Pipeline_Introduction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis of simulation with no error rate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### One by one query genome "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import packages\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as mpl\n",
    "import os\n",
    "\n",
    "os.getcwd()\n",
    "os.chdir('/Users/user/Documents/OUCRU/Graph_Genome')\n",
    "\n",
    "report = pd.read_csv(\"summarise.csv\",sep=\"\\t\",names=['gene',\"read_count\",\"length\",\"coverage\"])\n",
    "\n",
    "# dropping null value columns to avoid errors\n",
    "report.dropna(inplace = True)\n",
    "\n",
    "# new data frame with split value columns\n",
    "new = report[\"gene\"].str.split(\"_\", n = 1, expand = True)\n",
    "\n",
    "# making seperate first name column from new data frame\n",
    "report[\"accession_id\"] = new[0]\n",
    "\n",
    "# making seperate last name column from new data frame\n",
    "report[\"prot\"] = new[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Short gun DNA sequence data for each reference genome is simulated from fasta file with 10000 reads. Then each of query genome is aligned to genome graph built up to check whether "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>gene</th>\n",
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       "           gene  read_count  length coverage accession_id prot\n",
       "0  AF3267542_1C        1269     726     726M    AF3267542   1C\n",
       "1  AF3267542_2A         769     450   1D449M    AF3267542   2A\n",
       "2  AF3267542_1B        1344     765     765M    AF3267542   1B\n",
       "3  AF3267542_1A         217     207     207M    AF3267542   1A\n",
       "4  AF3267542_3A         336     255     255M    AF3267542   3A"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "report.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>U225211</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "prot          1A  1B  1C  1D  2A  2B  2C  3A  3C  3D  3Dpol  VP1  VP2  VP3  \\\n",
       "accession_id                                                                 \n",
       "AF3267542      1   1   1   1   1   1   1   1   1   1      0    0    0    0   \n",
       "AF3267612      1   1   1   1   1   1   1   1   1   1      0    0    0    0   \n",
       "AF3267642      1   1   1   1   1   1   1   1   1   1      0    0    0    0   \n",
       "AY4217601      0   0   0   0   1   1   1   1   1   1      0    1    1    1   \n",
       "AY4217611      0   0   0   0   1   1   1   1   1   1      0    1    1    1   \n",
       "AY4217621      0   0   0   0   1   1   1   1   1   1      0    1    1    1   \n",
       "AY4217641      0   0   0   0   1   1   1   1   1   1      0    1    1    1   \n",
       "AY4217651      0   0   0   0   1   1   1   1   1   1      0    1    1    1   \n",
       "AY4217661      0   0   0   0   1   1   1   1   1   1      0    1    1    1   \n",
       "AY4217681      0   0   0   0   1   1   1   1   1   1      0    1    1    1   \n",
       "AY4217691      0   0   0   0   1   1   1   1   1   1      0    1    1    1   \n",
       "AY6974581      1   1   1   1   1   1   1   1   1   1      0    0    0    0   \n",
       "AY6974591      1   1   1   1   1   1   1   1   1   1      0    0    0    0   \n",
       "AY6974601      1   1   1   1   1   1   1   1   1   0      0    0    0    0   \n",
       "AY6974611      1   1   1   1   1   1   1   1   1   1      0    0    0    0   \n",
       "EF6673441      1   1   1   1   1   1   1   1   1   1      0    0    0    0   \n",
       "KU3558761      0   0   0   0   0   1   1   1   1   0      1    0    0    0   \n",
       "KU3558771      0   0   0   0   0   1   1   1   1   0      1    0    0    0   \n",
       "U058761        0   0   0   0   1   1   1   1   1   1      0    1    1    1   \n",
       "U225211        0   0   0   0   1   1   1   1   1   1      0    1    1    1   \n",
       "All            8   8   8   8  18  20  20  20  20  17      2   10   10   10   \n",
       "\n",
       "prot          VP4  capsid  protease  All  \n",
       "accession_id                              \n",
       "AF3267542       0       0         0   10  \n",
       "AF3267612       0       0         0   10  \n",
       "AF3267642       0       0         0   10  \n",
       "AY4217601       1       0         0   10  \n",
       "AY4217611       1       0         0   10  \n",
       "AY4217621       1       0         0   10  \n",
       "AY4217641       1       0         0   10  \n",
       "AY4217651       1       0         0   10  \n",
       "AY4217661       1       0         0   10  \n",
       "AY4217681       1       0         0   10  \n",
