notebooks/T1_Method2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ![CellphoneDB Logo](https://www.cellphonedb.org/images/cellphonedb_logo_33.png) | CellphoneDB is a publicly available repository of curated receptors, ligands and their interactions."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CellphoneDB method 2 (statistical analysis)\n",
    "In this example we are using the method 2 (`statistical_analysis_method`) to study how cell-cell interactions change between a subset of immune cells and trophoblast cells as the trophoblast differentiate and invade the maternal uterus. This method will retrieve interactions where the mean expression of the interacting partners (proteins participating in the interaction) displays significant cell state specificity by employing a random shuffling methodology. \n",
    "\n",
    "This notebook assumes that you either know how two **download CellphoneDB database or to create your own database**. If this is not the case, please check `T0_BuildDBfromFiles.ipynb` or `T0_BuildDBfromRelease.ipynb`. In this notebook we will explain how to run CellphoneDB for the **statistical method**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check python version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import sys\n",
    "import os\n",
    "\n",
    "pd.set_option('display.max_columns', 100)\n",
    "# Define our base directory for the analysis\n",
    "os.chdir('/home/jovyan/cpdb_tutorial')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking that environment contains a Python >= 3.8 as required by CellphoneDB."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.10.9 | packaged by conda-forge | (main, Feb  2 2023, 20:20:04) [GCC 11.3.0]\n"
     ]
    }
   ],
   "source": [
    "print(sys.version)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Install CellphoneDB"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Installing last version of CellphoneDB in the current conda enviroment. \\\n",
    "Remove the `--quiet` flag in case you want to see a detailed description of the installation process."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> pip install --quiet cellphonedb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "### Input files\n",
    "The statistial method accepts 5 input files (3 mandatory).\n",
    "- **cpdb_file_path**: (mandatory) path to the database `cellphonedb.zip`.\n",
    "- **meta_file_path**: (mandatory) path to the meta file linking cell barcodes to cluster labels `metadata.tsv`.\n",
    "- **counts_file_path**: (mandatory) paths to normalized counts file (not z-transformed), either in text format or h5ad (recommended) `normalised_log_counts.h5ad`.\n",
    "- **microenvs_file_path** (optional) path to microenvironment file that groups cell clusters by microenvironments. When providing a microenvironment file, CellphoneDB will restrict the interactions to those cells within a microenvironment.\n",
    "- **active_tf_path**: (optional) to the active transcription factors.\n",
    "\n",
    "The `microenvs_file_path` content will depend on the biological question that the researcher wants to answer.\n",
    "\n",
    "> In this **example** we are studying how cell-cell interactions change between a subset of immune cells and trophoblast cells as the trophoblast differentiate and invade the maternal uterus. This module will randomly permute the cluster labels of all cells whitin each microenvironement (`microenvs_file_path`), 1,000 times (default), to test whether the mean average receptor expression level in a cluster and the average ligand expression level between the interacting clusters is higher than those of the rest cell pairs. This procedure generates a P-value for the likelihood of cell-type specificity of a given receptor–ligand complex."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "cpdb_file_path = 'db/v5/cellphonedb.zip'\n",
    "meta_file_path = 'data/metadata.tsv'\n",
    "counts_file_path = 'data/normalised_log_counts.h5ad'\n",
    "microenvs_file_path = 'data/microenvironment.tsv'\n",
    "active_tf_path = 'data/active_TFs.tsv'\n",
    "out_path = 'results/method2_withScore'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inspect input files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style=\"color:green\">**1)**</span> The **metadata** file is compossed of two columns:\n",
    "- **barcode_sample**: this column indicates the barcode of each cell in the experiment.\n",
    "- **cell_type**: this column denotes the cell label assigned."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>barcode_sample</th>\n",
       "      <th>cell_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AGCGATTAGTCTAACC-1_Pla_HDBR10917733</td>\n",
       "      <td>B_cells</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ATCCGTGAGGCTAGAA-1_Pla_Camb10714918</td>\n",
       "      <td>B_cells</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AGTAACCCATTAAAGG-1_Pla_HDBR10917733</td>\n",
       "      <td>B_cells</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        barcode_sample cell_type\n",
       "0  AGCGATTAGTCTAACC-1_Pla_HDBR10917733   B_cells\n",
       "1  ATCCGTGAGGCTAGAA-1_Pla_Camb10714918   B_cells\n",
       "2  AGTAACCCATTAAAGG-1_Pla_HDBR10917733   B_cells"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metadata = pd.read_csv(meta_file_path, sep = '\\t')\n",
    "metadata.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style=\"color:green\">**2)**</span>  The **counts** files is a scanpy h5ad object. The dimensions and order of this object must coincide with the dimensions of the metadata file (i.e. must have the same number of cells in both files)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3312, 30800)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import anndata\n",
    "\n",
    "adata = anndata.read_h5ad(counts_file_path)\n",
    "adata.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check barcodes in metadata and counts are the same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(adata.obs.index).sort() == list(metadata['barcode_sample']).sort()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style=\"color:green\">**3)**</span> **Micronevironments** defines the cell types that belong to a a given microenvironment. CellphoneDB will only calculate interactions between cells that belong to a given microenvironment. In this file we are defining just one microenvionment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>cell_type</th>\n",
       "      <th>microenvironment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PV MMP11</td>\n",
       "      <td>Env1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PV MYH11</td>\n",
       "      <td>Env1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PV STEAP4</td>\n",
       "      <td>Env1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cell_type microenvironment\n",
       "0   PV MMP11             Env1\n",
       "1   PV MYH11             Env1\n",
       "2  PV STEAP4             Env1"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "microenv = pd.read_csv(microenvs_file_path,\n",
    "                       sep = '\\t')\n",
    "microenv.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Displaying cells grouped per microenvironment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "microenvironment\n",
       "Env1    [PV MMP11, PV MYH11, PV STEAP4, EVT_1, EVT_2, ...\n",
       "Name: cell_type, dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "microenv.groupby('microenvironment', group_keys = False)['cell_type'].apply(lambda x : list(x.value_counts().index))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style=\"color:green\">**4)**</span> **Active transcription factors** defines trancription factors active in a given cell type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>cell_type</th>\n",
       "      <th>TF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EVT_1</td>\n",
       "      <td>ASCL2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>EVT_1</td>\n",
       "      <td>ELK3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EVT_1</td>\n",
       "      <td>GATA3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  cell_type     TF\n",
       "0     EVT_1  ASCL2\n",
       "1     EVT_1   ELK3\n",
       "2     EVT_1  GATA3"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv(active_tf_path, sep = '\\t') \\\n",
    "    .head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "____\n",
    "### Run statistical analysis\n",
    "The output of this method will be saved in `output_path` and also returned to the predefined variables.\n",
    "\n",
    "> The statisical method allows the user to downsample the data with the aim of speeding up the results (subsampling arguments). To this end, CellphoneDB employs a geometric sketching procedure (Hie *et al.* 2019) to preserve the structure of the data without losing information from lowly represented cells. For this tutorial, we have opted to manually downsample the count matrix and the metadata file accordingly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading user files...\n",
      "The following user files were loaded successfully:\n",
      "data/normalised_log_counts.h5ad\n",
      "data/metadata.tsv\n",
      "data/microenvironment.tsv\n",
      "data/active_TFs.tsv\n",
      "[ ][CORE][29/08/23-14:31:10][INFO] [Cluster Statistical Analysis] Threshold:0.1 Iterations:1000 Debug-seed:42 Threads:5 Precision:3\n",
      "[ ][CORE][29/08/23-14:31:10][WARNING] Debug random seed enabled. Set to 42\n",
      "[ ][CORE][29/08/23-14:31:10][INFO] Running Real Analysis\n",
      "[ ][CORE][29/08/23-14:31:10][INFO] Limiting cluster combinations using microenvironments\n",
      "[ ][CORE][29/08/23-14:31:10][INFO] Running Statistical Analysis\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1000/1000 [00:28<00:00, 35.28it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ ][CORE][29/08/23-14:31:39][INFO] Building Pvalues result\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ ][CORE][29/08/23-14:31:39][INFO] Building results\n",
      "[ ][CORE][29/08/23-14:31:41][INFO] Scoring interactions: Filtering genes per cell type..\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 42/42 [00:00<00:00, 227.29it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ ][CORE][29/08/23-14:31:41][INFO] Scoring interactions: Calculating mean expression of each gene per group/cell type..\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "100%|██████████| 42/42 [00:00<00:00, 655.70it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ ][CORE][29/08/23-14:31:42][INFO] Scoring interactions: Calculating scores for all interactions and cell types..\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 81/81 [00:17<00:00,  4.70it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved deconvoluted to results/method2_withScore/statistical_analysis_deconvoluted_08_29_2023_143159.txt\n",
      "Saved deconvoluted_percents to results/method2_withScore/statistical_analysis_deconvoluted_percents_08_29_2023_143159.txt\n",
      "Saved means to results/method2_withScore/statistical_analysis_means_08_29_2023_143159.txt\n",
      "Saved pvalues to results/method2_withScore/statistical_analysis_pvalues_08_29_2023_143159.txt\n",
      "Saved significant_means to results/method2_withScore/statistical_analysis_significant_means_08_29_2023_143159.txt\n",
      "Saved CellSign_active_interactions to results/method2_withScore/statistical_analysis_CellSign_active_interactions_08_29_2023_143159.txt\n",
