Motif_visualization.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c6f6fb7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import scanpy as sc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4fd54ba4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"C5.AML1.full_set.csv\",index_col=0)\n",
    "#id_dict = {\"MA0137.1\":\"STAT1\",\"MA0050.1\":\"IRF1\"}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b180da5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "keys = df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2eee5ff9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "15bfe903",
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "keys = [\"MEIS1\",\"PRDM1\",\"HOXA9\",\"ELF4\",\"IRF9\",\"MYBL2\",\"SMAD3\",\"CEBPE\",\"HOXA10\",\"HOXB2\",\"NFIL3\",\"GATA1\",\n",
    "              \"IRF1\",\"CEBPA\",\"STAT1\",\"BCL6\",\"BHLHE40\",\"FOSL1\",\"FOSL2\",\"KLF1\",\"HOXB3\",\"PBX3\",\"HOXB4\",\"ZBTB43\",\n",
    "              \"HOXA3\"]\n",
    "values = [\"MA0498.2\",\"MA0508.1\",\"MA0594.2\",\"MA0641.1\",\"MA0653.1\",\"MA0777.1\",\"MA0795.1\",\"MA0837.1\",\n",
    "              \"MA0899.1\",\"MA0902.1\",\"MA0025.1\",\"MA0035.4\",\"MA0050.1\",\"MA0102.3\",\"MA0137.1\",\"MA0463.2\",\n",
    "              \"MA0464.2\",\"MA0477.1\",\"MA0478.1\",\"MA0493.2\",\"MA0903.1\",\"MA1114.1\",\"MA1499.1\",\"UN0148.1\",\"UN0558.1\"]\n",
    "id_dict = dict(zip(values,keys))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f1b1e33d",
   "metadata": {},
   "outputs": [],
   "source": [
    "genes_not_nan = []\n",
    "for i in df[\"gr$nearestGene\"]:\n",
    "    if type(i)==str:\n",
    "        genes_not_nan.append(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "492761a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20335"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(np.unique(genes_not_nan))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3c4df18d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/lf2684/anaconda3/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3474: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/lf2684/anaconda3/lib/python3.8/site-packages/numpy/core/_methods.py:189: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n"
     ]
    }
   ],
   "source": [
    "mean_scores = pd.DataFrame()\n",
    "for j in df.columns:\n",
    "    if \"Gene\" not in j:\n",
    "        gene_names = []\n",
    "        score_list = []\n",
    "        for i in np.unique(genes_not_nan):#np.unique(df[\"gr$nearestGene\"]):\n",
    "            tmp=np.asarray(df[df[\"gr$nearestGene\"]==i][j])\n",
    "            mean_motif = np.mean(tmp[np.nonzero(tmp)[0]])\n",
    "            score_list.append(mean_motif)\n",
    "            gene_names.append(i)\n",
    "        mean_scores[j]=score_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "eb78efb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_scores.columns = [id_dict[i] for i in mean_scores.columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "864a3be1",
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_scores.index = gene_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "560bd2ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_scores.to_csv(\"AML1_b0_mean_motif_score_FULL.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ddb8d177",
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_scores = pd.read_csv(\"AML3_b0_mean_motif_score_FULL.csv\",index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b760a595",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyjaspar import jaspardb\n",
    "jdb_obj = jaspardb(release='JASPAR2020')\n",
    "id_dict = {}\n",
    "for i in mean_scores.columns:\n",
    "    if \"$\" not in i:\n",
    "        id_dict[i] = jdb_obj.fetch_motif_by_id(i).name\n",
    "#mean_scores.columns = [i+\" \"+id_dict[i] for i in mean_scores.columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6dcd9128",
