comparing_important_genes_for_aml.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Rough comparison of important genes for AML vs. normal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "project_directory = '/data/BIDS-HPC/private/projects/dmi2'\n",
    "my_important_genes_file = os.path.join(project_directory, 'data', 'important_genes_aml.pkl')\n",
    "their_important_genes_file = os.path.join(project_directory, 'data', 'glmQLFTest_AMLvsNBM_fc2fdr05.txt') # don't worry this directory is inaccessible to all but George and myself on Biowulf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load my AML-vs-normal dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "                place  norm_score  num_in_rank  raw_score  original score\nENSG00000068001   t-1         1.0            7   0.000433        0.000433\nENSG00000184669   t-1         1.0            7   0.000433        0.000433\nENSG00000087502   t-1         1.0            7   0.000433        0.000433\nENSG00000121440   t-1         1.0            7   0.000433        0.000433\nENSG00000181544   t-1         1.0            7   0.000433        0.000433\n...               ...         ...          ...        ...             ...\nENSG00000276026     0         0.0            0   0.000000        0.000000\nENSG00000135763     0         0.0            0   0.000000        0.000000\nENSG00000257550     0         0.0            0   0.000000        0.000000\nENSG00000213642     0         0.0            0   0.000000        0.000000\nENSGR0000281849     0         0.0            0   0.000000        0.000000\n\n[60483 rows x 5 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>place</th>\n      <th>norm_score</th>\n      <th>num_in_rank</th>\n      <th>raw_score</th>\n      <th>original score</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>ENSG00000068001</th>\n      <td>t-1</td>\n      <td>1.0</td>\n      <td>7</td>\n      <td>0.000433</td>\n      <td>0.000433</td>\n    </tr>\n    <tr>\n      <th>ENSG00000184669</th>\n      <td>t-1</td>\n      <td>1.0</td>\n      <td>7</td>\n      <td>0.000433</td>\n      <td>0.000433</td>\n    </tr>\n    <tr>\n      <th>ENSG00000087502</th>\n      <td>t-1</td>\n      <td>1.0</td>\n      <td>7</td>\n      <td>0.000433</td>\n      <td>0.000433</td>\n    </tr>\n    <tr>\n      <th>ENSG00000121440</th>\n      <td>t-1</td>\n      <td>1.0</td>\n      <td>7</td>\n      <td>0.000433</td>\n      <td>0.000433</td>\n    </tr>\n    <tr>\n      <th>ENSG00000181544</th>\n      <td>t-1</td>\n      <td>1.0</td>\n      <td>7</td>\n      <td>0.000433</td>\n      <td>0.000433</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>ENSG00000276026</th>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>ENSG00000135763</th>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>ENSG00000257550</th>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>ENSG00000213642</th>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>ENSGR0000281849</th>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n  </tbody>\n</table>\n<p>60483 rows × 5 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 2
    }
   ],
   "source": [
    "# Note I generated the my_important_genes_file file in main.ipynb using:\n",
    "#   import pickle, os\n",
    "#   filename = os.path.join(project_directory, 'data', 'important_genes_aml.pkl')\n",
    "#   with open(filename, 'wb') as f:\n",
    "#       pickle.dump(important_genes_aml, f)\n",
    "# Also note that my_scores here is exactly the same as important_genes_aml in main.ipynb\n",
    "\n",
    "import pickle\n",
    "with open(my_important_genes_file, 'rb') as f:\n",
    "    my_scores = pickle.load(f)\n",
    "my_scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load their DE analysis in to a Pandas dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "     GeneName           GeneID     logFC    logCPM           F        PValue  \\\n0       MYBL1  ENSG00000185697 -4.602894  1.553099  456.251266  3.104960e-72   \n1       FCRL6  ENSG00000181036 -5.693966  0.948122  425.910242  9.657346e-69   \n2      SPTSSB  ENSG00000196542 -5.372245 -2.798670  398.901307  1.558698e-65   \n3     FTH1P22  ENSG00000225079 -5.029037 -2.886335  373.477587  2.007676e-62   \n4     ASH2LP1  ENSG00000224144 -5.065626 -2.988322  352.915910  7.684420e-60   \n...       ...              ...       ...       ...         ...           ...   \n2620     NFIB  ENSG00000147862  2.069687 -0.030091    5.143154  2.376835e-02   \n2621    LAMP5  ENSG00000125869  2.524025  4.881743    5.056075  2.497884e-02   \n2622    DPP10  ENSG00000175497  2.199781  0.258309    5.052136  2.503509e-02   \n2623     PCLO  ENSG00000186472  2.200748  0.198458    4.965719  2.630296e-02   \n2624   RFPL4A  ENSG00000223638  2.022591 -0.466007    4.939167  2.670581e-02   \n\n               FDR  \n0     6.742110e-68  \n1     1.048498e-64  \n2     1.128186e-61  \n3     1.089867e-58  \n4     3.337190e-56  \n...            ...  \n2620  4.405785e-02  \n2621  4.599648e-02  \n2622  4.607662e-02  \n2623  4.810431e-02  \n2624  4.872616e-02  \n\n[2625 rows x 7 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>GeneName</th>\n      <th>GeneID</th>\n      <th>logFC</th>\n      <th>logCPM</th>\n      <th>F</th>\n      <th>PValue</th>\n      <th>FDR</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>MYBL1</td>\n      <td>ENSG00000185697</td>\n      <td>-4.602894</td>\n      <td>1.553099</td>\n      <td>456.251266</td>\n      <td>3.104960e-72</td>\n      <td>6.742110e-68</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>FCRL6</td>\n      <td>ENSG00000181036</td>\n      <td>-5.693966</td>\n      <td>0.948122</td>\n      <td>425.910242</td>\n      <td>9.657346e-69</td>\n      <td>1.048498e-64</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>SPTSSB</td>\n      <td>ENSG00000196542</td>\n      <td>-5.372245</td>\n      <td>-2.798670</td>\n      <td>398.901307</td>\n      <td>1.558698e-65</td>\n      <td>1.128186e-61</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>FTH1P22</td>\n      <td>ENSG00000225079</td>\n      <td>-5.029037</td>\n      <td>-2.886335</td>\n      <td>373.477587</td>\n      <td>2.007676e-62</td>\n      <td>1.089867e-58</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>ASH2LP1</td>\n      <td>ENSG00000224144</td>\n      <td>-5.065626</td>\n      <td>-2.988322</td>\n      <td>352.915910</td>\n      <td>7.684420e-60</td>\n      <td>3.337190e-56</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2620</th>\n      <td>NFIB</td>\n      <td>ENSG00000147862</td>\n      <td>2.069687</td>\n      <td>-0.030091</td>\n      <td>5.143154</td>\n      <td>2.376835e-02</td>\n      <td>4.405785e-02</td>\n    </tr>\n    <tr>\n      <th>2621</th>\n      <td>LAMP5</td>\n      <td>ENSG00000125869</td>\n      <td>2.524025</td>\n      <td>4.881743</td>\n      <td>5.056075</td>\n      <td>2.497884e-02</td>\n      <td>4.599648e-02</td>\n    </tr>\n    <tr>\n      <th>2622</th>\n      <td>DPP10</td>\n      <td>ENSG00000175497</td>\n      <td>2.199781</td>\n      <td>0.258309</td>\n      <td>5.052136</td>\n      <td>2.503509e-02</td>\n      <td>4.607662e-02</td>\n    </tr>\n    <tr>\n      <th>2623</th>\n      <td>PCLO</td>\n      <td>ENSG00000186472</td>\n      <td>2.200748</td>\n      <td>0.198458</td>\n      <td>4.965719</td>\n      <td>2.630296e-02</td>\n      <td>4.810431e-02</td>\n    </tr>\n    <tr>\n      <th>2624</th>\n      <td>RFPL4A</td>\n      <td>ENSG00000223638</td>\n      <td>2.022591</td>\n      <td>-0.466007</td>\n      <td>4.939167</td>\n      <td>2.670581e-02</td>\n      <td>4.872616e-02</td>\n    </tr>\n  </tbody>\n</table>\n<p>2625 rows × 7 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 3
    }
   ],
   "source": [
    "import pandas as pd\n",
    "their_scores = pd.read_csv(their_important_genes_file, sep='\\t')\n",
    "their_scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Add a normalized score for their data (0-1 range, just like I currently have for my results) and show what this score looks like"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "                GeneName     logFC    logCPM           F        PValue  \\\nGeneID                                                                   \nENSG00000185697    MYBL1 -4.602894  1.553099  456.251266  3.104960e-72   \nENSG00000181036    FCRL6 -5.693966  0.948122  425.910242  9.657346e-69   \nENSG00000196542   SPTSSB -5.372245 -2.798670  398.901307  1.558698e-65   \nENSG00000225079  FTH1P22 -5.029037 -2.886335  373.477587  2.007676e-62   \nENSG00000224144  ASH2LP1 -5.065626 -2.988322  352.915910  7.684420e-60   \n...                  ...       ...       ...         ...           ...   \nENSG00000147862     NFIB  2.069687 -0.030091    5.143154  2.376835e-02   \nENSG00000125869    LAMP5  2.524025  4.881743    5.056075  2.497884e-02   \nENSG00000175497    DPP10  2.199781  0.258309    5.052136  2.503509e-02   \nENSG00000186472     PCLO  2.200748  0.198458    4.965719  2.630296e-02   \nENSG00000223638   RFPL4A  2.022591 -0.466007    4.939167  2.670581e-02   \n\n                          FDR  norm_score  \nGeneID                                     \nENSG00000185697  6.742110e-68    1.000000  \nENSG00000181036  1.048498e-64    0.952483  \nENSG00000196542  1.128186e-61    0.907347  \nENSG00000225079  1.089867e-58    0.862909  \nENSG00000224144  3.337190e-56    0.825899  \n...                       ...         ...  \nENSG00000147862  4.405785e-02    0.020187  \nENSG00000125869  4.599648e-02    0.019908  \nENSG00000175497  4.607662e-02    0.019897  \nENSG00000186472  4.810431e-02    0.019619  \nENSG00000223638  4.872616e-02    0.019536  \n\n[2625 rows x 7 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>GeneName</th>\n      <th>logFC</th>\n      <th>logCPM</th>\n      <th>F</th>\n      <th>PValue</th>\n      <th>FDR</th>\n      <th>norm_score</th>\n    </tr>\n    <tr>\n      <th>GeneID</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>ENSG00000185697</th>\n      <td>MYBL1</td>\n      <td>-4.602894</td>\n      <td>1.553099</td>\n      <td>456.251266</td>\n      <td>3.104960e-72</td>\n      <td>6.742110e-68</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>ENSG00000181036</th>\n      <td>FCRL6</td>\n      <td>-5.693966</td>\n      <td>0.948122</td>\n      <td>425.910242</td>\n      <td>9.657346e-69</td>\n      <td>1.048498e-64</td>\n      <td>0.952483</td>\n    </tr>\n    <tr>\n      <th>ENSG00000196542</th>\n      <td>SPTSSB</td>\n      <td>-5.372245</td>\n      <td>-2.798670</td>\n      <td>398.901307</td>\n      <td>1.558698e-65</td>\n      <td>1.128186e-61</td>\n      <td>0.907347</td>\n    </tr>\n    <tr>\n      <th>ENSG00000225079</th>\n      <td>FTH1P22</td>\n      <td>-5.029037</td>\n      <td>-2.886335</td>\n      <td>373.477587</td>\n      <td>2.007676e-62</td>\n      <td>1.089867e-58</td>\n      <td>0.862909</td>\n    </tr>\n    <tr>\n      <th>ENSG00000224144</th>\n      <td>ASH2LP1</td>\n      <td>-5.065626</td>\n      <td>-2.988322</td>\n      <td>352.915910</td>\n      <td>7.684420e-60</td>\n      <td>3.337190e-56</td>\n      <td>0.825899</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>ENSG00000147862</th>\n      <td>NFIB</td>\n      <td>2.069687</td>\n      <td>-0.030091</td>\n      <td>5.143154</td>\n      <td>2.376835e-02</td>\n      <td>4.405785e-02</td>\n      <td>0.020187</td>\n    </tr>\n    <tr>\n      <th>ENSG00000125869</th>\n      <td>LAMP5</td>\n      <td>2.524025</td>\n      <td>4.881743</td>\n      <td>5.056075</td>\n      <td>2.497884e-02</td>\n      <td>4.599648e-02</td>\n      <td>0.019908</td>\n    </tr>\n    <tr>\n      <th>ENSG00000175497</th>\n      <td>DPP10</td>\n      <td>2.199781</td>\n      <td>0.258309</td>\n      <td>5.052136</td>\n      <td>2.503509e-02</td>\n      <td>4.607662e-02</td>\n      <td>0.019897</td>\n    </tr>\n    <tr>\n      <th>ENSG00000186472</th>\n      <td>PCLO</td>\n      <td>2.200748</td>\n      <td>0.198458</td>\n      <td>4.965719</td>\n      <td>2.630296e-02</td>\n      <td>4.810431e-02</td>\n      <td>0.019619</td>\n    </tr>\n    <tr>\n      <th>ENSG00000223638</th>\n      <td>RFPL4A</td>\n      <td>2.022591</td>\n      <td>-0.466007</td>\n      <td>4.939167</td>\n      <td>2.670581e-02</td>\n      <td>4.872616e-02</td>\n      <td>0.019536</td>\n    </tr>\n  </tbody>\n</table>\n<p>2625 rows × 7 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 4
