180405-1d-spatial/Tomo-seq analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "counts = pd.read_csv('GSE59873_RAW/GSM1448819_dr_15ss_TH2.txt.gz', sep='\\t', index_col=0).T\n",
    "\n",
    "sample_info = pd.DataFrame(index=counts.index)\n",
    "sample_info['x'] = (sample_info.index.values.astype(float) - 1) * 18\n",
    "\n",
    "# Make orientation head-to-tail because it's easier to think about\n",
    "sample_info['x'] = sample_info.x.max() - sample_info.x\n",
    "\n",
    "sample_info['total_count'] = counts.sum(1)\n",
    "sample_info['num_detected'] = (counts > 0).sum(1)\n",
    "sample_info['ERCC_counts'] = counts.loc[:, counts.columns.str.startswith('ERCC-')].sum(1)\n",
    "\n",
    "sample_info = sample_info.query('total_count > 1e4')  # Remove empty features\n",
    "counts = counts.loc[sample_info.index]\n",
    "\n",
    "counts = counts.T[counts.sum(0) >= 3].T  # Remove practically unobserved genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "gene_names = pd.read_csv('gene_info.txt', sep='\\t', index_col=0)['Gene name']\n",
    "name_getter = lambda s: str(s) + '_' + str(gene_names.get(s, s))\n",
    "counts.columns = counts.columns.map(name_getter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(95, 20317)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(sample_info.x, sample_info.total_count);\n",
    "plt.yscale('log');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(sample_info.x, sample_info.num_detected);\n",
    "plt.yscale('log');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(sample_info.total_count, sample_info.num_detected)\n",
    "plt.loglog();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.loglog()\n",
    "plt.scatter(counts.mean(0), counts.var(0))\n",
    "plt.plot([1e-2, 1e4], [1e-2, 1e4], c='k');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(sample_info.total_count, sample_info.ERCC_counts)\n",
    "plt.loglog();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import NaiveDE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfm = NaiveDE.stabilize(counts.T).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "res = NaiveDE.regress_out(sample_info, dfm.T, 'np.log(total_count) + np.log(ERCC_counts) + np.log(num_detected)').T\n",
    "res = res - res.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import SpatialDE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = sample_info.x.values[:, None]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Performing DE test\n",
      "INFO:root:Pre-calculating USU^T = K's ...\n",
      "INFO:root:Done: 0.014s\n",
      "INFO:root:Fitting gene models\n",
      "INFO:root:Model 1 of 10\n",
      "INFO:root:Model 2 of 10                               \n",
      "INFO:root:Model 3 of 10                               \n",
      "INFO:root:Model 4 of 10                               \n",
      "INFO:root:Model 5 of 10                               \n",
      "INFO:root:Model 6 of 10                               \n",
      "INFO:root:Model 7 of 10                               \n",
      "INFO:root:Model 8 of 10                               \n",
      "INFO:root:Model 9 of 10                               \n",
      "INFO:root:Model 10 of 10                              \n",
      "                                                      \r"
     ]
    }
   ],
   "source": [
    "results = SpatialDE.run(X, res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 933,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(95, 20317)"
      ]
     },
     "execution_count": 933,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>FSV</th>\n",
       "      <th>M</th>\n",
       "      <th>g</th>\n",
       "      <th>l</th>\n",
       "      <th>max_delta</th>\n",
       "      <th>max_ll</th>\n",
       "      <th>max_mu_hat</th>\n",
       "      <th>max_s2_t_hat</th>\n",
       "      <th>model</th>\n",
       "      <th>n</th>\n",
       "      <th>s2_FSV</th>\n",
       "      <th>s2_logdelta</th>\n",
       "      <th>time</th>\n",
       "      <th>BIC</th>\n",
       "      <th>max_ll_null</th>\n",
       "      <th>LLR</th>\n",
       "      <th>pval</th>\n",
       "      <th>qval</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.498856e-01</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000000001_slc35a5</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.655728e+00</td>\n",
       "      <td>12.687876</td>\n",
       "      <td>1.748343e-04</td>\n",
       "      <td>6.737526e-03</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>25.469732</td>\n",
       "      <td>1.472021e+03</td>\n",
       "      <td>0.001570</td>\n",
       "      <td>-7.160244</td>\n",
       "      <td>12.682495</td>\n",
       "      <td>0.005380</td>\n",
       "      <td>0.941526</td>\n",
       "      <td>0.958943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.055329e-09</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000000069_dap</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.851652e+08</td>\n",
       "      <td>-30.389941</td>\n",
       "      <td>2.027807e-12</td>\n",
       "      <td>2.288192e-10</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>0.719139</td>\n",
       "      <td>1.232609e+17</td>\n",
       "      <td>0.000958</td>\n",
       "      <td>78.995390</td>\n",
       "      <td>-30.392591</td>\n",
       "      <td>0.002650</td>\n",
       "      <td>0.958943</td>\n",
       "      <td>0.958943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.055329e-09</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000000086_itsn1</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.851652e+08</td>\n",
       "      <td>-69.062422</td>\n",
       "      <td>-2.051024e-12</td>\n",
       "      <td>5.165075e-10</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>0.714568</td>\n",
       "      <td>1.224775e+17</td>\n",
       "      <td>0.000689</td>\n",
       "      <td>156.340352</td>\n",
       "      <td>-69.065072</td>\n",
       "      <td>0.002650</td>\n",
       "      <td>0.958943</td>\n",
       "      <td>0.958943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.055329e-09</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000000103_myh10</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.851652e+08</td>\n",
       "      <td>-26.930872</td>\n",
       "      <td>-2.593145e-13</td>\n",
       "      <td>2.127483e-10</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>5.429598</td>\n",
       "      <td>9.306373e+17</td>\n",
       "      <td>0.000871</td>\n",
       "      <td>72.077251</td>\n",
       "      <td>-26.933522</td>\n",
       "      <td>0.002650</td>\n",
       "      <td>0.958943</td>\n",
       "      <td>0.958943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.758169e-01</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000000151_thraa</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.881474e-01</td>\n",
       "      <td>-6.268160</td>\n",
       "      <td>-3.834754e-05</td>\n",
       "      <td>5.243714e-02</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>13.522278</td>\n",
       "      <td>4.575089e+02</td>\n",
       "      <td>0.000729</td>\n",
       "      <td>30.751828</td>\n",
       "      <td>-6.713743</td>\n",
       "      <td>0.445583</td>\n",
       "      <td>0.504440</td>\n",
       "      <td>0.958943</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            FSV  M                           g    l     max_delta     max_ll  \\\n",
       "0  1.498856e-01  4  ENSDARG00000000001_slc35a5  9.0  5.655728e+00  12.687876   \n",
       "1  2.055329e-09  4      ENSDARG00000000069_dap  9.0  4.851652e+08 -30.389941   \n",
       "2  2.055329e-09  4    ENSDARG00000000086_itsn1  9.0  4.851652e+08 -69.062422   \n",
       "3  2.055329e-09  4    ENSDARG00000000103_myh10  9.0  4.851652e+08 -26.930872   \n",
       "4  7.758169e-01  4    ENSDARG00000000151_thraa  9.0  2.881474e-01  -6.268160   \n",
       "\n",
       "     max_mu_hat  max_s2_t_hat model   n     s2_FSV   s2_logdelta      time  \\\n",
       "0  1.748343e-04  6.737526e-03    SE  95  25.469732  1.472021e+03  0.001570   \n",
       "1  2.027807e-12  2.288192e-10    SE  95   0.719139  1.232609e+17  0.000958   \n",
       "2 -2.051024e-12  5.165075e-10    SE  95   0.714568  1.224775e+17  0.000689   \n",
       "3 -2.593145e-13  2.127483e-10    SE  95   5.429598  9.306373e+17  0.000871   \n",
       "4 -3.834754e-05  5.243714e-02    SE  95  13.522278  4.575089e+02  0.000729   \n",
       "\n",
       "          BIC  max_ll_null       LLR      pval      qval  \n",
       "0   -7.160244    12.682495  0.005380  0.941526  0.958943  \n",
       "1   78.995390   -30.392591  0.002650  0.958943  0.958943  \n",
       "2  156.340352   -69.065072  0.002650  0.958943  0.958943  \n",
       "3   72.077251   -26.933522  0.002650  0.958943  0.958943  \n",
       "4   30.751828    -6.713743  0.445583  0.504440  0.958943  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1010, 18)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.query('qval < 0.05').shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "results['pval'] = results['pval'].clip_lower(results.query('pval > 0')['pval'].min() / 2)\n",
    "results['qval'] = results['qval'].clip_lower(results.query('qval > 0')['qval'].min() / 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import SpatialDE.plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "SpatialDE.plot.FSV_sig(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "126.128432    366\n",
       "65.188408     301\n",
       "244.036967    154\n",
       "33.692075     147\n",
       "17.413462      32\n",
       "472.169837     10\n",
       "Name: l, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.query('qval < 0.05').l.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>g</th>\n",
       "      <th>l</th>\n",
       "      <th>qval</th>\n",
       "      <th>FSV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20259</th>\n",
       "      <td>ENSDARG00000070352_hoxc12a</td>\n",
       "      <td>472.169837</td>\n",
       "      <td>1.218306e-06</td>\n",
       "      <td>0.980114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17280</th>\n",
       "      <td>ENSDARG00000004712_tbr1b</td>\n",
       "      <td>126.128432</td>\n",
       "      <td>3.335185e-07</td>\n",
       "      <td>0.897112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20161</th>\n",
       "      <td>ENSDARG00000095715_BX465834.1</td>\n",
       "      <td>244.036967</td>\n",
       "      <td>4.331286e-02</td>\n",
       "      <td>0.889115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18017</th>\n",
       "      <td>ENSDARG00000036832_cyt1l</td>\n",
       "      <td>126.128432</td>\n",
       "      <td>7.281206e-10</td>\n",
       "      <td>0.855317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19951</th>\n",
       "      <td>ENSDARG00000070546_msgn1</td>\n",
       "      <td>244.036967</td>\n",
       "      <td>1.058936e-08</td>\n",
       "      <td>0.853156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19440</th>\n",
       "      <td>ENSDARG00000007099_cx43.4</td>\n",
       "      <td>244.036967</td>\n",
       "      <td>4.734528e-08</td>\n",
       "      <td>0.842311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19334</th>\n",
       "      <td>ENSDARG00000094041_krt17</td>\n",
       "      <td>126.128432</td>\n",
       "      <td>2.509206e-08</td>\n",
       "      <td>0.841733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17648</th>\n",
       "      <td>ENSDARG00000019365_zgc:110712</td>\n",
       "      <td>126.128432</td>\n",
       "      <td>1.990785e-07</td>\n",
       "      <td>0.840508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19982</th>\n",
       "      <td>ENSDARG00000074772_ccl44</td>\n",
       "      <td>244.036967</td>\n",
       "      <td>3.427289e-02</td>\n",
       "      <td>0.836121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19679</th>\n",
       "      <td>ENSDARG00000037840_ENSDARG00000037840</td>\n",
       "      <td>244.036967</td>\n",
       "      <td>1.679850e-08</td>\n",
       "      <td>0.831907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17448</th>\n",
       "      <td>ENSDARG00000011168_ENSDARG00000011168</td>\n",
       "      <td>126.128432</td>\n",
       "      <td>7.782124e-08</td>\n",
       "      <td>0.818406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18901</th>\n",
       "      <td>ENSDARG00000075107_nkx2.4a</td>\n",
       "      <td>126.128432</td>\n",
       "      <td>2.688908e-08</td>\n",
       "      <td>0.812340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17612</th>\n",
       "      <td>ENSDARG00000017917_her7</td>\n",
       "      <td>126.128432</td>\n",
       "      <td>1.380613e-08</td>\n",
       "      <td>0.806848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18803</th>\n",
       "      <td>ENSDARG00000070677_fezf2</td>\n",
       "      <td>126.128432</td>\n",
       "      <td>7.904765e-09</td>\n",
       "      <td>0.798747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19967</th>\n",
       "      <td>ENSDARG00000071586_tgfbi</td>\n",
       "      <td>244.036967</td>\n",
       "      <td>2.307520e-09</td>\n",
       "      <td>0.795773</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           g           l          qval  \\\n",
       "20259             ENSDARG00000070352_hoxc12a  472.169837  1.218306e-06   \n",
       "17280               ENSDARG00000004712_tbr1b  126.128432  3.335185e-07   \n",
       "20161          ENSDARG00000095715_BX465834.1  244.036967  4.331286e-02   \n",
       "18017               ENSDARG00000036832_cyt1l  126.128432  7.281206e-10   \n",
       "19951               ENSDARG00000070546_msgn1  244.036967  1.058936e-08   \n",
       "19440              ENSDARG00000007099_cx43.4  244.036967  4.734528e-08   \n",
       "19334               ENSDARG00000094041_krt17  126.128432  2.509206e-08   \n",
       "17648          ENSDARG00000019365_zgc:110712  126.128432  1.990785e-07   \n",
       "19982               ENSDARG00000074772_ccl44  244.036967  3.427289e-02   \n",
       "19679  ENSDARG00000037840_ENSDARG00000037840  244.036967  1.679850e-08   \n",
       "17448  ENSDARG00000011168_ENSDARG00000011168  126.128432  7.782124e-08   \n",
       "18901             ENSDARG00000075107_nkx2.4a  126.128432  2.688908e-08   \n",
       "17612                ENSDARG00000017917_her7  126.128432  1.380613e-08   \n",
       "18803               ENSDARG00000070677_fezf2  126.128432  7.904765e-09   \n",
       "19967               ENSDARG00000071586_tgfbi  244.036967  2.307520e-09   \n",
       "\n",
       "            FSV  \n",
       "20259  0.980114  \n",
       "17280  0.897112  \n",
       "20161  0.889115  \n",
       "18017  0.855317  \n",
       "19951  0.853156  \n",
       "19440  0.842311  \n",
       "19334  0.841733  \n",
       "17648  0.840508  \n",
       "19982  0.836121  \n",
       "19679  0.831907  \n",
       "17448  0.818406  \n",
       "18901  0.812340  \n",
       "17612  0.806848  \n",
       "18803  0.798747  \n",
       "19967  0.795773  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.query('qval < 0.05 & l > 100').sort_values('FSV', ascending=False).head(15)[['g', 'l', 'qval', 'FSV']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(sample_info.x, res['ENSDARG00000036832_cyt1l']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>FSV</th>\n",
       "      <td>0.149886</td>\n",
       "      <td>2.05533e-09</td>\n",
       "      <td>2.05533e-09</td>\n",
       "      <td>2.05533e-09</td>\n",
       "      <td>0.775817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>g</th>\n",
       "      <td>ENSDARG00000000001_slc35a5</td>\n",
       "      <td>ENSDARG00000000069_dap</td>\n",
       "      <td>ENSDARG00000000086_itsn1</td>\n",
       "      <td>ENSDARG00000000103_myh10</td>\n",
       "      <td>ENSDARG00000000151_thraa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>l</th>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max_delta</th>\n",
       "      <td>5.65573</td>\n",
       "      <td>4.85165e+08</td>\n",
       "      <td>4.85165e+08</td>\n",
       "      <td>4.85165e+08</td>\n",
       "      <td>0.288147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max_ll</th>\n",
       "      <td>12.6879</td>\n",
       "      <td>-30.3899</td>\n",
       "      <td>-69.0624</td>\n",
       "      <td>-26.9309</td>\n",
       "      <td>-6.26816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max_mu_hat</th>\n",
       "      <td>0.000174834</td>\n",
       "      <td>2.02781e-12</td>\n",
       "      <td>-2.05102e-12</td>\n",
       "      <td>-2.59315e-13</td>\n",
       "      <td>-3.83475e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max_s2_t_hat</th>\n",
       "      <td>0.00673753</td>\n",
       "      <td>2.28819e-10</td>\n",
       "      <td>5.16507e-10</td>\n",
       "      <td>2.12748e-10</td>\n",
       "      <td>0.0524371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <td>SE</td>\n",
       "      <td>SE</td>\n",
       "      <td>SE</td>\n",
       "      <td>SE</td>\n",
       "      <td>SE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n</th>\n",
       "      <td>95</td>\n",
       "      <td>95</td>\n",
       "      <td>95</td>\n",
       "      <td>95</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s2_FSV</th>\n",
       "      <td>25.4697</td>\n",
       "      <td>0.719139</td>\n",
       "      <td>0.714568</td>\n",
       "      <td>5.4296</td>\n",
       "      <td>13.5223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s2_logdelta</th>\n",
       "      <td>1472.02</td>\n",
       "      <td>1.23261e+17</td>\n",
       "      <td>1.22478e+17</td>\n",
       "      <td>9.30637e+17</td>\n",
       "      <td>457.509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <td>0.00157022</td>\n",
       "      <td>0.000957966</td>\n",
       "      <td>0.000688791</td>\n",
       "      <td>0.000870943</td>\n",
       "      <td>0.000729322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BIC</th>\n",
       "      <td>-7.16024</td>\n",
       "      <td>78.9954</td>\n",
       "      <td>156.34</td>\n",
       "      <td>72.0773</td>\n",
       "      <td>30.7518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max_ll_null</th>\n",
       "      <td>12.6825</td>\n",
       "      <td>-30.3926</td>\n",
       "      <td>-69.0651</td>\n",
       "      <td>-26.9335</td>\n",
       "      <td>-6.71374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LLR</th>\n",
       "      <td>0.00538048</td>\n",
       "      <td>0.00265019</td>\n",
       "      <td>0.00265019</td>\n",
       "      <td>0.00265019</td>\n",
       "      <td>0.445583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pval</th>\n",
       "      <td>0.941526</td>\n",
       "      <td>0.958943</td>\n",
       "      <td>0.958943</td>\n",
       "      <td>0.958943</td>\n",
       "      <td>0.50444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>qval</th>\n",
       "      <td>0.958943</td>\n",
       "      <td>0.958943</td>\n",
       "      <td>0.958943</td>\n",
       "      <td>0.958943</td>\n",
       "      <td>0.958943</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       0                       1  \\\n",
       "FSV                             0.149886             2.05533e-09   \n",
       "M                                      4                       4   \n",
       "g             ENSDARG00000000001_slc35a5  ENSDARG00000000069_dap   \n",
       "l                                      9                       9   \n",
       "max_delta                        5.65573             4.85165e+08   \n",