      "Saved CellSign_active_interactions_deconvoluted to results/method2_withScore/statistical_analysis_CellSign_active_interactions_deconvoluted_08_29_2023_143159.txt\n",
      "Saved interaction_scores to results/method2_withScore/statistical_analysis_interaction_scores_08_29_2023_143159.txt\n"
     ]
    }
   ],
   "source": [
    "from cellphonedb.src.core.methods import cpdb_statistical_analysis_method\n",
    "\n",
    "cpdb_results = cpdb_statistical_analysis_method.call(\n",
    "    cpdb_file_path = cpdb_file_path,                 # mandatory: CellphoneDB database zip file.\n",
    "    meta_file_path = meta_file_path,                 # mandatory: tsv file defining barcodes to cell label.\n",
    "    counts_file_path = counts_file_path,             # mandatory: normalized count matrix - a path to the counts file, or an in-memory AnnData object\n",
    "    counts_data = 'hgnc_symbol',                     # defines the gene annotation in counts matrix.\n",
    "    active_tfs_file_path = active_tf_path,           # optional: defines cell types and their active TFs.\n",
    "    microenvs_file_path = microenvs_file_path,       # optional (default: None): defines cells per microenvironment.\n",
    "    score_interactions = True,                       # optional: whether to score interactions or not. \n",
    "    iterations = 1000,                               # denotes the number of shufflings performed in the analysis.\n",
    "    threshold = 0.1,                                 # defines the min % of cells expressing a gene for this to be employed in the analysis.\n",
    "    threads = 5,                                     # number of threads to use in the analysis.\n",
    "    debug_seed = 42,                                 # debug randome seed. To disable >=0.\n",
    "    result_precision = 3,                            # Sets the rounding for the mean values in significan_means.\n",
    "    pvalue = 0.05,                                   # P-value threshold to employ for significance.\n",
    "    subsampling = False,                             # To enable subsampling the data (geometri sketching).\n",
    "    subsampling_log = False,                         # (mandatory) enable subsampling log1p for non log-transformed data inputs.\n",
    "    subsampling_num_pc = 100,                        # Number of componets to subsample via geometric skectching (dafault: 100).\n",
    "    subsampling_num_cells = 1000,                    # Number of cells to subsample (integer) (default: 1/3 of the dataset).\n",
    "    separator = '|',                                 # Sets the string to employ to separate cells in the results dataframes \"cellA|CellB\".\n",
    "    debug = False,                                   # Saves all intermediate tables employed during the analysis in pkl format.\n",
    "    output_path = out_path,                          # Path to save results.\n",
    "    output_suffix = None                             # Replaces the timestamp in the output files by a user defined string in the  (default: None).\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Results are save into the predefined file and as a dictionary in the `cpdb_results` variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['deconvoluted',\n",
       " 'deconvoluted_percents',\n",
       " 'means',\n",
       " 'pvalues',\n",
       " 'significant_means',\n",
       " 'CellSign_active_interactions',\n",
       " 'CellSign_active_interactions_deconvoluted',\n",
       " 'interaction_scores']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(cpdb_results.keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "### Description of output files\n",
    "Most output files share common columns:\n",
    "- **id_cp_interaction**: Unique CellphoneDB identifier for each interaction stored in the database.\n",
    "- **interacting_pair**: Name of the interacting pairs separated by “|”.\n",
    "- **partner A or B**: Identifier for the first interacting partner (A) or the second (B). It could be: UniProt (prefix simple:) or complex (prefix complex:)\n",
    "- **gene A or B**: Gene identifier for the first interacting partner (A) or the second (B). The identifier will depend on the input user list.\n",
    "- **secreted**: True if one of the partners is secreted.\n",
    "- **Receptor A or B**: True if the first interacting partner (A) or the second (B) is annotated as a receptor in our database.\n",
    "- **annotation_strategy**: Curated if the interaction was annotated by the CellphoneDB developers. Otherwise, the name of the database where the interaction has been downloaded from.\n",
    "- **is_integrin**: True if one of the partners is integrin.\n",
    "- **directionality**: Indiicates the directionality of the interaction and the charactersitics of the interactors.\n",
    "- **classification**: Pathway classification for the interacting partners."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Pvalues** fields:\n",
    "- **cell_a|cell_b**: The p-value resulting from the statistical analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>id_cp_interaction</th>\n",
       "      <th>interacting_pair</th>\n",
       "      <th>partner_a</th>\n",
       "      <th>partner_b</th>\n",
       "      <th>gene_a</th>\n",
       "      <th>gene_b</th>\n",
       "      <th>secreted</th>\n",
       "      <th>receptor_a</th>\n",
       "      <th>receptor_b</th>\n",
       "      <th>annotation_strategy</th>\n",
       "      <th>is_integrin</th>\n",
       "      <th>directionality</th>\n",
       "      <th>classification</th>\n",
       "      <th>PV MMP11|PV MMP11</th>\n",
       "      <th>PV MMP11|PV MYH11</th>\n",
       "      <th>PV MMP11|PV STEAP4</th>\n",
       "      <th>PV MMP11|EVT_1</th>\n",
       "      <th>PV MMP11|EVT_2</th>\n",
       "      <th>PV MMP11|iEVT</th>\n",
       "      <th>PV MMP11|VCT_CCC</th>\n",
       "      <th>PV MMP11|GC</th>\n",
       "      <th>PV MMP11|eEVT</th>\n",
       "      <th>PV MYH11|PV MMP11</th>\n",
       "      <th>PV MYH11|PV MYH11</th>\n",
       "      <th>PV MYH11|PV STEAP4</th>\n",
       "      <th>PV MYH11|EVT_1</th>\n",
       "      <th>PV MYH11|EVT_2</th>\n",
       "      <th>PV MYH11|iEVT</th>\n",
       "      <th>PV MYH11|VCT_CCC</th>\n",
       "      <th>PV MYH11|GC</th>\n",
       "      <th>PV MYH11|eEVT</th>\n",
       "      <th>PV STEAP4|PV MMP11</th>\n",
       "      <th>PV STEAP4|PV MYH11</th>\n",
       "      <th>PV STEAP4|PV STEAP4</th>\n",
       "      <th>PV STEAP4|EVT_1</th>\n",
       "      <th>PV STEAP4|EVT_2</th>\n",
       "      <th>PV STEAP4|iEVT</th>\n",
       "      <th>PV STEAP4|VCT_CCC</th>\n",
       "      <th>PV STEAP4|GC</th>\n",
       "      <th>PV STEAP4|eEVT</th>\n",
       "      <th>EVT_1|PV MMP11</th>\n",
       "      <th>EVT_1|PV MYH11</th>\n",
       "      <th>EVT_1|PV STEAP4</th>\n",
       "      <th>EVT_1|EVT_1</th>\n",
       "      <th>EVT_1|EVT_2</th>\n",
       "      <th>EVT_1|iEVT</th>\n",
       "      <th>EVT_1|VCT_CCC</th>\n",
       "      <th>EVT_1|GC</th>\n",
       "      <th>EVT_1|eEVT</th>\n",
       "      <th>EVT_2|PV MMP11</th>\n",
       "      <th>EVT_2|PV MYH11</th>\n",
       "      <th>EVT_2|PV STEAP4</th>\n",
       "      <th>EVT_2|EVT_1</th>\n",
       "      <th>EVT_2|EVT_2</th>\n",
       "      <th>EVT_2|iEVT</th>\n",
       "      <th>EVT_2|VCT_CCC</th>\n",
       "      <th>EVT_2|GC</th>\n",
       "      <th>EVT_2|eEVT</th>\n",
       "      <th>iEVT|PV MMP11</th>\n",
       "      <th>iEVT|PV MYH11</th>\n",
       "      <th>iEVT|PV STEAP4</th>\n",
       "      <th>iEVT|EVT_1</th>\n",
       "      <th>iEVT|EVT_2</th>\n",
       "      <th>iEVT|iEVT</th>\n",
       "      <th>iEVT|VCT_CCC</th>\n",
       "      <th>iEVT|GC</th>\n",
       "      <th>iEVT|eEVT</th>\n",
       "      <th>VCT_CCC|PV MMP11</th>\n",
       "      <th>VCT_CCC|PV MYH11</th>\n",
       "      <th>VCT_CCC|PV STEAP4</th>\n",
       "      <th>VCT_CCC|EVT_1</th>\n",
       "      <th>VCT_CCC|EVT_2</th>\n",
       "      <th>VCT_CCC|iEVT</th>\n",
       "      <th>VCT_CCC|VCT_CCC</th>\n",
       "      <th>VCT_CCC|GC</th>\n",
       "      <th>VCT_CCC|eEVT</th>\n",
       "      <th>GC|PV MMP11</th>\n",
       "      <th>GC|PV MYH11</th>\n",
       "      <th>GC|PV STEAP4</th>\n",
       "      <th>GC|EVT_1</th>\n",
       "      <th>GC|EVT_2</th>\n",
       "      <th>GC|iEVT</th>\n",
       "      <th>GC|VCT_CCC</th>\n",
       "      <th>GC|GC</th>\n",
       "      <th>GC|eEVT</th>\n",
       "      <th>eEVT|PV MMP11</th>\n",
       "      <th>eEVT|PV MYH11</th>\n",
       "      <th>eEVT|PV STEAP4</th>\n",
       "      <th>eEVT|EVT_1</th>\n",
       "      <th>eEVT|EVT_2</th>\n",
       "      <th>eEVT|iEVT</th>\n",
       "      <th>eEVT|VCT_CCC</th>\n",
       "      <th>eEVT|GC</th>\n",
       "      <th>eEVT|eEVT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CPI-SC0A2DB962D</td>\n",
       "      <td>CDH1_integrin_a2b1_complex</td>\n",
       "      <td>simple:P12830</td>\n",
       "      <td>complex:integrin_a2b1_complex</td>\n",
       "      <td>CDH1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>curated</td>\n",
       "      <td>True</td>\n",
       "      <td>Adhesion-Adhesion</td>\n",
       "      <td>Adhesion by Cadherin</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CPI-SC0B5CEA47D</td>\n",
       "      <td>COL10A1_integrin_a2b1_complex</td>\n",
       "      <td>simple:Q03692</td>\n",
       "      <td>complex:integrin_a2b1_complex</td>\n",
       "      <td>COL10A1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>curated</td>\n",
       "      <td>True</td>\n",
       "      <td>Adhesion-Adhesion</td>\n",
       "      <td>Adhesion by Collagen/Integrin</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  id_cp_interaction               interacting_pair      partner_a  \\\n",
       "0   CPI-SC0A2DB962D     CDH1_integrin_a2b1_complex  simple:P12830   \n",
       "1   CPI-SC0B5CEA47D  COL10A1_integrin_a2b1_complex  simple:Q03692   \n",
       "\n",
       "                       partner_b   gene_a gene_b  secreted  receptor_a  \\\n",
       "0  complex:integrin_a2b1_complex     CDH1    NaN     False       False   \n",
       "1  complex:integrin_a2b1_complex  COL10A1    NaN      True       False   \n",
       "\n",
       "   receptor_b annotation_strategy  is_integrin     directionality  \\\n",
       "0       False             curated         True  Adhesion-Adhesion   \n",
       "1       False             curated         True  Adhesion-Adhesion   \n",
       "\n",
       "                  classification  PV MMP11|PV MMP11  PV MMP11|PV MYH11  \\\n",
       "0           Adhesion by Cadherin                1.0                1.0   \n",
       "1  Adhesion by Collagen/Integrin                1.0                1.0   \n",
       "\n",
       "   PV MMP11|PV STEAP4  PV MMP11|EVT_1  PV MMP11|EVT_2  PV MMP11|iEVT  \\\n",
       "0                 1.0             1.0             1.0            1.0   \n",
       "1                 1.0             1.0             1.0            1.0   \n",
       "\n",
       "   PV MMP11|VCT_CCC  PV MMP11|GC  PV MMP11|eEVT  PV MYH11|PV MMP11  \\\n",
       "0               1.0          1.0            1.0                1.0   \n",
       "1               1.0          1.0            1.0                1.0   \n",
       "\n",
       "   PV MYH11|PV MYH11  PV MYH11|PV STEAP4  PV MYH11|EVT_1  PV MYH11|EVT_2  \\\n",
       "0                1.0                 1.0             1.0             1.0   \n",
       "1                1.0                 1.0             1.0             1.0   \n",
       "\n",
       "   PV MYH11|iEVT  PV MYH11|VCT_CCC  PV MYH11|GC  PV MYH11|eEVT  \\\n",
