pre-analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d435672e-e6b4-4661-9d03-12bcfee892ea",
   "metadata": {},
   "source": [
    "## [ Indonesian Rice SNPs - Information Content Pre-analysis ]\n",
    "`Prepared by` <b>Nicholas Dominic, S.Kom., M.T.I.</b>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59a222e2-d448-40b1-ace9-2f86f6dca9ae",
   "metadata": {},
   "source": [
    "### Define libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "602a39b5-e5a0-40ee-a55b-5c1a80b6ff8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from snp_info import load_dataset, SNPinfo\n",
    "from pandas import read_csv\n",
    "from numpy import log2\n",
    "from sklearn.metrics import mutual_info_score as mis\n",
    "from matplotlib import pyplot as plt\n",
    "from seaborn import distplot\n",
    "from warnings import filterwarnings as fw; fw(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "efd90284-496f-4ac9-a51f-8e21a059684b",
   "metadata": {},
   "outputs": [],
   "source": [
    "save_path, ext = \"./results/\", \".png\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ae0331e-9d44-43ce-95d8-b19de3c4ff29",
   "metadata": {},
   "source": [
    "### Gene co-expression (all SNPs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8089a466-1fb4-4c21-849f-60fded1ec66a",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id1000223_T</th>\n",
       "      <th>id1001073_G</th>\n",
       "      <th>id1001973_T</th>\n",
       "      <th>id1002158_T</th>\n",
       "      <th>id1002308_C</th>\n",
       "      <th>id1003036_G</th>\n",
       "      <th>id1003559_T</th>\n",
       "      <th>id1003838_T</th>\n",
       "      <th>id1004109_T</th>\n",
       "      <th>id1004591_G</th>\n",
       "      <th>...</th>\n",
       "      <th>id12008136_G</th>\n",
       "      <th>id12008285_C</th>\n",
       "      <th>id12008328_T</th>\n",
       "      <th>id12008665_T</th>\n",
       "      <th>id12008894_A</th>\n",
       "      <th>id12009153_A</th>\n",
       "      <th>id12009407_C</th>\n",
       "      <th>id12009654_A</th>\n",
       "      <th>id12009820_T</th>\n",
       "      <th>id12010152_T</th>\n",
       "    </tr>\n",
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       "      <th>id1000223_T</th>\n",
       "      <td>1.000000</td>\n",
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       "      <td>0.212633</td>\n",
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       "      <td>0.023536</td>\n",
       "      <td>...</td>\n",
       "      <td>0.084106</td>\n",
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       "      <td>0.213814</td>\n",
       "      <td>1.000000</td>\n",
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       "      <td>0.399764</td>\n",
       "      <td>-0.119492</td>\n",
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       "      <td>-0.161821</td>\n",
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       "      <td>0.274572</td>\n",
       "      <td>-0.253823</td>\n",
       "      <td>...</td>\n",
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       "      <td>-0.109022</td>\n",
       "      <td>0.219199</td>\n",
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       "      <td>-0.018151</td>\n",
       "      <td>-0.071092</td>\n",
       "      <td>-0.266771</td>\n",
       "      <td>-0.113474</td>\n",
       "      <td>0.244835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id1001973_T</th>\n",
       "      <td>0.138450</td>\n",
       "      <td>0.325534</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.824565</td>\n",
       "      <td>-0.346137</td>\n",
       "      <td>-0.234277</td>\n",
       "      <td>0.060567</td>\n",
       "      <td>0.071507</td>\n",
       "      <td>0.161289</td>\n",
       "      <td>-0.187252</td>\n",
       "      <td>...</td>\n",
       "      <td>0.098804</td>\n",
       "      <td>-0.135070</td>\n",
       "      <td>0.051473</td>\n",
       "      <td>0.108243</td>\n",
       "      <td>0.040103</td>\n",
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       "      <td>0.044448</td>\n",
       "      <td>0.045174</td>\n",
       "      <td>0.022047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id1002158_T</th>\n",
       "      <td>0.039201</td>\n",
       "      <td>0.399764</td>\n",