       "max_ll                           12.6879                -30.3899   \n",
       "max_mu_hat                   0.000174834             2.02781e-12   \n",
       "max_s2_t_hat                  0.00673753             2.28819e-10   \n",
       "model                                 SE                      SE   \n",
       "n                                     95                      95   \n",
       "s2_FSV                           25.4697                0.719139   \n",
       "s2_logdelta                      1472.02             1.23261e+17   \n",
       "time                          0.00157022             0.000957966   \n",
       "BIC                             -7.16024                 78.9954   \n",
       "max_ll_null                      12.6825                -30.3926   \n",
       "LLR                           0.00538048              0.00265019   \n",
       "pval                            0.941526                0.958943   \n",
       "qval                            0.958943                0.958943   \n",
       "\n",
       "                                     2                         3  \\\n",
       "FSV                        2.05533e-09               2.05533e-09   \n",
       "M                                    4                         4   \n",
       "g             ENSDARG00000000086_itsn1  ENSDARG00000000103_myh10   \n",
       "l                                    9                         9   \n",
       "max_delta                  4.85165e+08               4.85165e+08   \n",
       "max_ll                        -69.0624                  -26.9309   \n",
       "max_mu_hat                -2.05102e-12              -2.59315e-13   \n",
       "max_s2_t_hat               5.16507e-10               2.12748e-10   \n",
       "model                               SE                        SE   \n",
       "n                                   95                        95   \n",
       "s2_FSV                        0.714568                    5.4296   \n",
       "s2_logdelta                1.22478e+17               9.30637e+17   \n",
       "time                       0.000688791               0.000870943   \n",
       "BIC                             156.34                   72.0773   \n",
       "max_ll_null                   -69.0651                  -26.9335   \n",
       "LLR                         0.00265019                0.00265019   \n",
       "pval                          0.958943                  0.958943   \n",
       "qval                          0.958943                  0.958943   \n",
       "\n",
       "                                     4  \n",
       "FSV                           0.775817  \n",
       "M                                    4  \n",
       "g             ENSDARG00000000151_thraa  \n",
       "l                                    9  \n",
       "max_delta                     0.288147  \n",
       "max_ll                        -6.26816  \n",
       "max_mu_hat                -3.83475e-05  \n",
       "max_s2_t_hat                 0.0524371  \n",
       "model                               SE  \n",
       "n                                   95  \n",
       "s2_FSV                         13.5223  \n",
       "s2_logdelta                    457.509  \n",
       "time                       0.000729322  \n",
       "BIC                            30.7518  \n",
       "max_ll_null                   -6.71374  \n",
       "LLR                           0.445583  \n",
       "pval                           0.50444  \n",
       "qval                          0.958943  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.head().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_var = (1 + results.max_delta) * results.max_s2_t_hat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(total_var, results.max_s2_t_hat, marker='.', c='k')\n",
    "plt.loglog()\n",
    "plt.xlim(1e-3, 2e1)\n",
    "plt.ylim(1e-6, 2e1);\n",
    "plt.plot([2e-3, 5e0], [2e-3, 5e0], c='r');\n",
    "plt.xlabel('Total variance')\n",
    "plt.ylabel('Spatial variance');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "results['total_var'] = total_var"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>FSV</th>\n",
       "      <th>M</th>\n",
       "      <th>g</th>\n",
       "      <th>l</th>\n",
       "      <th>max_delta</th>\n",
       "      <th>max_ll</th>\n",
       "      <th>max_mu_hat</th>\n",
       "      <th>max_s2_t_hat</th>\n",
       "      <th>model</th>\n",
       "      <th>n</th>\n",
       "      <th>s2_FSV</th>\n",
       "      <th>s2_logdelta</th>\n",
       "      <th>time</th>\n",
       "      <th>BIC</th>\n",
       "      <th>max_ll_null</th>\n",
       "      <th>LLR</th>\n",
       "      <th>pval</th>\n",
       "      <th>qval</th>\n",
       "      <th>total_var</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20259</th>\n",
       "      <td>0.980114</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000070352_hoxc12a</td>\n",
       "      <td>472.169837</td>\n",
       "      <td>0.009566</td>\n",
       "      <td>-44.335273</td>\n",
       "      <td>1.554134</td>\n",
       "      <td>10.622827</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>0.000119</td>\n",
       "      <td>0.236804</td>\n",
       "      <td>0.000738</td>\n",
       "      <td>106.886053</td>\n",
       "      <td>-78.605278</td>\n",
       "      <td>34.270005</td>\n",
       "      <td>4.797187e-09</td>\n",
       "      <td>1.218306e-06</td>\n",
       "      <td>10.724446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19951</th>\n",
       "      <td>0.853156</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000070546_msgn1</td>\n",
       "      <td>244.036967</td>\n",
       "      <td>0.119441</td>\n",
       "      <td>-89.381996</td>\n",
       "      <td>0.608609</td>\n",
       "      <td>2.296660</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>0.001794</td>\n",
       "      <td>0.106805</td>\n",
       "      <td>0.000832</td>\n",
       "      <td>196.979500</td>\n",
       "      <td>-136.473248</td>\n",
       "      <td>47.091252</td>\n",
       "      <td>6.775691e-12</td>\n",
       "      <td>1.058936e-08</td>\n",
       "      <td>2.570976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19679</th>\n",
       "      <td>0.831907</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000037840_ENSDARG00000037840</td>\n",
       "      <td>244.036967</td>\n",
       "      <td>0.140217</td>\n",
       "      <td>-93.423331</td>\n",
       "      <td>-0.447397</td>\n",
       "      <td>2.155112</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>0.002479</td>\n",
       "      <td>0.121778</td>\n",
       "      <td>0.000813</td>\n",
       "      <td>205.062169</td>\n",
       "      <td>-139.329994</td>\n",
       "      <td>45.906664</td>\n",
       "      <td>1.240230e-11</td>\n",
       "      <td>1.679850e-08</td>\n",
       "      <td>2.457294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19853</th>\n",
       "      <td>0.728639</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000057911_ACTC1</td>\n",
       "      <td>244.036967</td>\n",
       "      <td>0.258440</td>\n",
       "      <td>-110.751398</td>\n",
       "      <td>-0.467278</td>\n",
       "      <td>1.776294</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>0.004725</td>\n",
       "      <td>0.129135</td>\n",
       "      <td>0.000686</td>\n",
       "      <td>239.718303</td>\n",
       "      <td>-146.327073</td>\n",
       "      <td>35.575675</td>\n",
       "      <td>2.453320e-09</td>\n",
       "      <td>7.120586e-07</td>\n",
       "      <td>2.235360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19636</th>\n",
       "      <td>0.795316</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000032242_tnnt2c</td>\n",
       "      <td>244.036967</td>\n",
       "      <td>0.178595</td>\n",
       "      <td>-94.090384</td>\n",
       "      <td>-0.512511</td>\n",
       "      <td>1.756339</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>0.002673</td>\n",
       "      <td>0.101143</td>\n",
       "      <td>0.000763</td>\n",
       "      <td>206.396275</td>\n",
       "      <td>-139.784499</td>\n",
       "      <td>45.694115</td>\n",
       "      <td>1.382372e-11</td>\n",
       "      <td>1.755353e-08</td>\n",
       "      <td>2.070012</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            FSV  M                                      g           l  \\\n",
       "20259  0.980114  4             ENSDARG00000070352_hoxc12a  472.169837   \n",
       "19951  0.853156  4               ENSDARG00000070546_msgn1  244.036967   \n",
       "19679  0.831907  4  ENSDARG00000037840_ENSDARG00000037840  244.036967   \n",
       "19853  0.728639  4               ENSDARG00000057911_ACTC1  244.036967   \n",
       "19636  0.795316  4              ENSDARG00000032242_tnnt2c  244.036967   \n",
       "\n",
       "       max_delta      max_ll  max_mu_hat  max_s2_t_hat model   n    s2_FSV  \\\n",
       "20259   0.009566  -44.335273    1.554134     10.622827    SE  95  0.000119   \n",
       "19951   0.119441  -89.381996    0.608609      2.296660    SE  95  0.001794   \n",
       "19679   0.140217  -93.423331   -0.447397      2.155112    SE  95  0.002479   \n",
       "19853   0.258440 -110.751398   -0.467278      1.776294    SE  95  0.004725   \n",
       "19636   0.178595  -94.090384   -0.512511      1.756339    SE  95  0.002673   \n",
       "\n",
       "       s2_logdelta      time         BIC  max_ll_null        LLR  \\\n",
       "20259     0.236804  0.000738  106.886053   -78.605278  34.270005   \n",
       "19951     0.106805  0.000832  196.979500  -136.473248  47.091252   \n",
       "19679     0.121778  0.000813  205.062169  -139.329994  45.906664   \n",
       "19853     0.129135  0.000686  239.718303  -146.327073  35.575675   \n",
       "19636     0.101143  0.000763  206.396275  -139.784499  45.694115   \n",
       "\n",
       "               pval          qval  total_var  \n",
       "20259  4.797187e-09  1.218306e-06  10.724446  \n",
       "19951  6.775691e-12  1.058936e-08   2.570976  \n",
       "19679  1.240230e-11  1.679850e-08   2.457294  \n",
       "19853  2.453320e-09  7.120586e-07   2.235360  \n",
       "19636  1.382372e-11  1.755353e-08   2.070012  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.sort_values('total_var', ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LfU9xoKmCEFbbybpPooq0tkV6oq7/KPWWfNaDMdKUOAiY2QDwdeA9wG7gfjPb5O6PNOz2ceAFd/8tM7sY+AJwUdJj511af6y9XNksKp3THACi9teqa1JknfTRNVZuylTxSaMlcDqwy90fBzCzW4HzgcYgcD5wdfD4X4Brzcw8r7moHOpV7S9qdMqAWWggCKvtqGYqRdVuRF9dWP9WWSo+aQSBUeCphue7gbdG7ePu+83sReANwC9SOL4kEJXOieoTKGptRyRMnI52gzn9W2Wq+ORqdJCZrQBWAIyNjWVcmmpolc4ZP+GY0tR2RML0akRfkSQeHWRmbwOudvdlwfPVAO5+TcM+m4N9/tvMDgd+Doy0SgcVfXSQiBRLmiP6stTp6KA0vix2P3CSmS0ys9cBFwObmvbZBFwaPP4gsEX9ASKSJ1l/8TIridNBQY7/MmAztSGiN7r7w2b2eWDC3TcBNwDfNrNdwPPUAoWISK6UKdcfVyp9Au5+B3BH07arGh7/CvjDNI4lIiLpqfTcQSIiVacgICJSYQoCIiIVpiAgIlJhCgIiIhWWq28Mi1RdmVaskmJQEBDJiVbLiCoQSK8oHSSSE+1WsxLpBbUEpHK6Sbn0I03TbhlRkV6oTBBQrlWgu5RLv9I0UXPbl30WS8lW6YJA2M0eUK5VgNYpl6hrod1r0qpgpLFUpyo70qlSLTQfNhWsEb4odF3YikFSXotW3d7xIuGtXvPli06JvOa6ubaS3MRbTYUMyVbCUnApjk6nki5VEFi6ZkuspeKaFXHO8CwV+YYQdY2MDg/NWT0qzmsgfNHxuiQBoVNR5Zx35CC/2vda1/PkdzLPfq/6W4p8zfVbpYNAVI0tjvpNIIuLrVfH7MX7prHwRpYds92Uv5sWZpheB4ROr/9Wga9R3MDZ6jw1f+b673NqembOuWz+fZRlsZd+6TQIlKpPIO6i0WH2TM9kMk67V8fs1ft2k1PvpFxJ+3TaBYtWy2lGaV6CsJsAQMNrpqZnuGL9JJevn0w1IHR6/U9Nz7Bo1e0MHzmIO7w4s2/W4/q5iTtqKezaaPzM9d8ZzP59Np/L5usp6TUnrZWqJRBWY4irVdM+bo2pG92kJ7J8325y6nHLFdUxesThhzE9sy/0/Rpvov2oMXabcmwlrdRKWi2W5rJFnf8BM15zP1i2K9ZPtj1WnBQaHOpvabX2b9xrrmoq3RJoV2MbGhzgwtNGuW3rVOgIjCvWT4a+by/HafdqbHiv3jfpMMZW5Yqq8bUK6o01zHZftkojndTu/HVz0w2r1XbTkkurxdJctl8bPIyhwYE55/ZAUIGst2ziHCvu9Td85GDbCt1hZnNaMuov6FzpvjG8fMko96w6iyfWvI8vX3TKnPVC/2b5yZHriEbdyHo5TrtXx4x6vQNLPn8np/z1nSxadTtL12xh4/ap2O+7ctlihgYHZm3rZBhjq8/bbYCq30SjXl+/gU5Nz+ANzzv53I3lDDM6PDTrmoNaQIgrTmolzreH69f/6PBQ4gBQN/3Kvll/MwM295PFPZZTu3m3MjQ4gDttW/QH3HHghVf2MT2z7+Dv9or1kyzs4tquqlK1BJpFrRcatT3uOO2knZSNrx8+cpDBw4x9rx36M+p0bHiYsM9S98Irh5r2nfYVdJNTh9YdgYMDxv++uj/yRhI2uqVZq/TCgFlq4/zbXSON11arz9ysObgkbclF7Wd03nfg1H7f9fOyaNXtsV4X9ZkPhKSg6/vOC2r1Uek/qP0+w96jsbzQv+8DFX3kUqmDQKfi3OCSdrg2v/6FV/YxOGAMDw3Oac4mubiaUwOtdNrJ1uli3M2f2Zn9R//yr/ZH/tEPDQ7wuQ+8BYj3WcJeHxU8uhkM0EkQjBsQwoJ+0rRbq9e3qiBEaTwvcYJInJx+c58C0LZcox22GHvdgVyGSf9K1TGctrCbcNRF3Y/hdt12csYZOtjLTrZux9nPC8n1QvsbReP7t/udRR2/l4MB4o6LT/L7b/f65tZo8+igdh3xcW7W9fMXdzBBu073evk7rQxkdW336vppp9Idw2mKivCtapVxJBlu122tJk7NrZf9Ht2mNhpTQPXzf80FJx+8EewJcvxhDGb9EUalcLIYDBCnJdVt2i3u69uVIerGvWd6JtYAjMaWTdxWTatz3jyUtpOWTB6v7TxREIgQdROOykem0UxvlObF1a7mlkYfRCvtPnPYz1rl8e9ZddbBm0FUTazxfLa6IUbVKvMwaVunabc0X9/udxaW6ooKVnH72qKO2Vyrbv59NrZemgOSUbu+lq7Z0pNcfRkm/VMQiBB1sz3gPifP3MlNNOkfRDcXV9QfTb+G1LX7zGE/i9viins+kw4GqJpOzku7YBO3VZPGMaP6XnqVqy/D9aMgEKFVraSeZ+5FM70u7Ysraa0yiTifOW7fS3MQ7HXapKrSPi/9SIE1HieshdiLTuIyXD/qGI6Qh/lKij70LIk8nH8prqTfbC+yvnYMm9kxwHpgIfAE8CF3fyFkvwNAfeKQn7n7eUmO2w95iPBZ1t6zlofzL8VVhlx9vyRqCZjZF4Hn3X2Nma0C5rn7X4Xs97K7H9XJe2fdEhCR4qpyS7LTlkDSaSPOB74VPP4WsDzh+4mIJLZ8yWjk9DAyW9KWwLS7DwePDXih/rxpv/3AJLAfWOPuGyPebwWwAmBsbOy0J598suuyiYhUUep9Amb2PeBNIT+6svGJu7uZRUWUE9x9ysxOBLaY2Q53/0nzTu6+DlgHtXRQ29KLiEgibYOAu7876mdm9rSZHevue83sWOCZiPeYCv5/3Mz+A1gCzAkCIiLSX0n7BDYBlwaPLwX+tXkHM5tnZkcEj+cDS4FHEh5XRERSkDQIrAHeY2aPAe8OnmNm42Z2fbDP7wITZvYAcDe1PgEFARGRHEj0PQF3fw54V8j2CeBPgsf/BZyc5DgiItIbpVtZTERE4svttBFm9iyQZIzofOAXKRWnH4pWXihemYtWXihemYtWXihemduV9wR3H4n7ZrkNAkmZ2UQnY2WzVrTyQvHKXLTyQvHKXLTyQvHKnHZ5lQ4SEakwBQERkQorcxBYl3UBOlS08kLxyly08kLxyly08kLxypxqeUvbJyAiIu2VuSUgIiJtlC4ImNnZZrbTzHYFaxxkzsyON7O7zewRM3vYzD4dbL/azKbMbDL4d27Da1YHn2GnmS3LqNxPmNmOoGwTwbZjzOwuM3ss+H9esN3M7GtBmR80s1P7XNbFDedx0sxeMrPL83aOzexGM3vGzB5q2NbxOTWzS4P9HzOzS8OO1eMyrzWzHwXl+o6Z1WcTXmhmMw3n+7qG15wWXE+7gs9lfSxvx9dBP+8lEWVe31DeJ8xsMtie7jl299L8AwaoTUx3IvA64AHgzTko17HAqcHjo4EfA28Grgb+ImT/NwdlPwJYFHymgQzK/QQwv2nbF4FVweNVwBeCx+cC36W2gt8ZwH0ZXwc/B07I2zkG3gmcCjzU7TkFjgEeD/6fFzye1+cyvxc4PHj8hYYyL2zcr+l9/if4HBZ8rnP6WN6OroN+30vCytz0878FrurFOS5bS+B0YJe7P+7u/wfcSm3hm0y5+1533xY8/iXwKNBqdYvzgVvd/VV3/ymwi9pny4OohYTOB27ymnuBYavNLJuFdwE/cfdWXzbM5By7+/eB50PK0sk5XQbc5e7Pe20517uAs/tZZne/0933B0/vBY5r9R5BuX/d3e/12t3qJnq0CFXEOY4SdR309V7SqsxBbf5DwC2t3qPbc1y2IDAKPNXwfDetb7Z9Z2YLqU2lfV+w6bKgSX1jPQ1Afj6HA3ea2VarLfgD8EZ33xs8/jnwxuBxXsoMcDGz/2DyfI6h83Oap7ID/DG1WmfdIjPbbmb/aWbvCLaNUitnXRZl7uQ6yNM5fgfwtLs/1rAttXNctiCQa2Z2FHAbcLm7vwR8A/hN4BRgL7UmX5683d1PBc4BPmlm72z8YVDbyNXwMjN7HXAe8M/Bpryf41nyeE5bMbMrqa0YeHOwaS8w5u5LgD8H/tHMfj2r8jUo1HXQ5MPMrtSkeo7LFgSmgOMbnh8XbMucmQ1SCwA3u/sGAHd/2t0PuPtrwN9zKB2Ri8/hhxYDegb4DrXyPV1P89jshYRyUWZqAWubuz8N+T/HgU7PaS7KbmYfBd4P/FEQvAjSKs8Fj7dSy6v/dlC+xpRRX8vcxXWQl3N8OHABsL6+Le1zXLYgcD9wkpktCmqEF1Nb+CZTQU7vBuBRd/9Sw/bGnPkfAPWRAZuAi83sCDNbBJxErcOnb8zs9WZ2dP0xtY7Ah4heSGgTcEkwouUM4MWGFEc/zao15fkcN+j0nG4G3mu1BZvmUfvdbO5ngc3sbOAvgfPc/ZWG7SNmNhA8PpHaeX08KPdLZnZG8PdwCSGLUPWwvJ1eB3m5l7wb+JG7H0zzpH6Oe9XbndU/aiMqfkwtOl6ZdXmCMr2dWhP/QWAy+Hcu8G1gR7B9E3Bsw2uuDD7DTno0iqJNmU+kNiLiAeDh+rkE3gD8O/AY8D3gmGC7AV8PyrwDGM+gzK8HngN+o2Fbrs4xtQC1F9hHLWf78W7OKbU8/K7g38cyKPMuajnz+vV8XbDvhcH1MglsAz7Q8D7j1G6+PwGuJfiyap/K2/F10M97SViZg+3fBP6sad9Uz7G+MSwiUmFlSweJiEgHFARERCpMQUBEpMIUBEREKkxBQESkwhQEREQqTEFARKTCFARERCrs/wHHxdQhf3SPOAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figsize(6, 4)\n",
    "plt.scatter(sample_info.x, res['ENSDARG00000070352_hoxc12a']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "sig_res = results.query('qval < 0.05').copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "126.128432    366\n",
       "65.188408     301\n",
       "244.036967    154\n",
       "33.692075     147\n",
       "17.413462      32\n",
       "472.169837     10\n",
       "Name: l, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sig_res.l.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iter 0, ELBO: -6.81e+10\n",
      "iter 1, ELBO: -3.34e+10, delta_ELBO: 3.47e+10\n",
      "iter 2, ELBO: -3.34e+10, delta_ELBO: 1.22e+04\n",
      "iter 3, ELBO: -3.34e+10, delta_ELBO: 8.83e+03\n",
      "iter 4, ELBO: -3.34e+10, delta_ELBO: 1.57e+03\n",
      "iter 5, ELBO: -3.34e+10, delta_ELBO: 6.03e+02\n",
      "iter 6, ELBO: -3.34e+10, delta_ELBO: 1.84e+02\n",
      "iter 7, ELBO: -3.34e+10, delta_ELBO: 8.54e+01\n",
      "iter 8, ELBO: -3.34e+10, delta_ELBO: 1.22e+02\n",
      "iter 9, ELBO: -3.34e+10, delta_ELBO: 1.59e+02\n",
      "iter 10, ELBO: -3.34e+10, delta_ELBO: 6.98e+01\n",
      "iter 11, ELBO: -3.34e+10, delta_ELBO: 1.45e+01\n",
      "iter 12, ELBO: -3.34e+10, delta_ELBO: 3.68e+00\n",
      "iter 13, ELBO: -3.34e+10, delta_ELBO: 8.78e-01\n",
      "iter 14, ELBO: -3.34e+10, delta_ELBO: 1.43e+00\n",
      "iter 15, ELBO: -3.34e+10, delta_ELBO: 9.44e-01\n",
      "iter 16, ELBO: -3.34e+10, delta_ELBO: 7.94e-01\n",
      "iter 17, ELBO: -3.34e+10, delta_ELBO: 1.52e+00\n",
      "iter 18, ELBO: -3.34e+10, delta_ELBO: 1.81e+00\n",
      "iter 19, ELBO: -3.34e+10, delta_ELBO: 1.72e+00\n",
      "iter 20, ELBO: -3.34e+10, delta_ELBO: 1.53e+00\n",
      "iter 21, ELBO: -3.34e+10, delta_ELBO: 1.36e+00\n",
      "iter 22, ELBO: -3.34e+10, delta_ELBO: 1.23e+00\n",
      "iter 23, ELBO: -3.34e+10, delta_ELBO: 1.09e+00\n",
      "iter 24, ELBO: -3.34e+10, delta_ELBO: 9.91e-01\n",
      "iter 25, ELBO: -3.34e+10, delta_ELBO: 9.18e-01\n",
      "iter 26, ELBO: -3.34e+10, delta_ELBO: 6.92e-01\n",
      "iter 27, ELBO: -3.34e+10, delta_ELBO: 1.14e+00\n",
      "iter 28, ELBO: -3.34e+10, delta_ELBO: 9.94e-01\n",
      "iter 29, ELBO: -3.34e+10, delta_ELBO: 9.88e-01\n",
      "iter 30, ELBO: -3.34e+10, delta_ELBO: 8.27e-01\n",
      "iter 31, ELBO: -3.34e+10, delta_ELBO: 5.47e-01\n",
      "iter 32, ELBO: -3.34e+10, delta_ELBO: 2.86e-01\n",
      "iter 33, ELBO: -3.34e+10, delta_ELBO: 1.31e-01\n",
      "iter 34, ELBO: -3.34e+10, delta_ELBO: 5.15e-02\n",
      "iter 35, ELBO: -3.34e+10, delta_ELBO: 1.47e-02\n",
      "iter 36, ELBO: -3.34e+10, delta_ELBO: 4.41e-03\n",
      "Converged on iter 36\n"
     ]
    }
   ],
   "source": [
    "pattern_results, patterns = SpatialDE.spatial_patterns(X, res, sig_res, 10, 150, verbosity=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figsize(10, 4)\n",
    "for p in patterns.columns:\n",
    "    C = patterns[p]\n",
    "    plt.subplot(2, 5, p + 1)\n",
    "    \n",
    "    plt.plot(sample_info.x, C)\n",
    "    plt.yticks([])\n",
    "    plt.title(f'Pattern {p}')\n",
    "    \n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    185\n",
       "1    135\n",
       "9    121\n",
       "8    109\n",
       "7     98\n",
       "2     94\n",
       "4     91\n",
       "5     81\n",
       "6     67\n",
       "0     29\n",
       "Name: pattern, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pattern_results.pattern.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "comb_results = sig_res.join(pattern_results.set_index('g'), on='g')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>FSV</th>\n",
       "      <th>M</th>\n",
       "      <th>g</th>\n",
       "      <th>l</th>\n",
       "      <th>max_delta</th>\n",
       "      <th>max_ll</th>\n",
       "      <th>max_mu_hat</th>\n",
       "      <th>max_s2_t_hat</th>\n",
       "      <th>model</th>\n",
       "      <th>n</th>\n",
       "      <th>...</th>\n",
       "      <th>s2_logdelta</th>\n",
       "      <th>time</th>\n",
       "      <th>BIC</th>\n",
       "      <th>max_ll_null</th>\n",
       "      <th>LLR</th>\n",
       "      <th>pval</th>\n",
       "      <th>qval</th>\n",
       "      <th>total_var</th>\n",
       "      <th>membership</th>\n",
       "      <th>pattern</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16637</th>\n",
       "      <td>0.704093</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000077121_cyp26b1</td>\n",
       "      <td>65.188408</td>\n",
       "      <td>0.385759</td>\n",
       "      <td>-81.700722</td>\n",
