sgatools.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ccd6d875-efae-44ca-966b-4b396e18ad7f",
   "metadata": {},
   "source": [
    "# Post-processing using `SGAtools`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d3ea0ec1-26c7-4a15-bcdd-fcfd62001a8d",
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "## logging\n",
    "# options(show.error.locations = TRUE)\n",
    "## loading required libraries\n",
    "source(\"rscripts/SGAtools.R\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c087b0f3-8587-4e30-805c-09b013c01147",
   "metadata": {
    "tags": [
     "parameters"
    ],
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "## parameters\n",
    "input_path=NULL#\"examples/inputs/image_example1.dat\"\n",
    "output_path=NULL#\"examples/outputs/image_example1.dat\"\n",
    "\n",
    "replicates=4\n",
    "linkage_cutoff=-1\n",
    "keep_large=FALSE \n",
    "overall_plate_median=510\n",
    "max_colony_size=1.5*overall_plate_median\n",
    "intermediate_data=F\n",
    "linkage_file=NULL\n",
    "linkage_genes=NULL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3aa67bbd-2b5f-45cf-b2d6-658b45e21f11",
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "## inferred parameters\n",
    "output_dir_path <- dirname(input_path)\n",
    "\n",
    "if (is.null(linkage_file)) {\n",
    "  linkage_file <- ''\n",
    "}\n",
    "print(linkage_file)\n",
    "if (is.null(linkage_genes)) {\n",
    "  linkage_genes <- character(0)\n",
    "}\n",
    "print(linkage_genes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9755e37d-d47d-43fe-acb4-935e03161f84",
   "metadata": {},
   "source": [
    "## Input data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c283b9ff-ea1b-4fc8-bf30-725a66b190f5",
   "metadata": {
    "tags": [],
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 1 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>row</th><th scope=col>col</th><th scope=col>size</th><th scope=col>circularity</th></tr>\n",
       "\t<tr><th></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></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>1</td><td>1</td><td>3861</td><td>0.861296</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 1 × 4\n",
       "\\begin{tabular}{r|llll}\n",
       "  & row & col & size & circularity\\\\\n",
       "  & <int> & <int> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t1 & 1 & 1 & 3861 & 0.861296\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 1 × 4\n",
       "\n",
       "| <!--/--> | row &lt;int&gt; | col &lt;int&gt; | size &lt;dbl&gt; | circularity &lt;dbl&gt; |\n",
       "|---|---|---|---|---|\n",
       "| 1 | 1 | 1 | 3861 | 0.861296 |\n",
       "\n"
      ],
      "text/plain": [
       "  row col size circularity\n",
       "1 1   1   3861 0.861296   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# pre-processed\n",
    "df01 <- read.table(input_path, sep = \"\\t\", header = TRUE)\n",
    "head(df01,1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65377888-8260-4423-9f33-c6312349e4ae",
   "metadata": {},
   "source": [
    "### Plots for the input data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d812c7e7-9172-4999-bd04-065b5d7ea2e9",
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "hist(df01$size)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "006a6712-6a2e-4228-8ca9-13492398eb65",
