{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "bcd14e3d",
   "metadata": {},
   "source": [
    "# 4. Running Tensor-cell2cell to perform a tensor factorization\n",
    "\n",
    "After generating a 4D-Communication Tensor, we can run Tensor-cell2cell to perform a tensor factorization and obtain the context-driven patterns of cell-cell communication (factors).\n",
    "\n",
    "This tutorial will show you how to load a previously exported 4D-Communication Tensor and run Tensor-cell2cell on this tensor."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5670971",
   "metadata": {},
   "source": [
    "## Initial Setup\n",
    "\n",
    "**Before running this notebook** \n",
    "\n",
    "GPUs can substantially speed-up this analysis. If you are planning to use a NVIDIA GPU, make sure to have a proper NVIDIA GPU driver (https://www.nvidia.com/Download/index.aspx) as well as the CUDA toolkit (https://developer.nvidia.com/cuda-toolkit) installed.\n",
    "\n",
    "Then, make sure to create an environment with PyTorch version >= v1.8 following these instructions to enable CUDA.\n",
    "\n",
    "https://pytorch.org/get-started/locally/"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb8efdbe",
   "metadata": {},
   "source": [
    "### Enabling GPU use\n",
    "\n",
    "If you are using a NVIDIA GPU, after installing PyTorch with CUDA enabled, specify `use_gpu = True`. Otherwise, `use_gpu = False`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "00c71f6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "use_gpu = True"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a547fcf",
   "metadata": {},
   "source": [
    "When using a GPU, first import tensorly and set the backend to be `'pytorch'`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1b1b5e31",
   "metadata": {},
   "outputs": [],
   "source": [
    "if use_gpu:\n",
    "    import tensorly as tl\n",
    "    tl.set_backend('pytorch')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08f75175",
   "metadata": {},
   "source": [
    "### Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "db13afe6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import cell2cell as c2c\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8f5b945f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d2cf899",
   "metadata": {},
   "source": [
    "## Directories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "14992aa1",
   "metadata": {},
   "outputs": [],
   "source": [
    "output_folder = '../../data/tc2c-outputs/'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06ab6da9",
   "metadata": {},
   "source": [
    "## Load Tensor\n",
    "\n",
    "Now, we can load our 4D-Communication Tensor that was previously exported with pickle."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9b3740ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "tensor = c2c.io.read_data.load_tensor(os.path.join(output_folder, 'BALF-Tensor.pkl'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a755f8b1",
   "metadata": {},
   "source": [
    "Load metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a933d0fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "meta_tensor = c2c.io.load_variable_with_pickle(os.path.join(output_folder, 'BALF-Tensor-Metadata.pkl'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be30b075",
   "metadata": {},
   "source": [
    "## Perform Tensor Factorization\n",
    "\n",
    "Tensor-cell2cell can be run with a simple pipeline command that executes all important steps to perform the tensor factorization.\n",
    "\n",
    "\n",
    "**Key parameters are:**\n",
    "\n",
    "- `copy_tensor` indicates whether modifying the original tensor passed as input to report the results or return a new version including the results.\n",
    "\n",
    "\n",
    "- `rank` is the number of factors or latent patterns we want to obtain from the analysis. You can either indicate a specific number or leave it as `None` to obtain a suggested number through an elbow analysis.\n",
    "\n",
    "\n",
    "- `tf_optimization` indicates whether running the analysis in the `'regular'` or the `'robust'` way. The regular way means that the tensor decomposition is run 10 times per rank evaluated in the elbow analysis, and 1 time in the final decomposition. Additionally, the optimization algorithm has less number of iterations in the regular than the robust case (100 vs 500) and less precision (tolerance of 1e-7 vs 1e-8). The robust case runs the tensor decomposition 20 times per rank evaluated in the elbow analysis, and 100 times in the final decomposition. Here we could use the ```tf_optimization='regular'```, which is faster but generates less robust results. We recommend using ```tf_optimization='robust```, which takes longer to run (more iteractions and more precise too). It is important to notice that multiple runs of a tensor decomposition differ in the initialization values (regardless of using `tf_init='random'` or `tf_init='svd'`).\n",
