plotData.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "library(CMplot)\n",
    "library(dplyr)\n",
    "library(tidyr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "trans_data <- read.table(\n",
    "    gzfile(\"Garb_01G000040_trans_MAF.5.txt.gz\"),\n",
    "    header = T,\n",
    "    sep=\",\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#TODO 切割获取染色体ID和SNP坐标\n",
    "trans_data <- separate(data = trans_data, col = \"variant_id\", into = c(\n",
    "    \"Chromosome\", \"Position\"\n",
    "), sep = \"_\", remove = FALSE)\n",
    "#调整列出现的顺序\n",
    "plotData=trans_data %>% select(c(\"variant_id\", \"Chromosome\", \"Position\",\"pval\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 7 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>variant_id</th><th scope=col>Chromosome</th><th scope=col>Position</th><th scope=col>pval</th></tr>\n",
       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>12_119343172</td><td>12</td><td>119343172</td><td>6.012033e-08</td></tr>\n",
       "\t<tr><td>12_119358264</td><td>12</td><td>119358264</td><td>6.936327e-08</td></tr>\n",
       "\t<tr><td>12_119359227</td><td>12</td><td>119359227</td><td>1.123156e-07</td></tr>\n",
       "\t<tr><td>12_119391118</td><td>12</td><td>119391118</td><td>3.781388e-08</td></tr>\n",
       "\t<tr><td>12_119401304</td><td>12</td><td>119401304</td><td>1.259025e-08</td></tr>\n",
       "\t<tr><td>12_119424092</td><td>12</td><td>119424092</td><td>9.132270e-08</td></tr>\n",
       "\t<tr><td>12_119485396</td><td>12</td><td>119485396</td><td>8.957855e-08</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 7 × 4\n",
       "\\begin{tabular}{llll}\n",
       " variant\\_id & Chromosome & Position & pval\\\\\n",
       " <fct> & <chr> & <chr> & <dbl>\\\\\n",
       "\\hline\n",
       "\t 12\\_119343172 & 12 & 119343172 & 6.012033e-08\\\\\n",
       "\t 12\\_119358264 & 12 & 119358264 & 6.936327e-08\\\\\n",
       "\t 12\\_119359227 & 12 & 119359227 & 1.123156e-07\\\\\n",
       "\t 12\\_119391118 & 12 & 119391118 & 3.781388e-08\\\\\n",
       "\t 12\\_119401304 & 12 & 119401304 & 1.259025e-08\\\\\n",
       "\t 12\\_119424092 & 12 & 119424092 & 9.132270e-08\\\\\n",
       "\t 12\\_119485396 & 12 & 119485396 & 8.957855e-08\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 7 × 4\n",
       "\n",
       "| variant_id &lt;fct&gt; | Chromosome &lt;chr&gt; | Position &lt;chr&gt; | pval &lt;dbl&gt; |\n",
       "|---|---|---|---|\n",
       "| 12_119343172 | 12 | 119343172 | 6.012033e-08 |\n",
       "| 12_119358264 | 12 | 119358264 | 6.936327e-08 |\n",
       "| 12_119359227 | 12 | 119359227 | 1.123156e-07 |\n",
       "| 12_119391118 | 12 | 119391118 | 3.781388e-08 |\n",
       "| 12_119401304 | 12 | 119401304 | 1.259025e-08 |\n",
       "| 12_119424092 | 12 | 119424092 | 9.132270e-08 |\n",
       "| 12_119485396 | 12 | 119485396 | 8.957855e-08 |\n",
       "\n"
      ],
      "text/plain": [
       "  variant_id   Chromosome Position  pval        \n",
       "1 12_119343172 12         119343172 6.012033e-08\n",
       "2 12_119358264 12         119358264 6.936327e-08\n",
       "3 12_119359227 12         119359227 1.123156e-07\n",
       "4 12_119391118 12         119391118 3.781388e-08\n",
       "5 12_119401304 12         119401304 1.259025e-08\n",
       "6 12_119424092 12         119424092 9.132270e-08\n",