   "metadata": {},
   "source": [
    "## Normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3efc15d1-195a-4958-aa4d-5e3ffefa35d3",
   "metadata": {
    "tags": [],
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[0m2024-01-19 15:49:19.794554 INFO::Normalizing plate: replicates = 4, overall.plate.median = 510, max.colony.size = 765\u001b[0m\u001b[22m\u001b[23m\u001b[24m\u001b[27m\u001b[28m\u001b[29m\u001b[39m\u001b[49m\u001b[0m\u001b[0m\u001b[22m\u001b[23m\u001b[24m\u001b[27m\u001b[28m\u001b[29m\u001b[39m\u001b[49m\n",
      "\u001b[0m2024-01-19 15:49:19.794554 INFO::Normalizing plate: replicates = 4, overall.plate.median = 510, max.colony.size = 765\u001b[0m\u001b[22m\u001b[23m\u001b[24m\u001b[27m\u001b[28m\u001b[29m\u001b[39m\u001b[49m\u001b[0m\u001b[0m\u001b[22m\u001b[23m\u001b[24m\u001b[27m\u001b[28m\u001b[29m\u001b[39m\u001b[49m\n",
      "\u001b[0m2024-01-19 15:49:19.800083 INFO::Assigning group ids?\u001b[0m\u001b[22m\u001b[23m\u001b[24m\u001b[27m\u001b[28m\u001b[29m\u001b[39m\u001b[49m\u001b[0m\u001b[0m\u001b[22m\u001b[23m\u001b[24m\u001b[27m\u001b[28m\u001b[29m\u001b[39m\u001b[49m\n",
      "\u001b[0m2024-01-19 15:49:19.800083 INFO::Assigning group ids?\u001b[0m\u001b[22m\u001b[23m\u001b[24m\u001b[27m\u001b[28m\u001b[29m\u001b[39m\u001b[49m\u001b[0m\u001b[0m\u001b[22m\u001b[23m\u001b[24m\u001b[27m\u001b[28m\u001b[29m\u001b[39m\u001b[49m\n",
      "\u001b[0m2024-01-19 15:49:19.807137 INFO::Preprocessing2\u001b[0m\u001b[22m\u001b[23m\u001b[24m\u001b[27m\u001b[28m\u001b[29m\u001b[39m\u001b[49m\u001b[0m\u001b[0m\u001b[22m\u001b[23m\u001b[24m\u001b[27m\u001b[28m\u001b[29m\u001b[39m\u001b[49m\n",
      "\u001b[0m2024-01-19 15:49:19.807137 INFO::Preprocessing2\u001b[0m\u001b[22m\u001b[23m\u001b[24m\u001b[27m\u001b[28m\u001b[29m\u001b[39m\u001b[49m\u001b[0m\u001b[0m\u001b[22m\u001b[23m\u001b[24m\u001b[27m\u001b[28m\u001b[29m\u001b[39m\u001b[49m\n",
      "\u001b[0m2024-01-19 15:49:19.807645 INFO::(F1) Linkage effect filter\u001b[0m\u001b[22m\u001b[23m\u001b[24m\u001b[27m\u001b[28m\u001b[29m\u001b[39m\u001b[49m\u001b[0m\u001b[0m\u001b[22m\u001b[23m\u001b[24m\u001b[27m\u001b[28m\u001b[29m\u001b[39m\u001b[49m\n",
      "\u001b[0m2024-01-19 15:49:19.807645 INFO::(F1) Linkage effect filter\u001b[0m\u001b[22m\u001b[23m\u001b[24m\u001b[27m\u001b[28m\u001b[29m\u001b[39m\u001b[49m\u001b[0m\u001b[0m\u001b[22m\u001b[23m\u001b[24m\u001b[27m\u001b[28m\u001b[29m\u001b[39m\u001b[49m\n",
      "\u001b[0m2024-01-19 15:49:19.820972 INFO::# Applying linkage filter, linkage.cutoff = -1\u001b[0m\u001b[22m\u001b[23m\u001b[24m\u001b[27m\u001b[28m\u001b[29m\u001b[39m\u001b[49m\u001b[0m\u001b[0m\u001b[22m\u001b[23m\u001b[24m\u001b[27m\u001b[28m\u001b[29m\u001b[39m\u001b[49m\n",
      "\u001b[0m2024-01-19 15:49:19.820972 INFO::# Applying linkage filter, linkage.cutoff = -1\u001b[0m\u001b[22m\u001b[23m\u001b[24m\u001b[27m\u001b[28m\u001b[29m\u001b[39m\u001b[49m\u001b[0m\u001b[0m\u001b[22m\u001b[23m\u001b[24m\u001b[27m\u001b[28m\u001b[29m\u001b[39m\u001b[49m\n",
      "\u001b[0m2024-01-19 15:49:19.821601 INFO::Linkage file is at:\u001b[0m\u001b[22m\u001b[23m\u001b[24m\u001b[27m\u001b[28m\u001b[29m\u001b[39m\u001b[49m\u001b[0m\u001b[0m\u001b[22m\u001b[23m\u001b[24m\u001b[27m\u001b[28m\u001b[29m\u001b[39m\u001b[49m\n",