       "AY4217691       1       0         0   10  \n",
       "AY6974581       0       0         0   10  \n",
       "AY6974591       0       0         0   10  \n",
       "AY6974601       0       0         0    9  \n",
       "AY6974611       0       0         0   10  \n",
       "EF6673441       0       0         0   10  \n",
       "KU3558761       0       1         1    7  \n",
       "KU3558771       0       1         1    7  \n",
       "U058761         1       0         0   10  \n",
       "U225211         1       0         0   10  \n",
       "All            10       2         2  193  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# \n",
    "pd.crosstab(report['accession_id'],report['prot'],margins=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x125da87b8>]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "table = pd.crosstab(report['accession_id'],report['accession_id'],margins=False)\n",
    "mpl.plot(report.groupby(['prot']).sum()['read_count'])#,report.groupby(['accession_id']).sum()['read_count'])\n",
    "mpl.plot(report3.groupby(['prot']).sum()['read_count'],color=\"red\")#,report.groupby(['accession_id']).sum()['read_count'])\n",
    "mpl.plot(report4.groupby(['prot']).sum()['read_count'],color=\"blue\")\n",
    "mpl.plot(report5.groupby(['prot']).sum()['read_count'],color=\"green\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mixture of 4 query genomes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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>gene</th>\n",
       "      <th>read_count</th>\n",
       "      <th>length</th>\n",
       "      <th>coverage</th>\n",
       "      <th>accession_id</th>\n",
       "      <th>prot</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KU3558771_protease</td>\n",
       "      <td>711</td>\n",
       "      <td>450</td>\n",
       "      <td>450M</td>\n",
       "      <td>KU3558771</td>\n",
       "      <td>protease</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AF3267642_1D</td>\n",
       "      <td>1578</td>\n",
       "      <td>873</td>\n",
       "      <td>873M</td>\n",
       "      <td>AF3267642</td>\n",
       "      <td>1D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>KU3558771_2C</td>\n",
       "      <td>1897</td>\n",
       "      <td>987</td>\n",
       "      <td>1D986M</td>\n",
       "      <td>KU3558771</td>\n",
       "      <td>2C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AF3267612_2C</td>\n",
       "      <td>1898</td>\n",
       "      <td>987</td>\n",
       "      <td>987M</td>\n",
       "      <td>AF3267612</td>\n",
       "      <td>2C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>KU3558761_capsid</td>\n",
       "      <td>1301</td>\n",
       "      <td>765</td>\n",
       "      <td>1D764M</td>\n",
       "      <td>KU3558761</td>\n",
       "      <td>capsid</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 gene  read_count  length coverage accession_id      prot\n",
       "0  KU3558771_protease         711     450     450M    KU3558771  protease\n",
       "1        AF3267642_1D        1578     873     873M    AF3267642        1D\n",
       "2        KU3558771_2C        1897     987   1D986M    KU3558771        2C\n",
       "3        AF3267612_2C        1898     987     987M    AF3267612        2C\n",
       "4    KU3558761_capsid        1301     765   1D764M    KU3558761    capsid"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mix1 = pd.read_csv(\"mixture1.csv\",sep=\"\\t\",names=['gene',\"read_count\",\"length\",\"coverage\"])\n",
    "# dropping null value columns to avoid errors\n",
    "mix1.dropna(inplace = True)\n",
    "\n",
    "# new data frame with split value columns\n",
    "new = mix1[\"gene\"].str.split(\"_\", n = 1, expand = True)\n",
    "\n",
    "# making seperate first name column from new data frame\n",
    "mix1[\"accession_id\"] = new[0]\n",
    "\n",
    "# making seperate last name column from new data frame\n",
    "mix1[\"prot\"] = new[1]\n",
    "mix1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mixture of 17 contamination genomes and 4 query genomes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Under this simulation case, 17 common contamination genomes are simulated with 10000 reads, no error and mutation rate and each read has length 100 bp. First, sample with 17 contamination genomes only are aligned by running this pipeline, result is as expected, no detection.\n",
    "\n",
    "Then create new sample include these 17 contamination genomes and 4 query genomes above and run pipeline again. Result is displayed in table below. All 4 query genomes are subtyped correctly and same as sample contains only these 4 query genomes above."
