Jupyter_notebooks/Figure_6/Nucleotide_analyses.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### To run this notebook, please make sure the following packages are installed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: numpy in /anaconda3/lib/python3.7/site-packages (1.18.2)\n",
      "Requirement already satisfied: pandas in /anaconda3/lib/python3.7/site-packages (0.25.1)\n",
      "Requirement already satisfied: pytz>=2017.2 in /anaconda3/lib/python3.7/site-packages (from pandas) (2018.7)\n",
      "Requirement already satisfied: numpy>=1.13.3 in /anaconda3/lib/python3.7/site-packages (from pandas) (1.18.2)\n",
      "Requirement already satisfied: python-dateutil>=2.6.1 in /anaconda3/lib/python3.7/site-packages (from pandas) (2.7.5)\n",
      "Requirement already satisfied: six>=1.5 in /anaconda3/lib/python3.7/site-packages (from python-dateutil>=2.6.1->pandas) (1.12.0)\n",
      "Requirement already satisfied: matplotlib in /anaconda3/lib/python3.7/site-packages (3.2.1)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in /anaconda3/lib/python3.7/site-packages (from matplotlib) (2.7.5)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /anaconda3/lib/python3.7/site-packages (from matplotlib) (1.0.1)\n",
      "Requirement already satisfied: numpy>=1.11 in /anaconda3/lib/python3.7/site-packages (from matplotlib) (1.18.2)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /anaconda3/lib/python3.7/site-packages (from matplotlib) (2.3.0)\n",
      "Requirement already satisfied: cycler>=0.10 in /anaconda3/lib/python3.7/site-packages (from matplotlib) (0.10.0)\n",
      "Requirement already satisfied: six>=1.5 in /anaconda3/lib/python3.7/site-packages (from python-dateutil>=2.1->matplotlib) (1.12.0)\n",
      "Requirement already satisfied: setuptools in /anaconda3/lib/python3.7/site-packages (from kiwisolver>=1.0.1->matplotlib) (40.6.3)\n",
      "Requirement already satisfied: seaborn in /anaconda3/lib/python3.7/site-packages (0.9.0)\n",
      "Requirement already satisfied: numpy>=1.9.3 in /anaconda3/lib/python3.7/site-packages (from seaborn) (1.18.2)\n",
      "Requirement already satisfied: scipy>=0.14.0 in /anaconda3/lib/python3.7/site-packages (from seaborn) (1.1.0)\n",
      "Requirement already satisfied: matplotlib>=1.4.3 in /anaconda3/lib/python3.7/site-packages (from seaborn) (3.2.1)\n",
      "Requirement already satisfied: pandas>=0.15.2 in /anaconda3/lib/python3.7/site-packages (from seaborn) (0.25.1)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /anaconda3/lib/python3.7/site-packages (from matplotlib>=1.4.3->seaborn) (1.0.1)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in /anaconda3/lib/python3.7/site-packages (from matplotlib>=1.4.3->seaborn) (2.7.5)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /anaconda3/lib/python3.7/site-packages (from matplotlib>=1.4.3->seaborn) (2.3.0)\n",
      "Requirement already satisfied: cycler>=0.10 in /anaconda3/lib/python3.7/site-packages (from matplotlib>=1.4.3->seaborn) (0.10.0)\n",
      "Requirement already satisfied: pytz>=2017.2 in /anaconda3/lib/python3.7/site-packages (from pandas>=0.15.2->seaborn) (2018.7)\n",
      "Requirement already satisfied: setuptools in /anaconda3/lib/python3.7/site-packages (from kiwisolver>=1.0.1->matplotlib>=1.4.3->seaborn) (40.6.3)\n",
      "Requirement already satisfied: six>=1.5 in /anaconda3/lib/python3.7/site-packages (from python-dateutil>=2.1->matplotlib>=1.4.3->seaborn) (1.12.0)\n",
      "Requirement already satisfied: scipy in /anaconda3/lib/python3.7/site-packages (1.1.0)\n",
      "Requirement already satisfied: pyCRAC in /anaconda3/lib/python3.7/site-packages (1.4.7)\n",
      "Requirement already satisfied: cython>=0.19 in /anaconda3/lib/python3.7/site-packages (from pyCRAC) (0.29.2)\n",
      "Requirement already satisfied: six>=1.9.0 in /anaconda3/lib/python3.7/site-packages (from pyCRAC) (1.12.0)\n",
      "Requirement already satisfied: pysam>=0.6 in /anaconda3/lib/python3.7/site-packages (from pyCRAC) (0.15.4)\n",
      "Requirement already satisfied: numpy>=1.5.1 in /anaconda3/lib/python3.7/site-packages (from pyCRAC) (1.18.2)\n"
     ]
    }
   ],
   "source": [
    "# REQUIRED PACKAGES\n",
    "! pip install numpy\n",
    "! pip install pandas\n",
    "! pip install matplotlib\n",
    "! pip install seaborn\n",
    "! pip install scipy\n",
    "! pip install pyCRAC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Bad key \"text.kerning_factor\" on line 4 in\n",
      "/anaconda3/lib/python3.7/site-packages/matplotlib/mpl-data/stylelib/_classic_test_patch.mplstyle.\n",
      "You probably need to get an updated matplotlibrc file from\n",
      "http://github.com/matplotlib/matplotlib/blob/master/matplotlibrc.template\n",
      "or from the matplotlib source distribution\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import math\n",
    "import sys\n",
    "import glob\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy.special import comb\n",
    "from scipy.stats import hypergeom,chisquare,fisher_exact\n",
    "from matplotlib import rcParams\n",
    "from collections import defaultdict\n",
    "from pyCRAC.Methods import sortbyvalue,contigousarray2Intervals\n",
    "\n",
    "rcParams['font.family'] = 'sans-serif'\n",
    "rcParams['font.sans-serif'] = ['Arial']\n",
