{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# This is a notebook where I show you what data is available for analysis, roughly how it is formatted, and how to make a few plots\n",
    "## It is meant to be a \"jump start\" for your own exploration of the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Resources\n",
    "### Figure80 article\n",
    "LINK TO BE ADDED\n",
    "### Paper\n",
    "https://science.sciencemag.org/content/366/6464/490\n",
    "### Preprint \n",
    "https://www.biorxiv.org/content/10.1101/675314v1\n",
    "### Github and archived Zenodo of the pipeline to get from raw reads to the processed data available here\n",
    "https://github.com/mjohnson11/TnSeq_Pipeline and https://zenodo.org/record/3402230#.Xc3brpNKhTY\n",
    "(this notebook is essentially a simplified version of the one in these repositories used to make the paper figures)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Requirements if using this locally:\n",
    "I think just a normal anaconda distribution of python should be fine for everything here, which you probably already have: https://www.anaconda.com/distribution/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing various useful libraries / setting up plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as pl\n",
    "import seaborn as sns\n",
    "sns.set_style(\"white\")\n",
    "sns.set_style(\"ticks\")\n",
    "colors = ['#FFB000', '#648FFF']\n",
    "%matplotlib notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reading data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:26: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n"
     ]
    }
   ],
   "source": [
    "## READING DATA\n",
    "def change_well_format(w):\n",
    "    if '_' in w:\n",
    "        plate = int(w[1:3])\n",
    "        t = 'LK' + str(plate) + '-'\n",
    "        n = int(w.split('_')[1])\n",
    "        lets = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H']\n",
    "        l = lets[int(np.floor((n-1)/12))]\n",
    "        return t + l + str(((n-1) % 12) + 1).zfill(2)\n",
    "    else:\n",
    "        return w\n",
    "\n",
    "def get_geno_matrix(seg_names):\n",
    "    # Data from https://www.nature.com/articles/nature11867, can also be dowloaded here http://genomics-pubs.princeton.edu/YeastCross_BYxRM/\n",
    "    d = pd.read_csv('files/BYxRM_GenoData.csv') \n",
    "    map_genos = {'B': 0, 'R': 1}\n",
    "    for w in d.keys():\n",
    "        if change_well_format(w) in seg_names:\n",
    "            d[change_well_format(w)] = d[w].map(map_genos)\n",
    "    assert len([s for s in seg_names if s in d.columns]) == len(seg_names)\n",
    "    return d[['marker'] + seg_names]\n",
    "\n",
    "\n",
    "# Reading information of segregant fitness in our focal environment (from Jerison et al. 2017)\n",
    "x_info = pd.read_csv('files/Clones_For_Tn96_Experiment.csv')\n",
    "seg_to_fit = {i[0]: i[1] for i in x_info.as_matrix(['segregant', 'initial fitness, YPD 30C'])}\n",
    "# Reading data files containing fitness effect information from the small library experiment (few (~100) mutations in many genetic backgrounds)\n",
    "tp_all = pd.read_csv('files/TP_data_by_edge.csv')\n",
    "# This excludes the neutral controls, a few mutations that were unintentionally included in this library, and a few controls that didn't end up getting good enough coverage to really analyze\n",
    "tp = tp_all.loc[tp_all['Type']=='Experiment']\n",
    "# Reading same thing for large library experiment (many mutations in a few genetic backgrounds)\n",
    "bt = pd.read_csv('files/BT_data_by_edge.csv')\n",
    "# Reading aggregate data on DFE (distribution of fitness effect) data statistics for each segregant\n",
    "tp_dfe = pd.read_csv('files/TP_DFE_statistics.csv')\n",
    "bt_dfe = pd.read_csv('files/BT_DFE_statistics.csv')\n",
    "# For bioinformatic reasons, the BT (large library) data has a longer edge sequence, changing so the two exps are comparable\n",
    "bt['Long.Edge'] = bt['Edge']\n",
    "bt['Edge'] = bt['Long.Edge'].str[:15]\n",
    "# Making a few dictionaries that point from the names I am used to to the dataframes\n",
    "dats = {'BT': bt, 'TP': tp, 'BT.DFE': bt_dfe, 'TP.DFE': tp_dfe}\n",
    "exps = {'BT': 'E1', 'TP': 'E2'}\n",
    "# Getting a list of segregants in each experiment by looking for columns like segregant.mean.s in the dataframe\n",
    "segs_all = {exp: [i.split('.')[0] for i in dats[exp] if '.mean.s' in i] for exp in exps}\n",
    "# Getting genotype information on these segregants using data from Bloom et al. 2013\n",
