coverage-plot.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "10bae546-4294-4399-b325-3cb84d511518",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Attaching package: ‘gridExtra’\n",
      "\n",
      "\n",
      "The following object is masked from ‘package:dplyr’:\n",
      "\n",
      "    combine\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "library(ggpubr)\n",
    "library(gridExtra)\n",
    "library(tidyverse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7d5e59a3-a4ff-4c86-acab-93fbc00cc206",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "“‘timedatectl’ indicates the non-existent timezone name ‘n/a’”\n",
      "Warning message:\n",
      "“Your system is mis-configured: ‘/etc/localtime’ is not a symlink”\n",
      "Warning message:\n",
      "“It is strongly recommended to set envionment variable TZ to ‘Europe/Brussels’ (or equivalent)”\n",
      "── \u001b[1mAttaching packages\u001b[22m ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.3.2 ──\n",
      "\u001b[32m✔\u001b[39m \u001b[34mggplot2\u001b[39m 3.4.0      \u001b[32m✔\u001b[39m \u001b[34mpurrr  \u001b[39m 0.3.5 \n",
      "\u001b[32m✔\u001b[39m \u001b[34mtibble \u001b[39m 3.1.8      \u001b[32m✔\u001b[39m \u001b[34mdplyr  \u001b[39m 1.0.10\n",
      "\u001b[32m✔\u001b[39m \u001b[34mtidyr  \u001b[39m 1.2.1      \u001b[32m✔\u001b[39m \u001b[34mstringr\u001b[39m 1.5.0 \n",
      "\u001b[32m✔\u001b[39m \u001b[34mreadr  \u001b[39m 2.1.3      \u001b[32m✔\u001b[39m \u001b[34mforcats\u001b[39m 0.5.2 \n",
      "── \u001b[1mConflicts\u001b[22m ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──\n",
      "\u001b[31m✖\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mfilter()\u001b[39m masks \u001b[34mstats\u001b[39m::filter()\n",
      "\u001b[31m✖\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mlag()\u001b[39m    masks \u001b[34mstats\u001b[39m::lag()\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>'MEMO005'</li><li>'MEMO078'</li><li>'NEMO46'</li><li>'MEMO141'</li><li>'NEMO42'</li><li>'MEMO085'</li><li>'MEMO073'</li><li>'NEMO35'</li><li>'MEMO108'</li><li>'MEMO139'</li><li>'MEMO057'</li><li>'MEMO056'</li><li>'MEMO105'</li><li>'MEMO129'</li><li>'MEMO100'</li><li>'MEMO043'</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'MEMO005'\n",
       "\\item 'MEMO078'\n",
       "\\item 'NEMO46'\n",
       "\\item 'MEMO141'\n",
       "\\item 'NEMO42'\n",
       "\\item 'MEMO085'\n",
       "\\item 'MEMO073'\n",
       "\\item 'NEMO35'\n",
       "\\item 'MEMO108'\n",
       "\\item 'MEMO139'\n",
       "\\item 'MEMO057'\n",
       "\\item 'MEMO056'\n",
       "\\item 'MEMO105'\n",
       "\\item 'MEMO129'\n",
       "\\item 'MEMO100'\n",
       "\\item 'MEMO043'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'MEMO005'\n",
       "2. 'MEMO078'\n",
       "3. 'NEMO46'\n",
       "4. 'MEMO141'\n",
       "5. 'NEMO42'\n",
       "6. 'MEMO085'\n",
       "7. 'MEMO073'\n",
       "8. 'NEMO35'\n",
       "9. 'MEMO108'\n",
       "10. 'MEMO139'\n",
       "11. 'MEMO057'\n",
       "12. 'MEMO056'\n",
       "13. 'MEMO105'\n",
       "14. 'MEMO129'\n",
       "15. 'MEMO100'\n",
       "16. 'MEMO043'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] \"MEMO005\" \"MEMO078\" \"NEMO46\"  \"MEMO141\" \"NEMO42\"  \"MEMO085\" \"MEMO073\"\n",
       " [8] \"NEMO35\"  \"MEMO108\" \"MEMO139\" \"MEMO057\" \"MEMO056\" \"MEMO105\" \"MEMO129\"\n",
       "[15] \"MEMO100\" \"MEMO043\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#args = commandArgs(trailingOnly=TRUE)\n",
    "args <- readLines(\"coverage-plots.txt\")\n",
    "args"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5fd3e8a8-814a-492e-b11c-2bd564269e77",
   "metadata": {},
   "outputs": [],
   "source": [
