notebooks/panel_evaluation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "# Show plots within the noteboook\n",
    "%matplotlib inline \n",
    "\n",
    "# Ignore warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# Required system Libraries \n",
    "import sys, os, datetime, time\n",
    "import random\n",
    "import cPickle as pickle\n",
    "from pprint import pprint\n",
    "\n",
    "# For plotting\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Numeric libraries\n",
    "import pandas as pd\n",
    "import numpy as np \n",
    "\n",
    "# Scikit learn stuff\n",
    "from sklearn.externals import joblib\n",
    "\n",
    "# Database connectors\n",
    "from memsql.common import database\n",
    "import urllib, json\n",
    "from urlparse import urlparse\n",
    "\n",
    "# orchid-ml\n",
    "from orchid_ml import MutationMatrix\n",
    "from orchid_ml import load_matrix\n",
    "\n",
    "\n",
    "# Options for modeling (mimics the options argparse class)\n",
    "class options:\n",
    "    prostate_dbi = 'REDACTED'\n",
    "    tag          = 'SVMv4_original_20171107' # A name for this dataset, available upon request\n",
    "    lookup_names = False    # Slow lookup of gene names if not present\n",
    "    std_by_len   = True     # Wether to downweight mutation scores by their gene lengths\n",
    "    #scale_by     = 'scaler' # Only scaler is used now\n",
    "    n_probes     = 7034     # A target number of loci to design probes for\n",
    "    \n",
    "os.chdir(\"../\"+options.tag)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "#Now in orchid.ml\n",
    "\n",
    "## Accent Grey: #2e3436\n",
    "## Muted Grey : #b2bec3\n",
    "#flatui = [\"#9b59b6\", \"#3498db\", \"#95a5a6\", \"#e74c3c\", \"#34495e\", \"#2ecc71\", \"#fca50e\"]\n",
    "#flatui = [\"#5deec5\", \"#85eceb\", \"#77bafc\", \"#a29dfb\", \"#fee9ab\", \"#fd9e4d\", \"#f8b1a2\", \"#fd7777\", \"#fb7ba8\"]\n",
    "flatui = [\"#22cec8\", \"#1cb794\", \"#1a86e0\", \"#6c61e4\", \"#fcca75\", \"#fd9e4d\", \"#df7059\", \"#d43237\", \"#e64793\"]\n",
    "pal = sns.color_palette(flatui)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "# Look up an ensembl_id gene name using their REST API\n",
    "def get_gene_name(mutation_id):\n",
    "    url = \"https://dcc.icgc.org/api/v1/mutations/%s/genes\" % (mutation_id)\n",
    "    response = urllib.urlopen(url)\n",
    "    data = json.loads(response.read())\n",
    "    if data['hits']==[]:\n",
    "        return (None, None)\n",
    "    return (data['hits'][0]['id'], data['hits'][0]['symbol'])\n",
    "\n",
    "# A plotting function to make nicer boxplots of number of mutations\n",
    "#   by binning 'Length', or 'log(Length)' (bin_on) \n",
    "#   into a desired number of bins (bin_on)\n",
    "# df must contain 'gene_name' and 'Transcript Length'\n",
    "def plot_counts(df, bin_on='length', bin_no=15, palette=pal):\n",
    "    # Get mutations per gene\n",
    "    mpg = df['gene_name'].value_counts()\n",
    "    # Get maximum transcript size per gene\n",
    "    mxl = df[['gene_name','Transcript Length']].groupby('gene_name').max()\n",
    "    # Merge the two, and add log(length)\n",
    "    df = pd.DataFrame(mpg).merge(mxl, how='left', left_index=True, right_index=True)\n",
    "    df.columns = ['number', 'Length']\n",
    "    df['log(Length)'] = np.log10(df['Length'])\n",
    "    # Divide into bin_no number of bins\n",
    "    df = df.dropna(subset=[bin_on])\n",
    "    bins = pd.cut(df[bin_on], bin_no, labels=range(1,bin_no+1), retbins=True)[0]\n",
    "    bins = pd.DataFrame(bins)\n",
    "    bins.columns = ['bin']\n",
    "    # Merge dataframe with bin membership\n",
    "    df = df.merge(bins, how=\"left\", left_index=True, right_index=True)\n",
    "    \n",
    "    # Set up the plot\n",
    "    sns.set_context(\"talk\", font_scale=1.8)\n",
    "    fig, ax = plt.subplots(figsize=(11.7, 8.27))\n",
    "    \n",
    "    # Define colors colors\n",
    "    colors = sns.color_palette(palette, bin_no).as_hex()\n",
    "\n",
    "    # Define flier properties\n",
    "    flierprops = dict(\n",
    "                        marker='o', \n",
    "                        markersize=8, \n",
    "                        linestyle='none', \n",
    "                        linewidth=2.5,\n",
    "                     )\n",
    "    # And generate the boxplots\n",
    "    ax = sns.boxplot(\n",
    "                        x='bin', \n",
    "                        y='number', \n",
    "                        data=df, \n",
    "                        whis=1.5, \n",
    "                        linewidth=2.5,\n",
    "                        palette=palette,\n",
    "                        flierprops=flierprops,\n",
    "                    )\n",
    "    \n",
    "    # Modify the axes a bit\n",
    "    ax.set(xlabel='Bin (%s)' % bin_on, ylabel='Mutations per Gene')\n",
    "    plt.yticks(xrange(0,21,2))\n",
    "    ax.set_ylim(0,20)\n",
    "    sns.despine()\n",
    "\n",
    "    medians = df.groupby(['bin'])['number'].median().values\n",
    "    median_labels = [str(np.round(s, 2)) for s in medians]\n",
    "\n",
