distal-enhancer-mapping.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d32c699c-29a7-4a98-a2c7-65db0d0d8319",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import bioframe as bf\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors as colors\n",
    "import bbi\n",
    "from gtfparse import read_gtf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a3327ea-1c7f-4a8d-ac3a-d17e644e5137",
   "metadata": {},
   "source": [
    "# Looking into relationship with distal enhancers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4956366d-fffb-40f3-a2b2-46232351d284",
   "metadata": {},
   "source": [
    "### Gathering references"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7cda0886-8184-4b88-ba77-3f744974acfb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TSS annotations\n",
    "# deseq2 results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9268560b-3fc0-46fd-8598-d4a5185f6ba3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing reference tables and data outputs for the RNA-seq analysis\n",
    "\n",
    "# outputs from basic-sleuth-mm9 mapping\n",
    "deseq_results_folder = \"/project/fudenber_735/GEO/nora_2017_rna-seq_SRP106652/deseq-mm9-results/\"\n",
    "\n",
    "# sample 2 conditions extracted from this project metadata\n",
    "s2c = pd.read_csv(\"/project/fudenber_735/GEO/nora_2017_rna-seq_SRP106652/supplementary/s2c.csv\", index_col = 0)\n",
    "\n",
    "# t2g to compare gene-level analysis\n",
    "t2g = pd.read_csv(\"/project/fudenber_735/genomes/mm9/ensembl/ensembl_relational_downloads/biomaRt_mm9_t2g.csv\", index_col = 0)\n",
    "\n",
    "# DE results reported by paper (FPKM)\n",
    "fpkm = pd.read_csv(\"/project/fudenber_735/GEO/nora_2017_rna-seq_SRP106652/supplementary/supplement-10_RNA_seq_FPKM.csv\", index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c1f550ea-206e-4a0f-be2d-24f41c418b0e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home1/pokorny/.conda/envs/bioenv/lib/python3.9/site-packages/gtfparse/read_gtf.py:82: FutureWarning: The error_bad_lines argument has been deprecated and will be removed in a future version. Use on_bad_lines in the future.\n",
      "\n",
      "\n",
      "  chunk_iterator = pd.read_csv(\n",
      "/home1/pokorny/.conda/envs/bioenv/lib/python3.9/site-packages/gtfparse/read_gtf.py:82: FutureWarning: The warn_bad_lines argument has been deprecated and will be removed in a future version. Use on_bad_lines in the future.\n",
      "\n",
      "\n",
      "  chunk_iterator = pd.read_csv(\n",
      "INFO:root:Extracted GTF attributes: ['transcript_id', 'gene_id']\n"
     ]
    }
   ],
   "source": [
    "# Gene annotations, references\n",
    "\n",
    "# Reading gtf file that contains only the parsed TSSes\n",
    "tss_df = read_gtf('/project/fudenber_735/genomes/mm9/ensembl/tsses.gtf')\n",
    "\n",
    "# # enhancer coordinates from elphege\n",
    "# enhancer_coords = bf.read_table('/project/fudenber_735/collaborations/karissa_2022/features/mm9/mESC_Enhancer_prediction_Chen12_BMCgenomics_mm9.bed',\n",
    "#                                schema='bedGraph',\n",
    "#                                skiprows=1)\n",
    "\n",
    "# # super enhancer coordinates from Whyte 2013\n",
    "# enhancer_coords = bf.read_table('/project/fudenber_735/collaborations/karissa_2022/features/mm9/mESC_superEnhancer_Whyte13_mm9_cell_6822_mmc1.bed',\n",
    "#                                schema='bed3',\n",
    "#                                skiprows=1)\n",
    "\n",
    "# cleaning out less-well defined chromosome numbers\n",
    "tss_df = tss_df.loc[False==( tss_df['seqname'].str.contains('NT_'))]\n",
    "tss_df = tss_df.loc[False==( tss_df['seqname'].str.contains('MT'))]\n",
    "\n",
    "# paste 'chr' to all chromosome names\n",
    "tss_df[\"seqname\"] = 'chr' + tss_df[\"seqname\"]\n",
    "\n",
    "# rename column to chrom to match bedframe/bioframe format\n",
    "tss_df = tss_df.rename(columns= {\"seqname\" : \"chrom\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "1304767e-1d7c-4d60-af8c-664b194b0b58",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Super-enhancers from whyte 2013\n",
    "enhancers = pd.read_excel('/project/fudenber_735/collaborations/karissa_2022/features/mm9/NIHMS465689-supplement-02.xls', \n",
    "                   sheet_name='Supplemental_Table_S1',\n",
    "                  header=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "e076613e-015c-4fcf-a220-7554701e0446",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8794, 15)"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "enhancers.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "8ffa777e-e7f3-4cdc-b1e9-5059cfb79af6",
   "metadata": {},
   "outputs": [],
   "source": [
    "super_enhancers = enhancers.iloc[np.where(enhancers['isSuper'] == 'YES')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "150d509e-f0e8-4b6b-8712-0732ff66f6ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(231, 15)"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "super_enhancers.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31724550-72b0-4f80-ac26-ab861edc5a2f",
   "metadata": {},
   "source": [
    "### Taking a look at feature data\n",
    "\n",
    "* tss_df was generated for mm9 using gtfutils (can see source in TSS_heatmaps_deseq)\n",
    "* enhancer_coords from chen 2012 --> to follow the paper we'll filter based on a probability threshold of 0.8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "aacc6db0-0970-4d86-977e-1b73f0d6b39a",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>chrom</th>\n",
       "      <th>source</th>\n",
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       "      <td>75098870</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>18811720</td>\n",
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       "      <th>95878</th>\n",
       "      <td>chr10</td>\n",
       "      <td>gffutils_derived</td>\n",
       "      <td>transcript</td>\n",
       "      <td>62020939</td>\n",
       "      <td>62020939</td>\n",
       "      <td>NaN</td>\n",
       "      <td>+</td>\n",
       "      <td>0</td>\n",
       "      <td>ENSMUST00000175629</td>\n",
       "      <td>ENSMUSG00000093370</td>\n",
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       "      <td>79964657</td>\n",
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       "      <td>ENSMUST00000175630</td>\n",
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       "      <td>126139118</td>\n",
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       "      <td>35351013</td>\n",
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       "       chrom            source     feature      start        end  score  \\\n",
       "0       chr3  gffutils_derived  transcript  107949065  107949065    NaN   \n",
       "1       chrX  gffutils_derived  transcript   75098963   75098963    NaN   \n",
       "2       chrX  gffutils_derived  transcript   75098870   75098870    NaN   \n",
       "3      chr16  gffutils_derived  transcript   18812066   18812066    NaN   \n",
       "4      chr16  gffutils_derived  transcript   18811720   18811720    NaN   \n",
       "...      ...               ...         ...        ...        ...    ...   \n",
       "95878  chr10  gffutils_derived  transcript   62020939   62020939    NaN   \n",
       "95879  chr10  gffutils_derived  transcript   79964657   79964657    NaN   \n",
       "95880   chr8  gffutils_derived  transcript  126139118  126139118    NaN   \n",
       "95881  chr19  gffutils_derived  transcript   35351013   35351013    NaN   \n",
       "95882   chr4  gffutils_derived  transcript  110112376  110112376    NaN   \n",
       "\n",
       "      strand frame       transcript_id             gene_id  \n",
       "0          -     0  ENSMUST00000000001  ENSMUSG00000000001  \n",
       "1          -     0  ENSMUST00000000003  ENSMUSG00000000003  \n",
       "2          -     0  ENSMUST00000114041  ENSMUSG00000000003  \n",
       "3          -     0  ENSMUST00000000028  ENSMUSG00000000028  \n",
       "4          -     0  ENSMUST00000096990  ENSMUSG00000000028  \n",
       "...      ...   ...                 ...                 ...  \n",
       "95878      +     0  ENSMUST00000175629  ENSMUSG00000093370  \n",
       "95879      -     0  ENSMUST00000175630  ENSMUSG00000093371  \n",
       "95880      -     0  ENSMUST00000175631  ENSMUSG00000093372  \n",
       "95881      -     0  ENSMUST00000175632  ENSMUSG00000093373  \n",
       "95882      -     0  ENSMUST00000175633  ENSMUSG00000093374  \n",
       "\n",
       "[95458 rows x 10 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tss_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "07c8a7c3-669a-45cb-a5c9-c2ca0fcc9407",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(19200, 4)"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#enhancer_coords = super_enhancers[['chrom', 'start', 'end']]\n",
    "enhancer_coords.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "815dce00-7fec-4cfd-a4c3-3cb741096e0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# replacing value string with a decimal\n",
    "enhancer_coords['value'] = enhancer_coords['value'].apply(lambda x: x.replace(',', '.')).astype(float)\n",
    "\n",
    "enhancer_coords = bf.sanitize_bedframe(enhancer_coords)\n",
    "\n",
    "bf.is_bedframe(enhancer_coords)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "f8c25d23-f644-4001-bbeb-ab93bdb2a5e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1277, 4)\n"
     ]
    },
    {
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       "      <th></th>\n",
       "      <th>chrom</th>\n",
       "      <th>start</th>\n",
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       "      <th>1272</th>\n",
       "      <td>chr18</td>\n",
       "      <td>77381001</td>\n",
       "      <td>77382000</td>\n",
       "      <td>0.8005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1273</th>\n",
       "      <td>chr9</td>\n",
       "      <td>78222001</td>\n",
       "      <td>78223000</td>\n",
       "      <td>0.8004</td>\n",
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       "      <th>1274</th>\n",
       "      <td>chr1</td>\n",
       "      <td>138467001</td>\n",
       "      <td>138468000</td>\n",
       "      <td>0.8003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1275</th>\n",
       "      <td>chr14</td>\n",
       "      <td>12940001</td>\n",
       "      <td>12941000</td>\n",
       "      <td>0.8000</td>\n",
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       "    <tr>\n",
       "      <th>1276</th>\n",
       "      <td>chr2</td>\n",
       "      <td>135388001</td>\n",
       "      <td>135389000</td>\n",
       "      <td>0.8000</td>\n",
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      "text/plain": [
       "      chrom      start        end   value\n",
       "1272  chr18   77381001   77382000  0.8005\n",
       "1273   chr9   78222001   78223000  0.8004\n",
       "1274   chr1  138467001  138468000  0.8003\n",
       "1275  chr14   12940001   12941000  0.8000\n",
       "1276   chr2  135388001  135389000  0.8000"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Taking cutoff\n",
    "enhancer_coords = enhancer_coords.loc[np.where(enhancer_coords['value'] >= 0.8)]\n",
    "\n",
    "print(enhancer_coords.shape)\n",
    "\n",
    "# Displaying last 5 --> the original data was ordered descending by 'value'\n",
    "enhancer_coords.tail(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "371dd9a5-af0f-43f1-900f-bec5744265b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# saving these coordinates with .8 cutoff so that can do liftover for mm10 later\n",
    "#enhancer_coords.to_csv('/project/fudenber_735/collaborations/karissa_2022/features/mm9/mESC_Enhancer_prediction_Chen12_BMCgenomics_mm9_above8.bed', sep='\\t')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f68b17d4-dfc3-4063-95e3-0c7fb930cb3d",
   "metadata": {},
   "source": [
    "## Variation 1: dCTCF DEGs mapped to mm9 (kallisto, DESeq)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "385e74d3-95db-4034-a271-f43c14d1c735",
   "metadata": {},
   "source": [
    "- [x] download enhancer coords\n",
    "- [x] apply TSS annotation to everything\n",
    "- [x] separate DEGs by up, down, or unchanged\n",
    "- [x] get all distances from tss to enhancers (bioframe func -- should be baby)\n",
    "- [x] sort by distances\n",
    "- [x] plottttyyyyy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "371c1535-ce4d-497c-8501-3c1e4b850fff",
   "metadata": {},
   "outputs": [],
   "source": [
    "# looks to be the right format\n",
    "#bf.SCHEMAS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "4205c29c-c026-44cf-ad02-db1ee3d14de6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>chrom</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>chr1</td>\n",
       "      <td>13049615</td>\n",
       "      <td>13094765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>chr1</td>\n",
       "      <td>34130107</td>\n",
       "      <td>34134640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>chr1</td>\n",
       "      <td>36070190</td>\n",
       "      <td>36074608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>chr1</td>\n",
       "      <td>36111164</td>\n",
       "      <td>36118698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>chr1</td>\n",
       "      <td>37039139</td>\n",
       "      <td>37045411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>chr19</td>\n",
       "      <td>23207455</td>\n",
       "      <td>23208806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>chr19</td>\n",
       "      <td>25553498</td>\n",
       "      <td>25564092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>chr19</td>\n",
       "      <td>53523440</td>\n",
       "      <td>53535319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>chrX</td>\n",
       "      <td>7578969</td>\n",
       "      <td>7597907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>chrX</td>\n",
       "      <td>50098631</td>\n",
       "      <td>50114110</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>231 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     chrom     start       end\n",
       "0     chr1  13049615  13094765\n",
       "1     chr1  34130107  34134640\n",
       "2     chr1  36070190  36074608\n",
       "3     chr1  36111164  36118698\n",
       "4     chr1  37039139  37045411\n",
       "..     ...       ...       ...\n",
       "226  chr19  23207455  23208806\n",
       "227  chr19  25553498  25564092\n",
       "228  chr19  53523440  53535319\n",
       "229   chrX   7578969   7597907\n",
       "230   chrX  50098631  50114110\n",
       "\n",
       "[231 rows x 3 columns]"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "enhancer_coords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "e583e769-96e1-4c11-a2a1-121f0b8a2dfc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import DEseq results\n",
    "\n",
    "# Importing the .csv outputs from sleuth WT for each of the conditions into pandas dataframes:\n",
    "basic_deseq_results = {}\n",
    "\n",
    "# list of all the conditions for which we generated results\n",
    "conditions = s2c['condition'].unique()\n",
    "conditions = [x.strip().replace(\"-\",\"_\").split(\"RNA_seq_\")[1] for x in conditions]\n",
    "\n",
    "# put em in dictionary so we can get results by name (I think there might be built-in objects that do that in daler's meta-seq package --> look into some day\n",
    "for c in conditions[1:]:\n",
    "    \n",
    "    df = pd.read_csv(deseq_results_folder+\"pairwise_deseq_wt_\"+c+\".csv\")\n",
    "    basic_deseq_results[c] = df.rename(columns={\"Unnamed: 0\" : 'gene_id'})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "f26b9197-f920-4dab-b513-b0b128acd33b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "26520\n",
      "25785\n"
     ]
    }
   ],
   "source": [
    "# when we merge the TSS into the result df --> how many do not have a TSS to map? how many entries are lost?\n",
    "print(len(basic_deseq_results['CTCF_AID_auxin1day']))\n",
    "tss_deseq_res = basic_deseq_results['CTCF_AID_auxin1day'].merge(tss_df.drop_duplicates(['gene_id']), how='inner', on='gene_id')\n",
    "print(len(tss_deseq_res))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "40241665-b3dd-4f6e-88cb-9801dc0356ed",
   "metadata": {},
   "outputs": [
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gene_id</th>\n",
       "      <th>baseMean</th>\n",
       "      <th>log2FoldChange</th>\n",
       "      <th>lfcSE</th>\n",
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       "      <td>0.236434</td>\n",
       "      <td>-0.141905</td>\n",
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       "      <td>9.829391</td>\n",
       "      <td>1.454489</td>\n",
       "      <td>1.157044</td>\n",
       "      <td>1.257073</td>\n",
       "      <td>2.087272e-01</td>\n",
       "      <td>9.998054e-01</td>\n",
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       "    <tr>\n",
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       "      <td>ENSMUSG00000084414</td>\n",
       "      <td>0.060269</td>\n",
       "      <td>-0.698860</td>\n",
       "      <td>4.055402</td>\n",
       "      <td>-0.172328</td>\n",
       "      <td>8.631796e-01</td>\n",
       "      <td>NaN</td>\n",
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      ],
      "text/plain": [
       "                  gene_id     baseMean  log2FoldChange     lfcSE       stat  \\\n",
       "0      ENSMUSG00000000001  4942.457855       -0.033551  0.236434  -0.141905   \n",
       "1      ENSMUSG00000000003     0.000000             NaN       NaN        NaN   \n",
       "2      ENSMUSG00000000028  2278.335221       -0.091383  0.163252  -0.559764   \n",
       "3      ENSMUSG00000000037   398.325060        2.463827  0.236823  10.403647   \n",
       "4      ENSMUSG00000000049     0.000000             NaN       NaN        NaN   \n",
       "...                   ...          ...             ...       ...        ...   \n",
       "26515  ENSMUSG00000084412     0.000000             NaN       NaN        NaN   \n",
       "26516  ENSMUSG00000084413     9.829391        1.454489  1.157044   1.257073   \n",
       "26517  ENSMUSG00000084414     0.060269       -0.698860  4.055402  -0.172328   \n",
       "26518  ENSMUSG00000084415     0.000000             NaN       NaN        NaN   \n",
       "26519  ENSMUSG00000084416     0.000000             NaN       NaN        NaN   \n",
       "\n",
       "             pvalue          padj  \n",
       "0      8.871554e-01  9.998054e-01  \n",
       "1               NaN           NaN  \n",
       "2      5.756404e-01  9.998054e-01  \n",
       "3      2.386216e-25  4.369956e-22  \n",
       "4               NaN           NaN  \n",
       "...             ...           ...  \n",
       "26515           NaN           NaN  \n",
       "26516  2.087272e-01  9.998054e-01  \n",
       "26517  8.631796e-01           NaN  \n",
       "26518           NaN           NaN  \n",
       "26519           NaN           NaN  \n",
       "\n",
       "[26520 rows x 7 columns]"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "basic_deseq_results['CTCF_AID_auxin1day']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "38f43e9b-c23b-4f12-94a3-be28c46d92d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number up up-regulated genes: 129\n",
      "number up down-regulated genes: 158\n"
     ]
    }
   ],
   "source": [
    "# Filtering by significant (padj<0.5) --> separate by up-regulated and down-regulated\n",
    "up_ix = np.where((tss_deseq_res['padj'] < 0.05) & (tss_deseq_res['log2FoldChange'] > 0))\n",
    "down_ix = np.where((tss_deseq_res['padj'] < 0.05) & (tss_deseq_res['log2FoldChange'] < 0))\n",
    "nonsig_ix = np.where(tss_deseq_res['padj'] >= 0.05)\n",
    "\n",
    "# Quick count to confirm this is filtering the same as earlier\n",
    "print(\"number up up-regulated genes: \" + str(len(tss_deseq_res.iloc[up_ix])))\n",
    "print(\"number up down-regulated genes: \" + str(len(tss_deseq_res.iloc[down_ix])))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69a55b05-d0e9-4899-bf1a-091bcfc83045",
   "metadata": {},
   "source": [
    "summary from DESEq2:\n",
    "\n",
    "```\n",
    "out of 22072 with nonzero total read count\n",
    "adjusted p-value < 0.05\n",
    "LFC > 0 (up)       : 130, 0.59%\n",
    "LFC < 0 (down)     : 159, 0.72%\n",
    "outliers [1]       : 86, 0.39%\n",
    "low counts [2]     : 5504, 25%\n",
    "(mean count < 5)\n",
    "[1] see 'cooksCutoff' argument of ?results\n",
    "[2] see 'independentFiltering' argument of ?results\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "60892788-192f-4ede-ae8d-353962ac1ca7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "47\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home1/pokorny/.conda/envs/bioenv/lib/python3.9/site-packages/pandas/core/arrays/masked.py:484: RuntimeWarning: divide by zero encountered in log10\n",
      "  result = getattr(ufunc, method)(*inputs2, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0       -inf\n",
       "100     -inf\n",
       "470     -inf\n",
       "473     -inf\n",
       "1461    -inf\n",
       "2273    -inf\n",
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       "22300   -inf\n",
       "23026   -inf\n",
       "23597   -inf\n",
       "23795   -inf\n",
       "23996   -inf\n",
       "Name: distance_to_enhancer, dtype: Float64"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Since we were getting a log-error, see if there are any distances that = 0\n",
    "closest_intervals = bf.closest(tss_deseq_res, enhancer_coords, suffixes=('_de_tss','_enhancer'))\n",
    "tss_deseq_res['distance_to_enhancer'] = closest_intervals['distance']\n",
    "\n",
    "print(len(tss_deseq_res[tss_deseq_res['distance_to_enhancer'] == 0]))\n",
    "np.log10(tss_deseq_res[tss_deseq_res['distance_to_enhancer'] == 0]['distance_to_enhancer'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "c9b2042b-f7ba-4a0b-8bfe-5c6e234568f0",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "unhashable type: 'Series'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/tmp/SLURM_7817857/ipykernel_160664/3855550558.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mbasic_deseq_results\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mbasic_deseq_results\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'CTCF_AID_auxin1day'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'log2FoldChange'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0.01\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: unhashable type: 'Series'"
     ]
    }
   ],
   "source": [
    "basic_deseq_results[basic_deseq_results['CTCF_AID_auxin1day']['log2FoldChange'] < 0.01]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "e80fd599-1051-480c-8da1-80f66dfd64b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12\n"
     ]
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       "      <th></th>\n",
       "      <th>gene_id</th>\n",
       "      <th>baseMean</th>\n",
       "      <th>log2FoldChange</th>\n",
       "      <th>lfcSE</th>\n",
       "      <th>stat</th>\n",
       "      <th>pvalue</th>\n",
       "      <th>padj</th>\n",
       "      <th>chrom</th>\n",
       "      <th>source</th>\n",
       "      <th>feature</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>score</th>\n",
       "      <th>strand</th>\n",
       "      <th>frame</th>\n",
       "      <th>transcript_id</th>\n",
       "      <th>distance_to_enhancer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25772</th>\n",
       "      <td>ENSMUSG00000084404</td>\n",
       "      <td>1.865468</td>\n",
       "      <td>1.088138</td>\n",
       "      <td>1.840053</td>\n",
       "      <td>0.591362</td>\n",
       "      <td>0.554278</td>\n",
       "      <td>NaN</td>\n",
       "      <td>chr4</td>\n",
       "      <td>gffutils_derived</td>\n",
       "      <td>transcript</td>\n",
       "      <td>55422180</td>\n",
       "      <td>55422180</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-</td>\n",
       "      <td>0</td>\n",
       "      <td>ENSMUST00000118617</td>\n",
       "      <td>13778530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25773</th>\n",
       "      <td>ENSMUSG00000084405</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>chr2</td>\n",
       "      <td>gffutils_derived</td>\n",
       "      <td>transcript</td>\n",
       "      <td>88037620</td>\n",
       "      <td>88037620</td>\n",
       "      <td>NaN</td>\n",
       "      <td>+</td>\n",
       "      <td>0</td>\n",
       "      <td>ENSMUST00000121437</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25774</th>\n",
       "      <td>ENSMUSG00000084406</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>chr4</td>\n",
       "      <td>gffutils_derived</td>\n",
       "      <td>transcript</td>\n",
       "      <td>121982890</td>\n",
       "      <td>121982890</td>\n",
       "      <td>NaN</td>\n",
       "      <td>+</td>\n",
       "      <td>0</td>\n",
       "      <td>ENSMUST00000118468</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25775</th>\n",
       "      <td>ENSMUSG00000084407</td>\n",
       "      <td>0.266680</td>\n",
       "      <td>1.835794</td>\n",
       "      <td>4.037416</td>\n",
       "      <td>0.454695</td>\n",
       "      <td>0.649329</td>\n",
       "      <td>NaN</td>\n",
       "      <td>chr2</td>\n",
       "      <td>gffutils_derived</td>\n",
       "      <td>transcript</td>\n",
       "      <td>121311167</td>\n",
       "      <td>121311167</td>\n",
       "      <td>NaN</td>\n",
       "      <td>+</td>\n",
       "      <td>0</td>\n",
       "      <td>ENSMUST00000119042</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25776</th>\n",
       "      <td>ENSMUSG00000084408</td>\n",
       "      <td>10.566498</td>\n",
       "      <td>-0.071127</td>\n",
       "      <td>0.791961</td>\n",
       "      <td>-0.089811</td>\n",
       "      <td>0.928437</td>\n",
       "      <td>0.999805</td>\n",
       "      <td>chr4</td>\n",
       "      <td>gffutils_derived</td>\n",
       "      <td>transcript</td>\n",
       "      <td>15939474</td>\n",
       "      <td>15939474</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-</td>\n",
       "      <td>0</td>\n",
       "      <td>ENSMUST00000119255</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25777</th>\n",
       "      <td>ENSMUSG00000084409</td>\n",
       "      <td>1.111532</td>\n",
       "      <td>-0.575257</td>\n",
       "      <td>2.867222</td>\n",
       "      <td>-0.200632</td>\n",
       "      <td>0.840986</td>\n",
       "      <td>NaN</td>\n",
       "      <td>chrX</td>\n",
       "      <td>gffutils_derived</td>\n",
       "      <td>transcript</td>\n",
       "      <td>66246106</td>\n",
       "      <td>66246106</td>\n",
       "      <td>NaN</td>\n",
       "      <td>+</td>\n",
       "      <td>0</td>\n",
       "      <td>ENSMUST00000118512</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25778</th>\n",
       "      <td>ENSMUSG00000084410</td>\n",
       "      <td>5.151212</td>\n",
       "      <td>1.207003</td>\n",
       "      <td>1.291019</td>\n",
       "      <td>0.934923</td>\n",
       "      <td>0.349828</td>\n",
       "      <td>0.999805</td>\n",
       "      <td>chrX</td>\n",
       "      <td>gffutils_derived</td>\n",
       "      <td>transcript</td>\n",
       "      <td>21376437</td>\n",
       "      <td>21376437</td>\n",
       "      <td>NaN</td>\n",
       "      <td>+</td>\n",
       "      <td>0</td>\n",
       "      <td>ENSMUST00000118331</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25779</th>\n",
       "      <td>ENSMUSG00000084411</td>\n",
       "      <td>7.408594</td>\n",
       "      <td>-0.560611</td>\n",
       "      <td>1.171068</td>\n",
       "      <td>-0.478718</td>\n",
       "      <td>0.632139</td>\n",
       "      <td>0.999805</td>\n",
       "      <td>chr11</td>\n",
       "      <td>gffutils_derived</td>\n",
       "      <td>transcript</td>\n",
       "      <td>88247017</td>\n",
       "      <td>88247017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-</td>\n",
       "      <td>0</td>\n",
       "      <td>ENSMUST00000120278</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25780</th>\n",
       "      <td>ENSMUSG00000084412</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>chr11</td>\n",
       "      <td>gffutils_derived</td>\n",
       "      <td>transcript</td>\n",
       "      <td>21351358</td>\n",
       "      <td>21351358</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-</td>\n",
       "      <td>0</td>\n",
       "      <td>ENSMUST00000120335</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25781</th>\n",
       "      <td>ENSMUSG00000084413</td>\n",
       "      <td>9.829391</td>\n",
       "      <td>1.454489</td>\n",
       "      <td>1.157044</td>\n",
       "      <td>1.257073</td>\n",
       "      <td>0.208727</td>\n",
       "      <td>0.999805</td>\n",
       "      <td>chr11</td>\n",
       "      <td>gffutils_derived</td>\n",
       "      <td>transcript</td>\n",
       "      <td>17874794</td>\n",
       "      <td>17874794</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-</td>\n",
       "      <td>0</td>\n",
       "      <td>ENSMUST00000122453</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25782</th>\n",
       "      <td>ENSMUSG00000084414</td>\n",
       "      <td>0.060269</td>\n",
       "      <td>-0.698860</td>\n",
       "      <td>4.055402</td>\n",
       "      <td>-0.172328</td>\n",
       "      <td>0.863180</td>\n",
       "      <td>NaN</td>\n",
       "      <td>chr13</td>\n",
       "      <td>gffutils_derived</td>\n",
       "      <td>transcript</td>\n",
       "      <td>21388588</td>\n",
       "      <td>21388588</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-</td>\n",
       "      <td>0</td>\n",
       "      <td>ENSMUST00000117685</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25783</th>\n",
       "      <td>ENSMUSG00000084415</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>chr4</td>\n",
       "      <td>gffutils_derived</td>\n",
       "      <td>transcript</td>\n",
       "      <td>115089060</td>\n",
       "      <td>115089060</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-</td>\n",
       "      <td>0</td>\n",
       "      <td>ENSMUST00000120131</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25784</th>\n",
       "      <td>ENSMUSG00000084416</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>chr1</td>\n",
       "      <td>gffutils_derived</td>\n",
       "      <td>transcript</td>\n",
       "      <td>65041179</td>\n",
       "      <td>65041179</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-</td>\n",
       "      <td>0</td>\n",
       "      <td>ENSMUST00000120360</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  gene_id   baseMean  log2FoldChange     lfcSE      stat  \\\n",
       "25772  ENSMUSG00000084404   1.865468        1.088138  1.840053  0.591362   \n",
       "25773  ENSMUSG00000084405   0.000000             NaN       NaN       NaN   \n",
       "25774  ENSMUSG00000084406   0.000000             NaN       NaN       NaN   \n",
       "25775  ENSMUSG00000084407   0.266680        1.835794  4.037416  0.454695   \n",
       "25776  ENSMUSG00000084408  10.566498       -0.071127  0.791961 -0.089811   \n",
       "25777  ENSMUSG00000084409   1.111532       -0.575257  2.867222 -0.200632   \n",
       "25778  ENSMUSG00000084410   5.151212        1.207003  1.291019  0.934923   \n",
       "25779  ENSMUSG00000084411   7.408594       -0.560611  1.171068 -0.478718   \n",
       "25780  ENSMUSG00000084412   0.000000             NaN       NaN       NaN   \n",
       "25781  ENSMUSG00000084413   9.829391        1.454489  1.157044  1.257073   \n",
       "25782  ENSMUSG00000084414   0.060269       -0.698860  4.055402 -0.172328   \n",
       "25783  ENSMUSG00000084415   0.000000             NaN       NaN       NaN   \n",
       "25784  ENSMUSG00000084416   0.000000             NaN       NaN       NaN   \n",
       "\n",
       "         pvalue      padj  chrom            source     feature      start  \\\n",
       "25772  0.554278       NaN   chr4  gffutils_derived  transcript   55422180   \n",
       "25773       NaN       NaN   chr2  gffutils_derived  transcript   88037620   \n",
       "25774       NaN       NaN   chr4  gffutils_derived  transcript  121982890   \n",
       "25775  0.649329       NaN   chr2  gffutils_derived  transcript  121311167   \n",
       "25776  0.928437  0.999805   chr4  gffutils_derived  transcript   15939474   \n",
       "25777  0.840986       NaN   chrX  gffutils_derived  transcript   66246106   \n",
       "25778  0.349828  0.999805   chrX  gffutils_derived  transcript   21376437   \n",
       "25779  0.632139  0.999805  chr11  gffutils_derived  transcript   88247017   \n",
       "25780       NaN       NaN  chr11  gffutils_derived  transcript   21351358   \n",
       "25781  0.208727  0.999805  chr11  gffutils_derived  transcript   17874794   \n",
       "25782  0.863180       NaN  chr13  gffutils_derived  transcript   21388588   \n",
       "25783       NaN       NaN   chr4  gffutils_derived  transcript  115089060   \n",
       "25784       NaN       NaN   chr1  gffutils_derived  transcript   65041179   \n",
       "\n",
       "             end  score strand frame       transcript_id  distance_to_enhancer  \n",
       "25772   55422180    NaN      -     0  ENSMUST00000118617              13778530  \n",
       "25773   88037620    NaN      +     0  ENSMUST00000121437                  <NA>  \n",
       "25774  121982890    NaN      +     0  ENSMUST00000118468                  <NA>  \n",
       "25775  121311167    NaN      +     0  ENSMUST00000119042                  <NA>  \n",
       "25776   15939474    NaN      -     0  ENSMUST00000119255                  <NA>  \n",
       "25777   66246106    NaN      +     0  ENSMUST00000118512                  <NA>  \n",
       "25778   21376437    NaN      +     0  ENSMUST00000118331                  <NA>  \n",
       "25779   88247017    NaN      -     0  ENSMUST00000120278                  <NA>  \n",
       "25780   21351358    NaN      -     0  ENSMUST00000120335                  <NA>  \n",
       "25781   17874794    NaN      -     0  ENSMUST00000122453                  <NA>  \n",
       "25782   21388588    NaN      -     0  ENSMUST00000117685                  <NA>  \n",
       "25783  115089060    NaN      -     0  ENSMUST00000120131                  <NA>  \n",
       "25784   65041179    NaN      -     0  ENSMUST00000120360                  <NA>  "
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(len(tss_deseq_res[pd.isna(tss_deseq_res['distance_to_enhancer'])]))\n",
    "tss_deseq_res.tail(13)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "287d79ec-247f-4a86-982c-4cd45e38d0d1",
   "metadata": {},
   "source": [
    "### A little more clean-up following finding enhancer-distance \n",
    "\n",
    "Taking a look --> none of the genes which could not find an enhancer seem to be DE genes (all have high / non-significant p_adj). \n",
    "\n",
    "* Removed genes that did not find an enhancer\n",
    "* Setting genes where distance from TSS to enhancer == 0 --> = 1 instead "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "589f6b22-5393-432c-bde2-3689f1e14ac8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25785\n",
      "25773\n"
     ]
    }
   ],
   "source": [
    "# To check: printing size before\n",
    "print(len(tss_deseq_res))\n",
    "\n",
    "# Replace distance_to_enhancer == 0\n",
    "tss_deseq_res['distance_to_enhancer'] = tss_deseq_res['distance_to_enhancer'].replace(0, 1)\n",
    "\n",
    "# Remove distance_to_enhancer = NA\n",
    "tss_deseq_res.dropna(subset='distance_to_enhancer', inplace=True)\n",
    "\n",
    "# How much did we remove\n",
    "print(len(tss_deseq_res))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "94263ade-ef8c-4835-887f-9637a1f039c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bf.is_bedframe(tss_deseq_res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "e8d108b0-d7a8-4966-af92-01e9961c89a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bf.is_bedframe(enhancer_coords)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "ceb2a390-3cc8-4d06-8087-f1feccfc47ce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(25773, 18)\n",
      "(287, 18)\n",
      "Total significantly over-expressed: 129\n",
      "Number DEGs within 10^5 of closest enhancer: 2\n"
     ]
    }
   ],
   "source": [
    "# investigate:\n",
    "\n",
    "# log10 distance to enhancer --> save as column\n",
    "tss_deseq_res['log10_distance_to_enhancer'] = np.log10(tss_deseq_res['distance_to_enhancer'])\n",
    "\n",
    "# drop lines where log2FC is way low\n",
    "\n",
    "# filter on p_adj below 0.05\n",
    "up_ix = np.where(tss_deseq_res['log2FoldChange'] > 0)\n",
    "sig_ix = np.where(tss_deseq_res['padj'] < 0.05)\n",
    "sig_up = np.where((tss_deseq_res['log2FoldChange'] > 0) & (tss_deseq_res['padj'] < 0.05))\n",
    "# print --> what is goin on in here? Where are the cutes within 3-5 range in original data?\n",
    "#tss_deseq_res.head(10)\n",
    "\n",
    "print(tss_deseq_res.shape)\n",
    "sig_tss_deseq = tss_deseq_res.iloc[sig_ix]\n",
    "print(sig_tss_deseq.shape)\n",
    "\n",
    "# how many of significant over-expressers are within 10^5 distance to closest enhancer\n",
    "print(\"Total significantly over-expressed: \" + str(len(tss_deseq_res.iloc[sig_up])))\n",
    "print(\"Number DEGs within 10^5 of closest enhancer: \" + str(len(tss_deseq_res.iloc[sig_up][tss_deseq_res.iloc[sig_up]['log10_distance_to_enhancer'] < 5])))\n",
    "\n",
    "# sort by log10 distance to enhancer\n",