    "rcParams['pdf.fonttype'] = 42\n",
    "rcParams['axes.formatter.useoffset'] = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading the big dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('../../Data/New_data_table_Xist.txt',\\\n",
    "                    sep=\"\\t\",\\\n",
    "                    header=0,\\\n",
    "                    index_col=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Removing data with no coverage:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2517\n"
     ]
    }
   ],
   "source": [
    "positionstomask = data[(data[\"SHAPE_reactivity_ex_vivo_1\"] < -900) | \n",
    "                       (data[\"SHAPE_reactivity_ex_vivo_2\"] < -900) |\n",
    "                       (data[\"SHAPE_reactivity_in_cell_1\"] < -900) | \n",
    "                       (data[\"SHAPE_reactivity_in_cell_2\"] < -900)].index\n",
    "print(len(positionstomask))\n",
    "data.loc[positionstomask,data.columns[11:]] = np.nan"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking what nucleotides were found to be modified in the ex_vivo and in_vivo data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "samples  = ['Xist_sequence','diffBUM_HMM_ex_vivo_norm','diffBUM_HMM_ex_vivo','diffBUM_HMM_in_vivo_norm','diffBUM_HMM_in_vivo',\n",
    "            'diffBUM_HMM_ex_vivo_norm_unique','diffBUM_HMM_ex_vivo_unique',\n",
    "            'diffBUM_HMM_in_vivo_norm_unique','diffBUM_HMM_in_vivo_unique',\n",
    "            'deltaSHAPE_ex_vivo_1','deltaSHAPE_ex_vivo_2','deltaSHAPE_in_vivo_1','deltaSHAPE_in_vivo_2']\n",
    "nucleotides = [\"A\",\"T\",\"C\",\"G\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "modinfo = pd.DataFrame(0,index=nucleotides,columns=samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Xist_sequence', 'diffBUM_HMM_ex_vivo_norm', 'diffBUM_HMM_ex_vivo',\n",
       "       'diffBUM_HMM_in_vivo_norm', 'diffBUM_HMM_in_vivo',\n",
       "       'diffBUM_HMM_ex_vivo_norm_unique', 'diffBUM_HMM_ex_vivo_unique',\n",
       "       'diffBUM_HMM_in_vivo_norm_unique', 'diffBUM_HMM_in_vivo_unique',\n",
       "       'deltaSHAPE_ex_vivo_1', 'deltaSHAPE_ex_vivo_2', 'deltaSHAPE_in_vivo_1',\n",
       "       'deltaSHAPE_in_vivo_2'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modinfo.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Xist sequence:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in data.nucleotide:\n",
    "    modinfo.loc[i,'Xist_sequence'] += 1 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setting a threshold for calling DRNs in the diffBUM-HMM data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "threshold = 0.95"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### First I need to figure out how long the stretches of DRNs are in the diffBUM-HMM and deltaSHAPE data:\n",
    "This will produce the data for Figure 6A."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    " intervallengthcounter = defaultdict(lambda: defaultdict(int))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### diffBUM-HMM:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "849\n",
      "1164\n",
      "127\n",
      "142\n"
     ]
    }
   ],
   "source": [
    "### How many DRNs are there in the data?:\n",
    "ex_vivo_pos = data[data.ex_vivo >= threshold].index\n",
    "\n",
    "### contigousarray2Intervals groups the DRNs that are next to each other in intervals.\n",
    "intervals = contigousarray2Intervals(ex_vivo_pos)\n",
    "\n",
    "### This prints the number of intervals that were detected and how many DRNs there were in the data.\n",
    "print(len(intervals))\n",
    "print(len(ex_vivo_pos))\n",
    "\n",
    "for (i,j) in intervals:\n",
    "    length = (j-i)+1\n",
    "    intervallengthcounter[\"diffBUM_HMM_ex_vivo\"][length] += 1\n",
    "        \n",
    "in_vivo_pos = data[data.in_vivo >= threshold].index\n",
    "intervals = contigousarray2Intervals(in_vivo_pos)\n",
    "\n",
    "print(len(intervals))\n",
    "print(len(in_vivo_pos))\n",
    "\n",
    "for (i,j) in intervals:\n",
    "    length = (j-i)+1\n",
    "    intervallengthcounter[\"diffBUM_HMM_in_vivo\"][length] += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### deltaSHAPE rep 1:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "162\n",
      "460\n",
      "120\n",
      "337\n"
     ]
    }
   ],
   "source": [
    "ex_vivo_pos = data[data.deltaSHAPE_rep1 > 0].index\n",
    "intervals = contigousarray2Intervals(ex_vivo_pos)\n",
    "\n",
    "print(len(intervals))\n",
    "print(len(ex_vivo_pos))\n",
    "\n",
    "\n",
    "for (i,j) in intervals:\n",
    "    length = (j-i)+1\n",
    "    intervallengthcounter[\"deltaSHAPE_rep1_ex_vivo\"][length] += 1\n",
    "    \n",
    "in_vivo_pos = data[data.deltaSHAPE_rep1 < 0].index\n",
    "intervals = contigousarray2Intervals(in_vivo_pos)\n",
    "\n",
    "print(len(intervals))\n",
    "print(len(in_vivo_pos))\n",
    "\n",
    "count = 0\n",
    "\n",
    "for (i,j) in intervals:\n",
    "    length = (j-i)+1\n",
    "    intervallengthcounter[\"deltaSHAPE_rep1_in_vivo\"][length] += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### deltaSHAPE rep 2:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "186\n",
      "510\n",
      "166\n",
      "428\n"
     ]
    }
   ],