    "    pos = range(len(medians))\n",
    "    for tick,label in zip(pos,ax.get_xticklabels()):\n",
    "        ax.text(\n",
    "                pos[tick], \n",
    "                0.3, #medians[tick] + 0.02, \n",
    "                median_labels[tick], \n",
    "                horizontalalignment='center', \n",
    "                size='xx-small', \n",
    "                color=colors[tick]\n",
    "               )\n",
    "    \n",
    "    # Only color the outside of the boxplots\n",
    "    for i,artist in enumerate(ax.artists):\n",
    "        # Set the linecolor on the artist to the facecolor, and set the facecolor to None\n",
    "        col = artist.get_facecolor()\n",
    "        artist.set_edgecolor(col)\n",
    "        artist.set_facecolor('None')\n",
    "        artist.set_zorder(10)\n",
    "\n",
    "        # Each box has 6 associated Line2D objects (to make the whiskers, fliers, etc.)\n",
    "        # Loop over them here, and use the same colour as above\n",
    "        for j in range(i*6,i*6+6):\n",
    "            line = ax.lines[j]\n",
    "            line.set_color(col)\n",
    "            line.set_mfc(col)\n",
    "            line.set_mec(col)\n",
    "            line.set_mew(1)\n",
    "\n",
    "            line.set_mfc((1,1,1,.3))\n",
    "    return df\n",
    "\n",
    "\n",
    "# Each box has 6 associated Line2D objects (to make the whiskers, fliers, etc.)\n",
    "# Loop over them here, and use the same colour as above\n",
    "#for j in range(i*6,i*6+6):\n",
    "#    line = ax.lines[j]\n",
    "#    line.set_color(col)\n",
    "#    line.set_mfc((1,1,1,.3))\n",
    "#    line.set_markeredgecolor(col)\n",
    "#    line.set_markeredgewidth(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load model and mutation scores (distances)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1min 46s, sys: 9.81 s, total: 1min 55s\n",
      "Wall time: 2min 56s\n"
     ]
    }
   ],
   "source": [
    "%%time \n",
    "mutations = None\n",
    "# Load the final model data\n",
    "mutations = load_matrix(\"prostate23_final.pkl\")\n",
    "# Make a copy of the original data structure as we modify the original\n",
    "all_mutations = mutations.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(147181, 306)\n",
      "(147181, 306)\n"
     ]
    }
   ],
   "source": [
    "# Focus just on the enriched mutations and rank by distance\n",
    "mutations = mutations[mutations['Distance']>0]\n",
    "mutations = mutations.sort_values(by='Distance', ascending=False)\n",
    "# Add a log(distance) column (natural log)\n",
    "mutations['Log Distance'] = np.log(mutations['Distance'])\n",
    "# Drop any duplicates\n",
    "print mutations.shape\n",
    "mutations = mutations.drop_duplicates()\n",
    "print mutations.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lookup gene and consequence information "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Annotating... done\n",
      "CPU times: user 336 ms, sys: 75.1 ms, total: 411 ms\n",
      "Wall time: 1.59 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# \n",
    "# This may cause the mutation list to expand significantly depending on the number of consequences per mutation\n",
    "# This can go over the transaction limit of some databases, so do this in 50K chunks\n",
    "chunk = 50000\n",
    "print \"Annotating...\",\n",
    "gene_info = None\n",
    "for i in xrange(0, mutations.shape[0]+1, chunk):\n",
    "    #print i\n",
    "    syntax = \"\"\"\n",
    "        SELECT c.mutation_id, c.gene_id, c.gene_name, c.consequence_type\n",
    "        FROM consequence AS c \n",
    "        WHERE c.mutation_id IN ('%s') \n",
    "    \"\"\" % (\"','\".join(list(mutations['mutation_id'][i:i+chunk])))\n",
    "    gene_info = pd.concat([gene_info, pd.DataFrame(pd.read_sql(syntax, options.prostate_dbi))])\n",
    "print \"done\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "# Drop some unnecessary columns\n",
    "mutations = \\\n",
    "mutations[['mutation_id','chromosome', 'start', 'end','Distance', 'Log Distance']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(939995, 9)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# And merge with the gene info\n",
    "mutations = \\\n",
    "mutations.merge(gene_info, how='left', left_on='mutation_id', right_on='mutation_id')\n",
    "mutations.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(487177, 9)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop duplicates and re-sort\n",
    "mutations = mutations.drop_duplicates()\n",
    "mutations.sort_values(by='Distance')\n",
    "mutations.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mutation_id</th>\n",
       "      <th>chromosome</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Log Distance</th>\n",
       "      <th>gene_id</th>\n",
       "      <th>gene_name</th>\n",
       "      <th>consequence_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MU3895825</td>\n",
       "      <td>15</td>\n",
       "      <td>91428783</td>\n",
       "      <td>91428783</td>\n",
       "      <td>23.649174</td>\n",
       "      <td>3.163328</td>\n",
