3-MRCA/MRCA_Estimation_Notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MRCA estimation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-------\n",
    "You can access your data via the dataset number. For example, ``handle = open(get(42), 'r')``.\n",
    "To save data, write your data to a file, and then call ``put('filename.txt')``. The dataset will then be available in your galaxy history.\n",
    "Notebooks can be saved to Galaxy by clicking the large green button at the top right of the IPython interface.<br>\n",
    "More help and informations can be found on the project [website](https://github.com/bgruening/galaxy-ipython)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inputs\n",
    "\n",
    "------\n",
    "\n",
    "This notebook expects two inputs from Galaxy history:\n",
    "\n",
    " 1. a comma separated list of accession numbers and corresponding collection dates\n",
    " 2. a phylogenetic tree (in newick format) in which OTU labels correspond to accession numbers from input 1\n",
    " \n",
    "Here is an example of input 1:\n",
    "```\n",
    "Accession,Collection_Date\n",
    "MT049951,2020-01-17\n",
    "MT019531,2019-12-30\n",
    "MT019529,2019-12-23\n",
    "MN975262,2020-01-11\n",
    "MN996528,2019-12-30\n",
    "MT019532,2019-12-30\n",
    "MT019530,2019-12-30\n",
    "MN994468,2020-01-22\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Set history items for datasets containing accession/dates and a maximum likelihood tree:\n",
    "# These numbers correspond to numbers of Galaxy datasets\n",
    "acc_date = 1\n",
    "tree = 116"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "!pip install --upgrade pip==20.0.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "!pip install --upgrade statsmodels==0.11.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "!pip install --upgrade pandas==0.24.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from Bio import Phylo as phylo\n",
    "from matplotlib import pyplot as plt\n",
    "import pandas as pd\n",
    "import datetime\n",
    "\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get accessions and dates\n",
    "acc_path = get(acc_date)\n",
    "# Get ML tree\n",
    "tree_path = get(tree)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "!mv {acc_path} acc_date.csv\n",
    "!mv {tree_path} tree.nwk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "col_dates = pd.read_csv('acc_date.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "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>Accession</th>\n",
       "      <th>Collection_Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MT019531</td>\n",
       "      <td>2019-12-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MT019529</td>\n",
       "      <td>2019-12-23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MT007544</td>\n",
       "      <td>2020-01-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MN975262</td>\n",
       "      <td>2020-01-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MN996528</td>\n",
       "      <td>2019-12-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>MT019532</td>\n",
       "      <td>2019-12-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>MT019530</td>\n",
       "      <td>2019-12-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>MN994468</td>\n",
       "      <td>2020-01-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>MT019533</td>\n",
       "      <td>2020-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>MT039888</td>\n",
       "      <td>2020-01-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>MT020880</td>\n",
       "      <td>2020-01-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>MN994467</td>\n",
       "      <td>2020-01-23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>MT020881</td>\n",
       "      <td>2020-01-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>MN988713</td>\n",
       "      <td>2020-01-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>MN997409</td>\n",
       "      <td>2020-01-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>MN985325</td>\n",
       "      <td>2020-01-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>MT044257</td>\n",
       "      <td>2020-01-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>MT027063</td>\n",
       "      <td>2020-01-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>MT027062</td>\n",
       "      <td>2020-01-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>MT027064</td>\n",
       "      <td>2020-01-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>MN988669</td>\n",
       "      <td>2020-01-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>MN988668</td>\n",
       "      <td>2020-01-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>MT039887</td>\n",
       "      <td>2020-01-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>MT044258</td>\n",
       "      <td>2020-01-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>MN996531</td>\n",
       "      <td>2019-12-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>MN996530</td>\n",
       "      <td>2019-12-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>MN996529</td>\n",
       "      <td>2019-12-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>MT020781</td>\n",
       "      <td>2020-01-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>MN938384</td>\n",
       "      <td>2020-01-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>MT039873</td>\n",
       "      <td>2020-01-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>MN996527</td>\n",
       "      <td>2019-12-30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Accession Collection_Date\n",
       "0   MT019531      2019-12-30\n",
       "1   MT019529      2019-12-23\n",
       "2   MT007544      2020-01-25\n",
       "3   MN975262      2020-01-11\n",