    "\n",
    "\n",
    "- `random_state` is the seed for randomization. It controls the randomization used when initializing the optimization algorithm that performs the tensor decomposition. It is useful for reproducing the same result every time that the analysis is run. If `None`, a different randomization will be used each time.\n",
    "\n",
    "\n",
    "- `device` indicates whether we are using the `'cpu'` or a GPU with `'cuda'` cores. See the beginning of this notebook to enable using a GPU.\n",
    "\n",
    "\n",
    "- `'elbow_metric'` is the metric to perform the elbow analysis to obtain a suggested number of factors. Options are `'error'` and `'similarity'`, indicating respectively the error of tensor reconstruction, and the similarity of tensor factorization across multiple runs for a given rank. The similarity metric may result in non-smooth curves given the highly variability especially at higher ranks. If so, we recommend using the parameter `smooth_elbow=True`.\n",
    "\n",
    "\n",
    "- `uppper_rank` is the max number of ranks to try in the elbow analysis.\n",
    "\n",
    "\n",
    "- `tf_init` is the initialization of the tensor decomposition algorithm. If your tensor contains a mask (`tensor.mask`), it will automatically use `tf_init='random'`. Options are `'random'` and `'svd'`. The latter helps to obtain results that are more close to a global optima from the optimization method behind the algorithm.\n",
    "\n",
    "\n",
    "- `tf_svd` is the method to perform the SVD to initialize the tensor factorization. This is only considered whe `tf_init='svd'`. \n",
    "\n",
    "\n",
    "- `output_folder` is the full path to the folder where the results will be saved. Make sure that this folder exists before passing it here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "896448c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running Elbow Analysis\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████| 25/25 [37:33<00:00, 90.12s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The rank at the elbow is: 10\n",
      "Running Tensor Factorization\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████| 100/100 [06:06<00:00,  3.66s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best model has a normalized error of: 0.505\n",
      "Generating Outputs\n",
      "Loadings of the tensor factorization were successfully saved into ../../data/tc2c-outputs//Loadings.xlsx\n",
      "CPU times: user 43min 58s, sys: 716 ms, total: 43min 59s\n",
      "Wall time: 43min 55s\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 400x225 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1300 with 40 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time\n",
    "tensor2 = c2c.analysis.run_tensor_cell2cell_pipeline(tensor,\n",
    "                                                     meta_tensor,\n",
    "                                                     copy_tensor=True, # Whether to output a new tensor or modifying the original\n",
    "                                                     rank=None, # Number of factors to perform the factorization. If None, it is automatically determined by an elbow analysis\n",
    "                                                     tf_optimization='robust', # To define how robust we want the analysis to be.\n",
    "                                                     random_state=0, # Random seed for reproducibility\n",
    "                                                     device='cuda', # Device to use. If using GPU and PyTorch, use 'cuda'. For CPU use 'cpu'\n",
    "                                                     elbow_metric='error', # Metric to use in the elbow analysis.\n",
    "                                                     smooth_elbow=False, # Whether smoothing the metric of the elbow analysis.\n",
    "                                                     upper_rank=25, # Max number of factors to try in the elbow analysis\n",
    "                                                     tf_init='random', # Initialization method of the tensor factorization\n",
    "                                                     tf_svd='numpy_svd', # Type of SVD to use if the initialization is 'svd'\n",
    "                                                     cmaps=None, # Color palettes to use in color each of the dimensions. Must be a list of palettes.\n",
    "                                                     sample_col='Element', # Columns containing the elements in the tensor metadata\n",
    "                                                     group_col='Category', # Columns containing the major groups in the tensor metadata\n",
    "                                                     fig_fontsize=14, # Fontsize of the figures generated\n",