       "      <td>ENSG00000182511</td>\n",
       "      <td>FES</td>\n",
       "      <td>missense_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MU3895825</td>\n",
       "      <td>15</td>\n",
       "      <td>91428783</td>\n",
       "      <td>91428783</td>\n",
       "      <td>23.649174</td>\n",
       "      <td>3.163328</td>\n",
       "      <td>ENSG00000140564</td>\n",
       "      <td>FURIN</td>\n",
       "      <td>downstream_gene_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MU3895825</td>\n",
       "      <td>15</td>\n",
       "      <td>91428783</td>\n",
       "      <td>91428783</td>\n",
       "      <td>23.649174</td>\n",
       "      <td>3.163328</td>\n",
       "      <td>ENSG00000182511</td>\n",
       "      <td>FES</td>\n",
       "      <td>3_prime_UTR_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MU3895825</td>\n",
       "      <td>15</td>\n",
       "      <td>91428783</td>\n",
       "      <td>91428783</td>\n",
       "      <td>23.649174</td>\n",
       "      <td>3.163328</td>\n",
       "      <td>ENSG00000182511</td>\n",
       "      <td>FES</td>\n",
       "      <td>intron_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MU3895825</td>\n",
       "      <td>15</td>\n",
       "      <td>91428783</td>\n",
       "      <td>91428783</td>\n",
       "      <td>23.649174</td>\n",
       "      <td>3.163328</td>\n",
       "      <td>ENSG00000182511</td>\n",
       "      <td>FES</td>\n",
       "      <td>downstream_gene_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>MU3895825</td>\n",
       "      <td>15</td>\n",
       "      <td>91428783</td>\n",
       "      <td>91428783</td>\n",
       "      <td>23.649174</td>\n",
       "      <td>3.163328</td>\n",
       "      <td>ENSG00000182511</td>\n",
       "      <td>FES</td>\n",
       "      <td>upstream_gene_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>MU3895825</td>\n",
       "      <td>15</td>\n",
       "      <td>91428783</td>\n",
       "      <td>91428783</td>\n",
       "      <td>23.649167</td>\n",
       "      <td>3.163328</td>\n",
       "      <td>ENSG00000182511</td>\n",
       "      <td>FES</td>\n",
       "      <td>missense_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>MU3895825</td>\n",
       "      <td>15</td>\n",
       "      <td>91428783</td>\n",
       "      <td>91428783</td>\n",
       "      <td>23.649167</td>\n",
       "      <td>3.163328</td>\n",
       "      <td>ENSG00000140564</td>\n",
       "      <td>FURIN</td>\n",
       "      <td>downstream_gene_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>MU3895825</td>\n",
       "      <td>15</td>\n",
       "      <td>91428783</td>\n",
       "      <td>91428783</td>\n",
       "      <td>23.649167</td>\n",
       "      <td>3.163328</td>\n",
       "      <td>ENSG00000182511</td>\n",
       "      <td>FES</td>\n",
       "      <td>3_prime_UTR_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>MU3895825</td>\n",
       "      <td>15</td>\n",
       "      <td>91428783</td>\n",
       "      <td>91428783</td>\n",
       "      <td>23.649167</td>\n",
       "      <td>3.163328</td>\n",
       "      <td>ENSG00000182511</td>\n",
       "      <td>FES</td>\n",
       "      <td>intron_variant</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mutation_id chromosome     start       end   Distance  Log Distance  \\\n",
       "0    MU3895825         15  91428783  91428783  23.649174      3.163328   \n",
       "1    MU3895825         15  91428783  91428783  23.649174      3.163328   \n",
       "2    MU3895825         15  91428783  91428783  23.649174      3.163328   \n",
       "3    MU3895825         15  91428783  91428783  23.649174      3.163328   \n",
       "4    MU3895825         15  91428783  91428783  23.649174      3.163328   \n",
       "6    MU3895825         15  91428783  91428783  23.649174      3.163328   \n",
       "10   MU3895825         15  91428783  91428783  23.649167      3.163328   \n",
       "11   MU3895825         15  91428783  91428783  23.649167      3.163328   \n",
       "12   MU3895825         15  91428783  91428783  23.649167      3.163328   \n",
       "13   MU3895825         15  91428783  91428783  23.649167      3.163328   \n",
       "\n",
       "            gene_id gene_name         consequence_type  \n",
       "0   ENSG00000182511       FES         missense_variant  \n",
       "1   ENSG00000140564     FURIN  downstream_gene_variant  \n",
       "2   ENSG00000182511       FES      3_prime_UTR_variant  \n",
       "3   ENSG00000182511       FES           intron_variant  \n",
       "4   ENSG00000182511       FES  downstream_gene_variant  \n",
       "6   ENSG00000182511       FES    upstream_gene_variant  \n",
       "10  ENSG00000182511       FES         missense_variant  \n",
       "11  ENSG00000140564     FURIN  downstream_gene_variant  \n",
       "12  ENSG00000182511       FES      3_prime_UTR_variant  \n",
       "13  ENSG00000182511       FES           intron_variant  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show\n",
    "mutations.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "54 unknown gene names...\n",