   "metadata": {},
   "outputs": [],
   "source": [
    "gene_names = mean_scores.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a56a1b59",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/lf2684/anaconda3/lib/python3.8/site-packages/anndata/compat/__init__.py:179: FutureWarning: Moving element from .uns['neighbors']['distances'] to .obsp['distances'].\n",
      "\n",
      "This is where adjacency matrices should go now.\n",
      "  warn(\n",
      "/home/lf2684/anaconda3/lib/python3.8/site-packages/anndata/compat/__init__.py:179: FutureWarning: Moving element from .uns['neighbors']['connectivities'] to .obsp['connectivities'].\n",
      "\n",
      "This is where adjacency matrices should go now.\n",
      "  warn(\n"
     ]
    }
   ],
   "source": [
    "#read in tet2 patients\n",
    "p08a=sc.read_h5ad('16H008_allsorted_cleaned-Copy1.h5') #AML2\n",
    "p89a=sc.read_h5ad('08H089_allsorted_cleaned-Copy1.h5') #AML1\n",
    "p7a=sc.read_h5ad('AML7_allsorted_cleaned-Copy1.h5') #AML3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "96d99ba4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/lf2684/anaconda3/lib/python3.8/site-packages/pandas/core/arrays/categorical.py:2487: FutureWarning: The `inplace` parameter in pandas.Categorical.remove_unused_categories is deprecated and will be removed in a future version.\n",
      "  res = method(*args, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "ad = p7a\n",
    "sc.pp.filter_genes(ad, min_counts=None, min_cells=ad.shape[0]*.01)\n",
    "\n",
    "tfs=pd.read_csv(\"Human_TF_MotifList_v_1.01.txt\",sep='\\t')\n",
    "import numpy as np\n",
    "tfs_in=[]\n",
    "for i in tfs[\"HGNC symbol\"]:\n",
    "    if i in ad.var_names and i in mean_scores.index:\n",
    "        tfs_in.append(i)\n",
    "        \n",
    "tfs_filtered=np.unique(tfs_in)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4561b0ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "#AML1\n",
    "timesrt = ['MAFB', 'PRDM1', 'HIF1A', 'ARID5B', 'NR4A1', 'NR4A3', 'FOXO3', 'SMAD3',\n",
    "       'ZNF267', 'BACH1', 'BHLHE40', 'AHR', 'GZF1', 'ZBTB43', 'MXD1', 'FOSL2',\n",
    "       'BCL6', 'ZBTB7A', 'NFIL3', 'ELF4', 'IRF8', 'MYBL2', 'RUNX2', 'TP53',\n",
    "       'CEBPE', 'HOXA3', 'HOXA10', 'HOXA9', 'CEBPA', 'MYC', 'MYB', 'KLF13',\n",
    "       'HOXB3', 'MAFK', 'IKZF1', 'HLX', 'MEIS1', 'ZZZ3', 'NFE2', 'HOXB4',\n",
    "       'PBX3']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5a70571f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#AML2 full\n",
    "timesrt = ['IRF8', 'MAFB', 'BCL6', 'CEBPB', 'HES4', 'JUNB', 'PRDM1', 'HIF1A',\n",
    "       'NFKB2', 'NFAT5', 'KLF7', 'FOSL2', 'MYBL2', 'E2F1', 'CEBPE', 'MXD1',\n",
    "       'IRX3', 'CREB5', 'BHLHE40', 'RFX2', 'NFIL3', 'EGR1', 'DDIT3', 'HIVEP1',\n",
    "       'KLF2', 'ATF3', 'JUN', 'HOXB3', 'ZNF117', 'AHR', 'HOXA3', 'ETS1',\n",
    "       'TCF3', 'ZNF711', 'NR4A1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a3def3bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "#AML3 full\n",
    "timesrt = ['SMAD3', 'HES4', 'POU2F2', 'RBPJ', 'MNT', 'JDP2', 'BACH1', 'BHLHE40',\n",
    "       'RXRA', 'KLF4', 'SPI1', 'MBD2', 'KLF6', 'ZBTB7A', 'NFIL3', 'ZNF787',\n",
    "       'ZBTB18', 'ZNF217', 'ARID3A', 'HLX', 'ELF4', 'MEF2D', 'KLF13', 'NR3C1',\n",
    "       'ZNF281', 'HOXA3', 'ERF', 'UBP1', 'HOXB3', 'HOXA10', 'MEIS1', 'RUNX1',\n",
    "       'HOXA9', 'GFI1', 'CREB3L2', 'BCL11A', 'ZNF90', 'MYBL2', 'CEBPE',\n",
    "       'DNMT1', 'GTF3A', 'NFE2', 'HMGA1', 'KLF1', 'MYC', 'MYB', 'ETV6', 'HHEX',\n",
    "       'HOXA7', 'LYL1', 'ZNF22']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "72938695",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "566"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(tfs_filtered)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "20f31b73",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 3600x1080 with 32 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(4,8,figsize=(50,15))\n",