       "      <td>-0.034786</td>\n",
       "      <td>0.494206</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>...</td>\n",
       "      <td>0.042931</td>\n",
       "      <td>0.000705</td>\n",
       "      <td>181.616951</td>\n",
       "      <td>-111.093054</td>\n",
       "      <td>29.392332</td>\n",
       "      <td>5.911107e-08</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.684850</td>\n",
       "      <td>0.999999</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18244</th>\n",
       "      <td>0.700630</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000044098_egr2a</td>\n",
       "      <td>126.128432</td>\n",
       "      <td>0.357475</td>\n",
       "      <td>-60.559674</td>\n",
       "      <td>-0.012502</td>\n",
       "      <td>0.402892</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>...</td>\n",
       "      <td>0.064570</td>\n",
       "      <td>0.000682</td>\n",
       "      <td>139.334855</td>\n",
       "      <td>-89.127449</td>\n",
       "      <td>28.567775</td>\n",
       "      <td>9.047545e-08</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.546916</td>\n",
       "      <td>0.999344</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15102</th>\n",
       "      <td>0.526268</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000023031_hoxa2b</td>\n",
       "      <td>65.188408</td>\n",
       "      <td>0.826259</td>\n",
       "      <td>-69.626080</td>\n",
       "      <td>-0.000957</td>\n",
       "      <td>0.208593</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>...</td>\n",
       "      <td>0.037549</td>\n",
       "      <td>0.000670</td>\n",
       "      <td>157.467668</td>\n",
       "      <td>-92.278801</td>\n",
       "      <td>22.652721</td>\n",
       "      <td>1.940852e-06</td>\n",
       "      <td>0.000163</td>\n",
       "      <td>0.380945</td>\n",
       "      <td>0.999947</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14099</th>\n",
       "      <td>0.664786</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000086393_dbx1a</td>\n",
       "      <td>33.692075</td>\n",
       "      <td>0.485034</td>\n",
       "      <td>-92.479162</td>\n",
       "      <td>-0.008599</td>\n",
       "      <td>0.421386</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>...</td>\n",
       "      <td>0.029051</td>\n",
       "      <td>0.000685</td>\n",
       "      <td>203.173831</td>\n",
       "      <td>-113.207555</td>\n",
       "      <td>20.728393</td>\n",
       "      <td>5.292539e-06</td>\n",
       "      <td>0.000376</td>\n",
       "      <td>0.625772</td>\n",
       "      <td>0.999923</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15027</th>\n",
       "      <td>0.506975</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000020219_dld</td>\n",
       "      <td>65.188408</td>\n",
       "      <td>0.892629</td>\n",
       "      <td>-39.482385</td>\n",
       "      <td>0.020865</td>\n",
       "      <td>0.103866</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>...</td>\n",
       "      <td>0.037674</td>\n",
       "      <td>0.000687</td>\n",
       "      <td>97.180277</td>\n",
       "      <td>-59.462025</td>\n",
       "      <td>19.979641</td>\n",
       "      <td>7.827112e-06</td>\n",
       "      <td>0.000519</td>\n",
       "      <td>0.196579</td>\n",
       "      <td>0.967334</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18201</th>\n",
       "      <td>0.514015</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000042826_egr2b</td>\n",
       "      <td>126.128432</td>\n",
       "      <td>0.790994</td>\n",
       "      <td>-102.069871</td>\n",
       "      <td>-0.018363</td>\n",
       "      <td>0.481099</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>...</td>\n",
       "      <td>0.076419</td>\n",
       "      <td>0.000676</td>\n",
       "      <td>222.355250</td>\n",
       "      <td>-121.896532</td>\n",
       "      <td>19.826661</td>\n",
       "      <td>8.479171e-06</td>\n",
       "      <td>0.000545</td>\n",
       "      <td>0.861646</td>\n",
       "      <td>0.969026</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15555</th>\n",
       "      <td>0.529013</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000039243_zgc:152791</td>\n",
       "      <td>65.188408</td>\n",
       "      <td>0.817207</td>\n",
       "      <td>5.546068</td>\n",
       "      <td>-0.006262</td>\n",
       "      <td>0.043245</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>...</td>\n",
       "      <td>0.035169</td>\n",
       "      <td>0.000742</td>\n",
       "      <td>7.123372</td>\n",
       "      <td>-14.268429</td>\n",
       "      <td>19.814497</td>\n",
       "      <td>8.533302e-06</td>\n",
       "      <td>0.000545</td>\n",
       "      <td>0.078586</td>\n",
       "      <td>0.903800</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13300</th>\n",
       "      <td>0.592982</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000055589_s100t</td>\n",
       "      <td>33.692075</td>\n",
       "      <td>0.660243</td>\n",
       "      <td>-27.802694</td>\n",
       "      <td>-0.006519</td>\n",
       "      <td>0.087766</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>...</td>\n",
       "      <td>0.024921</td>\n",
       "      <td>0.000748</td>\n",
       "      <td>73.820896</td>\n",
       "      <td>-44.756389</td>\n",
       "      <td>16.953695</td>\n",
       "      <td>3.830271e-05</td>\n",
       "      <td>0.001853</td>\n",
       "      <td>0.145712</td>\n",
       "      <td>0.973866</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12642</th>\n",
       "      <td>0.610757</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000020103_ENSDARG00000020103</td>\n",
       "      <td>33.692075</td>\n",
       "      <td>0.613033</td>\n",
       "      <td>-38.371963</td>\n",
       "      <td>0.002622</td>\n",
       "      <td>0.115326</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>...</td>\n",
       "      <td>0.042153</td>\n",
       "      <td>0.000675</td>\n",
       "      <td>94.959433</td>\n",
       "      <td>-53.929831</td>\n",
       "      <td>15.557868</td>\n",
       "      <td>8.001795e-05</td>\n",
       "      <td>0.003352</td>\n",
       "      <td>0.186025</td>\n",
       "      <td>0.660914</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14955</th>\n",
       "      <td>0.438060</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000018397_hpca</td>\n",
       "      <td>65.188408</td>\n",
       "      <td>1.177458</td>\n",
       "      <td>-90.230799</td>\n",
       "      <td>0.010007</td>\n",
       "      <td>0.240446</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>...</td>\n",
       "      <td>0.041856</td>\n",
       "      <td>0.000710</td>\n",
       "      <td>198.677106</td>\n",
       "      <td>-105.740322</td>\n",
       "      <td>15.509523</td>\n",
       "      <td>8.209065e-05</td>\n",
       "      <td>0.003418</td>\n",
       "      <td>0.523561</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12613</th>\n",
       "      <td>0.567649</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000019096_myl7</td>\n",
       "      <td>33.692075</td>\n",
       "      <td>0.732637</td>\n",
       "      <td>-19.422266</td>\n",
       "      <td>-0.000263</td>\n",
       "      <td>0.068459</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>...</td>\n",
       "      <td>0.036913</td>\n",
       "      <td>0.000709</td>\n",
       "      <td>57.060039</td>\n",
       "      <td>-32.616508</td>\n",
       "      <td>13.194242</td>\n",
       "      <td>2.808105e-04</td>\n",
       "      <td>0.009217</td>\n",
       "      <td>0.118615</td>\n",
       "      <td>0.982621</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12597</th>\n",
       "      <td>0.496354</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000018404_krt18</td>\n",
       "      <td>33.692075</td>\n",
       "      <td>0.976037</td>\n",
       "      <td>12.768417</td>\n",
       "      <td>-0.001165</td>\n",
       "      <td>0.028361</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>...</td>\n",
       "      <td>0.038086</td>\n",
       "      <td>0.000561</td>\n",
       "      <td>-7.321327</td>\n",
       "      <td>0.480842</td>\n",
       "      <td>12.287576</td>\n",
       "      <td>4.559837e-04</td>\n",
       "      <td>0.013485</td>\n",
       "      <td>0.056043</td>\n",
       "      <td>0.898883</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12850</th>\n",
       "      <td>0.531699</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000033234_stmn2a</td>\n",
       "      <td>33.692075</td>\n",
       "      <td>0.847211</td>\n",
       "      <td>-63.078804</td>\n",
       "      <td>-0.007778</td>\n",
       "      <td>0.154977</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>...</td>\n",
       "      <td>0.037900</td>\n",
       "      <td>0.000649</td>\n",
       "      <td>144.373116</td>\n",
       "      <td>-74.928943</td>\n",
       "      <td>11.850139</td>\n",
       "      <td>5.765705e-04</td>\n",
       "      <td>0.016180</td>\n",
       "      <td>0.286276</td>\n",
       "      <td>0.998711</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10880</th>\n",
       "      <td>0.746356</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000035904_crabp1b</td>\n",
       "      <td>17.413462</td>\n",
       "      <td>0.334810</td>\n",
       "      <td>-97.602585</td>\n",
       "      <td>-0.000735</td>\n",
       "      <td>0.429395</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>...</td>\n",
       "      <td>0.083612</td>\n",
       "      <td>0.000757</td>\n",
       "      <td>213.420678</td>\n",
       "      <td>-109.392058</td>\n",
       "      <td>11.789473</td>\n",
       "      <td>5.956660e-04</td>\n",
       "      <td>0.016580</td>\n",
       "      <td>0.573161</td>\n",
       "      <td>0.999927</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15536</th>\n",
       "      <td>0.382195</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000038609_mpz</td>\n",
       "      <td>65.188408</td>\n",
       "      <td>1.483736</td>\n",
       "      <td>-94.541028</td>\n",
       "      <td>-0.021101</td>\n",
       "      <td>0.216963</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>...</td>\n",
       "      <td>0.068479</td>\n",
       "      <td>0.000692</td>\n",
       "      <td>207.297564</td>\n",
       "      <td>-106.172789</td>\n",
       "      <td>11.631760</td>\n",
       "      <td>6.483517e-04</td>\n",
       "      <td>0.017825</td>\n",
       "      <td>0.538880</td>\n",
       "      <td>0.946939</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            FSV  M                                      g           l  \\\n",
       "16637  0.704093  4             ENSDARG00000077121_cyp26b1   65.188408   \n",
       "18244  0.700630  4               ENSDARG00000044098_egr2a  126.128432   \n",
       "15102  0.526268  4              ENSDARG00000023031_hoxa2b   65.188408   \n",
       "14099  0.664786  4               ENSDARG00000086393_dbx1a   33.692075   \n",
       "15027  0.506975  4                 ENSDARG00000020219_dld   65.188408   \n",
       "18201  0.514015  4               ENSDARG00000042826_egr2b  126.128432   \n",
       "15555  0.529013  4          ENSDARG00000039243_zgc:152791   65.188408   \n",
       "13300  0.592982  4               ENSDARG00000055589_s100t   33.692075   \n",
       "12642  0.610757  4  ENSDARG00000020103_ENSDARG00000020103   33.692075   \n",
       "14955  0.438060  4                ENSDARG00000018397_hpca   65.188408   \n",
       "12613  0.567649  4                ENSDARG00000019096_myl7   33.692075   \n",
       "12597  0.496354  4               ENSDARG00000018404_krt18   33.692075   \n",
       "12850  0.531699  4              ENSDARG00000033234_stmn2a   33.692075   \n",
       "10880  0.746356  4             ENSDARG00000035904_crabp1b   17.413462   \n",
       "15536  0.382195  4                 ENSDARG00000038609_mpz   65.188408   \n",
       "\n",
       "       max_delta      max_ll  max_mu_hat  max_s2_t_hat model   n   ...     \\\n",
       "16637   0.385759  -81.700722   -0.034786      0.494206    SE  95   ...      \n",
       "18244   0.357475  -60.559674   -0.012502      0.402892    SE  95   ...      \n",
       "15102   0.826259  -69.626080   -0.000957      0.208593    SE  95   ...      \n",
       "14099   0.485034  -92.479162   -0.008599      0.421386    SE  95   ...      \n",
       "15027   0.892629  -39.482385    0.020865      0.103866    SE  95   ...      \n",
       "18201   0.790994 -102.069871   -0.018363      0.481099    SE  95   ...      \n",
       "15555   0.817207    5.546068   -0.006262      0.043245    SE  95   ...      \n",
       "13300   0.660243  -27.802694   -0.006519      0.087766    SE  95   ...      \n",
       "12642   0.613033  -38.371963    0.002622      0.115326    SE  95   ...      \n",
       "14955   1.177458  -90.230799    0.010007      0.240446    SE  95   ...      \n",
       "12613   0.732637  -19.422266   -0.000263      0.068459    SE  95   ...      \n",
       "12597   0.976037   12.768417   -0.001165      0.028361    SE  95   ...      \n",
       "12850   0.847211  -63.078804   -0.007778      0.154977    SE  95   ...      \n",
       "10880   0.334810  -97.602585   -0.000735      0.429395    SE  95   ...      \n",
       "15536   1.483736  -94.541028   -0.021101      0.216963    SE  95   ...      \n",
       "\n",
       "       s2_logdelta      time         BIC  max_ll_null        LLR  \\\n",
       "16637     0.042931  0.000705  181.616951  -111.093054  29.392332   \n",
       "18244     0.064570  0.000682  139.334855   -89.127449  28.567775   \n",
       "15102     0.037549  0.000670  157.467668   -92.278801  22.652721   \n",
       "14099     0.029051  0.000685  203.173831  -113.207555  20.728393   \n",
       "15027     0.037674  0.000687   97.180277   -59.462025  19.979641   \n",
       "18201     0.076419  0.000676  222.355250  -121.896532  19.826661   \n",
       "15555     0.035169  0.000742    7.123372   -14.268429  19.814497   \n",
       "13300     0.024921  0.000748   73.820896   -44.756389  16.953695   \n",
       "12642     0.042153  0.000675   94.959433   -53.929831  15.557868   \n",
       "14955     0.041856  0.000710  198.677106  -105.740322  15.509523   \n",
       "12613     0.036913  0.000709   57.060039   -32.616508  13.194242   \n",
       "12597     0.038086  0.000561   -7.321327     0.480842  12.287576   \n",
       "12850     0.037900  0.000649  144.373116   -74.928943  11.850139   \n",
       "10880     0.083612  0.000757  213.420678  -109.392058  11.789473   \n",
       "15536     0.068479  0.000692  207.297564  -106.172789  11.631760   \n",
       "\n",
       "               pval      qval  total_var  membership  pattern  \n",
       "16637  5.911107e-08  0.000009   0.684850    0.999999        0  \n",
       "18244  9.047545e-08  0.000013   0.546916    0.999344        0  \n",
       "15102  1.940852e-06  0.000163   0.380945    0.999947        0  \n",
       "14099  5.292539e-06  0.000376   0.625772    0.999923        0  \n",
       "15027  7.827112e-06  0.000519   0.196579    0.967334        0  \n",
       "18201  8.479171e-06  0.000545   0.861646    0.969026        0  \n",
       "15555  8.533302e-06  0.000545   0.078586    0.903800        0  \n",
       "13300  3.830271e-05  0.001853   0.145712    0.973866        0  \n",
       "12642  8.001795e-05  0.003352   0.186025    0.660914        0  \n",
       "14955  8.209065e-05  0.003418   0.523561    1.000000        0  \n",
       "12613  2.808105e-04  0.009217   0.118615    0.982621        0  \n",
       "12597  4.559837e-04  0.013485   0.056043    0.898883        0  \n",
       "12850  5.765705e-04  0.016180   0.286276    0.998711        0  \n",
       "10880  5.956660e-04  0.016580   0.573161    0.999927        0  \n",
       "15536  6.483517e-04  0.017825   0.538880    0.946939        0  \n",
       "\n",
       "[15 rows x 21 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comb_results.query('pattern == 0').sort_values('pval').head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 770,
   "metadata": {},
   "outputs": [],
   "source": [
    "def nice_x_axis(n=7):\n",
    "    locs = np.linspace(0, 1800, n)\n",
    "    labs = locs.copy().astype(int).astype(str)\n",
    "    labs[0] = '0\\n(Head)'\n",
    "    labs[-1] = '1800\\n(Tail)'\n",
    "    plt.xticks(locs, labs)\n",
    "    plt.xlim(-100, 1850)\n",
    "    plt.xlabel('Position (µm)');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figsize(6, 4)\n",
    "g = 'ENSDARG00000077121_cyp26b1'\n",
    "plt.scatter(sample_info.x, res[g]);\n",
    "nice_x_axis()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(results.total_var, results.FSV, marker='.', c='k')\n",
    "plt.xscale('log')\n",
    "plt.xlim(1e-3, 2e1);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "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>FSV</th>\n",
       "      <th>M</th>\n",
       "      <th>g</th>\n",
       "      <th>l</th>\n",
       "      <th>max_delta</th>\n",
       "      <th>max_ll</th>\n",
       "      <th>max_mu_hat</th>\n",
       "      <th>max_s2_t_hat</th>\n",
       "      <th>model</th>\n",
       "      <th>n</th>\n",
       "      <th>s2_FSV</th>\n",
       "      <th>s2_logdelta</th>\n",
       "      <th>time</th>\n",
       "      <th>BIC</th>\n",
       "      <th>max_ll_null</th>\n",
       "      <th>LLR</th>\n",
       "      <th>pval</th>\n",
       "      <th>qval</th>\n",
       "      <th>total_var</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16266</th>\n",
       "      <td>0.018978</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000063021_rpain</td>\n",
       "      <td>65.188408</td>\n",
       "      <td>47.448912</td>\n",
       "      <td>-67.820149</td>\n",
       "      <td>0.000745</td>\n",
       "      <td>0.005044</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>0.044915</td>\n",
       "      <td>97.659304</td>\n",
       "      <td>0.002066</td>\n",
       "      <td>153.855806</td>\n",
       "      <td>-67.866237</td>\n",
       "      <td>0.046087</td>\n",
       "      <td>0.830017</td>\n",
       "      <td>0.958943</td>\n",
       "      <td>0.244377</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            FSV  M                         g          l  max_delta     max_ll  \\\n",
       "16266  0.018978  4  ENSDARG00000063021_rpain  65.188408  47.448912 -67.820149   \n",
       "\n",
       "       max_mu_hat  max_s2_t_hat model   n    s2_FSV  s2_logdelta      time  \\\n",
       "16266    0.000745      0.005044    SE  95  0.044915    97.659304  0.002066   \n",
       "\n",
       "              BIC  max_ll_null       LLR      pval      qval  total_var  \n",
       "16266  153.855806   -67.866237  0.046087  0.830017  0.958943   0.244377  "
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.query('total_var > 1e-1 & FSV < 0.1').sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figsize(6, 4)\n",
    "g = 'ENSDARG00000063021_rpain'\n",
    "plt.scatter(sample_info.x, res[g]);\n",
    "nice_x_axis()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(sample_info.x, counts[g]);\n",
    "nice_x_axis()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "gene_info = pd.DataFrame({'mean_counts': counts.mean()})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = results.join(gene_info, on='g')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(results.mean_counts, results.total_var, marker='.', c='k')\n",
    "tmp = results.query('FSV < 0.05')\n",
    "plt.scatter(tmp.mean_counts, tmp.total_var, marker='.', c='r')\n",
    "plt.xscale('log')\n",