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "import numpy as np\n",
    "tmp = -np.log10(their_scores['FDR']) # the non-normalized score is -log10(FDR)\n",
    "their_scores['norm_score'] = tmp / tmp.max(axis=0)\n",
    "their_scores = their_scores.set_index('GeneID') # set the dataframe index to the Ensembl ID\n",
    "their_scores['norm_score'].plot()\n",
    "their_scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Limit my data to roughly the most important genes (normalized score > 0.1) and similarly plot the scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "                place  norm_score  num_in_rank  raw_score  original score\nENSG00000068001   t-1    1.000000            7   0.000433        0.000433\nENSG00000184669   t-1    1.000000            7   0.000433        0.000433\nENSG00000087502   t-1    1.000000            7   0.000433        0.000433\nENSG00000121440   t-1    1.000000            7   0.000433        0.000433\nENSG00000181544   t-1    1.000000            7   0.000433        0.000433\n...               ...         ...          ...        ...             ...\nENSG00000139083  t-12    0.153846         1947   0.000067        0.000067\nENSG00000238111  t-12    0.153846         1947   0.000067        0.000067\nENSG00000102780  t-12    0.153846         1947   0.000067        0.000067\nENSG00000250746  t-12    0.153846         1947   0.000067        0.000067\nENSG00000276409  t-12    0.153846         1947   0.000067        0.000067\n\n[6444 rows x 5 columns]",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "my_scores = my_scores[my_scores['norm_score']>0.1]\n",
    "my_scores['norm_score'].plot()\n",
    "my_scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Perform an inner join on the Ensembl IDs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "                place  norm_score-me  num_in_rank  raw_score  original score  \\\nENSG00000153292   t-2       0.923077           12   0.000400        0.000400   \nENSG00000088836   t-2       0.923077           12   0.000400        0.000400   \nENSG00000104427   t-3       0.846154           38   0.000367        0.000367   \nENSG00000179403   t-3       0.846154           38   0.000367        0.000367   \nENSG00000225246   t-3       0.846154           38   0.000367        0.000367   \n...               ...            ...          ...        ...             ...   \nENSG00000100625  t-12       0.153846         1947   0.000067        0.000067   \nENSG00000189120  t-12       0.153846         1947   0.000067        0.000067   \nENSG00000204956  t-12       0.153846         1947   0.000067        0.000067   \nENSG00000235488  t-12       0.153846         1947   0.000067        0.000067   \nENSG00000115414  t-12       0.153846         1947   0.000067        0.000067   \n\n                   GeneName     logFC    logCPM          F        PValue  \\\nENSG00000153292      GPR110  2.401678 -1.966619   7.454756  6.552394e-03   \nENSG00000088836     SLC4A11  2.036883 -0.267762  11.523519  7.423866e-04   \nENSG00000104427     ZC2HC1A -3.272054 -1.336879  86.266845  4.955559e-19   \nENSG00000179403        VWA1  3.701116  0.883318  26.475403  3.854256e-07   \nENSG00000225246      RPS2P1  2.175516 -0.392433  15.769803  8.214427e-05   \n...                     ...       ...       ...        ...           ...   \nENSG00000100625        SIX4  2.909278 -2.134178  12.386449  4.723552e-04   \nENSG00000189120         SP6  3.038252 -1.145165  18.122807  2.478257e-05   \nENSG00000204956     PCDHGA1  2.182559  2.631066  12.700202  4.010324e-04   \nENSG00000235488  JARID2-AS1  2.449957  0.122012  35.808087  4.180172e-09   \nENSG00000115414         FN1  5.029102  4.865763  18.993782  1.595653e-05   \n\n                          FDR  norm_score-them  \nENSG00000153292  1.427774e-02         0.027472  \nENSG00000088836  2.112737e-03         0.039826  \nENSG00000104427  2.869467e-17         0.246269  \nENSG00000179403  2.774911e-06         0.082725  \nENSG00000225246  3.074781e-04         0.052287  \n...                       ...              ...  \nENSG00000100625  1.420992e-03         0.042390  \nENSG00000189120  1.072911e-04         0.059094  \nENSG00000204956  1.228038e-03         0.043334  \nENSG00000235488  4.752265e-08         0.109021  \nENSG00000115414  7.292782e-05         0.061590  \n\n[515 rows x 12 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>place</th>\n      <th>norm_score-me</th>\n      <th>num_in_rank</th>\n      <th>raw_score</th>\n      <th>original score</th>\n      <th>GeneName</th>\n      <th>logFC</th>\n      <th>logCPM</th>\n      <th>F</th>\n      <th>PValue</th>\n      <th>FDR</th>\n      <th>norm_score-them</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>ENSG00000153292</th>\n      <td>t-2</td>\n      <td>0.923077</td>\n      <td>12</td>\n      <td>0.000400</td>\n      <td>0.000400</td>\n      <td>GPR110</td>\n      <td>2.401678</td>\n      <td>-1.966619</td>\n      <td>7.454756</td>\n      <td>6.552394e-03</td>\n      <td>1.427774e-02</td>\n      <td>0.027472</td>\n    </tr>\n    <tr>\n      <th>ENSG00000088836</th>\n      <td>t-2</td>\n      <td>0.923077</td>\n      <td>12</td>\n      <td>0.000400</td>\n      <td>0.000400</td>\n      <td>SLC4A11</td>\n      <td>2.036883</td>\n      <td>-0.267762</td>\n      <td>11.523519</td>\n      <td>7.423866e-04</td>\n      <td>2.112737e-03</td>\n      <td>0.039826</td>\n    </tr>\n    <tr>\n      <th>ENSG00000104427</th>\n      <td>t-3</td>\n      <td>0.846154</td>\n      <td>38</td>\n      <td>0.000367</td>\n      <td>0.000367</td>\n      <td>ZC2HC1A</td>\n      <td>-3.272054</td>\n      <td>-1.336879</td>\n      <td>86.266845</td>\n      <td>4.955559e-19</td>\n      <td>2.869467e-17</td>\n      <td>0.246269</td>\n    </tr>\n    <tr>\n      <th>ENSG00000179403</th>\n      <td>t-3</td>\n      <td>0.846154</td>\n      <td>38</td>\n      <td>0.000367</td>\n      <td>0.000367</td>\n      <td>VWA1</td>\n      <td>3.701116</td>\n      <td>0.883318</td>\n      <td>26.475403</td>\n      <td>3.854256e-07</td>\n      <td>2.774911e-06</td>\n      <td>0.082725</td>\n    </tr>\n    <tr>\n      <th>ENSG00000225246</th>\n      <td>t-3</td>\n      <td>0.846154</td>\n      <td>38</td>\n      <td>0.000367</td>\n      <td>0.000367</td>\n      <td>RPS2P1</td>\n      <td>2.175516</td>\n      <td>-0.392433</td>\n      <td>15.769803</td>\n      <td>8.214427e-05</td>\n      <td>3.074781e-04</td>\n      <td>0.052287</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>ENSG00000100625</th>\n      <td>t-12</td>\n      <td>0.153846</td>\n      <td>1947</td>\n      <td>0.000067</td>\n      <td>0.000067</td>\n      <td>SIX4</td>\n      <td>2.909278</td>\n      <td>-2.134178</td>\n      <td>12.386449</td>\n      <td>4.723552e-04</td>\n      <td>1.420992e-03</td>\n      <td>0.042390</td>\n    </tr>\n    <tr>\n      <th>ENSG00000189120</th>\n      <td>t-12</td>\n      <td>0.153846</td>\n      <td>1947</td>\n      <td>0.000067</td>\n      <td>0.000067</td>\n      <td>SP6</td>\n      <td>3.038252</td>\n      <td>-1.145165</td>\n      <td>18.122807</td>\n      <td>2.478257e-05</td>\n      <td>1.072911e-04</td>\n      <td>0.059094</td>\n    </tr>\n    <tr>\n      <th>ENSG00000204956</th>\n      <td>t-12</td>\n      <td>0.153846</td>\n      <td>1947</td>\n      <td>0.000067</td>\n      <td>0.000067</td>\n      <td>PCDHGA1</td>\n      <td>2.182559</td>\n      <td>2.631066</td>\n      <td>12.700202</td>\n      <td>4.010324e-04</td>\n      <td>1.228038e-03</td>\n      <td>0.043334</td>\n    </tr>\n    <tr>\n      <th>ENSG00000235488</th>\n      <td>t-12</td>\n      <td>0.153846</td>\n      <td>1947</td>\n      <td>0.000067</td>\n      <td>0.000067</td>\n      <td>JARID2-AS1</td>\n      <td>2.449957</td>\n      <td>0.122012</td>\n      <td>35.808087</td>\n      <td>4.180172e-09</td>\n      <td>4.752265e-08</td>\n      <td>0.109021</td>\n    </tr>\n    <tr>\n      <th>ENSG00000115414</th>\n      <td>t-12</td>\n      <td>0.153846</td>\n      <td>1947</td>\n      <td>0.000067</td>\n      <td>0.000067</td>\n      <td>FN1</td>\n      <td>5.029102</td>\n      <td>4.865763</td>\n      <td>18.993782</td>\n      <td>1.595653e-05</td>\n      <td>7.292782e-05</td>\n      <td>0.061590</td>\n    </tr>\n  </tbody>\n</table>\n<p>515 rows × 12 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 6
    }
   ],
   "source": [
    "all_scores = my_scores.join(their_scores, how='inner', lsuffix='-me', rsuffix='-them')\n",
    "all_scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Average our scores together (with equal weighting) and normalize the result to the 0-1 range again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "                place  norm_score-me  num_in_rank  raw_score  original score  \\\nENSG00000153292   t-2       0.923077           12   0.000400        0.000400   \nENSG00000088836   t-2       0.923077           12   0.000400        0.000400   \nENSG00000104427   t-3       0.846154           38   0.000367        0.000367   \nENSG00000179403   t-3       0.846154           38   0.000367        0.000367   \nENSG00000225246   t-3       0.846154           38   0.000367        0.000367   \n...               ...            ...          ...        ...             ...   \nENSG00000100625  t-12       0.153846         1947   0.000067        0.000067   \nENSG00000189120  t-12       0.153846         1947   0.000067        0.000067   \nENSG00000204956  t-12       0.153846         1947   0.000067        0.000067   \nENSG00000235488  t-12       0.153846         1947   0.000067        0.000067   \nENSG00000115414  t-12       0.153846         1947   0.000067        0.000067   \n\n                   GeneName     logFC    logCPM          F        PValue  \\\nENSG00000153292      GPR110  2.401678 -1.966619   7.454756  6.552394e-03   \nENSG00000088836     SLC4A11  2.036883 -0.267762  11.523519  7.423866e-04   \nENSG00000104427     ZC2HC1A -3.272054 -1.336879  86.266845  4.955559e-19   \nENSG00000179403        VWA1  3.701116  0.883318  26.475403  3.854256e-07   \nENSG00000225246      RPS2P1  2.175516 -0.392433  15.769803  8.214427e-05   \n...                     ...       ...       ...        ...           ...   \nENSG00000100625        SIX4  2.909278 -2.134178  12.386449  4.723552e-04   \nENSG00000189120         SP6  3.038252 -1.145165  18.122807  2.478257e-05   \nENSG00000204956     PCDHGA1  2.182559  2.631066  12.700202  4.010324e-04   \nENSG00000235488  JARID2-AS1  2.449957  0.122012  35.808087  4.180172e-09   \nENSG00000115414         FN1  5.029102  4.865763  18.993782  1.595653e-05   \n\n                          FDR  norm_score-them  overall_score  \nENSG00000153292  1.427774e-02         0.027472       0.697587  \nENSG00000088836  2.112737e-03         0.039826       0.706653  \nENSG00000104427  2.869467e-17         0.246269       0.801705  \nENSG00000179403  2.774911e-06         0.082725       0.681684  \nENSG00000225246  3.074781e-04         0.052287       0.659346  \n...                       ...              ...            ...  \nENSG00000100625  1.420992e-03         0.042390       0.144014  \nENSG00000189120  1.072911e-04         0.059094       0.156272  \nENSG00000204956  1.228038e-03         0.043334       0.144706  \nENSG00000235488  4.752265e-08         0.109021       0.192913  \nENSG00000115414  7.292782e-05         0.061590       0.158104  \n\n[515 rows x 13 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>place</th>\n      <th>norm_score-me</th>\n      <th>num_in_rank</th>\n      <th>raw_score</th>\n      <th>original score</th>\n      <th>GeneName</th>\n      <th>logFC</th>\n      <th>logCPM</th>\n      <th>F</th>\n      <th>PValue</th>\n      <th>FDR</th>\n      <th>norm_score-them</th>\n      <th>overall_score</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>ENSG00000153292</th>\n      <td>t-2</td>\n      <td>0.923077</td>\n      <td>12</td>\n      <td>0.000400</td>\n      <td>0.000400</td>\n      <td>GPR110</td>\n      <td>2.401678</td>\n      <td>-1.966619</td>\n      <td>7.454756</td>\n      <td>6.552394e-03</td>\n      <td>1.427774e-02</td>\n      <td>0.027472</td>\n      <td>0.697587</td>\n    </tr>\n    <tr>\n      <th>ENSG00000088836</th>\n      <td>t-2</td>\n      <td>0.923077</td>\n      <td>12</td>\n      <td>0.000400</td>\n      <td>0.000400</td>\n      <td>SLC4A11</td>\n      <td>2.036883</td>\n      <td>-0.267762</td>\n      <td>11.523519</td>\n      <td>7.423866e-04</td>\n      <td>2.112737e-03</td>\n      <td>0.039826</td>\n      <td>0.706653</td>\n    </tr>\n    <tr>\n      <th>ENSG00000104427</th>\n      <td>t-3</td>\n      <td>0.846154</td>\n      <td>38</td>\n      <td>0.000367</td>\n      <td>0.000367</td>\n      <td>ZC2HC1A</td>\n      <td>-3.272054</td>\n      <td>-1.336879</td>\n      <td>86.266845</td>\n      <td>4.955559e-19</td>\n      <td>2.869467e-17</td>\n      <td>0.246269</td>\n      <td>0.801705</td>\n    </tr>\n    <tr>\n      <th>ENSG00000179403</th>\n      <td>t-3</td>\n      <td>0.846154</td>\n      <td>38</td>\n      <td>0.000367</td>\n      <td>0.000367</td>\n      <td>VWA1</td>\n      <td>3.701116</td>\n      <td>0.883318</td>\n      <td>26.475403</td>\n      <td>3.854256e-07</td>\n      <td>2.774911e-06</td>\n      <td>0.082725</td>\n      <td>0.681684</td>\n    </tr>\n    <tr>\n      <th>ENSG00000225246</th>\n      <td>t-3</td>\n      <td>0.846154</td>\n      <td>38</td>\n      <td>0.000367</td>\n      <td>0.000367</td>\n      <td>RPS2P1</td>\n      <td>2.175516</td>\n      <td>-0.392433</td>\n      <td>15.769803</td>\n      <td>8.214427e-05</td>\n      <td>3.074781e-04</td>\n      <td>0.052287</td>\n      <td>0.659346</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>ENSG00000100625</th>\n      <td>t-12</td>\n      <td>0.153846</td>\n      <td>1947</td>\n      <td>0.000067</td>\n      <td>0.000067</td>\n      <td>SIX4</td>\n      <td>2.909278</td>\n      <td>-2.134178</td>\n      <td>12.386449</td>\n      <td>4.723552e-04</td>\n      <td>1.420992e-03</td>\n      <td>0.042390</td>\n      <td>0.144014</td>\n    </tr>\n    <tr>\n      <th>ENSG00000189120</th>\n      <td>t-12</td>\n      <td>0.153846</td>\n      <td>1947</td>\n      <td>0.000067</td>\n      <td>0.000067</td>\n      <td>SP6</td>\n      <td>3.038252</td>\n      <td>-1.145165</td>\n      <td>18.122807</td>\n      <td>2.478257e-05</td>\n      <td>1.072911e-04</td>\n      <td>0.059094</td>\n      <td>0.156272</td>\n    </tr>\n    <tr>\n      <th>ENSG00000204956</th>\n      <td>t-12</td>\n      <td>0.153846</td>\n      <td>1947</td>\n      <td>0.000067</td>\n      <td>0.000067</td>\n      <td>PCDHGA1</td>\n      <td>2.182559</td>\n      <td>2.631066</td>\n      <td>12.700202</td>\n      <td>4.010324e-04</td>\n      <td>1.228038e-03</td>\n      <td>0.043334</td>\n      <td>0.144706</td>\n    </tr>\n    <tr>\n      <th>ENSG00000235488</th>\n      <td>t-12</td>\n      <td>0.153846</td>\n      <td>1947</td>\n      <td>0.000067</td>\n      <td>0.000067</td>\n      <td>JARID2-AS1</td>\n      <td>2.449957</td>\n      <td>0.122012</td>\n      <td>35.808087</td>\n      <td>4.180172e-09</td>\n      <td>4.752265e-08</td>\n      <td>0.109021</td>\n      <td>0.192913</td>\n    </tr>\n    <tr>\n      <th>ENSG00000115414</th>\n      <td>t-12</td>\n      <td>0.153846</td>\n      <td>1947</td>\n      <td>0.000067</td>\n      <td>0.000067</td>\n      <td>FN1</td>\n      <td>5.029102</td>\n      <td>4.865763</td>\n      <td>18.993782</td>\n      <td>1.595653e-05</td>\n      <td>7.292782e-05</td>\n      <td>0.061590</td>\n      <td>0.158104</td>\n    </tr>\n  </tbody>\n</table>\n<p>515 rows × 13 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 7
    }
   ],
   "source": [
    "tmp = all_scores['norm_score-me'] + all_scores['norm_score-them']\n",
    "all_scores['overall_score'] = tmp / tmp.max(axis=0)\n",
