{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stabilized ICA for transcriptomic data   \n",
    "\n",
    "In this jupyter notebook, we propose a short example for the use of our stabilized ICA algorithm. We study the extraction of ICA components for a transcriptomic data set gathering NSCLC patients.   \n",
    "\n",
    "**Note :** To run this jupyter notebook, you first need to extract the data set \"data.csv\" from the zip file \"data.zip\" (same directory)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 0. Load the data set\n",
    "\n",
    "This data set was extraced from [\"Defining the Biological Basis of Radiomic Phenotypes in Lung Cancer\" Grossman et al. 2017](https://elifesciences.org/articles/23421).   \n",
    "It contains the expression of 21,766 unique genes for 269 patients with Non-small cell lung cancer (NSCLC) treated at the H. Lee Moffitt Cancer Center, Tampa, Florida, USA. Gene expression values were measured on a custom Rosetta/Merck Affymetrix 2.0 microarray chipset and normalized with the robust multi-array average (RMA) algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <td>3.366466</td>\n",
       "      <td>5.495330</td>\n",
       "    </tr>\n",
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       "      <th>RadioGenomic-227</th>\n",
       "      <td>5.679276</td>\n",
       "      <td>7.747854</td>\n",
       "      <td>10.648704</td>\n",
       "      <td>9.127985</td>\n",
       "      <td>7.369421</td>\n",
       "      <td>7.203773</td>\n",
       "      <td>8.972255</td>\n",
       "      <td>8.328371</td>\n",
       "      <td>7.269232</td>\n",
       "      <td>7.449183</td>\n",
       "      <td>...</td>\n",
       "      <td>5.666999</td>\n",
       "      <td>4.316130</td>\n",
       "      <td>6.637855</td>\n",
       "      <td>6.248824</td>\n",
       "      <td>4.664228</td>\n",
       "      <td>5.767970</td>\n",
       "      <td>2.911470</td>\n",
       "      <td>8.674466</td>\n",
       "      <td>3.337194</td>\n",
       "      <td>6.308605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RadioGenomic-222</th>\n",
       "      <td>5.317341</td>\n",
       "      <td>7.196276</td>\n",
       "      <td>10.949771</td>\n",
       "      <td>8.098896</td>\n",
       "      <td>7.639882</td>\n",
       "      <td>7.971876</td>\n",
       "      <td>10.159637</td>\n",
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       "      <td>8.474250</td>\n",
       "      <td>7.271477</td>\n",
       "      <td>...</td>\n",
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       "      <td>9.099659</td>\n",
       "      <td>3.181781</td>\n",
       "      <td>5.740033</td>\n",
       "    </tr>\n",
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       "      <th>RadioGenomic-212</th>\n",
       "      <td>7.196904</td>\n",
       "      <td>9.346492</td>\n",
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       "      <td>6.601991</td>\n",
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       "      <td>2.945391</td>\n",
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       "                      3643     84263       7171      2934     11052      1241  \\\n",
       "RadioGenomic-017  5.205151  7.097989   9.559617  8.396808  7.603719  7.990605   \n",
       "RadioGenomic-055  5.615738  6.585052   9.777869  9.082415  8.639498  6.781274   \n",
       "RadioGenomic-227  5.679276  7.747854  10.648704  9.127985  7.369421  7.203773   \n",
       "RadioGenomic-222  5.317341  7.196276  10.949771  8.098896  7.639882  7.971876   \n",
       "RadioGenomic-212  7.196904  9.346492   9.673778  9.358636  8.741693  7.616498   \n",
       "\n",
       "                       6453     57541      9349     11165  ...    643669  \\\n",
       "RadioGenomic-017  10.044401  9.054930  7.383169  8.177010  ...  6.419273   \n",
       "RadioGenomic-055   9.541826  8.866110  6.422702  7.196294  ...  5.753828   \n",
       "RadioGenomic-227   8.972255  8.328371  7.269232  7.449183  ...  5.666999   \n",
       "RadioGenomic-222  10.159637  8.667702  8.474250  7.271477  ...  5.531060   \n",