    "gm = get_geno_matrix(segs_all['TP'])\n",
    "# Making restricted lists of segregants that have at least 50 mutations with s measured for DFE comparisons\n",
    "segs_use = {exp: [s for s in segs_all[exp] if len(dats[exp].loc[pd.notnull(dats[exp][s + '.mean.s'])])>=50] for exp in exps}\n",
    "# Making some \n",
    "sorted_segs = {exp: sorted(segs_use[exp], key=lambda x: seg_to_fit[x]) for exp in exps}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Let's just take a look at all this data, to see what's what\n",
    "for tp and bt, each row is a mutation\n",
    "for tp_dfe and bt_dfe, each row is a DFE statistic (or background fitness, which we can treat similarly)\n",
    "for gm, each row is a genotyped allele that is different between RM and BY (and takes each state in ~half the segregants)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Edge</th>\n",
       "      <th>Type</th>\n",
       "      <th>Edge.ID</th>\n",
       "      <th>Gene.Use</th>\n",
       "      <th>Gene_ORF</th>\n",
       "      <th>Gene_ORF.nearby</th>\n",
       "      <th>briefDescription</th>\n",
       "      <th>briefDescription.nearby</th>\n",
       "      <th>chromosome</th>\n",
       "      <th>description</th>\n",
       "      <th>...</th>\n",
       "      <th>full_model_p</th>\n",
       "      <th>full_model_aic</th>\n",
       "      <th>full_model_r2_95_conf_low</th>\n",
       "      <th>full_model_r2_95_conf_high</th>\n",
       "      <th>full_model_p_values</th>\n",
       "      <th>full_model_params</th>\n",
       "      <th>full_model_coeffs</th>\n",
       "      <th>qtls</th>\n",
       "      <th>resid.qtls</th>\n",
       "      <th>full.model.qtls</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TGATCATCACGGGAC</td>\n",
       "      <td>Experiment</td>\n",
       "      <td>888</td>\n",
       "      <td>in FLC2</td>\n",
       "      <td>FLC2</td>\n",
       "      <td>FLC2</td>\n",
       "      <td>Putative calcium channel involved in calcium r...</td>\n",
       "      <td>Putative calcium channel involved in calcium r...</td>\n",
       "      <td>chr01</td>\n",
       "      <td>Putative calcium channel involved in calcium r...</td>\n",
       "      <td>...</td>\n",
       "      <td>8.990535e-25</td>\n",
       "      <td>-1049.391200</td>\n",
       "      <td>0.484313</td>\n",
       "      <td>0.664302</td>\n",
       "      <td>3.3295688484889364e-32;1.8707009014831425e-05;...</td>\n",
       "      <td>x;locus_9649106_chr14_401893_C_T;locus_9717516...</td>\n",
       "      <td>-0.009667588479105496;-0.06737728192983725;0.3...</td>\n",
       "      <td>chr14_401893;chr14_374661;chr14_402771</td>\n",
       "      <td>chr14_470303;chr14_420065;chr14_492376</td>\n",
       "      <td>chr14_401893;chr14_374661;chr14_402771|chr14_4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TCGAAAGCACAGTAG</td>\n",
       "      <td>Experiment</td>\n",
       "      <td>377</td>\n",
       "      <td>nearby SRB2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SRB2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Subunit of the RNA polymerase II mediator complex</td>\n",
       "      <td>chr08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>5.640931e-01</td>\n",
       "      <td>-83.796365</td>\n",
       "      <td>0.000082</td>\n",
       "      <td>0.396975</td>\n",
       "      <td>0.00020013790535836575;0.5640930907526316</td>\n",
       "      <td>x</td>\n",
       "      <td>-0.049382849134784586;-0.08592702839231847</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GTTGAACTGGTTGTT</td>\n",
       "      <td>Experiment</td>\n",
       "      <td>197</td>\n",
       "      <td>in STM1</td>\n",
       "      <td>STM1</td>\n",
       "      <td>STM1</td>\n",
       "      <td>Protein required for optimal translation under...</td>\n",
       "      <td>Protein required for optimal translation under...</td>\n",
       "      <td>chr12</td>\n",
       "      <td>Protein required for optimal translation under...</td>\n",
       "      <td>...</td>\n",
       "      <td>2.500225e-07</td>\n",
       "      <td>-803.596564</td>\n",
       "      <td>0.103969</td>\n",
       "      <td>0.347860</td>\n",
       "      <td>1.4054000123711754e-50;0.9145877127045838;4.10...</td>\n",
       "      <td>x;locus_9680363_chr14_433150_G_A</td>\n",
       "      <td>-0.028546289780384954;-0.0024826738390553073;0...</td>\n",
       "      <td>chr14_433150;chr14_420065;chr14_485550</td>\n",
       "      <td>chr14_433150;chr14_420065;chr14_492376</td>\n",
