{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Similarity sequences analysis\n",
    "--------------------------------------------------------------------------------\n",
    "\n",
    "\n",
    "Author: Belén Otero Carrasco\n",
    "\n",
    "Last updated 24 April 2024\n",
    "\n",
    "--------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Usuario\\anaconda3\\lib\\site-packages\\pandas\\core\\computation\\expressions.py:20: UserWarning: Pandas requires version '2.7.3' or newer of 'numexpr' (version '2.7.1' currently installed).\n",
      "  from pandas.core.computation.check import NUMEXPR_INSTALLED\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from pandas import DataFrame\n",
    "from scipy import stats\n",
    "from sklearn.metrics import jaccard_score\n",
    "from sklearn.metrics import pairwise_distances\n",
    "from statsmodels.stats.diagnostic import lilliefors\n",
    "from scipy.stats import mannwhitneyu, levene\n",
    "import mysql.connector\n",
    "import re"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Similarity by datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "simi_treatvscan = pd.read_csv((\"AllProteins_SimilitudTreatmentvsCancers.csv\"),sep= \",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "simi_treat = pd.read_csv((\"AllProteins_SimilitudLungCancerTreatment.csv\"),sep= \",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "simi_full = pd.read_csv((\"AllProteins_SimilitudLungCancerFullConMismaProt.csv\"),sep= \",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "simi_dise_lung = pd.read_csv((\"AllProteins_SimilitudLungCancerDisease.csv\"),sep= \",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "simi_immune= pd.read_csv((\"AllProteins_%SimilitudAutoimmuneDisease.csv\"),sep= \",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "simi_rare = pd.read_csv((\"AllProteins_%SimilitudRareDisease.csv\"),sep= \",\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Describe data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Similaridad</th>\n",
       "      <th>SimilaridadAA</th>\n",
       "      <th>similaridadAA_2</th>\n",
       "      <th>similaridadBlosum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>302224.000000</td>\n",
       "      <td>302224.000000</td>\n",
       "      <td>302224.000000</td>\n",
       "      <td>302224.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>28.813926</td>\n",
       "      <td>50.549192</td>\n",
       "      <td>36.305730</td>\n",
       "      <td>58.704623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.601007</td>\n",
       "      <td>20.289527</td>\n",
       "      <td>11.544729</td>\n",
       "      <td>4.926356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.454953</td>\n",
       "      <td>0.454953</td>\n",
       "      <td>0.454953</td>\n",
       "      <td>42.046609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>23.052097</td>\n",
       "      <td>34.778182</td>\n",
       "      <td>28.503788</td>\n",
       "      <td>55.509709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>30.639809</td>\n",
       "      <td>52.897196</td>\n",
       "      <td>38.699690</td>\n",
       "      <td>59.215168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>36.097852</td>\n",
       "      <td>68.613861</td>\n",
       "      <td>46.052632</td>\n",
       "      <td>62.269854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>99.667406</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>99.833703</td>\n",
       "      <td>99.761791</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Similaridad  SimilaridadAA  similaridadAA_2  similaridadBlosum\n",
       "count  302224.000000  302224.000000    302224.000000      302224.000000\n",
       "mean       28.813926      50.549192        36.305730          58.704623\n",
       "std         8.601007      20.289527        11.544729           4.926356\n",
       "min         0.454953       0.454953         0.454953          42.046609\n",
       "25%        23.052097      34.778182        28.503788          55.509709\n",
       "50%        30.639809      52.897196        38.699690          59.215168\n",
       "75%        36.097852      68.613861        46.052632          62.269854\n",
       "max        99.667406     100.000000        99.833703          99.761791"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simi_treatvscan.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Similaridad</th>\n",
       "      <th>SimilaridadAA</th>\n",
       "      <th>similaridadAA_2</th>\n",
       "      <th>similaridadBlosum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2652.000000</td>\n",
       "      <td>2652.000000</td>\n",
       "      <td>2652.000000</td>\n",
       "      <td>2652.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>31.351628</td>\n",