   "source": [
    "ex_vivo_pos = data[data.deltaSHAPE_rep2 > 0].index\n",
    "intervals = contigousarray2Intervals(ex_vivo_pos)\n",
    "\n",
    "print(len(intervals))\n",
    "print(len(ex_vivo_pos))\n",
    "\n",
    "\n",
    "for (i,j) in intervals:\n",
    "    length = (j-i)+1\n",
    "    intervallengthcounter[\"deltaSHAPE_rep2_ex_vivo\"][length] += 1\n",
    "        \n",
    "in_vivo_pos = data[data.deltaSHAPE_rep2 < 0].index\n",
    "intervals = contigousarray2Intervals(in_vivo_pos)\n",
    "\n",
    "print(len(intervals))\n",
    "print(len(in_vivo_pos))\n",
    "\n",
    "count = 0\n",
    "\n",
    "for (i,j) in intervals:\n",
    "    length = (j-i)+1\n",
    "    intervallengthcounter[\"deltaSHAPE_rep2_in_vivo\"][length] += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now making the plots for Figure 6A:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "numberofplots = len(intervallengthcounter.keys())\n",
    "samples = list(intervallengthcounter.keys())\n",
    "fig,ax = plt.subplots(numberofplots,figsize=[3,10],sharex=True)\n",
    "for i in range(numberofplots):\n",
    "    sample = samples[i]\n",
    "    counts = intervallengthcounter[sample]\n",
    "    x = list(counts.keys())\n",
    "    y = list(counts.values())\n",
    "    sns.barplot(x,y,ax=ax[i],color=\"blue\")\n",
    "    ax[i].set_title(sample)\n",
    "    ax[i].set_ylabel('Counts')\n",
    "plt.tight_layout()\n",
    "fig.savefig(\"Figure_6A.pdf\",dpi=400)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now calculating the nucleotide composition of the DRNs and how frequently they are located in single-stranded regions:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### diffBUM_HMM:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "in_vivo = data[data.in_vivo >= threshold][\"nucleotide\"].values\n",
    "ex_vivo = data[data.ex_vivo >= threshold][\"nucleotide\"].values\n",
    "in_vivo_scaled = data[data.scaled_in_vivo >= threshold][\"nucleotide\"].values\n",
    "ex_vivo_scaled = data[data.scaled_ex_vivo >= threshold][\"nucleotide\"].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "for nuc in in_vivo:\n",
    "    modinfo.loc[nuc,\"diffBUM_HMM_in_vivo\"] += 1\n",
    "for nuc in in_vivo_scaled:\n",
    "    modinfo.loc[nuc,\"diffBUM_HMM_in_vivo_norm\"] += 1\n",
    "for nuc in ex_vivo:\n",
    "    modinfo.loc[nuc,\"diffBUM_HMM_ex_vivo\"] += 1\n",
    "for nuc in ex_vivo_scaled:\n",
    "    modinfo.loc[nuc,\"diffBUM_HMM_ex_vivo_norm\"] += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### diffBUM_HMM unique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "in_vivo_unique = data[(data.deltaSHAPE_rep1 == 0) & (data.deltaSHAPE_rep2 == 0) & \\\n",
    "                      (data.in_vivo >= threshold)][\"nucleotide\"].values\n",
    "ex_vivo_unique = data[(data.deltaSHAPE_rep1 == 0) & (data.deltaSHAPE_rep2 == 0) & \\\n",
    "                      (data.ex_vivo >= threshold)][\"nucleotide\"].values\n",
    "in_vivo_scaled_unique = data[(data.deltaSHAPE_rep1 == 0) & (data.deltaSHAPE_rep2 == 0) & \\\n",
    "                             (data.scaled_in_vivo >= threshold)][\"nucleotide\"].values\n",
    "ex_vivo_scaled_unique = data[(data.deltaSHAPE_rep1 == 0) & (data.deltaSHAPE_rep2 == 0) & \\\n",
    "                             (data.scaled_ex_vivo >= threshold)][\"nucleotide\"].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "for nuc in in_vivo_unique:\n",
    "    modinfo.loc[nuc,\"diffBUM_HMM_in_vivo_unique\"] += 1\n",
    "for nuc in in_vivo_scaled_unique:\n",
    "    modinfo.loc[nuc,\"diffBUM_HMM_in_vivo_norm_unique\"] += 1\n",
    "for nuc in ex_vivo_unique:\n",
    "    modinfo.loc[nuc,\"diffBUM_HMM_ex_vivo_unique\"] += 1\n",
    "for nuc in ex_vivo_scaled_unique:\n",
    "    modinfo.loc[nuc,\"diffBUM_HMM_ex_vivo_norm_unique\"] += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now deltaSHAPE data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "in_vivo_1 = data[data.deltaSHAPE_rep1 > 0][\"nucleotide\"].values\n",
    "ex_vivo_1 = data[data.deltaSHAPE_rep1 < 0][\"nucleotide\"].values\n",
    "in_vivo_2 = data[data.deltaSHAPE_rep2 > 0][\"nucleotide\"].values\n",
    "ex_vivo_2 = data[data.deltaSHAPE_rep2 < 0][\"nucleotide\"].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "for nuc in in_vivo_1:\n",
    "    modinfo.loc[nuc,\"deltaSHAPE_in_vivo_1\"] += 1\n",
    "for nuc in in_vivo_2:\n",
    "    modinfo.loc[nuc,\"deltaSHAPE_in_vivo_2\"] += 1\n",
    "for nuc in ex_vivo_1:\n",
    "    modinfo.loc[nuc,\"deltaSHAPE_ex_vivo_1\"] += 1\n",
    "for nuc in ex_vivo_2:\n",
    "    modinfo.loc[nuc,\"deltaSHAPE_ex_vivo_2\"] += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Xist_sequence</th>\n",
       "      <th>diffBUM_HMM_ex_vivo_norm</th>\n",
       "      <th>diffBUM_HMM_ex_vivo</th>\n",
       "      <th>diffBUM_HMM_in_vivo_norm</th>\n",
       "      <th>diffBUM_HMM_in_vivo</th>\n",
       "      <th>diffBUM_HMM_ex_vivo_norm_unique</th>\n",
       "      <th>diffBUM_HMM_ex_vivo_unique</th>\n",
       "      <th>diffBUM_HMM_in_vivo_norm_unique</th>\n",
       "      <th>diffBUM_HMM_in_vivo_unique</th>\n",
       "      <th>deltaSHAPE_ex_vivo_1</th>\n",
       "      <th>deltaSHAPE_ex_vivo_2</th>\n",
       "      <th>deltaSHAPE_in_vivo_1</th>\n",