       "RadioGenomic-212  10.376653  8.701461  6.601991  7.344651  ...  5.519642   \n",
       "\n",
       "                      1572      8551     26784     26783     26782     26779  \\\n",
       "RadioGenomic-017  3.809826  6.507880  6.572121  5.400848  5.951391  3.381860   \n",
       "RadioGenomic-055  4.186127  6.821582  7.031406  4.852417  6.140850  2.629760   \n",
       "RadioGenomic-227  4.316130  6.637855  6.248824  4.664228  5.767970  2.911470   \n",
       "RadioGenomic-222  3.403776  7.059419  6.201873  4.690005  6.256286  4.119688   \n",
       "RadioGenomic-212  3.796049  7.332635  6.050121  4.898523  6.537895  3.600895   \n",
       "\n",
       "                     26778     26777  100132941  \n",
       "RadioGenomic-017  9.825584  2.905091   5.622438  \n",
       "RadioGenomic-055  9.005145  3.366466   5.495330  \n",
       "RadioGenomic-227  8.674466  3.337194   6.308605  \n",
       "RadioGenomic-222  9.099659  3.181781   5.740033  \n",
       "RadioGenomic-212  8.792510  2.945391   5.835411  \n",
       "\n",
       "[5 rows x 21766 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"data/data.csv\" , index_col=0)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note :** in the following step, we center the expression of each gene (i.e the columns of the dataframe df). Indeed, we noticed that omitting this pre-processing step led to one ICA component entirely driven by the differences between the  mean expression of each gene. This component is not biologically relevant and we avoid it thanks to this centering step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.apply(func = lambda x : x - x.mean() , axis = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Most Stable Transcriptome Dimension (MSTD)   \n",
    "\n",
    "In order to select the number of ICA components we refer to [\"Determining the optimal number of independent components for reproducible transcriptomic data analysis\" Kairov et al. 2017](https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-017-4112-9). The idea is to find a trade-off between a sufficiently large number of components to capture all essential information and a restricted dimension to avoid ending up with a lot of very unstable components which only capture noise.   \n",
    "\n",
    "To do so we plot the stability distribution for a wide range of different number of components M = 5, ... , 100 and we observe which choice of M most satisfies the trade-off."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "model_id": "6e29c44f66744624b8cec7f8fa83ad9a",
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      },
      "text/plain": [
       "  0%|          | 0/49 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sica.base import MSTD\n",
    "MSTD(X.values , m = 5 , M = 100 , step = 2 , n_runs = 20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Please note that in the following we use a rough estimation of the optimal dimension (observed thanks to the graphs above). In fact more sophisticated techniques can be applied to estimate this dimension (ex: two-lines clustering) ; these can be found in the article cited above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "dim_opt = 45"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Stabilized ICA and Multidimensional Scaling (MDS)   \n",
    "\n",
    "This stabilized ICA algorithm is a copy of the ICASSO algorithm (implemented in MATLAB) ([\"ICASSO: software for investigating the reliability of ICA estimates by clustering and visualization\" Himber et al.](https://www.cs.helsinki.fi/u/ahyvarin/papers/Himberg03.pd) The main idea is to iterate several times the FastICA algorithm (ex: from sklearn), cluster the results (agglomerative hierarchical clustering) and define the final ICA components as the centrotype of each cluster.   \n",
    "\n",
    "Some explanation for Multidimensional Scaling can be found on the sklearn page : https://scikit-learn.org/stable/modules/generated/sklearn.manifold.MDS.html "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sica.base import StabilizedICA "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running time (min): 00:54.54\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "start = time.time()\n",
