "\n",
    "\n",
    "\n",
    "lower_lim = 0\n",
    "group_valid_value = 0.01\n",
    "\n",
    "\n",
    "B = (df_prot[\"Valid values_B\"] >= group_valid_value)\n",
    "BAT = (df_prot[\"Valid values_BAT\"] >= group_valid_value)\n",
    "H = (df_prot[\"Valid values_H\"] >= group_valid_value)\n",
    "K = (df_prot[\"Valid values_K\"] >= group_valid_value)\n",
    "L = (df_prot[\"Valid values_L\"] >= group_valid_value)\n",
    "S = (df_prot[\"Valid values_S\"] >= group_valid_value)\n",
    "SKM = (df_prot[\"Valid values_SKM\"] >= group_valid_value)\n",
    "\n",
    "new_lst = [list(x) for x in zip(B, BAT, H,K, L,S, SKM)]\n",
    "lists = [list(item) for item in set(tuple(row) for row in new_lst)]\n",
    "value= []\n",
    "for i in range(len(lists)):\n",
    "    value.append(new_lst.count(lists[i]))\n",
    "\n",
    "lists_index_filter = [i for i, e in enumerate(value) if e > lower_lim]\n",
    "value_filter = [ i for i in value if i > lower_lim ]\n",
    "\n",
    "index = pd.MultiIndex.from_tuples(lists, names=['Brain', 'BAT','Heart', 'Kidney'  , 'Liver','Spleen', 'SKM'])\n",
    "\n",
    "s = pd.Series(value_filter, index=index[lists_index_filter])\n",
    "\n",
    "#s = s.sort_values(ascending=False)[s>=20]\n",
    "\n",
    "plot(s, show_counts= True, sort_by= 'cardinality', totals_plot_elements= 5,intersection_plot_elements=7, facecolor='black')\n",
    "\n",
    "current_figure = plt.gcf()\n",
    "current_figure.axes[3].set_ylabel('Intersection size',size='xx-large', family='Arial')\n",
    "current_figure.axes[1].set_yticklabels(current_figure.axes[1].get_yticklabels(),size='x-large', family='Arial')\n",
    "current_figure.axes[3].set_xbound(-0.5,9.5)\n",
    "#current_figure.axes[3].texts = current_figure.axes[3].texts[:10]\n",
    "current_figure.set_size_inches(15,10)\n",
    "\n",
    "\n",
    "current_figure.axes[2].change_geometry(3,6,9)\n",
    "current_figure.axes[3].change_geometry(3,2,2)\n",
    "current_figure.axes[1].change_geometry(3,2,4)\n",
    "current_figure.axes[0].change_geometry(3,2,4)\n",
    "\n",
    "\n",
    "#current_figure.savefig((path_saving + r'Upsetplot_sites_1Valid.pdf'))\n",
    "\n",
    "\n",
    "exclusive_phos = len([i for i in range(len(new_lst)) if sum(new_lst[i]) == 1])\n",
    "\n",
    "common_phos = len([i for i in range(len(new_lst)) if sum(new_lst[i]) == 7])\n",
    "all_phos = len(new_lst)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "unusual-duplicate",
   "metadata": {},
   "source": [
    "<h1> Barplot: Protein+STYsites"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "painted-checkout",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 4))\n",
    "x = np.arange(2)\n",
    "bar_width = 0.2\n",
    "\n",
    "temp = \"ID_all\"\n",
    "#res = [val[temp] for key, val in site_dict_sorted.items() if temp in val]\n",
    "b1 = ax.bar(x, [all_prot,all_phos],width=bar_width, label='All identifications', color = '#737373', ec = 'black')\n",
    "temp = \"ID_ClassI\"\n",
    "#res = [val[temp] for key, val in site_dict_sorted.items() if temp in val]\n",
    "b2 = ax.bar(x+bar_width, [common_prot,common_phos],width=bar_width, label='Common to all tissues', color = '#969696', ec = 'black')\n",
    "temp = \"ID_Mito_ClassI\"\n",
    "#res = [val[temp] for key, val in site_dict_sorted.items() if temp in val]\n",
    "b3 = ax.bar(x+2*bar_width,[exclusive_prot,exclusive_phos] ,width=bar_width, label='Tissue exclusive', color = '#bdbdbd', ec = 'black')\n",