       "0            1.0               1.0          1.0            1.0   \n",
       "1            1.0               1.0          1.0            1.0   \n",
       "\n",
       "   PV STEAP4|PV MMP11  PV STEAP4|PV MYH11  PV STEAP4|PV STEAP4  \\\n",
       "0                 1.0                 1.0                  1.0   \n",
       "1                 1.0                 1.0                  1.0   \n",
       "\n",
       "   PV STEAP4|EVT_1  PV STEAP4|EVT_2  PV STEAP4|iEVT  PV STEAP4|VCT_CCC  \\\n",
       "0              1.0              1.0             1.0                1.0   \n",
       "1              1.0              1.0             1.0                1.0   \n",
       "\n",
       "   PV STEAP4|GC  PV STEAP4|eEVT  EVT_1|PV MMP11  EVT_1|PV MYH11  \\\n",
       "0           1.0             0.0             1.0             1.0   \n",
       "1           1.0             1.0             1.0             1.0   \n",
       "\n",
       "   EVT_1|PV STEAP4  EVT_1|EVT_1  EVT_1|EVT_2  EVT_1|iEVT  EVT_1|VCT_CCC  \\\n",
       "0              1.0          1.0          1.0         1.0            1.0   \n",
       "1              1.0          1.0          1.0         1.0            1.0   \n",
       "\n",
       "   EVT_1|GC  EVT_1|eEVT  EVT_2|PV MMP11  EVT_2|PV MYH11  EVT_2|PV STEAP4  \\\n",
       "0       1.0         0.0             1.0             1.0              1.0   \n",
       "1       1.0         1.0             1.0             1.0              1.0   \n",
       "\n",
       "   EVT_2|EVT_1  EVT_2|EVT_2  EVT_2|iEVT  EVT_2|VCT_CCC  EVT_2|GC  EVT_2|eEVT  \\\n",
       "0          1.0          1.0         1.0            1.0       1.0         0.0   \n",
       "1          1.0          1.0         1.0            1.0       1.0         1.0   \n",
       "\n",
       "   iEVT|PV MMP11  iEVT|PV MYH11  iEVT|PV STEAP4  iEVT|EVT_1  iEVT|EVT_2  \\\n",
       "0            1.0            1.0             1.0         1.0         1.0   \n",
       "1            1.0            1.0             1.0         1.0         1.0   \n",
       "\n",
       "   iEVT|iEVT  iEVT|VCT_CCC  iEVT|GC  iEVT|eEVT  VCT_CCC|PV MMP11  \\\n",
       "0        1.0           1.0      1.0        0.0               1.0   \n",
       "1        1.0           1.0      1.0        1.0               1.0   \n",
       "\n",
       "   VCT_CCC|PV MYH11  VCT_CCC|PV STEAP4  VCT_CCC|EVT_1  VCT_CCC|EVT_2  \\\n",
       "0               1.0                1.0            1.0            1.0   \n",
       "1               1.0                1.0            1.0            1.0   \n",
       "\n",
       "   VCT_CCC|iEVT  VCT_CCC|VCT_CCC  VCT_CCC|GC  VCT_CCC|eEVT  GC|PV MMP11  \\\n",
       "0           1.0              1.0         1.0           0.0          1.0   \n",
       "1           1.0              1.0         1.0           1.0          1.0   \n",
       "\n",
       "   GC|PV MYH11  GC|PV STEAP4  GC|EVT_1  GC|EVT_2  GC|iEVT  GC|VCT_CCC  GC|GC  \\\n",
       "0          1.0           1.0       1.0       1.0      1.0         1.0    1.0   \n",
       "1          1.0           1.0       1.0       1.0      1.0         1.0    1.0   \n",
       "\n",
       "   GC|eEVT  eEVT|PV MMP11  eEVT|PV MYH11  eEVT|PV STEAP4  eEVT|EVT_1  \\\n",
       "0      0.0            1.0            1.0             1.0         1.0   \n",
       "1      1.0            1.0            1.0             1.0         1.0   \n",
       "\n",
       "   eEVT|EVT_2  eEVT|iEVT  eEVT|VCT_CCC  eEVT|GC  eEVT|eEVT  \n",
       "0         1.0        1.0           1.0      1.0        0.0  \n",
       "1         1.0        1.0           1.0      1.0        1.0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cpdb_results['pvalues'].head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Means** fields:\n",
    "\n",
    "- **means**: Mean values for all the interacting partners: mean value refers to the total mean of the individual partner average expression values in the corresponding interacting pairs of cell types. If one of the mean values is 0, then the total mean is set to 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>id_cp_interaction</th>\n",
       "      <th>interacting_pair</th>\n",
       "      <th>partner_a</th>\n",
       "      <th>partner_b</th>\n",
       "      <th>gene_a</th>\n",
       "      <th>gene_b</th>\n",
       "      <th>secreted</th>\n",
       "      <th>receptor_a</th>\n",
       "      <th>receptor_b</th>\n",
       "      <th>annotation_strategy</th>\n",
       "      <th>is_integrin</th>\n",
       "      <th>directionality</th>\n",
       "      <th>classification</th>\n",
       "      <th>PV MMP11|PV MMP11</th>\n",
       "      <th>PV MMP11|PV MYH11</th>\n",
       "      <th>PV MMP11|PV STEAP4</th>\n",
       "      <th>PV MMP11|EVT_1</th>\n",
       "      <th>PV MMP11|EVT_2</th>\n",
       "      <th>PV MMP11|iEVT</th>\n",
       "      <th>PV MMP11|VCT_CCC</th>\n",
       "      <th>PV MMP11|GC</th>\n",
       "      <th>PV MMP11|eEVT</th>\n",
       "      <th>PV MYH11|PV MMP11</th>\n",
       "      <th>PV MYH11|PV MYH11</th>\n",
       "      <th>PV MYH11|PV STEAP4</th>\n",
       "      <th>PV MYH11|EVT_1</th>\n",
       "      <th>PV MYH11|EVT_2</th>\n",
       "      <th>PV MYH11|iEVT</th>\n",
       "      <th>PV MYH11|VCT_CCC</th>\n",
       "      <th>PV MYH11|GC</th>\n",
       "      <th>PV MYH11|eEVT</th>\n",
       "      <th>PV STEAP4|PV MMP11</th>\n",
       "      <th>PV STEAP4|PV MYH11</th>\n",
       "      <th>PV STEAP4|PV STEAP4</th>\n",
       "      <th>PV STEAP4|EVT_1</th>\n",
       "      <th>PV STEAP4|EVT_2</th>\n",
       "      <th>PV STEAP4|iEVT</th>\n",
       "      <th>PV STEAP4|VCT_CCC</th>\n",
       "      <th>PV STEAP4|GC</th>\n",
       "      <th>PV STEAP4|eEVT</th>\n",
       "      <th>EVT_1|PV MMP11</th>\n",
       "      <th>EVT_1|PV MYH11</th>\n",
       "      <th>EVT_1|PV STEAP4</th>\n",
       "      <th>EVT_1|EVT_1</th>\n",
       "      <th>EVT_1|EVT_2</th>\n",
       "      <th>EVT_1|iEVT</th>\n",
       "      <th>EVT_1|VCT_CCC</th>\n",
       "      <th>EVT_1|GC</th>\n",
       "      <th>EVT_1|eEVT</th>\n",
       "      <th>EVT_2|PV MMP11</th>\n",
       "      <th>EVT_2|PV MYH11</th>\n",
       "      <th>EVT_2|PV STEAP4</th>\n",
       "      <th>EVT_2|EVT_1</th>\n",
       "      <th>EVT_2|EVT_2</th>\n",
       "      <th>EVT_2|iEVT</th>\n",
       "      <th>EVT_2|VCT_CCC</th>\n",
       "      <th>EVT_2|GC</th>\n",
       "      <th>EVT_2|eEVT</th>\n",
       "      <th>iEVT|PV MMP11</th>\n",
       "      <th>iEVT|PV MYH11</th>\n",
       "      <th>iEVT|PV STEAP4</th>\n",
       "      <th>iEVT|EVT_1</th>\n",
       "      <th>iEVT|EVT_2</th>\n",
       "      <th>iEVT|iEVT</th>\n",
       "      <th>iEVT|VCT_CCC</th>\n",
       "      <th>iEVT|GC</th>\n",
       "      <th>iEVT|eEVT</th>\n",
       "      <th>VCT_CCC|PV MMP11</th>\n",
       "      <th>VCT_CCC|PV MYH11</th>\n",
       "      <th>VCT_CCC|PV STEAP4</th>\n",
       "      <th>VCT_CCC|EVT_1</th>\n",
       "      <th>VCT_CCC|EVT_2</th>\n",
       "      <th>VCT_CCC|iEVT</th>\n",
       "      <th>VCT_CCC|VCT_CCC</th>\n",
       "      <th>VCT_CCC|GC</th>\n",
       "      <th>VCT_CCC|eEVT</th>\n",
       "      <th>GC|PV MMP11</th>\n",
       "      <th>GC|PV MYH11</th>\n",
       "      <th>GC|PV STEAP4</th>\n",
       "      <th>GC|EVT_1</th>\n",
       "      <th>GC|EVT_2</th>\n",
       "      <th>GC|iEVT</th>\n",
       "      <th>GC|VCT_CCC</th>\n",
       "      <th>GC|GC</th>\n",
       "      <th>GC|eEVT</th>\n",
       "      <th>eEVT|PV MMP11</th>\n",
       "      <th>eEVT|PV MYH11</th>\n",
       "      <th>eEVT|PV STEAP4</th>\n",
       "      <th>eEVT|EVT_1</th>\n",
       "      <th>eEVT|EVT_2</th>\n",
       "      <th>eEVT|iEVT</th>\n",
       "      <th>eEVT|VCT_CCC</th>\n",
       "      <th>eEVT|GC</th>\n",
       "      <th>eEVT|eEVT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CPI-SC0A2DB962D</td>\n",
       "      <td>CDH1_integrin_a2b1_complex</td>\n",
       "      <td>simple:P12830</td>\n",
       "      <td>complex:integrin_a2b1_complex</td>\n",
       "      <td>CDH1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>curated</td>\n",
       "      <td>True</td>\n",
       "      <td>Adhesion-Adhesion</td>\n",
       "      <td>Adhesion by Cadherin</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.105</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.071</td>\n",
       "      <td>0.099</td>\n",
       "      <td>0.089</td>\n",
       "      <td>0.077</td>\n",
       "      <td>0.099</td>\n",
       "      <td>0.346</td>\n",
       "      <td>0.077</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.043</td>\n",
       "      <td>0.071</td>\n",
       "      <td>0.060</td>\n",
       "      <td>0.048</td>\n",
       "      <td>0.070</td>\n",
       "      <td>0.317</td>\n",
       "      <td>0.131</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.097</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.115</td>\n",
       "      <td>0.102</td>\n",
       "      <td>0.124</td>\n",
       "      <td>0.371</td>\n",
       "      <td>0.194</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.160</td>\n",
       "      <td>0.188</td>\n",
       "      <td>0.177</td>\n",
       "      <td>0.165</td>\n",
       "      <td>0.187</td>\n",
       "      <td>0.434</td>\n",
       "      <td>0.321</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.287</td>\n",
       "      <td>0.315</td>\n",
       "      <td>0.305</td>\n",
       "      <td>0.292</td>\n",
       "      <td>0.314</td>\n",
       "      <td>0.561</td>\n",
       "      <td>0.193</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.159</td>\n",
       "      <td>0.187</td>\n",
       "      <td>0.177</td>\n",
       "      <td>0.164</td>\n",
       "      <td>0.186</td>\n",
       "      <td>0.433</td>\n",
       "      <td>0.232</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.198</td>\n",
       "      <td>0.225</td>\n",
       "      <td>0.215</td>\n",
       "      <td>0.203</td>\n",
       "      <td>0.225</td>\n",
       "      <td>0.472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CPI-SC0B5CEA47D</td>\n",
       "      <td>COL10A1_integrin_a2b1_complex</td>\n",
       "      <td>simple:Q03692</td>\n",
       "      <td>complex:integrin_a2b1_complex</td>\n",
       "      <td>COL10A1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>curated</td>\n",
       "      <td>True</td>\n",
       "      <td>Adhesion-Adhesion</td>\n",
       "      <td>Adhesion by Collagen/Integrin</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.041</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.034</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.034</td>\n",
       "      <td>0.281</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.043</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.037</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0.036</td>\n",
       "      <td>0.283</td>\n",
       "      <td>0.042</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.035</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.035</td>\n",
       "      <td>0.282</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.049</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.043</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.042</td>\n",
       "      <td>0.289</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  id_cp_interaction               interacting_pair      partner_a  \\\n",
       "0   CPI-SC0A2DB962D     CDH1_integrin_a2b1_complex  simple:P12830   \n",
       "1   CPI-SC0B5CEA47D  COL10A1_integrin_a2b1_complex  simple:Q03692   \n",
       "\n",