    "\n",
    "sICA = StabilizedICA(n_components=dim_opt, n_runs=30, plot=True, n_jobs=-1)\n",
    "sICA.fit(X)\n",
    "\n",
    "end = time.time()\n",
    "\n",
    "minutes, seconds = divmod(end - start, 60)\n",
    "print(\"running time (min): \" + \"{:0>2}:{:05.2f}\".format(int(minutes),seconds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": [
     "nbsphinx-thumbnail"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running time (min): 00:31.51\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig , ax = plt.subplots(figsize = (10 , 7))\n",
    "\n",
    "start = time.time()\n",
    "\n",
    "sICA.projection(ax = ax , method = 'mds')\n",
    "ax.set_title(\"Multidimensional scaling for ICA components\")\n",
    "\n",
    "end = time.time() \n",
    "\n",
    "minutes, seconds = divmod(end - start, 60)\n",
    "print(\"running time (min): \" + \"{:0>2}:{:05.2f}\".format(int(minutes),seconds))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note :** by default, each stabilized ICA component (i.e a row of the S_ matrix) is normalized to unit variance and re-oriented towards positive heaviest tail. These operations facilitate the interpretation of the components and allow us to compare several components from different studies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>57541</th>\n",
       "      <th>9349</th>\n",
       "      <th>11165</th>\n",
       "      <th>...</th>\n",
       "      <th>643669</th>\n",
       "      <th>1572</th>\n",
       "      <th>8551</th>\n",
       "      <th>26784</th>\n",
       "      <th>26783</th>\n",
       "      <th>26782</th>\n",
       "      <th>26779</th>\n",
       "      <th>26778</th>\n",
       "      <th>26777</th>\n",
       "      <th>100132941</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>metagene 0</th>\n",
       "      <td>-0.502359</td>\n",
       "      <td>0.063330</td>\n",
       "      <td>0.619226</td>\n",
       "      <td>-0.584321</td>\n",
       "      <td>-0.034392</td>\n",
       "      <td>-0.645843</td>\n",
       "      <td>0.018026</td>\n",
       "      <td>0.165158</td>\n",
       "      <td>-0.390837</td>\n",
       "      <td>0.026501</td>\n",
       "      <td>...</td>\n",
       "      <td>0.211179</td>\n",
       "      <td>-0.369237</td>\n",
       "      <td>-0.975147</td>\n",
       "      <td>-0.142125</td>\n",
       "      <td>0.339079</td>\n",
       "      <td>0.118280</td>\n",
       "      <td>-0.761259</td>\n",
       "      <td>-0.183313</td>\n",
       "      <td>-0.006010</td>\n",
       "      <td>-0.323200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metagene 1</th>\n",
       "      <td>0.481376</td>\n",
       "      <td>-0.085962</td>\n",
       "      <td>-0.126218</td>\n",
       "      <td>0.490215</td>\n",
       "      <td>0.242829</td>\n",
       "      <td>0.751853</td>\n",
       "      <td>0.465473</td>\n",
       "      <td>-0.415335</td>\n",
       "      <td>0.587996</td>\n",
       "      <td>0.014006</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.362256</td>\n",
       "      <td>3.178655</td>\n",
       "      <td>-0.159200</td>\n",
       "      <td>-0.211356</td>\n",
       "      <td>-0.387652</td>\n",
       "      <td>-0.137837</td>\n",
       "      <td>-0.474530</td>\n",
       "      <td>0.204819</td>\n",
       "      <td>0.312316</td>\n",
       "      <td>-0.018258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metagene 2</th>\n",
       "      <td>-0.813705</td>\n",
       "      <td>-0.287146</td>\n",
       "      <td>0.135293</td>\n",
       "      <td>0.005913</td>\n",
       "      <td>1.361459</td>\n",
       "      <td>0.175264</td>\n",
       "      <td>-0.335616</td>\n",
       "      <td>-0.120486</td>\n",
       "      <td>-2.524144</td>\n",
       "      <td>0.933776</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.640554</td>\n",
       "      <td>-0.777672</td>\n",
       "      <td>-0.313629</td>\n",
       "      <td>0.010074</td>\n",
       "      <td>0.255665</td>\n",
       "      <td>1.612827</td>\n",
       "      <td>0.519558</td>\n",
