{

 "cells": [

  {

   "cell_type": "code",

   "execution_count": 1,

   "id": "colonial-revolution",

   "metadata": {},

   "outputs": [],

   "source": [

    "import pandas as pd\n",

    "import sys\n",

    "import glob\n",

    "import os\n",

    "\n",

    "pd.set_option('display.max_columns', 100)\n",

    "os.chdir('/home/jovyan/cpdb_tutorial')"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 2,

   "id": "hungarian-outreach",

   "metadata": {},

   "outputs": [

    {

     "name": "stdout",

     "output_type": "stream",

     "text": [

      "3.8.8 | packaged by conda-forge | (default, Feb 20 2021, 16:22:27) \n",

      "[GCC 9.3.0]\n"

     ]

    }

   ],

   "source": [

    "print(sys.version)"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 3,

   "id": "understanding-lambda",

   "metadata": {},

   "outputs": [

    {

     "data": {

      "text/html": [

       "<table class=\"striped\"><tr><th style=\"text-align:center\">Version</th><th style=\"text-align:center\">Release date</th></tr><td style=\"text-align:center\"><a class=\"teal-text\" href=\"https://github.com/ventolab/cellphonedb-data/releases/tag/v5.0.0\">v5.0.0</a></td><td style=\"text-align:center\">2023-10-31</td></tr><tr><td style=\"text-align:center\"><a class=\"teal-text\" href=\"https://github.com/ventolab/cellphonedb-data/releases/tag/v4.1.0\">v4.1.0</a></td><td style=\"text-align:center\">2023-03-09</td></tr><tr><tr><tr></table>"

      ],

      "text/plain": [

       "<IPython.core.display.HTML object>"

      ]

     },

     "metadata": {},

     "output_type": "display_data"

    }

   ],

   "source": [

    "from IPython.display import HTML, display\n",

    "from cellphonedb.utils import db_releases_utils\n",

    "\n",

    "display(HTML(db_releases_utils.get_remote_database_versions_html()['db_releases_html_table']))"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 4,

   "id": "phantom-manhattan",

   "metadata": {},

   "outputs": [],

   "source": [

    "# -- Version of the databse\n",

    "cpdb_version = 'v4.1.0'\n",

    "\n",

    "# -- Path where the input files to generate the database are located\n",

    "cpdb_target_dir = os.path.join('/home/jovyan/cpdb_tutorial/db', cpdb_version)"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 5,

   "id": "surgical-masters",

   "metadata": {},

   "outputs": [

    {

     "name": "stdout",

     "output_type": "stream",

     "text": [

      "Downloaded cellphonedb.zip into /home/jovyan/cpdb_tutorial/db/v4.1.0\n",

      "Downloaded complex_input.csv into /home/jovyan/cpdb_tutorial/db/v4.1.0\n",

      "Downloaded gene_input.csv into /home/jovyan/cpdb_tutorial/db/v4.1.0\n",

      "Downloaded interaction_input.csv into /home/jovyan/cpdb_tutorial/db/v4.1.0\n",

      "Downloaded protein_input.csv into /home/jovyan/cpdb_tutorial/db/v4.1.0\n",

      "Downloaded uniprot_synonyms.tsv into /home/jovyan/cpdb_tutorial/db/v4.1.0/sources\n"

     ]

    }

   ],

   "source": [

    "from cellphonedb.utils import db_utils\n",

    "\n",

    "db_utils.download_database(cpdb_target_dir, cpdb_version)"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 6,

   "id": "informative-rover",

   "metadata": {},

   "outputs": [],

   "source": [

    "cpdb_file_path = 'db/v4.1.0/cellphonedb.zip'\n",

    "meta_file_path = '/lustre/scratch126/cellgen/team205/rl20/CTCL/cellphoneDB_revision/CTCL_sub_nomore_100_per_donor_celltype_anno_meta.txt'\n",

    "counts_file_path = '/lustre/scratch126/cellgen/team205/rl20/CTCL/cellphoneDB_revision/CTCL_sub_nomore_100_per_donor_celltype_anno.h5ad'\n",

    "#microenvs_file_path = 'data/microenvironment.tsv'\n",

    "out_path = '/home/jovyan/farm/CTCL/cellphoneDB4_revision'"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 7,

   "id": "incorporate-treasurer",

   "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>AAACCTGAGCGTTCCG-0_CTCL1_CTCL1_CTCL1</td>\n",

       "      <td>Undifferentiated_KC</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>1</th>\n",

       "      <td>AAACCTGCACACCGCA-0_CTCL1_CTCL1_CTCL1</td>\n",

       "      <td>basal2</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>2</th>\n",

       "      <td>AAACCTGGTACCGTTA-0_CTCL1_CTCL1_CTCL1</td>\n",

       "      <td>DC1</td>\n",

       "    </tr>\n",

       "  </tbody>\n",

       "</table>\n",

       "</div>"

      ],

      "text/plain": [

       "                         barcode_sample            cell_type\n",

       "0  AAACCTGAGCGTTCCG-0_CTCL1_CTCL1_CTCL1  Undifferentiated_KC\n",

       "1  AAACCTGCACACCGCA-0_CTCL1_CTCL1_CTCL1               basal2\n",

       "2  AAACCTGGTACCGTTA-0_CTCL1_CTCL1_CTCL1                  DC1"

      ]

     },

     "execution_count": 7,

     "metadata": {},

     "output_type": "execute_result"

    }

   ],

   "source": [

    "metadata = pd.read_csv(meta_file_path, sep = '\\t')\n",

    "metadata.head(3)"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 8,

   "id": "portable-radical",

   "metadata": {},

   "outputs": [

    {

     "data": {

      "text/plain": [

       "(60256, 15790)"

