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Analysis of similarities - patterns significance _ DR .ipynb 380 KB
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Belen Otero Carrasco's avatar
Adding final results finding patterns  
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{
 "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": 98,
   "metadata": {},
   "outputs": [],
   "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\n",
    "from ast import literal_eval"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Similarity by datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "simi_treatvscan = pd.read_csv((\"AllProteins_SimilitudTreatmentvsCancers.csv\"),sep= \",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "simi_treat = pd.read_csv((\"AllProteins_SimilitudLungCancerTreatment.csv\"),sep= \",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "simi_full = pd.read_csv((\"AllProteins_SimilitudLungCancerFullConMismaProt.csv\"),sep= \",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "simi_dise_lung = pd.read_csv((\"AllProteins_SimilitudLungCancerDisease.csv\"),sep= \",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "simi_immune= pd.read_csv((\"AllProteins_%SimilitudAutoimmuneDisease.csv\"),sep= \",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "simi_rare = pd.read_csv((\"AllProteins_%SimilitudRareDisease.csv\"),sep= \",\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Describe data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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",
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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>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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simi_treatvscan.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\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>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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simi_treat.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_full = simi_full[simi_full['Proteina1'] != simi_full['Proteina2']]"
   ]
  },
  {
   "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>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": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_full.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simi_dise_lung.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simi_immune.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simi_rare.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot similarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "scrolled": true
   },
   "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": "code",
   "execution_count": 88,
   "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 = [\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",
    "legend_names = ['Needleman-Wunsch Weights', 'Class Amino Acids', 'Reduced Class Amino Acids', 'BLOSUM']\n",
    "\n",
    "fig, axs = plt.subplots(2, 3, figsize=(15, 10))\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, legend_name in zip(column_names, legend_names):\n",
    "        ax.hist(dataset[column_name], bins=20, alpha=0.5, label=legend_name, density=True)\n",
    "    ax.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"Histogram_six_datasets_new_names.svg\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "scrolled": true
   },
   "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": [
    "import matplotlib.pyplot as plt\n",
    "\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",
    "column_names = ['Similaridad', 'SimilaridadAA', 'similaridadAA_2', 'similaridadBlosum']\n",
    "legend_names = ['Needleman-Wunsch Weights', 'Class Amino Acids', 'Reduced Class Amino Acids', 'BLOSUM']\n",
    "colors = ['blue', 'orange', 'green', 'red']\n",
    "\n",
    "fig, axs = plt.subplots(2, 3, figsize=(15, 10))\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 j, (column_name, legend_name) in enumerate(zip(column_names, legend_names)):\n",
    "        ax.hist(dataset[column_name], bins=20, alpha=0.5, label=legend_name, color=colors[j], density=True)\n",
    "        \n",
    "# Plot legend outside the loop\n",
    "axs[1, 2].legend(loc='upper right', bbox_to_anchor=(1, 1))\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"Histogram_six_datasets_new.svg\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "scrolled": true
   },
   "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": [
    "import matplotlib.pyplot as plt\n",
    "\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",
    "column_names = ['Similaridad', 'SimilaridadAA', 'similaridadAA_2', 'similaridadBlosum']\n",
    "legend_names = ['Needleman-Wunsch Weights', 'Class Amino Acids', 'Reduced Class Amino Acids', 'BLOSUM']\n",
    "colors = ['skyblue', 'orange', 'lightgreen', 'lightcoral']\n",
    "\n",
    "fig, axs = plt.subplots(2, 3, figsize=(15, 10))\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 j, (column_name, legend_name) in enumerate(zip(column_names, legend_names)):\n",
    "        ax.hist(dataset[column_name], bins=20, alpha=0.7, label=legend_name, color=colors[j], density=True)\n",
    "        \n",
    "# Plot legend outside the loop\n",
