Building_pairwise_interactions_BEDPE_PromoterEnhancer_For_RescaledPileups.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "binding-acrobat",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "require([\"codemirror/keymap/sublime\", \"notebook/js/cell\", \"base/js/namespace\"],\n",
       "    function(sublime_keymap, cell, IPython) {\n",
       "        cell.Cell.options_default.cm_config.keyMap = 'sublime';\n",
       "        var cells = IPython.notebook.get_cells();\n",
       "        for(var cl=0; cl< cells.length ; cl++){\n",
       "            cells[cl].code_mirror.setOption('keyMap', 'sublime');\n",
       "        }\n",
       "    }\n",
       ");\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%javascript\n",
    "require([\"codemirror/keymap/sublime\", \"notebook/js/cell\", \"base/js/namespace\"],\n",
    "    function(sublime_keymap, cell, IPython) {\n",
    "        cell.Cell.options_default.cm_config.keyMap = 'sublime';\n",
    "        var cells = IPython.notebook.get_cells();\n",
    "        for(var cl=0; cl< cells.length ; cl++){\n",
    "            cells[cl].code_mirror.setOption('keyMap', 'sublime');\n",
    "        }\n",
    "    }\n",
    ");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cutting-tracy",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.container { width:85% !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# change the cell width\n",
    "from IPython.core.display import display, HTML\n",
    "display(HTML(\"<style>.container { width:85% !important; }</style>\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "judicial-backing",
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline\n",
    "%config InlineBackend.print_figure_kwargs={'facecolor' : \"w\"}\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "from more_itertools import chunked\n",
    "# import all relevant func and modules from a companion .py file\n",
    "from supp_lib import *\n",
    "# import all samples as dictionary ...\n",
    "from samples import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "racial-light",
   "metadata": {},
   "outputs": [],
   "source": [
    "# !scp ghpcc:/nl/umw_job_dekker/users/av90w/projects/for-paper/fig2/G4-CTCF-dots/intersect-all-NT-CTCF-NO-G4-centered-RAD21 ./\n",
    "# !scp ghpcc:/nl/umw_job_dekker/users/av90w/projects/for-paper/draft8-after-NSMB/CTCF-peaks-with-RAD21 ./"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "geographic-advocacy",
   "metadata": {},
   "outputs": [],
   "source": [
    "# consider switching to *ALL* CTCF with RAD21 (regardless of TSS) for the rescaled analysis ...\n",
    "# here is a location:\n",
    "# /nl/umw_job_dekker/users/av90w/projects/for-paper/draft8-after-NSMB/CTCF-peaks-with-RAD21"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "mathematical-thomas",
   "metadata": {},
   "outputs": [],
   "source": [
    "ctcf_df = bioframe.read_table(\n",
    "    \"CTCF-peaks-with-RAD21\",\n",
    "    names=[\"chrom\",\"start\",\"end\"],\n",
    "    usecols=[0,1,2]\n",
    ")\n",
    "ctcf_df = ctcf_df.sort_values([\"chrom\",\"start\"])\n",
    "ctcf_df = ctcf_df[ ctcf_df[\"chrom\"].isin(autosomal_chroms) ]\n",
    "ctcf_df[\"type\"] = \"ctcf\"\n",
    "ctcf_df = ctcf_df.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "retained-education",
   "metadata": {},
   "source": [
    "#### Annotate our CTCF ChipSeq peaks with the motifs ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "nutritional-management",
   "metadata": {},
   "outputs": [],
   "source": [
    "### CTCF motif: http://jaspar.genereg.net/matrix/MA0139.1/\n",
    "jaspar_url = 'http://expdata.cmmt.ubc.ca/JASPAR/downloads/UCSC_tracks/2022/hg19/'\n",
    "jaspar_motif_file = 'MA0139.1.tsv.gz'\n",
    "ctcf_motifs = bioframe.read_table(\n",
    "                jaspar_url+jaspar_motif_file,\n",
    "                skiprows=1,\n",
    "                names=[\"chrom\", \"start\", \"end\",\"name\",\"score\", \"pval\", \"strand\"]\n",
    ")\n",
    "ctcf_motifs = ctcf_motifs.sort_values([\"chrom\",\"start\"])\n",
    "ctcf_motifs = ctcf_motifs[ ctcf_motifs[\"chrom\"].isin(autosomal_chroms) ]\n",
    "ctcf_motifs = ctcf_motifs.reset_index(drop=True)\n",
    "\n",
    "# beforehand we used FIMO to find motifs in the hg19 - ourselves\n",
    "# it seems to match the published one - OK - so we'll use the published one:\n",
    "# # here is very-very liberal list of CTCF calls in hg19, based on \"MA0139.1.meme\" from JASPAR\n",
    "# # P-val cutoff was 1e-4 - let's see how it looks here ...\n",
    "# !scp ghpcc:/nl/umw_job_dekker/users/sv49w/ALV2021/CTCF_motif/CTCF.hist.txt ./CTCF.MA0139.1.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "protective-norfolk",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are 1222 CTCF-CHipSeq-peaks-with-RAD21 that do not have a MOTIF associated with them out of 12033\n"
     ]
    }
   ],
   "source": [
    "ctcf_motif_annotated = bioframe.overlap(\n",
    "    ctcf_df,\n",
    "    ctcf_motifs,\n",
    "    how=\"inner\",\n",
    "    suffixes=('','_')\n",
    ")\n",
    "# for a peak with several motifs, assign the \"strongest\" one:\n",
    "# NOTE pval here is actually -1 * log10(actual_pval) * 100 , i.e. 10^(-13) --> 1_300\n",
    "max_idx = ctcf_motif_annotated.groupby([\"chrom\",\"start\",\"end\"])[\"pval_\"].idxmax()\n",
    "\n",
    "_cols = [\"chrom\",\n",
    "        \"start\",\n",
    "        \"end\",\n",
    "        \"type\",\n",
    "        \"strand_\"]\n",
    "_rename_cols = {\"strand_\":\"strand\"}\n",
    "\n",
    "ctcf_motif_annotated = ctcf_motif_annotated \\\n",
    "                        .loc[max_idx,_cols] \\\n",
    "                        .rename(_rename_cols,axis=1) \\\n",
    "                        .reset_index(drop=True)\n",
    "ctcf_motif_annotated.head()\n",
    "print(f\"there are {len(ctcf_df) - len(ctcf_motif_annotated)} CTCF-CHipSeq-peaks-with-RAD21 that do not have a MOTIF associated with them out of {len(ctcf_df)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "incorrect-butler",
   "metadata": {},
   "outputs": [],
   "source": [
    "# merge full list of ctcf_df with the annotated subset - to get them all - \"-\",\"+\" and None-s\n",
    "ctcf_motif_annotated = pd.merge(ctcf_df, ctcf_motif_annotated, how=\"left\", on=[\"chrom\",\"start\",\"end\",\"type\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acting-filter",
   "metadata": {},
   "source": [
    "#### read a list of active TSSs ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "lucky-hours",
   "metadata": {},
   "outputs": [],
   "source": [
    "# this is how one can read a DataFrame into memory:\n",
    "# bf is short for bioframe here ...\n",
    "feature_of_interest = \"ActiveTSS-NO-CTCF-8849-1bp.bed\"\n",
    "tss_df = bioframe.read_table(\n",
    "    feature_of_interest,\n",
    "    names=[\"chrom\",\"start\",\"end\",\"strand\"]\n",
    ")\n",
    "tss_df = tss_df.sort_values([\"chrom\",\"start\"]) # sort ...\n",
    "tss_df = tss_df[tss_df[\"chrom\"].isin(autosomal_chroms)] # filter chroms\n",
    "tss_df[\"type\"] = \"tss\"\n",
    "tss_df = tss_df.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "buried-final",
   "metadata": {},
   "outputs": [],
   "source": [
    "# HAP1-Enhancers-K27minusTSSminusCTCFoverlapDNase_10443.bed\n",
    "\n",
    "# this is how one can read a DataFrame into memory:\n",
    "# bf is short for bioframe here ...\n",
    "feature_of_interest = \"HAP1-Enhancers-K27minusTSSminusCTCFoverlapDNase_10443.bed\"\n",
    "enhance_df = bioframe.read_table(\n",
    "    feature_of_interest,\n",
    "    names=[\"chrom\",\"start\",\"end\"]\n",
    ")\n",
    "enhance_df = enhance_df.sort_values([\"chrom\",\"start\"]) # sort ...\n",
    "enhance_df = enhance_df[enhance_df[\"chrom\"].isin(autosomal_chroms)] # filter chroms\n",
    "enhance_df[\"type\"] = \"enhancer\"\n",
    "enhance_df = enhance_df.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "national-irish",
   "metadata": {},
   "source": [
    "```python\n",
    "# just an example-prototype on how we can do pairwise combinations dissected by orientation/type/etc\n",
    "# also in this case we are combining 2 lists of intervals - to get to pairwise interactions between the two\n",
    "# let's assume that we have 2 dataframes - df and df1 :\n",
    "a = df.head().copy()\n",
    "b = df1.head().copy()\n",
    "a[\"type\"] = \"a\"\n",
    "b[\"type\"] = \"b\"\n",
    "\n",
    "xxx = pd.concat([a,b]).reset_index(drop=True)\n",
    "print(xxx)\n",
    "\n",
    "yyy = bioframe.pair_by_distance(xxx,min_sep=0,max_sep=100000000)\n",
    "# select only interactions between different types ...\n",
