{
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   "source": [
    "# Tutorial 3 - Generating metacells for reverse-engineering of ARACNe gene regulatory networks \n",
    "\n",
    "This tutorial explores different approaches to generate metacells from scRNA-seq data. Metacells are used as a means to counteract sparsity in single-cell transcriptomics and improve gene regulatory network (GRN) inference, for instance using the [ARACNe3](https://www.mdpi.com/1099-4300/25/3/542) and [ARACNe-AP](https://pubmed.ncbi.nlm.nih.gov/27153652/) algorithms for GRN reconstruction by mutual information estimation. After a brief description of the pyVIPER installation procedure and the modules needed, this notebook is organized in the following sections.\n",
    "\n",
    "**Table of Contents**\n",
    "\n",
    "[Step 1. Load a gene expression matrix and associated metadata](#1)\\\n",
    "[Step 2. Perform basic preprocessing and clustering](#2)\\\n",
    "[Step 3. Generate metacells](#3)\\\n",
    "[Key Takeaways](#4) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03b2b639",
   "metadata": {},
   "source": [
    "### Install Pyviper\n",
    "Install `pyviper` from PyPI using pip. Alternatively, refer to the README in the current GitHub to install from the local directory."
   ]
  },
  {
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   "source": [
    "#!pip install viper-in-python"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2590a58",
   "metadata": {},
   "source": [
    "### Import modules\n",
    "Load `pyviper` and additional modules required used in this tutorial."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "41321fbe",
   "metadata": {
    "execution": {
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   "source": [
    "import pyviper\n",
    "import scanpy as sc\n",
    "import anndata \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import rc_context\n",
    "from sklearn.metrics import silhouette_samples\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", message=\".*The 'nopython' keyword.*\") # for jit decorator issue with sc.pp.neighbors (09/30/2023)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d428eb41",
   "metadata": {},
   "source": [
    "### Step 1. Load a gene expression matrix and associated metadata\n",
    "<a id='1'></a>\n",
    "Load the same gene expression matrix (UMIs) used in Tutorial 2 and store it into an [AnnData](https://anndata.readthedocs.io/en/latest/) object to enable interoperability with [scanpy](https://scanpy-tutorials.readthedocs.io/en/latest/#). Cells used in this tutorial were sampled from scRNA-seq data published in [Peng et al., 2019](https://www.nature.com/articles/s41422-019-0195-y).  \n",
    "\n",
    "Display matrix dimensions (cells x genes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "03cb7f44",
   "metadata": {
    "execution": {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 4632 × 24005"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gExpr_url = \"https://zenodo.org/records/10059791/files/Tutorial_2_counts_mixed_4632.tsv.gz\" # path to gene expression matrix (UMI counts)\n",
    "adata_gExpr = pd.read_csv(gExpr_url, sep=\"\\t\") # read from remote \n",
    "adata_gExpr = sc.AnnData(adata_gExpr) # convert to AnnData object\n",
    "\n",
    "adata_gExpr"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "717ba697",
   "metadata": {},
   "source": [
    "Load cell-associated metadata."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d694957f",
   "metadata": {
    "execution": {
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     "shell.execute_reply.started": "2023-12-14T20:42:22.397901Z"
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   "outputs": [],
   "source": [
    "metadata_url = \"https://zenodo.org/records/10059791/files/Tutorial_2_metadata_mixed_4632.tsv.gz\" # path to cells metadata\n",
    "cells_metadata = pd.read_csv(metadata_url, sep=\"\\t\")  # load it"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "149296dd",
   "metadata": {},
   "source": [
    "Store the metadata in the `adata_gExpr` object and display the observation annotation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8b87dd88",
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    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cell_Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>T1_AACCATGCACAACTGT</th>\n",
       "      <td>Ductal cell type 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T1_AAGACCTAGTCATGCT</th>\n",
       "      <td>Ductal cell type 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T1_ACATACGAGACTCGGA</th>\n",
       "      <td>Ductal cell type 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T1_ACCAGTATCTTGCAAG</th>\n",
       "      <td>Ductal cell type 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T1_ACGATGTTCACGAAGG</th>\n",
       "      <td>Ductal cell type 2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Cell_Type\n",