   ]
  },
  {
   "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>gene</th>\n",
       "      <th>read_count</th>\n",
       "      <th>length</th>\n",
       "      <th>coverage</th>\n",
       "      <th>accession_id</th>\n",
       "      <th>prot</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KU3558761_3A</td>\n",
       "      <td>320</td>\n",
       "      <td>258</td>\n",
       "      <td>258M</td>\n",
       "      <td>KU3558761</td>\n",
       "      <td>3A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AF3267612_1A</td>\n",
       "      <td>212</td>\n",
       "      <td>207</td>\n",
       "      <td>207M</td>\n",
       "      <td>AF3267612</td>\n",
       "      <td>1A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>KU3558771_2C</td>\n",
       "      <td>1897</td>\n",
       "      <td>987</td>\n",
       "      <td>1D986M</td>\n",
       "      <td>KU3558771</td>\n",
       "      <td>2C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AF3267642_3D</td>\n",
       "      <td>2413</td>\n",
       "      <td>1386</td>\n",
       "      <td>1386M</td>\n",
       "      <td>AF3267642</td>\n",
       "      <td>3D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AF3267612_3D</td>\n",
       "      <td>2329</td>\n",
       "      <td>1386</td>\n",
       "      <td>1386M</td>\n",
       "      <td>AF3267612</td>\n",
       "      <td>3D</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           gene  read_count  length coverage accession_id prot\n",
       "0  KU3558761_3A         320     258     258M    KU3558761   3A\n",
       "1  AF3267612_1A         212     207     207M    AF3267612   1A\n",
       "2  KU3558771_2C        1897     987   1D986M    KU3558771   2C\n",
       "3  AF3267642_3D        2413    1386    1386M    AF3267642   3D\n",
       "4  AF3267612_3D        2329    1386    1386M    AF3267612   3D"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mix2 = pd.read_csv(\"mixture2.csv\",sep=\"\\t\",names=['gene',\"read_count\",\"length\",\"coverage\"])\n",
    "# dropping null value columns to avoid errors\n",
    "mix2.dropna(inplace = True)\n",
    "\n",
    "# new data frame with split value columns\n",
    "new = mix2[\"gene\"].str.split(\"_\", n = 1, expand = True)\n",
    "\n",
    "# making seperate first name column from new data frame\n",
    "mix2[\"accession_id\"] = new[0]\n",
    "\n",
    "# making seperate last name column from new data frame\n",
    "mix2[\"prot\"] = new[1]\n",
    "mix2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### False detection\n",
    "\n",
    "The number of mature peptides detected for each species is shown below. There is no false dicovery between subtypes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>accession_id</th>\n",
       "      <th>AF3267612</th>\n",
       "      <th>AF3267642</th>\n",
       "      <th>KU3558761</th>\n",
       "      <th>KU3558771</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>accession_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AF3267612</th>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>AF3267642</th>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KU3558761</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>KU3558771</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "accession_id  AF3267612  AF3267642  KU3558761  KU3558771\n",
       "accession_id                                            \n",
       "AF3267612            10          0          0          0\n",
       "AF3267642             0         10          0          0\n",
       "KU3558761             0          0          7          0\n",
       "KU3558771             0          0          0          7"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table1 = pd.crosstab(mix1['accession_id'],mix2['accession_id'],margins=False)\n",
    "table1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis of simulation with error and mutation rate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### One by one query genome"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Error rate due to sequencing technology as well as mutation rate of viruses are taken into account when simulating the NGS data. Under this situation, three values of error rate and mutation rate at 0.1\\%, 0.5\\% and 1\\%, respectively are used for simulated results. Results from these cases are compared to see how pipeline's performance in subtyping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "        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>gene</th>\n",
       "      <th>read_count</th>\n",
       "      <th>length</th>\n",
       "      <th>coverage</th>\n",
       "      <th>accession_id</th>\n",
       "      <th>prot</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AF3267542_3C</td>\n",
       "      <td>1302</td>\n",
       "      <td>549</td>\n",
       "      <td>549M</td>\n",
       "      <td>AF3267542</td>\n",
       "      <td>3C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AF3267542_1A</td>\n",
       "      <td>366</td>\n",
       "      <td>207</td>\n",
       "      <td>207M</td>\n",
       "      <td>AF3267542</td>\n",
       "      <td>1A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AF3267542_2A</td>\n",
       "      <td>1286</td>\n",
       "      <td>450</td>\n",
       "      <td>450M</td>\n",
       "      <td>AF3267542</td>\n",
       "      <td>2A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AF3267542_1C</td>\n",
       "      <td>1923</td>\n",
       "      <td>726</td>\n",
       "      <td>726M</td>\n",
       "      <td>AF3267542</td>\n",
       "      <td>1C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AF3267542_3A</td>\n",
       "      <td>570</td>\n",
       "      <td>255</td>\n",
       "      <td>255M</td>\n",
       "      <td>AF3267542</td>\n",
       "      <td>3A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           gene  read_count  length coverage accession_id prot\n",