       "      <td>55.800203</td>\n",
       "      <td>39.529462</td>\n",
       "      <td>60.427545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.018264</td>\n",
       "      <td>19.702628</td>\n",
       "      <td>11.532015</td>\n",
       "      <td>5.487288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.589251</td>\n",
       "      <td>2.589251</td>\n",
       "      <td>2.589251</td>\n",
       "      <td>45.132024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>26.906158</td>\n",
       "      <td>43.053245</td>\n",
       "      <td>33.692053</td>\n",
       "      <td>57.680352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>32.746333</td>\n",
       "      <td>58.359436</td>\n",
       "      <td>41.711608</td>\n",
       "      <td>60.456817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>37.541391</td>\n",
       "      <td>73.142415</td>\n",
       "      <td>48.134328</td>\n",
       "      <td>63.578275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>90.371457</td>\n",
       "      <td>97.182377</td>\n",
       "      <td>93.698770</td>\n",
       "      <td>96.598599</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Similaridad  SimilaridadAA  similaridadAA_2  similaridadBlosum\n",
       "count  2652.000000    2652.000000      2652.000000        2652.000000\n",
       "mean     31.351628      55.800203        39.529462          60.427545\n",
       "std       9.018264      19.702628        11.532015           5.487288\n",
       "min       2.589251       2.589251         2.589251          45.132024\n",
       "25%      26.906158      43.053245        33.692053          57.680352\n",
       "50%      32.746333      58.359436        41.711608          60.456817\n",
       "75%      37.541391      73.142415        48.134328          63.578275\n",
       "max      90.371457      97.182377        93.698770          96.598599"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simi_treat.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_full = simi_full[simi_full['Proteina1'] != simi_full['Proteina2']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Similaridad</th>\n",
       "      <th>SimilaridadAA</th>\n",
       "      <th>similaridadAA_2</th>\n",
       "      <th>similaridadBlosum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>909162.000000</td>\n",
       "      <td>909162.000000</td>\n",
       "      <td>909162.000000</td>\n",
       "      <td>909162.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>27.710122</td>\n",
       "      <td>47.830370</td>\n",
       "      <td>34.739893</td>\n",
       "      <td>57.857934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.014903</td>\n",
       "      <td>20.667222</td>\n",
       "      <td>11.953857</td>\n",
       "      <td>4.856046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.937175</td>\n",
       "      <td>0.937175</td>\n",
       "      <td>0.937175</td>\n",
       "      <td>42.213826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>21.208791</td>\n",
       "      <td>30.987395</td>\n",
       "      <td>26.030928</td>\n",
       "      <td>54.467975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>28.995902</td>\n",
       "      <td>48.697183</td>\n",
       "      <td>36.462729</td>\n",
       "      <td>58.269577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>35.392321</td>\n",
       "      <td>66.270431</td>\n",
       "      <td>44.975288</td>\n",
       "      <td>61.653818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>99.589041</td>\n",
       "      <td>99.794521</td>\n",
       "      <td>99.589041</td>\n",
       "      <td>99.707003</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Similaridad  SimilaridadAA  similaridadAA_2  similaridadBlosum\n",
       "count  909162.000000  909162.000000    909162.000000      909162.000000\n",
       "mean       27.710122      47.830370        34.739893          57.857934\n",
       "std         9.014903      20.667222        11.953857           4.856046\n",
       "min         0.937175       0.937175         0.937175          42.213826\n",
       "25%        21.208791      30.987395        26.030928          54.467975\n",
       "50%        28.995902      48.697183        36.462729          58.269577\n",
       "75%        35.392321      66.270431        44.975288          61.653818\n",
       "max        99.589041      99.794521        99.589041          99.707003"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_full.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Similaridad</th>\n",
       "      <th>SimilaridadAA</th>\n",
       "      <th>similaridadAA_2</th>\n",
       "      <th>similaridadBlosum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>812702.000000</td>\n",
       "      <td>812702.000000</td>\n",
       "      <td>812702.000000</td>\n",