       "                       partner_b   gene_a gene_b  secreted  receptor_a  \\\n",
       "0  complex:integrin_a2b1_complex     CDH1    NaN     False       False   \n",
       "1  complex:integrin_a2b1_complex  COL10A1    NaN      True       False   \n",
       "\n",
       "   receptor_b annotation_strategy  is_integrin     directionality  \\\n",
       "0       False             curated         True  Adhesion-Adhesion   \n",
       "1       False             curated         True  Adhesion-Adhesion   \n",
       "\n",
       "                  classification  PV MMP11|PV MMP11  PV MMP11|PV MYH11  \\\n",
       "0           Adhesion by Cadherin                0.0                0.0   \n",
       "1  Adhesion by Collagen/Integrin                0.0                0.0   \n",
       "\n",
       "   PV MMP11|PV STEAP4  PV MMP11|EVT_1  PV MMP11|EVT_2  PV MMP11|iEVT  \\\n",
       "0                 0.0             0.0             0.0            0.0   \n",
       "1                 0.0             0.0             0.0            0.0   \n",
       "\n",
       "   PV MMP11|VCT_CCC  PV MMP11|GC  PV MMP11|eEVT  PV MYH11|PV MMP11  \\\n",
       "0               0.0          0.0            0.0                0.0   \n",
       "1               0.0          0.0            0.0                0.0   \n",
       "\n",
       "   PV MYH11|PV MYH11  PV MYH11|PV STEAP4  PV MYH11|EVT_1  PV MYH11|EVT_2  \\\n",
       "0                0.0                 0.0             0.0             0.0   \n",
       "1                0.0                 0.0             0.0             0.0   \n",
       "\n",
       "   PV MYH11|iEVT  PV MYH11|VCT_CCC  PV MYH11|GC  PV MYH11|eEVT  \\\n",
       "0            0.0               0.0          0.0            0.0   \n",
       "1            0.0               0.0          0.0            0.0   \n",
       "\n",
       "   PV STEAP4|PV MMP11  PV STEAP4|PV MYH11  PV STEAP4|PV STEAP4  \\\n",
       "0               0.105                 0.0                  0.0   \n",
       "1               0.000                 0.0                  0.0   \n",
       "\n",
       "   PV STEAP4|EVT_1  PV STEAP4|EVT_2  PV STEAP4|iEVT  PV STEAP4|VCT_CCC  \\\n",
       "0            0.071            0.099           0.089              0.077   \n",
       "1            0.000            0.000           0.000              0.000   \n",
       "\n",
       "   PV STEAP4|GC  PV STEAP4|eEVT  EVT_1|PV MMP11  EVT_1|PV MYH11  \\\n",
       "0         0.099           0.346           0.077             0.0   \n",
       "1         0.000           0.000           0.041             0.0   \n",
       "\n",
       "   EVT_1|PV STEAP4  EVT_1|EVT_1  EVT_1|EVT_2  EVT_1|iEVT  EVT_1|VCT_CCC  \\\n",
       "0              0.0        0.043        0.071       0.060          0.048   \n",
       "1              0.0        0.006        0.034       0.024          0.012   \n",
       "\n",
       "   EVT_1|GC  EVT_1|eEVT  EVT_2|PV MMP11  EVT_2|PV MYH11  EVT_2|PV STEAP4  \\\n",
       "0     0.070       0.317           0.131             0.0              0.0   \n",
       "1     0.034       0.281           0.000             0.0              0.0   \n",
       "\n",
       "   EVT_2|EVT_1  EVT_2|EVT_2  EVT_2|iEVT  EVT_2|VCT_CCC  EVT_2|GC  EVT_2|eEVT  \\\n",
       "0        0.097        0.125       0.115          0.102     0.124       0.371   \n",
       "1        0.000        0.000       0.000          0.000     0.000       0.000   \n",
       "\n",
       "   iEVT|PV MMP11  iEVT|PV MYH11  iEVT|PV STEAP4  iEVT|EVT_1  iEVT|EVT_2  \\\n",
       "0          0.194            0.0             0.0       0.160       0.188   \n",
       "1          0.043            0.0             0.0       0.009       0.037   \n",
       "\n",
       "   iEVT|iEVT  iEVT|VCT_CCC  iEVT|GC  iEVT|eEVT  VCT_CCC|PV MMP11  \\\n",
       "0      0.177         0.165    0.187      0.434             0.321   \n",
       "1      0.026         0.014    0.036      0.283             0.042   \n",
       "\n",
       "   VCT_CCC|PV MYH11  VCT_CCC|PV STEAP4  VCT_CCC|EVT_1  VCT_CCC|EVT_2  \\\n",
       "0               0.0                0.0          0.287          0.315   \n",
       "1               0.0                0.0          0.007          0.035   \n",
       "\n",
       "   VCT_CCC|iEVT  VCT_CCC|VCT_CCC  VCT_CCC|GC  VCT_CCC|eEVT  GC|PV MMP11  \\\n",
       "0         0.305            0.292       0.314         0.561        0.193   \n",
       "1         0.025            0.013       0.035         0.282        0.000   \n",
       "\n",
       "   GC|PV MYH11  GC|PV STEAP4  GC|EVT_1  GC|EVT_2  GC|iEVT  GC|VCT_CCC  GC|GC  \\\n",
       "0          0.0           0.0     0.159     0.187    0.177       0.164  0.186   \n",
       "1          0.0           0.0     0.000     0.000    0.000       0.000  0.000   \n",
       "\n",
       "   GC|eEVT  eEVT|PV MMP11  eEVT|PV MYH11  eEVT|PV STEAP4  eEVT|EVT_1  \\\n",
       "0    0.433          0.232            0.0             0.0       0.198   \n",
       "1    0.000          0.049            0.0             0.0       0.015   \n",
       "\n",
       "   eEVT|EVT_2  eEVT|iEVT  eEVT|VCT_CCC  eEVT|GC  eEVT|eEVT  \n",
       "0       0.225      0.215         0.203    0.225      0.472  \n",
       "1       0.043      0.032         0.020    0.042      0.289  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cpdb_results['means'].head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Significant means** fields:\n",
    "- **significant_mean**: Significant mean calculation for all the interacting partners. If the interaction has been found relevant, the value will be the mean. Alternatively, the value is absent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>id_cp_interaction</th>\n",
       "      <th>interacting_pair</th>\n",
       "      <th>partner_a</th>\n",
       "      <th>partner_b</th>\n",
       "      <th>gene_a</th>\n",
       "      <th>gene_b</th>\n",
       "      <th>secreted</th>\n",
       "      <th>receptor_a</th>\n",
       "      <th>receptor_b</th>\n",
       "      <th>annotation_strategy</th>\n",
       "      <th>is_integrin</th>\n",
       "      <th>directionality</th>\n",
       "      <th>classification</th>\n",
       "      <th>rank</th>\n",
       "      <th>PV MMP11|PV MMP11</th>\n",
       "      <th>PV MMP11|PV MYH11</th>\n",
       "      <th>PV MMP11|PV STEAP4</th>\n",
       "      <th>PV MMP11|EVT_1</th>\n",
       "      <th>PV MMP11|EVT_2</th>\n",
       "      <th>PV MMP11|iEVT</th>\n",
       "      <th>PV MMP11|VCT_CCC</th>\n",
       "      <th>PV MMP11|GC</th>\n",
       "      <th>PV MMP11|eEVT</th>\n",
       "      <th>PV MYH11|PV MMP11</th>\n",
       "      <th>PV MYH11|PV MYH11</th>\n",
       "      <th>PV MYH11|PV STEAP4</th>\n",
       "      <th>PV MYH11|EVT_1</th>\n",
       "      <th>PV MYH11|EVT_2</th>\n",
       "      <th>PV MYH11|iEVT</th>\n",
       "      <th>PV MYH11|VCT_CCC</th>\n",
       "      <th>PV MYH11|GC</th>\n",
       "      <th>PV MYH11|eEVT</th>\n",
       "      <th>PV STEAP4|PV MMP11</th>\n",
       "      <th>PV STEAP4|PV MYH11</th>\n",
       "      <th>PV STEAP4|PV STEAP4</th>\n",
       "      <th>PV STEAP4|EVT_1</th>\n",
       "      <th>PV STEAP4|EVT_2</th>\n",
       "      <th>PV STEAP4|iEVT</th>\n",
       "      <th>PV STEAP4|VCT_CCC</th>\n",
       "      <th>PV STEAP4|GC</th>\n",
       "      <th>PV STEAP4|eEVT</th>\n",
       "      <th>EVT_1|PV MMP11</th>\n",
       "      <th>EVT_1|PV MYH11</th>\n",
       "      <th>EVT_1|PV STEAP4</th>\n",
       "      <th>EVT_1|EVT_1</th>\n",
       "      <th>EVT_1|EVT_2</th>\n",
       "      <th>EVT_1|iEVT</th>\n",
       "      <th>EVT_1|VCT_CCC</th>\n",
       "      <th>EVT_1|GC</th>\n",
       "      <th>EVT_1|eEVT</th>\n",
       "      <th>EVT_2|PV MMP11</th>\n",
       "      <th>EVT_2|PV MYH11</th>\n",
       "      <th>EVT_2|PV STEAP4</th>\n",
       "      <th>EVT_2|EVT_1</th>\n",
       "      <th>EVT_2|EVT_2</th>\n",
       "      <th>EVT_2|iEVT</th>\n",
       "      <th>EVT_2|VCT_CCC</th>\n",
       "      <th>EVT_2|GC</th>\n",
       "      <th>EVT_2|eEVT</th>\n",
       "      <th>iEVT|PV MMP11</th>\n",
       "      <th>iEVT|PV MYH11</th>\n",
       "      <th>iEVT|PV STEAP4</th>\n",
       "      <th>iEVT|EVT_1</th>\n",
       "      <th>iEVT|EVT_2</th>\n",
       "      <th>iEVT|iEVT</th>\n",
       "      <th>iEVT|VCT_CCC</th>\n",
       "      <th>iEVT|GC</th>\n",
       "      <th>iEVT|eEVT</th>\n",
       "      <th>VCT_CCC|PV MMP11</th>\n",
       "      <th>VCT_CCC|PV MYH11</th>\n",
       "      <th>VCT_CCC|PV STEAP4</th>\n",
       "      <th>VCT_CCC|EVT_1</th>\n",
       "      <th>VCT_CCC|EVT_2</th>\n",
       "      <th>VCT_CCC|iEVT</th>\n",
       "      <th>VCT_CCC|VCT_CCC</th>\n",
       "      <th>VCT_CCC|GC</th>\n",
       "      <th>VCT_CCC|eEVT</th>\n",
       "      <th>GC|PV MMP11</th>\n",
       "      <th>GC|PV MYH11</th>\n",
       "      <th>GC|PV STEAP4</th>\n",
       "      <th>GC|EVT_1</th>\n",
       "      <th>GC|EVT_2</th>\n",
       "      <th>GC|iEVT</th>\n",
       "      <th>GC|VCT_CCC</th>\n",
       "      <th>GC|GC</th>\n",
       "      <th>GC|eEVT</th>\n",
       "      <th>eEVT|PV MMP11</th>\n",
       "      <th>eEVT|PV MYH11</th>\n",
       "      <th>eEVT|PV STEAP4</th>\n",
       "      <th>eEVT|EVT_1</th>\n",
       "      <th>eEVT|EVT_2</th>\n",
       "      <th>eEVT|iEVT</th>\n",
       "      <th>eEVT|VCT_CCC</th>\n",
       "      <th>eEVT|GC</th>\n",
       "      <th>eEVT|eEVT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2581</th>\n",
       "      <td>CPI-SC012DB3194</td>\n",
       "      <td>WNT10A_FZD5_LRP5</td>\n",
       "      <td>simple:Q9GZT5</td>\n",
       "      <td>complex:FZD5_LRP5</td>\n",
       "      <td>WNT10A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>curated</td>\n",
       "      <td>False</td>\n",
       "      <td>Ligand-Receptor</td>\n",
       "      <td>Signaling by WNT</td>\n",
       "      <td>0.012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.071</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2427</th>\n",
       "      <td>CPI-SC0065F5B8A</td>\n",
       "      <td>WNT10A_FZD1_LRP6</td>\n",
       "      <td>simple:Q9GZT5</td>\n",
       "      <td>complex:FZD1_LRP6</td>\n",
       "      <td>WNT10A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>curated</td>\n",
       "      <td>False</td>\n",
       "      <td>Ligand-Receptor</td>\n",
       "      <td>Signaling by WNT</td>\n",
       "      <td>0.012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     id_cp_interaction  interacting_pair      partner_a          partner_b  \\\n",
       "2581   CPI-SC012DB3194  WNT10A_FZD5_LRP5  simple:Q9GZT5  complex:FZD5_LRP5   \n",
       "2427   CPI-SC0065F5B8A  WNT10A_FZD1_LRP6  simple:Q9GZT5  complex:FZD1_LRP6   \n",
       "\n",
       "      gene_a gene_b  secreted  receptor_a  receptor_b annotation_strategy  \\\n",
       "2581  WNT10A    NaN      True       False       False             curated   \n",
       "2427  WNT10A    NaN      True       False       False             curated   \n",
       "\n",
       "      is_integrin   directionality    classification   rank  \\\n",
       "2581        False  Ligand-Receptor  Signaling by WNT  0.012   \n",
       "2427        False  Ligand-Receptor  Signaling by WNT  0.012   \n",
       "\n",
       "      PV MMP11|PV MMP11  PV MMP11|PV MYH11  PV MMP11|PV STEAP4  \\\n",
       "2581                NaN                NaN                 NaN   \n",
       "2427                NaN                NaN                 NaN   \n",
       "\n",
       "      PV MMP11|EVT_1  PV MMP11|EVT_2  PV MMP11|iEVT  PV MMP11|VCT_CCC  \\\n",