    "                                                     output_folder=output_folder, # Whether to save the figures in files. If so, a folder pathname must be passed\n",
    "                                                     output_fig=True, # Whether to output the figures. If False, figures won't be saved a files if a folder was passed in output_folder.\n",
    "                                                     fig_format='pdf', # File format of the figures.\n",
    "                                                    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8260e88b",
   "metadata": {},
   "source": [
    "## Factorization Results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6838b4d1",
   "metadata": {},
   "source": [
    "After running the decomposition, the results are stored in the `factors` attribute (`tensor2.factors`). This attribute is a dictionary containing the loadings for each of the elements in every tensor dimension. Keys are the names of the different dimension."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0a508f90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "odict_keys(['Contexts', 'Ligand-Receptor Pairs', 'Sender Cells', 'Receiver Cells'])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tensor2.factors.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "456ebef3",
   "metadata": {},
   "source": [
    "We can inspect the loadings of the samples, for example, located under the key `'Contexts'`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0ea9b8a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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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>Factor 1</th>\n",
       "      <th>Factor 2</th>\n",
       "      <th>Factor 3</th>\n",
       "      <th>Factor 4</th>\n",
       "      <th>Factor 5</th>\n",
       "      <th>Factor 6</th>\n",
       "      <th>Factor 7</th>\n",
       "      <th>Factor 8</th>\n",
       "      <th>Factor 9</th>\n",
       "      <th>Factor 10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>HC1</th>\n",
       "      <td>0.708322</td>\n",
       "      <td>0.104737</td>\n",
       "      <td>0.090899</td>\n",
       "      <td>0.068597</td>\n",
       "      <td>0.056585</td>\n",
       "      <td>0.348333</td>\n",
       "      <td>0.011751</td>\n",
       "      <td>0.010677</td>\n",
       "      <td>0.155580</td>\n",
       "      <td>0.184025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HC2</th>\n",
       "      <td>0.583005</td>\n",
       "      <td>0.016801</td>\n",
       "      <td>0.075149</td>\n",
       "      <td>0.016643</td>\n",
       "      <td>0.206293</td>\n",
       "      <td>0.193095</td>\n",
       "      <td>0.105770</td>\n",
       "      <td>0.078638</td>\n",
       "      <td>0.062675</td>\n",
       "      <td>0.116539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HC3</th>\n",
       "      <td>0.229497</td>\n",
       "      <td>0.549203</td>\n",
       "      <td>0.279390</td>\n",
       "      <td>0.310239</td>\n",
       "      <td>0.006598</td>\n",
       "      <td>0.030883</td>\n",
       "      <td>0.135658</td>\n",
       "      <td>0.231957</td>\n",
       "      <td>0.370718</td>\n",
       "      <td>0.052132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M1</th>\n",
       "      <td>0.008884</td>\n",
       "      <td>0.478022</td>\n",
       "      <td>0.098610</td>\n",
       "      <td>0.334124</td>\n",
       "      <td>0.198149</td>\n",
       "      <td>0.500957</td>\n",
       "      <td>0.135596</td>\n",
       "      <td>0.623529</td>\n",
       "      <td>0.346221</td>\n",
       "      <td>0.329193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M2</th>\n",
       "      <td>0.004919</td>\n",
       "      <td>0.431901</td>\n",
       "      <td>0.140348</td>\n",
       "      <td>0.349510</td>\n",
       "      <td>0.194244</td>\n",
       "      <td>0.413197</td>\n",
       "      <td>0.127818</td>\n",
       "      <td>0.599028</td>\n",
       "      <td>0.348139</td>\n",
       "      <td>0.272664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M3</th>\n",
       "      <td>0.181776</td>\n",
       "      <td>0.388515</td>\n",
       "      <td>0.104921</td>\n",
       "      <td>0.298354</td>\n",
       "      <td>0.171414</td>\n",
       "      <td>0.446122</td>\n",
       "      <td>0.001239</td>\n",
       "      <td>0.110907</td>\n",
       "      <td>0.209971</td>\n",
       "      <td>0.164483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S1</th>\n",
       "      <td>0.225939</td>\n",
       "      <td>0.002075</td>\n",
       "      <td>0.478845</td>\n",
       "      <td>0.200954</td>\n",
       "      <td>0.412503</td>\n",
       "      <td>0.002279</td>\n",
       "      <td>0.345798</td>\n",
       "      <td>0.145365</td>\n",
       "      <td>0.205606</td>\n",