       "      <th>deltaSHAPE_in_vivo_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>A</td>\n",
       "      <td>4748</td>\n",
       "      <td>576</td>\n",
       "      <td>420</td>\n",
       "      <td>117</td>\n",
       "      <td>91</td>\n",
       "      <td>501</td>\n",
       "      <td>370</td>\n",
       "      <td>94</td>\n",
       "      <td>72</td>\n",
       "      <td>117</td>\n",
       "      <td>184</td>\n",
       "      <td>95</td>\n",
       "      <td>122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>T</td>\n",
       "      <td>5746</td>\n",
       "      <td>709</td>\n",
       "      <td>478</td>\n",
       "      <td>42</td>\n",
       "      <td>33</td>\n",
       "      <td>597</td>\n",
       "      <td>400</td>\n",
       "      <td>32</td>\n",
       "      <td>26</td>\n",
       "      <td>112</td>\n",
       "      <td>123</td>\n",
       "      <td>214</td>\n",
       "      <td>208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>C</td>\n",
       "      <td>3875</td>\n",
       "      <td>244</td>\n",
       "      <td>137</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>200</td>\n",
       "      <td>110</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>59</td>\n",
       "      <td>65</td>\n",
       "      <td>94</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>G</td>\n",
       "      <td>3549</td>\n",
       "      <td>210</td>\n",
       "      <td>129</td>\n",
       "      <td>19</td>\n",
       "      <td>12</td>\n",
       "      <td>180</td>\n",
       "      <td>118</td>\n",
       "      <td>17</td>\n",
       "      <td>10</td>\n",
       "      <td>49</td>\n",
       "      <td>56</td>\n",
       "      <td>57</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Xist_sequence  diffBUM_HMM_ex_vivo_norm  diffBUM_HMM_ex_vivo  \\\n",
       "A           4748                       576                  420   \n",
       "T           5746                       709                  478   \n",
       "C           3875                       244                  137   \n",
       "G           3549                       210                  129   \n",
       "\n",
       "   diffBUM_HMM_in_vivo_norm  diffBUM_HMM_in_vivo  \\\n",
       "A                       117                   91   \n",
       "T                        42                   33   \n",
       "C                        10                    6   \n",
       "G                        19                   12   \n",
       "\n",
       "   diffBUM_HMM_ex_vivo_norm_unique  diffBUM_HMM_ex_vivo_unique  \\\n",
       "A                              501                         370   \n",
       "T                              597                         400   \n",
       "C                              200                         110   \n",
       "G                              180                         118   \n",
       "\n",
       "   diffBUM_HMM_in_vivo_norm_unique  diffBUM_HMM_in_vivo_unique  \\\n",
       "A                               94                          72   \n",
       "T                               32                          26   \n",
       "C                                7                           4   \n",
       "G                               17                          10   \n",
       "\n",
       "   deltaSHAPE_ex_vivo_1  deltaSHAPE_ex_vivo_2  deltaSHAPE_in_vivo_1  \\\n",
       "A                   117                   184                    95   \n",
       "T                   112                   123                   214   \n",
       "C                    59                    65                    94   \n",
       "G                    49                    56                    57   \n",
       "\n",
       "   deltaSHAPE_in_vivo_2  \n",
       "A                   122  \n",
       "T                   208  \n",
       "C                   128  \n",
       "G                    52  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modinfo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now correct for total number of modifications:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "modinfonorm = modinfo.div(modinfo.sum(axis=0), axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now check if there is a statistical significant difference between the nucleotide frequencies in each dataset using a chi-square test:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Xist_sequence</th>\n",
       "      <th>diffBUM_HMM_ex_vivo_norm</th>\n",
       "      <th>diffBUM_HMM_ex_vivo</th>\n",
       "      <th>diffBUM_HMM_in_vivo_norm</th>\n",
       "      <th>diffBUM_HMM_in_vivo</th>\n",
       "      <th>diffBUM_HMM_ex_vivo_norm_unique</th>\n",
       "      <th>diffBUM_HMM_ex_vivo_unique</th>\n",
       "      <th>diffBUM_HMM_in_vivo_norm_unique</th>\n",
       "      <th>diffBUM_HMM_in_vivo_unique</th>\n",
       "      <th>deltaSHAPE_ex_vivo_1</th>\n",
       "      <th>deltaSHAPE_ex_vivo_2</th>\n",
       "      <th>deltaSHAPE_in_vivo_1</th>\n",
       "      <th>deltaSHAPE_in_vivo_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>A</td>\n",
       "      <td>0.264985</td>\n",
       "      <td>0.331225</td>\n",
       "      <td>0.360825</td>\n",
       "      <td>0.622340</td>\n",
       "      <td>0.640845</td>\n",