       "2581             NaN             NaN            NaN               NaN   \n",
       "2427             NaN             NaN            NaN               NaN   \n",
       "\n",
       "      PV MMP11|GC  PV MMP11|eEVT  PV MYH11|PV MMP11  PV MYH11|PV MYH11  \\\n",
       "2581          NaN            NaN                NaN                NaN   \n",
       "2427          NaN            NaN                NaN               0.08   \n",
       "\n",
       "      PV MYH11|PV STEAP4  PV MYH11|EVT_1  PV MYH11|EVT_2  PV MYH11|iEVT  \\\n",
       "2581                 NaN             NaN             NaN            NaN   \n",
       "2427                 NaN             NaN             NaN            NaN   \n",
       "\n",
       "      PV MYH11|VCT_CCC  PV MYH11|GC  PV MYH11|eEVT  PV STEAP4|PV MMP11  \\\n",
       "2581             0.071          NaN            NaN                 NaN   \n",
       "2427               NaN          NaN            NaN                 NaN   \n",
       "\n",
       "      PV STEAP4|PV MYH11  PV STEAP4|PV STEAP4  PV STEAP4|EVT_1  \\\n",
       "2581                 NaN                  NaN              NaN   \n",
       "2427                 NaN                  NaN              NaN   \n",
       "\n",
       "      PV STEAP4|EVT_2  PV STEAP4|iEVT  PV STEAP4|VCT_CCC  PV STEAP4|GC  \\\n",
       "2581              NaN             NaN                NaN           NaN   \n",
       "2427              NaN             NaN                NaN           NaN   \n",
       "\n",
       "      PV STEAP4|eEVT  EVT_1|PV MMP11  EVT_1|PV MYH11  EVT_1|PV STEAP4  \\\n",
       "2581             NaN             NaN             NaN              NaN   \n",
       "2427             NaN             NaN             NaN              NaN   \n",
       "\n",
       "      EVT_1|EVT_1  EVT_1|EVT_2  EVT_1|iEVT  EVT_1|VCT_CCC  EVT_1|GC  \\\n",
       "2581          NaN          NaN         NaN            NaN       NaN   \n",
       "2427          NaN          NaN         NaN            NaN       NaN   \n",
       "\n",
       "      EVT_1|eEVT  EVT_2|PV MMP11  EVT_2|PV MYH11  EVT_2|PV STEAP4  \\\n",
       "2581         NaN             NaN             NaN              NaN   \n",
       "2427         NaN             NaN             NaN              NaN   \n",
       "\n",
       "      EVT_2|EVT_1  EVT_2|EVT_2  EVT_2|iEVT  EVT_2|VCT_CCC  EVT_2|GC  \\\n",
       "2581          NaN          NaN         NaN            NaN       NaN   \n",
       "2427          NaN          NaN         NaN            NaN       NaN   \n",
       "\n",
       "      EVT_2|eEVT  iEVT|PV MMP11  iEVT|PV MYH11  iEVT|PV STEAP4  iEVT|EVT_1  \\\n",
       "2581         NaN            NaN            NaN             NaN         NaN   \n",
       "2427         NaN            NaN            NaN             NaN         NaN   \n",
       "\n",
       "      iEVT|EVT_2  iEVT|iEVT  iEVT|VCT_CCC  iEVT|GC  iEVT|eEVT  \\\n",
       "2581         NaN        NaN           NaN      NaN        NaN   \n",
       "2427         NaN        NaN           NaN      NaN        NaN   \n",
       "\n",
       "      VCT_CCC|PV MMP11  VCT_CCC|PV MYH11  VCT_CCC|PV STEAP4  VCT_CCC|EVT_1  \\\n",
       "2581               NaN               NaN                NaN            NaN   \n",
       "2427               NaN               NaN                NaN            NaN   \n",
       "\n",
       "      VCT_CCC|EVT_2  VCT_CCC|iEVT  VCT_CCC|VCT_CCC  VCT_CCC|GC  VCT_CCC|eEVT  \\\n",
       "2581            NaN           NaN              NaN         NaN           NaN   \n",
       "2427            NaN           NaN              NaN         NaN           NaN   \n",
       "\n",
       "      GC|PV MMP11  GC|PV MYH11  GC|PV STEAP4  GC|EVT_1  GC|EVT_2  GC|iEVT  \\\n",
       "2581          NaN          NaN           NaN       NaN       NaN      NaN   \n",
       "2427          NaN          NaN           NaN       NaN       NaN      NaN   \n",
       "\n",
       "      GC|VCT_CCC  GC|GC  GC|eEVT  eEVT|PV MMP11  eEVT|PV MYH11  \\\n",
       "2581         NaN    NaN      NaN            NaN            NaN   \n",
       "2427         NaN    NaN      NaN            NaN            NaN   \n",
       "\n",
       "      eEVT|PV STEAP4  eEVT|EVT_1  eEVT|EVT_2  eEVT|iEVT  eEVT|VCT_CCC  \\\n",
       "2581             NaN         NaN         NaN        NaN           NaN   \n",
       "2427             NaN         NaN         NaN        NaN           NaN   \n",
       "\n",
       "      eEVT|GC  eEVT|eEVT  \n",
       "2581      NaN        NaN  \n",
       "2427      NaN        NaN  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cpdb_results['significant_means'].head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Interaction scores** fields:\n",
    "- **scores**: scores ranging from 0 to 100. The higher the score is,  the more specific the interaction is expected to be."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>id_cp_interaction</th>\n",
       "      <th>interacting_pair</th>\n",
       "      <th>partner_a</th>\n",
       "      <th>partner_b</th>\n",
       "      <th>gene_a</th>\n",
       "      <th>gene_b</th>\n",
       "      <th>secreted</th>\n",
       "      <th>receptor_a</th>\n",
       "      <th>receptor_b</th>\n",
       "      <th>annotation_strategy</th>\n",
       "      <th>is_integrin</th>\n",
       "      <th>directionality</th>\n",
       "      <th>classification</th>\n",
       "      <th>PV MMP11|PV MMP11</th>\n",
       "      <th>PV MMP11|PV MYH11</th>\n",
       "      <th>PV MMP11|PV STEAP4</th>\n",
       "      <th>PV MMP11|EVT_1</th>\n",
       "      <th>PV MMP11|EVT_2</th>\n",
       "      <th>PV MMP11|iEVT</th>\n",
       "      <th>PV MMP11|VCT_CCC</th>\n",
       "      <th>PV MMP11|GC</th>\n",
       "      <th>PV MMP11|eEVT</th>\n",
       "      <th>PV MYH11|PV MMP11</th>\n",
       "      <th>PV MYH11|PV MYH11</th>\n",
       "      <th>PV MYH11|PV STEAP4</th>\n",
       "      <th>PV MYH11|EVT_1</th>\n",
       "      <th>PV MYH11|EVT_2</th>\n",
       "      <th>PV MYH11|iEVT</th>\n",
       "      <th>PV MYH11|VCT_CCC</th>\n",
       "      <th>PV MYH11|GC</th>\n",
       "      <th>PV MYH11|eEVT</th>\n",
       "      <th>PV STEAP4|PV MMP11</th>\n",
       "      <th>PV STEAP4|PV MYH11</th>\n",
       "      <th>PV STEAP4|PV STEAP4</th>\n",
       "      <th>PV STEAP4|EVT_1</th>\n",
       "      <th>PV STEAP4|EVT_2</th>\n",
       "      <th>PV STEAP4|iEVT</th>\n",
       "      <th>PV STEAP4|VCT_CCC</th>\n",
       "      <th>PV STEAP4|GC</th>\n",
       "      <th>PV STEAP4|eEVT</th>\n",
       "      <th>EVT_1|PV MMP11</th>\n",
       "      <th>EVT_1|PV MYH11</th>\n",
       "      <th>EVT_1|PV STEAP4</th>\n",
       "      <th>EVT_1|EVT_1</th>\n",
       "      <th>EVT_1|EVT_2</th>\n",
       "      <th>EVT_1|iEVT</th>\n",
       "      <th>EVT_1|VCT_CCC</th>\n",
       "      <th>EVT_1|GC</th>\n",
       "      <th>EVT_1|eEVT</th>\n",
       "      <th>EVT_2|PV MMP11</th>\n",
       "      <th>EVT_2|PV MYH11</th>\n",
       "      <th>EVT_2|PV STEAP4</th>\n",
       "      <th>EVT_2|EVT_1</th>\n",
       "      <th>EVT_2|EVT_2</th>\n",
       "      <th>EVT_2|iEVT</th>\n",
       "      <th>EVT_2|VCT_CCC</th>\n",
       "      <th>EVT_2|GC</th>\n",
       "      <th>EVT_2|eEVT</th>\n",
       "      <th>iEVT|PV MMP11</th>\n",
       "      <th>iEVT|PV MYH11</th>\n",
       "      <th>iEVT|PV STEAP4</th>\n",
       "      <th>iEVT|EVT_1</th>\n",
       "      <th>iEVT|EVT_2</th>\n",
       "      <th>iEVT|iEVT</th>\n",
       "      <th>iEVT|VCT_CCC</th>\n",
       "      <th>iEVT|GC</th>\n",
       "      <th>iEVT|eEVT</th>\n",
       "      <th>VCT_CCC|PV MMP11</th>\n",
       "      <th>VCT_CCC|PV MYH11</th>\n",
       "      <th>VCT_CCC|PV STEAP4</th>\n",
       "      <th>VCT_CCC|EVT_1</th>\n",
       "      <th>VCT_CCC|EVT_2</th>\n",
       "      <th>VCT_CCC|iEVT</th>\n",
       "      <th>VCT_CCC|VCT_CCC</th>\n",
       "      <th>VCT_CCC|GC</th>\n",
       "      <th>VCT_CCC|eEVT</th>\n",
       "      <th>GC|PV MMP11</th>\n",
       "      <th>GC|PV MYH11</th>\n",
       "      <th>GC|PV STEAP4</th>\n",
       "      <th>GC|EVT_1</th>\n",
       "      <th>GC|EVT_2</th>\n",
       "      <th>GC|iEVT</th>\n",
       "      <th>GC|VCT_CCC</th>\n",
       "      <th>GC|GC</th>\n",
       "      <th>GC|eEVT</th>\n",
       "      <th>eEVT|PV MMP11</th>\n",
       "      <th>eEVT|PV MYH11</th>\n",
       "      <th>eEVT|PV STEAP4</th>\n",
       "      <th>eEVT|EVT_1</th>\n",
       "      <th>eEVT|EVT_2</th>\n",
       "      <th>eEVT|iEVT</th>\n",
       "      <th>eEVT|VCT_CCC</th>\n",
       "      <th>eEVT|GC</th>\n",
       "      <th>eEVT|eEVT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CPI-SC0A2DB962D</td>\n",
       "      <td>CDH1_integrin_a2b1_complex</td>\n",
       "      <td>simple:P12830</td>\n",
       "      <td>complex:integrin_a2b1_complex</td>\n",
       "      <td>CDH1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>curated</td>\n",
       "      <td>True</td>\n",
       "      <td>Adhesion-Adhesion</td>\n",
       "      <td>Adhesion by Cadherin</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.346</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.843</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.095</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>41.533</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>74.911</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>41.35</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>51.391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CPI-SC0B5CEA47D</td>\n",
       "      <td>COL10A1_integrin_a2b1_complex</td>\n",
       "      <td>simple:Q03692</td>\n",
       "      <td>complex:integrin_a2b1_complex</td>\n",
       "      <td>COL10A1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>curated</td>\n",
       "      <td>True</td>\n",
       "      <td>Adhesion-Adhesion</td>\n",
       "      <td>Adhesion by Collagen/Integrin</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  id_cp_interaction               interacting_pair      partner_a  \\\n",
       "0   CPI-SC0A2DB962D     CDH1_integrin_a2b1_complex  simple:P12830   \n",
       "1   CPI-SC0B5CEA47D  COL10A1_integrin_a2b1_complex  simple:Q03692   \n",
       "\n",
       "                       partner_b   gene_a gene_b  secreted  receptor_a  \\\n",
       "0  complex:integrin_a2b1_complex     CDH1    NaN     False       False   \n",
       "1  complex:integrin_a2b1_complex  COL10A1    NaN      True       False   \n",
       "\n",
       "   receptor_b annotation_strategy  is_integrin     directionality  \\\n",
       "0       False             curated         True  Adhesion-Adhesion   \n",
       "1       False             curated         True  Adhesion-Adhesion   \n",
       "\n",
       "                  classification  PV MMP11|PV MMP11  PV MMP11|PV MYH11  \\\n",
       "0           Adhesion by Cadherin                0.0                0.0   \n",
       "1  Adhesion by Collagen/Integrin                0.0                0.0   \n",
       "\n",
       "   PV MMP11|PV STEAP4  PV MMP11|EVT_1  PV MMP11|EVT_2  PV MMP11|iEVT  \\\n",
       "0                 0.0             0.0             0.0            0.0   \n",
       "1                 0.0             0.0             0.0            0.0   \n",
       "\n",
       "   PV MMP11|VCT_CCC  PV MMP11|GC  PV MMP11|eEVT  PV MYH11|PV MMP11  \\\n",
       "0               0.0          0.0            0.0                0.0   \n",
       "1               0.0          0.0            0.0                0.0   \n",
       "\n",
       "   PV MYH11|PV MYH11  PV MYH11|PV STEAP4  PV MYH11|EVT_1  PV MYH11|EVT_2  \\\n",