       "      <td>0.824565</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.304522</td>\n",
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       "      <td>0.299592</td>\n",
       "      <td>-0.227596</td>\n",
       "      <td>...</td>\n",
       "      <td>0.018385</td>\n",
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       "      <td>0.122924</td>\n",
       "      <td>0.017586</td>\n",
       "      <td>-0.186104</td>\n",
       "      <td>0.069240</td>\n",
       "      <td>-0.104661</td>\n",
       "      <td>-0.163270</td>\n",
       "      <td>-0.094119</td>\n",
       "      <td>0.082398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id1002308_C</th>\n",
       "      <td>0.101684</td>\n",
       "      <td>-0.119492</td>\n",
       "      <td>-0.346137</td>\n",
       "      <td>-0.304522</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.419449</td>\n",
       "      <td>0.188837</td>\n",
       "      <td>0.160258</td>\n",
       "      <td>0.077974</td>\n",
       "      <td>-0.163096</td>\n",
       "      <td>...</td>\n",
       "      <td>0.041922</td>\n",
       "      <td>0.058337</td>\n",
       "      <td>-0.150585</td>\n",
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       "      <td>0.185969</td>\n",
       "      <td>0.222126</td>\n",
       "      <td>0.069089</td>\n",
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       "      <th>...</th>\n",
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       "      <th>id12009153_A</th>\n",
       "      <td>-0.018125</td>\n",
       "      <td>-0.018151</td>\n",
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       "      <td>-0.097667</td>\n",
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       "      <td>0.121029</td>\n",
       "      <td>-0.027030</td>\n",
       "      <td>-0.073895</td>\n",
       "      <td>...</td>\n",
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       "      <th>id12009654_A</th>\n",
       "      <td>0.104946</td>\n",
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       "      <td>0.044448</td>\n",
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       "      <td>0.487133</td>\n",
       "      <td>-0.134730</td>\n",
       "      <td>-0.058994</td>\n",
       "      <td>...</td>\n",
       "      <td>0.348439</td>\n",
       "      <td>-0.068737</td>\n",
       "      <td>-0.139761</td>\n",
       "      <td>0.288753</td>\n",
       "      <td>0.727638</td>\n",
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       "      <td>1.000000</td>\n",
       "      <td>0.442469</td>\n",
       "      <td>-0.126307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id12009820_T</th>\n",
       "      <td>0.173046</td>\n",
       "      <td>-0.113474</td>\n",
       "      <td>0.045174</td>\n",
       "      <td>-0.094119</td>\n",
       "      <td>0.222126</td>\n",
       "      <td>0.153191</td>\n",
       "      <td>0.270400</td>\n",
       "      <td>0.331975</td>\n",
       "      <td>-0.079827</td>\n",
       "      <td>-0.213065</td>\n",
       "      <td>...</td>\n",
       "      <td>0.367859</td>\n",
       "      <td>-0.188495</td>\n",
       "      <td>-0.128566</td>\n",
       "      <td>0.243271</td>\n",
       "      <td>0.571623</td>\n",
       "      <td>-0.087167</td>\n",
       "      <td>-0.063368</td>\n",
       "      <td>0.442469</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.283328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id12010152_T</th>\n",
       "      <td>-0.170585</td>\n",
       "      <td>0.244835</td>\n",
       "      <td>0.022047</td>\n",
       "      <td>0.082398</td>\n",
       "      <td>0.069089</td>\n",
       "      <td>-0.002704</td>\n",
       "      <td>-0.144814</td>\n",
       "      <td>-0.112762</td>\n",
       "      <td>0.174187</td>\n",
       "      <td>-0.268514</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.128769</td>\n",
       "      <td>0.029078</td>\n",
       "      <td>0.108699</td>\n",
       "      <td>-0.174629</td>\n",
       "      <td>-0.198877</td>\n",
       "      <td>0.074304</td>\n",
       "      <td>0.015897</td>\n",
       "      <td>-0.126307</td>\n",
       "      <td>-0.283328</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1232 rows × 1232 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              id1000223_T  id1001073_G  id1001973_T  id1002158_T  id1002308_C  \\\n",