       "      <td>0.338972</td>\n",
       "      <td>0.370741</td>\n",
       "      <td>0.626667</td>\n",
       "      <td>0.642857</td>\n",
       "      <td>0.347181</td>\n",
       "      <td>0.429907</td>\n",
       "      <td>0.206522</td>\n",
       "      <td>0.239216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>T</td>\n",
       "      <td>0.320683</td>\n",
       "      <td>0.407706</td>\n",
       "      <td>0.410653</td>\n",
       "      <td>0.223404</td>\n",
       "      <td>0.232394</td>\n",
       "      <td>0.403924</td>\n",
       "      <td>0.400802</td>\n",
       "      <td>0.213333</td>\n",
       "      <td>0.232143</td>\n",
       "      <td>0.332344</td>\n",
       "      <td>0.287383</td>\n",
       "      <td>0.465217</td>\n",
       "      <td>0.407843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>C</td>\n",
       "      <td>0.216263</td>\n",
       "      <td>0.140311</td>\n",
       "      <td>0.117698</td>\n",
       "      <td>0.053191</td>\n",
       "      <td>0.042254</td>\n",
       "      <td>0.135318</td>\n",
       "      <td>0.110220</td>\n",
       "      <td>0.046667</td>\n",
       "      <td>0.035714</td>\n",
       "      <td>0.175074</td>\n",
       "      <td>0.151869</td>\n",
       "      <td>0.204348</td>\n",
       "      <td>0.250980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>G</td>\n",
       "      <td>0.198069</td>\n",
       "      <td>0.120759</td>\n",
       "      <td>0.110825</td>\n",
       "      <td>0.101064</td>\n",
       "      <td>0.084507</td>\n",
       "      <td>0.121786</td>\n",
       "      <td>0.118236</td>\n",
       "      <td>0.113333</td>\n",
       "      <td>0.089286</td>\n",
       "      <td>0.145401</td>\n",
       "      <td>0.130841</td>\n",
       "      <td>0.123913</td>\n",
       "      <td>0.101961</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Xist_sequence  diffBUM_HMM_ex_vivo_norm  diffBUM_HMM_ex_vivo  \\\n",
       "A       0.264985                  0.331225             0.360825   \n",
       "T       0.320683                  0.407706             0.410653   \n",
       "C       0.216263                  0.140311             0.117698   \n",
       "G       0.198069                  0.120759             0.110825   \n",
       "\n",
       "   diffBUM_HMM_in_vivo_norm  diffBUM_HMM_in_vivo  \\\n",
       "A                  0.622340             0.640845   \n",
       "T                  0.223404             0.232394   \n",
       "C                  0.053191             0.042254   \n",
       "G                  0.101064             0.084507   \n",
       "\n",
       "   diffBUM_HMM_ex_vivo_norm_unique  diffBUM_HMM_ex_vivo_unique  \\\n",
       "A                         0.338972                    0.370741   \n",
       "T                         0.403924                    0.400802   \n",
       "C                         0.135318                    0.110220   \n",
       "G                         0.121786                    0.118236   \n",
       "\n",
       "   diffBUM_HMM_in_vivo_norm_unique  diffBUM_HMM_in_vivo_unique  \\\n",
       "A                         0.626667                    0.642857   \n",
       "T                         0.213333                    0.232143   \n",
       "C                         0.046667                    0.035714   \n",
       "G                         0.113333                    0.089286   \n",
       "\n",
       "   deltaSHAPE_ex_vivo_1  deltaSHAPE_ex_vivo_2  deltaSHAPE_in_vivo_1  \\\n",
       "A              0.347181              0.429907              0.206522   \n",
       "T              0.332344              0.287383              0.465217   \n",
       "C              0.175074              0.151869              0.204348   \n",
       "G              0.145401              0.130841              0.123913   \n",
       "\n",
       "   deltaSHAPE_in_vivo_2  \n",
       "A              0.239216  \n",
       "T              0.407843  \n",
       "C              0.250980  \n",
       "G              0.101961  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modinfonorm.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "diffBUM_HMM_ex_vivo_norm:\t0.9921921939576517\n",
      "diffBUM_HMM_ex_vivo:\t0.9861833528301358\n",
      "diffBUM_HMM_in_vivo_norm:\t0.8774512930475329\n",
      "diffBUM_HMM_in_vivo:\t0.8584003893945844\n",
      "diffBUM_HMM_ex_vivo_norm_unique:\t0.9916034877939462\n",
      "diffBUM_HMM_ex_vivo_unique:\t0.9857397075770127\n",
      "diffBUM_HMM_in_vivo_norm_unique:\t0.8734742568696059\n",
      "diffBUM_HMM_in_vivo_unique:\t0.8557288380621534\n",
      "deltaSHAPE_ex_vivo_1:\t0.99726258943411\n",
      "deltaSHAPE_ex_vivo_2:\t0.9854985448954785\n",
      "deltaSHAPE_in_vivo_1:\t0.9910509186704817\n",
      "deltaSHAPE_in_vivo_2:\t0.9942966910237633\n"
     ]
    }
   ],
   "source": [
    "for i in modinfonorm.columns[1:]:\n",
    "    f_exp = modinfonorm['Xist_sequence'].values\n",
    "    f_obs = modinfonorm[i].values\n",
    "    #print(f_exp,f_obs)\n",
    "    p_value = chisquare(f_obs,f_exp)[1]\n",
    "    print(\"%s:\\t%s\" % (i,p_value))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### So the difference is not statistically significant but the sample size for this test is too small. Should be at least 5. There must be a better approach."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now making the heatmap:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Xist_sequence</th>\n",