       "0  AF3267542_3C        1302     549     549M    AF3267542   3C\n",
       "1  AF3267542_1A         366     207     207M    AF3267542   1A\n",
       "2  AF3267542_2A        1286     450     450M    AF3267542   2A\n",
       "3  AF3267542_1C        1923     726     726M    AF3267542   1C\n",
       "4  AF3267542_3A         570     255     255M    AF3267542   3A"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "report4 = pd.read_csv(\"summarise3.csv\",sep=\"\\t\",names=['gene',\"read_count\",\"length\",\"coverage\"])\n",
    "\n",
    "# dropping null value columns to avoid errors\n",
    "report4.dropna(inplace = True)\n",
    "\n",
    "# new data frame with split value columns\n",
    "new = report4[\"gene\"].str.split(\"_\", n = 1, expand = True)\n",
    "\n",
    "# making seperate first name column from new data frame\n",
    "report4[\"accession_id\"] = new[0]\n",
    "\n",
    "# making seperate last name column from new data frame\n",
    "report4[\"prot\"] = new[1]\n",
    "\n",
    "report4.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The result with mutation rate at 0.1\\% and error rate are shown below. Number of mature peptides detected by this pipeline, compared to first case with 0\\% mutation and error rate, is decreased from 193 to 176."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>AY6974591</th>\n",
       "      <td>1</td>\n",
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       "      <th>AY6974611</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EF6673441</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KU3558761</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KU3558771</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>U058761</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>U225211</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>16</td>\n",
       "      <td>19</td>\n",
       "      <td>18</td>\n",
       "      <td>17</td>\n",
       "      <td>18</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>176</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "prot          1A  1B  1C  1D  2A  2B  2C  3A  3C  3D  3Dpol  VP1  VP2  VP3  \\\n",
       "accession_id                                                                 \n",
       "AF3267542      1   1   1   1   1   1   1   0   1   1      0    0    0    0   \n",
       "AF3267612      1   1   1   1   1   1   1   1   1   1      0    0    0    0   \n",
       "AF3267642      0   1   1   1   1   1   1   1   1   1      0    0    0    0   \n",
       "AY4217601      0   0   0   0   1   1   1   1   1   1      0    1    1    1   \n",
       "AY4217611      0   0   0   0   1   1   1   1   1   1      0    1    1    1   \n",
       "AY4217621      0   0   0   0   1   0   1   0   1   1      0    1    1    1   \n",
       "AY4217641      0   0   0   0   1   1   1   1   1   1      0    1    1    1   \n",
       "AY4217651      0   0   0   0   1   1   1   1   1   1      0    0    1    1   \n",
       "AY4217661      0   0   0   0   1   1   1   1   0   1      0    1    1    1   \n",
       "AY4217681      0   0   0   0   0   1   0   1   1   1      0    1    1    1   \n",
       "AY4217691      0   0   0   0   1   1   1   1   1   1      0    1    1    1   \n",
       "AY6974581      1   1   1   1   1   1   1   1   1   1      0    0    0    0   \n",
       "AY6974591      1   1   1   1   0   1   0   1   1   1      0    0    0    0   \n",
       "AY6974601      1   1   1   0   1   1   1   0   1   0      0    0    0    0   \n",
       "AY6974611      1   1   1   1   1   1   1   1   1   1      0    0    0    0   \n",
       "EF6673441      1   1   1   0   1   1   1   1   1   1      0    0    0    0   \n",
       "KU3558761      0   0   0   0   0   1   1   1   1   0      1    0    0    0   \n",
       "KU3558771      0   0   0   0   0   1   1   1   1   0      1    0    0    0   \n",
       "U058761        0   0   0   0   1   1   1   1   1   1      0    1    1    1   \n",
       "U225211        0   0   0   0   1   1   1   1   0   1      0    1    1    1   \n",
       "All            7   8   8   6  16  19  18  17  18  17      2    9   10   10   \n",
       "\n",
       "prot          VP4  capsid  protease  All  \n",
       "accession_id                              \n",
       "AF3267542       0       0         0    9  \n",
       "AF3267612       0       0         0   10  \n",
       "AF3267642       0       0         0    9  \n",
       "AY4217601       0       0         0    9  \n",
       "AY4217611       1       0         0   10  \n",
       "AY4217621       1       0         0    8  \n",
       "AY4217641       1       0         0   10  \n",
       "AY4217651       1       0         0    9  \n",
       "AY4217661       1       0         0    9  \n",
       "AY4217681       1       0         0    8  \n",
       "AY4217691       1       0         0   10  \n",
       "AY6974581       0       0         0   10  \n",
       "AY6974591       0       0         0    8  \n",
       "AY6974601       0       0         0    7  \n",
       "AY6974611       0       0         0   10  \n",
       "EF6673441       0       0         0    9  \n",
       "KU3558761       0       1         0    6  \n",
       "KU3558771       0       1         1    7  \n",
       "U058761         0       0         0    9  \n",
       "U225211         1       0         0    9  \n",
       "All             8       2         1  176  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(report2['accession_id'],report2['prot'],margins=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Figure below shows the number of mature peptides detected by the pipeline for each query genome. As we can see, if there is 0.1\\% mutation rate happen in whole genome, all species are still subtyped but there is decreas number of genes are subtyped.\n",
    "\n",
    "All cases with different values of mutation rate and error rate are shown is figure below. The higher mutation rate or error rate, the lower the number of mature peptide of each query genome are detected. Especially, when the mutation rate is 1\\% and error rate at 3\\%, there are some species are not detected\n",