    "#tss_deseq_res.iloc[sig_up].sort_values(by='log10_distance_to_enhancer').head(30)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "ac47b5f1-68b2-4a14-8e0b-d2ea1b32e1f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f5fd97f8280>]"
      ]
     },
     "execution_count": 115,
     "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": [
    "# figuring out y-axis: cumulative proportion of genes\n",
    "\n",
    "up_exp = tss_deseq_res.iloc[sig_up].sort_values(by='log10_distance_to_enhancer')\n",
    "\n",
    "cum_up = np.arange(0, len(up_exp), 1)\n",
    "cum_up = cum_up/ len(up_exp)\n",
    "\n",
    "#up_exp.head(10)\n",
    "\n",
    "plt.plot(up_exp['log10_distance_to_enhancer'], cum_up, color='red')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3eca6503-8e58-4a05-9e23-804407f01835",
   "metadata": {},
   "source": [
    "# Replicating Figure 6(d) from Nora 2017\n",
    "\n",
    "1. Using deseq2 results for 1 day, 2 days, or 4 days of auxin degradation treatment + mm9 enhancer coordinate predictions from Chen12_BMCgenomics (provided by Elphege)\n",
    "2. Using excel file of DE results reported by the paper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "1d16afce-5e94-4bdc-a0d2-4612a487aad3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=================\n",
      "For condition CTCF_AID_auxin1day, there are 287 DE genes out of 25773 total (that have a mappable TSS)\n",
      "Number of up-regulated genes: 129\n",
      "Number of down-regulated genes: 158\n",
      "\n",
      "=================\n",
      "For condition CTCF_AID_auxin2days, there are 885 DE genes out of 25773 total (that have a mappable TSS)\n",
      "Number of up-regulated genes: 568\n",
      "Number of down-regulated genes: 317\n",
      "\n",
      "=================\n",
      "For condition CTCF_AID_auxin4days, there are 3900 DE genes out of 25773 total (that have a mappable TSS)\n",
      "Number of up-regulated genes: 2184\n",
      "Number of down-regulated genes: 1716\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Only plotting the change/distance to coordinates for DE results that are 1. AID-tagged and 2. have had some auxin treatment\n",
    "only_treatment_results = [x for x in list(basic_deseq_results.keys()) if 'CTCF_AID_auxin' in x] \n",
    "\n",
    "# Generating a subplot for each of the conditions\n",
    "num_plots = len(only_treatment_results)\n",
    "fig, axs = plt.subplots(1, num_plots, figsize=[num_plots*10, 6])\n",
    "fig.suptitle(\"Genomic separation to 231 Super-Enhancers from White 2013\", fontsize=16)\n",
    "\n",
    "# plot log10 distance between TSS and closest enhancers, color-coded by up-regulated/down-regulated or non-regulated genes\n",
    "for condition, ax in zip(only_treatment_results, axs.ravel()):\n",
    "    \n",
    "    # adding TSS coordinates for the DEseq results\n",
    "    tss_deseq_res = basic_deseq_results[condition].merge(tss_df.drop_duplicates(['gene_id']),\n",
    "                                                         how='inner',\n",
    "                                                         on='gene_id')\n",
    "    \n",
    "    # Using bioframe to find distance between TSS and closest enhancer\n",
    "    closest_intervals = bf.closest(tss_deseq_res, enhancer_coords, suffixes=('_de_tss','_enhancer'))\n",
    "    tss_deseq_res['distance_to_enhancer'] = closest_intervals['distance'].replace(0,1) # replace any distances = 0 with value = 1 for log\n",
    "    \n",
    "    # Dropping invalid distance_to_enhancer values\n",
    "    tss_deseq_res.dropna(subset='distance_to_enhancer', inplace=True)\n",
    "    \n",
    "    # Adding log10 column\n",
    "    tss_deseq_res['log10_distance_to_enhancer'] = np.log10(tss_deseq_res['distance_to_enhancer'])\n",
    "    \n",
    "    # Filtering by significant (padj<0.5) --> separate by up-regulated and down-regulated\n",
    "    up_ix = np.where((tss_deseq_res['padj'] < 0.05) & (tss_deseq_res['log2FoldChange'] > 0))\n",
    "    down_ix = np.where((tss_deseq_res['padj'] < 0.05) & (tss_deseq_res['log2FoldChange'] < 0))\n",
    "    nonsig_ix = np.where(tss_deseq_res['padj'] >= 0.01)\n",
    "    \n",
    "    # Getting the sets of genes\n",
    "    up_reg_genes = tss_deseq_res.iloc[up_ix].sort_values(by='log10_distance_to_enhancer')\n",
    "    down_reg_genes = tss_deseq_res.iloc[down_ix].sort_values(by='log10_distance_to_enhancer')\n",
    "    nonsig_genes = tss_deseq_res.iloc[nonsig_ix].sort_values(by='log10_distance_to_enhancer')\n",
    "    \n",
    "    # Changing these values where distance between enhancer and tss is 0 to 1 (still close together)\n",
    "    # tss_deseq_res.loc[tss_deseq_res['distance_to_enhancer'] == 0, 'distance_to_enhancer'] = 1 --> not needed anymore with the .replace() cleanup step above\n",
    "    \n",
    "    # generate y axis: cumulative proportion of genes\n",
    "    cum_all = np.arange(0, len(nonsig_genes), 1)\n",
    "    cum_all = cum_all / len(nonsig_genes)\n",
    "\n",
    "    # plotting \n",
    "    line1, = ax.plot(nonsig_genes['log10_distance_to_enhancer'], \n",
    "                cum_all, \n",
    "                color='grey',\n",
    "                label='Non-regulated',\n",
    "                linewidth=2)\n",
    "\n",
    "\n",
    "    # generate y axis: cumulative proportion of genes\n",
    "    cum_up = np.arange(0, len(up_reg_genes), 1)\n",
    "    cum_up = cum_up/ len(up_reg_genes)\n",
    "\n",
    "    line2, = ax.plot(up_reg_genes['log10_distance_to_enhancer'], \n",
    "                cum_up, \n",
    "                color='red', \n",
    "                label='Up-regulated',\n",
    "                linewidth=2)\n",
    "\n",
    "    # generate y axis: cumulative proportion of genes\n",
    "    cum_down = np.arange(0, len(down_reg_genes), 1)\n",
    "    cum_down = cum_down/ len(down_reg_genes)\n",
    "\n",
    "    line3, = ax.plot(down_reg_genes['log10_distance_to_enhancer'],\n",
    "                cum_down,\n",
    "                color='blue',\n",
    "                label='Down-regulated',\n",
    "                linewidth=2)\n",
    "    \n",
    "    ax.set_title(condition.replace(\"CTCF_AID_\", \"\"))\n",
    "    ax.set_xlabel(\"log10 distance from TSS to the closest enhancer\")\n",
    "    ax.set_ylabel(\"cumulative portion of genes\")\n",
    "    ax.legend(handles=[line1, line2, line3])\n",
    "    \n",
    "    # Printing the raw number of significantly DE genes, for reference  \n",
    "    print(\"=================\")\n",
    "    print(\"For condition {}, there are {} DE genes out of {} total (that have a mappable TSS)\".format(condition, \n",
    "                                                                                                      len(up_reg_genes) + len(down_reg_genes), \n",
    "                                                                                                      len(tss_deseq_res)))\n",
    "    print(\"Number of up-regulated genes: {}\".format(len(up_reg_genes)))\n",
    "    print(\"Number of down-regulated genes: {}\".format(len(down_reg_genes)))\n",
    "    print()\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9652ede-44ed-4869-8f38-4e0cccb714ff",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Trouble-shooting / investigating: flipped pattern of down/up regulated enhancer binding \n",
    "\n",
    "Compared to figure 6(d) in Nora's 2017 paper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ec78c6df-8ac2-4a25-b361-9929523edc2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Looking again at the the filtering being done to generate the image below --> is it correct?\n",
    "tss_deseq_res = basic_deseq_results['CTCF_AID_auxin1day'].merge(tss_df.drop_duplicates(['gene_id']), how='inner', on='gene_id')\n",
    "up_ix = np.where((tss_deseq_res['padj'] < 0.05) & (tss_deseq_res['log2FoldChange'] > 0))\n",
    "\n",
    "upreg = tss_deseq_res.iloc[up_ix]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "680599c8-593e-4e09-95f5-2e038fd95619",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gene_id</th>\n",
       "      <th>baseMean</th>\n",
       "      <th>log2FoldChange</th>\n",
       "      <th>lfcSE</th>\n",
       "      <th>stat</th>\n",
       "      <th>pvalue</th>\n",
       "      <th>padj</th>\n",
       "      <th>chrom</th>\n",
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       "      <th>transcript_id</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ENSMUSG00000000037</td>\n",
       "      <td>398.325060</td>\n",
       "      <td>2.463827</td>\n",
       "      <td>0.236823</td>\n",
       "      <td>10.403647</td>\n",
       "      <td>2.386216e-25</td>\n",
       "      <td>4.369956e-22</td>\n",
       "      <td>chrX</td>\n",
       "      <td>gffutils_derived</td>\n",
       "      <td>transcript</td>\n",
       "      <td>157600703</td>\n",
       "      <td>157600703</td>\n",
       "      <td>NaN</td>\n",
       "      <td>+</td>\n",
       "      <td>0</td>\n",
       "      <td>ENSMUST00000019101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>ENSMUSG00000000489</td>\n",
       "      <td>454.955101</td>\n",
       "      <td>1.148861</td>\n",
       "      <td>0.303338</td>\n",
       "      <td>3.787397</td>\n",
       "      <td>1.522340e-04</td>\n",
       "      <td>1.172486e-02</td>\n",
       "      <td>chr15</td>\n",
       "      <td>gffutils_derived</td>\n",
       "      <td>transcript</td>\n",
       "      <td>79845239</td>\n",
       "      <td>79845239</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-</td>\n",
       "      <td>0</td>\n",
       "      <td>ENSMUST00000000500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267</th>\n",
       "      <td>ENSMUSG00000001510</td>\n",
       "      <td>91.377271</td>\n",
       "      <td>1.685140</td>\n",
       "      <td>0.349064</td>\n",
       "      <td>4.827593</td>\n",
       "      <td>1.381929e-06</td>\n",
       "      <td>1.851785e-04</td>\n",
       "      <td>chr11</td>\n",
       "      <td>gffutils_derived</td>\n",
       "      <td>transcript</td>\n",
       "      <td>94981402</td>\n",
       "      <td>94981402</td>\n",
       "      <td>NaN</td>\n",
       "      <td>+</td>\n",
       "      <td>0</td>\n",
       "      <td>ENSMUST00000092768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426</th>\n",
       "      <td>ENSMUSG00000002396</td>\n",
       "      <td>1467.523346</td>\n",
       "      <td>1.970582</td>\n",
       "      <td>0.431108</td>\n",
       "      <td>4.570968</td>\n",
       "      <td>4.854756e-06</td>\n",
       "      <td>5.556672e-04</td>\n",
       "      <td>chr8</td>\n",
       "      <td>gffutils_derived</td>\n",
       "      <td>transcript</td>\n",
       "      <td>73895237</td>\n",
       "      <td>73895237</td>\n",
       "      <td>NaN</td>\n",
       "      <td>+</td>\n",
       "      <td>0</td>\n",
       "      <td>ENSMUST00000002469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>535</th>\n",
       "      <td>ENSMUSG00000003154</td>\n",
       "      <td>302.933638</td>\n",
       "      <td>0.858210</td>\n",
       "      <td>0.200997</td>\n",
       "      <td>4.269765</td>\n",
       "      <td>1.956795e-05</td>\n",
       "      <td>1.990857e-03</td>\n",
       "      <td>chr6</td>\n",
       "      <td>gffutils_derived</td>\n",
       "      <td>transcript</td>\n",
       "      <td>122769931</td>\n",
       "      <td>122769931</td>\n",
       "      <td>NaN</td>\n",
       "      <td>+</td>\n",
       "      <td>0</td>\n",
       "      <td>ENSMUST00000003238</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>19735</th>\n",
       "      <td>ENSMUSG00000073043</td>\n",
       "      <td>170.623913</td>\n",
       "      <td>3.699361</td>\n",
       "      <td>0.529087</td>\n",
       "      <td>6.991968</td>\n",
       "      <td>2.710571e-12</td>\n",
       "      <td>1.116891e-09</td>\n",
       "      <td>chr6</td>\n",
       "      <td>gffutils_derived</td>\n",
       "      <td>transcript</td>\n",
       "      <td>64679139</td>\n",
       "      <td>64679139</td>\n",
       "      <td>NaN</td>\n",
       "      <td>+</td>\n",
       "      <td>0</td>\n",
       "      <td>ENSMUST00000101351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20840</th>\n",
       "      <td>ENSMUSG00000075707</td>\n",
       "      <td>212.361420</td>\n",
       "      <td>2.430096</td>\n",
       "      <td>0.281666</td>\n",
       "      <td>8.627590</td>\n",
       "      <td>6.265833e-18</td>\n",
       "      <td>5.163673e-15</td>\n",
       "      <td>chr12</td>\n",
       "      <td>gffutils_derived</td>\n",
       "      <td>transcript</td>\n",
       "      <td>111517439</td>\n",
       "      <td>111517439</td>\n",
       "      <td>NaN</td>\n",
       "      <td>+</td>\n",
       "      <td>0</td>\n",
       "      <td>ENSMUST00000097228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21583</th>\n",
       "      <td>ENSMUSG00000078670</td>\n",
       "      <td>304.359242</td>\n",
       "      <td>1.102548</td>\n",
       "      <td>0.200107</td>\n",
       "      <td>5.509782</td>\n",
       "      <td>3.592775e-08</td>\n",
       "      <td>7.791594e-06</td>\n",
       "      <td>chr7</td>\n",
       "      <td>gffutils_derived</td>\n",
       "      <td>transcript</td>\n",
       "      <td>80885192</td>\n",
       "      <td>80885192</td>\n",
       "      <td>NaN</td>\n",
       "      <td>+</td>\n",
       "      <td>0</td>\n",
       "      <td>ENSMUST00000107456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21797</th>\n",
       "      <td>ENSMUSG00000079034</td>\n",
       "      <td>620.173635</td>\n",
       "      <td>1.156824</td>\n",
       "      <td>0.273396</td>\n",
       "      <td>4.231307</td>\n",
       "      <td>2.323376e-05</td>\n",
       "      <td>2.293047e-03</td>\n",
       "      <td>chr12</td>\n",
       "      <td>gffutils_derived</td>\n",
       "      <td>transcript</td>\n",
       "      <td>88855265</td>\n",
       "      <td>88855265</td>\n",
       "      <td>NaN</td>\n",
       "      <td>+</td>\n",
       "      <td>0</td>\n",
       "      <td>ENSMUST00000110152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22249</th>\n",
       "      <td>ENSMUSG00000080242</td>\n",
       "      <td>45.403446</td>\n",
       "      <td>6.232422</td>\n",
       "      <td>1.670749</td>\n",
       "      <td>3.730317</td>\n",
       "      <td>1.912393e-04</td>\n",
       "      <td>1.419823e-02</td>\n",
       "      <td>chr6</td>\n",
       "      <td>gffutils_derived</td>\n",
       "      <td>transcript</td>\n",
       "      <td>37756085</td>\n",
       "      <td>37756085</td>\n",
       "      <td>NaN</td>\n",
       "      <td>+</td>\n",
       "      <td>0</td>\n",
       "      <td>ENSMUST00000116500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>129 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  gene_id     baseMean  log2FoldChange     lfcSE       stat  \\\n",
       "3      ENSMUSG00000000037   398.325060        2.463827  0.236823  10.403647   \n",
       "86     ENSMUSG00000000489   454.955101        1.148861  0.303338   3.787397   \n",
       "267    ENSMUSG00000001510    91.377271        1.685140  0.349064   4.827593   \n",
       "426    ENSMUSG00000002396  1467.523346        1.970582  0.431108   4.570968   \n",
       "535    ENSMUSG00000003154   302.933638        0.858210  0.200997   4.269765   \n",
       "...                   ...          ...             ...       ...        ...   \n",
       "19735  ENSMUSG00000073043   170.623913        3.699361  0.529087   6.991968   \n",
       "20840  ENSMUSG00000075707   212.361420        2.430096  0.281666   8.627590   \n",
       "21583  ENSMUSG00000078670   304.359242        1.102548  0.200107   5.509782   \n",
       "21797  ENSMUSG00000079034   620.173635        1.156824  0.273396   4.231307   \n",
       "22249  ENSMUSG00000080242    45.403446        6.232422  1.670749   3.730317   \n",
       "\n",
       "             pvalue          padj  chrom            source     feature  \\\n",
       "3      2.386216e-25  4.369956e-22   chrX  gffutils_derived  transcript   \n",
       "86     1.522340e-04  1.172486e-02  chr15  gffutils_derived  transcript   \n",
       "267    1.381929e-06  1.851785e-04  chr11  gffutils_derived  transcript   \n",
       "426    4.854756e-06  5.556672e-04   chr8  gffutils_derived  transcript   \n",
       "535    1.956795e-05  1.990857e-03   chr6  gffutils_derived  transcript   \n",
       "...             ...           ...    ...               ...         ...   \n",
       "19735  2.710571e-12  1.116891e-09   chr6  gffutils_derived  transcript   \n",
       "20840  6.265833e-18  5.163673e-15  chr12  gffutils_derived  transcript   \n",
       "21583  3.592775e-08  7.791594e-06   chr7  gffutils_derived  transcript   \n",
       "21797  2.323376e-05  2.293047e-03  chr12  gffutils_derived  transcript   \n",
       "22249  1.912393e-04  1.419823e-02   chr6  gffutils_derived  transcript   \n",
       "\n",
       "           start        end  score strand frame       transcript_id  \n",
       "3      157600703  157600703    NaN      +     0  ENSMUST00000019101  \n",
       "86      79845239   79845239    NaN      -     0  ENSMUST00000000500  \n",
       "267     94981402   94981402    NaN      +     0  ENSMUST00000092768  \n",
       "426     73895237   73895237    NaN      +     0  ENSMUST00000002469  \n",
       "535    122769931  122769931    NaN      +     0  ENSMUST00000003238  \n",
       "...          ...        ...    ...    ...   ...                 ...  \n",
       "19735   64679139   64679139    NaN      +     0  ENSMUST00000101351  \n",
       "20840  111517439  111517439    NaN      +     0  ENSMUST00000097228  \n",
       "21583   80885192   80885192    NaN      +     0  ENSMUST00000107456  \n",
       "21797   88855265   88855265    NaN      +     0  ENSMUST00000110152  \n",
       "22249   37756085   37756085    NaN      +     0  ENSMUST00000116500  \n",
       "\n",
       "[129 rows x 16 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "upreg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "78df8635-9e06-416a-9714-6e79828af546",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(24315, 14)\n"
     ]
    }
   ],
   "source": [
    "sdir = '/project/fudenber_735/GEO/nora_2017_GSE98671_downloads/Elphege_2017'\n",
    "\n",
    "#Supplmentary table 10 contains RNA-seq FPKM values, Related to figure 6 \n",
    "RNA_seq_FPKM = pd.read_excel(sdir+'/sup_data/NIHMS873912-supplement-10.xlsx', skiprows=6, usecols=\"A:K, O:Q\")\n",
    "\n",
    "RNA_seq_FPKM = RNA_seq_FPKM.rename(columns={'untreated': 'CTCF-AID_untreated_mean_FPKM', '1day': 'CTCF-AID_1day_mean_FPKM', '2days': 'CTCF-AID_2days_mean_FPKM', \n",
    "                                            '4days': 'CTCF-AID_4days_mean_FPKM', '2days_washoff_2days' : 'CTCF-AID_washoff_mean_FPKM', \n",
    "                                            'untreated.1' : 'WT_untreated_mean_FPKM', '2days.3' : 'WT_2days_mean_FPKM', '4days.3' : 'WT_4days_mean_FPKM'})\n",
    "print(RNA_seq_FPKM.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "57e5e3e0-9936-4fd0-b049-b31751b840ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(11589, 14)\n",
      "285\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tracking_id</th>\n",
       "      <th>locus</th>\n",
       "      <th>CTCF-AID_untreated_mean_FPKM</th>\n",
       "      <th>CTCF-AID_1day_mean_FPKM</th>\n",
       "      <th>CTCF-AID_2days_mean_FPKM</th>\n",
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       "      <th>CTCF-AID_washoff_mean_FPKM</th>\n",
       "      <th>1day.1</th>\n",
       "      <th>2days.1</th>\n",
       "      <th>4days.1</th>\n",
       "      <th>2days_washoff_2days.1</th>\n",
       "      <th>WT_untreated_mean_FPKM</th>\n",
       "      <th>WT_2days_mean_FPKM</th>\n",
       "      <th>WT_4days_mean_FPKM</th>\n",
       "      <th>1day.logFC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0610007P14Rik</td>\n",
       "      <td>chr12:87156404-87165495</td>\n",
       "      <td>71.716133</td>\n",
       "      <td>74.856200</td>\n",
       "      <td>63.412767</td>\n",
       "      <td>59.250033</td>\n",
       "      <td>67.995733</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>57.020867</td>\n",
       "      <td>49.716167</td>\n",
       "      <td>57.023633</td>\n",
       "      <td>0.061824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0610009B22Rik</td>\n",
       "      <td>chr11:51498886-51502136</td>\n",
       "      <td>69.529333</td>\n",
       "      <td>76.436433</td>\n",
       "      <td>61.336067</td>\n",
       "      <td>38.937500</td>\n",
       "      <td>58.353700</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>58.497000</td>\n",
       "      <td>46.990267</td>\n",
       "      <td>58.072767</td>\n",
       "      <td>0.136639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0610009O20Rik</td>\n",
       "      <td>chr18:38398058-38422283</td>\n",
       "      <td>17.565900</td>\n",
       "      <td>20.652933</td>\n",
       "      <td>20.476800</td>\n",
       "      <td>20.633733</td>\n",
       "      <td>18.857300</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>18.864133</td>\n",
       "      <td>18.413433</td>\n",
       "      <td>18.779333</td>\n",
       "      <td>0.233569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0610010F05Rik</td>\n",
       "      <td>chr11:23473775-23533631</td>\n",
       "      <td>4.500230</td>\n",
       "      <td>4.654253</td>\n",
       "      <td>3.784197</td>\n",
       "      <td>3.342397</td>\n",
       "      <td>4.257007</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>4.556360</td>\n",
       "      <td>3.955583</td>\n",
       "      <td>4.441780</td>\n",
       "      <td>0.048551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0610010K14Rik</td>\n",
       "      <td>chr11:70048705-70051416</td>\n",
       "      <td>115.794100</td>\n",
       "      <td>134.728000</td>\n",
       "      <td>124.419633</td>\n",
       "      <td>109.027433</td>\n",
       "      <td>95.227500</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>104.590300</td>\n",
       "      <td>129.307667</td>\n",
       "      <td>99.449367</td>\n",
       "      <td>0.218488</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     tracking_id                    locus  CTCF-AID_untreated_mean_FPKM  \\\n",
       "1  0610007P14Rik  chr12:87156404-87165495                     71.716133   \n",
       "2  0610009B22Rik  chr11:51498886-51502136                     69.529333   \n",
       "4  0610009O20Rik  chr18:38398058-38422283                     17.565900   \n",
       "6  0610010F05Rik  chr11:23473775-23533631                      4.500230   \n",
       "7  0610010K14Rik  chr11:70048705-70051416                    115.794100   \n",
       "\n",
       "   CTCF-AID_1day_mean_FPKM  CTCF-AID_2days_mean_FPKM  \\\n",
       "1                74.856200                 63.412767   \n",
       "2                76.436433                 61.336067   \n",
       "4                20.652933                 20.476800   \n",
       "6                 4.654253                  3.784197   \n",
       "7               134.728000                124.419633   \n",
       "\n",
       "   CTCF-AID_4days_mean_FPKM  CTCF-AID_washoff_mean_FPKM 1day.1 2days.1  \\\n",
       "1                 59.250033                   67.995733     no      no   \n",
       "2                 38.937500                   58.353700     no      no   \n",
       "4                 20.633733                   18.857300     no      no   \n",
       "6                  3.342397                    4.257007     no      no   \n",
       "7                109.027433                   95.227500     no      no   \n",
       "\n",
       "  4days.1 2days_washoff_2days.1  WT_untreated_mean_FPKM  WT_2days_mean_FPKM  \\\n",
       "1      no                    no               57.020867           49.716167   \n",
       "2     yes                    no               58.497000           46.990267   \n",
       "4      no                    no               18.864133           18.413433   \n",
       "6      no                    no                4.556360            3.955583   \n",
       "7      no                    no              104.590300          129.307667   \n",
       "\n",
       "   WT_4days_mean_FPKM  1day.logFC  \n",
       "1           57.023633    0.061824  \n",
       "2           58.072767    0.136639  \n",
       "4           18.779333    0.233569  \n",
       "6            4.441780    0.048551  \n",
       "7           99.449367    0.218488  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's compare with original results\n",
    "\n",
    "# Adding Filter for FPKM > 1.1\n",
    "RNA_seq_FPKM = RNA_seq_FPKM.iloc[np.where(RNA_seq_FPKM['CTCF-AID_untreated_mean_FPKM'] > 1.1)]\n",
    "print(RNA_seq_FPKM.shape)\n",
    "\n",
    "# how many 1-day treatment are differentially expressed\n",
    "print((RNA_seq_FPKM['1day.1'] == 'yes').sum())\n",
    "\n",
    "# add fold-change\n",
    "RNA_seq_FPKM['1day.logFC'] = np.log2(RNA_seq_FPKM['CTCF-AID_1day_mean_FPKM'] / RNA_seq_FPKM['CTCF-AID_untreated_mean_FPKM'])\n",
    "\n",
    "# what does this table looks like\n",
    "RNA_seq_FPKM[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "a0793b22-d174-4159-9da5-540bc6ad1885",
   "metadata": {},
   "outputs": [],
   "source": [
    "up_ix = np.where((RNA_seq_FPKM['1day.1'] == 'yes') & (RNA_seq_FPKM['1day.logFC'] > 0))\n",
    "\n",
    "upreg_supp = RNA_seq_FPKM.iloc[up_ix]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "c4517c00-1abe-405a-9888-e632658c980a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tracking_id</th>\n",
       "      <th>locus</th>\n",
       "      <th>CTCF-AID_untreated_mean_FPKM</th>\n",
       "      <th>CTCF-AID_1day_mean_FPKM</th>\n",
       "      <th>CTCF-AID_2days_mean_FPKM</th>\n",
       "      <th>CTCF-AID_4days_mean_FPKM</th>\n",
       "      <th>CTCF-AID_washoff_mean_FPKM</th>\n",
       "      <th>1day.1</th>\n",
       "      <th>2days.1</th>\n",
       "      <th>4days.1</th>\n",
       "      <th>2days_washoff_2days.1</th>\n",
       "      <th>WT_untreated_mean_FPKM</th>\n",
       "      <th>WT_2days_mean_FPKM</th>\n",
       "      <th>WT_4days_mean_FPKM</th>\n",
       "      <th>1day.logFC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1766</th>\n",
       "      <td>AU018091</td>\n",
       "      <td>chr7:3154659-3169204</td>\n",
       "      <td>52.181533</td>\n",
       "      <td>87.633233</td>\n",
       "      <td>82.740567</td>\n",
       "      <td>104.496500</td>\n",
       "      <td>56.954533</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>37.055900</td>\n",
       "      <td>35.258167</td>\n",
       "      <td>34.533933</td>\n",
       "      <td>0.747939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1792</th>\n",
       "      <td>AW554918</td>\n",
       "      <td>chr18:25327520-25625822</td>\n",
       "      <td>1.646903</td>\n",
       "      <td>3.264680</td>\n",
       "      <td>2.301877</td>\n",
       "      <td>2.484987</td>\n",
       "      <td>1.695870</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>1.220159</td>\n",
       "      <td>1.269099</td>\n",
       "      <td>1.172797</td>\n",
       "      <td>0.987186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1979</th>\n",
       "      <td>Acp5</td>\n",
       "      <td>chr9:21931170-21940190</td>\n",
       "      <td>2.433467</td>\n",
       "      <td>5.184907</td>\n",
       "      <td>4.383917</td>\n",
       "      <td>5.852107</td>\n",
       "      <td>3.821033</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>3.037120</td>\n",
       "      <td>3.514423</td>\n",
       "      <td>2.929810</td>\n",
       "      <td>1.091305</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     tracking_id                    locus  CTCF-AID_untreated_mean_FPKM  \\\n",
       "1766    AU018091     chr7:3154659-3169204                     52.181533   \n",
       "1792    AW554918  chr18:25327520-25625822                      1.646903   \n",
       "1979        Acp5   chr9:21931170-21940190                      2.433467   \n",
       "\n",
       "      CTCF-AID_1day_mean_FPKM  CTCF-AID_2days_mean_FPKM  \\\n",
       "1766                87.633233                 82.740567   \n",
       "1792                 3.264680                  2.301877   \n",
       "1979                 5.184907                  4.383917   \n",
       "\n",
       "      CTCF-AID_4days_mean_FPKM  CTCF-AID_washoff_mean_FPKM 1day.1 2days.1  \\\n",
       "1766                104.496500                   56.954533    yes     yes   \n",
       "1792                  2.484987                    1.695870    yes      no   \n",
       "1979                  5.852107                    3.821033    yes      no   \n",
       "\n",
       "     4days.1 2days_washoff_2days.1  WT_untreated_mean_FPKM  \\\n",
       "1766     yes                    no               37.055900   \n",
       "1792      no                    no                1.220159   \n",
       "1979     yes                    no                3.037120   \n",
       "\n",
       "      WT_2days_mean_FPKM  WT_4days_mean_FPKM  1day.logFC  \n",
       "1766           35.258167           34.533933    0.747939  \n",
       "1792            1.269099            1.172797    0.987186  \n",
       "1979            3.514423            2.929810    1.091305  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "upreg_supp.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "4b06acf6-3dd1-4075-95f0-ada1e79723a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home1/pokorny/.conda/envs/bioenv/lib/python3.9/site-packages/gtfparse/read_gtf.py:82: FutureWarning: The error_bad_lines argument has been deprecated and will be removed in a future version. Use on_bad_lines in the future.\n",
      "\n",
      "\n",
      "  chunk_iterator = pd.read_csv(\n",
      "/home1/pokorny/.conda/envs/bioenv/lib/python3.9/site-packages/gtfparse/read_gtf.py:82: FutureWarning: The warn_bad_lines argument has been deprecated and will be removed in a future version. Use on_bad_lines in the future.\n",
      "\n",
      "\n",
      "  chunk_iterator = pd.read_csv(\n",
      "INFO:root:Extracted GTF attributes: ['gene_id', 'transcript_id', 'gene_name']\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(11589, 19)\n",
      "Index(['tracking_id', 'locus', 'CTCF-AID_untreated_mean_FPKM',\n",
      "       'CTCF-AID_1day_mean_FPKM', 'CTCF-AID_2days_mean_FPKM',\n",
      "       'CTCF-AID_4days_mean_FPKM', 'CTCF-AID_washoff_mean_FPKM', '1day.1',\n",
      "       '2days.1', '4days.1', '2days_washoff_2days.1', 'WT_untreated_mean_FPKM',\n",
      "       'WT_2days_mean_FPKM', 'WT_4days_mean_FPKM', '1day.logFC', 'chrom',\n",
      "       'start', 'end', 'gene_id'],\n",
      "      dtype='object')\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tracking_id</th>\n",
       "      <th>locus</th>\n",
       "      <th>CTCF-AID_untreated_mean_FPKM</th>\n",
       "      <th>CTCF-AID_1day_mean_FPKM</th>\n",
       "      <th>CTCF-AID_2days_mean_FPKM</th>\n",
       "      <th>CTCF-AID_4days_mean_FPKM</th>\n",
       "      <th>CTCF-AID_washoff_mean_FPKM</th>\n",
       "      <th>1day.1</th>\n",
       "      <th>2days.1</th>\n",
       "      <th>4days.1</th>\n",
       "      <th>2days_washoff_2days.1</th>\n",
       "      <th>WT_untreated_mean_FPKM</th>\n",
       "      <th>WT_2days_mean_FPKM</th>\n",
       "      <th>WT_4days_mean_FPKM</th>\n",
       "      <th>1day.logFC</th>\n",
       "      <th>chrom</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>gene_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0610007P14Rik</td>\n",
       "      <td>chr12:87156404-87165495</td>\n",
       "      <td>71.716133</td>\n",
       "      <td>74.856200</td>\n",
       "      <td>63.412767</td>\n",
       "      <td>59.250033</td>\n",
       "      <td>67.995733</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>57.020867</td>\n",
       "      <td>49.716167</td>\n",
       "      <td>57.023633</td>\n",
       "      <td>0.061824</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0610009B22Rik</td>\n",
       "      <td>chr11:51498886-51502136</td>\n",
       "      <td>69.529333</td>\n",
       "      <td>76.436433</td>\n",
       "      <td>61.336067</td>\n",
       "      <td>38.937500</td>\n",
       "      <td>58.353700</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>58.497000</td>\n",
       "      <td>46.990267</td>\n",
       "      <td>58.072767</td>\n",
       "      <td>0.136639</td>\n",
       "      <td>chr11</td>\n",
       "      <td>51502137</td>\n",
       "      <td>51502137</td>\n",
       "      <td>0610009B22Rik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0610009O20Rik</td>\n",
       "      <td>chr18:38398058-38422283</td>\n",
       "      <td>17.565900</td>\n",
       "      <td>20.652933</td>\n",
       "      <td>20.476800</td>\n",
       "      <td>20.633733</td>\n",
       "      <td>18.857300</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>18.864133</td>\n",
       "      <td>18.413433</td>\n",
       "      <td>18.779333</td>\n",
       "      <td>0.233569</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     tracking_id                    locus  CTCF-AID_untreated_mean_FPKM  \\\n",
       "0  0610007P14Rik  chr12:87156404-87165495                     71.716133   \n",
       "1  0610009B22Rik  chr11:51498886-51502136                     69.529333   \n",
       "2  0610009O20Rik  chr18:38398058-38422283                     17.565900   \n",
       "\n",
       "   CTCF-AID_1day_mean_FPKM  CTCF-AID_2days_mean_FPKM  \\\n",
       "0                74.856200                 63.412767   \n",
       "1                76.436433                 61.336067   \n",
       "2                20.652933                 20.476800   \n",
       "\n",
       "   CTCF-AID_4days_mean_FPKM  CTCF-AID_washoff_mean_FPKM 1day.1 2days.1  \\\n",
       "0                 59.250033                   67.995733     no      no   \n",
       "1                 38.937500                   58.353700     no      no   \n",
       "2                 20.633733                   18.857300     no      no   \n",
       "\n",
       "  4days.1 2days_washoff_2days.1  WT_untreated_mean_FPKM  WT_2days_mean_FPKM  \\\n",
       "0      no                    no               57.020867           49.716167   \n",
       "1     yes                    no               58.497000           46.990267   \n",
       "2      no                    no               18.864133           18.413433   \n",
       "\n",
       "   WT_4days_mean_FPKM  1day.logFC  chrom     start       end        gene_id  \n",
       "0           57.023633    0.061824   <NA>      <NA>      <NA>            NaN  \n",
       "1           58.072767    0.136639  chr11  51502137  51502137  0610009B22Rik  \n",
       "2           18.779333    0.233569   <NA>      <NA>      <NA>            NaN  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "refseq_tss_df = read_gtf('/project/fudenber_735/genomes/mm9/RefSeq/tsses_from_mm9.refGene.gtf').rename(columns={'seqname' : 'chrom'})\n",
    "\n",
    "# Adding TSS annotation from refseq file\n",
    "RNA_seq_FPKM = RNA_seq_FPKM.merge(refseq_tss_df[['chrom', 'start', 'end', 'gene_id']].drop_duplicates(['gene_id']), how='left', left_on='tracking_id', right_on='gene_id')\n",
    "RNA_seq_FPKM = bf.sanitize_bedframe(RNA_seq_FPKM)\n",
    "print(RNA_seq_FPKM.shape)\n",
    "print(RNA_seq_FPKM.columns)\n",
    "RNA_seq_FPKM.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8c0f6ef5-bbb8-454a-a65c-1306dd3c03b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "refseq_tss_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "8b26afbb-aa6b-48fc-a144-b6f5ec9a2c0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bf.is_bedframe(RNA_seq_FPKM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "3c024331-11e6-4d88-86fe-656daebeed83",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bedframe?  True\n",
      "(1277, 4)\n"
     ]
    },
    {
     "data": {
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       "    }\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>chrom</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1272</th>\n",
       "      <td>chr18</td>\n",
       "      <td>77381001</td>\n",
       "      <td>77382000</td>\n",
       "      <td>0.8005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1273</th>\n",
       "      <td>chr9</td>\n",
       "      <td>78222001</td>\n",
       "      <td>78223000</td>\n",
       "      <td>0.8004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1274</th>\n",
       "      <td>chr1</td>\n",
       "      <td>138467001</td>\n",
       "      <td>138468000</td>\n",
       "      <td>0.8003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1275</th>\n",
       "      <td>chr14</td>\n",
       "      <td>12940001</td>\n",
       "      <td>12941000</td>\n",
       "      <td>0.8000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1276</th>\n",
       "      <td>chr2</td>\n",
       "      <td>135388001</td>\n",
       "      <td>135389000</td>\n",
       "      <td>0.8000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      chrom      start        end   value\n",
       "1272  chr18   77381001   77382000  0.8005\n",
       "1273   chr9   78222001   78223000  0.8004\n",
       "1274   chr1  138467001  138468000  0.8003\n",
       "1275  chr14   12940001   12941000  0.8000\n",
       "1276   chr2  135388001  135389000  0.8000"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "enhancer_coords = bf.read_table('/project/fudenber_735/collaborations/karissa_2022/features/mm9/mESC_Enhancer_prediction_Chen12_BMCgenomics_mm9.bed',\n",
    "                               schema='bedGraph',\n",
    "                               skiprows=1)\n",
    "# replacing value string with a decimal\n",
