Mechanism Enrichment Score Percentile Calculation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mechanism Enrichment Score Percentile Calculation\n",
    "\n",
    "This notebook executes simple statistics over the NeuroMMSig mechanism enrichment scores presented in [Step 2](https://github.com/cthoyt/epicom#step-2-1) of the comparitive mechanism enrichment analysis presented in Hoyt and Domingo-Fernandez *et al.*, 2018."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "import time\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%config InlineBackend.figure_format = 'svg'\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.6.4 (default, Mar 22 2018, 13:54:22) \n",
      "[GCC 4.2.1 Compatible Apple LLVM 9.0.0 (clang-900.0.39.2)]\n"
     ]
    }
   ],
   "source": [
    "print(sys.version)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Thu Apr 19 14:26:55 2018\n"
     ]
    }
   ],
   "source": [
    "print(time.asctime())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data\n",
    "\n",
    "Below is the original table from which the scores are extracted. \n",
    "\n",
    "<img src=\"screenshots/comparison-step2.png\" alt=\"Drawing\" style=\"width: 600px;\"/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores = [\n",
    "    0.75,\n",
    "    0.72,\n",
    "    0.48,\n",
    "    0.46,\n",
    "    0.33,\n",
    "    0.20,\n",
    "    0.14,\n",
    "    0.08,\n",
    "    0.05,\n",
    "    0.03,\n",
    "    0.02,\n",
    "    0.01,\n",
    "]\n",
    "\n",
    "scores = pd.Series(scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis\n",
    "\n",
    "Since the NeuroMMSig mechanism enrichment scores are only meant for ranking, their absolute values aren't so useful for comparion. Therefore, the 90th percentile was used as a cutoff to identify the most relevant subgraphs. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6960000000000001"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ninety_percentile = np.percentile(scores, 90)\n",
    "ninety_percentile"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A plot of the mechanism enrichment scores shows a pseudo-exponential distribution. The 90th percentile as well as the KDE plot are drawn to assist in interpretation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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    "plt.title('Distribution of Mechanism Enrichment Scores')\n",
    "plt.xlabel('Mechanism Enrichment Score')\n",
    "plt.ylabel('Frequency')\n",
    "sns.distplot(scores)\n",
    "plt.axvline(ninety_percentile)\n",
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