code/MBD_samples_genetic_analysisHCSS_5x.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Notebook for integrating methylation and genetic data  \n",
    "5x filtering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Attaching package: ‘dplyr’\n",
      "\n",
      "\n",
      "The following objects are masked from ‘package:stats’:\n",
      "\n",
      "    filter, lag\n",
      "\n",
      "\n",
      "The following objects are masked from ‘package:base’:\n",
      "\n",
      "    intersect, setdiff, setequal, union\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "library(spaa) #for correlating distance matrices\n",
    "library(dplyr) #distinct function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "kernel": "SoS"
   },
   "outputs": [],
   "source": [
    "SUFFIX = '../analyses/2bRAD/Inputs/HCSS_Afilt32m70_01'\n",
    "POP = '../analyses/2bRAD/PopGen/HCSS_Afilt32m70_01'\n",
    "INPUT = '../analyses/2bRAD/Inputs/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#function to run bash commands cleanly\n",
    "js <- function(shell_command){\n",
    "    cat(system(shell_command, intern=TRUE), sep='\\n')}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "# Correlate genetic and epigenetic distances"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "bash"
   },
   "source": [
    "### For all filtered MBD sites"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "kernel": "Bash"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\"SeqNum.row\",\"SeqNum.col\",\"dist.manh\",\"SampNum.row\",\"SampNum.col\"\n",
      "1,1,0,\"hc1_2\",\"hc1_2\"\n",
      "1,15,2627987.64277046,\"ss3_15\",\"hc1_2\"\n",
      "1,3,2759915.80320949,\"hc2_15\",\"hc1_2\"\n",
      "1,17,2729476.30606038,\"ss3_20\",\"hc1_2\"\n",
      "1,4,2673543.05735453,\"hc2_17\",\"hc1_2\"\n",
      "1,2,2833290.83588266,\"hc1_4\",\"hc1_2\"\n",
      "1,7,2757435.40167295,\"hc3_7\",\"hc1_2\"\n",
      "1,16,2684751.21460783,\"ss3_16\",\"hc1_2\"\n",
      "1,14,2633055.70898392,\"ss3_14\",\"hc1_2\"\n"
     ]
    }
   ],
   "source": [
    "js(\"head ../analyses/methylation-filtered/dist.manhat.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "kernel": "R"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A matrix: 18 x 18 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>hc1_2</th><th scope=col>hc1_4</th><th scope=col>hc2_15</th><th scope=col>hc2_17</th><th scope=col>hc3_1</th><th scope=col>hc3_5</th><th scope=col>hc3_7</th><th scope=col>hc3_10</th><th scope=col>hc3_11</th><th scope=col>ss2_9</th><th scope=col>ss2_14</th><th scope=col>ss2_18</th><th scope=col>ss3_3</th><th scope=col>ss3_14</th><th scope=col>ss3_15</th><th scope=col>ss3_16</th><th scope=col>ss3_20</th><th scope=col>ss5_18</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>hc1_2</th><td>      0</td><td>2833291</td><td>2759916</td><td>2673543</td><td>3086492</td><td>2747278</td><td>2757435</td><td>2596022</td><td>2886681</td><td>2864741</td><td>2816730</td><td>3174977</td><td>2673996</td><td>2633056</td><td>2627988</td><td>2684751</td><td>2729476</td><td>2805908</td></tr>\n",
       "\t<tr><th scope=row>hc1_4</th><td>2833291</td><td>      0</td><td>2752457</td><td>2607724</td><td>3034234</td><td>2984429</td><td>2714547</td><td>2749810</td><td>2935785</td><td>2941081</td><td>2921644</td><td>3186792</td><td>2748865</td><td>2722953</td><td>2725073</td><td>2774236</td><td>2794243</td><td>2838277</td></tr>\n",
       "\t<tr><th scope=row>hc2_15</th><td>2759916</td><td>2752457</td><td>      0</td><td>2553404</td><td>3004363</td><td>2952151</td><td>2599571</td><td>2689695</td><td>2898743</td><td>2865744</td><td>2806041</td><td>3206072</td><td>2675564</td><td>2630509</td><td>2625498</td><td>2683810</td><td>2742176</td><td>2802118</td></tr>\n",
       "\t<tr><th scope=row>hc2_17</th><td>2673543</td><td>2607724</td><td>2553404</td><td>      0</td><td>2992638</td><td>2838985</td><td>2565697</td><td>2523146</td><td>2854242</td><td>2765300</td><td>2684976</td><td>3174111</td><td>2578505</td><td>2532236</td><td>2455074</td><td>2586647</td><td>2595118</td><td>2717203</td></tr>\n",
       "\t<tr><th scope=row>hc3_1</th><td>3086492</td><td>3034234</td><td>3004363</td><td>2992638</td><td>      0</td><td>3199622</td><td>2832592</td><td>3074458</td><td>3098225</td><td>3179056</td><td>3217016</td><td>3272867</td><td>3016188</td><td>2990982</td><td>3038705</td><td>3050749</td><td>3075162</td><td>3050227</td></tr>\n",
       "\t<tr><th scope=row>hc3_5</th><td>2747278</td><td>2984429</td><td>2952151</td><td>2838985</td><td>3199622</td><td>      0</td><td>2969627</td><td>2732917</td><td>3070521</td><td>3065595</td><td>3014492</td><td>3298669</td><td>2906879</td><td>2875409</td><td>2833640</td><td>2917771</td><td>2910262</td><td>2972994</td></tr>\n",
       "\t<tr><th scope=row>hc3_7</th><td>2757435</td><td>2714547</td><td>2599571</td><td>2565697</td><td>2832592</td><td>2969627</td><td>      0</td><td>2713179</td><td>2867592</td><td>2850234</td><td>2829048</td><td>3169766</td><td>2644625</td><td>2611975</td><td>2626724</td><td>2666984</td><td>2743122</td><td>2787017</td></tr>\n",
       "\t<tr><th scope=row>hc3_10</th><td>2596022</td><td>2749810</td><td>2689695</td><td>2523146</td><td>3074458</td><td>2732917</td><td>2713179</td><td>      0</td><td>2908119</td><td>2815371</td><td>2693052</td><td>3197533</td><td>2629587</td><td>2572926</td><td>2471854</td><td>2611991</td><td>2611915</td><td>2759254</td></tr>\n",
       "\t<tr><th scope=row>hc3_11</th><td>2886681</td><td>2935785</td><td>2898743</td><td>2854242</td><td>3098225</td><td>3070521</td><td>2867592</td><td>2908119</td><td>      0</td><td>2937149</td><td>3018080</td><td>3135082</td><td>2755049</td><td>2732252</td><td>2822339</td><td>2790160</td><td>2853524</td><td>2831075</td></tr>\n",
       "\t<tr><th scope=row>ss2_9</th><td>2864741</td><td>2941081</td><td>2865744</td><td>2765300</td><td>3179056</td><td>3065595</td><td>2850234</td><td>2815371</td><td>2937149</td><td>      0</td><td>2631597</td><td>3058748</td><td>2498396</td><td>2494164</td><td>2613992</td><td>2525104</td><td>2682758</td><td>2702466</td></tr>\n",
       "\t<tr><th scope=row>ss2_14</th><td>2816730</td><td>2921644</td><td>2806041</td><td>2684976</td><td>3217016</td><td>3014492</td><td>2829048</td><td>2693052</td><td>3018080</td><td>2631597</td><td>      0</td><td>3211835</td><td>2488742</td><td>2486496</td><td>2471957</td><td>2456093</td><td>2637131</td><td>2757089</td></tr>\n",
       "\t<tr><th scope=row>ss2_18</th><td>3174977</td><td>3186792</td><td>3206072</td><td>3174111</td><td>3272867</td><td>3298669</td><td>3169766</td><td>3197533</td><td>3135082</td><td>3058748</td><td>3211835</td><td>      0</td><td>2828320</td><td>2874923</td><td>3101936</td><td>2988620</td><td>3086742</td><td>2826010</td></tr>\n",
       "\t<tr><th scope=row>ss3_3</th><td>2673996</td><td>2748865</td><td>2675564</td><td>2578505</td><td>3016188</td><td>2906879</td><td>2644625</td><td>2629587</td><td>2755049</td><td>2498396</td><td>2488742</td><td>2828320</td><td>      0</td><td>2244069</td><td>2395720</td><td>2285880</td><td>2509225</td><td>2374360</td></tr>\n",
       "\t<tr><th scope=row>ss3_14</th><td>2633056</td><td>2722953</td><td>2630509</td><td>2532236</td><td>2990982</td><td>2875409</td><td>2611975</td><td>2572926</td><td>2732252</td><td>2494164</td><td>2486496</td><td>2874923</td><td>2244069</td><td>      0</td><td>2331956</td><td>2237257</td><td>2475793</td><td>2415717</td></tr>\n",
       "\t<tr><th scope=row>ss3_15</th><td>2627988</td><td>2725073</td><td>2625498</td><td>2455074</td><td>3038705</td><td>2833640</td><td>2626724</td><td>2471854</td><td>2822339</td><td>2613992</td><td>2471957</td><td>3101936</td><td>2395720</td><td>2331956</td><td>      0</td><td>2360985</td><td>2441239</td><td>2602130</td></tr>\n",
       "\t<tr><th scope=row>ss3_16</th><td>2684751</td><td>2774236</td><td>2683810</td><td>2586647</td><td>3050749</td><td>2917771</td><td>2666984</td><td>2611991</td><td>2790160</td><td>2525104</td><td>2456093</td><td>2988620</td><td>2285880</td><td>2237257</td><td>2360985</td><td>      0</td><td>2508080</td><td>2520673</td></tr>\n",
       "\t<tr><th scope=row>ss3_20</th><td>2729476</td><td>2794243</td><td>2742176</td><td>2595118</td><td>3075162</td><td>2910262</td><td>2743122</td><td>2611915</td><td>2853524</td><td>2682758</td><td>2637131</td><td>3086742</td><td>2509225</td><td>2475793</td><td>2441239</td><td>2508080</td><td>      0</td><td>2657703</td></tr>\n",
       "\t<tr><th scope=row>ss5_18</th><td>2805908</td><td>2838277</td><td>2802118</td><td>2717203</td><td>3050227</td><td>2972994</td><td>2787017</td><td>2759254</td><td>2831075</td><td>2702466</td><td>2757089</td><td>2826010</td><td>2374360</td><td>2415717</td><td>2602130</td><td>2520673</td><td>2657703</td><td>      0</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 18 x 18 of type dbl\n",
       "\\begin{tabular}{r|llllllllllllllllll}\n",
       "  & hc1\\_2 & hc1\\_4 & hc2\\_15 & hc2\\_17 & hc3\\_1 & hc3\\_5 & hc3\\_7 & hc3\\_10 & hc3\\_11 & ss2\\_9 & ss2\\_14 & ss2\\_18 & ss3\\_3 & ss3\\_14 & ss3\\_15 & ss3\\_16 & ss3\\_20 & ss5\\_18\\\\\n",
       "\\hline\n",
       "\thc1\\_2 &       0 & 2833291 & 2759916 & 2673543 & 3086492 & 2747278 & 2757435 & 2596022 & 2886681 & 2864741 & 2816730 & 3174977 & 2673996 & 2633056 & 2627988 & 2684751 & 2729476 & 2805908\\\\\n",
       "\thc1\\_4 & 2833291 &       0 & 2752457 & 2607724 & 3034234 & 2984429 & 2714547 & 2749810 & 2935785 & 2941081 & 2921644 & 3186792 & 2748865 & 2722953 & 2725073 & 2774236 & 2794243 & 2838277\\\\\n",
       "\thc2\\_15 & 2759916 & 2752457 &       0 & 2553404 & 3004363 & 2952151 & 2599571 & 2689695 & 2898743 & 2865744 & 2806041 & 3206072 & 2675564 & 2630509 & 2625498 & 2683810 & 2742176 & 2802118\\\\\n",
       "\thc2\\_17 & 2673543 & 2607724 & 2553404 &       0 & 2992638 & 2838985 & 2565697 & 2523146 & 2854242 & 2765300 & 2684976 & 3174111 & 2578505 & 2532236 & 2455074 & 2586647 & 2595118 & 2717203\\\\\n",
       "\thc3\\_1 & 3086492 & 3034234 & 3004363 & 2992638 &       0 & 3199622 & 2832592 & 3074458 & 3098225 & 3179056 & 3217016 & 3272867 & 3016188 & 2990982 & 3038705 & 3050749 & 3075162 & 3050227\\\\\n",
       "\thc3\\_5 & 2747278 & 2984429 & 2952151 & 2838985 & 3199622 &       0 & 2969627 & 2732917 & 3070521 & 3065595 & 3014492 & 3298669 & 2906879 & 2875409 & 2833640 & 2917771 & 2910262 & 2972994\\\\\n",
       "\thc3\\_7 & 2757435 & 2714547 & 2599571 & 2565697 & 2832592 & 2969627 &       0 & 2713179 & 2867592 & 2850234 & 2829048 & 3169766 & 2644625 & 2611975 & 2626724 & 2666984 & 2743122 & 2787017\\\\\n",
       "\thc3\\_10 & 2596022 & 2749810 & 2689695 & 2523146 & 3074458 & 2732917 & 2713179 &       0 & 2908119 & 2815371 & 2693052 & 3197533 & 2629587 & 2572926 & 2471854 & 2611991 & 2611915 & 2759254\\\\\n",
       "\thc3\\_11 & 2886681 & 2935785 & 2898743 & 2854242 & 3098225 & 3070521 & 2867592 & 2908119 &       0 & 2937149 & 3018080 & 3135082 & 2755049 & 2732252 & 2822339 & 2790160 & 2853524 & 2831075\\\\\n",
       "\tss2\\_9 & 2864741 & 2941081 & 2865744 & 2765300 & 3179056 & 3065595 & 2850234 & 2815371 & 2937149 &       0 & 2631597 & 3058748 & 2498396 & 2494164 & 2613992 & 2525104 & 2682758 & 2702466\\\\\n",
       "\tss2\\_14 & 2816730 & 2921644 & 2806041 & 2684976 & 3217016 & 3014492 & 2829048 & 2693052 & 3018080 & 2631597 &       0 & 3211835 & 2488742 & 2486496 & 2471957 & 2456093 & 2637131 & 2757089\\\\\n",
       "\tss2\\_18 & 3174977 & 3186792 & 3206072 & 3174111 & 3272867 & 3298669 & 3169766 & 3197533 & 3135082 & 3058748 & 3211835 &       0 & 2828320 & 2874923 & 3101936 & 2988620 & 3086742 & 2826010\\\\\n",
       "\tss3\\_3 & 2673996 & 2748865 & 2675564 & 2578505 & 3016188 & 2906879 & 2644625 & 2629587 & 2755049 & 2498396 & 2488742 & 2828320 &       0 & 2244069 & 2395720 & 2285880 & 2509225 & 2374360\\\\\n",
       "\tss3\\_14 & 2633056 & 2722953 & 2630509 & 2532236 & 2990982 & 2875409 & 2611975 & 2572926 & 2732252 & 2494164 & 2486496 & 2874923 & 2244069 &       0 & 2331956 & 2237257 & 2475793 & 2415717\\\\\n",
       "\tss3\\_15 & 2627988 & 2725073 & 2625498 & 2455074 & 3038705 & 2833640 & 2626724 & 2471854 & 2822339 & 2613992 & 2471957 & 3101936 & 2395720 & 2331956 &       0 & 2360985 & 2441239 & 2602130\\\\\n",
       "\tss3\\_16 & 2684751 & 2774236 & 2683810 & 2586647 & 3050749 & 2917771 & 2666984 & 2611991 & 2790160 & 2525104 & 2456093 & 2988620 & 2285880 & 2237257 & 2360985 &       0 & 2508080 & 2520673\\\\\n",
       "\tss3\\_20 & 2729476 & 2794243 & 2742176 & 2595118 & 3075162 & 2910262 & 2743122 & 2611915 & 2853524 & 2682758 & 2637131 & 3086742 & 2509225 & 2475793 & 2441239 & 2508080 &       0 & 2657703\\\\\n",
       "\tss5\\_18 & 2805908 & 2838277 & 2802118 & 2717203 & 3050227 & 2972994 & 2787017 & 2759254 & 2831075 & 2702466 & 2757089 & 2826010 & 2374360 & 2415717 & 2602130 & 2520673 & 2657703 &       0\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 18 x 18 of type dbl\n",
       "\n",
       "| <!--/--> | hc1_2 | hc1_4 | hc2_15 | hc2_17 | hc3_1 | hc3_5 | hc3_7 | hc3_10 | hc3_11 | ss2_9 | ss2_14 | ss2_18 | ss3_3 | ss3_14 | ss3_15 | ss3_16 | ss3_20 | ss5_18 |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| hc1_2 |       0 | 2833291 | 2759916 | 2673543 | 3086492 | 2747278 | 2757435 | 2596022 | 2886681 | 2864741 | 2816730 | 3174977 | 2673996 | 2633056 | 2627988 | 2684751 | 2729476 | 2805908 |\n",
       "| hc1_4 | 2833291 |       0 | 2752457 | 2607724 | 3034234 | 2984429 | 2714547 | 2749810 | 2935785 | 2941081 | 2921644 | 3186792 | 2748865 | 2722953 | 2725073 | 2774236 | 2794243 | 2838277 |\n",
       "| hc2_15 | 2759916 | 2752457 |       0 | 2553404 | 3004363 | 2952151 | 2599571 | 2689695 | 2898743 | 2865744 | 2806041 | 3206072 | 2675564 | 2630509 | 2625498 | 2683810 | 2742176 | 2802118 |\n",
       "| hc2_17 | 2673543 | 2607724 | 2553404 |       0 | 2992638 | 2838985 | 2565697 | 2523146 | 2854242 | 2765300 | 2684976 | 3174111 | 2578505 | 2532236 | 2455074 | 2586647 | 2595118 | 2717203 |\n",
       "| hc3_1 | 3086492 | 3034234 | 3004363 | 2992638 |       0 | 3199622 | 2832592 | 3074458 | 3098225 | 3179056 | 3217016 | 3272867 | 3016188 | 2990982 | 3038705 | 3050749 | 3075162 | 3050227 |\n",
       "| hc3_5 | 2747278 | 2984429 | 2952151 | 2838985 | 3199622 |       0 | 2969627 | 2732917 | 3070521 | 3065595 | 3014492 | 3298669 | 2906879 | 2875409 | 2833640 | 2917771 | 2910262 | 2972994 |\n",
       "| hc3_7 | 2757435 | 2714547 | 2599571 | 2565697 | 2832592 | 2969627 |       0 | 2713179 | 2867592 | 2850234 | 2829048 | 3169766 | 2644625 | 2611975 | 2626724 | 2666984 | 2743122 | 2787017 |\n",
       "| hc3_10 | 2596022 | 2749810 | 2689695 | 2523146 | 3074458 | 2732917 | 2713179 |       0 | 2908119 | 2815371 | 2693052 | 3197533 | 2629587 | 2572926 | 2471854 | 2611991 | 2611915 | 2759254 |\n",
       "| hc3_11 | 2886681 | 2935785 | 2898743 | 2854242 | 3098225 | 3070521 | 2867592 | 2908119 |       0 | 2937149 | 3018080 | 3135082 | 2755049 | 2732252 | 2822339 | 2790160 | 2853524 | 2831075 |\n",
       "| ss2_9 | 2864741 | 2941081 | 2865744 | 2765300 | 3179056 | 3065595 | 2850234 | 2815371 | 2937149 |       0 | 2631597 | 3058748 | 2498396 | 2494164 | 2613992 | 2525104 | 2682758 | 2702466 |\n",
       "| ss2_14 | 2816730 | 2921644 | 2806041 | 2684976 | 3217016 | 3014492 | 2829048 | 2693052 | 3018080 | 2631597 |       0 | 3211835 | 2488742 | 2486496 | 2471957 | 2456093 | 2637131 | 2757089 |\n",
       "| ss2_18 | 3174977 | 3186792 | 3206072 | 3174111 | 3272867 | 3298669 | 3169766 | 3197533 | 3135082 | 3058748 | 3211835 |       0 | 2828320 | 2874923 | 3101936 | 2988620 | 3086742 | 2826010 |\n",
       "| ss3_3 | 2673996 | 2748865 | 2675564 | 2578505 | 3016188 | 2906879 | 2644625 | 2629587 | 2755049 | 2498396 | 2488742 | 2828320 |       0 | 2244069 | 2395720 | 2285880 | 2509225 | 2374360 |\n",
       "| ss3_14 | 2633056 | 2722953 | 2630509 | 2532236 | 2990982 | 2875409 | 2611975 | 2572926 | 2732252 | 2494164 | 2486496 | 2874923 | 2244069 |       0 | 2331956 | 2237257 | 2475793 | 2415717 |\n",
       "| ss3_15 | 2627988 | 2725073 | 2625498 | 2455074 | 3038705 | 2833640 | 2626724 | 2471854 | 2822339 | 2613992 | 2471957 | 3101936 | 2395720 | 2331956 |       0 | 2360985 | 2441239 | 2602130 |\n",
       "| ss3_16 | 2684751 | 2774236 | 2683810 | 2586647 | 3050749 | 2917771 | 2666984 | 2611991 | 2790160 | 2525104 | 2456093 | 2988620 | 2285880 | 2237257 | 2360985 |       0 | 2508080 | 2520673 |\n",
       "| ss3_20 | 2729476 | 2794243 | 2742176 | 2595118 | 3075162 | 2910262 | 2743122 | 2611915 | 2853524 | 2682758 | 2637131 | 3086742 | 2509225 | 2475793 | 2441239 | 2508080 |       0 | 2657703 |\n",
       "| ss5_18 | 2805908 | 2838277 | 2802118 | 2717203 | 3050227 | 2972994 | 2787017 | 2759254 | 2831075 | 2702466 | 2757089 | 2826010 | 2374360 | 2415717 | 2602130 | 2520673 | 2657703 |       0 |\n",
       "\n"
      ],
      "text/plain": [
       "       hc1_2   hc1_4   hc2_15  hc2_17  hc3_1   hc3_5   hc3_7   hc3_10  hc3_11 \n",
       "hc1_2        0 2833291 2759916 2673543 3086492 2747278 2757435 2596022 2886681\n",
       "hc1_4  2833291       0 2752457 2607724 3034234 2984429 2714547 2749810 2935785\n",
       "hc2_15 2759916 2752457       0 2553404 3004363 2952151 2599571 2689695 2898743\n",
       "hc2_17 2673543 2607724 2553404       0 2992638 2838985 2565697 2523146 2854242\n",
       "hc3_1  3086492 3034234 3004363 2992638       0 3199622 2832592 3074458 3098225\n",
       "hc3_5  2747278 2984429 2952151 2838985 3199622       0 2969627 2732917 3070521\n",
       "hc3_7  2757435 2714547 2599571 2565697 2832592 2969627       0 2713179 2867592\n",
       "hc3_10 2596022 2749810 2689695 2523146 3074458 2732917 2713179       0 2908119\n",
       "hc3_11 2886681 2935785 2898743 2854242 3098225 3070521 2867592 2908119       0\n",
       "ss2_9  2864741 2941081 2865744 2765300 3179056 3065595 2850234 2815371 2937149\n",
       "ss2_14 2816730 2921644 2806041 2684976 3217016 3014492 2829048 2693052 3018080\n",
       "ss2_18 3174977 3186792 3206072 3174111 3272867 3298669 3169766 3197533 3135082\n",
       "ss3_3  2673996 2748865 2675564 2578505 3016188 2906879 2644625 2629587 2755049\n",
       "ss3_14 2633056 2722953 2630509 2532236 2990982 2875409 2611975 2572926 2732252\n",
       "ss3_15 2627988 2725073 2625498 2455074 3038705 2833640 2626724 2471854 2822339\n",
       "ss3_16 2684751 2774236 2683810 2586647 3050749 2917771 2666984 2611991 2790160\n",
       "ss3_20 2729476 2794243 2742176 2595118 3075162 2910262 2743122 2611915 2853524\n",
       "ss5_18 2805908 2838277 2802118 2717203 3050227 2972994 2787017 2759254 2831075\n",
       "       ss2_9   ss2_14  ss2_18  ss3_3   ss3_14  ss3_15  ss3_16  ss3_20  ss5_18 \n",
       "hc1_2  2864741 2816730 3174977 2673996 2633056 2627988 2684751 2729476 2805908\n",
       "hc1_4  2941081 2921644 3186792 2748865 2722953 2725073 2774236 2794243 2838277\n",
       "hc2_15 2865744 2806041 3206072 2675564 2630509 2625498 2683810 2742176 2802118\n",
       "hc2_17 2765300 2684976 3174111 2578505 2532236 2455074 2586647 2595118 2717203\n",
       "hc3_1  3179056 3217016 3272867 3016188 2990982 3038705 3050749 3075162 3050227\n",
       "hc3_5  3065595 3014492 3298669 2906879 2875409 2833640 2917771 2910262 2972994\n",
       "hc3_7  2850234 2829048 3169766 2644625 2611975 2626724 2666984 2743122 2787017\n",
       "hc3_10 2815371 2693052 3197533 2629587 2572926 2471854 2611991 2611915 2759254\n",
       "hc3_11 2937149 3018080 3135082 2755049 2732252 2822339 2790160 2853524 2831075\n",
       "ss2_9        0 2631597 3058748 2498396 2494164 2613992 2525104 2682758 2702466\n",
       "ss2_14 2631597       0 3211835 2488742 2486496 2471957 2456093 2637131 2757089\n",
       "ss2_18 3058748 3211835       0 2828320 2874923 3101936 2988620 3086742 2826010\n",
       "ss3_3  2498396 2488742 2828320       0 2244069 2395720 2285880 2509225 2374360\n",
       "ss3_14 2494164 2486496 2874923 2244069       0 2331956 2237257 2475793 2415717\n",
       "ss3_15 2613992 2471957 3101936 2395720 2331956       0 2360985 2441239 2602130\n",
       "ss3_16 2525104 2456093 2988620 2285880 2237257 2360985       0 2508080 2520673\n",
       "ss3_20 2682758 2637131 3086742 2509225 2475793 2441239 2508080       0 2657703\n",
       "ss5_18 2702466 2757089 2826010 2374360 2415717 2602130 2520673 2657703       0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#read in epigenetic Manhattan distance, 5x coverage \n",
    "ep5 <-read.csv(\"../analyses/methylation-filtered/dist.manhat.csv\",header = T)\n",
    "ep5 <- ep5[,c(\"SampNum.row\",\"SampNum.col\",\"dist.manh\")]\n",
    "ep5 <- as.matrix(list2dist(ep5))\n",
    "mbdorder = c(\"hc1_2\",\"hc1_4\",\"hc2_15\",\"hc2_17\",\"hc3_1\",\"hc3_5\",\"hc3_7\",\"hc3_10\",\"hc3_11\",\n",
    "        \"ss2_9\",\"ss2_14\",\"ss2_18\",\"ss3_3\",\"ss3_14\",\"ss3_15\",\"ss3_16\",\"ss3_20\",\"ss5_18\")\n",
    "ep5 <- ep5[mbdorder, mbdorder]\n",
    "ep5\n",
    "Dep5 <- as.dist(ep5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "         hc1_2   hc1_4  hc2_15  hc2_17   hc3_1   hc3_5   hc3_7  hc3_10  hc3_11\n",
       "hc1_4  2833291                                                                \n",
       "hc2_15 2759916 2752457                                                        \n",
       "hc2_17 2673543 2607724 2553404                                                \n",
       "hc3_1  3086492 3034234 3004363 2992638                                        \n",
       "hc3_5  2747278 2984429 2952151 2838985 3199622                                \n",
       "hc3_7  2757435 2714547 2599571 2565697 2832592 2969627                        \n",
       "hc3_10 2596022 2749810 2689695 2523146 3074458 2732917 2713179                \n",
       "hc3_11 2886681 2935785 2898743 2854242 3098225 3070521 2867592 2908119        \n",
       "ss2_9  2864741 2941081 2865744 2765300 3179056 3065595 2850234 2815371 2937149\n",
       "ss2_14 2816730 2921644 2806041 2684976 3217016 3014492 2829048 2693052 3018080\n",
       "ss2_18 3174977 3186792 3206072 3174111 3272867 3298669 3169766 3197533 3135082\n",
       "ss3_3  2673996 2748865 2675564 2578505 3016188 2906879 2644625 2629587 2755049\n",
       "ss3_14 2633056 2722953 2630509 2532236 2990982 2875409 2611975 2572926 2732252\n",
       "ss3_15 2627988 2725073 2625498 2455074 3038705 2833640 2626724 2471854 2822339\n",
       "ss3_16 2684751 2774236 2683810 2586647 3050749 2917771 2666984 2611991 2790160\n",
       "ss3_20 2729476 2794243 2742176 2595118 3075162 2910262 2743122 2611915 2853524\n",
       "ss5_18 2805908 2838277 2802118 2717203 3050227 2972994 2787017 2759254 2831075\n",
       "         ss2_9  ss2_14  ss2_18   ss3_3  ss3_14  ss3_15  ss3_16  ss3_20\n",
       "hc1_4                                                                 \n",
       "hc2_15                                                                \n",
       "hc2_17                                                                \n",
       "hc3_1                                                                 \n",
       "hc3_5                                                                 \n",
       "hc3_7                                                                 \n",
       "hc3_10                                                                \n",
       "hc3_11                                                                \n",
       "ss2_9                                                                 \n",
       "ss2_14 2631597                                                        \n",
       "ss2_18 3058748 3211835                                                \n",
       "ss3_3  2498396 2488742 2828320                                        \n",
       "ss3_14 2494164 2486496 2874923 2244069                                \n",
       "ss3_15 2613992 2471957 3101936 2395720 2331956                        \n",
       "ss3_16 2525104 2456093 2988620 2285880 2237257 2360985                \n",
       "ss3_20 2682758 2637131 3086742 2509225 2475793 2441239 2508080        \n",
       "ss5_18 2702466 2757089 2826010 2374360 2415717 2602130 2520673 2657703"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Dep5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "kernel": "R"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 18 x 18</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>hc1_2</th><th scope=col>hc1_4</th><th scope=col>hc2_15</th><th scope=col>hc2_17</th><th scope=col>hc3_1</th><th scope=col>hc3_5</th><th scope=col>hc3_7</th><th scope=col>hc3_10</th><th scope=col>hc3_11</th><th scope=col>ss2_9</th><th scope=col>ss2_14</th><th scope=col>ss2_18</th><th scope=col>ss3_3</th><th scope=col>ss3_14</th><th scope=col>ss3_15</th><th scope=col>ss3_16</th><th scope=col>ss3_20</th><th scope=col>ss5_18</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>hc1_2</th><td>0.0000000</td><td>0.2155436</td><td>0.2054988</td><td>0.2245761</td><td>0.2140475</td><td>0.1251035</td><td>0.2137833</td><td>0.1941731</td><td>0.2196581</td><td>0.2191748</td><td>0.2201363</td><td>0.2251679</td><td>0.2293540</td><td>0.2303018</td><td>0.2273960</td><td>0.2267536</td><td>0.2241723</td><td>0.2393679</td></tr>\n",
       "\t<tr><th scope=row>hc1_4</th><td>0.2155436</td><td>0.0000000</td><td>0.1844240</td><td>0.1693271</td><td>0.1811331</td><td>0.2071129</td><td>0.1608409</td><td>0.2090009</td><td>0.2157563</td><td>0.2186305</td><td>0.2188330</td><td>0.2146309</td><td>0.2291905</td><td>0.2247412</td><td>0.2257357</td><td>0.2190871</td><td>0.2157204</td><td>0.2274161</td></tr>\n",
       "\t<tr><th scope=row>hc2_15</th><td>0.2054988</td><td>0.1844240</td><td>0.0000000</td><td>0.2041950</td><td>0.1854690</td><td>0.2021604</td><td>0.1823913</td><td>0.1592689</td><td>0.2168135</td><td>0.2041132</td><td>0.2085476</td><td>0.1910094</td><td>0.2042863</td><td>0.2045106</td><td>0.2061360</td><td>0.2040459</td><td>0.2013346</td><td>0.2098602</td></tr>\n",
       "\t<tr><th scope=row>hc2_17</th><td>0.2245761</td><td>0.1693271</td><td>0.2041950</td><td>0.0000000</td><td>0.1884048</td><td>0.2163634</td><td>0.1724402</td><td>0.2179260</td><td>0.2265039</td><td>0.2268210</td><td>0.2268209</td><td>0.2283067</td><td>0.2379735</td><td>0.2322090</td><td>0.2385235</td><td>0.2269364</td><td>0.2294361</td><td>0.2372695</td></tr>\n",
       "\t<tr><th scope=row>hc3_1</th><td>0.2140475</td><td>0.1811331</td><td>0.1854690</td><td>0.1884048</td><td>0.0000000</td><td>0.2084380</td><td>0.1141350</td><td>0.2070880</td><td>0.2092909</td><td>0.2195993</td><td>0.2160866</td><td>0.2155464</td><td>0.2254828</td><td>0.2196778</td><td>0.2258590</td><td>0.2205749</td><td>0.2200920</td><td>0.2156870</td></tr>\n",
       "\t<tr><th scope=row>hc3_5</th><td>0.1251035</td><td>0.2071129</td><td>0.2021604</td><td>0.2163634</td><td>0.2084380</td><td>0.0000000</td><td>0.2104185</td><td>0.1869989</td><td>0.2143060</td><td>0.2202222</td><td>0.2148287</td><td>0.2214425</td><td>0.2216537</td><td>0.2194587</td><td>0.2223524</td><td>0.2192765</td><td>0.2221298</td><td>0.2263948</td></tr>\n",
       "\t<tr><th scope=row>hc3_7</th><td>0.2137833</td><td>0.1608409</td><td>0.1823913</td><td>0.1724402</td><td>0.1141350</td><td>0.2104185</td><td>0.0000000</td><td>0.2098760</td><td>0.2154145</td><td>0.2211945</td><td>0.2233552</td><td>0.2236631</td><td>0.2265407</td><td>0.2248371</td><td>0.2269251</td><td>0.2224353</td><td>0.2260581</td><td>0.2286600</td></tr>\n",
       "\t<tr><th scope=row>hc3_10</th><td>0.1941731</td><td>0.2090009</td><td>0.1592689</td><td>0.2179260</td><td>0.2070880</td><td>0.1869989</td><td>0.2098760</td><td>0.0000000</td><td>0.2158190</td><td>0.2072541</td><td>0.2123815</td><td>0.2048036</td><td>0.2057382</td><td>0.2078799</td><td>0.1916720</td><td>0.2139469</td><td>0.2081097</td><td>0.2118349</td></tr>\n",
       "\t<tr><th scope=row>hc3_11</th><td>0.2196581</td><td>0.2157563</td><td>0.2168135</td><td>0.2265039</td><td>0.2092909</td><td>0.2143060</td><td>0.2154145</td><td>0.2158190</td><td>0.0000000</td><td>0.2204729</td><td>0.2206785</td><td>0.2201360</td><td>0.2362094</td><td>0.2252514</td><td>0.2307265</td><td>0.2282604</td><td>0.2200907</td><td>0.2270442</td></tr>\n",
       "\t<tr><th scope=row>ss2_9</th><td>0.2191748</td><td>0.2186305</td><td>0.2041132</td><td>0.2268210</td><td>0.2195993</td><td>0.2202222</td><td>0.2211945</td><td>0.2072541</td><td>0.2204729</td><td>0.0000000</td><td>0.1482022</td><td>0.1643140</td><td>0.1565610</td><td>0.1642693</td><td>0.2091080</td><td>0.1664055</td><td>0.2066793</td><td>0.1738464</td></tr>\n",
       "\t<tr><th scope=row>ss2_14</th><td>0.2201363</td><td>0.2188330</td><td>0.2085476</td><td>0.2268209</td><td>0.2160866</td><td>0.2148287</td><td>0.2233552</td><td>0.2123815</td><td>0.2206785</td><td>0.1482022</td><td>0.0000000</td><td>0.1741098</td><td>0.1484094</td><td>0.1731242</td><td>0.2053117</td><td>0.1555210</td><td>0.2028654</td><td>0.1736163</td></tr>\n",
       "\t<tr><th scope=row>ss2_18</th><td>0.2251679</td><td>0.2146309</td><td>0.1910094</td><td>0.2283067</td><td>0.2155464</td><td>0.2214425</td><td>0.2236631</td><td>0.2048036</td><td>0.2201360</td><td>0.1643140</td><td>0.1741098</td><td>0.0000000</td><td>0.1311476</td><td>0.1613894</td><td>0.2121693</td><td>0.1856383</td><td>0.1988239</td><td>0.1271048</td></tr>\n",
       "\t<tr><th scope=row>ss3_3</th><td>0.2293540</td><td>0.2291905</td><td>0.2042863</td><td>0.2379735</td><td>0.2254828</td><td>0.2216537</td><td>0.2265407</td><td>0.2057382</td><td>0.2362094</td><td>0.1565610</td><td>0.1484094</td><td>0.1311476</td><td>0.0000000</td><td>0.1692117</td><td>0.2093890</td><td>0.1462554</td><td>0.2056096</td><td>0.1244475</td></tr>\n",
       "\t<tr><th scope=row>ss3_14</th><td>0.2303018</td><td>0.2247412</td><td>0.2045106</td><td>0.2322090</td><td>0.2196778</td><td>0.2194587</td><td>0.2248371</td><td>0.2078799</td><td>0.2252514</td><td>0.1642693</td><td>0.1731242</td><td>0.1613894</td><td>0.1692117</td><td>0.0000000</td><td>0.2101518</td><td>0.1645175</td><td>0.2069574</td><td>0.1486115</td></tr>\n",
       "\t<tr><th scope=row>ss3_15</th><td>0.2273960</td><td>0.2257357</td><td>0.2061360</td><td>0.2385235</td><td>0.2258590</td><td>0.2223524</td><td>0.2269251</td><td>0.1916720</td><td>0.2307265</td><td>0.2091080</td><td>0.2053117</td><td>0.2121693</td><td>0.2093890</td><td>0.2101518</td><td>0.0000000</td><td>0.2127640</td><td>0.2091865</td><td>0.2141706</td></tr>\n",
       "\t<tr><th scope=row>ss3_16</th><td>0.2267536</td><td>0.2190871</td><td>0.2040459</td><td>0.2269364</td><td>0.2205749</td><td>0.2192765</td><td>0.2224353</td><td>0.2139469</td><td>0.2282604</td><td>0.1664055</td><td>0.1555210</td><td>0.1856383</td><td>0.1462554</td><td>0.1645175</td><td>0.2127640</td><td>0.0000000</td><td>0.2047189</td><td>0.1671745</td></tr>\n",
       "\t<tr><th scope=row>ss3_20</th><td>0.2241723</td><td>0.2157204</td><td>0.2013346</td><td>0.2294361</td><td>0.2200920</td><td>0.2221298</td><td>0.2260581</td><td>0.2081097</td><td>0.2200907</td><td>0.2066793</td><td>0.2028654</td><td>0.1988239</td><td>0.2056096</td><td>0.2069574</td><td>0.2091865</td><td>0.2047189</td><td>0.0000000</td><td>0.2039022</td></tr>\n",
       "\t<tr><th scope=row>ss5_18</th><td>0.2393679</td><td>0.2274161</td><td>0.2098602</td><td>0.2372695</td><td>0.2156870</td><td>0.2263948</td><td>0.2286600</td><td>0.2118349</td><td>0.2270442</td><td>0.1738464</td><td>0.1736163</td><td>0.1271048</td><td>0.1244475</td><td>0.1486115</td><td>0.2141706</td><td>0.1671745</td><td>0.2039022</td><td>0.0000000</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 18 x 18\n",
       "\\begin{tabular}{r|llllllllllllllllll}\n",
       "  & hc1\\_2 & hc1\\_4 & hc2\\_15 & hc2\\_17 & hc3\\_1 & hc3\\_5 & hc3\\_7 & hc3\\_10 & hc3\\_11 & ss2\\_9 & ss2\\_14 & ss2\\_18 & ss3\\_3 & ss3\\_14 & ss3\\_15 & ss3\\_16 & ss3\\_20 & ss5\\_18\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\thc1\\_2 & 0.0000000 & 0.2155436 & 0.2054988 & 0.2245761 & 0.2140475 & 0.1251035 & 0.2137833 & 0.1941731 & 0.2196581 & 0.2191748 & 0.2201363 & 0.2251679 & 0.2293540 & 0.2303018 & 0.2273960 & 0.2267536 & 0.2241723 & 0.2393679\\\\\n",
       "\thc1\\_4 & 0.2155436 & 0.0000000 & 0.1844240 & 0.1693271 & 0.1811331 & 0.2071129 & 0.1608409 & 0.2090009 & 0.2157563 & 0.2186305 & 0.2188330 & 0.2146309 & 0.2291905 & 0.2247412 & 0.2257357 & 0.2190871 & 0.2157204 & 0.2274161\\\\\n",
       "\thc2\\_15 & 0.2054988 & 0.1844240 & 0.0000000 & 0.2041950 & 0.1854690 & 0.2021604 & 0.1823913 & 0.1592689 & 0.2168135 & 0.2041132 & 0.2085476 & 0.1910094 & 0.2042863 & 0.2045106 & 0.2061360 & 0.2040459 & 0.2013346 & 0.2098602\\\\\n",
       "\thc2\\_17 & 0.2245761 & 0.1693271 & 0.2041950 & 0.0000000 & 0.1884048 & 0.2163634 & 0.1724402 & 0.2179260 & 0.2265039 & 0.2268210 & 0.2268209 & 0.2283067 & 0.2379735 & 0.2322090 & 0.2385235 & 0.2269364 & 0.2294361 & 0.2372695\\\\\n",
       "\thc3\\_1 & 0.2140475 & 0.1811331 & 0.1854690 & 0.1884048 & 0.0000000 & 0.2084380 & 0.1141350 & 0.2070880 & 0.2092909 & 0.2195993 & 0.2160866 & 0.2155464 & 0.2254828 & 0.2196778 & 0.2258590 & 0.2205749 & 0.2200920 & 0.2156870\\\\\n",
       "\thc3\\_5 & 0.1251035 & 0.2071129 & 0.2021604 & 0.2163634 & 0.2084380 & 0.0000000 & 0.2104185 & 0.1869989 & 0.2143060 & 0.2202222 & 0.2148287 & 0.2214425 & 0.2216537 & 0.2194587 & 0.2223524 & 0.2192765 & 0.2221298 & 0.2263948\\\\\n",
       "\thc3\\_7 & 0.2137833 & 0.1608409 & 0.1823913 & 0.1724402 & 0.1141350 & 0.2104185 & 0.0000000 & 0.2098760 & 0.2154145 & 0.2211945 & 0.2233552 & 0.2236631 & 0.2265407 & 0.2248371 & 0.2269251 & 0.2224353 & 0.2260581 & 0.2286600\\\\\n",
       "\thc3\\_10 & 0.1941731 & 0.2090009 & 0.1592689 & 0.2179260 & 0.2070880 & 0.1869989 & 0.2098760 & 0.0000000 & 0.2158190 & 0.2072541 & 0.2123815 & 0.2048036 & 0.2057382 & 0.2078799 & 0.1916720 & 0.2139469 & 0.2081097 & 0.2118349\\\\\n",
       "\thc3\\_11 & 0.2196581 & 0.2157563 & 0.2168135 & 0.2265039 & 0.2092909 & 0.2143060 & 0.2154145 & 0.2158190 & 0.0000000 & 0.2204729 & 0.2206785 & 0.2201360 & 0.2362094 & 0.2252514 & 0.2307265 & 0.2282604 & 0.2200907 & 0.2270442\\\\\n",
       "\tss2\\_9 & 0.2191748 & 0.2186305 & 0.2041132 & 0.2268210 & 0.2195993 & 0.2202222 & 0.2211945 & 0.2072541 & 0.2204729 & 0.0000000 & 0.1482022 & 0.1643140 & 0.1565610 & 0.1642693 & 0.2091080 & 0.1664055 & 0.2066793 & 0.1738464\\\\\n",
       "\tss2\\_14 & 0.2201363 & 0.2188330 & 0.2085476 & 0.2268209 & 0.2160866 & 0.2148287 & 0.2233552 & 0.2123815 & 0.2206785 & 0.1482022 & 0.0000000 & 0.1741098 & 0.1484094 & 0.1731242 & 0.2053117 & 0.1555210 & 0.2028654 & 0.1736163\\\\\n",
       "\tss2\\_18 & 0.2251679 & 0.2146309 & 0.1910094 & 0.2283067 & 0.2155464 & 0.2214425 & 0.2236631 & 0.2048036 & 0.2201360 & 0.1643140 & 0.1741098 & 0.0000000 & 0.1311476 & 0.1613894 & 0.2121693 & 0.1856383 & 0.1988239 & 0.1271048\\\\\n",
       "\tss3\\_3 & 0.2293540 & 0.2291905 & 0.2042863 & 0.2379735 & 0.2254828 & 0.2216537 & 0.2265407 & 0.2057382 & 0.2362094 & 0.1565610 & 0.1484094 & 0.1311476 & 0.0000000 & 0.1692117 & 0.2093890 & 0.1462554 & 0.2056096 & 0.1244475\\\\\n",
       "\tss3\\_14 & 0.2303018 & 0.2247412 & 0.2045106 & 0.2322090 & 0.2196778 & 0.2194587 & 0.2248371 & 0.2078799 & 0.2252514 & 0.1642693 & 0.1731242 & 0.1613894 & 0.1692117 & 0.0000000 & 0.2101518 & 0.1645175 & 0.2069574 & 0.1486115\\\\\n",
       "\tss3\\_15 & 0.2273960 & 0.2257357 & 0.2061360 & 0.2385235 & 0.2258590 & 0.2223524 & 0.2269251 & 0.1916720 & 0.2307265 & 0.2091080 & 0.2053117 & 0.2121693 & 0.2093890 & 0.2101518 & 0.0000000 & 0.2127640 & 0.2091865 & 0.2141706\\\\\n",
       "\tss3\\_16 & 0.2267536 & 0.2190871 & 0.2040459 & 0.2269364 & 0.2205749 & 0.2192765 & 0.2224353 & 0.2139469 & 0.2282604 & 0.1664055 & 0.1555210 & 0.1856383 & 0.1462554 & 0.1645175 & 0.2127640 & 0.0000000 & 0.2047189 & 0.1671745\\\\\n",
       "\tss3\\_20 & 0.2241723 & 0.2157204 & 0.2013346 & 0.2294361 & 0.2200920 & 0.2221298 & 0.2260581 & 0.2081097 & 0.2200907 & 0.2066793 & 0.2028654 & 0.1988239 & 0.2056096 & 0.2069574 & 0.2091865 & 0.2047189 & 0.0000000 & 0.2039022\\\\\n",
       "\tss5\\_18 & 0.2393679 & 0.2274161 & 0.2098602 & 0.2372695 & 0.2156870 & 0.2263948 & 0.2286600 & 0.2118349 & 0.2270442 & 0.1738464 & 0.1736163 & 0.1271048 & 0.1244475 & 0.1486115 & 0.2141706 & 0.1671745 & 0.2039022 & 0.0000000\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 18 x 18\n",
       "\n",
       "| <!--/--> | hc1_2 &lt;dbl&gt; | hc1_4 &lt;dbl&gt; | hc2_15 &lt;dbl&gt; | hc2_17 &lt;dbl&gt; | hc3_1 &lt;dbl&gt; | hc3_5 &lt;dbl&gt; | hc3_7 &lt;dbl&gt; | hc3_10 &lt;dbl&gt; | hc3_11 &lt;dbl&gt; | ss2_9 &lt;dbl&gt; | ss2_14 &lt;dbl&gt; | ss2_18 &lt;dbl&gt; | ss3_3 &lt;dbl&gt; | ss3_14 &lt;dbl&gt; | ss3_15 &lt;dbl&gt; | ss3_16 &lt;dbl&gt; | ss3_20 &lt;dbl&gt; | ss5_18 &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| hc1_2 | 0.0000000 | 0.2155436 | 0.2054988 | 0.2245761 | 0.2140475 | 0.1251035 | 0.2137833 | 0.1941731 | 0.2196581 | 0.2191748 | 0.2201363 | 0.2251679 | 0.2293540 | 0.2303018 | 0.2273960 | 0.2267536 | 0.2241723 | 0.2393679 |\n",
       "| hc1_4 | 0.2155436 | 0.0000000 | 0.1844240 | 0.1693271 | 0.1811331 | 0.2071129 | 0.1608409 | 0.2090009 | 0.2157563 | 0.2186305 | 0.2188330 | 0.2146309 | 0.2291905 | 0.2247412 | 0.2257357 | 0.2190871 | 0.2157204 | 0.2274161 |\n",
       "| hc2_15 | 0.2054988 | 0.1844240 | 0.0000000 | 0.2041950 | 0.1854690 | 0.2021604 | 0.1823913 | 0.1592689 | 0.2168135 | 0.2041132 | 0.2085476 | 0.1910094 | 0.2042863 | 0.2045106 | 0.2061360 | 0.2040459 | 0.2013346 | 0.2098602 |\n",
       "| hc2_17 | 0.2245761 | 0.1693271 | 0.2041950 | 0.0000000 | 0.1884048 | 0.2163634 | 0.1724402 | 0.2179260 | 0.2265039 | 0.2268210 | 0.2268209 | 0.2283067 | 0.2379735 | 0.2322090 | 0.2385235 | 0.2269364 | 0.2294361 | 0.2372695 |\n",
       "| hc3_1 | 0.2140475 | 0.1811331 | 0.1854690 | 0.1884048 | 0.0000000 | 0.2084380 | 0.1141350 | 0.2070880 | 0.2092909 | 0.2195993 | 0.2160866 | 0.2155464 | 0.2254828 | 0.2196778 | 0.2258590 | 0.2205749 | 0.2200920 | 0.2156870 |\n",
       "| hc3_5 | 0.1251035 | 0.2071129 | 0.2021604 | 0.2163634 | 0.2084380 | 0.0000000 | 0.2104185 | 0.1869989 | 0.2143060 | 0.2202222 | 0.2148287 | 0.2214425 | 0.2216537 | 0.2194587 | 0.2223524 | 0.2192765 | 0.2221298 | 0.2263948 |\n",
       "| hc3_7 | 0.2137833 | 0.1608409 | 0.1823913 | 0.1724402 | 0.1141350 | 0.2104185 | 0.0000000 | 0.2098760 | 0.2154145 | 0.2211945 | 0.2233552 | 0.2236631 | 0.2265407 | 0.2248371 | 0.2269251 | 0.2224353 | 0.2260581 | 0.2286600 |\n",
       "| hc3_10 | 0.1941731 | 0.2090009 | 0.1592689 | 0.2179260 | 0.2070880 | 0.1869989 | 0.2098760 | 0.0000000 | 0.2158190 | 0.2072541 | 0.2123815 | 0.2048036 | 0.2057382 | 0.2078799 | 0.1916720 | 0.2139469 | 0.2081097 | 0.2118349 |\n",
       "| hc3_11 | 0.2196581 | 0.2157563 | 0.2168135 | 0.2265039 | 0.2092909 | 0.2143060 | 0.2154145 | 0.2158190 | 0.0000000 | 0.2204729 | 0.2206785 | 0.2201360 | 0.2362094 | 0.2252514 | 0.2307265 | 0.2282604 | 0.2200907 | 0.2270442 |\n",
       "| ss2_9 | 0.2191748 | 0.2186305 | 0.2041132 | 0.2268210 | 0.2195993 | 0.2202222 | 0.2211945 | 0.2072541 | 0.2204729 | 0.0000000 | 0.1482022 | 0.1643140 | 0.1565610 | 0.1642693 | 0.2091080 | 0.1664055 | 0.2066793 | 0.1738464 |\n",
       "| ss2_14 | 0.2201363 | 0.2188330 | 0.2085476 | 0.2268209 | 0.2160866 | 0.2148287 | 0.2233552 | 0.2123815 | 0.2206785 | 0.1482022 | 0.0000000 | 0.1741098 | 0.1484094 | 0.1731242 | 0.2053117 | 0.1555210 | 0.2028654 | 0.1736163 |\n",
       "| ss2_18 | 0.2251679 | 0.2146309 | 0.1910094 | 0.2283067 | 0.2155464 | 0.2214425 | 0.2236631 | 0.2048036 | 0.2201360 | 0.1643140 | 0.1741098 | 0.0000000 | 0.1311476 | 0.1613894 | 0.2121693 | 0.1856383 | 0.1988239 | 0.1271048 |\n",
       "| ss3_3 | 0.2293540 | 0.2291905 | 0.2042863 | 0.2379735 | 0.2254828 | 0.2216537 | 0.2265407 | 0.2057382 | 0.2362094 | 0.1565610 | 0.1484094 | 0.1311476 | 0.0000000 | 0.1692117 | 0.2093890 | 0.1462554 | 0.2056096 | 0.1244475 |\n",
       "| ss3_14 | 0.2303018 | 0.2247412 | 0.2045106 | 0.2322090 | 0.2196778 | 0.2194587 | 0.2248371 | 0.2078799 | 0.2252514 | 0.1642693 | 0.1731242 | 0.1613894 | 0.1692117 | 0.0000000 | 0.2101518 | 0.1645175 | 0.2069574 | 0.1486115 |\n",
       "| ss3_15 | 0.2273960 | 0.2257357 | 0.2061360 | 0.2385235 | 0.2258590 | 0.2223524 | 0.2269251 | 0.1916720 | 0.2307265 | 0.2091080 | 0.2053117 | 0.2121693 | 0.2093890 | 0.2101518 | 0.0000000 | 0.2127640 | 0.2091865 | 0.2141706 |\n",
       "| ss3_16 | 0.2267536 | 0.2190871 | 0.2040459 | 0.2269364 | 0.2205749 | 0.2192765 | 0.2224353 | 0.2139469 | 0.2282604 | 0.1664055 | 0.1555210 | 0.1856383 | 0.1462554 | 0.1645175 | 0.2127640 | 0.0000000 | 0.2047189 | 0.1671745 |\n",
       "| ss3_20 | 0.2241723 | 0.2157204 | 0.2013346 | 0.2294361 | 0.2200920 | 0.2221298 | 0.2260581 | 0.2081097 | 0.2200907 | 0.2066793 | 0.2028654 | 0.1988239 | 0.2056096 | 0.2069574 | 0.2091865 | 0.2047189 | 0.0000000 | 0.2039022 |\n",
       "| ss5_18 | 0.2393679 | 0.2274161 | 0.2098602 | 0.2372695 | 0.2156870 | 0.2263948 | 0.2286600 | 0.2118349 | 0.2270442 | 0.1738464 | 0.1736163 | 0.1271048 | 0.1244475 | 0.1486115 | 0.2141706 | 0.1671745 | 0.2039022 | 0.0000000 |\n",
       "\n"
      ],
      "text/plain": [
       "       hc1_2     hc1_4     hc2_15    hc2_17    hc3_1     hc3_5     hc3_7    \n",
       "hc1_2  0.0000000 0.2155436 0.2054988 0.2245761 0.2140475 0.1251035 0.2137833\n",
       "hc1_4  0.2155436 0.0000000 0.1844240 0.1693271 0.1811331 0.2071129 0.1608409\n",
       "hc2_15 0.2054988 0.1844240 0.0000000 0.2041950 0.1854690 0.2021604 0.1823913\n",
       "hc2_17 0.2245761 0.1693271 0.2041950 0.0000000 0.1884048 0.2163634 0.1724402\n",