    "\n",
    "\n",
    "ax.set_xticks(x + bar_width)\n",
    "ax.set_xticklabels(['Proteins','STY sites'], size = 16)\n",
    "\n",
    "ax.legend(fontsize = 'large', facecolor='white', framealpha = 1)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['left'].set_visible(False)\n",
    "ax.spines['bottom'].set_color('black')\n",
    "ax.tick_params(bottom=False, left=False)\n",
    "ax.set_axisbelow(True)\n",
    "ax.yaxis.grid(True, color='black')\n",
    "#ax.xaxis.grid(False)\n",
    "\n",
    "#ax.set_xlabel('Tissues', labelpad=15,size = 18,family='Arial')\n",
    "ax.set_ylabel('# Identifications', labelpad=15,size = 18,family='Arial')\n",
    "ax.set_title('', pad=15)\n",
    "ax.set_facecolor(\"white\")\n",
    " \n",
    "fig.tight_layout()\n",
    "path_saving = os.path.dirname(os.path.abspath('__file__'))+'\\\\raw_figures\\\\BarPlots\\\\'\n",
    "#fig.savefig((path_saving + r'Protein_STYsites_1Valid.pdf'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "chicken-collect",
   "metadata": {},
   "source": [
    "<h1> Numbers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "collectible-positive",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mitoichondrial proteins: \n",
      "Tissue exclusive: 89\n",
      "Common to all tissues: 746\n",
      "All identifications: 1221\n",
      "\n",
      "Mitoichondrial STYsites: \n",
      "Tissue exclusive: 198\n",
      "Common to all tissues: 54\n",
      "All identifications: 618\n"
     ]
    }
   ],
   "source": [
    "print('Mitoichondrial proteins: \\n'\n",
    "      'Tissue exclusive: '+str(exclusive_prot)+'\\n'\n",
    "      'Common to all tissues: '+str(common_prot)+'\\n'\n",
    "      'All identifications: '+str(all_prot)+'\\n')\n",
    "\n",
    "print('Mitoichondrial STYsites: \\n'\n",
    "      'Tissue exclusive: '+str(exclusive_phos)+'\\n'\n",
    "      'Common to all tissues: '+str(common_phos)+'\\n'\n",
    "      'All identifications: '+str(all_phos))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aboriginal-lounge",
   "metadata": {},
   "source": [
    "<h1> Individual Barplot: Proteins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "executed-housing",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 324x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(4.5, 4))\n",
    "x = np.arange(1)\n",
    "bar_width = 0.2\n",
    "\n",
    "temp = \"ID_all\"\n",
    "#res = [val[temp] for key, val in site_dict_sorted.items() if temp in val]\n",
    "b1 = ax.bar(x, [all_prot],width=bar_width, label='All identifications', color = '#737373', ec = 'black')\n",
    "temp = \"ID_ClassI\"\n",
    "#res = [val[temp] for key, val in site_dict_sorted.items() if temp in val]\n",
    "b2 = ax.bar(x+bar_width, [common_prot],width=bar_width, label='Common to all tissues', color = '#969696', ec = 'black')\n",
    "temp = \"ID_Mito_ClassI\"\n",
    "#res = [val[temp] for key, val in site_dict_sorted.items() if temp in val]\n",
    "b3 = ax.bar(x+2*bar_width,[exclusive_prot] ,width=bar_width, label='Tissue exclusive', color = '#bdbdbd', ec = 'black')\n",
    "\n",
    "\n",
    "ax.set_xticks(x + bar_width)\n",
    "ax.set_xticklabels(['Proteins'], size = 16)\n",
    "\n",
    "ax.legend(fontsize = 'large', facecolor='white', framealpha = 1,loc='best', bbox_to_anchor=(0.5, 0., 0.0, 1.0))\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['left'].set_visible(False)\n",
    "ax.spines['bottom'].set_color('black')\n",
