analysis/10_fragsys_cluster_v_function.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EXTRACTING UNIPROT FEATURE ANNOTATIONS TO CLASSIFY SITES BASED ON FUNCTION"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### IMPORTING NECESSARY PACKAGES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fragsys_analysis import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## READING INPUT DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "main_dir = \"./../\"\n",
    "results_dir = os.path.join(main_dir, \"results\")\n",
    "figs_dir = os.path.join(main_dir, \"figs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "func_df = load_pickle(os.path.join(results_dir, \"prot_func_dict_comb.pkl\"))\n",
    "rsa_cluster_dict = load_pickle(os.path.join(results_dir, \"rsa_cluster_dict.pkl\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "293\n",
      "293\n"
     ]
    }
   ],
   "source": [
    "print(len(func_df))\n",
    "print(len(rsa_cluster_dict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "#func_df[func_df.bs_id.str.contains(\"P22557\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bs_id</th>\n",
       "      <th>auto</th>\n",
       "      <th>manual</th>\n",
       "      <th>comb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>H0Y4R8_0_BS0</td>\n",
       "      <td>UF</td>\n",
       "      <td>UF</td>\n",
       "      <td>UF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>O15178_0_BS0</td>\n",
       "      <td>UF</td>\n",
       "      <td>UF</td>\n",
       "      <td>UF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>O15178_0_BS1</td>\n",
       "      <td>UF</td>\n",
       "      <td>UF</td>\n",
       "      <td>UF</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          bs_id auto manual comb\n",
       "0  H0Y4R8_0_BS0   UF     UF   UF\n",
       "1  O15178_0_BS0   UF     UF   UF\n",
       "2  O15178_0_BS1   UF     UF   UF"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "func_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "func_df[\"rsa_cluster\"] = func_df.bs_id.map(rsa_cluster_dict)\n",
    "func_df = func_df.query('rsa_cluster == rsa_cluster').reset_index(drop = True)\n",
    "func_df.rsa_cluster = func_df.rsa_cluster.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "46"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(func_df.query('rsa_cluster == 1'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(func_df.query('rsa_cluster == 1 & comb == \"KF\"'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(func_df.query('rsa_cluster == 1 & comb == \"UF\"'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CALCULATING ENRICHMENT IN FUNCTION WITHIN CLUSTERS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>kf</th>\n",
       "      <th>uf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   kf   uf\n",
       "1  17   29\n",
       "2  21  106\n",
       "3   6   85\n",
       "4   0   29"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c_labs = sorted(func_df.rsa_cluster.unique().tolist())\n",
    "kf, uf = [[], []]\n",
    "for c in c_labs:\n",
    "    kf.append(len(func_df.query('rsa_cluster == @c and comb == \"KF\"')))\n",
    "    uf.append(len(func_df.query('rsa_cluster == @c and comb == \"UF\"')))\n",
    "cont_tab = pd.DataFrame(list(zip(kf, uf)), columns = [\"kf\", \"uf\"])\n",
    "cont_tab.index = c_labs\n",
    "cont_tab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1     46\n",
       "2    127\n",
       "3     91\n",
       "4     29\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cont_tab.kf + cont_tab.uf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "cont_tab = cont_tab + 1\n",
    "cont_tab[\"tot\"] = cont_tab.kf + cont_tab.uf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>kf</th>\n",
       "      <th>uf</th>\n",
       "      <th>tot</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18</td>\n",
       "      <td>30</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22</td>\n",
       "      <td>107</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>86</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   kf   uf  tot\n",
       "1  18   30   48\n",
       "2  22  107  129\n",
       "3   7   86   93\n",
       "4   1   30   31"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cont_tab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "import importlib\n",