    "print(yyy[yyy[\"type_1\"] != yyy[\"type_2\"]].reset_index(drop=True))\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fixed-miracle",
   "metadata": {},
   "source": [
    "### Create a list of all pairwise interactions between enhancers and promoters ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "annual-clearance",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  chrom1  start1    end1     type1 strand1 chrom2  start2    end2     type2  \\\n",
      "0  chr18  158556  158557       tss       +  chr18  634756  635309  enhancer   \n",
      "1  chr18  191023  191938  enhancer     NaN  chr18  657652  657653       tss   \n",
      "2  chr18  191023  191938  enhancer     NaN  chr18  658290  658291       tss   \n",
      "\n",
      "  strand2  \n",
      "0     NaN  \n",
      "1       +  \n",
      "2       -  \n"
     ]
    }
   ],
   "source": [
    "# enhance_df\n",
    "# ctcf_df\n",
    "# tss_df\n",
    "dfs_concat = pd.concat([enhance_df,tss_df]).reset_index(drop=True)\n",
    "bedpe = bioframe.pair_by_distance(dfs_concat, min_sep=50_000, max_sep=500_000, suffixes=('1','2'))\n",
    "bedpe = bedpe[ (bedpe[\"type1\"] != bedpe[\"type2\"]) ].reset_index(drop=True)\n",
    "print(bedpe.head(3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "isolated-audience",
   "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>chrom1</th>\n",
       "      <th>start1</th>\n",
       "      <th>end1</th>\n",
       "      <th>type1</th>\n",
       "      <th>strand1</th>\n",
       "      <th>chrom2</th>\n",
       "      <th>start2</th>\n",
       "      <th>end2</th>\n",
       "      <th>type2</th>\n",
       "      <th>strand2</th>\n",
       "      <th>chrom_</th>\n",
       "      <th>start_</th>\n",
       "      <th>end_</th>\n",
       "      <th>type_</th>\n",
       "      <th>strand_</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>chr18</td>\n",
       "      <td>158556.0</td>\n",
       "      <td>158557</td>\n",
       "      <td>tss</td>\n",
       "      <td>+</td>\n",
       "      <td>chr18</td>\n",
       "      <td>634756</td>\n",
       "      <td>635309.0</td>\n",
       "      <td>enhancer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>chr18</td>\n",
       "      <td>596929</td>\n",
       "      <td>597333</td>\n",
       "      <td>ctcf</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>chr18</td>\n",
       "      <td>191023.0</td>\n",
       "      <td>191938</td>\n",
       "      <td>enhancer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>chr18</td>\n",
       "      <td>657652</td>\n",
       "      <td>657653.0</td>\n",
       "      <td>tss</td>\n",
       "      <td>+</td>\n",
       "      <td>chr18</td>\n",
       "      <td>596929</td>\n",
       "      <td>597333</td>\n",
       "      <td>ctcf</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>chr18</td>\n",
       "      <td>191023.0</td>\n",
       "      <td>191938</td>\n",
       "      <td>enhancer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>chr18</td>\n",
       "      <td>658290</td>\n",
       "      <td>658291.0</td>\n",
       "      <td>tss</td>\n",
       "      <td>-</td>\n",
       "      <td>chr18</td>\n",
       "      <td>596929</td>\n",
       "      <td>597333</td>\n",
       "      <td>ctcf</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>chr18</td>\n",
       "      <td>268046.0</td>\n",
       "      <td>268047</td>\n",
       "      <td>tss</td>\n",
       "      <td>-</td>\n",
       "      <td>chr18</td>\n",
       "      <td>634756</td>\n",
       "      <td>635309.0</td>\n",
       "      <td>enhancer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>chr18</td>\n",
       "      <td>596929</td>\n",
       "      <td>597333</td>\n",
       "      <td>ctcf</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>chr18</td>\n",
       "      <td>268046.0</td>\n",
       "      <td>268047</td>\n",
       "      <td>tss</td>\n",
       "      <td>-</td>\n",
       "      <td>chr18</td>\n",
       "      <td>740677</td>\n",
       "      <td>741086.0</td>\n",
       "      <td>enhancer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>chr18</td>\n",
       "      <td>596929</td>\n",
       "      <td>597333</td>\n",
       "      <td>ctcf</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  chrom1    start1    end1     type1 strand1 chrom2  start2      end2  \\\n",
       "0  chr18  158556.0  158557       tss       +  chr18  634756  635309.0   \n",
       "1  chr18  191023.0  191938  enhancer     NaN  chr18  657652  657653.0   \n",
       "2  chr18  191023.0  191938  enhancer     NaN  chr18  658290  658291.0   \n",
       "3  chr18  268046.0  268047       tss       -  chr18  634756  635309.0   \n",
       "4  chr18  268046.0  268047       tss       -  chr18  740677  741086.0   \n",
       "\n",
       "      type2 strand2 chrom_  start_    end_ type_ strand_  \n",
       "0  enhancer     NaN  chr18  596929  597333  ctcf       +  \n",
       "1       tss       +  chr18  596929  597333  ctcf       +  \n",
       "2       tss       -  chr18  596929  597333  ctcf       +  \n",
       "3  enhancer     NaN  chr18  596929  597333  ctcf       +  \n",
       "4  enhancer     NaN  chr18  596929  597333  ctcf       +  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bedpe_ctcf_overlap = bioframe.overlap(\n",
    "    bedpe,\n",
    "    ctcf_motif_annotated,\n",
    "    how='left',\n",
    "    suffixes=('', '_'),\n",
    "    cols1=[\"chrom1\",\"end1\",\"start2\"],\n",
    ")\n",
    "bedpe_ctcf_overlap.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "abstract-craft",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'genomic separation between E and P, bp')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# some intermediate check - to see how often does a CTCF \"hide\" between E and P depending on the distance between them\n",
    "_1 = bedpe_ctcf_overlap[~bedpe_ctcf_overlap[\"chrom_\"].isna()] \\\n",
    "    .drop_duplicates(subset=[\"chrom1\",\"start1\",\"end1\",\"chrom2\",\"start2\",\"end2\"]) \\\n",
    "    .reset_index(drop=True)\n",
    "(_1[\"start2\"] - _1[\"end1\"]).hist(alpha=0.5,color=\"red\",label=\"EP ints with ctcf in between 27000\")\n",
    "\n",
    "_ = bedpe_ctcf_overlap[bedpe_ctcf_overlap[\"chrom_\"].isna()]\n",
    "(_[\"start2\"] - _[\"end1\"]).hist(alpha=0.5,color=\"blue\",label=\"EP ints without ctcf in between 11000\")\n",
    "plt.legend(frameon=False)\n",
    "plt.xlabel(\"genomic separation between E and P, bp\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "olive-heather",
   "metadata": {},
   "outputs": [],
   "source": [
    "# previous Anne-Laure's code - didn't take \"undetermined\" CTCFs into account - we might get back to it...\n",
    "# #\n",
    "# cols = ['chrom1','start1','end1','type1','strand1','chrom2','start2','end2','type2','strand2','type','strandc_']\n",
    "# #first drop *ALL* duplicates, this would keep 1 row per EP if there were CTCFs of same orientation, and 2 rows if they were +/-\n",
    "# bedpe_ctcf_overlap = bedpe_ctcf_overlap[cols].drop_duplicates().reset_index(drop=True).rename({\"type\": \"between_status\", \"strandc_\":\"strandc\"},axis=1)\n",
    "# #drop duplicates (on subset of EP-rows) to remove EP-s that have >1 CTCF of different orientation\n",
    "# bedpe_ctcf_overlap = bedpe_ctcf_overlap.drop_duplicates(subset=['chrom1', 'start1', 'end1', 'type1', 'strand1', 'chrom2', 'start2','end2', 'type2', 'strand2','between_status'], keep=False).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "adverse-capitol",
   "metadata": {},
   "outputs": [],
   "source": [
    "# repeat 1st step from Anne-Laure's analysis - to keep only one CTCF of a given orientation per EP:\n",
    "subset1 = ['chrom1','start1','end1','type1','strand1','chrom2','start2','end2','type2','strand2','type_','strand_']\n",
    "bedpe_ctcf_overlap = bedpe_ctcf_overlap \\\n",
    "                    .drop_duplicates(subset=subset1) \\\n",
    "                    .reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "parental-found",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([    0., 19749., 13322.,  4937.,     0.]),\n",
       " array([0, 1, 2, 3, 4, 5]),\n",
       " <BarContainer object of 5 artists>)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check the distribution of group sizes\n",
    "plt.hist(bedpe_ctcf_overlap.groupby(['chrom1','start1','chrom2','start2']).size(),bins=[0,1,2,3,4,5],width=0.8)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "entitled-framing",
   "metadata": {},
   "source": [
    "#### Sort out all types of EP according to the status of CTCFs in-between ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "reserved-latin",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mix+-      10920\n",
