Figure_3B_singleton_vs_cluster_size_distribution.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import pandas as pd\n",
    "import os\n",
    "import os.path as op\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.patches import Patch\n",
    "import math\n",
    "\n",
    "wd = \"/mnt/scgc/simon/simonsproject/gorg-tara_clustering/\"\n",
    "os.chdir(wd)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the length of the seed sequences for all GORG-TARA clusters identified in mmseqs2 analysis (see Figure 3A notebook for mmseqs2 analysis parameters).  Seed sequences found in mmseqs2 output file with \\*.faa suffix\n",
    "\n",
    "Then ran below script overnight:\n",
    "\n",
    "```\n",
    "import itertools\n",
    "\n",
    "def readfa(fh):\n",
    "    for header, group in itertools.groupby(fh, lambda line: line[0] == '>'):\n",
    "        if header:\n",
    "            line = next(group)\n",
    "            name = line[1:].strip()\n",
    "        else:\n",
    "            seq = ''.join(line.strip() for line in group)\n",
    "            yield name, seq\n",
    "\n",
    "\n",
    "filename = \"analyses/gorg_tara_tropics_80minid_m80_sizes.csv\"\n",
    "fasta = \"analyses/gorg_tara_tropics_80minid_m80.faa\"\n",
    "\n",
    "with open(filename, \"w\") as oh:\n",
    "    for name, seq in readfa(open(fasta)):\n",
    "        print(name, len(seq), file=oh, sep=\",\")\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load table used in generation of figure 3A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "scp = pd.read_csv(\"analyses/gorg_tara_tropics_80minid_kmer80_counts_by_source.csv\")[['c1','TARA','gorg']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load seed size table generated with above script"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sizedf = pd.read_csv(\"analyses/gorg_tara_tropics_80minid_m80_sizes.csv\", names = ['orf','aa_count'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add total column for cluster size (number of sequences in cluster) and combine tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "scp['total'] = scp['gorg'] + scp['TARA']\n",
    "scp = scp.merge(sizedf, left_on = 'c1', right_on='orf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>c1</th>\n",
       "      <th>TARA</th>\n",
       "      <th>gorg</th>\n",
       "      <th>total</th>\n",
       "      <th>orf</th>\n",
       "      <th>aa_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TARA_004_DCM_0.22-1.6_C22365543_1_gene422493</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>TARA_004_DCM_0.22-1.6_C22365543_1_gene422493</td>\n",
       "      <td>166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TARA_004_DCM_0.22-1.6_C22365545_1_gene422494</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>TARA_004_DCM_0.22-1.6_C22365545_1_gene422494</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TARA_004_DCM_0.22-1.6_C22365547_1_gene422497</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>TARA_004_DCM_0.22-1.6_C22365547_1_gene422497</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TARA_004_DCM_0.22-1.6_C22365549_1_gene422498</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>TARA_004_DCM_0.22-1.6_C22365549_1_gene422498</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TARA_004_DCM_0.22-1.6_C22365551_1_gene422500</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>TARA_004_DCM_0.22-1.6_C22365551_1_gene422500</td>\n",
       "      <td>166</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             c1  TARA  gorg  total  \\\n",
       "0  TARA_004_DCM_0.22-1.6_C22365543_1_gene422493   1.0   0.0    1.0   \n",
       "1  TARA_004_DCM_0.22-1.6_C22365545_1_gene422494   1.0   0.0    1.0   \n",
       "2  TARA_004_DCM_0.22-1.6_C22365547_1_gene422497   1.0   0.0    1.0   \n",
       "3  TARA_004_DCM_0.22-1.6_C22365549_1_gene422498   1.0   0.0    1.0   \n",
       "4  TARA_004_DCM_0.22-1.6_C22365551_1_gene422500   1.0   0.0    1.0   \n",
       "\n",
       "                                            orf  aa_count  \n",
       "0  TARA_004_DCM_0.22-1.6_C22365543_1_gene422493       166  \n",
       "1  TARA_004_DCM_0.22-1.6_C22365545_1_gene422494        50  \n",
       "2  TARA_004_DCM_0.22-1.6_C22365547_1_gene422497        39  \n",
       "3  TARA_004_DCM_0.22-1.6_C22365549_1_gene422498        26  \n",
       "4  TARA_004_DCM_0.22-1.6_C22365551_1_gene422500       166  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "binsplits = [0,1, 10, 100, 1000, 5000, 12000]\n",
    "scp['bin'] = pd.cut(scp['total'], bins=binsplits)\n",
    "\n",
    "# add column for bin labels\n",
    "ncd = dict(zip(list(scp['bin'].drop_duplicates()), ['1', '2-10','11-100','101-1000', '1001-5000','5001-12000']))\n",
    "scp['bin_label'] = [ncd[i] for i in scp['bin']]\n",
    "\n",
    "# take log of aa_count\n",
    "scp['log_aa'] = [math.log(i, 10) for i in scp['aa_count']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exploring seed size by bin size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/mnt/scgc_nfs/opt/common/anaconda3/4.0.0/lib/python3.5/site-packages/seaborn-0.7.1-py3.5.egg/seaborn/categorical.py:588: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
      "  kde_data = remove_na(group_data)\n",
      "/mnt/scgc_nfs/opt/common/anaconda3/4.0.0/lib/python3.5/site-packages/seaborn-0.7.1-py3.5.egg/seaborn/categorical.py:816: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
      "  violin_data = remove_na(group_data)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x2aabc17fb358>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfIAAAG/CAYAAACjV1ViAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIYQQImP/H4LzNYFrSSLFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2aacb90295c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8,6))\n",
    "\n",
    "sns.violinplot(x='bin', y='log_aa', data=scp)\n",
    "plt.xticks(rotation=90)\n",
    "plt.xlabel('Cluster Size')\n",
    "plt.ylabel('log(protein length)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compare the size distribution of singletons to bins with more than 10 members."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bin1 = list(scp[scp['total'] == 1]['log_aa'])\n",
    "bingt10 = list(scp[scp['total'] > 10]['log_aa'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x2aabb917ba90>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfoAAAGCCAYAAAAIZAHlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIiIiLqZALyIi4mIK9CIiIi72DeRYxNlyD0s8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2aacb9034a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cdict = {'singletons':'blue','clusters with >10 sequences':'green'}\n",
    "fig = plt.figure(figsize=(8,6))\n",
    "sns.set_style('whitegrid')\n",
    "\n",
    "sns.distplot(bin1, kde=False, norm_hist=True, color=\"blue\")\n",
    "sns.distplot(bingt10, kde=False, norm_hist=True, color = \"green\")\n",
    "plt.xlim(1,3.5)\n",
    "\n",
    "legend_elements = [Patch(facecolor=cdict[i], edgecolor=cdict[i], alpha=0.5, label=i) for i in cdict]\n",
    "plt.legend(handles=legend_elements, loc=2,\n",
    "          fancybox=False, shadow=False, frameon=False, ncol=1)\n",
    "\n",
    "plt.xlabel('Log(Amino Acid Sequence Length)')\n",
    "\n",
    "plt.ylabel('Normalized Density of Clusters')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.5.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}