notebook/Species Traits.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sqlalchemy import create_engine\n",
    "\n",
    "if os.getcwd().endswith('notebook'):\n",
    "    os.chdir('..')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set(palette='colorblind', font_scale=1.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "db_path = os.path.join(os.getcwd(), 'data/condensed_traits/db/seq.db')\n",
    "engine = create_engine(f'sqlite+pysqlite:///{db_path}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\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>species_taxid</th>\n",
       "      <th>species</th>\n",
       "      <th>genus</th>\n",
       "      <th>family</th>\n",
       "      <th>order</th>\n",
       "      <th>class</th>\n",
       "      <th>phylum</th>\n",
       "      <th>superkingdom</th>\n",
       "      <th>gram_stain</th>\n",
       "      <th>metabolism</th>\n",
       "      <th>...</th>\n",
       "      <th>genome_size.stdev</th>\n",
       "      <th>gc_content.stdev</th>\n",
       "      <th>coding_genes.stdev</th>\n",
       "      <th>optimum_tmp.stdev</th>\n",
       "      <th>optimum_ph.stdev</th>\n",
       "      <th>growth_tmp.stdev</th>\n",
       "      <th>rRNA16S_genes.stdev</th>\n",
       "      <th>tRNA_genes.stdev</th>\n",
       "      <th>data_source</th>\n",
       "      <th>ref_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>Azorhizobium caulinodans</td>\n",
       "      <td>Azorhizobium</td>\n",
       "      <td>Xanthobacteraceae</td>\n",
       "      <td>Rhizobiales</td>\n",
       "      <td>Alphaproteobacteria</td>\n",
       "      <td>Proteobacteria</td>\n",
       "      <td>Bacteria</td>\n",
       "      <td>negative</td>\n",
       "      <td>aerobic</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.572</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>engqvist, genbank, gold, jemma-refseq, kegg, p...</td>\n",
       "      <td>705, 6102, 13521, 13643, 15838, 19908, 19956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9</td>\n",
       "      <td>Buchnera aphidicola</td>\n",
       "      <td>Buchnera</td>\n",
       "      <td>Erwiniaceae</td>\n",
       "      <td>Enterobacterales</td>\n",
       "      <td>Gammaproteobacteria</td>\n",
       "      <td>Proteobacteria</td>\n",
       "      <td>Bacteria</td>\n",
       "      <td>negative</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>81529.139</td>\n",
       "      <td>1.855</td>\n",
       "      <td>82.983</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.566</td>\n",
       "      <td>genbank, gold, jemma-refseq, kegg, microbe-dir...</td>\n",
       "      <td>5601, 9112, 9158, 9864, 9910, 10313, 10358, 10...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11</td>\n",
       "      <td>Cellulomonas gilvus</td>\n",
       "      <td>Cellulomonas</td>\n",
       "      <td>Cellulomonadaceae</td>\n",
       "      <td>Micrococcales</td>\n",
       "      <td>Actinobacteria</td>\n",
       "      <td>Actinobacteria</td>\n",
       "      <td>Bacteria</td>\n",
       "      <td>positive</td>\n",
       "      <td>facultative</td>\n",
       "      <td>...</td>\n",
       "      <td>0.447</td>\n",
       "      <td>0.007</td>\n",
       "      <td>23.587</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>engqvist, faprotax, genbank, gold, jemma-refse...</td>\n",
       "      <td>705, 1030, 6289, 13521, 13874, 16639, 19891, 1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14</td>\n",
       "      <td>Dictyoglomus thermophilum</td>\n",
       "      <td>Dictyoglomus</td>\n",
       "      <td>Dictyoglomaceae</td>\n",
       "      <td>Dictyoglomales</td>\n",
       "      <td>Dictyoglomia</td>\n",
       "      <td>Dictyoglomi</td>\n",
       "      <td>Bacteria</td>\n",
       "      <td>negative</td>\n",
       "      <td>anaerobic</td>\n",
       "      <td>...</td>\n",
       "      <td>1.342</td>\n",
       "      <td>0.000</td>\n",
       "      <td>31.086</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.869</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>corkrey, engqvist, genbank, gold, jemma-refseq...</td>\n",
       "      <td>436, 705, 6230, 13521, 14083, 16003, 19891, 19...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19</td>\n",
       "      <td>Pelobacter carbinolicus</td>\n",
       "      <td>Pelobacter</td>\n",
       "      <td>Desulfuromonadaceae</td>\n",
       "      <td>Desulfuromonadales</td>\n",
       "      <td>Deltaproteobacteria</td>\n",
       "      <td>Proteobacteria</td>\n",
       "      <td>Bacteria</td>\n",
       "      <td>negative</td>\n",
       "      <td>anaerobic</td>\n",
       "      <td>...</td>\n",
       "      <td>138769.146</td>\n",
       "      <td>0.000</td>\n",