       "empty       8296\n",
       "-           5283\n",
       "+           5221\n",
       "all         4937\n",
       "almost-     1216\n",
       "almost+     1186\n",
       "NaN          949\n",
       "Name: status, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def assign_status(df):\n",
    "    # df[['type_','strand_']]\n",
    "    if len(df)==3:\n",
    "        return \"all\"\n",
    "    elif (len(df)==2)and(df[\"strand_\"].isnull().any()):\n",
    "        _orient = df.dropna(subset=[\"strand_\"])[\"strand_\"].iloc[0]\n",
    "        return f\"almost{_orient}\"\n",
    "    elif len(df)==2:\n",
    "        return \"mix+-\"\n",
    "    elif df[\"type_\"].iloc[0] == \"ctcf\":\n",
    "        return df[\"strand_\"].iloc[0]\n",
    "    else:\n",
    "        return \"empty\"\n",
    "\n",
    "res = bedpe_ctcf_overlap.groupby(['chrom1','start1','chrom2','start2']).apply(assign_status)\n",
    "res.name=\"status\"\n",
    "res.value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ambient-treasurer",
   "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>chrom1</th>\n",
       "      <th>start1</th>\n",
       "      <th>end1</th>\n",
       "      <th>type1</th>\n",
       "      <th>strand1</th>\n",
       "      <th>chrom2</th>\n",
       "      <th>start2</th>\n",
       "      <th>end2</th>\n",
       "      <th>type2</th>\n",
       "      <th>strand2</th>\n",
       "      <th>status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>chr1</td>\n",
       "      <td>762901.0</td>\n",
       "      <td>762902</td>\n",
       "      <td>tss</td>\n",
       "      <td>-</td>\n",
       "      <td>chr1</td>\n",
       "      <td>816241</td>\n",
       "      <td>817150.0</td>\n",
       "      <td>enhancer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>chr1</td>\n",
       "      <td>762901.0</td>\n",
       "      <td>762902</td>\n",
       "      <td>tss</td>\n",
       "      <td>-</td>\n",
       "      <td>chr1</td>\n",
       "      <td>969279</td>\n",
       "      <td>969493.0</td>\n",
       "      <td>enhancer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>all</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>chr1</td>\n",
       "      <td>762901.0</td>\n",
       "      <td>762902</td>\n",
       "      <td>tss</td>\n",
       "      <td>-</td>\n",
       "      <td>chr1</td>\n",
       "      <td>1014914</td>\n",
       "      <td>1015649.0</td>\n",
       "      <td>enhancer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>all</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>chr1</td>\n",
       "      <td>762970.0</td>\n",
       "      <td>762971</td>\n",
       "      <td>tss</td>\n",
       "      <td>+</td>\n",
       "      <td>chr1</td>\n",
       "      <td>816241</td>\n",
       "      <td>817150.0</td>\n",
       "      <td>enhancer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>chr1</td>\n",
       "      <td>762970.0</td>\n",
       "      <td>762971</td>\n",
       "      <td>tss</td>\n",
       "      <td>+</td>\n",
       "      <td>chr1</td>\n",
       "      <td>969279</td>\n",
       "      <td>969493.0</td>\n",
       "      <td>enhancer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>all</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  chrom1    start1    end1 type1 strand1 chrom2   start2       end2     type2  \\\n",
       "0   chr1  762901.0  762902   tss       -   chr1   816241   817150.0  enhancer   \n",
       "1   chr1  762901.0  762902   tss       -   chr1   969279   969493.0  enhancer   \n",
       "4   chr1  762901.0  762902   tss       -   chr1  1014914  1015649.0  enhancer   \n",
       "7   chr1  762970.0  762971   tss       +   chr1   816241   817150.0  enhancer   \n",
       "8   chr1  762970.0  762971   tss       +   chr1   969279   969493.0  enhancer   \n",
       "\n",
       "  strand2 status  \n",
       "0     NaN      +  \n",
       "1     NaN    all  \n",
       "4     NaN    all  \n",
       "7     NaN      +  \n",
       "8     NaN    all  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now, merge these EPs (formerly grouped by CTCF) with the original EP dataframe to get full info ...\n",
    "cols_output = ['chrom1','start1','end1','type1','strand1','chrom2','start2','end2','type2','strand2']\n",
    "output_df = pd.merge(\n",
    "    res.reset_index(),\n",
    "    bedpe_ctcf_overlap[cols_output],\n",
    "    how=\"left\",\n",
    "    on=['chrom1','start1','chrom2','start2']\n",
    ").drop_duplicates()[cols_output+[\"status\"]]\n",
    "# sample output\n",
    "output_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "adult-ghost",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EP_50-500kb_with_detailed_CTCF_status_stricte 100% 2681KB   3.2MB/s   00:00    \n",
      "EP_50-500kb_with_detailed_CTCF_status_stricte 100% 2681KB  10.6MB/s   00:00    \n"
     ]
    }
   ],
   "source": [
    "fname = \"EP_50-500kb_with_detailed_CTCF_status_stricter.bedpe\"\n",
    "output_df.to_csv(fname, sep=\"\\t\", index=False)\n",
    "! scp {fname} ghpcc:/nl/umw_job_dekker/users/sv49w/ALV2021/\n",
    "! scp {fname} shadow:/data/venevs/ALV-repo/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "appropriate-discount",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # cleanup output of the 'bedpe_ctcf_overlap':\n",
    "# _cols = ['chrom1', 'start1', 'end1', 'type1', 'strand1', 'chrom2', 'start2','end2', 'type2', 'strand2','type_']\n",
    "# bedpe_ctcf_overlap = bedpe_ctcf_overlap[_cols] \\\n",
    "#     .drop_duplicates() \\\n",
    "#     .reset_index(drop=True) \\\n",
    "#     .rename({\"type_\": \"between_status\"},axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "invalid-nothing",
   "metadata": {},
   "outputs": [],
   "source": [
    "# bedpe_ctcf_overlap.to_csv(\"EP_50-500kb_with_CTCF_status.bedpe\",sep=\"\\t\",index=False)\n",
    "# ! scp EP_50-500kb_with_CTCF_status.bedpe ghpcc:/nl/umw_job_dekker/users/sv49w/ALV2021/"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "complete-islam",
   "metadata": {},
   "source": [
    "### Try to deal with the neighbours analysis ..."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "quarterly-blank",
   "metadata": {},
   "source": [
    "#### strategy to address the neighbours ...\n",
    "\n",
    "1. for every CTCF (+/-) check \"closest\" TSS (+/-):\n",
    "     - first \"upstream\" - independently\n",
    "     - second \"downstream\" - indep.\n",
    "     - Note that this way >1 CTCF can \"interact\" with the same TSS\n",
    "2. For both of these lists - groupby TSS and pick nearest CTCF in a per TSS manner\n",
    "     - This way we ensure there is nothing between a given TSS and CTCF\n",
    "     - for a given TSS we also keep track of 2 closest CTCFs up- and down-stream\n",
    "3. Concatenate resulting two lists _up_ and _down_ -> this is the final list of CTCF-TSS interactions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "choice-license",
   "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>chrom1</th>\n",
       "      <th>start1</th>\n",
       "      <th>end1</th>\n",
       "      <th>type1</th>\n",
       "      <th>strand1</th>\n",
       "      <th>chrom2</th>\n",
       "      <th>start2</th>\n",
       "      <th>end2</th>\n",
       "      <th>strand2</th>\n",
       "      <th>type2</th>\n",
       "      <th>distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>chr1</td>\n",
       "      <td>10151</td>\n",
       "      <td>10508</td>\n",
       "      <td>ctcf</td>\n",
       "      <td>-</td>\n",
       "      <td>chr1</td>\n",
       "      <td>29369</td>\n",
       "      <td>29370</td>\n",
       "      <td>-</td>\n",
       "      <td>tss</td>\n",
       "      <td>18861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>chr1</td>\n",
       "      <td>873599</td>\n",
       "      <td>873857</td>\n",
       "      <td>ctcf</td>\n",
       "      <td>-</td>\n",
       "      <td>chr1</td>\n",
       "      <td>894635</td>\n",
       "      <td>894636</td>\n",
       "      <td>-</td>\n",
       "      <td>tss</td>\n",
       "      <td>20778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>chr1</td>\n",
       "      <td>968050</td>\n",
       "      <td>968716</td>\n",
       "      <td>ctcf</td>\n",
       "      <td>+</td>\n",
       "      <td>chr1</td>\n",
       "      <td>1004716</td>\n",
       "      <td>1004717</td>\n",
       "      <td>-</td>\n",
       "      <td>tss</td>\n",
       "      <td>36000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>chr1</td>\n",
       "      <td>1057249</td>\n",
       "      <td>1057885</td>\n",
       "      <td>ctcf</td>\n",