       "      <td>812702.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>27.552708</td>\n",
       "      <td>47.459438</td>\n",
       "      <td>34.520934</td>\n",
       "      <td>57.740908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.069390</td>\n",
       "      <td>20.715443</td>\n",
       "      <td>12.010812</td>\n",
       "      <td>4.835662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.937175</td>\n",
       "      <td>0.937175</td>\n",
       "      <td>0.937175</td>\n",
       "      <td>42.213826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>20.961887</td>\n",
       "      <td>30.469925</td>\n",
       "      <td>25.684394</td>\n",
       "      <td>54.330444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>28.792453</td>\n",
       "      <td>48.206522</td>\n",
       "      <td>36.183287</td>\n",
       "      <td>58.146147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>35.275623</td>\n",
       "      <td>65.932642</td>\n",
       "      <td>44.801980</td>\n",
       "      <td>61.558045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>99.589041</td>\n",
       "      <td>99.794521</td>\n",
       "      <td>99.589041</td>\n",
       "      <td>99.707003</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Similaridad  SimilaridadAA  similaridadAA_2  similaridadBlosum\n",
       "count  812702.000000  812702.000000    812702.000000      812702.000000\n",
       "mean       27.552708      47.459438        34.520934          57.740908\n",
       "std         9.069390      20.715443        12.010812           4.835662\n",
       "min         0.937175       0.937175         0.937175          42.213826\n",
       "25%        20.961887      30.469925        25.684394          54.330444\n",
       "50%        28.792453      48.206522        36.183287          58.146147\n",
       "75%        35.275623      65.932642        44.801980          61.558045\n",
       "max        99.589041      99.794521        99.589041          99.707003"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simi_dise_lung.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Similaridad</th>\n",
       "      <th>SimilaridadAA</th>\n",
       "      <th>similaridadAA_2</th>\n",
       "      <th>similaridadBlosum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>140244.000000</td>\n",
       "      <td>140244.000000</td>\n",
       "      <td>140244.000000</td>\n",
       "      <td>140244.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>29.471423</td>\n",
       "      <td>52.058989</td>\n",
       "      <td>37.194339</td>\n",
       "      <td>59.039481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.173877</td>\n",
       "      <td>19.557676</td>\n",
       "      <td>10.991107</td>\n",
       "      <td>4.755932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.318681</td>\n",
       "      <td>1.318681</td>\n",
       "      <td>1.318681</td>\n",
       "      <td>42.192479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>24.029024</td>\n",
       "      <td>37.007624</td>\n",
       "      <td>29.826863</td>\n",
       "      <td>56.054807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>31.414474</td>\n",
       "      <td>54.886299</td>\n",
       "      <td>39.692408</td>\n",
       "      <td>59.588976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>36.308204</td>\n",
       "      <td>69.353070</td>\n",
       "      <td>46.320157</td>\n",
       "      <td>62.390552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>98.202247</td>\n",
       "      <td>99.438202</td>\n",
       "      <td>98.932584</td>\n",
       "      <td>99.176556</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Similaridad  SimilaridadAA  similaridadAA_2  similaridadBlosum\n",
       "count  140244.000000  140244.000000    140244.000000      140244.000000\n",
       "mean       29.471423      52.058989        37.194339          59.039481\n",
       "std         8.173877      19.557676        10.991107           4.755932\n",
       "min         1.318681       1.318681         1.318681          42.192479\n",
       "25%        24.029024      37.007624        29.826863          56.054807\n",
       "50%        31.414474      54.886299        39.692408          59.588976\n",
       "75%        36.308204      69.353070        46.320157          62.390552\n",
       "max        98.202247      99.438202        98.932584          99.176556"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simi_immune.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>Similaridad</th>\n",
       "      <th>SimilaridadAA</th>\n",
       "      <th>similaridadAA_2</th>\n",
       "      <th>similaridadBlosum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1352.000000</td>\n",
       "      <td>1352.000000</td>\n",
       "      <td>1352.000000</td>\n",