    "plt.yscale('log')\n",
    "plt.ylim(1e-3, 1e1);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "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>FSV</th>\n",
       "      <th>M</th>\n",
       "      <th>g</th>\n",
       "      <th>l</th>\n",
       "      <th>max_delta</th>\n",
       "      <th>max_ll</th>\n",
       "      <th>max_mu_hat</th>\n",
       "      <th>max_s2_t_hat</th>\n",
       "      <th>model</th>\n",
       "      <th>n</th>\n",
       "      <th>s2_FSV</th>\n",
       "      <th>s2_logdelta</th>\n",
       "      <th>time</th>\n",
       "      <th>BIC</th>\n",
       "      <th>max_ll_null</th>\n",
       "      <th>LLR</th>\n",
       "      <th>pval</th>\n",
       "      <th>qval</th>\n",
       "      <th>total_var</th>\n",
       "      <th>mean_counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10296</th>\n",
       "      <td>2.055329e-09</td>\n",
       "      <td>4</td>\n",
       "      <td>ERCC-00096_ERCC-00096</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>4.851652e+08</td>\n",
       "      <td>100.458355</td>\n",
       "      <td>1.320887e-13</td>\n",
       "      <td>1.455932e-11</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>0.483538</td>\n",
       "      <td>8.287880e+16</td>\n",
       "      <td>0.000674</td>\n",
       "      <td>-182.701202</td>\n",
       "      <td>100.455704</td>\n",
       "      <td>0.002650</td>\n",
       "      <td>0.958943</td>\n",
       "      <td>0.958943</td>\n",
       "      <td>0.007064</td>\n",
       "      <td>275.494737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20199</th>\n",
       "      <td>3.591918e-02</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000021864_rplp1</td>\n",
       "      <td>472.169837</td>\n",
       "      <td>1.265443e+01</td>\n",
       "      <td>101.815270</td>\n",
       "      <td>-4.162727e-03</td>\n",
       "      <td>5.221713e-04</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>0.007431</td>\n",
       "      <td>4.830680e+00</td>\n",
       "      <td>0.000813</td>\n",
       "      <td>-185.415032</td>\n",
       "      <td>100.953345</td>\n",
       "      <td>0.861925</td>\n",
       "      <td>0.353200</td>\n",
       "      <td>0.958943</td>\n",
       "      <td>0.007130</td>\n",
       "      <td>1406.294737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2854</th>\n",
       "      <td>2.055329e-09</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000036629_rps14</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>4.851652e+08</td>\n",
       "      <td>83.493712</td>\n",
       "      <td>-9.562809e-13</td>\n",
       "      <td>2.080892e-11</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>0.657002</td>\n",
       "      <td>1.126106e+17</td>\n",
       "      <td>0.000653</td>\n",
       "      <td>-148.771916</td>\n",
       "      <td>83.491061</td>\n",
       "      <td>0.002650</td>\n",
       "      <td>0.958943</td>\n",
       "      <td>0.958943</td>\n",
       "      <td>0.010096</td>\n",
       "      <td>959.926316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14775</th>\n",
       "      <td>2.810682e-02</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000013012_ENSDARG00000013012</td>\n",
       "      <td>65.188408</td>\n",
       "      <td>3.173923e+01</td>\n",
       "      <td>81.534963</td>\n",
       "      <td>-2.557574e-05</td>\n",
       "      <td>3.220606e-04</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>0.024243</td>\n",
       "      <td>2.494009e+01</td>\n",
       "      <td>0.001466</td>\n",
       "      <td>-144.854419</td>\n",
       "      <td>81.433382</td>\n",
       "      <td>0.101581</td>\n",
       "      <td>0.749940</td>\n",
       "      <td>0.958943</td>\n",
       "      <td>0.010544</td>\n",
       "      <td>656.515789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10266</th>\n",
       "      <td>2.055329e-09</td>\n",
       "      <td>4</td>\n",
       "      <td>ERCC-00002_ERCC-00002</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>4.851652e+08</td>\n",
       "      <td>78.302741</td>\n",
       "      <td>-1.528203e-13</td>\n",
       "      <td>2.321191e-11</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>0.358753</td>\n",
       "      <td>6.149054e+16</td>\n",
       "      <td>0.000853</td>\n",
       "      <td>-138.389974</td>\n",
       "      <td>78.300090</td>\n",
       "      <td>0.002650</td>\n",
       "      <td>0.958943</td>\n",
       "      <td>0.958943</td>\n",
       "      <td>0.011262</td>\n",
       "      <td>524.084211</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                FSV  M                                      g           l  \\\n",
       "10296  2.055329e-09  4                  ERCC-00096_ERCC-00096    9.000000   \n",
       "20199  3.591918e-02  4               ENSDARG00000021864_rplp1  472.169837   \n",
       "2854   2.055329e-09  4               ENSDARG00000036629_rps14    9.000000   \n",
       "14775  2.810682e-02  4  ENSDARG00000013012_ENSDARG00000013012   65.188408   \n",
       "10266  2.055329e-09  4                  ERCC-00002_ERCC-00002    9.000000   \n",
       "\n",
       "          max_delta      max_ll    max_mu_hat  max_s2_t_hat model   n  \\\n",
       "10296  4.851652e+08  100.458355  1.320887e-13  1.455932e-11    SE  95   \n",
       "20199  1.265443e+01  101.815270 -4.162727e-03  5.221713e-04    SE  95   \n",
       "2854   4.851652e+08   83.493712 -9.562809e-13  2.080892e-11    SE  95   \n",
       "14775  3.173923e+01   81.534963 -2.557574e-05  3.220606e-04    SE  95   \n",
       "10266  4.851652e+08   78.302741 -1.528203e-13  2.321191e-11    SE  95   \n",
       "\n",
       "         s2_FSV   s2_logdelta      time         BIC  max_ll_null       LLR  \\\n",
       "10296  0.483538  8.287880e+16  0.000674 -182.701202   100.455704  0.002650   \n",
       "20199  0.007431  4.830680e+00  0.000813 -185.415032   100.953345  0.861925   \n",
       "2854   0.657002  1.126106e+17  0.000653 -148.771916    83.491061  0.002650   \n",
       "14775  0.024243  2.494009e+01  0.001466 -144.854419    81.433382  0.101581   \n",
       "10266  0.358753  6.149054e+16  0.000853 -138.389974    78.300090  0.002650   \n",
       "\n",
       "           pval      qval  total_var  mean_counts  \n",
       "10296  0.958943  0.958943   0.007064   275.494737  \n",
       "20199  0.353200  0.958943   0.007130  1406.294737  \n",
       "2854   0.958943  0.958943   0.010096   959.926316  \n",
       "14775  0.749940  0.958943   0.010544   656.515789  \n",
       "10266  0.958943  0.958943   0.011262   524.084211  "
      ]
     },
     "execution_count": 309,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.query('mean_counts > 1e2 & FSV < 0.05').sort_values('total_var', ascending=True).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figsize(6, 4)\n",
    "g = 'ENSDARG00000021864_rplp1'\n",
    "plt.scatter(sample_info.x, res[g]);\n",
    "nice_x_axis()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 10000.0)"
      ]
     },
     "execution_count": 322,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(sample_info.x, counts[g]);\n",
    "nice_x_axis()\n",
    "plt.yscale('log')\n",
    "plt.ylim(1 , 1e4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "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>FSV</th>\n",
       "      <th>M</th>\n",
       "      <th>g</th>\n",
       "      <th>l</th>\n",
       "      <th>max_delta</th>\n",
       "      <th>max_ll</th>\n",
       "      <th>max_mu_hat</th>\n",
       "      <th>max_s2_t_hat</th>\n",
       "      <th>model</th>\n",
       "      <th>n</th>\n",
       "      <th>s2_FSV</th>\n",
       "      <th>s2_logdelta</th>\n",
       "      <th>time</th>\n",
       "      <th>BIC</th>\n",
       "      <th>max_ll_null</th>\n",
       "      <th>LLR</th>\n",
       "      <th>pval</th>\n",
       "      <th>qval</th>\n",
       "      <th>total_var</th>\n",
       "      <th>mean_counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16784</th>\n",
       "      <td>0.479223</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000080337_AC024175.4</td>\n",
       "      <td>65.188408</td>\n",
       "      <td>0.997478</td>\n",
       "      <td>-23.911427</td>\n",
       "      <td>0.010474</td>\n",
       "      <td>0.068291</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>0.002952</td>\n",
       "      <td>0.055449</td>\n",
       "      <td>0.000582</td>\n",
       "      <td>66.038362</td>\n",
       "      <td>-40.642513</td>\n",
       "      <td>16.731086</td>\n",
       "      <td>0.000043</td>\n",
       "      <td>0.002017</td>\n",
       "      <td>0.136410</td>\n",
       "      <td>17614.505263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17688</th>\n",
       "      <td>0.292669</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000020850_eef1a1l2</td>\n",
       "      <td>126.128432</td>\n",
       "      <td>2.021958</td>\n",
       "      <td>60.082340</td>\n",
       "      <td>0.007496</td>\n",
       "      <td>0.006829</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>0.006124</td>\n",
       "      <td>0.155226</td>\n",
       "      <td>0.000663</td>\n",
       "      <td>-101.949173</td>\n",
       "      <td>53.319302</td>\n",
       "      <td>6.763038</td>\n",
       "      <td>0.009307</td>\n",
       "      <td>0.136619</td>\n",
       "      <td>0.020637</td>\n",
       "      <td>3620.568421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17078</th>\n",
       "      <td>0.231025</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000092807_RPL41</td>\n",
       "      <td>65.188408</td>\n",
       "      <td>3.055224</td>\n",
       "      <td>85.161883</td>\n",
       "      <td>-0.001778</td>\n",
       "      <td>0.002649</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>0.003730</td>\n",
       "      <td>0.121781</td>\n",
       "      <td>0.000749</td>\n",
       "      <td>-152.108258</td>\n",
       "      <td>80.067981</td>\n",
       "      <td>5.093902</td>\n",
       "      <td>0.024010</td>\n",
       "      <td>0.270857</td>\n",
       "      <td>0.010742</td>\n",
       "      <td>1873.526316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15505</th>\n",
       "      <td>0.498835</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000037870_actb2</td>\n",
       "      <td>65.188408</td>\n",
       "      <td>0.922176</td>\n",
       "      <td>37.053806</td>\n",
       "      <td>-0.002412</td>\n",
       "      <td>0.020180</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>0.003407</td>\n",
       "      <td>0.063808</td>\n",
       "      <td>0.000673</td>\n",
       "      <td>-55.892104</td>\n",
       "      <td>24.274019</td>\n",
       "      <td>12.779787</td>\n",
       "      <td>0.000350</td>\n",
       "      <td>0.011066</td>\n",
       "      <td>0.038790</td>\n",
       "      <td>1796.726316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20292</th>\n",
       "      <td>0.038566</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000088030_rpl35a</td>\n",
       "      <td>472.169837</td>\n",
       "      <td>11.753626</td>\n",
       "      <td>75.350598</td>\n",
       "      <td>-0.008926</td>\n",
       "      <td>0.000981</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>0.006216</td>\n",
       "      <td>3.542648</td>\n",
       "      <td>0.000679</td>\n",
       "      <td>-132.485688</td>\n",
       "      <td>74.470887</td>\n",
       "      <td>0.879711</td>\n",
       "      <td>0.348281</td>\n",
       "      <td>0.958943</td>\n",
       "      <td>0.012505</td>\n",
       "      <td>1755.305263</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            FSV  M                              g           l  max_delta  \\\n",
       "16784  0.479223  4  ENSDARG00000080337_AC024175.4   65.188408   0.997478   \n",
       "17688  0.292669  4    ENSDARG00000020850_eef1a1l2  126.128432   2.021958   \n",
       "17078  0.231025  4       ENSDARG00000092807_RPL41   65.188408   3.055224   \n",
       "15505  0.498835  4       ENSDARG00000037870_actb2   65.188408   0.922176   \n",
       "20292  0.038566  4      ENSDARG00000088030_rpl35a  472.169837  11.753626   \n",
       "\n",
       "          max_ll  max_mu_hat  max_s2_t_hat model   n    s2_FSV  s2_logdelta  \\\n",
       "16784 -23.911427    0.010474      0.068291    SE  95  0.002952     0.055449   \n",
       "17688  60.082340    0.007496      0.006829    SE  95  0.006124     0.155226   \n",
       "17078  85.161883   -0.001778      0.002649    SE  95  0.003730     0.121781   \n",
       "15505  37.053806   -0.002412      0.020180    SE  95  0.003407     0.063808   \n",
       "20292  75.350598   -0.008926      0.000981    SE  95  0.006216     3.542648   \n",
       "\n",
       "           time         BIC  max_ll_null        LLR      pval      qval  \\\n",
       "16784  0.000582   66.038362   -40.642513  16.731086  0.000043  0.002017   \n",
       "17688  0.000663 -101.949173    53.319302   6.763038  0.009307  0.136619   \n",
       "17078  0.000749 -152.108258    80.067981   5.093902  0.024010  0.270857   \n",
       "15505  0.000673  -55.892104    24.274019  12.779787  0.000350  0.011066   \n",
       "20292  0.000679 -132.485688    74.470887   0.879711  0.348281  0.958943   \n",
       "\n",
       "       total_var   mean_counts  \n",
       "16784   0.136410  17614.505263  \n",
       "17688   0.020637   3620.568421  \n",
       "17078   0.010742   1873.526316  \n",
       "15505   0.038790   1796.726316  \n",
       "20292   0.012505   1755.305263  "
      ]
     },
     "execution_count": 323,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.sort_values('mean_counts', ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figsize(6, 4)\n",
    "g = 'ENSDARG00000092807_RPL41'\n",
    "plt.scatter(sample_info.x, res[g]);\n",
    "nice_x_axis()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(sample_info.x, counts[g]);\n",
    "nice_x_axis()\n",
    "plt.yscale('log')\n",
    "plt.ylim(1 , 1e5);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "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>FSV</th>\n",
       "      <th>M</th>\n",
       "      <th>g</th>\n",
       "      <th>l</th>\n",
       "      <th>max_delta</th>\n",
       "      <th>max_ll</th>\n",
       "      <th>max_mu_hat</th>\n",
       "      <th>max_s2_t_hat</th>\n",
       "      <th>model</th>\n",
       "      <th>n</th>\n",
       "      <th>s2_FSV</th>\n",
       "      <th>s2_logdelta</th>\n",
       "      <th>time</th>\n",
       "      <th>BIC</th>\n",
       "      <th>max_ll_null</th>\n",
       "      <th>LLR</th>\n",
       "      <th>pval</th>\n",
       "      <th>qval</th>\n",
       "      <th>total_var</th>\n",
       "      <th>mean_counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18017</th>\n",
       "      <td>0.855317</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000036832_cyt1l</td>\n",
       "      <td>126.128432</td>\n",
       "      <td>0.141520</td>\n",
       "      <td>-16.064137</td>\n",
       "      <td>-0.142235</td>\n",
       "      <td>0.351501</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>0.000818</td>\n",
       "      <td>0.049767</td>\n",
       "      <td>0.000857</td>\n",
       "      <td>50.343781</td>\n",
       "      <td>-70.412792</td>\n",
       "      <td>54.348655</td>\n",
       "      <td>1.678657e-13</td>\n",
       "      <td>7.281206e-10</td>\n",
       "      <td>0.401246</td>\n",
       "      <td>436.063158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14933</th>\n",
       "      <td>0.795069</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000017624_krt4</td>\n",
       "      <td>65.188408</td>\n",
       "      <td>0.236588</td>\n",
       "      <td>-10.295000</td>\n",
       "      <td>-0.036410</td>\n",
       "      <td>0.160432</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>0.000582</td>\n",
       "      <td>0.021983</td>\n",
       "      <td>0.000960</td>\n",
       "      <td>38.805507</td>\n",
       "      <td>-59.648227</td>\n",
       "      <td>49.353227</td>\n",
       "      <td>2.137845e-12</td>\n",
       "      <td>6.204944e-09</td>\n",
       "      <td>0.198388</td>\n",
       "      <td>979.189474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19334</th>\n",
       "      <td>0.841733</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000094041_krt17</td>\n",
       "      <td>126.128432</td>\n",
       "      <td>0.157305</td>\n",
       "      <td>-30.758271</td>\n",
       "      <td>-0.199017</td>\n",
       "      <td>0.438606</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>0.001114</td>\n",
       "      <td>0.059573</td>\n",
       "      <td>0.000906</td>\n",
       "      <td>79.732049</td>\n",
       "      <td>-75.220388</td>\n",
       "      <td>44.462117</td>\n",
       "      <td>2.593270e-11</td>\n",
       "      <td>2.509206e-08</td>\n",
       "      <td>0.507601</td>\n",
       "      <td>216.978947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15463</th>\n",
       "      <td>0.768185</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000036834_krt92</td>\n",
       "      <td>65.188408</td>\n",
       "      <td>0.276991</td>\n",
       "      <td>-23.911880</td>\n",
       "      <td>-0.041632</td>\n",
       "      <td>0.189411</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>0.000715</td>\n",
       "      <td>0.023242</td>\n",
       "      <td>0.000737</td>\n",
       "      <td>66.039267</td>\n",
       "      <td>-67.304068</td>\n",
       "      <td>43.392188</td>\n",
       "      <td>4.479728e-11</td>\n",
       "      <td>3.640585e-08</td>\n",
       "      <td>0.241876</td>\n",
       "      <td>303.189474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19440</th>\n",
       "      <td>0.842311</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000007099_cx43.4</td>\n",
       "      <td>244.036967</td>\n",
       "      <td>0.129914</td>\n",
       "      <td>23.097924</td>\n",
       "      <td>0.223463</td>\n",
       "      <td>0.199959</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>0.001642</td>\n",
       "      <td>0.088258</td>\n",
       "      <td>0.001445</td>\n",
       "      <td>-27.980341</td>\n",
       "      <td>-19.703484</td>\n",
       "      <td>42.801408</td>\n",
       "      <td>6.058853e-11</td>\n",
       "      <td>4.734528e-08</td>\n",
       "      <td>0.225936</td>\n",
       "      <td>198.452632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17448</th>\n",
       "      <td>0.818406</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000011168_ENSDARG00000011168</td>\n",
       "      <td>126.128432</td>\n",
       "      <td>0.185635</td>\n",
       "      <td>-44.197398</td>\n",
       "      <td>-0.152408</td>\n",
       "      <td>0.503785</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>0.001099</td>\n",
       "      <td>0.048610</td>\n",
       "      <td>0.001211</td>\n",
       "      <td>106.610303</td>\n",
       "      <td>-85.747125</td>\n",
       "      <td>41.549727</td>\n",
       "      <td>1.149105e-10</td>\n",
       "      <td>7.782124e-08</td>\n",
       "      <td>0.597305</td>\n",
       "      <td>212.852632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18175</th>\n",
       "      <td>0.662926</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000042245_myl13</td>\n",
       "      <td>126.128432</td>\n",
       "      <td>0.425390</td>\n",
       "      <td>-111.447211</td>\n",
       "      <td>-0.181490</td>\n",
       "      <td>1.012938</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>0.002467</td>\n",
       "      <td>0.055237</td>\n",
