notebooks/02_analysis_scaling-curves.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Raw _P(s)_ curves by strand orientation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-08T02:08:11.042974Z",
     "start_time": "2018-05-08T02:08:09.811425Z"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import multiprocess as mp\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pypairix\n",
    "import bioframe\n",
    "import cooltools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-08T02:08:11.393617Z",
     "start_time": "2018-05-08T02:08:11.045491Z"
    }
   },
   "outputs": [],
   "source": [
    "mm9 = bioframe.fetch_chromsizes('mm9')\n",
    "chromsizes = bioframe.fetch_chromsizes('mm9')\n",
    "chromosomes = list(chromsizes.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-08T02:08:11.403280Z",
     "start_time": "2018-05-08T02:08:11.395458Z"
    }
   },
   "outputs": [],
   "source": [
    "conditions = ['WT', 'dN']\n",
    "binsize = 100000\n",
    "\n",
    "pairs_paths = {\n",
    "    'WT': 'data/UNTR_R1.nodups.pairs.gz',\n",
    "    'dN': 'data/NIPBL_R1.nodups.pairs.gz'\n",
    "}\n",
    "\n",
    "long_names = {\n",
    "    'WT': 'Wildtype',\n",
    "    'dN': 'NipblKO',\n",
    "}\n",
    "\n",
    "pal = sns.color_palette('colorblind')\n",
    "colors = {\n",
    "    'WT': pal[0],\n",
    "    'dN': pal[2],\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-08T02:08:14.996343Z",
     "start_time": "2018-05-08T02:08:13.492607Z"
    }
   },
   "outputs": [],
   "source": [
    "from cooltools.expected import compute_scaling\n",
    "import bioframe.dask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-08T02:08:16.831407Z",
     "start_time": "2018-05-08T02:08:16.388406Z"
    }
   },
   "outputs": [],
   "source": [
    "pairs_header = ['read_id', 'chrom1', 'pos1', 'chrom2', 'pos2', 'strand1', 'strand2', 'pair_type']\n",
    "chrom = 'chr1'\n",
    "\n",
    "df = bioframe.dask.read_pairix_block(\n",
    "    pairs_paths['WT'], \n",
    "    (chrom, chrom),\n",
    "    names=pairs_header,\n",
    "    chunk_level=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-08T02:08:35.876870Z",
     "start_time": "2018-05-08T02:08:16.902118Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4893134\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>read_id</th>\n",
       "      <th>chrom1</th>\n",
       "      <th>pos1</th>\n",
       "      <th>chrom2</th>\n",
       "      <th>pos2</th>\n",
       "      <th>strand1</th>\n",
       "      <th>strand2</th>\n",
       "      <th>pair_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>.</td>\n",
       "      <td>chr1</td>\n",
       "      <td>3000031</td>\n",
       "      <td>chr1</td>\n",
       "      <td>3002463</td>\n",
       "      <td>-</td>\n",
       "      <td>+</td>\n",
       "      <td>LL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>.</td>\n",
       "      <td>chr1</td>\n",
       "      <td>3000031</td>\n",
       "      <td>chr1</td>\n",
       "      <td>64746728</td>\n",
       "      <td>-</td>\n",
       "      <td>+</td>\n",
       "      <td>LL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>.</td>\n",
       "      <td>chr1</td>\n",
       "      <td>3000036</td>\n",
       "      <td>chr1</td>\n",
       "      <td>12271809</td>\n",
       "      <td>-</td>\n",
       "      <td>+</td>\n",
       "      <td>LL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>.</td>\n",
       "      <td>chr1</td>\n",
       "      <td>3000042</td>\n",
       "      <td>chr1</td>\n",
       "      <td>3009611</td>\n",
       "      <td>-</td>\n",
       "      <td>+</td>\n",
       "      <td>LL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>.</td>\n",
       "      <td>chr1</td>\n",
       "      <td>3000048</td>\n",
       "      <td>chr1</td>\n",
       "      <td>4637504</td>\n",
       "      <td>-</td>\n",
       "      <td>+</td>\n",
       "      <td>LL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  read_id chrom1     pos1 chrom2      pos2 strand1 strand2 pair_type\n",
       "0       .   chr1  3000031   chr1   3002463       -       +        LL\n",
       "1       .   chr1  3000031   chr1  64746728       -       +        LL\n",
       "2       .   chr1  3000036   chr1  12271809       -       +        LL\n",
       "3       .   chr1  3000042   chr1   3009611       -       +        LL\n",
       "4       .   chr1  3000048   chr1   4637504       -       +        LL"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.compute()\n",
    "print(len(df))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-08T02:08:40.994385Z",
     "start_time": "2018-05-08T02:08:35.878766Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7feace921be0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7feace8d59b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 12))\n",
    "\n",
    "dmin = 10\n",
    "dmax = int(1e7)\n",
    "\n",
    "orientations = [\n",
    "    ('+', '-'),\n",
    "    ('-', '+'),\n",
    "    ('+', '+'),\n",
    "    ('-', '-')\n",
    "]\n",
    "\n",
    "grouped = df.groupby(['strand1', 'strand2'])\n",
    "\n",
    "for s1, s2 in orientations:\n",
    "    group = grouped.get_group((s1, s2))\n",
    "    \n",
    "    dbins, obs, areas = compute_scaling(\n",
    "        group, \n",
    "        region1=[0, chromsizes[chrom]], \n",
    "        region2=[0, chromsizes[chrom]],\n",
    "        dmin=dmin, \n",
    "        dmax=dmax)\n",
    "    \n",
    "    dmeans = np.sqrt(dbins[:-1] * dbins[1:])\n",
    "    scaling = obs / areas\n",
    "    plt.loglog(dmeans, scaling, label='{} {}'.format(s1, s2))\n",
    "\n",
    "plt.xlabel(r'genomic separation $s$, bp')\n",
    "plt.ylabel('pair frequency')\n",
    "plt.legend()\n",
    "plt.axvline(6000, c='k', ls='--')\n",
    "plt.gca().set_aspect(1)\n",
    "plt.title(f'WT rep1, {chrom}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.6.2"
  },
  "toc": {
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
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   "toc_position": {},
   "toc_section_display": "block",
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}