example_usage.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example Usage of the HGT aware Gene Gain / Loss Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gene_model\n",
    "import gfs\n",
    "import color_scheme\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Simulation Parameters\n",
    "theta = 200 # Gene Gain rate\n",
    "rho = 0.1 # Gene Loss rate\n",
    "\n",
    "gene_conv = 0 # Gene Conversion rate\n",
    "recomb = 0 # Recombination rate\n",
    "hgt_rate = 0.0001 # HGT rate\n",
    "\n",
    "\n",
    "num_sites = 5000 # Number of sites to simulate\n",
    "num_samples = 5 # Number of samples / individuals"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compute a tskit.TreeSequence with mutations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Jules\\Documents\\pangenome-gene-transfer-simulation\\gene_model.py:229: RuntimeWarning: 3053 double mutation (present -> present) occured. It is recommended to increase the num_sites to 20000 or higher. Alternatily use the double_site_relocation or relocate_double_gene_gain option.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "            <div>\n",
       "              <style>\n",
       "                .tskit-table thead tr th {text-align: left;padding: 0.5em 0.5em;}\n",
       "                .tskit-table tbody tr td {padding: 0.5em 0.5em;}\n",
       "                .tskit-table tbody tr td:first-of-type {text-align: left;}\n",
       "                .tskit-details-label {vertical-align: top; padding-right:5px;}\n",
       "                .tskit-table-set {display: inline-flex;flex-wrap: wrap;margin: -12px 0 0 -12px;width: calc(100% + 12px);}\n",
       "                .tskit-table-set-table {margin: 12px 0 0 12px;}\n",
       "                details {display: inline-block;}\n",
       "                summary {cursor: pointer; outline: 0; display: list-item;}\n",
       "              </style>\n",
       "              <div class=\"tskit-table-set\">\n",
       "                <div class=\"tskit-table-set-table\">\n",
       "                  <table class=\"tskit-table\">\n",
       "                    <thead>\n",
       "                      <tr>\n",
       "                        <th style=\"padding:0;line-height:21px;\">\n",
       "                          <img style=\"height: 32px;display: inline-block;padding: 3px 5px 3px 0;\" src=\"https://raw.githubusercontent.com/tskit-dev/administrative/main/tskit_logo.svg\"/>\n",
       "                          <a target=\"_blank\" href=\"https://tskit.dev/tskit/docs/latest/python-api.html#the-treesequence-class\"> Tree Sequence </a>\n",
       "                        </th>\n",
       "                      </tr>\n",
       "                    </thead>\n",
       "                    <tbody>\n",
       "                      <tr><td>Trees</td><td>7</td></tr>\n",
       "                      <tr><td>Sequence Length</td><td>10000.0</td></tr>\n",
       "                      <tr><td>Time Units</td><td>unknown</td></tr>\n",
       "                      <tr><td>Sample Nodes</td><td>5</td></tr>\n",
       "                      <tr><td>Total Size</td><td>1.2 MiB</td></tr>\n",
       "                      <tr>\n",
       "                        <td>Metadata</td><td style=\"text-align: left;\">No Metadata</td></tr>\n",
       "                    </tbody>\n",
       "                  </table>\n",
       "                </div>\n",
       "                <div class=\"tskit-table-set-table\">\n",
       "                  <table class=\"tskit-table\">\n",
       "                    <thead>\n",
       "                      <tr>\n",
       "                        <th style=\"line-height:21px;\">Table</th>\n",
       "                        <th>Rows</th>\n",
       "                        <th>Size</th>\n",
       "                        <th>Has Metadata</th>\n",
       "                      </tr>\n",
       "                    </thead>\n",
       "                    <tbody>\n",
       "                    \n",
       "                  <tr>\n",
       "                    <td>Edges</td>\n",
       "                      <td>26</td>\n",
       "                      <td>840 Bytes</td>\n",
       "                      <td style=\"text-align: center;\">\n",
       "                        \n",
       "                      </td>\n",
       "                    </tr>\n",
       "                \n",
       "                  <tr>\n",
       "                    <td>Individuals</td>\n",
       "                      <td>0</td>\n",
       "                      <td>24 Bytes</td>\n",
       "                      <td style=\"text-align: center;\">\n",