       "id1000223_T      1.000000     0.213814     0.138450     0.039201     0.101684   \n",
       "id1001073_G      0.213814     1.000000     0.325534     0.399764    -0.119492   \n",
       "id1001973_T      0.138450     0.325534     1.000000     0.824565    -0.346137   \n",
       "id1002158_T      0.039201     0.399764     0.824565     1.000000    -0.304522   \n",
       "id1002308_C      0.101684    -0.119492    -0.346137    -0.304522     1.000000   \n",
       "...                   ...          ...          ...          ...          ...   \n",
       "id12009153_A    -0.018125    -0.018151     0.075768     0.069240    -0.097667   \n",
       "id12009407_C     0.015553    -0.071092    -0.013962    -0.104661    -0.094462   \n",
       "id12009654_A     0.104946    -0.266771     0.044448    -0.163270     0.185969   \n",
       "id12009820_T     0.173046    -0.113474     0.045174    -0.094119     0.222126   \n",
       "id12010152_T    -0.170585     0.244835     0.022047     0.082398     0.069089   \n",
       "\n",
       "              id1003036_G  id1003559_T  id1003838_T  id1004109_T  id1004591_G  \\\n",
       "id1000223_T     -0.081141     0.227860     0.212633    -0.014032     0.023536   \n",
       "id1001073_G     -0.183696    -0.161821    -0.239777     0.274572    -0.253823   \n",
       "id1001973_T     -0.234277     0.060567     0.071507     0.161289    -0.187252   \n",
       "id1002158_T      0.010848    -0.012002    -0.148243     0.299592    -0.227596   \n",
       "id1002308_C      0.419449     0.188837     0.160258     0.077974    -0.163096   \n",
       "...                   ...          ...          ...          ...          ...   \n",
       "id12009153_A    -0.067207     0.245860     0.121029    -0.027030    -0.073895   \n",
       "id12009407_C    -0.070103     0.072771     0.136017    -0.123088     0.068672   \n",
       "id12009654_A    -0.047543     0.159464     0.487133    -0.134730    -0.058994   \n",
       "id12009820_T     0.153191     0.270400     0.331975    -0.079827    -0.213065   \n",
       "id12010152_T    -0.002704    -0.144814    -0.112762     0.174187    -0.268514   \n",
       "\n",
       "              ...  id12008136_G  id12008285_C  id12008328_T  id12008665_T  \\\n",
       "id1000223_T   ...      0.084106     -0.001705     -0.136974      0.375078   \n",
       "id1001073_G   ...     -0.223192     -0.109022      0.219199     -0.121688   \n",
       "id1001973_T   ...      0.098804     -0.135070      0.051473      0.108243   \n",
       "id1002158_T   ...      0.018385     -0.087731      0.122924      0.017586   \n",
       "id1002308_C   ...      0.041922      0.058337     -0.150585      0.152978   \n",
       "...           ...           ...           ...           ...           ...   \n",
       "id12009153_A  ...     -0.042787      0.147546     -0.031327      0.012905   \n",
       "id12009407_C  ...     -0.003566      0.165777     -0.033518      0.036851   \n",
       "id12009654_A  ...      0.348439     -0.068737     -0.139761      0.288753   \n",
       "id12009820_T  ...      0.367859     -0.188495     -0.128566      0.243271   \n",
       "id12010152_T  ...     -0.128769      0.029078      0.108699     -0.174629   \n",
       "\n",
       "              id12008894_A  id12009153_A  id12009407_C  id12009654_A  \\\n",
       "id1000223_T       0.203607     -0.018125      0.015553      0.104946   \n",
       "id1001073_G      -0.232642     -0.018151     -0.071092     -0.266771   \n",
       "id1001973_T       0.040103      0.075768     -0.013962      0.044448   \n",
       "id1002158_T      -0.186104      0.069240     -0.104661     -0.163270   \n",
       "id1002308_C       0.249867     -0.097667     -0.094462      0.185969   \n",
       "...                    ...           ...           ...           ...   \n",
       "id12009153_A     -0.031740      1.000000      0.294846      0.042672   \n",
       "id12009407_C     -0.022705      0.294846      1.000000      0.058424   \n",
       "id12009654_A      0.727638      0.042672      0.058424      1.000000   \n",
       "id12009820_T      0.571623     -0.087167     -0.063368      0.442469   \n",
       "id12010152_T     -0.198877      0.074304      0.015897     -0.126307   \n",