    "results = pd.DataFrame()\n",
    "\n",
    "for j in range(mean_scores.shape[1]):\n",
    "    idd = mean_scores.columns[j]\n",
    "    gene = id_dict[idd]\n",
    "    sset_means = mean_scores.loc[[i for i in timesrt if i in gene_names]]\n",
    "    sset_means = sset_means[[i for i in mean_scores.columns if i in sset_means.columns]]\n",
    "    #drop the gene from the set of disrupted genes if it is there \n",
    "    if gene in sset_means.index:\n",
    "        sset_means = sset_means.drop(gene,axis=0)\n",
    "\n",
    "    #tfs = mean_scores.loc[tfs_in]\n",
    "    \n",
    "    other = [i for i in tfs_filtered if i not in timesrt]\n",
    "\n",
    "    null = mean_scores.loc[other]\n",
    "\n",
    "    our_value = sset_means[idd].fillna(0).mean()\n",
    "\n",
    "    samples = []\n",
    "    for i in range(10000):\n",
    "        sample = null.loc[np.random.choice(null.index,sset_means.shape[0])]\n",
    "        samples.append(sample[idd].fillna(0).mean())\n",
    "\n",
    "    counts = 0\n",
    "    for i in samples:\n",
    "        if i>=our_value:\n",
    "            counts+=1\n",
    "    p = counts/10000\n",
    "\n",
    "    ax[j//8][j%8].hist(samples,bins=12)\n",
    "    ax[j//8][j%8].plot([our_value for i in range(2500)],np.arange(0,2500))\n",
    "    ax[j//8][j%8].set_title(\"AML3:\"+gene+mean_scores.columns[j]+ \" regulating\\n disrupted TFs\\n, p=\"+str(p))\n",
    "    results[gene+mean_scores.columns[j]]=[p]\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"AML3 motif p values\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "db3c5866",
   "metadata": {},
   "outputs": [],
   "source": [
    "le_grand_results = pd.DataFrame()\n",
    "le_grand_results[\"AML1\"] = results.T[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "bbc7a1d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "le_grand_results[\"AML2\"] = results.T[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "99bd8339",
   "metadata": {},
   "outputs": [],
   "source": [
    "le_grand_results[\"AML3\"] = results.T[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e0e1f37d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>AML1</th>\n",
       "      <th>AML2</th>\n",
       "      <th>AML3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>FOSL1MA0477.1</th>\n",
       "      <td>0.0011</td>\n",
       "      <td>0.0057</td>\n",
       "      <td>0.0003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FOSL1MA0477.2</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0086</td>\n",
       "      <td>0.0001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HOXB4MA1499.1</th>\n",
       "      <td>0.0029</td>\n",
       "      <td>0.0458</td>\n",
       "      <td>0.0133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HOXB6MA1500.1</th>\n",
       "      <td>0.0007</td>\n",
       "      <td>0.0059</td>\n",
       "      <td>0.0320</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 AML1    AML2    AML3\n",
       "FOSL1MA0477.1  0.0011  0.0057  0.0003\n",
       "FOSL1MA0477.2  0.0000  0.0086  0.0001\n",
       "HOXB4MA1499.1  0.0029  0.0458  0.0133\n",
       "HOXB6MA1500.1  0.0007  0.0059  0.0320"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "le_grand_results[(le_grand_results[\"AML1\"]<.05)&(le_grand_results[\"AML2\"]<.05)&(le_grand_results[\"AML3\"]<.05)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e3837526",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\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>AML1</th>\n",
       "      <th>AML2</th>\n",
       "      <th>AML3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>GATA1MA0035.4</th>\n",
       "      <td>0.0007</td>\n",