    "ax.tick_params(bottom=False, left=False)\n",
    "ax.set_axisbelow(True)\n",
    "ax.yaxis.grid(True, color='black')\n",
    "#ax.xaxis.grid(False)\n",
    "\n",
    "#ax.set_xlabel('Tissues', labelpad=15,size = 18,family='Arial')\n",
    "ax.set_ylabel('# Identifications', labelpad=15,size = 18,family='Arial')\n",
    "ax.set_title('', pad=15)\n",
    "ax.set_facecolor(\"white\")\n",
    "#loc='best', bbox_to_anchor=(0.5, 0., 0.5, 0.5)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "lgd = ax.legend(handles, labels, loc='upper right', bbox_to_anchor=(1.1,1))\n",
    "text = ax.text(-0.2,1.05, \"\", transform=ax.transAxes)\n",
    "#fig.savefig((path + r'\\UpsetPlots\\OverallProteinMitoID_75%id_1shared.pdf'), bbox_extra_artists=(lgd,text), bbox_inches='tight')\n",
    "\n",
    "fig.tight_layout()\n",
    "path_saving = os.path.dirname(os.path.abspath('__file__'))+'\\\\raw_figures\\\\Figure_1\\\\'\n",
    "fig.savefig((path_saving + r'Protein_1Valid.pdf'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "broke-faculty",
   "metadata": {},
   "source": [
    "<h1> Individual Barplot: STYsites"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "demanding-preview",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 324x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(4.5, 4))\n",
    "x = np.arange(1)\n",
    "bar_width = 0.2\n",
    "\n",
    "temp = \"ID_all\"\n",
    "#res = [val[temp] for key, val in site_dict_sorted.items() if temp in val]\n",
    "b1 = ax.bar(x, [all_phos],width=bar_width, label='All identifications', color = '#737373', ec = 'black')\n",
    "temp = \"ID_ClassI\"\n",
    "#res = [val[temp] for key, val in site_dict_sorted.items() if temp in val]\n",
    "b2 = ax.bar(x+bar_width, [common_phos],width=bar_width, label='Common to all tissues', color = '#969696', ec = 'black')\n",
    "temp = \"ID_Mito_ClassI\"\n",
    "#res = [val[temp] for key, val in site_dict_sorted.items() if temp in val]\n",
    "b3 = ax.bar(x+2*bar_width,[exclusive_phos] ,width=bar_width, label='Tissue exclusive', color = '#bdbdbd', ec = 'black')\n",
    "\n",
    "\n",
    "ax.set_xticks(x + bar_width)\n",
    "ax.set_xticklabels(['STY sites'], size = 16)\n",
    "\n",
    "ax.legend(fontsize = 'large', facecolor='white', framealpha = 1,loc='best', bbox_to_anchor=(0.5, 0., 0.0, 1.0))\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['left'].set_visible(False)\n",
    "ax.spines['bottom'].set_color('black')\n",
    "ax.tick_params(bottom=False, left=False)\n",
    "ax.set_axisbelow(True)\n",
    "ax.yaxis.grid(True, color='black')\n",
    "\n",
    "#ax.xaxis.grid(False)\n",
    "\n",
    "#ax.set_xlabel('Tissues', labelpad=15,size = 18,family='Arial')\n",
    "ax.set_ylabel('# Identifications', labelpad=15,size = 18,family='Arial')\n",
    "ax.set_title('', pad=15)\n",
    "ax.set_facecolor(\"white\")\n",
    "#loc='best', bbox_to_anchor=(0.5, 0., 0.5, 0.5)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "lgd = ax.legend(handles, labels, loc='upper right', bbox_to_anchor=(1.1,1))\n",
    "text = ax.text(-0.2,1.05, \"\", transform=ax.transAxes)\n",
    "\n",
    "\n",
    "fig.tight_layout()\n",
    "path_saving = os.path.dirname(os.path.abspath('__file__'))+'\\\\raw_figures\\\\Figure_5\\\\'\n",
    "fig.savefig((path_saving + r'STYsites_1Valid.pdf'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "english-organic",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
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