       "\n",
       "              id12009820_T  id12010152_T  \n",
       "id1000223_T       0.173046     -0.170585  \n",
       "id1001073_G      -0.113474      0.244835  \n",
       "id1001973_T       0.045174      0.022047  \n",
       "id1002158_T      -0.094119      0.082398  \n",
       "id1002308_C       0.222126      0.069089  \n",
       "...                    ...           ...  \n",
       "id12009153_A     -0.087167      0.074304  \n",
       "id12009407_C     -0.063368      0.015897  \n",
       "id12009654_A      0.442469     -0.126307  \n",
       "id12009820_T      1.000000     -0.283328  \n",
       "id12010152_T     -0.283328      1.000000  \n",
       "\n",
       "[1232 rows x 1232 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "snps = load_dataset()\n",
    "snps.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c47d60bd-936a-4ee4-a61d-73b19998e820",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<snp_info.SNPinfo at 0x21fe18d5430>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "si = SNPinfo(snps=snps)\n",
    "si"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6899d0c-aa80-4f31-86f2-36215552ad73",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Entropy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d99f3c04-2d03-4690-a643-c0ac67ebfd3b",
   "metadata": {},
   "source": [
    "Properties of entropy:\n",
    "* Non negativity: H(X) ≥ 0, entropy is always non-negative. H(X) = 0 iff X is deterministic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4c8e9029-700c-45a8-b554-05060a7a3a7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.584962500721156"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Maximum entropy value, i.e., log2(n), where n = number of class\n",
    "# Formally, it can be written as H(n) <= log2(|n|)\n",
    "# In the rice SNPs, we have 3 classes (0, 1, 2) and therefore its entropy values range between 0 to log2(3)\n",
    "max_entropy_val = log2(len(set(snps.id1000223_T)))\n",
    "max_entropy_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "364fbf01-bf6b-4d84-8787-80a9f660f98d",
   "metadata": {},
   "outputs": [],
   "source": [
    "entropy_each_snp = {i : si.entropy(snps[i]) for i in snps.columns}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9d614f35-36fe-4db3-b310-a02ff49a2bab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 5))\n",
    "plt.title(\"Entropy Distribution\")\n",
    "plt.xlabel(\"Entropy value (bits)\")\n",
    "distplot(list(entropy_each_snp.values()), bins=15, color='g', rug=True, hist_kws=dict(edgecolor=\"white\", linewidth=2))\n",
    "plt.show()\n",
    "\n",
    "# To save this figure:\n",
    "# plt.savefig(fname=save_path + \"preanalysis/entropy-dist\" + ext, dpi=500, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "315cf32c-fe98-438b-a381-8221293186c7",
   "metadata": {},
   "source": [
    "### Joint Entropy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5476c10e-83ab-4376-b3db-4d5f8fb23ea0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11.211384176533803"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joint_entropy = si.entropy(snps.T.values, joint=True) # shape of prob. list = (1232, 3)\n",
    "joint_entropy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "db64993e-f663-47c0-a581-68e4f6d99141",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11.851749041416058"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_joint_entropy = log2(len(snps.columns)*3)\n",
    "max_joint_entropy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad562acc-de7b-4ff9-b4c5-62e14ba10e3f",
   "metadata": {},
   "source": [
    "### Total Correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "15b20ffa-e794-4ef8-b913-1df3d2d03329",
   "metadata": {},
   "outputs": [],
   "source": [
    "entropy_each_snp = [si.entropy(snps[i]) for i in snps.columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "bee237c1-4682-45b8-9385-8d1288f9f46f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1152.5329"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_correlation = round(sum(entropy_each_snp) - joint_entropy, 5)\n",
    "total_correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6a440c27-0b9e-4fd5-bebf-104fd3d5e582",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1162.1660203040674"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_total_correlation = sum(entropy_each_snp) - max(entropy_each_snp)\n",