       "hc3_1  0.2140475 0.1811331 0.1854690 0.1884048 0.0000000 0.2084380 0.1141350\n",
       "hc3_5  0.1251035 0.2071129 0.2021604 0.2163634 0.2084380 0.0000000 0.2104185\n",
       "hc3_7  0.2137833 0.1608409 0.1823913 0.1724402 0.1141350 0.2104185 0.0000000\n",
       "hc3_10 0.1941731 0.2090009 0.1592689 0.2179260 0.2070880 0.1869989 0.2098760\n",
       "hc3_11 0.2196581 0.2157563 0.2168135 0.2265039 0.2092909 0.2143060 0.2154145\n",
       "ss2_9  0.2191748 0.2186305 0.2041132 0.2268210 0.2195993 0.2202222 0.2211945\n",
       "ss2_14 0.2201363 0.2188330 0.2085476 0.2268209 0.2160866 0.2148287 0.2233552\n",
       "ss2_18 0.2251679 0.2146309 0.1910094 0.2283067 0.2155464 0.2214425 0.2236631\n",
       "ss3_3  0.2293540 0.2291905 0.2042863 0.2379735 0.2254828 0.2216537 0.2265407\n",
       "ss3_14 0.2303018 0.2247412 0.2045106 0.2322090 0.2196778 0.2194587 0.2248371\n",
       "ss3_15 0.2273960 0.2257357 0.2061360 0.2385235 0.2258590 0.2223524 0.2269251\n",
       "ss3_16 0.2267536 0.2190871 0.2040459 0.2269364 0.2205749 0.2192765 0.2224353\n",
       "ss3_20 0.2241723 0.2157204 0.2013346 0.2294361 0.2200920 0.2221298 0.2260581\n",
       "ss5_18 0.2393679 0.2274161 0.2098602 0.2372695 0.2156870 0.2263948 0.2286600\n",
       "       hc3_10    hc3_11    ss2_9     ss2_14    ss2_18    ss3_3     ss3_14   \n",
       "hc1_2  0.1941731 0.2196581 0.2191748 0.2201363 0.2251679 0.2293540 0.2303018\n",
       "hc1_4  0.2090009 0.2157563 0.2186305 0.2188330 0.2146309 0.2291905 0.2247412\n",
       "hc2_15 0.1592689 0.2168135 0.2041132 0.2085476 0.1910094 0.2042863 0.2045106\n",
       "hc2_17 0.2179260 0.2265039 0.2268210 0.2268209 0.2283067 0.2379735 0.2322090\n",
       "hc3_1  0.2070880 0.2092909 0.2195993 0.2160866 0.2155464 0.2254828 0.2196778\n",
       "hc3_5  0.1869989 0.2143060 0.2202222 0.2148287 0.2214425 0.2216537 0.2194587\n",
       "hc3_7  0.2098760 0.2154145 0.2211945 0.2233552 0.2236631 0.2265407 0.2248371\n",
       "hc3_10 0.0000000 0.2158190 0.2072541 0.2123815 0.2048036 0.2057382 0.2078799\n",
       "hc3_11 0.2158190 0.0000000 0.2204729 0.2206785 0.2201360 0.2362094 0.2252514\n",
       "ss2_9  0.2072541 0.2204729 0.0000000 0.1482022 0.1643140 0.1565610 0.1642693\n",
       "ss2_14 0.2123815 0.2206785 0.1482022 0.0000000 0.1741098 0.1484094 0.1731242\n",
       "ss2_18 0.2048036 0.2201360 0.1643140 0.1741098 0.0000000 0.1311476 0.1613894\n",
       "ss3_3  0.2057382 0.2362094 0.1565610 0.1484094 0.1311476 0.0000000 0.1692117\n",
       "ss3_14 0.2078799 0.2252514 0.1642693 0.1731242 0.1613894 0.1692117 0.0000000\n",
       "ss3_15 0.1916720 0.2307265 0.2091080 0.2053117 0.2121693 0.2093890 0.2101518\n",
       "ss3_16 0.2139469 0.2282604 0.1664055 0.1555210 0.1856383 0.1462554 0.1645175\n",
       "ss3_20 0.2081097 0.2200907 0.2066793 0.2028654 0.1988239 0.2056096 0.2069574\n",
       "ss5_18 0.2118349 0.2270442 0.1738464 0.1736163 0.1271048 0.1244475 0.1486115\n",
       "       ss3_15    ss3_16    ss3_20    ss5_18   \n",
       "hc1_2  0.2273960 0.2267536 0.2241723 0.2393679\n",
       "hc1_4  0.2257357 0.2190871 0.2157204 0.2274161\n",
       "hc2_15 0.2061360 0.2040459 0.2013346 0.2098602\n",
       "hc2_17 0.2385235 0.2269364 0.2294361 0.2372695\n",
       "hc3_1  0.2258590 0.2205749 0.2200920 0.2156870\n",
       "hc3_5  0.2223524 0.2192765 0.2221298 0.2263948\n",
       "hc3_7  0.2269251 0.2224353 0.2260581 0.2286600\n",
       "hc3_10 0.1916720 0.2139469 0.2081097 0.2118349\n",
       "hc3_11 0.2307265 0.2282604 0.2200907 0.2270442\n",
       "ss2_9  0.2091080 0.1664055 0.2066793 0.1738464\n",
       "ss2_14 0.2053117 0.1555210 0.2028654 0.1736163\n",
       "ss2_18 0.2121693 0.1856383 0.1988239 0.1271048\n",
       "ss3_3  0.2093890 0.1462554 0.2056096 0.1244475\n",
       "ss3_14 0.2101518 0.1645175 0.2069574 0.1486115\n",
       "ss3_15 0.0000000 0.2127640 0.2091865 0.2141706\n",
       "ss3_16 0.2127640 0.0000000 0.2047189 0.1671745\n",
       "ss3_20 0.2091865 0.2047189 0.0000000 0.2039022\n",
       "ss5_18 0.2141706 0.1671745 0.2039022 0.0000000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# read in genetic distances\n",
    "gen <- read.table(paste0(POP,\"_mbd.dist\"), row.names = 1, header=T)\n",
    "gen\n",
    "Dgen <- as.dist(gen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "distObj <- list(Dep5,Dgen)\n",
    "names(distObj) <- c(\"Dep5\",\"Dgen\")\n",
    "save(distObj,file=\"../analyses/2bRAD/MethGen/dist_allmeth5x_snp.Robj\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "kernel": "R",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPearson's product-moment correlation\n",
       "\n",
       "data:  Dep5 and Dgen\n",
       "t = 3.4343, df = 151, p-value = 0.0007671\n",
       "alternative hypothesis: true correlation is not equal to 0\n",
       "95 percent confidence interval:\n",
       " 0.1154177 0.4103193\n",
       "sample estimates:\n",
       "      cor \n",
       "0.2691662 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tSpearman's rank correlation rho\n",
       "\n",
       "data:  Dep5 and Dgen\n",
       "S = 466738, p-value = 0.006868\n",
       "alternative hypothesis: true rho is not equal to 0\n",
       "sample estimates:\n",
       "      rho \n",
       "0.2180686 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cor.test( ~ Dep5 + Dgen,\n",
    "         method = \"pearson\",\n",
    "         conf.level = 0.95)\n",
    "cor.test( ~ Dep5 + Dgen, \n",
    "         method = \"spearman\",\n",
    "         continuity = FALSE,\n",
    "         conf.level = 0.95)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "kernel": "R"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "lm(formula = Dep5 ~ Dgen)\n",
       "\n",
       "Residuals:\n",
       "    Min      1Q  Median      3Q     Max \n",
       "-461037 -159170  -14839  176003  504108 \n",
       "\n",
       "Coefficients:\n",
       "            Estimate Std. Error t value Pr(>|t|)    \n",
       "(Intercept)  2295830     142401  16.122  < 2e-16 ***\n",
       "Dgen         2365734     688851   3.434 0.000767 ***\n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 222200 on 151 degrees of freedom\n",
       "Multiple R-squared:  0.07245,\tAdjusted R-squared:  0.06631 \n",
       "F-statistic: 11.79 on 1 and 151 DF,  p-value: 0.0007671\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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SQf+5u5d4/mB+0OI/p5Tb5FNos2v0YXuEM2qunCD1hy7Q4j6jmYIcaItC8CiBTN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\nAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#library(MASS)\n",
    "#dens <- kde2d(Dgeo,DgenN, n=300)\n",
    "#myPal <- colorRampPalette(c(\"white\",\"blue\",\"gold\", \"orange\", \"red\"))\n",
    "plot(Dgen, Dep5, pch=20,cex=1,xlab = \"Genetic distance\", ylab = \"MBD Manhattan distance\")\n",
    "summary(lm(Dep5~Dgen),)\n",
    "R2 = round(summary(lm(Dep5~Dgen))$r.squared, 4)\n",
    "#image(dens, col=transp(myPal(300),.7), add=TRUE)\n",
    "abline(lm(Dep5~Dgen))\n",
    "text(0.13,3200000,label=paste(\"R^2=\",R2))\n",
    "text(0.13,3100000,label=\"r=0.27\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "bash"
   },
   "source": [
    "### For DMLs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "kernel": "R"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A matrix: 18 x 18 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>hc1_2</th><th scope=col>hc1_4</th><th scope=col>hc2_15</th><th scope=col>hc2_17</th><th scope=col>hc3_1</th><th scope=col>hc3_5</th><th scope=col>hc3_7</th><th scope=col>hc3_10</th><th scope=col>hc3_11</th><th scope=col>ss2_9</th><th scope=col>ss2_14</th><th scope=col>ss2_18</th><th scope=col>ss3_3</th><th scope=col>ss3_14</th><th scope=col>ss3_15</th><th scope=col>ss3_16</th><th scope=col>ss3_20</th><th scope=col>ss5_18</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>hc1_2</th><td>     0.00</td><td>122854.8</td><td>117833.69</td><td>121924.6</td><td>122048.45</td><td> 88251.19</td><td>122113.69</td><td>102701.5</td><td>117925.2</td><td>179529.92</td><td>180204.79</td><td>168741.42</td><td>180553.19</td><td>177851.85</td><td>158737.9</td><td>179176.20</td><td>151791.6</td><td>173015.35</td></tr>\n",
       "\t<tr><th scope=row>hc1_4</th><td>122854.80</td><td>     0.0</td><td>109179.36</td><td>101955.7</td><td>112350.01</td><td>121048.80</td><td>102968.43</td><td>121351.0</td><td>118987.8</td><td>187958.77</td><td>190402.67</td><td>179482.58</td><td>189540.66</td><td>186142.74</td><td>168699.4</td><td>187705.68</td><td>162244.6</td><td>181837.12</td></tr>\n",
       "\t<tr><th scope=row>hc2_15</th><td>117833.69</td><td>109179.4</td><td>     0.00</td><td>101404.9</td><td>109983.52</td><td>123337.37</td><td> 95894.53</td><td>116572.8</td><td>114520.9</td><td>190230.48</td><td>190926.63</td><td>178951.49</td><td>190496.05</td><td>188937.39</td><td>168712.1</td><td>189067.06</td><td>163370.8</td><td>183103.29</td></tr>\n",
       "\t<tr><th scope=row>hc2_17</th><td>121924.58</td><td>101955.7</td><td>101404.90</td><td>     0.0</td><td>116803.75</td><td>120192.08</td><td>104194.92</td><td>119223.2</td><td>120836.2</td><td>187025.52</td><td>189470.60</td><td>182139.42</td><td>189094.54</td><td>186588.74</td><td>166280.4</td><td>186926.44</td><td>161377.4</td><td>182154.17</td></tr>\n",
       "\t<tr><th scope=row>hc3_1</th><td>122048.45</td><td>112350.0</td><td>109983.52</td><td>116803.8</td><td>     0.00</td><td>124639.71</td><td> 84634.75</td><td>127938.6</td><td>117236.5</td><td>190032.93</td><td>190165.48</td><td>181890.70</td><td>193403.62</td><td>189630.02</td><td>170386.8</td><td>192128.15</td><td>164928.4</td><td>185042.82</td></tr>\n",
       "\t<tr><th scope=row>hc3_5</th><td> 88251.19</td><td>121048.8</td><td>123337.37</td><td>120192.1</td><td>124639.71</td><td>     0.00</td><td>124873.66</td><td>107091.3</td><td>119500.3</td><td>177061.13</td><td>176343.02</td><td>167518.30</td><td>178051.37</td><td>174936.90</td><td>157002.4</td><td>175158.45</td><td>152948.9</td><td>169232.11</td></tr>\n",
       "\t<tr><th scope=row>hc3_7</th><td>122113.69</td><td>102968.4</td><td> 95894.53</td><td>104194.9</td><td> 84634.75</td><td>124873.66</td><td>     0.00</td><td>122879.0</td><td>118978.7</td><td>196726.19</td><td>197363.50</td><td>186164.84</td><td>199380.37</td><td>198074.72</td><td>174234.3</td><td>198070.18</td><td>171071.9</td><td>191199.20</td></tr>\n",
       "\t<tr><th scope=row>hc3_10</th><td>102701.55</td><td>121351.0</td><td>116572.80</td><td>119223.2</td><td>127938.59</td><td>107091.25</td><td>122879.03</td><td>     0.0</td><td>119768.1</td><td>178788.12</td><td>180215.96</td><td>171130.51</td><td>181920.21</td><td>177760.85</td><td>160786.0</td><td>178066.79</td><td>155844.3</td><td>175961.75</td></tr>\n",
       "\t<tr><th scope=row>hc3_11</th><td>117925.23</td><td>118987.8</td><td>114520.90</td><td>120836.2</td><td>117236.50</td><td>119500.33</td><td>118978.71</td><td>119768.1</td><td>     0.0</td><td>174002.37</td><td>176288.17</td><td>166228.89</td><td>176696.28</td><td>173248.65</td><td>154512.7</td><td>173175.17</td><td>151397.1</td><td>167264.17</td></tr>\n",
       "\t<tr><th scope=row>ss2_9</th><td>179529.92</td><td>187958.8</td><td>190230.48</td><td>187025.5</td><td>190032.93</td><td>177061.13</td><td>196726.19</td><td>178788.1</td><td>174002.4</td><td>     0.00</td><td> 94644.82</td><td>101674.37</td><td> 92191.75</td><td> 96374.49</td><td>113633.2</td><td> 94374.59</td><td>118461.7</td><td>101448.22</td></tr>\n",
       "\t<tr><th scope=row>ss2_14</th><td>180204.79</td><td>190402.7</td><td>190926.63</td><td>189470.6</td><td>190165.48</td><td>176343.02</td><td>197363.50</td><td>180216.0</td><td>176288.2</td><td> 94644.82</td><td>     0.00</td><td>110417.84</td><td> 95430.27</td><td>102711.17</td><td>115362.6</td><td> 94180.91</td><td>120283.2</td><td>108619.67</td></tr>\n",
       "\t<tr><th scope=row>ss2_18</th><td>168741.42</td><td>179482.6</td><td>178951.49</td><td>182139.4</td><td>181890.70</td><td>167518.30</td><td>186164.84</td><td>171130.5</td><td>166228.9</td><td>101674.37</td><td>110417.84</td><td>     0.00</td><td> 88513.30</td><td> 97318.64</td><td>118027.4</td><td>108181.72</td><td>121558.4</td><td> 83907.52</td></tr>\n",
       "\t<tr><th scope=row>ss3_3</th><td>180553.19</td><td>189540.7</td><td>190496.05</td><td>189094.5</td><td>193403.62</td><td>178051.37</td><td>199380.37</td><td>181920.2</td><td>176696.3</td><td> 92191.75</td><td> 95430.27</td><td> 88513.30</td><td>     0.00</td><td> 90468.36</td><td>110760.4</td><td> 88394.44</td><td>115809.3</td><td> 75741.78</td></tr>\n",
       "\t<tr><th scope=row>ss3_14</th><td>177851.85</td><td>186142.7</td><td>188937.39</td><td>186588.7</td><td>189630.02</td><td>174936.90</td><td>198074.72</td><td>177760.8</td><td>173248.6</td><td> 96374.49</td><td>102711.17</td><td> 97318.64</td><td> 90468.36</td><td>     0.00</td><td>111859.9</td><td> 92047.80</td><td>115338.5</td><td> 86875.64</td></tr>\n",
       "\t<tr><th scope=row>ss3_15</th><td>158737.92</td><td>168699.4</td><td>168712.13</td><td>166280.4</td><td>170386.85</td><td>157002.43</td><td>174234.31</td><td>160786.0</td><td>154512.7</td><td>113633.21</td><td>115362.63</td><td>118027.40</td><td>110760.43</td><td>111859.89</td><td>     0.0</td><td>108633.76</td><td>112427.2</td><td>113421.34</td></tr>\n",
       "\t<tr><th scope=row>ss3_16</th><td>179176.20</td><td>187705.7</td><td>189067.06</td><td>186926.4</td><td>192128.15</td><td>175158.45</td><td>198070.18</td><td>178066.8</td><td>173175.2</td><td> 94374.59</td><td> 94180.91</td><td>108181.72</td><td> 88394.44</td><td> 92047.80</td><td>108633.8</td><td>     0.00</td><td>113836.0</td><td> 96165.50</td></tr>\n",
       "\t<tr><th scope=row>ss3_20</th><td>151791.56</td><td>162244.6</td><td>163370.79</td><td>161377.4</td><td>164928.43</td><td>152948.90</td><td>171071.91</td><td>155844.3</td><td>151397.1</td><td>118461.73</td><td>120283.15</td><td>121558.45</td><td>115809.30</td><td>115338.50</td><td>112427.2</td><td>113835.98</td><td>     0.0</td><td>116148.51</td></tr>\n",
       "\t<tr><th scope=row>ss5_18</th><td>173015.35</td><td>181837.1</td><td>183103.29</td><td>182154.2</td><td>185042.82</td><td>169232.11</td><td>191199.20</td><td>175961.8</td><td>167264.2</td><td>101448.22</td><td>108619.67</td><td> 83907.52</td><td> 75741.78</td><td> 86875.64</td><td>113421.3</td><td> 96165.50</td><td>116148.5</td><td>     0.00</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 18 x 18 of type dbl\n",
       "\\begin{tabular}{r|llllllllllllllllll}\n",
       "  & hc1\\_2 & hc1\\_4 & hc2\\_15 & hc2\\_17 & hc3\\_1 & hc3\\_5 & hc3\\_7 & hc3\\_10 & hc3\\_11 & ss2\\_9 & ss2\\_14 & ss2\\_18 & ss3\\_3 & ss3\\_14 & ss3\\_15 & ss3\\_16 & ss3\\_20 & ss5\\_18\\\\\n",
       "\\hline\n",
       "\thc1\\_2 &      0.00 & 122854.8 & 117833.69 & 121924.6 & 122048.45 &  88251.19 & 122113.69 & 102701.5 & 117925.2 & 179529.92 & 180204.79 & 168741.42 & 180553.19 & 177851.85 & 158737.9 & 179176.20 & 151791.6 & 173015.35\\\\\n",
       "\thc1\\_4 & 122854.80 &      0.0 & 109179.36 & 101955.7 & 112350.01 & 121048.80 & 102968.43 & 121351.0 & 118987.8 & 187958.77 & 190402.67 & 179482.58 & 189540.66 & 186142.74 & 168699.4 & 187705.68 & 162244.6 & 181837.12\\\\\n",
       "\thc2\\_15 & 117833.69 & 109179.4 &      0.00 & 101404.9 & 109983.52 & 123337.37 &  95894.53 & 116572.8 & 114520.9 & 190230.48 & 190926.63 & 178951.49 & 190496.05 & 188937.39 & 168712.1 & 189067.06 & 163370.8 & 183103.29\\\\\n",
       "\thc2\\_17 & 121924.58 & 101955.7 & 101404.90 &      0.0 & 116803.75 & 120192.08 & 104194.92 & 119223.2 & 120836.2 & 187025.52 & 189470.60 & 182139.42 & 189094.54 & 186588.74 & 166280.4 & 186926.44 & 161377.4 & 182154.17\\\\\n",
       "\thc3\\_1 & 122048.45 & 112350.0 & 109983.52 & 116803.8 &      0.00 & 124639.71 &  84634.75 & 127938.6 & 117236.5 & 190032.93 & 190165.48 & 181890.70 & 193403.62 & 189630.02 & 170386.8 & 192128.15 & 164928.4 & 185042.82\\\\\n",
       "\thc3\\_5 &  88251.19 & 121048.8 & 123337.37 & 120192.1 & 124639.71 &      0.00 & 124873.66 & 107091.3 & 119500.3 & 177061.13 & 176343.02 & 167518.30 & 178051.37 & 174936.90 & 157002.4 & 175158.45 & 152948.9 & 169232.11\\\\\n",
       "\thc3\\_7 & 122113.69 & 102968.4 &  95894.53 & 104194.9 &  84634.75 & 124873.66 &      0.00 & 122879.0 & 118978.7 & 196726.19 & 197363.50 & 186164.84 & 199380.37 & 198074.72 & 174234.3 & 198070.18 & 171071.9 & 191199.20\\\\\n",
       "\thc3\\_10 & 102701.55 & 121351.0 & 116572.80 & 119223.2 & 127938.59 & 107091.25 & 122879.03 &      0.0 & 119768.1 & 178788.12 & 180215.96 & 171130.51 & 181920.21 & 177760.85 & 160786.0 & 178066.79 & 155844.3 & 175961.75\\\\\n",
       "\thc3\\_11 & 117925.23 & 118987.8 & 114520.90 & 120836.2 & 117236.50 & 119500.33 & 118978.71 & 119768.1 &      0.0 & 174002.37 & 176288.17 & 166228.89 & 176696.28 & 173248.65 & 154512.7 & 173175.17 & 151397.1 & 167264.17\\\\\n",
       "\tss2\\_9 & 179529.92 & 187958.8 & 190230.48 & 187025.5 & 190032.93 & 177061.13 & 196726.19 & 178788.1 & 174002.4 &      0.00 &  94644.82 & 101674.37 &  92191.75 &  96374.49 & 113633.2 &  94374.59 & 118461.7 & 101448.22\\\\\n",
       "\tss2\\_14 & 180204.79 & 190402.7 & 190926.63 & 189470.6 & 190165.48 & 176343.02 & 197363.50 & 180216.0 & 176288.2 &  94644.82 &      0.00 & 110417.84 &  95430.27 & 102711.17 & 115362.6 &  94180.91 & 120283.2 & 108619.67\\\\\n",
       "\tss2\\_18 & 168741.42 & 179482.6 & 178951.49 & 182139.4 & 181890.70 & 167518.30 & 186164.84 & 171130.5 & 166228.9 & 101674.37 & 110417.84 &      0.00 &  88513.30 &  97318.64 & 118027.4 & 108181.72 & 121558.4 &  83907.52\\\\\n",
       "\tss3\\_3 & 180553.19 & 189540.7 & 190496.05 & 189094.5 & 193403.62 & 178051.37 & 199380.37 & 181920.2 & 176696.3 &  92191.75 &  95430.27 &  88513.30 &      0.00 &  90468.36 & 110760.4 &  88394.44 & 115809.3 &  75741.78\\\\\n",
       "\tss3\\_14 & 177851.85 & 186142.7 & 188937.39 & 186588.7 & 189630.02 & 174936.90 & 198074.72 & 177760.8 & 173248.6 &  96374.49 & 102711.17 &  97318.64 &  90468.36 &      0.00 & 111859.9 &  92047.80 & 115338.5 &  86875.64\\\\\n",
       "\tss3\\_15 & 158737.92 & 168699.4 & 168712.13 & 166280.4 & 170386.85 & 157002.43 & 174234.31 & 160786.0 & 154512.7 & 113633.21 & 115362.63 & 118027.40 & 110760.43 & 111859.89 &      0.0 & 108633.76 & 112427.2 & 113421.34\\\\\n",
       "\tss3\\_16 & 179176.20 & 187705.7 & 189067.06 & 186926.4 & 192128.15 & 175158.45 & 198070.18 & 178066.8 & 173175.2 &  94374.59 &  94180.91 & 108181.72 &  88394.44 &  92047.80 & 108633.8 &      0.00 & 113836.0 &  96165.50\\\\\n",
       "\tss3\\_20 & 151791.56 & 162244.6 & 163370.79 & 161377.4 & 164928.43 & 152948.90 & 171071.91 & 155844.3 & 151397.1 & 118461.73 & 120283.15 & 121558.45 & 115809.30 & 115338.50 & 112427.2 & 113835.98 &      0.0 & 116148.51\\\\\n",
       "\tss5\\_18 & 173015.35 & 181837.1 & 183103.29 & 182154.2 & 185042.82 & 169232.11 & 191199.20 & 175961.8 & 167264.2 & 101448.22 & 108619.67 &  83907.52 &  75741.78 &  86875.64 & 113421.3 &  96165.50 & 116148.5 &      0.00\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 18 x 18 of type dbl\n",
       "\n",
       "| <!--/--> | hc1_2 | hc1_4 | hc2_15 | hc2_17 | hc3_1 | hc3_5 | hc3_7 | hc3_10 | hc3_11 | ss2_9 | ss2_14 | ss2_18 | ss3_3 | ss3_14 | ss3_15 | ss3_16 | ss3_20 | ss5_18 |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| hc1_2 |      0.00 | 122854.8 | 117833.69 | 121924.6 | 122048.45 |  88251.19 | 122113.69 | 102701.5 | 117925.2 | 179529.92 | 180204.79 | 168741.42 | 180553.19 | 177851.85 | 158737.9 | 179176.20 | 151791.6 | 173015.35 |\n",
       "| hc1_4 | 122854.80 |      0.0 | 109179.36 | 101955.7 | 112350.01 | 121048.80 | 102968.43 | 121351.0 | 118987.8 | 187958.77 | 190402.67 | 179482.58 | 189540.66 | 186142.74 | 168699.4 | 187705.68 | 162244.6 | 181837.12 |\n",
       "| hc2_15 | 117833.69 | 109179.4 |      0.00 | 101404.9 | 109983.52 | 123337.37 |  95894.53 | 116572.8 | 114520.9 | 190230.48 | 190926.63 | 178951.49 | 190496.05 | 188937.39 | 168712.1 | 189067.06 | 163370.8 | 183103.29 |\n",
       "| hc2_17 | 121924.58 | 101955.7 | 101404.90 |      0.0 | 116803.75 | 120192.08 | 104194.92 | 119223.2 | 120836.2 | 187025.52 | 189470.60 | 182139.42 | 189094.54 | 186588.74 | 166280.4 | 186926.44 | 161377.4 | 182154.17 |\n",
       "| hc3_1 | 122048.45 | 112350.0 | 109983.52 | 116803.8 |      0.00 | 124639.71 |  84634.75 | 127938.6 | 117236.5 | 190032.93 | 190165.48 | 181890.70 | 193403.62 | 189630.02 | 170386.8 | 192128.15 | 164928.4 | 185042.82 |\n",
       "| hc3_5 |  88251.19 | 121048.8 | 123337.37 | 120192.1 | 124639.71 |      0.00 | 124873.66 | 107091.3 | 119500.3 | 177061.13 | 176343.02 | 167518.30 | 178051.37 | 174936.90 | 157002.4 | 175158.45 | 152948.9 | 169232.11 |\n",
       "| hc3_7 | 122113.69 | 102968.4 |  95894.53 | 104194.9 |  84634.75 | 124873.66 |      0.00 | 122879.0 | 118978.7 | 196726.19 | 197363.50 | 186164.84 | 199380.37 | 198074.72 | 174234.3 | 198070.18 | 171071.9 | 191199.20 |\n",
       "| hc3_10 | 102701.55 | 121351.0 | 116572.80 | 119223.2 | 127938.59 | 107091.25 | 122879.03 |      0.0 | 119768.1 | 178788.12 | 180215.96 | 171130.51 | 181920.21 | 177760.85 | 160786.0 | 178066.79 | 155844.3 | 175961.75 |\n",
       "| hc3_11 | 117925.23 | 118987.8 | 114520.90 | 120836.2 | 117236.50 | 119500.33 | 118978.71 | 119768.1 |      0.0 | 174002.37 | 176288.17 | 166228.89 | 176696.28 | 173248.65 | 154512.7 | 173175.17 | 151397.1 | 167264.17 |\n",
       "| ss2_9 | 179529.92 | 187958.8 | 190230.48 | 187025.5 | 190032.93 | 177061.13 | 196726.19 | 178788.1 | 174002.4 |      0.00 |  94644.82 | 101674.37 |  92191.75 |  96374.49 | 113633.2 |  94374.59 | 118461.7 | 101448.22 |\n",
       "| ss2_14 | 180204.79 | 190402.7 | 190926.63 | 189470.6 | 190165.48 | 176343.02 | 197363.50 | 180216.0 | 176288.2 |  94644.82 |      0.00 | 110417.84 |  95430.27 | 102711.17 | 115362.6 |  94180.91 | 120283.2 | 108619.67 |\n",
       "| ss2_18 | 168741.42 | 179482.6 | 178951.49 | 182139.4 | 181890.70 | 167518.30 | 186164.84 | 171130.5 | 166228.9 | 101674.37 | 110417.84 |      0.00 |  88513.30 |  97318.64 | 118027.4 | 108181.72 | 121558.4 |  83907.52 |\n",
       "| ss3_3 | 180553.19 | 189540.7 | 190496.05 | 189094.5 | 193403.62 | 178051.37 | 199380.37 | 181920.2 | 176696.3 |  92191.75 |  95430.27 |  88513.30 |      0.00 |  90468.36 | 110760.4 |  88394.44 | 115809.3 |  75741.78 |\n",
       "| ss3_14 | 177851.85 | 186142.7 | 188937.39 | 186588.7 | 189630.02 | 174936.90 | 198074.72 | 177760.8 | 173248.6 |  96374.49 | 102711.17 |  97318.64 |  90468.36 |      0.00 | 111859.9 |  92047.80 | 115338.5 |  86875.64 |\n",
       "| ss3_15 | 158737.92 | 168699.4 | 168712.13 | 166280.4 | 170386.85 | 157002.43 | 174234.31 | 160786.0 | 154512.7 | 113633.21 | 115362.63 | 118027.40 | 110760.43 | 111859.89 |      0.0 | 108633.76 | 112427.2 | 113421.34 |\n",
       "| ss3_16 | 179176.20 | 187705.7 | 189067.06 | 186926.4 | 192128.15 | 175158.45 | 198070.18 | 178066.8 | 173175.2 |  94374.59 |  94180.91 | 108181.72 |  88394.44 |  92047.80 | 108633.8 |      0.00 | 113836.0 |  96165.50 |\n",
       "| ss3_20 | 151791.56 | 162244.6 | 163370.79 | 161377.4 | 164928.43 | 152948.90 | 171071.91 | 155844.3 | 151397.1 | 118461.73 | 120283.15 | 121558.45 | 115809.30 | 115338.50 | 112427.2 | 113835.98 |      0.0 | 116148.51 |\n",
       "| ss5_18 | 173015.35 | 181837.1 | 183103.29 | 182154.2 | 185042.82 | 169232.11 | 191199.20 | 175961.8 | 167264.2 | 101448.22 | 108619.67 |  83907.52 |  75741.78 |  86875.64 | 113421.3 |  96165.50 | 116148.5 |      0.00 |\n",
       "\n"
      ],
      "text/plain": [
       "       hc1_2     hc1_4    hc2_15    hc2_17   hc3_1     hc3_5     hc3_7    \n",
       "hc1_2       0.00 122854.8 117833.69 121924.6 122048.45  88251.19 122113.69\n",
       "hc1_4  122854.80      0.0 109179.36 101955.7 112350.01 121048.80 102968.43\n",
       "hc2_15 117833.69 109179.4      0.00 101404.9 109983.52 123337.37  95894.53\n",
       "hc2_17 121924.58 101955.7 101404.90      0.0 116803.75 120192.08 104194.92\n",
       "hc3_1  122048.45 112350.0 109983.52 116803.8      0.00 124639.71  84634.75\n",
       "hc3_5   88251.19 121048.8 123337.37 120192.1 124639.71      0.00 124873.66\n",
       "hc3_7  122113.69 102968.4  95894.53 104194.9  84634.75 124873.66      0.00\n",
       "hc3_10 102701.55 121351.0 116572.80 119223.2 127938.59 107091.25 122879.03\n",
       "hc3_11 117925.23 118987.8 114520.90 120836.2 117236.50 119500.33 118978.71\n",
       "ss2_9  179529.92 187958.8 190230.48 187025.5 190032.93 177061.13 196726.19\n",
       "ss2_14 180204.79 190402.7 190926.63 189470.6 190165.48 176343.02 197363.50\n",
       "ss2_18 168741.42 179482.6 178951.49 182139.4 181890.70 167518.30 186164.84\n",
       "ss3_3  180553.19 189540.7 190496.05 189094.5 193403.62 178051.37 199380.37\n",
       "ss3_14 177851.85 186142.7 188937.39 186588.7 189630.02 174936.90 198074.72\n",
       "ss3_15 158737.92 168699.4 168712.13 166280.4 170386.85 157002.43 174234.31\n",
       "ss3_16 179176.20 187705.7 189067.06 186926.4 192128.15 175158.45 198070.18\n",
       "ss3_20 151791.56 162244.6 163370.79 161377.4 164928.43 152948.90 171071.91\n",
       "ss5_18 173015.35 181837.1 183103.29 182154.2 185042.82 169232.11 191199.20\n",
       "       hc3_10   hc3_11   ss2_9     ss2_14    ss2_18    ss3_3     ss3_14   \n",
       "hc1_2  102701.5 117925.2 179529.92 180204.79 168741.42 180553.19 177851.85\n",
       "hc1_4  121351.0 118987.8 187958.77 190402.67 179482.58 189540.66 186142.74\n",
       "hc2_15 116572.8 114520.9 190230.48 190926.63 178951.49 190496.05 188937.39\n",
       "hc2_17 119223.2 120836.2 187025.52 189470.60 182139.42 189094.54 186588.74\n",
       "hc3_1  127938.6 117236.5 190032.93 190165.48 181890.70 193403.62 189630.02\n",
       "hc3_5  107091.3 119500.3 177061.13 176343.02 167518.30 178051.37 174936.90\n",
       "hc3_7  122879.0 118978.7 196726.19 197363.50 186164.84 199380.37 198074.72\n",
       "hc3_10      0.0 119768.1 178788.12 180215.96 171130.51 181920.21 177760.85\n",
       "hc3_11 119768.1      0.0 174002.37 176288.17 166228.89 176696.28 173248.65\n",
       "ss2_9  178788.1 174002.4      0.00  94644.82 101674.37  92191.75  96374.49\n",
       "ss2_14 180216.0 176288.2  94644.82      0.00 110417.84  95430.27 102711.17\n",
       "ss2_18 171130.5 166228.9 101674.37 110417.84      0.00  88513.30  97318.64\n",
       "ss3_3  181920.2 176696.3  92191.75  95430.27  88513.30      0.00  90468.36\n",
       "ss3_14 177760.8 173248.6  96374.49 102711.17  97318.64  90468.36      0.00\n",
       "ss3_15 160786.0 154512.7 113633.21 115362.63 118027.40 110760.43 111859.89\n",
       "ss3_16 178066.8 173175.2  94374.59  94180.91 108181.72  88394.44  92047.80\n",
       "ss3_20 155844.3 151397.1 118461.73 120283.15 121558.45 115809.30 115338.50\n",
       "ss5_18 175961.8 167264.2 101448.22 108619.67  83907.52  75741.78  86875.64\n",
       "       ss3_15   ss3_16    ss3_20   ss5_18   \n",
       "hc1_2  158737.9 179176.20 151791.6 173015.35\n",
       "hc1_4  168699.4 187705.68 162244.6 181837.12\n",
       "hc2_15 168712.1 189067.06 163370.8 183103.29\n",
       "hc2_17 166280.4 186926.44 161377.4 182154.17\n",
       "hc3_1  170386.8 192128.15 164928.4 185042.82\n",
       "hc3_5  157002.4 175158.45 152948.9 169232.11\n",
       "hc3_7  174234.3 198070.18 171071.9 191199.20\n",
       "hc3_10 160786.0 178066.79 155844.3 175961.75\n",
       "hc3_11 154512.7 173175.17 151397.1 167264.17\n",
       "ss2_9  113633.2  94374.59 118461.7 101448.22\n",
       "ss2_14 115362.6  94180.91 120283.2 108619.67\n",
       "ss2_18 118027.4 108181.72 121558.4  83907.52\n",
       "ss3_3  110760.4  88394.44 115809.3  75741.78\n",
       "ss3_14 111859.9  92047.80 115338.5  86875.64\n",
       "ss3_15      0.0 108633.76 112427.2 113421.34\n",
       "ss3_16 108633.8      0.00 113836.0  96165.50\n",
       "ss3_20 112427.2 113835.98      0.0 116148.51\n",
       "ss5_18 113421.3  96165.50 116148.5      0.00"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#read in epigenetic Manhattan distance, 10 coverage, DML \n",
    "ep10 <-read.csv(\"../analyses/methylation-filtered/dist.manhat.DMLs.csv\",header = T)\n",
    "ep10 <- ep10[,c(\"SampNum.row\",\"SampNum.col\",\"dist.manh\")]\n",
    "ep10 <- as.matrix(list2dist(ep10))\n",
    "mbdorder = c(\"hc1_2\",\"hc1_4\",\"hc2_15\",\"hc2_17\",\"hc3_1\",\"hc3_5\",\"hc3_7\",\"hc3_10\",\"hc3_11\",\n",
    "        \"ss2_9\",\"ss2_14\",\"ss2_18\",\"ss3_3\",\"ss3_14\",\"ss3_15\",\"ss3_16\",\"ss3_20\",\"ss5_18\")\n",
    "ep10 <- ep10[mbdorder, mbdorder]\n",
    "ep10\n",
    "Dep10 <- as.dist(ep10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "distObj <- list(Dep10,Dgen)\n",
    "save(distObj,file=\"../analyses/2bRAD/MethGen/dist_DML5x_snp.Robj\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "kernel": "R",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPearson's product-moment correlation\n",
       "\n",
       "data:  Dep10 and Dgen\n",
       "t = 13.286, df = 151, p-value < 2.2e-16\n",
       "alternative hypothesis: true correlation is not equal to 0\n",
       "95 percent confidence interval:\n",
       " 0.6513392 0.7996648\n",
       "sample estimates:\n",
       "      cor \n",
       "0.7341414 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tSpearman's rank correlation rho\n",
       "\n",
       "data:  Dep10 and Dgen\n",
       "S = 177642, p-value < 2.2e-16\n",
       "alternative hypothesis: true rho is not equal to 0\n",
       "sample estimates:\n",
       "      rho \n",
       "0.7023944 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cor.test( ~ Dep10 + Dgen,\n",
    "         method = \"pearson\",\n",
    "         conf.level = 0.95)\n",
    "cor.test( ~ Dep10 + Dgen, \n",
    "         method = \"spearman\",\n",
    "         continuity = FALSE,\n",
    "         conf.level = 0.95)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "kernel": "R"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "lm(formula = Dep10 ~ Dgen)\n",
       "\n",
       "Residuals:\n",
       "   Min     1Q Median     3Q    Max \n",
       "-46810 -19691   3017  20734  47898 \n",
       "\n",
       "Coefficients:\n",
       "            Estimate Std. Error t value Pr(>|t|)    \n",
       "(Intercept)   -65864      16040  -4.106 6.57e-05 ***\n",
       "Dgen         1030929      77594  13.286  < 2e-16 ***\n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 25020 on 151 degrees of freedom\n",
       "Multiple R-squared:  0.539,\tAdjusted R-squared:  0.5359 \n",
       "F-statistic: 176.5 on 1 and 151 DF,  p-value: < 2.2e-16\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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T3pFWmxyl2els4DvXmOfyt2f4gZpBG0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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(Dgen, Dep10, pch=20,cex=1,xlab = \"Genetic distance\", ylab = \"MBD Manhattan distance, DMLs\")\n",
    "summary(lm(Dep10~Dgen),)\n",
    "R2 = round(summary(lm(Dep10~Dgen))$r.squared, 4)\n",
    "#image(dens, col=transp(myPal(300),.7), add=TRUE)\n",
    "abline(lm(Dep10~Dgen))\n",
    "text(0.13,160000,label=paste(\"R^2=\",R2))\n",
    "text(0.13,150000,label=\"r=0.73\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "bash"
   },
   "source": [
    "## Non CpG-SNPs (not done)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "kernel": "Bash"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/gpfs01/home/kes0132/CommonG_cp2/2019_Mapping/paper-oly-mbdbs-gen/code\n"
     ]
    }
   ],
   "source": [
    "\n",
    "js(\"pwd\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "kernel": "Bash"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\"SeqNum.row\",\"SeqNum.col\",\"dist.manh\",\"SampNum.row\",\"SampNum.col\"\n",
      "1,1,0,\"hc1_2\",\"hc1_2\"\n",
      "1,15,273985.441785306,\"ss3_15\",\"hc1_2\"\n",
      "1,3,290189.490642819,\"hc2_15\",\"hc1_2\"\n",
      "1,17,285476.927245925,\"ss3_20\",\"hc1_2\"\n",
      "1,4,281520.711361473,\"hc2_17\",\"hc1_2\"\n",
      "1,2,298303.029739484,\"hc1_4\",\"hc1_2\"\n",
      "1,7,298537.38668563,\"hc3_7\",\"hc1_2\"\n",
      "1,16,284208.486947856,\"ss3_16\",\"hc1_2\"\n",
      "1,14,281305.331808996,\"ss3_14\",\"hc1_2\"\n"
     ]
    }
   ],
   "source": [
    "js(\"head ../analyses/methylation-filtered/dist.manhat.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "kernel": "R"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A matrix: 18 × 18 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>hc1_2</th><th scope=col>hc1_4</th><th scope=col>hc2_15</th><th scope=col>hc2_17</th><th scope=col>hc3_1</th><th scope=col>hc3_5</th><th scope=col>hc3_7</th><th scope=col>hc3_10</th><th scope=col>hc3_11</th><th scope=col>ss2_9</th><th scope=col>ss2_14</th><th scope=col>ss2_18</th><th scope=col>ss3_3</th><th scope=col>ss3_14</th><th scope=col>ss3_15</th><th scope=col>ss3_16</th><th scope=col>ss3_20</th><th scope=col>ss5_18</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>hc1_2</th><td>     0.0</td><td>298303.0</td><td>290189.5</td><td>281520.7</td><td>323694.5</td><td>288274.1</td><td>298537.4</td><td>276634.8</td><td>311367.0</td><td>297793.0</td><td>280529.9</td><td>334528.1</td><td>284554.6</td><td>281305.3</td><td>273985.4</td><td>284208.5</td><td>285476.9</td><td>297048.1</td></tr>\n",
       "\t<tr><th scope=row>hc1_4</th><td>298303.0</td><td>     0.0</td><td>288401.4</td><td>272689.0</td><td>314054.6</td><td>308425.8</td><td>290930.3</td><td>292157.4</td><td>313295.9</td><td>306377.3</td><td>291403.7</td><td>334752.1</td><td>291934.5</td><td>287946.6</td><td>285750.2</td><td>292011.5</td><td>290794.1</td><td>299482.5</td></tr>\n",
       "\t<tr><th scope=row>hc2_15</th><td>290189.5</td><td>288401.4</td><td>     0.0</td><td>267996.3</td><td>312934.8</td><td>306314.1</td><td>276217.9</td><td>283512.3</td><td>309629.3</td><td>296792.4</td><td>273417.4</td><td>339043.0</td><td>280972.0</td><td>277302.7</td><td>270960.4</td><td>281246.4</td><td>283754.6</td><td>297864.8</td></tr>\n",
       "\t<tr><th scope=row>hc2_17</th><td>281520.7</td><td>272689.0</td><td>267996.3</td><td>     0.0</td><td>314787.3</td><td>299622.6</td><td>275369.7</td><td>267637.3</td><td>306306.9</td><td>287060.1</td><td>263776.1</td><td>339119.1</td><td>274036.3</td><td>268751.4</td><td>256317.1</td><td>272144.4</td><td>273361.0</td><td>290238.7</td></tr>\n",
       "\t<tr><th scope=row>hc3_1</th><td>323694.5</td><td>314054.6</td><td>312934.8</td><td>314787.3</td><td>     0.0</td><td>334641.8</td><td>299112.8</td><td>325937.0</td><td>324463.8</td><td>321179.5</td><td>320997.4</td><td>335280.4</td><td>315988.8</td><td>313767.2</td><td>321554.2</td><td>317127.8</td><td>317475.6</td><td>315139.7</td></tr>\n",
       "\t<tr><th scope=row>hc3_5</th><td>288274.1</td><td>308425.8</td><td>306314.1</td><td>299622.6</td><td>334641.8</td><td>     0.0</td><td>315610.7</td><td>288781.8</td><td>325942.9</td><td>316722.4</td><td>306762.5</td><td>346143.9</td><td>305665.1</td><td>303050.4</td><td>296746.5</td><td>308046.6</td><td>304834.4</td><td>313945.2</td></tr>\n",
       "\t<tr><th scope=row>hc3_7</th><td>298537.4</td><td>290930.3</td><td>276217.9</td><td>275369.7</td><td>299112.8</td><td>315610.7</td><td>     0.0</td><td>292111.6</td><td>315363.0</td><td>297745.1</td><td>282259.6</td><td>341069.9</td><td>284236.7</td><td>279628.7</td><td>277165.4</td><td>285466.0</td><td>287941.8</td><td>299663.9</td></tr>\n",
       "\t<tr><th scope=row>hc3_10</th><td>276634.8</td><td>292157.4</td><td>283512.3</td><td>267637.3</td><td>325937.0</td><td>288781.8</td><td>292111.6</td><td>     0.0</td><td>317230.3</td><td>297372.5</td><td>267563.6</td><td>345360.9</td><td>279921.4</td><td>274370.7</td><td>257816.0</td><td>277769.7</td><td>274299.4</td><td>294286.7</td></tr>\n",
       "\t<tr><th scope=row>hc3_11</th><td>311367.0</td><td>313295.9</td><td>309629.3</td><td>306306.9</td><td>324463.8</td><td>325942.9</td><td>315363.0</td><td>317230.3</td><td>     0.0</td><td>306749.7</td><td>303226.0</td><td>330648.3</td><td>295962.1</td><td>294657.6</td><td>302097.9</td><td>297720.5</td><td>300676.0</td><td>304098.3</td></tr>\n",
       "\t<tr><th scope=row>ss2_9</th><td>297793.0</td><td>306377.3</td><td>296792.4</td><td>287060.1</td><td>321179.5</td><td>316722.4</td><td>297745.1</td><td>297372.5</td><td>306749.7</td><td>     0.0</td><td>253556.8</td><td>313685.4</td><td>255231.0</td><td>253726.0</td><td>268911.5</td><td>256477.3</td><td>273957.8</td><td>278320.1</td></tr>\n",
       "\t<tr><th scope=row>ss2_14</th><td>280529.9</td><td>291403.7</td><td>273417.4</td><td>263776.1</td><td>320997.4</td><td>306762.5</td><td>282259.6</td><td>267563.6</td><td>303226.0</td><td>253556.8</td><td>     0.0</td><td>326677.5</td><td>241072.4</td><td>240035.5</td><td>237976.3</td><td>237509.6</td><td>258380.9</td><td>271803.4</td></tr>\n",
       "\t<tr><th scope=row>ss2_18</th><td>334528.1</td><td>334752.1</td><td>339043.0</td><td>339119.1</td><td>335280.4</td><td>346143.9</td><td>341069.9</td><td>345360.9</td><td>330648.3</td><td>313685.4</td><td>326677.5</td><td>     0.0</td><td>300672.3</td><td>306406.7</td><td>328384.0</td><td>317824.0</td><td>324077.2</td><td>300108.0</td></tr>\n",
       "\t<tr><th scope=row>ss3_3</th><td>284554.6</td><td>291934.5</td><td>280972.0</td><td>274036.3</td><td>315988.8</td><td>305665.1</td><td>284236.7</td><td>279921.4</td><td>295962.1</td><td>255231.0</td><td>241072.4</td><td>300672.3</td><td>     0.0</td><td>238988.6</td><td>251167.6</td><td>238332.2</td><td>262145.0</td><td>253721.5</td></tr>\n",
       "\t<tr><th scope=row>ss3_14</th><td>281305.3</td><td>287946.6</td><td>277302.7</td><td>268751.4</td><td>313767.2</td><td>303050.4</td><td>279628.7</td><td>274370.7</td><td>294657.6</td><td>253726.0</td><td>240035.5</td><td>306406.7</td><td>238988.6</td><td>     0.0</td><td>244038.7</td><td>235816.2</td><td>258085.7</td><td>257354.3</td></tr>\n",
       "\t<tr><th scope=row>ss3_15</th><td>273985.4</td><td>285750.2</td><td>270960.4</td><td>256317.1</td><td>321554.2</td><td>296746.5</td><td>277165.4</td><td>257816.0</td><td>302097.9</td><td>268911.5</td><td>237976.3</td><td>328384.0</td><td>251167.6</td><td>244038.7</td><td>     0.0</td><td>246521.3</td><td>253941.4</td><td>276798.6</td></tr>\n",
       "\t<tr><th scope=row>ss3_16</th><td>284208.5</td><td>292011.5</td><td>281246.4</td><td>272144.4</td><td>317127.8</td><td>308046.6</td><td>285466.0</td><td>277769.7</td><td>297720.5</td><td>256477.3</td><td>237509.6</td><td>317824.0</td><td>238332.2</td><td>235816.2</td><td>246521.3</td><td>     0.0</td><td>258804.6</td><td>267488.5</td></tr>\n",
       "\t<tr><th scope=row>ss3_20</th><td>285476.9</td><td>290794.1</td><td>283754.6</td><td>273361.0</td><td>317475.6</td><td>304834.4</td><td>287941.8</td><td>274299.4</td><td>300676.0</td><td>273957.8</td><td>258380.9</td><td>324077.2</td><td>262145.0</td><td>258085.7</td><td>253941.4</td><td>258804.6</td><td>     0.0</td><td>280486.1</td></tr>\n",
       "\t<tr><th scope=row>ss5_18</th><td>297048.1</td><td>299482.5</td><td>297864.8</td><td>290238.7</td><td>315139.7</td><td>313945.2</td><td>299663.9</td><td>294286.7</td><td>304098.3</td><td>278320.1</td><td>271803.4</td><td>300108.0</td><td>253721.5</td><td>257354.3</td><td>276798.6</td><td>267488.5</td><td>280486.1</td><td>     0.0</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 18 × 18 of type dbl\n",
       "\\begin{tabular}{r|llllllllllllllllll}\n",
       "  & hc1\\_2 & hc1\\_4 & hc2\\_15 & hc2\\_17 & hc3\\_1 & hc3\\_5 & hc3\\_7 & hc3\\_10 & hc3\\_11 & ss2\\_9 & ss2\\_14 & ss2\\_18 & ss3\\_3 & ss3\\_14 & ss3\\_15 & ss3\\_16 & ss3\\_20 & ss5\\_18\\\\\n",
       "\\hline\n",
       "\thc1\\_2 &      0.0 & 298303.0 & 290189.5 & 281520.7 & 323694.5 & 288274.1 & 298537.4 & 276634.8 & 311367.0 & 297793.0 & 280529.9 & 334528.1 & 284554.6 & 281305.3 & 273985.4 & 284208.5 & 285476.9 & 297048.1\\\\\n",
       "\thc1\\_4 & 298303.0 &      0.0 & 288401.4 & 272689.0 & 314054.6 & 308425.8 & 290930.3 & 292157.4 & 313295.9 & 306377.3 & 291403.7 & 334752.1 & 291934.5 & 287946.6 & 285750.2 & 292011.5 & 290794.1 & 299482.5\\\\\n",
       "\thc2\\_15 & 290189.5 & 288401.4 &      0.0 & 267996.3 & 312934.8 & 306314.1 & 276217.9 & 283512.3 & 309629.3 & 296792.4 & 273417.4 & 339043.0 & 280972.0 & 277302.7 & 270960.4 & 281246.4 & 283754.6 & 297864.8\\\\\n",
       "\thc2\\_17 & 281520.7 & 272689.0 & 267996.3 &      0.0 & 314787.3 & 299622.6 & 275369.7 & 267637.3 & 306306.9 & 287060.1 & 263776.1 & 339119.1 & 274036.3 & 268751.4 & 256317.1 & 272144.4 & 273361.0 & 290238.7\\\\\n",
       "\thc3\\_1 & 323694.5 & 314054.6 & 312934.8 & 314787.3 &      0.0 & 334641.8 & 299112.8 & 325937.0 & 324463.8 & 321179.5 & 320997.4 & 335280.4 & 315988.8 & 313767.2 & 321554.2 & 317127.8 & 317475.6 & 315139.7\\\\\n",
       "\thc3\\_5 & 288274.1 & 308425.8 & 306314.1 & 299622.6 & 334641.8 &      0.0 & 315610.7 & 288781.8 & 325942.9 & 316722.4 & 306762.5 & 346143.9 & 305665.1 & 303050.4 & 296746.5 & 308046.6 & 304834.4 & 313945.2\\\\\n",
       "\thc3\\_7 & 298537.4 & 290930.3 & 276217.9 & 275369.7 & 299112.8 & 315610.7 &      0.0 & 292111.6 & 315363.0 & 297745.1 & 282259.6 & 341069.9 & 284236.7 & 279628.7 & 277165.4 & 285466.0 & 287941.8 & 299663.9\\\\\n",
       "\thc3\\_10 & 276634.8 & 292157.4 & 283512.3 & 267637.3 & 325937.0 & 288781.8 & 292111.6 &      0.0 & 317230.3 & 297372.5 & 267563.6 & 345360.9 & 279921.4 & 274370.7 & 257816.0 & 277769.7 & 274299.4 & 294286.7\\\\\n",