    "enhancer_coords['value'] = enhancer_coords['value'].apply(lambda x: x.replace(',', '.')).astype(float)\n",
    "\n",
    "enhancer_coords = bf.sanitize_bedframe(enhancer_coords)\n",
    "\n",
    "print(\"bedframe? \" , str(bf.is_bedframe(enhancer_coords)))\n",
    "\n",
    "#Taking cutoff\n",
    "enhancer_coords = enhancer_coords.loc[np.where(enhancer_coords['value'] >= 0.8)]\n",
    "\n",
    "print(enhancer_coords.shape)\n",
    "\n",
    "# Displaying last 5 --> the original data was ordered descending by 'value'\n",
    "enhancer_coords.tail(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "d3d50e6d-adc4-41fa-8220-28bbdbf8b357",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get distance between TSSes and the closest enhancer\n",
    "closest_intervals_nora = bf.closest(RNA_seq_FPKM, enhancer_coords, suffixes=('_de_tss','_enhancer'))\n",
    "RNA_seq_FPKM['distance_to_enhancer'] = closest_intervals_nora['distance'].replace(0,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "6888ee4e-0102-49fb-ab82-99b1d1022e43",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "554\n"
     ]
    },
    {
     "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>tracking_id</th>\n",
       "      <th>locus</th>\n",
       "      <th>chrom</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>distance_to_enhancer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [tracking_id, locus, chrom, start, end, distance_to_enhancer]\n",
       "Index: []"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(RNA_seq_FPKM['distance_to_enhancer'].isna().sum())\n",
    "RNA_seq_FPKM.dropna(subset='distance_to_enhancer', inplace=True)\n",
    "             \n",
    "#RNA_seq_FPKM[RNA_seq_FPKM['distance_to_enhancer'].isna()][['tracking_id' , 'locus', 'chrom', 'start', 'end', 'distance_to_enhancer']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "c2f5fb85-e8e3-4b51-ad4d-b8a80604b88d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_distal_enhancers_supp_table(RNA_seq_FPKM)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f394861-6366-46f9-8519-a1052bb83c35",
   "metadata": {},
   "source": [
    "# Variation 2: DEG's from Nora 2017 supplementary table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "491ae861-dda3-45d6-803b-957b3a7f638c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "7caf3230-5ea5-4d3e-b4a1-1a4511fb1c5d",
   "metadata": {},
   "source": [
    "## Detour: extracting TSS compatible with original genome build used in STAR alignment\n",
    "\n",
    "Found that RefSeq archives are likely most compatible with the data set in supplementary table 11. We have two download choices:\n",
    "1. `/project/fudenber_735/genomes/mm9/RefSeq/mm9.refGene.gtf` from hgdownload.cse.ucsc.edu/goldenPath/mm9/bigZips/\n",
    "2. `/project/fudenber_735/genomes/mm9/gencode/gencode.vM1.annotation.gtf.gz` from https://www.gencodegenes.org/mouse/release_M1.html \n",
    "\n",
    "Going to 1. see how many have TSSes annotated already, 2. Compare the approximate range to the locus in the supplementary table file (might not be possible for some since they're annotated exons instead of genes. Maybe gffutils can help with this 3. use gff utils to extract a singe TSS for each tracking_id and add it to the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0d3a9eb6-cfc9-4b7c-9c1d-ce78eab3bfe1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import gffutils\n",
    "import pybedtools\n",
    "from pybedtools.featurefuncs import TSS\n",
    "from gffutils.helpers import asinterval\n",
    "import bioframe as bf\n",
    "from gtfparse import read_gtf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "00f41897-7d71-4296-bedb-cf29fb7bb57d",
   "metadata": {},
   "outputs": [],
   "source": [
    "refGene_df = pd.read_table(\"/project/fudenber_735/genomes/mm9/RefSeq/mm9.refGene.gtf\", header=None, \n",
    "                          names=['seqname', 'type', 'feature', 'start', 'end', 'score', 'strand', 'frame',\n",
    "                                'attributes'])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "32b9497f-fc15-4524-bd6d-14c217081a46",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of transcript entries in refGene annotation file? 44308\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of transcript entries in refGene annotation file? \" + str(len(refGene_df[refGene_df['feature'] == 'transcript'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "206ed711-44df-49bd-b3a6-471780876c68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['gene_id \"Rbfox2\"', ' transcript_id \"NM_001110827\"', '  gene_name \"Rbfox2\"']"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "refGene_df['attributes'][0].split(\";\")[0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "23d2b5cd-8373-4bea-9a6b-83e4bb27acb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# parses the string list of attributes from gtf file into a list\n",
    "def split_ensembl_atts(attributeString):\n",
    "    \n",
    "    groups = attributeString.split(\";\")[0:3]\n",
    "    data = [x.split('\"')[1].strip() for x in groups]\n",
    "    labels = [x.split('\"')[0].strip() for x in groups]\n",
    "\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ad2357be-acf6-41e8-83c4-d74989650a19",
   "metadata": {},
   "outputs": [],
   "source": [
    "attributes_listed =  [split_ensembl_atts(line) for line in refGene_df['attributes']]\n",
    "\n",
    "attribute_df = pd.DataFrame(attributes_listed, columns =['gene_id', 'transcript_id', 'gene_name'])\n",
    "\n",
    "refGene_df = pd.concat([refGene_df, attribute_df], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8cea7eda-36fe-4c6b-a256-a417ae43e8f8",
   "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>seqname</th>\n",
       "      <th>type</th>\n",
       "      <th>feature</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>score</th>\n",
       "      <th>strand</th>\n",
       "      <th>frame</th>\n",
       "      <th>attributes</th>\n",
       "      <th>gene_id</th>\n",
       "      <th>transcript_id</th>\n",
       "      <th>gene_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>chr15</td>\n",
       "      <td>refGene</td>\n",
       "      <td>transcript</td>\n",
       "      <td>76909420</td>\n",
       "      <td>77137483</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "      <td>.</td>\n",
       "      <td>gene_id \"Rbfox2\"; transcript_id \"NM_001110827\"...</td>\n",
       "      <td>Rbfox2</td>\n",
       "      <td>NM_001110827</td>\n",
       "      <td>Rbfox2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>chr15</td>\n",
       "      <td>refGene</td>\n",
       "      <td>exon</td>\n",
       "      <td>76909420</td>\n",
       "      <td>76914636</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "      <td>.</td>\n",
       "      <td>gene_id \"Rbfox2\"; transcript_id \"NM_001110827\"...</td>\n",
       "      <td>Rbfox2</td>\n",
       "      <td>NM_001110827</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>chr15</td>\n",
       "      <td>refGene</td>\n",
       "      <td>3UTR</td>\n",
       "      <td>76909420</td>\n",
       "      <td>76914591</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "      <td>.</td>\n",
       "      <td>gene_id \"Rbfox2\"; transcript_id \"NM_001110827\"...</td>\n",
       "      <td>Rbfox2</td>\n",
       "      <td>NM_001110827</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  seqname     type     feature     start       end score strand frame  \\\n",
       "0   chr15  refGene  transcript  76909420  77137483     .      -     .   \n",
       "1   chr15  refGene        exon  76909420  76914636     .      -     .   \n",
       "2   chr15  refGene        3UTR  76909420  76914591     .      -     .   \n",
       "\n",
       "                                          attributes gene_id transcript_id  \\\n",
       "0  gene_id \"Rbfox2\"; transcript_id \"NM_001110827\"...  Rbfox2  NM_001110827   \n",
       "1  gene_id \"Rbfox2\"; transcript_id \"NM_001110827\"...  Rbfox2  NM_001110827   \n",
       "2  gene_id \"Rbfox2\"; transcript_id \"NM_001110827\"...  Rbfox2  NM_001110827   \n",
       "\n",
       "  gene_name  \n",
       "0    Rbfox2  \n",
       "1         1  \n",
       "2         1  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "refGene_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3993660a-e633-4244-957e-0667666b1295",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "de60357a-0039-42df-b10a-964347c3233e",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Run this once, to generate db from gtf file\n",
    "# db = gffutils.create_db(\"/project/fudenber_735/genomes/mm9/RefSeq/mm9.refGene.gtf\", \n",
    "#                         \"/project/fudenber_735/genomes/mm9/RefSeq/mm9.refGene.gtf.db\", \n",
    "#                         force=True,\n",
    "#                         disable_infer_transcripts=True)\n",
    "\n",
    "refGene_db = gffutils.FeatureDB(\"/project/fudenber_735/genomes/mm9/RefSeq/mm9.refGene.gtf.db\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "3d0302e0-fda3-4cfb-94c8-ac6a4ad65fec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['', 'project', 'fudenber_735', 'genomes', 'mm9', 'RefSeq', 'mm9.refGene.gtf']"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filename = '/project/fudenber_735/genomes/mm9/RefSeq/mm9.refGene.gtf'\n",
    "\n",
    "filename.split('/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5396f27c-e2ad-45f4-99f3-c01d29294395",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def tss_generator():\n",
    "    \"\"\"\n",
    "    Generator function to yield TSS of each annotated transcript\n",
    "    \"\"\"\n",
    "    for transcript in refGene_db.features_of_type('transcript'):\n",
    "        yield TSS(asinterval(transcript), upstream=1, downstream=0)\n",
    "\n",
    "tsses = pybedtools.BedTool(tss_generator()).saveas('/project/fudenber_735/genomes/mm9/RefSeq/tsses_from_mm9.refGene.gtf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2bf0c169-87e4-4312-a320-cf88c2f798b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home1/pokorny/.conda/envs/bioenv/lib/python3.9/site-packages/gtfparse/read_gtf.py:82: FutureWarning: The error_bad_lines argument has been deprecated and will be removed in a future version. Use on_bad_lines in the future.\n",
      "\n",
      "\n",
      "  chunk_iterator = pd.read_csv(\n",
      "/home1/pokorny/.conda/envs/bioenv/lib/python3.9/site-packages/gtfparse/read_gtf.py:82: FutureWarning: The warn_bad_lines argument has been deprecated and will be removed in a future version. Use on_bad_lines in the future.\n",
      "\n",
      "\n",
      "  chunk_iterator = pd.read_csv(\n",
      "INFO:root:Extracted GTF attributes: ['gene_id', 'transcript_id', 'gene_name']\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of transcript entries in gff utils generated tss annotation file: 44308\n"
     ]
    }
   ],
   "source": [
    "refseq_tss_df = read_gtf('/project/fudenber_735/genomes/mm9/RefSeq/tsses_from_mm9.refGene.gtf')\n",
    "\n",
    "#print(\"Number of transcript entries in refGene annotation file? \" + str(len(refGene_df[refGene_df['feature'] == 'transcript'])))\n",
    "print(\"Number of transcript entries in gff utils generated tss annotation file: \" + str(refseq_tss_df.shape[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "cc5c95ef-a677-420e-b293-0f695b3abb33",
   "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>seqname</th>\n",
       "      <th>source</th>\n",
       "      <th>feature</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>score</th>\n",
       "      <th>strand</th>\n",
       "      <th>frame</th>\n",
       "      <th>gene_id</th>\n",
       "      <th>transcript_id</th>\n",
       "      <th>gene_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>chr15</td>\n",
       "      <td>refGene</td>\n",
       "      <td>transcript</td>\n",
       "      <td>77137484</td>\n",
       "      <td>77137484</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-</td>\n",
       "      <td>0</td>\n",
       "      <td>Rbfox2</td>\n",
       "      <td>NM_001110827</td>\n",
       "      <td>Rbfox2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>chr2</td>\n",
       "      <td>refGene</td>\n",
       "      <td>transcript</td>\n",
       "      <td>104334647</td>\n",
       "      <td>104334647</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-</td>\n",
       "      <td>0</td>\n",
       "      <td>Hipk3</td>\n",
       "      <td>NM_001145824</td>\n",
       "      <td>Hipk3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>chr2</td>\n",
       "      <td>refGene</td>\n",
       "      <td>transcript</td>\n",
       "      <td>11423718</td>\n",
       "      <td>11423718</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-</td>\n",
       "      <td>0</td>\n",
       "      <td>Pfkfb3</td>\n",
       "      <td>NM_001177756</td>\n",
       "      <td>Pfkfb3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>chr2</td>\n",
       "      <td>refGene</td>\n",
       "      <td>transcript</td>\n",
       "      <td>57090088</td>\n",
       "      <td>57090088</td>\n",
       "      <td>NaN</td>\n",
       "      <td>+</td>\n",
       "      <td>0</td>\n",
       "      <td>Gpd2</td>\n",
       "      <td>NM_001145820</td>\n",
       "      <td>Gpd2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>chr6</td>\n",
       "      <td>refGene</td>\n",
       "      <td>transcript</td>\n",
       "      <td>135317510</td>\n",
       "      <td>135317510</td>\n",
       "      <td>NaN</td>\n",
       "      <td>+</td>\n",
       "      <td>0</td>\n",
       "      <td>Emp1</td>\n",
       "      <td>NM_001288627</td>\n",
       "      <td>Emp1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>chr10</td>\n",
       "      <td>refGene</td>\n",
       "      <td>transcript</td>\n",
       "      <td>79389787</td>\n",
       "      <td>79389787</td>\n",
       "      <td>NaN</td>\n",
       "      <td>+</td>\n",
       "      <td>0</td>\n",
       "      <td>Arid3a</td>\n",
       "      <td>NM_001288626</td>\n",
       "      <td>Arid3a</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  seqname   source     feature      start        end  score strand frame  \\\n",
       "0   chr15  refGene  transcript   77137484   77137484    NaN      -     0   \n",
       "1    chr2  refGene  transcript  104334647  104334647    NaN      -     0   \n",
       "2    chr2  refGene  transcript   11423718   11423718    NaN      -     0   \n",
       "3    chr2  refGene  transcript   57090088   57090088    NaN      +     0   \n",
       "4    chr6  refGene  transcript  135317510  135317510    NaN      +     0   \n",
       "5   chr10  refGene  transcript   79389787   79389787    NaN      +     0   \n",
       "\n",
       "  gene_id transcript_id gene_name  \n",
       "0  Rbfox2  NM_001110827    Rbfox2  \n",
       "1   Hipk3  NM_001145824     Hipk3  \n",
       "2  Pfkfb3  NM_001177756    Pfkfb3  \n",
       "3    Gpd2  NM_001145820      Gpd2  \n",
       "4    Emp1  NM_001288627      Emp1  \n",
       "5  Arid3a  NM_001288626    Arid3a  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "refseq_tss_df.head(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "4b491dc3-0409-4c8a-82a0-d2a57b6447f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tracking_id</th>\n",
       "      <th>locus</th>\n",
       "      <th>CTCF-AID_untreated_mean_FPKM</th>\n",
       "      <th>CTCF-AID_1day_mean_FPKM</th>\n",
       "      <th>CTCF-AID_2days_mean_FPKM</th>\n",
       "      <th>CTCF-AID_4days_mean_FPKM</th>\n",
       "      <th>CTCF-AID_washoff_mean_FPKM</th>\n",
       "      <th>1day.1</th>\n",
       "      <th>2days.1</th>\n",
       "      <th>4days.1</th>\n",
       "      <th>...</th>\n",
       "      <th>source</th>\n",
       "      <th>feature</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
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       "      <th>gene_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0610005C13Rik</td>\n",
       "      <td>chr7:52823164-52845080</td>\n",
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       "      <td>0.804255</td>\n",
       "      <td>1.040841</td>\n",
       "      <td>2.176798</td>\n",
       "      <td>1.209881</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>refGene</td>\n",
       "      <td>transcript</td>\n",
       "      <td>52830547.0</td>\n",
       "      <td>52830547.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-</td>\n",
       "      <td>0</td>\n",
       "      <td>0610005C13Rik</td>\n",
       "      <td>NR_038165</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0610007P14Rik</td>\n",
       "      <td>chr12:87156404-87165495</td>\n",
       "      <td>71.716133</td>\n",
       "      <td>74.856200</td>\n",
       "      <td>63.412767</td>\n",
       "      <td>59.250033</td>\n",
       "      <td>67.995733</td>\n",
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       "      <td>no</td>\n",
       "      <td>no</td>\n",
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       "      <td>NaN</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0610009B22Rik</td>\n",
       "      <td>chr11:51498886-51502136</td>\n",
       "      <td>69.529333</td>\n",
       "      <td>76.436433</td>\n",
       "      <td>61.336067</td>\n",
       "      <td>38.937500</td>\n",
       "      <td>58.353700</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>...</td>\n",
       "      <td>refGene</td>\n",
       "      <td>transcript</td>\n",
       "      <td>51502137.0</td>\n",
       "      <td>51502137.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-</td>\n",
       "      <td>0</td>\n",
       "      <td>0610009B22Rik</td>\n",
       "      <td>NM_025319</td>\n",
       "      <td>0610009B22Rik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0610009L18Rik</td>\n",
       "      <td>chr11:120209991-120212504</td>\n",
       "      <td>0.944842</td>\n",
       "      <td>1.154941</td>\n",
       "      <td>0.905838</td>\n",
       "      <td>1.097459</td>\n",
       "      <td>0.889548</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>refGene</td>\n",
       "      <td>transcript</td>\n",
       "      <td>120209991.0</td>\n",
       "      <td>120209991.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>+</td>\n",
       "      <td>0</td>\n",
       "      <td>0610009L18Rik</td>\n",
       "      <td>NR_038126</td>\n",
       "      <td>0610009L18Rik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0610009O20Rik</td>\n",
       "      <td>chr18:38398058-38422283</td>\n",
       "      <td>17.565900</td>\n",
       "      <td>20.652933</td>\n",
       "      <td>20.476800</td>\n",
       "      <td>20.633733</td>\n",
       "      <td>18.857300</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0610010B08Rik</td>\n",
       "      <td>chr2:174952492-175261278</td>\n",
       "      <td>0.057933</td>\n",
       "      <td>0.051192</td>\n",
       "      <td>0.080296</td>\n",
       "      <td>0.091421</td>\n",
       "      <td>0.090390</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0610010F05Rik</td>\n",
       "      <td>chr11:23473775-23533631</td>\n",
       "      <td>4.500230</td>\n",
       "      <td>4.654253</td>\n",
       "      <td>3.784197</td>\n",
       "      <td>3.342397</td>\n",
       "      <td>4.257007</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>refGene</td>\n",
       "      <td>transcript</td>\n",
       "      <td>23533632.0</td>\n",
       "      <td>23533632.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-</td>\n",
       "      <td>0</td>\n",
       "      <td>0610010F05Rik</td>\n",
       "      <td>NR_156450</td>\n",
       "      <td>0610010F05Rik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0610010K14Rik</td>\n",
       "      <td>chr11:70048705-70051416</td>\n",
       "      <td>115.794100</td>\n",
       "      <td>134.728000</td>\n",
       "      <td>124.419633</td>\n",
       "      <td>109.027433</td>\n",
       "      <td>95.227500</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>refGene</td>\n",
       "      <td>transcript</td>\n",
       "      <td>70051417.0</td>\n",
       "      <td>70051417.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-</td>\n",
       "      <td>0</td>\n",
       "      <td>0610010K14Rik</td>\n",
       "      <td>NM_001177607</td>\n",
       "      <td>0610010K14Rik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0610011F06Rik</td>\n",
       "      <td>chr17:26012444-26014108</td>\n",
       "      <td>12.449400</td>\n",
       "      <td>12.249967</td>\n",
       "      <td>11.492490</td>\n",
       "      <td>12.151867</td>\n",
       "      <td>11.099847</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0610012G03Rik</td>\n",
       "      <td>chr16:31947136-31948607</td>\n",
       "      <td>34.492833</td>\n",
       "      <td>40.396067</td>\n",
       "      <td>36.419533</td>\n",
       "      <td>30.946500</td>\n",
       "      <td>30.611233</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>refGene</td>\n",
       "      <td>transcript</td>\n",
       "      <td>31948592.0</td>\n",
       "      <td>31948592.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-</td>\n",
       "      <td>0</td>\n",
       "      <td>0610012G03Rik</td>\n",
       "      <td>NM_134107</td>\n",
       "      <td>0610012G03Rik</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     tracking_id                      locus  CTCF-AID_untreated_mean_FPKM  \\\n",
       "0  0610005C13Rik     chr7:52823164-52845080                      0.784511   \n",
       "1  0610007P14Rik    chr12:87156404-87165495                     71.716133   \n",
       "2  0610009B22Rik    chr11:51498886-51502136                     69.529333   \n",
       "3  0610009L18Rik  chr11:120209991-120212504                      0.944842   \n",
       "4  0610009O20Rik    chr18:38398058-38422283                     17.565900   \n",
       "5  0610010B08Rik   chr2:174952492-175261278                      0.057933   \n",
       "6  0610010F05Rik    chr11:23473775-23533631                      4.500230   \n",
       "7  0610010K14Rik    chr11:70048705-70051416                    115.794100   \n",
       "8  0610011F06Rik    chr17:26012444-26014108                     12.449400   \n",
       "9  0610012G03Rik    chr16:31947136-31948607                     34.492833   \n",
       "\n",
       "   CTCF-AID_1day_mean_FPKM  CTCF-AID_2days_mean_FPKM  \\\n",
       "0                 0.804255                  1.040841   \n",
       "1                74.856200                 63.412767   \n",
       "2                76.436433                 61.336067   \n",
       "3                 1.154941                  0.905838   \n",
       "4                20.652933                 20.476800   \n",
       "5                 0.051192                  0.080296   \n",
       "6                 4.654253                  3.784197   \n",
       "7               134.728000                124.419633   \n",
       "8                12.249967                 11.492490   \n",
       "9                40.396067                 36.419533   \n",
       "\n",
       "   CTCF-AID_4days_mean_FPKM  CTCF-AID_washoff_mean_FPKM 1day.1 2days.1  \\\n",
       "0                  2.176798                    1.209881     no      no   \n",
       "1                 59.250033                   67.995733     no      no   \n",
       "2                 38.937500                   58.353700     no      no   \n",
       "3                  1.097459                    0.889548     no      no   \n",
       "4                 20.633733                   18.857300     no      no   \n",
       "5                  0.091421                    0.090390     no      no   \n",
       "6                  3.342397                    4.257007     no      no   \n",
       "7                109.027433                   95.227500     no      no   \n",
       "8                 12.151867                   11.099847     no      no   \n",
       "9                 30.946500                   30.611233     no      no   \n",
       "\n",
       "  4days.1  ...   source     feature        start          end score strand  \\\n",
       "0      no  ...  refGene  transcript   52830547.0   52830547.0   NaN      -   \n",
       "1      no  ...      NaN         NaN          NaN          NaN   NaN    NaN   \n",
       "2     yes  ...  refGene  transcript   51502137.0   51502137.0   NaN      -   \n",
       "3      no  ...  refGene  transcript  120209991.0  120209991.0   NaN      +   \n",
       "4      no  ...      NaN         NaN          NaN          NaN   NaN    NaN   \n",
       "5      no  ...      NaN         NaN          NaN          NaN   NaN    NaN   \n",
       "6      no  ...  refGene  transcript   23533632.0   23533632.0   NaN      -   \n",
       "7      no  ...  refGene  transcript   70051417.0   70051417.0   NaN      -   \n",
       "8      no  ...      NaN         NaN          NaN          NaN   NaN    NaN   \n",
       "9      no  ...  refGene  transcript   31948592.0   31948592.0   NaN      -   \n",
       "\n",
       "  frame        gene_id  transcript_id      gene_name  \n",
       "0     0  0610005C13Rik      NR_038165  0610005C13Rik  \n",
       "1   NaN            NaN            NaN            NaN  \n",
       "2     0  0610009B22Rik      NM_025319  0610009B22Rik  \n",
       "3     0  0610009L18Rik      NR_038126  0610009L18Rik  \n",
       "4   NaN            NaN            NaN            NaN  \n",
       "5   NaN            NaN            NaN            NaN  \n",
       "6     0  0610010F05Rik      NR_156450  0610010F05Rik  \n",
       "7     0  0610010K14Rik   NM_001177607  0610010K14Rik  \n",
       "8   NaN            NaN            NaN            NaN  \n",
       "9     0  0610012G03Rik      NM_134107  0610012G03Rik  \n",
       "\n",
       "[10 rows x 25 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add TSSes pulled from RefSeq db \n",
    "fpkm_refseq = fpkm.merge(refseq_tss_df.drop_duplicates('gene_name'), how='left', left_on='tracking_id', right_on='gene_name')\n",
    "fpkm_refseq.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "df89be2b-8d4c-48a5-8dfd-42c33fc7e509",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24315\n",
      "24315\n",
      "Number of entries that did not have a TSS entry in refseq annotations: \n",
      "1063\n"
     ]
    }
   ],
   "source": [
    "# Checking shapes and dimensions to make sure everything made it into the  \n",
    "print(fpkm.shape[0])\n",
    "print(fpkm_refseq.shape[0])\n",
    "\n",
    "print(\"Number of entries that did not have a TSS entry in refseq annotations: \")\n",
    "print(fpkm_refseq[fpkm_refseq['gene_id'].isna()].shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d6240fc-809b-416b-84b7-5e9cc128a1ab",
   "metadata": {},
   "source": [
    "### Again, with gencode db"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "2b639a6c-c9f1-4a2c-b16a-feeebee68139",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Generating database for Gencode (repeating same as above) -- MOVE TO SEPARATE HEADER TOO CONFUSING mixed in liket this\n",
    "# gencode_db = gffutils.create_db(\"/project/fudenber_735/genomes/mm9/gencode/gencode.vM1.annotation.gtf.gz\", \n",
    "#                         \"/project/fudenber_735/genomes/mm9/gencode/gencode.vM1.annotation.gtf.db\", \n",
    "#                         force=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "c9045231-513a-43fa-b228-5993ebb6ef18",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m/tmp/SLURM_5480641/ipykernel_4775/4046824229.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      6\u001b[0m         \u001b[0;32myield\u001b[0m \u001b[0mTSS\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0masinterval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtranscript\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mupstream\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdownstream\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mtsses\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpybedtools\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mBedTool\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtss_generator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msaveas\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/project/fudenber_735/genomes/mm9/gencode/tsses_from_gencode.vM1.annotation.gtf'\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      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/bioenv/lib/python3.9/site-packages/pybedtools/bedtool.py\u001b[0m in \u001b[0;36mdecorated\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    921\u001b[0m             \u001b[0;31m# this calls the actual method in the first place; *result* is\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    922\u001b[0m             \u001b[0;31m# whatever you get back\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 923\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    924\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    925\u001b[0m             \u001b[0;31m# add appropriate tags\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/bioenv/lib/python3.9/site-packages/pybedtools/bedtool.py\u001b[0m in \u001b[0;36msaveas\u001b[0;34m(self, fn, trackline, compressed)\u001b[0m\n\u001b[1;32m   3360\u001b[0m         \u001b[0min_compressed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstring_types\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0misGZIP\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3361\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3362\u001b[0;31m         fn = self._collapse(\n\u001b[0m\u001b[1;32m   3363\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3364\u001b[0m             \u001b[0mfn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/bioenv/lib/python3.9/site-packages/pybedtools/bedtool.py\u001b[0m in \u001b[0;36m_collapse\u001b[0;34m(self, iterable, fn, trackline, in_compressed, out_compressed)\u001b[0m\n\u001b[1;32m   1420\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1421\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mout_open_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"wt\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mout_\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1422\u001b[0;31m                 \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0miterable\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   1423\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\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   1424\u001b[0m                         \u001b[0mi\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcreate_interval_from_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\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;32m~/.conda/envs/bioenv/lib/python3.9/site-packages/pybedtools/cbedtools.pyx\u001b[0m in \u001b[0;36mpybedtools.cbedtools.IntervalIterator.__next__\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m/tmp/SLURM_5480641/ipykernel_4775/4046824229.py\u001b[0m in \u001b[0;36mtss_generator\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0mGenerator\u001b[0m \u001b[0mfunction\u001b[0m \u001b[0mto\u001b[0m \u001b[0;32myield\u001b[0m \u001b[0mTSS\u001b[0m \u001b[0mof\u001b[0m \u001b[0meach\u001b[0m \u001b[0mannotated\u001b[0m \u001b[0mtranscript\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \"\"\"\n\u001b[0;32m----> 5\u001b[0;31m     \u001b[0;32mfor\u001b[0m \u001b[0mtranscript\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mgencode_db\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfeatures_of_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'transcript'\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[0m\u001b[1;32m      6\u001b[0m         \u001b[0;32myield\u001b[0m \u001b[0mTSS\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0masinterval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtranscript\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mupstream\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdownstream\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/bioenv/lib/python3.9/site-packages/gffutils/interface.py\u001b[0m in \u001b[0;36mfeatures_of_type\u001b[0;34m(self, featuretype, limit, strand, order_by, reverse, completely_within)\u001b[0m\n\u001b[1;32m    339\u001b[0m         )\n\u001b[1;32m    340\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 341\u001b[0;31m         \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_execute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mquery\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\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[0m\u001b[1;32m    342\u001b[0m             \u001b[0;32myield\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_feature_returner\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    343\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "def tss_generator():\n",
    "    \"\"\"\n",
    "    Generator function to yield TSS of each annotated transcript\n",
    "    \"\"\"\n",
    "    for transcript in gencode_db.features_of_type('transcript'):\n",
    "        yield TSS(asinterval(transcript), upstream=1, downstream=0)\n",
    "\n",
    "tsses = pybedtools.BedTool(tss_generator()).saveas('/project/fudenber_735/genomes/mm9/gencode/tsses_from_gencode.vM1.annotation.gtf')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "c92ad889-3c0d-4592-bb12-1be679fa91f6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home1/pokorny/.conda/envs/bioenv/lib/python3.9/site-packages/gtfparse/read_gtf.py:82: FutureWarning: The error_bad_lines argument has been deprecated and will be removed in a future version. Use on_bad_lines in the future.\n",
      "\n",
      "\n",
      "  chunk_iterator = pd.read_csv(\n",
      "/home1/pokorny/.conda/envs/bioenv/lib/python3.9/site-packages/gtfparse/read_gtf.py:82: FutureWarning: The warn_bad_lines argument has been deprecated and will be removed in a future version. Use on_bad_lines in the future.\n",
      "\n",
      "\n",
      "  chunk_iterator = pd.read_csv(\n",
      "INFO:root:Extracted GTF attributes: ['gene_id', 'transcript_id', 'gene_type', 'gene_status', 'gene_name', 'transcript_type', 'transcript_status', 'transcript_name', 'level', 'havana_gene', 'ont', 'havana_transcript', 'exon_number', 'exon_id', 'tag', 'ccdsid']\n",
      "/home1/pokorny/.conda/envs/bioenv/lib/python3.9/site-packages/gtfparse/read_gtf.py:82: FutureWarning: The error_bad_lines argument has been deprecated and will be removed in a future version. Use on_bad_lines in the future.\n",
      "\n",
      "\n",
      "  chunk_iterator = pd.read_csv(\n",
      "/home1/pokorny/.conda/envs/bioenv/lib/python3.9/site-packages/gtfparse/read_gtf.py:82: FutureWarning: The warn_bad_lines argument has been deprecated and will be removed in a future version. Use on_bad_lines in the future.\n",
      "\n",
      "\n",
      "  chunk_iterator = pd.read_csv(\n",
      "INFO:root:Extracted GTF attributes: ['gene_id', 'transcript_id', 'gene_type', 'gene_status', 'gene_name', 'transcript_type', 'transcript_status', 'transcript_name', 'level', 'ont', 'havana_gene', 'havana_transcript', 'tag', 'ccdsid']\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of transcript entries in refGene annotation file? 95495\n",
      "Number of transcript entries in gff utils generated tss annotation file: 73915\n"
     ]
    }
   ],
   "source": [
    "gencode_df = read_gtf(\"/project/fudenber_735/genomes/mm9/gencode/gencode.vM1.annotation.gtf.gz\")\n",
    "\n",
    "gencode_tss_df = read_gtf('/project/fudenber_735/genomes/mm9/gencode/tsses_from_gencode.vM1.annotation.gtf')\n",
    "\n",
    "print(\"Number of transcript entries in refGene annotation file? \" + str(len(gencode_df[gencode_df['feature'] == 'transcript'])))\n",
    "print(\"Number of transcript entries in gff utils generated tss annotation file: \" + str(gencode_tss_df.shape[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "a0e3d154-bdfb-4ad2-b7aa-3418977e6a0d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of transcript entries in gencode annotation file? 95495\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of transcript entries in gencode annotation file? \" + str(len(gencode_df[gencode_df['feature'] == 'transcript'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8ce1c6b9-c821-4974-9cc4-f659068b6931",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'refseq_tss_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/tmp/SLURM_7936489/ipykernel_236650/1115408594.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Add TSSes pulled from RefSeq db\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mfpkm_gencode\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfpkm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmerge\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrefseq_tss_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop_duplicates\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'gene_name'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'left'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mleft_on\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'tracking_id'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright_on\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'gene_name'\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      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfpkm_gencode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\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[0mfpkm_gencode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\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 'refseq_tss_df' is not defined"
     ]
    }
   ],
   "source": [
    "# Add TSSes pulled from RefSeq db \n",
    "fpkm_gencode = fpkm.merge(refseq_tss_df.drop_duplicates('gene_name'), how='left', left_on='tracking_id', right_on='gene_name')\n",