    "#df_merged <- read.table(paste0(\"bowtie/\",args[1],\".bowtie.depth\"), header = T)\n",
    "phageblast <- read.table(\"phage_blast.tsv\", header=F, sep=\"\\t\")\n",
    "phageblast <- phageblast[phageblast$V4 > 2000,]\n",
    "\n",
    "rRNA <- data.frame(name=c(\"16S\", \"23S\", \"5S\"),\n",
    "  start=c(1136001, 1235938, 1238768),\n",
    "  end=c(1137446, 1238682, 1238878)\n",
    ")\n",
    "\n",
    "prophage <- data.frame(\n",
    "  name=c(\"p1\", \"p2\", \"p3\", \"p4\", \"p5\"),\n",
    "  start=c(250652, 320006, 346157, 444716, 1376656),\n",
    "  end=c(266846, 337904, 360657, 483289, 1413160)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a9404b50-a3f6-48e9-a8c9-671d7bbef1a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_list <- list()\n",
    "\n",
    "for (file in args) {\n",
    "  # Read the TSV file into a data frame\n",
    "  df <- read.table(paste0(\"bowtie/\",file,\".bowtie.depth\"), header=TRUE, sep=\"\\t\")\n",
    "  \n",
    "  # Append the data frame to the list\n",
    "  df_list[[file]] <- df\n",
    "}\n",
    "\n",
    "df_merged <- Reduce(function(x, y) merge(x, y, by=c(\"strain\", \"pos\"), all=TRUE), df_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "c69dec4c-a2c5-4686-8316-a6ab2107aa23",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "g <- lapply(args, function(sample) {\n",
    "    \n",
    "    if (max(df_list[[sample]][3])<1000){\n",
    "\n",
    "        ggplot(df_list[[sample]], aes_string(x=\"pos\", y=sample))+\n",
    "                geom_line()+\n",
    "                geom_rect(data = phageblast, inherit.aes=FALSE, aes(xmin=V9, xmax=V10, ymin=-max(df_list[[sample]][3])*0.066, ymax=-max(df_list[[sample]][3])*0.043,\n",
    "                                                                  fill=\"#3977AF\", color=\"#3977AF\"), alpha=1)+\n",
    "                geom_rect(data = rRNA, inherit.aes=FALSE, aes(xmin=start, xmax=end, ymin=-max(df_list[[sample]][3])*0.036, ymax=-max(df_list[[sample]][3])*0.012,\n",
    "                                                                  fill=\"red\", color=\"red\"), alpha=1)+\n",
    "                geom_rect(data = prophage, inherit.aes=FALSE, aes(xmin=start, xmax=end, ymin=-max(df_list[[sample]][3])*0.036, ymax=-max(df_list[[sample]][3])*0.012,\n",
    "                                                                    fill=\"limegreen\", color=\"limegreen\"), alpha=1)+\n",
    "                labs(x=\"Nucleotide position\", y=\"Read depth\")+\n",
    "                ggtitle(sample)+\n",
    "                scale_fill_identity(name = \"\",\n",
    "                                    breaks = c(\"#3977AF\", \"limegreen\", \"red\"),\n",
    "                                    labels = c(\"phageWO prophage region (BLASTn)\", \"prophage regions (Genbank)\", \"rRNA genes\"),\n",
    "                                    guide = \"legend\")+\n",
    "                scale_color_identity(name = \"\",\n",
    "                                    #breaks = c(\"#3977AF\", \"red\"),\n",
    "                                    #labels = c(\"phageWO prophage region\", \"rRNA genes\"),\n",
    "                                    guide = \"none\",#\"legend\"\n",
    "                                    )+\n",
    "                theme_bw()+\n",
    "                theme(legend.position = \"top\", \n",
    "                      legend.background=element_rect(fill=NA, color=NA),\n",
    "                      legend.margin = margin(t = 0, r = 0, b = 0, l = 0, unit='cm'),\n",
    "                      legend.key.size = unit(3, 'mm'),\n",
    "                      plot.title = element_text(size=9, face=\"bold\"),\n",
    "                      aspect.ratio=5/7.5,\n",