    "max_total_correlation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d344481-ae1e-45c0-ac38-26fdf2915a31",
   "metadata": {},
   "source": [
    "### Mutual Information (MI)\n",
    "* Note: Positive Pointwise MI (PPMI) = max(MI, 0) is unnecessary, since there is no MI with negative value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a1df45bb-ca7b-483e-b57e-0ec323b5aa8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "mutual_info = [round(mis(snps[i], snps[snps.columns[len(snps.columns)-1]]), 5) for i in snps.columns]\n",
    "# for i in snps.columns:\n",
    "#     print(\"{}: {:.5f}\".format(i, mis(snps[i], snps[snps.columns[len(snps.columns)-1]])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "bcfe6f98-ab90-4f7b-bbd3-d52ba810c6be",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "any(mi < 0 for mi in mutual_info)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77bec5b3-1024-41e2-9f78-04caccbfcb45",
   "metadata": {},
   "source": [
    "### Estimation of Combined Information of Multiple SNPs\n",
    "* The greater the number of SNPs, the greater the gap between lower and upper bounds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d3301d57-d1c4-45dd-a641-6598315cf304",
   "metadata": {},
   "outputs": [],
   "source": [
    "joint_entropy_mi = si.entropy(snps.T.values[:-1], joint=True)\n",
    "joint_entropy_lower_bound = joint_entropy_mi + si.entropy(snps.T.values[-1]) - max(mutual_info)\n",
    "joint_entropy_upper_bound = joint_entropy_mi + si.entropy(snps.T.values[-1]) - round(sum(mutual_info), 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e2717579-e11c-426c-9f44-37839ac40b23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1152.21485"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lower_bound_tc = round(sum(entropy_each_snp) - joint_entropy_lower_bound, 5)\n",
    "lower_bound_tc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2ce7f405-be84-4e46-b126-1fa498a64d4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1199.10815"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "upper_bound_tc = round(sum(entropy_each_snp) - joint_entropy_upper_bound, 5)\n",
    "upper_bound_tc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24a96d0a-c0aa-4693-ae8b-4965918743b5",
   "metadata": {},
   "source": [
    "### Summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "00bbbc0c-5a48-48a6-8824-701c6ee0fbe9",
   "metadata": {},
   "outputs": [],
   "source": [
    "file_name = \"preanalysis/snps_combined_information.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a85e4c57-ed7c-401c-b963-c78be79bbaa4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# je, tc, etcl, etcu = [], [], [], []\n",
    "# for i in range(1, len(snps.columns)+1):\n",
    "#     joint_entropy_, total_corr_, est_total_corr_lower, est_total_corr_upper = si.snp_combined_info(end_idx=i)\n",
    "#     je.append(joint_entropy_)\n",
    "#     tc.append(total_corr_)\n",
    "#     etcl.append(est_total_corr_lower)\n",
    "#     etcu.append(est_total_corr_upper)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1afaed30-e4ef-45c8-8315-e918b4cf897a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# from pandas import DataFrame\n",
    "# DataFrame({\n",
    "#     \"joint_entropy_\" : je,\n",
    "#     \"total_corr_\" : tc,\n",
    "#     \"est_total_corr_lower\" : etcl,\n",
    "#     \"est_total_corr_upper\" : etcu\n",
    "# }).to_csv(save_path + file_name, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b7d67b58-bd48-4788-8b4f-e0f8608747a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>joint_entropy_</th>\n",
       "      <th>total_corr_</th>\n",
       "      <th>est_total_corr_lower</th>\n",
       "      <th>est_total_corr_upper</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00940</td>\n",
       "      <td>1.30380</td>\n",
       "      <td>-10.04522</td>\n",
       "      <td>-10.04522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.01899</td>\n",
       "      <td>2.83989</td>\n",
       "      <td>-8.33840</td>\n",
       "      <td>-8.25618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02844</td>\n",
       "      <td>4.21156</td>\n",
       "      <td>-7.07135</td>\n",
       "      <td>-6.89588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03797</td>\n",