    "print(fpkm_gencode.columns)\n",
    "fpkm_gencode.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "381ec9fd-d67a-4a99-b7da-9f85bb2363e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], dtype: int64)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(fpkm_gencode['gene_id'] == fpkm_gencode['gene_name']).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "8c13f959-6841-4eb8-b0a2-5766a3ece008",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24315\n",
      "24315\n",
      "Number of entries that did not have a TSS entry in Gencode annotations: \n",
      "1063\n"
     ]
    }
   ],
   "source": [
    "# Looking for unfound gene_ids \n",
    "print(fpkm.shape[0])\n",
    "print(fpkm_gencode.shape[0])\n",
    "\n",
    "print(\"Number of entries that did not have a TSS entry in Gencode annotations: \")\n",
    "print(fpkm_gencode[fpkm_gencode['gene_id'].isna()].shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "002eeb53-411c-40e2-8c15-a3e922a5705f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True     23252\n",
       "False     1063\n",
       "dtype: int64"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# comparing annotations from the two, since appears to be the same number\n",
    "(fpkm_gencode['gene_id'] == fpkm_refseq['gene_name']).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "32f5dd62-289e-4c5f-a375-45ac6640255a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True    23252\n",
       "Name: start, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check if the TSS index annotated are the same\n",
    "match_ix = np.where(fpkm_gencode['gene_id'] == fpkm_refseq['gene_id'])\n",
    "\n",
    "(fpkm_gencode.loc[match_ix]['start'] == fpkm_refseq.loc[match_ix]['start']).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "cb2b53e7-3cd8-4ffa-9eff-907aa42dd0d3",
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tracking_id</th>\n",
       "      <th>locus</th>\n",
       "      <th>CTCF-AID_untreated_mean_FPKM</th>\n",
       "      <th>CTCF-AID_1day_mean_FPKM</th>\n",
       "      <th>CTCF-AID_2days_mean_FPKM</th>\n",
       "      <th>CTCF-AID_4days_mean_FPKM</th>\n",
       "      <th>CTCF-AID_washoff_mean_FPKM</th>\n",
       "      <th>1day.1</th>\n",
       "      <th>2days.1</th>\n",
       "      <th>4days.1</th>\n",
       "      <th>...</th>\n",
       "      <th>source</th>\n",
       "      <th>feature</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>score</th>\n",
       "      <th>strand</th>\n",
       "      <th>frame</th>\n",
       "      <th>gene_id</th>\n",
       "      <th>transcript_id</th>\n",
       "      <th>gene_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0610007P14Rik</td>\n",
       "      <td>chr12:87156404-87165495</td>\n",
       "      <td>71.716133</td>\n",
       "      <td>74.856200</td>\n",
       "      <td>63.412767</td>\n",
       "      <td>59.250033</td>\n",
       "      <td>67.995733</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0610009O20Rik</td>\n",
       "      <td>chr18:38398058-38422283</td>\n",
       "      <td>17.565900</td>\n",
       "      <td>20.652933</td>\n",
       "      <td>20.476800</td>\n",
       "      <td>20.633733</td>\n",
       "      <td>18.857300</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0610010B08Rik</td>\n",
       "      <td>chr2:174952492-175261278</td>\n",
       "      <td>0.057933</td>\n",
       "      <td>0.051192</td>\n",
       "      <td>0.080296</td>\n",
       "      <td>0.091421</td>\n",
       "      <td>0.090390</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     tracking_id                     locus  CTCF-AID_untreated_mean_FPKM  \\\n",
       "1  0610007P14Rik   chr12:87156404-87165495                     71.716133   \n",
       "4  0610009O20Rik   chr18:38398058-38422283                     17.565900   \n",
       "5  0610010B08Rik  chr2:174952492-175261278                      0.057933   \n",
       "\n",
       "   CTCF-AID_1day_mean_FPKM  CTCF-AID_2days_mean_FPKM  \\\n",
       "1                74.856200                 63.412767   \n",
       "4                20.652933                 20.476800   \n",
       "5                 0.051192                  0.080296   \n",
       "\n",
       "   CTCF-AID_4days_mean_FPKM  CTCF-AID_washoff_mean_FPKM 1day.1 2days.1  \\\n",
       "1                 59.250033                   67.995733     no      no   \n",
       "4                 20.633733                   18.857300     no      no   \n",
       "5                  0.091421                    0.090390     no      no   \n",
       "\n",
       "  4days.1  ... source  feature  start  end score strand frame  gene_id  \\\n",
       "1      no  ...    NaN      NaN    NaN  NaN   NaN    NaN   NaN      NaN   \n",
       "4      no  ...    NaN      NaN    NaN  NaN   NaN    NaN   NaN      NaN   \n",
       "5      no  ...    NaN      NaN    NaN  NaN   NaN    NaN   NaN      NaN   \n",
       "\n",
       "   transcript_id  gene_name  \n",
       "1            NaN        NaN  \n",
       "4            NaN        NaN  \n",
       "5            NaN        NaN  \n",
       "\n",
       "[3 rows x 25 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking to see if any of the gene_ids which didn't have an annotated transcript exists in another form in the annotation\n",
    "fpkm_gencode[fpkm_gencode['gene_id'].isna()].head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "c1242867-b212-4ac4-a717-4299d29b786c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Searching Gencode annotation GTF for: 0610007P14Rik\n",
      "Stopped search in pandas --> but grep returned multiple entries for this gene id??\n"
     ]
    }
   ],
   "source": [
    "unlabeled_id = fpkm_gencode[fpkm_gencode['gene_id'].isna()].iloc[0]['tracking_id']\n",
    "\n",
    "print(\"Searching Gencode annotation GTF for: \" + str(unlabeled_id))\n",
    "## gencode_df.apply(lambda row: row.astype(str).str.contains(unlabeled_id).any(), axis=1)\n",
    "print(\"Stopped search in pandas --> but grep returned multiple entries for this gene id??\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34668ffd-1e68-4be5-9fc4-6cb4b1771ef1",
   "metadata": {},
   "source": [
    "My guess for missing values: either the gencode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "a0d67629-fc4f-4e47-a075-364d8e66c7af",
   "metadata": {},
   "outputs": [],
   "source": [
    "gencode_orig_tsses = gencode_df[gencode_df['feature'] == 'start_codon']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "cf9fb8c4-9ce9-4aa7-9843-99d06ecba91b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "50189\n",
      "16    Xkr4\n",
      "39     Rp1\n",
      "52     Rp1\n",
      "Name: gene_name, dtype: object\n"
     ]
    },
    {
     "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>seqname</th>\n",
       "      <th>source</th>\n",
       "      <th>feature</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>score</th>\n",
       "      <th>strand</th>\n",
       "      <th>frame</th>\n",
       "      <th>gene_id</th>\n",
       "      <th>transcript_id</th>\n",
       "      <th>...</th>\n",
       "      <th>transcript_status</th>\n",
       "      <th>transcript_name</th>\n",
       "      <th>level</th>\n",
       "      <th>havana_gene</th>\n",
       "      <th>ont</th>\n",
       "      <th>havana_transcript</th>\n",
       "      <th>exon_number</th>\n",
       "      <th>exon_id</th>\n",
       "      <th>tag</th>\n",
       "      <th>ccdsid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>chr1</td>\n",
       "      <td>HAVANA</td>\n",
       "      <td>start_codon</td>\n",
       "      <td>3661427</td>\n",
       "      <td>3661429</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-</td>\n",
       "      <td>0</td>\n",
       "      <td>ENSMUSG00000051951.5</td>\n",
       "      <td>ENSMUST00000070533.4</td>\n",
       "      <td>...</td>\n",
       "      <td>KNOWN</td>\n",
       "      <td>Xkr4-001</td>\n",
       "      <td>2</td>\n",
       "      <td>OTTMUSG00000026353.2</td>\n",
       "      <td></td>\n",
       "      <td>OTTMUST00000065166.1</td>\n",
       "      <td>1</td>\n",
       "      <td>ENSMUSE00000485541.3</td>\n",
       "      <td>basic,CCDS</td>\n",
       "      <td>CCDS14803.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>chr1</td>\n",
       "      <td>ENSEMBL</td>\n",
       "      <td>start_codon</td>\n",
       "      <td>4342904</td>\n",
       "      <td>4342906</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-</td>\n",
       "      <td>0</td>\n",
       "      <td>ENSMUSG00000025900.4</td>\n",
       "      <td>ENSMUST00000027032.3</td>\n",
       "      <td>...</td>\n",
       "      <td>KNOWN</td>\n",
       "      <td>Rp1-201</td>\n",
       "      <td>3</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>2</td>\n",
       "      <td>ENSMUSE00000232073.3</td>\n",
       "      <td>basic,CCDS</td>\n",
       "      <td>CCDS14804.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>chr1</td>\n",
       "      <td>ENSEMBL</td>\n",
       "      <td>start_codon</td>\n",
       "      <td>4342904</td>\n",
       "      <td>4342906</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-</td>\n",
       "      <td>0</td>\n",
       "      <td>ENSMUSG00000025900.4</td>\n",
       "      <td>ENSMUST00000115540.1</td>\n",
       "      <td>...</td>\n",
       "      <td>KNOWN</td>\n",
       "      <td>Rp1-202</td>\n",
       "      <td>3</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>2</td>\n",
       "      <td>ENSMUSE00000232073.3</td>\n",
       "      <td>basic</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>chr1</td>\n",
       "      <td>ENSEMBL</td>\n",
       "      <td>start_codon</td>\n",
       "      <td>4483485</td>\n",
       "      <td>4483487</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-</td>\n",
       "      <td>0</td>\n",
       "      <td>ENSMUSG00000025902.7</td>\n",
       "      <td>ENSMUST00000027035.3</td>\n",
       "      <td>...</td>\n",
       "      <td>KNOWN</td>\n",
       "      <td>Sox17-201</td>\n",
       "      <td>3</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>4</td>\n",
       "      <td>ENSMUSE00000154039.3</td>\n",
       "      <td>basic,CCDS</td>\n",
       "      <td>CCDS14805.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>chr1</td>\n",
       "      <td>ENSEMBL</td>\n",
       "      <td>start_codon</td>\n",
       "      <td>4483485</td>\n",
       "      <td>4483487</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-</td>\n",
       "      <td>0</td>\n",
       "      <td>ENSMUSG00000025902.7</td>\n",
       "      <td>ENSMUST00000116652.2</td>\n",
       "      <td>...</td>\n",
       "      <td>KNOWN</td>\n",
       "      <td>Sox17-202</td>\n",
       "      <td>3</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>3</td>\n",
       "      <td>ENSMUSE00000878088.1</td>\n",
       "      <td>basic,CCDS</td>\n",
       "      <td>CCDS14805.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>chr1</td>\n",
       "      <td>HAVANA</td>\n",
       "      <td>start_codon</td>\n",
       "      <td>4775756</td>\n",
       "      <td>4775758</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-</td>\n",
       "      <td>0</td>\n",
       "      <td>ENSMUSG00000033845.7</td>\n",
       "      <td>ENSMUST00000130201.1</td>\n",
       "      <td>...</td>\n",
       "      <td>KNOWN</td>\n",
       "      <td>Mrpl15-002</td>\n",
       "      <td>2</td>\n",
       "      <td>OTTMUSG00000029329.1</td>\n",
       "      <td></td>\n",
       "      <td>OTTMUST00000072660.1</td>\n",
       "      <td>1</td>\n",
       "      <td>ENSMUSE00000450421.7</td>\n",
       "      <td>basic,CCDS</td>\n",
       "      <td>CCDS35504.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>chr1</td>\n",
       "      <td>HAVANA</td>\n",
       "      <td>start_codon</td>\n",
       "      <td>4775756</td>\n",
       "      <td>4775758</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-</td>\n",
       "      <td>0</td>\n",
       "      <td>ENSMUSG00000033845.7</td>\n",
       "      <td>ENSMUST00000156816.1</td>\n",
       "      <td>...</td>\n",
       "      <td>KNOWN</td>\n",
       "      <td>Mrpl15-001</td>\n",
       "      <td>2</td>\n",
       "      <td>OTTMUSG00000029329.1</td>\n",
       "      <td></td>\n",
       "      <td>OTTMUST00000072659.1</td>\n",
       "      <td>1</td>\n",
       "      <td>ENSMUSE00000931984.1</td>\n",
       "      <td>basic,CCDS</td>\n",
       "      <td>CCDS48211.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>chr1</td>\n",
       "      <td>HAVANA</td>\n",
       "      <td>start_codon</td>\n",
       "      <td>4775756</td>\n",
       "      <td>4775758</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-</td>\n",
       "      <td>0</td>\n",
       "      <td>ENSMUSG00000033845.7</td>\n",
       "      <td>ENSMUST00000045689.7</td>\n",
       "      <td>...</td>\n",
       "      <td>KNOWN</td>\n",
       "      <td>Mrpl15-003</td>\n",
       "      <td>2</td>\n",
       "      <td>OTTMUSG00000029329.1</td>\n",
       "      <td></td>\n",
       "      <td>OTTMUST00000072661.1</td>\n",
       "      <td>1</td>\n",
       "      <td>ENSMUSE00000789568.1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>chr1</td>\n",
       "      <td>HAVANA</td>\n",
       "      <td>start_codon</td>\n",
       "      <td>4797995</td>\n",
       "      <td>4797997</td>\n",
       "      <td>NaN</td>\n",
       "      <td>+</td>\n",
       "      <td>0</td>\n",
       "      <td>ENSMUSG00000025903.8</td>\n",
       "      <td>ENSMUST00000027036.4</td>\n",
       "      <td>...</td>\n",
       "      <td>KNOWN</td>\n",
       "      <td>Lypla1-001</td>\n",
       "      <td>2</td>\n",
       "      <td>OTTMUSG00000021562.1</td>\n",
       "      <td></td>\n",
       "      <td>OTTMUST00000051162.1</td>\n",
       "      <td>1</td>\n",
       "      <td>ENSMUSE00000792454.1</td>\n",
       "      <td>basic,CCDS</td>\n",
       "      <td>CCDS14806.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231</th>\n",
       "      <td>chr1</td>\n",
       "      <td>HAVANA</td>\n",
       "      <td>start_codon</td>\n",
       "      <td>4797995</td>\n",
       "      <td>4797997</td>\n",
       "      <td>NaN</td>\n",
       "      <td>+</td>\n",
       "      <td>0</td>\n",
       "      <td>ENSMUSG00000025903.8</td>\n",
       "      <td>ENSMUST00000137887.1</td>\n",
       "      <td>...</td>\n",
       "      <td>NOVEL</td>\n",
       "      <td>Lypla1-007</td>\n",
       "      <td>2</td>\n",
       "      <td>OTTMUSG00000021562.1</td>\n",
       "      <td></td>\n",
       "      <td>OTTMUST00000051168.1</td>\n",
       "      <td>1</td>\n",
       "      <td>ENSMUSE00000805214.1</td>\n",
       "      <td>mRNA_end_NF,cds_end_NF</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    seqname   source      feature    start      end  score strand frame  \\\n",
       "16     chr1   HAVANA  start_codon  3661427  3661429    NaN      -     0   \n",
       "39     chr1  ENSEMBL  start_codon  4342904  4342906    NaN      -     0   \n",
       "52     chr1  ENSEMBL  start_codon  4342904  4342906    NaN      -     0   \n",
       "68     chr1  ENSEMBL  start_codon  4483485  4483487    NaN      -     0   \n",
       "82     chr1  ENSEMBL  start_codon  4483485  4483487    NaN      -     0   \n",
       "100    chr1   HAVANA  start_codon  4775756  4775758    NaN      -     0   \n",
       "115    chr1   HAVANA  start_codon  4775756  4775758    NaN      -     0   \n",
       "130    chr1   HAVANA  start_codon  4775756  4775758    NaN      -     0   \n",
       "179    chr1   HAVANA  start_codon  4797995  4797997    NaN      +     0   \n",
       "231    chr1   HAVANA  start_codon  4797995  4797997    NaN      +     0   \n",
       "\n",
       "                  gene_id         transcript_id  ... transcript_status  \\\n",
       "16   ENSMUSG00000051951.5  ENSMUST00000070533.4  ...             KNOWN   \n",
       "39   ENSMUSG00000025900.4  ENSMUST00000027032.3  ...             KNOWN   \n",
       "52   ENSMUSG00000025900.4  ENSMUST00000115540.1  ...             KNOWN   \n",
       "68   ENSMUSG00000025902.7  ENSMUST00000027035.3  ...             KNOWN   \n",
       "82   ENSMUSG00000025902.7  ENSMUST00000116652.2  ...             KNOWN   \n",
       "100  ENSMUSG00000033845.7  ENSMUST00000130201.1  ...             KNOWN   \n",
       "115  ENSMUSG00000033845.7  ENSMUST00000156816.1  ...             KNOWN   \n",
       "130  ENSMUSG00000033845.7  ENSMUST00000045689.7  ...             KNOWN   \n",
       "179  ENSMUSG00000025903.8  ENSMUST00000027036.4  ...             KNOWN   \n",
       "231  ENSMUSG00000025903.8  ENSMUST00000137887.1  ...             NOVEL   \n",
       "\n",
       "    transcript_name level           havana_gene ont     havana_transcript  \\\n",
       "16         Xkr4-001     2  OTTMUSG00000026353.2      OTTMUST00000065166.1   \n",
       "39          Rp1-201     3                                                   \n",
       "52          Rp1-202     3                                                   \n",
       "68        Sox17-201     3                                                   \n",
       "82        Sox17-202     3                                                   \n",
       "100      Mrpl15-002     2  OTTMUSG00000029329.1      OTTMUST00000072660.1   \n",
       "115      Mrpl15-001     2  OTTMUSG00000029329.1      OTTMUST00000072659.1   \n",
       "130      Mrpl15-003     2  OTTMUSG00000029329.1      OTTMUST00000072661.1   \n",
       "179      Lypla1-001     2  OTTMUSG00000021562.1      OTTMUST00000051162.1   \n",
       "231      Lypla1-007     2  OTTMUSG00000021562.1      OTTMUST00000051168.1   \n",
       "\n",
       "    exon_number               exon_id                     tag       ccdsid  \n",
       "16            1  ENSMUSE00000485541.3              basic,CCDS  CCDS14803.1  \n",
       "39            2  ENSMUSE00000232073.3              basic,CCDS  CCDS14804.1  \n",
       "52            2  ENSMUSE00000232073.3                   basic               \n",
       "68            4  ENSMUSE00000154039.3              basic,CCDS  CCDS14805.1  \n",
       "82            3  ENSMUSE00000878088.1              basic,CCDS  CCDS14805.1  \n",
       "100           1  ENSMUSE00000450421.7              basic,CCDS  CCDS35504.1  \n",
       "115           1  ENSMUSE00000931984.1              basic,CCDS  CCDS48211.1  \n",
       "130           1  ENSMUSE00000789568.1                                       \n",
       "179           1  ENSMUSE00000792454.1              basic,CCDS  CCDS14806.1  \n",
       "231           1  ENSMUSE00000805214.1  mRNA_end_NF,cds_end_NF               \n",
       "\n",
       "[10 rows x 24 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(gencode_orig_tsses.shape[0])\n",
    "print(gencode_orig_tsses['gene_name'].head(3))\n",
    "gencode_orig_tsses.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "2b0f73c4-897b-4537-80a8-de5d553fbc9e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['tracking_id', 'locus', 'CTCF-AID_untreated_mean_FPKM',\n",
      "       'CTCF-AID_1day_mean_FPKM', 'CTCF-AID_2days_mean_FPKM',\n",
      "       'CTCF-AID_4days_mean_FPKM', 'CTCF-AID_washoff_mean_FPKM', '1day.1',\n",
      "       '2days.1', '4days.1', '2days_washoff_2days.1', 'WT_untreated_mean_FPKM',\n",
      "       'WT_2days_mean_FPKM', 'WT_4days_mean_FPKM', 'seqname', 'source',\n",
      "       'feature', 'start', 'end', 'score', 'strand', 'frame', 'gene_id',\n",
      "       'transcript_id', 'gene_type', 'gene_status', 'gene_name',\n",
      "       'transcript_type', 'transcript_status', 'transcript_name', 'level',\n",
      "       'havana_gene', 'ont', 'havana_transcript', 'exon_number', 'exon_id',\n",
      "       'tag', 'ccdsid'],\n",
      "      dtype='object')\n"
     ]
    },
    {
     "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>tracking_id</th>\n",
       "      <th>locus</th>\n",
       "      <th>CTCF-AID_untreated_mean_FPKM</th>\n",
       "      <th>CTCF-AID_1day_mean_FPKM</th>\n",
       "      <th>CTCF-AID_2days_mean_FPKM</th>\n",
       "      <th>CTCF-AID_4days_mean_FPKM</th>\n",
       "      <th>CTCF-AID_washoff_mean_FPKM</th>\n",
       "      <th>1day.1</th>\n",
       "      <th>2days.1</th>\n",
       "      <th>4days.1</th>\n",
       "      <th>...</th>\n",
       "      <th>transcript_status</th>\n",
       "      <th>transcript_name</th>\n",
       "      <th>level</th>\n",
       "      <th>havana_gene</th>\n",
       "      <th>ont</th>\n",
       "      <th>havana_transcript</th>\n",
       "      <th>exon_number</th>\n",
       "      <th>exon_id</th>\n",
       "      <th>tag</th>\n",
       "      <th>ccdsid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0610005C13Rik</td>\n",
       "      <td>chr7:52823164-52845080</td>\n",
       "      <td>0.784511</td>\n",
       "      <td>0.804255</td>\n",
       "      <td>1.040841</td>\n",
       "      <td>2.176798</td>\n",
       "      <td>1.209881</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0610007P14Rik</td>\n",
       "      <td>chr12:87156404-87165495</td>\n",
       "      <td>71.716133</td>\n",
       "      <td>74.856200</td>\n",
       "      <td>63.412767</td>\n",
       "      <td>59.250033</td>\n",
       "      <td>67.995733</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>KNOWN</td>\n",
       "      <td>0610007P14Rik-001</td>\n",
       "      <td>2</td>\n",
       "      <td>OTTMUSG00000032571.1</td>\n",
       "      <td></td>\n",
       "      <td>OTTMUST00000081014.1</td>\n",
       "      <td>2</td>\n",
       "      <td>ENSMUSE00000295744.3</td>\n",
       "      <td>basic,CCDS</td>\n",
       "      <td>CCDS26063.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0610009B22Rik</td>\n",
       "      <td>chr11:51498886-51502136</td>\n",
       "      <td>69.529333</td>\n",
       "      <td>76.436433</td>\n",
       "      <td>61.336067</td>\n",
       "      <td>38.937500</td>\n",
       "      <td>58.353700</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>...</td>\n",
       "      <td>KNOWN</td>\n",
       "      <td>0610009B22Rik-001</td>\n",
       "      <td>2</td>\n",
       "      <td>OTTMUSG00000005608.1</td>\n",
       "      <td></td>\n",
       "      <td>OTTMUST00000012478.1</td>\n",
       "      <td>2</td>\n",
       "      <td>ENSMUSE00000679023.1</td>\n",
       "      <td>basic,CCDS</td>\n",
       "      <td>CCDS36149.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0610009L18Rik</td>\n",
       "      <td>chr11:120209991-120212504</td>\n",
       "      <td>0.944842</td>\n",
       "      <td>1.154941</td>\n",
       "      <td>0.905838</td>\n",
       "      <td>1.097459</td>\n",
       "      <td>0.889548</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0610009O20Rik</td>\n",
       "      <td>chr18:38398058-38422283</td>\n",
       "      <td>17.565900</td>\n",
       "      <td>20.652933</td>\n",
       "      <td>20.476800</td>\n",
       "      <td>20.633733</td>\n",
       "      <td>18.857300</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>KNOWN</td>\n",
       "      <td>0610009O20Rik-201</td>\n",
       "      <td>3</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>ENSMUSE00000269236.4</td>\n",
       "      <td>basic,CCDS</td>\n",
       "      <td>CCDS50260.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0610010B08Rik</td>\n",
       "      <td>chr2:174952492-175261278</td>\n",
       "      <td>0.057933</td>\n",
       "      <td>0.051192</td>\n",
       "      <td>0.080296</td>\n",
       "      <td>0.091421</td>\n",
       "      <td>0.090390</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0610010F05Rik</td>\n",
       "      <td>chr11:23473775-23533631</td>\n",
       "      <td>4.500230</td>\n",
       "      <td>4.654253</td>\n",
       "      <td>3.784197</td>\n",
       "      <td>3.342397</td>\n",
       "      <td>4.257007</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>KNOWN</td>\n",
       "      <td>0610010F05Rik-003</td>\n",
       "      <td>2</td>\n",
       "      <td>OTTMUSG00000005276.2</td>\n",
       "      <td></td>\n",
       "      <td>OTTMUST00000011758.2</td>\n",
       "      <td>1</td>\n",
       "      <td>ENSMUSE00000934484.1</td>\n",
       "      <td>basic,CCDS</td>\n",
       "      <td>CCDS36119.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0610010K14Rik</td>\n",
       "      <td>chr11:70048705-70051416</td>\n",
       "      <td>115.794100</td>\n",
       "      <td>134.728000</td>\n",
       "      <td>124.419633</td>\n",
       "      <td>109.027433</td>\n",
       "      <td>95.227500</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>NOVEL</td>\n",
       "      <td>0610010K14Rik-005</td>\n",
       "      <td>2</td>\n",
       "      <td>OTTMUSG00000006039.2</td>\n",
       "      <td></td>\n",
       "      <td>OTTMUST00000013588.1</td>\n",
       "      <td>1</td>\n",
       "      <td>ENSMUSE00000458761.4</td>\n",
       "      <td>basic,CCDS</td>\n",
       "      <td>CCDS48831.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0610011F06Rik</td>\n",
       "      <td>chr17:26012444-26014108</td>\n",
       "      <td>12.449400</td>\n",
       "      <td>12.249967</td>\n",
       "      <td>11.492490</td>\n",
       "      <td>12.151867</td>\n",
       "      <td>11.099847</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>KNOWN</td>\n",
       "      <td>0610011F06Rik-005</td>\n",
       "      <td>2</td>\n",
       "      <td>OTTMUSG00000035873.2</td>\n",
       "      <td></td>\n",
       "      <td>OTTMUST00000091993.1</td>\n",
       "      <td>1</td>\n",
       "      <td>ENSMUSE00000916485.1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0610012G03Rik</td>\n",
       "      <td>chr16:31947136-31948607</td>\n",
       "      <td>34.492833</td>\n",
       "      <td>40.396067</td>\n",
       "      <td>36.419533</td>\n",
       "      <td>30.946500</td>\n",
       "      <td>30.611233</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     tracking_id                      locus  CTCF-AID_untreated_mean_FPKM  \\\n",
       "0  0610005C13Rik     chr7:52823164-52845080                      0.784511   \n",
       "1  0610007P14Rik    chr12:87156404-87165495                     71.716133   \n",
       "2  0610009B22Rik    chr11:51498886-51502136                     69.529333   \n",
       "3  0610009L18Rik  chr11:120209991-120212504                      0.944842   \n",
       "4  0610009O20Rik    chr18:38398058-38422283                     17.565900   \n",
       "5  0610010B08Rik   chr2:174952492-175261278                      0.057933   \n",
       "6  0610010F05Rik    chr11:23473775-23533631                      4.500230   \n",
       "7  0610010K14Rik    chr11:70048705-70051416                    115.794100   \n",
       "8  0610011F06Rik    chr17:26012444-26014108                     12.449400   \n",
       "9  0610012G03Rik    chr16:31947136-31948607                     34.492833   \n",
       "\n",
       "   CTCF-AID_1day_mean_FPKM  CTCF-AID_2days_mean_FPKM  \\\n",
       "0                 0.804255                  1.040841   \n",
       "1                74.856200                 63.412767   \n",
       "2                76.436433                 61.336067   \n",
       "3                 1.154941                  0.905838   \n",
       "4                20.652933                 20.476800   \n",
       "5                 0.051192                  0.080296   \n",
       "6                 4.654253                  3.784197   \n",
       "7               134.728000                124.419633   \n",
       "8                12.249967                 11.492490   \n",
       "9                40.396067                 36.419533   \n",
       "\n",
       "   CTCF-AID_4days_mean_FPKM  CTCF-AID_washoff_mean_FPKM 1day.1 2days.1  \\\n",
       "0                  2.176798                    1.209881     no      no   \n",
       "1                 59.250033                   67.995733     no      no   \n",
       "2                 38.937500                   58.353700     no      no   \n",
       "3                  1.097459                    0.889548     no      no   \n",
       "4                 20.633733                   18.857300     no      no   \n",
       "5                  0.091421                    0.090390     no      no   \n",
       "6                  3.342397                    4.257007     no      no   \n",
       "7                109.027433                   95.227500     no      no   \n",
       "8                 12.151867                   11.099847     no      no   \n",
       "9                 30.946500                   30.611233     no      no   \n",
       "\n",
       "  4days.1  ... transcript_status    transcript_name  level  \\\n",
       "0      no  ...               NaN                NaN    NaN   \n",
       "1      no  ...             KNOWN  0610007P14Rik-001      2   \n",
       "2     yes  ...             KNOWN  0610009B22Rik-001      2   \n",
       "3      no  ...               NaN                NaN    NaN   \n",
       "4      no  ...             KNOWN  0610009O20Rik-201      3   \n",
       "5      no  ...               NaN                NaN    NaN   \n",
       "6      no  ...             KNOWN  0610010F05Rik-003      2   \n",
       "7      no  ...             NOVEL  0610010K14Rik-005      2   \n",
       "8      no  ...             KNOWN  0610011F06Rik-005      2   \n",
       "9      no  ...               NaN                NaN    NaN   \n",
       "\n",
       "            havana_gene  ont     havana_transcript exon_number  \\\n",
       "0                   NaN  NaN                   NaN         NaN   \n",
       "1  OTTMUSG00000032571.1       OTTMUST00000081014.1           2   \n",
       "2  OTTMUSG00000005608.1       OTTMUST00000012478.1           2   \n",
       "3                   NaN  NaN                   NaN         NaN   \n",
       "4                                                            1   \n",
       "5                   NaN  NaN                   NaN         NaN   \n",
       "6  OTTMUSG00000005276.2       OTTMUST00000011758.2           1   \n",
       "7  OTTMUSG00000006039.2       OTTMUST00000013588.1           1   \n",
       "8  OTTMUSG00000035873.2       OTTMUST00000091993.1           1   \n",
       "9                   NaN  NaN                   NaN         NaN   \n",
       "\n",
       "                exon_id         tag       ccdsid  \n",
       "0                   NaN         NaN          NaN  \n",
       "1  ENSMUSE00000295744.3  basic,CCDS  CCDS26063.1  \n",
       "2  ENSMUSE00000679023.1  basic,CCDS  CCDS36149.1  \n",
       "3                   NaN         NaN          NaN  \n",
       "4  ENSMUSE00000269236.4  basic,CCDS  CCDS50260.1  \n",
       "5                   NaN         NaN          NaN  \n",
       "6  ENSMUSE00000934484.1  basic,CCDS  CCDS36119.1  \n",
       "7  ENSMUSE00000458761.4  basic,CCDS  CCDS48831.1  \n",
       "8  ENSMUSE00000916485.1                           \n",
       "9                   NaN         NaN          NaN  \n",
       "\n",
       "[10 rows x 38 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Try labelling FPKM data with just the already annotated TSSes\n",
    "fpkm_orig_gencode = fpkm.merge(gencode_orig_tsses.drop_duplicates('gene_name'), how='left', left_on='tracking_id', right_on='gene_name')\n",
    "                               \n",
    "print(fpkm_orig_gencode.columns)\n",
    "fpkm_orig_gencode.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "4d828a1f-e91b-4f5e-9b49-9956ed67b305",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24315\n",
      "24315\n",
      "Number of entries that did not have a TSS entry annotated gencode start_codon: \n",
      "5305\n"
     ]
    }
   ],
   "source": [
    "print(fpkm.shape[0])\n",
    "print(fpkm_orig_gencode.shape[0])\n",
    "\n",
    "print(\"Number of entries that did not have a TSS entry annotated gencode start_codon: \")\n",
    "print(fpkm_orig_gencode[fpkm_orig_gencode['gene_id'].isna()].shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7bde9751-5046-4700-bff9-40c37a5c9014",
   "metadata": {},
   "source": [
    "This is missing way more --> will go with the gffutils generated TSSes\n",
    "\n",
    "Perhaps we can merge them?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "ca85665b-66e8-4eee-a1ef-efe3c579b47c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "How many did not have a gene ID in either TSS set?\n"
     ]
    },
    {
     "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>tracking_id</th>\n",
       "      <th>locus</th>\n",
       "      <th>CTCF-AID_untreated_mean_FPKM</th>\n",
       "      <th>CTCF-AID_1day_mean_FPKM</th>\n",
       "      <th>CTCF-AID_2days_mean_FPKM</th>\n",
       "      <th>CTCF-AID_4days_mean_FPKM</th>\n",
       "      <th>CTCF-AID_washoff_mean_FPKM</th>\n",
       "      <th>1day.1</th>\n",
       "      <th>2days.1</th>\n",
       "      <th>4days.1</th>\n",
       "      <th>...</th>\n",
       "      <th>transcript_status</th>\n",
       "      <th>transcript_name</th>\n",
       "      <th>level</th>\n",
       "      <th>havana_gene</th>\n",
       "      <th>ont</th>\n",
       "      <th>havana_transcript</th>\n",
       "      <th>exon_number</th>\n",
       "      <th>exon_id</th>\n",
       "      <th>tag</th>\n",
       "      <th>ccdsid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0610010B08Rik</td>\n",
       "      <td>chr2:174952492-175261278</td>\n",
       "      <td>0.057933</td>\n",
       "      <td>0.051192</td>\n",
       "      <td>0.080296</td>\n",
       "      <td>0.091421</td>\n",
       "      <td>0.090390</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1010001N08Rik</td>\n",
       "      <td>chr18:11042034-11085633</td>\n",
       "      <td>0.968879</td>\n",
       "      <td>1.514376</td>\n",
       "      <td>1.701958</td>\n",
       "      <td>3.042223</td>\n",
       "      <td>1.917330</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>1190002F15Rik</td>\n",
       "      <td>chr6:134879109-134901736</td>\n",
       "      <td>12.463803</td>\n",
       "      <td>13.283183</td>\n",
       "      <td>9.771923</td>\n",
       "      <td>5.705407</td>\n",
       "      <td>11.291177</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>1190003K10Rik</td>\n",
       "      <td>chr13:64582553-64599100</td>\n",
       "      <td>0.010722</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.013394</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>1500011K16Rik</td>\n",
       "      <td>chr2:127617113-127618224</td>\n",
       "      <td>51.659667</td>\n",
       "      <td>63.654233</td>\n",
       "      <td>53.027700</td>\n",
       "      <td>33.302900</td>\n",
       "      <td>41.839033</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23085</th>\n",
       "      <td>Vmn1r207-ps</td>\n",
       "      <td>chr13:22817552-22818491</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.020214</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23396</th>\n",
       "      <td>Vwa9</td>\n",
       "      <td>chr9:64808638-64834788</td>\n",
       "      <td>16.963900</td>\n",
       "      <td>18.241167</td>\n",
       "      <td>16.307667</td>\n",
       "      <td>14.447500</td>\n",
       "      <td>16.970300</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23411</th>\n",
       "      <td>Wash1</td>\n",
       "      <td>chr17:66460885-66469843</td>\n",
       "      <td>11.520233</td>\n",
       "      <td>13.488233</td>\n",
       "      <td>14.300033</td>\n",
       "      <td>15.053533</td>\n",
       "      <td>12.177333</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23680</th>\n",
       "      <td>Zak</td>\n",
       "      <td>chr2:72123693-72280667</td>\n",
       "      <td>3.417830</td>\n",
       "      <td>2.966437</td>\n",
       "      <td>4.427577</td>\n",
       "      <td>5.465733</td>\n",
       "      <td>4.726070</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24147</th>\n",
       "      <td>Zfp91Cntf</td>\n",
       "      <td>chr19:12838017-12870613</td>\n",
       "      <td>1.759253</td>\n",
       "      <td>1.061577</td>\n",
       "      <td>1.381043</td>\n",
       "      <td>1.334933</td>\n",
       "      <td>1.611156</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>227 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         tracking_id                     locus  CTCF-AID_untreated_mean_FPKM  \\\n",
       "5      0610010B08Rik  chr2:174952492-175261278                      0.057933   \n",
       "20     1010001N08Rik   chr18:11042034-11085633                      0.968879   \n",
       "51     1190002F15Rik  chr6:134879109-134901736                     12.463803   \n",
       "53     1190003K10Rik   chr13:64582553-64599100                      0.010722   \n",
       "63     1500011K16Rik  chr2:127617113-127618224                     51.659667   \n",
       "...              ...                       ...                           ...   \n",
       "23085    Vmn1r207-ps   chr13:22817552-22818491                      0.010000   \n",
       "23396           Vwa9    chr9:64808638-64834788                     16.963900   \n",
       "23411          Wash1   chr17:66460885-66469843                     11.520233   \n",
       "23680            Zak    chr2:72123693-72280667                      3.417830   \n",
       "24147      Zfp91Cntf   chr19:12838017-12870613                      1.759253   \n",
       "\n",
       "       CTCF-AID_1day_mean_FPKM  CTCF-AID_2days_mean_FPKM  \\\n",
       "5                     0.051192                  0.080296   \n",
       "20                    1.514376                  1.701958   \n",
       "51                   13.283183                  9.771923   \n",
       "53                    0.010000                  0.010000   \n",