       "T1_AACCATGCACAACTGT  Ductal cell type 2\n",
       "T1_AAGACCTAGTCATGCT  Ductal cell type 2\n",
       "T1_ACATACGAGACTCGGA  Ductal cell type 2\n",
       "T1_ACCAGTATCTTGCAAG  Ductal cell type 2\n",
       "T1_ACGATGTTCACGAAGG  Ductal cell type 2"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata_gExpr.obs = pd.merge(adata_gExpr.obs, cells_metadata, how=\"left\",left_index=True, right_index=True) # store cell-specific metadata as annotation observation\n",
    "adata_gExpr.obs.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15319a83",
   "metadata": {},
   "source": [
    "The observation annotations include the patient from which each cell was sequenced and the annotated cell type. List the number of cells for each cell types."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "87a47a53",
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    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cell_Type</th>\n",
       "      <th>n</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>B cell</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ductal cell type 2</td>\n",
       "      <td>1455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Fibroblast cell</td>\n",
       "      <td>1277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Stellate cell</td>\n",
       "      <td>900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Cell_Type     n\n",
       "0              B cell  1000\n",
       "1  Ductal cell type 2  1455\n",
       "2     Fibroblast cell  1277\n",
       "3       Stellate cell   900"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata_gExpr.obs.groupby('Cell_Type').size().reset_index(name='n') # show cell types and number of cells for each type in AnnData"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "808dc5f7",
   "metadata": {},
   "source": [
    "### Step 2. Perform basic preprocessing and clustering\n",
    "<a id='2'></a>\n",
    "Perform some standard preprocessing. The UMI matrix can be processed using the standard Scanpy preprocessing workflow. Since the quality of the provided data was pre-assessed and found to be high, cell filtering will be minimal. For a more detailed explanation of quality control steps, refer to the preprocessing tutorials by [Scanpy](https://scanpy-tutorials.readthedocs.io/en/latest/pbmc3k.html) or [Seurat](https://satijalab.org/seurat/articles/pbmc3k_tutorial). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "08d3b344",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-14T20:42:23.044788Z",
     "iopub.status.busy": "2023-12-14T20:42:23.044597Z",
     "iopub.status.idle": "2023-12-14T20:42:31.500470Z",
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   },
   "outputs": [],
   "source": [
    "sc.pp.calculate_qc_metrics(adata_gExpr, inplace=True)\n",
    "sc.pp.filter_cells(adata_gExpr, min_genes=200) # filter out cells with <200 genes expressed\n",
    "sc.pp.filter_genes(adata_gExpr, min_counts=10) # filter out genes that are present with <10 UMIs\n",
    "\n",
    "#adata_gExpr = calcMitoRiboPercent(adata_gExpr)\n",
    "#adata_gExpr = filterCellsHighMito(adata_gExpr,0.2)\n",
    "\n",
    "# Store UMI in .raw\n",
    "adata_gExpr.raw = adata_gExpr\n",
    "\n",
    "sc.pp.normalize_total(adata_gExpr, inplace=True,target_sum=1e4)\n",
    "sc.pp.log1p(adata_gExpr)\n",
    "sc.pp.highly_variable_genes(adata_gExpr, flavor=\"seurat\", n_top_genes=3000, inplace=True)\n",
    "\n",
    "sc.pp.scale(adata_gExpr)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02e141f6",
   "metadata": {},
   "source": [
    "scRNA-seq data are known to exhibit high sparsity, typically >80-90%. Compute the sparsity of cells labelled as Fibroblasts as the percentage of zero-entries in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a859ee5f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sparsity (fibroblasts, single cells): 85.0%\n"
     ]
    }
   ],
   "source": [
    "Fibroblast_matrix = adata_gExpr[adata_gExpr.obs['Cell_Type'] == 'Fibroblast cell']\n",
    "sparsity = np.mean(Fibroblast_matrix.raw.X == 0)\n",
    "del Fibroblast_matrix\n",
    "\n",
    "print(\"Sparsity (fibroblasts, single cells): \" + str(round(sparsity,2)*100) + \"%\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7432b70c",
   "metadata": {},
   "source": [
    "### 3. Generate Metacells \n",
    "<a id='3'></a>\n",
    "Compute a distance matrix between each pair of cells on the PCA-projected space in the dataset using the `corr_distance` function. Any distance matrix can be used for metacell  generation by setting 'dist_slot' to the corresponding value in the `repr_metacells` function (see below)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d700cd3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "sc.tl.pca(adata_gExpr, svd_solver='arpack', random_state=0)\n",
    "pyviper.pp.corr_distance(adata_gExpr) # compute correlation distance"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "227ea7cd",
   "metadata": {},
   "source": [