       "      <td>0.000683</td>\n",
       "      <td>241.109930</td>\n",
       "      <td>-151.532459</td>\n",
       "      <td>40.085247</td>\n",
       "      <td>2.431181e-10</td>\n",
       "      <td>1.400625e-07</td>\n",
       "      <td>1.443831</td>\n",
       "      <td>118.010526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16690</th>\n",
       "      <td>0.739336</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000078234_si:dkey-253d23.12</td>\n",
       "      <td>65.188408</td>\n",
       "      <td>0.323614</td>\n",
       "      <td>19.951841</td>\n",
       "      <td>-0.019858</td>\n",
       "      <td>0.066689</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>0.000693</td>\n",
       "      <td>0.019757</td>\n",
       "      <td>0.000752</td>\n",
       "      <td>-21.688174</td>\n",
       "      <td>-19.619232</td>\n",
       "      <td>39.571073</td>\n",
       "      <td>3.163344e-10</td>\n",
       "      <td>1.591382e-07</td>\n",
       "      <td>0.088270</td>\n",
       "      <td>413.031579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18524</th>\n",
       "      <td>0.783836</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000058371_krt5</td>\n",
       "      <td>126.128432</td>\n",
       "      <td>0.230720</td>\n",
       "      <td>-19.104014</td>\n",
       "      <td>-0.125405</td>\n",
       "      <td>0.246766</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>0.001766</td>\n",
       "      <td>0.062443</td>\n",
       "      <td>0.000744</td>\n",
       "      <td>56.423536</td>\n",
       "      <td>-58.324458</td>\n",
       "      <td>39.220444</td>\n",
       "      <td>3.785555e-10</td>\n",
       "      <td>1.747980e-07</td>\n",
       "      <td>0.303700</td>\n",
       "      <td>381.263158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17648</th>\n",
       "      <td>0.840508</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000019365_zgc:110712</td>\n",
       "      <td>126.128432</td>\n",
       "      <td>0.158754</td>\n",
       "      <td>-52.043942</td>\n",
       "      <td>-0.279440</td>\n",
       "      <td>0.682305</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>0.001833</td>\n",
       "      <td>0.096924</td>\n",
       "      <td>0.001103</td>\n",
       "      <td>122.303392</td>\n",
       "      <td>-90.966579</td>\n",
       "      <td>38.922636</td>\n",
       "      <td>4.409378e-10</td>\n",
       "      <td>1.990785e-07</td>\n",
       "      <td>0.790623</td>\n",
       "      <td>112.589474</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            FSV  M                                      g           l  \\\n",
       "18017  0.855317  4               ENSDARG00000036832_cyt1l  126.128432   \n",
       "14933  0.795069  4                ENSDARG00000017624_krt4   65.188408   \n",
       "19334  0.841733  4               ENSDARG00000094041_krt17  126.128432   \n",
       "15463  0.768185  4               ENSDARG00000036834_krt92   65.188408   \n",
       "19440  0.842311  4              ENSDARG00000007099_cx43.4  244.036967   \n",
       "17448  0.818406  4  ENSDARG00000011168_ENSDARG00000011168  126.128432   \n",
       "18175  0.662926  4               ENSDARG00000042245_myl13  126.128432   \n",
       "16690  0.739336  4   ENSDARG00000078234_si:dkey-253d23.12   65.188408   \n",
       "18524  0.783836  4                ENSDARG00000058371_krt5  126.128432   \n",
       "17648  0.840508  4          ENSDARG00000019365_zgc:110712  126.128432   \n",
       "\n",
       "       max_delta      max_ll  max_mu_hat  max_s2_t_hat model   n    s2_FSV  \\\n",
       "18017   0.141520  -16.064137   -0.142235      0.351501    SE  95  0.000818   \n",
       "14933   0.236588  -10.295000   -0.036410      0.160432    SE  95  0.000582   \n",
       "19334   0.157305  -30.758271   -0.199017      0.438606    SE  95  0.001114   \n",
       "15463   0.276991  -23.911880   -0.041632      0.189411    SE  95  0.000715   \n",
       "19440   0.129914   23.097924    0.223463      0.199959    SE  95  0.001642   \n",
       "17448   0.185635  -44.197398   -0.152408      0.503785    SE  95  0.001099   \n",
       "18175   0.425390 -111.447211   -0.181490      1.012938    SE  95  0.002467   \n",
       "16690   0.323614   19.951841   -0.019858      0.066689    SE  95  0.000693   \n",
       "18524   0.230720  -19.104014   -0.125405      0.246766    SE  95  0.001766   \n",
       "17648   0.158754  -52.043942   -0.279440      0.682305    SE  95  0.001833   \n",
       "\n",
       "       s2_logdelta      time         BIC  max_ll_null        LLR  \\\n",
       "18017     0.049767  0.000857   50.343781   -70.412792  54.348655   \n",
       "14933     0.021983  0.000960   38.805507   -59.648227  49.353227   \n",
       "19334     0.059573  0.000906   79.732049   -75.220388  44.462117   \n",
       "15463     0.023242  0.000737   66.039267   -67.304068  43.392188   \n",
       "19440     0.088258  0.001445  -27.980341   -19.703484  42.801408   \n",
       "17448     0.048610  0.001211  106.610303   -85.747125  41.549727   \n",
       "18175     0.055237  0.000683  241.109930  -151.532459  40.085247   \n",
       "16690     0.019757  0.000752  -21.688174   -19.619232  39.571073   \n",
       "18524     0.062443  0.000744   56.423536   -58.324458  39.220444   \n",
       "17648     0.096924  0.001103  122.303392   -90.966579  38.922636   \n",
       "\n",
       "               pval          qval  total_var  mean_counts  \n",
       "18017  1.678657e-13  7.281206e-10   0.401246   436.063158  \n",
       "14933  2.137845e-12  6.204944e-09   0.198388   979.189474  \n",
       "19334  2.593270e-11  2.509206e-08   0.507601   216.978947  \n",
       "15463  4.479728e-11  3.640585e-08   0.241876   303.189474  \n",
       "19440  6.058853e-11  4.734528e-08   0.225936   198.452632  \n",
       "17448  1.149105e-10  7.782124e-08   0.597305   212.852632  \n",
       "18175  2.431181e-10  1.400625e-07   1.443831   118.010526  \n",
       "16690  3.163344e-10  1.591382e-07   0.088270   413.031579  \n",
       "18524  3.785555e-10  1.747980e-07   0.303700   381.263158  \n",
       "17648  4.409378e-10  1.990785e-07   0.790623   112.589474  "
      ]
     },
     "execution_count": 332,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.query('mean_counts > 100').sort_values('pval').head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figsize(6, 4)\n",
    "g = 'ENSDARG00000042245_myl13'\n",
    "plt.scatter(sample_info.x, res[g]);\n",
    "nice_x_axis()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 10000.0)"
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(sample_info.x, counts[g] + 1);\n",
    "nice_x_axis()\n",
    "plt.yscale('log')\n",
    "plt.ylim(1 , 1e4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "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>FSV</th>\n",
       "      <th>M</th>\n",
       "      <th>g</th>\n",
       "      <th>l</th>\n",
       "      <th>max_delta</th>\n",
       "      <th>max_ll</th>\n",
       "      <th>max_mu_hat</th>\n",
       "      <th>max_s2_t_hat</th>\n",
       "      <th>model</th>\n",
       "      <th>n</th>\n",
       "      <th>s2_FSV</th>\n",
       "      <th>s2_logdelta</th>\n",
       "      <th>time</th>\n",
       "      <th>BIC</th>\n",
       "      <th>max_ll_null</th>\n",
       "      <th>LLR</th>\n",
       "      <th>pval</th>\n",
       "      <th>qval</th>\n",
       "      <th>total_var</th>\n",
       "      <th>mean_counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20227</th>\n",
       "      <td>0.572236</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000039701_emx2</td>\n",
       "      <td>472.169837</td>\n",
       "      <td>0.352440</td>\n",
       "      <td>-88.827840</td>\n",
       "      <td>0.138372</td>\n",
       "      <td>0.908495</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>0.015420</td>\n",
       "      <td>0.298592</td>\n",
       "      <td>0.000822</td>\n",
       "      <td>195.871188</td>\n",
       "      <td>-103.369463</td>\n",
       "      <td>14.541622</td>\n",
       "      <td>0.000137</td>\n",
       "      <td>0.005086</td>\n",
       "      <td>1.228684</td>\n",
       "      <td>6.210526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14221</th>\n",
       "      <td>0.597139</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000089806_si:dkey-239j18.3</td>\n",
       "      <td>33.692075</td>\n",
       "      <td>0.648950</td>\n",
       "      <td>-128.487072</td>\n",
       "      <td>-0.006220</td>\n",
       "      <td>0.739582</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>0.001331</td>\n",
       "      <td>0.026497</td>\n",
       "      <td>0.000682</td>\n",
       "      <td>275.189652</td>\n",
       "      <td>-144.239103</td>\n",
       "      <td>15.752031</td>\n",
       "      <td>0.000072</td>\n",
       "      <td>0.003082</td>\n",
       "      <td>1.219534</td>\n",
       "      <td>25.736842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12990</th>\n",
       "      <td>0.619656</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000039173_ctslb</td>\n",
       "      <td>33.692075</td>\n",
       "      <td>0.590416</td>\n",
       "      <td>-134.187502</td>\n",
       "      <td>0.004063</td>\n",
       "      <td>0.889275</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>0.001366</td>\n",
       "      <td>0.028082</td>\n",
       "      <td>0.000656</td>\n",
       "      <td>286.590512</td>\n",
       "      <td>-150.928889</td>\n",
       "      <td>16.741387</td>\n",
       "      <td>0.000043</td>\n",
       "      <td>0.002015</td>\n",
       "      <td>1.414317</td>\n",
       "      <td>65.073684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12696</th>\n",
       "      <td>0.604168</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000023656_he1a</td>\n",
       "      <td>33.692075</td>\n",
       "      <td>0.630210</td>\n",
       "      <td>-133.226527</td>\n",
       "      <td>-0.002913</td>\n",
       "      <td>0.833706</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>0.001183</td>\n",
       "      <td>0.023761</td>\n",
       "      <td>0.000654</td>\n",
       "      <td>284.668562</td>\n",
       "      <td>-150.295156</td>\n",
       "      <td>17.068628</td>\n",
       "      <td>0.000036</td>\n",
       "      <td>0.001774</td>\n",
       "      <td>1.359116</td>\n",
       "      <td>40.168421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14269</th>\n",
       "      <td>0.612878</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000090918_ENSDARG00000090918</td>\n",
       "      <td>33.692075</td>\n",
       "      <td>0.607583</td>\n",
       "      <td>-133.419423</td>\n",
       "      <td>0.009180</td>\n",
       "      <td>0.858199</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>0.001236</td>\n",
       "      <td>0.025147</td>\n",
       "      <td>0.000661</td>\n",
       "      <td>285.054354</td>\n",
       "      <td>-150.993325</td>\n",
       "      <td>17.573902</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.001432</td>\n",
       "      <td>1.379627</td>\n",
       "      <td>47.473684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14031</th>\n",
       "      <td>0.628752</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000079376_zgc:174153</td>\n",
       "      <td>33.692075</td>\n",
       "      <td>0.567959</td>\n",
       "      <td>-129.687176</td>\n",
       "      <td>0.007225</td>\n",
       "      <td>0.830327</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>0.001085</td>\n",
       "      <td>0.022666</td>\n",
       "      <td>0.000930</td>\n",
       "      <td>277.589860</td>\n",
       "      <td>-148.860374</td>\n",
       "      <td>19.173198</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>0.000709</td>\n",
       "      <td>1.301920</td>\n",
       "      <td>83.926316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13796</th>\n",
       "      <td>0.643604</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000074306_ctslb</td>\n",
       "      <td>33.692075</td>\n",
       "      <td>0.532654</td>\n",
       "      <td>-120.869845</td>\n",
       "      <td>0.002696</td>\n",
       "      <td>0.719934</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>0.001133</td>\n",
       "      <td>0.024335</td>\n",
       "      <td>0.000744</td>\n",
       "      <td>259.955198</td>\n",
       "      <td>-140.353379</td>\n",
       "      <td>19.483533</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000614</td>\n",
       "      <td>1.103410</td>\n",
       "      <td>21.673684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14398</th>\n",
       "      <td>0.647294</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000095254_si:dkey-239j18.2</td>\n",
       "      <td>33.692075</td>\n",
       "      <td>0.524134</td>\n",
       "      <td>-127.119719</td>\n",
       "      <td>0.014531</td>\n",
       "      <td>0.830082</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>0.001101</td>\n",
       "      <td>0.023826</td>\n",
       "      <td>0.000795</td>\n",
       "      <td>272.454946</td>\n",
       "      <td>-146.884834</td>\n",
       "      <td>19.765115</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.000553</td>\n",
       "      <td>1.265156</td>\n",
       "      <td>58.578947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20210</th>\n",
       "      <td>0.674268</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000031483_col9a1b</td>\n",
       "      <td>472.169837</td>\n",
       "      <td>0.227763</td>\n",
       "      <td>-92.870375</td>\n",
       "      <td>-0.845062</td>\n",
       "      <td>1.513510</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>0.018058</td>\n",
       "      <td>0.415812</td>\n",
       "      <td>0.000656</td>\n",
       "      <td>203.956258</td>\n",
       "      <td>-112.670310</td>\n",
       "      <td>19.799935</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.000545</td>\n",
       "      <td>1.858232</td>\n",
       "      <td>47.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19462</th>\n",
       "      <td>0.639769</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000009461_ENSDARG00000009461</td>\n",
       "      <td>244.036967</td>\n",
       "      <td>0.390736</td>\n",
       "      <td>-93.575967</td>\n",
       "      <td>0.128039</td>\n",
       "      <td>0.842681</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>0.006295</td>\n",
       "      <td>0.134161</td>\n",
       "      <td>0.000671</td>\n",
       "      <td>205.367441</td>\n",
       "      <td>-114.084327</td>\n",
       "      <td>20.508360</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.000416</td>\n",
       "      <td>1.171946</td>\n",
       "      <td>8.600000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            FSV  M                                      g           l  \\\n",
       "20227  0.572236  4                ENSDARG00000039701_emx2  472.169837   \n",
       "14221  0.597139  4    ENSDARG00000089806_si:dkey-239j18.3   33.692075   \n",
       "12990  0.619656  4               ENSDARG00000039173_ctslb   33.692075   \n",
       "12696  0.604168  4                ENSDARG00000023656_he1a   33.692075   \n",
       "14269  0.612878  4  ENSDARG00000090918_ENSDARG00000090918   33.692075   \n",
       "14031  0.628752  4          ENSDARG00000079376_zgc:174153   33.692075   \n",
       "13796  0.643604  4               ENSDARG00000074306_ctslb   33.692075   \n",
       "14398  0.647294  4    ENSDARG00000095254_si:dkey-239j18.2   33.692075   \n",
       "20210  0.674268  4             ENSDARG00000031483_col9a1b  472.169837   \n",
       "19462  0.639769  4  ENSDARG00000009461_ENSDARG00000009461  244.036967   \n",
       "\n",
       "       max_delta      max_ll  max_mu_hat  max_s2_t_hat model   n    s2_FSV  \\\n",
       "20227   0.352440  -88.827840    0.138372      0.908495    SE  95  0.015420   \n",
       "14221   0.648950 -128.487072   -0.006220      0.739582    SE  95  0.001331   \n",
       "12990   0.590416 -134.187502    0.004063      0.889275    SE  95  0.001366   \n",
       "12696   0.630210 -133.226527   -0.002913      0.833706    SE  95  0.001183   \n",
       "14269   0.607583 -133.419423    0.009180      0.858199    SE  95  0.001236   \n",
       "14031   0.567959 -129.687176    0.007225      0.830327    SE  95  0.001085   \n",
       "13796   0.532654 -120.869845    0.002696      0.719934    SE  95  0.001133   \n",
       "14398   0.524134 -127.119719    0.014531      0.830082    SE  95  0.001101   \n",
       "20210   0.227763  -92.870375   -0.845062      1.513510    SE  95  0.018058   \n",
       "19462   0.390736  -93.575967    0.128039      0.842681    SE  95  0.006295   \n",
       "\n",
       "       s2_logdelta      time         BIC  max_ll_null        LLR      pval  \\\n",
       "20227     0.298592  0.000822  195.871188  -103.369463  14.541622  0.000137   \n",
       "14221     0.026497  0.000682  275.189652  -144.239103  15.752031  0.000072   \n",
       "12990     0.028082  0.000656  286.590512  -150.928889  16.741387  0.000043   \n",
       "12696     0.023761  0.000654  284.668562  -150.295156  17.068628  0.000036   \n",
       "14269     0.025147  0.000661  285.054354  -150.993325  17.573902  0.000028   \n",
       "14031     0.022666  0.000930  277.589860  -148.860374  19.173198  0.000012   \n",
       "13796     0.024335  0.000744  259.955198  -140.353379  19.483533  0.000010   \n",
       "14398     0.023826  0.000795  272.454946  -146.884834  19.765115  0.000009   \n",
       "20210     0.415812  0.000656  203.956258  -112.670310  19.799935  0.000009   \n",
       "19462     0.134161  0.000671  205.367441  -114.084327  20.508360  0.000006   \n",
       "\n",
       "           qval  total_var  mean_counts  \n",
       "20227  0.005086   1.228684     6.210526  \n",
       "14221  0.003082   1.219534    25.736842  \n",
       "12990  0.002015   1.414317    65.073684  \n",
       "12696  0.001774   1.359116    40.168421  \n",
       "14269  0.001432   1.379627    47.473684  \n",
       "14031  0.000709   1.301920    83.926316  \n",
       "13796  0.000614   1.103410    21.673684  \n",
       "14398  0.000553   1.265156    58.578947  \n",
       "20210  0.000545   1.858232    47.200000  \n",
       "19462  0.000416   1.171946     8.600000  "
      ]
     },
     "execution_count": 333,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.query('1.1 < total_var').sort_values('pval', ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figsize(6, 4)\n",
    "g = 'ENSDARG00000079376_zgc:174153'\n",
    "plt.scatter(sample_info.x, res[g]);\n",
    "nice_x_axis()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 10000.0)"
      ]
     },
     "execution_count": 287,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(sample_info.x, counts[g] + 1);\n",
    "nice_x_axis()\n",
    "plt.yscale('log')\n",
    "plt.ylim(1 , 1e4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "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>FSV</th>\n",
       "      <th>M</th>\n",
       "      <th>g</th>\n",
       "      <th>l</th>\n",
       "      <th>max_delta</th>\n",
       "      <th>max_ll</th>\n",
       "      <th>max_mu_hat</th>\n",
       "      <th>max_s2_t_hat</th>\n",
       "      <th>model</th>\n",
       "      <th>n</th>\n",
       "      <th>s2_FSV</th>\n",
       "      <th>s2_logdelta</th>\n",
       "      <th>time</th>\n",
       "      <th>BIC</th>\n",
       "      <th>max_ll_null</th>\n",
       "      <th>LLR</th>\n",
       "      <th>pval</th>\n",
       "      <th>qval</th>\n",
       "      <th>total_var</th>\n",