       "                        \n",
       "                      </td>\n",
       "                    </tr>\n",
       "                \n",
       "                  <tr>\n",
       "                    <td>Migrations</td>\n",
       "                      <td>0</td>\n",
       "                      <td>8 Bytes</td>\n",
       "                      <td style=\"text-align: center;\">\n",
       "                        \n",
       "                      </td>\n",
       "                    </tr>\n",
       "                \n",
       "                  <tr>\n",
       "                    <td>Mutations</td>\n",
       "                      <td>23214</td>\n",
       "                      <td>962.2 KiB</td>\n",
       "                      <td style=\"text-align: center;\">\n",
       "                        \n",
       "                      </td>\n",
       "                    </tr>\n",
       "                \n",
       "                  <tr>\n",
       "                    <td>Nodes</td>\n",
       "                      <td>21</td>\n",
       "                      <td>596 Bytes</td>\n",
       "                      <td style=\"text-align: center;\">\n",
       "                        \n",
       "                      </td>\n",
       "                    </tr>\n",
       "                \n",
       "                  <tr>\n",
       "                    <td>Populations</td>\n",
       "                      <td>1</td>\n",
       "                      <td>16 Bytes</td>\n",
       "                      <td style=\"text-align: center;\">\n",
       "                        \n",
       "                      </td>\n",
       "                    </tr>\n",
       "                \n",
       "                  <tr>\n",
       "                    <td>Provenances</td>\n",
       "                      <td>0</td>\n",
       "                      <td>16 Bytes</td>\n",
       "                      <td style=\"text-align: center;\">\n",
       "                        \n",
       "                      </td>\n",
       "                    </tr>\n",
       "                \n",
       "                  <tr>\n",
       "                    <td>Sites</td>\n",
       "                      <td>7247</td>\n",
       "                      <td>214.2 KiB</td>\n",
       "                      <td style=\"text-align: center;\">\n",
       "                        \n",
       "                      </td>\n",
       "                    </tr>\n",
       "                \n",
       "                    </tbody>\n",
       "                  </table>\n",
       "                </div>\n",
       "              </div>\n",
       "            </div>\n",
       "            "
      ],
      "text/plain": [
       "<tskit.trees.TreeSequence at 0x24ecbe43e30>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mts = gene_model.gene_model(\n",
    "    theta=theta,\n",
    "    rho=rho,\n",
    "    gene_conversion_rate=gene_conv,\n",
    "    recombination_rate=recomb,\n",
    "    hgt_rate=hgt_rate,\n",
    "    num_samples=num_samples,\n",
    "    num_sites=num_sites,\n",
    "    double_site_relocation=True, # Fix double gene gain events, won't hide the warning.\n",
    ")\n",
    "mts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compute multiple GFS to get a distribution for each GF Class for a fixed tree."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_simulations = 200\n",
    "processes = 10\n",
    "nwk = \"(((A:0.1, B:0.1):0.15, (C:0.05, D:0.05):0.2):0.2, E:0.35);\"\n",
    "\n",
    "#         /\\\n",
    "#      __/  \\\n",
    "#     /   \\  \\\n",
    "#    /\\    \\  \\\n",
    "#   |  |   /\\  \\\n",
    "#   A  B  C  D  E     \n",
    "\n",
    "gm = gene_model.multi_genotype_matrices(\n",
    "    n=num_simulations,\n",
    "    processes=processes,\n",
    "    theta=theta,\n",
    "    rho=rho,\n",
    "    num_sites=num_sites,\n",
    "    num_samples=num_samples,\n",
    "    gene_conversion_rate=gene_conv,\n",
    "    recombination_rate=recomb,\n",
    "    hgt_rate=hgt_rate,\n",
    "    ce_from_nwk=nwk,\n",
    "    check_double_gene_gain=False,\n",
    "    double_site_relocation=True,\n",
    ")\n",
    "\n",
    "# Compute GFS out of genotyle matrices\n",
    "gfs_list = gfs.padded_gfs_from_gm(gm, num_samples=num_samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 4))\n",
    "sns.violinplot(data=gfs_list, color=color_scheme.primary)\n",
    "plt.title(\"Distribution per GF-class\")\n",
    "plt.xlabel(\"GF-Class\");"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "MA",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}