       "      <td>chr14_433150;chr14_420065;chr14_485550</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 1696 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Edge        Type  Edge.ID     Gene.Use Gene_ORF Gene_ORF.nearby  \\\n",
       "0  TGATCATCACGGGAC  Experiment      888      in FLC2     FLC2            FLC2   \n",
       "1  TCGAAAGCACAGTAG  Experiment      377  nearby SRB2      NaN            SRB2   \n",
       "2  GTTGAACTGGTTGTT  Experiment      197      in STM1     STM1            STM1   \n",
       "\n",
       "                                    briefDescription  \\\n",
       "0  Putative calcium channel involved in calcium r...   \n",
       "1                                                NaN   \n",
       "2  Protein required for optimal translation under...   \n",
       "\n",
       "                             briefDescription.nearby chromosome  \\\n",
       "0  Putative calcium channel involved in calcium r...      chr01   \n",
       "1  Subunit of the RNA polymerase II mediator complex      chr08   \n",
       "2  Protein required for optimal translation under...      chr12   \n",
       "\n",
       "                                         description  \\\n",
       "0  Putative calcium channel involved in calcium r...   \n",
       "1                                                NaN   \n",
       "2  Protein required for optimal translation under...   \n",
       "\n",
       "                         ...                          full_model_p  \\\n",
       "0                        ...                          8.990535e-25   \n",
       "1                        ...                          5.640931e-01   \n",
       "2                        ...                          2.500225e-07   \n",
       "\n",
       "   full_model_aic full_model_r2_95_conf_low full_model_r2_95_conf_high  \\\n",
       "0    -1049.391200                  0.484313                   0.664302   \n",
       "1      -83.796365                  0.000082                   0.396975   \n",
       "2     -803.596564                  0.103969                   0.347860   \n",
       "\n",
       "                                 full_model_p_values  \\\n",
       "0  3.3295688484889364e-32;1.8707009014831425e-05;...   \n",
       "1          0.00020013790535836575;0.5640930907526316   \n",
       "2  1.4054000123711754e-50;0.9145877127045838;4.10...   \n",
       "\n",
       "                                   full_model_params  \\\n",
       "0  x;locus_9649106_chr14_401893_C_T;locus_9717516...   \n",
       "1                                                  x   \n",
       "2                   x;locus_9680363_chr14_433150_G_A   \n",
       "\n",
       "                                   full_model_coeffs  \\\n",
       "0  -0.009667588479105496;-0.06737728192983725;0.3...   \n",
       "1         -0.049382849134784586;-0.08592702839231847   \n",
       "2  -0.028546289780384954;-0.0024826738390553073;0...   \n",
       "\n",
       "                                     qtls  \\\n",
       "0  chr14_401893;chr14_374661;chr14_402771   \n",
       "1                                     NaN   \n",
       "2  chr14_433150;chr14_420065;chr14_485550   \n",
       "\n",
       "                               resid.qtls  \\\n",
       "0  chr14_470303;chr14_420065;chr14_492376   \n",
       "1                                     NaN   \n",
       "2  chr14_433150;chr14_420065;chr14_492376   \n",
       "\n",
       "                                     full.model.qtls  \n",
       "0  chr14_401893;chr14_374661;chr14_402771|chr14_4...  \n",
       "1                                                NaN  \n",
       "2             chr14_433150;chr14_420065;chr14_485550  \n",
       "\n",
       "[3 rows x 1696 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tp.iloc[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Edge.ID</th>\n",
       "      <th>Edge</th>\n",
       "      <th>Gene.Use</th>\n",
       "      <th>Gene_ORF</th>\n",
       "      <th>Gene_ORF.nearby</th>\n",
       "      <th>briefDescription</th>\n",
       "      <th>briefDescription.nearby</th>\n",
       "      <th>chromosome</th>\n",
       "      <th>description</th>\n",
       "      <th>description.nearby</th>\n",
       "      <th>...</th>\n",
       "      <th>full_model_r2</th>\n",
       "      <th>full_model_p</th>\n",
       "      <th>full_model_aic</th>\n",
       "      <th>full_model_p_values</th>\n",
       "      <th>full_model_params</th>\n",
       "      <th>full_model_coeffs</th>\n",
       "      <th>qtls</th>\n",
       "      <th>resid.qtls</th>\n",