       "      <td>0.3939</td>\n",
       "      <td>0.4695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GATA2MA0036.1</th>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.5604</td>\n",
       "      <td>0.0115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GATA2MA0036.2</th>\n",
       "      <td>0.0423</td>\n",
       "      <td>0.0154</td>\n",
       "      <td>0.1355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GATA2MA0036.3</th>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0309</td>\n",
       "      <td>0.2044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MYCMA0147.3</th>\n",
       "      <td>0.0450</td>\n",
       "      <td>0.2716</td>\n",
       "      <td>0.0001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NR4A2MA0160.1</th>\n",
       "      <td>0.0272</td>\n",
       "      <td>0.2301</td>\n",
       "      <td>0.0016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ERGMA0474.2</th>\n",
       "      <td>0.6634</td>\n",
       "      <td>0.8829</td>\n",
       "      <td>0.8635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FOSL1MA0477.1</th>\n",
       "      <td>0.0011</td>\n",
       "      <td>0.0057</td>\n",
       "      <td>0.0003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FOSL1MA0477.2</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0086</td>\n",
       "      <td>0.0001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAFFMA0495.1</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.2989</td>\n",
       "      <td>0.0365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAFFMA0495.2</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.2773</td>\n",
       "      <td>0.1295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAFFMA0495.3</th>\n",
       "      <td>0.0011</td>\n",
       "      <td>0.1498</td>\n",
       "      <td>0.1656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MEIS1MA0498.2</th>\n",
       "      <td>0.0049</td>\n",
       "      <td>0.6191</td>\n",
       "      <td>0.1122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HOXA9MA0594.2</th>\n",
       "      <td>0.2314</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ATF3MA0605.2</th>\n",
       "      <td>0.0316</td>\n",
       "      <td>0.0219</td>\n",
       "      <td>0.4456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IRF9MA0653.1</th>\n",
       "      <td>0.1370</td>\n",
       "      <td>0.6629</td>\n",
       "      <td>0.4351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NFKB2MA0778.1</th>\n",
       "      <td>0.0292</td>\n",
       "      <td>0.8464</td>\n",
       "      <td>0.0158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HOXB2MA0902.1</th>\n",
       "      <td>0.0565</td>\n",
       "      <td>0.0028</td>\n",
       "      <td>0.0828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HOXB2MA0902.2</th>\n",
       "      <td>0.1153</td>\n",
       "      <td>0.1383</td>\n",
       "      <td>0.0938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HOXB5MA0904.2</th>\n",
       "      <td>0.7507</td>\n",
       "      <td>0.3165</td>\n",
       "      <td>0.1582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RELBMA1117.1</th>\n",
       "      <td>0.0109</td>\n",
       "      <td>0.2713</td>\n",
       "      <td>0.0036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HOXB4MA1499.1</th>\n",
       "      <td>0.0029</td>\n",
       "      <td>0.0458</td>\n",
       "      <td>0.0133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HOXB6MA1500.1</th>\n",
       "      <td>0.0007</td>\n",
       "      <td>0.0059</td>\n",
       "      <td>0.0320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KLF2MA1515.1</th>\n",
       "      <td>0.0468</td>\n",
       "      <td>0.0675</td>\n",
       "      <td>0.0009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZNF274MA1592.1</th>\n",