      ]

     },

     "execution_count": 8,

     "metadata": {},

     "output_type": "execute_result"

    }

   ],

   "source": [

    "import anndata\n",

    "\n",

    "adata = anndata.read_h5ad(counts_file_path)\n",

    "adata.shape"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 9,

   "id": "olive-african",

   "metadata": {},

   "outputs": [

    {

     "data": {

      "text/plain": [

       "True"

      ]

     },

     "execution_count": 9,

     "metadata": {},

     "output_type": "execute_result"

    }

   ],

   "source": [

    "list(adata.obs.index).sort() == list(metadata['barcode_sample']).sort()"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 10,

   "id": "aquatic-butter",

   "metadata": {},

   "outputs": [

    {

     "name": "stdout",

     "output_type": "stream",

     "text": [

      "Reading user files...\n",

      "The following user files were loaded successfully:\n",

      "/lustre/scratch126/cellgen/team205/rl20/CTCL/cellphoneDB_revision/CTCL_sub_nomore_100_per_donor_celltype_anno.h5ad\n",

      "/lustre/scratch126/cellgen/team205/rl20/CTCL/cellphoneDB_revision/CTCL_sub_nomore_100_per_donor_celltype_anno_meta.txt\n",

      "[ ][CORE][09/07/24-13:46:18][INFO] [Cluster Statistical Analysis] Threshold:0.1 Iterations:1000 Debug-seed:42 Threads:4 Precision:3\n",

      "[ ][CORE][09/07/24-13:46:18][WARNING] Debug random seed enabled. Set to 42\n",

      "[ ][CORE][09/07/24-13:46:25][INFO] Running Real Analysis\n",

      "[ ][CORE][09/07/24-13:46:25][INFO] Running Statistical Analysis\n"

     ]

    },

    {

     "name": "stderr",

     "output_type": "stream",

     "text": [

      "100%|██████████| 1000/1000 [17:35<00:00,  1.06s/it]"

     ]

    },

    {

     "name": "stdout",

     "output_type": "stream",

     "text": [

      "[ ][CORE][09/07/24-14:04:01][INFO] Building Pvalues result\n"

     ]

    },

    {

     "name": "stderr",

     "output_type": "stream",

     "text": [

      "\n"

     ]

    },

    {

     "name": "stdout",

     "output_type": "stream",

     "text": [

      "[ ][CORE][09/07/24-14:04:10][INFO] Building results\n",

      "Saved deconvoluted to /home/jovyan/farm/CTCL/cellphoneDB4_revision/statistical_analysis_deconvoluted_07_09_2024_14:04:11.txt\n",

      "Saved means to /home/jovyan/farm/CTCL/cellphoneDB4_revision/statistical_analysis_means_07_09_2024_14:04:11.txt\n",

      "Saved pvalues to /home/jovyan/farm/CTCL/cellphoneDB4_revision/statistical_analysis_pvalues_07_09_2024_14:04:11.txt\n",

      "Saved significant_means to /home/jovyan/farm/CTCL/cellphoneDB4_revision/statistical_analysis_significant_means_07_09_2024_14:04:11.txt\n"

     ]

    }

   ],

   "source": [

    "from cellphonedb.src.core.methods import cpdb_statistical_analysis_method\n",

    "\n",

    "deconvoluted, means, pvalues, significant_means = 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.\n",

    "    counts_data = 'hgnc_symbol',                     # defines the gene annotation in counts matrix.\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 = 4,                                     # 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": "code",

   "execution_count": 11,

   "id": "extreme-split",

   "metadata": {},

   "outputs": [],

   "source": [

    "import ktplotspy as kpy"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 12,

   "id": "related-switzerland",

   "metadata": {},

   "outputs": [],

   "source": [

    "adata = anndata.read_h5ad(counts_file_path)"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 13,

   "id": "intellectual-twelve",

   "metadata": {},

   "outputs": [

    {

     "data": {

      "text/plain": [

       "<60256x15790 sparse matrix of type '<class 'numpy.float32'>'\n",

       "\twith 97273288 stored elements in Compressed Sparse Row format>"

      ]

     },

     "execution_count": 13,

     "metadata": {},

     "output_type": "execute_result"

    }

   ],

   "source": [

    "adata.X"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 15,

   "id": "buried-portrait",

   "metadata": {},

   "outputs": [],

   "source": [

    "means = pd.read_csv(\"/home/jovyan/farm/CTCL/cellphoneDB4_revision/statistical_analysis_means_07_05_2024_18:27:35.txt\", sep=\"\\t\")\n",

    "pvals = pd.read_csv(\"/home/jovyan/farm/CTCL/cellphoneDB4_revision/statistical_analysis_pvalues_07_05_2024_18:27:35.txt\", sep=\"\\t\")\n",

    "decon = pd.read_csv(\"/home/jovyan/farm/CTCL/cellphoneDB4_revision/statistical_analysis_deconvoluted_07_05_2024_18:27:35.txt\", sep=\"\\t\")"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 16,

   "id": "cordless-percentage",

   "metadata": {},

   "outputs": [

    {

     "data": {

      "text/plain": [

       "<seaborn.matrix.ClusterGrid at 0x7f90116fe640>"

      ]

     },

     "execution_count": 16,

     "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=pvals, figsize=(5, 5), title=\"Sum of significant interactions\")"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "id": "multiple-shareware",

   "metadata": {},

   "outputs": [],

   "source": []

  }

 ],

 "metadata": {

  "kernelspec": {

   "display_name": "cpdb4",

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