       "      <td>0.661906</td>\n",
       "      <td>0.016296</td>\n",
       "      <td>-0.335104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metagene 3</th>\n",
       "      <td>0.029950</td>\n",
       "      <td>0.171725</td>\n",
       "      <td>0.607405</td>\n",
       "      <td>1.403389</td>\n",
       "      <td>-0.513840</td>\n",
       "      <td>2.216392</td>\n",
       "      <td>3.532994</td>\n",
       "      <td>0.286914</td>\n",
       "      <td>-1.342845</td>\n",
       "      <td>1.290781</td>\n",
       "      <td>...</td>\n",
       "      <td>0.489110</td>\n",
       "      <td>0.356925</td>\n",
       "      <td>0.674017</td>\n",
       "      <td>-1.592262</td>\n",
       "      <td>-1.058137</td>\n",
       "      <td>0.383702</td>\n",
       "      <td>0.648317</td>\n",
       "      <td>-0.687954</td>\n",
       "      <td>-0.364239</td>\n",
       "      <td>-0.733333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metagene 4</th>\n",
       "      <td>1.338965</td>\n",
       "      <td>-0.540206</td>\n",
       "      <td>-0.242459</td>\n",
       "      <td>-1.213108</td>\n",
       "      <td>0.724185</td>\n",
       "      <td>4.785730</td>\n",
       "      <td>0.466036</td>\n",
       "      <td>-0.455820</td>\n",
       "      <td>-0.756496</td>\n",
       "      <td>-0.013456</td>\n",
       "      <td>...</td>\n",
       "      <td>0.950401</td>\n",
       "      <td>0.123224</td>\n",
       "      <td>0.326030</td>\n",
       "      <td>2.161343</td>\n",
       "      <td>0.678265</td>\n",
       "      <td>1.191926</td>\n",
       "      <td>1.467889</td>\n",
       "      <td>1.275098</td>\n",
       "      <td>0.168216</td>\n",
       "      <td>-0.796021</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21766 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                3643     84263      7171      2934     11052      1241  \\\n",
       "metagene 0 -0.502359  0.063330  0.619226 -0.584321 -0.034392 -0.645843   \n",
       "metagene 1  0.481376 -0.085962 -0.126218  0.490215  0.242829  0.751853   \n",
       "metagene 2 -0.813705 -0.287146  0.135293  0.005913  1.361459  0.175264   \n",
       "metagene 3  0.029950  0.171725  0.607405  1.403389 -0.513840  2.216392   \n",
       "metagene 4  1.338965 -0.540206 -0.242459 -1.213108  0.724185  4.785730   \n",
       "\n",
       "                6453     57541      9349     11165  ...    643669      1572  \\\n",
       "metagene 0  0.018026  0.165158 -0.390837  0.026501  ...  0.211179 -0.369237   \n",
       "metagene 1  0.465473 -0.415335  0.587996  0.014006  ... -0.362256  3.178655   \n",
       "metagene 2 -0.335616 -0.120486 -2.524144  0.933776  ... -0.640554 -0.777672   \n",
       "metagene 3  3.532994  0.286914 -1.342845  1.290781  ...  0.489110  0.356925   \n",
       "metagene 4  0.466036 -0.455820 -0.756496 -0.013456  ...  0.950401  0.123224   \n",
       "\n",
       "                8551     26784     26783     26782     26779     26778  \\\n",
       "metagene 0 -0.975147 -0.142125  0.339079  0.118280 -0.761259 -0.183313   \n",
       "metagene 1 -0.159200 -0.211356 -0.387652 -0.137837 -0.474530  0.204819   \n",
       "metagene 2 -0.313629  0.010074  0.255665  1.612827  0.519558  0.661906   \n",
       "metagene 3  0.674017 -1.592262 -1.058137  0.383702  0.648317 -0.687954   \n",
       "metagene 4  0.326030  2.161343  0.678265  1.191926  1.467889  1.275098   \n",
       "\n",
       "               26777  100132941  \n",
       "metagene 0 -0.006010  -0.323200  \n",
       "metagene 1  0.312316  -0.018258  \n",
       "metagene 2  0.016296  -0.335104  \n",
       "metagene 3 -0.364239  -0.733333  \n",
       "metagene 4  0.168216  -0.796021  \n",
       "\n",
       "[5 rows x 21766 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Metagenes = pd.DataFrame(sICA.S_ , columns = df.columns , index = ['metagene ' + str(i) for i in range(sICA.S_.shape[0])])\n",
    "Metagenes.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can check that we do obtain sparse ICA components by plotting the distribution of \"metagene 1\" values (i.e the coefficients associated with \"metagene 1\" for each feature)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequency')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def distribution(Components ,  name , bins):  \n",