       "      <td>0.0090</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MEIS1(var.2)MA1639.1</th>\n",
       "      <td>0.0034</td>\n",
       "      <td>0.6398</td>\n",
       "      <td>0.1038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZNF32UN0177.1</th>\n",
       "      <td>0.0578</td>\n",
       "      <td>0.7853</td>\n",
       "      <td>0.5935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STAT1MA0137.1</th>\n",
       "      <td>0.0009</td>\n",
       "      <td>0.8863</td>\n",
       "      <td>0.0357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STAT1MA0137.2</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.4507</td>\n",
       "      <td>0.1163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STAT1MA0137.3</th>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.5511</td>\n",
       "      <td>0.1273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IRF1MA0050.1</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.8653</td>\n",
       "      <td>0.0057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IRF1MA0050.2</th>\n",
       "      <td>0.0031</td>\n",
       "      <td>0.4868</td>\n",
       "      <td>0.0216</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        AML1    AML2    AML3\n",
       "GATA1MA0035.4         0.0007  0.3939  0.4695\n",
       "GATA2MA0036.1         0.0001  0.5604  0.0115\n",
       "GATA2MA0036.2         0.0423  0.0154  0.1355\n",
       "GATA2MA0036.3         0.0003  0.0309  0.2044\n",
       "MYCMA0147.3           0.0450  0.2716  0.0001\n",
       "NR4A2MA0160.1         0.0272  0.2301  0.0016\n",
       "ERGMA0474.2           0.6634  0.8829  0.8635\n",
       "FOSL1MA0477.1         0.0011  0.0057  0.0003\n",
       "FOSL1MA0477.2         0.0000  0.0086  0.0001\n",
       "MAFFMA0495.1          0.0000  0.2989  0.0365\n",
       "MAFFMA0495.2          0.0000  0.2773  0.1295\n",
       "MAFFMA0495.3          0.0011  0.1498  0.1656\n",
       "MEIS1MA0498.2         0.0049  0.6191  0.1122\n",
       "HOXA9MA0594.2         0.2314  1.0000  1.0000\n",
       "ATF3MA0605.2          0.0316  0.0219  0.4456\n",
       "IRF9MA0653.1          0.1370  0.6629  0.4351\n",
       "NFKB2MA0778.1         0.0292  0.8464  0.0158\n",
       "HOXB2MA0902.1         0.0565  0.0028  0.0828\n",
       "HOXB2MA0902.2         0.1153  0.1383  0.0938\n",
       "HOXB5MA0904.2         0.7507  0.3165  0.1582\n",
       "RELBMA1117.1          0.0109  0.2713  0.0036\n",
       "HOXB4MA1499.1         0.0029  0.0458  0.0133\n",
       "HOXB6MA1500.1         0.0007  0.0059  0.0320\n",
       "KLF2MA1515.1          0.0468  0.0675  0.0009\n",
       "ZNF274MA1592.1        0.0090  1.0000  0.0046\n",
       "MEIS1(var.2)MA1639.1  0.0034  0.6398  0.1038\n",
       "ZNF32UN0177.1         0.0578  0.7853  0.5935\n",
       "STAT1MA0137.1         0.0009  0.8863  0.0357\n",
       "STAT1MA0137.2         0.0000  0.4507  0.1163\n",
       "STAT1MA0137.3         0.0003  0.5511  0.1273\n",
       "IRF1MA0050.1          0.0000  0.8653  0.0057\n",
       "IRF1MA0050.2          0.0031  0.4868  0.0216"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "le_grand_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fd87c693",
   "metadata": {},
   "outputs": [],
   "source": [
    "IFN1 = ['IRF1',\n",
    "'IFIH1',\n",
    "'IFITM3',\n",
    "'DDX58',\n",
    "'IFI44L',\n",
    "'IFI6',\n",
    "'IFITM2',\n",
    "'NAMPT',\n",
    "'OASL',\n",
    "'RTP4',\n",
    "'TREX1',\n",
    "'ADAR',\n",
    "'FAM46C',\n",
    "'LY6E',\n",
    "'MCOLN2',\n",
    "'APOBEC3G',\n",
    "'IL15',\n",
    "'ISG15',\n",
    "'MX1',\n",
    "'TLR3',]\n",
    "\n",
    "timesrt = ['GATA1','KLF1', 'FOSL1', 'CREB5',\n",
    "       'HOXB2', 'PBX3', 'HOXB4',\n",
    "       'MEIS1', 'HLX', 'HOXB3', 'CEBPA', 'HOXA9', 'HOXA3', 'CEBPE',\n",
    "       'MYBL2', 'ELF4', 'NFIL3', 'BCL6', 'FOSL2', 'ZBTB43', 'AHR', 'BHLHE40',\n",