    "Generate a set of metacell matrices for each cellular population (cluster), as represented by the 'Cell_Type' annotation in .obs. The metacell generation function allows several combination of inputs. Specifically, exactly two of the following parameters must be set: \n",
    "**1.** 'size', **2a.** 'min_median_depth' or **2b.** 'n_cells_per_metacell', **3a.** 'perc_data_to_use' or **3b.** 'perc_incl_data_reused'. \n",
    "\n",
    "<u>Please notice that 2a-2b and 3a-3b are mutually exclusive, i.e. they cannot be set together.</u>\n",
    "\n",
    "- **Example 1**: generate 400 metacells per cell population requiring an attempt minimum depth of 12,000 UMI/metacell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ce8c7727",
   "metadata": {
    "execution": {
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     "iopub.status.idle": "2023-12-14T20:42:33.299039Z",
     "shell.execute_reply": "2023-12-14T20:42:33.298309Z",
     "shell.execute_reply.started": "2023-12-14T20:42:31.501506Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "cluster metacells:  25%|██▌       | 1/4 [00:00<00:02,  1.17it/s]/Users/lucazanella7/mambaforge/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/Users/lucazanella7/mambaforge/lib/python3.10/site-packages/numpy/core/_methods.py:192: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "/Users/lucazanella7/mambaforge/lib/python3.10/site-packages/numpy/core/_methods.py:269: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/Users/lucazanella7/mambaforge/lib/python3.10/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "/Users/lucazanella7/mambaforge/lib/python3.10/site-packages/numpy/core/_methods.py:261: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "cluster metacells: 100%|██████████| 4/4 [00:03<00:00,  1.14it/s]\n"
     ]
    }
   ],
   "source": [
    "n_metacells=400\n",
    "min_target_depth=12000\n",
    "\n",
    "pyviper.pp.repr_metacells(adata_gExpr, \n",
    "                          counts=None, \n",
    "                          pca_slot='X_pca', \n",
    "                          dist_slot='corr_dist', \n",
    "                          size=n_metacells, \n",
    "                          min_median_depth=min_target_depth, \n",
    "                          clusters_slot=\"Cell_Type\",\n",
    "                          key_added=\"metacells_approach_1\")  # size=n_meta, n_cells_per_metacell=k, clusters_slot=\"putative_tumor_infercnv_output\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e28733da",
   "metadata": {},
   "source": [
    "While the generation of the minimum target depth cannot be guaranteed in all cases, pyVIPER attempts to satisfy this request by tuning the number of nearest neighbors used in metacell generation. \n",
    "\n",
    "Display `adata_gExpr`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8bb06d47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 4632 × 18160\n",
       "    obs: 'Cell_Type', 'n_genes_by_counts', 'log1p_n_genes_by_counts', 'total_counts', 'log1p_total_counts', 'pct_counts_in_top_50_genes', 'pct_counts_in_top_100_genes', 'pct_counts_in_top_200_genes', 'pct_counts_in_top_500_genes', 'n_genes'\n",
       "    var: 'n_cells_by_counts', 'mean_counts', 'log1p_mean_counts', 'pct_dropout_by_counts', 'total_counts', 'log1p_total_counts', 'n_counts', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'mean', 'std'\n",
       "    uns: 'log1p', 'hvg', 'pca', 'metacells_approach_1_B cell', 'metacells_approach_1_Ductal cell type 2', 'metacells_approach_1_Fibroblast cell', 'metacells_approach_1_Stellate cell'\n",
       "    obsm: 'X_pca'\n",
       "    varm: 'PCs'\n",
       "    obsp: 'corr_dist'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata_gExpr"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fb28d00",
   "metadata": {},
   "source": [
    "4 new matrices, one for each cell type, were stored as Pandas dataframe in `adata.uns`. \n",
    "\n",
    "Display the metacell matrix for the population of Ductal cell type 2. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f2d1a02e",
   "metadata": {},
   "outputs": [
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       "      <th>AL627309.1</th>\n",
       "      <th>AP006222.2</th>\n",
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       "      <th>RP11-206L10.9</th>\n",
       "      <th>LINC00115</th>\n",
       "      <th>FAM41C</th>\n",
       "      <th>RP11-54O7.3</th>\n",
       "      <th>SAMD11</th>\n",
       "      <th>NOC2L</th>\n",
       "      <th>...</th>\n",
       "      <th>RP11-332K15.1</th>\n",
       "      <th>RP11-157E21.1</th>\n",
       "      <th>DMRT2</th>\n",
       "      <th>RP11-309M23.1</th>\n",
       "      <th>NLRP7</th>\n",
       "      <th>IGLL1</th>\n",
       "      <th>EGFR-AS1</th>\n",
       "      <th>RSPO2</th>\n",
       "      <th>KCNC2</th>\n",