       "      <td>1352.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>25.918008</td>\n",
       "      <td>45.042138</td>\n",
       "      <td>32.624567</td>\n",
       "      <td>57.261413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>11.008288</td>\n",
       "      <td>23.660605</td>\n",
       "      <td>14.598808</td>\n",
       "      <td>5.929105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.201422</td>\n",
       "      <td>0.201422</td>\n",
       "      <td>0.201422</td>\n",
       "      <td>43.513301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>19.384968</td>\n",
       "      <td>26.946498</td>\n",
       "      <td>23.630079</td>\n",
       "      <td>53.830268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>29.109537</td>\n",
       "      <td>48.787263</td>\n",
       "      <td>36.887019</td>\n",
       "      <td>58.286203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>34.752498</td>\n",
       "      <td>65.594774</td>\n",
       "      <td>44.378321</td>\n",
       "      <td>61.608040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>41.616162</td>\n",
       "      <td>83.823529</td>\n",
       "      <td>53.482587</td>\n",
       "      <td>83.784318</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Similaridad  SimilaridadAA  similaridadAA_2  similaridadBlosum\n",
       "count  1352.000000    1352.000000      1352.000000        1352.000000\n",
       "mean     25.918008      45.042138        32.624567          57.261413\n",
       "std      11.008288      23.660605        14.598808           5.929105\n",
       "min       0.201422       0.201422         0.201422          43.513301\n",
       "25%      19.384968      26.946498        23.630079          53.830268\n",
       "50%      29.109537      48.787263        36.887019          58.286203\n",
       "75%      34.752498      65.594774        44.378321          61.608040\n",
       "max      41.616162      83.823529        53.482587          83.784318"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simi_rare.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot similarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "datasets = [simi_dise_lung,simi_treat ,df_full, simi_treatvscan, simi_immune, simi_rare]\n",
    "datasets = [\n",
    "    ('Disease Lung Cancer', simi_dise_lung),\n",
    "    ('Treatment Lung Cancer', simi_treat),\n",
    "    ('Full protein Lung Cancer', df_full),\n",
    "    ('Cancers', simi_treatvscan),\n",
    "    ('Autoimmune diseases', simi_immune),\n",
    "    ('Rare diseases', simi_rare)\n",
    "]\n",
    "\n",
    "\n",
    "column_names = ['Similaridad', 'SimilaridadAA', 'similaridadAA_2', 'similaridadBlosum']\n",
    "\n",
    "\n",
    "fig, axs = plt.subplots(2, 3, figsize=(15, 10))\n",
    "\n",
    "\n",
    "for i, (dataset_name, dataset) in enumerate(datasets):\n",
    "    row = i // 3\n",
    "    col = i % 3\n",
    "    ax = axs[row, col]\n",
    "    ax.set_title(f'Histogram Dataset {dataset_name}')\n",
    "    ax.set_xlabel('Value')\n",
    "    ax.set_ylabel('Frequency')\n",
    "    for column_name in column_names:\n",
    "        ax.hist(dataset[column_name], bins=20, alpha=0.5, label=column_name, density=True)\n",
    "    ax.legend()\n",
    "\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"Histogram_six_datasets.svg\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Statistics "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_combined_simi = pd.concat([df_full['SimilaridadAA'], simi_dise_lung['SimilaridadAA'], simi_treat['SimilaridadAA'], simi_treatvscan['SimilaridadAA'], simi_immune['SimilaridadAA'],simi_rare['SimilaridadAA']], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_combined_simi.columns = [\"full\",\"disease\",\"treatment\",\"cancers\",\"immune\",\"rare\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>full</th>\n",
       "      <th>disease</th>\n",
       "      <th>treatment</th>\n",
       "      <th>cancers</th>\n",
       "      <th>immune</th>\n",
       "      <th>rare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>47.877046</td>\n",
       "      <td>16.585582</td>\n",
       "      <td>43.165888</td>\n",
       "      <td>61.635945</td>\n",
       "      <td>29.341927</td>\n",
       "      <td>36.693548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16.585582</td>\n",
       "      <td>44.183989</td>\n",
       "      <td>76.708428</td>\n",
       "      <td>59.913793</td>\n",
       "      <td>12.686567</td>\n",
       "      <td>76.069519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>44.183989</td>\n",
       "      <td>17.868200</td>\n",
       "      <td>52.044393</td>\n",
       "      <td>60.136674</td>\n",
       "      <td>29.630258</td>\n",
       "      <td>36.275626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17.868200</td>\n",