    "    s = Components.loc[name]\n",
    "    s.hist(bins = bins , weights=np.zeros_like(s) + 1. / s.size , grid = False)\n",
    "    return\n",
    "\n",
    "plt.figure(figsize = (10 , 7))\n",
    "distribution(Metagenes , name = 'metagene 1' , bins = 30)\n",
    "plt.title(\"Distribution for metagene 1\")\n",
    "plt.xlabel(\"Coefficient value\")\n",
    "plt.ylabel(\"Frequency\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Metagene annotation with sica-omics toolbox\n",
    "\n",
    "Finally, we provide a complementary Python package, called sica-omics, which gathers specific tools for analyzing the results of stabilized-ica applied on omics data. In particular, it provides tools to perform enrichment analysis and characterize each metagene with relevant biological annotations. These tools gather two major operations : first, a list of the most expressed genes is retrieved for each metagene, and then an enrichment analysis is performed on this list using [reactome](https://reactome.org/) or [ToppFun](https://toppgene.cchmc.org/enrichment.jsp).  \n",
    "\n",
    "A conversion tool (https://pypi.org/project/mygene/) is included in both methods. It can handle usual gene identifiers like Entrez gene ids, Ensemble gene ids, NCBI RefSeq ids, official gene symbols, HUGO Gene Nomenclature, UniProt ids, Affymetrix probeset ids, and a mix of everything. It automatically converts them to Entrez gene IDs. For ToppFun this conversion is necessary for the analysis to work."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 Reactome enrichment analysis   \n",
    "\n",
    "**Note :** This tool is based on the python package [reactome2py](https://pypi.org/project/reactome2py/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sicaomics.annotate import reactome"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The user can choose different strategies for selecting the most expressed genes of each metagene :\n",
    "- The user can choose to only look at the right tail of the metagene distribution, the left tail, both or the heaviest one.\n",
    "- The user can choose whether the thresholds will be computed with deviations from the mean of the distribution (i.e $\\mu \\pm k*\\sigma$) or with its quantiles.\n",
    "- The user can fine tune these thresholds with the threshold parameter.\n",
    "\n",
    "**Note :** Here we will set the parameter ``convert_ids`` to False since genes are already identified by Entrez gene ids and thus the conversion is not necessary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ncaptier\\Documents\\GitHub\\sica-omics\\sicaomics\\annotate\\reactome.py:77: UserWarning: If convert_ids is False ReactomeAnalysis will assume that the inputs are valid gene ids for Reactome analysis ((e.g HUGO gene symbols, EntrezGene , Uniprot ...). No conversion will be performed. See https://reactome.org/userguide/analysis for more details.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "Rannot = reactome.ReactomeAnalysis(data = Metagenes , threshold = 3 , method = 'std' , tail = 'heaviest' , convert_ids = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note :** By default, an arbitrary threshold of 0.05 is used to filter Entities pValue. The user can change it with the p_value parameter. The user can also pass a list of species to filter the results as well as min and max thresholds for the number of entities per pathway."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pathway identifier</th>\n",
       "      <th>Pathway name</th>\n",
       "      <th>#Entities found</th>\n",
       "      <th>#Entities total</th>\n",
       "      <th>Entities ratio</th>\n",
       "      <th>Entities pValue</th>\n",
       "      <th>Entities FDR</th>\n",
       "      <th>#Reactions found</th>\n",
       "      <th>#Reactions total</th>\n",
       "      <th>Reactions ratio</th>\n",
       "      <th>Species identifier</th>\n",
       "      <th>Species name</th>\n",