       "\thc3\\_11 & 311367.0 & 313295.9 & 309629.3 & 306306.9 & 324463.8 & 325942.9 & 315363.0 & 317230.3 &      0.0 & 306749.7 & 303226.0 & 330648.3 & 295962.1 & 294657.6 & 302097.9 & 297720.5 & 300676.0 & 304098.3\\\\\n",
       "\tss2\\_9 & 297793.0 & 306377.3 & 296792.4 & 287060.1 & 321179.5 & 316722.4 & 297745.1 & 297372.5 & 306749.7 &      0.0 & 253556.8 & 313685.4 & 255231.0 & 253726.0 & 268911.5 & 256477.3 & 273957.8 & 278320.1\\\\\n",
       "\tss2\\_14 & 280529.9 & 291403.7 & 273417.4 & 263776.1 & 320997.4 & 306762.5 & 282259.6 & 267563.6 & 303226.0 & 253556.8 &      0.0 & 326677.5 & 241072.4 & 240035.5 & 237976.3 & 237509.6 & 258380.9 & 271803.4\\\\\n",
       "\tss2\\_18 & 334528.1 & 334752.1 & 339043.0 & 339119.1 & 335280.4 & 346143.9 & 341069.9 & 345360.9 & 330648.3 & 313685.4 & 326677.5 &      0.0 & 300672.3 & 306406.7 & 328384.0 & 317824.0 & 324077.2 & 300108.0\\\\\n",
       "\tss3\\_3 & 284554.6 & 291934.5 & 280972.0 & 274036.3 & 315988.8 & 305665.1 & 284236.7 & 279921.4 & 295962.1 & 255231.0 & 241072.4 & 300672.3 &      0.0 & 238988.6 & 251167.6 & 238332.2 & 262145.0 & 253721.5\\\\\n",
       "\tss3\\_14 & 281305.3 & 287946.6 & 277302.7 & 268751.4 & 313767.2 & 303050.4 & 279628.7 & 274370.7 & 294657.6 & 253726.0 & 240035.5 & 306406.7 & 238988.6 &      0.0 & 244038.7 & 235816.2 & 258085.7 & 257354.3\\\\\n",
       "\tss3\\_15 & 273985.4 & 285750.2 & 270960.4 & 256317.1 & 321554.2 & 296746.5 & 277165.4 & 257816.0 & 302097.9 & 268911.5 & 237976.3 & 328384.0 & 251167.6 & 244038.7 &      0.0 & 246521.3 & 253941.4 & 276798.6\\\\\n",
       "\tss3\\_16 & 284208.5 & 292011.5 & 281246.4 & 272144.4 & 317127.8 & 308046.6 & 285466.0 & 277769.7 & 297720.5 & 256477.3 & 237509.6 & 317824.0 & 238332.2 & 235816.2 & 246521.3 &      0.0 & 258804.6 & 267488.5\\\\\n",
       "\tss3\\_20 & 285476.9 & 290794.1 & 283754.6 & 273361.0 & 317475.6 & 304834.4 & 287941.8 & 274299.4 & 300676.0 & 273957.8 & 258380.9 & 324077.2 & 262145.0 & 258085.7 & 253941.4 & 258804.6 &      0.0 & 280486.1\\\\\n",
       "\tss5\\_18 & 297048.1 & 299482.5 & 297864.8 & 290238.7 & 315139.7 & 313945.2 & 299663.9 & 294286.7 & 304098.3 & 278320.1 & 271803.4 & 300108.0 & 253721.5 & 257354.3 & 276798.6 & 267488.5 & 280486.1 &      0.0\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 18 × 18 of type dbl\n",
       "\n",
       "| <!--/--> | hc1_2 | hc1_4 | hc2_15 | hc2_17 | hc3_1 | hc3_5 | hc3_7 | hc3_10 | hc3_11 | ss2_9 | ss2_14 | ss2_18 | ss3_3 | ss3_14 | ss3_15 | ss3_16 | ss3_20 | ss5_18 |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| hc1_2 |      0.0 | 298303.0 | 290189.5 | 281520.7 | 323694.5 | 288274.1 | 298537.4 | 276634.8 | 311367.0 | 297793.0 | 280529.9 | 334528.1 | 284554.6 | 281305.3 | 273985.4 | 284208.5 | 285476.9 | 297048.1 |\n",
       "| hc1_4 | 298303.0 |      0.0 | 288401.4 | 272689.0 | 314054.6 | 308425.8 | 290930.3 | 292157.4 | 313295.9 | 306377.3 | 291403.7 | 334752.1 | 291934.5 | 287946.6 | 285750.2 | 292011.5 | 290794.1 | 299482.5 |\n",
       "| hc2_15 | 290189.5 | 288401.4 |      0.0 | 267996.3 | 312934.8 | 306314.1 | 276217.9 | 283512.3 | 309629.3 | 296792.4 | 273417.4 | 339043.0 | 280972.0 | 277302.7 | 270960.4 | 281246.4 | 283754.6 | 297864.8 |\n",
       "| hc2_17 | 281520.7 | 272689.0 | 267996.3 |      0.0 | 314787.3 | 299622.6 | 275369.7 | 267637.3 | 306306.9 | 287060.1 | 263776.1 | 339119.1 | 274036.3 | 268751.4 | 256317.1 | 272144.4 | 273361.0 | 290238.7 |\n",
       "| hc3_1 | 323694.5 | 314054.6 | 312934.8 | 314787.3 |      0.0 | 334641.8 | 299112.8 | 325937.0 | 324463.8 | 321179.5 | 320997.4 | 335280.4 | 315988.8 | 313767.2 | 321554.2 | 317127.8 | 317475.6 | 315139.7 |\n",
       "| hc3_5 | 288274.1 | 308425.8 | 306314.1 | 299622.6 | 334641.8 |      0.0 | 315610.7 | 288781.8 | 325942.9 | 316722.4 | 306762.5 | 346143.9 | 305665.1 | 303050.4 | 296746.5 | 308046.6 | 304834.4 | 313945.2 |\n",
       "| hc3_7 | 298537.4 | 290930.3 | 276217.9 | 275369.7 | 299112.8 | 315610.7 |      0.0 | 292111.6 | 315363.0 | 297745.1 | 282259.6 | 341069.9 | 284236.7 | 279628.7 | 277165.4 | 285466.0 | 287941.8 | 299663.9 |\n",
       "| hc3_10 | 276634.8 | 292157.4 | 283512.3 | 267637.3 | 325937.0 | 288781.8 | 292111.6 |      0.0 | 317230.3 | 297372.5 | 267563.6 | 345360.9 | 279921.4 | 274370.7 | 257816.0 | 277769.7 | 274299.4 | 294286.7 |\n",
       "| hc3_11 | 311367.0 | 313295.9 | 309629.3 | 306306.9 | 324463.8 | 325942.9 | 315363.0 | 317230.3 |      0.0 | 306749.7 | 303226.0 | 330648.3 | 295962.1 | 294657.6 | 302097.9 | 297720.5 | 300676.0 | 304098.3 |\n",
       "| ss2_9 | 297793.0 | 306377.3 | 296792.4 | 287060.1 | 321179.5 | 316722.4 | 297745.1 | 297372.5 | 306749.7 |      0.0 | 253556.8 | 313685.4 | 255231.0 | 253726.0 | 268911.5 | 256477.3 | 273957.8 | 278320.1 |\n",
       "| ss2_14 | 280529.9 | 291403.7 | 273417.4 | 263776.1 | 320997.4 | 306762.5 | 282259.6 | 267563.6 | 303226.0 | 253556.8 |      0.0 | 326677.5 | 241072.4 | 240035.5 | 237976.3 | 237509.6 | 258380.9 | 271803.4 |\n",
       "| ss2_18 | 334528.1 | 334752.1 | 339043.0 | 339119.1 | 335280.4 | 346143.9 | 341069.9 | 345360.9 | 330648.3 | 313685.4 | 326677.5 |      0.0 | 300672.3 | 306406.7 | 328384.0 | 317824.0 | 324077.2 | 300108.0 |\n",
       "| ss3_3 | 284554.6 | 291934.5 | 280972.0 | 274036.3 | 315988.8 | 305665.1 | 284236.7 | 279921.4 | 295962.1 | 255231.0 | 241072.4 | 300672.3 |      0.0 | 238988.6 | 251167.6 | 238332.2 | 262145.0 | 253721.5 |\n",
       "| ss3_14 | 281305.3 | 287946.6 | 277302.7 | 268751.4 | 313767.2 | 303050.4 | 279628.7 | 274370.7 | 294657.6 | 253726.0 | 240035.5 | 306406.7 | 238988.6 |      0.0 | 244038.7 | 235816.2 | 258085.7 | 257354.3 |\n",
       "| ss3_15 | 273985.4 | 285750.2 | 270960.4 | 256317.1 | 321554.2 | 296746.5 | 277165.4 | 257816.0 | 302097.9 | 268911.5 | 237976.3 | 328384.0 | 251167.6 | 244038.7 |      0.0 | 246521.3 | 253941.4 | 276798.6 |\n",
       "| ss3_16 | 284208.5 | 292011.5 | 281246.4 | 272144.4 | 317127.8 | 308046.6 | 285466.0 | 277769.7 | 297720.5 | 256477.3 | 237509.6 | 317824.0 | 238332.2 | 235816.2 | 246521.3 |      0.0 | 258804.6 | 267488.5 |\n",
       "| ss3_20 | 285476.9 | 290794.1 | 283754.6 | 273361.0 | 317475.6 | 304834.4 | 287941.8 | 274299.4 | 300676.0 | 273957.8 | 258380.9 | 324077.2 | 262145.0 | 258085.7 | 253941.4 | 258804.6 |      0.0 | 280486.1 |\n",
       "| ss5_18 | 297048.1 | 299482.5 | 297864.8 | 290238.7 | 315139.7 | 313945.2 | 299663.9 | 294286.7 | 304098.3 | 278320.1 | 271803.4 | 300108.0 | 253721.5 | 257354.3 | 276798.6 | 267488.5 | 280486.1 |      0.0 |\n",
       "\n"
      ],
      "text/plain": [
       "       hc1_2    hc1_4    hc2_15   hc2_17   hc3_1    hc3_5    hc3_7    hc3_10  \n",
       "hc1_2       0.0 298303.0 290189.5 281520.7 323694.5 288274.1 298537.4 276634.8\n",
       "hc1_4  298303.0      0.0 288401.4 272689.0 314054.6 308425.8 290930.3 292157.4\n",
       "hc2_15 290189.5 288401.4      0.0 267996.3 312934.8 306314.1 276217.9 283512.3\n",
       "hc2_17 281520.7 272689.0 267996.3      0.0 314787.3 299622.6 275369.7 267637.3\n",
       "hc3_1  323694.5 314054.6 312934.8 314787.3      0.0 334641.8 299112.8 325937.0\n",
       "hc3_5  288274.1 308425.8 306314.1 299622.6 334641.8      0.0 315610.7 288781.8\n",
       "hc3_7  298537.4 290930.3 276217.9 275369.7 299112.8 315610.7      0.0 292111.6\n",
       "hc3_10 276634.8 292157.4 283512.3 267637.3 325937.0 288781.8 292111.6      0.0\n",
       "hc3_11 311367.0 313295.9 309629.3 306306.9 324463.8 325942.9 315363.0 317230.3\n",
       "ss2_9  297793.0 306377.3 296792.4 287060.1 321179.5 316722.4 297745.1 297372.5\n",
       "ss2_14 280529.9 291403.7 273417.4 263776.1 320997.4 306762.5 282259.6 267563.6\n",
       "ss2_18 334528.1 334752.1 339043.0 339119.1 335280.4 346143.9 341069.9 345360.9\n",
       "ss3_3  284554.6 291934.5 280972.0 274036.3 315988.8 305665.1 284236.7 279921.4\n",
       "ss3_14 281305.3 287946.6 277302.7 268751.4 313767.2 303050.4 279628.7 274370.7\n",
       "ss3_15 273985.4 285750.2 270960.4 256317.1 321554.2 296746.5 277165.4 257816.0\n",
       "ss3_16 284208.5 292011.5 281246.4 272144.4 317127.8 308046.6 285466.0 277769.7\n",
       "ss3_20 285476.9 290794.1 283754.6 273361.0 317475.6 304834.4 287941.8 274299.4\n",
       "ss5_18 297048.1 299482.5 297864.8 290238.7 315139.7 313945.2 299663.9 294286.7\n",
       "       hc3_11   ss2_9    ss2_14   ss2_18   ss3_3    ss3_14   ss3_15   ss3_16  \n",
       "hc1_2  311367.0 297793.0 280529.9 334528.1 284554.6 281305.3 273985.4 284208.5\n",
       "hc1_4  313295.9 306377.3 291403.7 334752.1 291934.5 287946.6 285750.2 292011.5\n",
       "hc2_15 309629.3 296792.4 273417.4 339043.0 280972.0 277302.7 270960.4 281246.4\n",
       "hc2_17 306306.9 287060.1 263776.1 339119.1 274036.3 268751.4 256317.1 272144.4\n",
       "hc3_1  324463.8 321179.5 320997.4 335280.4 315988.8 313767.2 321554.2 317127.8\n",
       "hc3_5  325942.9 316722.4 306762.5 346143.9 305665.1 303050.4 296746.5 308046.6\n",
       "hc3_7  315363.0 297745.1 282259.6 341069.9 284236.7 279628.7 277165.4 285466.0\n",
       "hc3_10 317230.3 297372.5 267563.6 345360.9 279921.4 274370.7 257816.0 277769.7\n",
       "hc3_11      0.0 306749.7 303226.0 330648.3 295962.1 294657.6 302097.9 297720.5\n",
       "ss2_9  306749.7      0.0 253556.8 313685.4 255231.0 253726.0 268911.5 256477.3\n",
       "ss2_14 303226.0 253556.8      0.0 326677.5 241072.4 240035.5 237976.3 237509.6\n",
       "ss2_18 330648.3 313685.4 326677.5      0.0 300672.3 306406.7 328384.0 317824.0\n",
       "ss3_3  295962.1 255231.0 241072.4 300672.3      0.0 238988.6 251167.6 238332.2\n",
       "ss3_14 294657.6 253726.0 240035.5 306406.7 238988.6      0.0 244038.7 235816.2\n",
       "ss3_15 302097.9 268911.5 237976.3 328384.0 251167.6 244038.7      0.0 246521.3\n",
       "ss3_16 297720.5 256477.3 237509.6 317824.0 238332.2 235816.2 246521.3      0.0\n",
       "ss3_20 300676.0 273957.8 258380.9 324077.2 262145.0 258085.7 253941.4 258804.6\n",
       "ss5_18 304098.3 278320.1 271803.4 300108.0 253721.5 257354.3 276798.6 267488.5\n",
       "       ss3_20   ss5_18  \n",
       "hc1_2  285476.9 297048.1\n",
       "hc1_4  290794.1 299482.5\n",
       "hc2_15 283754.6 297864.8\n",
       "hc2_17 273361.0 290238.7\n",
       "hc3_1  317475.6 315139.7\n",
       "hc3_5  304834.4 313945.2\n",
       "hc3_7  287941.8 299663.9\n",
       "hc3_10 274299.4 294286.7\n",
       "hc3_11 300676.0 304098.3\n",
       "ss2_9  273957.8 278320.1\n",
       "ss2_14 258380.9 271803.4\n",
       "ss2_18 324077.2 300108.0\n",
       "ss3_3  262145.0 253721.5\n",
       "ss3_14 258085.7 257354.3\n",
       "ss3_15 253941.4 276798.6\n",
       "ss3_16 258804.6 267488.5\n",
       "ss3_20      0.0 280486.1\n",
       "ss5_18 280486.1      0.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#read in epigenetic Manhattan distance, 10 coverage \n",
    "library(spaa)\n",
    "ep10 <-read.csv(\"../analyses/methylation-filtered/dist.manhat.csv\",header = T)\n",
    "ep10 <- ep10[,c(\"SampNum.row\",\"SampNum.col\",\"dist.manh\")]\n",
    "ep10 <- as.matrix(list2dist(ep10))\n",
    "mbdorder = c(\"hc1_2\",\"hc1_4\",\"hc2_15\",\"hc2_17\",\"hc3_1\",\"hc3_5\",\"hc3_7\",\"hc3_10\",\"hc3_11\",\n",
    "        \"ss2_9\",\"ss2_14\",\"ss2_18\",\"ss3_3\",\"ss3_14\",\"ss3_15\",\"ss3_16\",\"ss3_20\",\"ss5_18\")\n",
    "ep10 <- ep10[mbdorder, mbdorder]\n",
    "ep10\n",
    "Dep10 <- as.dist(ep10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "           hc1_2     hc1_4    hc2_15    hc2_17     hc3_1     hc3_5     hc3_7\n",
       "hc1_4  10545.532                                                            \n",
       "hc2_15 10351.204  9064.440                                                  \n",
       "hc2_17  9385.573  7984.184  8032.958                                        \n",
       "hc3_1  10610.214  8727.395  9008.169  9827.633                              \n",
       "hc3_5   6363.300  8905.577  9760.334  8017.600  8808.350                    \n",
       "hc3_7  10611.443  8699.207  7750.867  8785.571  6567.108  9189.478          \n",
       "hc3_10  8551.481 10736.499 10319.292  9408.603 10436.642  7834.229 10443.443\n",
       "hc3_11  9068.337 10026.701 11194.320 10454.902 10554.475  9119.644 10804.160\n",
       "ss2_9  14788.300 17346.810 16995.519 16400.962 16222.809 15202.564 17348.604\n",
       "ss2_14 14887.002 16626.609 16120.010 16108.636 16014.164 15975.183 16705.442\n",
       "ss2_18 13635.256 15169.867 15450.882 15381.091 13934.951 13292.572 14868.123\n",
       "ss3_3  14606.952 16480.535 16418.206 15743.180 15248.147 15959.683 16044.562\n",
       "ss3_14 14484.494 16347.990 16417.512 15586.173 14915.306 15121.119 16375.903\n",
       "ss3_15 11693.997 14253.680 13740.327 13138.397 12793.947 11756.756 13350.965\n",
       "ss3_16 14072.044 15345.568 15822.143 14668.390 14521.674 15334.174 16117.654\n",
       "ss3_20 11702.183 13291.645 13357.002 12158.790 12503.211 12440.562 13837.969\n",
       "ss5_18 13190.286 15195.551 15649.929 14422.239 13721.460 14534.806 15293.315\n",
       "          hc3_10    hc3_11     ss2_9    ss2_14    ss2_18     ss3_3    ss3_14\n",
       "hc1_4                                                                       \n",
       "hc2_15                                                                      \n",
       "hc2_17                                                                      \n",
       "hc3_1                                                                       \n",
       "hc3_5                                                                       \n",
       "hc3_7                                                                       \n",
       "hc3_10                                                                      \n",
       "hc3_11 10148.679                                                            \n",
       "ss2_9  15486.501 15102.614                                                  \n",
       "ss2_14 14988.730 14709.279  9317.558                                        \n",
       "ss2_18 13497.210 13012.517  8747.628 10653.366                              \n",
       "ss3_3  15209.257 14188.195  8413.878  9278.006  7351.714                    \n",
       "ss3_14 14489.565 14113.226  9119.600  9505.802  8692.706  8503.102          \n",
       "ss3_15 12354.985 11779.373 10149.293 10936.765 10026.944  9899.829 10568.719\n",
       "ss3_16 14441.498 13574.423  8682.160  8530.524 10471.814  8283.191  9230.876\n",
       "ss3_20 11705.987 12187.148 11387.520 11322.262 11078.749 11215.385 11030.208\n",
       "ss5_18 14281.504 13623.710  8899.327 10281.724  7483.073  6997.712  8055.097\n",
       "          ss3_15    ss3_16    ss3_20\n",
       "hc1_4                               \n",
       "hc2_15                              \n",
       "hc2_17                              \n",
       "hc3_1                               \n",
       "hc3_5                               \n",
       "hc3_7                               \n",
       "hc3_10                              \n",
       "hc3_11                              \n",
       "ss2_9                               \n",
       "ss2_14                              \n",
       "ss2_18                              \n",
       "ss3_3                               \n",
       "ss3_14                              \n",
       "ss3_15                              \n",
       "ss3_16 10193.024                    \n",
       "ss3_20 10433.139 10436.720          \n",
       "ss5_18  9796.022  8725.894 10959.480"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Dep10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "kernel": "R"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 18 × 18</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>hc1_2</th><th scope=col>hc1_4</th><th scope=col>hc2_15</th><th scope=col>hc2_17</th><th scope=col>hc3_1</th><th scope=col>hc3_5</th><th scope=col>hc3_7</th><th scope=col>hc3_10</th><th scope=col>hc3_11</th><th scope=col>ss2_9</th><th scope=col>ss2_14</th><th scope=col>ss2_18</th><th scope=col>ss3_3</th><th scope=col>ss3_14</th><th scope=col>ss3_15</th><th scope=col>ss3_16</th><th scope=col>ss3_20</th><th scope=col>ss5_18</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>hc1_2</th><td>0.0000000</td><td>0.2155436</td><td>0.2054988</td><td>0.2245761</td><td>0.2140475</td><td>0.1251035</td><td>0.2137833</td><td>0.1941731</td><td>0.2196581</td><td>0.2191748</td><td>0.2201363</td><td>0.2251679</td><td>0.2293540</td><td>0.2303018</td><td>0.2273960</td><td>0.2267536</td><td>0.2241723</td><td>0.2393679</td></tr>\n",
       "\t<tr><th scope=row>hc1_4</th><td>0.2155436</td><td>0.0000000</td><td>0.1844240</td><td>0.1693271</td><td>0.1811331</td><td>0.2071129</td><td>0.1608409</td><td>0.2090009</td><td>0.2157563</td><td>0.2186305</td><td>0.2188330</td><td>0.2146309</td><td>0.2291905</td><td>0.2247412</td><td>0.2257357</td><td>0.2190871</td><td>0.2157204</td><td>0.2274161</td></tr>\n",
       "\t<tr><th scope=row>hc2_15</th><td>0.2054988</td><td>0.1844240</td><td>0.0000000</td><td>0.2041950</td><td>0.1854690</td><td>0.2021604</td><td>0.1823913</td><td>0.1592689</td><td>0.2168135</td><td>0.2041132</td><td>0.2085476</td><td>0.1910094</td><td>0.2042863</td><td>0.2045106</td><td>0.2061360</td><td>0.2040459</td><td>0.2013346</td><td>0.2098602</td></tr>\n",
       "\t<tr><th scope=row>hc2_17</th><td>0.2245761</td><td>0.1693271</td><td>0.2041950</td><td>0.0000000</td><td>0.1884048</td><td>0.2163634</td><td>0.1724402</td><td>0.2179260</td><td>0.2265039</td><td>0.2268210</td><td>0.2268209</td><td>0.2283067</td><td>0.2379735</td><td>0.2322090</td><td>0.2385235</td><td>0.2269364</td><td>0.2294361</td><td>0.2372695</td></tr>\n",
       "\t<tr><th scope=row>hc3_1</th><td>0.2140475</td><td>0.1811331</td><td>0.1854690</td><td>0.1884048</td><td>0.0000000</td><td>0.2084380</td><td>0.1141350</td><td>0.2070880</td><td>0.2092909</td><td>0.2195993</td><td>0.2160866</td><td>0.2155464</td><td>0.2254828</td><td>0.2196778</td><td>0.2258590</td><td>0.2205749</td><td>0.2200920</td><td>0.2156870</td></tr>\n",
       "\t<tr><th scope=row>hc3_5</th><td>0.1251035</td><td>0.2071129</td><td>0.2021604</td><td>0.2163634</td><td>0.2084380</td><td>0.0000000</td><td>0.2104185</td><td>0.1869989</td><td>0.2143060</td><td>0.2202222</td><td>0.2148287</td><td>0.2214425</td><td>0.2216537</td><td>0.2194587</td><td>0.2223524</td><td>0.2192765</td><td>0.2221298</td><td>0.2263948</td></tr>\n",
       "\t<tr><th scope=row>hc3_7</th><td>0.2137833</td><td>0.1608409</td><td>0.1823913</td><td>0.1724402</td><td>0.1141350</td><td>0.2104185</td><td>0.0000000</td><td>0.2098760</td><td>0.2154145</td><td>0.2211945</td><td>0.2233552</td><td>0.2236631</td><td>0.2265407</td><td>0.2248371</td><td>0.2269251</td><td>0.2224353</td><td>0.2260581</td><td>0.2286600</td></tr>\n",
       "\t<tr><th scope=row>hc3_10</th><td>0.1941731</td><td>0.2090009</td><td>0.1592689</td><td>0.2179260</td><td>0.2070880</td><td>0.1869989</td><td>0.2098760</td><td>0.0000000</td><td>0.2158190</td><td>0.2072541</td><td>0.2123815</td><td>0.2048036</td><td>0.2057382</td><td>0.2078799</td><td>0.1916720</td><td>0.2139469</td><td>0.2081097</td><td>0.2118349</td></tr>\n",
       "\t<tr><th scope=row>hc3_11</th><td>0.2196581</td><td>0.2157563</td><td>0.2168135</td><td>0.2265039</td><td>0.2092909</td><td>0.2143060</td><td>0.2154145</td><td>0.2158190</td><td>0.0000000</td><td>0.2204729</td><td>0.2206785</td><td>0.2201360</td><td>0.2362094</td><td>0.2252514</td><td>0.2307265</td><td>0.2282604</td><td>0.2200907</td><td>0.2270442</td></tr>\n",
       "\t<tr><th scope=row>ss2_9</th><td>0.2191748</td><td>0.2186305</td><td>0.2041132</td><td>0.2268210</td><td>0.2195993</td><td>0.2202222</td><td>0.2211945</td><td>0.2072541</td><td>0.2204729</td><td>0.0000000</td><td>0.1482022</td><td>0.1643140</td><td>0.1565610</td><td>0.1642693</td><td>0.2091080</td><td>0.1664055</td><td>0.2066793</td><td>0.1738464</td></tr>\n",
       "\t<tr><th scope=row>ss2_14</th><td>0.2201363</td><td>0.2188330</td><td>0.2085476</td><td>0.2268209</td><td>0.2160866</td><td>0.2148287</td><td>0.2233552</td><td>0.2123815</td><td>0.2206785</td><td>0.1482022</td><td>0.0000000</td><td>0.1741098</td><td>0.1484094</td><td>0.1731242</td><td>0.2053117</td><td>0.1555210</td><td>0.2028654</td><td>0.1736163</td></tr>\n",
       "\t<tr><th scope=row>ss2_18</th><td>0.2251679</td><td>0.2146309</td><td>0.1910094</td><td>0.2283067</td><td>0.2155464</td><td>0.2214425</td><td>0.2236631</td><td>0.2048036</td><td>0.2201360</td><td>0.1643140</td><td>0.1741098</td><td>0.0000000</td><td>0.1311476</td><td>0.1613894</td><td>0.2121693</td><td>0.1856383</td><td>0.1988239</td><td>0.1271048</td></tr>\n",
       "\t<tr><th scope=row>ss3_3</th><td>0.2293540</td><td>0.2291905</td><td>0.2042863</td><td>0.2379735</td><td>0.2254828</td><td>0.2216537</td><td>0.2265407</td><td>0.2057382</td><td>0.2362094</td><td>0.1565610</td><td>0.1484094</td><td>0.1311476</td><td>0.0000000</td><td>0.1692117</td><td>0.2093890</td><td>0.1462554</td><td>0.2056096</td><td>0.1244475</td></tr>\n",
       "\t<tr><th scope=row>ss3_14</th><td>0.2303018</td><td>0.2247412</td><td>0.2045106</td><td>0.2322090</td><td>0.2196778</td><td>0.2194587</td><td>0.2248371</td><td>0.2078799</td><td>0.2252514</td><td>0.1642693</td><td>0.1731242</td><td>0.1613894</td><td>0.1692117</td><td>0.0000000</td><td>0.2101518</td><td>0.1645175</td><td>0.2069574</td><td>0.1486115</td></tr>\n",
       "\t<tr><th scope=row>ss3_15</th><td>0.2273960</td><td>0.2257357</td><td>0.2061360</td><td>0.2385235</td><td>0.2258590</td><td>0.2223524</td><td>0.2269251</td><td>0.1916720</td><td>0.2307265</td><td>0.2091080</td><td>0.2053117</td><td>0.2121693</td><td>0.2093890</td><td>0.2101518</td><td>0.0000000</td><td>0.2127640</td><td>0.2091865</td><td>0.2141706</td></tr>\n",
       "\t<tr><th scope=row>ss3_16</th><td>0.2267536</td><td>0.2190871</td><td>0.2040459</td><td>0.2269364</td><td>0.2205749</td><td>0.2192765</td><td>0.2224353</td><td>0.2139469</td><td>0.2282604</td><td>0.1664055</td><td>0.1555210</td><td>0.1856383</td><td>0.1462554</td><td>0.1645175</td><td>0.2127640</td><td>0.0000000</td><td>0.2047189</td><td>0.1671745</td></tr>\n",
       "\t<tr><th scope=row>ss3_20</th><td>0.2241723</td><td>0.2157204</td><td>0.2013346</td><td>0.2294361</td><td>0.2200920</td><td>0.2221298</td><td>0.2260581</td><td>0.2081097</td><td>0.2200907</td><td>0.2066793</td><td>0.2028654</td><td>0.1988239</td><td>0.2056096</td><td>0.2069574</td><td>0.2091865</td><td>0.2047189</td><td>0.0000000</td><td>0.2039022</td></tr>\n",
       "\t<tr><th scope=row>ss5_18</th><td>0.2393679</td><td>0.2274161</td><td>0.2098602</td><td>0.2372695</td><td>0.2156870</td><td>0.2263948</td><td>0.2286600</td><td>0.2118349</td><td>0.2270442</td><td>0.1738464</td><td>0.1736163</td><td>0.1271048</td><td>0.1244475</td><td>0.1486115</td><td>0.2141706</td><td>0.1671745</td><td>0.2039022</td><td>0.0000000</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 18 × 18\n",
       "\\begin{tabular}{r|llllllllllllllllll}\n",
       "  & hc1\\_2 & hc1\\_4 & hc2\\_15 & hc2\\_17 & hc3\\_1 & hc3\\_5 & hc3\\_7 & hc3\\_10 & hc3\\_11 & ss2\\_9 & ss2\\_14 & ss2\\_18 & ss3\\_3 & ss3\\_14 & ss3\\_15 & ss3\\_16 & ss3\\_20 & ss5\\_18\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\thc1\\_2 & 0.0000000 & 0.2155436 & 0.2054988 & 0.2245761 & 0.2140475 & 0.1251035 & 0.2137833 & 0.1941731 & 0.2196581 & 0.2191748 & 0.2201363 & 0.2251679 & 0.2293540 & 0.2303018 & 0.2273960 & 0.2267536 & 0.2241723 & 0.2393679\\\\\n",
       "\thc1\\_4 & 0.2155436 & 0.0000000 & 0.1844240 & 0.1693271 & 0.1811331 & 0.2071129 & 0.1608409 & 0.2090009 & 0.2157563 & 0.2186305 & 0.2188330 & 0.2146309 & 0.2291905 & 0.2247412 & 0.2257357 & 0.2190871 & 0.2157204 & 0.2274161\\\\\n",
       "\thc2\\_15 & 0.2054988 & 0.1844240 & 0.0000000 & 0.2041950 & 0.1854690 & 0.2021604 & 0.1823913 & 0.1592689 & 0.2168135 & 0.2041132 & 0.2085476 & 0.1910094 & 0.2042863 & 0.2045106 & 0.2061360 & 0.2040459 & 0.2013346 & 0.2098602\\\\\n",
       "\thc2\\_17 & 0.2245761 & 0.1693271 & 0.2041950 & 0.0000000 & 0.1884048 & 0.2163634 & 0.1724402 & 0.2179260 & 0.2265039 & 0.2268210 & 0.2268209 & 0.2283067 & 0.2379735 & 0.2322090 & 0.2385235 & 0.2269364 & 0.2294361 & 0.2372695\\\\\n",
       "\thc3\\_1 & 0.2140475 & 0.1811331 & 0.1854690 & 0.1884048 & 0.0000000 & 0.2084380 & 0.1141350 & 0.2070880 & 0.2092909 & 0.2195993 & 0.2160866 & 0.2155464 & 0.2254828 & 0.2196778 & 0.2258590 & 0.2205749 & 0.2200920 & 0.2156870\\\\\n",
       "\thc3\\_5 & 0.1251035 & 0.2071129 & 0.2021604 & 0.2163634 & 0.2084380 & 0.0000000 & 0.2104185 & 0.1869989 & 0.2143060 & 0.2202222 & 0.2148287 & 0.2214425 & 0.2216537 & 0.2194587 & 0.2223524 & 0.2192765 & 0.2221298 & 0.2263948\\\\\n",
       "\thc3\\_7 & 0.2137833 & 0.1608409 & 0.1823913 & 0.1724402 & 0.1141350 & 0.2104185 & 0.0000000 & 0.2098760 & 0.2154145 & 0.2211945 & 0.2233552 & 0.2236631 & 0.2265407 & 0.2248371 & 0.2269251 & 0.2224353 & 0.2260581 & 0.2286600\\\\\n",
       "\thc3\\_10 & 0.1941731 & 0.2090009 & 0.1592689 & 0.2179260 & 0.2070880 & 0.1869989 & 0.2098760 & 0.0000000 & 0.2158190 & 0.2072541 & 0.2123815 & 0.2048036 & 0.2057382 & 0.2078799 & 0.1916720 & 0.2139469 & 0.2081097 & 0.2118349\\\\\n",
       "\thc3\\_11 & 0.2196581 & 0.2157563 & 0.2168135 & 0.2265039 & 0.2092909 & 0.2143060 & 0.2154145 & 0.2158190 & 0.0000000 & 0.2204729 & 0.2206785 & 0.2201360 & 0.2362094 & 0.2252514 & 0.2307265 & 0.2282604 & 0.2200907 & 0.2270442\\\\\n",
       "\tss2\\_9 & 0.2191748 & 0.2186305 & 0.2041132 & 0.2268210 & 0.2195993 & 0.2202222 & 0.2211945 & 0.2072541 & 0.2204729 & 0.0000000 & 0.1482022 & 0.1643140 & 0.1565610 & 0.1642693 & 0.2091080 & 0.1664055 & 0.2066793 & 0.1738464\\\\\n",
       "\tss2\\_14 & 0.2201363 & 0.2188330 & 0.2085476 & 0.2268209 & 0.2160866 & 0.2148287 & 0.2233552 & 0.2123815 & 0.2206785 & 0.1482022 & 0.0000000 & 0.1741098 & 0.1484094 & 0.1731242 & 0.2053117 & 0.1555210 & 0.2028654 & 0.1736163\\\\\n",
       "\tss2\\_18 & 0.2251679 & 0.2146309 & 0.1910094 & 0.2283067 & 0.2155464 & 0.2214425 & 0.2236631 & 0.2048036 & 0.2201360 & 0.1643140 & 0.1741098 & 0.0000000 & 0.1311476 & 0.1613894 & 0.2121693 & 0.1856383 & 0.1988239 & 0.1271048\\\\\n",
       "\tss3\\_3 & 0.2293540 & 0.2291905 & 0.2042863 & 0.2379735 & 0.2254828 & 0.2216537 & 0.2265407 & 0.2057382 & 0.2362094 & 0.1565610 & 0.1484094 & 0.1311476 & 0.0000000 & 0.1692117 & 0.2093890 & 0.1462554 & 0.2056096 & 0.1244475\\\\\n",
       "\tss3\\_14 & 0.2303018 & 0.2247412 & 0.2045106 & 0.2322090 & 0.2196778 & 0.2194587 & 0.2248371 & 0.2078799 & 0.2252514 & 0.1642693 & 0.1731242 & 0.1613894 & 0.1692117 & 0.0000000 & 0.2101518 & 0.1645175 & 0.2069574 & 0.1486115\\\\\n",
       "\tss3\\_15 & 0.2273960 & 0.2257357 & 0.2061360 & 0.2385235 & 0.2258590 & 0.2223524 & 0.2269251 & 0.1916720 & 0.2307265 & 0.2091080 & 0.2053117 & 0.2121693 & 0.2093890 & 0.2101518 & 0.0000000 & 0.2127640 & 0.2091865 & 0.2141706\\\\\n",
       "\tss3\\_16 & 0.2267536 & 0.2190871 & 0.2040459 & 0.2269364 & 0.2205749 & 0.2192765 & 0.2224353 & 0.2139469 & 0.2282604 & 0.1664055 & 0.1555210 & 0.1856383 & 0.1462554 & 0.1645175 & 0.2127640 & 0.0000000 & 0.2047189 & 0.1671745\\\\\n",
       "\tss3\\_20 & 0.2241723 & 0.2157204 & 0.2013346 & 0.2294361 & 0.2200920 & 0.2221298 & 0.2260581 & 0.2081097 & 0.2200907 & 0.2066793 & 0.2028654 & 0.1988239 & 0.2056096 & 0.2069574 & 0.2091865 & 0.2047189 & 0.0000000 & 0.2039022\\\\\n",
       "\tss5\\_18 & 0.2393679 & 0.2274161 & 0.2098602 & 0.2372695 & 0.2156870 & 0.2263948 & 0.2286600 & 0.2118349 & 0.2270442 & 0.1738464 & 0.1736163 & 0.1271048 & 0.1244475 & 0.1486115 & 0.2141706 & 0.1671745 & 0.2039022 & 0.0000000\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 18 × 18\n",
       "\n",
       "| <!--/--> | hc1_2 &lt;dbl&gt; | hc1_4 &lt;dbl&gt; | hc2_15 &lt;dbl&gt; | hc2_17 &lt;dbl&gt; | hc3_1 &lt;dbl&gt; | hc3_5 &lt;dbl&gt; | hc3_7 &lt;dbl&gt; | hc3_10 &lt;dbl&gt; | hc3_11 &lt;dbl&gt; | ss2_9 &lt;dbl&gt; | ss2_14 &lt;dbl&gt; | ss2_18 &lt;dbl&gt; | ss3_3 &lt;dbl&gt; | ss3_14 &lt;dbl&gt; | ss3_15 &lt;dbl&gt; | ss3_16 &lt;dbl&gt; | ss3_20 &lt;dbl&gt; | ss5_18 &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| hc1_2 | 0.0000000 | 0.2155436 | 0.2054988 | 0.2245761 | 0.2140475 | 0.1251035 | 0.2137833 | 0.1941731 | 0.2196581 | 0.2191748 | 0.2201363 | 0.2251679 | 0.2293540 | 0.2303018 | 0.2273960 | 0.2267536 | 0.2241723 | 0.2393679 |\n",
       "| hc1_4 | 0.2155436 | 0.0000000 | 0.1844240 | 0.1693271 | 0.1811331 | 0.2071129 | 0.1608409 | 0.2090009 | 0.2157563 | 0.2186305 | 0.2188330 | 0.2146309 | 0.2291905 | 0.2247412 | 0.2257357 | 0.2190871 | 0.2157204 | 0.2274161 |\n",
       "| hc2_15 | 0.2054988 | 0.1844240 | 0.0000000 | 0.2041950 | 0.1854690 | 0.2021604 | 0.1823913 | 0.1592689 | 0.2168135 | 0.2041132 | 0.2085476 | 0.1910094 | 0.2042863 | 0.2045106 | 0.2061360 | 0.2040459 | 0.2013346 | 0.2098602 |\n",
       "| hc2_17 | 0.2245761 | 0.1693271 | 0.2041950 | 0.0000000 | 0.1884048 | 0.2163634 | 0.1724402 | 0.2179260 | 0.2265039 | 0.2268210 | 0.2268209 | 0.2283067 | 0.2379735 | 0.2322090 | 0.2385235 | 0.2269364 | 0.2294361 | 0.2372695 |\n",
       "| hc3_1 | 0.2140475 | 0.1811331 | 0.1854690 | 0.1884048 | 0.0000000 | 0.2084380 | 0.1141350 | 0.2070880 | 0.2092909 | 0.2195993 | 0.2160866 | 0.2155464 | 0.2254828 | 0.2196778 | 0.2258590 | 0.2205749 | 0.2200920 | 0.2156870 |\n",
       "| hc3_5 | 0.1251035 | 0.2071129 | 0.2021604 | 0.2163634 | 0.2084380 | 0.0000000 | 0.2104185 | 0.1869989 | 0.2143060 | 0.2202222 | 0.2148287 | 0.2214425 | 0.2216537 | 0.2194587 | 0.2223524 | 0.2192765 | 0.2221298 | 0.2263948 |\n",
       "| hc3_7 | 0.2137833 | 0.1608409 | 0.1823913 | 0.1724402 | 0.1141350 | 0.2104185 | 0.0000000 | 0.2098760 | 0.2154145 | 0.2211945 | 0.2233552 | 0.2236631 | 0.2265407 | 0.2248371 | 0.2269251 | 0.2224353 | 0.2260581 | 0.2286600 |\n",
       "| hc3_10 | 0.1941731 | 0.2090009 | 0.1592689 | 0.2179260 | 0.2070880 | 0.1869989 | 0.2098760 | 0.0000000 | 0.2158190 | 0.2072541 | 0.2123815 | 0.2048036 | 0.2057382 | 0.2078799 | 0.1916720 | 0.2139469 | 0.2081097 | 0.2118349 |\n",
       "| hc3_11 | 0.2196581 | 0.2157563 | 0.2168135 | 0.2265039 | 0.2092909 | 0.2143060 | 0.2154145 | 0.2158190 | 0.0000000 | 0.2204729 | 0.2206785 | 0.2201360 | 0.2362094 | 0.2252514 | 0.2307265 | 0.2282604 | 0.2200907 | 0.2270442 |\n",
       "| ss2_9 | 0.2191748 | 0.2186305 | 0.2041132 | 0.2268210 | 0.2195993 | 0.2202222 | 0.2211945 | 0.2072541 | 0.2204729 | 0.0000000 | 0.1482022 | 0.1643140 | 0.1565610 | 0.1642693 | 0.2091080 | 0.1664055 | 0.2066793 | 0.1738464 |\n",
       "| ss2_14 | 0.2201363 | 0.2188330 | 0.2085476 | 0.2268209 | 0.2160866 | 0.2148287 | 0.2233552 | 0.2123815 | 0.2206785 | 0.1482022 | 0.0000000 | 0.1741098 | 0.1484094 | 0.1731242 | 0.2053117 | 0.1555210 | 0.2028654 | 0.1736163 |\n",
       "| ss2_18 | 0.2251679 | 0.2146309 | 0.1910094 | 0.2283067 | 0.2155464 | 0.2214425 | 0.2236631 | 0.2048036 | 0.2201360 | 0.1643140 | 0.1741098 | 0.0000000 | 0.1311476 | 0.1613894 | 0.2121693 | 0.1856383 | 0.1988239 | 0.1271048 |\n",
       "| ss3_3 | 0.2293540 | 0.2291905 | 0.2042863 | 0.2379735 | 0.2254828 | 0.2216537 | 0.2265407 | 0.2057382 | 0.2362094 | 0.1565610 | 0.1484094 | 0.1311476 | 0.0000000 | 0.1692117 | 0.2093890 | 0.1462554 | 0.2056096 | 0.1244475 |\n",
       "| ss3_14 | 0.2303018 | 0.2247412 | 0.2045106 | 0.2322090 | 0.2196778 | 0.2194587 | 0.2248371 | 0.2078799 | 0.2252514 | 0.1642693 | 0.1731242 | 0.1613894 | 0.1692117 | 0.0000000 | 0.2101518 | 0.1645175 | 0.2069574 | 0.1486115 |\n",
       "| ss3_15 | 0.2273960 | 0.2257357 | 0.2061360 | 0.2385235 | 0.2258590 | 0.2223524 | 0.2269251 | 0.1916720 | 0.2307265 | 0.2091080 | 0.2053117 | 0.2121693 | 0.2093890 | 0.2101518 | 0.0000000 | 0.2127640 | 0.2091865 | 0.2141706 |\n",
       "| ss3_16 | 0.2267536 | 0.2190871 | 0.2040459 | 0.2269364 | 0.2205749 | 0.2192765 | 0.2224353 | 0.2139469 | 0.2282604 | 0.1664055 | 0.1555210 | 0.1856383 | 0.1462554 | 0.1645175 | 0.2127640 | 0.0000000 | 0.2047189 | 0.1671745 |\n",
       "| ss3_20 | 0.2241723 | 0.2157204 | 0.2013346 | 0.2294361 | 0.2200920 | 0.2221298 | 0.2260581 | 0.2081097 | 0.2200907 | 0.2066793 | 0.2028654 | 0.1988239 | 0.2056096 | 0.2069574 | 0.2091865 | 0.2047189 | 0.0000000 | 0.2039022 |\n",
       "| ss5_18 | 0.2393679 | 0.2274161 | 0.2098602 | 0.2372695 | 0.2156870 | 0.2263948 | 0.2286600 | 0.2118349 | 0.2270442 | 0.1738464 | 0.1736163 | 0.1271048 | 0.1244475 | 0.1486115 | 0.2141706 | 0.1671745 | 0.2039022 | 0.0000000 |\n",
       "\n"
      ],
      "text/plain": [
       "       hc1_2     hc1_4     hc2_15    hc2_17    hc3_1     hc3_5     hc3_7    \n",
       "hc1_2  0.0000000 0.2155436 0.2054988 0.2245761 0.2140475 0.1251035 0.2137833\n",
       "hc1_4  0.2155436 0.0000000 0.1844240 0.1693271 0.1811331 0.2071129 0.1608409\n",
       "hc2_15 0.2054988 0.1844240 0.0000000 0.2041950 0.1854690 0.2021604 0.1823913\n",
       "hc2_17 0.2245761 0.1693271 0.2041950 0.0000000 0.1884048 0.2163634 0.1724402\n",
       "hc3_1  0.2140475 0.1811331 0.1854690 0.1884048 0.0000000 0.2084380 0.1141350\n",
       "hc3_5  0.1251035 0.2071129 0.2021604 0.2163634 0.2084380 0.0000000 0.2104185\n",
       "hc3_7  0.2137833 0.1608409 0.1823913 0.1724402 0.1141350 0.2104185 0.0000000\n",
       "hc3_10 0.1941731 0.2090009 0.1592689 0.2179260 0.2070880 0.1869989 0.2098760\n",
       "hc3_11 0.2196581 0.2157563 0.2168135 0.2265039 0.2092909 0.2143060 0.2154145\n",
       "ss2_9  0.2191748 0.2186305 0.2041132 0.2268210 0.2195993 0.2202222 0.2211945\n",
       "ss2_14 0.2201363 0.2188330 0.2085476 0.2268209 0.2160866 0.2148287 0.2233552\n",
       "ss2_18 0.2251679 0.2146309 0.1910094 0.2283067 0.2155464 0.2214425 0.2236631\n",
       "ss3_3  0.2293540 0.2291905 0.2042863 0.2379735 0.2254828 0.2216537 0.2265407\n",
       "ss3_14 0.2303018 0.2247412 0.2045106 0.2322090 0.2196778 0.2194587 0.2248371\n",
       "ss3_15 0.2273960 0.2257357 0.2061360 0.2385235 0.2258590 0.2223524 0.2269251\n",
       "ss3_16 0.2267536 0.2190871 0.2040459 0.2269364 0.2205749 0.2192765 0.2224353\n",
       "ss3_20 0.2241723 0.2157204 0.2013346 0.2294361 0.2200920 0.2221298 0.2260581\n",
       "ss5_18 0.2393679 0.2274161 0.2098602 0.2372695 0.2156870 0.2263948 0.2286600\n",
       "       hc3_10    hc3_11    ss2_9     ss2_14    ss2_18    ss3_3     ss3_14   \n",
       "hc1_2  0.1941731 0.2196581 0.2191748 0.2201363 0.2251679 0.2293540 0.2303018\n",
       "hc1_4  0.2090009 0.2157563 0.2186305 0.2188330 0.2146309 0.2291905 0.2247412\n",
       "hc2_15 0.1592689 0.2168135 0.2041132 0.2085476 0.1910094 0.2042863 0.2045106\n",
       "hc2_17 0.2179260 0.2265039 0.2268210 0.2268209 0.2283067 0.2379735 0.2322090\n",
       "hc3_1  0.2070880 0.2092909 0.2195993 0.2160866 0.2155464 0.2254828 0.2196778\n",
       "hc3_5  0.1869989 0.2143060 0.2202222 0.2148287 0.2214425 0.2216537 0.2194587\n",
       "hc3_7  0.2098760 0.2154145 0.2211945 0.2233552 0.2236631 0.2265407 0.2248371\n",
       "hc3_10 0.0000000 0.2158190 0.2072541 0.2123815 0.2048036 0.2057382 0.2078799\n",
       "hc3_11 0.2158190 0.0000000 0.2204729 0.2206785 0.2201360 0.2362094 0.2252514\n",
       "ss2_9  0.2072541 0.2204729 0.0000000 0.1482022 0.1643140 0.1565610 0.1642693\n",
       "ss2_14 0.2123815 0.2206785 0.1482022 0.0000000 0.1741098 0.1484094 0.1731242\n",
       "ss2_18 0.2048036 0.2201360 0.1643140 0.1741098 0.0000000 0.1311476 0.1613894\n",
       "ss3_3  0.2057382 0.2362094 0.1565610 0.1484094 0.1311476 0.0000000 0.1692117\n",
       "ss3_14 0.2078799 0.2252514 0.1642693 0.1731242 0.1613894 0.1692117 0.0000000\n",
       "ss3_15 0.1916720 0.2307265 0.2091080 0.2053117 0.2121693 0.2093890 0.2101518\n",
       "ss3_16 0.2139469 0.2282604 0.1664055 0.1555210 0.1856383 0.1462554 0.1645175\n",
       "ss3_20 0.2081097 0.2200907 0.2066793 0.2028654 0.1988239 0.2056096 0.2069574\n",
       "ss5_18 0.2118349 0.2270442 0.1738464 0.1736163 0.1271048 0.1244475 0.1486115\n",
       "       ss3_15    ss3_16    ss3_20    ss5_18   \n",
       "hc1_2  0.2273960 0.2267536 0.2241723 0.2393679\n",
       "hc1_4  0.2257357 0.2190871 0.2157204 0.2274161\n",
       "hc2_15 0.2061360 0.2040459 0.2013346 0.2098602\n",
       "hc2_17 0.2385235 0.2269364 0.2294361 0.2372695\n",
       "hc3_1  0.2258590 0.2205749 0.2200920 0.2156870\n",
       "hc3_5  0.2223524 0.2192765 0.2221298 0.2263948\n",
       "hc3_7  0.2269251 0.2224353 0.2260581 0.2286600\n",
       "hc3_10 0.1916720 0.2139469 0.2081097 0.2118349\n",
       "hc3_11 0.2307265 0.2282604 0.2200907 0.2270442\n",
       "ss2_9  0.2091080 0.1664055 0.2066793 0.1738464\n",
       "ss2_14 0.2053117 0.1555210 0.2028654 0.1736163\n",
       "ss2_18 0.2121693 0.1856383 0.1988239 0.1271048\n",
       "ss3_3  0.2093890 0.1462554 0.2056096 0.1244475\n",
       "ss3_14 0.2101518 0.1645175 0.2069574 0.1486115\n",
       "ss3_15 0.0000000 0.2127640 0.2091865 0.2141706\n",
       "ss3_16 0.2127640 0.0000000 0.2047189 0.1671745\n",
       "ss3_20 0.2091865 0.2047189 0.0000000 0.2039022\n",
       "ss5_18 0.2141706 0.1671745 0.2039022 0.0000000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# read in genetic distances\n",
    "gen <- read.table(paste0(POP,\"_mbd.dist\"), row.names = 1, header=T)\n",
    "gen\n",
    "Dgen <- as.dist(gen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "distObj <- list(Dep10,Dgen)\n",
    "names(distObj) <- c(\"Dep10\",\"Dgen\")\n",
    "save(distObj,file=\"../analyses/2bRAD/MethGen/dist_allmeth_snp.Robj\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "kernel": "R"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "lm(formula = Dep10 ~ Dgen)\n",
       "\n",
       "Residuals:\n",
       "   Min     1Q Median     3Q    Max \n",
       "-53094 -17259   -937  18870  54443 \n",
       "\n",
       "Coefficients:\n",
       "            Estimate Std. Error t value Pr(>|t|)    \n",
       "(Intercept)   229505      15591  14.720  < 2e-16 ***\n",
       "Dgen          299862      75420   3.976 0.000108 ***\n",
       "---\n",
       "Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
       "\n",
       "Residual standard error: 24320 on 151 degrees of freedom\n",
       "Multiple R-squared:  0.09477,\tAdjusted R-squared:  0.08877 \n",
       "F-statistic: 15.81 on 1 and 151 DF,  p-value: 0.0001084\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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5J6VAOXdggRAmo4Vt3mUQmjJon1AkKE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\nAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#library(MASS)\n",
    "#dens <- kde2d(Dgeo,DgenN, n=300)\n",
    "#myPal <- colorRampPalette(c(\"white\",\"blue\",\"gold\", \"orange\", \"red\"))\n",
    "plot(Dgen, Dep10, pch=20,cex=1,xlab = \"Genetic distance\", ylab = \"MBD Manhattan distance\")\n",
    "summary(lm(Dep10~Dgen),)\n",
    "R2 = round(summary(lm(Dep10~Dgen))$r.squared, 4)\n",
    "#image(dens, col=transp(myPal(300),.7), add=TRUE)\n",
    "abline(lm(Dep10~Dgen))\n",
    "text(0.13,320000,label=paste(\"R^2=\",R2))\n",
    "text(0.13,310000,label=\"r=0.31\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "kernel": "R",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPearson's product-moment correlation\n",
       "\n",
       "data:  Dep10 and Dgen\n",
       "t = 3.9759, df = 151, p-value = 0.0001084\n",
       "alternative hypothesis: true correlation is not equal to 0\n",
       "95 percent confidence interval:\n",
       " 0.1568240 0.4447926\n",
       "sample estimates:\n",
       "      cor \n",
       "0.3078416 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tSpearman's rank correlation rho\n",
       "\n",
       "data:  Dep10 and Dgen\n",
       "S = 444614, p-value = 0.001505\n",
       "alternative hypothesis: true rho is not equal to 0\n",
       "sample estimates:\n",
       "      rho \n",
       "0.2551332 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cor.test( ~ Dep10 + Dgen,\n",
    "         method = \"pearson\",\n",
    "         conf.level = 0.95)\n",
    "cor.test( ~ Dep10 + Dgen, \n",
    "         method = \"spearman\",\n",
    "         continuity = FALSE,\n",
    "         conf.level = 0.95)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "bash"
   },
   "source": [
    "### For DMLs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "kernel": "R"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A matrix: 18 × 18 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>hc1_2</th><th scope=col>hc1_4</th><th scope=col>hc2_15</th><th scope=col>hc2_17</th><th scope=col>hc3_1</th><th scope=col>hc3_5</th><th scope=col>hc3_7</th><th scope=col>hc3_10</th><th scope=col>hc3_11</th><th scope=col>ss2_9</th><th scope=col>ss2_14</th><th scope=col>ss2_18</th><th scope=col>ss3_3</th><th scope=col>ss3_14</th><th scope=col>ss3_15</th><th scope=col>ss3_16</th><th scope=col>ss3_20</th><th scope=col>ss5_18</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>hc1_2</th><td>    0.000</td><td>10545.532</td><td>10351.204</td><td> 9385.573</td><td>10610.214</td><td> 6363.300</td><td>10611.443</td><td> 8551.481</td><td> 9068.337</td><td>14788.300</td><td>14887.002</td><td>13635.256</td><td>14606.952</td><td>14484.494</td><td>11693.997</td><td>14072.044</td><td>11702.18</td><td>13190.286</td></tr>\n",
       "\t<tr><th scope=row>hc1_4</th><td>10545.532</td><td>    0.000</td><td> 9064.440</td><td> 7984.184</td><td> 8727.395</td><td> 8905.577</td><td> 8699.207</td><td>10736.499</td><td>10026.701</td><td>17346.810</td><td>16626.609</td><td>15169.867</td><td>16480.535</td><td>16347.990</td><td>14253.680</td><td>15345.568</td><td>13291.64</td><td>15195.551</td></tr>\n",