    "\n",
    "Under this situation, there is no false detection also."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x125e95cf8>]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mpl.plot(report.groupby(['accession_id']).count()['prot'],color=\"blue\")\n",
    "\n",
    "mpl.plot(report2.groupby(['accession_id']).count()['prot'],color=\"red\")\n",
    "\n",
    "mpl.plot(report3.groupby(['accession_id']).count()['prot'],color=\"green\")\n",
    "\n",
    "mpl.plot(report4.groupby(['accession_id']).count()['prot'],color=\"yellow\")\n",
    "\n",
    "mpl.plot(report5.groupby(['accession_id']).count()['prot'],color=\"black\")\n",
    "\n",
    "mpl.plot(report6.groupby(['accession_id']).count()['prot'],color=\".8\")\n",
    "\n",
    "mpl.plot(report7.groupby(['accession_id']).count()['prot'],color=\"cyan\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>accession_id</th>\n",
       "      <th>AF3267542</th>\n",
       "      <th>AF3267612</th>\n",
       "      <th>AF3267642</th>\n",
       "      <th>AY4217601</th>\n",
       "      <th>AY4217611</th>\n",
       "      <th>AY4217621</th>\n",
       "      <th>AY4217641</th>\n",
       "      <th>AY4217651</th>\n",
       "      <th>AY4217661</th>\n",
       "      <th>AY4217681</th>\n",
       "      <th>AY4217691</th>\n",
       "      <th>AY6974581</th>\n",
       "      <th>AY6974591</th>\n",
       "      <th>AY6974601</th>\n",
       "      <th>AY6974611</th>\n",
       "      <th>EF6673441</th>\n",
       "      <th>KU3558761</th>\n",
       "      <th>KU3558771</th>\n",
       "      <th>U058761</th>\n",
       "      <th>U225211</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>accession_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AF3267542</th>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AF3267612</th>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AF3267642</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AY4217601</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AY4217611</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "accession_id  AF3267542  AF3267612  AF3267642  AY4217601  AY4217611  \\\n",
       "accession_id                                                          \n",
       "AF3267542             9          0          0          0          0   \n",
       "AF3267612             0         10          0          0          0   \n",
       "AF3267642             0          0          9          0          0   \n",
       "AY4217601             0          0          0          9          0   \n",
       "AY4217611             0          0          0          0         10   \n",
       "\n",
       "accession_id  AY4217621  AY4217641  AY4217651  AY4217661  AY4217681  \\\n",
       "accession_id                                                          \n",
       "AF3267542             0          0          0          0          0   \n",
       "AF3267612             0          0          0          0          0   \n",
       "AF3267642             0          0          0          0          0   \n",
       "AY4217601             0          0          0          0          0   \n",
       "AY4217611             0          0          0          0          0   \n",
       "\n",
       "accession_id  AY4217691  AY6974581  AY6974591  AY6974601  AY6974611  \\\n",
       "accession_id                                                          \n",
       "AF3267542             0          0          0          0          0   \n",
       "AF3267612             0          0          0          0          0   \n",
       "AF3267642             0          0          0          0          0   \n",
       "AY4217601             0          0          0          0          0   \n",
       "AY4217611             0          0          0          0          0   \n",
       "\n",
       "accession_id  EF6673441  KU3558761  KU3558771  U058761  U225211  \n",
       "accession_id                                                     \n",
       "AF3267542             0          0          0        0        0  \n",
       "AF3267612             0          0          0        0        0  \n",
       "AF3267642             0          0          0        0        0  \n",
       "AY4217601             0          0          0        0        0  \n",
       "AY4217611             0          0          0        0        0  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table2 = pd.crosstab(report2['accession_id'],report2['accession_id'],margins=False)\n",
    "table2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below result is from simulated with 1\\% error rate and 1\\% mutation rate for each of query genome, then each sample will run under the pipeline."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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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>gene</th>\n",
       "      <th>read_count</th>\n",
       "      <th>length</th>\n",
       "      <th>coverage</th>\n",
       "      <th>accession_id</th>\n",
       "      <th>prot</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AF3267542_1C</td>\n",
       "      <td>1692</td>\n",
       "      <td>726</td>\n",
       "      <td>726M</td>\n",
       "      <td>AF3267542</td>\n",
       "      <td>1C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AF3267542_3D</td>\n",
       "      <td>2329</td>\n",
       "      <td>1386</td>\n",
       "      <td>435M1D950M</td>\n",
       "      <td>AF3267542</td>\n",
       "      <td>3D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AF3267612_1A</td>\n",
       "      <td>325</td>\n",
       "      <td>207</td>\n",
       "      <td>207M</td>\n",
       "      <td>AF3267612</td>\n",
       "      <td>1A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AF3267612_2B</td>\n",
       "      <td>629</td>\n",