       "0                0.0                 0.0             0.0             0.0   \n",
       "1                0.0                 0.0             0.0             0.0   \n",
       "\n",
       "   PV MYH11|iEVT  PV MYH11|VCT_CCC  PV MYH11|GC  PV MYH11|eEVT  \\\n",
       "0            0.0               0.0          0.0            0.0   \n",
       "1            0.0               0.0          0.0            0.0   \n",
       "\n",
       "   PV STEAP4|PV MMP11  PV STEAP4|PV MYH11  PV STEAP4|PV STEAP4  \\\n",
       "0                 0.0                 0.0                  0.0   \n",
       "1                 0.0                 0.0                  0.0   \n",
       "\n",
       "   PV STEAP4|EVT_1  PV STEAP4|EVT_2  PV STEAP4|iEVT  PV STEAP4|VCT_CCC  \\\n",
       "0              0.0              0.0             0.0                0.0   \n",
       "1              0.0              0.0             0.0                0.0   \n",
       "\n",
       "   PV STEAP4|GC  PV STEAP4|eEVT  EVT_1|PV MMP11  EVT_1|PV MYH11  \\\n",
       "0           0.0          18.346             0.0             0.0   \n",
       "1           0.0           0.000             0.0             0.0   \n",
       "\n",
       "   EVT_1|PV STEAP4  EVT_1|EVT_1  EVT_1|EVT_2  EVT_1|iEVT  EVT_1|VCT_CCC  \\\n",
       "0              0.0          0.0          0.0         0.0            0.0   \n",
       "1              0.0          0.0          0.0         0.0            0.0   \n",
       "\n",
       "   EVT_1|GC  EVT_1|eEVT  EVT_2|PV MMP11  EVT_2|PV MYH11  EVT_2|PV STEAP4  \\\n",
       "0       0.0      10.843             0.0             0.0              0.0   \n",
       "1       0.0       0.000             0.0             0.0              0.0   \n",
       "\n",
       "   EVT_2|EVT_1  EVT_2|EVT_2  EVT_2|iEVT  EVT_2|VCT_CCC  EVT_2|GC  EVT_2|eEVT  \\\n",
       "0          0.0          0.0         0.0            0.0       0.0      25.095   \n",
       "1          0.0          0.0         0.0            0.0       0.0       0.000   \n",
       "\n",
       "   iEVT|PV MMP11  iEVT|PV MYH11  iEVT|PV STEAP4  iEVT|EVT_1  iEVT|EVT_2  \\\n",
       "0            0.0            0.0             0.0         0.0         0.0   \n",
       "1            0.0            0.0             0.0         0.0         0.0   \n",
       "\n",
       "   iEVT|iEVT  iEVT|VCT_CCC  iEVT|GC  iEVT|eEVT  VCT_CCC|PV MMP11  \\\n",
       "0        0.0           0.0      0.0     41.533               0.0   \n",
       "1        0.0           0.0      0.0      0.000               0.0   \n",
       "\n",
       "   VCT_CCC|PV MYH11  VCT_CCC|PV STEAP4  VCT_CCC|EVT_1  VCT_CCC|EVT_2  \\\n",
       "0               0.0                0.0            0.0            0.0   \n",
       "1               0.0                0.0            0.0            0.0   \n",
       "\n",
       "   VCT_CCC|iEVT  VCT_CCC|VCT_CCC  VCT_CCC|GC  VCT_CCC|eEVT  GC|PV MMP11  \\\n",
       "0           0.0              0.0         0.0        74.911          0.0   \n",
       "1           0.0              0.0         0.0         0.000          0.0   \n",
       "\n",
       "   GC|PV MYH11  GC|PV STEAP4  GC|EVT_1  GC|EVT_2  GC|iEVT  GC|VCT_CCC  GC|GC  \\\n",
       "0          0.0           0.0       0.0       0.0      0.0         0.0    0.0   \n",
       "1          0.0           0.0       0.0       0.0      0.0         0.0    0.0   \n",
       "\n",
       "   GC|eEVT  eEVT|PV MMP11  eEVT|PV MYH11  eEVT|PV STEAP4  eEVT|EVT_1  \\\n",
       "0    41.35            0.0            0.0             0.0         0.0   \n",
       "1     0.00            0.0            0.0             0.0         0.0   \n",
       "\n",
       "   eEVT|EVT_2  eEVT|iEVT  eEVT|VCT_CCC  eEVT|GC  eEVT|eEVT  \n",
       "0         0.0        0.0           0.0      0.0     51.391  \n",
       "1         0.0        0.0           0.0      0.0      0.000  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cpdb_results['interaction_scores'].head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**CellSign** fields:\n",
    "- **interacting_pair**: Name of the interacting pairs.\n",
    "- **partner A or B**: Identifier for the first interacting partner (A) or the second (B). It could be: UniProt (prefix simple:) or complex (prefix complex:)\n",
    "- **gene A or B**: Gene identifier for the first interacting partner (A) or the second (B). The identifier will depend on the input user list.\n",
    "- **Receptor A or B**: True if the first interacting partner (A) or the second (B) is annotated as a receptor in our database.\n",
    "- **TF relationship**: a 1 denotes that the TF downstream the receptor is active."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>id_cp_interaction</th>\n",
       "      <th>interacting_pair</th>\n",
       "      <th>partner_a</th>\n",
       "      <th>partner_b</th>\n",
       "      <th>gene_a</th>\n",
       "      <th>gene_b</th>\n",
       "      <th>secreted</th>\n",
       "      <th>receptor_a</th>\n",
       "      <th>receptor_b</th>\n",
       "      <th>annotation_strategy</th>\n",
       "      <th>is_integrin</th>\n",
       "      <th>directionality</th>\n",
       "      <th>classification</th>\n",
       "      <th>rank</th>\n",
       "      <th>PV MMP11|PV MMP11</th>\n",
       "      <th>PV MMP11|PV MYH11</th>\n",
       "      <th>PV MMP11|PV STEAP4</th>\n",
       "      <th>PV MMP11|EVT_1</th>\n",
       "      <th>PV MMP11|EVT_2</th>\n",
       "      <th>PV MMP11|iEVT</th>\n",
       "      <th>PV MMP11|VCT_CCC</th>\n",
       "      <th>PV MMP11|GC</th>\n",
       "      <th>PV MMP11|eEVT</th>\n",
       "      <th>PV MYH11|PV MMP11</th>\n",
       "      <th>PV MYH11|PV MYH11</th>\n",
       "      <th>PV MYH11|PV STEAP4</th>\n",
       "      <th>PV MYH11|EVT_1</th>\n",
       "      <th>PV MYH11|EVT_2</th>\n",
       "      <th>PV MYH11|iEVT</th>\n",
       "      <th>PV MYH11|VCT_CCC</th>\n",
       "      <th>PV MYH11|GC</th>\n",
       "      <th>PV MYH11|eEVT</th>\n",
       "      <th>PV STEAP4|PV MMP11</th>\n",
       "      <th>PV STEAP4|PV MYH11</th>\n",
       "      <th>PV STEAP4|PV STEAP4</th>\n",
       "      <th>PV STEAP4|EVT_1</th>\n",
       "      <th>PV STEAP4|EVT_2</th>\n",
       "      <th>PV STEAP4|iEVT</th>\n",
       "      <th>PV STEAP4|VCT_CCC</th>\n",
       "      <th>PV STEAP4|GC</th>\n",
       "      <th>PV STEAP4|eEVT</th>\n",
       "      <th>EVT_1|PV MMP11</th>\n",
       "      <th>EVT_1|PV MYH11</th>\n",
       "      <th>EVT_1|PV STEAP4</th>\n",
       "      <th>EVT_1|EVT_1</th>\n",
       "      <th>EVT_1|EVT_2</th>\n",
       "      <th>EVT_1|iEVT</th>\n",
       "      <th>EVT_1|VCT_CCC</th>\n",
       "      <th>EVT_1|GC</th>\n",
       "      <th>EVT_1|eEVT</th>\n",
       "      <th>EVT_2|PV MMP11</th>\n",
       "      <th>EVT_2|PV MYH11</th>\n",
       "      <th>EVT_2|PV STEAP4</th>\n",
       "      <th>EVT_2|EVT_1</th>\n",
       "      <th>EVT_2|EVT_2</th>\n",
       "      <th>EVT_2|iEVT</th>\n",
       "      <th>EVT_2|VCT_CCC</th>\n",
       "      <th>EVT_2|GC</th>\n",
       "      <th>EVT_2|eEVT</th>\n",
       "      <th>iEVT|PV MMP11</th>\n",
       "      <th>iEVT|PV MYH11</th>\n",
       "      <th>iEVT|PV STEAP4</th>\n",
       "      <th>iEVT|EVT_1</th>\n",
       "      <th>iEVT|EVT_2</th>\n",
       "      <th>iEVT|iEVT</th>\n",
       "      <th>iEVT|VCT_CCC</th>\n",
       "      <th>iEVT|GC</th>\n",
       "      <th>iEVT|eEVT</th>\n",
       "      <th>VCT_CCC|PV MMP11</th>\n",
       "      <th>VCT_CCC|PV MYH11</th>\n",
       "      <th>VCT_CCC|PV STEAP4</th>\n",
       "      <th>VCT_CCC|EVT_1</th>\n",
       "      <th>VCT_CCC|EVT_2</th>\n",
       "      <th>VCT_CCC|iEVT</th>\n",
       "      <th>VCT_CCC|VCT_CCC</th>\n",
       "      <th>VCT_CCC|GC</th>\n",
       "      <th>VCT_CCC|eEVT</th>\n",
       "      <th>GC|PV MMP11</th>\n",
       "      <th>GC|PV MYH11</th>\n",
       "      <th>GC|PV STEAP4</th>\n",
       "      <th>GC|EVT_1</th>\n",
       "      <th>GC|EVT_2</th>\n",
       "      <th>GC|iEVT</th>\n",
       "      <th>GC|VCT_CCC</th>\n",
       "      <th>GC|GC</th>\n",
       "      <th>GC|eEVT</th>\n",
       "      <th>eEVT|PV MMP11</th>\n",
       "      <th>eEVT|PV MYH11</th>\n",
       "      <th>eEVT|PV STEAP4</th>\n",
       "      <th>eEVT|EVT_1</th>\n",
       "      <th>eEVT|EVT_2</th>\n",
       "      <th>eEVT|iEVT</th>\n",
       "      <th>eEVT|VCT_CCC</th>\n",
       "      <th>eEVT|GC</th>\n",
       "      <th>eEVT|eEVT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CPI-SS0CC966B71</td>\n",
       "      <td>BTC_EGFR</td>\n",
       "      <td>simple:P35070</td>\n",
       "      <td>simple:P00533</td>\n",
       "      <td>BTC</td>\n",
       "      <td>EGFR</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>curated</td>\n",
       "      <td>False</td>\n",
       "      <td>Ligand-Receptor</td>\n",
       "      <td>Signaling by Epidermal growth factor</td>\n",
       "      <td>0.062</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",
       "      <td>0</td>\n",
       "      <td>0</td>\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>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>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>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>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>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>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>1</th>\n",
       "      <td>CPI-SS076940BCD</td>\n",
       "      <td>LGALS3_MERTK</td>\n",
       "      <td>simple:P17931</td>\n",
       "      <td>simple:Q12866</td>\n",
       "      <td>LGALS3</td>\n",
       "      <td>MERTK</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>curated</td>\n",
       "      <td>False</td>\n",
       "      <td>Ligand-Receptor</td>\n",
       "      <td>Signaling by Galectin</td>\n",
       "      <td>0.012</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",
       "      <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>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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\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>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>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>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>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",
       "</div>"
      ],
      "text/plain": [
       "  id_cp_interaction interacting_pair      partner_a      partner_b  gene_a  \\\n",
       "0   CPI-SS0CC966B71         BTC_EGFR  simple:P35070  simple:P00533     BTC   \n",
       "1   CPI-SS076940BCD     LGALS3_MERTK  simple:P17931  simple:Q12866  LGALS3   \n",
       "\n",
       "  gene_b  secreted  receptor_a  receptor_b annotation_strategy  is_integrin  \\\n",
       "0   EGFR      True       False        True             curated        False   \n",
       "1  MERTK      True       False        True             curated        False   \n",
       "\n",
       "    directionality                        classification   rank  \\\n",
       "0  Ligand-Receptor  Signaling by Epidermal growth factor  0.062   \n",
       "1  Ligand-Receptor                 Signaling by Galectin  0.012   \n",
       "\n",
       "   PV MMP11|PV MMP11  PV MMP11|PV MYH11  PV MMP11|PV STEAP4  PV MMP11|EVT_1  \\\n",
       "0                  0                  0                   0               0   \n",
       "1                  0                  0                   0               0   \n",
       "\n",
       "   PV MMP11|EVT_2  PV MMP11|iEVT  PV MMP11|VCT_CCC  PV MMP11|GC  \\\n",