    "axs[1, 2].legend(loc='upper right')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"Histogram_six_datasets_new.svg\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "scrolled": true
   },
   "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": [
    "import matplotlib.pyplot as plt\n",
    "\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",
    "column_names = ['Similaridad', 'SimilaridadAA', 'similaridadAA_2', 'similaridadBlosum']\n",
    "legend_names = ['Needleman-Wunsch Weights', 'Class Amino Acids', 'Reduced Class Amino Acids', 'BLOSUM']\n",
    "colors = ['skyblue', 'orange', 'lightgreen', 'lightcoral']\n",
    "\n",
    "fig, axs = plt.subplots(2, 3, figsize=(15, 10))\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 j, (column_name, legend_name) in enumerate(zip(column_names, legend_names)):\n",
    "        ax.hist(dataset[column_name], bins=20, alpha=0.7, label=legend_name, color=colors[j], density=True)\n",
    "\n",
    "# Plot legend outside the subplots\n",
    "plt.legend(loc='upper left', bbox_to_anchor=(1.15, 1))\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"Histogram_six_datasets_new.svg\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Statistics "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_combined_simi = pd.concat([df_full['Similaridad'], simi_dise_lung['Similaridad'], simi_treat['Similaridad'], simi_treatvscan['Similaridad'], simi_immune['Similaridad'],simi_rare['Similaridad']], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_combined_simi.columns = [\"full\",\"disease\",\"treatment\",\"cancers\",\"immune\",\"rare\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>29.179567</td>\n",
       "      <td>13.544892</td>\n",
       "      <td>26.460280</td>\n",
       "      <td>34.447005</td>\n",
       "      <td>20.844640</td>\n",
       "      <td>23.271889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13.544892</td>\n",
       "      <td>27.742149</td>\n",
       "      <td>38.553531</td>\n",
       "      <td>32.500000</td>\n",
       "      <td>11.143148</td>\n",
       "      <td>36.764706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>27.742149</td>\n",
       "      <td>14.595312</td>\n",
       "      <td>30.315421</td>\n",
       "      <td>33.200456</td>\n",
       "      <td>21.031208</td>\n",
       "      <td>23.006834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14.595312</td>\n",
       "      <td>16.331269</td>\n",
       "      <td>38.785047</td>\n",
       "      <td>37.068966</td>\n",
       "      <td>13.263229</td>\n",
       "      <td>35.300429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>16.331269</td>\n",
       "      <td>16.386555</td>\n",
       "      <td>36.039720</td>\n",
       "      <td>35.368957</td>\n",
       "      <td>19.267300</td>\n",
       "      <td>24.173028</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>6.380952</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>28.027140</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>16.000000</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>32.638889</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       29.179567  13.544892  26.460280  34.447005  20.844640  23.271889\n",
       "2       13.544892  27.742149  38.553531  32.500000  11.143148  36.764706\n",
       "3       27.742149  14.595312  30.315421  33.200456  21.031208  23.006834\n",
       "4       14.595312  16.331269  38.785047  37.068966  13.263229  35.300429\n",
       "5       16.331269  16.386555  36.039720  35.368957  19.267300  24.173028\n",
       "...           ...        ...        ...        ...        ...        ...\n",
       "807930        NaN   6.380952        NaN        NaN        NaN        NaN\n",
       "808885        NaN  28.027140        NaN        NaN        NaN        NaN\n",
       "809840        NaN   1.909059        NaN        NaN        NaN        NaN\n",
       "810795        NaN  16.000000        NaN        NaN        NaN        NaN\n",
       "811750        NaN  32.638889        NaN        NaN        NaN        NaN\n",
       "\n",
       "[910013 rows x 6 columns]"
      ]
     },
     "execution_count": 33,
     "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": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.07289683365344113, 0.0009999999999998899)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " lilliefors(df_full['Similaridad'], dist ='norm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0713468648504968, 0.0009999999999998899)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " lilliefors(simi_dise_lung['Similaridad'], dist ='norm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.12772944936671882, 0.0009999999999998899)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " lilliefors(simi_treat['Similaridad'], dist ='norm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.09153056659669923, 0.0009999999999998899)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " lilliefors(simi_treatvscan['Similaridad'], dist ='norm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.09965017459478354, 0.0009999999999998899)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " lilliefors(simi_immune['Similaridad'], dist ='norm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.1232704899783637, 0.0009999999999998899)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " lilliefors(simi_rare['Similaridad'], dist ='norm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "### MannWhitney-U Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MannwhitneyuResult(statistic=930436868.0, pvalue=3.832336401936878e-92)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mannwhitneyu(simi_treat['Similaridad'], df_full['Similaridad'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MannwhitneyuResult(statistic=821694398.0, pvalue=1.3698183384024696e-99)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mannwhitneyu(simi_treat['Similaridad'], simi_dise_lung['Similaridad'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MannwhitneyuResult(statistic=337091335.0, pvalue=1.7248436793974533e-45)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mannwhitneyu(simi_treat['Similaridad'], simi_treatvscan['Similaridad'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MannwhitneyuResult(statistic=162676932.0, pvalue=9.259851826692811e-29)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mannwhitneyu(simi_treat['Similaridad'], simi_immune['Similaridad'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MannwhitneyuResult(statistic=1308205.0, pvalue=7.047478815221771e-45)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mannwhitneyu(simi_treat['Similaridad'], simi_rare['Similaridad'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### full vs cancers, immune, rare "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MannwhitneyuResult(statistic=127150065646.0, pvalue=0.0)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mannwhitneyu(df_full['Similaridad'], simi_treatvscan['Similaridad'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MannwhitneyuResult(statistic=56490400616.0, pvalue=0.0)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mannwhitneyu(df_full['Similaridad'], simi_immune['Similaridad'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MannwhitneyuResult(statistic=577481679.0, pvalue=6.081335888818508e-05)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mannwhitneyu(df_full['Similaridad'], simi_rare['Similaridad'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BOXPLOT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "treat = simi_treat['Similaridad']\n",
    "treat = pd.DataFrame(treat)\n",
    "disease = simi_dise_lung['Similaridad']\n",
    "disease = pd.DataFrame(disease)\n",
    "full = df_full['Similaridad']\n",
    "full = pd.DataFrame(full)\n",
    "treatvscan = simi_treatvscan['Similaridad']\n",
    "treatvscan = pd.DataFrame(treatvscan)\n",
    "immune = simi_immune[\"Similaridad\"]\n",
    "immune = pd.DataFrame(immune)\n",
    "rare = simi_rare[\"Similaridad\"]\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": 161,
   "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>Group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>38.997696</td>\n",
       "      <td>Treatment_lung</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>26.460280</td>\n",
       "      <td>Treatment_lung</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38.553531</td>\n",
       "      <td>Treatment_lung</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30.315421</td>\n",
       "      <td>Treatment_lung</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>38.785047</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>38.195173</td>\n",
       "      <td>Rare_diseases</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1348</th>\n",
       "      <td>30.862697</td>\n",
       "      <td>Rare_diseases</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1349</th>\n",
       "      <td>40.218712</td>\n",
       "      <td>Rare_diseases</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1350</th>\n",
       "      <td>30.346294</td>\n",
       "      <td>Rare_diseases</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1351</th>\n",
       "      <td>32.320778</td>\n",
       "      <td>Rare_diseases</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2168336 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Similaridad           Group\n",
       "0       38.997696  Treatment_lung\n",
       "1       26.460280  Treatment_lung\n",
       "2       38.553531  Treatment_lung\n",
       "3       30.315421  Treatment_lung\n",
       "4       38.785047  Treatment_lung\n",
       "...           ...             ...\n",
       "1347    38.195173   Rare_diseases\n",
       "1348    30.862697   Rare_diseases\n",
       "1349    40.218712   Rare_diseases\n",
       "1350    30.346294   Rare_diseases\n",
       "1351    32.320778   Rare_diseases\n",
       "\n",
       "[2168336 rows x 2 columns]"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "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 = [\"#023e8a\", \"#0077b6\", \"#0096c7\", \"#00b4d8\", \"#90e0ef\", \"#caf0f8\"]\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=\"Similaridad\", 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\")\n",
    "sns.despine()\n",
    "\n",
    "# Guardar el gráfico como SVG\n",
    "plt.savefig(\"plot_Test_6groups_name.svg\")\n",
    "\n",