       "      <td>0.303407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S2</th>\n",
       "      <td>0.032451</td>\n",
       "      <td>0.011212</td>\n",
       "      <td>0.443589</td>\n",
       "      <td>0.346494</td>\n",
       "      <td>0.464173</td>\n",
       "      <td>0.080372</td>\n",
       "      <td>0.481296</td>\n",
       "      <td>0.310248</td>\n",
       "      <td>0.390459</td>\n",
       "      <td>0.406946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S3</th>\n",
       "      <td>0.026430</td>\n",
       "      <td>0.118665</td>\n",
       "      <td>0.250201</td>\n",
       "      <td>0.367463</td>\n",
       "      <td>0.311271</td>\n",
       "      <td>0.215456</td>\n",
       "      <td>0.033697</td>\n",
       "      <td>0.141541</td>\n",
       "      <td>0.206051</td>\n",
       "      <td>0.125222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S4</th>\n",
       "      <td>0.028198</td>\n",
       "      <td>0.292207</td>\n",
       "      <td>0.364289</td>\n",
       "      <td>0.371689</td>\n",
       "      <td>0.297431</td>\n",
       "      <td>0.067789</td>\n",
       "      <td>0.463423</td>\n",
       "      <td>0.125962</td>\n",
       "      <td>0.410685</td>\n",
       "      <td>0.436202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S5</th>\n",
       "      <td>0.048065</td>\n",
       "      <td>0.124125</td>\n",
       "      <td>0.461640</td>\n",
       "      <td>0.194442</td>\n",
       "      <td>0.378922</td>\n",
       "      <td>0.018785</td>\n",
       "      <td>0.534163</td>\n",
       "      <td>0.027190</td>\n",
       "      <td>0.206305</td>\n",
       "      <td>0.435308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S6</th>\n",
       "      <td>0.129097</td>\n",
       "      <td>0.078312</td>\n",
       "      <td>0.181984</td>\n",
       "      <td>0.323308</td>\n",
       "      <td>0.365061</td>\n",
       "      <td>0.402339</td>\n",
       "      <td>0.288267</td>\n",
       "      <td>0.161089</td>\n",
       "      <td>0.317591</td>\n",
       "      <td>0.294891</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Factor 1  Factor 2  Factor 3  Factor 4  Factor 5  Factor 6  Factor 7  \\\n",
       "HC1  0.708322  0.104737  0.090899  0.068597  0.056585  0.348333  0.011751   \n",
       "HC2  0.583005  0.016801  0.075149  0.016643  0.206293  0.193095  0.105770   \n",
       "HC3  0.229497  0.549203  0.279390  0.310239  0.006598  0.030883  0.135658   \n",
       "M1   0.008884  0.478022  0.098610  0.334124  0.198149  0.500957  0.135596   \n",
       "M2   0.004919  0.431901  0.140348  0.349510  0.194244  0.413197  0.127818   \n",
       "M3   0.181776  0.388515  0.104921  0.298354  0.171414  0.446122  0.001239   \n",
       "S1   0.225939  0.002075  0.478845  0.200954  0.412503  0.002279  0.345798   \n",
       "S2   0.032451  0.011212  0.443589  0.346494  0.464173  0.080372  0.481296   \n",
       "S3   0.026430  0.118665  0.250201  0.367463  0.311271  0.215456  0.033697   \n",
       "S4   0.028198  0.292207  0.364289  0.371689  0.297431  0.067789  0.463423   \n",
       "S5   0.048065  0.124125  0.461640  0.194442  0.378922  0.018785  0.534163   \n",
       "S6   0.129097  0.078312  0.181984  0.323308  0.365061  0.402339  0.288267   \n",
       "\n",
       "     Factor 8  Factor 9  Factor 10  \n",
       "HC1  0.010677  0.155580   0.184025  \n",
       "HC2  0.078638  0.062675   0.116539  \n",
       "HC3  0.231957  0.370718   0.052132  \n",
       "M1   0.623529  0.346221   0.329193  \n",
       "M2   0.599028  0.348139   0.272664  \n",
       "M3   0.110907  0.209971   0.164483  \n",
       "S1   0.145365  0.205606   0.303407  \n",
       "S2   0.310248  0.390459   0.406946  \n",
       "S3   0.141541  0.206051   0.125222  \n",
       "S4   0.125962  0.410685   0.436202  \n",
       "S5   0.027190  0.206305   0.435308  \n",
       "S6   0.161089  0.317591   0.294891  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tensor2.factors['Contexts']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ad4559e",
   "metadata": {},
   "source": [
    "If we are interested in the top elements of a given dimension, here for example `'Ligand-Receptor Pairs'`, we can easily obtain them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "090917d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "COL9A3^CD44                  0.323394\n",
       "SLPI^PLSCR1                  0.154680\n",
       "HP^TLR4                      0.142395\n",
       "MDK^TSPAN1                   0.132488\n",
       "VCAN^TLR1                    0.132404\n",
       "CD59^CD2                     0.125173\n",
       "MDK^NCL                      0.118178\n",
       "TGFB1^ACVR1_TGFBR1_TGFBR2    0.112601\n",
       "CADM1^CRTAM                  0.111984\n",
       "VCAN^TLR2                    0.109834\n",
       "Name: Factor 10, dtype: float32"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Top 10 LR pairs in the Factor 10\n",