    "                      #panel.grid.major = element_line(size = .25),\n",
    "                      panel.grid=element_blank())+\n",
    "                guides(fill = guide_legend(override.aes = list(alpha = 1)))\n",
    "        }\n",
    "    else {\n",
    "        ggplot(df_list[[sample]], aes_string(x=\"pos\", y=sample))+\n",
    "                    geom_line()+\n",
    "                    geom_rect(data = phageblast, inherit.aes=FALSE, aes(xmin=V9, xmax=V10, ymin=-1000*0.066, ymax=-1000*0.043,\n",
    "                                                                      fill=\"#3977AF\", color=\"#3977AF\"), alpha=1)+\n",
    "                    geom_rect(data = rRNA, inherit.aes=FALSE, aes(xmin=start, xmax=end, ymin=-1000*0.036, ymax=-1000*0.012,\n",
    "                                                                      fill=\"red\", color=\"red\"), alpha=1)+\n",
    "                    geom_rect(data = prophage, inherit.aes=FALSE, aes(xmin=start, xmax=end, ymin=-1000*0.036, ymax=-1000*0.012,\n",
    "                                                                        fill=\"limegreen\", color=\"limegreen\"), alpha=1)+\n",
    "                    labs(x=\"Nucleotide position\", y=\"Read depth\")+\n",
    "                    ggtitle(sample)+\n",
    "                    scale_fill_identity(name = \"\",\n",
    "                                        breaks = c(\"#3977AF\", \"limegreen\", \"red\"),\n",
    "                                        labels = c(\"phageWO prophage region (BLASTn)\", \"prophage regions (Genbank)\", \"rRNA genes\"),\n",
    "                                        guide = \"legend\")+\n",
    "                    scale_color_identity(name = \"\",\n",
    "                                        #breaks = c(\"#3977AF\", \"red\"),\n",
    "                                        #labels = c(\"phageWO prophage region\", \"rRNA genes\"),\n",
    "                                        guide = \"none\",#\"legend\"\n",
    "                                        )+\n",
    "                    scale_y_continuous(limits=c(NA,1000))+\n",
    "                    theme_bw()+\n",
    "                    theme(legend.position = \"top\", \n",
    "                          legend.background=element_rect(fill=NA, color=NA),\n",
    "                          legend.margin = margin(t = 0, r = 0, b = 0, l = 0, unit='cm'),\n",
    "                          legend.key.size = unit(3, 'mm'),\n",
    "                          plot.title = element_text(size=9, face=\"bold\"),\n",
    "                          aspect.ratio=5/7.5,\n",
    "                          #panel.grid.major = element_line(size = .25),\n",
    "                          panel.grid=element_blank())+\n",
    "                    guides(fill = guide_legend(override.aes = list(alpha = 1)))\n",
    "        }\n",
    "    })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "61661a8b-d348-469c-b80a-1f40c4294708",
   "metadata": {},
   "outputs": [],
   "source": [
    "p <- ggarrange(plotlist = g, ncol = 4, nrow=4, common.legend=T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "bc7900f3-c1b3-4fe7-8e1f-d1a595521326",
   "metadata": {},
   "outputs": [],
   "source": [
    "ggsave(file=\"test.png\", plot=p, dpi=300, height=10, width=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c80bcc93-ac44-4676-877b-3637e48562a3",
   "metadata": {},
   "source": [
    "--------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f57dfbc8-4612-4b0d-9e5b-987a739b8909",
   "metadata": {},
   "outputs": [],
   "source": [
    "plotlist <- list()\n",
    "count <- 0\n",
    "max <- c()\n",
    "for (sample in args){\n",
    "    count <- count+1\n",
    "    max[[count]] <- max(df_list[[sample]][3])\n",