    "# Mostrar el gráfico\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importance of patterns + Drug repurposing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## load data of pattern found "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "prot_by_lung_005 = pd.read_excel(\"ProtByPatternLung005_summary.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "prot_by_lung_01 = pd.read_excel(\"ProtByPatternLung01_summary.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "prot_by_cancer_005 = pd.read_excel(\"ProtByPatternCanc005_summary.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "prot_by_cancer_01 = pd.read_excel(\"ProtByPatternCanc01_summary.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "prot_by_immune_005 = pd.read_excel(\"ProtByPatternImmun005_summary.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "prot_by_immune_01=pd.read_excel(\"ProtByPatternImmun01_summary.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "prot_by_rare_005 = pd.read_excel(\"ProtByPatternRare005_summary.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "prot_by_rare_01=pd.read_excel(\"ProtByPatternRare01_summary.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "def obtained_simi_by_duplas (df_simi, df_pattern):\n",
    "    \n",
    "  # loop to obtein only IDs proteins and proteins_treat  \n",
    "    df_postprocessed = pd.DataFrame(columns=['pattern', 'Proteina2', 'Proteina1'])\n",
    "    for n, row in df_pattern.iterrows():\n",
    "        #if n > 0:\n",
    "        #    break\n",
    "\n",
    "        pattern: str = row[\"pattern\"]\n",
    "        proteins: list = literal_eval(row[\"proteins\"])\n",
    "        proteins_treat: dict = literal_eval(row[\"proteins_treat\"])\n",
    "        for p in proteins:\n",
    "            for pt in proteins_treat:\n",
    "                new_row = pd.DataFrame([{\n",
    "                    'pattern': pattern, \n",
    "                    'Proteina2': p[0],\n",
    "                    'Proteina1': pt}])\n",
    "\n",
    "                df_postprocessed = pd.concat([df_postprocessed, new_row], ignore_index=True)\n",
    "                \n",
    "    # merge df_simi with df_pattern\n",
    "    df_simi_postpro = df_postprocessed.merge(df_simi.drop_duplicates(), on=['Proteina1', 'Proteina2'], how='left')\n",
    "    \n",
    "    return df_simi_postpro"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "simi_immune_005 = obtained_simi_by_duplas(simi_immune, prot_by_immune_005)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "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>69736.000000</td>\n",
       "      <td>69736.000000</td>\n",
       "      <td>69736.000000</td>\n",
       "      <td>69736.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>28.516888</td>\n",
       "      <td>49.120833</td>\n",
       "      <td>35.712802</td>\n",
       "      <td>58.581339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.489777</td>\n",
       "      <td>20.853536</td>\n",
       "      <td>12.307930</td>\n",
       "      <td>5.309439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.490852</td>\n",
       "      <td>1.490852</td>\n",
       "      <td>1.490852</td>\n",
       "      <td>42.650005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>22.082310</td>\n",
       "      <td>32.336210</td>\n",
       "      <td>27.020400</td>\n",
       "      <td>55.082971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>29.955947</td>\n",
       "      <td>50.677431</td>\n",
       "      <td>37.694223</td>\n",
       "      <td>58.953499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>35.959339</td>\n",
       "      <td>67.609797</td>\n",
       "      <td>45.750262</td>\n",
       "      <td>62.189256</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  69736.000000   69736.000000     69736.000000       69736.000000\n",
       "mean      28.516888      49.120833        35.712802          58.581339\n",
       "std        9.489777      20.853536        12.307930           5.309439\n",
       "min        1.490852       1.490852         1.490852          42.650005\n",
       "25%       22.082310      32.336210        27.020400          55.082971\n",
       "50%       29.955947      50.677431        37.694223          58.953499\n",
       "75%       35.959339      67.609797        45.750262          62.189256\n",
       "max       98.202247      99.438202        98.932584          99.176556"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simi_immune_005.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### DR list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "immune_005_95 = simi_immune_005[simi_immune_005[\"Similaridad\"]>95]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-124-77b55bc88f9c>:1: UserWarning: Pandas requires version '1.4.3' or newer of 'xlsxwriter' (version '1.3.7' currently installed).\n",
      "  immune_005_95.to_excel(\"DR_005_immune_95%.xlsx\")\n"
     ]
    }
   ],
   "source": [
    "immune_005_95.to_excel(\"DR_005_immune_95%.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Proteina2  Proteina1\n",
       "P68371     P07437       74\n",
       "dtype: int64"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "immune_005_95.groupby(['Proteina2',\"Proteina1\"]).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "immune_005_5 = simi_immune_005[simi_immune_005[\"Similaridad\"]<5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-129-4b5d941fe93d>:1: UserWarning: Pandas requires version '1.4.3' or newer of 'xlsxwriter' (version '1.3.7' currently installed).\n",