    "tensor2.get_top_factor_elements(order_name='Ligand-Receptor Pairs', \n",
    "                                factor_name='Factor 10', \n",
    "                                top_number=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a1df134",
   "metadata": {},
   "source": [
    "## Supplementary Information about the Tensor Decomposition"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b8cd5be",
   "metadata": {},
   "source": [
    "To show the steps here, we will make a copy of the original tensor object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "fba3929b",
   "metadata": {},
   "outputs": [],
   "source": [
    "interaction_tensor = tensor.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5498757b",
   "metadata": {},
   "source": [
    "### The one-line command used to run Tensor-cell2cell (`c2c.analysis.run_tensor_cell2cell_pipeline()`) performs three main things under-the-hood:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97f8fc49",
   "metadata": {},
   "source": [
    "**1- Allocates the tensor in the device to use**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "21deb9da",
   "metadata": {},
   "outputs": [],
   "source": [
    "device = 'cuda'\n",
    "interaction_tensor.to_device(device)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "daf4ed37",
   "metadata": {},
   "source": [
    "**2- If no rank was specified, it performs the elbow analysis**\n",
    "\n",
    "Main parameters are:\n",
    "\n",
    "- `uppper_rank` is the max number of ranks to try in the elbow analysis.\n",
    "\n",
    "\n",
    "- `runs` is the number of tensor decompositions (using different randomizations for initializing the algorithm) to perform per rank. If more than 1 is passed, the plot will show the mean of the metric and its standard deviation for each evaluated rank.\n",
    "\n",
    "\n",
    "- `init` is the initialization of the tensor decomposition algorithm. If your tensor contains a mask (`tensor.mask`), it will automatically use `init='random'`. Options are `'random'` and `'svd'`. The latter helps to obtain results that are more close to a global optima from the optimization method behind the algorithm.\n",
    "\n",
    "\n",
    "- `svd` is the method to perform the SVD to initialize the tensor factorization. This is only considered whe `init='svd'`. \n",
    "\n",
    "\n",
    "- `automatic_elbow` indicates whether suggesting a rank number to perform the decomposition. If `False`, this analysis will only report the curve, then the user can make a decision of what rank number to use in the final decomposition.\n",
    "\n",
    "\n",
    "- `'metric'` is the metric to perform the elbow analysis to obtain a suggested number of factors. Options are `'error'` and `'similarity'`, indicating respectively the error of tensor reconstruction, and the similarity of tensor factorization across multiple runs for a given rank. The similarity metric may result in non-smooth curves given the highly variability especially at higher ranks. If so, we recommend using the parameter `smooth_elbow=True`.\n",
    "\n",
    "\n",
    "- `random_state` is the seed for randomization. It controls the randomization used when initializing the optimization algorithm that performs the tensor decomposition. It is useful for reproducing the same result every time that the analysis is run. If `None`, a different randomization will be used each time.\n",
    "\n",
    "\n",
    "- `filename` is the full path name of the file wherein the figure is saved (even indicating the extension). For example, '/home/user/Elbow.pdf'. If `None` the figure will not be saved."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44baa0d0",
   "metadata": {},
   "source": [
    "Here we can also specify the `tol`and the `n_iter_max`, which are the parameters associated with the `tf_optimization` parameter in the `c2c.analysis.run_tensor_cell2cell_pipeline()` function.\n",
    "\n",
    "For 'regular' we use `tol=1e-7` and `n_iter_max=100`, while for 'robust' these parameters are `tol=1e-8` and `n_iter_max=500`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a08e966a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████| 25/25 [03:47<00:00,  9.12s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The rank at the elbow is: 11\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x225 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig1, error = interaction_tensor.elbow_rank_selection(upper_rank=25,\n",
    "                                                      runs=10,\n",
    "                                                      init='random',\n",
    "                                                      svd='numpy_svd',\n",
    "                                                      automatic_elbow=True,\n",
    "                                                      metric='error',\n",