       "      <th>AGTR2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>metacell_0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metacell_1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metacell_2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metacell_3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metacell_4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metacell_395</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metacell_396</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metacell_397</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metacell_398</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metacell_399</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>400 rows × 18160 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              AL627309.1  AP006222.2  RP11-206L10.3  RP11-206L10.2  \\\n",
       "metacell_0             0           0              0              0   \n",
       "metacell_1             0           0              0              0   \n",
       "metacell_2             0           0              0              0   \n",
       "metacell_3             0           0              0              0   \n",
       "metacell_4             0           0              0              0   \n",
       "...                  ...         ...            ...            ...   \n",
       "metacell_395           0           0              0              0   \n",
       "metacell_396           0           0              0              0   \n",
       "metacell_397           0           1              0              0   \n",
       "metacell_398           0           1              0              0   \n",
       "metacell_399           0           0              0              0   \n",
       "\n",
       "              RP11-206L10.9  LINC00115  FAM41C  RP11-54O7.3  SAMD11  NOC2L  \\\n",
       "metacell_0                0          0       0            0       0      0   \n",
       "metacell_1                0          0       0            0       0      0   \n",
       "metacell_2                0          0       0            0       0      0   \n",
       "metacell_3                0          0       0            0       0      0   \n",
       "metacell_4                0          0       0            0       0      0   \n",
       "...                     ...        ...     ...          ...     ...    ...   \n",
       "metacell_395              0          0       0            0       0      2   \n",
       "metacell_396              0          0       0            0       0      0   \n",
       "metacell_397              0          0       2            0       0      0   \n",
       "metacell_398              0          0       0            0       0      0   \n",
       "metacell_399              0          0       0            0       0      0   \n",
       "\n",
       "              ...  RP11-332K15.1  RP11-157E21.1  DMRT2  RP11-309M23.1  NLRP7  \\\n",
       "metacell_0    ...              0              0      0              0      0   \n",
       "metacell_1    ...              0              0      0              0      0   \n",
       "metacell_2    ...              0              0      0              0      0   \n",
       "metacell_3    ...              0              0      0              0      0   \n",
       "metacell_4    ...              0              0      0              0      0   \n",
       "...           ...            ...            ...    ...            ...    ...   \n",
       "metacell_395  ...              0              0      0              0      0   \n",
       "metacell_396  ...              0              0      0              0      0   \n",
       "metacell_397  ...              0              0      0              0      0   \n",
       "metacell_398  ...              0              0      0              0      0   \n",
       "metacell_399  ...              0              0      0              0      0   \n",
       "\n",
       "              IGLL1  EGFR-AS1  RSPO2  KCNC2  AGTR2  \n",
       "metacell_0        0         0      0      0      0  \n",
       "metacell_1        0         0      0      0      0  \n",
       "metacell_2        0         0      0      0      0  \n",
       "metacell_3        0         0      0      0      0  \n",
       "metacell_4        0         0      0      0      0  \n",
       "...             ...       ...    ...    ...    ...  \n",
       "metacell_395      0         0      0      0      0  \n",
       "metacell_396      0         0      0      0      0  \n",
       "metacell_397      0         0      0      0      0  \n",
       "metacell_398      0         0      0      0      0  \n",
       "metacell_399      0         0      0      0      0  \n",
       "\n",
       "[400 rows x 18160 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata_gExpr.uns[\"metacells_approach_1_Ductal cell type 2\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb11effd",
   "metadata": {},
   "source": [