       "      <td>72.016</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>engqvist, faprotax, genbank, gold, jemma-refse...</td>\n",
       "      <td>705, 760, 5944, 13521, 14696, 15514, 19891, 19...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 79 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   species_taxid                    species         genus  \\\n",
       "0              7   Azorhizobium caulinodans  Azorhizobium   \n",
       "1              9        Buchnera aphidicola      Buchnera   \n",
       "2             11        Cellulomonas gilvus  Cellulomonas   \n",
       "3             14  Dictyoglomus thermophilum  Dictyoglomus   \n",
       "4             19    Pelobacter carbinolicus    Pelobacter   \n",
       "\n",
       "                family               order                class  \\\n",
       "0    Xanthobacteraceae         Rhizobiales  Alphaproteobacteria   \n",
       "1          Erwiniaceae    Enterobacterales  Gammaproteobacteria   \n",
       "2    Cellulomonadaceae       Micrococcales       Actinobacteria   \n",
       "3      Dictyoglomaceae      Dictyoglomales         Dictyoglomia   \n",
       "4  Desulfuromonadaceae  Desulfuromonadales  Deltaproteobacteria   \n",
       "\n",
       "           phylum superkingdom gram_stain   metabolism  ... genome_size.stdev  \\\n",
       "0  Proteobacteria     Bacteria   negative      aerobic  ...             1.000   \n",
       "1  Proteobacteria     Bacteria   negative         None  ...         81529.139   \n",
       "2  Actinobacteria     Bacteria   positive  facultative  ...             0.447   \n",
       "3     Dictyoglomi     Bacteria   negative    anaerobic  ...             1.342   \n",
       "4  Proteobacteria     Bacteria   negative    anaerobic  ...        138769.146   \n",
       "\n",
       "  gc_content.stdev coding_genes.stdev optimum_tmp.stdev optimum_ph.stdev  \\\n",
       "0              NaN              7.572               NaN              NaN   \n",
       "1            1.855             82.983               NaN              NaN   \n",
       "2            0.007             23.587               0.0              NaN   \n",
       "3            0.000             31.086               0.0              NaN   \n",
       "4            0.000             72.016               NaN              NaN   \n",
       "\n",
       "  growth_tmp.stdev rRNA16S_genes.stdev tRNA_genes.stdev  \\\n",
       "0              NaN                 0.0            0.000   \n",
       "1            0.000                 0.0            0.566   \n",
       "2              NaN                 0.0            0.000   \n",
       "3            5.869                 0.0            0.000   \n",
       "4              NaN                 0.0            0.000   \n",
       "\n",
       "                                         data_source  \\\n",
       "0  engqvist, genbank, gold, jemma-refseq, kegg, p...   \n",
       "1  genbank, gold, jemma-refseq, kegg, microbe-dir...   \n",
       "2  engqvist, faprotax, genbank, gold, jemma-refse...   \n",
       "3  corkrey, engqvist, genbank, gold, jemma-refseq...   \n",
       "4  engqvist, faprotax, genbank, gold, jemma-refse...   \n",
       "\n",
       "                                              ref_id  \n",
       "0       705, 6102, 13521, 13643, 15838, 19908, 19956  \n",
       "1  5601, 9112, 9158, 9864, 9910, 10313, 10358, 10...  \n",
       "2  705, 1030, 6289, 13521, 13874, 16639, 19891, 1...  \n",
       "3  436, 705, 6230, 13521, 14083, 16003, 19891, 19...  \n",
       "4  705, 760, 5944, 13521, 14696, 15514, 19891, 19...  \n",
       "\n",
       "[5 rows x 79 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"select * from species_traits\"\n",
    "traits = pd.read_sql(query, engine)\n",
    "traits.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['species_taxid', 'species', 'genus', 'family', 'order', 'class',\n",
       "       'phylum', 'superkingdom', 'gram_stain', 'metabolism', 'pathways',\n",
       "       'carbon_substrates', 'sporulation', 'motility', 'range_tmp',\n",
       "       'range_salinity', 'cell_shape', 'isolation_source', 'd1_lo', 'd1_up',\n",
       "       'd2_lo', 'd2_up', 'doubling_h', 'genome_size', 'gc_content',\n",
       "       'coding_genes', 'optimum_tmp', 'optimum_ph', 'growth_tmp',\n",
       "       'rRNA16S_genes', 'tRNA_genes', 'gram_stain.count', 'metabolism.count',\n",
       "       'pathways.count', 'carbon_substrates.count', 'sporulation.count',\n",
       "       'motility.count', 'range_tmp.count', 'range_salinity.count',\n",
       "       'cell_shape.count', 'isolation_source.count', 'gram_stain.prop',\n",
       "       'metabolism.prop', 'pathways.prop', 'carbon_substrates.prop',\n",
       "       'sporulation.prop', 'motility.prop', 'range_tmp.prop',\n",