    "                                                      random_state=0,\n",
    "                                                      fontsize=14,\n",
    "                                                      filename=None,\n",
    "                                                      tol=1e-7, n_iter_max=100,\n",
    "                                                      )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e7739d0",
   "metadata": {},
   "source": [
    "Here, the error measures how different is the original tensor from the sum of R rank-1 tensors used to approximate it. The idea is to find a good trade-off between a small number of factors and a small error.\n",
    "\n",
    "![factors](https://earmingol.github.io/cell2cell/tutorials/ASD/figures/tensor-approx.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69eee788",
   "metadata": {},
   "source": [
    "**3- Tensor Factorization/Decomposition**\n",
    "\n",
    "After knowing that number of factors (rank) to use, we can perform the tensor decompositon.\n",
    "\n",
    "Main parameters are:\n",
    "\n",
    "- `rank` is the number of factors or latent patterns to obtain from the tensor decomposition.\n",
    "\n",
    "\n",
    "- `runs` is the number of tensor decompositions (using different randomizations for initializing the algorithm) to perform. If more. than 1 is assigned, the result that minimizes the tensor-reconstruction error will be output.\n",
    "\n",
    "\n",
    "- `init` is the initialization of the tensor decomposition algorithm. If your tensor contains a mask (`tensor.mask`), it will automatically use `init='random'`. Options are `'random'` and `'svd'`. The latter helps to obtain results that are more close to a global optima from the optimization method behind the algorithm.\n",
    "\n",
    "\n",
    "- `svd` is the method to perform the SVD to initialize the tensor factorization. This is only considered whe `init='svd'`. \n",
    "\n",
    "\n",
    "- `random_state` is the seed for randomization. It controls the randomization used when initializing the optimization algorithm that performs the tensor decomposition. It is useful for reproducing the same result every time that the analysis is run. If `None`, a different randomization will be used each time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "939c81cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# From the elbow analysis, the suggested rank is found in\n",
    "interaction_tensor.rank"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da678305",
   "metadata": {},
   "source": [
    "Here we can also specify the `tol`and the `n_iter_max`, which are the parameters associated with the `tf_optimization` parameter in the `c2c.analysis.run_tensor_cell2cell_pipeline()` function.\n",
    "\n",
    "For 'regular' we use `tol=1e-7` and `n_iter_max=100`, while for 'robust' these parameters are `tol=1e-8` and `n_iter_max=500`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "358c97e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████| 100/100 [06:28<00:00,  3.88s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best model has a normalized error of: 0.503\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "interaction_tensor.compute_tensor_factorization(rank=interaction_tensor.rank,\n",
    "                                                init='random',\n",
    "                                                svd='numpy_svd',\n",
    "                                                random_state=0,\n",
    "                                                runs=100,\n",
    "                                                normalize_loadings=True,\n",
    "                                                tol=1e-8, n_iter_max=500,\n",
    "                                                )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31251910",
   "metadata": {},
   "source": [
    "The tensor decomposition performed here is a non-negative canonical polyadic decomposition (CPD) (same performed in the elbow analysis). Briefly, tensor decomposition identifies a low-rank tensor (here, a rank of 11) that approximated the full tensor. This low-rank tensor can be represented as the sum of a set of rank-1 tensors (10 of them in this case). Each rank-1 tensor represents a factor in the decomposition and can be further represented as the outer product of n vectors, where n represents the number of tensor dimensions. Each vector represents one of the n tensor dimensions for that factor and its values, corresponding to individual elements in each dimension, represent the factor loadings. In our case, each factor will contain loadings for the context, LR pair, sender-cell, and receiver-cell dimensions. Those elements within each factor that contain high loadings contribute to the factor-specific communication pattern.\n",
    "\n",