      "Looking up 54 unknown gene names . . . . . .CPU times: user 1min 14s, sys: 99.2 ms, total: 1min 14s\n",
      "Wall time: 1min 30s\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Look up gene names if requested\n",
    "to_lookup = mutations[(mutations['consequence_type']!='intergenic_region') & \\\n",
    "                      (mutations['gene_name'].isnull())]\n",
    "if options.lookup_names: \n",
    "    print \"Looking up %d unknown gene names\" % to_lookup.shape[0],\n",
    "    for i,g in to_lookup.drop_duplicates(subset='mutation_id').reset_index().iterrows():\n",
    "        if i%10==0:\n",
    "            print '.',\n",
    "        #print \"Looking up %s\" % g['mutation_id']\n",
    "        id_, sym = get_gene_name(g['mutation_id'])\n",
    "        mutations['gene_id'][mutations['mutation_id']==i] = id_\n",
    "        mutations['gene_name'][mutations['mutation_id']==i] = sym\n",
    "else: \n",
    "    print \"Ignoring\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "downstream_gene_variant            117659\n",
       "upstream_gene_variant              103520\n",
       "missense_variant                    90770\n",
       "intron_variant                      77785\n",
       "3_prime_UTR_variant                 38807\n",
       "synonymous_variant                  34315\n",
       "intergenic_region                    9513\n",
       "5_prime_UTR_variant                  9076\n",
       "stop_gained                          3210\n",
       "5_prime_UTR_premature_start_cod      1583\n",
       "splice_region_variant                 490\n",
       "start_lost                            251\n",
       "stop_lost                             135\n",
       "intragenic_variant                      5\n",
       "initiator_codon_variant                 4\n",
       "Name: consequence_type, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A break down of consequence type counts\n",
    "mutations['consequence_type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "# Re rank mutations by distance\n",
    "ranked_mutations = mutations.copy().sort_values('Distance', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adjust scores based on gene length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "# If standardizing by length, show a histogram of scale factors\n",
    "if options.std_by_len:\n",
    "    ucsc_dbi = \"mysql://genome@genome-mysql.cse.ucsc.edu:3306/hg19\"\n",
    "    query = \"\"\"\n",
    "        SELECT X.geneSymbol, K.name as transcript, \n",
    "        K.chrom, (K.txEnd - K.txStart) as transcriptLen \n",
    "        FROM kgXref as X, knownGene as K \n",
    "        WHERE X.geneSymbol IN ('%s') \n",
    "        AND X.kgId = K.name\n",
    "    \"\"\" % \"', '\".join(ranked_mutations['gene_name'].dropna().unique())\n",
    "    gene_lengths = pd.read_sql(query, ucsc_dbi)\n",
    "    gene_lengths = pd.DataFrame(gene_lengths.groupby('geneSymbol')['transcriptLen'].max())\n",
    "    gene_lengths.columns = ['Transcript Length']\n",
    "    \n",
    "    gene_lengths['z-score'] = \\\n",
    "    (gene_lengths['Transcript Length']-(gene_lengths['Transcript Length'].mean()))\\\n",
    "    /(gene_lengths['Transcript Length'].std())\n",
    "    gene_lengths['tanh'] = np.tanh(gene_lengths['z-score'])\n",
    "    gene_lengths['scaler'] = 1-(gene_lengths['tanh']*.95)\n",
    "    gene_lengths['normalizer'] = gene_lengths['Transcript Length'] / gene_lengths['z-score']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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vQHkP3EKpB31atUJXk6D6jNqC8ve2NmX19a2UmxYXjuebAxHxIMoK/o0p7+fb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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x253887710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# If standardizing by length, show a histogram of gene lengths\n",
    "if options.std_by_len:\n",
    "    plt.figure(figsize=(16,12))\n",
    "    sns.set_context(\"talk\", font_scale=2)\n",
    "    sns.set_style(\"white\")\n",
    "    ax = sns.distplot(gene_lengths['Transcript Length'], kde=True, color='#31E173')\n",
    "    ax.set(xlabel='Gene Length', ylabel='Density', title='Gene Length Histogram')\n",
    "    sns.despine()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Supplemental Figure 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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MqLT9x8XFiY6/ugUEBAg9QXR0dLBnzx6Fv19tbW2MGTMGHTp0wIQJE5CdnQ2ZTIYTJ04I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\nr3DbFRcvXtTmzZuN7dDQUM2ePbtKLYFLf8/9ud8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XWFh4cHmjZtWqv2Z2dn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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12a287710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show the transforms if standardizing by length\n",
    "if options.std_by_len:\n",
    "    sns.set_style(\"white\")\n",
    "    sns.set_style(\"ticks\")\n",
    "    f, axes = plt.subplots(2, 2, figsize=(20,10))\n",
    "\n",
    "    sns.distplot(gene_lengths['Transcript Length'], color='#22cec8', axlabel='', ax=axes[0][0], kde=False)\n",
