03-22-2019_Number_of_Unique_Genus.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "suppressPackageStartupMessages({\n",
    "    library(tidyverse)\n",
    "    library(reshape2)\n",
    "    library(ggpubr)\n",
    "    library(matrixStats)\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = read.csv(\"all_samples_genus.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>taxa</th><th scope=col>V1V2_HNO17_S8_R1</th><th scope=col>V1V2_HNO19_S9_R1</th><th scope=col>V1V2_HNO209_S14_R1</th><th scope=col>V1V2_HNO223_S15_R1</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>A2            </td><td>0             </td><td>0             </td><td>0             </td><td>0             </td></tr>\n",
       "\t<tr><td>ASF356        </td><td>0             </td><td>0             </td><td>0             </td><td>0             </td></tr>\n",
       "\t<tr><td>Abiotrophia   </td><td>1             </td><td>1             </td><td>0             </td><td>1             </td></tr>\n",
       "\t<tr><td>Acetitomaculum</td><td>0             </td><td>0             </td><td>0             </td><td>0             </td></tr>\n",
       "\t<tr><td>Acholeplasma  </td><td>0             </td><td>0             </td><td>0             </td><td>0             </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllll}\n",
       " taxa & V1V2\\_HNO17\\_S8\\_R1 & V1V2\\_HNO19\\_S9\\_R1 & V1V2\\_HNO209\\_S14\\_R1 & V1V2\\_HNO223\\_S15\\_R1\\\\\n",
       "\\hline\n",
       "\t A2             & 0              & 0              & 0              & 0             \\\\\n",
       "\t ASF356         & 0              & 0              & 0              & 0             \\\\\n",
       "\t Abiotrophia    & 1              & 1              & 0              & 1             \\\\\n",
       "\t Acetitomaculum & 0              & 0              & 0              & 0             \\\\\n",
       "\t Acholeplasma   & 0              & 0              & 0              & 0             \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| taxa | V1V2_HNO17_S8_R1 | V1V2_HNO19_S9_R1 | V1V2_HNO209_S14_R1 | V1V2_HNO223_S15_R1 |\n",
       "|---|---|---|---|---|\n",
       "| A2             | 0              | 0              | 0              | 0              |\n",
       "| ASF356         | 0              | 0              | 0              | 0              |\n",
       "| Abiotrophia    | 1              | 1              | 0              | 1              |\n",
       "| Acetitomaculum | 0              | 0              | 0              | 0              |\n",
       "| Acholeplasma   | 0              | 0              | 0              | 0              |\n",
       "\n"
      ],
      "text/plain": [
       "  taxa           V1V2_HNO17_S8_R1 V1V2_HNO19_S9_R1 V1V2_HNO209_S14_R1\n",
       "1 A2             0                0                0                 \n",
       "2 ASF356         0                0                0                 \n",
       "3 Abiotrophia    1                1                0                 \n",
       "4 Acetitomaculum 0                0                0                 \n",
       "5 Acholeplasma   0                0                0                 \n",
       "  V1V2_HNO223_S15_R1\n",
       "1 0                 \n",
       "2 0                 \n",
       "3 1                 \n",
       "4 0                 \n",
       "5 0                 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[1:5,1:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2_mat = as.matrix( df[-1])\n",
    "rownames(df2_mat) = df$taxa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>'V1V2'</li>\n",
       "\t<li>'V2V3'</li>\n",
       "\t<li>'V3V4'</li>\n",
       "\t<li>'V4V5'</li>\n",
       "\t<li>'V5V7'</li>\n",
       "\t<li>'V7V9'</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'V1V2'\n",
       "\\item 'V2V3'\n",
       "\\item 'V3V4'\n",
       "\\item 'V4V5'\n",
       "\\item 'V5V7'\n",
       "\\item 'V7V9'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'V1V2'\n",
       "2. 'V2V3'\n",
       "3. 'V3V4'\n",
       "4. 'V4V5'\n",
       "5. 'V5V7'\n",
       "6. 'V7V9'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] \"V1V2\" \"V2V3\" \"V3V4\" \"V4V5\" \"V5V7\" \"V7V9\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sets\n",
    "primer_sets = unique( sapply(strsplit(colnames(df2_mat),'_'),'[[',1) )\n",
    "primer_sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"V1V2\"\n",
      "[1] \"V2V3\"\n",
      "[1] \"V3V4\"\n",
      "[1] \"V4V5\"\n",
      "[1] \"V5V7\"\n",
      "[1] \"V7V9\"\n"
     ]
    }
   ],
   "source": [
    "new_df = list();\n",
    "for( i in 1:length(primer_sets) ){\n",
    "    primer= primer_sets[i]\n",
    "    print(primer)\n",
    "    new_df[[primer]] = rowMeans(df2_mat[ , grep( primer, colnames(df2_mat))])\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df = as.data.frame( new_df )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>V1V2</th><th scope=col>V2V3</th><th scope=col>V3V4</th><th scope=col>V4V5</th><th scope=col>V5V7</th><th scope=col>V7V9</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>A2</th><td>0.08695652</td><td>0.13043478</td><td>0.04347826</td><td>0.13043478</td><td>0.04347826</td><td>0.04347826</td></tr>\n",
