main_work/suppl_work/meanL_distribution__perGofOrg.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3e0571fd",
   "metadata": {},
   "source": [
    "# Mean of the distribution of lengths in the different group of organisms\n",
    "**in the protein coding gene length distributions for the different species**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21807dcd",
   "metadata": {},
   "source": [
    "## Import python modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b336e97e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "from plotnine import *\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import sys\n",
    "sys.path.append('../../gl_lib/')\n",
    "import gl_constants as c"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8325132a",
   "metadata": {},
   "source": [
    "## The protein coding gene length distributions for the different species"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e601f90b",
   "metadata": {},
   "source": [
    "### Retrieving the statistical descriptions of the gene length's distributions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9a656a1",
   "metadata": {},
   "source": [
    "#### Statistical description "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f656fca4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The statistical descriptions of the protein coding gene distributions for the different species is in:\n",
      " /home/emuro/git/github/EM_geneLength_nature/main_work/suppl_work/../../gl_lib/../main_tables/stat_protCodGenes.tsv \n",
      "\n"
     ]
    },
    {
     "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>division_both_dbs</th>\n",
       "      <th>species</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>var</th>\n",
       "      <th>log10_mean</th>\n",
       "      <th>log10_var</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>33087</th>\n",
       "      <td>protists</td>\n",
       "      <td>plasmodium_gonderi_gca_002157705</td>\n",
       "      <td>5916</td>\n",
       "      <td>2426.989689</td>\n",
       "      <td>6.049565e+06</td>\n",
       "      <td>3.246751</td>\n",
       "      <td>0.111318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10044</th>\n",
       "      <td>bacteria</td>\n",
       "      <td>marinobacter_sp__gca_003539345</td>\n",
       "      <td>3602</td>\n",
       "      <td>760.666574</td>\n",
       "      <td>3.083007e+05</td>\n",
       "      <td>2.772226</td>\n",
       "      <td>0.101309</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      division_both_dbs                           species  count         mean  \\\n",
       "33087          protists  plasmodium_gonderi_gca_002157705   5916  2426.989689   \n",
       "10044          bacteria    marinobacter_sp__gca_003539345   3602   760.666574   \n",
       "\n",
       "                var  log10_mean  log10_var  \n",
       "33087  6.049565e+06    3.246751   0.111318  \n",
       "10044  3.083007e+05    2.772226   0.101309  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(33627, 7)\n",
      "division_both_dbs\n",
      "bacteria         30714\n",
      "archaea           1229\n",
      "fungi             1014\n",
      "protists           237\n",
      "vertebrates        222\n",
      "invertebrates      115\n",
      "plants              96\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# statistics on length distributions \n",
    "stat_file = c.STAT_G_FILE\n",
    "print(\"The statistical descriptions of the protein coding gene distributions \" + \\\n",
    "    \"for the different species is in:\\n\", stat_file, \"\\n\")\n",
    "\n",
    "# retrieve data and diminish the number of columns\n",
    "stat_df = pd.read_csv(stat_file, low_memory=False, sep=\"\\t\")\n",
    "stat_df = stat_df[[\"division_both_dbs\", \"species\", \"count\", \"mean\", \"var\", \"log10_mean\", \"log10_var\"]]\n",
    "\n",
    "# visualize data\n",
    "if 1:\n",
    "    pd.set_option('display.max_columns', len(stat_df.columns))\n",
    "    display(stat_df.sample(2))\n",
    "    print(stat_df.shape)\n",
    "    print(stat_df[\"division_both_dbs\"].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c47fb994",
   "metadata": {},
   "source": [
    "## $<L>$ histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7c5a300d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "archaea\n",
      "count    1229.000000\n",
      "mean      834.838258\n",
      "std        80.439432\n",
      "min       395.933333\n",
      "25%       791.161435\n",
      "50%       846.641304\n",
      "75%       879.990714\n",
      "max      1181.906073\n",
      "Name: mean, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bacteria\n",
      "count    30714.000000\n",
      "mean       942.991035\n",
      "std         76.549769\n",
      "min        452.684851\n",
      "25%        902.371160\n",
      "50%        944.827334\n",
      "75%        985.652610\n",
      "max       1306.264577\n",
      "Name: mean, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "protists\n",
      "count      237.000000\n",
      "mean      2059.139453\n",
      "std       1089.415802\n",
      "min        679.553838\n",
      "25%       1487.206671\n",
      "50%       1798.289527\n",
      "75%       2358.855279\n",
      "max      12164.651890\n",
      "Name: mean, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fungi\n",
      "count    1014.000000\n",
      "mean     1672.224559\n",
      "std       304.288468\n",
      "min       530.847826\n",
      "25%      1493.055354\n",
      "50%      1620.808764\n",
      "75%      1801.365068\n",
      "max      2580.356803\n",
      "Name: mean, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "col_x = \"mean\"  # prots_mean\n",
    "col_y = \"var\"   # prots_var\n",
    "legends_by = \"division_both_dbs\"\n",
    "#\n",
    "title_template = \"Protein coding genes\" \n",
    "x_lab = \"Mean gene length\"   \n",
    "y_lab = \"Counts\"\n",
    "#\n",
    "#\n",
    "Lg_df = stat_df.copy()\n",
    "Lg_df = Lg_df[['division_both_dbs', 'mean', 'var']] \n",
    "\n",
    "# sort by groups of organisms\n",
    "Lg_df[legends_by] = pd.Categorical(Lg_df[legends_by], \n",
    "                                  categories=c.ORG_GROUPS,\n",
    "                                  ordered=True)\n",
    "Lg_df.sort_values(legends_by, inplace=True)\n",
    "\n",
    "for group in [\"archaea\", \"bacteria\", \"protists\", \"fungi\"]:\n",
    "    group_Lg_df = Lg_df.loc[Lg_df[legends_by]==group]\n",
    "    print(group, group_Lg_df['mean'].describe(), sep=\"\\n\")\n",
    "\n",
    "    title = title_template + \" (\" + str(group_Lg_df.shape[0]) + \" \" + str(group) + \")\"\n",
    "    sns.set_theme()\n",
    "    fig, ax = plt.subplots(figsize=(10, 5))  \n",
    "    ax.set(title=title, xlabel=x_lab, ylabel=y_lab)\n",
    "    sns.histplot(data=group_Lg_df, x=\"mean\", bins=100, color=c.COLOR_FOR_DIST[\"genes\"]) #, stat=\"density\")\n",
    "    plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}