    "Each metacell matrix can be used as input to ARACNe3 to reverse engineer the corresponding GRN which can then be provided as an input to VIPER. Each metacell matrix is associated with a dictionary collecting relevant metacell statistics. \n",
    "\n",
    "Display metacell-associated statistics for the population of fibroblasts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c5d7dc15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>size</th>\n",
       "      <td>400.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n_cells_per_metacell</th>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min_median_depth</th>\n",
       "      <td>12000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median_depth</th>\n",
       "      <td>18104.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>perc_total_samples_used</th>\n",
       "      <td>58.418168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>perc_included_samples_used_mult_times</th>\n",
       "      <td>6.970509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_n_times_samples_used</th>\n",
       "      <td>1.072386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stdev_n_times_samples_used</th>\n",
       "      <td>0.269274</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             Values\n",
       "size                                     400.000000\n",
       "n_cells_per_metacell                       2.000000\n",
       "min_median_depth                       12000.000000\n",
       "median_depth                           18104.000000\n",
       "perc_total_samples_used                   58.418168\n",
       "perc_included_samples_used_mult_times      6.970509\n",
       "mean_n_times_samples_used                  1.072386\n",
       "stdev_n_times_samples_used                 0.269274"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "attrs_dict = adata_gExpr.uns[\"metacells_approach_1_Fibroblast cell\"].attrs\n",
    "attrs_df = pd.DataFrame.from_dict(attrs_dict, orient='index', columns=['Values']) # convert to df\n",
    "\n",
    "attrs_df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "942b47b3",
   "metadata": {},
   "source": [
    "The report is highly informative. The metacell matrix contains 400 metacells, each generated by aggregation of 2 nearest neighbors after requesting an \"attempt\" minimum target depth of 12,000 UMI/metacell. The median depth for the inferred metacells is ~18,000 UMI/metacell. 58% of the original single-cells from the fibroblasts population was used for metacell generation, with <7% of cells being aggregated in more than 1 metacell. \n",
    "\n",
    "The data sparsity is expected to be decreased in the metacells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c4f2268b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sparsity (fibroblasts, metacells example 1): 77.0%\n"
     ]
    }
   ],
   "source": [
    "sparsity = np.mean(np.array(adata_gExpr.uns[\"metacells_approach_1_Fibroblast cell\"]) == 0)\n",
    "print(\"Sparsity (fibroblasts, metacells example 1): \" + str(round(sparsity,2)*100) + \"%\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6410f82c",
   "metadata": {},
   "source": [
    "Display the statistics for metacells of another cluster (B cells)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e3f4b56b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>size</th>\n",
       "      <td>400.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n_cells_per_metacell</th>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min_median_depth</th>\n",
       "      <td>12000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median_depth</th>\n",
       "      <td>11097.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>perc_total_samples_used</th>\n",
       "      <td>81.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>perc_included_samples_used_mult_times</th>\n",
       "      <td>30.929095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_n_times_samples_used</th>\n",
       "      <td>1.466993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stdev_n_times_samples_used</th>\n",
       "      <td>0.907459</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             Values\n",
       "size                                     400.000000\n",
       "n_cells_per_metacell                       3.000000\n",
       "min_median_depth                       12000.000000\n",
       "median_depth                           11097.500000\n",
       "perc_total_samples_used                   81.800000\n",
       "perc_included_samples_used_mult_times     30.929095\n",
       "mean_n_times_samples_used                  1.466993\n",
       "stdev_n_times_samples_used                 0.907459"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "attrs_dict = adata_gExpr.uns[\"metacells_approach_1_B cell\"].attrs\n",
    "attrs_df = pd.DataFrame.from_dict(attrs_dict, orient='index', columns=['Values']) # convert to df\n",
    "\n",
    "attrs_df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a08d5fec",
   "metadata": {},
   "source": [