       "      <td>21.229544</td>\n",
       "      <td>75.934579</td>\n",
       "      <td>71.120690</td>\n",
       "      <td>15.807327</td>\n",
       "      <td>65.879828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>21.229544</td>\n",
       "      <td>21.273773</td>\n",
       "      <td>66.939252</td>\n",
       "      <td>65.903308</td>\n",
       "      <td>26.662144</td>\n",
       "      <td>40.139949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>807930</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6.666667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>808885</th>\n",
       "      <td>NaN</td>\n",
       "      <td>50.521921</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>809840</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.909059</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>810795</th>\n",
       "      <td>NaN</td>\n",
       "      <td>20.837209</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>811750</th>\n",
       "      <td>NaN</td>\n",
       "      <td>59.406566</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>910013 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             full    disease  treatment    cancers     immune       rare\n",
       "1       47.877046  16.585582  43.165888  61.635945  29.341927  36.693548\n",
       "2       16.585582  44.183989  76.708428  59.913793  12.686567  76.069519\n",
       "3       44.183989  17.868200  52.044393  60.136674  29.630258  36.275626\n",
       "4       17.868200  21.229544  75.934579  71.120690  15.807327  65.879828\n",
       "5       21.229544  21.273773  66.939252  65.903308  26.662144  40.139949\n",
       "...           ...        ...        ...        ...        ...        ...\n",
       "807930        NaN   6.666667        NaN        NaN        NaN        NaN\n",
       "808885        NaN  50.521921        NaN        NaN        NaN        NaN\n",
       "809840        NaN   1.909059        NaN        NaN        NaN        NaN\n",
       "810795        NaN  20.837209        NaN        NaN        NaN        NaN\n",
       "811750        NaN  59.406566        NaN        NaN        NaN        NaN\n",
       "\n",
       "[910013 rows x 6 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_combined_simi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### check normality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.06678582003123557, 0.0009999999999998899)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " lilliefors(df_full['SimilaridadAA'], dist ='norm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0660039739215218, 0.0009999999999998899)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " lilliefors(simi_dise_lung['SimilaridadAA'], dist ='norm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.08928327114518153, 0.0009999999999998899)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " lilliefors(simi_treat['SimilaridadAA'], dist ='norm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.07614832209326139, 0.0009999999999998899)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " lilliefors(simi_treatvscan['SimilaridadAA'], dist ='norm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.08322363214534123, 0.0009999999999998899)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " lilliefors(simi_immune['SimilaridadAA'], dist ='norm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0908226753983673, 0.0009999999999998899)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " lilliefors(simi_rare['SimilaridadAA'], dist ='norm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "### MannWhitney-U Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MannwhitneyuResult(statistic=932346904.0, pvalue=6.726641267914384e-91)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mannwhitneyu(simi_treat['SimilaridadAA'], df_full['SimilaridadAA'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MannwhitneyuResult(statistic=823136118.0, pvalue=1.699618462349326e-98)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mannwhitneyu(simi_treat['SimilaridadAA'], simi_dise_lung['SimilaridadAA'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MannwhitneyuResult(statistic=338897583.0, pvalue=4.640824468030087e-43)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mannwhitneyu(simi_treat['SimilaridadAA'], simi_treatvscan['SimilaridadAA'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MannwhitneyuResult(statistic=163722019.0, pvalue=2.084823990292487e-26)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mannwhitneyu(simi_treat['SimilaridadAA'], simi_immune['SimilaridadAA'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MannwhitneyuResult(statistic=1323813.0, pvalue=3.652658300767717e-42)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mannwhitneyu(simi_treat['SimilaridadAA'], simi_rare['SimilaridadAA'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "### full vs cancers, immune, rare "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MannwhitneyuResult(statistic=126867920668.0, pvalue=0.0)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mannwhitneyu(df_full['SimilaridadAA'], simi_treatvscan['SimilaridadAA'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MannwhitneyuResult(statistic=56291704649.0, pvalue=0.