    "       'SMAD3', 'PRDM1', 'MAFB']\n",
    "\n",
    "\n",
    "sset_means = mean_scores.loc[[i for i in IFN1 if i in gene_names]]\n",
    "sset_means = sset_means[[i for i in timesrt if i in sset_means.columns]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "eb88759c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GATA1 0.4456\n",
      "FOSL1 0.6447\n",
      "HOXB2 0.9488\n",
      "PBX3 0.9851\n",
      "HOXB4 0.9427\n",
      "MEIS1 0.0\n",
      "HOXB3 0.9656\n",
      "CEBPA 0.0027\n",
      "HOXA9 0.4399\n",
      "CEBPE 0.2717\n",
      "MYBL2 0.5478\n",
      "ELF4 0.3058\n",
      "NFIL3 0.9599\n",
      "BCL6 0.642\n",
      "FOSL2 0.9789\n",
      "ZBTB43 0.9679\n",
      "BHLHE40 0.8383\n",
      "SMAD3 0.741\n",
      "PRDM1 0.9941\n"
     ]
    }
   ],
   "source": [
    "for gene in timesrt:\n",
    "    if gene in mean_scores.columns:\n",
    "        our_value = sset_means[gene].fillna(0).mean()\n",
    "\n",
    "        samples = []\n",
    "        for i in range(10000):\n",
    "            null = mean_scores.loc[np.random.choice(mean_scores.index,len(IFN1))]\n",
    "            null = null[gene]\n",
    "            samples.append(null.mean())\n",
    "\n",
    "        counts = 0\n",
    "        for i in samples:\n",
    "            if i>our_value:\n",
    "                counts+=1\n",
    "        p = counts/10000\n",
    "        print(gene, p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "a721e7b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Significance of disrupted TFs \\nregulating IFN1 vs other genes, p=1.0')"
      ]
     },
     "execution_count": 162,
     "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.hist(samples,bins=12)\n",
    "plt.plot([our_value for i in range(2500)],np.arange(0,2500))\n",
    "plt.title(\"Significance of disrupted TFs \\nregulating IFN1 vs other genes, p=\"+str(p))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "59baa44e",
   "metadata": {},
   "outputs": [],
   "source": [
    "mhc_genes = [\n",
    "    \"HLA-DM\", \"HLA-DMA\", \"HLA-DMB\", \"HLA-DO\", \"HLA-DOA\", \"HLA-DOB\",\n",
    "    \"HLA-DP\", \"HLA-DPA1\", \"HLA-DPB1\", \"HLA-DQ\", \"HLA-DQA1\", \"HLA-DQA2\",\n",
    "    \"HLA-DQB1\", \"HLA-DQB2\", \"HLA-DR\", \"HLA-DRA\", \"HLA-DRB1\", \"HLA-DRB3\",\n",
    "    \"HLA-DRB4\", \"HLA-DRB5\"\n",
    "]\n",
    "\n",
    "sset_means = mean_scores.loc[[i for i in mhc_genes if i in gene_names]]\n",
    "sset_means = sset_means[[i for i in timesrt if i in sset_means.columns]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "906c1a16",
   "metadata": {},
   "outputs": [],
   "source": [
    "our_value = sset_means.fillna(0).mean().mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "44f48279",
   "metadata": {},
   "outputs": [],
   "source": [
    "samples = []\n",
    "for i in range(10000):\n",
    "    null = mean_scores.loc[np.random.choice(mean_scores.index,len(mhc_genes))]\n",
    "    null = null[[i for i in timesrt if i in sset_means.columns]]\n",
    "    samples.append(null.mean().mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "b45c7b13",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n"
     ]
    }
   ],
   "source": [
    "counts = 0\n",
    "for i in samples:\n",
    "    if i>our_value:\n",
    "        counts+=1\n",
    "p = counts/10000\n",
    "print(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "c3d160ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Significance of disrupted TFs \\nregulating MHC-II vs other genes, p=1.0')"
      ]
     },
     "execution_count": 169,
     "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.hist(samples,bins=12)\n",
    "plt.plot([our_value for i in range(2500)],np.arange(0,2500))\n",
    "plt.title(\"Significance of disrupted TFs \\nregulating MHC-II vs other genes, p=\"+str(p))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}