    "    sns.distplot(gene_lengths['z-score'],  color='#1a86e0', axlabel='', ax=axes[0][1], kde=False)\n",
    "    sns.distplot(gene_lengths['tanh'],     color='#fcca75', axlabel='', ax=axes[1][0], kde=False)\n",
    "    sns.distplot(gene_lengths['scaler'],   color='#d43237', axlabel='', ax=axes[1][1], kde=False)\n",
    "    #sns.distplot(gene_lengths['normalizer'], color='r', ax=axes[1, 1], kde_kws={'bw':5000000})\n",
    "    #ax.set(ylabel='Density', title='Histogram')\n",
    "\n",
    "    f.suptitle(\"Gene Length Histograms\", fontsize=28)\n",
    "    axes[0,0].set_title(\"Raw Length\", size=30)\n",
    "    axes[0,1].set_title(\"Standard Score\", size=30)\n",
    "    axes[1,0].set_xlabel(\"Tanh Transformed\", size=30)\n",
    "    axes[1,1].set_xlabel(\"Scale Factor\", size=30)\n",
    "\n",
    "    sns.despine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(265308, 15)\n"
     ]
    }
   ],
   "source": [
    "# Downweight mutation scores if they come from large genes, and upweight small gene mutations\n",
    "# Based on the scaler metric used above\n",
    "#scale_by = options.scale_by\n",
    "scale_by = 'scaler'\n",
    "if options.std_by_len:\n",
    "    ranked_mutations= \\\n",
    "    ranked_mutations.merge(gene_lengths, how=\"left\", left_on='gene_name', right_index=True)\n",
    "    ranked_mutations[scale_by] = ranked_mutations[scale_by].fillna(1)\n",
    "    ranked_mutations['Scaled Distance'] = ranked_mutations['Distance'] * ranked_mutations[scale_by]\n",
    "    ranked_mutations = ranked_mutations.sort_values('Scaled Distance', ascending=False)\n",
    "    ranked_mutations = \\\n",
    "    ranked_mutations.drop_duplicates(subset=['Scaled Distance', 'mutation_id', 'gene_id'])\n",
    "    print ranked_mutations.shape\n",
    "    ranked_mutations.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mutation_id</th>\n",
       "      <th>gene_name</th>\n",
       "      <th>Transcript Length</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Log Distance</th>\n",
       "      <th>Scaled Distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MU100562</td>\n",
       "      <td>KCNJ12</td>\n",
       "      <td>43481.0</td>\n",
       "      <td>5.696561</td>\n",
       "      <td>1.739863</td>\n",
       "      <td>7.182002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MU100583</td>\n",
       "      <td>KCNJ12</td>\n",
       "      <td>43481.0</td>\n",
       "      <td>11.649934</td>\n",
       "      <td>2.455300</td>\n",
       "      <td>14.687783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MU10165919</td>\n",
       "      <td>PCDH15</td>\n",
       "      <td>1806764.0</td>\n",
       "      <td>1.928305</td>\n",
       "      <td>0.656641</td>\n",
       "      <td>0.096415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MU10165919</td>\n",
       "      <td>RP11-168O22.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.928305</td>\n",
       "      <td>0.656641</td>\n",
       "      <td>1.928305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MU10169762</td>\n",
       "      <td>ANK3</td>\n",
       "      <td>707229.0</td>\n",
       "      <td>1.730665</td>\n",
       "      <td>0.548506</td>\n",
       "      <td>0.088195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>MU10208</td>\n",
       "      <td>TP53</td>\n",
       "      <td>19149.0</td>\n",
       "      <td>0.448353</td>\n",
       "      <td>-0.813130</td>\n",
       "      <td>0.621402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>MU1029760</td>\n",
       "      <td>CTB-147C22.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.728778</td>\n",
       "      <td>-0.316403</td>\n",
       "      <td>0.728778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>MU1029760</td>\n",
       "      <td>KLK8</td>\n",
       "      <td>7253.0</td>\n",
       "      <td>0.728778</td>\n",
       "      <td>-0.316403</td>\n",
       "      <td>1.051006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>MU1029760</td>\n",
       "      <td>KLK9</td>\n",
       "      <td>9211.0</td>\n",
       "      <td>0.728778</td>\n",
       "      <td>-0.316403</td>\n",
       "      <td>1.044448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>MU10390749</td>\n",
       "      <td>NINJ2</td>\n",
       "      <td>99294.0</td>\n",
       "      <td>5.401764</td>\n",
       "      <td>1.686726</td>\n",
       "      <td>5.088733</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  mutation_id      gene_name  Transcript Length   Distance  Log Distance  \\\n",
       "0    MU100562         KCNJ12            43481.0   5.696561      1.739863   \n",
       "1    MU100583         KCNJ12            43481.0  11.649934      2.455300   \n",
       "2  MU10165919         PCDH15          1806764.0   1.928305      0.656641   \n",
       "3  MU10165919  RP11-168O22.1                NaN   1.928305      0.656641   \n",
       "4  MU10169762           ANK3           707229.0   1.730665      0.548506   \n",
       "5     MU10208           TP53            19149.0   0.448353     -0.813130   \n",
       "6   MU1029760   CTB-147C22.9                NaN   0.728778     -0.316403   \n",