       "      <th>mean_counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17115</th>\n",
       "      <td>8.033182e-02</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000093831_znf1121</td>\n",
       "      <td>65.188408</td>\n",
       "      <td>1.050832e+01</td>\n",
       "      <td>-108.336627</td>\n",
       "      <td>1.341855e-03</td>\n",
       "      <td>5.055415e-02</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>0.007927</td>\n",
       "      <td>1.227055e+00</td>\n",
       "      <td>0.002178</td>\n",
       "      <td>234.888761</td>\n",
       "      <td>-109.098839</td>\n",
       "      <td>0.762212</td>\n",
       "      <td>0.382637</td>\n",
       "      <td>0.958943</td>\n",
       "      <td>0.581793</td>\n",
       "      <td>5.168421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16930</th>\n",
       "      <td>3.226590e-01</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000088967_wnk4b</td>\n",
       "      <td>65.188408</td>\n",
       "      <td>1.926873e+00</td>\n",
       "      <td>-97.900499</td>\n",
       "      <td>-2.329415e-02</td>\n",
       "      <td>1.864995e-01</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>0.003182</td>\n",
       "      <td>7.384806e-02</td>\n",
       "      <td>0.000676</td>\n",
       "      <td>214.016505</td>\n",
       "      <td>-106.544060</td>\n",
       "      <td>8.643561</td>\n",
       "      <td>0.003282</td>\n",
       "      <td>0.061574</td>\n",
       "      <td>0.545860</td>\n",
       "      <td>6.568421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9160</th>\n",
       "      <td>2.055329e-09</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000091334_ENSDARG00000091334</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>4.851652e+08</td>\n",
       "      <td>-105.468715</td>\n",
       "      <td>-3.566685e-12</td>\n",
       "      <td>1.111580e-09</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>0.185267</td>\n",
       "      <td>3.175497e+16</td>\n",
       "      <td>0.000856</td>\n",
       "      <td>229.152937</td>\n",
       "      <td>-105.471365</td>\n",
       "      <td>0.002650</td>\n",
       "      <td>0.958943</td>\n",
       "      <td>0.958943</td>\n",
       "      <td>0.539300</td>\n",
       "      <td>41.905263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17823</th>\n",
       "      <td>3.528868e-01</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000029179_foxd1</td>\n",
       "      <td>126.128432</td>\n",
       "      <td>1.534163e+00</td>\n",
       "      <td>-90.390587</td>\n",
       "      <td>7.296788e-02</td>\n",
       "      <td>2.084791e-01</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>0.013578</td>\n",
       "      <td>2.936426e-01</td>\n",
       "      <td>0.000701</td>\n",
       "      <td>198.996682</td>\n",
       "      <td>-98.467489</td>\n",
       "      <td>8.076902</td>\n",
       "      <td>0.004483</td>\n",
       "      <td>0.077986</td>\n",
       "      <td>0.528320</td>\n",
       "      <td>5.284211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19944</th>\n",
       "      <td>3.861092e-01</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000070339_hoxc3a</td>\n",
       "      <td>244.036967</td>\n",
       "      <td>1.103333e+00</td>\n",
       "      <td>-81.162510</td>\n",
       "      <td>1.039455e-02</td>\n",
       "      <td>2.475075e-01</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>0.015143</td>\n",
       "      <td>3.085721e-01</td>\n",
       "      <td>0.000581</td>\n",
       "      <td>180.540527</td>\n",
       "      <td>-89.797261</td>\n",
       "      <td>8.634751</td>\n",
       "      <td>0.003298</td>\n",
       "      <td>0.061758</td>\n",
       "      <td>0.520591</td>\n",
       "      <td>16.210526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17563</th>\n",
       "      <td>2.450090e-01</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000015775_ENSDARG00000015775</td>\n",
       "      <td>126.128432</td>\n",
       "      <td>2.578022e+00</td>\n",
       "      <td>-95.245210</td>\n",
       "      <td>-4.000416e-02</td>\n",
       "      <td>1.440185e-01</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>0.007224</td>\n",
       "      <td>2.204473e-01</td>\n",
       "      <td>0.000652</td>\n",
       "      <td>208.705927</td>\n",
       "      <td>-100.729566</td>\n",
       "      <td>5.484356</td>\n",
       "      <td>0.019187</td>\n",
       "      <td>0.231904</td>\n",
       "      <td>0.515301</td>\n",
       "      <td>5.578947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11717</th>\n",
       "      <td>3.728283e-01</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000076434_ARHGAP22 (1 of many)</td>\n",
       "      <td>17.413462</td>\n",
       "      <td>1.657287e+00</td>\n",
       "      <td>-100.763434</td>\n",
       "      <td>6.169905e-03</td>\n",
       "      <td>1.930320e-01</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>0.010739</td>\n",
       "      <td>2.236363e-01</td>\n",
       "      <td>0.000682</td>\n",
       "      <td>219.742376</td>\n",
       "      <td>-103.295423</td>\n",
       "      <td>2.531988</td>\n",
       "      <td>0.111560</td>\n",
       "      <td>0.729438</td>\n",
       "      <td>0.512941</td>\n",
       "      <td>9.978947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16363</th>\n",
       "      <td>3.106932e-01</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000069503_cldnh</td>\n",
       "      <td>65.188408</td>\n",
       "      <td>2.036434e+00</td>\n",
       "      <td>-93.482896</td>\n",
       "      <td>-1.756111e-02</td>\n",
       "      <td>1.620474e-01</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>0.004317</td>\n",
       "      <td>1.035393e-01</td>\n",
       "      <td>0.000656</td>\n",
       "      <td>205.181299</td>\n",
       "      <td>-100.594906</td>\n",
       "      <td>7.112010</td>\n",
       "      <td>0.007657</td>\n",
       "      <td>0.118521</td>\n",
       "      <td>0.492046</td>\n",
       "      <td>4.126316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12742</th>\n",
       "      <td>2.913011e-01</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000027279_numb</td>\n",
       "      <td>33.692075</td>\n",
       "      <td>2.340191e+00</td>\n",
       "      <td>-96.868979</td>\n",
       "      <td>-2.334198e-02</td>\n",
       "      <td>1.461008e-01</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>0.008178</td>\n",
       "      <td>2.082265e-01</td>\n",
       "      <td>0.000647</td>\n",
       "      <td>211.953465</td>\n",
       "      <td>-99.549476</td>\n",
       "      <td>2.680497</td>\n",
       "      <td>0.101584</td>\n",
       "      <td>0.693714</td>\n",
       "      <td>0.488005</td>\n",
       "      <td>25.263158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17365</th>\n",
       "      <td>4.012571e-01</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000007641_msx1a</td>\n",
       "      <td>126.128432</td>\n",
       "      <td>1.248373e+00</td>\n",
       "      <td>-83.225616</td>\n",
       "      <td>-2.415600e-02</td>\n",
       "      <td>2.154986e-01</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>0.010967</td>\n",
       "      <td>2.187335e-01</td>\n",
       "      <td>0.000679</td>\n",
       "      <td>184.666741</td>\n",
       "      <td>-91.620386</td>\n",
       "      <td>8.394769</td>\n",
       "      <td>0.003763</td>\n",
       "      <td>0.067898</td>\n",
       "      <td>0.484521</td>\n",
       "      <td>11.526316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17507</th>\n",
       "      <td>2.051659e-01</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000013838_sulf2b</td>\n",
       "      <td>126.128432</td>\n",
       "      <td>3.241141e+00</td>\n",
       "      <td>-93.940143</td>\n",
       "      <td>-3.131293e-02</td>\n",
       "      <td>1.135528e-01</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>0.005966</td>\n",
       "      <td>2.250821e-01</td>\n",
       "      <td>0.000691</td>\n",
       "      <td>206.095793</td>\n",
       "      <td>-100.822631</td>\n",
       "      <td>6.882489</td>\n",
       "      <td>0.008704</td>\n",
       "      <td>0.130227</td>\n",
       "      <td>0.481593</td>\n",
       "      <td>6.863158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19488</th>\n",
       "      <td>2.426675e-01</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000011821_plod2</td>\n",
       "      <td>244.036967</td>\n",
       "      <td>2.165711e+00</td>\n",
       "      <td>-87.053794</td>\n",
       "      <td>-1.233989e-01</td>\n",
       "      <td>1.493339e-01</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>0.010335</td>\n",
       "      <td>3.188342e-01</td>\n",
       "      <td>0.000657</td>\n",
       "      <td>192.323095</td>\n",
       "      <td>-95.888015</td>\n",
       "      <td>8.834222</td>\n",
       "      <td>0.002956</td>\n",
       "      <td>0.056917</td>\n",
       "      <td>0.472748</td>\n",
       "      <td>15.305263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17224</th>\n",
       "      <td>3.292581e-01</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000002131_celf2</td>\n",
       "      <td>126.128432</td>\n",
       "      <td>1.704299e+00</td>\n",
       "      <td>-86.218179</td>\n",
       "      <td>2.331038e-02</td>\n",
       "      <td>1.734296e-01</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>0.007942</td>\n",
       "      <td>1.812470e-01</td>\n",
       "      <td>0.000700</td>\n",
       "      <td>190.651866</td>\n",
       "      <td>-94.044351</td>\n",
       "      <td>7.826172</td>\n",
       "      <td>0.005150</td>\n",
       "      <td>0.086752</td>\n",
       "      <td>0.469005</td>\n",
       "      <td>16.094737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12498</th>\n",
       "      <td>4.093639e-01</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000014651_svilb</td>\n",
       "      <td>33.692075</td>\n",
       "      <td>1.387849e+00</td>\n",
       "      <td>-90.868460</td>\n",
       "      <td>-1.487163e-02</td>\n",
       "      <td>1.937378e-01</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>0.002990</td>\n",
       "      <td>5.903730e-02</td>\n",
       "      <td>0.000649</td>\n",
       "      <td>199.952428</td>\n",
       "      <td>-98.704697</td>\n",
       "      <td>7.836237</td>\n",
       "      <td>0.005121</td>\n",
       "      <td>0.086533</td>\n",
       "      <td>0.462617</td>\n",
       "      <td>6.757895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20067</th>\n",
       "      <td>1.207924e-01</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000082180_ENSDARG00000082180</td>\n",
       "      <td>244.036967</td>\n",
       "      <td>5.050998e+00</td>\n",
       "      <td>-93.473487</td>\n",
       "      <td>3.057024e-02</td>\n",
       "      <td>7.633925e-02</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>0.007130</td>\n",
       "      <td>5.694103e-01</td>\n",
       "      <td>0.000686</td>\n",
       "      <td>205.162481</td>\n",
       "      <td>-96.636999</td>\n",
       "      <td>3.163512</td>\n",
       "      <td>0.075301</td>\n",
       "      <td>0.577317</td>\n",
       "      <td>0.461929</td>\n",
       "      <td>22.905263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19744</th>\n",
       "      <td>1.639778e-01</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000043686_ENSDARG00000043686</td>\n",
       "      <td>244.036967</td>\n",
       "      <td>3.538005e+00</td>\n",
       "      <td>-90.354348</td>\n",
       "      <td>-4.078718e-02</td>\n",
       "      <td>1.003376e-01</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>0.010153</td>\n",
       "      <td>5.163428e-01</td>\n",
       "      <td>0.000743</td>\n",
       "      <td>198.924203</td>\n",
       "      <td>-96.397920</td>\n",
       "      <td>6.043573</td>\n",
       "      <td>0.013957</td>\n",
       "      <td>0.182945</td>\n",
       "      <td>0.455333</td>\n",
       "      <td>6.115789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15407</th>\n",
       "      <td>3.439770e-01</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000035652_sat1a.2</td>\n",
       "      <td>65.188408</td>\n",
       "      <td>1.750569e+00</td>\n",
       "      <td>-88.071431</td>\n",
       "      <td>-2.059294e-02</td>\n",
       "      <td>1.645842e-01</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>0.003534</td>\n",
       "      <td>7.790962e-02</td>\n",
       "      <td>0.000755</td>\n",
       "      <td>194.358370</td>\n",
       "      <td>-96.912685</td>\n",
       "      <td>8.841254</td>\n",
       "      <td>0.002945</td>\n",
       "      <td>0.056875</td>\n",
       "      <td>0.452700</td>\n",
       "      <td>5.694737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11711</th>\n",
       "      <td>3.973786e-01</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000076364_nan</td>\n",
       "      <td>17.413462</td>\n",
       "      <td>1.494033e+00</td>\n",
       "      <td>-94.047021</td>\n",
       "      <td>3.922662e-03</td>\n",
       "      <td>1.797707e-01</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>0.015891</td>\n",
       "      <td>3.185970e-01</td>\n",
       "      <td>0.000859</td>\n",
       "      <td>206.309550</td>\n",
       "      <td>-96.087862</td>\n",
       "      <td>2.040840</td>\n",
       "      <td>0.153125</td>\n",
       "      <td>0.861069</td>\n",
       "      <td>0.448354</td>\n",
       "      <td>2.631579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16277</th>\n",
       "      <td>3.269530e-01</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000063313_plbd1</td>\n",
       "      <td>65.188408</td>\n",
       "      <td>1.889512e+00</td>\n",
       "      <td>-88.356027</td>\n",
       "      <td>-2.226128e-02</td>\n",
       "      <td>1.551325e-01</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>0.003562</td>\n",
       "      <td>8.176477e-02</td>\n",
       "      <td>0.000677</td>\n",
       "      <td>194.927561</td>\n",
       "      <td>-97.224850</td>\n",
       "      <td>8.868823</td>\n",
       "      <td>0.002901</td>\n",
       "      <td>0.056237</td>\n",
       "      <td>0.448257</td>\n",
       "      <td>6.410526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20190</th>\n",
       "      <td>2.106675e-01</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000015016_kif26ab</td>\n",
       "      <td>472.169837</td>\n",
       "      <td>1.766518e+00</td>\n",
       "      <td>-79.859140</td>\n",
       "      <td>1.898702e-01</td>\n",
       "      <td>1.618910e-01</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>0.017881</td>\n",
       "      <td>6.526206e-01</td>\n",
       "      <td>0.000754</td>\n",
       "      <td>177.933788</td>\n",
       "      <td>-86.458659</td>\n",
       "      <td>6.599519</td>\n",
       "      <td>0.010201</td>\n",
       "      <td>0.146360</td>\n",
       "      <td>0.447874</td>\n",
       "      <td>4.684211</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                FSV  M                                        g           l  \\\n",
       "17115  8.033182e-02  4               ENSDARG00000093831_znf1121   65.188408   \n",
       "16930  3.226590e-01  4                 ENSDARG00000088967_wnk4b   65.188408   \n",
       "9160   2.055329e-09  4    ENSDARG00000091334_ENSDARG00000091334    9.000000   \n",
       "17823  3.528868e-01  4                 ENSDARG00000029179_foxd1  126.128432   \n",
       "19944  3.861092e-01  4                ENSDARG00000070339_hoxc3a  244.036967   \n",
       "17563  2.450090e-01  4    ENSDARG00000015775_ENSDARG00000015775  126.128432   \n",
       "11717  3.728283e-01  4  ENSDARG00000076434_ARHGAP22 (1 of many)   17.413462   \n",
       "16363  3.106932e-01  4                 ENSDARG00000069503_cldnh   65.188408   \n",
       "12742  2.913011e-01  4                  ENSDARG00000027279_numb   33.692075   \n",
       "17365  4.012571e-01  4                 ENSDARG00000007641_msx1a  126.128432   \n",
       "17507  2.051659e-01  4                ENSDARG00000013838_sulf2b  126.128432   \n",
       "19488  2.426675e-01  4                 ENSDARG00000011821_plod2  244.036967   \n",
       "17224  3.292581e-01  4                 ENSDARG00000002131_celf2  126.128432   \n",
       "12498  4.093639e-01  4                 ENSDARG00000014651_svilb   33.692075   \n",
       "20067  1.207924e-01  4    ENSDARG00000082180_ENSDARG00000082180  244.036967   \n",
       "19744  1.639778e-01  4    ENSDARG00000043686_ENSDARG00000043686  244.036967   \n",
       "15407  3.439770e-01  4               ENSDARG00000035652_sat1a.2   65.188408   \n",
       "11711  3.973786e-01  4                   ENSDARG00000076364_nan   17.413462   \n",
       "16277  3.269530e-01  4                 ENSDARG00000063313_plbd1   65.188408   \n",
       "20190  2.106675e-01  4               ENSDARG00000015016_kif26ab  472.169837   \n",
       "\n",
       "          max_delta      max_ll    max_mu_hat  max_s2_t_hat model   n  \\\n",
       "17115  1.050832e+01 -108.336627  1.341855e-03  5.055415e-02    SE  95   \n",
       "16930  1.926873e+00  -97.900499 -2.329415e-02  1.864995e-01    SE  95   \n",
       "9160   4.851652e+08 -105.468715 -3.566685e-12  1.111580e-09    SE  95   \n",
       "17823  1.534163e+00  -90.390587  7.296788e-02  2.084791e-01    SE  95   \n",
       "19944  1.103333e+00  -81.162510  1.039455e-02  2.475075e-01    SE  95   \n",
       "17563  2.578022e+00  -95.245210 -4.000416e-02  1.440185e-01    SE  95   \n",
       "11717  1.657287e+00 -100.763434  6.169905e-03  1.930320e-01    SE  95   \n",
       "16363  2.036434e+00  -93.482896 -1.756111e-02  1.620474e-01    SE  95   \n",
       "12742  2.340191e+00  -96.868979 -2.334198e-02  1.461008e-01    SE  95   \n",
       "17365  1.248373e+00  -83.225616 -2.415600e-02  2.154986e-01    SE  95   \n",
       "17507  3.241141e+00  -93.940143 -3.131293e-02  1.135528e-01    SE  95   \n",
       "19488  2.165711e+00  -87.053794 -1.233989e-01  1.493339e-01    SE  95   \n",
       "17224  1.704299e+00  -86.218179  2.331038e-02  1.734296e-01    SE  95   \n",
       "12498  1.387849e+00  -90.868460 -1.487163e-02  1.937378e-01    SE  95   \n",
       "20067  5.050998e+00  -93.473487  3.057024e-02  7.633925e-02    SE  95   \n",
       "19744  3.538005e+00  -90.354348 -4.078718e-02  1.003376e-01    SE  95   \n",
       "15407  1.750569e+00  -88.071431 -2.059294e-02  1.645842e-01    SE  95   \n",
       "11711  1.494033e+00  -94.047021  3.922662e-03  1.797707e-01    SE  95   \n",
       "16277  1.889512e+00  -88.356027 -2.226128e-02  1.551325e-01    SE  95   \n",
       "20190  1.766518e+00  -79.859140  1.898702e-01  1.618910e-01    SE  95   \n",
       "\n",
       "         s2_FSV   s2_logdelta      time         BIC  max_ll_null       LLR  \\\n",
       "17115  0.007927  1.227055e+00  0.002178  234.888761  -109.098839  0.762212   \n",
       "16930  0.003182  7.384806e-02  0.000676  214.016505  -106.544060  8.643561   \n",
       "9160   0.185267  3.175497e+16  0.000856  229.152937  -105.471365  0.002650   \n",
       "17823  0.013578  2.936426e-01  0.000701  198.996682   -98.467489  8.076902   \n",
       "19944  0.015143  3.085721e-01  0.000581  180.540527   -89.797261  8.634751   \n",
       "17563  0.007224  2.204473e-01  0.000652  208.705927  -100.729566  5.484356   \n",
       "11717  0.010739  2.236363e-01  0.000682  219.742376  -103.295423  2.531988   \n",
       "16363  0.004317  1.035393e-01  0.000656  205.181299  -100.594906  7.112010   \n",
       "12742  0.008178  2.082265e-01  0.000647  211.953465   -99.549476  2.680497   \n",