       "      <th>full.model.qtls</th>\n",
       "      <th>Long.Edge</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>TCTCCAAGGGATACT</td>\n",
       "      <td>in YMR085W</td>\n",
       "      <td>YMR085W</td>\n",
       "      <td>YMR085W</td>\n",
       "      <td>Putative protein of unknown function</td>\n",
       "      <td>Putative protein of unknown function</td>\n",
       "      <td>chr13</td>\n",
       "      <td>Putative protein of unknown function; YMR085W ...</td>\n",
       "      <td>Putative protein of unknown function; YMR085W ...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.010223</td>\n",
       "      <td>0.781072</td>\n",
       "      <td>-80.547553</td>\n",
       "      <td>0.26814250806982043;0.7810722126668491</td>\n",
       "      <td>x</td>\n",
       "      <td>0.001490528225059142;0.006953626038516742</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TCTCCAAGGGATACTTAACGTTATTCCTTT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>TGTGTCGATTTAGTG</td>\n",
       "      <td>in RKM3</td>\n",
       "      <td>RKM3</td>\n",
       "      <td>RKM3</td>\n",
       "      <td>Ribosomal lysine methyltransferase</td>\n",
       "      <td>Ribosomal lysine methyltransferase</td>\n",
       "      <td>chr02</td>\n",
       "      <td>Ribosomal lysine methyltransferase; specific f...</td>\n",
       "      <td>Ribosomal lysine methyltransferase; specific f...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.030625</td>\n",
       "      <td>0.567432</td>\n",
       "      <td>-114.446981</td>\n",
       "      <td>0.15944797128735522;0.5674322326468317</td>\n",
       "      <td>x</td>\n",
       "      <td>0.001162120166529229;-0.009006462611979207</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TGTGTCGATTTAGTGTTAAAGAATGACGTC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>TATGGTGCAGAAAAG</td>\n",
       "      <td>nearby YNL143C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MEP2|YNL143C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Ammonium permease involved in regulation of ps...</td>\n",
       "      <td>chr14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Ammonium permease involved in regulation of ps...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.144110</td>\n",
       "      <td>0.132863</td>\n",
       "      <td>-171.801874</td>\n",
       "      <td>0.10689612722798757;0.13286265303302433</td>\n",
       "      <td>x</td>\n",
       "      <td>0.0006108064927804045;0.012645873311052722</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TATGGTGCAGAAAAGTGGCTCGGAATGAAC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 246 columns</p>\n",
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      ],
      "text/plain": [
       "   Edge.ID             Edge        Gene.Use Gene_ORF Gene_ORF.nearby  \\\n",
       "0        0  TCTCCAAGGGATACT      in YMR085W  YMR085W         YMR085W   \n",
       "1        1  TGTGTCGATTTAGTG         in RKM3     RKM3            RKM3   \n",
       "2        2  TATGGTGCAGAAAAG  nearby YNL143C      NaN    MEP2|YNL143C   \n",
       "\n",
       "                       briefDescription  \\\n",
       "0  Putative protein of unknown function   \n",
       "1    Ribosomal lysine methyltransferase   \n",
       "2                                   NaN   \n",
       "\n",
       "                             briefDescription.nearby chromosome  \\\n",
       "0               Putative protein of unknown function      chr13   \n",
       "1                 Ribosomal lysine methyltransferase      chr02   \n",
       "2  Ammonium permease involved in regulation of ps...      chr14   \n",
       "\n",
       "                                         description  \\\n",
       "0  Putative protein of unknown function; YMR085W ...   \n",
       "1  Ribosomal lysine methyltransferase; specific f...   \n",
       "2                                                NaN   \n",
       "\n",
       "                                  description.nearby  \\\n",
       "0  Putative protein of unknown function; YMR085W ...   \n",
       "1  Ribosomal lysine methyltransferase; specific f...   \n",
       "2  Ammonium permease involved in regulation of ps...   \n",
       "\n",
       "                ...               full_model_r2 full_model_p full_model_aic  \\\n",
       "0               ...                    0.010223     0.781072     -80.547553   \n",
       "1               ...                    0.030625     0.567432    -114.446981   \n",
       "2               ...                    0.144110     0.132863    -171.801874   \n",