       "63                   63.654233                 53.027700   \n",
       "...                        ...                       ...   \n",
       "23085                 0.010000                  0.010000   \n",
       "23396                18.241167                 16.307667   \n",
       "23411                13.488233                 14.300033   \n",
       "23680                 2.966437                  4.427577   \n",
       "24147                 1.061577                  1.381043   \n",
       "\n",
       "       CTCF-AID_4days_mean_FPKM  CTCF-AID_washoff_mean_FPKM 1day.1 2days.1  \\\n",
       "5                      0.091421                    0.090390     no      no   \n",
       "20                     3.042223                    1.917330     no      no   \n",
       "51                     5.705407                   11.291177     no      no   \n",
       "53                     0.013394                    0.010000     no      no   \n",
       "63                    33.302900                   41.839033     no      no   \n",
       "...                         ...                         ...    ...     ...   \n",
       "23085                  0.010000                    0.020214     no      no   \n",
       "23396                 14.447500                   16.970300     no      no   \n",
       "23411                 15.053533                   12.177333     no      no   \n",
       "23680                  5.465733                    4.726070     no      no   \n",
       "24147                  1.334933                    1.611156     no      no   \n",
       "\n",
       "      4days.1  ... transcript_status  transcript_name  level  havana_gene  \\\n",
       "5          no  ...               NaN              NaN    NaN          NaN   \n",
       "20         no  ...               NaN              NaN    NaN          NaN   \n",
       "51        yes  ...               NaN              NaN    NaN          NaN   \n",
       "53         no  ...               NaN              NaN    NaN          NaN   \n",
       "63        yes  ...               NaN              NaN    NaN          NaN   \n",
       "...       ...  ...               ...              ...    ...          ...   \n",
       "23085      no  ...               NaN              NaN    NaN          NaN   \n",
       "23396      no  ...               NaN              NaN    NaN          NaN   \n",
       "23411      no  ...               NaN              NaN    NaN          NaN   \n",
       "23680      no  ...               NaN              NaN    NaN          NaN   \n",
       "24147      no  ...               NaN              NaN    NaN          NaN   \n",
       "\n",
       "       ont havana_transcript exon_number  exon_id  tag  ccdsid  \n",
       "5      NaN               NaN         NaN      NaN  NaN     NaN  \n",
       "20     NaN               NaN         NaN      NaN  NaN     NaN  \n",
       "51     NaN               NaN         NaN      NaN  NaN     NaN  \n",
       "53     NaN               NaN         NaN      NaN  NaN     NaN  \n",
       "63     NaN               NaN         NaN      NaN  NaN     NaN  \n",
       "...    ...               ...         ...      ...  ...     ...  \n",
       "23085  NaN               NaN         NaN      NaN  NaN     NaN  \n",
       "23396  NaN               NaN         NaN      NaN  NaN     NaN  \n",
       "23411  NaN               NaN         NaN      NaN  NaN     NaN  \n",
       "23680  NaN               NaN         NaN      NaN  NaN     NaN  \n",
       "24147  NaN               NaN         NaN      NaN  NaN     NaN  \n",
       "\n",
       "[227 rows x 38 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Comparing annotated gencode start_codons with gff_utils generated TSSes\n",
    "\n",
    "print(\"How many did not have a gene ID in either TSS set?\")\n",
    "missing_ix = np.where(fpkm_orig_gencode['gene_name'].isna() & fpkm_gencode['gene_id'].isna())\n",
    "\n",
    "fpkm_orig_gencode.loc[missing_ix]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "ab6703f2-7e73-4f70-9799-162eccd9b6dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        0610005C13Rik\n",
       "1                  NaN\n",
       "2        0610009B22Rik\n",
       "3        0610009L18Rik\n",
       "4                  NaN\n",
       "             ...      \n",
       "24310              Zyx\n",
       "24311            Zzef1\n",
       "24312             Zzz3\n",
       "24313                a\n",
       "24314              NaN\n",
       "Name: gene_name, Length: 24315, dtype: object"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fpkm_gencode['gene_name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "2a21d441-96e4-43af-a069-d5fe984fa5e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                  NaN\n",
       "1        0610007P14Rik\n",
       "2        0610009B22Rik\n",
       "3                  NaN\n",
       "4        0610009O20Rik\n",
       "             ...      \n",
       "24310              Zyx\n",
       "24311            Zzef1\n",
       "24312             Zzz3\n",
       "24313                a\n",
       "24314            l7Rn6\n",
       "Name: gene_name, Length: 24315, dtype: object"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fpkm_orig_gencode['gene_name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "c1507432-d12c-454e-81bb-c9fee8238cba",
   "metadata": {},
   "outputs": [],
   "source": [
    "fpkm_gencode[['seqname', 'source', 'feature', 'start', 'end', 'score', 'strand', 'frame', 'gene_name']] = \\\n",
    "    fpkm_gencode[['seqname', 'source', 'feature', 'start', 'end', 'score', 'strand', 'frame', 'gene_name']].fillna(\n",
    "        fpkm_orig_gencode[['seqname', 'source', 'feature', 'start', 'end', 'score', 'strand', 'frame', 'gene_name']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "26407a46-0852-47f3-9655-d88ff54e3620",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "227"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fpkm_gencode[fpkm_gencode['gene_name'].isna()].shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "80d6e28f-62f7-441e-9879-acdfc6c2d94e",
   "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>tracking_id</th>\n",
       "      <th>locus</th>\n",
       "      <th>CTCF-AID_untreated_mean_FPKM</th>\n",
       "      <th>CTCF-AID_1day_mean_FPKM</th>\n",
       "      <th>CTCF-AID_2days_mean_FPKM</th>\n",
       "      <th>CTCF-AID_4days_mean_FPKM</th>\n",
       "      <th>CTCF-AID_washoff_mean_FPKM</th>\n",
       "      <th>1day.1</th>\n",
       "      <th>2days.1</th>\n",
       "      <th>4days.1</th>\n",
       "      <th>...</th>\n",
       "      <th>source</th>\n",
       "      <th>feature</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>score</th>\n",
       "      <th>strand</th>\n",
       "      <th>frame</th>\n",
       "      <th>gene_id</th>\n",
       "      <th>transcript_id</th>\n",
       "      <th>gene_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0610005C13Rik</td>\n",
       "      <td>chr7:52823164-52845080</td>\n",
       "      <td>0.784511</td>\n",
       "      <td>0.804255</td>\n",
       "      <td>1.040841</td>\n",
       "      <td>2.176798</td>\n",
       "      <td>1.209881</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>refGene</td>\n",
       "      <td>transcript</td>\n",
       "      <td>52830547.0</td>\n",
       "      <td>52830547.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-</td>\n",
       "      <td>0</td>\n",
       "      <td>0610005C13Rik</td>\n",
       "      <td>NR_038165</td>\n",
       "      <td>0610005C13Rik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0610007P14Rik</td>\n",
       "      <td>chr12:87156404-87165495</td>\n",
       "      <td>71.716133</td>\n",
       "      <td>74.856200</td>\n",
       "      <td>63.412767</td>\n",
       "      <td>59.250033</td>\n",
       "      <td>67.995733</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>HAVANA</td>\n",
       "      <td>start_codon</td>\n",
       "      <td>87163168.0</td>\n",
       "      <td>87163170.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0610007P14Rik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0610009B22Rik</td>\n",
       "      <td>chr11:51498886-51502136</td>\n",
       "      <td>69.529333</td>\n",
       "      <td>76.436433</td>\n",
       "      <td>61.336067</td>\n",
       "      <td>38.937500</td>\n",
       "      <td>58.353700</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>...</td>\n",
       "      <td>refGene</td>\n",
       "      <td>transcript</td>\n",
       "      <td>51502137.0</td>\n",
       "      <td>51502137.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-</td>\n",
       "      <td>0</td>\n",
       "      <td>0610009B22Rik</td>\n",
       "      <td>NM_025319</td>\n",
       "      <td>0610009B22Rik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0610009L18Rik</td>\n",
       "      <td>chr11:120209991-120212504</td>\n",
       "      <td>0.944842</td>\n",
       "      <td>1.154941</td>\n",
       "      <td>0.905838</td>\n",
       "      <td>1.097459</td>\n",
       "      <td>0.889548</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>refGene</td>\n",
       "      <td>transcript</td>\n",
       "      <td>120209991.0</td>\n",
       "      <td>120209991.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>+</td>\n",
       "      <td>0</td>\n",
       "      <td>0610009L18Rik</td>\n",
       "      <td>NR_038126</td>\n",
       "      <td>0610009L18Rik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0610009O20Rik</td>\n",
       "      <td>chr18:38398058-38422283</td>\n",
       "      <td>17.565900</td>\n",
       "      <td>20.652933</td>\n",
       "      <td>20.476800</td>\n",
       "      <td>20.633733</td>\n",
       "      <td>18.857300</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>ENSEMBL</td>\n",
       "      <td>start_codon</td>\n",
       "      <td>38410023.0</td>\n",
       "      <td>38410025.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>+</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0610009O20Rik</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24310</th>\n",
       "      <td>Zyx</td>\n",
       "      <td>chr6:42299826-42330557</td>\n",
       "      <td>8.531693</td>\n",
       "      <td>8.734813</td>\n",
       "      <td>11.727350</td>\n",
       "      <td>24.111000</td>\n",
       "      <td>11.359220</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>...</td>\n",
       "      <td>refGene</td>\n",
       "      <td>transcript</td>\n",
       "      <td>42299826.0</td>\n",
       "      <td>42299826.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>+</td>\n",
       "      <td>0</td>\n",
       "      <td>Zyx</td>\n",
       "      <td>NM_001289619</td>\n",
       "      <td>Zyx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24311</th>\n",
       "      <td>Zzef1</td>\n",
       "      <td>chr11:72609727-72740622</td>\n",
       "      <td>2.597283</td>\n",
       "      <td>2.411157</td>\n",
       "      <td>2.507930</td>\n",
       "      <td>3.121180</td>\n",
       "      <td>2.856007</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>refGene</td>\n",
       "      <td>transcript</td>\n",
       "      <td>72609727.0</td>\n",
       "      <td>72609727.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>+</td>\n",
       "      <td>0</td>\n",
       "      <td>Zzef1</td>\n",
       "      <td>NM_001045536</td>\n",
       "      <td>Zzef1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24312</th>\n",
       "      <td>Zzz3</td>\n",
       "      <td>chr3:152058966-152344396</td>\n",
       "      <td>9.986867</td>\n",
       "      <td>8.012557</td>\n",
       "      <td>7.069317</td>\n",
       "      <td>8.471867</td>\n",
       "      <td>10.256167</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>refGene</td>\n",
       "      <td>transcript</td>\n",
       "      <td>152059647.0</td>\n",
       "      <td>152059647.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>+</td>\n",
       "      <td>0</td>\n",
       "      <td>Zzz3</td>\n",
       "      <td>NM_001287139</td>\n",
       "      <td>Zzz3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24313</th>\n",
       "      <td>a</td>\n",
       "      <td>chr2:154839305-154876748</td>\n",
       "      <td>0.087051</td>\n",
       "      <td>0.167859</td>\n",
       "      <td>0.370351</td>\n",
       "      <td>0.402713</td>\n",
       "      <td>0.090154</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>refGene</td>\n",
       "      <td>transcript</td>\n",
       "      <td>154839305.0</td>\n",
       "      <td>154839305.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>+</td>\n",
       "      <td>0</td>\n",
       "      <td>a</td>\n",
       "      <td>NM_015770</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24314</th>\n",
       "      <td>l7Rn6</td>\n",
       "      <td>chr7:97067194-97089714</td>\n",
       "      <td>159.055667</td>\n",
       "      <td>170.891333</td>\n",
       "      <td>126.653000</td>\n",
       "      <td>84.032633</td>\n",
       "      <td>126.660000</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>...</td>\n",
       "      <td>HAVANA</td>\n",
       "      <td>start_codon</td>\n",
       "      <td>97089506.0</td>\n",
       "      <td>97089508.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>l7Rn6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>24315 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         tracking_id                      locus  CTCF-AID_untreated_mean_FPKM  \\\n",
       "0      0610005C13Rik     chr7:52823164-52845080                      0.784511   \n",
       "1      0610007P14Rik    chr12:87156404-87165495                     71.716133   \n",
       "2      0610009B22Rik    chr11:51498886-51502136                     69.529333   \n",
       "3      0610009L18Rik  chr11:120209991-120212504                      0.944842   \n",
       "4      0610009O20Rik    chr18:38398058-38422283                     17.565900   \n",
       "...              ...                        ...                           ...   \n",
       "24310            Zyx     chr6:42299826-42330557                      8.531693   \n",
       "24311          Zzef1    chr11:72609727-72740622                      2.597283   \n",
       "24312           Zzz3   chr3:152058966-152344396                      9.986867   \n",
       "24313              a   chr2:154839305-154876748                      0.087051   \n",
       "24314          l7Rn6     chr7:97067194-97089714                    159.055667   \n",
       "\n",
       "       CTCF-AID_1day_mean_FPKM  CTCF-AID_2days_mean_FPKM  \\\n",
       "0                     0.804255                  1.040841   \n",
       "1                    74.856200                 63.412767   \n",
       "2                    76.436433                 61.336067   \n",
       "3                     1.154941                  0.905838   \n",
       "4                    20.652933                 20.476800   \n",
       "...                        ...                       ...   \n",
       "24310                 8.734813                 11.727350   \n",
       "24311                 2.411157                  2.507930   \n",
       "24312                 8.012557                  7.069317   \n",
       "24313                 0.167859                  0.370351   \n",
       "24314               170.891333                126.653000   \n",
       "\n",
       "       CTCF-AID_4days_mean_FPKM  CTCF-AID_washoff_mean_FPKM 1day.1 2days.1  \\\n",
       "0                      2.176798                    1.209881     no      no   \n",
       "1                     59.250033                   67.995733     no      no   \n",
       "2                     38.937500                   58.353700     no      no   \n",
       "3                      1.097459                    0.889548     no      no   \n",
       "4                     20.633733                   18.857300     no      no   \n",
       "...                         ...                         ...    ...     ...   \n",
       "24310                 24.111000                   11.359220     no      no   \n",
       "24311                  3.121180                    2.856007     no      no   \n",
       "24312                  8.471867                   10.256167     no      no   \n",
       "24313                  0.402713                    0.090154     no      no   \n",
       "24314                 84.032633                  126.660000     no      no   \n",
       "\n",
       "      4days.1  ...   source      feature        start          end score  \\\n",
       "0          no  ...  refGene   transcript   52830547.0   52830547.0   NaN   \n",
       "1          no  ...   HAVANA  start_codon   87163168.0   87163170.0   NaN   \n",
       "2         yes  ...  refGene   transcript   51502137.0   51502137.0   NaN   \n",
       "3          no  ...  refGene   transcript  120209991.0  120209991.0   NaN   \n",
       "4          no  ...  ENSEMBL  start_codon   38410023.0   38410025.0   NaN   \n",
       "...       ...  ...      ...          ...          ...          ...   ...   \n",
       "24310     yes  ...  refGene   transcript   42299826.0   42299826.0   NaN   \n",
       "24311      no  ...  refGene   transcript   72609727.0   72609727.0   NaN   \n",
       "24312      no  ...  refGene   transcript  152059647.0  152059647.0   NaN   \n",
       "24313      no  ...  refGene   transcript  154839305.0  154839305.0   NaN   \n",
       "24314     yes  ...   HAVANA  start_codon   97089506.0   97089508.0   NaN   \n",
       "\n",
       "      strand frame        gene_id  transcript_id      gene_name  \n",
       "0          -     0  0610005C13Rik      NR_038165  0610005C13Rik  \n",
       "1          -     0            NaN            NaN  0610007P14Rik  \n",
       "2          -     0  0610009B22Rik      NM_025319  0610009B22Rik  \n",
       "3          +     0  0610009L18Rik      NR_038126  0610009L18Rik  \n",
       "4          +     0            NaN            NaN  0610009O20Rik  \n",
       "...      ...   ...            ...            ...            ...  \n",
       "24310      +     0            Zyx   NM_001289619            Zyx  \n",
       "24311      +     0          Zzef1   NM_001045536          Zzef1  \n",
       "24312      +     0           Zzz3   NM_001287139           Zzz3  \n",
       "24313      +     0              a      NM_015770              a  \n",
       "24314      -     0            NaN            NaN          l7Rn6  \n",
       "\n",
       "[24315 rows x 25 columns]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fpkm_gencode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "edef74c3-f530-4b56-a291-ec47dfc33281",
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tracking_id</th>\n",
       "      <th>locus</th>\n",
       "      <th>CTCF-AID_untreated_mean_FPKM</th>\n",
       "      <th>CTCF-AID_1day_mean_FPKM</th>\n",
       "      <th>CTCF-AID_2days_mean_FPKM</th>\n",
       "      <th>CTCF-AID_4days_mean_FPKM</th>\n",
       "      <th>CTCF-AID_washoff_mean_FPKM</th>\n",
       "      <th>1day.1</th>\n",
       "      <th>2days.1</th>\n",
       "      <th>4days.1</th>\n",
       "      <th>...</th>\n",
       "      <th>source</th>\n",
       "      <th>feature</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>score</th>\n",
       "      <th>strand</th>\n",
       "      <th>frame</th>\n",
       "      <th>gene_id</th>\n",
       "      <th>transcript_id</th>\n",
       "      <th>gene_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0610005C13Rik</td>\n",
       "      <td>chr7:52823164-52845080</td>\n",
       "      <td>0.784511</td>\n",
       "      <td>0.804255</td>\n",
       "      <td>1.040841</td>\n",
       "      <td>2.176798</td>\n",
       "      <td>1.209881</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>refGene</td>\n",
       "      <td>transcript</td>\n",
       "      <td>52830547.0</td>\n",
       "      <td>52830547.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-</td>\n",
       "      <td>0</td>\n",
       "      <td>0610005C13Rik</td>\n",
       "      <td>NR_038165</td>\n",
       "      <td>0610005C13Rik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0610007P14Rik</td>\n",
       "      <td>chr12:87156404-87165495</td>\n",
       "      <td>71.716133</td>\n",
       "      <td>74.856200</td>\n",
       "      <td>63.412767</td>\n",
       "      <td>59.250033</td>\n",
       "      <td>67.995733</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>HAVANA</td>\n",
       "      <td>start_codon</td>\n",
       "      <td>87163170.0</td>\n",
       "      <td>87163170.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0610007P14Rik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0610009B22Rik</td>\n",
       "      <td>chr11:51498886-51502136</td>\n",
       "      <td>69.529333</td>\n",
       "      <td>76.436433</td>\n",
       "      <td>61.336067</td>\n",
       "      <td>38.937500</td>\n",
       "      <td>58.353700</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>...</td>\n",
       "      <td>refGene</td>\n",
       "      <td>transcript</td>\n",
       "      <td>51502137.0</td>\n",
       "      <td>51502137.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-</td>\n",
       "      <td>0</td>\n",
       "      <td>0610009B22Rik</td>\n",
       "      <td>NM_025319</td>\n",
       "      <td>0610009B22Rik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0610009L18Rik</td>\n",
       "      <td>chr11:120209991-120212504</td>\n",
       "      <td>0.944842</td>\n",
       "      <td>1.154941</td>\n",
       "      <td>0.905838</td>\n",
       "      <td>1.097459</td>\n",
       "      <td>0.889548</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>refGene</td>\n",
       "      <td>transcript</td>\n",
       "      <td>120209991.0</td>\n",
       "      <td>120209991.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>+</td>\n",
       "      <td>0</td>\n",
       "      <td>0610009L18Rik</td>\n",
       "      <td>NR_038126</td>\n",
       "      <td>0610009L18Rik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0610009O20Rik</td>\n",
       "      <td>chr18:38398058-38422283</td>\n",
       "      <td>17.565900</td>\n",
       "      <td>20.652933</td>\n",
       "      <td>20.476800</td>\n",
       "      <td>20.633733</td>\n",
       "      <td>18.857300</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>ENSEMBL</td>\n",
       "      <td>start_codon</td>\n",
       "      <td>38410023.0</td>\n",
       "      <td>38410023.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>+</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0610009O20Rik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0610010B08Rik</td>\n",
       "      <td>chr2:174952492-175261278</td>\n",
       "      <td>0.057933</td>\n",
       "      <td>0.051192</td>\n",
       "      <td>0.080296</td>\n",
       "      <td>0.091421</td>\n",
       "      <td>0.090390</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0610010F05Rik</td>\n",
       "      <td>chr11:23473775-23533631</td>\n",
       "      <td>4.500230</td>\n",
       "      <td>4.654253</td>\n",
       "      <td>3.784197</td>\n",
       "      <td>3.342397</td>\n",
       "      <td>4.257007</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>refGene</td>\n",
       "      <td>transcript</td>\n",
       "      <td>23533632.0</td>\n",
       "      <td>23533632.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-</td>\n",
       "      <td>0</td>\n",
       "      <td>0610010F05Rik</td>\n",
       "      <td>NR_156450</td>\n",
       "      <td>0610010F05Rik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0610010K14Rik</td>\n",
       "      <td>chr11:70048705-70051416</td>\n",
       "      <td>115.794100</td>\n",
       "      <td>134.728000</td>\n",
       "      <td>124.419633</td>\n",
       "      <td>109.027433</td>\n",
       "      <td>95.227500</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>refGene</td>\n",
       "      <td>transcript</td>\n",
       "      <td>70051417.0</td>\n",
       "      <td>70051417.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-</td>\n",
       "      <td>0</td>\n",
       "      <td>0610010K14Rik</td>\n",
       "      <td>NM_001177607</td>\n",
       "      <td>0610010K14Rik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0610011F06Rik</td>\n",
       "      <td>chr17:26012444-26014108</td>\n",
       "      <td>12.449400</td>\n",
       "      <td>12.249967</td>\n",
       "      <td>11.492490</td>\n",
       "      <td>12.151867</td>\n",
       "      <td>11.099847</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>HAVANA</td>\n",
       "      <td>start_codon</td>\n",
       "      <td>26012475.0</td>\n",
       "      <td>26012475.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>+</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0610011F06Rik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0610012G03Rik</td>\n",
       "      <td>chr16:31947136-31948607</td>\n",
       "      <td>34.492833</td>\n",
       "      <td>40.396067</td>\n",
       "      <td>36.419533</td>\n",
       "      <td>30.946500</td>\n",
       "      <td>30.611233</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>refGene</td>\n",
       "      <td>transcript</td>\n",
       "      <td>31948592.0</td>\n",
       "      <td>31948592.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-</td>\n",
       "      <td>0</td>\n",
       "      <td>0610012G03Rik</td>\n",
       "      <td>NM_134107</td>\n",
       "      <td>0610012G03Rik</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     tracking_id                      locus  CTCF-AID_untreated_mean_FPKM  \\\n",
       "0  0610005C13Rik     chr7:52823164-52845080                      0.784511   \n",
       "1  0610007P14Rik    chr12:87156404-87165495                     71.716133   \n",
       "2  0610009B22Rik    chr11:51498886-51502136                     69.529333   \n",
       "3  0610009L18Rik  chr11:120209991-120212504                      0.944842   \n",
       "4  0610009O20Rik    chr18:38398058-38422283                     17.565900   \n",
       "5  0610010B08Rik   chr2:174952492-175261278                      0.057933   \n",
       "6  0610010F05Rik    chr11:23473775-23533631                      4.500230   \n",
       "7  0610010K14Rik    chr11:70048705-70051416                    115.794100   \n",
       "8  0610011F06Rik    chr17:26012444-26014108                     12.449400   \n",
       "9  0610012G03Rik    chr16:31947136-31948607                     34.492833   \n",
       "\n",
       "   CTCF-AID_1day_mean_FPKM  CTCF-AID_2days_mean_FPKM  \\\n",
       "0                 0.804255                  1.040841   \n",
       "1                74.856200                 63.412767   \n",
       "2                76.436433                 61.336067   \n",
       "3                 1.154941                  0.905838   \n",
       "4                20.652933                 20.476800   \n",
       "5                 0.051192                  0.080296   \n",
       "6                 4.654253                  3.784197   \n",
       "7               134.728000                124.419633   \n",
       "8                12.249967                 11.492490   \n",
       "9                40.396067                 36.419533   \n",
       "\n",
       "   CTCF-AID_4days_mean_FPKM  CTCF-AID_washoff_mean_FPKM 1day.1 2days.1  \\\n",
       "0                  2.176798                    1.209881     no      no   \n",
       "1                 59.250033                   67.995733     no      no   \n",
       "2                 38.937500                   58.353700     no      no   \n",
       "3                  1.097459                    0.889548     no      no   \n",
       "4                 20.633733                   18.857300     no      no   \n",
       "5                  0.091421                    0.090390     no      no   \n",
       "6                  3.342397                    4.257007     no      no   \n",
       "7                109.027433                   95.227500     no      no   \n",
       "8                 12.151867                   11.099847     no      no   \n",
       "9                 30.946500                   30.611233     no      no   \n",
       "\n",
       "  4days.1  ...   source      feature        start          end score strand  \\\n",
       "0      no  ...  refGene   transcript   52830547.0   52830547.0   NaN      -   \n",
       "1      no  ...   HAVANA  start_codon   87163170.0   87163170.0   NaN      -   \n",
       "2     yes  ...  refGene   transcript   51502137.0   51502137.0   NaN      -   \n",
       "3      no  ...  refGene   transcript  120209991.0  120209991.0   NaN      +   \n",
       "4      no  ...  ENSEMBL  start_codon   38410023.0   38410023.0   NaN      +   \n",
       "5      no  ...      NaN          NaN          NaN          NaN   NaN    NaN   \n",
       "6      no  ...  refGene   transcript   23533632.0   23533632.0   NaN      -   \n",
       "7      no  ...  refGene   transcript   70051417.0   70051417.0   NaN      -   \n",
       "8      no  ...   HAVANA  start_codon   26012475.0   26012475.0   NaN      +   \n",
       "9      no  ...  refGene   transcript   31948592.0   31948592.0   NaN      -   \n",
       "\n",
       "  frame        gene_id  transcript_id      gene_name  \n",
       "0     0  0610005C13Rik      NR_038165  0610005C13Rik  \n",
       "1     0            NaN            NaN  0610007P14Rik  \n",
       "2     0  0610009B22Rik      NM_025319  0610009B22Rik  \n",
       "3     0  0610009L18Rik      NR_038126  0610009L18Rik  \n",
       "4     0            NaN            NaN  0610009O20Rik  \n",
       "5   NaN            NaN            NaN            NaN  \n",
       "6     0  0610010F05Rik      NR_156450  0610010F05Rik  \n",
       "7     0  0610010K14Rik   NM_001177607  0610010K14Rik  \n",
       "8     0            NaN            NaN  0610011F06Rik  \n",
       "9     0  0610012G03Rik      NM_134107  0610012G03Rik  \n",
       "\n",
       "[10 rows x 25 columns]"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Replace the Start codon indices with single values\n",
    "ix_start_codons_fwd = np.where((fpkm_gencode['feature'] == 'start_codon') & (fpkm_gencode['strand'] == '+'))\n",
    "ix_start_codons_rev = np.where((fpkm_gencode['feature'] == 'start_codon') & (fpkm_gencode['strand'] == '-'))\n",
    "\n",
    "\n",
    "# Replace the end position of forward strands \n",
    "fpkm_gencode[\"end\"].loc[ix_start_codons_fwd].replace(fpkm_gencode.loc[ix_start_codons_fwd][\"start\"])\n",
    "\n",
    "fpkm_gencode[\"start\"].loc[ix_start_codons_rev].replace(fpkm_gencode.loc[ix_start_codons_rev][\"end\"])\n",
    "\n",
    "# Replace the start position of reverse strands \n",
    "\n",
    "fpkm_gencode.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7db7ecf-7b9d-46f3-a1a2-de23cac8a188",
   "metadata": {},
   "source": [
    "## Replicating Figure 6(D) using Supplementary table"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed7e762a-40ab-4200-aa56-7cf4748351af",
   "metadata": {},
   "source": [
    "## Formatting and labelling supplementary table of DE results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "79f66aaa-13fc-49cc-8cbd-2f660bbaa1c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of FPKM results before dropping null values: 24315\n",
      "Size after dropping null values: 23252\n"
     ]
    }
   ],
   "source": [
    "# Reformatting to use bioframe operations\n",
    "\n",
    "# dropping null entries (anything that didn't have a match in the TSS database\n",
    "print(\"number of FPKM results before dropping null values: \" + str(fpkm_gencode.shape[0]))\n",
    "fpkm_gencode.dropna(subset=['gene_id'], inplace=True)\n",
    "print(\"Size after dropping null values: \" + str(fpkm_gencode.shape[0]))\n",
    "\n",
    "fpkm_gencode = fpkm_gencode.rename(columns = {'seqname' : 'chrom'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "20332c1d-f688-4a79-b4ef-1a7414b9f50e",
   "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>tracking_id</th>\n",
       "      <th>locus</th>\n",
       "      <th>CTCF-AID_untreated_mean_FPKM</th>\n",
       "      <th>CTCF-AID_1day_mean_FPKM</th>\n",
       "      <th>CTCF-AID_2days_mean_FPKM</th>\n",
       "      <th>CTCF-AID_4days_mean_FPKM</th>\n",
       "      <th>CTCF-AID_washoff_mean_FPKM</th>\n",
       "      <th>1day.1</th>\n",
       "      <th>2days.1</th>\n",
       "      <th>4days.1</th>\n",
       "      <th>...</th>\n",
       "      <th>source</th>\n",
       "      <th>feature</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>score</th>\n",
       "      <th>strand</th>\n",
       "      <th>frame</th>\n",
       "      <th>gene_id</th>\n",
       "      <th>transcript_id</th>\n",
       "      <th>gene_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0610005C13Rik</td>\n",
       "      <td>chr7:52823164-52845080</td>\n",
       "      <td>0.784511</td>\n",
       "      <td>0.804255</td>\n",
       "      <td>1.040841</td>\n",
       "      <td>2.176798</td>\n",
       "      <td>1.209881</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>refGene</td>\n",
       "      <td>transcript</td>\n",
       "      <td>52830547.0</td>\n",
       "      <td>52830547.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-</td>\n",
       "      <td>0</td>\n",
       "      <td>0610005C13Rik</td>\n",
       "      <td>NR_038165</td>\n",
       "      <td>0610005C13Rik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0610009B22Rik</td>\n",
       "      <td>chr11:51498886-51502136</td>\n",
       "      <td>69.529333</td>\n",
       "      <td>76.436433</td>\n",
       "      <td>61.336067</td>\n",
       "      <td>38.937500</td>\n",
       "      <td>58.353700</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>...</td>\n",
       "      <td>refGene</td>\n",
       "      <td>transcript</td>\n",
       "      <td>51502137.0</td>\n",
       "      <td>51502137.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-</td>\n",
       "      <td>0</td>\n",
       "      <td>0610009B22Rik</td>\n",
       "      <td>NM_025319</td>\n",
       "      <td>0610009B22Rik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0610009L18Rik</td>\n",
       "      <td>chr11:120209991-120212504</td>\n",
       "      <td>0.944842</td>\n",
       "      <td>1.154941</td>\n",
       "      <td>0.905838</td>\n",
       "      <td>1.097459</td>\n",
       "      <td>0.889548</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>refGene</td>\n",
       "      <td>transcript</td>\n",
       "      <td>120209991.0</td>\n",
       "      <td>120209991.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>+</td>\n",
       "      <td>0</td>\n",
       "      <td>0610009L18Rik</td>\n",
       "      <td>NR_038126</td>\n",
       "      <td>0610009L18Rik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0610010F05Rik</td>\n",
       "      <td>chr11:23473775-23533631</td>\n",
       "      <td>4.500230</td>\n",
       "      <td>4.654253</td>\n",
       "      <td>3.784197</td>\n",
       "      <td>3.342397</td>\n",
       "      <td>4.257007</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>refGene</td>\n",
       "      <td>transcript</td>\n",
       "      <td>23533632.0</td>\n",
       "      <td>23533632.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-</td>\n",
       "      <td>0</td>\n",
       "      <td>0610010F05Rik</td>\n",
       "      <td>NR_156450</td>\n",
       "      <td>0610010F05Rik</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     tracking_id                      locus  CTCF-AID_untreated_mean_FPKM  \\\n",
       "0  0610005C13Rik     chr7:52823164-52845080                      0.784511   \n",
       "2  0610009B22Rik    chr11:51498886-51502136                     69.529333   \n",
       "3  0610009L18Rik  chr11:120209991-120212504                      0.944842   \n",
       "6  0610010F05Rik    chr11:23473775-23533631                      4.500230   \n",
       "\n",
       "   CTCF-AID_1day_mean_FPKM  CTCF-AID_2days_mean_FPKM  \\\n",
       "0                 0.804255                  1.040841   \n",
       "2                76.436433                 61.336067   \n",
       "3                 1.154941                  0.905838   \n",
       "6                 4.654253                  3.784197   \n",
       "\n",
       "   CTCF-AID_4days_mean_FPKM  CTCF-AID_washoff_mean_FPKM 1day.1 2days.1  \\\n",
       "0                  2.176798                    1.209881     no      no   \n",
       "2                 38.937500                   58.353700     no      no   \n",
       "3                  1.097459                    0.889548     no      no   \n",
       "6                  3.342397                    4.257007     no      no   \n",
       "\n",
       "  4days.1  ...   source     feature        start          end score strand  \\\n",
       "0      no  ...  refGene  transcript   52830547.0   52830547.0   NaN      -   \n",
       "2     yes  ...  refGene  transcript   51502137.0   51502137.0   NaN      -   \n",
       "3      no  ...  refGene  transcript  120209991.0  120209991.0   NaN      +   \n",
       "6      no  ...  refGene  transcript   23533632.0   23533632.0   NaN      -   \n",
       "\n",
       "  frame        gene_id  transcript_id      gene_name  \n",
       "0     0  0610005C13Rik      NR_038165  0610005C13Rik  \n",
       "2     0  0610009B22Rik      NM_025319  0610009B22Rik  \n",
       "3     0  0610009L18Rik      NR_038126  0610009L18Rik  \n",
       "6     0  0610010F05Rik      NR_156450  0610010F05Rik  \n",
       "\n",
       "[4 rows x 25 columns]"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fpkm_gencode.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "161e4ef4-6cd8-4d9d-91f7-a86674ca23ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fpkm_gencode = bf.sanitize_bedframe(fpkm_gencode)\n",