       "      <td>297</td>\n",
       "      <td>297M</td>\n",
       "      <td>AF3267612</td>\n",
       "      <td>2B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AF3267612_2A</td>\n",
       "      <td>283</td>\n",
       "      <td>450</td>\n",
       "      <td>300M1D149M</td>\n",
       "      <td>AF3267612</td>\n",
       "      <td>2A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           gene  read_count  length    coverage accession_id prot\n",
       "0  AF3267542_1C        1692     726        726M    AF3267542   1C\n",
       "1  AF3267542_3D        2329    1386  435M1D950M    AF3267542   3D\n",
       "2  AF3267612_1A         325     207        207M    AF3267612   1A\n",
       "3  AF3267612_2B         629     297        297M    AF3267612   2B\n",
       "4  AF3267612_2A         283     450  300M1D149M    AF3267612   2A"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "report4 = pd.read_csv(\"summarise4.csv\",sep=\"\\t\",names=['gene',\"read_count\",\"length\",\"coverage\"])\n",
    "\n",
    "# dropping null value columns to avoid errors\n",
    "report4.dropna(inplace = True)\n",
    "\n",
    "# new data frame with split value columns\n",
    "new = report4[\"gene\"].str.split(\"_\", n = 1, expand = True)\n",
    "\n",
    "# making seperate first name column from new data frame\n",
    "report4[\"accession_id\"] = new[0]\n",
    "\n",
    "# making seperate last name column from new data frame\n",
    "report4[\"prot\"] = new[1]\n",
    "\n",
    "report4.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AY4217661</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AY4217681</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>AY4217691</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AY6974581</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>AY6974611</th>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EF6673441</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>2</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KU3558761</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KU3558771</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>U058761</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>U225211</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "accession_id  AF3267542  AF3267612  AF3267642  AY4217601  AY4217611  \\\n",
       "accession_id                                                          \n",
       "AF3267542             2          0          0          0          0   \n",
       "AF3267612             0          5          0          0          0   \n",
       "AF3267642             0          0          2          0          0   \n",
       "AY4217601             0          0          0          2          0   \n",
       "AY4217611             0          0          0          0          2   \n",
       "AY4217621             0          0          0          0          0   \n",
       "AY4217641             0          0          0          0          0   \n",
       "AY4217651             0          0          0          0          0   \n",
       "AY4217661             0          0          0          0          0   \n",
       "AY4217681             0          0          0          0          0   \n",
       "AY4217691             0          0          0          0          0   \n",
       "AY6974581             0          0          0          0          0   \n",
       "AY6974591             0          0          0          0          0   \n",
       "AY6974601             0          0          0          0          0   \n",
       "AY6974611             0          0          0          0          0   \n",
       "EF6673441             0          0          0          0          0   \n",
       "KU3558761             0          0          0          0          0   \n",
       "KU3558771             0          0          0          0          0   \n",
       "U058761               0          0          0          0          0   \n",
       "U225211               0          0          0          0          0   \n",
       "\n",
       "accession_id  AY4217621  AY4217641  AY4217651  AY4217661  AY4217681  \\\n",
       "accession_id                                                          \n",
       "AF3267542             0          0          0          0          0   \n",
       "AF3267612             0          0          0          0          0   \n",
       "AF3267642             0          0          0          0          0   \n",
       "AY4217601             0          0          0          0          0   \n",
       "AY4217611             0          0          0          0          0   \n",
       "AY4217621             2          0          0          0          0   \n",
       "AY4217641             0          4          0          0          0   \n",
       "AY4217651             0          0          2          0          0   \n",
       "AY4217661             0          0          0          4          0   \n",
       "AY4217681             0          0          0          0          4   \n",
       "AY4217691             0          0          0          0          0   \n",
       "AY6974581             0          0          0          0          0   \n",
       "AY6974591             0          0          0          0          0   \n",
       "AY6974601             0          0          0          0          0   \n",
       "AY6974611             0          0          0          0          0   \n",
       "EF6673441             0          0          0          0          0   \n",
       "KU3558761             0          0          0          0          0   \n",
       "KU3558771             0          0          0          0          0   \n",
       "U058761               0          0          0          0          0   \n",
       "U225211               0          0          0          0          0   \n",
       "\n",
       "accession_id  AY4217691  AY6974581  AY6974591  AY6974601  AY6974611  \\\n",