       "\t<tr><th scope=row>hc2_15</th><td>10351.204</td><td> 9064.440</td><td>    0.000</td><td> 8032.958</td><td> 9008.169</td><td> 9760.334</td><td> 7750.867</td><td>10319.292</td><td>11194.320</td><td>16995.519</td><td>16120.010</td><td>15450.882</td><td>16418.206</td><td>16417.512</td><td>13740.327</td><td>15822.143</td><td>13357.00</td><td>15649.929</td></tr>\n",
       "\t<tr><th scope=row>hc2_17</th><td> 9385.573</td><td> 7984.184</td><td> 8032.958</td><td>    0.000</td><td> 9827.633</td><td> 8017.600</td><td> 8785.571</td><td> 9408.603</td><td>10454.902</td><td>16400.962</td><td>16108.636</td><td>15381.091</td><td>15743.180</td><td>15586.173</td><td>13138.397</td><td>14668.390</td><td>12158.79</td><td>14422.239</td></tr>\n",
       "\t<tr><th scope=row>hc3_1</th><td>10610.214</td><td> 8727.395</td><td> 9008.169</td><td> 9827.633</td><td>    0.000</td><td> 8808.350</td><td> 6567.108</td><td>10436.642</td><td>10554.475</td><td>16222.809</td><td>16014.164</td><td>13934.951</td><td>15248.147</td><td>14915.306</td><td>12793.947</td><td>14521.674</td><td>12503.21</td><td>13721.460</td></tr>\n",
       "\t<tr><th scope=row>hc3_5</th><td> 6363.300</td><td> 8905.577</td><td> 9760.334</td><td> 8017.600</td><td> 8808.350</td><td>    0.000</td><td> 9189.478</td><td> 7834.229</td><td> 9119.644</td><td>15202.564</td><td>15975.183</td><td>13292.572</td><td>15959.683</td><td>15121.119</td><td>11756.756</td><td>15334.174</td><td>12440.56</td><td>14534.806</td></tr>\n",
       "\t<tr><th scope=row>hc3_7</th><td>10611.443</td><td> 8699.207</td><td> 7750.867</td><td> 8785.571</td><td> 6567.108</td><td> 9189.478</td><td>    0.000</td><td>10443.443</td><td>10804.160</td><td>17348.604</td><td>16705.442</td><td>14868.123</td><td>16044.562</td><td>16375.903</td><td>13350.965</td><td>16117.654</td><td>13837.97</td><td>15293.315</td></tr>\n",
       "\t<tr><th scope=row>hc3_10</th><td> 8551.481</td><td>10736.499</td><td>10319.292</td><td> 9408.603</td><td>10436.642</td><td> 7834.229</td><td>10443.443</td><td>    0.000</td><td>10148.679</td><td>15486.501</td><td>14988.730</td><td>13497.210</td><td>15209.257</td><td>14489.565</td><td>12354.985</td><td>14441.498</td><td>11705.99</td><td>14281.504</td></tr>\n",
       "\t<tr><th scope=row>hc3_11</th><td> 9068.337</td><td>10026.701</td><td>11194.320</td><td>10454.902</td><td>10554.475</td><td> 9119.644</td><td>10804.160</td><td>10148.679</td><td>    0.000</td><td>15102.614</td><td>14709.279</td><td>13012.517</td><td>14188.195</td><td>14113.226</td><td>11779.373</td><td>13574.423</td><td>12187.15</td><td>13623.710</td></tr>\n",
       "\t<tr><th scope=row>ss2_9</th><td>14788.300</td><td>17346.810</td><td>16995.519</td><td>16400.962</td><td>16222.809</td><td>15202.564</td><td>17348.604</td><td>15486.501</td><td>15102.614</td><td>    0.000</td><td> 9317.558</td><td> 8747.628</td><td> 8413.878</td><td> 9119.600</td><td>10149.293</td><td> 8682.160</td><td>11387.52</td><td> 8899.327</td></tr>\n",
       "\t<tr><th scope=row>ss2_14</th><td>14887.002</td><td>16626.609</td><td>16120.010</td><td>16108.636</td><td>16014.164</td><td>15975.183</td><td>16705.442</td><td>14988.730</td><td>14709.279</td><td> 9317.558</td><td>    0.000</td><td>10653.366</td><td> 9278.006</td><td> 9505.802</td><td>10936.765</td><td> 8530.524</td><td>11322.26</td><td>10281.724</td></tr>\n",
       "\t<tr><th scope=row>ss2_18</th><td>13635.256</td><td>15169.867</td><td>15450.882</td><td>15381.091</td><td>13934.951</td><td>13292.572</td><td>14868.123</td><td>13497.210</td><td>13012.517</td><td> 8747.628</td><td>10653.366</td><td>    0.000</td><td> 7351.714</td><td> 8692.706</td><td>10026.944</td><td>10471.814</td><td>11078.75</td><td> 7483.073</td></tr>\n",
       "\t<tr><th scope=row>ss3_3</th><td>14606.952</td><td>16480.535</td><td>16418.206</td><td>15743.180</td><td>15248.147</td><td>15959.683</td><td>16044.562</td><td>15209.257</td><td>14188.195</td><td> 8413.878</td><td> 9278.006</td><td> 7351.714</td><td>    0.000</td><td> 8503.102</td><td> 9899.829</td><td> 8283.191</td><td>11215.38</td><td> 6997.712</td></tr>\n",
       "\t<tr><th scope=row>ss3_14</th><td>14484.494</td><td>16347.990</td><td>16417.512</td><td>15586.173</td><td>14915.306</td><td>15121.119</td><td>16375.903</td><td>14489.565</td><td>14113.226</td><td> 9119.600</td><td> 9505.802</td><td> 8692.706</td><td> 8503.102</td><td>    0.000</td><td>10568.719</td><td> 9230.876</td><td>11030.21</td><td> 8055.097</td></tr>\n",
       "\t<tr><th scope=row>ss3_15</th><td>11693.997</td><td>14253.680</td><td>13740.327</td><td>13138.397</td><td>12793.947</td><td>11756.756</td><td>13350.965</td><td>12354.985</td><td>11779.373</td><td>10149.293</td><td>10936.765</td><td>10026.944</td><td> 9899.829</td><td>10568.719</td><td>    0.000</td><td>10193.024</td><td>10433.14</td><td> 9796.022</td></tr>\n",
       "\t<tr><th scope=row>ss3_16</th><td>14072.044</td><td>15345.568</td><td>15822.143</td><td>14668.390</td><td>14521.674</td><td>15334.174</td><td>16117.654</td><td>14441.498</td><td>13574.423</td><td> 8682.160</td><td> 8530.524</td><td>10471.814</td><td> 8283.191</td><td> 9230.876</td><td>10193.024</td><td>    0.000</td><td>10436.72</td><td> 8725.894</td></tr>\n",
       "\t<tr><th scope=row>ss3_20</th><td>11702.183</td><td>13291.645</td><td>13357.002</td><td>12158.790</td><td>12503.211</td><td>12440.562</td><td>13837.969</td><td>11705.987</td><td>12187.148</td><td>11387.520</td><td>11322.262</td><td>11078.749</td><td>11215.385</td><td>11030.208</td><td>10433.139</td><td>10436.720</td><td>    0.00</td><td>10959.480</td></tr>\n",
       "\t<tr><th scope=row>ss5_18</th><td>13190.286</td><td>15195.551</td><td>15649.929</td><td>14422.239</td><td>13721.460</td><td>14534.806</td><td>15293.315</td><td>14281.504</td><td>13623.710</td><td> 8899.327</td><td>10281.724</td><td> 7483.073</td><td> 6997.712</td><td> 8055.097</td><td> 9796.022</td><td> 8725.894</td><td>10959.48</td><td>    0.000</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 18 × 18 of type dbl\n",
       "\\begin{tabular}{r|llllllllllllllllll}\n",
       "  & hc1\\_2 & hc1\\_4 & hc2\\_15 & hc2\\_17 & hc3\\_1 & hc3\\_5 & hc3\\_7 & hc3\\_10 & hc3\\_11 & ss2\\_9 & ss2\\_14 & ss2\\_18 & ss3\\_3 & ss3\\_14 & ss3\\_15 & ss3\\_16 & ss3\\_20 & ss5\\_18\\\\\n",
       "\\hline\n",
       "\thc1\\_2 &     0.000 & 10545.532 & 10351.204 &  9385.573 & 10610.214 &  6363.300 & 10611.443 &  8551.481 &  9068.337 & 14788.300 & 14887.002 & 13635.256 & 14606.952 & 14484.494 & 11693.997 & 14072.044 & 11702.18 & 13190.286\\\\\n",
       "\thc1\\_4 & 10545.532 &     0.000 &  9064.440 &  7984.184 &  8727.395 &  8905.577 &  8699.207 & 10736.499 & 10026.701 & 17346.810 & 16626.609 & 15169.867 & 16480.535 & 16347.990 & 14253.680 & 15345.568 & 13291.64 & 15195.551\\\\\n",
       "\thc2\\_15 & 10351.204 &  9064.440 &     0.000 &  8032.958 &  9008.169 &  9760.334 &  7750.867 & 10319.292 & 11194.320 & 16995.519 & 16120.010 & 15450.882 & 16418.206 & 16417.512 & 13740.327 & 15822.143 & 13357.00 & 15649.929\\\\\n",
       "\thc2\\_17 &  9385.573 &  7984.184 &  8032.958 &     0.000 &  9827.633 &  8017.600 &  8785.571 &  9408.603 & 10454.902 & 16400.962 & 16108.636 & 15381.091 & 15743.180 & 15586.173 & 13138.397 & 14668.390 & 12158.79 & 14422.239\\\\\n",
       "\thc3\\_1 & 10610.214 &  8727.395 &  9008.169 &  9827.633 &     0.000 &  8808.350 &  6567.108 & 10436.642 & 10554.475 & 16222.809 & 16014.164 & 13934.951 & 15248.147 & 14915.306 & 12793.947 & 14521.674 & 12503.21 & 13721.460\\\\\n",
       "\thc3\\_5 &  6363.300 &  8905.577 &  9760.334 &  8017.600 &  8808.350 &     0.000 &  9189.478 &  7834.229 &  9119.644 & 15202.564 & 15975.183 & 13292.572 & 15959.683 & 15121.119 & 11756.756 & 15334.174 & 12440.56 & 14534.806\\\\\n",
       "\thc3\\_7 & 10611.443 &  8699.207 &  7750.867 &  8785.571 &  6567.108 &  9189.478 &     0.000 & 10443.443 & 10804.160 & 17348.604 & 16705.442 & 14868.123 & 16044.562 & 16375.903 & 13350.965 & 16117.654 & 13837.97 & 15293.315\\\\\n",
       "\thc3\\_10 &  8551.481 & 10736.499 & 10319.292 &  9408.603 & 10436.642 &  7834.229 & 10443.443 &     0.000 & 10148.679 & 15486.501 & 14988.730 & 13497.210 & 15209.257 & 14489.565 & 12354.985 & 14441.498 & 11705.99 & 14281.504\\\\\n",
       "\thc3\\_11 &  9068.337 & 10026.701 & 11194.320 & 10454.902 & 10554.475 &  9119.644 & 10804.160 & 10148.679 &     0.000 & 15102.614 & 14709.279 & 13012.517 & 14188.195 & 14113.226 & 11779.373 & 13574.423 & 12187.15 & 13623.710\\\\\n",
       "\tss2\\_9 & 14788.300 & 17346.810 & 16995.519 & 16400.962 & 16222.809 & 15202.564 & 17348.604 & 15486.501 & 15102.614 &     0.000 &  9317.558 &  8747.628 &  8413.878 &  9119.600 & 10149.293 &  8682.160 & 11387.52 &  8899.327\\\\\n",
       "\tss2\\_14 & 14887.002 & 16626.609 & 16120.010 & 16108.636 & 16014.164 & 15975.183 & 16705.442 & 14988.730 & 14709.279 &  9317.558 &     0.000 & 10653.366 &  9278.006 &  9505.802 & 10936.765 &  8530.524 & 11322.26 & 10281.724\\\\\n",
       "\tss2\\_18 & 13635.256 & 15169.867 & 15450.882 & 15381.091 & 13934.951 & 13292.572 & 14868.123 & 13497.210 & 13012.517 &  8747.628 & 10653.366 &     0.000 &  7351.714 &  8692.706 & 10026.944 & 10471.814 & 11078.75 &  7483.073\\\\\n",
       "\tss3\\_3 & 14606.952 & 16480.535 & 16418.206 & 15743.180 & 15248.147 & 15959.683 & 16044.562 & 15209.257 & 14188.195 &  8413.878 &  9278.006 &  7351.714 &     0.000 &  8503.102 &  9899.829 &  8283.191 & 11215.38 &  6997.712\\\\\n",
       "\tss3\\_14 & 14484.494 & 16347.990 & 16417.512 & 15586.173 & 14915.306 & 15121.119 & 16375.903 & 14489.565 & 14113.226 &  9119.600 &  9505.802 &  8692.706 &  8503.102 &     0.000 & 10568.719 &  9230.876 & 11030.21 &  8055.097\\\\\n",
       "\tss3\\_15 & 11693.997 & 14253.680 & 13740.327 & 13138.397 & 12793.947 & 11756.756 & 13350.965 & 12354.985 & 11779.373 & 10149.293 & 10936.765 & 10026.944 &  9899.829 & 10568.719 &     0.000 & 10193.024 & 10433.14 &  9796.022\\\\\n",
       "\tss3\\_16 & 14072.044 & 15345.568 & 15822.143 & 14668.390 & 14521.674 & 15334.174 & 16117.654 & 14441.498 & 13574.423 &  8682.160 &  8530.524 & 10471.814 &  8283.191 &  9230.876 & 10193.024 &     0.000 & 10436.72 &  8725.894\\\\\n",
       "\tss3\\_20 & 11702.183 & 13291.645 & 13357.002 & 12158.790 & 12503.211 & 12440.562 & 13837.969 & 11705.987 & 12187.148 & 11387.520 & 11322.262 & 11078.749 & 11215.385 & 11030.208 & 10433.139 & 10436.720 &     0.00 & 10959.480\\\\\n",
       "\tss5\\_18 & 13190.286 & 15195.551 & 15649.929 & 14422.239 & 13721.460 & 14534.806 & 15293.315 & 14281.504 & 13623.710 &  8899.327 & 10281.724 &  7483.073 &  6997.712 &  8055.097 &  9796.022 &  8725.894 & 10959.48 &     0.000\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 18 × 18 of type dbl\n",
       "\n",
       "| <!--/--> | hc1_2 | hc1_4 | hc2_15 | hc2_17 | hc3_1 | hc3_5 | hc3_7 | hc3_10 | hc3_11 | ss2_9 | ss2_14 | ss2_18 | ss3_3 | ss3_14 | ss3_15 | ss3_16 | ss3_20 | ss5_18 |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| hc1_2 |     0.000 | 10545.532 | 10351.204 |  9385.573 | 10610.214 |  6363.300 | 10611.443 |  8551.481 |  9068.337 | 14788.300 | 14887.002 | 13635.256 | 14606.952 | 14484.494 | 11693.997 | 14072.044 | 11702.18 | 13190.286 |\n",
       "| hc1_4 | 10545.532 |     0.000 |  9064.440 |  7984.184 |  8727.395 |  8905.577 |  8699.207 | 10736.499 | 10026.701 | 17346.810 | 16626.609 | 15169.867 | 16480.535 | 16347.990 | 14253.680 | 15345.568 | 13291.64 | 15195.551 |\n",
       "| hc2_15 | 10351.204 |  9064.440 |     0.000 |  8032.958 |  9008.169 |  9760.334 |  7750.867 | 10319.292 | 11194.320 | 16995.519 | 16120.010 | 15450.882 | 16418.206 | 16417.512 | 13740.327 | 15822.143 | 13357.00 | 15649.929 |\n",
       "| hc2_17 |  9385.573 |  7984.184 |  8032.958 |     0.000 |  9827.633 |  8017.600 |  8785.571 |  9408.603 | 10454.902 | 16400.962 | 16108.636 | 15381.091 | 15743.180 | 15586.173 | 13138.397 | 14668.390 | 12158.79 | 14422.239 |\n",
       "| hc3_1 | 10610.214 |  8727.395 |  9008.169 |  9827.633 |     0.000 |  8808.350 |  6567.108 | 10436.642 | 10554.475 | 16222.809 | 16014.164 | 13934.951 | 15248.147 | 14915.306 | 12793.947 | 14521.674 | 12503.21 | 13721.460 |\n",
       "| hc3_5 |  6363.300 |  8905.577 |  9760.334 |  8017.600 |  8808.350 |     0.000 |  9189.478 |  7834.229 |  9119.644 | 15202.564 | 15975.183 | 13292.572 | 15959.683 | 15121.119 | 11756.756 | 15334.174 | 12440.56 | 14534.806 |\n",
       "| hc3_7 | 10611.443 |  8699.207 |  7750.867 |  8785.571 |  6567.108 |  9189.478 |     0.000 | 10443.443 | 10804.160 | 17348.604 | 16705.442 | 14868.123 | 16044.562 | 16375.903 | 13350.965 | 16117.654 | 13837.97 | 15293.315 |\n",
       "| hc3_10 |  8551.481 | 10736.499 | 10319.292 |  9408.603 | 10436.642 |  7834.229 | 10443.443 |     0.000 | 10148.679 | 15486.501 | 14988.730 | 13497.210 | 15209.257 | 14489.565 | 12354.985 | 14441.498 | 11705.99 | 14281.504 |\n",
       "| hc3_11 |  9068.337 | 10026.701 | 11194.320 | 10454.902 | 10554.475 |  9119.644 | 10804.160 | 10148.679 |     0.000 | 15102.614 | 14709.279 | 13012.517 | 14188.195 | 14113.226 | 11779.373 | 13574.423 | 12187.15 | 13623.710 |\n",
       "| ss2_9 | 14788.300 | 17346.810 | 16995.519 | 16400.962 | 16222.809 | 15202.564 | 17348.604 | 15486.501 | 15102.614 |     0.000 |  9317.558 |  8747.628 |  8413.878 |  9119.600 | 10149.293 |  8682.160 | 11387.52 |  8899.327 |\n",
       "| ss2_14 | 14887.002 | 16626.609 | 16120.010 | 16108.636 | 16014.164 | 15975.183 | 16705.442 | 14988.730 | 14709.279 |  9317.558 |     0.000 | 10653.366 |  9278.006 |  9505.802 | 10936.765 |  8530.524 | 11322.26 | 10281.724 |\n",
       "| ss2_18 | 13635.256 | 15169.867 | 15450.882 | 15381.091 | 13934.951 | 13292.572 | 14868.123 | 13497.210 | 13012.517 |  8747.628 | 10653.366 |     0.000 |  7351.714 |  8692.706 | 10026.944 | 10471.814 | 11078.75 |  7483.073 |\n",
       "| ss3_3 | 14606.952 | 16480.535 | 16418.206 | 15743.180 | 15248.147 | 15959.683 | 16044.562 | 15209.257 | 14188.195 |  8413.878 |  9278.006 |  7351.714 |     0.000 |  8503.102 |  9899.829 |  8283.191 | 11215.38 |  6997.712 |\n",
       "| ss3_14 | 14484.494 | 16347.990 | 16417.512 | 15586.173 | 14915.306 | 15121.119 | 16375.903 | 14489.565 | 14113.226 |  9119.600 |  9505.802 |  8692.706 |  8503.102 |     0.000 | 10568.719 |  9230.876 | 11030.21 |  8055.097 |\n",
       "| ss3_15 | 11693.997 | 14253.680 | 13740.327 | 13138.397 | 12793.947 | 11756.756 | 13350.965 | 12354.985 | 11779.373 | 10149.293 | 10936.765 | 10026.944 |  9899.829 | 10568.719 |     0.000 | 10193.024 | 10433.14 |  9796.022 |\n",
       "| ss3_16 | 14072.044 | 15345.568 | 15822.143 | 14668.390 | 14521.674 | 15334.174 | 16117.654 | 14441.498 | 13574.423 |  8682.160 |  8530.524 | 10471.814 |  8283.191 |  9230.876 | 10193.024 |     0.000 | 10436.72 |  8725.894 |\n",
       "| ss3_20 | 11702.183 | 13291.645 | 13357.002 | 12158.790 | 12503.211 | 12440.562 | 13837.969 | 11705.987 | 12187.148 | 11387.520 | 11322.262 | 11078.749 | 11215.385 | 11030.208 | 10433.139 | 10436.720 |     0.00 | 10959.480 |\n",
       "| ss5_18 | 13190.286 | 15195.551 | 15649.929 | 14422.239 | 13721.460 | 14534.806 | 15293.315 | 14281.504 | 13623.710 |  8899.327 | 10281.724 |  7483.073 |  6997.712 |  8055.097 |  9796.022 |  8725.894 | 10959.48 |     0.000 |\n",
       "\n"
      ],
      "text/plain": [
       "       hc1_2     hc1_4     hc2_15    hc2_17    hc3_1     hc3_5     hc3_7    \n",
       "hc1_2      0.000 10545.532 10351.204  9385.573 10610.214  6363.300 10611.443\n",
       "hc1_4  10545.532     0.000  9064.440  7984.184  8727.395  8905.577  8699.207\n",
       "hc2_15 10351.204  9064.440     0.000  8032.958  9008.169  9760.334  7750.867\n",
       "hc2_17  9385.573  7984.184  8032.958     0.000  9827.633  8017.600  8785.571\n",
       "hc3_1  10610.214  8727.395  9008.169  9827.633     0.000  8808.350  6567.108\n",
       "hc3_5   6363.300  8905.577  9760.334  8017.600  8808.350     0.000  9189.478\n",
       "hc3_7  10611.443  8699.207  7750.867  8785.571  6567.108  9189.478     0.000\n",
       "hc3_10  8551.481 10736.499 10319.292  9408.603 10436.642  7834.229 10443.443\n",
       "hc3_11  9068.337 10026.701 11194.320 10454.902 10554.475  9119.644 10804.160\n",
       "ss2_9  14788.300 17346.810 16995.519 16400.962 16222.809 15202.564 17348.604\n",
       "ss2_14 14887.002 16626.609 16120.010 16108.636 16014.164 15975.183 16705.442\n",
       "ss2_18 13635.256 15169.867 15450.882 15381.091 13934.951 13292.572 14868.123\n",
       "ss3_3  14606.952 16480.535 16418.206 15743.180 15248.147 15959.683 16044.562\n",
       "ss3_14 14484.494 16347.990 16417.512 15586.173 14915.306 15121.119 16375.903\n",
       "ss3_15 11693.997 14253.680 13740.327 13138.397 12793.947 11756.756 13350.965\n",
       "ss3_16 14072.044 15345.568 15822.143 14668.390 14521.674 15334.174 16117.654\n",
       "ss3_20 11702.183 13291.645 13357.002 12158.790 12503.211 12440.562 13837.969\n",
       "ss5_18 13190.286 15195.551 15649.929 14422.239 13721.460 14534.806 15293.315\n",
       "       hc3_10    hc3_11    ss2_9     ss2_14    ss2_18    ss3_3     ss3_14   \n",
       "hc1_2   8551.481  9068.337 14788.300 14887.002 13635.256 14606.952 14484.494\n",
       "hc1_4  10736.499 10026.701 17346.810 16626.609 15169.867 16480.535 16347.990\n",
       "hc2_15 10319.292 11194.320 16995.519 16120.010 15450.882 16418.206 16417.512\n",
       "hc2_17  9408.603 10454.902 16400.962 16108.636 15381.091 15743.180 15586.173\n",
       "hc3_1  10436.642 10554.475 16222.809 16014.164 13934.951 15248.147 14915.306\n",
       "hc3_5   7834.229  9119.644 15202.564 15975.183 13292.572 15959.683 15121.119\n",
       "hc3_7  10443.443 10804.160 17348.604 16705.442 14868.123 16044.562 16375.903\n",
       "hc3_10     0.000 10148.679 15486.501 14988.730 13497.210 15209.257 14489.565\n",
       "hc3_11 10148.679     0.000 15102.614 14709.279 13012.517 14188.195 14113.226\n",
       "ss2_9  15486.501 15102.614     0.000  9317.558  8747.628  8413.878  9119.600\n",
       "ss2_14 14988.730 14709.279  9317.558     0.000 10653.366  9278.006  9505.802\n",
       "ss2_18 13497.210 13012.517  8747.628 10653.366     0.000  7351.714  8692.706\n",
       "ss3_3  15209.257 14188.195  8413.878  9278.006  7351.714     0.000  8503.102\n",
       "ss3_14 14489.565 14113.226  9119.600  9505.802  8692.706  8503.102     0.000\n",
       "ss3_15 12354.985 11779.373 10149.293 10936.765 10026.944  9899.829 10568.719\n",
       "ss3_16 14441.498 13574.423  8682.160  8530.524 10471.814  8283.191  9230.876\n",
       "ss3_20 11705.987 12187.148 11387.520 11322.262 11078.749 11215.385 11030.208\n",
       "ss5_18 14281.504 13623.710  8899.327 10281.724  7483.073  6997.712  8055.097\n",
       "       ss3_15    ss3_16    ss3_20   ss5_18   \n",
       "hc1_2  11693.997 14072.044 11702.18 13190.286\n",
       "hc1_4  14253.680 15345.568 13291.64 15195.551\n",
       "hc2_15 13740.327 15822.143 13357.00 15649.929\n",
       "hc2_17 13138.397 14668.390 12158.79 14422.239\n",
       "hc3_1  12793.947 14521.674 12503.21 13721.460\n",
       "hc3_5  11756.756 15334.174 12440.56 14534.806\n",
       "hc3_7  13350.965 16117.654 13837.97 15293.315\n",
       "hc3_10 12354.985 14441.498 11705.99 14281.504\n",
       "hc3_11 11779.373 13574.423 12187.15 13623.710\n",
       "ss2_9  10149.293  8682.160 11387.52  8899.327\n",
       "ss2_14 10936.765  8530.524 11322.26 10281.724\n",
       "ss2_18 10026.944 10471.814 11078.75  7483.073\n",
       "ss3_3   9899.829  8283.191 11215.38  6997.712\n",
       "ss3_14 10568.719  9230.876 11030.21  8055.097\n",
       "ss3_15     0.000 10193.024 10433.14  9796.022\n",
       "ss3_16 10193.024     0.000 10436.72  8725.894\n",
       "ss3_20 10433.139 10436.720     0.00 10959.480\n",
       "ss5_18  9796.022  8725.894 10959.48     0.000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#read in epigenetic Manhattan distance, 5 coverage, DML \n",
    "ep10 <-read.csv(\"../analyses/methylation-filtered/dist.manhat.DMLs.csv\",header = T)\n",
    "ep10 <- ep10[,c(\"SampNum.row\",\"SampNum.col\",\"dist.manh\")]\n",
    "ep10 <- as.matrix(list2dist(ep10))\n",
    "mbdorder = c(\"hc1_2\",\"hc1_4\",\"hc2_15\",\"hc2_17\",\"hc3_1\",\"hc3_5\",\"hc3_7\",\"hc3_10\",\"hc3_11\",\n",
    "        \"ss2_9\",\"ss2_14\",\"ss2_18\",\"ss3_3\",\"ss3_14\",\"ss3_15\",\"ss3_16\",\"ss3_20\",\"ss5_18\")\n",
    "ep10 <- ep10[mbdorder, mbdorder]\n",
    "ep10\n",
    "Dep10 <- as.dist(ep10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "distObj <- list(Dep10,Dgen)\n",
    "save(distObj,file=\"../analyses/2bRAD/MethGen/dist_DML_snp.Robj\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "kernel": "R"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "lm(formula = Dep10 ~ Dgen)\n",
       "\n",
       "Residuals:\n",
       "   Min     1Q Median     3Q    Max \n",
       " -5032  -1730    -81   1508   4874 \n",
       "\n",
       "Coefficients:\n",
       "            Estimate Std. Error t value Pr(>|t|)    \n",
       "(Intercept)    -3343       1374  -2.433   0.0161 *  \n",
       "Dgen           75762       6646  11.400   <2e-16 ***\n",
       "---\n",
       "Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
       "\n",
       "Residual standard error: 2143 on 151 degrees of freedom\n",
       "Multiple R-squared:  0.4625,\tAdjusted R-squared:  0.459 \n",
       "F-statistic:   130 on 1 and 151 DF,  p-value: < 2.2e-16\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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4OAAddmPj1JPDHdyRoWj+pd0Kg/NvRY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\n+H9MiUFbrWuULwAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#library(MASS)\n",
    "#dens <- kde2d(Dgeo,DgenN, n=300)\n",
    "#myPal <- colorRampPalette(c(\"white\",\"blue\",\"gold\", \"orange\", \"red\"))\n",
    "plot(Dgen, Dep10, pch=20,cex=1,xlab = \"Genetic distance\", ylab = \"MBD Manhattan distance, DMLs\")\n",
    "summary(lm(Dep10~Dgen),)\n",
    "R2 = round(summary(lm(Dep10~Dgen))$r.squared, 4)\n",
    "#image(dens, col=transp(myPal(300),.7), add=TRUE)\n",
    "abline(lm(Dep10~Dgen))\n",
    "text(0.13,16000,label=paste(\"R^2=\",R2))\n",
    "text(0.13,15000,label=\"r=0.68\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "kernel": "R",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPearson's product-moment correlation\n",
       "\n",
       "data:  Dep10 and Dgen\n",
       "t = 11.529, df = 151, p-value < 2.2e-16\n",
       "alternative hypothesis: true correlation is not equal to 0\n",
       "95 percent confidence interval:\n",
       " 0.5895371 0.7603386\n",
       "sample estimates:\n",
       "      cor \n",
       "0.6842097 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tSpearman's rank correlation rho\n",
       "\n",
       "data:  Dep10 and Dgen\n",
       "S = 209532, p-value < 2.2e-16\n",
       "alternative hypothesis: true rho is not equal to 0\n",
       "sample estimates:\n",
       "      rho \n",
       "0.6489687 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cor.test( ~ Dep10 + Dgen,\n",
    "         method = \"pearson\",\n",
    "         conf.level = 0.95)\n",
    "cor.test( ~ Dep10 + Dgen, \n",
    "         method = \"spearman\",\n",
    "         continuity = FALSE,\n",
    "         conf.level = 0.95)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "R",
    "tags": []
   },
   "source": [
    "# Correlate Pst and Fst  \n",
    "maybe redo gene oveerlap if interestied  \n",
    "## For 10kb bins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "kernel": "bash"
   },
   "outputs": [],
   "source": [
    "%%bash\n",
    "# use previously created genome fai file as the bedfile input\n",
    "awk 'FS=OFS=\"\\t\"{print $1, 0, $2}' ../../Olurida_v081.fa.fai \\\n",
    "| bedtools makewindows -b - -w 10000 > ../genome-features/Olurida_v081_10kbIntervals.bed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "kernel": "bash"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Contig0\t0\t10000\n",
      "Contig0\t10000\t20000\n",
      "Contig0\t20000\t30000\n",
      "Contig0\t30000\t40000\n",
      "Contig0\t40000\t50000\n",
      "Contig0\t50000\t60000\n",
      "Contig0\t60000\t70000\n",
      "Contig0\t70000\t80000\n",
      "Contig0\t80000\t90000\n",
      "Contig0\t90000\t100000\n"
     ]
    }
   ],
   "source": [
    "js(\"head ../analyses/2bRAD/Inputs/Olurida_v081_10kbIntervals.bed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "kernel": "R"
   },
   "outputs": [],
   "source": [
    "# reading regions\n",
    "regions=read.table(\"../genome-features/Olurida_v081_10kbIntervals.bed\")\n",
    "names(regions)=c(\"contig\",\"start\",\"end\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "kernel": "R"
   },
   "outputs": [],
   "source": [
    "# read in fst data\n",
    "fst01=read.table(\"../analyses/2bRAD/HCSS_sfsm70_fst2pop.fst\")\n",
    "names(fst01)=c(\"contig\",\"pos\",\"a\",\"b\")\n",
    "\n",
    "fst01$contig=as.character(fst01$contig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "kernel": "R"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "chh\n",
       " FALSE   TRUE \n",
       "363405   5882 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 x 4</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>contig</th><th scope=col>pos</th><th scope=col>a</th><th scope=col>b</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>64</th><td>Contig0</td><td> 74432</td><td>0.000</td><td>0.009</td></tr>\n",
       "\t<tr><th scope=row>82</th><td>Contig0</td><td>109103</td><td>0.034</td><td>0.457</td></tr>\n",
       "\t<tr><th scope=row>98</th><td>Contig0</td><td>109119</td><td>0.003</td><td>0.052</td></tr>\n",
       "\t<tr><th scope=row>102</th><td>Contig0</td><td>109123</td><td>0.002</td><td>0.067</td></tr>\n",
       "\t<tr><th scope=row>114</th><td>Contig1</td><td> 40362</td><td>0.000</td><td>0.009</td></tr>\n",
       "\t<tr><th scope=row>169</th><td>Contig1</td><td> 42880</td><td>0.003</td><td>0.078</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 x 4\n",
       "\\begin{tabular}{r|llll}\n",
       "  & contig & pos & a & b\\\\\n",
       "  & <chr> & <int> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t64 & Contig0 &  74432 & 0.000 & 0.009\\\\\n",
       "\t82 & Contig0 & 109103 & 0.034 & 0.457\\\\\n",
       "\t98 & Contig0 & 109119 & 0.003 & 0.052\\\\\n",
       "\t102 & Contig0 & 109123 & 0.002 & 0.067\\\\\n",
       "\t114 & Contig1 &  40362 & 0.000 & 0.009\\\\\n",
       "\t169 & Contig1 &  42880 & 0.003 & 0.078\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 x 4\n",
       "\n",
       "| <!--/--> | contig &lt;chr&gt; | pos &lt;int&gt; | a &lt;dbl&gt; | b &lt;dbl&gt; |\n",
       "|---|---|---|---|---|\n",
       "| 64 | Contig0 |  74432 | 0.000 | 0.009 |\n",
       "| 82 | Contig0 | 109103 | 0.034 | 0.457 |\n",
       "| 98 | Contig0 | 109119 | 0.003 | 0.052 |\n",
       "| 102 | Contig0 | 109123 | 0.002 | 0.067 |\n",
       "| 114 | Contig1 |  40362 | 0.000 | 0.009 |\n",
       "| 169 | Contig1 |  42880 | 0.003 | 0.078 |\n",
       "\n"
      ],
      "text/plain": [
       "    contig  pos    a     b    \n",
       "64  Contig0  74432 0.000 0.009\n",
       "82  Contig0 109103 0.034 0.457\n",
       "98  Contig0 109119 0.003 0.052\n",
       "102 Contig0 109123 0.002 0.067\n",
       "114 Contig1  40362 0.000 0.009\n",
       "169 Contig1  42880 0.003 0.078"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# removing zero-only (invariant) bases\n",
    "fst01[,3:4]=round(fst01[,3:4],3)\n",
    "ch01=apply(fst01[,3:4],1,sum)\n",
    "chh=(ch01>0)\n",
    "table(chh)\n",
    "\n",
    "fst01=fst01[chh,]\n",
    "head(fst01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "kernel": "R"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================================================"
     ]
    }
   ],
   "source": [
    "# computing weighted Fst per region\n",
    "i=1;gfst01=c();ns01=0;snp=c();a=c();b=c()\n",
    "pb=txtProgressBar(0,nrow(regions))\n",
    "for (i in 1:nrow(regions)) {\n",
    "\tsetTxtProgressBar(pb,i)\n",
    "\tsub=subset(fst01,contig==regions$contig[i] & pos>=regions$start[i] & pos<=regions$end[i])\n",
    "\tif (is.null(sub[1,1]) | sum(sub$b)==0) { \n",
    "\t\tgfst01=append(gfst01,NA)\n",
    "\t} else {\n",
    "\t\tgfst01=append(gfst01,sum(sub$a)/sum(sub$b))\n",
    "\t\tns01=ns01+nrow(sub)\n",
    "        snp=append(snp,paste(sub$contig,sub$pos,sep=\"_\"))\n",
    "        a=append(a,sub$a)\n",
    "        b=append(b,sub$b)\n",
    "\t}\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>5882</li><li>3</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 5882\n",
       "\\item 3\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 5882\n",
       "2. 3\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 5882    3"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "geneSites <- cbind(as.data.frame(snp,stringsAsFactors = F),a,b,stringsAsFactors = F)\n",
    "geneSites <- distinct(geneSites)\n",
    "dim(geneSites)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 x 3</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>snp</th><th scope=col>a</th><th scope=col>b</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>Contig0_74432 </td><td>0.000</td><td>0.009</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>Contig0_109103</td><td>0.034</td><td>0.457</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>Contig0_109119</td><td>0.003</td><td>0.052</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>Contig0_109123</td><td>0.002</td><td>0.067</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>Contig1_40362 </td><td>0.000</td><td>0.009</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>Contig1_42880 </td><td>0.003</td><td>0.078</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 x 3\n",
       "\\begin{tabular}{r|lll}\n",
       "  & snp & a & b\\\\\n",
       "  & <chr> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t1 & Contig0\\_74432  & 0.000 & 0.009\\\\\n",
       "\t2 & Contig0\\_109103 & 0.034 & 0.457\\\\\n",
       "\t3 & Contig0\\_109119 & 0.003 & 0.052\\\\\n",
       "\t4 & Contig0\\_109123 & 0.002 & 0.067\\\\\n",
       "\t5 & Contig1\\_40362  & 0.000 & 0.009\\\\\n",
       "\t6 & Contig1\\_42880  & 0.003 & 0.078\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 x 3\n",
       "\n",
       "| <!--/--> | snp &lt;chr&gt; | a &lt;dbl&gt; | b &lt;dbl&gt; |\n",
       "|---|---|---|---|\n",
       "| 1 | Contig0_74432  | 0.000 | 0.009 |\n",
       "| 2 | Contig0_109103 | 0.034 | 0.457 |\n",
       "| 3 | Contig0_109119 | 0.003 | 0.052 |\n",
       "| 4 | Contig0_109123 | 0.002 | 0.067 |\n",
       "| 5 | Contig1_40362  | 0.000 | 0.009 |\n",
       "| 6 | Contig1_42880  | 0.003 | 0.078 |\n",
       "\n"
      ],
      "text/plain": [
       "  snp            a     b    \n",
       "1 Contig0_74432  0.000 0.009\n",
       "2 Contig0_109103 0.034 0.457\n",
       "3 Contig0_109119 0.003 0.052\n",
       "4 Contig0_109123 0.002 0.067\n",
       "5 Contig1_40362  0.000 0.009\n",
       "6 Contig1_42880  0.003 0.078"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(geneSites)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "0.097069447184321"
      ],
      "text/latex": [
       "0.097069447184321"
      ],
      "text/markdown": [
       "0.097069447184321"
      ],
      "text/plain": [
       "[1] 0.09706945"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#weighted Fst for SNPs in 10kb regions\n",
    "sum(geneSites$a)/sum(geneSites$b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "0.0595914491109701"
      ],
      "text/latex": [
       "0.0595914491109701"
      ],
      "text/markdown": [
       "0.0595914491109701"
      ],
      "text/plain": [
       "[1] 0.05959145"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#unweighted Fst for SNPs in 10kb regions\n",
    "rat <- geneSites$a/geneSites$b\n",
    "mean(rat,na.rm=T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "kernel": "R"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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N5r4tmnNASpS39/Y3EeITCKoUEq3l28eZwOS+3OwY7fUfzWrt1gf2N/pQvKVgOg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\nQaUYjkgkUGcAAAAASUVORK5CYII=",
      "text/plain": [
       "Plot with title “density.default(x = na.omit(gfst01))”"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# density plots of per-region Fst\n",
    "plot(density(na.omit(gfst01)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "kernel": "R"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 x 4</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>contig</th><th scope=col>start</th><th scope=col>end</th><th scope=col>fst01</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>8</th><td>Contig0</td><td> 70000</td><td> 80000</td><td>0.00000000</td></tr>\n",
       "\t<tr><th scope=row>11</th><td>Contig0</td><td>100000</td><td>110000</td><td>0.06770833</td></tr>\n",
       "\t<tr><th scope=row>17</th><td>Contig1</td><td> 40000</td><td> 50000</td><td>0.03448276</td></tr>\n",
       "\t<tr><th scope=row>23</th><td>Contig2</td><td> 10000</td><td> 20000</td><td>0.02127660</td></tr>\n",
       "\t<tr><th scope=row>35</th><td>Contig2</td><td>130000</td><td>139250</td><td>0.00000000</td></tr>\n",
       "\t<tr><th scope=row>37</th><td>Contig3</td><td> 10000</td><td> 20000</td><td>0.25287356</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 x 4\n",
       "\\begin{tabular}{r|llll}\n",
       "  & contig & start & end & fst01\\\\\n",
       "  & <chr> & <int> & <int> & <dbl>\\\\\n",
       "\\hline\n",
       "\t8 & Contig0 &  70000 &  80000 & 0.00000000\\\\\n",
       "\t11 & Contig0 & 100000 & 110000 & 0.06770833\\\\\n",
       "\t17 & Contig1 &  40000 &  50000 & 0.03448276\\\\\n",
       "\t23 & Contig2 &  10000 &  20000 & 0.02127660\\\\\n",
       "\t35 & Contig2 & 130000 & 139250 & 0.00000000\\\\\n",
       "\t37 & Contig3 &  10000 &  20000 & 0.25287356\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 x 4\n",
       "\n",
       "| <!--/--> | contig &lt;chr&gt; | start &lt;int&gt; | end &lt;int&gt; | fst01 &lt;dbl&gt; |\n",
       "|---|---|---|---|---|\n",
       "| 8 | Contig0 |  70000 |  80000 | 0.00000000 |\n",
       "| 11 | Contig0 | 100000 | 110000 | 0.06770833 |\n",
       "| 17 | Contig1 |  40000 |  50000 | 0.03448276 |\n",
       "| 23 | Contig2 |  10000 |  20000 | 0.02127660 |\n",
       "| 35 | Contig2 | 130000 | 139250 | 0.00000000 |\n",
       "| 37 | Contig3 |  10000 |  20000 | 0.25287356 |\n",
       "\n"
      ],
      "text/plain": [
       "   contig  start  end    fst01     \n",
       "8  Contig0  70000  80000 0.00000000\n",
       "11 Contig0 100000 110000 0.06770833\n",
       "17 Contig1  40000  50000 0.03448276\n",
       "23 Contig2  10000  20000 0.02127660\n",
       "35 Contig2 130000 139250 0.00000000\n",
       "37 Contig3  10000  20000 0.25287356"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#adding results to regions table, saving\n",
    "regions$fst01=gfst01\n",
    "regionsNA <- regions[!is.na(regions$fst01),]\n",
    "head(regionsNA)\n",
    "#save bed file of intervals only with fst data\n",
    "write.csv(regionsNA,file=\"../analyses/2bRAD/MethGen/HCSS_sfsm70_Per10kbFst.csv\",quote=F,row.names=F)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "0.0664118377114643"
      ],
      "text/latex": [
       "0.0664118377114643"
      ],
      "text/markdown": [
       "0.0664118377114643"
      ],
      "text/plain": [
       "[1] 0.06641184"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean(regionsNA$fst01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "kernel": "R"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "3782"
      ],
      "text/latex": [
       "3782"
      ],
      "text/markdown": [
       "3782"
      ],
      "text/plain": [
       "[1] 3782"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nrow(regionsNA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "kernel": "R"
   },
   "outputs": [],
   "source": [
    "filtbed <- regionsNA[,c(\"contig\",\"start\",\"end\")]\n",
    "write.table(filtbed, \"../analyses/2bRAD/MethGen/HCSS_sfsm70_Per10kb.bed\",quote=F,row.names=F,col.names = F)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "bash"
   },
   "source": [
    "#### Compare with overlappin Pst bins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "kernel": "R"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 x 5</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>id</th><th scope=col>contig</th><th scope=col>start</th><th scope=col>end</th><th scope=col>fst01</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>Contig0_70000_80000  </td><td>Contig0</td><td> 70000</td><td> 80000</td><td>0.00000000</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>Contig0_100000_110000</td><td>Contig0</td><td>100000</td><td>110000</td><td>0.06770833</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>Contig1_40000_50000  </td><td>Contig1</td><td> 40000</td><td> 50000</td><td>0.03448276</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>Contig2_10000_20000  </td><td>Contig2</td><td> 10000</td><td> 20000</td><td>0.02127660</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>Contig2_130000_139250</td><td>Contig2</td><td>130000</td><td>139250</td><td>0.00000000</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>Contig3_10000_20000  </td><td>Contig3</td><td> 10000</td><td> 20000</td><td>0.25287356</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 x 5\n",
       "\\begin{tabular}{r|lllll}\n",
       "  & id & contig & start & end & fst01\\\\\n",
       "  & <chr> & <chr> & <int> & <int> & <dbl>\\\\\n",
       "\\hline\n",
       "\t1 & Contig0\\_70000\\_80000   & Contig0 &  70000 &  80000 & 0.00000000\\\\\n",
       "\t2 & Contig0\\_100000\\_110000 & Contig0 & 100000 & 110000 & 0.06770833\\\\\n",
       "\t3 & Contig1\\_40000\\_50000   & Contig1 &  40000 &  50000 & 0.03448276\\\\\n",
       "\t4 & Contig2\\_10000\\_20000   & Contig2 &  10000 &  20000 & 0.02127660\\\\\n",
       "\t5 & Contig2\\_130000\\_139250 & Contig2 & 130000 & 139250 & 0.00000000\\\\\n",
       "\t6 & Contig3\\_10000\\_20000   & Contig3 &  10000 &  20000 & 0.25287356\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 x 5\n",
       "\n",
       "| <!--/--> | id &lt;chr&gt; | contig &lt;chr&gt; | start &lt;int&gt; | end &lt;int&gt; | fst01 &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|\n",
       "| 1 | Contig0_70000_80000   | Contig0 |  70000 |  80000 | 0.00000000 |\n",
       "| 2 | Contig0_100000_110000 | Contig0 | 100000 | 110000 | 0.06770833 |\n",
       "| 3 | Contig1_40000_50000   | Contig1 |  40000 |  50000 | 0.03448276 |\n",
       "| 4 | Contig2_10000_20000   | Contig2 |  10000 |  20000 | 0.02127660 |\n",
       "| 5 | Contig2_130000_139250 | Contig2 | 130000 | 139250 | 0.00000000 |\n",
       "| 6 | Contig3_10000_20000   | Contig3 |  10000 |  20000 | 0.25287356 |\n",
       "\n"
      ],
      "text/plain": [
       "  id                    contig  start  end    fst01     \n",
       "1 Contig0_70000_80000   Contig0  70000  80000 0.00000000\n",
       "2 Contig0_100000_110000 Contig0 100000 110000 0.06770833\n",
       "3 Contig1_40000_50000   Contig1  40000  50000 0.03448276\n",
       "4 Contig2_10000_20000   Contig2  10000  20000 0.02127660\n",
       "5 Contig2_130000_139250 Contig2 130000 139250 0.00000000\n",
       "6 Contig3_10000_20000   Contig3  10000  20000 0.25287356"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "regionsNA <- read.csv(\"../analyses/2bRAD/MethGen/HCSS_sfsm70_Per10kbFst.csv\")\n",
    "id <- paste(regionsNA$contig,regionsNA$start,regionsNA$end, sep = \"_\")\n",
    "regionsNA <- cbind(id,regionsNA)\n",
    "head(regionsNA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "kernel": "R"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 x 7</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>id</th><th scope=col>contig</th><th scope=col>start</th><th scope=col>end</th><th scope=col>pst</th><th scope=col>pst.lowCI</th><th scope=col>pst.highCI</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>Contig0_70000_80000     </td><td>Contig0     </td><td>70000</td><td>80000</td><td>0.03413975</td><td>0.0004093522</td><td>0.7746472</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>Contig100484_10000_13621</td><td>Contig100484</td><td>10000</td><td>13621</td><td>0.04039275</td><td>0.0010121740</td><td>0.7577370</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>Contig10074_0_6621      </td><td>Contig10074 </td><td>    0</td><td> 6621</td><td>0.36138305</td><td>0.0022606581</td><td>0.8092501</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>Contig100771_0_1852     </td><td>Contig100771</td><td>    0</td><td> 1852</td><td>0.01811047</td><td>0.0008525946</td><td>0.7631854</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>Contig102229_0_10000    </td><td>Contig102229</td><td>    0</td><td>10000</td><td>0.05933308</td><td>0.0006847296</td><td>0.7844142</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>Contig10259_0_9151      </td><td>Contig10259 </td><td>    0</td><td> 9151</td><td>0.22438574</td><td>0.0018436786</td><td>0.7552582</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 x 7\n",
       "\\begin{tabular}{r|lllllll}\n",
       "  & id & contig & start & end & pst & pst.lowCI & pst.highCI\\\\\n",
       "  & <chr> & <chr> & <int> & <int> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t1 & Contig0\\_70000\\_80000      & Contig0      & 70000 & 80000 & 0.03413975 & 0.0004093522 & 0.7746472\\\\\n",