       "       'range_salinity.prop', 'cell_shape.prop', 'isolation_source.prop',\n",
       "       'd1_lo.count', 'd1_up.count', 'd2_lo.count', 'd2_up.count',\n",
       "       'doubling_h.count', 'genome_size.count', 'gc_content.count',\n",
       "       'coding_genes.count', 'optimum_tmp.count', 'optimum_ph.count',\n",
       "       'growth_tmp.count', 'rRNA16S_genes.count', 'tRNA_genes.count',\n",
       "       'd1_lo.stdev', 'd1_up.stdev', 'd2_lo.stdev', 'd2_up.stdev',\n",
       "       'doubling_h.stdev', 'genome_size.stdev', 'gc_content.stdev',\n",
       "       'coding_genes.stdev', 'optimum_tmp.stdev', 'optimum_ph.stdev',\n",
       "       'growth_tmp.stdev', 'rRNA16S_genes.stdev', 'tRNA_genes.stdev',\n",
       "       'data_source', 'ref_id'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "traits.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>species_taxid</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rRNA16S_genes</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2.000</th>\n",
       "      <td>316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.000</th>\n",
       "      <td>265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.000</th>\n",
       "      <td>233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.000</th>\n",
       "      <td>207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.000</th>\n",
       "      <td>139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.933</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.900</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.800</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.667</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17.000</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>127 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               species_taxid\n",
       "rRNA16S_genes               \n",
       "2.000                    316\n",
       "3.000                    265\n",
       "4.000                    233\n",
       "1.000                    207\n",
       "6.000                    139\n",
       "...                      ...\n",
       "4.933                      1\n",
       "4.900                      1\n",
       "4.800                      1\n",
       "4.667                      1\n",
       "17.000                     1\n",
       "\n",
       "[127 rows x 1 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trait = 'rRNA16S_genes'\n",
    "traits[['species_taxid', trait]].groupby(trait).count().sort_values('species_taxid', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1740.000000\n",
       "mean        4.295890\n",
       "std         2.745868\n",
       "min         1.000000\n",
       "25%         2.000000\n",
       "50%         4.000000\n",
       "75%         6.000000\n",
       "max        17.000000\n",
       "Name: rRNA16S_genes, dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "traits['rRNA16S_genes'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, ax = plt.subplots(1, 1, figsize=(12, 6))\n",
    "traits[traits['growth_tmp.stdev'].notnull()]['growth_tmp.stdev'].hist(ax=ax, bins=100);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "candidates = traits[\n",
    "    (traits['growth_tmp.stdev'].notnull()) &\n",
    "    (traits['growth_tmp.stdev'] > 0) &\n",
    "    (traits['growth_tmp.stdev'] < 5)\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6       engqvist, genbank, gold, jemma-refseq, kegg, m...\n",
       "7            engqvist, genbank, gold, kegg, patric, rrndb\n",
       "12      engqvist, faprotax, genbank, gold, jemma-refse...\n",
       "17      engqvist, faprotax, genbank, gold, jemma-refse...\n",
       "23      corkrey, engqvist, genbank, gold, jemma-refseq...\n",
       "                              ...                        \n",
       "2164          campedelli, engqvist, genbank, gold, patric\n",
       "2166                                    corkrey, engqvist\n",
       "2171                    corkrey, engqvist, fierer, patric\n",
       "2182                 campedelli, engqvist, fierer, patric\n",
       "2264                      engqvist, genbank, gold, patric\n",
       "Name: data_source, Length: 525, dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "candidates['data_source']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['growth_tmp', 'growth_tmp.count', 'growth_tmp.stdev']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[c for c in traits.columns if 'growth_tmp' in c]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "species       Elizabethkingia meningoseptica\n",
       "growth_tmp                                34\n",
       "Name: 53, dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "candidates.iloc[10][['species', 'growth_tmp']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}