      "  immune_005_5.to_excel(\"DR_005_immune_5%.xlsx\")\n"
     ]
    }
   ],
   "source": [
    "immune_005_5.to_excel(\"DR_005_immune_5%.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "108"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(immune_005_5.groupby(['Proteina2',\"Proteina1\"]).size())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "simi_immune_010 = obtained_simi_by_duplas(simi_immune, prot_by_immune_01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "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>6604.000000</td>\n",
       "      <td>6604.000000</td>\n",
       "      <td>6604.000000</td>\n",
       "      <td>6604.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>29.680393</td>\n",
       "      <td>51.783472</td>\n",
       "      <td>37.199914</td>\n",
       "      <td>59.035342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.166719</td>\n",
       "      <td>20.259029</td>\n",
       "      <td>11.794235</td>\n",
       "      <td>4.964961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.371003</td>\n",
       "      <td>2.398256</td>\n",
       "      <td>2.398256</td>\n",
       "      <td>42.706844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>23.755385</td>\n",
       "      <td>36.251292</td>\n",
       "      <td>29.324527</td>\n",
       "      <td>55.886727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>31.063111</td>\n",
       "      <td>53.778798</td>\n",
       "      <td>39.019150</td>\n",
       "      <td>59.473552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>36.857293</td>\n",
       "      <td>70.188002</td>\n",
       "      <td>46.848010</td>\n",
       "      <td>62.522322</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  6604.000000    6604.000000      6604.000000        6604.000000\n",
       "mean     29.680393      51.783472        37.199914          59.035342\n",
       "std       9.166719      20.259029        11.794235           4.964961\n",
       "min       2.371003       2.398256         2.398256          42.706844\n",
       "25%      23.755385      36.251292        29.324527          55.886727\n",
       "50%      31.063111      53.778798        39.019150          59.473552\n",
       "75%      36.857293      70.188002        46.848010          62.522322\n",
       "max      98.202247      99.438202        98.932584          99.176556"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simi_immune_010.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### DR list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "immune_010_95 = simi_immune_010[simi_immune_010[\"Similaridad\"]>95]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-134-9ccb4d5ffc9f>:1: UserWarning: Pandas requires version '1.4.3' or newer of 'xlsxwriter' (version '1.3.7' currently installed).\n",
      "  immune_010_95.to_excel(\"DR_010_immune_95%.xlsx\")\n"
     ]
    }
   ],
   "source": [
    "immune_010_95.to_excel(\"DR_010_immune_95%.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Proteina2  Proteina1\n",
       "P68371     P07437       5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "immune_010_95.groupby(['Proteina2',\"Proteina1\"]).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "immune_010_5 = simi_immune_010[simi_immune_010[\"Similaridad\"]<5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-140-0f5e9468f48e>:1: UserWarning: Pandas requires version '1.4.3' or newer of 'xlsxwriter' (version '1.3.7' currently installed).\n",
      "  immune_010_5.to_excel(\"DR_010_immune_5%.xlsx\")\n"
     ]
    }
   ],
   "source": [
    "immune_010_5.to_excel(\"DR_010_immune_5%.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(immune_010_5.groupby(['Proteina2',\"Proteina1\"]).size())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "simi_rare_005 = obtained_simi_by_duplas(simi_rare, prot_by_rare_005)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "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>3659.000000</td>\n",
       "      <td>3659.000000</td>\n",
       "      <td>3659.000000</td>\n",
       "      <td>3659.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>12.445468</td>\n",
       "      <td>19.217269</td>\n",
       "      <td>15.015796</td>\n",
       "      <td>50.360458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>12.345569</td>\n",
       "      <td>23.313831</td>\n",
       "      <td>15.981478</td>\n",
       "      <td>6.462279</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>2.334666</td>\n",
       "      <td>2.334666</td>\n",
       "      <td>2.334666</td>\n",
       "      <td>44.773821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.601682</td>\n",
       "      <td>6.865977</td>\n",
       "      <td>6.857451</td>\n",
       "      <td>47.595571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>21.188630</td>\n",
       "      <td>30.594315</td>\n",
       "      <td>25.697674</td>\n",
       "      <td>55.367352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>41.616162</td>\n",
       "      <td>81.263158</td>\n",
       "      <td>53.102625</td>\n",
       "      <td>78.072727</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Similaridad  SimilaridadAA  similaridadAA_2  similaridadBlosum\n",
       "count  3659.000000    3659.000000      3659.000000        3659.000000\n",