      "\u001b[0m2024-01-19 15:49:19.821601 INFO::Linkage file is at:\u001b[0m\u001b[22m\u001b[23m\u001b[24m\u001b[27m\u001b[28m\u001b[29m\u001b[39m\u001b[49m\u001b[0m\u001b[0m\u001b[22m\u001b[23m\u001b[24m\u001b[27m\u001b[28m\u001b[29m\u001b[39m\u001b[49m\n",
      "\u001b[0m2024-01-19 15:49:19.822065 INFO::Chromosome coordinates file does not exist, returning empty data frame\u001b[0m\u001b[22m\u001b[23m\u001b[24m\u001b[27m\u001b[28m\u001b[29m\u001b[39m\u001b[49m\u001b[0m\u001b[0m\u001b[22m\u001b[23m\u001b[24m\u001b[27m\u001b[28m\u001b[29m\u001b[39m\u001b[49m\n",
      "\u001b[0m2024-01-19 15:49:19.822065 INFO::Chromosome coordinates file does not exist, returning empty data frame\u001b[0m\u001b[22m\u001b[23m\u001b[24m\u001b[27m\u001b[28m\u001b[29m\u001b[39m\u001b[49m\u001b[0m\u001b[0m\u001b[22m\u001b[23m\u001b[24m\u001b[27m\u001b[28m\u001b[29m\u001b[39m\u001b[49m\n",
      "\u001b[0m2024-01-19 15:49:19.822544 INFO::Skipping linkage correction...\u001b[0m\u001b[22m\u001b[23m\u001b[24m\u001b[27m\u001b[28m\u001b[29m\u001b[39m\u001b[49m\u001b[0m\u001b[0m\u001b[22m\u001b[23m\u001b[24m\u001b[27m\u001b[28m\u001b[29m\u001b[39m\u001b[49m\n",
      "\u001b[0m2024-01-19 15:49:19.822544 INFO::Skipping linkage correction...\u001b[0m\u001b[22m\u001b[23m\u001b[24m\u001b[27m\u001b[28m\u001b[29m\u001b[39m\u001b[49m\u001b[0m\u001b[0m\u001b[22m\u001b[23m\u001b[24m\u001b[27m\u001b[28m\u001b[29m\u001b[39m\u001b[49m\n",
      "\u001b[0m2024-01-19 15:49:19.840516 INFO::(N1) Plate normalization\u001b[0m\u001b[22m\u001b[23m\u001b[24m\u001b[27m\u001b[28m\u001b[29m\u001b[39m\u001b[49m\u001b[0m\u001b[0m\u001b[22m\u001b[23m\u001b[24m\u001b[27m\u001b[28m\u001b[29m\u001b[39m\u001b[49m\n",
      "\u001b[0m2024-01-19 15:49:19.840516 INFO::(N1) Plate normalization\u001b[0m\u001b[22m\u001b[23m\u001b[24m\u001b[27m\u001b[28m\u001b[29m\u001b[39m\u001b[49m\u001b[0m\u001b[0m\u001b[22m\u001b[23m\u001b[24m\u001b[27m\u001b[28m\u001b[29m\u001b[39m\u001b[49m\n",
      "\u001b[0m2024-01-19 15:49:19.846845 INFO::# Normalizing for plate effect, default.overall.median = 510\u001b[0m\u001b[22m\u001b[23m\u001b[24m\u001b[27m\u001b[28m\u001b[29m\u001b[39m\u001b[49m\u001b[0m\u001b[0m\u001b[22m\u001b[23m\u001b[24m\u001b[27m\u001b[28m\u001b[29m\u001b[39m\u001b[49m\n",
      "\u001b[0m2024-01-19 15:49:19.846845 INFO::# Normalizing for plate effect, default.overall.median = 510\u001b[0m\u001b[22m\u001b[23m\u001b[24m\u001b[27m\u001b[28m\u001b[29m\u001b[39m\u001b[49m\u001b[0m\u001b[0m\u001b[22m\u001b[23m\u001b[24m\u001b[27m\u001b[28m\u001b[29m\u001b[39m\u001b[49m\n",
      "\u001b[0m2024-01-19 15:49:19.847809 INFO::Done plate normalization\u001b[0m\u001b[22m\u001b[23m\u001b[24m\u001b[27m\u001b[28m\u001b[29m\u001b[39m\u001b[49m\u001b[0m\u001b[0m\u001b[22m\u001b[23m\u001b[24m\u001b[27m\u001b[28m\u001b[29m\u001b[39m\u001b[49m\n",
      "\u001b[0m2024-01-19 15:49:19.847809 INFO::Done plate normalization\u001b[0m\u001b[22m\u001b[23m\u001b[24m\u001b[27m\u001b[28m\u001b[29m\u001b[39m\u001b[49m\u001b[0m\u001b[0m\u001b[22m\u001b[23m\u001b[24m\u001b[27m\u001b[28m\u001b[29m\u001b[39m\u001b[49m\n",
      "\u001b[0m2024-01-19 15:49:19.855242 INFO::# Applying big replicates filter, max.colony.size = 765\u001b[0m\u001b[22m\u001b[23m\u001b[24m\u001b[27m\u001b[28m\u001b[29m\u001b[39m\u001b[49m\u001b[0m\u001b[0m\u001b[22m\u001b[23m\u001b[24m\u001b[27m\u001b[28m\u001b[29m\u001b[39m\u001b[49m\n",