    "\n",
    "bf.is_bedframe(fpkm_gencode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f32b5458-eafe-42bb-8bb0-68d4d72220e5",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'fpkm_gencode' 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/tmp/SLURM_7936489/ipykernel_236650/3443116088.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Get distance between TSSes and the closest enhancer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mclosest_intervals_nora\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclosest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfpkm_gencode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0menhancer_coords\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msuffixes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'_de_tss'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'_enhancer'\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[0m\u001b[1;32m      3\u001b[0m \u001b[0mfpkm_gencode\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'distance_to_enhancer'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclosest_intervals_nora\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'distance'\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 'fpkm_gencode' is not defined"
     ]
    }
   ],
   "source": [
    "# Get distance between TSSes and the closest enhancer\n",
    "closest_intervals_nora = bf.closest(fpkm_gencode, enhancer_coords, suffixes=('_de_tss','_enhancer'))\n",
    "fpkm_gencode['distance_to_enhancer'] = closest_intervals_nora['distance']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "47043421-4976-4b71-b3be-69135b2e6527",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of genes that have distance from TSS to enhancer = 0 is : 4\n",
      "Number of genes that did not find a closest enhancer: 1120\n"
     ]
    }
   ],
   "source": [
    "# How many?\n",
    "print(\"Number of genes that have distance from TSS to enhancer = 0 is : \" + str(len(fpkm_gencode[fpkm_gencode['distance_to_enhancer'] == 0]))) \n",
    "\n",
    "# Changing these values to = 1 \n",
    "fpkm_gencode.loc[fpkm_gencode['distance_to_enhancer'] == 0, 'distance_to_enhancer'] = 1\n",
    "\n",
    "# How many have no enhancer to call home\n",
    "print(\"Number of genes that did not find a closest enhancer: \" + str(len(fpkm_gencode[pd.isna(fpkm_gencode['distance_to_enhancer'])])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "7eaa14ab-0e73-4c3d-b25e-19b81660d7b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "fpkm_gencode.dropna(subset='distance_to_enhancer', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "6e1c98ca-8b2b-437f-8bda-7548e0a14f39",
   "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>chrom</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1272</th>\n",
       "      <td>chr18</td>\n",
       "      <td>77381001</td>\n",
       "      <td>77382000</td>\n",
       "      <td>0.8005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1273</th>\n",
       "      <td>chr9</td>\n",
       "      <td>78222001</td>\n",
       "      <td>78223000</td>\n",
       "      <td>0.8004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1274</th>\n",
       "      <td>chr1</td>\n",
       "      <td>138467001</td>\n",
       "      <td>138468000</td>\n",
       "      <td>0.8003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1275</th>\n",
       "      <td>chr14</td>\n",
       "      <td>12940001</td>\n",
       "      <td>12941000</td>\n",
       "      <td>0.8000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1276</th>\n",
       "      <td>chr2</td>\n",
       "      <td>135388001</td>\n",
       "      <td>135389000</td>\n",
       "      <td>0.8000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      chrom      start        end   value\n",
       "1272  chr18   77381001   77382000  0.8005\n",
       "1273   chr9   78222001   78223000  0.8004\n",
       "1274   chr1  138467001  138468000  0.8003\n",
       "1275  chr14   12940001   12941000  0.8000\n",
       "1276   chr2  135388001  135389000  0.8000"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking to make sure filter is on enhancer coords\n",
    "enhancer_coords.tail(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "7d56d80b-6fcb-4f37-9a19-dec88c9a6284",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_distal_enhancers_supp_table(fpkm_gencode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "53f46419-2bcd-4e57-8298-730fb5555957",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def plot_distal_enhancers_supp_table(table):\n",
    "    \n",
    "    days = ['1day', '2days', '4days']\n",
    "\n",
    "\n",
    "    # Generating a subplot for each of the conditions\n",
    "    num_plots = len(days)\n",
    "    fig, axs = plt.subplots(1, num_plots, figsize=[num_plots*10, 6])\n",
    "    fig.suptitle(\"Genomic separation to enhancers\", fontsize=24)\n",
    "\n",
    "    for day, ax in zip(days, axs.ravel()):\n",
    "\n",
    "        up_reg = table.iloc[np.where((table[day + '.1'] == 'yes') & ((table['CTCF-AID_untreated_mean_FPKM'] - table['CTCF-AID_' + day + '_mean_FPKM']) > 0))]\n",
    "        down_reg = table.iloc[np.where((table[day + '.1'] == 'yes') & ((table['CTCF-AID_untreated_mean_FPKM'] - table['CTCF-AID_' + day + '_mean_FPKM']) < 0))]\n",
    "        non_reg = table.iloc[np.where(table[day + '.1'] == 'no')]\n",
    "\n",
    "        # generate y axis: cumulative proportion of genes\n",
    "        cum_all = np.arange(0, len(non_reg), 1)\n",
    "        cum_all = cum_all/ len(non_reg)\n",
    "\n",
    "        # plotting \n",
    "        ax.plot(np.log10(non_reg['distance_to_enhancer'].sort_values()), \n",
    "                cum_all, \n",
    "                color='grey',\n",
    "                label='Non-regulated',\n",
    "                linewidth=2)\n",
    "\n",
    "\n",
    "        # generate y axis: cumulative proportion of genes\n",
    "        cum_up = np.arange(0, len(up_reg), 1)\n",
    "        cum_up = cum_up/ len(up_reg)\n",
    "\n",
    "        ax.plot(np.log10(up_reg['distance_to_enhancer'].sort_values()), \n",
    "                cum_up, \n",
    "                color='red', \n",
    "                label='Up-regulated',\n",
    "                linewidth=2)\n",
    "\n",
    "        # generate y axis: cumulative proportion of genes\n",
    "        cum_down = np.arange(0, len(down_reg), 1)\n",
    "        cum_down = cum_down/ len(down_reg)\n",
    "\n",
    "        ax.plot(np.log10(down_reg['distance_to_enhancer'].sort_values()),\n",
    "                cum_down,\n",
    "                color='blue',\n",
    "                label='Down-regulated',\n",
    "                linewidth=2)\n",
    "\n",
    "        ax.set_title(\"Auxin treatment: \" + day, fontsize=18)\n",
    "        ax.set_xlabel(\"log10 distance from TSS to the closest enhancer\", fontsize=14)\n",
    "        ax.set_ylabel(\"cumulative portion of genes\", fontsize=14)\n",
    "\n",
    "\n",
    "    handles, labels = ax.get_legend_handles_labels()\n",
    "    fig.legend(handles, labels, fontsize=14)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac17f35a-0c1d-452e-8c45-bb7166cca0f7",
   "metadata": {},
   "source": [
    "### Unsuccessful method: matching tracking_id (MGI ids http://www.informatics.jax.org/) to ensembl ids\n",
    "\n",
    "- Attempt using MRK_ENSEMBL.rpt downloaded from MGI website http://www.informatics.jax.org/downloads/reports/index.html\n",
    "  - issues: no versioning associated?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "df4eac67-5585-45a6-9b6c-5bea129541df",
   "metadata": {},
   "outputs": [],
   "source": [
    "# mold fpkm data to be more like the above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "e383bf6c-d91e-426a-b14b-ff365da4f471",
   "metadata": {},
   "outputs": [],
   "source": [
    "mgi_ensembl = pd.read_csv(\"/project/fudenber_735/genomes/mm9/ensembl/ensembl_relational_downloads/MRK_ENSEMBL.rpt\", sep='\\t', header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "fc251257-45d1-4879-85f7-1d17cf46168a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "55341\n"
     ]
    },
    {
     "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MGI:1915733</td>\n",
       "      <td>1110002O04Rik</td>\n",
       "      <td>RIKEN cDNA 1110002O04 gene</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>1</td>\n",
       "      <td>ENSMUSG00000102531</td>\n",
       "      <td>ENSMUST00000194261</td>\n",
       "      <td>NaN</td>\n",
       "      <td>lncRNA gene</td>\n",
       "      <td>35918926</td>\n",
       "      <td>35920200</td>\n",
       "      <td>+</td>\n",
       "      <td>ncRNA|lncRNA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MGI:1919275</td>\n",
       "      <td>1600012P17Rik</td>\n",
       "      <td>RIKEN cDNA 1600012P17 gene</td>\n",
       "      <td>68.49</td>\n",
       "      <td>1</td>\n",
       "      <td>ENSMUSG00000047661</td>\n",
       "      <td>ENSMUST00000062159 ENSMUST00000162474</td>\n",
       "      <td>NaN</td>\n",
       "      <td>lncRNA gene</td>\n",
       "      <td>158795271</td>\n",
       "      <td>158808032</td>\n",
       "      <td>-</td>\n",
       "      <td>lncRNA|ncRNA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MGI:1914753</td>\n",
       "      <td>1700001G17Rik</td>\n",
       "      <td>RIKEN cDNA 1700001G17 gene</td>\n",
       "      <td>12.78</td>\n",
       "      <td>1</td>\n",
       "      <td>ENSMUSG00000103746</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>lncRNA gene</td>\n",
       "      <td>33708905</td>\n",
       "      <td>33709793</td>\n",
       "      <td>+</td>\n",
       "      <td>TEC|ncRNA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            0              1                           2      3  4   \\\n",
       "0  MGI:1915733  1110002O04Rik  RIKEN cDNA 1110002O04 gene  -1.00  1   \n",
       "1  MGI:1919275  1600012P17Rik  RIKEN cDNA 1600012P17 gene  68.49  1   \n",
       "2  MGI:1914753  1700001G17Rik  RIKEN cDNA 1700001G17 gene  12.78  1   \n",
       "\n",
       "                   5                                      6    7   \\\n",
       "0  ENSMUSG00000102531                     ENSMUST00000194261  NaN   \n",
       "1  ENSMUSG00000047661  ENSMUST00000062159 ENSMUST00000162474  NaN   \n",
       "2  ENSMUSG00000103746                                    NaN  NaN   \n",
       "\n",
       "            8          9          10 11            12  \n",
       "0  lncRNA gene   35918926   35920200  +  ncRNA|lncRNA  \n",
       "1  lncRNA gene  158795271  158808032  -  lncRNA|ncRNA  \n",
       "2  lncRNA gene   33708905   33709793  +     TEC|ncRNA  "
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(mgi_ensembl.shape[0])\n",
    "mgi_ensembl.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "e2f7abc7-955e-47e5-ac8c-9e3113773cee",
   "metadata": {},
   "outputs": [],
   "source": [
    "# add column names\n",
    "mgi_ensembl = mgi_ensembl.rename(columns={0 : 'mgi_id',\n",
    "                            1 : 'mgi_gene',\n",
    "                            2 : 'description',\n",
    "                            3 : 'not_sure',\n",
    "                            4 : 'chrom',\n",
    "                            5 : 'ensembl_gene_id',\n",
    "                            6 : 'ensembl_transcript_ids',\n",
    "                            7 : 'ensembl_protein_id',\n",
    "                            8 : 'biotype',\n",
    "                            9 : 'start',\n",
    "                            10 : 'end',\n",
    "                            11 : 'strand',\n",
    "                            12 : 'possible_biotypes'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "b0ff83b5-6a51-47a1-93f5-102481d33ab2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of unique id's in DEseq results (from kallisto mapping): 26520\n"
     ]
    },
    {
     "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>gene_id</th>\n",
       "      <th>baseMean</th>\n",
       "      <th>log2FoldChange</th>\n",
       "      <th>lfcSE</th>\n",
       "      <th>stat</th>\n",
       "      <th>pvalue</th>\n",
       "      <th>padj</th>\n",
       "      <th>mgi_id</th>\n",
       "      <th>mgi_gene</th>\n",
       "      <th>description</th>\n",
       "      <th>not_sure</th>\n",
       "      <th>chrom</th>\n",
       "      <th>ensembl_gene_id</th>\n",
       "      <th>ensembl_transcript_ids</th>\n",
       "      <th>ensembl_protein_id</th>\n",
       "      <th>biotype</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>strand</th>\n",
       "      <th>possible_biotypes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ENSMUSG00000000001</td>\n",
       "      <td>4942.457855</td>\n",
       "      <td>-0.033551</td>\n",
       "      <td>0.236434</td>\n",
       "      <td>-0.141905</td>\n",
       "      <td>8.871554e-01</td>\n",
       "      <td>9.998054e-01</td>\n",
       "      <td>MGI:95773</td>\n",
       "      <td>Gnai3</td>\n",
       "      <td>guanine nucleotide binding protein (G protein)...</td>\n",
       "      <td>46.83</td>\n",
       "      <td>3</td>\n",
       "      <td>ENSMUSG00000000001</td>\n",
       "      <td>ENSMUST00000000001</td>\n",
       "      <td>ENSMUSP00000000001</td>\n",
       "      <td>protein coding gene</td>\n",
       "      <td>108014596.0</td>\n",
       "      <td>108053462.0</td>\n",
       "      <td>-</td>\n",
       "      <td>protein-coding|protein_coding</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ENSMUSG00000000003</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MGI:1860484</td>\n",
       "      <td>Pbsn</td>\n",
       "      <td>probasin</td>\n",
       "      <td>38.32</td>\n",
       "      <td>X</td>\n",
       "      <td>ENSMUSG00000000003</td>\n",
       "      <td>ENSMUST00000000003 ENSMUST00000114041</td>\n",
       "      <td>ENSMUSP00000000003 ENSMUSP00000109675</td>\n",
       "      <td>protein coding gene</td>\n",
       "      <td>76881504.0</td>\n",
       "      <td>76897106.0</td>\n",
       "      <td>-</td>\n",
       "      <td>protein_coding|protein-coding</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ENSMUSG00000000028</td>\n",
       "      <td>2278.335221</td>\n",
       "      <td>-0.091383</td>\n",
       "      <td>0.163252</td>\n",
       "      <td>-0.559764</td>\n",
       "      <td>5.756404e-01</td>\n",
       "      <td>9.998054e-01</td>\n",
       "      <td>MGI:1338073</td>\n",
       "      <td>Cdc45</td>\n",
       "      <td>cell division cycle 45</td>\n",
       "      <td>11.63</td>\n",
       "      <td>16</td>\n",
       "      <td>ENSMUSG00000000028</td>\n",
       "      <td>ENSMUST00000231819 ENSMUST00000096990 ENSMUST0...</td>\n",
       "      <td>ENSMUSP00000094753 ENSMUSP00000111248 ENSMUSP0...</td>\n",
       "      <td>protein coding gene</td>\n",
       "      <td>18599197.0</td>\n",
       "      <td>18630737.0</td>\n",
       "      <td>-</td>\n",
       "      <td>protein_coding|protein-coding</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ENSMUSG00000000037</td>\n",
       "      <td>398.325060</td>\n",
       "      <td>2.463827</td>\n",
       "      <td>0.236823</td>\n",
       "      <td>10.403647</td>\n",
       "      <td>2.386216e-25</td>\n",
       "      <td>4.369956e-22</td>\n",
       "      <td>MGI:1340042</td>\n",
       "      <td>Scml2</td>\n",
       "      <td>Scm polycomb group protein like 2</td>\n",
       "      <td>73.95</td>\n",
       "      <td>X</td>\n",
       "      <td>ENSMUSG00000000037</td>\n",
       "      <td>ENSMUST00000238603 ENSMUST00000077375 ENSMUST0...</td>\n",
       "      <td>ENSMUSP00000076593 ENSMUSP00000019101 ENSMUSP0...</td>\n",
       "      <td>protein coding gene</td>\n",
       "      <td>159865521.0</td>\n",
       "      <td>160041209.0</td>\n",
       "      <td>+</td>\n",
       "      <td>protein-coding|protein_coding</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ENSMUSG00000000049</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MGI:88058</td>\n",
       "      <td>Apoh</td>\n",
       "      <td>apolipoprotein H</td>\n",
       "      <td>71.80</td>\n",
       "      <td>11</td>\n",
       "      <td>ENSMUSG00000000049</td>\n",
       "      <td>ENSMUST00000152958 ENSMUST00000146050 ENSMUST0...</td>\n",
       "      <td>ENSMUSP00000123486 ENSMUSP00000114214 ENSMUSP0...</td>\n",
       "      <td>protein coding gene</td>\n",
       "      <td>108286123.0</td>\n",
       "      <td>108305222.0</td>\n",
       "      <td>+</td>\n",
       "      <td>protein_coding|protein-coding</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26516</th>\n",
       "      <td>ENSMUSG00000084412</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MGI:3651841</td>\n",
       "      <td>Gm12044</td>\n",
       "      <td>predicted gene 12044</td>\n",
       "      <td>13.89</td>\n",
       "      <td>11</td>\n",
       "      <td>ENSMUSG00000084412</td>\n",
       "      <td>ENSMUST00000120335</td>\n",
       "      <td>NaN</td>\n",
       "      <td>pseudogene</td>\n",
       "      <td>21401142.0</td>\n",
       "      <td>21401354.0</td>\n",
       "      <td>-</td>\n",
       "      <td>processed_pseudogene</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26517</th>\n",
       "      <td>ENSMUSG00000084413</td>\n",
       "      <td>9.829391</td>\n",
       "      <td>1.454489</td>\n",
       "      <td>1.157044</td>\n",
       "      <td>1.257073</td>\n",
       "      <td>2.087272e-01</td>\n",
       "      <td>9.998054e-01</td>\n",
       "      <td>MGI:3651377</td>\n",
       "      <td>Gm12017</td>\n",
       "      <td>predicted gene 12017</td>\n",
       "      <td>10.45</td>\n",
       "      <td>11</td>\n",
       "      <td>ENSMUSG00000084413</td>\n",
       "      <td>ENSMUST00000122453</td>\n",
       "      <td>NaN</td>\n",
       "      <td>pseudogene</td>\n",
       "      <td>17923788.0</td>\n",
       "      <td>17924790.0</td>\n",
       "      <td>-</td>\n",
       "      <td>processed_pseudogene|pseudo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26518</th>\n",
       "      <td>ENSMUSG00000084414</td>\n",
       "      <td>0.060269</td>\n",
       "      <td>-0.698860</td>\n",
       "      <td>4.055402</td>\n",
       "      <td>-0.172328</td>\n",
       "      <td>8.631796e-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MGI:3649599</td>\n",
       "      <td>Gm11270</td>\n",
       "      <td>predicted gene 11270</td>\n",
       "      <td>7.73</td>\n",
       "      <td>13</td>\n",
       "      <td>ENSMUSG00000084414</td>\n",
       "      <td>ENSMUST00000117685</td>\n",
       "      <td>NaN</td>\n",
       "      <td>pseudogene</td>\n",
       "      <td>21480418.0</td>\n",
       "      <td>21480888.0</td>\n",
       "      <td>-</td>\n",
       "      <td>pseudo|processed_pseudogene</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26519</th>\n",
       "      <td>ENSMUSG00000084415</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MGI:3649425</td>\n",
       "      <td>Gm12837</td>\n",
       "      <td>predicted gene 12837</td>\n",
       "      <td>52.99</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSMUSG00000084415</td>\n",
       "      <td>ENSMUST00000120131</td>\n",
       "      <td>NaN</td>\n",
       "      <td>pseudogene</td>\n",
       "      <td>115273279.0</td>\n",
       "      <td>115273651.0</td>\n",
       "      <td>-</td>\n",
       "      <td>pseudo|processed_pseudogene</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26520</th>\n",
       "      <td>ENSMUSG00000084416</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MGI:3705885</td>\n",
       "      <td>Rpl10a-ps1</td>\n",
       "      <td>ribosomal protein L10A, pseudogene 1</td>\n",
       "      <td>32.80</td>\n",
       "      <td>1</td>\n",
       "      <td>ENSMUSG00000084416</td>\n",
       "      <td>ENSMUST00000120360</td>\n",
       "      <td>NaN</td>\n",
       "      <td>pseudogene</td>\n",
       "      <td>65033110.0</td>\n",
       "      <td>65033763.0</td>\n",
       "      <td>-</td>\n",
       "      <td>processed_pseudogene|pseudo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>26521 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  gene_id     baseMean  log2FoldChange     lfcSE       stat  \\\n",
       "0      ENSMUSG00000000001  4942.457855       -0.033551  0.236434  -0.141905   \n",
       "1      ENSMUSG00000000003     0.000000             NaN       NaN        NaN   \n",
       "2      ENSMUSG00000000028  2278.335221       -0.091383  0.163252  -0.559764   \n",
       "3      ENSMUSG00000000037   398.325060        2.463827  0.236823  10.403647   \n",
       "4      ENSMUSG00000000049     0.000000             NaN       NaN        NaN   \n",
       "...                   ...          ...             ...       ...        ...   \n",
       "26516  ENSMUSG00000084412     0.000000             NaN       NaN        NaN   \n",
       "26517  ENSMUSG00000084413     9.829391        1.454489  1.157044   1.257073   \n",
       "26518  ENSMUSG00000084414     0.060269       -0.698860  4.055402  -0.172328   \n",
       "26519  ENSMUSG00000084415     0.000000             NaN       NaN        NaN   \n",
       "26520  ENSMUSG00000084416     0.000000             NaN       NaN        NaN   \n",
       "\n",
       "             pvalue          padj       mgi_id    mgi_gene  \\\n",
       "0      8.871554e-01  9.998054e-01    MGI:95773       Gnai3   \n",
       "1               NaN           NaN  MGI:1860484        Pbsn   \n",
       "2      5.756404e-01  9.998054e-01  MGI:1338073       Cdc45   \n",
       "3      2.386216e-25  4.369956e-22  MGI:1340042       Scml2   \n",
       "4               NaN           NaN    MGI:88058        Apoh   \n",
       "...             ...           ...          ...         ...   \n",
       "26516           NaN           NaN  MGI:3651841     Gm12044   \n",
       "26517  2.087272e-01  9.998054e-01  MGI:3651377     Gm12017   \n",
       "26518  8.631796e-01           NaN  MGI:3649599     Gm11270   \n",
       "26519           NaN           NaN  MGI:3649425     Gm12837   \n",
       "26520           NaN           NaN  MGI:3705885  Rpl10a-ps1   \n",
       "\n",
       "                                             description  not_sure chrom  \\\n",
       "0      guanine nucleotide binding protein (G protein)...     46.83     3   \n",
       "1                                               probasin     38.32     X   \n",
       "2                                 cell division cycle 45     11.63    16   \n",
       "3                      Scm polycomb group protein like 2     73.95     X   \n",
       "4                                       apolipoprotein H     71.80    11   \n",
       "...                                                  ...       ...   ...   \n",
       "26516                               predicted gene 12044     13.89    11   \n",
       "26517                               predicted gene 12017     10.45    11   \n",
       "26518                               predicted gene 11270      7.73    13   \n",
       "26519                               predicted gene 12837     52.99     4   \n",
       "26520               ribosomal protein L10A, pseudogene 1     32.80     1   \n",
       "\n",
       "          ensembl_gene_id                             ensembl_transcript_ids  \\\n",
       "0      ENSMUSG00000000001                                 ENSMUST00000000001   \n",
       "1      ENSMUSG00000000003              ENSMUST00000000003 ENSMUST00000114041   \n",
       "2      ENSMUSG00000000028  ENSMUST00000231819 ENSMUST00000096990 ENSMUST0...   \n",
       "3      ENSMUSG00000000037  ENSMUST00000238603 ENSMUST00000077375 ENSMUST0...   \n",
       "4      ENSMUSG00000000049  ENSMUST00000152958 ENSMUST00000146050 ENSMUST0...   \n",
       "...                   ...                                                ...   \n",
       "26516  ENSMUSG00000084412                                 ENSMUST00000120335   \n",
       "26517  ENSMUSG00000084413                                 ENSMUST00000122453   \n",
       "26518  ENSMUSG00000084414                                 ENSMUST00000117685   \n",
       "26519  ENSMUSG00000084415                                 ENSMUST00000120131   \n",
       "26520  ENSMUSG00000084416                                 ENSMUST00000120360   \n",
       "\n",
       "                                      ensembl_protein_id              biotype  \\\n",
       "0                                     ENSMUSP00000000001  protein coding gene   \n",
       "1                  ENSMUSP00000000003 ENSMUSP00000109675  protein coding gene   \n",
       "2      ENSMUSP00000094753 ENSMUSP00000111248 ENSMUSP0...  protein coding gene   \n",
       "3      ENSMUSP00000076593 ENSMUSP00000019101 ENSMUSP0...  protein coding gene   \n",
       "4      ENSMUSP00000123486 ENSMUSP00000114214 ENSMUSP0...  protein coding gene   \n",
       "...                                                  ...                  ...   \n",
       "26516                                                NaN           pseudogene   \n",
       "26517                                                NaN           pseudogene   \n",
       "26518                                                NaN           pseudogene   \n",
       "26519                                                NaN           pseudogene   \n",
       "26520                                                NaN           pseudogene   \n",
       "\n",
       "             start          end strand              possible_biotypes  \n",
       "0      108014596.0  108053462.0      -  protein-coding|protein_coding  \n",
       "1       76881504.0   76897106.0      -  protein_coding|protein-coding  \n",
       "2       18599197.0   18630737.0      -  protein_coding|protein-coding  \n",
       "3      159865521.0  160041209.0      +  protein-coding|protein_coding  \n",
       "4      108286123.0  108305222.0      +  protein_coding|protein-coding  \n",
       "...            ...          ...    ...                            ...  \n",
       "26516   21401142.0   21401354.0      -           processed_pseudogene  \n",
       "26517   17923788.0   17924790.0      -    processed_pseudogene|pseudo  \n",
       "26518   21480418.0   21480888.0      -    pseudo|processed_pseudogene  \n",
       "26519  115273279.0  115273651.0      -    pseudo|processed_pseudogene  \n",
       "26520   65033110.0   65033763.0      -    processed_pseudogene|pseudo  \n",
       "\n",
       "[26521 rows x 20 columns]"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# first, mapping to deseq results to see compatibility/completeness of the two sets\n",
    "print(\"number of unique id's in DEseq results (from kallisto mapping): \" + str(basic_deseq_results['CTCF_AID_auxin1day']['gene_id'].nunique()))\n",
    "\n",
    "basic_deseq_results['CTCF_AID_auxin1day'].merge(mgi_ensembl, how='left', left_on='gene_id', right_on='ensembl_gene_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "7a3fa823-a202-4f26-9883-e0b595409f58",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2537"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_df = basic_deseq_results['CTCF_AID_auxin1day'].copy()\n",
    "\n",
    "# Let's add this label to our results dataset\n",
    "result_df['mgi_gene'] = result_df.merge(mgi_ensembl, how='left', left_on='gene_id', right_on='ensembl_gene_id')['mgi_gene']\n",
    "\n",
    "# count any that didn't have an mgi_gene match\n",
    "result_df['mgi_gene'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "e8041a27-609e-4cea-b5b7-92ff7ce01bd5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\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>gene_id</th>\n",
       "      <th>baseMean</th>\n",
       "      <th>log2FoldChange</th>\n",
       "      <th>lfcSE</th>\n",
       "      <th>stat</th>\n",
       "      <th>pvalue</th>\n",
       "      <th>padj</th>\n",
       "      <th>mgi_gene</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>ENSMUSG00000000325</td>\n",
       "      <td>1176.624728</td>\n",
       "      <td>0.139254</td>\n",
       "      <td>0.489379</td>\n",
       "      <td>0.284553</td>\n",
       "      <td>0.775987</td>\n",
       "      <td>0.999805</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>ENSMUSG00000000702</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>ENSMUSG00000001379</td>\n",
       "      <td>627.512892</td>\n",
       "      <td>-0.133893</td>\n",
       "      <td>0.172369</td>\n",
       "      <td>-0.776780</td>\n",
       "      <td>0.437289</td>\n",
       "      <td>0.999805</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406</th>\n",
       "      <td>ENSMUSG00000002290</td>\n",
       "      <td>12.458554</td>\n",
       "      <td>0.148792</td>\n",
       "      <td>0.751600</td>\n",
       "      <td>0.197967</td>\n",
       "      <td>0.843071</td>\n",
       "      <td>0.999805</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>539</th>\n",
       "      <td>ENSMUSG00000003178</td>\n",
       "      <td>468.462225</td>\n",
       "      <td>-0.240071</td>\n",
       "      <td>0.199192</td>\n",
       "      <td>-1.205225</td>\n",
       "      <td>0.228117</td>\n",
       "      <td>0.999805</td>\n",
       "      <td>NaN</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26502</th>\n",
       "      <td>ENSMUSG00000084399</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26506</th>\n",
       "      <td>ENSMUSG00000084403</td>\n",
       "      <td>2.354672</td>\n",
       "      <td>-0.070210</td>\n",
       "      <td>1.486532</td>\n",
       "      <td>-0.047231</td>\n",
       "      <td>0.962329</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26510</th>\n",
       "      <td>ENSMUSG00000084407</td>\n",
       "      <td>0.266680</td>\n",
       "      <td>1.835794</td>\n",
       "      <td>4.037416</td>\n",
       "      <td>0.454695</td>\n",
       "      <td>0.649329</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26513</th>\n",
       "      <td>ENSMUSG00000084410</td>\n",
       "      <td>5.151212</td>\n",
       "      <td>1.207003</td>\n",
       "      <td>1.291019</td>\n",
       "      <td>0.934923</td>\n",
       "      <td>0.349828</td>\n",
       "      <td>0.999805</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26514</th>\n",
       "      <td>ENSMUSG00000084411</td>\n",
       "      <td>7.408594</td>\n",
       "      <td>-0.560611</td>\n",
       "      <td>1.171068</td>\n",
       "      <td>-0.478718</td>\n",
       "      <td>0.632139</td>\n",
       "      <td>0.999805</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2537 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  gene_id     baseMean  log2FoldChange     lfcSE      stat  \\\n",
       "59     ENSMUSG00000000325  1176.624728        0.139254  0.489379  0.284553   \n",
       "117    ENSMUSG00000000702     0.000000             NaN       NaN       NaN   \n",
       "240    ENSMUSG00000001379   627.512892       -0.133893  0.172369 -0.776780   \n",
       "406    ENSMUSG00000002290    12.458554        0.148792  0.751600  0.197967   \n",
       "539    ENSMUSG00000003178   468.462225       -0.240071  0.199192 -1.205225   \n",
       "...                   ...          ...             ...       ...       ...   \n",
       "26502  ENSMUSG00000084399     0.000000             NaN       NaN       NaN   \n",
       "26506  ENSMUSG00000084403     2.354672       -0.070210  1.486532 -0.047231   \n",
       "26510  ENSMUSG00000084407     0.266680        1.835794  4.037416  0.454695   \n",
       "26513  ENSMUSG00000084410     5.151212        1.207003  1.291019  0.934923   \n",
       "26514  ENSMUSG00000084411     7.408594       -0.560611  1.171068 -0.478718   \n",
       "\n",
       "         pvalue      padj mgi_gene  \n",
       "59     0.775987  0.999805      NaN  \n",
       "117         NaN       NaN      NaN  \n",
       "240    0.437289  0.999805      NaN  \n",
       "406    0.843071  0.999805      NaN  \n",
       "539    0.228117  0.999805      NaN  \n",
       "...         ...       ...      ...  \n",
       "26502       NaN       NaN      NaN  \n",
       "26506  0.962329       NaN      NaN  \n",
       "26510  0.649329       NaN      NaN  \n",
       "26513  0.349828  0.999805      NaN  \n",
       "26514  0.632139  0.999805      NaN  \n",
       "\n",
       "[2537 rows x 8 columns]"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_df[result_df['mgi_gene'].isna()]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89d46226-5b05-473f-beca-7162e3c89d6e",
   "metadata": {},
   "source": [
    "### Using MGI relationship table to add ensembl_id's to FPKM table (mapping mgi_id --> ensembl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "4c4aee42-64ae-40d4-bcf4-027d7c4a6825",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\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>tracking_id</th>\n",
       "      <th>locus</th>\n",
       "      <th>CTCF-AID_untreated_mean_FPKM</th>\n",
       "      <th>CTCF-AID_1day_mean_FPKM</th>\n",
       "      <th>CTCF-AID_2days_mean_FPKM</th>\n",
       "      <th>CTCF-AID_4days_mean_FPKM</th>\n",
       "      <th>CTCF-AID_washoff_mean_FPKM</th>\n",
       "      <th>1day.1</th>\n",
       "      <th>2days.1</th>\n",
       "      <th>4days.1</th>\n",
       "      <th>...</th>\n",
       "      <th>not_sure</th>\n",
       "      <th>chrom</th>\n",
       "      <th>ensembl_gene_id_y</th>\n",
       "      <th>ensembl_transcript_ids</th>\n",
       "      <th>ensembl_protein_id</th>\n",
       "      <th>biotype</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>strand</th>\n",
       "      <th>possible_biotypes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0610005C13Rik</td>\n",
       "      <td>chr7:52823164-52845080</td>\n",
       "      <td>0.784511</td>\n",
       "      <td>0.804255</td>\n",
       "      <td>1.040841</td>\n",
       "      <td>2.176798</td>\n",
       "      <td>1.209881</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>29.36</td>\n",
       "      <td>7</td>\n",
       "      <td>ENSMUSG00000109644</td>\n",
       "      <td>ENSMUST00000210965 ENSMUST00000209510 ENSMUST0...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>lncRNA gene</td>\n",
       "      <td>45217219.0</td>\n",
       "      <td>45224600.0</td>\n",
       "      <td>-</td>\n",
       "      <td>ncRNA|lncRNA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0610007P14Rik</td>\n",
       "      <td>chr12:87156404-87165495</td>\n",
       "      <td>71.716133</td>\n",
       "      <td>74.856200</td>\n",
       "      <td>63.412767</td>\n",
       "      <td>59.250033</td>\n",
       "      <td>67.995733</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0610009B22Rik</td>\n",
       "      <td>chr11:51498886-51502136</td>\n",
       "      <td>69.529333</td>\n",
       "      <td>76.436433</td>\n",
       "      <td>61.336067</td>\n",
       "      <td>38.937500</td>\n",
       "      <td>58.353700</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>...</td>\n",
       "      <td>31.26</td>\n",
       "      <td>11</td>\n",
       "      <td>ENSMUSG00000007777</td>\n",
       "      <td>ENSMUST00000007921 ENSMUST00000109098</td>\n",
       "      <td>ENSMUSP00000104726 ENSMUSP00000007921</td>\n",
       "      <td>protein coding gene</td>\n",
       "      <td>51576213.0</td>\n",
       "      <td>51579701.0</td>\n",
       "      <td>-</td>\n",
       "      <td>protein_coding|protein-coding</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0610009L18Rik</td>\n",
       "      <td>chr11:120209991-120212504</td>\n",
       "      <td>0.944842</td>\n",
       "      <td>1.154941</td>\n",
       "      <td>0.905838</td>\n",
       "      <td>1.097459</td>\n",
       "      <td>0.889548</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>84.07</td>\n",
       "      <td>11</td>\n",
       "      <td>ENSMUSG00000043644</td>\n",
       "      <td>ENSMUST00000143813</td>\n",
       "      <td>NaN</td>\n",
       "      <td>lncRNA gene</td>\n",
       "      <td>120239504.0</td>\n",
       "      <td>120242016.0</td>\n",
       "      <td>+</td>\n",
       "      <td>ncRNA|lncRNA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0610009O20Rik</td>\n",
       "      <td>chr18:38398058-38422283</td>\n",
       "      <td>17.565900</td>\n",
       "      <td>20.652933</td>\n",
       "      <td>20.476800</td>\n",
       "      <td>20.633733</td>\n",
       "      <td>18.857300</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24343</th>\n",
       "      <td>Zyx</td>\n",
       "      <td>chr6:42299826-42330557</td>\n",
       "      <td>8.531693</td>\n",
       "      <td>8.734813</td>\n",
       "      <td>11.727350</td>\n",
       "      <td>24.111000</td>\n",
       "      <td>11.359220</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>...</td>\n",
       "      <td>20.64</td>\n",
       "      <td>6</td>\n",
       "      <td>ENSMUSG00000029860</td>\n",
       "      <td>ENSMUST00000203401 ENSMUST00000204480 ENSMUST0...</td>\n",
       "      <td>ENSMUSP00000145236 ENSMUSP00000145052 ENSMUSP0...</td>\n",
       "      <td>protein coding gene</td>\n",
       "      <td>42326564.0</td>\n",
       "      <td>42337147.0</td>\n",
       "      <td>+</td>\n",
       "      <td>protein_coding|protein-coding</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24344</th>\n",
       "      <td>Zzef1</td>\n",
       "      <td>chr11:72609727-72740622</td>\n",
       "      <td>2.597283</td>\n",
       "      <td>2.411157</td>\n",
       "      <td>2.507930</td>\n",
       "      <td>3.121180</td>\n",
       "      <td>2.856007</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>44.56</td>\n",
       "      <td>11</td>\n",