       "0               0              0                 0            0   \n",
       "1               0              0                 0            0   \n",
       "\n",
       "   PV MMP11|eEVT  PV MYH11|PV MMP11  PV MYH11|PV MYH11  PV MYH11|PV STEAP4  \\\n",
       "0              0                  0                  0                   0   \n",
       "1              0                  0                  0                   0   \n",
       "\n",
       "   PV MYH11|EVT_1  PV MYH11|EVT_2  PV MYH11|iEVT  PV MYH11|VCT_CCC  \\\n",
       "0               0               1              0                 0   \n",
       "1               0               0              0                 0   \n",
       "\n",
       "   PV MYH11|GC  PV MYH11|eEVT  PV STEAP4|PV MMP11  PV STEAP4|PV MYH11  \\\n",
       "0            0              0                   0                   0   \n",
       "1            0              0                   0                   0   \n",
       "\n",
       "   PV STEAP4|PV STEAP4  PV STEAP4|EVT_1  PV STEAP4|EVT_2  PV STEAP4|iEVT  \\\n",
       "0                    0                0                0               0   \n",
       "1                    0                0                0               0   \n",
       "\n",
       "   PV STEAP4|VCT_CCC  PV STEAP4|GC  PV STEAP4|eEVT  EVT_1|PV MMP11  \\\n",
       "0                  0             0               0               0   \n",
       "1                  0             0               0               0   \n",
       "\n",
       "   EVT_1|PV MYH11  EVT_1|PV STEAP4  EVT_1|EVT_1  EVT_1|EVT_2  EVT_1|iEVT  \\\n",
       "0               0                0            0            0           0   \n",
       "1               0                0            0            0           0   \n",
       "\n",
       "   EVT_1|VCT_CCC  EVT_1|GC  EVT_1|eEVT  EVT_2|PV MMP11  EVT_2|PV MYH11  \\\n",
       "0              0         0           0               0               0   \n",
       "1              0         0           1               0               0   \n",
       "\n",
       "   EVT_2|PV STEAP4  EVT_2|EVT_1  EVT_2|EVT_2  EVT_2|iEVT  EVT_2|VCT_CCC  \\\n",
       "0                0            0            0           0              0   \n",
       "1                0            0            0           0              0   \n",
       "\n",
       "   EVT_2|GC  EVT_2|eEVT  iEVT|PV MMP11  iEVT|PV MYH11  iEVT|PV STEAP4  \\\n",
       "0         0           0              0              0               0   \n",
       "1         0           0              0              0               0   \n",
       "\n",
       "   iEVT|EVT_1  iEVT|EVT_2  iEVT|iEVT  iEVT|VCT_CCC  iEVT|GC  iEVT|eEVT  \\\n",
       "0           0           0          0             0        0          0   \n",
       "1           0           0          0             0        0          0   \n",
       "\n",
       "   VCT_CCC|PV MMP11  VCT_CCC|PV MYH11  VCT_CCC|PV STEAP4  VCT_CCC|EVT_1  \\\n",
       "0                 0                 0                  0              0   \n",
       "1                 0                 0                  0              0   \n",
       "\n",
       "   VCT_CCC|EVT_2  VCT_CCC|iEVT  VCT_CCC|VCT_CCC  VCT_CCC|GC  VCT_CCC|eEVT  \\\n",
       "0              0             0                0           0             0   \n",
       "1              0             0                0           0             0   \n",
       "\n",
       "   GC|PV MMP11  GC|PV MYH11  GC|PV STEAP4  GC|EVT_1  GC|EVT_2  GC|iEVT  \\\n",
       "0            0            0             0         0         0        0   \n",
       "1            0            0             0         0         0        0   \n",
       "\n",
       "   GC|VCT_CCC  GC|GC  GC|eEVT  eEVT|PV MMP11  eEVT|PV MYH11  eEVT|PV STEAP4  \\\n",
       "0           0      0        0              0              0               0   \n",
       "1           0      0        0              0              0               0   \n",
       "\n",
       "   eEVT|EVT_1  eEVT|EVT_2  eEVT|iEVT  eEVT|VCT_CCC  eEVT|GC  eEVT|eEVT  \n",
       "0           0           0          0             0        0          0  \n",
       "1           0           0          0             0        0          0  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cpdb_results['CellSign_active_interactions'].head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Deconvoluted** fields:\n",
    "- **gene_name**: Gene identifier for one of the subunits that are participating in the interaction defined in “means.csv” file. The identifier will depend on the input of the user list.\n",
    "- **uniprot**: UniProt identifier for one of the subunits that are participating in the interaction defined in “means.csv” file.\n",
    "- **is_complex**: True if the subunit is part of a complex. Single if it is not, complex if it is.\n",
    "- **protein_name**: Protein name for one of the subunits that are participating in the interaction defined in “means.csv” file.\n",
    "- **complex_name**: Complex name if the subunit is part of a complex. Empty if not.\n",
    "- **mean**: Mean expression of the corresponding gene in each cluster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>gene_name</th>\n",
       "      <th>uniprot</th>\n",
       "      <th>is_complex</th>\n",
       "      <th>protein_name</th>\n",
       "      <th>complex_name</th>\n",
       "      <th>id_cp_interaction</th>\n",
       "      <th>gene</th>\n",
       "      <th>B_cells</th>\n",
       "      <th>DC</th>\n",
       "      <th>EVT_1</th>\n",
       "      <th>EVT_2</th>\n",
       "      <th>Endo_F</th>\n",
       "      <th>Endo_L</th>\n",
       "      <th>Endo_M</th>\n",
       "      <th>GC</th>\n",
       "      <th>Granulocytes</th>\n",
       "      <th>HOFB</th>\n",
       "      <th>ILC3</th>\n",
       "      <th>M3</th>\n",
       "      <th>MO</th>\n",
       "      <th>NK</th>\n",
       "      <th>PV MMP11</th>\n",
       "      <th>PV MYH11</th>\n",
       "      <th>PV STEAP4</th>\n",
       "      <th>Plasma</th>\n",
       "      <th>SCT</th>\n",
       "      <th>T_cells</th>\n",
       "      <th>VCT</th>\n",
       "      <th>VCT_CCC</th>\n",
       "      <th>VCT_fusing</th>\n",
       "      <th>VCT_p</th>\n",
       "      <th>dDC</th>\n",
       "      <th>dEpi_lumenal</th>\n",
       "      <th>dEpi_secretory</th>\n",
       "      <th>dM1</th>\n",
       "      <th>dM2</th>\n",
       "      <th>dNK1</th>\n",
       "      <th>dNK2</th>\n",
       "      <th>dNK3</th>\n",
       "      <th>dS1</th>\n",
       "      <th>dS2</th>\n",
       "      <th>dS3</th>\n",
       "      <th>dT_cells</th>\n",
       "      <th>dT_regs</th>\n",
       "      <th>eEVT</th>\n",
       "      <th>fF1</th>\n",
       "      <th>fF2</th>\n",
       "      <th>iEVT</th>\n",
       "      <th>uSMC</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>multidata_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1355</th>\n",
       "      <td>UBASH3B</td>\n",
       "      <td>Q8TF42</td>\n",
       "      <td>True</td>\n",
       "      <td>UBS3B_HUMAN</td>\n",
       "      <td>Dehydroepiandrosterone_bySTS</td>\n",
       "      <td>CPI-CS09B8977D7</td>\n",
       "      <td>UBASH3B</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.621</td>\n",
       "      <td>1.06</td>\n",
       "      <td>0.105</td>\n",
       "      <td>0.212</td>\n",
       "      <td>0.216</td>\n",
       "      <td>1.424</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.411</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.465</td>\n",
       "      <td>0.068</td>\n",
       "      <td>0.127</td>\n",
       "      <td>0.114</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.161</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.484</td>\n",
       "      <td>0.576</td>\n",
       "      <td>0.158</td>\n",
       "      <td>0.454</td>\n",
       "      <td>0.608</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.124</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.36</td>\n",
       "      <td>1.134</td>\n",
       "      <td>0.632</td>\n",
       "      <td>0.437</td>\n",
       "      <td>0.118</td>\n",
       "      <td>0.224</td>\n",
       "      <td>0.162</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.124</td>\n",
       "      <td>1.182</td>\n",
       "      <td>0.055</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.402</td>\n",
       "      <td>0.338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1355</th>\n",
       "      <td>UBASH3B</td>\n",
       "      <td>Q8TF42</td>\n",
       "      <td>True</td>\n",
       "      <td>UBS3B_HUMAN</td>\n",
       "      <td>Dehydroepiandrosterone_bySTS</td>\n",
       "      <td>CPI-CS05760BB78</td>\n",
       "      <td>UBASH3B</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.621</td>\n",
       "      <td>1.06</td>\n",
       "      <td>0.105</td>\n",
       "      <td>0.212</td>\n",
       "      <td>0.216</td>\n",
       "      <td>1.424</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.411</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.465</td>\n",
       "      <td>0.068</td>\n",
       "      <td>0.127</td>\n",
       "      <td>0.114</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.161</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.484</td>\n",
       "      <td>0.576</td>\n",
       "      <td>0.158</td>\n",
       "      <td>0.454</td>\n",
       "      <td>0.608</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.124</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.36</td>\n",
       "      <td>1.134</td>\n",
       "      <td>0.632</td>\n",
       "      <td>0.437</td>\n",
       "      <td>0.118</td>\n",
       "      <td>0.224</td>\n",
       "      <td>0.162</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.124</td>\n",
       "      <td>1.182</td>\n",
       "      <td>0.055</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.402</td>\n",
       "      <td>0.338</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             gene_name uniprot  is_complex protein_name  \\\n",
       "multidata_id                                              \n",
       "1355           UBASH3B  Q8TF42        True  UBS3B_HUMAN   \n",
       "1355           UBASH3B  Q8TF42        True  UBS3B_HUMAN   \n",
       "\n",
       "                              complex_name id_cp_interaction     gene  \\\n",
       "multidata_id                                                            \n",
       "1355          Dehydroepiandrosterone_bySTS   CPI-CS09B8977D7  UBASH3B   \n",
       "1355          Dehydroepiandrosterone_bySTS   CPI-CS05760BB78  UBASH3B   \n",
       "\n",
       "              B_cells   DC  EVT_1  EVT_2  Endo_F  Endo_L  Endo_M     GC  \\\n",
       "multidata_id                                                              \n",
       "1355             0.24  0.0  0.621   1.06   0.105   0.212   0.216  1.424   \n",
       "1355             0.24  0.0  0.621   1.06   0.105   0.212   0.216  1.424   \n",
       "\n",
       "              Granulocytes   HOFB  ILC3     M3     MO     NK  PV MMP11  \\\n",
       "multidata_id                                                             \n",
       "1355                   0.0  0.411  0.25  0.465  0.068  0.127     0.114   \n",
       "1355                   0.0  0.411  0.25  0.465  0.068  0.127     0.114   \n",
       "\n",
       "              PV MYH11  PV STEAP4  Plasma    SCT  T_cells    VCT  VCT_CCC  \\\n",
       "multidata_id                                                                \n",
       "1355               0.0        0.0     0.0  0.161      0.0  0.484    0.576   \n",