       "\n",
       "                       full_model_p_values  full_model_params  \\\n",
       "0   0.26814250806982043;0.7810722126668491                  x   \n",
       "1   0.15944797128735522;0.5674322326468317                  x   \n",
       "2  0.10689612722798757;0.13286265303302433                  x   \n",
       "\n",
       "                            full_model_coeffs qtls resid.qtls full.model.qtls  \\\n",
       "0   0.001490528225059142;0.006953626038516742  NaN        NaN             NaN   \n",
       "1  0.001162120166529229;-0.009006462611979207  NaN        NaN             NaN   \n",
       "2  0.0006108064927804045;0.012645873311052722  NaN        NaN             NaN   \n",
       "\n",
       "                        Long.Edge  \n",
       "0  TCTCCAAGGGATACTTAACGTTATTCCTTT  \n",
       "1  TGTGTCGATTTAGTGTTAAAGAATGACGTC  \n",
       "2  TATGGTGCAGAAAAGTGGCTCGGAATGAAC  \n",
       "\n",
       "[3 rows x 246 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bt.iloc[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DFE.statistic</th>\n",
       "      <th>LK4-G06</th>\n",
       "      <th>LK3-G08</th>\n",
       "      <th>LK2-G07</th>\n",
       "      <th>LK4-H03</th>\n",
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       "      <th>LK1-D12</th>\n",
       "      <th>LK6-C05</th>\n",
       "      <th>LK4-C09</th>\n",
       "      <th>...</th>\n",
       "      <th>full_resid_seg_model_r2</th>\n",
       "      <th>full_resid_seg_model_p</th>\n",
       "      <th>full_resid_seg_model_r2_95_conf_low</th>\n",
       "      <th>full_resid_seg_model_r2_95_conf_high</th>\n",
       "      <th>full_resid_seg_model_p_values</th>\n",
       "      <th>full_resid_seg_model_params</th>\n",
       "      <th>full_resid_seg_model_coeffs</th>\n",
       "      <th>qtls</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>background.fitness</td>\n",
       "      <td>0.017536</td>\n",
       "      <td>0.093806</td>\n",
       "      <td>-0.019195</td>\n",
       "      <td>0.022961</td>\n",
       "      <td>-0.060829</td>\n",
       "      <td>0.032745</td>\n",
       "      <td>-0.057404</td>\n",
       "      <td>-0.059662</td>\n",
       "      <td>-0.087799</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>chr14_381891;chr14_368185;chr14_393024|chr15_1...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>chr14_381891;chr14_368185;chr14_393024|chr15_1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>mean</td>\n",
       "      <td>-0.021606</td>\n",
       "      <td>-0.038078</td>\n",
       "      <td>-0.022692</td>\n",
       "      <td>-0.033220</td>\n",
       "      <td>-0.011229</td>\n",
       "      <td>-0.026932</td>\n",
       "      <td>-0.010239</td>\n",
       "      <td>-0.010303</td>\n",
       "      <td>-0.016691</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>chr14_381891;chr14_371959;chr14_393024|chr04_4...</td>\n",
       "      <td>chr14_459667;chr14_414983;chr14_481897</td>\n",
       "      <td>chr14_381891;chr14_371959;chr14_393024|chr04_4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>median</td>\n",
       "      <td>-0.017210</td>\n",
       "      <td>-0.022665</td>\n",
       "      <td>-0.012889</td>\n",
       "      <td>-0.025198</td>\n",
       "      <td>-0.012115</td>\n",
       "      <td>-0.016404</td>\n",
       "      <td>-0.010974</td>\n",
       "      <td>-0.009748</td>\n",
       "      <td>-0.010328</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>chr14_376315;chr14_368185;chr14_393024</td>\n",
       "      <td>chr14_433150;chr14_414983;chr14_485550</td>\n",
       "      <td>chr14_376315;chr14_368185;chr14_393024|chr14_4...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 213 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        DFE.statistic   LK4-G06   LK3-G08   LK2-G07   LK4-H03   LK4-D11  \\\n",
       "0  background.fitness  0.017536  0.093806 -0.019195  0.022961 -0.060829   \n",
       "1                mean -0.021606 -0.038078 -0.022692 -0.033220 -0.011229   \n",
       "2              median -0.017210 -0.022665 -0.012889 -0.025198 -0.012115   \n",
       "\n",
       "    LK3-E06   LK1-D12   LK6-C05   LK4-C09  \\\n",
       "0  0.032745 -0.057404 -0.059662 -0.087799   \n",
       "1 -0.026932 -0.010239 -0.010303 -0.016691   \n",
       "2 -0.016404 -0.010974 -0.009748 -0.010328   \n",
       "\n",
       "                         ...                          full_resid_seg_model_r2  \\\n",
       "0                        ...                                              NaN   \n",