       "7   MU1029760           KLK8             7253.0   0.728778     -0.316403   \n",
       "8   MU1029760           KLK9             9211.0   0.728778     -0.316403   \n",
       "9  MU10390749          NINJ2            99294.0   5.401764      1.686726   \n",
       "\n",
       "   Scaled Distance  \n",
       "0         7.182002  \n",
       "1        14.687783  \n",
       "2         0.096415  \n",
       "3         1.928305  \n",
       "4         0.088195  \n",
       "5         0.621402  \n",
       "6         0.728778  \n",
       "7         1.051006  \n",
       "8         1.044448  \n",
       "9         5.088733  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For each mutation, get it's mean distance values\n",
    "mmg = ranked_mutations.groupby(['mutation_id','gene_name']).mean().reset_index()\n",
    "mmg = mmg[[\n",
    "            'mutation_id', \n",
    "            'gene_name', \n",
    "            'Transcript Length', \n",
    "            'Distance', \n",
    "            'Log Distance', \n",
    "            'Scaled Distance'\n",
    "         ]]\n",
    "mmg.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Figure 2A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
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JNE19/fXXKigouO7xe/fulWEYuuWWW5wtBQAAAAAAOMjp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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2360b0910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Bin the genes into \n",
    "if options.std_by_len:\n",
    "    before=mmg.copy().sort_values('Distance', ascending=False)[0:options.n_probes]\n",
    "    bf = plot_counts(before, bin_on='Length', bin_no=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before length representation correlation 0.199, p-val= 6.03e-39\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import pearsonr\n",
    "representation = before[['gene_name', 'Transcript Length']].drop_duplicates()\\\n",
    ".merge(pd.DataFrame(before.groupby(['gene_name']).size(), columns=['Number']),\n",
    "       how=\"left\", left_on='gene_name', right_index=True)\\\n",
    ".dropna()\n",
    "cor = pearsonr(representation['Transcript Length'], representation['Number'])\n",
    "print \"Before length representation correlation %.3f, p-val= %.2e\" % (cor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1     3730\n",
      "2      328\n",
      "3       88\n",
      "4       39\n",
      "5       20\n",
      "6        9\n",
      "7       10\n",
      "8        4\n",
      "9        4\n",
      "10       3\n",
      "Name: bin, dtype: int64\n",
      "\n",
      "Number of genes before:   5392\n",
      "Number non-coding before:  0\n",
      "Avg mutations per gene before 1.30452522255\n"
     ]
    }
   ],
   "source": [
    "if options.std_by_len:\n",
    "    print bf['bin'].value_counts(sort=False)\n",
    "    print \"\\nNumber of genes before:  \", before['gene_name'].unique().shape[0]\n",
    "    print \"Number non-coding before: \", sum(before['gene_name'].isnull())\n",
    "    print \"Avg mutations per gene before\", before.groupby('gene_name')['mutation_id'].count().mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Figure 2B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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a5bY6jUnorcTwSJXUVSv9UJHirOF6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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x235e5ef50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if options.std_by_len:\n",
    "    #after  = ranked_mutations.sort_values('Scaled Distance', ascending=False).drop_duplicates(subset=['mutation_id','gene_name'])\n",
    "    after = mmg.sort_values('Scaled Distance', ascending=False)[0:options.n_probes]\n",
    "    af = plot_counts(after, bin_on='Length', bin_no=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before length representation correlation 0.048, p-val= 1.50e-03\n"
     ]
    }
   ],
   "source": [
    "representation = after[['gene_name', 'Transcript Length']].drop_duplicates()\\\n",
    ".merge(pd.DataFrame(after.groupby(['gene_name']).size(), columns=['Number']),\n",
    "       how=\"left\", left_on='gene_name', right_index=True)\\\n",
    ".dropna()\n",
    "cor = pearsonr(representation['Transcript Length'], representation['Number'])\n",
    "print \"Before length representation correlation %.3f, p-val= %.2e\" % (cor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1     2306\n",
       "2      904\n",
       "3      460\n",
       "4      300\n",
       "5      178\n",
       "6      122\n",
       "7       59\n",
       "8       28\n",
       "9        9\n",
       "10       5\n",
       "Name: bin, dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "af['bin'].value_counts(sort=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1     2306\n",
      "2      904\n",
      "3      460\n",
      "4      300\n",
      "5      178\n",
      "6      122\n",
      "7       59\n",
      "8       28\n",
      "9        9\n",
      "10       5\n",
      "Name: bin, dtype: int64\n",
      "\n",
      "Number of genes before:   5567\n",