    "\n",
    "    plotlist[[sample]] <- ggplot(df_list[[sample]], aes_string(x=\"pos\", y=sample))+\n",
    "        geom_line()+\n",
    "        geom_rect(data = phageblast, inherit.aes=FALSE, aes(xmin=V9, xmax=V10, ymin=-max[[count]]*0.066, ymax=-max[[count]]*0.043,\n",
    "                                                          fill=\"#3977AF\", color=\"#3977AF\"), alpha=1)+\n",
    "        geom_rect(data = rRNA, inherit.aes=FALSE, aes(xmin=start, xmax=end, ymin=-max[[count]]*0.036, ymax=-max[[count]]*0.012,\n",
    "                                                          fill=\"red\", color=\"red\"), alpha=1)+\n",
    "        geom_rect(data = prophage, inherit.aes=FALSE, aes(xmin=start, xmax=end, ymin=-max[[count]]*0.036, ymax=-max[[count]]*0.012),\n",
    "                                                            fill=\"limegreen\", color=\"limegreen\"), alpha=1)+\n",
    "        labs(x=\"Nucleotide position\", y=\"Read depth\")+\n",
    "        ggtitle(sample)+\n",
    "        scale_fill_identity(name = \"\",\n",
    "                            breaks = c(\"#3977AF\", \"limegreen\", \"red\"),\n",
    "                            labels = c(\"phageWO prophage region (BLASTn)\", \"prophage regions (Genbank)\", \"rRNA genes\"),\n",
    "                            guide = \"legend\")+\n",
    "        scale_color_identity(name = \"\",\n",
    "                            #breaks = c(\"#3977AF\", \"red\"),\n",
    "                            #labels = c(\"phageWO prophage region\", \"rRNA genes\"),\n",
    "                            guide = \"none\",#\"legend\"\n",
    "                            )+\n",
    "        theme_bw()+\n",
    "        theme(legend.position = \"top\", \n",
    "              legend.background=element_rect(fill=NA, color=NA),\n",
    "              legend.margin = margin(t = 0, r = 0, b = 0, l = 0, unit='cm'),\n",
    "              legend.key.size = unit(3, 'mm'),\n",
    "              plot.title = element_text(size=9, face=\"bold\"),\n",
    "              aspect.ratio=5/7.5,\n",
    "              #panel.grid.major = element_line(size = .25),\n",
    "              panel.grid=element_blank())+\n",
    "        guides(fill = guide_legend(override.aes = list(alpha = 1)))\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c075b284-a3f4-48de-8d91-6e43f15364cb",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "for (sample in args) {\n",
    "    ggsave(paste0(sample,\"-covplot.pdf\"), plotlist[[sample]], dpi=300, width = 7.5, height=5)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4ff32a1a-1ed4-4b51-b513-e12e575cbddf",
   "metadata": {},
   "outputs": [],
   "source": [
    "for (sample in args) {\n",
    "    print(max(df_list[[sample]][3]))\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "70afb5b8-c81d-47eb-99c1-131837746a11",
   "metadata": {},
   "outputs": [],
   "source": [
    "for (file in args) {\n",
    "    df_list[[file]][\"ymax\"] <- max(df_list[[file]][3])\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b95c5a03-cdc0-47e2-b742-cc3811945621",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_merged <- df_merged %>%\n",
    "    pivot_longer(cols = \"MEMO005\":\"MEMO043\", names_to=\"Sample\", values_to = \"coverage\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8a1b11b0-dc21-4845-91fc-09ebaa3cb5aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_merged <- df_merged %>%\n",
    "    group_by(Sample) %>%\n",
    "    summarise(max = max(coverage)) %>%\n",
    "    left_join(df_merged, by=\"Sample\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14bce33a-1009-4415-b541-59ce80ed2d26",
   "metadata": {},
   "source": [