       "      <td>5.66875</td>\n",
       "      <td>-5.54527</td>\n",
       "      <td>-4.88815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.04715</td>\n",
       "      <td>6.71345</td>\n",
       "      <td>-4.77757</td>\n",
       "      <td>-4.51317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1227</th>\n",
       "      <td>11.17541</td>\n",
       "      <td>1149.31078</td>\n",
       "      <td>1148.39210</td>\n",
       "      <td>1175.65899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1228</th>\n",
       "      <td>11.18397</td>\n",
       "      <td>1149.58562</td>\n",
       "      <td>1148.69736</td>\n",
       "      <td>1171.56609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1229</th>\n",
       "      <td>11.19301</td>\n",
       "      <td>1150.44745</td>\n",
       "      <td>1149.97545</td>\n",
       "      <td>1236.28416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1230</th>\n",
       "      <td>11.20219</td>\n",
       "      <td>1151.47562</td>\n",
       "      <td>1151.12819</td>\n",
       "      <td>1229.79093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1231</th>\n",
       "      <td>11.21138</td>\n",
       "      <td>1152.53291</td>\n",
       "      <td>1152.21485</td>\n",
       "      <td>1199.10815</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1232 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      joint_entropy_  total_corr_  est_total_corr_lower  est_total_corr_upper\n",
       "0            0.00940      1.30380             -10.04522             -10.04522\n",
       "1            0.01899      2.83989              -8.33840              -8.25618\n",
       "2            0.02844      4.21156              -7.07135              -6.89588\n",
       "3            0.03797      5.66875              -5.54527              -4.88815\n",
       "4            0.04715      6.71345              -4.77757              -4.51317\n",
       "...              ...          ...                   ...                   ...\n",
       "1227        11.17541   1149.31078            1148.39210            1175.65899\n",
       "1228        11.18397   1149.58562            1148.69736            1171.56609\n",
       "1229        11.19301   1150.44745            1149.97545            1236.28416\n",
       "1230        11.20219   1151.47562            1151.12819            1229.79093\n",
       "1231        11.21138   1152.53291            1152.21485            1199.10815\n",
       "\n",
       "[1232 rows x 4 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "snp_info = read_csv(\"./results/\" + file_name)\n",
    "snp_info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ca37a638-0f41-47f2-bfb1-e039ccc5db30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(range(1, len(snps.columns)+1), snp_info.est_total_corr_lower, label=\"Lower Bound\", color=\"xkcd:turquoise green\")\n",
    "plt.plot(range(1, len(snps.columns)+1), snp_info.est_total_corr_upper, label=\"Upper Bound\", color=\"xkcd:yellowish orange\")\n",
    "plt.title(\"Total Correlation Distribution\")\n",
    "plt.xlabel(\"Number of SNPs\")\n",
    "plt.ylabel(\"Total Correlation\")\n",
    "plt.legend()\n",
    "# plt.show()\n",
    "\n",
    "# To save this figure:\n",
    "plt.savefig(fname=save_path + \"preanalysis/total-corr-dist\" + ext, dpi=500, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "834563c7-3db2-4f17-b0c0-2109a1c72564",
   "metadata": {},
   "source": [
    "### Copyright 2022 Authors\n",
    "* Do <b>NOT</b> use or redistribute this file <b>EXCEPT</b> there is an official permission from authors.\n",
    "* Please cite our paper whenever you take references from this research.\n",
    "* Any questions can be addressed to `nicholas.dominic@binus.ac.id` or `bdsrc@binus.edu`.\n",
    "\n",
    "Bioinformatics and Data Science Research Center [(visit page)](https://research.binus.ac.id/bdsrc/)\n",
    "<br>NVIDIA - BINUS Artificial Intelligence Research and Development Center [(visit page)](https://research.binus.ac.id/airdc/)\n",
    "<br><b>BINUS Higher Education</b>, part of BINUS Group [(visit page)](https://www.binus.edu/) [(visit QS page)](https://www.topuniversities.com/universities/bina-nusantara-university-binus)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}