    "As mentioned before, `repr_metacells` allows several combinations of parameters. Let's consider another combination.\n",
    "\n",
    "- **Example 2**: generate 400 metacells per cluster such that each metacell should incorporate 20 single-cells (`n_cells_per_metacell=20`; `min_median_depth=None`).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b9ceab10",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "cluster metacells: 100%|██████████| 4/4 [00:07<00:00,  1.79s/it]\n"
     ]
    }
   ],
   "source": [
    "n_cells_per_metacell=20\n",
    "\n",
    "pyviper.pp.repr_metacells(adata_gExpr, \n",
    "                          counts=None, \n",
    "                          pca_slot='X_pca', \n",
    "                          dist_slot='corr_dist', \n",
    "                          size=400,\n",
    "                          n_cells_per_metacell=n_cells_per_metacell,\n",
    "                          min_median_depth=None, \n",
    "                          clusters_slot=\"Cell_Type\",\n",
    "                          key_added=\"metacells_approach_2\") "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28b82629",
   "metadata": {},
   "source": [
    "Display metacell-associated statistics for the Fibroblast population. The median depth is expected to be much higher than before, due to the larger number of cells per metacells specified."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "da4d17ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>Values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>size</th>\n",
       "      <td>400.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n_cells_per_metacell</th>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min_median_depth</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median_depth</th>\n",
       "      <td>196668.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>perc_total_samples_used</th>\n",
       "      <td>97.963978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>perc_included_samples_used_mult_times</th>\n",
       "      <td>89.928058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_n_times_samples_used</th>\n",
       "      <td>6.394884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stdev_n_times_samples_used</th>\n",
       "      <td>4.219301</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              Values\n",
       "size                                      400.000000\n",
       "n_cells_per_metacell                       20.000000\n",
       "min_median_depth                                 NaN\n",
       "median_depth                           196668.500000\n",
       "perc_total_samples_used                    97.963978\n",
       "perc_included_samples_used_mult_times      89.928058\n",
       "mean_n_times_samples_used                   6.394884\n",
       "stdev_n_times_samples_used                  4.219301"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "attrs_dict = adata_gExpr.uns[\"metacells_approach_2_Fibroblast cell\"].attrs\n",
    "attrs_df = pd.DataFrame.from_dict(attrs_dict, orient='index', columns=['Values']) # convert to df\n",
    "\n",
    "attrs_df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd9642f0",
   "metadata": {},
   "source": [
    "Data sparsity is also strongly decreased due to the higher number of nearest neighbors being aggregated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "fd05b2d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sparsity (fibroblasts, metacells example 2): 40.0%\n"
     ]
    }
   ],
   "source": [
    "sparsity = np.mean(np.array(adata_gExpr.uns[\"metacells_approach_2_Fibroblast cell\"]) == 0)\n",
    "\n",
    "print(\"Sparsity (fibroblasts, metacells example 2): \" + str(round(sparsity,2)*100) + \"%\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3311b6df",
   "metadata": {},
   "source": [
    "- **Example 3**: Generate 400 metacells and allow for only the 20% of the cells to be incorporated in more than 1 metacell (`perc_incl_data_reused=20`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d1bf7600",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "cluster metacells: 100%|██████████| 4/4 [00:04<00:00,  1.08s/it]\n"
     ]
    }
   ],
   "source": [
    "perc_incl_data_reused=20\n",
    "\n",
    "pyviper.pp.repr_metacells(adata_gExpr, \n",
    "                          counts=None, \n",
    "                          pca_slot='X_pca', \n",
    "                          dist_slot='corr_dist', \n",
    "                          size=400,\n",
    "                          perc_incl_data_reused=perc_incl_data_reused,\n",
    "                          min_median_depth=None, \n",
    "                          clusters_slot=\"Cell_Type\",\n",
    "                          key_added=\"metacells_approach_3\") \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db3c16d3",
   "metadata": {},
   "source": [