    "#df_merged$Sample <- fct_reorder(df_merged, data$y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5981e1fc-c951-462e-b98b-c360fbb1d88e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_merged %>%\n",
    "    ggplot(aes(x=pos, y=coverage))+\n",
    "    facet_wrap(~Sample, ncol = 2, scales = \"free_y\")+\n",
    "    geom_line()+\n",
    "    geom_rect(data = phageblast, inherit.aes=FALSE, aes(xmin=V9, xmax=V10, ymin=-max(df_merged[3])*0.066, ymax=-max(df_merged[3])*0.043,\n",
    "                                                      fill=\"#3977AF\", color=\"#3977AF\"), alpha=1)+\n",
    "    geom_rect(data = rRNA, inherit.aes=FALSE, aes(xmin=start, xmax=end, ymin=-max(df_merged[3])*0.036, ymax=-max(df_merged[3])*0.012,\n",
    "                                                      fill=\"red\", color=\"red\"), alpha=1)+\n",
    "    geom_rect(data = prophage, inherit.aes=FALSE, aes(xmin=start, xmax=end, ymin=-max(df_merged[3])*0.036, ymax=-max(df_merged[3])*0.012,\n",
    "                                                        fill=\"limegreen\", color=\"limegreen\"), alpha=1)+\n",
    "    labs(x=\"Nucleotide position\", y=\"Read depth\")+\n",
    "    #ggtitle(Sample)+\n",
    "    scale_fill_identity(name = \"\",\n",
    "                        breaks = c(\"#3977AF\", \"limegreen\", \"red\"),\n",
    "                        labels = c(\"phageWO prophage region (BLASTn)\", \"prophage regions (Genbank)\", \"rRNA genes\"),\n",
    "                        guide = \"legend\")+\n",
    "    scale_color_identity(name = \"\",\n",
    "                        #breaks = c(\"#3977AF\", \"red\"),\n",
    "                        #labels = c(\"phageWO prophage region\", \"rRNA genes\"),\n",
    "                        guide = \"none\",#\"legend\"\n",
    "                        )+\n",
    "    theme_bw()+\n",
    "    theme(legend.position = \"top\", \n",
    "          legend.background=element_rect(fill=NA, color=NA),\n",
    "          legend.margin = margin(t = 0, r = 0, b = 0, l = 0, unit='cm'),\n",
    "          legend.key.size = unit(3, 'mm'),\n",
    "          plot.title = element_text(size=9, face=\"bold\"),\n",
    "          #aspect.ratio=5/7.5,\n",
    "          #panel.grid.major = element_line(size = .25),\n",
    "          panel.grid=element_blank())+\n",
    "    guides(fill = guide_legend(override.aes = list(alpha = 1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "325f164a-6e66-4b76-86c9-8455af1e889a",
   "metadata": {},
   "outputs": [],
   "source": [
    "covplot <- ggplot(df_merged, aes(x=pos, y=MEMO005))+\n",
    "  geom_line()+\n",
    "  geom_rect(data = phageblast, inherit.aes=FALSE, aes(xmin=V9, xmax=V10, ymin=-max(df_merged[3])*0.066, ymax=-max(df_merged[3])*0.043,\n",
    "                                                      fill=\"#3977AF\", color=\"#3977AF\"), alpha=1)+\n",
    "  geom_rect(data = rRNA, inherit.aes=FALSE, aes(xmin=start, xmax=end, ymin=-max(df_merged[3])*0.036, ymax=-max(df_merged[3])*0.012,\n",
    "                                                      fill=\"red\", color=\"red\"), alpha=1)+\n",
    "  geom_rect(data = prophage, inherit.aes=FALSE, aes(xmin=start, xmax=end, ymin=-max(df_merged[3])*0.036, ymax=-max(df_merged[3])*0.012,\n",
    "                                                      fill=\"limegreen\", color=\"limegreen\"), alpha=1)+\n",
    "  labs(x=\"Nucleotide position\", y=\"Read depth\")+\n",
    "  ggtitle(args[1])+\n",
    "  scale_fill_identity(name = \"\",\n",
    "                      breaks = c(\"#3977AF\", \"limegreen\", \"red\"),\n",
    "                      labels = c(\"phageWO prophage region (BLASTn)\", \"prophage regions (Genbank)\", \"rRNA genes\"),\n",
    "                      guide = \"legend\")+\n",