       "mean     12.445468      19.217269        15.015796          50.360458\n",
       "std      12.345569      23.313831        15.981478           6.462279\n",
       "min       0.201422       0.201422         0.201422          43.513301\n",
       "25%       2.334666       2.334666         2.334666          44.773821\n",
       "50%       6.601682       6.865977         6.857451          47.595571\n",
       "75%      21.188630      30.594315        25.697674          55.367352\n",
       "max      41.616162      81.263158        53.102625          78.072727"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simi_rare_005.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## DR list "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "rare_005_95 = simi_rare_005[simi_rare_005[\"Similaridad\"]>95]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "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>pattern</th>\n",
       "      <th>Proteina2</th>\n",
       "      <th>Proteina1</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [pattern, Proteina2, Proteina1, Similaridad, SimilaridadAA, similaridadAA_2, similaridadBlosum]\n",
       "Index: []"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rare_005_95"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "rare_005_5 = simi_rare_005[simi_rare_005[\"Similaridad\"]<5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-147-78df00f2a873>:1: UserWarning: Pandas requires version '1.4.3' or newer of 'xlsxwriter' (version '1.3.7' currently installed).\n",
      "  rare_005_5.to_excel(\"DR_005_rare_5%.xlsx\")\n"
     ]
    }
   ],
   "source": [
    "rare_005_5.to_excel(\"DR_005_rare_5%.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "89"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(rare_005_5.groupby(['Proteina2',\"Proteina1\"]).size())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "simi_rare_010 = obtained_simi_by_duplas(simi_rare, prot_by_rare_01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>275.000000</td>\n",
       "      <td>275.000000</td>\n",
       "      <td>275.000000</td>\n",
       "      <td>275.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>12.251715</td>\n",
       "      <td>18.490582</td>\n",
       "      <td>14.707264</td>\n",
       "      <td>50.408430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>11.595286</td>\n",
       "      <td>21.862052</td>\n",
       "      <td>15.044851</td>\n",
       "      <td>6.028837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.329600</td>\n",
       "      <td>0.329600</td>\n",
       "      <td>0.329600</td>\n",
       "      <td>43.587137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.937667</td>\n",
       "      <td>2.937667</td>\n",
       "      <td>2.937667</td>\n",
       "      <td>45.078439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.459695</td>\n",
       "      <td>6.809139</td>\n",
       "      <td>6.800614</td>\n",
       "      <td>47.665773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>20.158167</td>\n",
       "      <td>28.340865</td>\n",
       "      <td>24.031633</td>\n",
       "      <td>54.787648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>39.900990</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>51.876173</td>\n",
       "      <td>65.576324</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Similaridad  SimilaridadAA  similaridadAA_2  similaridadBlosum\n",
       "count   275.000000     275.000000       275.000000         275.000000\n",
       "mean     12.251715      18.490582        14.707264          50.408430\n",
       "std      11.595286      21.862052        15.044851           6.028837\n",
       "min       0.329600       0.329600         0.329600          43.587137\n",
       "25%       2.937667       2.937667         2.937667          45.078439\n",
       "50%       6.459695       6.809139         6.800614          47.665773\n",
       "75%      20.158167      28.340865        24.031633          54.787648\n",
       "max      39.900990      80.000000        51.876173          65.576324"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simi_rare_010.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "rare_010_5 = simi_rare_010[simi_rare_010[\"Similaridad\"]<5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-152-542963272225>:1: UserWarning: Pandas requires version '1.4.3' or newer of 'xlsxwriter' (version '1.3.7' currently installed).\n",
      "  rare_010_5.to_excel(\"DR_010_rare_5%.xlsx\")\n"
     ]
    }
   ],
   "source": [
    "rare_010_5.to_excel(\"DR_010_rare_5%.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "45"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(rare_010_5.groupby(['Proteina2',\"Proteina1\"]).size())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "rare_010_95 = simi_rare_010[simi_rare_010[\"Similaridad\"]>95]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "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>pattern</th>\n",
       "      <th>Proteina2</th>\n",
       "      <th>Proteina1</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [pattern, Proteina2, Proteina1, Similaridad, SimilaridadAA, similaridadAA_2, similaridadBlosum]\n",
       "Index: []"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rare_010_95"
   ]
  }
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