    "Display metacell statistics for the B cell population: the percentage of included cells is now lower than in the example 1. This approach can be useful if the goal is to generate metacells that are independent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2b0a08fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>size</th>\n",
       "      <td>400.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n_cells_per_metacell</th>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min_median_depth</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median_depth</th>\n",
       "      <td>7523.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>perc_total_samples_used</th>\n",
       "      <td>69.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>perc_included_samples_used_mult_times</th>\n",
       "      <td>12.034384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_n_times_samples_used</th>\n",
       "      <td>1.146132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stdev_n_times_samples_used</th>\n",
       "      <td>0.436669</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            Values\n",
       "size                                    400.000000\n",
       "n_cells_per_metacell                      2.000000\n",
       "min_median_depth                               NaN\n",
       "median_depth                           7523.000000\n",
       "perc_total_samples_used                  69.800000\n",
       "perc_included_samples_used_mult_times    12.034384\n",
       "mean_n_times_samples_used                 1.146132\n",
       "stdev_n_times_samples_used                0.436669"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "attrs_dict = adata_gExpr.uns[\"metacells_approach_3_B cell\"].attrs\n",
    "attrs_df = pd.DataFrame.from_dict(attrs_dict, orient='index', columns=['Values']) # convert to df\n",
    "\n",
    "attrs_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bc2a264",
   "metadata": {},
   "source": [
    "##### Comparing metacell and original single-cells\n",
    "As a simple and quick sanity check, we compare the original cells and the derived cell metacells. Extract all the metacell matrices generated with the first approach."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "becc4fdf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract and concatenate all population-specific metacell matrices\n",
    "B_meta=adata_gExpr.uns[\"metacells_approach_1_B cell\"]\n",
    "Ductal_meta=adata_gExpr.uns[\"metacells_approach_1_Ductal cell type 2\"]\n",
    "Fibroblast_meta=adata_gExpr.uns[\"metacells_approach_1_Fibroblast cell\"]\n",
    "Stellate_meta=adata_gExpr.uns[\"metacells_approach_1_Stellate cell\"]\n",
    "\n",
    "# rename all indexes before merging\n",
    "B_meta.index = \"B_\"+B_meta.index\n",
    "Ductal_meta.index = \"Ductal_\"+Ductal_meta.index\n",
    "Fibroblast_meta.index = \"Fibroblast_\"+Fibroblast_meta.index\n",
    "Stellate_meta.index = \"Stellate_\"+Stellate_meta.index\n",
    "\n",
    "# concatenate all dataframes\n",
    "metacells_df = pd.concat([B_meta, Ductal_meta, Fibroblast_meta, Stellate_meta], ignore_index=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5997567a",
   "metadata": {},
   "source": [
    "Annotate each metacell with its cell type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2ebb53ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dictionary mapping index prefixes to cell types\n",
    "cell_type_map = {\n",
    "    'B': 'B cell',\n",
    "    'Ductal': 'Ductal cell type 2',\n",
    "    'Fibroblast': 'Fibroblast cell',\n",
    "    'Stellate': 'Stellate cell'\n",
    "}\n",
    "\n",
    "# Generate the \"Cell_Type\" column based on the index\n",
    "cell_types = [cell_type_map[index.split('_')[0]] for index in metacells_df.index]\n",
    "\n",
    "# metacell observation dataframe\n",
    "observation_df = pd.DataFrame({'Cell_Type': cell_types}, index=metacells_df.index)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5c0341f",
   "metadata": {},
   "source": [
    "Generate an AnnData object from metacells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "be6a2422",
   "metadata": {},
   "outputs": [],
   "source": [
    "adata_metacell = anndata.AnnData(X=metacells_df, obs=observation_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26989bfe",
   "metadata": {},
   "source": [
    "Preprocess the concatenated metacells for PCA and UMAP visualization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "4a241691",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n"
     ]
    }
   ],
   "source": [
    "adata_metacell.raw = adata_metacell\n",
    "\n",
    "sc.pp.normalize_total(adata_metacell, inplace=True,target_sum=1e4)\n",
    "sc.pp.log1p(adata_metacell)\n",
    "sc.pp.highly_variable_genes(adata_metacell, flavor=\"seurat\", n_top_genes=3000, inplace=True)\n",
    "\n",
    "sc.pp.scale(adata_metacell)\n",
    "\n",
    "sc.tl.pca(adata_metacell, svd_solver='arpack', random_state=0)\n",
    "sc.pp.neighbors(adata_metacell, n_neighbors=15, n_pcs=30)\n",