       "accession_id                                                          \n",
       "AF3267542             0          0          0          0          0   \n",
       "AF3267612             0          0          0          0          0   \n",
       "AF3267642             0          0          0          0          0   \n",
       "AY4217601             0          0          0          0          0   \n",
       "AY4217611             0          0          0          0          0   \n",
       "AY4217621             0          0          0          0          0   \n",
       "AY4217641             0          0          0          0          0   \n",
       "AY4217651             0          0          0          0          0   \n",
       "AY4217661             0          0          0          0          0   \n",
       "AY4217681             0          0          0          0          0   \n",
       "AY4217691             4          0          0          0          0   \n",
       "AY6974581             0          2          0          0          0   \n",
       "AY6974591             0          0          5          0          0   \n",
       "AY6974601             0          0          0          1          0   \n",
       "AY6974611             0          0          0          0          4   \n",
       "EF6673441             0          0          0          0          0   \n",
       "KU3558761             0          0          0          0          0   \n",
       "KU3558771             0          0          0          0          0   \n",
       "U058761               0          0          0          0          0   \n",
       "U225211               0          0          0          0          0   \n",
       "\n",
       "accession_id  EF6673441  KU3558761  KU3558771  U058761  U225211  \n",
       "accession_id                                                     \n",
       "AF3267542             0          0          0        0        0  \n",
       "AF3267612             0          0          0        0        0  \n",
       "AF3267642             0          0          0        0        0  \n",
       "AY4217601             0          0          0        0        0  \n",
       "AY4217611             0          0          0        0        0  \n",
       "AY4217621             0          0          0        0        0  \n",
       "AY4217641             0          0          0        0        0  \n",
       "AY4217651             0          0          0        0        0  \n",
       "AY4217661             0          0          0        0        0  \n",
       "AY4217681             0          0          0        0        0  \n",
       "AY4217691             0          0          0        0        0  \n",
       "AY6974581             0          0          0        0        0  \n",
       "AY6974591             0          0          0        0        0  \n",
       "AY6974601             0          0          0        0        0  \n",
       "AY6974611             0          0          0        0        0  \n",
       "EF6673441             2          0          0        0        0  \n",
       "KU3558761             0          4          0        0        0  \n",
       "KU3558771             0          0          1        0        0  \n",
       "U058761               0          0          0        4        0  \n",
       "U225211               0          0          0        0        3  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table4 = pd.crosstab(report4['accession_id'],report4['accession_id'],margins=False)\n",
    "table4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Subtypes are still detected when error rate at 3\\% based on mature peptides detected. However, this can't be confirmed that which virus is presented in the sample due to homogeneity. The graph is constructed by viruses of interest only, thus, all other species or strains which might be contain same mature peptides not covered in the graph library could be potential species."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>accession_id</th>\n",
       "      <th>AF3267542</th>\n",
       "      <th>AF3267612</th>\n",
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       "      <th>EF6673441</th>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AY4217611</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AY4217621</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AY4217641</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AY4217651</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AY4217661</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AY4217681</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AY6974581</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AY6974591</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AY6974601</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AY6974611</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EF6673441</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KU3558761</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>U058761</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>U225211</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "accession_id  AF3267542  AF3267612  AF3267642  AY4217601  AY4217611  \\\n",
       "accession_id                                                          \n",
       "AF3267542             1          0          0          0          0   \n",
       "AF3267612             0          3          0          0          0   \n",
       "AF3267642             0          0          1          0          0   \n",
       "AY4217601             0          0          0          4          0   \n",
       "AY4217611             0          0          0          0          3   \n",
       "AY4217621             0          0          0          0          0   \n",
       "AY4217641             0          0          0          0          0   \n",