       "1                        ...                                              NaN   \n",
       "2                        ...                                              NaN   \n",
       "\n",
       "   full_resid_seg_model_p  full_resid_seg_model_r2_95_conf_low  \\\n",
       "0                     NaN                                  NaN   \n",
       "1                     NaN                                  NaN   \n",
       "2                     NaN                                  NaN   \n",
       "\n",
       "   full_resid_seg_model_r2_95_conf_high  full_resid_seg_model_p_values  \\\n",
       "0                                   NaN                            NaN   \n",
       "1                                   NaN                            NaN   \n",
       "2                                   NaN                            NaN   \n",
       "\n",
       "   full_resid_seg_model_params  full_resid_seg_model_coeffs  \\\n",
       "0                          NaN                          NaN   \n",
       "1                          NaN                          NaN   \n",
       "2                          NaN                          NaN   \n",
       "\n",
       "                                                qtls  \\\n",
       "0  chr14_381891;chr14_368185;chr14_393024|chr15_1...   \n",
       "1  chr14_381891;chr14_371959;chr14_393024|chr04_4...   \n",
       "2             chr14_376315;chr14_368185;chr14_393024   \n",
       "\n",
       "                               resid.qtls  \\\n",
       "0                                     NaN   \n",
       "1  chr14_459667;chr14_414983;chr14_481897   \n",
       "2  chr14_433150;chr14_414983;chr14_485550   \n",
       "\n",
       "                                     full.model.qtls  \n",
       "0  chr14_381891;chr14_368185;chr14_393024|chr15_1...  \n",
       "1  chr14_381891;chr14_371959;chr14_393024|chr04_4...  \n",
       "2  chr14_376315;chr14_368185;chr14_393024|chr14_4...  \n",
       "\n",
       "[3 rows x 213 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
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  {
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   "execution_count": 6,
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   "outputs": [
    {
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       "      <th>DFE.statistic</th>\n",
       "      <th>LK4-A04</th>\n",
       "      <th>LK3-D08</th>\n",
       "      <th>LK3-G02</th>\n",
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       "      <th>0</th>\n",
       "      <td>background.fitness</td>\n",
       "      <td>0.036559</td>\n",
       "      <td>0.023625</td>\n",
       "      <td>-0.021251</td>\n",
       "      <td>0.035349</td>\n",
       "      <td>0.001306</td>\n",
       "      <td>-0.021838</td>\n",
       "      <td>0.097835</td>\n",
       "      <td>-0.029612</td>\n",
       "      <td>-0.043350</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>mean</td>\n",
       "      <td>-0.005015</td>\n",
       "      <td>-0.005416</td>\n",
       "      <td>-0.003760</td>\n",
       "      <td>-0.004865</td>\n",
       "      <td>-0.002509</td>\n",
       "      <td>-0.003774</td>\n",
       "      <td>-0.003754</td>\n",
       "      <td>-0.002583</td>\n",
       "      <td>-0.003060</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>median</td>\n",
       "      <td>0.001086</td>\n",
       "      <td>0.000173</td>\n",
       "      <td>0.000602</td>\n",
       "      <td>0.000504</td>\n",
       "      <td>0.000646</td>\n",
       "      <td>0.000713</td>\n",
       "      <td>0.000439</td>\n",
       "      <td>0.000787</td>\n",
       "      <td>0.000182</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 85 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        DFE.statistic   LK4-A04   LK3-D08   LK3-G02   LK2-F11   LK1-E09  \\\n",
       "0  background.fitness  0.036559  0.023625 -0.021251  0.035349  0.001306   \n",
       "1                mean -0.005015 -0.005416 -0.003760 -0.004865 -0.002509   \n",
       "2              median  0.001086  0.000173  0.000602  0.000504  0.000646   \n",
       "\n",
       "    LK4-E02   LK1-C09   LK2-A12   LK4-H11       ...         \\\n",
       "0 -0.021838  0.097835 -0.029612 -0.043350       ...          \n",
       "1 -0.003774 -0.003754 -0.002583 -0.003060       ...          \n",
       "2  0.000713  0.000439  0.000787  0.000182       ...          \n",
       "\n",
       "   full_resid_seg_model_r2  full_resid_seg_model_p  \\\n",
       "0                      NaN                     NaN   \n",