       "1355               0.0        0.0     0.0  0.161      0.0  0.484    0.576   \n",
       "\n",
       "              VCT_fusing  VCT_p    dDC  dEpi_lumenal  dEpi_secretory   dM1  \\\n",
       "multidata_id                                                                 \n",
       "1355               0.158  0.454  0.608           0.0           0.124  0.24   \n",
       "1355               0.158  0.454  0.608           0.0           0.124  0.24   \n",
       "\n",
       "               dM2   dNK1   dNK2   dNK3    dS1    dS2    dS3  dT_cells  \\\n",
       "multidata_id                                                             \n",
       "1355          0.36  1.134  0.632  0.437  0.118  0.224  0.162     0.025   \n",
       "1355          0.36  1.134  0.632  0.437  0.118  0.224  0.162     0.025   \n",
       "\n",
       "              dT_regs   eEVT    fF1  fF2   iEVT   uSMC  \n",
       "multidata_id                                            \n",
       "1355            0.124  1.182  0.055  0.0  1.402  0.338  \n",
       "1355            0.124  1.182  0.055  0.0  1.402  0.338  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cpdb_results['deconvoluted'].head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explore CellphoneDB results\n",
    "This module allows to filter CellphoneDB results by specifying: cell types pairs, genes or specific interactions. \\\n",
    "\n",
    "We can specify two list of cell types (`query_cell_type_1`and `query_cell_type_2`), the method will perform a pairwise comparison between the cell types defined in each list and will subset the interactions to those pairs of cells. Cell types within each list will not be paired. If we are interested in filtering interactions ocurring between a given cell to all the rest of cells in the dataset we can define `query_cell_type_1 = 'All'` and `query_cell_type_2 = ['cellA', 'cellB', ...]`. The argument `genes` allows the user to filter interactions in which a gene participates, the users can also define specific interactions based on the interaction name `query_interactions`.\n",
    "\n",
    "> In **this example** we are going to filter interactions in which a subset of trophoblast interact with the PV cells, furthermore, we will select those interactions in which the gene TGFBR1 participates and a specific interaction named CSF1_CSF1R.\n",
    "\n",
    "This method filters the rows and columns of the significant_means file; NaN value correspond to interacting pairs found not significant, non-NaN value correspond to those interacting pairs found relevant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>interacting_pair</th>\n",
       "      <th>partner_a</th>\n",
       "      <th>partner_b</th>\n",
       "      <th>gene_a</th>\n",
       "      <th>gene_b</th>\n",
       "      <th>directionality</th>\n",
       "      <th>classification</th>\n",
       "      <th>interacting_cells</th>\n",
       "      <th>significant_mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GDF11_TGFR_AVR2B</td>\n",
       "      <td>simple:O95390</td>\n",
       "      <td>complex:TGFR_AVR2B</td>\n",
       "      <td>GDF11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Ligand-Receptor</td>\n",
       "      <td>Signaling by Growth differentiation factor</td>\n",
       "      <td>PV MMP11|EVT_2</td>\n",
       "      <td>0.105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TGFB1_TGFBR3</td>\n",
       "      <td>simple:P01137</td>\n",
       "      <td>simple:Q03167</td>\n",
       "      <td>TGFB1</td>\n",
       "      <td>TGFBR3</td>\n",
       "      <td>Ligand-Receptor</td>\n",
       "      <td>Signaling by Transforming growth factor</td>\n",
       "      <td>PV MMP11|EVT_2</td>\n",
       "      <td>0.590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>GDF11_TGFR_AVR2B</td>\n",
       "      <td>simple:O95390</td>\n",
       "      <td>complex:TGFR_AVR2B</td>\n",
       "      <td>GDF11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Ligand-Receptor</td>\n",
       "      <td>Signaling by Growth differentiation factor</td>\n",
       "      <td>PV MMP11|iEVT</td>\n",
       "      <td>0.105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>TGFB1_TGFbeta_receptor1</td>\n",
       "      <td>simple:P01137</td>\n",
       "      <td>complex:TGFbeta_receptor1</td>\n",
       "      <td>TGFB1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Ligand-Receptor</td>\n",
       "      <td>Signaling by Transforming growth factor</td>\n",
       "      <td>PV MMP11|iEVT</td>\n",
       "      <td>0.446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>TGFB1_TGFBR3</td>\n",
       "      <td>simple:P01137</td>\n",
       "      <td>simple:Q03167</td>\n",
       "      <td>TGFB1</td>\n",
       "      <td>TGFBR3</td>\n",
       "      <td>Ligand-Receptor</td>\n",
       "      <td>Signaling by Transforming growth factor</td>\n",
       "      <td>PV MMP11|iEVT</td>\n",
       "      <td>0.771</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          interacting_pair      partner_a                  partner_b gene_a  \\\n",
       "0         GDF11_TGFR_AVR2B  simple:O95390         complex:TGFR_AVR2B  GDF11   \n",
       "4             TGFB1_TGFBR3  simple:P01137              simple:Q03167  TGFB1   \n",
       "5         GDF11_TGFR_AVR2B  simple:O95390         complex:TGFR_AVR2B  GDF11   \n",
       "8  TGFB1_TGFbeta_receptor1  simple:P01137  complex:TGFbeta_receptor1  TGFB1   \n",
       "9             TGFB1_TGFBR3  simple:P01137              simple:Q03167  TGFB1   \n",
       "\n",
       "   gene_b   directionality                              classification  \\\n",
       "0     NaN  Ligand-Receptor  Signaling by Growth differentiation factor   \n",
       "4  TGFBR3  Ligand-Receptor     Signaling by Transforming growth factor   \n",
       "5     NaN  Ligand-Receptor  Signaling by Growth differentiation factor   \n",
       "8     NaN  Ligand-Receptor     Signaling by Transforming growth factor   \n",
       "9  TGFBR3  Ligand-Receptor     Signaling by Transforming growth factor   \n",
       "\n",
       "  interacting_cells  significant_mean  \n",
       "0    PV MMP11|EVT_2             0.105  \n",
       "4    PV MMP11|EVT_2             0.590  \n",
       "5     PV MMP11|iEVT             0.105  \n",
       "8     PV MMP11|iEVT             0.446  \n",
       "9     PV MMP11|iEVT             0.771  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from cellphonedb.utils import search_utils\n",
    "\n",
    "search_results = search_utils.search_analysis_results(\n",
    "    query_cell_types_1 = ['EVT_1', 'EVT_2', 'GC', 'eEVT', 'iEVT'],  # List of cells 1, will be paired to cells 2 (list or 'All').\n",
    "    query_cell_types_2 = ['PV MMP11', 'PV MYH11', 'PV STEAP4'],     # List of cells 2, will be paired to cells 1 (list or 'All').\n",
    "    query_genes = ['TGFBR1'],                                       # filter interactions based on the genes participating (list).\n",
    "    query_interactions = ['CSF1_CSF1R'],                            # filter intereactions based on their name (list).\n",
    "    significant_means = cpdb_results['significant_means'],          # significant_means file generated by CellphoneDB.\n",
    "    deconvoluted = cpdb_results['deconvoluted'],                    # devonvoluted file generated by CellphoneDB.\n",
    "    interaction_scores = cpdb_results['interaction_scores'],        # interaction score generated by CellphoneDB.\n",
    "    query_minimum_score = 50,                                       # minimum score that an interaction must have to be filtered.\n",
    "    separator = '|',                                                # separator (default: |) employed to split cells (cellA|cellB).\n",
    "    long_format = True,                                             # converts the output into a wide table, removing non-significant interactions\n",
    "    query_classifications = ['Signaling by Transforming growth factor']\n",
    ")\n",
    "\n",
    "search_results.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Basic analysis and plotting\n",
    "In this section of the tutorial we will make use of Kelvin's implementations ([kt-plots tutorial](https://ktplotspy.readthedocs.io/en/latest/index.html)) to perform some exploratory analysis of the results obtained from CellphoneDB. It is of great importance that the user is familiarized with the biological context of the data to obtain meaninful results from CellphoneDB results. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import anndata as ad\n",
    "import pandas as pd\n",
    "import ktplotspy as kpy\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Display as heatmap the number of significant interactions found by CellphoneDB between all of the cell pairs. Only cells found in the microenvironment will be represented in this plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.matrix.ClusterGrid at 0x7efd8a269cc0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 500x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kpy.plot_cpdb_heatmap(pvals = cpdb_results['pvalues'],\n",
    "                      degs_analysis = False,\n",
    "                      figsize = (5, 5),\n",
    "                      title = \"Sum of significant interactions\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we are plotting the interactions between the PVs and the trophoblasts that are mediated by TGFB2 and CSF1R."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 300,
       "width": 1000
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure Size: (1000 x 300)>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kpy.plot_cpdb(\n",
    "    adata = adata,\n",
    "    cell_type1 = \"PV MYH11|PV STEAP4|PV MMPP11\",\n",
    "    cell_type2 = \"EVT_1|EVT_2|GC|iEVT|eEVT|VCT_CCC\",\n",
    "    means = cpdb_results['means'],\n",
    "    pvals = cpdb_results['pvalues'],\n",
    "    celltype_key = \"cell_labels\",\n",
    "    genes = [\"TGFB2\", \"CSF1R\"],\n",
    "    figsize = (10, 3),\n",
    "    title = \"Interactions between\\nPV and trophoblast\",\n",
    "    max_size = 3,\n",
    "    highlight_size = 0.75,\n",
    "    degs_analysis = False,\n",
    "    standard_scale = True,\n",
    "    interaction_scores = cpdb_results['interaction_scores'],\n",
    "    scale_alpha_by_interaction_scores = True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interactions can also be plotted grouped by pathway."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x800 with 3 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 800,
       "width": 1200
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure Size: (1200 x 800)>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from plotnine import facet_wrap\n",
    "\n",
    "p = kpy.plot_cpdb(\n",
    "    adata = adata,\n",
    "    cell_type1 = \"PV MYH11\",\n",
    "    cell_type2 = \"EVT_1|EVT_2|GC|iEVT|eEVT|VCT_CCC\",\n",
    "    means = cpdb_results['means'],\n",
    "    pvals = cpdb_results['pvalues'],\n",
    "    celltype_key = \"cell_labels\",\n",
    "    genes = [\"TGFB2\", \"CSF1R\", \"COL1A1\"],\n",
    "    figsize = (12, 8),\n",
    "    title = \"Interactions between PV and trophoblast\\ns grouped by classification\",\n",
    "    max_size = 6,\n",
    "    highlight_size = 0.75,\n",
    "    degs_analysis = False,\n",
    "    standard_scale = True,\n",
    ")\n",
    "p + facet_wrap(\"~ classification\", ncol = 1)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "cpdb-bare-score",
   "language": "python",
   "name": "cpdb-bare-score"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}