       "      <th>Submitted entities found</th>\n",
       "      <th>Mapped entities</th>\n",
       "      <th>Found reaction identifiers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>R-HSA-1474228</td>\n",
       "      <td>Degradation of the extracellular matrix</td>\n",
       "      <td>40.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>0.009799</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>0.007612</td>\n",
       "      <td>9606</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>50509;1284;1282;1281;176;1634;1513;80781;2006;...</td>\n",
       "      <td></td>\n",
       "      <td>R-HSA-8940554;R-HSA-1602458;R-HSA-1474196;R-HS...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>R-HSA-2022090</td>\n",
       "      <td>Assembly of collagen fibrils and other multime...</td>\n",
       "      <td>26.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.004436</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.001885</td>\n",
       "      <td>9606</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>50509;1284;1282;1281;80781;1278;1277;5118;7837...</td>\n",
       "      <td></td>\n",
       "      <td>R-HSA-2395302;R-HSA-2022141;R-HSA-2299620;R-HS...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>R-HSA-1442490</td>\n",
       "      <td>Collagen degradation</td>\n",
       "      <td>29.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>0.004568</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.002465</td>\n",
       "      <td>9606</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>50509;1284;1282;1281;1634;1513;80781;1278;1277...</td>\n",
       "      <td></td>\n",
       "      <td>R-HSA-1564117;R-HSA-1474196;R-HSA-1474197;R-HS...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "0 Pathway identifier                                       Pathway name  \\\n",
       "2      R-HSA-1474228            Degradation of the extracellular matrix   \n",
       "3      R-HSA-2022090  Assembly of collagen fibrils and other multime...   \n",
       "4      R-HSA-1442490                               Collagen degradation   \n",
       "\n",
       "0 #Entities found #Entities total Entities ratio Entities pValue Entities FDR  \\\n",
       "2            40.0           148.0       0.009799             0.0          0.0   \n",
       "3            26.0            67.0       0.004436             0.0          0.0   \n",
       "4            29.0            69.0       0.004568             0.0          0.0   \n",
       "\n",
       "0 #Reactions found #Reactions total Reactions ratio Species identifier  \\\n",
       "2             87.0            105.0        0.007612               9606   \n",
       "3             21.0             26.0        0.001885               9606   \n",
       "4             27.0             34.0        0.002465               9606   \n",
       "\n",
       "0  Species name                           Submitted entities found  \\\n",
       "2  Homo sapiens  50509;1284;1282;1281;176;1634;1513;80781;2006;...   \n",
       "3  Homo sapiens  50509;1284;1282;1281;80781;1278;1277;5118;7837...   \n",
       "4  Homo sapiens  50509;1284;1282;1281;1634;1513;80781;1278;1277...   \n",
       "\n",
       "0 Mapped entities                         Found reaction identifiers  \n",
       "2                  R-HSA-8940554;R-HSA-1602458;R-HSA-1474196;R-HS...  \n",
       "3                  R-HSA-2395302;R-HSA-2022141;R-HSA-2299620;R-HS...  \n",
       "4                  R-HSA-1564117;R-HSA-1474196;R-HSA-1474197;R-HS...  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Rannot.get_analysis(metagene = 'metagene 6' , species = 'Homo sapiens' , p_value=0.05).head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, the complete analysis can be browsed and visualized in reactome web portal (you need an internet access for this function)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "Rannot.open_full_analysis(metagene = 'metagene 6')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 Toppfun enrichment analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sicaomics.annotate import toppfun"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note :** ToppFunAnalysis intializes the same way as ReactomeAnalysis. Please refer to the precedent section for some explanations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ncaptier\\Documents\\GitHub\\sica-omics\\sicaomics\\annotate\\toppfun.py:77: UserWarning: If convert_ids is False ToppFunAnalysis will assume that the inputs are already Entrez gene IDs. No conversion will be performed. \n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "Tannot = toppfun.ToppFunAnalysis(data = Metagenes , threshold = 3 , method = 'std' , tail = 'heaviest' , convert_ids = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note :** By default, an arbitrary threshold of 0.05 is used to filter pvalues. The user can change it with the p_value parameter. The user can also choose which annotation features he wants to look at with the type_list parameter. All the available features are enriched by default. Finaly, the user can play with several other parameters to filter the results (e.g min_entities , max_entities...)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enrichment analysis success!\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Category</th>\n",
       "      <th>ID</th>\n",
       "      <th>Name</th>\n",
       "      <th>PValue</th>\n",
       "      <th>QValueFDRBH</th>\n",
       "      <th>QValueFDRBY</th>\n",
       "      <th>QValueBonferroni</th>\n",
       "      <th>TotalGenes</th>\n",
       "      <th>GenesInTerm</th>\n",
       "      <th>GenesInQuery</th>\n",
       "      <th>GenesInTermInQuery</th>\n",
       "      <th>Source</th>\n",
       "      <th>URL</th>\n",
       "      <th>Gene_Symbol</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GeneOntologyMolecularFunction</td>\n",
       "      <td>GO:0045296</td>\n",
       "      <td>cadherin binding</td>\n",
       "      <td>5.468367e-10</td>\n",
       "      <td>3.253678e-07</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>3.253678e-07</td>\n",
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       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>VAPB,ADD1,AHNAK,DHX29,VCL,LIMA1,EPN2,USO1,PKN2...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GeneOntologyMolecularFunction</td>\n",
       "      <td>GO:0003779</td>\n",
       "      <td>actin binding</td>\n",
       "      <td>4.007864e-06</td>\n",
       "      <td>1.192340e-03</td>\n",
       "      <td>0.008307</td>\n",
       "      <td>2.384679e-03</td>\n",
       "      <td>19869</td>\n",
       "      <td>492</td>\n",
       "      <td>408</td>\n",
       "      <td>27</td>\n",
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       "      <td></td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GeneOntologyMolecularFunction</td>\n",
       "      <td>GO:0042393</td>\n",
       "      <td>histone binding</td>\n",
       "      <td>6.316044e-06</td>\n",
       "      <td>1.252682e-03</td>\n",
       "      <td>0.008727</td>\n",
       "      <td>3.758046e-03</td>\n",
       "      <td>19869</td>\n",
       "      <td>256</td>\n",
       "      <td>408</td>\n",
       "      <td>18</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>CHD1,EZH1,PHF2,SET,BRD9,SMARCC1,SART3,ZMYND11,...</td>\n",
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      "text/plain": [
       "                        Category          ID              Name        PValue  \\\n",
       "0  GeneOntologyMolecularFunction  GO:0045296  cadherin binding  5.468367e-10   \n",
       "1  GeneOntologyMolecularFunction  GO:0003779     actin binding  4.007864e-06   \n",
       "2  GeneOntologyMolecularFunction  GO:0042393   histone binding  6.316044e-06   \n",
       "\n",
       "    QValueFDRBH  QValueFDRBY  QValueBonferroni  TotalGenes  GenesInTerm  \\\n",
       "0  3.253678e-07     0.000002      3.253678e-07       19869          341   \n",
       "1  1.192340e-03     0.008307      2.384679e-03       19869          492   \n",
       "2  1.252682e-03     0.008727      3.758046e-03       19869          256   \n",
       "\n",
       "   GenesInQuery  GenesInTermInQuery Source URL  \\\n",
       "0           408                  28              \n",