       "17365  0.010967  2.187335e-01  0.000679  184.666741   -91.620386  8.394769   \n",
       "17507  0.005966  2.250821e-01  0.000691  206.095793  -100.822631  6.882489   \n",
       "19488  0.010335  3.188342e-01  0.000657  192.323095   -95.888015  8.834222   \n",
       "17224  0.007942  1.812470e-01  0.000700  190.651866   -94.044351  7.826172   \n",
       "12498  0.002990  5.903730e-02  0.000649  199.952428   -98.704697  7.836237   \n",
       "20067  0.007130  5.694103e-01  0.000686  205.162481   -96.636999  3.163512   \n",
       "19744  0.010153  5.163428e-01  0.000743  198.924203   -96.397920  6.043573   \n",
       "15407  0.003534  7.790962e-02  0.000755  194.358370   -96.912685  8.841254   \n",
       "11711  0.015891  3.185970e-01  0.000859  206.309550   -96.087862  2.040840   \n",
       "16277  0.003562  8.176477e-02  0.000677  194.927561   -97.224850  8.868823   \n",
       "20190  0.017881  6.526206e-01  0.000754  177.933788   -86.458659  6.599519   \n",
       "\n",
       "           pval      qval  total_var  mean_counts  \n",
       "17115  0.382637  0.958943   0.581793     5.168421  \n",
       "16930  0.003282  0.061574   0.545860     6.568421  \n",
       "9160   0.958943  0.958943   0.539300    41.905263  \n",
       "17823  0.004483  0.077986   0.528320     5.284211  \n",
       "19944  0.003298  0.061758   0.520591    16.210526  \n",
       "17563  0.019187  0.231904   0.515301     5.578947  \n",
       "11717  0.111560  0.729438   0.512941     9.978947  \n",
       "16363  0.007657  0.118521   0.492046     4.126316  \n",
       "12742  0.101584  0.693714   0.488005    25.263158  \n",
       "17365  0.003763  0.067898   0.484521    11.526316  \n",
       "17507  0.008704  0.130227   0.481593     6.863158  \n",
       "19488  0.002956  0.056917   0.472748    15.305263  \n",
       "17224  0.005150  0.086752   0.469005    16.094737  \n",
       "12498  0.005121  0.086533   0.462617     6.757895  \n",
       "20067  0.075301  0.577317   0.461929    22.905263  \n",
       "19744  0.013957  0.182945   0.455333     6.115789  \n",
       "15407  0.002945  0.056875   0.452700     5.694737  \n",
       "11711  0.153125  0.861069   0.448354     2.631579  \n",
       "16277  0.002901  0.056237   0.448257     6.410526  \n",
       "20190  0.010201  0.146360   0.447874     4.684211  "
      ]
     },
     "execution_count": 338,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.sort_values('total_var', ascending=False).query('qval > 0.05').head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figsize(6, 4)\n",
    "g = 'ENSDARG00000091334_ENSDARG00000091334'\n",
    "plt.scatter(sample_info.x, res[g]);\n",
    "nice_x_axis()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 10000.0)"
      ]
     },
     "execution_count": 344,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(sample_info.x, counts[g] + 1);\n",
    "nice_x_axis()\n",
    "plt.yscale('log')\n",
    "plt.ylim(1 , 1e4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 521,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "126.128432    366\n",
       "65.188408     301\n",
       "244.036967    154\n",
       "33.692075     147\n",
       "17.413462      32\n",
       "472.169837     10\n",
       "Name: l, dtype: int64"
      ]
     },
     "execution_count": 521,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comb_results.l.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 526,
   "metadata": {},
   "outputs": [],
   "source": [
    "Sigma = SpatialDE.base.SE_kernel(X, 126)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 527,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 558,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def se_kernel_pred(X1, X2, l):\n",
    "    X1sq = np.sum(np.square(X1), 1)\n",
    "    X2sq = np.sum(np.square(X2), 1)\n",
    "    R2 = -2. * np.dot(X1, X2.T) + (X1sq[:, None] + X2sq[None, :])\n",
    "    R2 = np.clip(R2, 1e-12, np.inf)\n",
    "    \n",
    "    return np.exp(-R2 / (2 * l ** 2))\n",
    "\n",
    "xx = np.linspace(0, 1800, 512)[:, None]\n",
    "K_s = se_kernel_pred(xx, X, 126)\n",
    "\n",
    "\n",
    "def predict_gp(X_new, Y, K, K_s, s2, delta, mu):\n",
    "    Y_new = mu + s2 * K_s.dot(np.linalg.solve(s2 * K + s2 * delta * np.eye(K.shape[0]), Y - mu))\n",
    "    return Y_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 796,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figsize(10, 6)\n",
    "\n",
    "with plt.rc_context(rc={'font.size': 14}):\n",
    "    i = 0\n",
    "    for fsv in [1., 0.9, 0.8, 0.6, 0.3, 0.1]:\n",
    "        delta = 1 / fsv - 1\n",
    "\n",
    "        i += 1\n",
    "        plt.subplot(2, 3, i)\n",
    "\n",
    "        K = Sigma + np.eye(95) * (delta + 1e-8)\n",
    "        yy = stats.multivariate_normal(np.zeros((95,)), K).rvs()\n",
    "        plt.scatter(X, yy, c='k')\n",
    "\n",
    "        yyf = predict_gp(xx, yy, K, K_s, 1, delta, 0)\n",
    "        plt.plot(xx, yyf, c='r')\n",
    "\n",
    "        nice_x_axis(5)\n",
    "        plt.yticks([])\n",
    "        plt.title(f'FSV = {fsv:0.2}')\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.savefig('3.png', dpi=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 578,
   "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>FSV</th>\n",
       "      <th>M</th>\n",
       "      <th>g</th>\n",
       "      <th>l</th>\n",
       "      <th>max_delta</th>\n",
       "      <th>max_ll</th>\n",
       "      <th>max_mu_hat</th>\n",
       "      <th>max_s2_t_hat</th>\n",
       "      <th>model</th>\n",
       "      <th>n</th>\n",
       "      <th>s2_FSV</th>\n",
       "      <th>s2_logdelta</th>\n",
       "      <th>time</th>\n",
       "      <th>BIC</th>\n",
       "      <th>max_ll_null</th>\n",
       "      <th>LLR</th>\n",
       "      <th>pval</th>\n",
       "      <th>qval</th>\n",
       "      <th>total_var</th>\n",
       "      <th>mean_counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18017</th>\n",
       "      <td>0.855317</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000036832_cyt1l</td>\n",
       "      <td>126.128432</td>\n",
       "      <td>0.141520</td>\n",
       "      <td>-16.064137</td>\n",
       "      <td>-0.142235</td>\n",
       "      <td>0.351501</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>0.000818</td>\n",
       "      <td>0.049767</td>\n",
       "      <td>0.000857</td>\n",
       "      <td>50.343781</td>\n",
       "      <td>-70.412792</td>\n",
       "      <td>54.348655</td>\n",
       "      <td>1.678657e-13</td>\n",
       "      <td>7.281206e-10</td>\n",
       "      <td>0.401246</td>\n",
       "      <td>436.063158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19334</th>\n",
       "      <td>0.841733</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000094041_krt17</td>\n",
       "      <td>126.128432</td>\n",
       "      <td>0.157305</td>\n",
       "      <td>-30.758271</td>\n",
       "      <td>-0.199017</td>\n",
       "      <td>0.438606</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>0.001114</td>\n",
       "      <td>0.059573</td>\n",
       "      <td>0.000906</td>\n",
       "      <td>79.732049</td>\n",
       "      <td>-75.220388</td>\n",
       "      <td>44.462117</td>\n",
       "      <td>2.593270e-11</td>\n",
       "      <td>2.509206e-08</td>\n",
       "      <td>0.507601</td>\n",
       "      <td>216.978947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17448</th>\n",
       "      <td>0.818406</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000011168_ENSDARG00000011168</td>\n",
       "      <td>126.128432</td>\n",
       "      <td>0.185635</td>\n",
       "      <td>-44.197398</td>\n",
       "      <td>-0.152408</td>\n",
       "      <td>0.503785</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>0.001099</td>\n",
       "      <td>0.048610</td>\n",
       "      <td>0.001211</td>\n",
       "      <td>106.610303</td>\n",
       "      <td>-85.747125</td>\n",
       "      <td>41.549727</td>\n",
       "      <td>1.149105e-10</td>\n",
       "      <td>7.782124e-08</td>\n",
       "      <td>0.597305</td>\n",
       "      <td>212.852632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19440</th>\n",
       "      <td>0.842311</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000007099_cx43.4</td>\n",
       "      <td>244.036967</td>\n",
       "      <td>0.129914</td>\n",
       "      <td>23.097924</td>\n",
       "      <td>0.223463</td>\n",
       "      <td>0.199959</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>0.001642</td>\n",
       "      <td>0.088258</td>\n",
       "      <td>0.001445</td>\n",
       "      <td>-27.980341</td>\n",
       "      <td>-19.703484</td>\n",
       "      <td>42.801408</td>\n",
       "      <td>6.058853e-11</td>\n",
       "      <td>4.734528e-08</td>\n",
       "      <td>0.225936</td>\n",
       "      <td>198.452632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17648</th>\n",
       "      <td>0.840508</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000019365_zgc:110712</td>\n",
       "      <td>126.128432</td>\n",
       "      <td>0.158754</td>\n",
       "      <td>-52.043942</td>\n",
       "      <td>-0.279440</td>\n",
       "      <td>0.682305</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>0.001833</td>\n",
       "      <td>0.096924</td>\n",
       "      <td>0.001103</td>\n",
       "      <td>122.303392</td>\n",
       "      <td>-90.966579</td>\n",
       "      <td>38.922636</td>\n",
       "      <td>4.409378e-10</td>\n",
       "      <td>1.990785e-07</td>\n",
       "      <td>0.790623</td>\n",
       "      <td>112.589474</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            FSV  M                                      g           l  \\\n",
       "18017  0.855317  4               ENSDARG00000036832_cyt1l  126.128432   \n",
       "19334  0.841733  4               ENSDARG00000094041_krt17  126.128432   \n",
       "17448  0.818406  4  ENSDARG00000011168_ENSDARG00000011168  126.128432   \n",
       "19440  0.842311  4              ENSDARG00000007099_cx43.4  244.036967   \n",
       "17648  0.840508  4          ENSDARG00000019365_zgc:110712  126.128432   \n",
       "\n",
       "       max_delta     max_ll  max_mu_hat  max_s2_t_hat model   n    s2_FSV  \\\n",
       "18017   0.141520 -16.064137   -0.142235      0.351501    SE  95  0.000818   \n",
       "19334   0.157305 -30.758271   -0.199017      0.438606    SE  95  0.001114   \n",
       "17448   0.185635 -44.197398   -0.152408      0.503785    SE  95  0.001099   \n",
       "19440   0.129914  23.097924    0.223463      0.199959    SE  95  0.001642   \n",
       "17648   0.158754 -52.043942   -0.279440      0.682305    SE  95  0.001833   \n",
       "\n",
       "       s2_logdelta      time         BIC  max_ll_null        LLR  \\\n",
       "18017     0.049767  0.000857   50.343781   -70.412792  54.348655   \n",
       "19334     0.059573  0.000906   79.732049   -75.220388  44.462117   \n",
       "17448     0.048610  0.001211  106.610303   -85.747125  41.549727   \n",
       "19440     0.088258  0.001445  -27.980341   -19.703484  42.801408   \n",
       "17648     0.096924  0.001103  122.303392   -90.966579  38.922636   \n",
       "\n",
       "               pval          qval  total_var  mean_counts  \n",
       "18017  1.678657e-13  7.281206e-10   0.401246   436.063158  \n",
       "19334  2.593270e-11  2.509206e-08   0.507601   216.978947  \n",
       "17448  1.149105e-10  7.782124e-08   0.597305   212.852632  \n",
       "19440  6.058853e-11  4.734528e-08   0.225936   198.452632  \n",
       "17648  4.409378e-10  1.990785e-07   0.790623   112.589474  "
      ]
     },
     "execution_count": 578,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.query('FSV > 0.8 & l > 120').sort_values('mean_counts', ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 595,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figsize(6, 4)\n",
    "g = 'ENSDARG00000094041_krt17'\n",
    "plt.scatter(sample_info.x, res[g]);\n",
    "nice_x_axis()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 580,
   "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>FSV</th>\n",
       "      <th>M</th>\n",
       "      <th>g</th>\n",
       "      <th>l</th>\n",
       "      <th>max_delta</th>\n",
       "      <th>max_ll</th>\n",
       "      <th>max_mu_hat</th>\n",
       "      <th>max_s2_t_hat</th>\n",
       "      <th>model</th>\n",
       "      <th>n</th>\n",
       "      <th>s2_FSV</th>\n",
       "      <th>s2_logdelta</th>\n",
       "      <th>time</th>\n",
       "      <th>BIC</th>\n",
       "      <th>max_ll_null</th>\n",
       "      <th>LLR</th>\n",
       "      <th>pval</th>\n",
       "      <th>qval</th>\n",
       "      <th>total_var</th>\n",
       "      <th>mean_counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20046</th>\n",
       "      <td>0.648662</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000079193_ENSDARG00000079193</td>\n",
       "      <td>244.036967</td>\n",
       "      <td>0.375866</td>\n",
       "      <td>3.653397</td>\n",
       "      <td>0.117062</td>\n",
       "      <td>0.113205</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>0.005798</td>\n",
       "      <td>0.125788</td>\n",
       "      <td>0.000785</td>\n",
       "      <td>10.908714</td>\n",
       "      <td>-30.545499</td>\n",
       "      <td>34.198896</td>\n",
       "      <td>4.975717e-09</td>\n",
       "      <td>1.248045e-06</td>\n",
       "      <td>0.155755</td>\n",
       "      <td>1443.947368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19613</th>\n",
       "      <td>0.669583</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000029150_hsp90ab1</td>\n",
       "      <td>244.036967</td>\n",
       "      <td>0.342439</td>\n",
       "      <td>56.624647</td>\n",
       "      <td>0.073013</td>\n",
       "      <td>0.040456</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>0.007482</td>\n",
       "      <td>0.170260</td>\n",
       "      <td>0.000678</td>\n",
       "      <td>-95.033786</td>\n",
       "      <td>20.776528</td>\n",
       "      <td>35.848118</td>\n",
       "      <td>2.133139e-09</td>\n",
       "      <td>6.496761e-07</td>\n",
       "      <td>0.054310</td>\n",
       "      <td>1102.505263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18524</th>\n",
       "      <td>0.783836</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000058371_krt5</td>\n",
       "      <td>126.128432</td>\n",
       "      <td>0.230720</td>\n",
       "      <td>-19.104014</td>\n",
       "      <td>-0.125405</td>\n",
       "      <td>0.246766</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>0.001766</td>\n",
       "      <td>0.062443</td>\n",
       "      <td>0.000744</td>\n",
       "      <td>56.423536</td>\n",
       "      <td>-58.324458</td>\n",
       "      <td>39.220444</td>\n",
       "      <td>3.785555e-10</td>\n",
       "      <td>1.747980e-07</td>\n",
       "      <td>0.303700</td>\n",
       "      <td>381.263158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18957</th>\n",
       "      <td>0.614813</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000076532_si:ch211-222l21.1</td>\n",
       "      <td>126.128432</td>\n",
       "      <td>0.524151</td>\n",
       "      <td>28.087970</td>\n",
       "      <td>0.019003</td>\n",
       "      <td>0.044654</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>0.002885</td>\n",
       "      <td>0.058872</td>\n",
       "      <td>0.000677</td>\n",
       "      <td>-37.960432</td>\n",
       "      <td>-7.563954</td>\n",
       "      <td>35.651924</td>\n",
       "      <td>2.359148e-09</td>\n",
       "      <td>6.946493e-07</td>\n",
       "      <td>0.068059</td>\n",
       "      <td>352.631579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19376</th>\n",
       "      <td>0.661007</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000095863_afp4</td>\n",
       "      <td>126.128432</td>\n",
       "      <td>0.429054</td>\n",
       "      <td>-100.215447</td>\n",
       "      <td>-0.120663</td>\n",
       "      <td>0.792962</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>0.002398</td>\n",
       "      <td>0.053465</td>\n",
       "      <td>0.000684</td>\n",
       "      <td>218.646401</td>\n",
       "      <td>-130.029890</td>\n",
       "      <td>29.814444</td>\n",
       "      <td>4.754367e-08</td>\n",
       "      <td>7.437557e-06</td>\n",
       "      <td>1.133187</td>\n",
       "      <td>181.705263</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            FSV  M                                      g           l  \\\n",
       "20046  0.648662  4  ENSDARG00000079193_ENSDARG00000079193  244.036967   \n",
       "19613  0.669583  4            ENSDARG00000029150_hsp90ab1  244.036967   \n",
       "18524  0.783836  4                ENSDARG00000058371_krt5  126.128432   \n",
       "18957  0.614813  4   ENSDARG00000076532_si:ch211-222l21.1  126.128432   \n",
       "19376  0.661007  4                ENSDARG00000095863_afp4  126.128432   \n",
       "\n",
       "       max_delta      max_ll  max_mu_hat  max_s2_t_hat model   n    s2_FSV  \\\n",
       "20046   0.375866    3.653397    0.117062      0.113205    SE  95  0.005798   \n",
       "19613   0.342439   56.624647    0.073013      0.040456    SE  95  0.007482   \n",
       "18524   0.230720  -19.104014   -0.125405      0.246766    SE  95  0.001766   \n",
       "18957   0.524151   28.087970    0.019003      0.044654    SE  95  0.002885   \n",
       "19376   0.429054 -100.215447   -0.120663      0.792962    SE  95  0.002398   \n",
       "\n",
       "       s2_logdelta      time         BIC  max_ll_null        LLR  \\\n",
       "20046     0.125788  0.000785   10.908714   -30.545499  34.198896   \n",
       "19613     0.170260  0.000678  -95.033786    20.776528  35.848118   \n",
       "18524     0.062443  0.000744   56.423536   -58.324458  39.220444   \n",
       "18957     0.058872  0.000677  -37.960432    -7.563954  35.651924   \n",
       "19376     0.053465  0.000684  218.646401  -130.029890  29.814444   \n",
       "\n",
       "               pval          qval  total_var  mean_counts  \n",
       "20046  4.975717e-09  1.248045e-06   0.155755  1443.947368  \n",
       "19613  2.133139e-09  6.496761e-07   0.054310  1102.505263  \n",
       "18524  3.785555e-10  1.747980e-07   0.303700   381.263158  \n",
       "18957  2.359148e-09  6.946493e-07   0.068059   352.631579  \n",
       "19376  4.754367e-08  7.437557e-06   1.133187   181.705263  "
      ]
     },
     "execution_count": 580,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.query('0.6 < FSV < 0.8 & l > 120').sort_values('mean_counts', ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 583,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figsize(6, 4)\n",
    "g = 'ENSDARG00000079193_ENSDARG00000079193'\n",
    "plt.scatter(sample_info.x, res[g]);\n",
    "nice_x_axis()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 584,
   "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>FSV</th>\n",
       "      <th>M</th>\n",
       "      <th>g</th>\n",
       "      <th>l</th>\n",
       "      <th>max_delta</th>\n",
       "      <th>max_ll</th>\n",
       "      <th>max_mu_hat</th>\n",
       "      <th>max_s2_t_hat</th>\n",
       "      <th>model</th>\n",
       "      <th>n</th>\n",
       "      <th>s2_FSV</th>\n",
       "      <th>s2_logdelta</th>\n",
       "      <th>time</th>\n",
       "      <th>BIC</th>\n",
       "      <th>max_ll_null</th>\n",
       "      <th>LLR</th>\n",
       "      <th>pval</th>\n",
       "      <th>qval</th>\n",
       "      <th>total_var</th>\n",