       "\t2 & Contig100484\\_10000\\_13621 & Contig100484 & 10000 & 13621 & 0.04039275 & 0.0010121740 & 0.7577370\\\\\n",
       "\t3 & Contig10074\\_0\\_6621       & Contig10074  &     0 &  6621 & 0.36138305 & 0.0022606581 & 0.8092501\\\\\n",
       "\t4 & Contig100771\\_0\\_1852      & Contig100771 &     0 &  1852 & 0.01811047 & 0.0008525946 & 0.7631854\\\\\n",
       "\t5 & Contig102229\\_0\\_10000     & Contig102229 &     0 & 10000 & 0.05933308 & 0.0006847296 & 0.7844142\\\\\n",
       "\t6 & Contig10259\\_0\\_9151       & Contig10259  &     0 &  9151 & 0.22438574 & 0.0018436786 & 0.7552582\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 x 7\n",
       "\n",
       "| <!--/--> | id &lt;chr&gt; | contig &lt;chr&gt; | start &lt;int&gt; | end &lt;int&gt; | pst &lt;dbl&gt; | pst.lowCI &lt;dbl&gt; | pst.highCI &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|\n",
       "| 1 | Contig0_70000_80000      | Contig0      | 70000 | 80000 | 0.03413975 | 0.0004093522 | 0.7746472 |\n",
       "| 2 | Contig100484_10000_13621 | Contig100484 | 10000 | 13621 | 0.04039275 | 0.0010121740 | 0.7577370 |\n",
       "| 3 | Contig10074_0_6621       | Contig10074  |     0 |  6621 | 0.36138305 | 0.0022606581 | 0.8092501 |\n",
       "| 4 | Contig100771_0_1852      | Contig100771 |     0 |  1852 | 0.01811047 | 0.0008525946 | 0.7631854 |\n",
       "| 5 | Contig102229_0_10000     | Contig102229 |     0 | 10000 | 0.05933308 | 0.0006847296 | 0.7844142 |\n",
       "| 6 | Contig10259_0_9151       | Contig10259  |     0 |  9151 | 0.22438574 | 0.0018436786 | 0.7552582 |\n",
       "\n"
      ],
      "text/plain": [
       "  id                       contig       start end   pst        pst.lowCI   \n",
       "1 Contig0_70000_80000      Contig0      70000 80000 0.03413975 0.0004093522\n",
       "2 Contig100484_10000_13621 Contig100484 10000 13621 0.04039275 0.0010121740\n",
       "3 Contig10074_0_6621       Contig10074      0  6621 0.36138305 0.0022606581\n",
       "4 Contig100771_0_1852      Contig100771     0  1852 0.01811047 0.0008525946\n",
       "5 Contig102229_0_10000     Contig102229     0 10000 0.05933308 0.0006847296\n",
       "6 Contig10259_0_9151       Contig10259      0  9151 0.22438574 0.0018436786\n",
       "  pst.highCI\n",
       "1 0.7746472 \n",
       "2 0.7577370 \n",
       "3 0.8092501 \n",
       "4 0.7631854 \n",
       "5 0.7844142 \n",
       "6 0.7552582 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "827"
      ],
      "text/latex": [
       "827"
      ],
      "text/markdown": [
       "827"
      ],
      "text/plain": [
       "[1] 827"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# read in Pst data\n",
    "pst10kb <- read.csv(\"../analyses/methylation-filtered/Pst_bins_10kb.tab\", sep=\"\\t\",header=T,stringsAsFactors = F)\n",
    "colnames(pst10kb) <- c(\"id\",\"contig\",\"start\",\"end\",\"pst\",\"pst.lowCI\",\"pst.highCI\")\n",
    "head(pst10kb)\n",
    "nrow(pst10kb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "R"
   },
   "source": [
    "#### 827 overlapping bins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "kernel": "R",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 x 11</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>id</th><th scope=col>contig.x</th><th scope=col>start.x</th><th scope=col>end.x</th><th scope=col>pst</th><th scope=col>pst.lowCI</th><th scope=col>pst.highCI</th><th scope=col>contig.y</th><th scope=col>start.y</th><th scope=col>end.y</th><th scope=col>fst01</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>Contig0_70000_80000     </td><td>Contig0     </td><td>70000</td><td>80000</td><td>0.03413975</td><td>0.0004093522</td><td>0.7746472</td><td>Contig0     </td><td>70000</td><td>80000</td><td>0.00000000</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>Contig100484_10000_13621</td><td>Contig100484</td><td>10000</td><td>13621</td><td>0.04039275</td><td>0.0010121740</td><td>0.7577370</td><td>Contig100484</td><td>10000</td><td>13621</td><td>0.05769231</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>Contig10074_0_6621      </td><td>Contig10074 </td><td>    0</td><td> 6621</td><td>0.36138305</td><td>0.0022606581</td><td>0.8092501</td><td>Contig10074 </td><td>    0</td><td> 6621</td><td>0.19201995</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>Contig100771_0_1852     </td><td>Contig100771</td><td>    0</td><td> 1852</td><td>0.01811047</td><td>0.0008525946</td><td>0.7631854</td><td>Contig100771</td><td>    0</td><td> 1852</td><td>0.14919355</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>Contig102229_0_10000    </td><td>Contig102229</td><td>    0</td><td>10000</td><td>0.05933308</td><td>0.0006847296</td><td>0.7844142</td><td>Contig102229</td><td>    0</td><td>10000</td><td>0.00000000</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>Contig10259_0_9151      </td><td>Contig10259 </td><td>    0</td><td> 9151</td><td>0.22438574</td><td>0.0018436786</td><td>0.7552582</td><td>Contig10259 </td><td>    0</td><td> 9151</td><td>0.10266940</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 x 11\n",
       "\\begin{tabular}{r|lllllllllll}\n",
       "  & id & contig.x & start.x & end.x & pst & pst.lowCI & pst.highCI & contig.y & start.y & end.y & fst01\\\\\n",
       "  & <chr> & <chr> & <int> & <int> & <dbl> & <dbl> & <dbl> & <chr> & <int> & <int> & <dbl>\\\\\n",
       "\\hline\n",
       "\t1 & Contig0\\_70000\\_80000      & Contig0      & 70000 & 80000 & 0.03413975 & 0.0004093522 & 0.7746472 & Contig0      & 70000 & 80000 & 0.00000000\\\\\n",
       "\t2 & Contig100484\\_10000\\_13621 & Contig100484 & 10000 & 13621 & 0.04039275 & 0.0010121740 & 0.7577370 & Contig100484 & 10000 & 13621 & 0.05769231\\\\\n",
       "\t3 & Contig10074\\_0\\_6621       & Contig10074  &     0 &  6621 & 0.36138305 & 0.0022606581 & 0.8092501 & Contig10074  &     0 &  6621 & 0.19201995\\\\\n",
       "\t4 & Contig100771\\_0\\_1852      & Contig100771 &     0 &  1852 & 0.01811047 & 0.0008525946 & 0.7631854 & Contig100771 &     0 &  1852 & 0.14919355\\\\\n",
       "\t5 & Contig102229\\_0\\_10000     & Contig102229 &     0 & 10000 & 0.05933308 & 0.0006847296 & 0.7844142 & Contig102229 &     0 & 10000 & 0.00000000\\\\\n",
       "\t6 & Contig10259\\_0\\_9151       & Contig10259  &     0 &  9151 & 0.22438574 & 0.0018436786 & 0.7552582 & Contig10259  &     0 &  9151 & 0.10266940\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 x 11\n",
       "\n",
       "| <!--/--> | id &lt;chr&gt; | contig.x &lt;chr&gt; | start.x &lt;int&gt; | end.x &lt;int&gt; | pst &lt;dbl&gt; | pst.lowCI &lt;dbl&gt; | pst.highCI &lt;dbl&gt; | contig.y &lt;chr&gt; | start.y &lt;int&gt; | end.y &lt;int&gt; | fst01 &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 1 | Contig0_70000_80000      | Contig0      | 70000 | 80000 | 0.03413975 | 0.0004093522 | 0.7746472 | Contig0      | 70000 | 80000 | 0.00000000 |\n",
       "| 2 | Contig100484_10000_13621 | Contig100484 | 10000 | 13621 | 0.04039275 | 0.0010121740 | 0.7577370 | Contig100484 | 10000 | 13621 | 0.05769231 |\n",
       "| 3 | Contig10074_0_6621       | Contig10074  |     0 |  6621 | 0.36138305 | 0.0022606581 | 0.8092501 | Contig10074  |     0 |  6621 | 0.19201995 |\n",
       "| 4 | Contig100771_0_1852      | Contig100771 |     0 |  1852 | 0.01811047 | 0.0008525946 | 0.7631854 | Contig100771 |     0 |  1852 | 0.14919355 |\n",
       "| 5 | Contig102229_0_10000     | Contig102229 |     0 | 10000 | 0.05933308 | 0.0006847296 | 0.7844142 | Contig102229 |     0 | 10000 | 0.00000000 |\n",
       "| 6 | Contig10259_0_9151       | Contig10259  |     0 |  9151 | 0.22438574 | 0.0018436786 | 0.7552582 | Contig10259  |     0 |  9151 | 0.10266940 |\n",
       "\n"
      ],
      "text/plain": [
       "  id                       contig.x     start.x end.x pst        pst.lowCI   \n",
       "1 Contig0_70000_80000      Contig0      70000   80000 0.03413975 0.0004093522\n",
       "2 Contig100484_10000_13621 Contig100484 10000   13621 0.04039275 0.0010121740\n",
       "3 Contig10074_0_6621       Contig10074      0    6621 0.36138305 0.0022606581\n",
       "4 Contig100771_0_1852      Contig100771     0    1852 0.01811047 0.0008525946\n",
       "5 Contig102229_0_10000     Contig102229     0   10000 0.05933308 0.0006847296\n",
       "6 Contig10259_0_9151       Contig10259      0    9151 0.22438574 0.0018436786\n",
       "  pst.highCI contig.y     start.y end.y fst01     \n",
       "1 0.7746472  Contig0      70000   80000 0.00000000\n",
       "2 0.7577370  Contig100484 10000   13621 0.05769231\n",
       "3 0.8092501  Contig10074      0    6621 0.19201995\n",
       "4 0.7631854  Contig100771     0    1852 0.14919355\n",
       "5 0.7844142  Contig102229     0   10000 0.00000000\n",
       "6 0.7552582  Contig10259      0    9151 0.10266940"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "both = merge(pst10kb,regionsNA, by =\"id\")\n",
    "#both <- both[,c(\"id\",\"pst\",\"fst01\")]\n",
    "head(both)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "0.0654780581529713"
      ],
      "text/latex": [
       "0.0654780581529713"
      ],
      "text/markdown": [
       "0.0654780581529713"
      ],
      "text/plain": [
       "[1] 0.06547806"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "0.303080092561533"
      ],
      "text/latex": [
       "0.303080092561533"
      ],
      "text/markdown": [
       "0.303080092561533"
      ],
      "text/plain": [
       "[1] 0.3030801"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean(both$fst01)\n",
    "mean(both$pst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "kernel": "R",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPearson's product-moment correlation\n",
       "\n",
       "data:  both$pst and both$fst01\n",
       "t = -0.15004, df = 825, p-value = 0.8808\n",
       "alternative hypothesis: true correlation is not equal to 0\n",
       "95 percent confidence interval:\n",
       " -0.07337011  0.06297153\n",
       "sample estimates:\n",
       "         cor \n",
       "-0.005223564 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in cor.test.default(x = c(0.0341397522278473, 0.0403927503300008, :\n",
      "“Cannot compute exact p-value with ties”\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tSpearman's rank correlation rho\n",
       "\n",
       "data:  both$pst and both$fst01\n",
       "S = 94490042, p-value = 0.9461\n",
       "alternative hypothesis: true rho is not equal to 0\n",
       "sample estimates:\n",
       "         rho \n",
       "-0.002354627 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cor.test( ~ both$pst + both$fst01,\n",
    "         method = \"pearson\",\n",
    "         conf.level = 0.95)\n",
    "cor.test( ~ both$pst + both$fst01, \n",
    "         method = \"spearman\",\n",
    "         continuity = FALSE,\n",
    "         conf.level = 0.95)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "kernel": "R"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "lm(formula = both$pst ~ both$fst01)\n",
       "\n",
       "Residuals:\n",
       "     Min       1Q   Median       3Q      Max \n",
       "-0.30386 -0.23723 -0.05892  0.19445  0.70007 \n",
       "\n",
       "Coefficients:\n",
       "            Estimate Std. Error t value Pr(>|t|)    \n",
       "(Intercept)  0.30403    0.01091   27.87   <2e-16 ***\n",
       "both$fst01  -0.01444    0.09622   -0.15    0.881    \n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 0.2561 on 825 degrees of freedom\n",
       "Multiple R-squared:  2.729e-05,\tAdjusted R-squared:  -0.001185 \n",
       "F-statistic: 0.02251 on 1 and 825 DF,  p-value: 0.8808\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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deCeJgAEdL2cHovjbGZH8vutVj3euWn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M8wtn7fHemNOTX5jzFKs5R9QXTHytQZNp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\nAElFTkSuQmCC",
      "text/plain": [
       "Plot with title “Pst vs Fst, 10kb bins, 827 bins”"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(both$fst01, both$pst, pch=20,cex=1,xlab = \"Fst\", ylab = \"Pst\")\n",
    "title(\"Pst vs Fst, 10kb bins, 827 bins\")\n",
    "summary(lm(both$pst~both$fst01),)\n",
    "R2 = round(summary(lm(both$pst~both$fst01))$r.squared, 4)\n",
    "#image(dens, col=transp(myPal(300),.7), add=TRUE)\n",
    "abline(lm(both$pst~both$fst01))\n",
    "text(0.4,0.8,label=paste(\"R^2=\",R2))\n",
    "text(0.4,0.75,label=\"r= -0.005\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "R"
   },
   "source": [
    "# Correlating PC scores for genetic and epigenetic data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "R"
   },
   "source": [
    "### All methylation data (5x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "kernel": "R"
   },
   "outputs": [],
   "source": [
    "# Read in PC scores from epigenetics\n",
    "ep <- read.table(\"../analyses/methylation-filtered/PC-scores-filtered-methylation.tab\",\n",
    "                 header=T, sep=\"\\t\",row.names = \"sample\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "R"
   },
   "source": [
    "Add sample names to PC score matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "kernel": "R"
   },
   "outputs": [],
   "source": [
    "key = read.csv(\"../data/sample-key.csv\",colClasses = c(\"character\",\"character\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "kernel": "R"
   },
   "outputs": [],
   "source": [
    "samples = rownames(ep)\n",
    "mapdf <- data.frame(old=key$MBD.FILENAME,new=key$SAMPLE)\n",
    "rownames(ep) <- mapdf$new[match(samples,mapdf$old)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "kernel": "R"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 18 x 18</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>PC1</th><th scope=col>PC2</th><th scope=col>PC3</th><th scope=col>PC4</th><th scope=col>PC5</th><th scope=col>PC6</th><th scope=col>PC7</th><th scope=col>PC8</th><th scope=col>PC9</th><th scope=col>PC10</th><th scope=col>PC11</th><th scope=col>PC12</th><th scope=col>PC13</th><th scope=col>PC14</th><th scope=col>PC15</th><th scope=col>PC16</th><th scope=col>PC17</th><th scope=col>PC18</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>hc1_2</th><td> -41.855179</td><td>  48.129186</td><td> -89.423468</td><td>  20.22070</td><td> -29.7202460</td><td> 158.846259</td><td>  63.693423</td><td>  95.04688</td><td>-240.359208</td><td>  -1.583968</td><td> -51.04567</td><td> -39.903419</td><td>  22.8062963</td><td>  19.642542</td><td>  -5.516195</td><td>  -6.89146481</td><td>  -4.1565798</td><td>-8.954237e-13</td></tr>\n",
       "\t<tr><th scope=row>hc1_4</th><td>  -3.141767</td><td>  68.092352</td><td>  33.976913</td><td> 181.69572</td><td>  71.4365693</td><td>-237.591754</td><td> -13.896974</td><td>  70.05871</td><td> -89.446720</td><td> -25.612906</td><td> -47.69838</td><td>  -7.094666</td><td> -51.7706657</td><td> -22.686702</td><td>  -9.008882</td><td>  -0.86495842</td><td>   2.5512477</td><td>-1.078092e-12</td></tr>\n",
       "\t<tr><th scope=row>hc2_15</th><td> -38.552940</td><td>  64.282695</td><td>  55.518975</td><td> 117.51097</td><td>  22.9760019</td><td> 116.363538</td><td> -47.778507</td><td>-188.55458</td><td>  33.498908</td><td>-176.912782</td><td> -63.28670</td><td> -25.898024</td><td> -47.3524892</td><td>  -7.459777</td><td>  -5.933131</td><td>  -0.81121599</td><td>  -5.6725954</td><td>-6.715565e-13</td></tr>\n",
       "\t<tr><th scope=row>hc2_17</th><td> -82.093747</td><td>  44.408345</td><td>  27.834560</td><td>  70.94997</td><td>  50.9966792</td><td> -10.599225</td><td>   3.198294</td><td>   8.42252</td><td>  46.912464</td><td> -14.378083</td><td>  70.47019</td><td>  16.195739</td><td> 252.9834935</td><td>  85.447893</td><td>  57.180445</td><td>  -4.80047455</td><td>  -2.9124148</td><td>-4.472849e-13</td></tr>\n",
       "\t<tr><th scope=row>hc3_1</th><td> 224.746062</td><td> 240.034234</td><td> 133.312802</td><td>-170.12095</td><td>  27.4689155</td><td>  -2.215026</td><td>  16.470975</td><td>  44.05080</td><td>   6.833667</td><td> -41.934739</td><td>  -8.28425</td><td>   4.153790</td><td>  10.6881919</td><td>  -8.717818</td><td>   1.262954</td><td>   3.09348631</td><td>  -0.1519688</td><td>-7.986172e-13</td></tr>\n",
       "\t<tr><th scope=row>hc3_5</th><td> -31.510415</td><td> 124.975628</td><td>-317.775086</td><td> -90.90335</td><td>   9.5736442</td><td> -71.192278</td><td> -70.182743</td><td> -80.20398</td><td>  37.175811</td><td>  20.276913</td><td> -17.84866</td><td> -13.758503</td><td>   3.0338443</td><td> -11.204631</td><td>   4.449811</td><td>   2.95571290</td><td>  -0.7183613</td><td>-2.464503e-13</td></tr>\n",
       "\t<tr><th scope=row>hc3_7</th><td>   2.266559</td><td>  88.206573</td><td>  80.134766</td><td>  76.71013</td><td>  24.0733767</td><td>  58.501761</td><td> -54.978134</td><td>-103.41131</td><td> -20.776915</td><td> 246.470031</td><td>  86.25975</td><td> -12.393247</td><td> -58.4778249</td><td>  18.914360</td><td>  -9.712800</td><td>  -6.72069143</td><td>  -1.3720118</td><td>-7.084814e-14</td></tr>\n",
       "\t<tr><th scope=row>hc3_10</th><td>-107.031724</td><td>  36.136923</td><td> -44.786067</td><td>  10.04223</td><td>  53.0399616</td><td>  50.226984</td><td>  77.862837</td><td> 104.50437</td><td>  69.139601</td><td> -62.148411</td><td> 151.70808</td><td> 138.668209</td><td>-133.1581378</td><td>  38.464803</td><td>  30.817861</td><td>   5.40631998</td><td>  -7.5503503</td><td>-5.897121e-13</td></tr>\n",
       "\t<tr><th scope=row>hc3_11</th><td>  90.595770</td><td>   8.032785</td><td> -17.444734</td><td>  79.06294</td><td>-312.3872200</td><td> -43.718287</td><td>  90.965691</td><td> -25.50297</td><td>  37.011081</td><td>  -6.446748</td><td>  39.40510</td><td>  17.974319</td><td>   9.6832275</td><td>  -8.125046</td><td>  -6.630260</td><td>   0.80489946</td><td>  -0.5893811</td><td>-8.516853e-14</td></tr>\n",
       "\t<tr><th scope=row>ss2_9</th><td> -11.358211</td><td>-113.456315</td><td>  49.069336</td><td> -83.86651</td><td> -76.0809820</td><td> -24.882964</td><td>-246.758699</td><td>  34.16348</td><td> -82.315434</td><td> -68.740251</td><td> 107.56343</td><td>  -7.909964</td><td> -11.1992441</td><td>  39.397959</td><td> -29.070913</td><td> -13.94348907</td><td>  -3.4553305</td><td>-2.719016e-13</td></tr>\n",
       "\t<tr><th scope=row>ss2_14</th><td>-117.071913</td><td> -98.917400</td><td>  68.920326</td><td>-156.54863</td><td>  28.1731043</td><td>-105.184934</td><td> 160.580208</td><td>-151.09389</td><td> -87.467111</td><td> -10.676948</td><td>  23.99284</td><td> -22.227690</td><td> -15.1185183</td><td>  52.617219</td><td> -17.522872</td><td>  -6.87423444</td><td>   3.5161844</td><td> 1.921395e-12</td></tr>\n",
       "\t<tr><th scope=row>ss2_18</th><td> 299.601511</td><td>-188.189376</td><td> -96.851537</td><td>  59.00188</td><td> 120.8222085</td><td>  26.332985</td><td>  48.205011</td><td> -28.06381</td><td>  -4.796050</td><td> -16.692753</td><td>  74.45781</td><td> -27.257034</td><td>  13.7540282</td><td> -20.170889</td><td>  -7.358440</td><td> -11.46049368</td><td>  -1.5348860</td><td>-1.200846e-12</td></tr>\n",
       "\t<tr><th scope=row>ss3_3</th><td>  -9.179039</td><td> -74.881938</td><td>  24.907495</td><td> -22.73858</td><td>  -0.4820002</td><td>   8.096835</td><td> -19.353492</td><td>  11.39722</td><td>  12.137654</td><td>  33.784758</td><td> -70.36688</td><td>  41.302083</td><td>   6.6781367</td><td> -36.567637</td><td>  47.968655</td><td> 214.58327327</td><td>-123.3770819</td><td> 2.459107e-12</td></tr>\n",
       "\t<tr><th scope=row>ss3_14</th><td> -25.735038</td><td> -57.218259</td><td>  21.458449</td><td> -19.63399</td><td>  -0.7972375</td><td>  19.601002</td><td> -15.831340</td><td>  16.14609</td><td>  17.748587</td><td>  22.182114</td><td> -49.09610</td><td>  32.619746</td><td>   5.5818412</td><td> -63.710984</td><td>  54.642634</td><td>  66.87267604</td><td> 236.1110885</td><td>-3.225787e-13</td></tr>\n",
       "\t<tr><th scope=row>ss3_15</th><td> -97.216062</td><td> -15.810181</td><td>  20.834465</td><td> -14.51974</td><td>  23.8500668</td><td>  25.143062</td><td>  20.735511</td><td>  45.56274</td><td>  58.624737</td><td>  12.518932</td><td>  10.89437</td><td>   5.577832</td><td>  59.6023077</td><td>-160.577564</td><td>-210.784437</td><td> -22.02139631</td><td> -17.8009361</td><td> 1.366052e-12</td></tr>\n",
       "\t<tr><th scope=row>ss3_16</th><td> -41.385837</td><td> -64.878720</td><td>  34.962514</td><td> -37.65652</td><td>  -9.0995393</td><td>   7.508962</td><td> -13.463855</td><td>  12.96011</td><td>  14.433709</td><td>  29.387305</td><td> -56.02326</td><td>  26.969135</td><td>  -0.5315889</td><td>-137.182070</td><td> 161.909305</td><td>-150.28022609</td><td> -66.1613066</td><td>-2.043060e-13</td></tr>\n",
       "\t<tr><th scope=row>ss3_20</th><td> -54.362674</td><td> -32.056607</td><td>  13.809393</td><td> -10.95988</td><td>  -4.3353746</td><td>  12.777216</td><td>  23.024209</td><td> 105.54750</td><td> 129.418154</td><td>  10.700222</td><td> -21.39494</td><td>-249.492408</td><td> -54.2720570</td><td>  64.762593</td><td>  17.241905</td><td>   0.08408483</td><td>  -2.4032965</td><td>-8.109339e-13</td></tr>\n",
       "\t<tr><th scope=row>ss5_18</th><td>  43.284641</td><td> -76.889925</td><td>   1.540898</td><td>  -8.24637</td><td>   0.4920718</td><td>  11.985864</td><td> -22.492415</td><td>  28.97013</td><td>  62.227063</td><td>  49.807315</td><td>-179.70674</td><td> 122.474104</td><td> -12.9308413</td><td> 157.155749</td><td> -73.935641</td><td> -69.13180798</td><td>  -4.3220198</td><td> 1.732399e-12</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 18 x 18\n",
       "\\begin{tabular}{r|llllllllllllllllll}\n",
       "  & PC1 & PC2 & PC3 & PC4 & PC5 & PC6 & PC7 & PC8 & PC9 & PC10 & PC11 & PC12 & PC13 & PC14 & PC15 & PC16 & PC17 & PC18\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\thc1\\_2 &  -41.855179 &   48.129186 &  -89.423468 &   20.22070 &  -29.7202460 &  158.846259 &   63.693423 &   95.04688 & -240.359208 &   -1.583968 &  -51.04567 &  -39.903419 &   22.8062963 &   19.642542 &   -5.516195 &   -6.89146481 &   -4.1565798 & -8.954237e-13\\\\\n",
       "\thc1\\_4 &   -3.141767 &   68.092352 &   33.976913 &  181.69572 &   71.4365693 & -237.591754 &  -13.896974 &   70.05871 &  -89.446720 &  -25.612906 &  -47.69838 &   -7.094666 &  -51.7706657 &  -22.686702 &   -9.008882 &   -0.86495842 &    2.5512477 & -1.078092e-12\\\\\n",
       "\thc2\\_15 &  -38.552940 &   64.282695 &   55.518975 &  117.51097 &   22.9760019 &  116.363538 &  -47.778507 & -188.55458 &   33.498908 & -176.912782 &  -63.28670 &  -25.898024 &  -47.3524892 &   -7.459777 &   -5.933131 &   -0.81121599 &   -5.6725954 & -6.715565e-13\\\\\n",
       "\thc2\\_17 &  -82.093747 &   44.408345 &   27.834560 &   70.94997 &   50.9966792 &  -10.599225 &    3.198294 &    8.42252 &   46.912464 &  -14.378083 &   70.47019 &   16.195739 &  252.9834935 &   85.447893 &   57.180445 &   -4.80047455 &   -2.9124148 & -4.472849e-13\\\\\n",
       "\thc3\\_1 &  224.746062 &  240.034234 &  133.312802 & -170.12095 &   27.4689155 &   -2.215026 &   16.470975 &   44.05080 &    6.833667 &  -41.934739 &   -8.28425 &    4.153790 &   10.6881919 &   -8.717818 &    1.262954 &    3.09348631 &   -0.1519688 & -7.986172e-13\\\\\n",
       "\thc3\\_5 &  -31.510415 &  124.975628 & -317.775086 &  -90.90335 &    9.5736442 &  -71.192278 &  -70.182743 &  -80.20398 &   37.175811 &   20.276913 &  -17.84866 &  -13.758503 &    3.0338443 &  -11.204631 &    4.449811 &    2.95571290 &   -0.7183613 & -2.464503e-13\\\\\n",
       "\thc3\\_7 &    2.266559 &   88.206573 &   80.134766 &   76.71013 &   24.0733767 &   58.501761 &  -54.978134 & -103.41131 &  -20.776915 &  246.470031 &   86.25975 &  -12.393247 &  -58.4778249 &   18.914360 &   -9.712800 &   -6.72069143 &   -1.3720118 & -7.084814e-14\\\\\n",
       "\thc3\\_10 & -107.031724 &   36.136923 &  -44.786067 &   10.04223 &   53.0399616 &   50.226984 &   77.862837 &  104.50437 &   69.139601 &  -62.148411 &  151.70808 &  138.668209 & -133.1581378 &   38.464803 &   30.817861 &    5.40631998 &   -7.5503503 & -5.897121e-13\\\\\n",
       "\thc3\\_11 &   90.595770 &    8.032785 &  -17.444734 &   79.06294 & -312.3872200 &  -43.718287 &   90.965691 &  -25.50297 &   37.011081 &   -6.446748 &   39.40510 &   17.974319 &    9.6832275 &   -8.125046 &   -6.630260 &    0.80489946 &   -0.5893811 & -8.516853e-14\\\\\n",
       "\tss2\\_9 &  -11.358211 & -113.456315 &   49.069336 &  -83.86651 &  -76.0809820 &  -24.882964 & -246.758699 &   34.16348 &  -82.315434 &  -68.740251 &  107.56343 &   -7.909964 &  -11.1992441 &   39.397959 &  -29.070913 &  -13.94348907 &   -3.4553305 & -2.719016e-13\\\\\n",
       "\tss2\\_14 & -117.071913 &  -98.917400 &   68.920326 & -156.54863 &   28.1731043 & -105.184934 &  160.580208 & -151.09389 &  -87.467111 &  -10.676948 &   23.99284 &  -22.227690 &  -15.1185183 &   52.617219 &  -17.522872 &   -6.87423444 &    3.5161844 &  1.921395e-12\\\\\n",
       "\tss2\\_18 &  299.601511 & -188.189376 &  -96.851537 &   59.00188 &  120.8222085 &   26.332985 &   48.205011 &  -28.06381 &   -4.796050 &  -16.692753 &   74.45781 &  -27.257034 &   13.7540282 &  -20.170889 &   -7.358440 &  -11.46049368 &   -1.5348860 & -1.200846e-12\\\\\n",
       "\tss3\\_3 &   -9.179039 &  -74.881938 &   24.907495 &  -22.73858 &   -0.4820002 &    8.096835 &  -19.353492 &   11.39722 &   12.137654 &   33.784758 &  -70.36688 &   41.302083 &    6.6781367 &  -36.567637 &   47.968655 &  214.58327327 & -123.3770819 &  2.459107e-12\\\\\n",
       "\tss3\\_14 &  -25.735038 &  -57.218259 &   21.458449 &  -19.63399 &   -0.7972375 &   19.601002 &  -15.831340 &   16.14609 &   17.748587 &   22.182114 &  -49.09610 &   32.619746 &    5.5818412 &  -63.710984 &   54.642634 &   66.87267604 &  236.1110885 & -3.225787e-13\\\\\n",
       "\tss3\\_15 &  -97.216062 &  -15.810181 &   20.834465 &  -14.51974 &   23.8500668 &   25.143062 &   20.735511 &   45.56274 &   58.624737 &   12.518932 &   10.89437 &    5.577832 &   59.6023077 & -160.577564 & -210.784437 &  -22.02139631 &  -17.8009361 &  1.366052e-12\\\\\n",
       "\tss3\\_16 &  -41.385837 &  -64.878720 &   34.962514 &  -37.65652 &   -9.0995393 &    7.508962 &  -13.463855 &   12.96011 &   14.433709 &   29.387305 &  -56.02326 &   26.969135 &   -0.5315889 & -137.182070 &  161.909305 & -150.28022609 &  -66.1613066 & -2.043060e-13\\\\\n",
       "\tss3\\_20 &  -54.362674 &  -32.056607 &   13.809393 &  -10.95988 &   -4.3353746 &   12.777216 &   23.024209 &  105.54750 &  129.418154 &   10.700222 &  -21.39494 & -249.492408 &  -54.2720570 &   64.762593 &   17.241905 &    0.08408483 &   -2.4032965 & -8.109339e-13\\\\\n",
       "\tss5\\_18 &   43.284641 &  -76.889925 &    1.540898 &   -8.24637 &    0.4920718 &   11.985864 &  -22.492415 &   28.97013 &   62.227063 &   49.807315 & -179.70674 &  122.474104 &  -12.9308413 &  157.155749 &  -73.935641 &  -69.13180798 &   -4.3220198 &  1.732399e-12\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 18 x 18\n",
       "\n",
       "| <!--/--> | PC1 &lt;dbl&gt; | PC2 &lt;dbl&gt; | PC3 &lt;dbl&gt; | PC4 &lt;dbl&gt; | PC5 &lt;dbl&gt; | PC6 &lt;dbl&gt; | PC7 &lt;dbl&gt; | PC8 &lt;dbl&gt; | PC9 &lt;dbl&gt; | PC10 &lt;dbl&gt; | PC11 &lt;dbl&gt; | PC12 &lt;dbl&gt; | PC13 &lt;dbl&gt; | PC14 &lt;dbl&gt; | PC15 &lt;dbl&gt; | PC16 &lt;dbl&gt; | PC17 &lt;dbl&gt; | PC18 &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| hc1_2 |  -41.855179 |   48.129186 |  -89.423468 |   20.22070 |  -29.7202460 |  158.846259 |   63.693423 |   95.04688 | -240.359208 |   -1.583968 |  -51.04567 |  -39.903419 |   22.8062963 |   19.642542 |   -5.516195 |   -6.89146481 |   -4.1565798 | -8.954237e-13 |\n",
       "| hc1_4 |   -3.141767 |   68.092352 |   33.976913 |  181.69572 |   71.4365693 | -237.591754 |  -13.896974 |   70.05871 |  -89.446720 |  -25.612906 |  -47.69838 |   -7.094666 |  -51.7706657 |  -22.686702 |   -9.008882 |   -0.86495842 |    2.5512477 | -1.078092e-12 |\n",
       "| hc2_15 |  -38.552940 |   64.282695 |   55.518975 |  117.51097 |   22.9760019 |  116.363538 |  -47.778507 | -188.55458 |   33.498908 | -176.912782 |  -63.28670 |  -25.898024 |  -47.3524892 |   -7.459777 |   -5.933131 |   -0.81121599 |   -5.6725954 | -6.715565e-13 |\n",
       "| hc2_17 |  -82.093747 |   44.408345 |   27.834560 |   70.94997 |   50.9966792 |  -10.599225 |    3.198294 |    8.42252 |   46.912464 |  -14.378083 |   70.47019 |   16.195739 |  252.9834935 |   85.447893 |   57.180445 |   -4.80047455 |   -2.9124148 | -4.472849e-13 |\n",
       "| hc3_1 |  224.746062 |  240.034234 |  133.312802 | -170.12095 |   27.4689155 |   -2.215026 |   16.470975 |   44.05080 |    6.833667 |  -41.934739 |   -8.28425 |    4.153790 |   10.6881919 |   -8.717818 |    1.262954 |    3.09348631 |   -0.1519688 | -7.986172e-13 |\n",
       "| hc3_5 |  -31.510415 |  124.975628 | -317.775086 |  -90.90335 |    9.5736442 |  -71.192278 |  -70.182743 |  -80.20398 |   37.175811 |   20.276913 |  -17.84866 |  -13.758503 |    3.0338443 |  -11.204631 |    4.449811 |    2.95571290 |   -0.7183613 | -2.464503e-13 |\n",
       "| hc3_7 |    2.266559 |   88.206573 |   80.134766 |   76.71013 |   24.0733767 |   58.501761 |  -54.978134 | -103.41131 |  -20.776915 |  246.470031 |   86.25975 |  -12.393247 |  -58.4778249 |   18.914360 |   -9.712800 |   -6.72069143 |   -1.3720118 | -7.084814e-14 |\n",
       "| hc3_10 | -107.031724 |   36.136923 |  -44.786067 |   10.04223 |   53.0399616 |   50.226984 |   77.862837 |  104.50437 |   69.139601 |  -62.148411 |  151.70808 |  138.668209 | -133.1581378 |   38.464803 |   30.817861 |    5.40631998 |   -7.5503503 | -5.897121e-13 |\n",
       "| hc3_11 |   90.595770 |    8.032785 |  -17.444734 |   79.06294 | -312.3872200 |  -43.718287 |   90.965691 |  -25.50297 |   37.011081 |   -6.446748 |   39.40510 |   17.974319 |    9.6832275 |   -8.125046 |   -6.630260 |    0.80489946 |   -0.5893811 | -8.516853e-14 |\n",
       "| ss2_9 |  -11.358211 | -113.456315 |   49.069336 |  -83.86651 |  -76.0809820 |  -24.882964 | -246.758699 |   34.16348 |  -82.315434 |  -68.740251 |  107.56343 |   -7.909964 |  -11.1992441 |   39.397959 |  -29.070913 |  -13.94348907 |   -3.4553305 | -2.719016e-13 |\n",
       "| ss2_14 | -117.071913 |  -98.917400 |   68.920326 | -156.54863 |   28.1731043 | -105.184934 |  160.580208 | -151.09389 |  -87.467111 |  -10.676948 |   23.99284 |  -22.227690 |  -15.1185183 |   52.617219 |  -17.522872 |   -6.87423444 |    3.5161844 |  1.921395e-12 |\n",
       "| ss2_18 |  299.601511 | -188.189376 |  -96.851537 |   59.00188 |  120.8222085 |   26.332985 |   48.205011 |  -28.06381 |   -4.796050 |  -16.692753 |   74.45781 |  -27.257034 |   13.7540282 |  -20.170889 |   -7.358440 |  -11.46049368 |   -1.5348860 | -1.200846e-12 |\n",
       "| ss3_3 |   -9.179039 |  -74.881938 |   24.907495 |  -22.73858 |   -0.4820002 |    8.096835 |  -19.353492 |   11.39722 |   12.137654 |   33.784758 |  -70.36688 |   41.302083 |    6.6781367 |  -36.567637 |   47.968655 |  214.58327327 | -123.3770819 |  2.459107e-12 |\n",
       "| ss3_14 |  -25.735038 |  -57.218259 |   21.458449 |  -19.63399 |   -0.7972375 |   19.601002 |  -15.831340 |   16.14609 |   17.748587 |   22.182114 |  -49.09610 |   32.619746 |    5.5818412 |  -63.710984 |   54.642634 |   66.87267604 |  236.1110885 | -3.225787e-13 |\n",
       "| ss3_15 |  -97.216062 |  -15.810181 |   20.834465 |  -14.51974 |   23.8500668 |   25.143062 |   20.735511 |   45.56274 |   58.624737 |   12.518932 |   10.89437 |    5.577832 |   59.6023077 | -160.577564 | -210.784437 |  -22.02139631 |  -17.8009361 |  1.366052e-12 |\n",
       "| ss3_16 |  -41.385837 |  -64.878720 |   34.962514 |  -37.65652 |   -9.0995393 |    7.508962 |  -13.463855 |   12.96011 |   14.433709 |   29.387305 |  -56.02326 |   26.969135 |   -0.5315889 | -137.182070 |  161.909305 | -150.28022609 |  -66.1613066 | -2.043060e-13 |\n",
       "| ss3_20 |  -54.362674 |  -32.056607 |   13.809393 |  -10.95988 |   -4.3353746 |   12.777216 |   23.024209 |  105.54750 |  129.418154 |   10.700222 |  -21.39494 | -249.492408 |  -54.2720570 |   64.762593 |   17.241905 |    0.08408483 |   -2.4032965 | -8.109339e-13 |\n",
       "| ss5_18 |   43.284641 |  -76.889925 |    1.540898 |   -8.24637 |    0.4920718 |   11.985864 |  -22.492415 |   28.97013 |   62.227063 |   49.807315 | -179.70674 |  122.474104 |  -12.9308413 |  157.155749 |  -73.935641 |  -69.13180798 |   -4.3220198 |  1.732399e-12 |\n",
       "\n"
      ],
      "text/plain": [
       "       PC1         PC2         PC3         PC4        PC5          PC6        \n",
       "hc1_2   -41.855179   48.129186  -89.423468   20.22070  -29.7202460  158.846259\n",
       "hc1_4    -3.141767   68.092352   33.976913  181.69572   71.4365693 -237.591754\n",
       "hc2_15  -38.552940   64.282695   55.518975  117.51097   22.9760019  116.363538\n",
       "hc2_17  -82.093747   44.408345   27.834560   70.94997   50.9966792  -10.599225\n",
       "hc3_1   224.746062  240.034234  133.312802 -170.12095   27.4689155   -2.215026\n",
       "hc3_5   -31.510415  124.975628 -317.775086  -90.90335    9.5736442  -71.192278\n",
       "hc3_7     2.266559   88.206573   80.134766   76.71013   24.0733767   58.501761\n",
       "hc3_10 -107.031724   36.136923  -44.786067   10.04223   53.0399616   50.226984\n",
       "hc3_11   90.595770    8.032785  -17.444734   79.06294 -312.3872200  -43.718287\n",
       "ss2_9   -11.358211 -113.456315   49.069336  -83.86651  -76.0809820  -24.882964\n",
       "ss2_14 -117.071913  -98.917400   68.920326 -156.54863   28.1731043 -105.184934\n",
       "ss2_18  299.601511 -188.189376  -96.851537   59.00188  120.8222085   26.332985\n",
       "ss3_3    -9.179039  -74.881938   24.907495  -22.73858   -0.4820002    8.096835\n",
       "ss3_14  -25.735038  -57.218259   21.458449  -19.63399   -0.7972375   19.601002\n",
       "ss3_15  -97.216062  -15.810181   20.834465  -14.51974   23.8500668   25.143062\n",
       "ss3_16  -41.385837  -64.878720   34.962514  -37.65652   -9.0995393    7.508962\n",
       "ss3_20  -54.362674  -32.056607   13.809393  -10.95988   -4.3353746   12.777216\n",
       "ss5_18   43.284641  -76.889925    1.540898   -8.24637    0.4920718   11.985864\n",
       "       PC7         PC8        PC9         PC10        PC11       PC12       \n",
       "hc1_2    63.693423   95.04688 -240.359208   -1.583968  -51.04567  -39.903419\n",
       "hc1_4   -13.896974   70.05871  -89.446720  -25.612906  -47.69838   -7.094666\n",
       "hc2_15  -47.778507 -188.55458   33.498908 -176.912782  -63.28670  -25.898024\n",
       "hc2_17    3.198294    8.42252   46.912464  -14.378083   70.47019   16.195739\n",
       "hc3_1    16.470975   44.05080    6.833667  -41.934739   -8.28425    4.153790\n",
       "hc3_5   -70.182743  -80.20398   37.175811   20.276913  -17.84866  -13.758503\n",
       "hc3_7   -54.978134 -103.41131  -20.776915  246.470031   86.25975  -12.393247\n",
       "hc3_10   77.862837  104.50437   69.139601  -62.148411  151.70808  138.668209\n",
       "hc3_11   90.965691  -25.50297   37.011081   -6.446748   39.40510   17.974319\n",
       "ss2_9  -246.758699   34.16348  -82.315434  -68.740251  107.56343   -7.909964\n",
       "ss2_14  160.580208 -151.09389  -87.467111  -10.676948   23.99284  -22.227690\n",
       "ss2_18   48.205011  -28.06381   -4.796050  -16.692753   74.45781  -27.257034\n",
       "ss3_3   -19.353492   11.39722   12.137654   33.784758  -70.36688   41.302083\n",
       "ss3_14  -15.831340   16.14609   17.748587   22.182114  -49.09610   32.619746\n",
       "ss3_15   20.735511   45.56274   58.624737   12.518932   10.89437    5.577832\n",
       "ss3_16  -13.463855   12.96011   14.433709   29.387305  -56.02326   26.969135\n",
       "ss3_20   23.024209  105.54750  129.418154   10.700222  -21.39494 -249.492408\n",
       "ss5_18  -22.492415   28.97013   62.227063   49.807315 -179.70674  122.474104\n",
       "       PC13         PC14        PC15        PC16          PC17        \n",
       "hc1_2    22.8062963   19.642542   -5.516195   -6.89146481   -4.1565798\n",
       "hc1_4   -51.7706657  -22.686702   -9.008882   -0.86495842    2.5512477\n",
       "hc2_15  -47.3524892   -7.459777   -5.933131   -0.81121599   -5.6725954\n",
       "hc2_17  252.9834935   85.447893   57.180445   -4.80047455   -2.9124148\n",
       "hc3_1    10.6881919   -8.717818    1.262954    3.09348631   -0.1519688\n",
       "hc3_5     3.0338443  -11.204631    4.449811    2.95571290   -0.7183613\n",
       "hc3_7   -58.4778249   18.914360   -9.712800   -6.72069143   -1.3720118\n",
       "hc3_10 -133.1581378   38.464803   30.817861    5.40631998   -7.5503503\n",
       "hc3_11    9.6832275   -8.125046   -6.630260    0.80489946   -0.5893811\n",
       "ss2_9   -11.1992441   39.397959  -29.070913  -13.94348907   -3.4553305\n",
       "ss2_14  -15.1185183   52.617219  -17.522872   -6.87423444    3.5161844\n",
       "ss2_18   13.7540282  -20.170889   -7.358440  -11.46049368   -1.5348860\n",
       "ss3_3     6.6781367  -36.567637   47.968655  214.58327327 -123.3770819\n",
       "ss3_14    5.5818412  -63.710984   54.642634   66.87267604  236.1110885\n",
       "ss3_15   59.6023077 -160.577564 -210.784437  -22.02139631  -17.8009361\n",
       "ss3_16   -0.5315889 -137.182070  161.909305 -150.28022609  -66.1613066\n",
       "ss3_20  -54.2720570   64.762593   17.241905    0.08408483   -2.4032965\n",
       "ss5_18  -12.9308413  157.155749  -73.935641  -69.13180798   -4.3220198\n",
       "       PC18         \n",
       "hc1_2  -8.954237e-13\n",
       "hc1_4  -1.078092e-12\n",
       "hc2_15 -6.715565e-13\n",
       "hc2_17 -4.472849e-13\n",
       "hc3_1  -7.986172e-13\n",
       "hc3_5  -2.464503e-13\n",
       "hc3_7  -7.084814e-14\n",
       "hc3_10 -5.897121e-13\n",
       "hc3_11 -8.516853e-14\n",
       "ss2_9  -2.719016e-13\n",
       "ss2_14  1.921395e-12\n",
       "ss2_18 -1.200846e-12\n",
       "ss3_3   2.459107e-12\n",
       "ss3_14 -3.225787e-13\n",
       "ss3_15  1.366052e-12\n",
       "ss3_16 -2.043060e-13\n",
       "ss3_20 -8.109339e-13\n",
       "ss5_18  1.732399e-12"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "kernel": "R"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 18 x 19</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>PC1</th><th scope=col>PC2</th><th scope=col>PC3</th><th scope=col>PC4</th><th scope=col>PC5</th><th scope=col>PC6</th><th scope=col>PC7</th><th scope=col>PC8</th><th scope=col>PC9</th><th scope=col>PC10</th><th scope=col>PC11</th><th scope=col>PC12</th><th scope=col>PC13</th><th scope=col>PC14</th><th scope=col>PC15</th><th scope=col>PC16</th><th scope=col>PC17</th><th scope=col>PC18</th><th scope=col>Pop</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>hc1_2</th><td>-0.169157009</td><td> 0.32979333</td><td>-0.0925416296</td><td> 0.50520867</td><td>-0.294133348</td><td> 0.26493543</td><td>-0.06140564</td><td>-0.006872229</td><td> 0.04954539</td><td>-0.03079583</td><td> 0.16689447</td><td>-0.019328195</td><td>-0.35883554</td><td> 0.29791521</td><td>-0.122308974</td><td>-0.111055481</td><td>-0.375556625</td><td> 0.144280831</td><td>HC</td></tr>\n",
       "\t<tr><th scope=row>hc1_4</th><td>-0.271757005</td><td>-0.34897316</td><td>-0.2228580558</td><td> 0.07179135</td><td> 0.140243789</td><td> 0.05948359</td><td> 0.23457074</td><td>-0.431635637</td><td> 0.58806689</td><td>-0.31398495</td><td>-0.05176609</td><td> 0.072893855</td><td>-0.08775922</td><td>-0.04412887</td><td> 0.022912601</td><td>-0.146603418</td><td> 0.022401047</td><td>-0.020879124</td><td>HC</td></tr>\n",
       "\t<tr><th scope=row>hc2_15</th><td>-0.071626776</td><td>-0.08966038</td><td>-0.0397213904</td><td>-0.09019719</td><td> 0.008315516</td><td>-0.08914973</td><td>-0.13072719</td><td>-0.577920092</td><td>-0.10440079</td><td> 0.71562241</td><td>-0.01947412</td><td> 0.022977518</td><td>-0.18519793</td><td> 0.22058976</td><td> 0.071916895</td><td>-0.041643594</td><td> 0.064886645</td><td> 0.004739740</td><td>HC</td></tr>\n",
       "\t<tr><th scope=row>hc2_17</th><td>-0.298667880</td><td>-0.46065814</td><td> 0.1709509477</td><td> 0.50626010</td><td> 0.477551173</td><td>-0.14189869</td><td>-0.12229645</td><td> 0.248062189</td><td>-0.22116443</td><td> 0.09107086</td><td> 0.10440924</td><td>-0.118132992</td><td>-0.02734497</td><td> 0.03632917</td><td> 0.021668054</td><td>-0.059933906</td><td>-0.003101467</td><td> 0.013541656</td><td>HC</td></tr>\n",
       "\t<tr><th scope=row>hc3_1</th><td>-0.256597468</td><td>-0.26360918</td><td>-0.1601116901</td><td>-0.25708967</td><td>-0.429531489</td><td>-0.01684808</td><td>-0.01589535</td><td> 0.228308475</td><td>-0.20952811</td><td>-0.03248925</td><td> 0.06665936</td><td>-0.095151431</td><td>-0.09836574</td><td> 0.02562810</td><td> 0.010099832</td><td>-0.650256453</td><td> 0.209656174</td><td>-0.009993740</td><td>HC</td></tr>\n",
       "\t<tr><th scope=row>hc3_10</th><td>-0.065071796</td><td> 0.29433533</td><td>-0.1021351748</td><td> 0.05611128</td><td> 0.049239062</td><td>-0.72913326</td><td> 0.52168553</td><td>-0.007792869</td><td>-0.08157511</td><td>-0.05036670</td><td> 0.21913731</td><td> 0.012004485</td><td>-0.11929153</td><td> 0.11770265</td><td> 0.002913704</td><td>-0.032029642</td><td> 0.047016400</td><td>-0.024292181</td><td>HC</td></tr>\n",
       "\t<tr><th scope=row>hc3_11</th><td>-0.125488142</td><td> 0.01373902</td><td> 0.8988541987</td><td>-0.11639476</td><td>-0.143160319</td><td>-0.01167964</td><td> 0.05355827</td><td>-0.120221710</td><td> 0.11441735</td><td>-0.16274347</td><td> 0.03271447</td><td>-0.005991747</td><td>-0.19713438</td><td> 0.15824428</td><td> 0.058861658</td><td>-0.028844206</td><td> 0.117987901</td><td>-0.042665776</td><td>HC</td></tr>\n",