       "1                      NaN                     NaN   \n",
       "2                      NaN                     NaN   \n",
       "\n",
       "   full_resid_seg_model_r2_95_conf_low  full_resid_seg_model_r2_95_conf_high  \\\n",
       "0                                  NaN                                   NaN   \n",
       "1                                  NaN                                   NaN   \n",
       "2                                  NaN                                   NaN   \n",
       "\n",
       "   full_resid_seg_model_p_values  full_resid_seg_model_params  \\\n",
       "0                            NaN                          NaN   \n",
       "1                            NaN                          NaN   \n",
       "2                            NaN                          NaN   \n",
       "\n",
       "   full_resid_seg_model_coeffs  qtls  resid.qtls  full.model.qtls  \n",
       "0                          NaN   NaN         NaN              NaN  \n",
       "1                          NaN   NaN         NaN              NaN  \n",
       "2                          NaN   NaN         NaN              NaN  \n",
       "\n",
       "[3 rows x 85 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
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   "source": [
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  {
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   "execution_count": 7,
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   "outputs": [
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       "      <th>0</th>\n",
       "      <td>27915_chr01_27915_T_C</td>\n",
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       "      <td>28323_chr01_28323_G_A</td>\n",
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       "      <td>1</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 163 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  marker  LK4-G06  LK3-G08  LK2-G07  LK4-A04  LK4-H03  \\\n",
       "0  27915_chr01_27915_T_C        1        0        1        1        0   \n",
       "1  28323_chr01_28323_G_A        1        0        1        1        0   \n",
       "2  28652_chr01_28652_G_T        1        0        1        1        0   \n",
       "\n",
       "   LK4-D11  LK3-E06  LK1-D10  LK1-D12   ...     LK2-D09  LK1-D06  LK3-A01  \\\n",
       "0        0        0        1        1   ...           0        0        1   \n",
       "1        0        0        1        1   ...           0        0        1   \n",
       "2        0        0        1        1   ...           0        0        1   \n",
       "\n",
       "   LK2-D05  LK1-C11  LK1-G03  LK3-D04  LK3-C06  LK2-A01  LK2-G10  \n",
       "0        0        1        0        1        1        1        1  \n",
       "1        0        1        0        1        1        1        1  \n",
       "2        0        1        0        1        1        1        1  \n",
       "\n",
       "[3 rows x 163 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gm.iloc[:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Here are some basic statistics on the data from the two experiments:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "tp_seg_edges_measured = [len(tp.loc[pd.notnull(tp[s+'.mean.s'])]) for s in segs_all['TP']]\n",
    "bt_seg_edges_measured = [len(bt.loc[pd.notnull(bt[s+'.mean.s'])]) for s in segs_all['BT']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BT. Tried 20 segregants.\n",
      "18  segregants included. 413.8888888888889 mutations measured on average, min: 180 max: 614\n",
      "996 mutations. 710 measured in at least one seg. of those, they are measured in 10.492957746478874 on average.\n",
      "Measured in 10.492957746478874 segs on average\n",
      "253 significant in at least one seg, 457 in none, 0.643661971830986 percent\n"
     ]
    }
   ],
   "source": [
    "print('BT. Tried 20 segregants.')\n",
    "print(len(segs_all['BT']), ' segregants included.', np.mean(bt_seg_edges_measured), 'mutations measured on average,', 'min:', np.min(bt_seg_edges_measured), 'max:', np.max(bt_seg_edges_measured))\n",
    "print(len(bt), 'mutations.', len(bt.loc[bt['num.measured']>0]), 'measured in at least one seg.', 'of those, they are measured in', np.mean(bt.loc[bt['num.measured']>0]['num.measured']), 'on average.')\n",
    "print('Measured in', np.nanmean(bt.loc[bt['num.measured']>0]['num.measured']), 'segs on average')\n",
    "print(len(bt.loc[bt['num.sig']>0]), 'significant in at least one seg,', len(bt.loc[bt['num.measured']>0].loc[bt['num.sig']==0]), 'in none,', \n",