    "  scale_color_identity(name = \"\",\n",
    "                      #breaks = c(\"#3977AF\", \"red\"),\n",
    "                      #labels = c(\"phageWO prophage region\", \"rRNA genes\"),\n",
    "                      guide = \"none\",#\"legend\"\n",
    "                      )+\n",
    "  theme_bw()+\n",
    "  theme(legend.position = \"top\", \n",
    "        legend.background=element_rect(fill=NA, color=NA),\n",
    "        legend.margin = margin(t = 0, r = 0, b = 0, l = 0, unit='cm'),\n",
    "        legend.key.size = unit(3, 'mm'),\n",
    "        plot.title = element_text(size=9, face=\"bold\"),\n",
    "        aspect.ratio=5/7.5,\n",
    "        #panel.grid.major = element_line(size = .25),\n",
    "        panel.grid=element_blank())+\n",
    "  guides(fill = guide_legend(override.aes = list(alpha = 1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d38f439a-d53d-4455-bbac-99a25202ce8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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yIIdnH/LVgc8VyvZ0pmNNzdu+lYab93+7WqXcGzipfvsN5NIjdvCVIMtgMAAMAE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A3bt3P/zww0FKgDkI7AwwmnvUuszVQF+T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\nAAAAgEUgsAMAAACwiP8HrQNm+0vfOdwAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "covplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8a0af024-3f60-4b6b-b2c9-a015f5c7287f",
   "metadata": {},
   "outputs": [],
   "source": [
    "covplot <- ggplot(df_merged, aes_string(x = \"pos\", y=names(df_merged[3])))+\n",
    "  geom_line()+\n",
    "  geom_rect(data = phageblast, inherit.aes=FALSE, aes(xmin=V9, xmax=V10, ymin=-max(df_merged[3])*0.066, ymax=-max(df_merged[3])*0.043,\n",
    "                                                      fill=\"#3977AF\", color=\"#3977AF\"), alpha=1)+\n",
    "  geom_rect(data = rRNA, inherit.aes=FALSE, aes(xmin=start, xmax=end, ymin=-max(df_merged[3])*0.036, ymax=-max(df_merged[3])*0.012,\n",
    "                                                      fill=\"red\", color=\"red\"), alpha=1)+\n",
    "  geom_rect(data = prophage, inherit.aes=FALSE, aes(xmin=start, xmax=end, ymin=-max(df_merged[3])*0.036, ymax=-max(df_merged[3])*0.012,\n",
    "                                                      fill=\"limegreen\", color=\"limegreen\"), alpha=1)+\n",
    "  labs(x=\"Nucleotide position\", y=\"Read depth\")+\n",
    "  ggtitle(args[1])+\n",
    "  scale_fill_identity(name = \"\",\n",
    "                      breaks = c(\"#3977AF\", \"limegreen\", \"red\"),\n",
    "                      labels = c(\"phageWO prophage region (BLASTn)\", \"prophage regions (Genbank)\", \"rRNA genes\"),\n",
    "                      guide = \"legend\")+\n",
    "  scale_color_identity(name = \"\",\n",
    "                      #breaks = c(\"#3977AF\", \"red\"),\n",
    "                      #labels = c(\"phageWO prophage region\", \"rRNA genes\"),\n",
    "                      guide = \"none\",#\"legend\"\n",
    "                      )+\n",
    "  theme_bw()+\n",
    "  theme(legend.position = \"top\", \n",
    "        legend.background=element_rect(fill=NA, color=NA),\n",
    "        legend.margin = margin(t = 0, r = 0, b = 0, l = 0, unit='cm'),\n",
    "        legend.key.size = unit(3, 'mm'),\n",
    "        plot.title = element_text(size=9, face=\"bold\"),\n",
    "        #panel.grid.major = element_line(size = .25),\n",
    "        panel.grid=element_blank())+\n",
    "  guides(fill = guide_legend(override.aes = list(alpha = 1)))\n",
    "\n",
    "ggsave(paste0(args[1],\"-covplot.pdf\"), covplot, dpi=300, width = 7.5, height=5)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "4.2.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}