       "      <td>+</td>\n",
       "      <td>chr1</td>\n",
       "      <td>1167380</td>\n",
       "      <td>1167381</td>\n",
       "      <td>-</td>\n",
       "      <td>tss</td>\n",
       "      <td>109495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>chr1</td>\n",
       "      <td>1227273</td>\n",
       "      <td>1227656</td>\n",
       "      <td>ctcf</td>\n",
       "      <td>-</td>\n",
       "      <td>chr1</td>\n",
       "      <td>1243959</td>\n",
       "      <td>1243960</td>\n",
       "      <td>+</td>\n",
       "      <td>tss</td>\n",
       "      <td>16303</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8723</th>\n",
       "      <td>chr9</td>\n",
       "      <td>139565964</td>\n",
       "      <td>139566655</td>\n",
       "      <td>ctcf</td>\n",
       "      <td>-</td>\n",
       "      <td>chr9</td>\n",
       "      <td>139581908</td>\n",
       "      <td>139581909</td>\n",
       "      <td>-</td>\n",
       "      <td>tss</td>\n",
       "      <td>15253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8724</th>\n",
       "      <td>chr9</td>\n",
       "      <td>139588664</td>\n",
       "      <td>139589215</td>\n",
       "      <td>ctcf</td>\n",
       "      <td>-</td>\n",
       "      <td>chr9</td>\n",
       "      <td>139607023</td>\n",
       "      <td>139607024</td>\n",
       "      <td>+</td>\n",
       "      <td>tss</td>\n",
       "      <td>17808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8725</th>\n",
       "      <td>chr9</td>\n",
       "      <td>139929536</td>\n",
       "      <td>139930441</td>\n",
       "      <td>ctcf</td>\n",
       "      <td>+</td>\n",
       "      <td>chr9</td>\n",
       "      <td>139948512</td>\n",
       "      <td>139948513</td>\n",
       "      <td>-</td>\n",
       "      <td>tss</td>\n",
       "      <td>18071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8726</th>\n",
       "      <td>chr9</td>\n",
       "      <td>140121819</td>\n",
       "      <td>140122389</td>\n",
       "      <td>ctcf</td>\n",
       "      <td>-</td>\n",
       "      <td>chr9</td>\n",
       "      <td>140149778</td>\n",
       "      <td>140149779</td>\n",
       "      <td>+</td>\n",
       "      <td>tss</td>\n",
       "      <td>27389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8727</th>\n",
       "      <td>chr9</td>\n",
       "      <td>140188936</td>\n",
       "      <td>140190350</td>\n",
       "      <td>ctcf</td>\n",
       "      <td>+</td>\n",
       "      <td>chr9</td>\n",
       "      <td>140335808</td>\n",
       "      <td>140335809</td>\n",
       "      <td>-</td>\n",
       "      <td>tss</td>\n",
       "      <td>145458</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3486 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     chrom1     start1       end1 type1 strand1 chrom2     start2       end2  \\\n",
       "0      chr1      10151      10508  ctcf       -   chr1      29369      29370   \n",
       "3      chr1     873599     873857  ctcf       -   chr1     894635     894636   \n",
       "5      chr1     968050     968716  ctcf       +   chr1    1004716    1004717   \n",
       "6      chr1    1057249    1057885  ctcf       +   chr1    1167380    1167381   \n",
       "8      chr1    1227273    1227656  ctcf       -   chr1    1243959    1243960   \n",
       "...     ...        ...        ...   ...     ...    ...        ...        ...   \n",
       "8723   chr9  139565964  139566655  ctcf       -   chr9  139581908  139581909   \n",
       "8724   chr9  139588664  139589215  ctcf       -   chr9  139607023  139607024   \n",
       "8725   chr9  139929536  139930441  ctcf       +   chr9  139948512  139948513   \n",
       "8726   chr9  140121819  140122389  ctcf       -   chr9  140149778  140149779   \n",
       "8727   chr9  140188936  140190350  ctcf       +   chr9  140335808  140335809   \n",
       "\n",
       "     strand2 type2  distance  \n",
       "0          -   tss     18861  \n",
       "3          -   tss     20778  \n",
       "5          -   tss     36000  \n",
       "6          -   tss    109495  \n",
       "8          +   tss     16303  \n",
       "...      ...   ...       ...  \n",
       "8723       -   tss     15253  \n",
       "8724       +   tss     17808  \n",
       "8725       -   tss     18071  \n",
       "8726       +   tss     27389  \n",
       "8727       -   tss    145458  \n",
       "\n",
       "[3486 rows x 11 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# for every CTCF find nearest TSS downstream\n",
    "_down = bioframe.closest(\n",
    "    ctcf_motif_annotated,\n",
    "    tss_df,\n",
    "    k=1,\n",
    "    ignore_overlaps=True,\n",
    "    ignore_upstream=True,\n",
    "    ignore_downstream=False,\n",
    "    suffixes=('1', '2'),\n",
    ")\n",
    "# groupby on TSSs - to select the closest CTCF\n",
    "# and make sure we get CTCF-TSS interactions with nothing in between\n",
    "# display(_down)\n",
    "_down_tss = _down.loc[_down.groupby([\"chrom2\",\"start2\",\"end2\"])[\"distance\"].idxmin()]\n",
    "display(_down_tss)\n",
    "(_down_tss[\"start2\"]>_down_tss[\"end1\"]).all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "satisfactory-metabolism",
   "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>chrom1</th>\n",
       "      <th>start1</th>\n",
       "      <th>end1</th>\n",
       "      <th>type1</th>\n",
       "      <th>strand1</th>\n",
       "      <th>chrom2</th>\n",
       "      <th>start2</th>\n",
       "      <th>end2</th>\n",
       "      <th>strand2</th>\n",
       "      <th>type2</th>\n",
       "      <th>distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>chr1</td>\n",
       "      <td>804937</td>\n",
       "      <td>805691</td>\n",
       "      <td>ctcf</td>\n",
       "      <td>+</td>\n",
       "      <td>chr1</td>\n",
       "      <td>762970</td>\n",
       "      <td>762971</td>\n",
       "      <td>+</td>\n",
       "      <td>tss</td>\n",
       "      <td>41966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>chr1</td>\n",
       "      <td>919017</td>\n",
       "      <td>919979</td>\n",
       "      <td>ctcf</td>\n",
       "      <td>+</td>\n",
       "      <td>chr1</td>\n",
       "      <td>901861</td>\n",
       "      <td>901862</td>\n",
       "      <td>+</td>\n",
       "      <td>tss</td>\n",
       "      <td>17155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>chr1</td>\n",
       "      <td>1057249</td>\n",
       "      <td>1057885</td>\n",
       "      <td>ctcf</td>\n",
       "      <td>+</td>\n",
       "      <td>chr1</td>\n",
       "      <td>1051468</td>\n",
       "      <td>1051469</td>\n",
       "      <td>-</td>\n",
       "      <td>tss</td>\n",
       "      <td>5780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>chr1</td>\n",
       "      <td>1225501</td>\n",
       "      <td>1225951</td>\n",
       "      <td>ctcf</td>\n",
       "      <td>-</td>\n",
       "      <td>chr1</td>\n",
       "      <td>1209233</td>\n",
       "      <td>1209234</td>\n",
       "      <td>-</td>\n",
       "      <td>tss</td>\n",
       "      <td>16267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>chr1</td>\n",
       "      <td>1590639</td>\n",
       "      <td>1591017</td>\n",
       "      <td>ctcf</td>\n",
       "      <td>-</td>\n",
       "      <td>chr1</td>\n",
       "      <td>1550794</td>\n",
       "      <td>1550795</td>\n",
       "      <td>+</td>\n",
       "      <td>tss</td>\n",
       "      <td>39844</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8725</th>\n",
       "      <td>chr9</td>\n",
       "      <td>139588664</td>\n",
       "      <td>139589215</td>\n",
       "      <td>ctcf</td>\n",
       "      <td>-</td>\n",
       "      <td>chr9</td>\n",
       "      <td>139581908</td>\n",
       "      <td>139581909</td>\n",
       "      <td>-</td>\n",
       "      <td>tss</td>\n",
       "      <td>6755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8726</th>\n",
       "      <td>chr9</td>\n",
       "      <td>139929536</td>\n",
       "      <td>139930441</td>\n",
       "      <td>ctcf</td>\n",
       "      <td>+</td>\n",
       "      <td>chr9</td>\n",
       "      <td>139886873</td>\n",
       "      <td>139886874</td>\n",
       "      <td>+</td>\n",
       "      <td>tss</td>\n",
       "      <td>42662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8727</th>\n",
       "      <td>chr9</td>\n",
       "      <td>140121819</td>\n",
       "      <td>140122389</td>\n",
       "      <td>ctcf</td>\n",
       "      <td>-</td>\n",
       "      <td>chr9</td>\n",
       "      <td>140115774</td>\n",