       "1           408                  27              \n",
       "2           408                  18              \n",
       "\n",
       "                                         Gene_Symbol  \n",
       "0  VAPB,ADD1,AHNAK,DHX29,VCL,LIMA1,EPN2,USO1,PKN2...  \n",
       "1  MAP1B,ADD1,LMOD1,PLS3,VCL,FLII,LIMA1,ENC1,YWHA...  \n",
       "2  CHD1,EZH1,PHF2,SET,BRD9,SMARCC1,SART3,ZMYND11,...  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Tannot.get_analysis(metagene = 'metagene 3').head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For both methods (i.e ReactomeAnalysis and ToppFunAnalysis) the top_genes_ attribute gives access to the list of extreme expressed genes for each metagene. The 'inputs' column contains the original gene IDs, the 'entrezgene' column contains the converted gene IDs (in Entrez IDs), and the 'notfound' column contains the original gene IDs which were not found by the conversion tool.   \n",
    "\n",
    "**Note :** For cases with no conversion( i.e convert_to_entrez parameter was set to False), 'entregene' and 'notfound' columns contain only None values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>inputs</th>\n",
       "      <th>entrezgene</th>\n",
       "      <th>notfound</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>metagene 0</th>\n",
       "      <td>[6192, 9086, 8653, 8284, 8287, 246126, 7404, 6...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metagene 1</th>\n",
       "      <td>[128602, 92747, 399949, 127003, 144448, 133690...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metagene 2</th>\n",
       "      <td>[388468, 400968, 29044, 228, 160857, 83417, 64...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metagene 3</th>\n",
       "      <td>[79832, 23347, 7403, 659, 83448, 1038, 163590,...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metagene 4</th>\n",
       "      <td>[283131, 84719, 79923, 29896, 100642175, 27099...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metagene 5</th>\n",
       "      <td>[8685, 366, 1118, 81501, 6348, 126014, 597, 11...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metagene 6</th>\n",
       "      <td>[1301, 131578, 6423, 8483, 26585, 84624, 22801...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metagene 7</th>\n",
       "      <td>[9787, 55872, 83540, 259266, 4751, 1063, 55635...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metagene 8</th>\n",
       "      <td>[23440, 7200, 80309, 4842, 10842, 5354, 6857, ...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metagene 9</th>\n",
       "      <td>[260436, 931, 10563, 83416, 3507, 973, 1380, 6...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                       inputs entrezgene  \\\n",
       "metagene 0  [6192, 9086, 8653, 8284, 8287, 246126, 7404, 6...       None   \n",
       "metagene 1  [128602, 92747, 399949, 127003, 144448, 133690...       None   \n",
       "metagene 2  [388468, 400968, 29044, 228, 160857, 83417, 64...       None   \n",
       "metagene 3  [79832, 23347, 7403, 659, 83448, 1038, 163590,...       None   \n",
       "metagene 4  [283131, 84719, 79923, 29896, 100642175, 27099...       None   \n",
       "metagene 5  [8685, 366, 1118, 81501, 6348, 126014, 597, 11...       None   \n",
       "metagene 6  [1301, 131578, 6423, 8483, 26585, 84624, 22801...       None   \n",
       "metagene 7  [9787, 55872, 83540, 259266, 4751, 1063, 55635...       None   \n",
       "metagene 8  [23440, 7200, 80309, 4842, 10842, 5354, 6857, ...       None   \n",
       "metagene 9  [260436, 931, 10563, 83416, 3507, 973, 1380, 6...       None   \n",
       "\n",
       "           notfound  \n",
       "metagene 0     None  \n",
       "metagene 1     None  \n",
       "metagene 2     None  \n",
       "metagene 3     None  \n",
       "metagene 4     None  \n",
       "metagene 5     None  \n",
       "metagene 6     None  \n",
       "metagene 7     None  \n",
       "metagene 8     None  \n",
       "metagene 9     None  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Tannot.top_genes_.head(10)"
   ]
  }
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