    "all_scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sort by the overall score (descending), re-arrange the columns, and print the top 20 genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "                 overall_score  norm_score-me  norm_score-them   GeneName  \\\nENSG00000168447       1.000000       0.846154         0.516470     SCNN1B   \nENSG00000104427       0.801705       0.846154         0.246269    ZC2HC1A   \nENSG00000130558       0.737913       0.615385         0.390113      OLFM1   \nENSG00000133574       0.706798       0.461538         0.501561     GIMAP4   \nENSG00000088836       0.706653       0.923077         0.039826    SLC4A11   \nENSG00000153292       0.697587       0.923077         0.027472     GPR110   \nENSG00000179403       0.681684       0.846154         0.082725       VWA1   \nENSG00000144824       0.678111       0.769231         0.154780     PHLDB2   \nENSG00000054654       0.667341       0.769231         0.140104      SYNE2   \nENSG00000143365       0.663473       0.538462         0.365603       RORC   \nENSG00000156475       0.660311       0.615385         0.284372    PPP2R2B   \nENSG00000225246       0.659346       0.846154         0.052287     RPS2P1   \nENSG00000197077       0.652714       0.692308         0.197097   KIAA1671   \nENSG00000169418       0.650499       0.846154         0.040232       NPR1   \nENSG00000006555       0.641708       0.461538         0.412869      TTC22   \nENSG00000180596       0.641191       0.692308         0.181394  HIST1H2BC   \nENSG00000107679       0.630767       0.615385         0.244113    PLEKHA1   \nENSG00000115884       0.628934       0.461538         0.395462       SDC1   \nENSG00000112796       0.623953       0.384615         0.465599      ENPP5   \nENSG00000135605       0.612302       0.615385         0.218953        TEC   \n\n                place  num_in_rank  raw_score  original score     logFC  \\\nENSG00000168447   t-3           38   0.000367        0.000367 -4.458596   \nENSG00000104427   t-3           38   0.000367        0.000367 -3.272054   \nENSG00000130558   t-6          233   0.000267        0.000267 -4.275702   \nENSG00000133574   t-8          544   0.000200        0.000200 -4.165698   \nENSG00000088836   t-2           12   0.000400        0.000400  2.036883   \nENSG00000153292   t-2           12   0.000400        0.000400  2.401678   \nENSG00000179403   t-3           38   0.000367        0.000367  3.701116   \nENSG00000144824   t-4           70   0.000333        0.000333 -2.356385   \nENSG00000054654   t-4           70   0.000333        0.000333 -2.333268   \nENSG00000143365   t-7          380   0.000233        0.000233 -3.667802   \nENSG00000156475   t-6          233   0.000267        0.000267 -3.351588   \nENSG00000225246   t-3           38   0.000367        0.000367  2.175516   \nENSG00000197077   t-5          107   0.000300        0.000300 -2.913504   \nENSG00000169418   t-3           38   0.000367        0.000367  3.334806   \nENSG00000006555   t-8          544   0.000200        0.000200 -3.682236   \nENSG00000180596   t-5          107   0.000300        0.000300  3.781744   \nENSG00000107679   t-6          233   0.000267        0.000267 -2.447003   \nENSG00000115884   t-8          544   0.000200        0.000200 -3.623720   \nENSG00000112796   t-9          757   0.000167        0.000167 -4.591047   \nENSG00000135605   t-6          233   0.000267        0.000267  2.115200   \n\n                   logCPM           F        PValue           FDR  \nENSG00000168447 -2.764723  198.898325  3.276383e-38  2.032668e-35  \nENSG00000104427 -1.336879   86.266845  4.955559e-19  2.869467e-17  \nENSG00000130558 -1.994927  143.544314  3.193224e-29  6.246637e-27  \nENSG00000133574  2.900366  192.184955  3.698145e-37  2.039506e-34  \nENSG00000088836 -0.267762   11.523519  7.423866e-04  2.112737e-03  \nENSG00000153292 -1.966619    7.454756  6.552394e-03  1.427774e-02  \nENSG00000179403  0.883318   26.475403  3.854256e-07  2.774911e-06  \nENSG00000144824  0.312400   52.343174  1.784429e-12  4.011086e-11  \nENSG00000054654  5.135965   47.024643  2.096949e-11  3.881770e-10  \nENSG00000143365 -1.045537  133.451199  1.694758e-27  2.766915e-25  \nENSG00000156475 -1.057759  100.899341  9.877331e-22  7.914257e-20  \nENSG00000225246 -0.392433   15.769803  8.214427e-05  3.074781e-04  \nENSG00000197077  0.165653   67.830821  1.598232e-15  5.764785e-14  \nENSG00000169418 -1.301356   11.662977  6.899612e-04  1.984084e-03  \nENSG00000006555 -0.707274  153.202518  7.581605e-31  1.849741e-28  \nENSG00000180596  4.417731   62.051190  2.138127e-14  6.539056e-13  \nENSG00000107679  2.513195   85.438031  7.082981e-19  4.005204e-17  \nENSG00000115884 -0.775661  145.810777  1.320573e-29  2.730945e-27  \nENSG00000112796 -1.731787  175.845505  1.467533e-34  5.311004e-32  \nENSG00000135605  4.824003   76.015323  4.273674e-17  1.961915e-15  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>overall_score</th>\n      <th>norm_score-me</th>\n      <th>norm_score-them</th>\n      <th>GeneName</th>\n      <th>place</th>\n      <th>num_in_rank</th>\n      <th>raw_score</th>\n      <th>original score</th>\n      <th>logFC</th>\n      <th>logCPM</th>\n      <th>F</th>\n      <th>PValue</th>\n      <th>FDR</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>ENSG00000168447</th>\n      <td>1.000000</td>\n      <td>0.846154</td>\n      <td>0.516470</td>\n      <td>SCNN1B</td>\n      <td>t-3</td>\n      <td>38</td>\n      <td>0.000367</td>\n      <td>0.000367</td>\n      <td>-4.458596</td>\n      <td>-2.764723</td>\n      <td>198.898325</td>\n      <td>3.276383e-38</td>\n      <td>2.032668e-35</td>\n    </tr>\n    <tr>\n      <th>ENSG00000104427</th>\n      <td>0.801705</td>\n      <td>0.846154</td>\n      <td>0.246269</td>\n      <td>ZC2HC1A</td>\n      <td>t-3</td>\n      <td>38</td>\n      <td>0.000367</td>\n      <td>0.000367</td>\n      <td>-3.272054</td>\n      <td>-1.336879</td>\n      <td>86.266845</td>\n      <td>4.955559e-19</td>\n      <td>2.869467e-17</td>\n    </tr>\n    <tr>\n      <th>ENSG00000130558</th>\n      <td>0.737913</td>\n      <td>0.615385</td>\n      <td>0.390113</td>\n      <td>OLFM1</td>\n      <td>t-6</td>\n      <td>233</td>\n      <td>0.000267</td>\n      <td>0.000267</td>\n      <td>-4.275702</td>\n      <td>-1.994927</td>\n      <td>143.544314</td>\n      <td>3.193224e-29</td>\n      <td>6.246637e-27</td>\n    </tr>\n    <tr>\n      <th>ENSG00000133574</th>\n      <td>0.706798</td>\n      <td>0.461538</td>\n      <td>0.501561</td>\n      <td>GIMAP4</td>\n      <td>t-8</td>\n      <td>544</td>\n      <td>0.000200</td>\n      <td>0.000200</td>\n      <td>-4.165698</td>\n      <td>2.900366</td>\n      <td>192.184955</td>\n      <td>3.698145e-37</td>\n      <td>2.039506e-34</td>\n    </tr>\n    <tr>\n      <th>ENSG00000088836</th>\n      <td>0.706653</td>\n      <td>0.923077</td>\n      <td>0.039826</td>\n      <td>SLC4A11</td>\n      <td>t-2</td>\n      <td>12</td>\n      <td>0.000400</td>\n      <td>0.000400</td>\n      <td>2.036883</td>\n      <td>-0.267762</td>\n      <td>11.523519</td>\n      <td>7.423866e-04</td>\n      <td>2.112737e-03</td>\n    </tr>\n    <tr>\n      <th>ENSG00000153292</th>\n      <td>0.697587</td>\n      <td>0.923077</td>\n      <td>0.027472</td>\n      <td>GPR110</td>\n      <td>t-2</td>\n      <td>12</td>\n      <td>0.000400</td>\n      <td>0.000400</td>\n      <td>2.401678</td>\n      <td>-1.966619</td>\n      <td>7.454756</td>\n      <td>6.552394e-03</td>\n      <td>1.427774e-02</td>\n    </tr>\n    <tr>\n      <th>ENSG00000179403</th>\n      <td>0.681684</td>\n      <td>0.846154</td>\n      <td>0.082725</td>\n      <td>VWA1</td>\n      <td>t-3</td>\n      <td>38</td>\n      <td>0.000367</td>\n      <td>0.000367</td>\n      <td>3.701116</td>\n      <td>0.883318</td>\n      <td>26.475403</td>\n      <td>3.854256e-07</td>\n      <td>2.774911e-06</td>\n    </tr>\n    <tr>\n      <th>ENSG00000144824</th>\n      <td>0.678111</td>\n      <td>0.769231</td>\n      <td>0.154780</td>\n      <td>PHLDB2</td>\n      <td>t-4</td>\n      <td>70</td>\n      <td>0.000333</td>\n      <td>0.000333</td>\n      <td>-2.356385</td>\n      <td>0.312400</td>\n      <td>52.343174</td>\n      <td>1.784429e-12</td>\n      <td>4.011086e-11</td>\n    </tr>\n    <tr>\n      <th>ENSG00000054654</th>\n      <td>0.667341</td>\n      <td>0.769231</td>\n      <td>0.140104</td>\n      <td>SYNE2</td>\n      <td>t-4</td>\n      <td>70</td>\n      <td>0.000333</td>\n      <td>0.000333</td>\n      <td>-2.333268</td>\n      <td>5.135965</td>\n      <td>47.024643</td>\n      <td>2.096949e-11</td>\n      <td>3.881770e-10</td>\n    </tr>\n    <tr>\n      <th>ENSG00000143365</th>\n      <td>0.663473</td>\n      <td>0.538462</td>\n      <td>0.365603</td>\n      <td>RORC</td>\n      <td>t-7</td>\n      <td>380</td>\n      <td>0.000233</td>\n      <td>0.000233</td>\n      <td>-3.667802</td>\n      <td>-1.045537</td>\n      <td>133.451199</td>\n      <td>1.694758e-27</td>\n      <td>2.766915e-25</td>\n    </tr>\n    <tr>\n      <th>ENSG00000156475</th>\n      <td>0.660311</td>\n      <td>0.615385</td>\n      <td>0.284372</td>\n      <td>PPP2R2B</td>\n      <td>t-6</td>\n      <td>233</td>\n      <td>0.000267</td>\n      <td>0.000267</td>\n      <td>-3.351588</td>\n      <td>-1.057759</td>\n      <td>100.899341</td>\n      <td>9.877331e-22</td>\n      <td>7.914257e-20</td>\n    </tr>\n    <tr>\n      <th>ENSG00000225246</th>\n      <td>0.659346</td>\n      <td>0.846154</td>\n      <td>0.052287</td>\n      <td>RPS2P1</td>\n      <td>t-3</td>\n      <td>38</td>\n      <td>0.000367</td>\n      <td>0.000367</td>\n      <td>2.175516</td>\n      <td>-0.392433</td>\n      <td>15.769803</td>\n      <td>8.214427e-05</td>\n      <td>3.074781e-04</td>\n    </tr>\n    <tr>\n      <th>ENSG00000197077</th>\n      <td>0.652714</td>\n      <td>0.692308</td>\n      <td>0.197097</td>\n      <td>KIAA1671</td>\n      <td>t-5</td>\n      <td>107</td>\n      <td>0.000300</td>\n      <td>0.000300</td>\n      <td>-2.913504</td>\n      <td>0.165653</td>\n      <td>67.830821</td>\n      <td>1.598232e-15</td>\n      <td>5.764785e-14</td>\n    </tr>\n    <tr>\n      <th>ENSG00000169418</th>\n      <td>0.650499</td>\n      <td>0.846154</td>\n      <td>0.040232</td>\n      <td>NPR1</td>\n      <td>t-3</td>\n      <td>38</td>\n      <td>0.000367</td>\n      <td>0.000367</td>\n      <td>3.334806</td>\n      <td>-1.301356</td>\n      <td>11.662977</td>\n      <td>6.899612e-04</td>\n      <td>1.984084e-03</td>\n    </tr>\n    <tr>\n      <th>ENSG00000006555</th>\n      <td>0.641708</td>\n      <td>0.461538</td>\n      <td>0.412869</td>\n      <td>TTC22</td>\n      <td>t-8</td>\n      <td>544</td>\n      <td>0.000200</td>\n      <td>0.000200</td>\n      <td>-3.682236</td>\n      <td>-0.707274</td>\n      <td>153.202518</td>\n      <td>7.581605e-31</td>\n      <td>1.849741e-28</td>\n    </tr>\n    <tr>\n      <th>ENSG00000180596</th>\n      <td>0.641191</td>\n      <td>0.692308</td>\n      <td>0.181394</td>\n      <td>HIST1H2BC</td>\n      <td>t-5</td>\n      <td>107</td>\n      <td>0.000300</td>\n      <td>0.000300</td>\n      <td>3.781744</td>\n      <td>4.417731</td>\n      <td>62.051190</td>\n      <td>2.138127e-14</td>\n      <td>6.539056e-13</td>\n    </tr>\n    <tr>\n      <th>ENSG00000107679</th>\n      <td>0.630767</td>\n      <td>0.615385</td>\n      <td>0.244113</td>\n      <td>PLEKHA1</td>\n      <td>t-6</td>\n      <td>233</td>\n      <td>0.000267</td>\n      <td>0.000267</td>\n      <td>-2.447003</td>\n      <td>2.513195</td>\n      <td>85.438031</td>\n      <td>7.082981e-19</td>\n      <td>4.005204e-17</td>\n    </tr>\n    <tr>\n      <th>ENSG00000115884</th>\n      <td>0.628934</td>\n      <td>0.461538</td>\n      <td>0.395462</td>\n      <td>SDC1</td>\n      <td>t-8</td>\n      <td>544</td>\n      <td>0.000200</td>\n      <td>0.000200</td>\n      <td>-3.623720</td>\n      <td>-0.775661</td>\n      <td>145.810777</td>\n      <td>1.320573e-29</td>\n      <td>2.730945e-27</td>\n    </tr>\n    <tr>\n      <th>ENSG00000112796</th>\n      <td>0.623953</td>\n      <td>0.384615</td>\n      <td>0.465599</td>\n      <td>ENPP5</td>\n      <td>t-9</td>\n      <td>757</td>\n      <td>0.000167</td>\n      <td>0.000167</td>\n      <td>-4.591047</td>\n      <td>-1.731787</td>\n      <td>175.845505</td>\n      <td>1.467533e-34</td>\n      <td>5.311004e-32</td>\n    </tr>\n    <tr>\n      <th>ENSG00000135605</th>\n      <td>0.612302</td>\n      <td>0.615385</td>\n      <td>0.218953</td>\n      <td>TEC</td>\n      <td>t-6</td>\n      <td>233</td>\n      <td>0.000267</td>\n      <td>0.000267</td>\n      <td>2.115200</td>\n      <td>4.824003</td>\n      <td>76.015323</td>\n      <td>4.273674e-17</td>\n      <td>1.961915e-15</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 8