       "      <td>140115775</td>\n",
       "      <td>-</td>\n",
       "      <td>tss</td>\n",
       "      <td>6044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8728</th>\n",
       "      <td>chr9</td>\n",
       "      <td>140188936</td>\n",
       "      <td>140190350</td>\n",
       "      <td>ctcf</td>\n",
       "      <td>+</td>\n",
       "      <td>chr9</td>\n",
       "      <td>140149778</td>\n",
       "      <td>140149779</td>\n",
       "      <td>+</td>\n",
       "      <td>tss</td>\n",
       "      <td>39157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8729</th>\n",
       "      <td>chr9</td>\n",
       "      <td>140567033</td>\n",
       "      <td>140568019</td>\n",
       "      <td>ctcf</td>\n",
       "      <td>+</td>\n",
       "      <td>chr9</td>\n",
       "      <td>140513456</td>\n",
       "      <td>140513457</td>\n",
       "      <td>+</td>\n",
       "      <td>tss</td>\n",
       "      <td>53576</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3486 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     chrom1     start1       end1 type1 strand1 chrom2     start2       end2  \\\n",
       "0      chr1     804937     805691  ctcf       +   chr1     762970     762971   \n",
       "3      chr1     919017     919979  ctcf       +   chr1     901861     901862   \n",
       "5      chr1    1057249    1057885  ctcf       +   chr1    1051468    1051469   \n",
       "6      chr1    1225501    1225951  ctcf       -   chr1    1209233    1209234   \n",
       "8      chr1    1590639    1591017  ctcf       -   chr1    1550794    1550795   \n",
       "...     ...        ...        ...   ...     ...    ...        ...        ...   \n",
       "8725   chr9  139588664  139589215  ctcf       -   chr9  139581908  139581909   \n",
       "8726   chr9  139929536  139930441  ctcf       +   chr9  139886873  139886874   \n",
       "8727   chr9  140121819  140122389  ctcf       -   chr9  140115774  140115775   \n",
       "8728   chr9  140188936  140190350  ctcf       +   chr9  140149778  140149779   \n",
       "8729   chr9  140567033  140568019  ctcf       +   chr9  140513456  140513457   \n",
       "\n",
       "     strand2 type2  distance  \n",
       "0          +   tss     41966  \n",
       "3          +   tss     17155  \n",
       "5          -   tss      5780  \n",
       "6          -   tss     16267  \n",
       "8          +   tss     39844  \n",
       "...      ...   ...       ...  \n",
       "8725       -   tss      6755  \n",
       "8726       +   tss     42662  \n",
       "8727       -   tss      6044  \n",
       "8728       +   tss     39157  \n",
       "8729       +   tss     53576  \n",
       "\n",
       "[3486 rows x 11 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# for every CTCF find nearest TSS downstream\n",
    "_up = bioframe.closest(\n",
    "    ctcf_motif_annotated,\n",
    "    tss_df,\n",
    "    k=1,\n",
    "    ignore_overlaps=True,\n",
    "    ignore_upstream=False,\n",
    "    ignore_downstream=True,\n",
    "    suffixes=('1', '2'),\n",
    ")\n",
    "# groupby on TSSs - to select the closest CTCF\n",
    "# and make sure we get CTCF-TSS interactions with nothing in between\n",
    "# display(_up)\n",
    "_up_tss = _up.loc[_up.groupby([\"chrom2\",\"start2\",\"end2\"])[\"distance\"].idxmin()]\n",
    "display(_up_tss)\n",
    "(_up_tss[\"start2\"]<_up_tss[\"end1\"]).all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "secure-avenue",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there should be 0 duplicated interactions in our list - we got 0\n",
      "there are 1454 TSS that show up >1 in the interactions list (they have >1 nearest CTCF)\n",
      "there are 1413 CTCF that show up >1 in the interactions list\n",
      "there are 5518 TSS we have in the interaction list, out of 8849 total (3331 unused ...)\n",
      "there are 0 super short range CTCF-TSS interactions in our final list\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#####\n",
    "# Concatenating up and down CTCF-TSS as is ! without flipping - just to produce some\n",
    "# important informational messages ...\n",
    "# to be redone for output and other checks ...\n",
    "#####\n",
    "# concat upp and down CTCF-TSS interactions:\n",
    "_ctcf_tss_df = pd.concat([_up_tss,_down_tss]).reset_index(drop=True)\n",
    "# are there any duplicated interactions for whatever reason - should be none ...\n",
    "dups_ints = _ctcf_tss_df.duplicated(subset=[\"chrom1\",\"start1\",\"end1\",\"chrom2\",\"start2\",\"end2\"]).sum()\n",
    "print(f\"there should be 0 duplicated interactions in our list - we got {dups_ints}\")\n",
    "# how many TSS show up twice in the list ?\n",
    "dups_tss = _ctcf_tss_df.duplicated(subset=[\"chrom2\",\"start2\",\"end2\"]).sum()\n",
    "print(f\"there are {dups_tss} TSS that show up >1 in the interactions list (they have >1 nearest CTCF)\")\n",
    "# how many CTCF show up twice in the list ?\n",
    "dups_ctcf = _ctcf_tss_df.duplicated(subset=[\"chrom1\",\"start1\",\"end1\"]).sum()\n",
    "print(f\"there are {dups_ctcf} CTCF that show up >1 in the interactions list\")\n",
    "# how many unique TSS are we dealing with ?\n",
    "uniq_tss = len(_ctcf_tss_df.drop_duplicates(subset=[\"chrom2\",\"start2\",\"end2\"]))\n",
    "print(f\"there are {uniq_tss} TSS we have in the interaction list, out of {len(tss_df)} total ({len(tss_df)-uniq_tss} unused ...)\")\n",
    "# how many of the interactions are closer than 2kb ? - should be none! - by construction of the input CTCF and TSS lists...\n",
    "short_range_ints = (_ctcf_tss_df[\"distance\"]<2_000).sum()\n",
    "print(f\"there are {short_range_ints} super short range CTCF-TSS interactions in our final list\")\n",
    "\n",
    "# check out the CTCF-TSS interaction distance ditribution as well!\n",
    "bins = np.linspace(2_000,1_000_000,100)\n",
    "_ctcf_tss_df[\"distance\"].plot.hist(bins=bins)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "homeless-proposition",
   "metadata": {},
   "source": [
    "* Results o such procedure (steps 1-3):\n",
    "     - ~3000 TSS are not included at all (they must have other TSSs around them, closer then the nearest CTCF - check that?)\n",
    "     - ~1500 TSS have both upstream and downstream nearest CTCF (we kept both interactions in the list for now)\n",
    "     - ~4000 TSS have only single nearest CTCF on one side\n",
    "     - remember - we are considering CTCFs with cohesin only ! there are other CTCFs around of course ..."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "individual-identification",
   "metadata": {},
   "source": [
    "### Now prepare the final list of CTCF-TSS interactions, such that it corresponds to the \"upper triangle\" of a Hi-C heatmap !\n",
    "#### flip to make sure `chom2,start2,end2 >= chom1,start1,end1` throughout !"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "worthy-temple",
   "metadata": {},
   "outputs": [],
   "source": [
    "# this looks like a dangerous cyclic flipping 1->2, 2->1 - but it seemd to work fine !\n",
    "_cyclic_rename = {\"chrom1\":\"chrom2\",\n",
    "        \"start1\":\"start2\",\n",
    "        \"end1\":\"end2\",\n",
    "        \"type1\":\"type2\",\n",
    "        \"strand1\":\"strand2\",\n",
    "        \"chrom2\":\"chrom1\",\n",
    "        \"start2\":\"start1\",\n",
    "        \"end2\":\"end1\",\n",
    "        \"type2\":\"type1\",\n",
    "        \"strand2\":\"strand1\"}\n",
    "_up_tss_flipped = _up_tss.rename(_cyclic_rename,axis=1).loc[:,_down_tss.columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "hairy-algorithm",
   "metadata": {},
   "outputs": [],
   "source": [
    "ctcf_tss_df = pd.concat([_up_tss_flipped,_down_tss]).sort_values([\"chrom1\",\"start1\"]).reset_index(drop=True)\n",
    "# make sure all interactions are from triu !\n",
    "(ctcf_tss_df[\"start2\"]>ctcf_tss_df[\"end1\"]).all()\n",
    "# save it !\n",
    "ctcf_tss_df.to_csv(\"nearestCTCF_TSS.bedpe\",index=False,sep=\"\\t\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "rough-habitat",
   "metadata": {},
   "source": [
    "#### more ultimate checks could be done to see if there is anything between a pair of \"interacting\" CTCF and TSS ...\n",
    "#### this could even be used to further filter out some of the unwanted interactions\n",
    "#### - e.g. those that have a CTCF-without-motif stuck between CTCF and TSS ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "funny-january",
   "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>chrom1</th>\n",
       "      <th>start1</th>\n",
       "      <th>end1</th>\n",
       "      <th>type1</th>\n",
       "      <th>strand1</th>\n",