       "4   MN996528      2019-12-30\n",
       "5   MT019532      2019-12-30\n",
       "6   MT019530      2019-12-30\n",
       "7   MN994468      2020-01-22\n",
       "8   MT019533      2020-01-01\n",
       "9   MT039888      2020-01-29\n",
       "10  MT020880      2020-01-25\n",
       "11  MN994467      2020-01-23\n",
       "12  MT020881      2020-01-25\n",
       "13  MN988713      2020-01-21\n",
       "14  MN997409      2020-01-22\n",
       "15  MN985325      2020-01-19\n",
       "16  MT044257      2020-01-28\n",
       "17  MT027063      2020-01-29\n",
       "18  MT027062      2020-01-29\n",
       "19  MT027064      2020-01-29\n",
       "20  MN988669      2020-01-02\n",
       "21  MN988668      2020-01-02\n",
       "22  MT039887      2020-01-31\n",
       "23  MT044258      2020-01-27\n",
       "24  MN996531      2019-12-30\n",
       "25  MN996530      2019-12-30\n",
       "26  MN996529      2019-12-30\n",
       "27  MT020781      2020-01-29\n",
       "28  MN938384      2020-01-10\n",
       "29  MT039873      2020-01-20\n",
       "30  MN996527      2019-12-30"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tree = next( phylo.parse( 'tree.nwk', \"newick\" ) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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wYUIGDRqUpqam/OIXv0iSXHHFFWlsbEy1Wk21Ws3tt9+eJFmyZEl9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VYP9DQAgMJYwQPS3t4uNgAACmAFD0iyM/LYf6yWAgD7ghU8AACAQgg8AACAQgg8AACA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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7feb41435e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = [15, 50] \n",
    "phylo.draw( tree )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def root_to_tip( tree, date_df ):\n",
    "    accum = []\n",
    "    def tree_walker( clade, total_branch_length ):\n",
    "        for child in clade.clades:\n",
    "            if child.is_terminal:\n",
    "                if child.name is not None:\n",
    "                    date = date_df[date_df['Accession']==child.name]['Collection_Date'].to_string(index=False)\n",
    "                    accum.append( ( child.name, date, total_branch_length + child.branch_length ) )\n",
    "            tree_walker( child, total_branch_length + child.branch_length  )\n",
    "    tree_walker( tree.clade, 0 )\n",
    "    return pd.DataFrame( accum, columns=[\"name\",\"date\",\"distance_to_root\"] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for clade in list( tree.find_clades() ):\n",
    "    tree.root_with_outgroup( clade )\n",
    "    df = root_to_tip( tree, col_dates )\n",
    "    df['date'] = pd.to_datetime(df['date']) \n",
    "    df['date_as_numeric'] = [d.year + (d.dayofyear-1)/365 for d in df['date']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7feb418a65c0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3htJp6eI7N+ie9W/QZgrUgH1tpc6yvu6i2ZmhNLSWTk6urbRYlcO5dbrOgnAI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\nW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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7feb3db99a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = [15, 10] \n",
    "df.plot( x=\"date\", y=\"distance_to_root\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['date_as_numeric'] = [d.year + (d.dayofyear-1)/365 for d in df['date']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MRCA timing is ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:       distance_to_root   R-squared:                       0.056\n",
      "Model:                            OLS   Adj. R-squared:                  0.023\n",
      "Method:                 Least Squares   F-statistic:                     1.671\n",
      "Date:                Thu, 13 Feb 2020   Prob (F-statistic):              0.207\n",
      "Time:                        19:40:50   Log-Likelihood:                 246.10\n",
      "No. Observations:                  30   AIC:                            -488.2\n",
      "Df Residuals:                      28   BIC:                            -485.4\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===================================================================================\n",
      "                      coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-----------------------------------------------------------------------------------\n",
      "Intercept          -0.9146      0.708     -1.293      0.207      -2.364       0.535\n",
      "date_as_numeric     0.0005      0.000      1.293      0.207      -0.000       0.001\n",
      "==============================================================================\n",
      "Omnibus:                        3.224   Durbin-Watson:                   1.763\n",
      "Prob(Omnibus):                  0.199   Jarque-Bera (JB):                2.585\n",
      "Skew:                           0.716   Prob(JB):                        0.275\n",
      "Kurtosis:                       2.868   Cond. No.                     1.14e+08\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 1.14e+08. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n",
      "Root predicted at 2019-11-14 00:00:00\n"
     ]
    }
   ],
   "source": [
    "import datetime\n",
    "def decimal_to_calendar (decimal):\n",
    "    years = int (decimal)\n",
    "    d = datetime.datetime (years, 1,1) + datetime.timedelta (days = int ((decimal-years)*365))\n",
    "    return d\n",
    "\n",
    "model = smf.ols(formula='distance_to_root ~ date_as_numeric ', data=df)\n",
    "results = model.fit()\n",
    "print( results.summary() )\n",
    "print (\"Root predicted at {}\".format(decimal_to_calendar(-results.params.Intercept/results.params.date_as_numeric)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
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 },
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