       "      <th>diffBUM_HMM_ex_vivo_norm</th>\n",
       "      <th>diffBUM_HMM_ex_vivo</th>\n",
       "      <th>diffBUM_HMM_in_vivo_norm</th>\n",
       "      <th>diffBUM_HMM_in_vivo</th>\n",
       "      <th>diffBUM_HMM_ex_vivo_norm_unique</th>\n",
       "      <th>diffBUM_HMM_ex_vivo_unique</th>\n",
       "      <th>diffBUM_HMM_in_vivo_norm_unique</th>\n",
       "      <th>diffBUM_HMM_in_vivo_unique</th>\n",
       "      <th>deltaSHAPE_ex_vivo_1</th>\n",
       "      <th>deltaSHAPE_ex_vivo_2</th>\n",
       "      <th>deltaSHAPE_in_vivo_1</th>\n",
       "      <th>deltaSHAPE_in_vivo_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>A</td>\n",
       "      <td>0.264985</td>\n",
       "      <td>0.331225</td>\n",
       "      <td>0.360825</td>\n",
       "      <td>0.622340</td>\n",
       "      <td>0.640845</td>\n",
       "      <td>0.338972</td>\n",
       "      <td>0.370741</td>\n",
       "      <td>0.626667</td>\n",
       "      <td>0.642857</td>\n",
       "      <td>0.347181</td>\n",
       "      <td>0.429907</td>\n",
       "      <td>0.206522</td>\n",
       "      <td>0.239216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>T</td>\n",
       "      <td>0.320683</td>\n",
       "      <td>0.407706</td>\n",
       "      <td>0.410653</td>\n",
       "      <td>0.223404</td>\n",
       "      <td>0.232394</td>\n",
       "      <td>0.403924</td>\n",
       "      <td>0.400802</td>\n",
       "      <td>0.213333</td>\n",
       "      <td>0.232143</td>\n",
       "      <td>0.332344</td>\n",
       "      <td>0.287383</td>\n",
       "      <td>0.465217</td>\n",
       "      <td>0.407843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>C</td>\n",
       "      <td>0.216263</td>\n",
       "      <td>0.140311</td>\n",
       "      <td>0.117698</td>\n",
       "      <td>0.053191</td>\n",
       "      <td>0.042254</td>\n",
       "      <td>0.135318</td>\n",
       "      <td>0.110220</td>\n",
       "      <td>0.046667</td>\n",
       "      <td>0.035714</td>\n",
       "      <td>0.175074</td>\n",
       "      <td>0.151869</td>\n",
       "      <td>0.204348</td>\n",
       "      <td>0.250980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>G</td>\n",
       "      <td>0.198069</td>\n",
       "      <td>0.120759</td>\n",
       "      <td>0.110825</td>\n",
       "      <td>0.101064</td>\n",
       "      <td>0.084507</td>\n",
       "      <td>0.121786</td>\n",
       "      <td>0.118236</td>\n",
       "      <td>0.113333</td>\n",
       "      <td>0.089286</td>\n",
       "      <td>0.145401</td>\n",
       "      <td>0.130841</td>\n",
       "      <td>0.123913</td>\n",
       "      <td>0.101961</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Xist_sequence  diffBUM_HMM_ex_vivo_norm  diffBUM_HMM_ex_vivo  \\\n",
       "A       0.264985                  0.331225             0.360825   \n",
       "T       0.320683                  0.407706             0.410653   \n",
       "C       0.216263                  0.140311             0.117698   \n",
       "G       0.198069                  0.120759             0.110825   \n",
       "\n",
       "   diffBUM_HMM_in_vivo_norm  diffBUM_HMM_in_vivo  \\\n",
       "A                  0.622340             0.640845   \n",
       "T                  0.223404             0.232394   \n",
       "C                  0.053191             0.042254   \n",
       "G                  0.101064             0.084507   \n",
       "\n",
       "   diffBUM_HMM_ex_vivo_norm_unique  diffBUM_HMM_ex_vivo_unique  \\\n",
       "A                         0.338972                    0.370741   \n",
       "T                         0.403924                    0.400802   \n",
       "C                         0.135318                    0.110220   \n",
       "G                         0.121786                    0.118236   \n",
       "\n",
       "   diffBUM_HMM_in_vivo_norm_unique  diffBUM_HMM_in_vivo_unique  \\\n",
       "A                         0.626667                    0.642857   \n",
       "T                         0.213333                    0.232143   \n",
       "C                         0.046667                    0.035714   \n",
       "G                         0.113333                    0.089286   \n",
       "\n",
       "   deltaSHAPE_ex_vivo_1  deltaSHAPE_ex_vivo_2  deltaSHAPE_in_vivo_1  \\\n",
       "A              0.347181              0.429907              0.206522   \n",
       "T              0.332344              0.287383              0.465217   \n",
       "C              0.175074              0.151869              0.204348   \n",
       "G              0.145401              0.130841              0.123913   \n",
       "\n",
       "   deltaSHAPE_in_vivo_2  \n",
       "A              0.239216  \n",
       "T              0.407843  \n",
       "C              0.250980  \n",
       "G              0.101961  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modinfonorm.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now adding data for how many of the DRNs are located in regions predicted to be single-stranded in Xist:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "totalss = len(data[data.base_paired_to == 0].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "deltaSHAPE_ex_vivo_rep1_total = len(data[data.deltaSHAPE_rep1 > 0].index)\n",