       "\t<tr><th scope=row>hc3_5</th><td>-0.143120769</td><td> 0.25833924</td><td>-0.0773359071</td><td> 0.39861545</td><td>-0.166206946</td><td> 0.13312088</td><td>-0.01994485</td><td> 0.001502115</td><td> 0.08777540</td><td> 0.06530467</td><td>-0.01659665</td><td>-0.105096992</td><td> 0.07948560</td><td>-0.09322778</td><td> 0.197338624</td><td> 0.138111410</td><td> 0.761771924</td><td>-0.159821780</td><td>HC</td></tr>\n",
       "\t<tr><th scope=row>hc3_7</th><td>-0.318831783</td><td>-0.31672402</td><td>-0.1685526135</td><td>-0.20871865</td><td>-0.302968493</td><td>-0.06214955</td><td>-0.05833547</td><td> 0.105436774</td><td>-0.07224308</td><td>-0.10760250</td><td> 0.15323289</td><td>-0.041115290</td><td>-0.18920247</td><td> 0.18938030</td><td>-0.009664227</td><td> 0.707551090</td><td> 0.006904790</td><td>-0.038549729</td><td>HC</td></tr>\n",
       "\t<tr><th scope=row>ss2_14</th><td> 0.243532694</td><td>-0.05325667</td><td>-0.0112504003</td><td> 0.02660362</td><td> 0.010415915</td><td> 0.05821443</td><td>-0.07413337</td><td>-0.389989744</td><td>-0.30450396</td><td>-0.25018469</td><td> 0.43696327</td><td>-0.402573092</td><td>-0.25713079</td><td>-0.44736762</td><td>-0.003418302</td><td> 0.017336869</td><td> 0.024482783</td><td> 0.021291751</td><td>SS</td></tr>\n",
       "\t<tr><th scope=row>ss2_18</th><td> 0.286774500</td><td>-0.18692917</td><td> 0.0607321050</td><td> 0.07293295</td><td>-0.096864786</td><td>-0.06099770</td><td>-0.01245880</td><td> 0.204805726</td><td> 0.33236915</td><td> 0.26832962</td><td> 0.32740659</td><td> 0.276267253</td><td>-0.19667989</td><td>-0.18134206</td><td>-0.581828420</td><td> 0.009179303</td><td> 0.205992957</td><td> 0.002802899</td><td>SS</td></tr>\n",
       "\t<tr><th scope=row>ss2_9</th><td> 0.217164350</td><td>-0.13668071</td><td>-0.0359391218</td><td> 0.12504185</td><td>-0.034491284</td><td> 0.03055297</td><td>-0.09330715</td><td>-0.158611623</td><td>-0.28458837</td><td>-0.26532185</td><td> 0.23254672</td><td> 0.712236517</td><td> 0.07543774</td><td> 0.19810597</td><td> 0.262751244</td><td>-0.030782937</td><td> 0.152386118</td><td> 0.174226060</td><td>SS</td></tr>\n",
       "\t<tr><th scope=row>ss3_14</th><td> 0.284005355</td><td>-0.13343295</td><td>-0.0453763001</td><td> 0.14950017</td><td>-0.064156555</td><td>-0.06947412</td><td> 0.06337588</td><td> 0.096367552</td><td>-0.11549845</td><td>-0.04674180</td><td>-0.50853613</td><td> 0.140984955</td><td>-0.58760720</td><td>-0.12835534</td><td> 0.161687478</td><td> 0.015612647</td><td>-0.044728107</td><td>-0.413190543</td><td>SS</td></tr>\n",
       "\t<tr><th scope=row>ss3_15</th><td> 0.008320234</td><td> 0.25839268</td><td>-0.1403021067</td><td>-0.25629503</td><td> 0.376373342</td><td>-0.17472840</td><td>-0.59718904</td><td> 0.111137946</td><td> 0.22635953</td><td>-0.19824638</td><td> 0.09764560</td><td>-0.041632615</td><td>-0.32747893</td><td> 0.23209975</td><td> 0.054120731</td><td>-0.092662237</td><td> 0.176367549</td><td>-0.032818887</td><td>SS</td></tr>\n",
       "\t<tr><th scope=row>ss3_16</th><td> 0.252509868</td><td>-0.11857098</td><td>-0.0431228268</td><td> 0.08965063</td><td> 0.019221920</td><td>-0.02240088</td><td> 0.04005040</td><td>-0.180833165</td><td>-0.20228145</td><td>-0.25125342</td><td>-0.34995704</td><td>-0.255014779</td><td> 0.06612093</td><td> 0.46156279</td><td>-0.516495088</td><td> 0.009494130</td><td> 0.266183507</td><td> 0.174456979</td><td>SS</td></tr>\n",
       "\t<tr><th scope=row>ss3_20</th><td> 0.037809434</td><td> 0.04616455</td><td>-0.0589178291</td><td>-0.24481162</td><td> 0.392205366</td><td> 0.53899828</td><td> 0.49733542</td><td> 0.176921496</td><td>-0.11761826</td><td> 0.10437955</td><td> 0.19390864</td><td>-0.015883140</td><td>-0.25973585</td><td> 0.22674915</td><td> 0.052554708</td><td> 0.009970900</td><td> 0.158389076</td><td>-0.025277071</td><td>SS</td></tr>\n",
       "\t<tr><th scope=row>ss3_3</th><td> 0.372932794</td><td>-0.18422417</td><td>-0.0197498927</td><td> 0.09594917</td><td>-0.116801908</td><td>-0.01000246</td><td>-0.01186712</td><td> 0.018885450</td><td> 0.18829491</td><td> 0.01801898</td><td> 0.31317672</td><td>-0.239693297</td><td> 0.26625812</td><td> 0.40494020</td><td> 0.193512101</td><td>-0.069335517</td><td>-0.127424254</td><td>-0.564333793</td><td>SS</td></tr>\n",
       "\t<tr><th scope=row>ss5_18</th><td> 0.348617184</td><td>-0.18623575</td><td>-0.0002393696</td><td> 0.05522893</td><td>-0.120708591</td><td>-0.10013944</td><td> 0.07737120</td><td> 0.187529089</td><td> 0.27635145</td><td> 0.11537599</td><td>-0.04890382</td><td>-0.260466163</td><td>-0.13365163</td><td> 0.06453839</td><td> 0.438908477</td><td> 0.035969106</td><td> 0.047603910</td><td> 0.629948894</td><td>SS</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 18 x 19\n",
       "\\begin{tabular}{r|lllllllllllllllllll}\n",
       "  & PC1 & PC2 & PC3 & PC4 & PC5 & PC6 & PC7 & PC8 & PC9 & PC10 & PC11 & PC12 & PC13 & PC14 & PC15 & PC16 & PC17 & PC18 & Pop\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <chr>\\\\\n",
       "\\hline\n",
       "\thc1\\_2 & -0.169157009 &  0.32979333 & -0.0925416296 &  0.50520867 & -0.294133348 &  0.26493543 & -0.06140564 & -0.006872229 &  0.04954539 & -0.03079583 &  0.16689447 & -0.019328195 & -0.35883554 &  0.29791521 & -0.122308974 & -0.111055481 & -0.375556625 &  0.144280831 & HC\\\\\n",
       "\thc1\\_4 & -0.271757005 & -0.34897316 & -0.2228580558 &  0.07179135 &  0.140243789 &  0.05948359 &  0.23457074 & -0.431635637 &  0.58806689 & -0.31398495 & -0.05176609 &  0.072893855 & -0.08775922 & -0.04412887 &  0.022912601 & -0.146603418 &  0.022401047 & -0.020879124 & HC\\\\\n",
       "\thc2\\_15 & -0.071626776 & -0.08966038 & -0.0397213904 & -0.09019719 &  0.008315516 & -0.08914973 & -0.13072719 & -0.577920092 & -0.10440079 &  0.71562241 & -0.01947412 &  0.022977518 & -0.18519793 &  0.22058976 &  0.071916895 & -0.041643594 &  0.064886645 &  0.004739740 & HC\\\\\n",
       "\thc2\\_17 & -0.298667880 & -0.46065814 &  0.1709509477 &  0.50626010 &  0.477551173 & -0.14189869 & -0.12229645 &  0.248062189 & -0.22116443 &  0.09107086 &  0.10440924 & -0.118132992 & -0.02734497 &  0.03632917 &  0.021668054 & -0.059933906 & -0.003101467 &  0.013541656 & HC\\\\\n",
       "\thc3\\_1 & -0.256597468 & -0.26360918 & -0.1601116901 & -0.25708967 & -0.429531489 & -0.01684808 & -0.01589535 &  0.228308475 & -0.20952811 & -0.03248925 &  0.06665936 & -0.095151431 & -0.09836574 &  0.02562810 &  0.010099832 & -0.650256453 &  0.209656174 & -0.009993740 & HC\\\\\n",
       "\thc3\\_10 & -0.065071796 &  0.29433533 & -0.1021351748 &  0.05611128 &  0.049239062 & -0.72913326 &  0.52168553 & -0.007792869 & -0.08157511 & -0.05036670 &  0.21913731 &  0.012004485 & -0.11929153 &  0.11770265 &  0.002913704 & -0.032029642 &  0.047016400 & -0.024292181 & HC\\\\\n",
       "\thc3\\_11 & -0.125488142 &  0.01373902 &  0.8988541987 & -0.11639476 & -0.143160319 & -0.01167964 &  0.05355827 & -0.120221710 &  0.11441735 & -0.16274347 &  0.03271447 & -0.005991747 & -0.19713438 &  0.15824428 &  0.058861658 & -0.028844206 &  0.117987901 & -0.042665776 & HC\\\\\n",
       "\thc3\\_5 & -0.143120769 &  0.25833924 & -0.0773359071 &  0.39861545 & -0.166206946 &  0.13312088 & -0.01994485 &  0.001502115 &  0.08777540 &  0.06530467 & -0.01659665 & -0.105096992 &  0.07948560 & -0.09322778 &  0.197338624 &  0.138111410 &  0.761771924 & -0.159821780 & HC\\\\\n",
       "\thc3\\_7 & -0.318831783 & -0.31672402 & -0.1685526135 & -0.20871865 & -0.302968493 & -0.06214955 & -0.05833547 &  0.105436774 & -0.07224308 & -0.10760250 &  0.15323289 & -0.041115290 & -0.18920247 &  0.18938030 & -0.009664227 &  0.707551090 &  0.006904790 & -0.038549729 & HC\\\\\n",
       "\tss2\\_14 &  0.243532694 & -0.05325667 & -0.0112504003 &  0.02660362 &  0.010415915 &  0.05821443 & -0.07413337 & -0.389989744 & -0.30450396 & -0.25018469 &  0.43696327 & -0.402573092 & -0.25713079 & -0.44736762 & -0.003418302 &  0.017336869 &  0.024482783 &  0.021291751 & SS\\\\\n",
       "\tss2\\_18 &  0.286774500 & -0.18692917 &  0.0607321050 &  0.07293295 & -0.096864786 & -0.06099770 & -0.01245880 &  0.204805726 &  0.33236915 &  0.26832962 &  0.32740659 &  0.276267253 & -0.19667989 & -0.18134206 & -0.581828420 &  0.009179303 &  0.205992957 &  0.002802899 & SS\\\\\n",
       "\tss2\\_9 &  0.217164350 & -0.13668071 & -0.0359391218 &  0.12504185 & -0.034491284 &  0.03055297 & -0.09330715 & -0.158611623 & -0.28458837 & -0.26532185 &  0.23254672 &  0.712236517 &  0.07543774 &  0.19810597 &  0.262751244 & -0.030782937 &  0.152386118 &  0.174226060 & SS\\\\\n",
       "\tss3\\_14 &  0.284005355 & -0.13343295 & -0.0453763001 &  0.14950017 & -0.064156555 & -0.06947412 &  0.06337588 &  0.096367552 & -0.11549845 & -0.04674180 & -0.50853613 &  0.140984955 & -0.58760720 & -0.12835534 &  0.161687478 &  0.015612647 & -0.044728107 & -0.413190543 & SS\\\\\n",
       "\tss3\\_15 &  0.008320234 &  0.25839268 & -0.1403021067 & -0.25629503 &  0.376373342 & -0.17472840 & -0.59718904 &  0.111137946 &  0.22635953 & -0.19824638 &  0.09764560 & -0.041632615 & -0.32747893 &  0.23209975 &  0.054120731 & -0.092662237 &  0.176367549 & -0.032818887 & SS\\\\\n",
       "\tss3\\_16 &  0.252509868 & -0.11857098 & -0.0431228268 &  0.08965063 &  0.019221920 & -0.02240088 &  0.04005040 & -0.180833165 & -0.20228145 & -0.25125342 & -0.34995704 & -0.255014779 &  0.06612093 &  0.46156279 & -0.516495088 &  0.009494130 &  0.266183507 &  0.174456979 & SS\\\\\n",
       "\tss3\\_20 &  0.037809434 &  0.04616455 & -0.0589178291 & -0.24481162 &  0.392205366 &  0.53899828 &  0.49733542 &  0.176921496 & -0.11761826 &  0.10437955 &  0.19390864 & -0.015883140 & -0.25973585 &  0.22674915 &  0.052554708 &  0.009970900 &  0.158389076 & -0.025277071 & SS\\\\\n",
       "\tss3\\_3 &  0.372932794 & -0.18422417 & -0.0197498927 &  0.09594917 & -0.116801908 & -0.01000246 & -0.01186712 &  0.018885450 &  0.18829491 &  0.01801898 &  0.31317672 & -0.239693297 &  0.26625812 &  0.40494020 &  0.193512101 & -0.069335517 & -0.127424254 & -0.564333793 & SS\\\\\n",
       "\tss5\\_18 &  0.348617184 & -0.18623575 & -0.0002393696 &  0.05522893 & -0.120708591 & -0.10013944 &  0.07737120 &  0.187529089 &  0.27635145 &  0.11537599 & -0.04890382 & -0.260466163 & -0.13365163 &  0.06453839 &  0.438908477 &  0.035969106 &  0.047603910 &  0.629948894 & SS\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 18 x 19\n",
       "\n",
       "| <!--/--> | PC1 &lt;dbl&gt; | PC2 &lt;dbl&gt; | PC3 &lt;dbl&gt; | PC4 &lt;dbl&gt; | PC5 &lt;dbl&gt; | PC6 &lt;dbl&gt; | PC7 &lt;dbl&gt; | PC8 &lt;dbl&gt; | PC9 &lt;dbl&gt; | PC10 &lt;dbl&gt; | PC11 &lt;dbl&gt; | PC12 &lt;dbl&gt; | PC13 &lt;dbl&gt; | PC14 &lt;dbl&gt; | PC15 &lt;dbl&gt; | PC16 &lt;dbl&gt; | PC17 &lt;dbl&gt; | PC18 &lt;dbl&gt; | Pop &lt;chr&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| hc1_2 | -0.169157009 |  0.32979333 | -0.0925416296 |  0.50520867 | -0.294133348 |  0.26493543 | -0.06140564 | -0.006872229 |  0.04954539 | -0.03079583 |  0.16689447 | -0.019328195 | -0.35883554 |  0.29791521 | -0.122308974 | -0.111055481 | -0.375556625 |  0.144280831 | HC |\n",
       "| hc1_4 | -0.271757005 | -0.34897316 | -0.2228580558 |  0.07179135 |  0.140243789 |  0.05948359 |  0.23457074 | -0.431635637 |  0.58806689 | -0.31398495 | -0.05176609 |  0.072893855 | -0.08775922 | -0.04412887 |  0.022912601 | -0.146603418 |  0.022401047 | -0.020879124 | HC |\n",
       "| hc2_15 | -0.071626776 | -0.08966038 | -0.0397213904 | -0.09019719 |  0.008315516 | -0.08914973 | -0.13072719 | -0.577920092 | -0.10440079 |  0.71562241 | -0.01947412 |  0.022977518 | -0.18519793 |  0.22058976 |  0.071916895 | -0.041643594 |  0.064886645 |  0.004739740 | HC |\n",
       "| hc2_17 | -0.298667880 | -0.46065814 |  0.1709509477 |  0.50626010 |  0.477551173 | -0.14189869 | -0.12229645 |  0.248062189 | -0.22116443 |  0.09107086 |  0.10440924 | -0.118132992 | -0.02734497 |  0.03632917 |  0.021668054 | -0.059933906 | -0.003101467 |  0.013541656 | HC |\n",
       "| hc3_1 | -0.256597468 | -0.26360918 | -0.1601116901 | -0.25708967 | -0.429531489 | -0.01684808 | -0.01589535 |  0.228308475 | -0.20952811 | -0.03248925 |  0.06665936 | -0.095151431 | -0.09836574 |  0.02562810 |  0.010099832 | -0.650256453 |  0.209656174 | -0.009993740 | HC |\n",
       "| hc3_10 | -0.065071796 |  0.29433533 | -0.1021351748 |  0.05611128 |  0.049239062 | -0.72913326 |  0.52168553 | -0.007792869 | -0.08157511 | -0.05036670 |  0.21913731 |  0.012004485 | -0.11929153 |  0.11770265 |  0.002913704 | -0.032029642 |  0.047016400 | -0.024292181 | HC |\n",
       "| hc3_11 | -0.125488142 |  0.01373902 |  0.8988541987 | -0.11639476 | -0.143160319 | -0.01167964 |  0.05355827 | -0.120221710 |  0.11441735 | -0.16274347 |  0.03271447 | -0.005991747 | -0.19713438 |  0.15824428 |  0.058861658 | -0.028844206 |  0.117987901 | -0.042665776 | HC |\n",
       "| hc3_5 | -0.143120769 |  0.25833924 | -0.0773359071 |  0.39861545 | -0.166206946 |  0.13312088 | -0.01994485 |  0.001502115 |  0.08777540 |  0.06530467 | -0.01659665 | -0.105096992 |  0.07948560 | -0.09322778 |  0.197338624 |  0.138111410 |  0.761771924 | -0.159821780 | HC |\n",
       "| hc3_7 | -0.318831783 | -0.31672402 | -0.1685526135 | -0.20871865 | -0.302968493 | -0.06214955 | -0.05833547 |  0.105436774 | -0.07224308 | -0.10760250 |  0.15323289 | -0.041115290 | -0.18920247 |  0.18938030 | -0.009664227 |  0.707551090 |  0.006904790 | -0.038549729 | HC |\n",
       "| ss2_14 |  0.243532694 | -0.05325667 | -0.0112504003 |  0.02660362 |  0.010415915 |  0.05821443 | -0.07413337 | -0.389989744 | -0.30450396 | -0.25018469 |  0.43696327 | -0.402573092 | -0.25713079 | -0.44736762 | -0.003418302 |  0.017336869 |  0.024482783 |  0.021291751 | SS |\n",
       "| ss2_18 |  0.286774500 | -0.18692917 |  0.0607321050 |  0.07293295 | -0.096864786 | -0.06099770 | -0.01245880 |  0.204805726 |  0.33236915 |  0.26832962 |  0.32740659 |  0.276267253 | -0.19667989 | -0.18134206 | -0.581828420 |  0.009179303 |  0.205992957 |  0.002802899 | SS |\n",
       "| ss2_9 |  0.217164350 | -0.13668071 | -0.0359391218 |  0.12504185 | -0.034491284 |  0.03055297 | -0.09330715 | -0.158611623 | -0.28458837 | -0.26532185 |  0.23254672 |  0.712236517 |  0.07543774 |  0.19810597 |  0.262751244 | -0.030782937 |  0.152386118 |  0.174226060 | SS |\n",
       "| ss3_14 |  0.284005355 | -0.13343295 | -0.0453763001 |  0.14950017 | -0.064156555 | -0.06947412 |  0.06337588 |  0.096367552 | -0.11549845 | -0.04674180 | -0.50853613 |  0.140984955 | -0.58760720 | -0.12835534 |  0.161687478 |  0.015612647 | -0.044728107 | -0.413190543 | SS |\n",
       "| ss3_15 |  0.008320234 |  0.25839268 | -0.1403021067 | -0.25629503 |  0.376373342 | -0.17472840 | -0.59718904 |  0.111137946 |  0.22635953 | -0.19824638 |  0.09764560 | -0.041632615 | -0.32747893 |  0.23209975 |  0.054120731 | -0.092662237 |  0.176367549 | -0.032818887 | SS |\n",
       "| ss3_16 |  0.252509868 | -0.11857098 | -0.0431228268 |  0.08965063 |  0.019221920 | -0.02240088 |  0.04005040 | -0.180833165 | -0.20228145 | -0.25125342 | -0.34995704 | -0.255014779 |  0.06612093 |  0.46156279 | -0.516495088 |  0.009494130 |  0.266183507 |  0.174456979 | SS |\n",
       "| ss3_20 |  0.037809434 |  0.04616455 | -0.0589178291 | -0.24481162 |  0.392205366 |  0.53899828 |  0.49733542 |  0.176921496 | -0.11761826 |  0.10437955 |  0.19390864 | -0.015883140 | -0.25973585 |  0.22674915 |  0.052554708 |  0.009970900 |  0.158389076 | -0.025277071 | SS |\n",
       "| ss3_3 |  0.372932794 | -0.18422417 | -0.0197498927 |  0.09594917 | -0.116801908 | -0.01000246 | -0.01186712 |  0.018885450 |  0.18829491 |  0.01801898 |  0.31317672 | -0.239693297 |  0.26625812 |  0.40494020 |  0.193512101 | -0.069335517 | -0.127424254 | -0.564333793 | SS |\n",
       "| ss5_18 |  0.348617184 | -0.18623575 | -0.0002393696 |  0.05522893 | -0.120708591 | -0.10013944 |  0.07737120 |  0.187529089 |  0.27635145 |  0.11537599 | -0.04890382 | -0.260466163 | -0.13365163 |  0.06453839 |  0.438908477 |  0.035969106 |  0.047603910 |  0.629948894 | SS |\n",
       "\n"
      ],
      "text/plain": [
       "       PC1          PC2         PC3           PC4         PC5         \n",
       "hc1_2  -0.169157009  0.32979333 -0.0925416296  0.50520867 -0.294133348\n",
       "hc1_4  -0.271757005 -0.34897316 -0.2228580558  0.07179135  0.140243789\n",
       "hc2_15 -0.071626776 -0.08966038 -0.0397213904 -0.09019719  0.008315516\n",
       "hc2_17 -0.298667880 -0.46065814  0.1709509477  0.50626010  0.477551173\n",
       "hc3_1  -0.256597468 -0.26360918 -0.1601116901 -0.25708967 -0.429531489\n",
       "hc3_10 -0.065071796  0.29433533 -0.1021351748  0.05611128  0.049239062\n",
       "hc3_11 -0.125488142  0.01373902  0.8988541987 -0.11639476 -0.143160319\n",
       "hc3_5  -0.143120769  0.25833924 -0.0773359071  0.39861545 -0.166206946\n",
       "hc3_7  -0.318831783 -0.31672402 -0.1685526135 -0.20871865 -0.302968493\n",
       "ss2_14  0.243532694 -0.05325667 -0.0112504003  0.02660362  0.010415915\n",
       "ss2_18  0.286774500 -0.18692917  0.0607321050  0.07293295 -0.096864786\n",
       "ss2_9   0.217164350 -0.13668071 -0.0359391218  0.12504185 -0.034491284\n",
       "ss3_14  0.284005355 -0.13343295 -0.0453763001  0.14950017 -0.064156555\n",
       "ss3_15  0.008320234  0.25839268 -0.1403021067 -0.25629503  0.376373342\n",
       "ss3_16  0.252509868 -0.11857098 -0.0431228268  0.08965063  0.019221920\n",
       "ss3_20  0.037809434  0.04616455 -0.0589178291 -0.24481162  0.392205366\n",
       "ss3_3   0.372932794 -0.18422417 -0.0197498927  0.09594917 -0.116801908\n",
       "ss5_18  0.348617184 -0.18623575 -0.0002393696  0.05522893 -0.120708591\n",
       "       PC6         PC7         PC8          PC9         PC10        PC11       \n",
       "hc1_2   0.26493543 -0.06140564 -0.006872229  0.04954539 -0.03079583  0.16689447\n",
       "hc1_4   0.05948359  0.23457074 -0.431635637  0.58806689 -0.31398495 -0.05176609\n",
       "hc2_15 -0.08914973 -0.13072719 -0.577920092 -0.10440079  0.71562241 -0.01947412\n",
       "hc2_17 -0.14189869 -0.12229645  0.248062189 -0.22116443  0.09107086  0.10440924\n",
       "hc3_1  -0.01684808 -0.01589535  0.228308475 -0.20952811 -0.03248925  0.06665936\n",
       "hc3_10 -0.72913326  0.52168553 -0.007792869 -0.08157511 -0.05036670  0.21913731\n",
       "hc3_11 -0.01167964  0.05355827 -0.120221710  0.11441735 -0.16274347  0.03271447\n",
       "hc3_5   0.13312088 -0.01994485  0.001502115  0.08777540  0.06530467 -0.01659665\n",
       "hc3_7  -0.06214955 -0.05833547  0.105436774 -0.07224308 -0.10760250  0.15323289\n",
       "ss2_14  0.05821443 -0.07413337 -0.389989744 -0.30450396 -0.25018469  0.43696327\n",
       "ss2_18 -0.06099770 -0.01245880  0.204805726  0.33236915  0.26832962  0.32740659\n",
       "ss2_9   0.03055297 -0.09330715 -0.158611623 -0.28458837 -0.26532185  0.23254672\n",
       "ss3_14 -0.06947412  0.06337588  0.096367552 -0.11549845 -0.04674180 -0.50853613\n",
       "ss3_15 -0.17472840 -0.59718904  0.111137946  0.22635953 -0.19824638  0.09764560\n",
       "ss3_16 -0.02240088  0.04005040 -0.180833165 -0.20228145 -0.25125342 -0.34995704\n",
       "ss3_20  0.53899828  0.49733542  0.176921496 -0.11761826  0.10437955  0.19390864\n",
       "ss3_3  -0.01000246 -0.01186712  0.018885450  0.18829491  0.01801898  0.31317672\n",
       "ss5_18 -0.10013944  0.07737120  0.187529089  0.27635145  0.11537599 -0.04890382\n",
       "       PC12         PC13        PC14        PC15         PC16        \n",
       "hc1_2  -0.019328195 -0.35883554  0.29791521 -0.122308974 -0.111055481\n",
       "hc1_4   0.072893855 -0.08775922 -0.04412887  0.022912601 -0.146603418\n",
       "hc2_15  0.022977518 -0.18519793  0.22058976  0.071916895 -0.041643594\n",
       "hc2_17 -0.118132992 -0.02734497  0.03632917  0.021668054 -0.059933906\n",
       "hc3_1  -0.095151431 -0.09836574  0.02562810  0.010099832 -0.650256453\n",
       "hc3_10  0.012004485 -0.11929153  0.11770265  0.002913704 -0.032029642\n",
       "hc3_11 -0.005991747 -0.19713438  0.15824428  0.058861658 -0.028844206\n",
       "hc3_5  -0.105096992  0.07948560 -0.09322778  0.197338624  0.138111410\n",
       "hc3_7  -0.041115290 -0.18920247  0.18938030 -0.009664227  0.707551090\n",
       "ss2_14 -0.402573092 -0.25713079 -0.44736762 -0.003418302  0.017336869\n",
       "ss2_18  0.276267253 -0.19667989 -0.18134206 -0.581828420  0.009179303\n",
       "ss2_9   0.712236517  0.07543774  0.19810597  0.262751244 -0.030782937\n",
       "ss3_14  0.140984955 -0.58760720 -0.12835534  0.161687478  0.015612647\n",
       "ss3_15 -0.041632615 -0.32747893  0.23209975  0.054120731 -0.092662237\n",
       "ss3_16 -0.255014779  0.06612093  0.46156279 -0.516495088  0.009494130\n",
       "ss3_20 -0.015883140 -0.25973585  0.22674915  0.052554708  0.009970900\n",
       "ss3_3  -0.239693297  0.26625812  0.40494020  0.193512101 -0.069335517\n",
       "ss5_18 -0.260466163 -0.13365163  0.06453839  0.438908477  0.035969106\n",
       "       PC17         PC18         Pop\n",
       "hc1_2  -0.375556625  0.144280831 HC \n",
       "hc1_4   0.022401047 -0.020879124 HC \n",
       "hc2_15  0.064886645  0.004739740 HC \n",
       "hc2_17 -0.003101467  0.013541656 HC \n",
       "hc3_1   0.209656174 -0.009993740 HC \n",
       "hc3_10  0.047016400 -0.024292181 HC \n",
       "hc3_11  0.117987901 -0.042665776 HC \n",
       "hc3_5   0.761771924 -0.159821780 HC \n",
       "hc3_7   0.006904790 -0.038549729 HC \n",
       "ss2_14  0.024482783  0.021291751 SS \n",
       "ss2_18  0.205992957  0.002802899 SS \n",
       "ss2_9   0.152386118  0.174226060 SS \n",
       "ss3_14 -0.044728107 -0.413190543 SS \n",
       "ss3_15  0.176367549 -0.032818887 SS \n",
       "ss3_16  0.266183507  0.174456979 SS \n",
       "ss3_20  0.158389076 -0.025277071 SS \n",
       "ss3_3  -0.127424254 -0.564333793 SS \n",
       "ss5_18  0.047603910  0.629948894 SS "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#covmat is different order than adegent pca\n",
    "gen <- read.table(\"../analyses/2bRAD/MethGen/MBD_pca_covMat.tsv\")\n",
    "mbdorder = c(\"hc1_2\",\"hc1_4\",\"hc2_15\",\"hc2_17\",\"hc3_1\",\"hc3_10\",\"hc3_11\",\"hc3_5\",\"hc3_7\",\n",
    "        \"ss2_14\",\"ss2_18\",\"ss2_9\",\"ss3_14\",\"ss3_15\",\"ss3_16\",\"ss3_20\",\"ss3_3\",\"ss5_18\")\n",
    "rownames(gen) <- mbdorder\n",
    "gen\n",
    "#make sure ep and gen in same order\n",
    "ep <- ep[mbdorder,]\n",
    "pop = as.factor(c(rep(\"Hood Canal\",9),rep(\"South Sound\",9)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "kernel": "R"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>Hood Canal</li><li>Hood Canal</li><li>Hood Canal</li><li>Hood Canal</li><li>Hood Canal</li><li>Hood Canal</li><li>Hood Canal</li><li>Hood Canal</li><li>Hood Canal</li><li>South Sound</li><li>South Sound</li><li>South Sound</li><li>South Sound</li><li>South Sound</li><li>South Sound</li><li>South Sound</li><li>South Sound</li><li>South Sound</li></ol>\n",
       "\n",
       "<details>\n",
       "\t<summary style=display:list-item;cursor:pointer>\n",
       "\t\t<strong>Levels</strong>:\n",
       "\t</summary>\n",
       "\t<style>\n",
       "\t.list-inline {list-style: none; margin:0; padding: 0}\n",
       "\t.list-inline>li {display: inline-block}\n",
       "\t.list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "\t</style>\n",
       "\t<ol class=list-inline><li>'Hood Canal'</li><li>'South Sound'</li></ol>\n",
       "</details>"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item Hood Canal\n",
       "\\item Hood Canal\n",
       "\\item Hood Canal\n",
       "\\item Hood Canal\n",
       "\\item Hood Canal\n",
       "\\item Hood Canal\n",
       "\\item Hood Canal\n",
       "\\item Hood Canal\n",
       "\\item Hood Canal\n",
       "\\item South Sound\n",
       "\\item South Sound\n",
       "\\item South Sound\n",
       "\\item South Sound\n",
       "\\item South Sound\n",
       "\\item South Sound\n",
       "\\item South Sound\n",
       "\\item South Sound\n",
       "\\item South Sound\n",
       "\\end{enumerate*}\n",
       "\n",
       "\\emph{Levels}: \\begin{enumerate*}\n",
       "\\item 'Hood Canal'\n",
       "\\item 'South Sound'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. Hood Canal\n",
       "2. Hood Canal\n",
       "3. Hood Canal\n",
       "4. Hood Canal\n",
       "5. Hood Canal\n",
       "6. Hood Canal\n",
       "7. Hood Canal\n",
       "8. Hood Canal\n",
       "9. Hood Canal\n",
       "10. South Sound\n",
       "11. South Sound\n",
       "12. South Sound\n",
       "13. South Sound\n",
       "14. South Sound\n",
       "15. South Sound\n",
       "16. South Sound\n",
       "17. South Sound\n",
       "18. South Sound\n",
       "\n",
       "\n",
       "\n",
       "**Levels**: 1. 'Hood Canal'\n",
       "2. 'South Sound'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] Hood Canal  Hood Canal  Hood Canal  Hood Canal  Hood Canal  Hood Canal \n",
       " [7] Hood Canal  Hood Canal  Hood Canal  South Sound South Sound South Sound\n",
       "[13] South Sound South Sound South Sound South Sound South Sound South Sound\n",
       "Levels: Hood Canal South Sound"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "kernel": "R"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPearson's product-moment correlation\n",
       "\n",
       "data:  ep$PC2 and gen$PC1\n",
       "t = -6.0608, df = 16, p-value = 1.652e-05\n",
       "alternative hypothesis: true correlation is not equal to 0\n",
       "95 percent confidence interval:\n",
       " -0.9365517 -0.6025227\n",
       "sample estimates:\n",
       "       cor \n",
       "-0.8346156 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tSpearman's rank correlation rho\n",
       "\n",
       "data:  ep$PC2 and gen$PC1\n",
       "S = 1800, p-value < 2.2e-16\n",
       "alternative hypothesis: true rho is not equal to 0\n",
       "sample estimates:\n",
       "       rho \n",
       "-0.8575851 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cor.test( ~ ep$PC2 + gen$PC1,\n",
    "         method = \"pearson\",\n",
    "         conf.level = 0.95)\n",
    "cor.test( ~ ep$PC2 + gen$PC1, \n",
    "         method = \"spearman\",\n",
    "         continuity = FALSE,\n",
    "         conf.level = 0.95)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "kernel": "R"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "lm(formula = gen$PC1 ~ ep$PC2)\n",
       "\n",
       "Residuals:\n",
       "     Min       1Q   Median       3Q      Max \n",
       "-0.22844 -0.10331 -0.01952  0.10043  0.20507 \n",
       "\n",
       "Coefficients:\n",
       "              Estimate Std. Error t value Pr(>|t|)    \n",
       "(Intercept)  0.0184082  0.0323590   0.569    0.577    \n",
       "ep$PC2      -0.0019959  0.0003293  -6.061 1.65e-05 ***\n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 0.1373 on 16 degrees of freedom\n",
       "Multiple R-squared:  0.6966,\tAdjusted R-squared:  0.6776 \n",
       "F-statistic: 36.73 on 1 and 16 DF,  p-value: 1.652e-05\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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rhc7oIFC27fvq2pqcl2LAAAAAANgtyMiq2OQCAoLy/ncrlcrrwmqQAAAAAyIfeJ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yt7cPCgq6cuWKhDYAlo7BDgAMqri4noO2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\nrkJggg==",
      "text/plain": [
       "Plot with title “Filtered 5x methylated sites (PC2) vs filtered SNPs (PC1)”"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "palette(c(\"red\",\"blue\"))\n",
    "plot(ep$PC2,gen$PC1, pch=20,cex=1,xlab = \"PC2 methylation\", ylab = \"PC1 SNPs\",col=pop)\n",
    "legend('topright', legend = levels(pop), col = c(\"red\",\"blue\"), cex = 1, pch = 20)\n",
    "title(\"Filtered 5x methylated sites (PC2) vs filtered SNPs (PC1)\")\n",
    "summary(lm(gen$PC1~ep$PC2),)\n",
    "R2 = round(summary(lm(gen$PC1~ep$PC2))$r.squared, 4)\n",
    "#image(dens, col=transp(myPal(300),.7), add=TRUE)\n",
    "abline(lm(gen$PC1~ep$PC2))\n",
    "text(30,0.3,label=paste(\"R^2=\",R2))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "R"
   },
   "source": [
    "### DMLs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "kernel": "R"
   },
   "outputs": [],
   "source": [
    "# Read in PC scores from epigenetics\n",
    "ep <- read.table(\"../../paper-oly-mbdbs-gen/analyses/DMLs/PC-scores-DMLs.tab\",\n",
    "                 header=T, sep=\"\\t\",row.names = \"sample\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "R"
   },
   "source": [
    "Add sample names to PC score matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "kernel": "R"
   },
   "outputs": [],
   "source": [
    "key = read.csv(\"../../paper-oly-mbdbs-gen/data/sample-key.csv\",colClasses = c(\"character\",\"character\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "kernel": "R"
   },
   "outputs": [],
   "source": [
    "samples = rownames(ep)\n",
    "mapdf <- data.frame(old=key$MBD.FILENAME,new=key$SAMPLE)\n",
    "rownames(ep) <- mapdf$new[match(samples,mapdf$old)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "kernel": "R"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 18 x 18</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>PC1</th><th scope=col>PC2</th><th scope=col>PC3</th><th scope=col>PC4</th><th scope=col>PC5</th><th scope=col>PC6</th><th scope=col>PC7</th><th scope=col>PC8</th><th scope=col>PC9</th><th scope=col>PC10</th><th scope=col>PC11</th><th scope=col>PC12</th><th scope=col>PC13</th><th scope=col>PC14</th><th scope=col>PC15</th><th scope=col>PC16</th><th scope=col>PC17</th><th scope=col>PC18</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>hc1_2</th><td>-21.19546</td><td> 25.55281133</td><td> -2.393988</td><td>  5.591844</td><td> -4.896916</td><td>  5.7768050</td><td> -4.128529439</td><td>  5.57650316</td><td> -3.85203413</td><td>  2.9231433</td><td> -0.7657338</td><td>  4.4360063</td><td> -4.4771879</td><td>  8.7981682</td><td>-17.2358795</td><td>  0.9513672</td><td> -0.8182284</td><td> 1.720716e-14</td></tr>\n",
       "\t<tr><th scope=row>hc1_4</th><td>-26.17505</td><td> -9.85545673</td><td> -1.541117</td><td>-10.753280</td><td>  8.005603</td><td> 10.5847020</td><td>  4.757230355</td><td> 15.40325160</td><td> 18.67185587</td><td> -4.8192035</td><td> -5.9023911</td><td>  7.8951770</td><td> -2.6919361</td><td> -1.4044176</td><td>  0.1171435</td><td>  1.7351011</td><td>  0.6697971</td><td>-1.264874e-14</td></tr>\n",
       "\t<tr><th scope=row>hc2_15</th><td>-27.44207</td><td> -9.93683657</td><td> -1.977504</td><td> -4.045792</td><td>  5.006737</td><td> -5.8281511</td><td>  2.850467381</td><td>-16.40317370</td><td>-11.63784009</td><td>  2.6068492</td><td> -9.5852781</td><td> 15.2182081</td><td> -5.9624166</td><td> -2.7677115</td><td>  0.5300971</td><td>  6.4510671</td><td>  1.0285543</td><td>-3.407431e-14</td></tr>\n",
       "\t<tr><th scope=row>hc2_17</th><td>-26.78017</td><td> -8.88494286</td><td>  1.616390</td><td>-14.824815</td><td> 12.381558</td><td>  7.3275247</td><td>  5.852446442</td><td>  3.36382822</td><td>-13.21606471</td><td>  6.4167259</td><td>  9.0711493</td><td>-12.5874488</td><td>  7.4232076</td><td>  4.0397190</td><td> -1.7964671</td><td>  2.2744603</td><td>  0.6141236</td><td>-2.575609e-14</td></tr>\n",
       "\t<tr><th scope=row>hc3_1</th><td>-26.12304</td><td>-16.27841110</td><td> -2.015371</td><td> 17.218358</td><td>-13.041637</td><td>  2.0462520</td><td> -9.522806180</td><td> -0.53596836</td><td>  3.84218496</td><td> -3.4599005</td><td>  7.0833796</td><td> -4.4688671</td><td>  4.5761089</td><td> -2.0321508</td><td>  0.2791371</td><td> 13.6215489</td><td>  3.3053655</td><td>-1.901517e-14</td></tr>\n",
       "\t<tr><th scope=row>hc3_5</th><td>-21.16350</td><td> 23.73935592</td><td> -6.121172</td><td>  3.914822</td><td> -3.441625</td><td>  8.1002522</td><td> -1.098953210</td><td>  8.29100230</td><td> -9.86400158</td><td> -3.0539660</td><td>  0.1558437</td><td>  1.3297368</td><td> -0.3305647</td><td> -9.0950639</td><td> 16.0432798</td><td> -2.4147457</td><td> -0.3670638</td><td>-1.212225e-14</td></tr>\n",
       "\t<tr><th scope=row>hc3_7</th><td>-30.92843</td><td>-18.85346769</td><td> -4.714808</td><td>  8.145754</td><td> -5.213806</td><td>  2.8217210</td><td> -3.423819013</td><td> -5.59599819</td><td>  0.45624539</td><td> -0.0675766</td><td>  0.2144223</td><td> -2.7433394</td><td> -3.7633990</td><td>  1.6825471</td><td> -0.8846641</td><td>-19.4150542</td><td> -4.7047574</td><td>-1.759183e-14</td></tr>\n",
       "\t<tr><th scope=row>hc3_10</th><td>-21.03439</td><td> 21.19788276</td><td> -5.169549</td><td> -1.486571</td><td>  6.959634</td><td> -6.8396262</td><td>  6.314141625</td><td>-19.46781373</td><td> 15.73262170</td><td> -1.9548204</td><td>  1.6908580</td><td> -8.0732548</td><td>  5.3095863</td><td> -0.9271814</td><td>  0.8063040</td><td> -0.0301668</td><td>  0.6053540</td><td>-1.797174e-15</td></tr>\n",
       "\t<tr><th scope=row>hc3_11</th><td>-18.36727</td><td>  0.85014804</td><td> 10.747881</td><td> -2.272608</td><td> -2.600135</td><td>-31.3607291</td><td>  0.487915045</td><td> 13.80828266</td><td> -1.06536157</td><td> -2.3706240</td><td> -0.4811927</td><td> -2.0388925</td><td> -0.6142991</td><td>  0.8118330</td><td>  0.7590647</td><td> -1.2628954</td><td> -1.0649637</td><td>-8.077740e-15</td></tr>\n",
       "\t<tr><th scope=row>ss2_9</th><td> 27.92738</td><td> -0.99474729</td><td> -1.306689</td><td>  7.344412</td><td> 10.033103</td><td> -2.5032820</td><td>  3.658758146</td><td>  1.63333350</td><td>  3.54838446</td><td> 11.4095109</td><td> 17.3094919</td><td>  2.8043906</td><td>-15.0397080</td><td> -7.3643725</td><td> -0.7990890</td><td> -0.2474286</td><td>  2.6488365</td><td> 5.981327e-15</td></tr>\n",
       "\t<tr><th scope=row>ss2_14</th><td> 27.26662</td><td> -2.32389839</td><td> -2.323917</td><td> 19.380787</td><td> 19.529981</td><td> -1.1692254</td><td> -3.021897612</td><td>  5.06715081</td><td> -1.42090243</td><td>  5.0275361</td><td>-12.9202010</td><td> -0.5300017</td><td> 11.5702126</td><td>  0.8631428</td><td> -0.4268394</td><td> -1.7591284</td><td>  3.0005723</td><td> 4.294481e-14</td></tr>\n",
       "\t<tr><th scope=row>ss2_18</th><td> 24.22251</td><td> -1.47916612</td><td>-13.251917</td><td> -5.960620</td><td>-11.663632</td><td> -1.9182301</td><td>  0.182622973</td><td>  0.59229757</td><td>  4.22888400</td><td> 13.9204231</td><td> -2.9325512</td><td>  0.5503040</td><td> -1.0779615</td><td> 15.9687410</td><td> 10.3597386</td><td>  2.2814881</td><td> -0.4422527</td><td>-2.360959e-15</td></tr>\n",
       "\t<tr><th scope=row>ss3_3</th><td> 30.17793</td><td> -2.62223772</td><td> -8.365908</td><td> -4.693003</td><td> -3.722243</td><td>  0.3392242</td><td>  0.005355216</td><td>  0.68648534</td><td> -1.08833183</td><td> -0.5716599</td><td> -8.2498927</td><td> -9.5772992</td><td> -3.7059466</td><td>-10.1130966</td><td> -5.1669301</td><td>  5.2915805</td><td>-15.6854936</td><td> 2.349163e-14</td></tr>\n",
       "\t<tr><th scope=row>ss3_14</th><td> 27.60295</td><td> -0.88708311</td><td> -4.250512</td><td> -5.234454</td><td> -2.130024</td><td> -1.8039344</td><td> -1.187514046</td><td> -1.30621468</td><td> -0.91727159</td><td> -7.2434185</td><td> 13.5491349</td><td> 15.5270600</td><td> 16.1776729</td><td> -0.1864911</td><td> -2.1980922</td><td> -2.7053248</td><td> -5.9626650</td><td> 3.373690e-14</td></tr>\n",
       "\t<tr><th scope=row>ss3_15</th><td> 14.98480</td><td> -0.07093903</td><td> 24.062688</td><td>  6.650366</td><td>-12.631309</td><td>  7.7039480</td><td> 23.790870371</td><td> -2.33859580</td><td>  0.07552961</td><td>  2.1434425</td><td> -3.2239836</td><td>  0.4467441</td><td>  2.6433451</td><td> -0.3533197</td><td>  0.1384903</td><td> -0.6236540</td><td>  0.1086379</td><td> 9.041379e-15</td></tr>\n",
       "\t<tr><th scope=row>ss3_16</th><td> 28.46982</td><td> -1.30184420</td><td>  2.017514</td><td>  2.014892</td><td>  7.727253</td><td>  2.3645664</td><td>  0.249636220</td><td> -3.06564357</td><td> -4.58421007</td><td>-22.3348854</td><td>  1.4004324</td><td> -4.0588603</td><td>-10.5830820</td><td> 11.4598793</td><td>  3.2359805</td><td>  1.6399255</td><td>  1.2558372</td><td>-1.663600e-14</td></tr>\n",
       "\t<tr><th scope=row>ss3_20</th><td> 11.77351</td><td>  4.01502430</td><td> 24.663484</td><td> -9.506869</td><td>  1.016381</td><td>  5.5700022</td><td>-24.698986518</td><td> -5.74788184</td><td>  3.56884937</td><td>  4.8499062</td><td> -1.7295599</td><td> -0.2129407</td><td> -0.6234259</td><td> -1.3172753</td><td>  2.6181064</td><td> -1.0812665</td><td> -0.2115781</td><td> 1.012298e-14</td></tr>\n",
       "\t<tr><th scope=row>ss5_18</th><td> 26.78386</td><td> -1.86619154</td><td> -9.675504</td><td>-11.483224</td><td>-11.318924</td><td> -1.2118194</td><td> -1.066937756</td><td>  0.03915472</td><td> -2.47853737</td><td> -3.4214823</td><td> -4.6839281</td><td> -3.9167225</td><td>  1.1697940</td><td> -8.0629501</td><td> -6.3793807</td><td> -4.7068743</td><td> 16.0199245</td><td> 1.033136e-15</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 18 x 18\n",
       "\\begin{tabular}{r|llllllllllllllllll}\n",
       "  & PC1 & PC2 & PC3 & PC4 & PC5 & PC6 & PC7 & PC8 & PC9 & PC10 & PC11 & PC12 & PC13 & PC14 & PC15 & PC16 & PC17 & PC18\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\thc1\\_2 & -21.19546 &  25.55281133 &  -2.393988 &   5.591844 &  -4.896916 &   5.7768050 &  -4.128529439 &   5.57650316 &  -3.85203413 &   2.9231433 &  -0.7657338 &   4.4360063 &  -4.4771879 &   8.7981682 & -17.2358795 &   0.9513672 &  -0.8182284 &  1.720716e-14\\\\\n",
       "\thc1\\_4 & -26.17505 &  -9.85545673 &  -1.541117 & -10.753280 &   8.005603 &  10.5847020 &   4.757230355 &  15.40325160 &  18.67185587 &  -4.8192035 &  -5.9023911 &   7.8951770 &  -2.6919361 &  -1.4044176 &   0.1171435 &   1.7351011 &   0.6697971 & -1.264874e-14\\\\\n",
       "\thc2\\_15 & -27.44207 &  -9.93683657 &  -1.977504 &  -4.045792 &   5.006737 &  -5.8281511 &   2.850467381 & -16.40317370 & -11.63784009 &   2.6068492 &  -9.5852781 &  15.2182081 &  -5.9624166 &  -2.7677115 &   0.5300971 &   6.4510671 &   1.0285543 & -3.407431e-14\\\\\n",
       "\thc2\\_17 & -26.78017 &  -8.88494286 &   1.616390 & -14.824815 &  12.381558 &   7.3275247 &   5.852446442 &   3.36382822 & -13.21606471 &   6.4167259 &   9.0711493 & -12.5874488 &   7.4232076 &   4.0397190 &  -1.7964671 &   2.2744603 &   0.6141236 & -2.575609e-14\\\\\n",
       "\thc3\\_1 & -26.12304 & -16.27841110 &  -2.015371 &  17.218358 & -13.041637 &   2.0462520 &  -9.522806180 &  -0.53596836 &   3.84218496 &  -3.4599005 &   7.0833796 &  -4.4688671 &   4.5761089 &  -2.0321508 &   0.2791371 &  13.6215489 &   3.3053655 & -1.901517e-14\\\\\n",
       "\thc3\\_5 & -21.16350 &  23.73935592 &  -6.121172 &   3.914822 &  -3.441625 &   8.1002522 &  -1.098953210 &   8.29100230 &  -9.86400158 &  -3.0539660 &   0.1558437 &   1.3297368 &  -0.3305647 &  -9.0950639 &  16.0432798 &  -2.4147457 &  -0.3670638 & -1.212225e-14\\\\\n",
       "\thc3\\_7 & -30.92843 & -18.85346769 &  -4.714808 &   8.145754 &  -5.213806 &   2.8217210 &  -3.423819013 &  -5.59599819 &   0.45624539 &  -0.0675766 &   0.2144223 &  -2.7433394 &  -3.7633990 &   1.6825471 &  -0.8846641 & -19.4150542 &  -4.7047574 & -1.759183e-14\\\\\n",
       "\thc3\\_10 & -21.03439 &  21.19788276 &  -5.169549 &  -1.486571 &   6.959634 &  -6.8396262 &   6.314141625 & -19.46781373 &  15.73262170 &  -1.9548204 &   1.6908580 &  -8.0732548 &   5.3095863 &  -0.9271814 &   0.8063040 &  -0.0301668 &   0.6053540 & -1.797174e-15\\\\\n",
       "\thc3\\_11 & -18.36727 &   0.85014804 &  10.747881 &  -2.272608 &  -2.600135 & -31.3607291 &   0.487915045 &  13.80828266 &  -1.06536157 &  -2.3706240 &  -0.4811927 &  -2.0388925 &  -0.6142991 &   0.8118330 &   0.7590647 &  -1.2628954 &  -1.0649637 & -8.077740e-15\\\\\n",
       "\tss2\\_9 &  27.92738 &  -0.99474729 &  -1.306689 &   7.344412 &  10.033103 &  -2.5032820 &   3.658758146 &   1.63333350 &   3.54838446 &  11.4095109 &  17.3094919 &   2.8043906 & -15.0397080 &  -7.3643725 &  -0.7990890 &  -0.2474286 &   2.6488365 &  5.981327e-15\\\\\n",
       "\tss2\\_14 &  27.26662 &  -2.32389839 &  -2.323917 &  19.380787 &  19.529981 &  -1.1692254 &  -3.021897612 &   5.06715081 &  -1.42090243 &   5.0275361 & -12.9202010 &  -0.5300017 &  11.5702126 &   0.8631428 &  -0.4268394 &  -1.7591284 &   3.0005723 &  4.294481e-14\\\\\n",
       "\tss2\\_18 &  24.22251 &  -1.47916612 & -13.251917 &  -5.960620 & -11.663632 &  -1.9182301 &   0.182622973 &   0.59229757 &   4.22888400 &  13.9204231 &  -2.9325512 &   0.5503040 &  -1.0779615 &  15.9687410 &  10.3597386 &   2.2814881 &  -0.4422527 & -2.360959e-15\\\\\n",
       "\tss3\\_3 &  30.17793 &  -2.62223772 &  -8.365908 &  -4.693003 &  -3.722243 &   0.3392242 &   0.005355216 &   0.68648534 &  -1.08833183 &  -0.5716599 &  -8.2498927 &  -9.5772992 &  -3.7059466 & -10.1130966 &  -5.1669301 &   5.2915805 & -15.6854936 &  2.349163e-14\\\\\n",
       "\tss3\\_14 &  27.60295 &  -0.88708311 &  -4.250512 &  -5.234454 &  -2.130024 &  -1.8039344 &  -1.187514046 &  -1.30621468 &  -0.91727159 &  -7.2434185 &  13.5491349 &  15.5270600 &  16.1776729 &  -0.1864911 &  -2.1980922 &  -2.7053248 &  -5.9626650 &  3.373690e-14\\\\\n",