       "7 12_119485396 12         119485396 8.957855e-08"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotData %>% filter(pval<=1/nrow(plotData))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>variant_id</th><th scope=col>Chromosome</th><th scope=col>Position</th><th scope=col>pval</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>1_784 </td><td>1</td><td>784 </td><td>0.337817038</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>1_831 </td><td>1</td><td>831 </td><td>0.749978448</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>1_3045</td><td>1</td><td>3045</td><td>0.006713613</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>1_3169</td><td>1</td><td>3169</td><td>0.017767390</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>1_7848</td><td>1</td><td>7848</td><td>0.978508259</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>1_7900</td><td>1</td><td>7900</td><td>0.183733228</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 4\n",
       "\\begin{tabular}{r|llll}\n",
       "  & variant\\_id & Chromosome & Position & pval\\\\\n",
       "  & <fct> & <chr> & <chr> & <dbl>\\\\\n",
       "\\hline\n",
       "\t1 & 1\\_784  & 1 & 784  & 0.337817038\\\\\n",
       "\t2 & 1\\_831  & 1 & 831  & 0.749978448\\\\\n",
       "\t3 & 1\\_3045 & 1 & 3045 & 0.006713613\\\\\n",
       "\t4 & 1\\_3169 & 1 & 3169 & 0.017767390\\\\\n",
       "\t5 & 1\\_7848 & 1 & 7848 & 0.978508259\\\\\n",
       "\t6 & 1\\_7900 & 1 & 7900 & 0.183733228\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 4\n",
       "\n",
       "| <!--/--> | variant_id &lt;fct&gt; | Chromosome &lt;chr&gt; | Position &lt;chr&gt; | pval &lt;dbl&gt; |\n",
       "|---|---|---|---|---|\n",
       "| 1 | 1_784  | 1 | 784  | 0.337817038 |\n",
       "| 2 | 1_831  | 1 | 831  | 0.749978448 |\n",
       "| 3 | 1_3045 | 1 | 3045 | 0.006713613 |\n",
       "| 4 | 1_3169 | 1 | 3169 | 0.017767390 |\n",
       "| 5 | 1_7848 | 1 | 7848 | 0.978508259 |\n",
       "| 6 | 1_7900 | 1 | 7900 | 0.183733228 |\n",
       "\n"
      ],
      "text/plain": [
       "  variant_id Chromosome Position pval       \n",
       "1 1_784      1          784      0.337817038\n",
       "2 1_831      1          831      0.749978448\n",
       "3 1_3045     1          3045     0.006713613\n",
       "4 1_3169     1          3169     0.017767390\n",
       "5 1_7848     1          7848     0.978508259\n",
       "6 1_7900     1          7900     0.183733228"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(plotData)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Rectangular-Manhattan Plotting pval.\n",
      " Plots are stored in: /public/home/zpliu/github/eQTL_pipeline \n"
     ]
    }
   ],
   "source": [
    "CMplot(plotData,\n",
    "    plot.type = \"m\",\n",
    "    col = c(\"grey30\", \"grey60\"),\n",
    "    LOG10 = TRUE,\n",
    "    threshold=1/nrow(plotData),\n",
    "    chr.den.col = NULL,\n",
    "    file = \"jpg\", memo = \"\",\n",
    "    dpi = 600,\n",
    "    chr.labels=c(paste(\"A\",sprintf('%02d',seq(1,13)),sep=\"\")),\n",
    "    width = 20, height = 6,\n",
    "    file.output = TRUE, verbose = TRUE\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "For example usage please run: vignette('qqman')\n",
      "\n",
      "\n",
      "\n",
      "Citation appreciated but not required:\n",
      "\n",
      "Turner, (2018). qqman: an R package for visualizing GWAS results using Q-Q and manhattan plots. Journal of Open Source Software, 3(25), 731, https://doi.org/10.21105/joss.00731.\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#TODO 绘制QQ-plot\n",
    "library(qqman)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "qq(plotData$pval)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R 3.6",
   "language": "R",
   "name": "ir32"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "3.6.0"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}