       "      <td>ENSMUSG00000055670</td>\n",
       "      <td>ENSMUST00000150551 ENSMUST00000145393 ENSMUST0...</td>\n",
       "      <td>ENSMUSP00000130515 ENSMUSP00000147028 ENSMUSP0...</td>\n",
       "      <td>protein coding gene</td>\n",
       "      <td>72687052.0</td>\n",
       "      <td>72817946.0</td>\n",
       "      <td>+</td>\n",
       "      <td>protein_coding|protein-coding</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24345</th>\n",
       "      <td>Zzz3</td>\n",
       "      <td>chr3:152058966-152344396</td>\n",
       "      <td>9.986867</td>\n",
       "      <td>8.012557</td>\n",
       "      <td>7.069317</td>\n",
       "      <td>8.471867</td>\n",
       "      <td>10.256167</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>77.16</td>\n",
       "      <td>3</td>\n",
       "      <td>ENSMUSG00000039068</td>\n",
       "      <td>ENSMUST00000198468 ENSMUST00000089982 ENSMUST0...</td>\n",
       "      <td>ENSMUSP00000087428 ENSMUSP00000101709 ENSMUSP0...</td>\n",
       "      <td>protein coding gene</td>\n",
       "      <td>152101110.0</td>\n",
       "      <td>152168463.0</td>\n",
       "      <td>+</td>\n",
       "      <td>protein_coding|protein-coding</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24346</th>\n",
       "      <td>a</td>\n",
       "      <td>chr2:154839305-154876748</td>\n",
       "      <td>0.087051</td>\n",
       "      <td>0.167859</td>\n",
       "      <td>0.370351</td>\n",
       "      <td>0.402713</td>\n",
       "      <td>0.090154</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>76.83</td>\n",
       "      <td>2</td>\n",
       "      <td>ENSMUSG00000027596</td>\n",
       "      <td>ENSMUST00000148402 ENSMUST00000109697 ENSMUST0...</td>\n",
       "      <td>ENSMUSP00000122261 ENSMUSP00000029123 ENSMUSP0...</td>\n",
       "      <td>protein coding gene</td>\n",
       "      <td>154792519.0</td>\n",
       "      <td>154892932.0</td>\n",
       "      <td>+</td>\n",
       "      <td>protein_coding|protein-coding</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24347</th>\n",
       "      <td>l7Rn6</td>\n",
       "      <td>chr7:97067194-97089714</td>\n",
       "      <td>159.055667</td>\n",
       "      <td>170.891333</td>\n",
       "      <td>126.653000</td>\n",
       "      <td>84.032633</td>\n",
       "      <td>126.660000</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>24348 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         tracking_id                      locus  CTCF-AID_untreated_mean_FPKM  \\\n",
       "0      0610005C13Rik     chr7:52823164-52845080                      0.784511   \n",
       "1      0610007P14Rik    chr12:87156404-87165495                     71.716133   \n",
       "2      0610009B22Rik    chr11:51498886-51502136                     69.529333   \n",
       "3      0610009L18Rik  chr11:120209991-120212504                      0.944842   \n",
       "4      0610009O20Rik    chr18:38398058-38422283                     17.565900   \n",
       "...              ...                        ...                           ...   \n",
       "24343            Zyx     chr6:42299826-42330557                      8.531693   \n",
       "24344          Zzef1    chr11:72609727-72740622                      2.597283   \n",
       "24345           Zzz3   chr3:152058966-152344396                      9.986867   \n",
       "24346              a   chr2:154839305-154876748                      0.087051   \n",
       "24347          l7Rn6     chr7:97067194-97089714                    159.055667   \n",
       "\n",
       "       CTCF-AID_1day_mean_FPKM  CTCF-AID_2days_mean_FPKM  \\\n",
       "0                     0.804255                  1.040841   \n",
       "1                    74.856200                 63.412767   \n",
       "2                    76.436433                 61.336067   \n",
       "3                     1.154941                  0.905838   \n",
       "4                    20.652933                 20.476800   \n",
       "...                        ...                       ...   \n",
       "24343                 8.734813                 11.727350   \n",
       "24344                 2.411157                  2.507930   \n",
       "24345                 8.012557                  7.069317   \n",
       "24346                 0.167859                  0.370351   \n",
       "24347               170.891333                126.653000   \n",
       "\n",
       "       CTCF-AID_4days_mean_FPKM  CTCF-AID_washoff_mean_FPKM 1day.1 2days.1  \\\n",
       "0                      2.176798                    1.209881     no      no   \n",
       "1                     59.250033                   67.995733     no      no   \n",
       "2                     38.937500                   58.353700     no      no   \n",
       "3                      1.097459                    0.889548     no      no   \n",
       "4                     20.633733                   18.857300     no      no   \n",
       "...                         ...                         ...    ...     ...   \n",
       "24343                 24.111000                   11.359220     no      no   \n",
       "24344                  3.121180                    2.856007     no      no   \n",
       "24345                  8.471867                   10.256167     no      no   \n",
       "24346                  0.402713                    0.090154     no      no   \n",
       "24347                 84.032633                  126.660000     no      no   \n",
       "\n",
       "      4days.1  ... not_sure  chrom   ensembl_gene_id_y  \\\n",
       "0          no  ...    29.36      7  ENSMUSG00000109644   \n",
       "1          no  ...      NaN    NaN                 NaN   \n",
       "2         yes  ...    31.26     11  ENSMUSG00000007777   \n",
       "3          no  ...    84.07     11  ENSMUSG00000043644   \n",
       "4          no  ...      NaN    NaN                 NaN   \n",
       "...       ...  ...      ...    ...                 ...   \n",
       "24343     yes  ...    20.64      6  ENSMUSG00000029860   \n",
       "24344      no  ...    44.56     11  ENSMUSG00000055670   \n",
       "24345      no  ...    77.16      3  ENSMUSG00000039068   \n",
       "24346      no  ...    76.83      2  ENSMUSG00000027596   \n",
       "24347     yes  ...      NaN    NaN                 NaN   \n",
       "\n",
       "                                  ensembl_transcript_ids  \\\n",
       "0      ENSMUST00000210965 ENSMUST00000209510 ENSMUST0...   \n",
       "1                                                    NaN   \n",
       "2                  ENSMUST00000007921 ENSMUST00000109098   \n",
       "3                                     ENSMUST00000143813   \n",
       "4                                                    NaN   \n",
       "...                                                  ...   \n",
       "24343  ENSMUST00000203401 ENSMUST00000204480 ENSMUST0...   \n",
       "24344  ENSMUST00000150551 ENSMUST00000145393 ENSMUST0...   \n",
       "24345  ENSMUST00000198468 ENSMUST00000089982 ENSMUST0...   \n",
       "24346  ENSMUST00000148402 ENSMUST00000109697 ENSMUST0...   \n",
       "24347                                                NaN   \n",
       "\n",
       "                                      ensembl_protein_id              biotype  \\\n",
       "0                                                    NaN          lncRNA gene   \n",
       "1                                                    NaN                  NaN   \n",
       "2                  ENSMUSP00000104726 ENSMUSP00000007921  protein coding gene   \n",
       "3                                                    NaN          lncRNA gene   \n",
       "4                                                    NaN                  NaN   \n",
       "...                                                  ...                  ...   \n",
       "24343  ENSMUSP00000145236 ENSMUSP00000145052 ENSMUSP0...  protein coding gene   \n",
       "24344  ENSMUSP00000130515 ENSMUSP00000147028 ENSMUSP0...  protein coding gene   \n",
       "24345  ENSMUSP00000087428 ENSMUSP00000101709 ENSMUSP0...  protein coding gene   \n",
       "24346  ENSMUSP00000122261 ENSMUSP00000029123 ENSMUSP0...  protein coding gene   \n",
       "24347                                                NaN                  NaN   \n",
       "\n",
       "             start          end  strand              possible_biotypes  \n",
       "0       45217219.0   45224600.0       -                   ncRNA|lncRNA  \n",
       "1              NaN          NaN     NaN                            NaN  \n",
       "2       51576213.0   51579701.0       -  protein_coding|protein-coding  \n",
       "3      120239504.0  120242016.0       +                   ncRNA|lncRNA  \n",
       "4              NaN          NaN     NaN                            NaN  \n",
       "...            ...          ...     ...                            ...  \n",
       "24343   42326564.0   42337147.0       +  protein_coding|protein-coding  \n",
       "24344   72687052.0   72817946.0       +  protein_coding|protein-coding  \n",
       "24345  152101110.0  152168463.0       +  protein_coding|protein-coding  \n",
       "24346  154792519.0  154892932.0       +  protein_coding|protein-coding  \n",
       "24347          NaN          NaN     NaN                            NaN  \n",
       "\n",
       "[24348 rows x 28 columns]"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fpkm.merge(mgi_ensembl, how='left', left_on='tracking_id', right_on='mgi_gene')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "b59877ab-5d51-4041-ac0b-213daada6a5f",
   "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>tracking_id</th>\n",
       "      <th>locus</th>\n",
       "      <th>CTCF-AID_untreated_mean_FPKM</th>\n",
       "      <th>CTCF-AID_1day_mean_FPKM</th>\n",
       "      <th>CTCF-AID_2days_mean_FPKM</th>\n",
       "      <th>CTCF-AID_4days_mean_FPKM</th>\n",
       "      <th>CTCF-AID_washoff_mean_FPKM</th>\n",
       "      <th>1day.1</th>\n",
       "      <th>2days.1</th>\n",
       "      <th>4days.1</th>\n",
       "      <th>2days_washoff_2days.1</th>\n",
       "      <th>WT_untreated_mean_FPKM</th>\n",
       "      <th>WT_2days_mean_FPKM</th>\n",
       "      <th>WT_4days_mean_FPKM</th>\n",
       "      <th>ensembl_gene_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0610005C13Rik</td>\n",
       "      <td>chr7:52823164-52845080</td>\n",
       "      <td>0.784511</td>\n",
       "      <td>0.804255</td>\n",
       "      <td>1.040841</td>\n",
       "      <td>2.176798</td>\n",
       "      <td>1.209881</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0.531110</td>\n",
       "      <td>0.539697</td>\n",
       "      <td>0.414068</td>\n",
       "      <td>ENSMUSG00000109644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0610007P14Rik</td>\n",
       "      <td>chr12:87156404-87165495</td>\n",
       "      <td>71.716133</td>\n",
       "      <td>74.856200</td>\n",
       "      <td>63.412767</td>\n",
       "      <td>59.250033</td>\n",
       "      <td>67.995733</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>57.020867</td>\n",
       "      <td>49.716167</td>\n",
       "      <td>57.023633</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0610009B22Rik</td>\n",
       "      <td>chr11:51498886-51502136</td>\n",
       "      <td>69.529333</td>\n",
       "      <td>76.436433</td>\n",
       "      <td>61.336067</td>\n",
       "      <td>38.937500</td>\n",
       "      <td>58.353700</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>58.497000</td>\n",
       "      <td>46.990267</td>\n",
       "      <td>58.072767</td>\n",
       "      <td>ENSMUSG00000007777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0610009L18Rik</td>\n",
       "      <td>chr11:120209991-120212504</td>\n",
       "      <td>0.944842</td>\n",
       "      <td>1.154941</td>\n",
       "      <td>0.905838</td>\n",
       "      <td>1.097459</td>\n",
       "      <td>0.889548</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>1.180704</td>\n",
       "      <td>1.977405</td>\n",
       "      <td>0.825640</td>\n",
       "      <td>ENSMUSG00000043644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0610009O20Rik</td>\n",
       "      <td>chr18:38398058-38422283</td>\n",
       "      <td>17.565900</td>\n",
       "      <td>20.652933</td>\n",
       "      <td>20.476800</td>\n",
       "      <td>20.633733</td>\n",
       "      <td>18.857300</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>18.864133</td>\n",
       "      <td>18.413433</td>\n",
       "      <td>18.779333</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0610010B08Rik</td>\n",
       "      <td>chr2:174952492-175261278</td>\n",
       "      <td>0.057933</td>\n",
       "      <td>0.051192</td>\n",
       "      <td>0.080296</td>\n",
       "      <td>0.091421</td>\n",
       "      <td>0.090390</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0.071852</td>\n",
       "      <td>0.105181</td>\n",
       "      <td>0.096969</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0610010F05Rik</td>\n",
       "      <td>chr11:23473775-23533631</td>\n",
       "      <td>4.500230</td>\n",
       "      <td>4.654253</td>\n",
       "      <td>3.784197</td>\n",
       "      <td>3.342397</td>\n",
       "      <td>4.257007</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>4.556360</td>\n",
       "      <td>3.955583</td>\n",
       "      <td>4.441780</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0610010K14Rik</td>\n",
       "      <td>chr11:70048705-70051416</td>\n",
       "      <td>115.794100</td>\n",
       "      <td>134.728000</td>\n",
       "      <td>124.419633</td>\n",
       "      <td>109.027433</td>\n",
       "      <td>95.227500</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>104.590300</td>\n",
       "      <td>129.307667</td>\n",
       "      <td>99.449367</td>\n",
       "      <td>ENSMUSG00000020831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0610011F06Rik</td>\n",
       "      <td>chr17:26012444-26014108</td>\n",
       "      <td>12.449400</td>\n",
       "      <td>12.249967</td>\n",
       "      <td>11.492490</td>\n",
       "      <td>12.151867</td>\n",
       "      <td>11.099847</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>10.334770</td>\n",
       "      <td>14.981610</td>\n",
       "      <td>11.079557</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0610012G03Rik</td>\n",
       "      <td>chr16:31947136-31948607</td>\n",
       "      <td>34.492833</td>\n",
       "      <td>40.396067</td>\n",
       "      <td>36.419533</td>\n",
       "      <td>30.946500</td>\n",
       "      <td>30.611233</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>26.746667</td>\n",
       "      <td>35.935333</td>\n",
       "      <td>23.776233</td>\n",
       "      <td>ENSMUSG00000107002</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     tracking_id                      locus  CTCF-AID_untreated_mean_FPKM  \\\n",
       "0  0610005C13Rik     chr7:52823164-52845080                      0.784511   \n",
       "1  0610007P14Rik    chr12:87156404-87165495                     71.716133   \n",
       "2  0610009B22Rik    chr11:51498886-51502136                     69.529333   \n",
       "3  0610009L18Rik  chr11:120209991-120212504                      0.944842   \n",
       "4  0610009O20Rik    chr18:38398058-38422283                     17.565900   \n",
       "5  0610010B08Rik   chr2:174952492-175261278                      0.057933   \n",
       "6  0610010F05Rik    chr11:23473775-23533631                      4.500230   \n",
       "7  0610010K14Rik    chr11:70048705-70051416                    115.794100   \n",
       "8  0610011F06Rik    chr17:26012444-26014108                     12.449400   \n",
       "9  0610012G03Rik    chr16:31947136-31948607                     34.492833   \n",
       "\n",
       "   CTCF-AID_1day_mean_FPKM  CTCF-AID_2days_mean_FPKM  \\\n",
       "0                 0.804255                  1.040841   \n",
       "1                74.856200                 63.412767   \n",
       "2                76.436433                 61.336067   \n",
       "3                 1.154941                  0.905838   \n",
       "4                20.652933                 20.476800   \n",
       "5                 0.051192                  0.080296   \n",
       "6                 4.654253                  3.784197   \n",
       "7               134.728000                124.419633   \n",
       "8                12.249967                 11.492490   \n",
       "9                40.396067                 36.419533   \n",
       "\n",
       "   CTCF-AID_4days_mean_FPKM  CTCF-AID_washoff_mean_FPKM 1day.1 2days.1  \\\n",
       "0                  2.176798                    1.209881     no      no   \n",
       "1                 59.250033                   67.995733     no      no   \n",
       "2                 38.937500                   58.353700     no      no   \n",
       "3                  1.097459                    0.889548     no      no   \n",
       "4                 20.633733                   18.857300     no      no   \n",
       "5                  0.091421                    0.090390     no      no   \n",
       "6                  3.342397                    4.257007     no      no   \n",
       "7                109.027433                   95.227500     no      no   \n",
       "8                 12.151867                   11.099847     no      no   \n",
       "9                 30.946500                   30.611233     no      no   \n",
       "\n",
       "  4days.1 2days_washoff_2days.1  WT_untreated_mean_FPKM  WT_2days_mean_FPKM  \\\n",
       "0      no                    no                0.531110            0.539697   \n",
       "1      no                    no               57.020867           49.716167   \n",
       "2     yes                    no               58.497000           46.990267   \n",
       "3      no                    no                1.180704            1.977405   \n",
       "4      no                    no               18.864133           18.413433   \n",
       "5      no                    no                0.071852            0.105181   \n",
       "6      no                    no                4.556360            3.955583   \n",
       "7      no                    no              104.590300          129.307667   \n",
       "8      no                    no               10.334770           14.981610   \n",
       "9      no                    no               26.746667           35.935333   \n",
       "\n",
       "   WT_4days_mean_FPKM     ensembl_gene_id  \n",
       "0            0.414068  ENSMUSG00000109644  \n",
       "1           57.023633                 NaN  \n",
       "2           58.072767  ENSMUSG00000007777  \n",
       "3            0.825640  ENSMUSG00000043644  \n",
       "4           18.779333                 NaN  \n",
       "5            0.096969                 NaN  \n",
       "6            4.441780                 NaN  \n",
       "7           99.449367  ENSMUSG00000020831  \n",
       "8           11.079557                 NaN  \n",
       "9           23.776233  ENSMUSG00000107002  "
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fpkm['ensembl_gene_id'] = fpkm.merge(mgi_ensembl, how='left', left_on='tracking_id', right_on='mgi_gene')['ensembl_gene_id']\n",
    "fpkm.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "6ecbd8a9-1940-4330-ab58-2ecc8141d23a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Out of 24315 genes labeled by mgi gene name in Nora results table, 1562 are missing a corresponding ensembl id, 6.424018095825621%.\n"
     ]
    }
   ],
   "source": [
    "# how many are missing\n",
    "missing = fpkm['ensembl_gene_id'].isna().sum()\n",
    "tot = fpkm.shape[0]\n",
    "\n",
    "print(\"Out of {} genes labeled by mgi gene name in Nora results table, {} are missing a corresponding ensembl id, {}%.\".format(tot, \n",
    "                                                                                                                               missing,\n",
    "                                                                                                                               (float) (missing/tot) * 100))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1add463-77ee-4d25-b5b5-cfb0d9f50954",
   "metadata": {},
   "source": [
    "For the purposes of checking/verifying consistency, we'll just toss the un-matched ones. If things look really weird, find a better reference?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "4295a1f5-8e9f-4cc2-8e19-2fb4e6ad4162",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Out of 24315 genes in results table, 20188 had a corresponding TSS (so 4127 were dropped)\n"
     ]
    }
   ],
   "source": [
    "fpkm = fpkm.merge(tss_df.drop_duplicates(['gene_id']), how='inner', left_on='ensembl_gene_id', right_on='gene_id', suffixes=('_data', '_tss')) \n",
    "\n",
    "new_len = fpkm.shape[0]\n",
    "\n",
    "print(\"Out of {} genes in results table, {} had a corresponding TSS (so {} were dropped)\".format(tot, new_len, tot-new_len))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "b167d3c5-717a-487d-98c9-572de24c101c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tracking_id</th>\n",
       "      <th>locus</th>\n",
       "      <th>CTCF-AID_untreated_mean_FPKM</th>\n",
       "      <th>CTCF-AID_1day_mean_FPKM</th>\n",
       "      <th>CTCF-AID_2days_mean_FPKM</th>\n",
       "      <th>CTCF-AID_4days_mean_FPKM</th>\n",
       "      <th>CTCF-AID_washoff_mean_FPKM</th>\n",
       "      <th>1day.1</th>\n",
       "      <th>2days.1</th>\n",
       "      <th>4days.1</th>\n",
       "      <th>...</th>\n",
       "      <th>chrom</th>\n",
       "      <th>source</th>\n",
       "      <th>feature</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>score</th>\n",
       "      <th>strand</th>\n",
       "      <th>frame</th>\n",
       "      <th>transcript_id</th>\n",
       "      <th>gene_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0610009B22Rik</td>\n",
       "      <td>chr11:51498886-51502136</td>\n",
       "      <td>69.529333</td>\n",
       "      <td>76.436433</td>\n",
       "      <td>61.336067</td>\n",
       "      <td>38.937500</td>\n",
       "      <td>58.353700</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>...</td>\n",
       "      <td>chr11</td>\n",
       "      <td>gffutils_derived</td>\n",
       "      <td>transcript</td>\n",
       "      <td>51502156</td>\n",
       "      <td>51502156</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-</td>\n",
       "      <td>0</td>\n",
       "      <td>ENSMUST00000007921</td>\n",
       "      <td>ENSMUSG00000007777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0610009L18Rik</td>\n",
       "      <td>chr11:120209991-120212504</td>\n",
       "      <td>0.944842</td>\n",
       "      <td>1.154941</td>\n",
       "      <td>0.905838</td>\n",
       "      <td>1.097459</td>\n",
       "      <td>0.889548</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>chr11</td>\n",
       "      <td>gffutils_derived</td>\n",
       "      <td>transcript</td>\n",
       "      <td>120209991</td>\n",
       "      <td>120209991</td>\n",
       "      <td>NaN</td>\n",
       "      <td>+</td>\n",
       "      <td>0</td>\n",
       "      <td>ENSMUST00000143813</td>\n",
       "      <td>ENSMUSG00000043644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0610010K14Rik</td>\n",
       "      <td>chr11:70048705-70051416</td>\n",
       "      <td>115.794100</td>\n",
       "      <td>134.728000</td>\n",
       "      <td>124.419633</td>\n",
       "      <td>109.027433</td>\n",
       "      <td>95.227500</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>chr11</td>\n",
       "      <td>gffutils_derived</td>\n",
       "      <td>transcript</td>\n",
       "      <td>70051417</td>\n",
       "      <td>70051417</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-</td>\n",
       "      <td>0</td>\n",
       "      <td>ENSMUST00000021180</td>\n",
       "      <td>ENSMUSG00000020831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0610030E20Rik</td>\n",
       "      <td>chr6:72297310-72303154</td>\n",
       "      <td>3.961530</td>\n",
       "      <td>4.223233</td>\n",
       "      <td>4.699287</td>\n",
       "      <td>5.407897</td>\n",
       "      <td>4.271537</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>chr6</td>\n",
       "      <td>gffutils_derived</td>\n",
       "      <td>transcript</td>\n",
       "      <td>72297310</td>\n",
       "      <td>72297310</td>\n",
       "      <td>NaN</td>\n",
       "      <td>+</td>\n",
       "      <td>0</td>\n",
       "      <td>ENSMUST00000077783</td>\n",
       "      <td>ENSMUSG00000058706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0610039K10Rik</td>\n",
       "      <td>chr2:163449008-163471536</td>\n",
       "      <td>0.337451</td>\n",
       "      <td>0.253110</td>\n",
       "      <td>0.320109</td>\n",
       "      <td>0.318655</td>\n",
       "      <td>0.178876</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>chr2</td>\n",
       "      <td>gffutils_derived</td>\n",
       "      <td>transcript</td>\n",
       "      <td>163470585</td>\n",
       "      <td>163470585</td>\n",
       "      <td>NaN</td>\n",
       "      <td>+</td>\n",
       "      <td>0</td>\n",
       "      <td>ENSMUST00000076667</td>\n",
       "      <td>ENSMUSG00000058812</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     tracking_id                      locus  CTCF-AID_untreated_mean_FPKM  \\\n",
       "0  0610009B22Rik    chr11:51498886-51502136                     69.529333   \n",
       "1  0610009L18Rik  chr11:120209991-120212504                      0.944842   \n",
       "2  0610010K14Rik    chr11:70048705-70051416                    115.794100   \n",
       "3  0610030E20Rik     chr6:72297310-72303154                      3.961530   \n",
       "4  0610039K10Rik   chr2:163449008-163471536                      0.337451   \n",
       "\n",
       "   CTCF-AID_1day_mean_FPKM  CTCF-AID_2days_mean_FPKM  \\\n",
       "0                76.436433                 61.336067   \n",
       "1                 1.154941                  0.905838   \n",
       "2               134.728000                124.419633   \n",
       "3                 4.223233                  4.699287   \n",
       "4                 0.253110                  0.320109   \n",
       "\n",
       "   CTCF-AID_4days_mean_FPKM  CTCF-AID_washoff_mean_FPKM 1day.1 2days.1  \\\n",
       "0                 38.937500                   58.353700     no      no   \n",
       "1                  1.097459                    0.889548     no      no   \n",
       "2                109.027433                   95.227500     no      no   \n",
       "3                  5.407897                    4.271537     no      no   \n",
       "4                  0.318655                    0.178876     no      no   \n",
       "\n",
       "  4days.1  ...  chrom            source     feature      start        end  \\\n",
       "0     yes  ...  chr11  gffutils_derived  transcript   51502156   51502156   \n",
       "1      no  ...  chr11  gffutils_derived  transcript  120209991  120209991   \n",
       "2      no  ...  chr11  gffutils_derived  transcript   70051417   70051417   \n",
       "3      no  ...   chr6  gffutils_derived  transcript   72297310   72297310   \n",
       "4      no  ...   chr2  gffutils_derived  transcript  163470585  163470585   \n",
       "\n",
       "  score strand frame       transcript_id             gene_id  \n",
       "0   NaN      -     0  ENSMUST00000007921  ENSMUSG00000007777  \n",
       "1   NaN      +     0  ENSMUST00000143813  ENSMUSG00000043644  \n",
       "2   NaN      -     0  ENSMUST00000021180  ENSMUSG00000020831  \n",
       "3   NaN      +     0  ENSMUST00000077783  ENSMUSG00000058706  \n",
       "4   NaN      +     0  ENSMUST00000076667  ENSMUSG00000058812  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fpkm.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "dd6ae488-1bb6-4245-9596-46b3ff10c897",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get distance between TSSes and the closest enhancer\n",
    "closest_intervals_nora = bf.closest(fpkm, enhancer_coords, suffixes=('_de_tss','_enhancer'))\n",
    "fpkm['distance_to_enhancer'] = closest_intervals_nora['distance']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "17a57616-b9ae-40e2-b93d-74e3d5614197",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tracking_id</th>\n",
       "      <th>locus</th>\n",
       "      <th>CTCF-AID_untreated_mean_FPKM</th>\n",
       "      <th>CTCF-AID_1day_mean_FPKM</th>\n",
       "      <th>CTCF-AID_2days_mean_FPKM</th>\n",
       "      <th>CTCF-AID_4days_mean_FPKM</th>\n",
       "      <th>CTCF-AID_washoff_mean_FPKM</th>\n",
       "      <th>1day.1</th>\n",
       "      <th>2days.1</th>\n",
       "      <th>4days.1</th>\n",
       "      <th>...</th>\n",
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       "      <th>feature</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
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       "      <th>strand</th>\n",
       "      <th>frame</th>\n",
       "      <th>transcript_id</th>\n",
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       "      <th>distance_to_enhancer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0610009B22Rik</td>\n",
       "      <td>chr11:51498886-51502136</td>\n",
       "      <td>69.529333</td>\n",
       "      <td>76.436433</td>\n",
       "      <td>61.336067</td>\n",
       "      <td>38.937500</td>\n",
       "      <td>58.353700</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>...</td>\n",
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       "      <td>51502156</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-</td>\n",
       "      <td>0</td>\n",
       "      <td>ENSMUST00000007921</td>\n",
       "      <td>ENSMUSG00000007777</td>\n",
       "      <td>202016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0610009L18Rik</td>\n",
       "      <td>chr11:120209991-120212504</td>\n",
       "      <td>0.944842</td>\n",
       "      <td>1.154941</td>\n",
       "      <td>0.905838</td>\n",
       "      <td>1.097459</td>\n",
       "      <td>0.889548</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>gffutils_derived</td>\n",
       "      <td>transcript</td>\n",
       "      <td>120209991</td>\n",
       "      <td>120209991</td>\n",
       "      <td>NaN</td>\n",
       "      <td>+</td>\n",
       "      <td>0</td>\n",
       "      <td>ENSMUST00000143813</td>\n",
       "      <td>ENSMUSG00000043644</td>\n",
       "      <td>3158057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0610010K14Rik</td>\n",
       "      <td>chr11:70048705-70051416</td>\n",
       "      <td>115.794100</td>\n",
       "      <td>134.728000</td>\n",
       "      <td>124.419633</td>\n",
       "      <td>109.027433</td>\n",
       "      <td>95.227500</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>gffutils_derived</td>\n",
       "      <td>transcript</td>\n",
       "      <td>70051417</td>\n",
       "      <td>70051417</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-</td>\n",
       "      <td>0</td>\n",
       "      <td>ENSMUST00000021180</td>\n",
       "      <td>ENSMUSG00000020831</td>\n",
       "      <td>233462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0610030E20Rik</td>\n",
       "      <td>chr6:72297310-72303154</td>\n",
       "      <td>3.961530</td>\n",
       "      <td>4.223233</td>\n",
       "      <td>4.699287</td>\n",
       "      <td>5.407897</td>\n",
       "      <td>4.271537</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>gffutils_derived</td>\n",
       "      <td>transcript</td>\n",
       "      <td>72297310</td>\n",
       "      <td>72297310</td>\n",
       "      <td>NaN</td>\n",
       "      <td>+</td>\n",
       "      <td>0</td>\n",
       "      <td>ENSMUST00000077783</td>\n",
       "      <td>ENSMUSG00000058706</td>\n",
       "      <td>312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0610039K10Rik</td>\n",
       "      <td>chr2:163449008-163471536</td>\n",
       "      <td>0.337451</td>\n",
       "      <td>0.253110</td>\n",
       "      <td>0.320109</td>\n",
       "      <td>0.318655</td>\n",
       "      <td>0.178876</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>gffutils_derived</td>\n",
       "      <td>transcript</td>\n",
       "      <td>163470585</td>\n",
       "      <td>163470585</td>\n",
       "      <td>NaN</td>\n",
       "      <td>+</td>\n",
       "      <td>0</td>\n",
       "      <td>ENSMUST00000076667</td>\n",
       "      <td>ENSMUSG00000058812</td>\n",
       "      <td>742451</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20183</th>\n",
       "      <td>Zyx</td>\n",
       "      <td>chr6:42299826-42330557</td>\n",
       "      <td>8.531693</td>\n",
       "      <td>8.734813</td>\n",
       "      <td>11.727350</td>\n",
       "      <td>24.111000</td>\n",
       "      <td>11.359220</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>...</td>\n",
       "      <td>gffutils_derived</td>\n",
       "      <td>transcript</td>\n",
       "      <td>23737822</td>\n",
       "      <td>23737822</td>\n",
       "      <td>NaN</td>\n",
       "      <td>+</td>\n",
       "      <td>0</td>\n",
       "      <td>ENSMUST00000095595</td>\n",
       "      <td>ENSMUSG00000023902</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20184</th>\n",
       "      <td>Zzef1</td>\n",
       "      <td>chr11:72609727-72740622</td>\n",
       "      <td>2.597283</td>\n",
       "      <td>2.411157</td>\n",
       "      <td>2.507930</td>\n",
       "      <td>3.121180</td>\n",
       "      <td>2.856007</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>gffutils_derived</td>\n",
       "      <td>transcript</td>\n",
       "      <td>21454695</td>\n",
       "      <td>21454695</td>\n",
       "      <td>NaN</td>\n",
       "      <td>+</td>\n",
       "      <td>0</td>\n",
       "      <td>ENSMUST00000053293</td>\n",
       "      <td>ENSMUSG00000036721</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20185</th>\n",
       "      <td>Zzz3</td>\n",
       "      <td>chr3:152058966-152344396</td>\n",
       "      <td>9.986867</td>\n",
       "      <td>8.012557</td>\n",
       "      <td>7.069317</td>\n",
       "      <td>8.471867</td>\n",
       "      <td>10.256167</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>gffutils_derived</td>\n",
       "      <td>transcript</td>\n",
       "      <td>13361008</td>\n",
       "      <td>13361008</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-</td>\n",
       "      <td>0</td>\n",
       "      <td>ENSMUST00000046245</td>\n",
       "      <td>ENSMUSG00000070822</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20186</th>\n",
       "      <td>a</td>\n",
       "      <td>chr2:154839305-154876748</td>\n",
       "      <td>0.087051</td>\n",
       "      <td>0.167859</td>\n",
       "      <td>0.370351</td>\n",
       "      <td>0.402713</td>\n",
       "      <td>0.090154</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>gffutils_derived</td>\n",
       "      <td>transcript</td>\n",
       "      <td>88006993</td>\n",
       "      <td>88006993</td>\n",
       "      <td>NaN</td>\n",
       "      <td>+</td>\n",
       "      <td>0</td>\n",
       "      <td>ENSMUST00000044115</td>\n",
       "      <td>ENSMUSG00000038797</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20187</th>\n",
       "      <td>l7Rn6</td>\n",
       "      <td>chr7:97067194-97089714</td>\n",
       "      <td>159.055667</td>\n",
       "      <td>170.891333</td>\n",
       "      <td>126.653000</td>\n",
       "      <td>84.032633</td>\n",
       "      <td>126.660000</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>...</td>\n",
       "      <td>gffutils_derived</td>\n",
       "      <td>transcript</td>\n",
       "      <td>128267207</td>\n",
       "      <td>128267207</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-</td>\n",
       "      <td>0</td>\n",
       "      <td>ENSMUST00000084276</td>\n",
       "      <td>ENSMUSG00000061894</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20188 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         tracking_id                      locus  CTCF-AID_untreated_mean_FPKM  \\\n",
       "0      0610009B22Rik    chr11:51498886-51502136                     69.529333   \n",
       "1      0610009L18Rik  chr11:120209991-120212504                      0.944842   \n",
       "2      0610010K14Rik    chr11:70048705-70051416                    115.794100   \n",
       "3      0610030E20Rik     chr6:72297310-72303154                      3.961530   \n",
       "4      0610039K10Rik   chr2:163449008-163471536                      0.337451   \n",
       "...              ...                        ...                           ...   \n",
       "20183            Zyx     chr6:42299826-42330557                      8.531693   \n",
       "20184          Zzef1    chr11:72609727-72740622                      2.597283   \n",
       "20185           Zzz3   chr3:152058966-152344396                      9.986867   \n",
       "20186              a   chr2:154839305-154876748                      0.087051   \n",
       "20187          l7Rn6     chr7:97067194-97089714                    159.055667   \n",
       "\n",
       "       CTCF-AID_1day_mean_FPKM  CTCF-AID_2days_mean_FPKM  \\\n",
       "0                    76.436433                 61.336067   \n",
       "1                     1.154941                  0.905838   \n",
       "2                   134.728000                124.419633   \n",