    "import fragsys_analysis\n",
    "importlib.reload(fragsys_analysis)\n",
    "from fragsys_analysis import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 [18, 30, 30, 223]\n",
      "2 [22, 26, 107, 146]\n",
      "3 [7, 41, 86, 167]\n",
      "4 [1, 47, 30, 223]\n"
     ]
    }
   ],
   "source": [
    "cont_tab_or = get_OR(cont_tab, c_labs, \"tot\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>kf</th>\n",
       "      <th>uf</th>\n",
       "      <th>tot</th>\n",
       "      <th>oddsratio</th>\n",
       "      <th>log_oddsratio</th>\n",
       "      <th>pvalue</th>\n",
       "      <th>ci_dist</th>\n",
       "      <th>lo_95ci_or</th>\n",
       "      <th>hi_95ci_or</th>\n",
       "      <th>lo_95ci_or_dist</th>\n",
       "      <th>hi_95ci_or_dist</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18</td>\n",
       "      <td>30</td>\n",
       "      <td>48</td>\n",
       "      <td>4.46</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.70</td>\n",
       "      <td>2.22</td>\n",
       "      <td>8.96</td>\n",
       "      <td>2.24</td>\n",
       "      <td>4.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22</td>\n",
       "      <td>107</td>\n",
       "      <td>129</td>\n",
       "      <td>1.15</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.62</td>\n",
       "      <td>2.15</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>86</td>\n",
       "      <td>93</td>\n",
       "      <td>0.33</td>\n",
       "      <td>-1.10</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>31</td>\n",
       "      <td>0.16</td>\n",
       "      <td>-1.84</td>\n",
       "      <td>0.04</td>\n",
       "      <td>2.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>1.19</td>\n",
       "      <td>0.14</td>\n",
       "      <td>1.03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   kf   uf  tot  oddsratio  log_oddsratio  pvalue  ci_dist  lo_95ci_or  \\\n",
       "1  18   30   48       4.46           1.50    0.00     0.70        2.22   \n",
       "2  22  107  129       1.15           0.14    0.75     0.62        0.62   \n",
       "3   7   86   93       0.33          -1.10    0.01     0.84        0.14   \n",
       "4   1   30   31       0.16          -1.84    0.04     2.02        0.02   \n",
       "\n",
       "   hi_95ci_or  lo_95ci_or_dist  hi_95ci_or_dist  \n",
       "1        8.96             2.24             4.50  \n",
       "2        2.15             0.53             0.99  \n",
       "3        0.77             0.19             0.44  \n",
       "4        1.19             0.14             1.03  "
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cont_tab_or"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.9\n",
      "13.5\n",
      "27.9\n"
     ]
    }
   ],
   "source": [
    "print(round(4.46/1.15, 1))\n",
    "print(round(4.46/0.33, 1))\n",
    "print(round(4.46/0.16, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.37\n",
      "0.17\n",
      "0.07\n",
      "0.0\n"
     ]
    }
   ],
   "source": [
    "print(round(17/46,2))\n",
    "print(round(21/127,2))\n",
    "print(round(6/91,2))\n",
    "print(round(0/29,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "colss = sample_colors[:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (5, 5), dpi = 100)\n",
    "for i, idx in enumerate(cont_tab_or.index.tolist()):\n",
    "    plt.errorbar(\n",
    "        c_labs[i], cont_tab_or.loc[idx, \"oddsratio\"],\n",
    "        yerr = tuple([[cont_tab_or.loc[idx, \"lo_95ci_or_dist\"] ], [cont_tab_or.loc[idx, \"hi_95ci_or_dist\"]]]),\n",
    "        ecolor = colss[i], elinewidth = 1.5, capsize = 3, capthick = 1.5, fmt = \"o\", c = colss[i], ls = 'None'\n",
    "    )\n",
    "    \n",
    "plt.tick_params(width = 1, length = 7.5, which = 'major')\n",
    "plt.tick_params(width = 1, length = 3.5, which = 'minor')\n",
    "plt.axhline(y = 1, linestyle = \"--\", linewidth = 1, c = \"black\")\n",
    "plt.xticks([1, 2, 3, 4], [\"C1\", \"C2\", \"C3\", \"C4\"])\n",
    "plt.xlabel(\"RSA Cluster\")\n",
    "plt.ylabel(\"Odds Ratio\")\n",
    "plt.yscale('log')\n",
    "#plt.savefig(os.path.join(figs_dir, \"RSA_clusters_func_OR.png\"))\n",
    "plt.show()"
   ]
  }
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