    "sc.tl.umap(adata_metacell)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b356970e",
   "metadata": {},
   "source": [
    "Visualize the original single-cells in PCA and UMAP embeddings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "168c8717",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/lucazanella7/mambaforge/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:369: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored\n",
      "  ax.scatter(\n",
      "/Users/lucazanella7/mambaforge/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:379: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored\n",
      "  ax.scatter(\n",
      "/Users/lucazanella7/mambaforge/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored\n",
      "  cax = scatter(\n",
      "/Users/lucazanella7/mambaforge/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:369: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored\n",
      "  ax.scatter(\n",
      "/Users/lucazanella7/mambaforge/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:379: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored\n",
      "  ax.scatter(\n",
      "/Users/lucazanella7/mambaforge/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored\n",
      "  cax = scatter(\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x350 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sc.pp.neighbors(adata_gExpr, n_neighbors=15, n_pcs=30)\n",
    "sc.tl.umap(adata_gExpr)\n",
    "\n",
    "fig, axs = plt.subplots(1, 2, figsize=(10, 3.5))\n",
    "\n",
    "with rc_context({'figure.figsize': (3.5, 3.5)}):\n",
    "    sc.pl.pca(adata_gExpr, color=\"Cell_Type\", use_raw=False, palette=\"Set2\", title=\"PCA by Cell Types\", add_outline=True, ax=axs[0], show=False)\n",
    "with rc_context({'figure.figsize': (3.5, 3.5)}):\n",
    "    sc.pl.umap(adata_gExpr, color=\"Cell_Type\", use_raw=False, palette=\"Set2\", title=\"UMAP by Cell Types\", add_outline=True, ax=axs[1], show=False)\n",
    "\n",
    "plt.suptitle(\"Gene expression - single cells\")\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "213559ad",
   "metadata": {},
   "source": [
    "Visualize the cell population-specific metacells in PCA and UMAP embeddings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "aa3ab719",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/lucazanella7/mambaforge/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:369: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored\n",
      "  ax.scatter(\n",
      "/Users/lucazanella7/mambaforge/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:379: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored\n",
      "  ax.scatter(\n",
      "/Users/lucazanella7/mambaforge/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored\n",
      "  cax = scatter(\n",
      "/Users/lucazanella7/mambaforge/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:369: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored\n",
      "  ax.scatter(\n",
      "/Users/lucazanella7/mambaforge/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:379: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored\n",
      "  ax.scatter(\n",
      "/Users/lucazanella7/mambaforge/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored\n",
      "  cax = scatter(\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x350 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 2, figsize=(10, 3.5))\n",
    "\n",
    "with rc_context({'figure.figsize': (3.5, 3.5)}):\n",
    "    sc.pl.pca(adata_metacell, color=\"Cell_Type\", use_raw=False, palette=\"Set2\", title=\"PCA by Cell Types\", add_outline=True, ax=axs[0], show=False)\n",
    "with rc_context({'figure.figsize': (3.5, 3.5)}):\n",
    "    sc.pl.umap(adata_metacell, color=\"Cell_Type\", use_raw=False, palette=\"Set2\", title=\"UMAP by Cell Types\", add_outline=True, ax=axs[1], show=False)\n",
    "plt.suptitle(\"Gene expression - metacells\")\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d598b66e",
   "metadata": {},
   "source": [
    "The cell type distribution is consistent between single-cells and metacells, with stellate cells and fibroblasts partially overlapping in the PCA as expected, and all the three lineages been separated along the directions of maximal variance."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1e82953",
   "metadata": {},
   "source": [
    "### Key takeaways\n",
    "<a id='4'></a>\n",
    "This Tutorial describes how to generate metacells with pyVIPER. Metacells can be used for pseudo-bulk analysis of specific cellular populations or as input to ARACNe, when the original cell depth is not adequate to robustly infer regulatory networks (typically below 10,000 UMI/cell). The `repr_metacell` function computes several metacell-associated statistics and allows the users to specify several parameter combinations, among which the current Tutorial provides three examples."
   ]
  }
 ],
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