       "3                     4.223233                  4.699287   \n",
       "4                     0.253110                  0.320109   \n",
       "...                        ...                       ...   \n",
       "20183                 8.734813                 11.727350   \n",
       "20184                 2.411157                  2.507930   \n",
       "20185                 8.012557                  7.069317   \n",
       "20186                 0.167859                  0.370351   \n",
       "20187               170.891333                126.653000   \n",
       "\n",
       "       CTCF-AID_4days_mean_FPKM  CTCF-AID_washoff_mean_FPKM 1day.1 2days.1  \\\n",
       "0                     38.937500                   58.353700     no      no   \n",
       "1                      1.097459                    0.889548     no      no   \n",
       "2                    109.027433                   95.227500     no      no   \n",
       "3                      5.407897                    4.271537     no      no   \n",
       "4                      0.318655                    0.178876     no      no   \n",
       "...                         ...                         ...    ...     ...   \n",
       "20183                 24.111000                   11.359220     no      no   \n",
       "20184                  3.121180                    2.856007     no      no   \n",
       "20185                  8.471867                   10.256167     no      no   \n",
       "20186                  0.402713                    0.090154     no      no   \n",
       "20187                 84.032633                  126.660000     no      no   \n",
       "\n",
       "      4days.1  ...            source     feature      start        end score  \\\n",
       "0         yes  ...  gffutils_derived  transcript   51502156   51502156   NaN   \n",
       "1          no  ...  gffutils_derived  transcript  120209991  120209991   NaN   \n",
       "2          no  ...  gffutils_derived  transcript   70051417   70051417   NaN   \n",
       "3          no  ...  gffutils_derived  transcript   72297310   72297310   NaN   \n",
       "4          no  ...  gffutils_derived  transcript  163470585  163470585   NaN   \n",
       "...       ...  ...               ...         ...        ...        ...   ...   \n",
       "20183     yes  ...  gffutils_derived  transcript   23737822   23737822   NaN   \n",
       "20184      no  ...  gffutils_derived  transcript   21454695   21454695   NaN   \n",
       "20185      no  ...  gffutils_derived  transcript   13361008   13361008   NaN   \n",
       "20186      no  ...  gffutils_derived  transcript   88006993   88006993   NaN   \n",
       "20187     yes  ...  gffutils_derived  transcript  128267207  128267207   NaN   \n",
       "\n",
       "      strand frame       transcript_id             gene_id  \\\n",
       "0          -     0  ENSMUST00000007921  ENSMUSG00000007777   \n",
       "1          +     0  ENSMUST00000143813  ENSMUSG00000043644   \n",
       "2          -     0  ENSMUST00000021180  ENSMUSG00000020831   \n",
       "3          +     0  ENSMUST00000077783  ENSMUSG00000058706   \n",
       "4          +     0  ENSMUST00000076667  ENSMUSG00000058812   \n",
       "...      ...   ...                 ...                 ...   \n",
       "20183      +     0  ENSMUST00000095595  ENSMUSG00000023902   \n",
       "20184      +     0  ENSMUST00000053293  ENSMUSG00000036721   \n",
       "20185      -     0  ENSMUST00000046245  ENSMUSG00000070822   \n",
       "20186      +     0  ENSMUST00000044115  ENSMUSG00000038797   \n",
       "20187      -     0  ENSMUST00000084276  ENSMUSG00000061894   \n",
       "\n",
       "       distance_to_enhancer  \n",
       "0                    202016  \n",
       "1                   3158057  \n",
       "2                    233462  \n",
       "3                       312  \n",
       "4                    742451  \n",
       "...                     ...  \n",
       "20183                  <NA>  \n",
       "20184                  <NA>  \n",
       "20185                  <NA>  \n",
       "20186                  <NA>  \n",
       "20187                  <NA>  \n",
       "\n",
       "[20188 rows x 26 columns]"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fpkm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "0aa365ac-67c9-41d7-aee6-22436f8341db",
   "metadata": {},
   "outputs": [],
   "source": [
    "# todo:\n",
    "\n",
    "# assign TSSes to fpkm\n",
    "# get distance between these TSSes and the enhancer data\n",
    "# for each day condition:\n",
    "    # filter for 'sig' by 1day.1, 2days.1, 4days.1\n",
    "    # graph the distances, sorted in each of these three groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "4e659cd1-b76c-4032-a278-775249a10499",
   "metadata": {},
   "outputs": [],
   "source": [
    "up = fpkm.iloc[np.where((fpkm['1day.1'] == 'yes') & ((fpkm['CTCF-AID_untreated_mean_FPKM'] - fpkm['CTCF-AID_1day_mean_FPKM']) < 0))]\n",
    "down = fpkm.iloc[np.where((fpkm['1day.1'] == 'yes') & ((fpkm['CTCF-AID_untreated_mean_FPKM'] - fpkm['CTCF-AID_1day_mean_FPKM']) > 0))]\n",
    "non = fpkm.iloc[np.where(fpkm['1day.1'] == 'no')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "ec22bc75-0840-49dd-9c14-c9846e4e14d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fae5ada8c70>]"
      ]
     },
     "execution_count": 147,
     "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": [
    "plt.plot(np.log10(up['distance_to_enhancer'].sort_values()), np.arange(0, len(up), 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "dab63391-9ad7-4928-9488-29a9ada13d08",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of genes that have distance from TSS to enhancer = 0 is : 0\n",
      "Number of genes that did not find a closest enhancer: 10\n"
     ]
    }
   ],
   "source": [
    "# How many?\n",
    "print(\"Number of genes that have distance from TSS to enhancer = 0 is : \" + str(len(fpkm[fpkm['distance_to_enhancer'] == 0]))) \n",
    "\n",
    "# Changing these values to = 1 \n",
    "fpkm.loc[fpkm['distance_to_enhancer'] == 0, 'distance_to_enhancer'] = 1\n",
    "\n",
    "# How many have no enhancer to call home\n",
    "print(\"Number of genes that did not find a closest enhancer: \" + str(len(fpkm[pd.isna(fpkm['distance_to_enhancer'])])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "16f35292-2247-4ef3-99f6-ef5880a7c5b5",
   "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>tracking_id</th>\n",
       "      <th>locus</th>\n",
       "      <th>CTCF-AID_untreated_mean_FPKM</th>\n",
       "      <th>CTCF-AID_1day_mean_FPKM</th>\n",
       "      <th>CTCF-AID_2days_mean_FPKM</th>\n",
       "      <th>CTCF-AID_4days_mean_FPKM</th>\n",
       "      <th>CTCF-AID_washoff_mean_FPKM</th>\n",
       "      <th>1day.1</th>\n",
       "      <th>2days.1</th>\n",
       "      <th>4days.1</th>\n",
       "      <th>...</th>\n",
       "      <th>source</th>\n",
       "      <th>feature</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>score</th>\n",
       "      <th>strand</th>\n",
       "      <th>frame</th>\n",
       "      <th>transcript_id</th>\n",
       "      <th>gene_id</th>\n",
       "      <th>distance_to_enhancer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20178</th>\n",
       "      <td>Zxda</td>\n",
       "      <td>chrX:92036623-92043628</td>\n",
       "      <td>0.065962</td>\n",
       "      <td>0.127417</td>\n",
       "      <td>0.120941</td>\n",
       "      <td>0.171184</td>\n",
       "      <td>0.077726</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>gffutils_derived</td>\n",
       "      <td>transcript</td>\n",
       "      <td>140141266</td>\n",
       "      <td>140141266</td>\n",
       "      <td>NaN</td>\n",
       "      <td>+</td>\n",
       "      <td>0</td>\n",
       "      <td>ENSMUST00000033265</td>\n",
       "      <td>ENSMUSG00000030967</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20179</th>\n",
       "      <td>Zxdb</td>\n",
       "      <td>chrX:91969907-91975530</td>\n",
       "      <td>0.185390</td>\n",
       "      <td>0.117669</td>\n",
       "      <td>0.193258</td>\n",
       "      <td>0.247512</td>\n",
       "      <td>0.138311</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>gffutils_derived</td>\n",
       "      <td>transcript</td>\n",
       "      <td>157197322</td>\n",
       "      <td>157197322</td>\n",
       "      <td>NaN</td>\n",
       "      <td>+</td>\n",
       "      <td>0</td>\n",
       "      <td>ENSMUST00000029831</td>\n",
       "      <td>ENSMUSG00000028180</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20180</th>\n",
       "      <td>Zxdc</td>\n",
       "      <td>chr6:90319487-90353480</td>\n",
       "      <td>4.497293</td>\n",
       "      <td>4.802807</td>\n",
       "      <td>5.840457</td>\n",
       "      <td>6.868297</td>\n",
       "      <td>5.241337</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>...</td>\n",
       "      <td>gffutils_derived</td>\n",
       "      <td>transcript</td>\n",
       "      <td>129999031</td>\n",
       "      <td>129999031</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-</td>\n",
       "      <td>0</td>\n",
       "      <td>ENSMUST00000037426</td>\n",
       "      <td>ENSMUSG00000036086</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20181</th>\n",
       "      <td>Zyg11a</td>\n",
       "      <td>chr4:107854538-107890527</td>\n",
       "      <td>2.508323</td>\n",
       "      <td>3.991237</td>\n",
       "      <td>3.020370</td>\n",
       "      <td>3.450447</td>\n",
       "      <td>4.242610</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>gffutils_derived</td>\n",
       "      <td>transcript</td>\n",
       "      <td>22873214</td>\n",
       "      <td>22873214</td>\n",
       "      <td>NaN</td>\n",
       "      <td>+</td>\n",
       "      <td>0</td>\n",
       "      <td>ENSMUST00000049506</td>\n",
       "      <td>ENSMUSG00000044068</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20182</th>\n",
       "      <td>Zyg11b</td>\n",
       "      <td>chr4:107900359-107973695</td>\n",
       "      <td>4.213553</td>\n",
       "      <td>3.829030</td>\n",
       "      <td>3.525300</td>\n",
       "      <td>4.269620</td>\n",
       "      <td>4.373337</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>gffutils_derived</td>\n",
       "      <td>transcript</td>\n",
       "      <td>160396531</td>\n",
       "      <td>160396531</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-</td>\n",
       "      <td>0</td>\n",
       "      <td>ENSMUST00000033736</td>\n",
       "      <td>ENSMUSG00000031370</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20183</th>\n",
       "      <td>Zyx</td>\n",
       "      <td>chr6:42299826-42330557</td>\n",
       "      <td>8.531693</td>\n",
       "      <td>8.734813</td>\n",
       "      <td>11.727350</td>\n",
       "      <td>24.111000</td>\n",
       "      <td>11.359220</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>...</td>\n",
       "      <td>gffutils_derived</td>\n",
       "      <td>transcript</td>\n",
       "      <td>23737822</td>\n",
       "      <td>23737822</td>\n",
       "      <td>NaN</td>\n",
       "      <td>+</td>\n",
       "      <td>0</td>\n",
       "      <td>ENSMUST00000095595</td>\n",
       "      <td>ENSMUSG00000023902</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20184</th>\n",
       "      <td>Zzef1</td>\n",
       "      <td>chr11:72609727-72740622</td>\n",
       "      <td>2.597283</td>\n",
       "      <td>2.411157</td>\n",
       "      <td>2.507930</td>\n",
       "      <td>3.121180</td>\n",
       "      <td>2.856007</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>gffutils_derived</td>\n",
       "      <td>transcript</td>\n",
       "      <td>21454695</td>\n",
       "      <td>21454695</td>\n",
       "      <td>NaN</td>\n",
       "      <td>+</td>\n",
       "      <td>0</td>\n",
       "      <td>ENSMUST00000053293</td>\n",
       "      <td>ENSMUSG00000036721</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20185</th>\n",
       "      <td>Zzz3</td>\n",
       "      <td>chr3:152058966-152344396</td>\n",
       "      <td>9.986867</td>\n",
       "      <td>8.012557</td>\n",
       "      <td>7.069317</td>\n",
       "      <td>8.471867</td>\n",
       "      <td>10.256167</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>gffutils_derived</td>\n",
       "      <td>transcript</td>\n",
       "      <td>13361008</td>\n",
       "      <td>13361008</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-</td>\n",
       "      <td>0</td>\n",
       "      <td>ENSMUST00000046245</td>\n",
       "      <td>ENSMUSG00000070822</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20186</th>\n",
       "      <td>a</td>\n",
       "      <td>chr2:154839305-154876748</td>\n",
       "      <td>0.087051</td>\n",
       "      <td>0.167859</td>\n",
       "      <td>0.370351</td>\n",
       "      <td>0.402713</td>\n",
       "      <td>0.090154</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>gffutils_derived</td>\n",
       "      <td>transcript</td>\n",
       "      <td>88006993</td>\n",
       "      <td>88006993</td>\n",
       "      <td>NaN</td>\n",
       "      <td>+</td>\n",
       "      <td>0</td>\n",
       "      <td>ENSMUST00000044115</td>\n",
       "      <td>ENSMUSG00000038797</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20187</th>\n",
       "      <td>l7Rn6</td>\n",
       "      <td>chr7:97067194-97089714</td>\n",
       "      <td>159.055667</td>\n",
       "      <td>170.891333</td>\n",
       "      <td>126.653000</td>\n",
       "      <td>84.032633</td>\n",
       "      <td>126.660000</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>...</td>\n",
       "      <td>gffutils_derived</td>\n",
       "      <td>transcript</td>\n",
       "      <td>128267207</td>\n",
       "      <td>128267207</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-</td>\n",
       "      <td>0</td>\n",
       "      <td>ENSMUST00000084276</td>\n",
       "      <td>ENSMUSG00000061894</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      tracking_id                     locus  CTCF-AID_untreated_mean_FPKM  \\\n",
       "20178        Zxda    chrX:92036623-92043628                      0.065962   \n",
       "20179        Zxdb    chrX:91969907-91975530                      0.185390   \n",
       "20180        Zxdc    chr6:90319487-90353480                      4.497293   \n",
       "20181      Zyg11a  chr4:107854538-107890527                      2.508323   \n",
       "20182      Zyg11b  chr4:107900359-107973695                      4.213553   \n",
       "20183         Zyx    chr6:42299826-42330557                      8.531693   \n",
       "20184       Zzef1   chr11:72609727-72740622                      2.597283   \n",
       "20185        Zzz3  chr3:152058966-152344396                      9.986867   \n",
       "20186           a  chr2:154839305-154876748                      0.087051   \n",
       "20187       l7Rn6    chr7:97067194-97089714                    159.055667   \n",
       "\n",
       "       CTCF-AID_1day_mean_FPKM  CTCF-AID_2days_mean_FPKM  \\\n",
       "20178                 0.127417                  0.120941   \n",
       "20179                 0.117669                  0.193258   \n",
       "20180                 4.802807                  5.840457   \n",
       "20181                 3.991237                  3.020370   \n",
       "20182                 3.829030                  3.525300   \n",
       "20183                 8.734813                 11.727350   \n",
       "20184                 2.411157                  2.507930   \n",
       "20185                 8.012557                  7.069317   \n",
       "20186                 0.167859                  0.370351   \n",
       "20187               170.891333                126.653000   \n",
       "\n",
       "       CTCF-AID_4days_mean_FPKM  CTCF-AID_washoff_mean_FPKM 1day.1 2days.1  \\\n",
       "20178                  0.171184                    0.077726     no      no   \n",
       "20179                  0.247512                    0.138311     no      no   \n",
       "20180                  6.868297                    5.241337     no      no   \n",
       "20181                  3.450447                    4.242610     no      no   \n",
       "20182                  4.269620                    4.373337     no      no   \n",
       "20183                 24.111000                   11.359220     no      no   \n",
       "20184                  3.121180                    2.856007     no      no   \n",
       "20185                  8.471867                   10.256167     no      no   \n",
       "20186                  0.402713                    0.090154     no      no   \n",
       "20187                 84.032633                  126.660000     no      no   \n",
       "\n",
       "      4days.1  ...            source     feature      start        end score  \\\n",
       "20178      no  ...  gffutils_derived  transcript  140141266  140141266   NaN   \n",
       "20179      no  ...  gffutils_derived  transcript  157197322  157197322   NaN   \n",
       "20180     yes  ...  gffutils_derived  transcript  129999031  129999031   NaN   \n",
       "20181      no  ...  gffutils_derived  transcript   22873214   22873214   NaN   \n",
       "20182      no  ...  gffutils_derived  transcript  160396531  160396531   NaN   \n",
       "20183     yes  ...  gffutils_derived  transcript   23737822   23737822   NaN   \n",
       "20184      no  ...  gffutils_derived  transcript   21454695   21454695   NaN   \n",
       "20185      no  ...  gffutils_derived  transcript   13361008   13361008   NaN   \n",
       "20186      no  ...  gffutils_derived  transcript   88006993   88006993   NaN   \n",
       "20187     yes  ...  gffutils_derived  transcript  128267207  128267207   NaN   \n",
       "\n",
       "      strand frame       transcript_id             gene_id  \\\n",
       "20178      +     0  ENSMUST00000033265  ENSMUSG00000030967   \n",
       "20179      +     0  ENSMUST00000029831  ENSMUSG00000028180   \n",
       "20180      -     0  ENSMUST00000037426  ENSMUSG00000036086   \n",
       "20181      +     0  ENSMUST00000049506  ENSMUSG00000044068   \n",
       "20182      -     0  ENSMUST00000033736  ENSMUSG00000031370   \n",
       "20183      +     0  ENSMUST00000095595  ENSMUSG00000023902   \n",
       "20184      +     0  ENSMUST00000053293  ENSMUSG00000036721   \n",
       "20185      -     0  ENSMUST00000046245  ENSMUSG00000070822   \n",
       "20186      +     0  ENSMUST00000044115  ENSMUSG00000038797   \n",
       "20187      -     0  ENSMUST00000084276  ENSMUSG00000061894   \n",
       "\n",
       "       distance_to_enhancer  \n",
       "20178                  <NA>  \n",
       "20179                  <NA>  \n",
       "20180                  <NA>  \n",
       "20181                  <NA>  \n",
       "20182                  <NA>  \n",
       "20183                  <NA>  \n",
       "20184                  <NA>  \n",
       "20185                  <NA>  \n",
       "20186                  <NA>  \n",
       "20187                  <NA>  \n",
       "\n",
       "[10 rows x 26 columns]"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Interesting that these are similar number to the above -- going to print out the entries before dropping to compare\n",
    "fpkm[pd.isna(fpkm['distance_to_enhancer'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "ef6a2efc-6de6-44a5-b4e3-d03858b13809",
   "metadata": {},
   "outputs": [],
   "source": [
    "fpkm.dropna(subset='distance_to_enhancer', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "3766ec74-4c5b-4cf1-9284-dee81ea8df0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 2160x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "days = ['1day', '2days', '4days']\n",
    "\n",
    "# Generating a subplot for each of the conditions\n",
    "num_plots = len(days)\n",
    "fig, axs = plt.subplots(1, num_plots, figsize=[num_plots*10, 6])\n",
    "fig.suptitle(\"Genomic separation to enhancers\", fontsize=24)\n",
    "\n",
    "for day, ax in zip(days, axs.ravel()):\n",
    "    \n",
    "    up_reg = fpkm.iloc[np.where((fpkm[day + '.1'] == 'yes') & ((fpkm['CTCF-AID_untreated_mean_FPKM'] - fpkm['CTCF-AID_' + day + '_mean_FPKM']) > 0))]\n",
    "    down_reg = fpkm.iloc[np.where((fpkm[day + '.1'] == 'yes') & ((fpkm['CTCF-AID_untreated_mean_FPKM'] - fpkm['CTCF-AID_' + day + '_mean_FPKM']) < 0))]\n",
    "    non_reg = fpkm.iloc[np.where(fpkm[day + '.1'] == 'no')]\n",
    "    \n",
    "    # generate y axis: cumulative proportion of genes\n",
    "    cum_all = np.arange(0, len(non_reg), 1)\n",
    "    cum_all = cum_all/ len(non_reg)\n",
    "\n",
    "    # plotting \n",
    "    ax.plot(np.log10(non_reg['distance_to_enhancer'].sort_values()), \n",
    "                cum_all, \n",
    "                color='grey',\n",
    "                label='Non-regulated',\n",
    "                linewidth=2)\n",
    "\n",
    "\n",
    "    # generate y axis: cumulative proportion of genes\n",
    "    cum_up = np.arange(0, len(up_reg), 1)\n",
    "    cum_up = cum_up/ len(up_reg)\n",
    "\n",
    "    ax.plot(np.log10(up_reg['distance_to_enhancer'].sort_values()), \n",
    "                cum_up, \n",
    "                color='red', \n",
    "                label='Up-regulated',\n",
    "                linewidth=2)\n",
    "\n",
    "    # generate y axis: cumulative proportion of genes\n",
    "    cum_down = np.arange(0, len(down_reg), 1)\n",
    "    cum_down = cum_down/ len(down_reg)\n",
    "\n",
    "    ax.plot(np.log10(down_reg['distance_to_enhancer'].sort_values()),\n",
    "                cum_down,\n",
    "                color='blue',\n",
    "                label='Down-regulated',\n",
    "                linewidth=2)\n",
    "    \n",
    "    ax.set_title(\"Auxin treatment: \" + day, fontsize=18)\n",
    "    ax.set_xlabel(\"log10 distance from TSS to the closest enhancer\", fontsize=14)\n",
    "    ax.set_ylabel(\"cumulative portion of genes\", fontsize=14)\n",
    "    \n",
    "    \n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "fig.legend(handles, labels, fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4633e66f-2776-4049-a091-e2d26b0cd61c",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Scratch work / misc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "daf7490f-6b2c-4898-8412-d27d1640421c",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Below is work for generating first graph, was put into the for loop, above, but keeping in case process is easier to decipher in steps below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "7d3a7276-3c38-412f-b38c-b5e1f49f1861",
   "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>gene_id_de_tss</th>\n",
       "      <th>baseMean_de_tss</th>\n",
       "      <th>log2FoldChange_de_tss</th>\n",
       "      <th>lfcSE_de_tss</th>\n",
       "      <th>stat_de_tss</th>\n",
       "      <th>pvalue_de_tss</th>\n",
       "      <th>padj_de_tss</th>\n",
       "      <th>chrom_de_tss</th>\n",
       "      <th>source_de_tss</th>\n",
       "      <th>feature_de_tss</th>\n",
       "      <th>...</th>\n",
       "      <th>score_de_tss</th>\n",
       "      <th>strand_de_tss</th>\n",
       "      <th>frame_de_tss</th>\n",
       "      <th>transcript_id_de_tss</th>\n",
       "      <th>distance_to_enhancer_de_tss</th>\n",
       "      <th>chrom_enhancer</th>\n",
       "      <th>start_enhancer</th>\n",
       "      <th>end_enhancer</th>\n",
       "      <th>value_enhancer</th>\n",
       "      <th>distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ENSMUSG00000000544</td>\n",
       "      <td>2291.751812</td>\n",
       "      <td>-0.178754</td>\n",
       "      <td>0.255600</td>\n",
       "      <td>-0.699352</td>\n",
       "      <td>0.484332</td>\n",
       "      <td>0.999805</td>\n",
       "      <td>chr1</td>\n",
       "      <td>gffutils_derived</td>\n",
       "      <td>transcript</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>+</td>\n",
       "      <td>0</td>\n",
       "      <td>ENSMUST00000027847</td>\n",
       "      <td>25376</td>\n",
       "      <td>chr1</td>\n",
       "      <td>168055001</td>\n",
       "      <td>168056000</td>\n",
       "      <td>0.9624</td>\n",
       "      <td>4547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ENSMUSG00000000817</td>\n",
       "      <td>1.790351</td>\n",
       "      <td>-0.737808</td>\n",
       "      <td>1.908559</td>\n",
       "      <td>-0.386579</td>\n",
       "      <td>0.699068</td>\n",
       "      <td>NaN</td>\n",
       "      <td>chr1</td>\n",
       "      <td>gffutils_derived</td>\n",
       "      <td>transcript</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-</td>\n",
       "      <td>0</td>\n",
       "      <td>ENSMUST00000000834</td>\n",
       "      <td>981964</td>\n",
       "      <td>chr1</td>\n",
       "      <td>162240001</td>\n",
       "      <td>162241000</td>\n",
       "      <td>0.8637</td>\n",
       "      <td>1477627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ENSMUSG00000001138</td>\n",
       "      <td>640.593647</td>\n",
       "      <td>-0.257097</td>\n",
       "      <td>0.262880</td>\n",
       "      <td>-0.978004</td>\n",
       "      <td>0.328073</td>\n",
       "      <td>0.999805</td>\n",
       "      <td>chr1</td>\n",
       "      <td>gffutils_derived</td>\n",
       "      <td>transcript</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>+</td>\n",
       "      <td>0</td>\n",
       "      <td>ENSMUST00000001166</td>\n",
       "      <td>3236924</td>\n",
       "      <td>chr1</td>\n",
       "      <td>36118001</td>\n",
       "      <td>36119000</td>\n",
       "      <td>0.8449</td>\n",
       "      <td>449711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ENSMUSG00000001143</td>\n",
       "      <td>115.038360</td>\n",
       "      <td>-0.475708</td>\n",
       "      <td>0.728265</td>\n",
       "      <td>-0.653207</td>\n",
       "      <td>0.513623</td>\n",
       "      <td>0.999805</td>\n",
       "      <td>chr1</td>\n",
       "      <td>gffutils_derived</td>\n",
       "      <td>transcript</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-</td>\n",
       "      <td>0</td>\n",
       "      <td>ENSMUST00000001171</td>\n",
       "      <td>3096612</td>\n",
       "      <td>chr1</td>\n",
       "      <td>36118001</td>\n",
       "      <td>36119000</td>\n",
       "      <td>0.8449</td>\n",
       "      <td>383117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ENSMUSG00000001305</td>\n",
       "      <td>4027.317920</td>\n",
       "      <td>-0.101227</td>\n",
       "      <td>0.297815</td>\n",
       "      <td>-0.339898</td>\n",
       "      <td>0.733933</td>\n",
       "      <td>0.999805</td>\n",
       "      <td>chr1</td>\n",
       "      <td>gffutils_derived</td>\n",
       "      <td>transcript</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-</td>\n",
       "      <td>0</td>\n",
       "      <td>ENSMUST00000001339</td>\n",
       "      <td>45628</td>\n",
       "      <td>chr1</td>\n",
       "      <td>188746001</td>\n",
       "      <td>188747000</td>\n",
       "      <td>0.8166</td>\n",
       "      <td>172763</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25780</th>\n",
       "      <td>ENSMUSG00000069044</td>\n",
       "      <td>6.949773</td>\n",
       "      <td>1.272515</td>\n",
       "      <td>1.052337</td>\n",
       "      <td>1.209228</td>\n",
       "      <td>0.226575</td>\n",
       "      <td>0.999805</td>\n",
       "      <td>chrY</td>\n",
       "      <td>gffutils_derived</td>\n",
       "      <td>transcript</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-</td>\n",
       "      <td>0</td>\n",
       "      <td>ENSMUST00000091188</td>\n",
       "      <td>2768326</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25781</th>\n",
       "      <td>ENSMUSG00000069045</td>\n",
       "      <td>160.679467</td>\n",
       "      <td>-0.292968</td>\n",
       "      <td>0.501614</td>\n",
       "      <td>-0.584051</td>\n",
       "      <td>0.559186</td>\n",
       "      <td>0.999805</td>\n",
       "      <td>chrY</td>\n",
       "      <td>gffutils_derived</td>\n",
       "      <td>transcript</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-</td>\n",
       "      <td>0</td>\n",
       "      <td>ENSMUST00000091190</td>\n",
       "      <td>1453387</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25782</th>\n",
       "      <td>ENSMUSG00000069049</td>\n",
       "      <td>937.139720</td>\n",
       "      <td>0.148641</td>\n",
       "      <td>0.174857</td>\n",
       "      <td>0.850074</td>\n",
       "      <td>0.395284</td>\n",
       "      <td>0.999805</td>\n",
       "      <td>chrY</td>\n",
       "      <td>gffutils_derived</td>\n",
       "      <td>transcript</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>+</td>\n",
       "      <td>0</td>\n",
       "      <td>ENSMUST00000091197</td>\n",
       "      <td>179695</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25783</th>\n",
       "      <td>ENSMUSG00000069053</td>\n",
       "      <td>84.431660</td>\n",
       "      <td>-0.005782</td>\n",
       "      <td>0.369600</td>\n",
       "      <td>-0.015643</td>\n",
       "      <td>0.987519</td>\n",
       "      <td>0.999805</td>\n",
       "      <td>chrY</td>\n",
       "      <td>gffutils_derived</td>\n",
       "      <td>transcript</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>+</td>\n",
       "      <td>0</td>\n",
       "      <td>ENSMUST00000115894</td>\n",
       "      <td>67333</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25784</th>\n",
       "      <td>ENSMUSG00000071960</td>\n",
       "      <td>2.420455</td>\n",
       "      <td>-4.657119</td>\n",
       "      <td>3.928769</td>\n",
       "      <td>-1.185389</td>\n",
       "      <td>0.235864</td>\n",
       "      <td>NaN</td>\n",
       "      <td>chrY</td>\n",
       "      <td>gffutils_derived</td>\n",
       "      <td>transcript</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>+</td>\n",
       "      <td>0</td>\n",
       "      <td>ENSMUST00000078383</td>\n",
       "      <td>273831</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25785 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           gene_id_de_tss  baseMean_de_tss  log2FoldChange_de_tss  \\\n",
       "0      ENSMUSG00000000544      2291.751812              -0.178754   \n",
       "1      ENSMUSG00000000817         1.790351              -0.737808   \n",
       "2      ENSMUSG00000001138       640.593647              -0.257097   \n",
       "3      ENSMUSG00000001143       115.038360              -0.475708   \n",
       "4      ENSMUSG00000001305      4027.317920              -0.101227   \n",
       "...                   ...              ...                    ...   \n",
       "25780  ENSMUSG00000069044         6.949773               1.272515   \n",
       "25781  ENSMUSG00000069045       160.679467              -0.292968   \n",
       "25782  ENSMUSG00000069049       937.139720               0.148641   \n",
       "25783  ENSMUSG00000069053        84.431660              -0.005782   \n",
       "25784  ENSMUSG00000071960         2.420455              -4.657119   \n",
       "\n",
       "       lfcSE_de_tss  stat_de_tss  pvalue_de_tss  padj_de_tss chrom_de_tss  \\\n",
       "0          0.255600    -0.699352       0.484332     0.999805         chr1   \n",
       "1          1.908559    -0.386579       0.699068          NaN         chr1   \n",
       "2          0.262880    -0.978004       0.328073     0.999805         chr1   \n",
       "3          0.728265    -0.653207       0.513623     0.999805         chr1   \n",
       "4          0.297815    -0.339898       0.733933     0.999805         chr1   \n",
       "...             ...          ...            ...          ...          ...   \n",
       "25780      1.052337     1.209228       0.226575     0.999805         chrY   \n",
       "25781      0.501614    -0.584051       0.559186     0.999805         chrY   \n",
       "25782      0.174857     0.850074       0.395284     0.999805         chrY   \n",
       "25783      0.369600    -0.015643       0.987519     0.999805         chrY   \n",
       "25784      3.928769    -1.185389       0.235864          NaN         chrY   \n",
       "\n",
       "          source_de_tss feature_de_tss  ...  score_de_tss  strand_de_tss  \\\n",
       "0      gffutils_derived     transcript  ...           NaN              +   \n",
       "1      gffutils_derived     transcript  ...           NaN              -   \n",
       "2      gffutils_derived     transcript  ...           NaN              +   \n",
       "3      gffutils_derived     transcript  ...           NaN              -   \n",
       "4      gffutils_derived     transcript  ...           NaN              -   \n",
       "...                 ...            ...  ...           ...            ...   \n",
       "25780  gffutils_derived     transcript  ...           NaN              -   \n",
       "25781  gffutils_derived     transcript  ...           NaN              -   \n",
       "25782  gffutils_derived     transcript  ...           NaN              +   \n",
       "25783  gffutils_derived     transcript  ...           NaN              +   \n",
       "25784  gffutils_derived     transcript  ...           NaN              +   \n",
       "\n",
       "       frame_de_tss transcript_id_de_tss distance_to_enhancer_de_tss  \\\n",
       "0                 0   ENSMUST00000027847                       25376   \n",
       "1                 0   ENSMUST00000000834                      981964   \n",
       "2                 0   ENSMUST00000001166                     3236924   \n",
       "3                 0   ENSMUST00000001171                     3096612   \n",
       "4                 0   ENSMUST00000001339                       45628   \n",
       "...             ...                  ...                         ...   \n",
       "25780             0   ENSMUST00000091188                     2768326   \n",
       "25781             0   ENSMUST00000091190                     1453387   \n",
       "25782             0   ENSMUST00000091197                      179695   \n",
       "25783             0   ENSMUST00000115894                       67333   \n",
       "25784             0   ENSMUST00000078383                      273831   \n",
       "\n",
       "      chrom_enhancer  start_enhancer end_enhancer  value_enhancer  distance  \n",
       "0               chr1       168055001    168056000          0.9624      4547  \n",
       "1               chr1       162240001    162241000          0.8637   1477627  \n",
       "2               chr1        36118001     36119000          0.8449    449711  \n",
       "3               chr1        36118001     36119000          0.8449    383117  \n",
       "4               chr1       188746001    188747000          0.8166    172763  \n",
       "...              ...             ...          ...             ...       ...  \n",
       "25780           <NA>            <NA>         <NA>            <NA>      <NA>  \n",
       "25781           <NA>            <NA>         <NA>            <NA>      <NA>  \n",
       "25782           <NA>            <NA>         <NA>            <NA>      <NA>  \n",
       "25783           <NA>            <NA>         <NA>            <NA>      <NA>  \n",
       "25784           <NA>            <NA>         <NA>            <NA>      <NA>  \n",
       "\n",
       "[25785 rows x 22 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "25785"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "closest_intervals = bf.closest(tss_deseq_res, enhancer_coords, suffixes=('_de_tss','_enhancer'))\n",
    "display(closest_intervals)\n",
    "len(closest_intervals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "f17c8c8e-e66a-4ded-b095-919230316c7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "tss_deseq_res['distance_to_enhancer'] = closest_intervals['distance']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "27edcce0-72f2-4097-bde6-d7634825d8e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f5de6551a90>"
      ]
     },
     "execution_count": 101,
     "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": [
    "# generate y axis: cumulative proportion of genes\n",
    "cum_all = np.arange(0, len(tss_deseq_res.iloc[nonsig_ix]), 1)\n",
    "cum_all = cum_all/ len(tss_deseq_res.iloc[nonsig_ix])\n",
    "\n",
    "plt.scatter(np.log10(tss_deseq_res.iloc[nonsig_ix]['distance_to_enhancer'].sort_values()), \n",
    "            cum_all, \n",
    "            color='grey',\n",
    "            s=10,\n",
    "            label='Non-regulated')\n",
    "\n",
    "\n",
    "# generate y axis: cumulative proportion of genes\n",
    "cum_up = np.arange(0, len(tss_deseq_res.iloc[up_ix]), 1)\n",
    "cum_up = cum_up/ len(tss_deseq_res.iloc[up_ix])\n",
    "\n",
    "plt.scatter(np.log10(tss_deseq_res.iloc[up_ix]['distance_to_enhancer'].sort_values()), \n",
    "            cum_up, \n",
    "            color='red', \n",
    "            s=10,\n",
    "            label='Up-regulated')\n",
    "\n",
    "# generate y axis: cumulative proportion of genes\n",
    "cum_down = np.arange(0, len(tss_deseq_res.iloc[down_ix]), 1)\n",
    "cum_down = cum_down/ len(tss_deseq_res.iloc[down_ix])\n",
    "\n",
    "plt.scatter(np.log10(tss_deseq_res.iloc[down_ix]['distance_to_enhancer'].sort_values()),\n",
    "            cum_down,\n",
    "            color='blue',\n",
    "            s=10,\n",
    "            label='Down-regulated')\n",
    "            "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Bioframe Env",
   "language": "python",
   "name": "bioenv"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}