      "Number non-coding before:  0\n",
      "Also number of genes before? Avg mutations per gene before 1.26351715466\n"
     ]
    }
   ],
   "source": [
    "if options.std_by_len:\n",
    "    print af['bin'].value_counts(sort=False)\n",
    "    print \"\\nNumber of genes before:  \", after['gene_name'].unique().shape[0]\n",
    "    print \"Number non-coding before: \", sum(after['gene_name'].isnull())\n",
    "    print \"Also number of genes before?\", \n",
    "    print \"Avg mutations per gene before\", after.groupby('gene_name')['mutation_id'].count().mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "only_after = mutations[mutations['mutation_id']\\\n",
    ".isin(set(after['mutation_id']).difference(set(before['mutation_id'])))]['gene_name']\\\n",
    ".dropna().drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "only_before = mutations[mutations['mutation_id']\\\n",
    ".isin(set(before['mutation_id']).difference(set(after['mutation_id'])))]['gene_name']\\\n",
    ".dropna().drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "both = mutations[mutations['mutation_id']\\\n",
    ".isin(set(before['mutation_id']).intersection(set(after['mutation_id'])))]['gene_name']\\\n",
    ".dropna().drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1597"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(only_after)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "702"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(only_before)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4833"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(both)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Coding/Noncoding Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "## Convert consequence_type to a category column\n",
    "ranked_mutations['consequence_type'] = ranked_mutations['consequence_type'].astype(\"category\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "high = [\n",
    "         'missense_variant', 'stop_gained', 'stop_lost', 'splice_donor_variant', \n",
    "         'splice_acceptor_variant'\n",
    "      ]\n",
    "moderate = [\n",
    "       'synonymous_variant', 'exon_variant', 'initiator_codon_variant',\n",
    "       'splice_region_variant'\n",
    "      ]\n",
    "low = [\n",
    "        '5_prime_UTR_premature_start_codon_gain_variant', '5_prime_UTR_variant', \n",
    "        '3_prime_UTR_variant', 'stop_retained_variant'\n",
    "      ]\n",
    "modifier = [\n",
    "        'intron_variant', 'intragenic_variant', 'downstream_gene_variant', \n",
    "        'upstream_gene_variant',  'intergenic_region'\n",
    "      ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "## Define variant priority\n",
    "coding_cats = high + moderate + low\n",
    "noncoding_cats = modifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "## Set the catgory priorities\n",
    "ranked_mutations['consequence_type'].cat.set_categories(coding_cats + noncoding_cats, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 76.1 ms, sys: 16.3 ms, total: 92.4 ms\n",
      "Wall time: 90.7 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "## Sort the consequence_type by priority\n",
    "ranked_mutations = ranked_mutations.sort_values(by=['mutation_id', 'consequence_type'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 62.3 ms, sys: 15.1 ms, total: 77.4 ms\n",
      "Wall time: 76.5 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "## Drop missing and then the less priortized consequences\n",
    "ranked_mutations = ranked_mutations.dropna(subset=['consequence_type'])\n",
    "ranked_mutations = ranked_mutations.drop_duplicates(subset=['mutation_id'], keep='first')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "#if options.std_by_len:\n",
    "#    ranked_mutations['Distance'] = ranked_mutations['Scaled Distance']\n",
    "\n",
    "ranked_mutations = ranked_mutations.sort_values(by='Distance', ascending=False)\n",
    "ranked_mutations = ranked_mutations.drop_duplicates(\\\n",
    "        subset=['mutation_id', 'gene_name', 'consequence_type'],\n",
    "        keep='first')\n",
    "ranked_mutations = ranked_mutations.reset_index(drop=True)\n",
    "ranked_mutations.index = ranked_mutations.index + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "## Finally, add the column type and colors\n",
    "ranked_mutations['type'] = None\n",
    "ranked_mutations['type'][ranked_mutations['consequence_type'].isin(coding_cats)]    = 'coding'\n",
    "ranked_mutations['type'][ranked_mutations['consequence_type'].isin(noncoding_cats)] = 'noncoding'\n",
    "\n",
    "ranked_mutations['color'] = None\n",
    "ranked_mutations['color'][ranked_mutations['type']=='coding'] = '#31e173'\n",
    "ranked_mutations['color'][ranked_mutations['type']=='noncoding'] = 'black'\n",
    "\n",
    "ranked_mutations['impact'] = None\n",
    "ranked_mutations['impact'][ranked_mutations['consequence_type'].isin(high)]     = 'high'\n",
    "ranked_mutations['impact'][ranked_mutations['consequence_type'].isin(moderate)] = 'moderate'\n",
    "ranked_mutations['impact'][ranked_mutations['consequence_type'].isin(low)]      = 'low'\n",