       "\tss3\\_15 &  14.98480 &  -0.07093903 &  24.062688 &   6.650366 & -12.631309 &   7.7039480 &  23.790870371 &  -2.33859580 &   0.07552961 &   2.1434425 &  -3.2239836 &   0.4467441 &   2.6433451 &  -0.3533197 &   0.1384903 &  -0.6236540 &   0.1086379 &  9.041379e-15\\\\\n",
       "\tss3\\_16 &  28.46982 &  -1.30184420 &   2.017514 &   2.014892 &   7.727253 &   2.3645664 &   0.249636220 &  -3.06564357 &  -4.58421007 & -22.3348854 &   1.4004324 &  -4.0588603 & -10.5830820 &  11.4598793 &   3.2359805 &   1.6399255 &   1.2558372 & -1.663600e-14\\\\\n",
       "\tss3\\_20 &  11.77351 &   4.01502430 &  24.663484 &  -9.506869 &   1.016381 &   5.5700022 & -24.698986518 &  -5.74788184 &   3.56884937 &   4.8499062 &  -1.7295599 &  -0.2129407 &  -0.6234259 &  -1.3172753 &   2.6181064 &  -1.0812665 &  -0.2115781 &  1.012298e-14\\\\\n",
       "\tss5\\_18 &  26.78386 &  -1.86619154 &  -9.675504 & -11.483224 & -11.318924 &  -1.2118194 &  -1.066937756 &   0.03915472 &  -2.47853737 &  -3.4214823 &  -4.6839281 &  -3.9167225 &   1.1697940 &  -8.0629501 &  -6.3793807 &  -4.7068743 &  16.0199245 &  1.033136e-15\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 18 x 18\n",
       "\n",
       "| <!--/--> | PC1 &lt;dbl&gt; | PC2 &lt;dbl&gt; | PC3 &lt;dbl&gt; | PC4 &lt;dbl&gt; | PC5 &lt;dbl&gt; | PC6 &lt;dbl&gt; | PC7 &lt;dbl&gt; | PC8 &lt;dbl&gt; | PC9 &lt;dbl&gt; | PC10 &lt;dbl&gt; | PC11 &lt;dbl&gt; | PC12 &lt;dbl&gt; | PC13 &lt;dbl&gt; | PC14 &lt;dbl&gt; | PC15 &lt;dbl&gt; | PC16 &lt;dbl&gt; | PC17 &lt;dbl&gt; | PC18 &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| hc1_2 | -21.19546 |  25.55281133 |  -2.393988 |   5.591844 |  -4.896916 |   5.7768050 |  -4.128529439 |   5.57650316 |  -3.85203413 |   2.9231433 |  -0.7657338 |   4.4360063 |  -4.4771879 |   8.7981682 | -17.2358795 |   0.9513672 |  -0.8182284 |  1.720716e-14 |\n",
       "| hc1_4 | -26.17505 |  -9.85545673 |  -1.541117 | -10.753280 |   8.005603 |  10.5847020 |   4.757230355 |  15.40325160 |  18.67185587 |  -4.8192035 |  -5.9023911 |   7.8951770 |  -2.6919361 |  -1.4044176 |   0.1171435 |   1.7351011 |   0.6697971 | -1.264874e-14 |\n",
       "| hc2_15 | -27.44207 |  -9.93683657 |  -1.977504 |  -4.045792 |   5.006737 |  -5.8281511 |   2.850467381 | -16.40317370 | -11.63784009 |   2.6068492 |  -9.5852781 |  15.2182081 |  -5.9624166 |  -2.7677115 |   0.5300971 |   6.4510671 |   1.0285543 | -3.407431e-14 |\n",
       "| hc2_17 | -26.78017 |  -8.88494286 |   1.616390 | -14.824815 |  12.381558 |   7.3275247 |   5.852446442 |   3.36382822 | -13.21606471 |   6.4167259 |   9.0711493 | -12.5874488 |   7.4232076 |   4.0397190 |  -1.7964671 |   2.2744603 |   0.6141236 | -2.575609e-14 |\n",
       "| hc3_1 | -26.12304 | -16.27841110 |  -2.015371 |  17.218358 | -13.041637 |   2.0462520 |  -9.522806180 |  -0.53596836 |   3.84218496 |  -3.4599005 |   7.0833796 |  -4.4688671 |   4.5761089 |  -2.0321508 |   0.2791371 |  13.6215489 |   3.3053655 | -1.901517e-14 |\n",
       "| hc3_5 | -21.16350 |  23.73935592 |  -6.121172 |   3.914822 |  -3.441625 |   8.1002522 |  -1.098953210 |   8.29100230 |  -9.86400158 |  -3.0539660 |   0.1558437 |   1.3297368 |  -0.3305647 |  -9.0950639 |  16.0432798 |  -2.4147457 |  -0.3670638 | -1.212225e-14 |\n",
       "| hc3_7 | -30.92843 | -18.85346769 |  -4.714808 |   8.145754 |  -5.213806 |   2.8217210 |  -3.423819013 |  -5.59599819 |   0.45624539 |  -0.0675766 |   0.2144223 |  -2.7433394 |  -3.7633990 |   1.6825471 |  -0.8846641 | -19.4150542 |  -4.7047574 | -1.759183e-14 |\n",
       "| hc3_10 | -21.03439 |  21.19788276 |  -5.169549 |  -1.486571 |   6.959634 |  -6.8396262 |   6.314141625 | -19.46781373 |  15.73262170 |  -1.9548204 |   1.6908580 |  -8.0732548 |   5.3095863 |  -0.9271814 |   0.8063040 |  -0.0301668 |   0.6053540 | -1.797174e-15 |\n",
       "| hc3_11 | -18.36727 |   0.85014804 |  10.747881 |  -2.272608 |  -2.600135 | -31.3607291 |   0.487915045 |  13.80828266 |  -1.06536157 |  -2.3706240 |  -0.4811927 |  -2.0388925 |  -0.6142991 |   0.8118330 |   0.7590647 |  -1.2628954 |  -1.0649637 | -8.077740e-15 |\n",
       "| ss2_9 |  27.92738 |  -0.99474729 |  -1.306689 |   7.344412 |  10.033103 |  -2.5032820 |   3.658758146 |   1.63333350 |   3.54838446 |  11.4095109 |  17.3094919 |   2.8043906 | -15.0397080 |  -7.3643725 |  -0.7990890 |  -0.2474286 |   2.6488365 |  5.981327e-15 |\n",
       "| ss2_14 |  27.26662 |  -2.32389839 |  -2.323917 |  19.380787 |  19.529981 |  -1.1692254 |  -3.021897612 |   5.06715081 |  -1.42090243 |   5.0275361 | -12.9202010 |  -0.5300017 |  11.5702126 |   0.8631428 |  -0.4268394 |  -1.7591284 |   3.0005723 |  4.294481e-14 |\n",
       "| ss2_18 |  24.22251 |  -1.47916612 | -13.251917 |  -5.960620 | -11.663632 |  -1.9182301 |   0.182622973 |   0.59229757 |   4.22888400 |  13.9204231 |  -2.9325512 |   0.5503040 |  -1.0779615 |  15.9687410 |  10.3597386 |   2.2814881 |  -0.4422527 | -2.360959e-15 |\n",
       "| ss3_3 |  30.17793 |  -2.62223772 |  -8.365908 |  -4.693003 |  -3.722243 |   0.3392242 |   0.005355216 |   0.68648534 |  -1.08833183 |  -0.5716599 |  -8.2498927 |  -9.5772992 |  -3.7059466 | -10.1130966 |  -5.1669301 |   5.2915805 | -15.6854936 |  2.349163e-14 |\n",
       "| ss3_14 |  27.60295 |  -0.88708311 |  -4.250512 |  -5.234454 |  -2.130024 |  -1.8039344 |  -1.187514046 |  -1.30621468 |  -0.91727159 |  -7.2434185 |  13.5491349 |  15.5270600 |  16.1776729 |  -0.1864911 |  -2.1980922 |  -2.7053248 |  -5.9626650 |  3.373690e-14 |\n",
       "| ss3_15 |  14.98480 |  -0.07093903 |  24.062688 |   6.650366 | -12.631309 |   7.7039480 |  23.790870371 |  -2.33859580 |   0.07552961 |   2.1434425 |  -3.2239836 |   0.4467441 |   2.6433451 |  -0.3533197 |   0.1384903 |  -0.6236540 |   0.1086379 |  9.041379e-15 |\n",
       "| ss3_16 |  28.46982 |  -1.30184420 |   2.017514 |   2.014892 |   7.727253 |   2.3645664 |   0.249636220 |  -3.06564357 |  -4.58421007 | -22.3348854 |   1.4004324 |  -4.0588603 | -10.5830820 |  11.4598793 |   3.2359805 |   1.6399255 |   1.2558372 | -1.663600e-14 |\n",
       "| ss3_20 |  11.77351 |   4.01502430 |  24.663484 |  -9.506869 |   1.016381 |   5.5700022 | -24.698986518 |  -5.74788184 |   3.56884937 |   4.8499062 |  -1.7295599 |  -0.2129407 |  -0.6234259 |  -1.3172753 |   2.6181064 |  -1.0812665 |  -0.2115781 |  1.012298e-14 |\n",
       "| ss5_18 |  26.78386 |  -1.86619154 |  -9.675504 | -11.483224 | -11.318924 |  -1.2118194 |  -1.066937756 |   0.03915472 |  -2.47853737 |  -3.4214823 |  -4.6839281 |  -3.9167225 |   1.1697940 |  -8.0629501 |  -6.3793807 |  -4.7068743 |  16.0199245 |  1.033136e-15 |\n",
       "\n"
      ],
      "text/plain": [
       "       PC1       PC2          PC3        PC4        PC5        PC6        \n",
       "hc1_2  -21.19546  25.55281133  -2.393988   5.591844  -4.896916   5.7768050\n",
       "hc1_4  -26.17505  -9.85545673  -1.541117 -10.753280   8.005603  10.5847020\n",
       "hc2_15 -27.44207  -9.93683657  -1.977504  -4.045792   5.006737  -5.8281511\n",
       "hc2_17 -26.78017  -8.88494286   1.616390 -14.824815  12.381558   7.3275247\n",
       "hc3_1  -26.12304 -16.27841110  -2.015371  17.218358 -13.041637   2.0462520\n",
       "hc3_5  -21.16350  23.73935592  -6.121172   3.914822  -3.441625   8.1002522\n",
       "hc3_7  -30.92843 -18.85346769  -4.714808   8.145754  -5.213806   2.8217210\n",
       "hc3_10 -21.03439  21.19788276  -5.169549  -1.486571   6.959634  -6.8396262\n",
       "hc3_11 -18.36727   0.85014804  10.747881  -2.272608  -2.600135 -31.3607291\n",
       "ss2_9   27.92738  -0.99474729  -1.306689   7.344412  10.033103  -2.5032820\n",
       "ss2_14  27.26662  -2.32389839  -2.323917  19.380787  19.529981  -1.1692254\n",
       "ss2_18  24.22251  -1.47916612 -13.251917  -5.960620 -11.663632  -1.9182301\n",
       "ss3_3   30.17793  -2.62223772  -8.365908  -4.693003  -3.722243   0.3392242\n",
       "ss3_14  27.60295  -0.88708311  -4.250512  -5.234454  -2.130024  -1.8039344\n",
       "ss3_15  14.98480  -0.07093903  24.062688   6.650366 -12.631309   7.7039480\n",
       "ss3_16  28.46982  -1.30184420   2.017514   2.014892   7.727253   2.3645664\n",
       "ss3_20  11.77351   4.01502430  24.663484  -9.506869   1.016381   5.5700022\n",
       "ss5_18  26.78386  -1.86619154  -9.675504 -11.483224 -11.318924  -1.2118194\n",
       "       PC7           PC8          PC9          PC10        PC11       \n",
       "hc1_2   -4.128529439   5.57650316  -3.85203413   2.9231433  -0.7657338\n",
       "hc1_4    4.757230355  15.40325160  18.67185587  -4.8192035  -5.9023911\n",
       "hc2_15   2.850467381 -16.40317370 -11.63784009   2.6068492  -9.5852781\n",
       "hc2_17   5.852446442   3.36382822 -13.21606471   6.4167259   9.0711493\n",
       "hc3_1   -9.522806180  -0.53596836   3.84218496  -3.4599005   7.0833796\n",
       "hc3_5   -1.098953210   8.29100230  -9.86400158  -3.0539660   0.1558437\n",
       "hc3_7   -3.423819013  -5.59599819   0.45624539  -0.0675766   0.2144223\n",
       "hc3_10   6.314141625 -19.46781373  15.73262170  -1.9548204   1.6908580\n",
       "hc3_11   0.487915045  13.80828266  -1.06536157  -2.3706240  -0.4811927\n",
       "ss2_9    3.658758146   1.63333350   3.54838446  11.4095109  17.3094919\n",
       "ss2_14  -3.021897612   5.06715081  -1.42090243   5.0275361 -12.9202010\n",
       "ss2_18   0.182622973   0.59229757   4.22888400  13.9204231  -2.9325512\n",
       "ss3_3    0.005355216   0.68648534  -1.08833183  -0.5716599  -8.2498927\n",
       "ss3_14  -1.187514046  -1.30621468  -0.91727159  -7.2434185  13.5491349\n",
       "ss3_15  23.790870371  -2.33859580   0.07552961   2.1434425  -3.2239836\n",
       "ss3_16   0.249636220  -3.06564357  -4.58421007 -22.3348854   1.4004324\n",
       "ss3_20 -24.698986518  -5.74788184   3.56884937   4.8499062  -1.7295599\n",
       "ss5_18  -1.066937756   0.03915472  -2.47853737  -3.4214823  -4.6839281\n",
       "       PC12        PC13        PC14        PC15        PC16        PC17       \n",
       "hc1_2    4.4360063  -4.4771879   8.7981682 -17.2358795   0.9513672  -0.8182284\n",
       "hc1_4    7.8951770  -2.6919361  -1.4044176   0.1171435   1.7351011   0.6697971\n",
       "hc2_15  15.2182081  -5.9624166  -2.7677115   0.5300971   6.4510671   1.0285543\n",
       "hc2_17 -12.5874488   7.4232076   4.0397190  -1.7964671   2.2744603   0.6141236\n",
       "hc3_1   -4.4688671   4.5761089  -2.0321508   0.2791371  13.6215489   3.3053655\n",
       "hc3_5    1.3297368  -0.3305647  -9.0950639  16.0432798  -2.4147457  -0.3670638\n",
       "hc3_7   -2.7433394  -3.7633990   1.6825471  -0.8846641 -19.4150542  -4.7047574\n",
       "hc3_10  -8.0732548   5.3095863  -0.9271814   0.8063040  -0.0301668   0.6053540\n",
       "hc3_11  -2.0388925  -0.6142991   0.8118330   0.7590647  -1.2628954  -1.0649637\n",
       "ss2_9    2.8043906 -15.0397080  -7.3643725  -0.7990890  -0.2474286   2.6488365\n",
       "ss2_14  -0.5300017  11.5702126   0.8631428  -0.4268394  -1.7591284   3.0005723\n",
       "ss2_18   0.5503040  -1.0779615  15.9687410  10.3597386   2.2814881  -0.4422527\n",
       "ss3_3   -9.5772992  -3.7059466 -10.1130966  -5.1669301   5.2915805 -15.6854936\n",
       "ss3_14  15.5270600  16.1776729  -0.1864911  -2.1980922  -2.7053248  -5.9626650\n",
       "ss3_15   0.4467441   2.6433451  -0.3533197   0.1384903  -0.6236540   0.1086379\n",
       "ss3_16  -4.0588603 -10.5830820  11.4598793   3.2359805   1.6399255   1.2558372\n",
       "ss3_20  -0.2129407  -0.6234259  -1.3172753   2.6181064  -1.0812665  -0.2115781\n",
       "ss5_18  -3.9167225   1.1697940  -8.0629501  -6.3793807  -4.7068743  16.0199245\n",
       "       PC18         \n",
       "hc1_2   1.720716e-14\n",
       "hc1_4  -1.264874e-14\n",
       "hc2_15 -3.407431e-14\n",
       "hc2_17 -2.575609e-14\n",
       "hc3_1  -1.901517e-14\n",
       "hc3_5  -1.212225e-14\n",
       "hc3_7  -1.759183e-14\n",
       "hc3_10 -1.797174e-15\n",
       "hc3_11 -8.077740e-15\n",
       "ss2_9   5.981327e-15\n",
       "ss2_14  4.294481e-14\n",
       "ss2_18 -2.360959e-15\n",
       "ss3_3   2.349163e-14\n",
       "ss3_14  3.373690e-14\n",
       "ss3_15  9.041379e-15\n",
       "ss3_16 -1.663600e-14\n",
       "ss3_20  1.012298e-14\n",
       "ss5_18  1.033136e-15"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "kernel": "R"
   },
   "outputs": [],
   "source": [
    "#make sure ep and gen in same order\n",
    "ep <- ep[mbdorder,]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "kernel": "R",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPearson's product-moment correlation\n",
       "\n",
       "data:  ep$PC1 and gen$PC1\n",
       "t = 11.909, df = 16, p-value = 2.297e-09\n",
       "alternative hypothesis: true correlation is not equal to 0\n",
       "95 percent confidence interval:\n",
       " 0.8630461 0.9807661\n",
       "sample estimates:\n",
       "      cor \n",
       "0.9479559 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tSpearman's rank correlation rho\n",
       "\n",
       "data:  ep$PC1 and gen$PC1\n",
       "S = 106, p-value < 2.2e-16\n",
       "alternative hypothesis: true rho is not equal to 0\n",
       "sample estimates:\n",
       "      rho \n",
       "0.8906089 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cor.test( ~ ep$PC1 + gen$PC1,\n",
    "         method = \"pearson\",\n",
    "         conf.level = 0.95)\n",
    "cor.test( ~ ep$PC1 + gen$PC1, \n",
    "         method = \"spearman\",\n",
    "         continuity = FALSE,\n",
    "         conf.level = 0.95)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "kernel": "R"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "lm(formula = gen$PC1 ~ ep$PC1)\n",
       "\n",
       "Residuals:\n",
       "      Min        1Q    Median        3Q       Max \n",
       "-0.144231 -0.054698 -0.008395  0.045627  0.155625 \n",
       "\n",
       "Coefficients:\n",
       "             Estimate Std. Error t value Pr(>|t|)    \n",
       "(Intercept) 0.0184082  0.0187047   0.984     0.34    \n",
       "ep$PC1      0.0089519  0.0007517  11.909  2.3e-09 ***\n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 0.07936 on 16 degrees of freedom\n",
       "Multiple R-squared:  0.8986,\tAdjusted R-squared:  0.8923 \n",
       "F-statistic: 141.8 on 1 and 16 DF,  p-value: 2.297e-09\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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USUhEnH4/EiIyOTk5PLy8vFy8XPiBBia2urqfn/qQiLxTIxMbl//34zmgIaEjsA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\nQhDsAAAAFIJgBwAAoBAEOwAAAIX4D/wptCx3y9JPAAAAAElFTkSuQmCC",
      "text/plain": [
       "Plot with title “DMLs (PC1) vs filtered SNPs (PC1)”"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "palette(c(\"red\",\"blue\"))\n",
    "plot(ep$PC1,gen$PC1, pch=20,cex=1,xlab = \"PC1 methylation\", ylab = \"PC1 SNPs\",col=pop)\n",
    "legend('topright', legend = levels(pop), col = c(\"red\",\"blue\"), cex = 1, pch = 20)\n",
    "title(\"DMLs (PC1) vs filtered SNPs (PC1)\")\n",
    "summary(lm(gen$PC1~ep$PC1),)\n",
    "R2 = round(summary(lm(gen$PC1~ep$PC1))$r.squared, 4)\n",
    "#image(dens, col=transp(myPal(300),.7), add=TRUE)\n",
    "abline(lm(gen$PC1~ep$PC1))\n",
    "text(3,0.3,label=paste(\"R^2=\",R2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "R"
   },
   "source": [
    "# CpG-SNPs  not updated\n",
    "count all CpG-SNPs in genome using maf > 0.01 SNPs from full dataset, as well as GBS data  [site](https://www.bioconductor.org/packages/release/bioc/vignettes/ramwas/inst/doc/RW2_CpG_sets.html)  \n",
    "see how many CpGs are in cis/trans sites\n",
    "Do MACAU  \n",
    "* correct for cis-SNPs, trans-SNPs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "ERROR",
     "evalue": "Error in library(ramwas): there is no package called ‘ramwas’\n",
     "output_type": "error",
     "traceback": [
      "Error in library(ramwas): there is no package called ‘ramwas’\nTraceback:\n",
      "1. library(ramwas)"
     ]
    }
   ],
   "source": [
    "library(ramwas)\n",
    "library(Biostrings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read in genome\n",
    "genomeO = readDNAStringSet(\"../../Olurida_v081.fa\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "genome = genomeO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "  A DNAStringSet instance of length 6\n",
       "     width seq                                              names               \n",
       "[1] 116746 CCTTTGAAGGGGGTCTGGTCCTT...AATATTCGTAAGACAGTTATTT Contig0\n",
       "[2]  87411 ACACCGCATTCACGGCGTTTTAC...GAAGGCTCGAACCATGATTTTT Contig1\n",
       "[3] 139250 CGTATCGTACCAATGTGCCGTGA...ACCAAAAAAAGGCACAGATTTG Contig2\n",
       "[4] 141657 CAAAATTCCTGGATCCGCCCCTG...GGGTAGATCATATGCCTGATGT Contig3\n",
       "[5]  95692 CCTTCAACTAGGGACGACCCCCC...GTAAGGGGAGGGGGGGCCAAGT Contig4\n",
       "[6] 130522 CATGAATTCATTATAAGCATGTT...AAATATGAAGCAGATATGTCTG Contig5"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(genome)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "159429"
      ],
      "text/latex": [
       "159429"
      ],
      "text/markdown": [
       "159429"
      ],
      "text/plain": [
       "[1] 159429"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "length(genome)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in system(\"vcftools --vcf ../analyses/2bRAD/Inputs/HCSS_Afilt32m70_01_pp90.vcf --counts --out ../analyses/2bRAD/MethGen/count_HCSS_Afilt32m70_01_pp90.txt\"):\n",
      "\"error in running command\"\n"
     ]
    }
   ],
   "source": [
    "js(\"vcftools --vcf ../analyses/2bRAD/Inputs/HCSS_Afilt32m70_01_pp90.vcf --counts --out ../analyses/2bRAD/MethGen/count_HCSS_Afilt32m70_01_pp90\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>'Contig0'</li><li>'Contig1'</li><li>'Contig2'</li><li>'Contig3'</li><li>'Contig4'</li><li>'Contig5'</li><li>'Contig6'</li><li>'Contig7'</li><li>'Contig8'</li><li>'Contig9'</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'Contig0'\n",
       "\\item 'Contig1'\n",
       "\\item 'Contig2'\n",
       "\\item 'Contig3'\n",
       "\\item 'Contig4'\n",
       "\\item 'Contig5'\n",
       "\\item 'Contig6'\n",
       "\\item 'Contig7'\n",
       "\\item 'Contig8'\n",
       "\\item 'Contig9'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'Contig0'\n",
       "2. 'Contig1'\n",
       "3. 'Contig2'\n",
       "4. 'Contig3'\n",
       "5. 'Contig4'\n",
       "6. 'Contig5'\n",
       "7. 'Contig6'\n",
       "8. 'Contig7'\n",
       "9. 'Contig8'\n",
       "10. 'Contig9'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] \"Contig0\" \"Contig1\" \"Contig2\" \"Contig3\" \"Contig4\" \"Contig5\" \"Contig6\"\n",
       " [8] \"Contig7\" \"Contig8\" \"Contig9\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "names(genome)[1:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "chr = c()\n",
    "pos=c()\n",
    "r = readLines(\"../analyses/2bRAD/MethGen/count_HCSS_Afilt32m70_01_pp90.frq.count\")\n",
    "for (i in names(genome)){\n",
    "    g = paste0(i,\"\\t\")\n",
    "     freq <- c(r[1],r[grep(g,r)])\n",
    "    if(length(freq)>1){\n",
    "            genome[i] = injectSNPsMAF(gensequence = unlist(genome[i]),frqcount = freq,\n",
    "        MAF = 0.01)\n",
    "    }\n",
    "\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "  A DNAStringSet instance of length 159429\n",
       "          width seq                                         names               \n",
       "     [1] 116746 CCTTTGAAGGGGGTCTGGTC...TATTCGTAAGACAGTTATTT Contig0\n",
       "     [2]  87411 ACACCGCATTCACGGCGTTT...AGGCTCGAACCATGATTTTT Contig1\n",
       "     [3] 139250 CGTATCGTACCAATGTGCCG...CAAAAAAAGGCACAGATTTG Contig2\n",
       "     [4] 141657 CAAAATTCCTGGATCCGCCC...GTAGATCATATGCCTGATGT Contig3\n",
       "     [5]  95692 CCTTCAACTAGGGACGACCC...AAGGGGAGGGGGGGCCAAGT Contig4\n",
       "     ...    ... ...\n",
       "[159425]   1633 GGGGGGGGGGGTTTAGCCCA...AAAGAATCTCTTTCTCTTTT Contig696726\n",
       "[159426]   2007 TGTCTGCAATTTGTATATAT...TATTTTTTTTTTTTGGTTTC Contig696755\n",
       "[159427]   2014 GGGAGTCAAGGCTAACTCTG...CATATACGGTACCAATTTTT Contig696768\n",
       "[159428]   7680 GTATATGCACGTGCAATTTC...GCAGTGCTCAGGGACGATGT Contig696807\n",
       "[159429]   1455 ATATTGTAAATGTCCTCTAT...CGGGACAATAATTATAGATA Contig696856"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "genome"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Find the CpGs\n",
    "cpgset = getCpGsetALL(genome)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Find the CpGs\n",
    "cpgsetO = getCpGsetALL(genomeO)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "159429"
      ],
      "text/latex": [
       "159429"
      ],
      "text/markdown": [
       "159429"
      ],
      "text/plain": [
       "[1] 159429"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "length(cpgsetO)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "27332429"
      ],
      "text/latex": [
       "27332429"
      ],
      "text/markdown": [
       "27332429"
      ],
      "text/plain": [
       "[1] 27332429"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "snpsCPG <- sum(lengths(cpgset))\n",
    "snpsCPG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "CPGO <- sum(lengths(cpgsetO))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "27331887"
      ],
      "text/latex": [
       "27331887"
      ],
      "text/markdown": [
       "27331887"
      ],
      "text/plain": [
       "[1] 27331887"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "CPGO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "542"
      ],
      "text/latex": [
       "542"
      ],
      "text/markdown": [
       "542"
      ],
      "text/plain": [
       "[1] 542"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "snpsCPG - CPGO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "save(cpgset,cpgsetO,genome,genomeO,file=\"cpgset.RData\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "load(\"cpgset.RData\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>36</li><li>95</li><li>133</li><li>146</li><li>150</li><li>159</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 36\n",
       "\\item 95\n",
       "\\item 133\n",
       "\\item 146\n",
       "\\item 150\n",
       "\\item 159\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 36\n",
       "2. 95\n",
       "3. 133\n",
       "4. 146\n",
       "5. 150\n",
       "6. 159\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1]  36  95 133 146 150 159"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(cpgset$'Contig0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>109103</li><li>109119</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 109103\n",
       "\\item 109119\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 109103\n",
       "2. 109119\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 109103 109119"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "setdiff(cpgset[['Contig0']],cpgsetO$Contig0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "lappend <- function (lst, ...){\n",
    "lst <- c(lst, list(...))\n",
    "  return(lst)\n",
    "}\n",
    "\n",
    "lst=list()\n",
    "for (c in names(cpgset)){\n",
    "    d <- setdiff(cpgset[[c]],cpgsetO[[c]])\n",
    "    lst <- c(lst,list(d))\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol>\n",
       "\t<li><style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>109103</li><li>109119</li></ol>\n",
       "</li>\n",
       "\t<li></li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate}\n",
       "\\item \\begin{enumerate*}\n",
       "\\item 109103\n",
       "\\item 109119\n",
       "\\end{enumerate*}\n",
       "\n",
       "\\item \n",
       "\\end{enumerate}\n"
      ],
      "text/markdown": [
       "1. 1. 109103\n",
       "2. 109119\n",
       "\n",
       "\n",
       "\n",
       "2. \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[[1]]\n",
       "[1] 109103 109119\n",
       "\n",
       "[[2]]\n",
       "integer(0)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lst"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 542 of SNPs create a new CpG"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check how many SNPs are in CpG set and extract"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "######################\n",
      "#### bedr v1.0.7 ####\n",
      "######################\n",
      "\n",
      "checking binary availability...\n",
      "  * Checking path for bedtools... PASS\n",
      "    /tools/bedtools-2.29.0/bin/bedtools\n",
      "  * Checking path for bedops... PASS\n",
      "    /home/kes0132/miniconda3/envs/D/bin/bedops\n",
      "  * Checking path for tabix... PASS\n",
      "    /tools/tabix-0.2.7/tabix\n",
      "tests and examples will be skipped on R CMD check if binaries are missing\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "library(bedr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "READING VCF\n",
      " * checking if file exists... PASS\n",
      " * Reading vcf header...\n",
      "   Done\n",
      " * Reading vcf body...\n",
      "   Done\n",
      " * Parse vcf header...\n",
      "   Done\n",
      "CONVERT VCF TO BED\n"
     ]
    }
   ],
   "source": [
    "x <- read.vcf(\"../analyses/2bRAD/Inputs/HCSS_Afilt32m70_01_pp90.vcf\")\n",
    "x <- vcf2bed(x, filename = NULL, header = FALSE, other = NULL, verbose = TRUE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 6 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>chr</th><th scope=col>start</th><th scope=col>end</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>Contig0</td><td>109102</td><td>109103</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>Contig0</td><td>109118</td><td>109119</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>Contig0</td><td>109119</td><td>109120</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>Contig0</td><td>109122</td><td>109123</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>Contig1</td><td> 42879</td><td> 42880</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>Contig3</td><td> 14913</td><td> 14914</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 3\n",
       "\\begin{tabular}{r|lll}\n",
       "  & chr & start & end\\\\\n",
       "  & <chr> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t1 & Contig0 & 109102 & 109103\\\\\n",
       "\t2 & Contig0 & 109118 & 109119\\\\\n",
       "\t3 & Contig0 & 109119 & 109120\\\\\n",
       "\t4 & Contig0 & 109122 & 109123\\\\\n",
       "\t5 & Contig1 &  42879 &  42880\\\\\n",
       "\t6 & Contig3 &  14913 &  14914\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 3\n",
       "\n",
       "| <!--/--> | chr &lt;chr&gt; | start &lt;dbl&gt; | end &lt;dbl&gt; |\n",
       "|---|---|---|---|\n",
       "| 1 | Contig0 | 109102 | 109103 |\n",
       "| 2 | Contig0 | 109118 | 109119 |\n",
       "| 3 | Contig0 | 109119 | 109120 |\n",
       "| 4 | Contig0 | 109122 | 109123 |\n",
       "| 5 | Contig1 |  42879 |  42880 |\n",
       "| 6 | Contig3 |  14913 |  14914 |\n",
       "\n"
      ],
      "text/plain": [
       "  chr     start  end   \n",
       "1 Contig0 109102 109103\n",
       "2 Contig0 109118 109119\n",
       "3 Contig0 109119 109120\n",
       "4 Contig0 109122 109123\n",
       "5 Contig1  42879  42880\n",
       "6 Contig3  14913  14914"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 6 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>V1</th><th scope=col>V2</th><th scope=col>V3</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>Contig0</td><td>109103</td><td>109103</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>Contig0</td><td>109119</td><td>109119</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>Contig0</td><td>109120</td><td>109120</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>Contig0</td><td>109123</td><td>109123</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>Contig1</td><td> 42880</td><td> 42880</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>Contig3</td><td> 14914</td><td> 14914</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 3\n",
       "\\begin{tabular}{r|lll}\n",
       "  & V1 & V2 & V3\\\\\n",
       "  & <fct> & <int> & <int>\\\\\n",
       "\\hline\n",
       "\t1 & Contig0 & 109103 & 109103\\\\\n",
       "\t2 & Contig0 & 109119 & 109119\\\\\n",
       "\t3 & Contig0 & 109120 & 109120\\\\\n",
       "\t4 & Contig0 & 109123 & 109123\\\\\n",
       "\t5 & Contig1 &  42880 &  42880\\\\\n",
       "\t6 & Contig3 &  14914 &  14914\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 3\n",
       "\n",
       "| <!--/--> | V1 &lt;fct&gt; | V2 &lt;int&gt; | V3 &lt;int&gt; |\n",
       "|---|---|---|---|\n",
       "| 1 | Contig0 | 109103 | 109103 |\n",
       "| 2 | Contig0 | 109119 | 109119 |\n",
       "| 3 | Contig0 | 109120 | 109120 |\n",
       "| 4 | Contig0 | 109123 | 109123 |\n",
       "| 5 | Contig1 |  42880 |  42880 |\n",
       "| 6 | Contig3 |  14914 |  14914 |\n",
       "\n"
      ],
      "text/plain": [
       "  V1      V2     V3    \n",
       "1 Contig0 109103 109103\n",
       "2 Contig0 109119 109119\n",
       "3 Contig0 109120 109120\n",
       "4 Contig0 109123 109123\n",
       "5 Contig1  42880  42880\n",
       "6 Contig3  14914  14914"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x[,2] <- x[,3]\n",
    "head(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>5269</li><li>3</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 5269\n",
       "\\item 3\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 5269\n",
       "2. 3\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 5269    3"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "write.table(x,\"../analyses/2bRAD/Inputs/HCSS_Afilt32m70_01_pp90.BED\",row.names = F,quote = F,col.names = F,sep=\"\\t\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "contigs <- names(cpgset)\n",
    "radcpgSnps <- data.frame(chr=character(),\n",
    "                 start=double(), \n",
    "                 end=double(), \n",
    "                 stringsAsFactors=FALSE)\n",
    "for(row in 1:nrow(x)){\n",
    "    c <- x[row,\"chr\"]\n",
    "    if(c %in% contigs){\n",
    "        snps <- cpgset[[c]]\n",
    "        if(x[row,\"end\"] %in% snps){\n",
    "            radcpgSnps <- rbind(radcpgSnps,x[row,])\n",
    "        }\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>651</li><li>3</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 651\n",
       "\\item 3\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 651\n",
       "2. 3\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 651   3"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(radcpgSnps)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 651 of our 5269 SNPs are CpG-SNPs (12.4%), 266 CpG-SNPs (40.9%) within 350bp of a methylated site (5x) vs 1591 (34.5%) of non CpG-SNPs "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "radcpgSnps$start <- radcpgSnps$end\n",
    "write.table(radcpgSnps,file=\"../analyses/2bRAD/MethGen/cpgsnps.bed\",quote = F, row.names = F,sep = \"\\t\",col.names = F)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "4618 ../analyses/2bRAD/Inputs/non-cpgsnps.bed\n"
     ]
    }
   ],
   "source": [
    "# bed file with non-CpG-SNPs\n",
    "js(\"bedtools subtract -a ../analyses/2bRAD/Inputs/HCSS_Afilt32m70_01_pp90.BED -b ../analyses/2bRAD/MethGen/cpgsnps.bed  >  ../analyses/2bRAD/Inputs/non-cpgsnps.bed\")\n",
    "js(\"wc -l ../analyses/2bRAD/Inputs/non-cpgsnps.bed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "266 ../analyses/2bRAD/MethGen/meth-cpgsnp-350.bed\n"
     ]
    }
   ],
   "source": [
    "# how many Cpg-snps are within 350bp of a methylated site?\n",
    "js(\"bedtools window -w 350 -u -b ../analyses/methylation-characteristics/all_methylated_5x.bed -a ../analyses/2bRAD/MethGen/cpgsnps.bed  >  ../analyses/2bRAD/MethGen/meth-cpgsnp-350.bed\")\n",
    "js(\"wc -l ../analyses/2bRAD/MethGen/meth-cpgsnp-350.bed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "650 ../analyses/2bRAD/MethGen/allCpG-cpgsnp-350.bed\n"
     ]
    }
   ],
   "source": [
    "# how many CpG-SNPs within 350bp of a CpG\n",
    "js(\"bedtools window -w 350 -u -b ../genome-features/Olurida_v081_CG-motif.gff -a ../analyses/2bRAD/MethGen/cpgsnps.bed  >  ../analyses/2bRAD/MethGen/allCpG-cpgsnp-350.bed\")\n",
    "js(\"wc -l ../analyses/2bRAD/MethGen/allCpG-cpgsnp-350.bed\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1591 ../analyses/2bRAD/MethGen/meth-nonCpGsnp-350.bed\n"
     ]
    }
   ],
   "source": [
    "# how many non cpg-SNPs near meth\n",
    "js(\"bedtools window -w 350 -u -b ../analyses/methylation-characteristics/all_methylated_5x.bed -a ../analyses/2bRAD/Inputs/non-cpgsnps.bed  >  ../analyses/2bRAD/MethGen/meth-nonCpGsnp-350.bed\")\n",
    "js(\"wc -l ../analyses/2bRAD/MethGen/meth-nonCpGsnp-350.bed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "4609 ../analyses/2bRAD/MethGen/allCpG-nonCpGsnp-350.bed\n"
     ]
    }
   ],
   "source": [
    "# how many non CpG-SNPs near cpg\n",
    "js(\"bedtools window -w 350 -u -b ../genome-features/Olurida_v081_CG-motif.gff -a ../analyses/2bRAD/Inputs/non-cpgsnps.bed  >  ../analyses/2bRAD/MethGen/allCpG-nonCpGsnp-350.bed\")\n",
    "js(\"wc -l ../analyses/2bRAD/MethGen/allCpG-nonCpGsnp-350.bed\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Is methylation associated with CpG-SNP status?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "cm <- 266\n",
    "cnm <- 650-cm\n",
    "ncm <- 1591\n",
    "ncnm <- 4609-1591"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A matrix: 2 × 2 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>cpg</th><th scope=col>non-cpg</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>meth</th><td>266</td><td>1591</td></tr>\n",
       "\t<tr><th scope=row>nonmeth</th><td>384</td><td>3018</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 2 × 2 of type dbl\n",
       "\\begin{tabular}{r|ll}\n",
       "  & cpg & non-cpg\\\\\n",
       "\\hline\n",
       "\tmeth & 266 & 1591\\\\\n",
       "\tnonmeth & 384 & 3018\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 2 × 2 of type dbl\n",
       "\n",
       "| <!--/--> | cpg | non-cpg |\n",
       "|---|---|---|\n",
       "| meth | 266 | 1591 |\n",
       "| nonmeth | 384 | 3018 |\n",
       "\n"
      ],
      "text/plain": [
       "        cpg non-cpg\n",
       "meth    266 1591   \n",
       "nonmeth 384 3018   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mat <- matrix(c(cm,cnm,ncm,ncnm),ncol=2)\n",
    "dimnames(mat) <- list(c(\"meth\",\"nonmeth\"),c(\"cpg\",\"non-cpg\"))\n",
    "mat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPearson's Chi-squared test with Yates' continuity correction\n",
       "\n",
       "data:  mat\n",
       "X-squared = 9.9482, df = 1, p-value = 0.00161\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test <- chisq.test(mat)\n",
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A matrix: 2 × 2 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>cpg</th><th scope=col>non-cpg</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>meth</th><td>229.4377</td><td>1627.562</td></tr>\n",
       "\t<tr><th scope=row>nonmeth</th><td>421.5623</td><td>2990.438</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 2 × 2 of type dbl\n",
       "\\begin{tabular}{r|ll}\n",
       "  & cpg & non-cpg\\\\\n",
       "\\hline\n",
       "\tmeth & 229.4377 & 1627.562\\\\\n",
       "\tnonmeth & 421.5623 & 2990.438\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 2 × 2 of type dbl\n",
       "\n",
       "| <!--/--> | cpg | non-cpg |\n",
       "|---|---|---|\n",
       "| meth | 229.4377 | 1627.562 |\n",
       "| nonmeth | 421.5623 | 2990.438 |\n",
       "\n"
      ],
      "text/plain": [
       "        cpg      non-cpg \n",
       "meth    229.4377 1627.562\n",
       "nonmeth 421.5623 2990.438"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test$expected"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "40.9230769230769"
      ],
      "text/latex": [
       "40.9230769230769"
      ],
      "text/markdown": [
       "40.9230769230769"
      ],
      "text/plain": [
       "[1] 40.92308"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# %cpg-snps with meth site\n",
    "cm/(cm+cnm) *100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "34.5194185289651"
      ],
      "text/latex": [
       "34.5194185289651"
      ],
      "text/markdown": [
       "34.5194185289651"
      ],
      "text/plain": [
       "[1] 34.51942"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# % non cpg with meth site\n",
    "ncm/(ncm+ncnm) *100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CpG-SNPs and DMLs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "6 ../analyses/2bRAD/MethGen/cpgsnps-DMLs-350bp.bed\n"
     ]
    }
   ],
   "source": [
    "# how many CpG-SNPs are near a dml\n",
    "js(\"bedtools window -w 350 -u -a ../analyses/2bRAD/MethGen/cpgsnps.bed -b ../analyses/DMLs/dml25_forIGV.bed > ../analyses/2bRAD/MethGen/cpgsnps-DMLs-350bp.bed\")\n",
    "js(\"wc -l ../analyses/2bRAD/MethGen/cpgsnps-DMLs-350bp.bed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "26 ../analyses/2bRAD/MethGen/Allsnps-DMLs-350bp.bed\n"
     ]
    }
   ],
   "source": [
    "# how many all SNPs are near a dml\n",
    "js(\"bedtools window -w 350 -u -a ../analyses/2bRAD/Inputs/HCSS_Afilt32m70_01_pp90.BED -b ../analyses/DMLs/dml25_forIGV.bed > ../analyses/2bRAD/MethGen/Allsnps-DMLs-350bp.bed\")\n",
    "js(\"wc -l ../analyses/2bRAD/MethGen/Allsnps-DMLs-350bp.bed\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "cm <- 6\n",
    "cnm <- 650-66\n",
    "ncm <- 20\n",
    "ncnm <- 5269-26"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A matrix: 2 x 2 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>cpg</th><th scope=col>non-cpg</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>meth</th><td>  6</td><td>  20</td></tr>\n",
       "\t<tr><th scope=row>nonmeth</th><td>584</td><td>5243</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 2 x 2 of type dbl\n",
       "\\begin{tabular}{r|ll}\n",
       "  & cpg & non-cpg\\\\\n",
       "\\hline\n",
       "\tmeth &   6 &   20\\\\\n",
       "\tnonmeth & 584 & 5243\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 2 x 2 of type dbl\n",
       "\n",
       "| <!--/--> | cpg | non-cpg |\n",
       "|---|---|---|\n",
       "| meth |   6 |   20 |\n",
       "| nonmeth | 584 | 5243 |\n",
       "\n"
      ],
      "text/plain": [
       "        cpg non-cpg\n",
       "meth      6   20   \n",
       "nonmeth 584 5243   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mat <- matrix(c(cm,cnm,ncm,ncnm),ncol=2)\n",
    "dimnames(mat) <- list(c(\"meth\",\"nonmeth\"),c(\"cpg\",\"non-cpg\"))\n",
    "mat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\tFisher's Exact Test for Count Data\n",
       "\n",
       "data:  mat\n",
       "p-value = 0.04086\n",
       "alternative hypothesis: true odds ratio is not equal to 1\n",
       "95 percent confidence interval:\n",
       " 0.8812717 6.9879996\n",
       "sample estimates:\n",
       "odds ratio \n",
       "  2.692662 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test <- fisher.test(mat)\n",
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "0.00921658986175115"
      ],
      "text/latex": [
       "0.00921658986175115"
      ],
      "text/markdown": [
       "0.00921658986175115"
      ],
      "text/plain": [
       "[1] 0.00921659"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "6/651"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "0.0043308791684712"
      ],
      "text/latex": [
       "0.0043308791684712"
      ],
      "text/markdown": [
       "0.0043308791684712"
      ],
      "text/plain": [
       "[1] 0.004330879"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "20/(5269-651)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "10 ../analyses/2bRAD/MethGen/DMLs-by-cpgsnps-350bp.bed\n"
     ]
    }
   ],
   "source": [
    "# how many dmls are near a CpG-SNP\n",
    "js(\"bedtools window -w 350 -u -b ../analyses/2bRAD/MethGen/cpgsnps.bed -a ../analyses/DMLs/dml25_forIGV.bed > ../analyses/2bRAD/MethGen/DMLs-by-cpgsnps-350bp.bed\")\n",
    "js(\"wc -l ../analyses/2bRAD/MethGen/DMLs-by-cpgsnps-350bp.bed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3963 ../analyses/DMLs/dml25_forIGV.bed\n"
     ]
    }
   ],
   "source": [
    "js(\"wc -l ../analyses/DMLs/dml25_forIGV.bed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "23 ../analyses/2bRAD/MethGen/DMLs-by-allsnps-350bp.bed\n"
     ]
    }
   ],
   "source": [
    "# how many dmls are near all snps\n",
    "js(\"bedtools window -w 350 -u -b ../analyses/2bRAD/Inputs/HCSS_Afilt32m70_01_pp90.BED -a ../analyses/DMLs/dml25_forIGV.bed > ../analyses/2bRAD/MethGen/DMLs-by-allsnps-350bp.bed\")\n",
    "js(\"wc -l ../analyses/2bRAD/MethGen/DMLs-by-allsnps-350bp.bed\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "###### "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CpG-SNPs with GBS SNPs added"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "js(\"vcftools --vcf ../data/Oly-ref-c90-t10-m70-mac4.recode.vcf --counts --out ../analyses/2bRAD/MethGen/Oly-ref-c90-t10-m70-mac4\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "genomeG <- genome"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "chr = c()\n",
    "pos=c()\n",
    "r = readLines(\"../analyses/2bRAD/MethGen/Oly-ref-c90-t10-m70-mac4.frq.count\")\n",
    "for (i in names(genomeG)){\n",
    "    g = paste0(i,\"\\t\")\n",
    "     freq <- c(r[1],r[grep(g,r)])\n",
    "    if(length(freq)>1){\n",
