single_cell_gene_co-expression.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a664602f",
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "Are two selected genes co-expressed?\n",
    "\n",
    "Are any genes co-expressed with a selected gene?\n",
    "\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "afc181a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import scanpy as sc\n",
    "from scipy import stats\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e3b218c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jrlab2019/miniconda3/envs/sc/lib/python3.9/site-packages/scanpy/preprocessing/_normalization.py:170: UserWarning: Received a view of an AnnData. Making a copy.\n",
      "  view_to_actual(adata)\n"
     ]
    }
   ],
   "source": [
    "#simple import and preprocessing\n",
    "adata = sc.read_10x_mtx('../tutorial_sample/outs/filtered_feature_bc_matrix/')\n",
    "sc.pp.filter_cells(adata, min_genes=200)\n",
    "sc.pp.filter_genes(adata, min_cells=3)\n",
    "adata.var['mt'] = adata.var_names.str.startswith('MT-') \n",
    "sc.pp.calculate_qc_metrics(adata, qc_vars=['mt'], percent_top=None, log1p=False, inplace=True)\n",
    "adata = adata[adata.obs.pct_counts_mt < 20]\n",
    "sc.pp.normalize_total(adata, target_sum=1e4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fb29b19e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 8093 × 21949\n",
       "    obs: 'n_genes', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt'\n",
       "    var: 'gene_ids', 'feature_types', 'n_cells', 'mt', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "89fb17ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['AL627309.1', 'AL627309.5', 'LINC01409', 'FAM87B', 'LINC01128',\n",
       "       'LINC00115', 'FAM41C', 'AL645608.2', 'AL645608.4', 'LINC02593',\n",
       "       ...\n",
       "       'MT-CYB', 'BX004987.1', 'AC145212.1', 'MAFIP', 'AC011043.1',\n",
       "       'AL354822.1', 'AL592183.1', 'AC240274.1', 'AC007325.4', 'zika'],\n",
       "      dtype='object', length=21949)"
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     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.var_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b060f1a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "i1 = np.where(adata.var_names == 'zika')[0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6e02a079",
   "metadata": {},
   "outputs": [],
   "source": [
    "i2 = np.where(adata.var_names == 'IFITM1')[0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "87801fdb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8093, 21949)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = adata.X.toarray()\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4a024686",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.        ,  0.3849559 ,  0.47232196, ..., 83.49873   ,\n",
       "        0.        ,  0.        ], dtype=float32)"
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     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "data[:, i1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c4f5a8ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.004018901858421179, 0.7177328380898428)"
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     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "stats.pearsonr(data[:, i1], data[:, i2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4914ceda",
   "metadata": {},
   "outputs": [],
   "source": [
    "out = []\n",
    "for gene in adata.var_names:\n",
    "    i2 = np.where(adata.var_names == gene)[0][0]\n",
    "    res = stats.pearsonr(data[:, i1], data[:, i2])\n",
    "    out.append([gene, res[0], res[1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8ae63171",
   "metadata": {},
   "outputs": [
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       "      <th>21947</th>\n",
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       "      <td>7.034826e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21948</th>\n",
       "      <td>zika</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
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       "  </tbody>\n",
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      "text/plain": [
       "             gene         r             p\n",
       "0      AL627309.1  0.007712  4.878679e-01\n",
       "1      AL627309.5 -0.000773  9.445847e-01\n",
       "2       LINC01409  0.037610  7.140523e-04\n",
       "3          FAM87B -0.003992  7.195206e-01\n",
       "4       LINC01128 -0.008512  4.438714e-01\n",
       "...           ...       ...           ...\n",
       "21944  AL354822.1 -0.013347  2.299292e-01\n",
       "21945  AL592183.1  0.004540  6.830051e-01\n",
       "21946  AC240274.1  0.110236  2.633133e-23\n",
       "21947  AC007325.4  0.020117  7.034826e-02\n",
       "21948        zika  1.000000  0.000000e+00\n",
       "\n",
       "[21949 rows x 3 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(out, columns = ['gene', 'r', 'p'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6dd2805b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
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     "text": [
      "/home/jrlab2019/miniconda3/envs/sc/lib/python3.9/site-packages/pandas/core/arraylike.py:397: RuntimeWarning: divide by zero encountered in log10\n",
      "  result = getattr(ufunc, method)(*inputs, **kwargs)\n"
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       "      <td>1.152747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21948</th>\n",
       "      <td>zika</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>inf</td>\n",