       "\t<tr><th scope=row>ASF356</th><td>0.08695652</td><td>0.04347826</td><td>0.04347826</td><td>0.08695652</td><td>0.04347826</td><td>0.04347826</td></tr>\n",
       "\t<tr><th scope=row>Abiotrophia</th><td>0.52173913</td><td>0.08695652</td><td>0.34782609</td><td>0.34782609</td><td>0.39130435</td><td>0.00000000</td></tr>\n",
       "\t<tr><th scope=row>Acetitomaculum</th><td>0.00000000</td><td>0.04347826</td><td>0.00000000</td><td>0.00000000</td><td>0.00000000</td><td>0.00000000</td></tr>\n",
       "\t<tr><th scope=row>Acholeplasma</th><td>0.04347826</td><td>0.04347826</td><td>0.04347826</td><td>0.04347826</td><td>0.04347826</td><td>0.00000000</td></tr>\n",
       "\t<tr><th scope=row>Acinetobacter</th><td>0.00000000</td><td>0.04347826</td><td>0.00000000</td><td>0.00000000</td><td>0.00000000</td><td>0.00000000</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllll}\n",
       "  & V1V2 & V2V3 & V3V4 & V4V5 & V5V7 & V7V9\\\\\n",
       "\\hline\n",
       "\tA2 & 0.08695652 & 0.13043478 & 0.04347826 & 0.13043478 & 0.04347826 & 0.04347826\\\\\n",
       "\tASF356 & 0.08695652 & 0.04347826 & 0.04347826 & 0.08695652 & 0.04347826 & 0.04347826\\\\\n",
       "\tAbiotrophia & 0.52173913 & 0.08695652 & 0.34782609 & 0.34782609 & 0.39130435 & 0.00000000\\\\\n",
       "\tAcetitomaculum & 0.00000000 & 0.04347826 & 0.00000000 & 0.00000000 & 0.00000000 & 0.00000000\\\\\n",
       "\tAcholeplasma & 0.04347826 & 0.04347826 & 0.04347826 & 0.04347826 & 0.04347826 & 0.00000000\\\\\n",
       "\tAcinetobacter & 0.00000000 & 0.04347826 & 0.00000000 & 0.00000000 & 0.00000000 & 0.00000000\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | V1V2 | V2V3 | V3V4 | V4V5 | V5V7 | V7V9 |\n",
       "|---|---|---|---|---|---|---|\n",
       "| A2 | 0.08695652 | 0.13043478 | 0.04347826 | 0.13043478 | 0.04347826 | 0.04347826 |\n",
       "| ASF356 | 0.08695652 | 0.04347826 | 0.04347826 | 0.08695652 | 0.04347826 | 0.04347826 |\n",
       "| Abiotrophia | 0.52173913 | 0.08695652 | 0.34782609 | 0.34782609 | 0.39130435 | 0.00000000 |\n",
       "| Acetitomaculum | 0.00000000 | 0.04347826 | 0.00000000 | 0.00000000 | 0.00000000 | 0.00000000 |\n",
       "| Acholeplasma | 0.04347826 | 0.04347826 | 0.04347826 | 0.04347826 | 0.04347826 | 0.00000000 |\n",
       "| Acinetobacter | 0.00000000 | 0.04347826 | 0.00000000 | 0.00000000 | 0.00000000 | 0.00000000 |\n",
       "\n"
      ],
      "text/plain": [
       "               V1V2       V2V3       V3V4       V4V5       V5V7      \n",
       "A2             0.08695652 0.13043478 0.04347826 0.13043478 0.04347826\n",
       "ASF356         0.08695652 0.04347826 0.04347826 0.08695652 0.04347826\n",
       "Abiotrophia    0.52173913 0.08695652 0.34782609 0.34782609 0.39130435\n",
       "Acetitomaculum 0.00000000 0.04347826 0.00000000 0.00000000 0.00000000\n",
       "Acholeplasma   0.04347826 0.04347826 0.04347826 0.04347826 0.04347826\n",
       "Acinetobacter  0.00000000 0.04347826 0.00000000 0.00000000 0.00000000\n",
       "               V7V9      \n",
       "A2             0.04347826\n",
       "ASF356         0.04347826\n",
       "Abiotrophia    0.00000000\n",
       "Acetitomaculum 0.00000000\n",
       "Acholeplasma   0.00000000\n",
       "Acinetobacter  0.00000000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_df %>% head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "length( unique((rownames(new_df[ new_df$V1V2 !=0,][\"V1V2\"]))) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "length( unique((rownames(new_df[ new_df$V2V3 !=0,][\"V2V3\"]))) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "length( unique((rownames(new_df[ new_df$V4V5 !=0,][\"V4V5\"]))) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "length( unique((rownames(new_df[ new_df$V3V4 !=0,][\"V3V4\"]))) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "length( unique((rownames(new_df[ new_df$V5V7 !=0,][\"V5V7\"]))) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/latex": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "length( unique((rownames(new_df[ new_df$V5V7 !=0,][\"V7V9\"]))) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "v1v2 = length( unique((rownames(new_df[ new_df$V1V2 !=0,][\"V1V2\"]))) )\n",
    "v2v3 = length( unique((rownames(new_df[ new_df$V2V3 !=0,][\"V2V3\"]))) )\n",
    "v3v4 = length( unique((rownames(new_df[ new_df$V3V4 !=0,][\"V3V4\"]))) )\n",
    "v4v5 = length( unique((rownames(new_df[ new_df$V4V5 !=0,][\"V4V5\"]))) )\n",
    "v5v7 = length( unique((rownames(new_df[ new_df$V5V7 !=0,][\"V5V7\"]))) )\n",
    "v7v9 = length( unique((rownames(new_df[ new_df$V5V7 !=0,][\"V7V9\"]))) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IBSU9QhvP/++ydPntzS0rJ+/fqDBw/2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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.height=4, repr.plot.width=5)\n",
    "barplot( cbind( v1v2, v2v3, v3v4, v4v5, v5v7, v7v9 ) , las=1)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R 3.5",
   "language": "R",
   "name": "ir35"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}