    "      len(bt.loc[bt['num.measured']>0].loc[bt['num.sig']==0])/len(bt.loc[bt['num.measured']>0]), 'percent')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TP. Tried 176 segregants.\n",
      "162  segregants included. 65.70987654320987 mutations measured on average, min: 1 max: 82\n",
      "91 mutations. Measured in 116.97802197802197 segs on average\n",
      "9 is the minimum # segs w measurements for one mutation, and the min # sig effects is: 0\n"
     ]
    }
   ],
   "source": [
    "print('TP. Tried 176 segregants.')\n",
    "print(len(segs_all['TP']), ' segregants included.', np.mean(tp_seg_edges_measured), 'mutations measured on average,', 'min:', np.min(tp_seg_edges_measured), 'max:', np.max(tp_seg_edges_measured))\n",
    "print(len(tp), 'mutations.', 'Measured in', np.nanmean(tp['num.measured']), 'segs on average')\n",
    "print(np.min(tp['num.measured']),'is the minimum # segs w measurements for one mutation, and the min # sig effects is:', np.min(tp['num.sig']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Additional columns in these datasets describe the results of the modeling described in the paper, in terms of the R^2, AIC, etc. for various models of epistasis, the models are background fitness (x), QTLs (qtl), full (both), or resid versions (resid_qtl is the qtl model after regressing out x effects), and the columns like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['x_model_r2', 'x_model_p', 'x_model_aic', 'qtl_model_r2', 'qtl_model_p', 'qtl_model_aic', 'resid_qtl_model_r2', 'resid_qtl_model_p', 'resid_qtl_model_aic', 'resid_x_model_r2', 'resid_x_model_p', 'resid_x_model_aic', 'full_model_r2', 'full_model_p', 'full_model_aic']\n"
     ]
    }
   ],
   "source": [
    "print([i for i in tp if 'aic' in i or i[-2:] in ['r2', '_p']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot fitness effect vs. background fitness for a particular mutation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "def plot_simple(sub, df_row, segs):\n",
    "    measured = [seg for seg in segs if pd.notnull(df_row[seg + '.mean.s'])]\n",
    "    xs = [seg_to_fit[measured[s]] for s in range(len(measured))]\n",
    "    ys = [df_row[measured[s] + '.mean.s'] for s in range(len(measured))]\n",
    "    ye = [df_row[measured[s] + '.stderr.s'] for s in range(len(measured))]\n",
    "    sub.axhline(y=0, xmin=0, xmax=1, color='#333333', linestyle='dashed', alpha=0.5, lw=1)\n",
    "    sub.errorbar(x=xs, y=ys, yerr=ye, marker='o', c='k', linestyle='')   \n",
    "    sub.set_xlim([-0.16, 0.12])\n",
    "    sub.set_ylim([-0.2, 0.08])\n",
    "    sub.set_xlabel('X')\n",
    "    sub.set_ylabel('S')\n",
    "    sns.despine()\n",
    "\n",
    "f, sub = pl.subplots(1,1)\n",
    "dataframe_row = tp[tp['Gene.Use']=='in RPL16A'].iloc[0]\n",
    "plot_simple(sub, dataframe_row, segs_use['TP'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot a couple DFEs\n",
    "Note in the paper we plot combined DFEs by background fitness quartiles to make them less noisey, which is why this is different:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "def get_dfe(df, segname):\n",
    "    use_df = df.loc[pd.notnull(df[segname + '.mean.s'])]\n",
    "    return list(use_df[segname + '.mean.s'])\n",
    "\n",
    "def simple_plot_dfe_compare(exp):\n",
    "    f, sub = pl.subplots(1, 1, figsize=(6, 4))\n",
    "    lowest_fit_dfe = get_dfe(dats[exp], sorted_segs[exp][0])\n",
    "    highest_fit_dfe = get_dfe(dats[exp], sorted_segs[exp][-1])\n",
    "    bin_lefts = [(-16.15+i)*0.015-0.005 for i in range(22)]\n",
    "    sub.hist(lowest_fit_dfe, bins=bin_lefts, label='Least Fit Seg', histtype=\"step\", color=\"r\", weights=np.ones_like(lowest_fit_dfe)/float(len(lowest_fit_dfe)))\n",
    "    sub.hist(highest_fit_dfe, bins=bin_lefts, label='Most Fit Seg', histtype=\"step\", color=\"b\", weights=np.ones_like(highest_fit_dfe)/float(len(highest_fit_dfe)))\n",
    "    sub.legend(loc='upper left')\n",
    "    sns.despine()\n",
    "    \n",
    "simple_plot_dfe_compare('BT')\n",
    "simple_plot_dfe_compare('TP')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# OK that concludes the jump-start! \n",
    "\n",
    "If you want to get deeper into the plotting / data exploration you can also check out the (much longer but less well commented) jupyter notebook on https://github.com/mjohnson11/TnSeq_Pipeline\n",
    "\n",
    "Please email me if you have questions: milo.s.johnson.13@gmail.com\n"
   ]
  }
 ],
 "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}