    "deltaSHAPE_ex_vivo_rep2_total = len(data[data.deltaSHAPE_rep2 > 0].index)\n",
    "deltaSHAPE_in_vivo_rep1_total = len(data[data.deltaSHAPE_rep1 < 0].index)\n",
    "deltaSHAPE_in_vivo_rep2_total = len(data[data.deltaSHAPE_rep2 < 0].index)\n",
    "in_vivo_total = len(data[(data.in_vivo >= threshold)].index)\n",
    "ex_vivo_total = len(data[(data.ex_vivo >= threshold)].index)\n",
    "in_vivo_unique_total = len(data[(data.deltaSHAPE_rep1 == 0) & (data.deltaSHAPE_rep2 == 0) & \\\n",
    "                      (data.in_vivo >= threshold)].index)\n",
    "ex_vivo_unique_total = len(data[(data.deltaSHAPE_rep1 == 0) & (data.deltaSHAPE_rep2 == 0) & \\\n",
    "                      (data.ex_vivo >= threshold)].index)\n",
    "in_vivo_scaled_total = len(data[(data.scaled_in_vivo >= threshold)].index)\n",
    "ex_vivo_scaled_total = len(data[(data.scaled_ex_vivo >= threshold)].index)\n",
    "in_vivo_scaled_unique_total = len(data[(data.deltaSHAPE_rep1 == 0) & (data.deltaSHAPE_rep2 == 0) & \\\n",
    "                             (data.scaled_in_vivo >= threshold)].index)\n",
    "ex_vivo_scaled_unique_total = len(data[(data.deltaSHAPE_rep1 == 0) & (data.deltaSHAPE_rep2 == 0) & \\\n",
    "                             (data.scaled_ex_vivo >= threshold)].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "deltaSHAPE_ex_vivo_rep1 = len(data[(data.deltaSHAPE_rep1 > 0) & (data.base_paired_to == 0)].index)\n",
    "deltaSHAPE_ex_vivo_rep2 = len(data[(data.deltaSHAPE_rep2 > 0) & (data.base_paired_to == 0)].index)\n",
    "deltaSHAPE_in_vivo_rep1 = len(data[(data.deltaSHAPE_rep1 < 0) & (data.base_paired_to == 0)].index)\n",
    "deltaSHAPE_in_vivo_rep2 = len(data[(data.deltaSHAPE_rep2 < 0) & (data.base_paired_to == 0)].index)\n",
    "in_vivo = len(data[(data.in_vivo >= threshold) & (data.base_paired_to == 0)].index)\n",
    "ex_vivo = len(data[(data.ex_vivo >= threshold) & (data.base_paired_to == 0)].index)\n",
    "in_vivo_unique = len(data[(data.deltaSHAPE_rep1 == 0) & (data.deltaSHAPE_rep2 == 0) & \\\n",
    "                      (data.in_vivo >= threshold) & (data.base_paired_to == 0)].index)\n",
    "ex_vivo_unique = len(data[(data.deltaSHAPE_rep1 == 0) & (data.deltaSHAPE_rep2 == 0) & \\\n",
    "                      (data.ex_vivo >= threshold) & (data.base_paired_to == 0)].index)\n",
    "in_vivo_scaled = len(data[(data.scaled_in_vivo >= threshold) & (data.base_paired_to == 0)].index)\n",
    "ex_vivo_scaled = len(data[(data.scaled_ex_vivo >= threshold) & (data.base_paired_to == 0)].index)\n",
    "in_vivo_scaled_unique = len(data[(data.deltaSHAPE_rep1 == 0) & (data.deltaSHAPE_rep2 == 0) & \\\n",
    "                                 (data.scaled_in_vivo >= threshold) & (data.base_paired_to == 0)].index)\n",
    "ex_vivo_scaled_unique = len(data[(data.deltaSHAPE_rep1 == 0) & (data.deltaSHAPE_rep2 == 0) & \\\n",
    "                                 (data.scaled_ex_vivo >= threshold) & (data.base_paired_to == 0)].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "samples = modinfonorm.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Xist_sequence', 'diffBUM_HMM_ex_vivo_norm', 'diffBUM_HMM_ex_vivo',\n",
       "       'diffBUM_HMM_in_vivo_norm', 'diffBUM_HMM_in_vivo',\n",
       "       'diffBUM_HMM_ex_vivo_norm_unique', 'diffBUM_HMM_ex_vivo_unique',\n",
       "       'diffBUM_HMM_in_vivo_norm_unique', 'diffBUM_HMM_in_vivo_unique',\n",
       "       'deltaSHAPE_ex_vivo_1', 'deltaSHAPE_ex_vivo_2', 'deltaSHAPE_in_vivo_1',\n",
       "       'deltaSHAPE_in_vivo_2'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "frequencies = [ totalss/len(data.index),\n",
    "                ex_vivo_scaled/ex_vivo_scaled_total,\n",
    "                ex_vivo/ex_vivo_total,\n",
    "                in_vivo_scaled/in_vivo_scaled_total,\n",
    "                in_vivo/in_vivo_total,\n",
    "                ex_vivo_scaled_unique/ex_vivo_scaled_unique_total,\n",
    "                ex_vivo_unique/ex_vivo_unique_total,\n",
    "                in_vivo_scaled_unique/in_vivo_scaled_unique_total,\n",
    "                in_vivo_unique/in_vivo_unique_total,\n",
    "                deltaSHAPE_ex_vivo_rep1/deltaSHAPE_ex_vivo_rep1_total,\n",
    "                deltaSHAPE_ex_vivo_rep2/deltaSHAPE_ex_vivo_rep2_total,\n",
    "                deltaSHAPE_in_vivo_rep1/deltaSHAPE_in_vivo_rep1_total,\n",
    "                deltaSHAPE_in_vivo_rep2/deltaSHAPE_in_vivo_rep2_total]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.47120214309632774,\n",
       " 0.8418631397354802,\n",
       " 0.8737113402061856,\n",
       " 0.5319148936170213,\n",
       " 0.5985915492957746,\n",
       " 0.8281461434370772,\n",
       " 0.8627254509018036,\n",
       " 0.5,\n",
       " 0.5803571428571429,\n",
       " 0.7956521739130434,\n",
       " 0.7764705882352941,\n",
       " 0.6053412462908012,\n",
       " 0.5817757009345794]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frequencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(frequencies,columns=[\"frequencies\"],index=samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>frequencies</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Xist_sequence</td>\n",