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mannwhitneyu(df_full['SimilaridadAA'], simi_immune['SimilaridadAA'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MannwhitneyuResult(statistic=580959003.0, pvalue=0.0002481393097134617)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mannwhitneyu(df_full['SimilaridadAA'], simi_rare['SimilaridadAA'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BOXPLOT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "treat = simi_treat['SimilaridadAA']\n",
    "treat = pd.DataFrame(treat)\n",
    "disease = simi_dise_lung['SimilaridadAA']\n",
    "disease = pd.DataFrame(disease)\n",
    "full = df_full['SimilaridadAA']\n",
    "full = pd.DataFrame(full)\n",
    "treatvscan = simi_treatvscan['SimilaridadAA']\n",
    "treatvscan = pd.DataFrame(treatvscan)\n",
    "immune = simi_immune[\"SimilaridadAA\"]\n",
    "immune = pd.DataFrame(immune)\n",
    "rare = simi_rare[\"SimilaridadAA\"]\n",
    "rare = pd.DataFrame(rare)\n",
    "\n",
    "treat[\"Group\"] = \"Treatment_lung\"\n",
    "disease[\"Group\"] = \"Disease_lung\"\n",
    "full[\"Group\"] = \"Full_protein_lung\"\n",
    "treatvscan[\"Group\"] = \"Cancers\"\n",
    "immune[\"Group\"] = \"Immune_diseases\"\n",
    "rare[\"Group\"] = \"Rare_diseases\"\n",
    "\n",
    "groups = pd.concat([treat,disease,full,treatvscan,immune,rare])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>SimilaridadAA</th>\n",
       "      <th>Group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>77.649770</td>\n",
       "      <td>Treatment_lung</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>43.165888</td>\n",
       "      <td>Treatment_lung</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>76.708428</td>\n",
       "      <td>Treatment_lung</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>52.044393</td>\n",
       "      <td>Treatment_lung</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>75.934579</td>\n",
       "      <td>Treatment_lung</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1347</th>\n",
       "      <td>76.469045</td>\n",
       "      <td>Rare_diseases</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1348</th>\n",
       "      <td>54.313487</td>\n",
       "      <td>Rare_diseases</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1349</th>\n",
       "      <td>80.619684</td>\n",
       "      <td>Rare_diseases</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1350</th>\n",
       "      <td>51.913730</td>\n",
       "      <td>Rare_diseases</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1351</th>\n",
       "      <td>55.832321</td>\n",
       "      <td>Rare_diseases</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2168336 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      SimilaridadAA           Group\n",
       "0         77.649770  Treatment_lung\n",
       "1         43.165888  Treatment_lung\n",
       "2         76.708428  Treatment_lung\n",
       "3         52.044393  Treatment_lung\n",
       "4         75.934579  Treatment_lung\n",
       "...             ...             ...\n",
       "1347      76.469045   Rare_diseases\n",
       "1348      54.313487   Rare_diseases\n",
       "1349      80.619684   Rare_diseases\n",
       "1350      51.913730   Rare_diseases\n",
       "1351      55.832321   Rare_diseases\n",
       "\n",
       "[2168336 rows x 2 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Definir una paleta de colores derivados del azul\n",
    "my_colors = [\"#ff4800\", \"#ff6000\", \"#ff8500\", \"#ff9100\", \"#ffb627\", \"#ffc971\"]\n",
    "\n",
    "# Configurar la paleta de colores\n",
    "sns.set_palette(my_colors)\n",
    "\n",
    "# Crear la figura\n",
    "fig = plt.figure(figsize=(12, 6))\n",
    "\n",
    "# Graficar el boxenplot\n",
    "sns.boxenplot(x=\"SimilaridadAA\", y=\"Group\", data=groups, \n",
    "              width=0.7, order=[\"Treatment_lung\", \"Disease_lung\", \"Full_protein_lung\", \"Cancers\",\"Immune_diseases\",\"Rare_diseases\"],\n",
    "              showfliers=True)\n",
    "\n",
    "# Personalizar ejes y eliminar despine\n",
    "plt.xlabel(\"\")\n",
    "#plt.ylabel(\"Value Similarity_AA\")\n",
    "sns.despine()\n",
    "\n",
    "# Guardar el gráfico como SVG\n",
    "plt.savefig(\"plot_Test_6groups_SIMIAA_name.svg\")\n",
    "\n",
    "# Mostrar el gráfico\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}