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      "text/plain": [
       "             gene         r             p           bon   -log10_p\n",
       "0      AL627309.1  0.007712  4.878679e-01  1.070821e+04   0.311698\n",
       "1      AL627309.5 -0.000773  9.445847e-01  2.073269e+04   0.024759\n",
       "2       LINC01409  0.037610  7.140523e-04  1.567273e+01   3.146270\n",
       "3          FAM87B -0.003992  7.195206e-01  1.579276e+04   0.142957\n",
       "4       LINC01128 -0.008512  4.438714e-01  9.742533e+03   0.352743\n",
       "...           ...       ...           ...           ...        ...\n",
       "21944  AL354822.1 -0.013347  2.299292e-01  5.046717e+03   0.638406\n",
       "21945  AL592183.1  0.004540  6.830051e-01  1.499128e+04   0.165576\n",
       "21946  AC240274.1  0.110236  2.633133e-23  5.779463e-19  22.579527\n",
       "21947  AC007325.4  0.020117  7.034826e-02  1.544074e+03   1.152747\n",
       "21948        zika  1.000000  0.000000e+00  0.000000e+00        inf\n",
       "\n",
       "[21949 rows x 5 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['bon'] = df.p * len(df)\n",
    "df['-log10_p'] = -np.log10(df.p)\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e58c8438",
   "metadata": {},
   "outputs": [
    {
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       "      <th>r</th>\n",
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       "      <th>0</th>\n",
       "      <td>zika</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>IFIT2</td>\n",
       "      <td>0.319031</td>\n",
       "      <td>7.119685e-191</td>\n",
       "      <td>1.562700e-186</td>\n",
       "      <td>190.147539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>OASL</td>\n",
       "      <td>0.299120</td>\n",
       "      <td>6.186299e-167</td>\n",
       "      <td>1.357831e-162</td>\n",
       "      <td>166.208569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IFIT1</td>\n",
       "      <td>0.247911</td>\n",
       "      <td>1.291226e-113</td>\n",
       "      <td>2.834112e-109</td>\n",
       "      <td>112.888998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DDX58</td>\n",
       "      <td>0.243737</td>\n",
       "      <td>9.033933e-110</td>\n",
       "      <td>1.982858e-105</td>\n",
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       "    <tr>\n",
       "      <th>693</th>\n",
       "      <td>RAE1</td>\n",
       "      <td>0.052628</td>\n",
       "      <td>2.169152e-06</td>\n",
       "      <td>4.761072e-02</td>\n",
       "      <td>5.663710</td>\n",
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       "    <tr>\n",
       "      <th>694</th>\n",
       "      <td>EXOSC4</td>\n",
       "      <td>0.052628</td>\n",
       "      <td>2.169169e-06</td>\n",
       "      <td>4.761109e-02</td>\n",
       "      <td>5.663707</td>\n",
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       "    <tr>\n",
       "      <th>695</th>\n",
       "      <td>AC016747.1</td>\n",
       "      <td>0.052623</td>\n",
       "      <td>2.173228e-06</td>\n",
       "      <td>4.770018e-02</td>\n",
       "      <td>5.662895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>696</th>\n",
       "      <td>PPM1L</td>\n",
       "      <td>0.052611</td>\n",
       "      <td>2.184895e-06</td>\n",
       "      <td>4.795627e-02</td>\n",
       "      <td>5.660569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>697</th>\n",
       "      <td>FUNDC2</td>\n",
       "      <td>-0.052538</td>\n",
       "      <td>2.256946e-06</td>\n",
       "      <td>4.953771e-02</td>\n",
       "      <td>5.646479</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>698 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           gene         r              p            bon    -log10_p\n",
       "0          zika  1.000000   0.000000e+00   0.000000e+00         inf\n",
       "1         IFIT2  0.319031  7.119685e-191  1.562700e-186  190.147539\n",
       "2          OASL  0.299120  6.186299e-167  1.357831e-162  166.208569\n",
       "3         IFIT1  0.247911  1.291226e-113  2.834112e-109  112.888998\n",
       "4         DDX58  0.243737  9.033933e-110  1.982858e-105  109.044123\n",
       "..          ...       ...            ...            ...         ...\n",
       "693        RAE1  0.052628   2.169152e-06   4.761072e-02    5.663710\n",
       "694      EXOSC4  0.052628   2.169169e-06   4.761109e-02    5.663707\n",
       "695  AC016747.1  0.052623   2.173228e-06   4.770018e-02    5.662895\n",
       "696       PPM1L  0.052611   2.184895e-06   4.795627e-02    5.660569\n",
       "697      FUNDC2 -0.052538   2.256946e-06   4.953771e-02    5.646479\n",
       "\n",
       "[698 rows x 5 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[df.bon < 0.05].sort_values('bon').reset_index(drop = True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "14cacf15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize = (3,5))\n",
    "\n",
    "ax = sns.barplot(data = df[1:16], x = '-log10_p', y = 'gene', color = 'grey')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cb162306",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b9e4915d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c7c9b80d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3f4d5cd6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9aad4a5c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aeee62a7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fae68899",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9a14a5f8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "45697472",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}