    "            genomeG[i] = injectSNPsMAF(gensequence = unlist(genomeG[i]),frqcount = freq,\n",
    "        MAF = 0.01)\n",
    "    }\n",
    "\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "  A DNAStringSet instance of length 159429\n",
       "          width seq                                         names               \n",
       "     [1] 116746 CCTTTGAAGGGGGTCTGGTC...TATTCGTAAGACAGTTATTT Contig0\n",
       "     [2]  87411 ACACCGCATTCACGGCGTTT...AGGCTCGAACCATGATTTTT Contig1\n",
       "     [3] 139250 CGTATCGTACCAATGTGCCG...CAAAAAAAGGCACAGATTTG Contig2\n",
       "     [4] 141657 CAAAATTCCTGGATCCGCCC...GTAGATCATATGCCTGATGT Contig3\n",
       "     [5]  95692 CCTTCAACTAGGGACGACCC...AAGGGGAGGGGGGGCCAAGT Contig4\n",
       "     ...    ... ...\n",
       "[159425]   1633 GGGGGGGGGGGTTTAGCCCA...AAAGAATCTCTTTCTCTTTT Contig696726\n",
       "[159426]   2007 TGTCTGCAATTTGTATATAT...TATTTTTTTTTTTTGGTTTC Contig696755\n",
       "[159427]   2014 GGGAGTCAAGGCTAACTCTG...CATATACGGTACCAATTTTT Contig696768\n",
       "[159428]   7680 GTATATGCACGTGCAATTTC...GCAGTGCTCAGGGACGATGT Contig696807\n",
       "[159429]   1455 ATATTGTAAATGTCCTCTAT...CGGGACAATAATTATAGATA Contig696856"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "genomeG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find the CpGs\n",
    "cpgsetG = getCpGsetALL(genomeG)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "27338139"
      ],
      "text/latex": [
       "27338139"
      ],
      "text/markdown": [
       "27338139"
      ],
      "text/plain": [
       "[1] 27338139"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "snpsCPGg <- sum(lengths(cpgsetG))\n",
    "snpsCPGg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "27332429"
      ],
      "text/latex": [
       "27332429"
      ],
      "text/markdown": [
       "27332429"
      ],
      "text/plain": [
       "[1] 27332429"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "snpsCPG <- sum(lengths(cpgset))\n",
    "snpsCPG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "CPGO <- sum(lengths(cpgsetO))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "5710"
      ],
      "text/latex": [
       "5710"
      ],
      "text/markdown": [
       "5710"
      ],
      "text/plain": [
       "[1] 5710"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "snpsCPGg - snpsCPG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "6252"
      ],
      "text/latex": [
       "6252"
      ],
      "text/markdown": [
       "6252"
      ],
      "text/plain": [
       "[1] 6252"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "snpsCPGg - CPGO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "542"
      ],
      "text/latex": [
       "542"
      ],
      "text/markdown": [
       "542"
      ],
      "text/plain": [
       "[1] 542"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "snpsCPG - CPGO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "save(cpgset,cpgsetO,cpgsetG,genomeG,genome,genomeO,file=\"cpgset.RData\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "load(\"cpgset.RData\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>36</li><li>95</li><li>133</li><li>146</li><li>150</li><li>159</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 36\n",
       "\\item 95\n",
       "\\item 133\n",
       "\\item 146\n",
       "\\item 150\n",
       "\\item 159\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 36\n",
       "2. 95\n",
       "3. 133\n",
       "4. 146\n",
       "5. 150\n",
       "6. 159\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1]  36  95 133 146 150 159"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(cpgsetG$'Contig0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>109103</li><li>109119</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 109103\n",
       "\\item 109119\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 109103\n",
       "2. 109119\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 109103 109119"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "setdiff(cpgsetG[['Contig0']],cpgsetO$Contig0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dont run\n",
    "lappend <- function (lst, ...){\n",
    "lst <- c(lst, list(...))\n",
    "  return(lst)\n",
    "}\n",
    "\n",
    "lst=list()\n",
    "for (c in names(cpgset)){\n",
    "    d <- setdiff(cpgset[[c]],cpgsetO[[c]])\n",
    "    lst <- c(lst,list(d))\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol>\n",
       "\t<li><style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>109103</li><li>109119</li></ol>\n",
       "</li>\n",
       "\t<li></li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate}\n",
       "\\item \\begin{enumerate*}\n",
       "\\item 109103\n",
       "\\item 109119\n",
       "\\end{enumerate*}\n",
       "\n",
       "\\item \n",
       "\\end{enumerate}\n"
      ],
      "text/markdown": [
       "1. 1. 109103\n",
       "2. 109119\n",
       "\n",
       "\n",
       "\n",
       "2. \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[[1]]\n",
       "[1] 109103 109119\n",
       "\n",
       "[[2]]\n",
       "integer(0)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lst"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6252 of SNPs create a new CpG"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check how many SNPs are in CpG set and extract"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "######################\n",
      "#### bedr v1.0.7 ####\n",
      "######################\n",
      "\n",
      "checking binary availability...\n",
      "  * Checking path for bedtools... PASS\n",
      "    /tools/bedtools-2.30.0/bin/bedtools\n",
      "  * Checking path for bedops... FAIL\n",
      "  * Checking path for tabix... PASS\n",
      "    /tools/tabix-0.2.7/tabix\n",
      "tests and examples will be skipped on R CMD check if binaries are missing\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "library(bedr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "READING VCF\n",
      " * checking if file exists... PASS\n",
      " * Reading vcf header...\n",
      "   Done\n",
      " * Reading vcf body...\n",
      "   Done\n",
      " * Parse vcf header...\n",
      "   Done\n",
      "CONVERT VCF TO BED\n"
     ]
    }
   ],
   "source": [
    "x <- read.vcf(\"../analyses/2bRAD/Inputs/HCSS_Afilt32m70_01_pp90.vcf\")\n",
    "x <- vcf2bed(x, filename = NULL, header = FALSE, other = NULL, verbose = TRUE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "READING VCF\n",
      " * checking if file exists... PASS\n",
      " * Reading vcf header...\n",
      "   Done\n",
      " * Reading vcf body...\n",
      "   Done\n",
      " * Parse vcf header...\n",
      "   Done\n",
      "CONVERT VCF TO BED\n"
     ]
    }
   ],
   "source": [
    "y <- read.vcf(\"../data/Oly-ref-c90-t10-m70-mac4.recode.vcf\")\n",
    "y <- vcf2bed(y, filename = NULL, header = FALSE, other = NULL, verbose = TRUE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 6 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>chr</th><th scope=col>start</th><th scope=col>end</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>Contig0</td><td>109103</td><td>109103</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>Contig0</td><td>109119</td><td>109119</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>Contig0</td><td>109120</td><td>109120</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>Contig0</td><td>109123</td><td>109123</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>Contig1</td><td> 42880</td><td> 42880</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>Contig3</td><td> 14914</td><td> 14914</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 3\n",
       "\\begin{tabular}{r|lll}\n",
       "  & chr & start & end\\\\\n",
       "  & <chr> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t1 & Contig0 & 109103 & 109103\\\\\n",
       "\t2 & Contig0 & 109119 & 109119\\\\\n",
       "\t3 & Contig0 & 109120 & 109120\\\\\n",
       "\t4 & Contig0 & 109123 & 109123\\\\\n",
       "\t5 & Contig1 &  42880 &  42880\\\\\n",
       "\t6 & Contig3 &  14914 &  14914\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 3\n",
       "\n",
       "| <!--/--> | chr &lt;chr&gt; | start &lt;dbl&gt; | end &lt;dbl&gt; |\n",
       "|---|---|---|---|\n",
       "| 1 | Contig0 | 109103 | 109103 |\n",
       "| 2 | Contig0 | 109119 | 109119 |\n",
       "| 3 | Contig0 | 109120 | 109120 |\n",
       "| 4 | Contig0 | 109123 | 109123 |\n",
       "| 5 | Contig1 |  42880 |  42880 |\n",
       "| 6 | Contig3 |  14914 |  14914 |\n",
       "\n"
      ],
      "text/plain": [
       "  chr     start  end   \n",
       "1 Contig0 109103 109103\n",
       "2 Contig0 109119 109119\n",
       "3 Contig0 109120 109120\n",
       "4 Contig0 109123 109123\n",
       "5 Contig1  42880  42880\n",
       "6 Contig3  14914  14914"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>5269</li><li>3</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 5269\n",
       "\\item 3\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 5269\n",
       "2. 3\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 5269    3"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(x)\n",
    "dim(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 6 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>chr</th><th scope=col>start</th><th scope=col>end</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>Contig0</td><td> 7285</td><td> 7285</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>Contig0</td><td>34735</td><td>34735</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>Contig1</td><td> 2414</td><td> 2414</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>Contig1</td><td> 2422</td><td> 2422</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>Contig1</td><td> 2431</td><td> 2431</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>Contig1</td><td> 2436</td><td> 2436</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 3\n",
       "\\begin{tabular}{r|lll}\n",
       "  & chr & start & end\\\\\n",
       "  & <chr> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t1 & Contig0 &  7285 &  7285\\\\\n",
       "\t2 & Contig0 & 34735 & 34735\\\\\n",
       "\t3 & Contig1 &  2414 &  2414\\\\\n",
       "\t4 & Contig1 &  2422 &  2422\\\\\n",
       "\t5 & Contig1 &  2431 &  2431\\\\\n",
       "\t6 & Contig1 &  2436 &  2436\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 3\n",
       "\n",
       "| <!--/--> | chr &lt;chr&gt; | start &lt;dbl&gt; | end &lt;dbl&gt; |\n",
       "|---|---|---|---|\n",
       "| 1 | Contig0 |  7285 |  7285 |\n",
       "| 2 | Contig0 | 34735 | 34735 |\n",
       "| 3 | Contig1 |  2414 |  2414 |\n",
       "| 4 | Contig1 |  2422 |  2422 |\n",
       "| 5 | Contig1 |  2431 |  2431 |\n",
       "| 6 | Contig1 |  2436 |  2436 |\n",
       "\n"
      ],
      "text/plain": [
       "  chr     start end  \n",
       "1 Contig0  7285  7285\n",
       "2 Contig0 34735 34735\n",
       "3 Contig1  2414  2414\n",
       "4 Contig1  2422  2422\n",
       "5 Contig1  2431  2431\n",
       "6 Contig1  2436  2436"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>58126</li><li>3</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 58126\n",
       "\\item 3\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 58126\n",
       "2. 3\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 58126     3"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(y)\n",
    "dim(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 6 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>chr</th><th scope=col>start</th><th scope=col>end</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>Contig0</td><td>109103</td><td>109103</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>Contig0</td><td>109119</td><td>109119</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>Contig0</td><td>109120</td><td>109120</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>Contig0</td><td>109123</td><td>109123</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>Contig1</td><td> 42880</td><td> 42880</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>Contig3</td><td> 14914</td><td> 14914</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 3\n",
       "\\begin{tabular}{r|lll}\n",
       "  & chr & start & end\\\\\n",
       "  & <chr> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t1 & Contig0 & 109103 & 109103\\\\\n",
       "\t2 & Contig0 & 109119 & 109119\\\\\n",
       "\t3 & Contig0 & 109120 & 109120\\\\\n",
       "\t4 & Contig0 & 109123 & 109123\\\\\n",
       "\t5 & Contig1 &  42880 &  42880\\\\\n",
       "\t6 & Contig3 &  14914 &  14914\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 3\n",
       "\n",
       "| <!--/--> | chr &lt;chr&gt; | start &lt;dbl&gt; | end &lt;dbl&gt; |\n",
       "|---|---|---|---|\n",
       "| 1 | Contig0 | 109103 | 109103 |\n",
       "| 2 | Contig0 | 109119 | 109119 |\n",
       "| 3 | Contig0 | 109120 | 109120 |\n",
       "| 4 | Contig0 | 109123 | 109123 |\n",
       "| 5 | Contig1 |  42880 |  42880 |\n",
       "| 6 | Contig3 |  14914 |  14914 |\n",
       "\n"
      ],
      "text/plain": [
       "  chr     start  end   \n",
       "1 Contig0 109103 109103\n",
       "2 Contig0 109119 109119\n",
       "3 Contig0 109120 109120\n",
       "4 Contig0 109123 109123\n",
       "5 Contig1  42880  42880\n",
       "6 Contig3  14914  14914"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x[,2] <- x[,3]\n",
    "head(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 6 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>chr</th><th scope=col>start</th><th scope=col>end</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>Contig0</td><td> 7285</td><td> 7285</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>Contig0</td><td>34735</td><td>34735</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>Contig1</td><td> 2414</td><td> 2414</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>Contig1</td><td> 2422</td><td> 2422</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>Contig1</td><td> 2431</td><td> 2431</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>Contig1</td><td> 2436</td><td> 2436</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 3\n",
       "\\begin{tabular}{r|lll}\n",
       "  & chr & start & end\\\\\n",
       "  & <chr> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t1 & Contig0 &  7285 &  7285\\\\\n",
       "\t2 & Contig0 & 34735 & 34735\\\\\n",
       "\t3 & Contig1 &  2414 &  2414\\\\\n",
       "\t4 & Contig1 &  2422 &  2422\\\\\n",
       "\t5 & Contig1 &  2431 &  2431\\\\\n",
       "\t6 & Contig1 &  2436 &  2436\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 3\n",
       "\n",
       "| <!--/--> | chr &lt;chr&gt; | start &lt;dbl&gt; | end &lt;dbl&gt; |\n",
       "|---|---|---|---|\n",
       "| 1 | Contig0 |  7285 |  7285 |\n",
       "| 2 | Contig0 | 34735 | 34735 |\n",
       "| 3 | Contig1 |  2414 |  2414 |\n",
       "| 4 | Contig1 |  2422 |  2422 |\n",
       "| 5 | Contig1 |  2431 |  2431 |\n",
       "| 6 | Contig1 |  2436 |  2436 |\n",
       "\n"
      ],
      "text/plain": [
       "  chr     start end  \n",
       "1 Contig0  7285  7285\n",
       "2 Contig0 34735 34735\n",
       "3 Contig1  2414  2414\n",
       "4 Contig1  2422  2422\n",
       "5 Contig1  2431  2431\n",
       "6 Contig1  2436  2436"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y[,2] <- y[,3]\n",
    "head(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SORTING\n",
      "VALIDATE REGIONS\n",
      " * Checking input type... PASS\n",
      "   Input is in bed format\n",
      " * Check if index is a string... PASS\n",
      " * Check index pattern... PASS\n",
      " * Check for missing values... PASS\n",
      " * Check for larger start position... PASS.\n",
      " * Check if zero based... FAIL\n",
      "   There are identical start and stop positions.\n",
      "   This usually indicates 1 based point variants.\n",
      "      chr  start    end\n",
      "1 Contig0 109103 109103\n",
      "2 Contig0 109119 109119\n",
      "3 Contig0 109120 109120\n",
      "4 Contig0 109123 109123\n",
      "5 Contig1  42880  42880\n",
      "6 Contig3  14914  14914\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>63395</li><li>3</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 63395\n",
       "\\item 3\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 63395\n",
       "2. 3\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 63395     3"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 6 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>chr</th><th scope=col>start</th><th scope=col>end</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>5270</th><td>Contig0</td><td>  7285</td><td>  7285</td></tr>\n",
       "\t<tr><th scope=row>5271</th><td>Contig0</td><td> 34735</td><td> 34735</td></tr>\n",
       "\t<tr><th scope=row>1</th><td>Contig0</td><td>109103</td><td>109103</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>Contig0</td><td>109119</td><td>109119</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>Contig0</td><td>109120</td><td>109120</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>Contig0</td><td>109123</td><td>109123</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 3\n",
       "\\begin{tabular}{r|lll}\n",
       "  & chr & start & end\\\\\n",
       "  & <chr> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t5270 & Contig0 &   7285 &   7285\\\\\n",
       "\t5271 & Contig0 &  34735 &  34735\\\\\n",
       "\t1 & Contig0 & 109103 & 109103\\\\\n",
       "\t2 & Contig0 & 109119 & 109119\\\\\n",
       "\t3 & Contig0 & 109120 & 109120\\\\\n",
       "\t4 & Contig0 & 109123 & 109123\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 3\n",
       "\n",
       "| <!--/--> | chr &lt;chr&gt; | start &lt;dbl&gt; | end &lt;dbl&gt; |\n",
       "|---|---|---|---|\n",
       "| 5270 | Contig0 |   7285 |   7285 |\n",
       "| 5271 | Contig0 |  34735 |  34735 |\n",
       "| 1 | Contig0 | 109103 | 109103 |\n",
       "| 2 | Contig0 | 109119 | 109119 |\n",
       "| 3 | Contig0 | 109120 | 109120 |\n",
       "| 4 | Contig0 | 109123 | 109123 |\n",
       "\n"
      ],
      "text/plain": [
       "     chr     start  end   \n",
       "5270 Contig0   7285   7285\n",
       "5271 Contig0  34735  34735\n",
       "1    Contig0 109103 109103\n",
       "2    Contig0 109119 109119\n",
       "3    Contig0 109120 109120\n",
       "4    Contig0 109123 109123"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xy <- rbind(x,y)\n",
    "xy.sort <- bedr.sort.region(xy,check.chr=FALSE)\n",
    "dim(xy.sort)\n",
    "head(xy.sort)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "FALSE"
      ],
      "text/latex": [
       "FALSE"
      ],
      "text/markdown": [
       "FALSE"
      ],
      "text/plain": [
       "[1] FALSE"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "is.merged <- is.merged.region(xy.sort,check.zero.based=F,check.chr = F)\n",
    "is.merged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 6 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>chr</th><th scope=col>start</th><th scope=col>end</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>4803</th><td>Contig139210</td><td> 906</td><td> 906</td></tr>\n",
       "\t<tr><th scope=row>58367</th><td>Contig139210</td><td> 906</td><td> 906</td></tr>\n",
       "\t<tr><th scope=row>4804</th><td>Contig139210</td><td> 908</td><td> 908</td></tr>\n",
       "\t<tr><th scope=row>58368</th><td>Contig139210</td><td> 908</td><td> 908</td></tr>\n",
       "\t<tr><th scope=row>275</th><td>Contig1607  </td><td>8412</td><td>8412</td></tr>\n",
       "\t<tr><th scope=row>8582</th><td>Contig1607  </td><td>8412</td><td>8412</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 3\n",
       "\\begin{tabular}{r|lll}\n",
       "  & chr & start & end\\\\\n",
       "  & <chr> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t4803 & Contig139210 &  906 &  906\\\\\n",
       "\t58367 & Contig139210 &  906 &  906\\\\\n",
       "\t4804 & Contig139210 &  908 &  908\\\\\n",
       "\t58368 & Contig139210 &  908 &  908\\\\\n",
       "\t275 & Contig1607   & 8412 & 8412\\\\\n",
       "\t8582 & Contig1607   & 8412 & 8412\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 3\n",
       "\n",
       "| <!--/--> | chr &lt;chr&gt; | start &lt;dbl&gt; | end &lt;dbl&gt; |\n",
       "|---|---|---|---|\n",
       "| 4803 | Contig139210 |  906 |  906 |\n",
       "| 58367 | Contig139210 |  906 |  906 |\n",
       "| 4804 | Contig139210 |  908 |  908 |\n",
       "| 58368 | Contig139210 |  908 |  908 |\n",
       "| 275 | Contig1607   | 8412 | 8412 |\n",
       "| 8582 | Contig1607   | 8412 | 8412 |\n",
       "\n"
      ],
      "text/plain": [
       "      chr          start end \n",
       "4803  Contig139210  906   906\n",
       "58367 Contig139210  906   906\n",
       "4804  Contig139210  908   908\n",
       "58368 Contig139210  908   908\n",
       "275   Contig1607   8412  8412\n",
       "8582  Contig1607   8412  8412"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(xy.sort[duplicated(xy.sort)| duplicated(xy.sort, fromLast = TRUE),])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>33</li><li>3</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 33\n",
       "\\item 3\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 33\n",
       "2. 3\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 33  3"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(xy.sort[duplicated(xy.sort),])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "33 SNPS overlap between datasets, so remove them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>63362</li><li>3</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 63362\n",
       "\\item 3\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 63362\n",
       "2. 3\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 63362     3"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xy.merge <- xy.sort[!duplicated(xy.sort),]\n",
    "dim(xy.merge)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "write.table(xy.merge,\"../analyses/2bRAD/Inputs/GBS_2bRAD_SNPs_maf01.BED\",row.names = F,quote = F,col.names = F,sep=\"\\t\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "contigs <- names(cpgsetG)\n",
    "radcpgSnps <- data.frame(chr=character(),\n",
    "                 start=double(), \n",
    "                 end=double(), \n",
    "                 stringsAsFactors=FALSE)\n",
    "for(row in 1:nrow(xy.merge)){\n",
    "    c <- xy.merge[row,\"chr\"]\n",
    "    if(c %in% contigs){\n",
    "        snps <- cpgsetG[[c]]\n",
    "        if(xy.merge[row,\"end\"] %in% snps){\n",
    "            radcpgSnps <- rbind(radcpgSnps,xy.merge[row,])\n",
    "        }\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>7234</li><li>3</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 7234\n",
       "\\item 3\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 7234\n",
       "2. 3\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 7234    3"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(radcpgSnps)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7234 of our 63,362 SNPs are CpG-SNPs (11.4%), 3601 CpG-SNPs (49.78% of all, 49.8 of near CpG)) within 350bp of a methylated site (5x) vs 22884 (40.78%, 40.83%) of non CpG-SNPs "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 6 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>chr</th><th scope=col>start</th><th scope=col>end</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>Contig0 </td><td>109103</td><td>109103</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>Contig0 </td><td>109119</td><td>109119</td></tr>\n",
       "\t<tr><th scope=row>5285</th><td>Contig1 </td><td>  4525</td><td>  4525</td></tr>\n",
       "\t<tr><th scope=row>5318</th><td>Contig10</td><td> 44712</td><td> 44712</td></tr>\n",
       "\t<tr><th scope=row>5320</th><td>Contig10</td><td> 44785</td><td> 44785</td></tr>\n",
       "\t<tr><th scope=row>5322</th><td>Contig10</td><td> 44794</td><td> 44794</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 3\n",
       "\\begin{tabular}{r|lll}\n",
       "  & chr & start & end\\\\\n",
       "  & <chr> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t1 & Contig0  & 109103 & 109103\\\\\n",
       "\t2 & Contig0  & 109119 & 109119\\\\\n",
       "\t5285 & Contig1  &   4525 &   4525\\\\\n",
       "\t5318 & Contig10 &  44712 &  44712\\\\\n",
       "\t5320 & Contig10 &  44785 &  44785\\\\\n",
       "\t5322 & Contig10 &  44794 &  44794\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 3\n",
       "\n",
       "| <!--/--> | chr &lt;chr&gt; | start &lt;dbl&gt; | end &lt;dbl&gt; |\n",
       "|---|---|---|---|\n",
       "| 1 | Contig0  | 109103 | 109103 |\n",
       "| 2 | Contig0  | 109119 | 109119 |\n",
       "| 5285 | Contig1  |   4525 |   4525 |\n",
       "| 5318 | Contig10 |  44712 |  44712 |\n",
       "| 5320 | Contig10 |  44785 |  44785 |\n",
       "| 5322 | Contig10 |  44794 |  44794 |\n",
       "\n"
      ],
      "text/plain": [
       "     chr      start  end   \n",
       "1    Contig0  109103 109103\n",
       "2    Contig0  109119 109119\n",
       "5285 Contig1    4525   4525\n",
       "5318 Contig10  44712  44712\n",
       "5320 Contig10  44785  44785\n",
       "5322 Contig10  44794  44794"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(radcpgSnps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "radcpgSnps$start <- radcpgSnps$end\n",
    "write.table(radcpgSnps,file=\"../analyses/2bRAD/MethGen/cpgsnps_GBS2b.bed\",quote = F, row.names = F,sep = \"\\t\",col.names = F)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "56122 ../analyses/2bRAD/Inputs/non-cpgsnps_GBS2b.bed\n"
     ]
    }
   ],
   "source": [
    "# bed file with non-CpG-SNPs\n",
    "js(\"bedtools subtract -a ../analyses/2bRAD/Inputs/GBS_2bRAD_SNPs_maf01.BED -b ../analyses/2bRAD/MethGen/cpgsnps_GBS2b.bed  >  ../analyses/2bRAD/Inputs/non-cpgsnps_GBS2b.bed\")\n",
    "js(\"wc -l ../analyses/2bRAD/Inputs/non-cpgsnps_GBS2b.bed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "3601 ../analyses/2bRAD/MethGen/meth-cpgsnp-350_GBS2b.bed\n"
     ]
    }
   ],
   "source": [
    "# how many Cpg-snps are within 350bp of a methylated site?\n",
    "js(\"bedtools window -w 350 -u -b ../analyses/methylation-characteristics/all_methylated_5x.bed -a ../analyses/2bRAD/MethGen/cpgsnps_GBS2b.bed  >  ../analyses/2bRAD/MethGen/meth-cpgsnp-350_GBS2b.bed\")\n",
    "js(\"wc -l ../analyses/2bRAD/MethGen/meth-cpgsnp-350_GBS2b.bed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "22884 ../analyses/2bRAD/MethGen/meth-non-cpgsnp-350_GBS2b.bed\n"
     ]
    }
   ],
   "source": [
    "# how many non-Cpg-snps are within 350bp of a methylated site?\n",
    "js(\"bedtools window -w 350 -u -b ../analyses/methylation-characteristics/all_methylated_5x.bed -a ../analyses/2bRAD/Inputs/non-cpgsnps_GBS2b.bed  >  ../analyses/2bRAD/MethGen/meth-non-cpgsnp-350_GBS2b.bed\")\n",
    "js(\"wc -l ../analyses/2bRAD/MethGen/meth-non-cpgsnp-350_GBS2b.bed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "7231 ../analyses/2bRAD/MethGen/allCpG-cpgsnp-350_GBS2b.bed\n"
     ]
    }
   ],
   "source": [
    "# how many CpG-SNPs within 350bp of a CpG\n",
    "js(\"bedtools window -w 350 -u -b ../genome-features/Olurida_v081_CG-motif.gff -a ../analyses/2bRAD/MethGen/cpgsnps_GBS2b.bed  >  ../analyses/2bRAD/MethGen/allCpG-cpgsnp-350_GBS2b.bed\")\n",
    "js(\"wc -l ../analyses/2bRAD/MethGen/allCpG-cpgsnp-350_GBS2b.bed\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "56049 ../analyses/2bRAD/MethGen/allCpG-nonCpGsnp-350_GBS2b.bed\n"
     ]
    }
   ],
   "source": [
    "# how many non-CpG SNPs near cpg\n",
    "js(\"bedtools window -w 350 -u -b ../genome-features/Olurida_v081_CG-motif.gff -a ../analyses/2bRAD/Inputs/non-cpgsnps_GBS2b.bed  >  ../analyses/2bRAD/MethGen/allCpG-nonCpGsnp-350_GBS2b.bed\")\n",
    "js(\"wc -l ../analyses/2bRAD/MethGen/allCpG-nonCpGsnp-350_GBS2b.bed\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Is methylation associated with CpG-SNP status?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "cm <- 3601\n",
    "cnm <- 7231-cm\n",
    "ncm <- 22884\n",
    "ncnm <- 56049-ncm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A matrix: 2 × 2 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>cpg</th><th scope=col>non-cpg</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>meth</th><td>3601</td><td>22884</td></tr>\n",
       "\t<tr><th scope=row>nonmeth</th><td>3630</td><td>33165</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 2 × 2 of type dbl\n",
       "\\begin{tabular}{r|ll}\n",
       "  & cpg & non-cpg\\\\\n",
       "\\hline\n",
       "\tmeth & 3601 & 22884\\\\\n",
       "\tnonmeth & 3630 & 33165\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 2 × 2 of type dbl\n",
       "\n",
       "| <!--/--> | cpg | non-cpg |\n",
       "|---|---|---|\n",
       "| meth | 3601 | 22884 |\n",
       "| nonmeth | 3630 | 33165 |\n",
       "\n"
      ],
      "text/plain": [
       "        cpg  non-cpg\n",
       "meth    3601 22884  \n",
       "nonmeth 3630 33165  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mat <- matrix(c(cm,cnm,ncm,ncnm),ncol=2)\n",
    "dimnames(mat) <- list(c(\"meth\",\"nonmeth\"),c(\"cpg\",\"non-cpg\"))\n",
    "mat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPearson's Chi-squared test with Yates' continuity correction\n",
       "\n",
       "data:  mat\n",
       "X-squared = 211.43, df = 1, p-value < 2.2e-16\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test <- chisq.test(mat)\n",
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A matrix: 2 × 2 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>cpg</th><th scope=col>non-cpg</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>meth</th><td>3026.439</td><td>23458.56</td></tr>\n",
       "\t<tr><th scope=row>nonmeth</th><td>4204.561</td><td>32590.44</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 2 × 2 of type dbl\n",
       "\\begin{tabular}{r|ll}\n",
       "  & cpg & non-cpg\\\\\n",
       "\\hline\n",
       "\tmeth & 3026.439 & 23458.56\\\\\n",
       "\tnonmeth & 4204.561 & 32590.44\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 2 × 2 of type dbl\n",
       "\n",
       "| <!--/--> | cpg | non-cpg |\n",
       "|---|---|---|\n",
       "| meth | 3026.439 | 23458.56 |\n",
       "| nonmeth | 4204.561 | 32590.44 |\n",
       "\n"
      ],
      "text/plain": [
       "        cpg      non-cpg \n",
       "meth    3026.439 23458.56\n",
       "nonmeth 4204.561 32590.44"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test$expected"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "49.7994744848569"
      ],
      "text/latex": [
       "49.7994744848569"
      ],
      "text/markdown": [
       "49.7994744848569"
      ],
      "text/plain": [
       "[1] 49.79947"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# %cpg-snps with meth site\n",
    "cm/(cm+cnm) *100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "40.8285607236525"
      ],
      "text/latex": [
       "40.8285607236525"
      ],
      "text/markdown": [
       "40.8285607236525"
      ],
      "text/plain": [
       "[1] 40.82856"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# % non cpg with meth site\n",
    "ncm/(ncm+ncnm) *100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TsTv ratio (mutation rate bias)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TsTv in all SNPs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "VCFtools - 0.1.17\n",
      "(C) Adam Auton and Anthony Marcketta 2009\n",
      "\n",
      "Parameters as interpreted:\n",
      "\t--vcf ../analyses/2bRAD/Inputs/HCSS_Afilt32m70_01_pp90.vcf\n",
      "\t--out ../analyses/2bRAD/MethGen/nonCpGsnps\n",
      "\t--TsTv-summary\n",
      "\t--exclude-bed ../analyses/2bRAD/MethGen/cpgsnps.bed\n",
      "\n",
      "Warning: Expected at least 2 parts in FORMAT entry: ID=GT,Type=String,Number=1,Description=\"Genotype call probabilities, threshholded at 0.90\">\n",
      "After filtering, kept 114 out of 114 Individuals\n",
      "Outputting Ts/Tv summary\n",
      "\tRead 651 BED file entries.\n",
      "Ts/Tv ratio: 1.312\n",
      "After filtering, kept 4619 out of a possible 5269 Sites\n",
      "Run Time = 1.00 seconds\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "#change to python kernel\n",
    "vcftools --vcf ../analyses/2bRAD/Inputs/HCSS_Afilt32m70_01_pp90.vcf --exclude-bed ../analyses/2bRAD/MethGen/cpgsnps.bed --TsTv-summary --out ../analyses/2bRAD/MethGen/nonCpGsnps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "VCFtools - 0.1.17\n",
      "(C) Adam Auton and Anthony Marcketta 2009\n",
      "\n",
      "Parameters as interpreted:\n",
      "\t--vcf ../analyses/2bRAD/Inputs/HCSS_Afilt32m70_01_pp90.vcf\n",
      "\t--out ../analyses/2bRAD/MethGen/cpgsnp\n",
      "\t--TsTv-summary\n",
      "\t--bed ../analyses/2bRAD/MethGen/cpgsnps.bed\n",
      "\n",
      "Warning: Expected at least 2 parts in FORMAT entry: ID=GT,Type=String,Number=1,Description=\"Genotype call probabilities, threshholded at 0.90\">\n",
      "After filtering, kept 114 out of 114 Individuals\n",
      "Outputting Ts/Tv summary\n",
      "\tRead 651 BED file entries.\n",
      "Ts/Tv ratio: 2.672\n",
      "After filtering, kept 650 out of a possible 5269 Sites\n",
      "Run Time = 0.00 seconds\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "#change to python kernel, cpgsnps\n",
    "vcftools --vcf ../analyses/2bRAD/Inputs/HCSS_Afilt32m70_01_pp90.vcf --bed ../analyses/2bRAD/MethGen/cpgsnps.bed --TsTv-summary --out ../analyses/2bRAD/MethGen/cpgsnp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 8 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>MODEL</th><th scope=col>COUNT</th><th scope=col>prop</th></tr>\n",
       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>AC</td><td> 487</td><td>0.10543408</td></tr>\n",
       "\t<tr><td>AG</td><td>1487</td><td>0.32193115</td></tr>\n",
       "\t<tr><td>AT</td><td> 720</td><td>0.15587790</td></tr>\n",
       "\t<tr><td>CG</td><td> 200</td><td>0.04329942</td></tr>\n",
       "\t<tr><td>CT</td><td>1134</td><td>0.24550769</td></tr>\n",
       "\t<tr><td>GT</td><td> 591</td><td>0.12794977</td></tr>\n",
       "\t<tr><td>Ts</td><td>2621</td><td>0.56743884</td></tr>\n",
       "\t<tr><td>Tv</td><td>1998</td><td>0.43256116</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 8 × 3\n",
       "\\begin{tabular}{lll}\n",
       " MODEL & COUNT & prop\\\\\n",
       " <fct> & <int> & <dbl>\\\\\n",
       "\\hline\n",
       "\t AC &  487 & 0.10543408\\\\\n",
       "\t AG & 1487 & 0.32193115\\\\\n",
       "\t AT &  720 & 0.15587790\\\\\n",
       "\t CG &  200 & 0.04329942\\\\\n",
       "\t CT & 1134 & 0.24550769\\\\\n",
       "\t GT &  591 & 0.12794977\\\\\n",
       "\t Ts & 2621 & 0.56743884\\\\\n",
       "\t Tv & 1998 & 0.43256116\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 8 × 3\n",
       "\n",
       "| MODEL &lt;fct&gt; | COUNT &lt;int&gt; | prop &lt;dbl&gt; |\n",
       "|---|---|---|\n",
       "| AC |  487 | 0.10543408 |\n",
       "| AG | 1487 | 0.32193115 |\n",
       "| AT |  720 | 0.15587790 |\n",
       "| CG |  200 | 0.04329942 |\n",
       "| CT | 1134 | 0.24550769 |\n",
       "| GT |  591 | 0.12794977 |\n",
       "| Ts | 2621 | 0.56743884 |\n",
       "| Tv | 1998 | 0.43256116 |\n",
       "\n"
      ],
      "text/plain": [
       "  MODEL COUNT prop      \n",
       "1 AC     487  0.10543408\n",
       "2 AG    1487  0.32193115\n",
       "3 AT     720  0.15587790\n",
       "4 CG     200  0.04329942\n",
       "5 CT    1134  0.24550769\n",
       "6 GT     591  0.12794977\n",
       "7 Ts    2621  0.56743884\n",
       "8 Tv    1998  0.43256116"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#change back to R kernel\n",
    "nt <- read.table(\"../analyses/2bRAD/MethGen/nonCpGsnps.TsTv.summary\",header = T)\n",
    "total = nt[7,\"COUNT\"] + nt[8,\"COUNT\"]\n",
    "nt <- nt %>% mutate(prop=COUNT/total)\n",
    "nt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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OtSXO/1KY539fWlue57E5kJ/FDPeL02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\ngESUQEAiSiAgESUQkIgSCEhECfT/1KnIj37nvDAAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g <- ggplot(nt,aes(x = MODEL,y=prop)) + \n",
    "            geom_bar(stat=\"identity\", width = 0.5)  + \n",
    "            xlab(\"Substitution Type\") + ylab(\"SNP frequency (%)\") +\n",
    "        ggtitle(\"All SNPs\")\n",
    "\n",
    "g\n",
    "#ggsave(\"TsTv/TsTv_Am4s2n.png\", plot = g,device = \"png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 8 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>MODEL</th><th scope=col>COUNT</th><th scope=col>prop</th></tr>\n",
       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>AC</td><td>114</td><td>0.17538462</td></tr>\n",
       "\t<tr><td>AG</td><td>  0</td><td>0.00000000</td></tr>\n",
       "\t<tr><td>AT</td><td>  0</td><td>0.00000000</td></tr>\n",
       "\t<tr><td>CG</td><td> 63</td><td>0.09692308</td></tr>\n",
       "\t<tr><td>CT</td><td>473</td><td>0.72769231</td></tr>\n",
       "\t<tr><td>GT</td><td>  0</td><td>0.00000000</td></tr>\n",
       "\t<tr><td>Ts</td><td>473</td><td>0.72769231</td></tr>\n",
       "\t<tr><td>Tv</td><td>177</td><td>0.27230769</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 8 × 3\n",
       "\\begin{tabular}{lll}\n",
       " MODEL & COUNT & prop\\\\\n",
       " <fct> & <int> & <dbl>\\\\\n",
       "\\hline\n",
       "\t AC & 114 & 0.17538462\\\\\n",
       "\t AG &   0 & 0.00000000\\\\\n",
       "\t AT &   0 & 0.00000000\\\\\n",
       "\t CG &  63 & 0.09692308\\\\\n",
       "\t CT & 473 & 0.72769231\\\\\n",
       "\t GT &   0 & 0.00000000\\\\\n",
       "\t Ts & 473 & 0.72769231\\\\\n",
       "\t Tv & 177 & 0.27230769\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 8 × 3\n",
       "\n",
       "| MODEL &lt;fct&gt; | COUNT &lt;int&gt; | prop &lt;dbl&gt; |\n",
       "|---|---|---|\n",
       "| AC | 114 | 0.17538462 |\n",
       "| AG |   0 | 0.00000000 |\n",
       "| AT |   0 | 0.00000000 |\n",
       "| CG |  63 | 0.09692308 |\n",
       "| CT | 473 | 0.72769231 |\n",
       "| GT |   0 | 0.00000000 |\n",
       "| Ts | 473 | 0.72769231 |\n",
       "| Tv | 177 | 0.27230769 |\n",
       "\n"
      ],
      "text/plain": [
       "  MODEL COUNT prop      \n",
       "1 AC    114   0.17538462\n",
       "2 AG      0   0.00000000\n",
       "3 AT      0   0.00000000\n",
       "4 CG     63   0.09692308\n",
       "5 CT    473   0.72769231\n",
       "6 GT      0   0.00000000\n",
       "7 Ts    473   0.72769231\n",
       "8 Tv    177   0.27230769"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#change back to R kernel\n",
    "t <- read.table(\"../analyses/2bRAD/MethGen/cpgsnp.TsTv.summary\",header = T)\n",
    "total = t[7,\"COUNT\"] + t[8,\"COUNT\"]\n",
    "t <- t %>% mutate(prop=COUNT/total)\n",
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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oAkBSQgGQOSFJCAZAxIUkACkjEgSQEJ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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g <- ggplot(t,aes(x = MODEL,y=prop)) + \n",
    "            geom_bar(stat=\"identity\", width = 0.5)  + \n",
    "            xlab(\"Substitution Type\") + ylab(\"SNP frequency (%)\") +\n",
    "       ggtitle(\"CpG-SNPs\")\n",
    "\n",
    "g\n",
    "#ggsave(\"TsTv/TsTv_Am4s2n.png\", plot = g,device = \"png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "nct <- nt$COUNT[5]\n",
    "ntotal <- nt[7,\"COUNT\"] + nt[8,\"COUNT\"]\n",
    "nother <- ntotal - nct\n",
    "\n",
    "ct <- t$COUNT[5]\n",
    "total <- t[7,\"COUNT\"] + t[8,\"COUNT\"]\n",
    "other <- total - ct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A matrix: 2 × 2 of type int</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>cpg</th><th scope=col>non-cpg</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>CT</th><td>473</td><td>1134</td></tr>\n",
       "\t<tr><th scope=row>nonCT</th><td>177</td><td>3485</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 2 × 2 of type int\n",
       "\\begin{tabular}{r|ll}\n",
       "  & cpg & non-cpg\\\\\n",
       "\\hline\n",
       "\tCT & 473 & 1134\\\\\n",
       "\tnonCT & 177 & 3485\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 2 × 2 of type int\n",
       "\n",
       "| <!--/--> | cpg | non-cpg |\n",
       "|---|---|---|\n",
       "| CT | 473 | 1134 |\n",
       "| nonCT | 177 | 3485 |\n",
       "\n"
      ],
      "text/plain": [
       "      cpg non-cpg\n",
       "CT    473 1134   \n",
       "nonCT 177 3485   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mat <- matrix(c(ct,other,nct,nother),ncol=2)\n",
    "dimnames(mat) <- list(c(\"CT\",\"nonCT\"),c(\"cpg\",\"non-cpg\"))\n",
    "mat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPearson's Chi-squared test with Yates' continuity correction\n",
       "\n",
       "data:  mat\n",
       "X-squared = 622.73, df = 1, p-value < 2.2e-16\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test <- chisq.test(mat)\n",
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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