    "ranked_mutations['impact'][ranked_mutations['consequence_type'].isin(modifier)] = 'modifier'\n",
    "\n",
    "\n",
    "#ranked_mutations['color'] = None\n",
    "#ranked_mutations['color'][ranked_mutations['impact']=='high']     = '#EA4335'\n",
    "#ranked_mutations['color'][ranked_mutations['impact']=='moderate'] = 'darkorange'\n",
    "#ranked_mutations['color'][ranked_mutations['impact']=='low']      = '#FBBC05'\n",
    "#ranked_mutations['color'][ranked_mutations['impact']=='modifier'] = '#4285F4'\n",
    "\n",
    "## #31e173"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Figure 2C"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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mdu3aPrmelE7g/ZRcRHIdeWSa/buohbeluDL9XYKIiIiI+IlhGOzPD8x1hE5Z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7bkII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\nUMv976TP1eeVQVIzf9NRylg89N8sW0eNRRkHAAAAoPnPzVDypn1StytNR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1YAABThSURB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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x236a293d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# PLOT\n",
    "num_to_plot=options.n_probes\n",
    "\n",
    "sns.set(style=\"white\")\n",
    "sns.set_context(\"poster\")#, font_scale=1.5, rc={\"lines.linewidth\": 2})\n",
    "fig, ax = plt.subplots()\n",
    "# the size of A4 paper\n",
    "fig.set_size_inches(11.7, 8.27)\n",
    "\n",
    "cats = ranked_mutations[0:num_to_plot]\n",
    "xs   = (np.log(cats.index)+1)\n",
    "ys   = (cats['Distance'])\n",
    "ss   = 500./(np.log(.7*(1+xs)))\n",
    "leg  = []\n",
    "\n",
    "markers = ['o','o']\n",
    "alphas  = [.4,.17] \n",
    "for i,cat in enumerate(['coding', 'noncoding']):\n",
    "    idx = cats['type'] == cat\n",
    "    leg.append(plt.scatter(\n",
    "                    xs[idx],\n",
    "                    ys[idx],  \n",
    "                    #s=100,\n",
    "                    s=ss[idx],\n",
    "                    c=cats[idx]['color'], \n",
    "                    marker=markers[i], \n",
    "                    alpha=alphas[i], \n",
    "                    linewidths=0.05, \n",
    "                    edgecolors='black'\n",
    "    ))\n",
    "\n",
    "    \n",
    "#labels = zip(ranked_mutations['gene_name'], xs, ys, ss)[0:5]\n",
    "#switch = 1 \n",
    "#for label, x, y, ss in labels:\n",
    "#    switch = switch * -1\n",
    "#    plt.annotate(\n",
    "#        label,\n",
    "#        xy=(x, y), xytext=(0, 0),\n",
    "#        textcoords='offset points', ha='center', va='bottom',\n",
    "#        size=ss/100,\n",
    "#        color = \"black\"\n",
    "#    )\n",
    "        \n",
    "    \n",
    "#plt.xticks(size=18)\n",
    "#plt.yticks(size=18)\n",
    "plt.title(\"Mutation Coding Type\")#, size=32)\n",
    "plt.ylabel(\"Distance from Hyperplane\")#, size=26)\n",
    "plt.xlabel(\"Log Rank\")#, size=26)\n",
    "\n",
    "#plt.xlim(None,6.6) #len(ranked_mutations)+1000)\n",
    "#plt.ylim(0, 20) \n",
    "\n",
    "plt.legend(leg,\n",
    "           ('Coding', 'Non-coding'),\n",
    "           scatterpoints=1,\n",
    "           loc='upper right',\n",
    "           ncol=1)#,\n",
    "           #fontsize=20)\n",
    "sns.despine()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Figure 2D (data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "type\n",
      "coding       4629\n",
      "noncoding    2405\n",
      "Name: type, dtype: int64\n",
      "\n",
      "\n",
      "consequence_type\n",
      "missense_variant                                  3386\n",
      "stop_gained                                         50\n",
      "stop_lost                                            0\n",
      "splice_donor_variant                                 0\n",
      "splice_acceptor_variant                              0\n",
      "synonymous_variant                                1142\n",
      "exon_variant                                         0\n",
      "initiator_codon_variant                              0\n",
      "splice_region_variant                                0\n",
      "5_prime_UTR_premature_start_codon_gain_variant       0\n",
      "5_prime_UTR_variant                                 16\n",
      "3_prime_UTR_variant                                 35\n",
      "stop_retained_variant                                0\n",
      "intron_variant                                    1155\n",
      "intragenic_variant                                   0\n",
      "downstream_gene_variant                            332\n",
      "upstream_gene_variant                              257\n",
      "intergenic_region                                  661\n",
      "Name: consequence_type, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Category count breakdowns\n",
    "print ranked_mutations[0:options.n_probes]\\\n",
    ".groupby(['type'])['type'].apply(lambda x: x.count())\n",
    "print \"\\n\"\n",
    "print ranked_mutations[0:options.n_probes]\\\n",
    ".groupby(['consequence_type'])['consequence_type'].apply(lambda x: x.count())"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}