       "AY4217651             0          0          0          0          0   \n",
       "AY4217661             0          0          0          0          0   \n",
       "AY4217681             0          0          0          0          0   \n",
       "AY6974581             0          0          0          0          0   \n",
       "AY6974591             0          0          0          0          0   \n",
       "AY6974601             0          0          0          0          0   \n",
       "AY6974611             0          0          0          0          0   \n",
       "EF6673441             0          0          0          0          0   \n",
       "KU3558761             0          0          0          0          0   \n",
       "U058761               0          0          0          0          0   \n",
       "U225211               0          0          0          0          0   \n",
       "All                   1          3          1          4          3   \n",
       "\n",
       "accession_id  AY4217621  AY4217641  AY4217651  AY4217661  AY4217681  \\\n",
       "accession_id                                                          \n",
       "AF3267542             0          0          0          0          0   \n",
       "AF3267612             0          0          0          0          0   \n",
       "AF3267642             0          0          0          0          0   \n",
       "AY4217601             0          0          0          0          0   \n",
       "AY4217611             0          0          0          0          0   \n",
       "AY4217621             5          0          0          0          0   \n",
       "AY4217641             0          2          0          0          0   \n",
       "AY4217651             0          0          4          0          0   \n",
       "AY4217661             0          0          0          1          0   \n",
       "AY4217681             0          0          0          0          2   \n",
       "AY6974581             0          0          0          0          0   \n",
       "AY6974591             0          0          0          0          0   \n",
       "AY6974601             0          0          0          0          0   \n",
       "AY6974611             0          0          0          0          0   \n",
       "EF6673441             0          0          0          0          0   \n",
       "KU3558761             0          0          0          0          0   \n",
       "U058761               0          0          0          0          0   \n",
       "U225211               0          0          0          0          0   \n",
       "All                   5          2          4          1          2   \n",
       "\n",
       "accession_id  AY6974581  AY6974591  AY6974601  AY6974611  EF6673441  \\\n",
       "accession_id                                                          \n",
       "AF3267542             0          0          0          0          0   \n",
       "AF3267612             0          0          0          0          0   \n",
       "AF3267642             0          0          0          0          0   \n",
       "AY4217601             0          0          0          0          0   \n",
       "AY4217611             0          0          0          0          0   \n",
       "AY4217621             0          0          0          0          0   \n",
       "AY4217641             0          0          0          0          0   \n",
       "AY4217651             0          0          0          0          0   \n",
       "AY4217661             0          0          0          0          0   \n",
       "AY4217681             0          0          0          0          0   \n",
       "AY6974581             2          0          0          0          0   \n",
       "AY6974591             0          2          0          0          0   \n",
       "AY6974601             0          0          3          0          0   \n",
       "AY6974611             0          0          0          2          0   \n",
       "EF6673441             0          0          0          0          1   \n",
       "KU3558761             0          0          0          0          0   \n",
       "U058761               0          0          0          0          0   \n",
       "U225211               0          0          0          0          0   \n",
       "All                   2          2          3          2          1   \n",
       "\n",
       "accession_id  KU3558761  U058761  U225211  All  \n",
       "accession_id                                    \n",
       "AF3267542             0        0        0    1  \n",
       "AF3267612             0        0        0    3  \n",
       "AF3267642             0        0        0    1  \n",
       "AY4217601             0        0        0    4  \n",
       "AY4217611             0        0        0    3  \n",
       "AY4217621             0        0        0    5  \n",
       "AY4217641             0        0        0    2  \n",
       "AY4217651             0        0        0    4  \n",
       "AY4217661             0        0        0    1  \n",
       "AY4217681             0        0        0    2  \n",
       "AY6974581             0        0        0    2  \n",
       "AY6974591             0        0        0    2  \n",
       "AY6974601             0        0        0    3  \n",
       "AY6974611             0        0        0    2  \n",
       "EF6673441             0        0        0    1  \n",
       "KU3558761             3        0        0    3  \n",
       "U058761               0        5        0    5  \n",
       "U225211               0        0        2    2  \n",
       "All                   3        5        2   46  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table7 = pd.crosstab(report7['accession_id'],report7['accession_id'],margins=True)\n",
    "table7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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