    "![TF](https://earmingol.github.io/cell2cell/tutorials/ASD/figures/tensor-factorization.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40f7b985",
   "metadata": {},
   "source": [
    "We can plot the results with:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6ccbacea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1400 with 44 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "factors, axes = c2c.plotting.tensor_factors_plot(interaction_tensor=interaction_tensor,\n",
    "                                                 metadata = meta_tensor, # This is the metadata for each dimension\n",
    "                                                 sample_col='Element',\n",
    "                                                 group_col='Category',\n",
    "                                                 meta_cmaps = ['plasma', 'Dark2_r', 'tab20', 'tab20'],\n",
    "                                                 fontsize=10, # Font size of the figures generated\n",
    "                                                 filename=None # Path to save the figure. E.g. '/home/user/TF.pdf'\n",
    "                                                 )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc53af2d",
   "metadata": {},
   "source": [
    "### Subset of a Tensor\n",
    "\n",
    "\n",
    "From this analysis we may also be interested in a subset of the original tensor to inspect the cell-cell communication of a subset of samples, LR pairs, sender cells, or receiver cells.\n",
    "\n",
    "We can subset the original tensor without having to build it from the beginning. For example, we are interested in the CCC of NKs and T cells as the senders, and B cells, Macrophages, mDC and pDC cells as receivers. We can do so by creating a dictionary specifying the elements of the dimensions that we want to subset. Here, the dimensions corresponds to:\n",
    "- `0` for the Contexts\n",
    "- `1` for the Ligand-Receptor Pairs\n",
    "- `2` for the Sender Cells\n",
    "- `3` for the Receiver Cells\n",
    "\n",
    "If we do not include a dimension in the following dictionary, that dimension will not be subset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "470ee580",
   "metadata": {},
   "outputs": [],
   "source": [
    "subset_dict = {2: ['NK', 'T'], # Sender Cells\n",
    "               3: ['B', 'Macrophages', 'mDC', 'pDC'] # Receiver Cells\n",
    "              }"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e469885",
   "metadata": {},
   "source": [
    "Some key parameters here are:\n",
    "\n",
    "- `original_order` indicates whether keeping the element order as in the original tensor or as in the subset list.\n",
    "\n",
    "- `remove_duplicates` indicates whether removing duplicate elements in the subset lists. Duplicates can be introduced especially when passing these lists manually.\n",
    "\n",
    "- `keep` indicates what duplicated element to keep. Options are:\n",
    "    - 'first' : Drop duplicates except for the first occurrence.\n",
    "    - 'last' : Drop duplicates except for the last occurrence.\n",
    "    - False : Drop all duplicates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "68dd7c53",
   "metadata": {},
   "outputs": [],
   "source": [
    "subset_tensor = c2c.tensor.subset_tensor(interaction_tensor=tensor,\n",
    "                                         subset_dict=subset_dict,\n",
    "                                         original_order=True \n",
    "                                        )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba0a34ed",
   "metadata": {},
   "source": [
    "If we compare the size of the original tensor with the subset one, we can see that we only modified the senders and receiver cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "98b939b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([12, 1054, 10, 10]) torch.Size([12, 1054, 2, 4])\n"
     ]
    }
   ],
   "source": [
    "print(tensor.tensor.shape, subset_tensor.tensor.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "328f0867",
   "metadata": {},
   "source": [
    "Now with this subset tensor, we have to subset the metadata as well. With the following function we can pass the subset tensor as reference to adapt the metadata correspondingly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0c0b6725",
   "metadata": {},
   "outputs": [],
   "source": [
    "subset_meta = c2c.tensor.subset_metadata(tensor_metadata=meta_tensor,\n",
    "                                         interaction_tensor=subset_tensor\n",
    "                                        )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00378491",
   "metadata": {},
   "source": [
    "**With this subset tensor and metadata (`subset_tensor` and `subset_meta`), we can run the Tensor-cell2cell analysis as indicated earlier in this tutorial**"
   ]
  }
 ],
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