    }
   ],
   "source": [
    "all_scores = all_scores.sort_values('overall_score', ascending=False)\n",
    "cols = all_scores.columns.tolist()\n",
    "first_four = ['overall_score', 'norm_score-me', 'norm_score-them', 'GeneName']\n",
    "cols_new = first_four + [x for x in filter(lambda y: y not in first_four, cols)]\n",
    "all_scores = all_scores[cols_new]\n",
    "all_scores[:20]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While quite a few of these are involved in various cancers, cell proliferation, etc., only the fourth (GIMAP4) explicitly *may* have anything to do with AML; from its summary at NCBI: \"...The encoded protein of this gene may be negatively regulated by T-cell acute lymphocytic leukemia 1 (TAL1)....\" (I recognize this is ALL not AML).\n",
    "\n",
    "Based on this and the fact that my \"most promising gene\" (NOL3, i.e., ARC [ENSG00000140939], tied for second most important) is not in their list while their *potentially* \"most promising gene\" (MYBL1 [ENSG00000185697], their most differentially expressed gene) is not in my list..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "My \"most promising\" gene, ENSG00000140939, is NOT in their list\nTheir \"most promising\" gene, ENSG00000185697, is NOT in my list\n"
    }
   ],
   "source": [
    "# See whether my most promising gene is in their list\n",
    "my_most_promising_gene = 'ENSG00000140939'\n",
    "try:\n",
    "    their_scores.loc[my_most_promising_gene,:]\n",
    "    print('My \"most promising\" gene, ' + my_most_promising_gene + ', IS in their list')\n",
    "except:\n",
    "    print('My \"most promising\" gene, ' + my_most_promising_gene + ', is NOT in their list')\n",
    "\n",
    "# See whether their most promising gene is in my list\n",
    "their_most_promising_gene = 'ENSG00000185697'\n",
    "try:\n",
    "    my_scores.loc[their_most_promising_gene,:]\n",
    "    print('Their \"most promising\" gene, ' + their_most_promising_gene + ', IS in my list')\n",
    "except:\n",
    "    print('Their \"most promising\" gene, ' + their_most_promising_gene + ', is NOT in my list')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "...I am developing the feeling that our lists either fundamentally do not agree or that they are not on \"equal footing\" because they seem to be using GRCh37 while we use GRCh38. (I am unsure how much that actually matters!) Note again that I did not run the most rigorous feature-importance (i.e., gene-importance) study."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Write a function to get the current gene names from a list of Ensembl IDs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This is based off the example at https://rest.ensembl.org/documentation/info/lookup_post\n",
    "def get_display_names(list_of_ensembl_ids):\n",
    "    string_version_of_list = str(list_of_ensembl_ids).replace('\\'', '\"')\n",
    "    import requests, sys\n",
    "    server = \"https://rest.ensembl.org\"\n",
    "    ext = \"/lookup/id\"\n",
    "    headers={ \"Content-Type\" : \"application/json\", \"Accept\" : \"application/json\"}\n",
    "    r = requests.post(server+ext, headers=headers, data='{ \"ids\" : ' + string_version_of_list + ' }')\n",
    "    if not r.ok:\n",
    "        r.raise_for_status()\n",
    "        sys.exit()\n",
    "    decoded = r.json()\n",
    "    return([decoded[x]['display_name'] if decoded[x] is not None else None for x in list_of_ensembl_ids]) # return the display names in the same order as the inputted Ensembl IDs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get the gene names for my most important genes and add them to my dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "On call #1 to the Ensembl REST API...\nOn call #2 to the Ensembl REST API...\nOn call #3 to the Ensembl REST API...\nOn call #4 to the Ensembl REST API...\nOn call #5 to the Ensembl REST API...\nOn call #6 to the Ensembl REST API...\nOn call #7 to the Ensembl REST API...\n"
    }
   ],
   "source": [
    "chunk_size = 1000 # maximum call to POST method for the Ensembl REST API\n",
    "full_list = my_scores.index.to_list()\n",
    "full_list_size = len(full_list)\n",
    "ncalls = int(np.ceil(full_list_size/chunk_size))\n",
    "start_ind = 0\n",
    "display_name_list = []\n",
    "for icall in range(ncalls):\n",
    "    print('On call #{} to the Ensembl REST API...'.format(icall+1))\n",
    "    if icall != ncalls-1:\n",
    "        end_ind = start_ind + chunk_size\n",
    "    else:\n",
    "        end_ind = full_list_size\n",
    "    display_name_list = display_name_list + get_display_names(full_list[start_ind:end_ind])\n",
    "    start_ind = end_ind\n",
    "my_scores['display_name'] = display_name_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Write the scores to disk in order to give my larger list of important gene names to Daoud's group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_scores.to_csv(os.path.join(project_directory, 'data', 'andrews_aml_vs_normal_scores.tsv'), sep='\\t', float_format='%8.6f')"
   ]
  }
 ]
}