       "      <th>mean_counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19016</th>\n",
       "      <td>0.463928</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000077777_tmsb4x</td>\n",
       "      <td>126.128432</td>\n",
       "      <td>0.966715</td>\n",
       "      <td>-1.588167</td>\n",
       "      <td>-0.034152</td>\n",
       "      <td>0.048616</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>0.005931</td>\n",
       "      <td>0.112014</td>\n",
       "      <td>0.000759</td>\n",
       "      <td>21.391841</td>\n",
       "      <td>-18.580126</td>\n",
       "      <td>16.991959</td>\n",
       "      <td>0.000038</td>\n",
       "      <td>0.001820</td>\n",
       "      <td>0.095613</td>\n",
       "      <td>1599.642105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19580</th>\n",
       "      <td>0.332612</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000021838_rps23</td>\n",
       "      <td>244.036967</td>\n",
       "      <td>1.392405</td>\n",
       "      <td>56.582536</td>\n",
       "      <td>-0.009194</td>\n",
       "      <td>0.010959</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>0.017354</td>\n",
       "      <td>0.392795</td>\n",
       "      <td>0.000674</td>\n",
       "      <td>-94.949564</td>\n",
       "      <td>48.848797</td>\n",
       "      <td>7.733739</td>\n",
       "      <td>0.005420</td>\n",
       "      <td>0.090110</td>\n",
       "      <td>0.026218</td>\n",
       "      <td>1123.105263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17806</th>\n",
       "      <td>0.366173</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000028335_hmga1a</td>\n",
       "      <td>126.128432</td>\n",
       "      <td>1.448141</td>\n",
       "      <td>67.032389</td>\n",
       "      <td>0.016347</td>\n",
       "      <td>0.007989</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>0.004936</td>\n",
       "      <td>0.104013</td>\n",
       "      <td>0.000647</td>\n",
       "      <td>-115.849270</td>\n",
       "      <td>55.014923</td>\n",
       "      <td>12.017466</td>\n",
       "      <td>0.000527</td>\n",
       "      <td>0.015039</td>\n",
       "      <td>0.019559</td>\n",
       "      <td>785.726316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19947</th>\n",
       "      <td>0.340518</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000070437_rpl22</td>\n",
       "      <td>244.036967</td>\n",
       "      <td>1.343968</td>\n",
       "      <td>64.862549</td>\n",
       "      <td>-0.014978</td>\n",
       "      <td>0.009521</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>0.007965</td>\n",
       "      <td>0.176962</td>\n",
       "      <td>0.000686</td>\n",
       "      <td>-111.509591</td>\n",
       "      <td>52.950442</td>\n",
       "      <td>11.912108</td>\n",
       "      <td>0.000558</td>\n",
       "      <td>0.015781</td>\n",
       "      <td>0.022318</td>\n",
       "      <td>630.505263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17662</th>\n",
       "      <td>0.301907</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000019778_rps6</td>\n",
       "      <td>126.128432</td>\n",
       "      <td>1.934492</td>\n",
       "      <td>59.770379</td>\n",
       "      <td>-0.001750</td>\n",
       "      <td>0.007152</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>0.005512</td>\n",
       "      <td>0.135694</td>\n",
       "      <td>0.000785</td>\n",
       "      <td>-101.325251</td>\n",
       "      <td>49.670373</td>\n",
       "      <td>10.100007</td>\n",
       "      <td>0.001483</td>\n",
       "      <td>0.033620</td>\n",
       "      <td>0.020989</td>\n",
       "      <td>622.768421</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            FSV  M                          g           l  max_delta  \\\n",
       "19016  0.463928  4  ENSDARG00000077777_tmsb4x  126.128432   0.966715   \n",
       "19580  0.332612  4   ENSDARG00000021838_rps23  244.036967   1.392405   \n",
       "17806  0.366173  4  ENSDARG00000028335_hmga1a  126.128432   1.448141   \n",
       "19947  0.340518  4   ENSDARG00000070437_rpl22  244.036967   1.343968   \n",
       "17662  0.301907  4    ENSDARG00000019778_rps6  126.128432   1.934492   \n",
       "\n",
       "          max_ll  max_mu_hat  max_s2_t_hat model   n    s2_FSV  s2_logdelta  \\\n",
       "19016  -1.588167   -0.034152      0.048616    SE  95  0.005931     0.112014   \n",
       "19580  56.582536   -0.009194      0.010959    SE  95  0.017354     0.392795   \n",
       "17806  67.032389    0.016347      0.007989    SE  95  0.004936     0.104013   \n",
       "19947  64.862549   -0.014978      0.009521    SE  95  0.007965     0.176962   \n",
       "17662  59.770379   -0.001750      0.007152    SE  95  0.005512     0.135694   \n",
       "\n",
       "           time         BIC  max_ll_null        LLR      pval      qval  \\\n",
       "19016  0.000759   21.391841   -18.580126  16.991959  0.000038  0.001820   \n",
       "19580  0.000674  -94.949564    48.848797   7.733739  0.005420  0.090110   \n",
       "17806  0.000647 -115.849270    55.014923  12.017466  0.000527  0.015039   \n",
       "19947  0.000686 -111.509591    52.950442  11.912108  0.000558  0.015781   \n",
       "17662  0.000785 -101.325251    49.670373  10.100007  0.001483  0.033620   \n",
       "\n",
       "       total_var  mean_counts  \n",
       "19016   0.095613  1599.642105  \n",
       "19580   0.026218  1123.105263  \n",
       "17806   0.019559   785.726316  \n",
       "19947   0.022318   630.505263  \n",
       "17662   0.020989   622.768421  "
      ]
     },
     "execution_count": 584,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.query('0.3 < FSV < 0.6 & l > 120').sort_values('mean_counts', ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 585,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figsize(6, 4)\n",
    "g = 'ENSDARG00000077777_tmsb4x'\n",
    "plt.scatter(sample_info.x, res[g]);\n",
    "nice_x_axis()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 586,
   "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>FSV</th>\n",
       "      <th>M</th>\n",
       "      <th>g</th>\n",
       "      <th>l</th>\n",
       "      <th>max_delta</th>\n",
       "      <th>max_ll</th>\n",
       "      <th>max_mu_hat</th>\n",
       "      <th>max_s2_t_hat</th>\n",
       "      <th>model</th>\n",
       "      <th>n</th>\n",
       "      <th>s2_FSV</th>\n",
       "      <th>s2_logdelta</th>\n",
       "      <th>time</th>\n",
       "      <th>BIC</th>\n",
       "      <th>max_ll_null</th>\n",
       "      <th>LLR</th>\n",
       "      <th>pval</th>\n",
       "      <th>qval</th>\n",
       "      <th>total_var</th>\n",
       "      <th>mean_counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17688</th>\n",
       "      <td>0.292669</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000020850_eef1a1l2</td>\n",
       "      <td>126.128432</td>\n",
       "      <td>2.021958</td>\n",
       "      <td>60.082340</td>\n",
       "      <td>0.007496</td>\n",
       "      <td>0.006829</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>0.006124</td>\n",
       "      <td>0.155226</td>\n",
       "      <td>0.000663</td>\n",
       "      <td>-101.949173</td>\n",
       "      <td>53.319302</td>\n",
       "      <td>6.763038</td>\n",
       "      <td>0.009307</td>\n",
       "      <td>0.136619</td>\n",
       "      <td>0.020637</td>\n",
       "      <td>3620.568421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20292</th>\n",
       "      <td>0.038566</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000088030_rpl35a</td>\n",
       "      <td>472.169837</td>\n",
       "      <td>11.753626</td>\n",
       "      <td>75.350598</td>\n",
       "      <td>-0.008926</td>\n",
       "      <td>0.000981</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>0.006216</td>\n",
       "      <td>3.542648</td>\n",
       "      <td>0.000679</td>\n",
       "      <td>-132.485688</td>\n",
       "      <td>74.470887</td>\n",
       "      <td>0.879711</td>\n",
       "      <td>0.348281</td>\n",
       "      <td>0.958943</td>\n",
       "      <td>0.012505</td>\n",
       "      <td>1755.305263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19481</th>\n",
       "      <td>0.140701</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000011405_rps9</td>\n",
       "      <td>244.036967</td>\n",
       "      <td>4.238102</td>\n",
       "      <td>76.738287</td>\n",
       "      <td>0.001653</td>\n",
       "      <td>0.002506</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>0.011704</td>\n",
       "      <td>0.741970</td>\n",
       "      <td>0.000679</td>\n",
       "      <td>-135.261066</td>\n",
       "      <td>74.423673</td>\n",
       "      <td>2.314614</td>\n",
       "      <td>0.128163</td>\n",
       "      <td>0.784713</td>\n",
       "      <td>0.013127</td>\n",
       "      <td>1549.115789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20199</th>\n",
       "      <td>0.035919</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000021864_rplp1</td>\n",
       "      <td>472.169837</td>\n",
       "      <td>12.654426</td>\n",
       "      <td>101.815270</td>\n",
       "      <td>-0.004163</td>\n",
       "      <td>0.000522</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>0.007431</td>\n",
       "      <td>4.830680</td>\n",
       "      <td>0.000813</td>\n",
       "      <td>-185.415032</td>\n",
       "      <td>100.953345</td>\n",
       "      <td>0.861925</td>\n",
       "      <td>0.353200</td>\n",
       "      <td>0.958943</td>\n",
       "      <td>0.007130</td>\n",
       "      <td>1406.294737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17985</th>\n",
       "      <td>0.235456</td>\n",
       "      <td>4</td>\n",
       "      <td>ENSDARG00000036044_rps20</td>\n",
       "      <td>126.128432</td>\n",
       "      <td>2.716564</td>\n",
       "      <td>45.953971</td>\n",
       "      <td>-0.004389</td>\n",
       "      <td>0.007021</td>\n",
       "      <td>SE</td>\n",
       "      <td>95</td>\n",
       "      <td>0.003966</td>\n",
       "      <td>0.126669</td>\n",
       "      <td>0.000742</td>\n",
       "      <td>-73.692435</td>\n",
       "      <td>37.360426</td>\n",
       "      <td>8.593545</td>\n",
       "      <td>0.003374</td>\n",
       "      <td>0.062693</td>\n",
       "      <td>0.026095</td>\n",
       "      <td>1295.894737</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            FSV  M                            g           l  max_delta  \\\n",
       "17688  0.292669  4  ENSDARG00000020850_eef1a1l2  126.128432   2.021958   \n",
       "20292  0.038566  4    ENSDARG00000088030_rpl35a  472.169837  11.753626   \n",
       "19481  0.140701  4      ENSDARG00000011405_rps9  244.036967   4.238102   \n",
       "20199  0.035919  4     ENSDARG00000021864_rplp1  472.169837  12.654426   \n",
       "17985  0.235456  4     ENSDARG00000036044_rps20  126.128432   2.716564   \n",
       "\n",
       "           max_ll  max_mu_hat  max_s2_t_hat model   n    s2_FSV  s2_logdelta  \\\n",
       "17688   60.082340    0.007496      0.006829    SE  95  0.006124     0.155226   \n",
       "20292   75.350598   -0.008926      0.000981    SE  95  0.006216     3.542648   \n",
       "19481   76.738287    0.001653      0.002506    SE  95  0.011704     0.741970   \n",
       "20199  101.815270   -0.004163      0.000522    SE  95  0.007431     4.830680   \n",
       "17985   45.953971   -0.004389      0.007021    SE  95  0.003966     0.126669   \n",
       "\n",
       "           time         BIC  max_ll_null       LLR      pval      qval  \\\n",
       "17688  0.000663 -101.949173    53.319302  6.763038  0.009307  0.136619   \n",
       "20292  0.000679 -132.485688    74.470887  0.879711  0.348281  0.958943   \n",
       "19481  0.000679 -135.261066    74.423673  2.314614  0.128163  0.784713   \n",
       "20199  0.000813 -185.415032   100.953345  0.861925  0.353200  0.958943   \n",
       "17985  0.000742  -73.692435    37.360426  8.593545  0.003374  0.062693   \n",
       "\n",
       "       total_var  mean_counts  \n",
       "17688   0.020637  3620.568421  \n",
       "20292   0.012505  1755.305263  \n",
       "19481   0.013127  1549.115789  \n",
       "20199   0.007130  1406.294737  \n",
       "17985   0.026095  1295.894737  "
      ]
     },
     "execution_count": 586,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.query('FSV < 0.3 & l > 120').sort_values('mean_counts', ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 590,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figsize(6, 4)\n",
    "g = 'ENSDARG00000088030_rpl35a'\n",
    "plt.scatter(sample_info.x, res[g]);\n",
    "nice_x_axis()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "matplotlib.rcParams.update()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 767,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "DEBUG:matplotlib.font_manager:findfont: Matching :family=sans-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=16.8 to DejaVu Sans ('/anaconda3/lib/python3.6/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000\n",
      "DEBUG:matplotlib.font_manager:findfont: Matching :family=sans-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=14.0 to DejaVu Sans ('/anaconda3/lib/python3.6/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figsize(12, 8)\n",
    "\n",
    "example_genes = ['ENSDARG00000094041_krt17', 'ENSDARG00000079193_ENSDARG00000079193',\n",
    "                 'ENSDARG00000077777_tmsb4x', 'ENSDARG00000088030_rpl35a']\n",
    "\n",
    "with plt.rc_context(rc={'font.size': 14}):\n",
    "    i = 0\n",
    "    for g in  example_genes:\n",
    "\n",
    "        i += 1\n",
    "        plt.subplot(2, 2, i)\n",
    "        g_fit = results.set_index('g').loc[g]\n",
    "\n",
    "        plt.scatter(sample_info.x, res[g], c='k');\n",
    "\n",
    "        nice_x_axis()\n",
    "        plt.yticks([])\n",
    "        plt.ylabel('Expression level');\n",
    "\n",
    "        g_name = g.split('_')[-1]\n",
    "        plt.title(f'{g_name}: FSV = {g_fit.FSV:.2}');\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.savefig('4.png', dpi=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 811,
   "metadata": {},
   "outputs": [],
   "source": [
    "junker_genes = ['ENSDARG00000068761_mespab', 'ENSDARG00000017078_mespaa', 'ENSDARG00000042826_egr2b',\n",
    "                'ENSDARG00000058008_six3a', 'ENSDARG00000036292_cdx4', 'ENSDARG00000015072_dmrt2a',\n",
    "                'ENSDARG00000070535_ripply2', 'ENSDARG00000054103_ripply1', 'ENSDARG00000006467_pcdh8',\n",
    "                'ENSDARG00000011785_tbx6', 'ENSDARG00000013477_gata1a', 'ENSDARG00000019920_lft1',\n",
    "                'ENSDARG00000019096_myl7', 'ENSDARG00000059073_gsc', ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 760,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figsize(6, 4)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 932,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figsize(12, 4)\n",
    "\n",
    "manual_place = {\n",
    "    'pcdh8': (0.72, 1e-15),\n",
    "    'six3a': (0.92, 1e-9),\n",
    "    'mespab': (0.51, 2e-15),\n",
    "    'mespaa': (0.85, 3e-7),\n",
    "    'tbx6': (0.9, 5e-14),\n",
    "    'cdx4': (0.3, 5e-10),\n",
    "    'egr2b': (0.4, 4e-9),\n",
    "    'lft1': (0.25, 5e-9),\n",
    "    'ripply1': (0.15, 1e-7),\n",
    "    'dmrt2a': (0.1, 2e-6),\n",
    "    'gata1a': (0.06, 4e-5),\n",
    "    'ENSDARG00000079193': (0.05, 1e-13),\n",
    "    'rpl35a': (0.0, 1e-3),\n",
    "    'krt17': (0.92, 3e-12),\n",
    "    'ripply2': (0.52, 1e-11),\n",
    "    'tmsb4x': (0.75, 2e-5)\n",
    "}\n",
    "\n",
    "with plt.rc_context(rc={'font.size': 14}):\n",
    "    \n",
    "    plt.subplot(1, 2, 1)\n",
    "    \n",
    "    plt.scatter(results.total_var, results.max_s2_t_hat, marker='.', c=results.FSV, vmin=0, vmax=1)\n",
    "    plt.colorbar(label='FSV')\n",
    "    plt.loglog()\n",
    "    plt.xlim(1e-3, 2e1)\n",
    "    plt.ylim(1e-6, 2e1);\n",
    "    plt.plot([2e-3, 5e0], [2e-3, 5e0], c='r', lw=1, zorder=0);\n",
    "    plt.xlabel('Total variance')\n",
    "    plt.ylabel('Spatial variance');\n",
    "    \n",
    "    plt.subplot(1, 2, 2)\n",
    "    \n",
    "    SpatialDE.plot.FSV_sig(results)\n",
    "\n",
    "    tmp = results.query('g in @junker_genes')\n",
    "\n",
    "    for i, r in tmp.iterrows():\n",
    "        xy = (r['FSV'], r['pval'])\n",
    "        xyt = (xy[0] - 0.4, xy[1] * 1e-3)\n",
    "        gn = r['g'].split('_')[-1]\n",
    "        plt.annotate(gn, xy, manual_place.get(gn, xyt), arrowprops={'arrowstyle': '-', 'color': 'tab:red'})\n",
    "        \n",
    "    tmp = results.query('g in @example_genes')\n",
    "\n",
    "    for i, r in tmp.iterrows():\n",
    "        xy = (r['FSV'], r['pval'])\n",
    "        xyt = (xy[0], xy[1])\n",
    "        gn = r['g'].split('_')[-1]\n",
    "        plt.annotate(gn, xy, manual_place.get(gn, xyt), arrowprops={'arrowstyle': '-', 'color': 'tab:red'})\n",
    "        \n",
    "    plt.gca().legend_.remove()\n",
    "        \n",
    "plt.tight_layout()\n",
    "plt.savefig('5.png', dpi=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 793,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figsize(12, 4)\n",
    "\n",
    "with plt.rc_context(rc={'font.size': 14}):\n",
    "\n",
    "    plt.subplot(1, 3, 1)\n",
    "\n",
    "    i = -1\n",
    "    plt.plot(X, Sigma[i], c='C1')\n",
    "    plt.axvline(X[i], ymax=Sigma[i, i], c='C1', ls=':')\n",
    "    plt.axvline(X[i - 5], ymax=Sigma[i, i - 5], c='k', ls=':')\n",
    "    plt.annotate('Tip of the head', (X[i] + 30, Sigma[i, i] - 0.05))\n",
    "    plt.annotate('5 slices away \\n(90 µm)', (X[i - 5] + 30, Sigma[i, i - 5] - 0.05))\n",
    "    plt.annotate('10 slices away \\n(180 µm)', (X[i - 10] + 30, Sigma[i, i - 10] - 0.05))\n",
    "    plt.axvline(X[i - 10], ymax=Sigma[i, i - 10], c='k', ls=':')\n",
    "    nice_x_axis(5)\n",
    "    plt.ylabel('Covariance');\n",
    "\n",
    "    plt.title('Covariance relative to head')\n",
    "\n",
    "    plt.subplot(1, 3, 2)\n",
    "\n",
    "    plt.pcolormesh(Sigma)\n",
    "\n",
    "    locs = np.linspace(0, 95, 5)\n",
    "    labs = (locs * 18).astype(int).astype(str)\n",
    "    labs[0] = '0\\n(Head)'\n",
    "    labs[-1] = '1800\\n(Tail)'\n",
    "    plt.xticks(locs, labs)\n",
    "    plt.xlabel('Position (µm)');\n",
    "    plt.yticks(locs, labs)\n",
    "    plt.axhline(X[i], c='C1', lw=4)\n",
    "\n",
    "    plt.title('Covariance matrix')\n",
    "\n",
    "    plt.subplot(1, 3, 3)\n",
    "\n",
    "    for j in range(2):\n",
    "        K = Sigma + np.eye(95) * 1e-8\n",
    "        yy = stats.multivariate_normal(np.zeros((95,)), K).rvs()\n",
    "        plt.plot(X, yy, c='w', lw=5)\n",
    "        plt.plot(X, yy, c='r')\n",
    "\n",
    "    nice_x_axis(5)\n",
    "    plt.yticks([])\n",
    "    plt.ylabel('Expression level')\n",
    "\n",
    "    plt.title('Example spatial functions')\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.savefig('2.png', dpi=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 784,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.360447788597821, 0.7748374288832494)"
      ]
     },
     "execution_count": 784,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Sigma[-1, -11], Sigma[-1, -6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 649,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(90, 180)"
      ]
     },
     "execution_count": 649,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "5 * 18, 10 * 18"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}