       "      <th>chrom2</th>\n",
       "      <th>start2</th>\n",
       "      <th>end2</th>\n",
       "      <th>strand2</th>\n",
       "      <th>type2</th>\n",
       "      <th>distance</th>\n",
       "      <th>chrom_</th>\n",
       "      <th>start_</th>\n",
       "      <th>end_</th>\n",
       "      <th>strand_</th>\n",
       "      <th>type_</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [chrom1, start1, end1, type1, strand1, chrom2, start2, end2, strand2, type2, distance, chrom_, start_, end_, strand_, type_]\n",
       "Index: []"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>chrom1</th>\n",
       "      <th>start1</th>\n",
       "      <th>end1</th>\n",
       "      <th>type1</th>\n",
       "      <th>strand1</th>\n",
       "      <th>chrom2</th>\n",
       "      <th>start2</th>\n",
       "      <th>end2</th>\n",
       "      <th>strand2</th>\n",
       "      <th>type2</th>\n",
       "      <th>distance</th>\n",
       "      <th>chrom_</th>\n",
       "      <th>start_</th>\n",
       "      <th>end_</th>\n",
       "      <th>type_</th>\n",
       "      <th>strand_</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [chrom1, start1, end1, type1, strand1, chrom2, start2, end2, strand2, type2, distance, chrom_, start_, end_, type_, strand_]\n",
       "Index: []"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>index</th>\n",
       "      <th>chrom1</th>\n",
       "      <th>start1</th>\n",
       "      <th>end1</th>\n",
       "      <th>type1</th>\n",
       "      <th>strand1</th>\n",
       "      <th>chrom2</th>\n",
       "      <th>start2</th>\n",
       "      <th>end2</th>\n",
       "      <th>strand2</th>\n",
       "      <th>type2</th>\n",
       "      <th>distance</th>\n",
       "      <th>index_</th>\n",
       "      <th>chrom_</th>\n",
       "      <th>start_</th>\n",
       "      <th>end_</th>\n",
       "      <th>type_</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>804</td>\n",
       "      <td>chr10</td>\n",
       "      <td>23446754</td>\n",
       "      <td>23447770</td>\n",
       "      <td>ctcf</td>\n",
       "      <td>+</td>\n",
       "      <td>chr10</td>\n",
       "      <td>23633725</td>\n",
       "      <td>23633726</td>\n",
       "      <td>-</td>\n",
       "      <td>tss</td>\n",
       "      <td>185955</td>\n",
       "      <td>1081</td>\n",
       "      <td>chr10</td>\n",
       "      <td>23582010</td>\n",
       "      <td>23582505</td>\n",
       "      <td>ctcf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>814</td>\n",
       "      <td>chr10</td>\n",
       "      <td>27847443</td>\n",
       "      <td>27847908</td>\n",
       "      <td>ctcf</td>\n",
       "      <td>+</td>\n",
       "      <td>chr10</td>\n",
       "      <td>28288007</td>\n",
       "      <td>28288008</td>\n",
       "      <td>-</td>\n",
       "      <td>tss</td>\n",
       "      <td>440099</td>\n",
       "      <td>1093</td>\n",
       "      <td>chr10</td>\n",
       "      <td>28030975</td>\n",
       "      <td>28031425</td>\n",
       "      <td>ctcf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>831</td>\n",
       "      <td>chr10</td>\n",
       "      <td>35379541</td>\n",
       "      <td>35379542</td>\n",
       "      <td>tss</td>\n",
       "      <td>-</td>\n",
       "      <td>chr10</td>\n",
       "      <td>35507265</td>\n",
       "      <td>35507893</td>\n",
       "      <td>-</td>\n",
       "      <td>ctcf</td>\n",
       "      <td>127723</td>\n",
       "      <td>1124</td>\n",
       "      <td>chr10</td>\n",
       "      <td>35484867</td>\n",
       "      <td>35485395</td>\n",
       "      <td>ctcf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>836</td>\n",
       "      <td>chr10</td>\n",
       "      <td>43843824</td>\n",
       "      <td>43844433</td>\n",
       "      <td>ctcf</td>\n",
       "      <td>-</td>\n",
       "      <td>chr10</td>\n",
       "      <td>43932295</td>\n",
       "      <td>43932296</td>\n",
       "      <td>+</td>\n",
       "      <td>tss</td>\n",
       "      <td>87862</td>\n",
       "      <td>1140</td>\n",
       "      <td>chr10</td>\n",
       "      <td>43857798</td>\n",
       "      <td>43858199</td>\n",
       "      <td>ctcf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>875</td>\n",
       "      <td>chr10</td>\n",
       "      <td>64564479</td>\n",
       "      <td>64564480</td>\n",
       "      <td>tss</td>\n",
       "      <td>+</td>\n",
       "      <td>chr10</td>\n",
       "      <td>64578436</td>\n",
       "      <td>64578761</td>\n",
       "      <td>+</td>\n",
       "      <td>ctcf</td>\n",
       "      <td>13956</td>\n",
       "      <td>1188</td>\n",
       "      <td>chr10</td>\n",
       "      <td>64577608</td>\n",
       "      <td>64578177</td>\n",
       "      <td>ctcf</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>265</th>\n",
       "      <td>3988</td>\n",
       "      <td>chr2</td>\n",
       "      <td>175640989</td>\n",
       "      <td>175641328</td>\n",
       "      <td>ctcf</td>\n",
       "      <td>-</td>\n",
       "      <td>chr2</td>\n",
       "      <td>176032436</td>\n",
       "      <td>176032437</td>\n",
       "      <td>-</td>\n",
       "      <td>tss</td>\n",
       "      <td>391108</td>\n",
       "      <td>5259</td>\n",
       "      <td>chr2</td>\n",
       "      <td>175855007</td>\n",
       "      <td>175855392</td>\n",
       "      <td>ctcf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266</th>\n",
       "      <td>4052</td>\n",
       "      <td>chr2</td>\n",
       "      <td>216878028</td>\n",
       "      <td>216878604</td>\n",
       "      <td>ctcf</td>\n",
       "      <td>-</td>\n",
       "      <td>chr2</td>\n",
       "      <td>216946531</td>\n",
       "      <td>216946532</td>\n",
       "      <td>-</td>\n",
       "      <td>tss</td>\n",
       "      <td>67927</td>\n",
       "      <td>5362</td>\n",
       "      <td>chr2</td>\n",
       "      <td>216879026</td>\n",
       "      <td>216879255</td>\n",
       "      <td>ctcf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267</th>\n",
       "      <td>4076</td>\n",
       "      <td>chr2</td>\n",
       "      <td>231964625</td>\n",
       "      <td>231964917</td>\n",
       "      <td>ctcf</td>\n",
       "      <td>+</td>\n",
       "      <td>chr2</td>\n",
       "      <td>232063344</td>\n",
       "      <td>232063345</td>\n",
       "      <td>+</td>\n",
       "      <td>tss</td>\n",
       "      <td>98427</td>\n",
       "      <td>5428</td>\n",
       "      <td>chr2</td>\n",
       "      <td>231968867</td>\n",
       "      <td>231969170</td>\n",
       "      <td>ctcf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>268</th>\n",
       "      <td>4077</td>\n",
       "      <td>chr2</td>\n",
       "      <td>232063344</td>\n",
       "      <td>232063345</td>\n",
       "      <td>tss</td>\n",
       "      <td>+</td>\n",
       "      <td>chr2</td>\n",
       "      <td>232276934</td>\n",
       "      <td>232277641</td>\n",
       "      <td>-</td>\n",
       "      <td>ctcf</td>\n",
       "      <td>213589</td>\n",
       "      <td>5429</td>\n",
       "      <td>chr2</td>\n",
       "      <td>232270421</td>\n",
       "      <td>232270859</td>\n",
       "      <td>ctcf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>269</th>\n",
       "      <td>4077</td>\n",
       "      <td>chr2</td>\n",
       "      <td>232063344</td>\n",
       "      <td>232063345</td>\n",
       "      <td>tss</td>\n",
       "      <td>+</td>\n",
       "      <td>chr2</td>\n",
       "      <td>232276934</td>\n",
       "      <td>232277641</td>\n",
       "      <td>-</td>\n",
       "      <td>ctcf</td>\n",
       "      <td>213589</td>\n",
       "      <td>5430</td>\n",
       "      <td>chr2</td>\n",
       "      <td>232274726</td>\n",
       "      <td>232275403</td>\n",
       "      <td>ctcf</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>270 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     index chrom1     start1       end1 type1 strand1 chrom2     start2  \\\n",
       "0      804  chr10   23446754   23447770  ctcf       +  chr10   23633725   \n",
       "1      814  chr10   27847443   27847908  ctcf       +  chr10   28288007   \n",
       "2      831  chr10   35379541   35379542   tss       -  chr10   35507265   \n",
       "3      836  chr10   43843824   43844433  ctcf       -  chr10   43932295   \n",
       "4      875  chr10   64564479   64564480   tss       +  chr10   64578436   \n",
       "..     ...    ...        ...        ...   ...     ...    ...        ...   \n",