      "\u001b[0m2024-01-19 15:49:19.855242 INFO::# Applying big replicates filter, max.colony.size = 765\u001b[0m\u001b[22m\u001b[23m\u001b[24m\u001b[27m\u001b[28m\u001b[29m\u001b[39m\u001b[49m\u001b[0m\u001b[0m\u001b[22m\u001b[23m\u001b[24m\u001b[27m\u001b[28m\u001b[29m\u001b[39m\u001b[49m\n",
      "\u001b[0m2024-01-19 15:49:19.856225 INFO::Done big replicates filter, total ignored = 0\u001b[0m\u001b[22m\u001b[23m\u001b[24m\u001b[27m\u001b[28m\u001b[29m\u001b[39m\u001b[49m\u001b[0m\u001b[0m\u001b[22m\u001b[23m\u001b[24m\u001b[27m\u001b[28m\u001b[29m\u001b[39m\u001b[49m\n",
      "\u001b[0m2024-01-19 15:49:19.856225 INFO::Done big replicates filter, total ignored = 0\u001b[0m\u001b[22m\u001b[23m\u001b[24m\u001b[27m\u001b[28m\u001b[29m\u001b[39m\u001b[49m\u001b[0m\u001b[0m\u001b[22m\u001b[23m\u001b[24m\u001b[27m\u001b[28m\u001b[29m\u001b[39m\u001b[49m\n",
      "\u001b[0m2024-01-19 15:49:19.874004 INFO::# Normalizing for spatial effect\u001b[0m\u001b[22m\u001b[23m\u001b[24m\u001b[27m\u001b[28m\u001b[29m\u001b[39m\u001b[49m\u001b[0m\u001b[0m\u001b[22m\u001b[23m\u001b[24m\u001b[27m\u001b[28m\u001b[29m\u001b[39m\u001b[49m\n",
      "\u001b[0m2024-01-19 15:49:19.874004 INFO::# Normalizing for spatial effect\u001b[0m\u001b[22m\u001b[23m\u001b[24m\u001b[27m\u001b[28m\u001b[29m\u001b[39m\u001b[49m\u001b[0m\u001b[0m\u001b[22m\u001b[23m\u001b[24m\u001b[27m\u001b[28m\u001b[29m\u001b[39m\u001b[49m\n",
      "\u001b[0m2024-01-19 15:49:19.944841 INFO::# Normalizing for row column effect\u001b[0m\u001b[22m\u001b[23m\u001b[24m\u001b[27m\u001b[28m\u001b[29m\u001b[39m\u001b[49m\u001b[0m\u001b[0m\u001b[22m\u001b[23m\u001b[24m\u001b[27m\u001b[28m\u001b[29m\u001b[39m\u001b[49m\n",
      "\u001b[0m2024-01-19 15:49:19.944841 INFO::# Normalizing for row column effect\u001b[0m\u001b[22m\u001b[23m\u001b[24m\u001b[27m\u001b[28m\u001b[29m\u001b[39m\u001b[49m\u001b[0m\u001b[0m\u001b[22m\u001b[23m\u001b[24m\u001b[27m\u001b[28m\u001b[29m\u001b[39m\u001b[49m\n",
      "\u001b[0m2024-01-19 15:49:19.973011 INFO::# Applying jackknife filter\u001b[0m\u001b[22m\u001b[23m\u001b[24m\u001b[27m\u001b[28m\u001b[29m\u001b[39m\u001b[49m\u001b[0m\u001b[0m\u001b[22m\u001b[23m\u001b[24m\u001b[27m\u001b[28m\u001b[29m\u001b[39m\u001b[49m\n",
      "\u001b[0m2024-01-19 15:49:19.973011 INFO::# Applying jackknife filter\u001b[0m\u001b[22m\u001b[23m\u001b[24m\u001b[27m\u001b[28m\u001b[29m\u001b[39m\u001b[49m\u001b[0m\u001b[0m\u001b[22m\u001b[23m\u001b[24m\u001b[27m\u001b[28m\u001b[29m\u001b[39m\u001b[49m\n",
      "\u001b[0m2024-01-19 15:49:19.973692 INFO::Done jackknife filter, total ignored = 0\u001b[0m\u001b[22m\u001b[23m\u001b[24m\u001b[27m\u001b[28m\u001b[29m\u001b[39m\u001b[49m\u001b[0m\u001b[0m\u001b[22m\u001b[23m\u001b[24m\u001b[27m\u001b[28m\u001b[29m\u001b[39m\u001b[49m\n",
      "\u001b[0m2024-01-19 15:49:19.973692 INFO::Done jackknife filter, total ignored = 0\u001b[0m\u001b[22m\u001b[23m\u001b[24m\u001b[27m\u001b[28m\u001b[29m\u001b[39m\u001b[49m\u001b[0m\u001b[0m\u001b[22m\u001b[23m\u001b[24m\u001b[27m\u001b[28m\u001b[29m\u001b[39m\u001b[49m\n",