       "      <td>0.471202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>diffBUM_HMM_ex_vivo_norm</td>\n",
       "      <td>0.841863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>diffBUM_HMM_ex_vivo</td>\n",
       "      <td>0.873711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>diffBUM_HMM_in_vivo_norm</td>\n",
       "      <td>0.531915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>diffBUM_HMM_in_vivo</td>\n",
       "      <td>0.598592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>diffBUM_HMM_ex_vivo_norm_unique</td>\n",
       "      <td>0.828146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>diffBUM_HMM_ex_vivo_unique</td>\n",
       "      <td>0.862725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>diffBUM_HMM_in_vivo_norm_unique</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>diffBUM_HMM_in_vivo_unique</td>\n",
       "      <td>0.580357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>deltaSHAPE_ex_vivo_1</td>\n",
       "      <td>0.795652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>deltaSHAPE_ex_vivo_2</td>\n",
       "      <td>0.776471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>deltaSHAPE_in_vivo_1</td>\n",
       "      <td>0.605341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>deltaSHAPE_in_vivo_2</td>\n",
       "      <td>0.581776</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 frequencies\n",
       "Xist_sequence                       0.471202\n",
       "diffBUM_HMM_ex_vivo_norm            0.841863\n",
       "diffBUM_HMM_ex_vivo                 0.873711\n",
       "diffBUM_HMM_in_vivo_norm            0.531915\n",
       "diffBUM_HMM_in_vivo                 0.598592\n",
       "diffBUM_HMM_ex_vivo_norm_unique     0.828146\n",
       "diffBUM_HMM_ex_vivo_unique          0.862725\n",
       "diffBUM_HMM_in_vivo_norm_unique     0.500000\n",
       "diffBUM_HMM_in_vivo_unique          0.580357\n",
       "deltaSHAPE_ex_vivo_1                0.795652\n",
       "deltaSHAPE_ex_vivo_2                0.776471\n",
       "deltaSHAPE_in_vivo_1                0.605341\n",
       "deltaSHAPE_in_vivo_2                0.581776"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x252 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(figsize=[9,3.5])\n",
    "ax = sns.barplot(df.index,df.frequencies,color=\"blue\")\n",
    "ax.set_yticks(np.arange(0,1.2,0.2))\n",
    "ax.set_yticklabels(np.arange(0,1.2,0.2))\n",
    "ax.set_xticklabels(df.index,rotation=45,ha='right')\n",
    "fig.savefig(\"Nucleotide_ssOverlap_counts.pdf\",dpi=400)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "modinfonorm = modinfonorm.append(df.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x252 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(figsize=[9,3.5])\n",
    "ax = sns.heatmap(modinfonorm,\\\n",
    "            cmap='Blues',\\\n",
    "            linewidths=.5,\\\n",
    "            annot=True)\n",
    "ax.set_yticklabels(modinfonorm.index,rotation=0,fontsize=14)\n",
    "\n",
    "fig.savefig(\"Figure_6B.pdf\",dpi=400)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### There seem to be a lot of nucleotides in the diffBUM-HMM in vivo data that are A-rich but according to the secondary structure data do not seem to be part of single-stranded regions? Could this have biological significance?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "A_and_double_stranded = data[(data.nucleotide == \"A\") & \\\n",
    "                             (data.in_vivo >= threshold) & \\\n",
    "                             (data.base_paired_to > 0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "36\n"
     ]
    }
   ],
   "source": [
    "print(len(A_and_double_stranded.index))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How many DRNs were in the in vivo data?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "totaldrnsinvivo = data[data.in_vivo >= threshold]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "142\n"
     ]
    }
   ],
   "source": [
    "print(len(totaldrnsinvivo.index))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "totaldrnsinvivo.to_csv(\"diffBUM-HMM_DRNs_in_vivo_locations.txt\",sep=\"\\t\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### That is quite a reasonable fraction.\n",
    "Where are they located?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([  179,   180,   236,   773,   777,  7272,  7461,  7471,  7547,\n",
       "             8346,  8462,  8525,  8744,  8862,  8917,  8959,  9014,  9150,\n",
       "             9219,  9325, 11181, 11265, 11281, 11434, 11578, 11592, 11617,\n",
       "            12145, 12263, 12280, 12282, 12316, 12414, 12577, 12849, 15165],\n",
       "           dtype='int64')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A_and_double_stranded.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### They don't seem to cluster very well...\n",
    "Perhaps add something about this to the discussion."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "A_and_double_stranded.to_csv(\"diffBUM-HMM_DRNs_in_As_in_vivo_locations.txt\",sep=\"\\t\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}