       "265   3988   chr2  175640989  175641328  ctcf       -   chr2  176032436   \n",
       "266   4052   chr2  216878028  216878604  ctcf       -   chr2  216946531   \n",
       "267   4076   chr2  231964625  231964917  ctcf       +   chr2  232063344   \n",
       "268   4077   chr2  232063344  232063345   tss       +   chr2  232276934   \n",
       "269   4077   chr2  232063344  232063345   tss       +   chr2  232276934   \n",
       "\n",
       "          end2 strand2 type2  distance  index_ chrom_     start_       end_  \\\n",
       "0     23633726       -   tss    185955    1081  chr10   23582010   23582505   \n",
       "1     28288008       -   tss    440099    1093  chr10   28030975   28031425   \n",
       "2     35507893       -  ctcf    127723    1124  chr10   35484867   35485395   \n",
       "3     43932296       +   tss     87862    1140  chr10   43857798   43858199   \n",
       "4     64578761       +  ctcf     13956    1188  chr10   64577608   64578177   \n",
       "..         ...     ...   ...       ...     ...    ...        ...        ...   \n",
       "265  176032437       -   tss    391108    5259   chr2  175855007  175855392   \n",
       "266  216946532       -   tss     67927    5362   chr2  216879026  216879255   \n",
       "267  232063345       +   tss     98427    5428   chr2  231968867  231969170   \n",
       "268  232277641       -  ctcf    213589    5429   chr2  232270421  232270859   \n",
       "269  232277641       -  ctcf    213589    5430   chr2  232274726  232275403   \n",
       "\n",
       "    type_  \n",
       "0    ctcf  \n",
       "1    ctcf  \n",
       "2    ctcf  \n",
       "3    ctcf  \n",
       "4    ctcf  \n",
       "..    ...  \n",
       "265  ctcf  \n",
       "266  ctcf  \n",
       "267  ctcf  \n",
       "268  ctcf  \n",
       "269  ctcf  \n",
       "\n",
       "[270 rows x 17 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(bioframe.overlap(\n",
    "    ctcf_tss_df,\n",
    "    tss_df,\n",
    "    how=\"inner\",\n",
    "    cols1 = [\"chrom1\",\"end1\",\"start2\"],\n",
    "    cols2=None\n",
    "))\n",
    "display(bioframe.overlap(\n",
    "    ctcf_tss_df,\n",
    "    ctcf_motif_annotated,\n",
    "    how=\"inner\",\n",
    "    cols1 = [\"chrom1\",\"end1\",\"start2\"],\n",
    "    cols2=None\n",
    "))\n",
    "display(bioframe.overlap(\n",
    "    ctcf_tss_df,\n",
    "    ctcf_df,\n",
    "    how=\"inner\",\n",
    "    return_index=True,\n",
    "    cols1 = [\"chrom1\",\"end1\",\"start2\"],\n",
    "    cols2=None\n",
    "))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "brilliant-digest",
   "metadata": {},
   "source": [
    "##### we could actually just select interactions between CTCF and TSS - and figure out splitting by oriewntation later on in the pile up script itself !"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "intimate-baltimore",
   "metadata": {},
   "outputs": [],
   "source": [
    "bedpe[(bedpe[\"type1\"]!=bedpe[\"type2\"])].reset_index(drop=True).to_csv(\"allCTCF_TSS.bedpe\",index=False,sep=\"\\t\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "recovered-submission",
   "metadata": {},
   "source": [
    "### Breaking it down by orientation of TSS ...\n",
    "\n",
    "remember - we are alway looking at the upper triangle of the Hi-C heatmap\n",
    "so in our BEDPEs left coordinate is always going to be \"smaller\" then the right one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "impressed-houston",
   "metadata": {},
   "outputs": [],
   "source": [
    "# likely not needed at this point\n",
    "BEDPE_cols = ['chrom1','start1','end1','chrom2','start2','end2']\n",
    "\n",
    "bedpe[((bedpe[\"type1\"]==\"ctcf\")&(bedpe[\"type2\"]==\"tss\")&(bedpe[\"strand2\"]==\"+\"))][BEDPE_cols] \\\n",
    "    .reset_index(drop=True) \\\n",
    "    .to_csv(\"upstreamCTCF_pTSS.bedpe\",index=False,sep=\"\\t\")\n",
    "\n",
    "bedpe[((bedpe[\"type2\"]==\"ctcf\")&(bedpe[\"type1\"]==\"tss\")&(bedpe[\"strand1\"]==\"-\"))][BEDPE_cols] \\\n",
    "    .reset_index(drop=True) \\\n",
    "    .to_csv(\"downstreamCTCF_mTSS.bedpe\",index=False,sep=\"\\t\")\n",
    "\n",
    "bedpe[((bedpe[\"type1\"]==\"ctcf\")&(bedpe[\"type2\"]==\"tss\")&(bedpe[\"strand2\"]==\"-\"))][BEDPE_cols] \\\n",
    "    .reset_index(drop=True) \\\n",
    "    .to_csv(\"upstreamCTCF_mTSS_ctrl.bedpe\",index=False,sep=\"\\t\")\n",
    "\n",
    "bedpe[((bedpe[\"type2\"]==\"ctcf\")&(bedpe[\"type1\"]==\"tss\")&(bedpe[\"strand1\"]==\"+\"))][BEDPE_cols] \\\n",
    "    .reset_index(drop=True) \\\n",
    "    .to_csv(\"downstreamCTCF_pTSS_ctrl.bedpe\",index=False,sep=\"\\t\")\n",
    "\n",
    "# !scp upstreamCTCF_pTSS.bedpe ghpcc:/nl/umw_job_dekker/users/sv49w/ALV2021/\n",
    "# !scp downstreamCTCF_mTSS.bedpe ghpcc:/nl/umw_job_dekker/users/sv49w/ALV2021/\n",
    "# !scp downstreamCTCF_pTSS_ctrl.bedpe ghpcc:/nl/umw_job_dekker/users/sv49w/ALV2021/\n",
    "# !scp upstreamCTCF_mTSS_ctrl.bedpe ghpcc:/nl/umw_job_dekker/users/sv49w/ALV2021/"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "theoretical-particle",
   "metadata": {},
   "source": [
    "### Let's do all pairwise interaction again , but having CTCF orientation in mind!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "consecutive-procedure",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'df' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-47-d01ae6da198a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdfs_concat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mctcf_motif_annotated\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreset_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;31m# print(dfs_concat.head(3))\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mbedpe\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbioframe\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpair_by_distance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdfs_concat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmin_sep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m50_000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_sep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m400_000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msuffixes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'1'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'2'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbedpe\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'df' is not defined"
     ]
    }
   ],
   "source": [
    "dfs_concat = pd.concat([df,ctcf_motif_annotated]).reset_index(drop=True)\n",
    "# print(dfs_concat.head(3))\n",
    "bedpe = bioframe.pair_by_distance(dfs_concat, min_sep=50_000, max_sep=400_000, suffixes=('1','2'))\n",
    "print(bedpe.head(3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "friendly-palace",
   "metadata": {},
   "outputs": [],
   "source": [
    "# feature1: CTCF upstream, +; TSS+ downstream\n",
    "orientation_cond1 = (bedpe[\"type1\"]==\"ctcf\") & (bedpe[\"strand1\"]==\"+\") \\\n",
    "                    & (bedpe[\"type2\"]==\"tss\") &(bedpe[\"strand2\"]==\"+\")\n",
    "# bedpe[orientation_cond][BEDPE_cols] \\\n",
    "#     .reset_index(drop=True) \\\n",
    "#     .to_csv(\"upstreamCTCF_pTSS.bedpe\",index=False,sep=\"\\t\")\n",
    "\n",
    "# feature1: TSS upstream, -; CTCF- downstream\n",
    "orientation_cond2 = (bedpe[\"type1\"]==\"tss\") & (bedpe[\"strand1\"]==\"-\") \\\n",
    "                    & (bedpe[\"type2\"]==\"ctcf\") &(bedpe[\"strand2\"]==\"-\")\n",
    "\n",
    "\n",
    "bedpe[orientation_cond1 | orientation_cond2][BEDPE_cols+[\"strand1\"]] \\\n",
    "    .reset_index(drop=True) \\\n",
    "    .to_csv(\"CTCF_TSS_same_strand.bedpe\",index=False,sep=\"\\t\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "proud-training",
   "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>chrom1_1</th>\n",
       "      <th>start1_1</th>\n",
       "      <th>end1_1</th>\n",
       "      <th>type1_1</th>\n",
       "      <th>strand1_1</th>\n",
       "      <th>chrom2_1</th>\n",
       "      <th>start2_1</th>\n",
       "      <th>end2_1</th>\n",
       "      <th>strand2_1</th>\n",
       "      <th>type2_1</th>\n",
       "      <th>distance_1</th>\n",
       "      <th>chrom_2</th>\n",
       "      <th>start_2</th>\n",
       "      <th>end_2</th>\n",
       "      <th>type_2</th>\n",
       "      <th>strand_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>chr1</td>\n",
       "      <td>10151.0</td>\n",
       "      <td>10508.0</td>\n",
       "      <td>ctcf</td>\n",
       "      <td>-</td>\n",
       "      <td>chr1</td>\n",
       "      <td>29365.0</td>\n",
       "      <td>29370.0</td>\n",
       "      <td>-</td>\n",
       "      <td>tss</td>\n",
       "      <td>18857.0</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>chr1</td>\n",
       "      <td>2313115.0</td>\n",
       "      <td>2314622.0</td>\n",
       "      <td>ctcf</td>\n",
       "      <td>-</td>\n",
       "      <td>chr1</td>\n",
       "      <td>2322988.0</td>\n",
       "      <td>2322993.0</td>\n",
       "      <td>-</td>\n",
       "      <td>tss</td>\n",
       "      <td>8366.0</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>chr1</td>\n",
       "      <td>3640901.0</td>\n",
       "      <td>3641906.0</td>\n",
       "      <td>ctcf</td>\n",
       "      <td>-</td>\n",
       "      <td>chr1</td>\n",
       "      <td>3663932.0</td>\n",
       "      <td>3663937.0</td>\n",
       "      <td>-</td>\n",
       "      <td>tss</td>\n",
       "      <td>22026.0</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>chr1</td>\n",
       "      <td>6464684.0</td>\n",
       "      <td>6465436.0</td>\n",
       "      <td>ctcf</td>\n",
       "      <td>-</td>\n",
       "      <td>chr1</td>\n",
       "      <td>6479974.0</td>\n",
       "      <td>6479979.0</td>\n",
       "      <td>-</td>\n",
       "      <td>tss</td>\n",
       "      <td>14538.0</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>chr1</td>\n",
       "      <td>6549687.0</td>\n",
       "      <td>6550641.0</td>\n",
       "      <td>ctcf</td>\n",
       "      <td>-</td>\n",
       "      <td>chr1</td>\n",
       "      <td>6640053.0</td>\n",
       "      <td>6640058.0</td>\n",
       "      <td>+</td>\n",
       "      <td>tss</td>\n",
       "      <td>89412.0</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2738</th>\n",
       "      <td>chr9</td>\n",
       "      <td>136964232.0</td>\n",
       "      <td>136964900.0</td>\n",
       "      <td>ctcf</td>\n",
       "      <td>-</td>\n",
       "      <td>chr9</td>\n",
       "      <td>137000320.0</td>\n",
       "      <td>137000325.0</td>\n",
       "      <td>+</td>\n",
       "      <td>tss</td>\n",
       "      <td>35420.0</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2739</th>\n",
       "      <td>chr9</td>\n",
       "      <td>139565964.0</td>\n",
       "      <td>139566655.0</td>\n",
       "      <td>ctcf</td>\n",
       "      <td>-</td>\n",
       "      <td>chr9</td>\n",
       "      <td>139581904.0</td>\n",
       "      <td>139581909.0</td>\n",
       "      <td>-</td>\n",
       "      <td>tss</td>\n",
       "      <td>15249.0</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2740</th>\n",
       "      <td>chr9</td>\n",
       "      <td>139588664.0</td>\n",
       "      <td>139589215.0</td>\n",
       "      <td>ctcf</td>\n",
       "      <td>-</td>\n",
       "      <td>chr9</td>\n",
       "      <td>139607023.0</td>\n",
       "      <td>139607028.0</td>\n",
       "      <td>+</td>\n",
       "      <td>tss</td>\n",
       "      <td>17808.0</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2741</th>\n",
       "      <td>chr9</td>\n",
       "      <td>139622478.0</td>\n",
       "      <td>139623139.0</td>\n",
       "      <td>ctcf</td>\n",
       "      <td>-</td>\n",
       "      <td>chr9</td>\n",
       "      <td>139702399.0</td>\n",
       "      <td>139702404.0</td>\n",
       "      <td>+</td>\n",
       "      <td>tss</td>\n",
       "      <td>79260.0</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2742</th>\n",
       "      <td>chr9</td>\n",
       "      <td>140339533.0</td>\n",
       "      <td>140340365.0</td>\n",
       "      <td>ctcf</td>\n",
       "      <td>-</td>\n",
       "      <td>chr9</td>\n",
       "      <td>140353781.0</td>\n",
       "      <td>140353786.0</td>\n",
       "      <td>-</td>\n",
       "      <td>tss</td>\n",
       "      <td>13416.0</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1593 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     chrom1_1     start1_1       end1_1 type1_1 strand1_1 chrom2_1  \\\n",
       "127      chr1      10151.0      10508.0    ctcf         -     chr1   \n",
       "128      chr1    2313115.0    2314622.0    ctcf         -     chr1   \n",
       "129      chr1    3640901.0    3641906.0    ctcf         -     chr1   \n",
       "130      chr1    6464684.0    6465436.0    ctcf         -     chr1   \n",
       "131      chr1    6549687.0    6550641.0    ctcf         -     chr1   \n",
       "...       ...          ...          ...     ...       ...      ...   \n",
       "2738     chr9  136964232.0  136964900.0    ctcf         -     chr9   \n",
       "2739     chr9  139565964.0  139566655.0    ctcf         -     chr9   \n",
       "2740     chr9  139588664.0  139589215.0    ctcf         -     chr9   \n",
       "2741     chr9  139622478.0  139623139.0    ctcf         -     chr9   \n",
       "2742     chr9  140339533.0  140340365.0    ctcf         -     chr9   \n",
       "\n",
       "         start2_1       end2_1 strand2_1 type2_1  distance_1 chrom_2  start_2  \\\n",
       "127       29365.0      29370.0         -     tss     18857.0    None      NaN   \n",
       "128     2322988.0    2322993.0         -     tss      8366.0    None      NaN   \n",
       "129     3663932.0    3663937.0         -     tss     22026.0    None      NaN   \n",
       "130     6479974.0    6479979.0         -     tss     14538.0    None      NaN   \n",
       "131     6640053.0    6640058.0         +     tss     89412.0    None      NaN   \n",
       "...           ...          ...       ...     ...         ...     ...      ...   \n",
       "2738  137000320.0  137000325.0         +     tss     35420.0    None      NaN   \n",
       "2739  139581904.0  139581909.0         -     tss     15249.0    None      NaN   \n",
       "2740  139607023.0  139607028.0         +     tss     17808.0    None      NaN   \n",
       "2741  139702399.0  139702404.0         +     tss     79260.0    None      NaN   \n",
       "2742  140353781.0  140353786.0         -     tss     13416.0    None      NaN   \n",
       "\n",
       "      end_2 type_2 strand_2  \n",
       "127     NaN   None     None  \n",
       "128     NaN   None     None  \n",
       "129     NaN   None     None  \n",
       "130     NaN   None     None  \n",
       "131     NaN   None     None  \n",
       "...     ...    ...      ...  \n",
       "2738    NaN   None     None  \n",
       "2739    NaN   None     None  \n",
       "2740    NaN   None     None  \n",
       "2741    NaN   None     None  \n",
       "2742    NaN   None     None  \n",
       "\n",
       "[1593 rows x 16 columns]"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xxx = bioframe.overlap(\n",
    "    pctcf_downtss,\n",
    "    ctcf_motif_annotated[ctcf_motif_annotated[\"strand\"]==\"+\"],\n",
    "    how=\"left\",\n",
    "    cols1=[\"chrom1\",\"end1\",\"end2\"], #[\"chrom2\",\"end2\",\"start1\"]\n",
    "    cols2=None,\n",
    ")\n",
    "# print(xxx)\n",
    "# xxx.drop_duplicates(subset=[\"chrom1_1\",\"end1_1\",\"end2_1\"])\n",
    "yyy = xxx[pd.isna(xxx[\"start_2\"])]#.drop_duplicates(subset=[\"chrom1_1\",\"end1_1\",\"end2_1\"])\n",
    "yyy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "closing-yield",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = np.linspace(0,1_000_000,100)\n",
    "_,bins,_ = plt.hist(pctcf_downtss[\"distance\"],bins=bins,alpha=0.6,color=\"red\")\n",
    "# plt.hist(xxx.drop_duplicates(subset=[\"chrom1_1\",\"end1_1\",\"end2_1\"])[\"distance_1\"],bins=bins)\n",
    "plt.hist(xxx[pd.isna(xxx[\"start_2\"])][\"distance_1\"],bins=bins);\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "assisted-denver",
   "metadata": {},
   "outputs": [],
   "source": [
    "yyy[(yyy[\"distance_1\"]>100_000)&(yyy[\"strand2_1\"]==\"+\")].rename(\n",
    "    {\"chrom1_1\":\"chrom1\",\n",
    "    \"start1_1\":\"start1\",\n",
    "    \"end1_1\":\"end1\",\n",
    "    \"chrom2_1\":\"chrom2\",\n",
    "    \"start2_1\":\"start2\",\n",
    "    \"end2_1\":\"end2\"},\n",
    "    axis=1\n",
    "    )[BEDPE_cols].astype(\n",
    "    {\"start1\":np.int,\n",
    "    \"end1\":np.int,\n",
    "    \"start2\":np.int,\n",
    "    \"end2\":np.int}\n",
    ").reset_index(drop=True).to_csv(\"mctcf_ptss_nothingbetween.bedpe\",index=False,sep=\"\\t\")\n",
    "\n",
    "\n",
    "\n",
    "# \"chrom1_1\",\"start1_1\",\"end1_1\",\"type1_1\",\"strand1_1\",\"chrom2_1\",\"start2_1\",\"end2_1\"\n",
    "# \"chrom1_1\":\"chrom1\",\n",
    "# \"start1_1\":\"start1\",\n",
    "# \"end1_1\":\"end1\",\n",
    "# \"chrom2_1\":\"chrom2\",\n",
    "# \"start2_1\":\"start2\",\n",
    "# \"end2_1\":\"end2\""
   ]
  }
 ],
 "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}