      "\u001b[0m2024-01-19 15:49:19.986893 INFO::# Normalizing for plate effect, default.overall.median = 510\u001b[0m\u001b[22m\u001b[23m\u001b[24m\u001b[27m\u001b[28m\u001b[29m\u001b[39m\u001b[49m\u001b[0m\u001b[0m\u001b[22m\u001b[23m\u001b[24m\u001b[27m\u001b[28m\u001b[29m\u001b[39m\u001b[49m\n",
      "\u001b[0m2024-01-19 15:49:19.986893 INFO::# Normalizing for plate effect, default.overall.median = 510\u001b[0m\u001b[22m\u001b[23m\u001b[24m\u001b[27m\u001b[28m\u001b[29m\u001b[39m\u001b[49m\u001b[0m\u001b[0m\u001b[22m\u001b[23m\u001b[24m\u001b[27m\u001b[28m\u001b[29m\u001b[39m\u001b[49m\n",
      "\u001b[0m2024-01-19 15:49:19.987678 INFO::Done plate normalization\u001b[0m\u001b[22m\u001b[23m\u001b[24m\u001b[27m\u001b[28m\u001b[29m\u001b[39m\u001b[49m\u001b[0m\u001b[0m\u001b[22m\u001b[23m\u001b[24m\u001b[27m\u001b[28m\u001b[29m\u001b[39m\u001b[49m\n",
      "\u001b[0m2024-01-19 15:49:19.987678 INFO::Done plate normalization\u001b[0m\u001b[22m\u001b[23m\u001b[24m\u001b[27m\u001b[28m\u001b[29m\u001b[39m\u001b[49m\u001b[0m\u001b[0m\u001b[22m\u001b[23m\u001b[24m\u001b[27m\u001b[28m\u001b[29m\u001b[39m\u001b[49m\n"
     ]
    }
   ],
   "source": [
    "df1 = normalizeSGA(\n",
    "    df01,\n",
    "    replicates=replicates,\n",
    "    linkage.cutoff=linkage_cutoff,\n",
    "    keep.large=keep_large, #?\n",
    "    overall.plate.median=overall_plate_median,\n",
    "    max.colony.size=max_colony_size,\n",
    "    intermediate.data=intermediate_data,\n",
    "    # linkage.file=linkage_file, #todo\n",
    "    linkage.genes=linkage_genes,\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00de9fcb-27da-46cc-87fe-ad64257163c7",
   "metadata": {},
   "source": [
    "## Output data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "77c5dd18-766e-4b70-8523-b31f32bb09ae",
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "if (!file.exists(output_dir_path)) {\n",
    "  dir.create(output_dir_path, recursive = TRUE)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4c75bc35-6def-4f59-ba54-dfbdacc2b328",
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "# str(df1)\n",
    "df1$kvp <- sapply(df1$kvp, toString)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c4b2e150-30d7-4007-b9c4-43d75f34a54e",
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 6</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>row</th><th scope=col>col</th><th scope=col>size</th><th scope=col>circularity</th><th scope=col>colonysize</th><th scope=col>ncolonysize</th></tr>\n",
       "\t<tr><th></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;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>1</td><td>1</td><td>3861</td><td>0.8612960</td><td>3861</td><td>1.0046823</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>1</td><td>2</td><td>3060</td><td>0.8894521</td><td>3060</td><td>0.9251287</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>1</td><td>3</td><td>2962</td><td>0.9150902</td><td>2962</td><td>1.0517116</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>1</td><td>4</td><td>2721</td><td>0.9170527</td><td>2721</td><td>1.0196817</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>1</td><td>5</td><td>2557</td><td>0.9042184</td><td>2557</td><td>0.9883676</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>1</td><td>6</td><td>3119</td><td>0.9169053</td><td>3119</td><td>1.2351216</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 6\n",
       "\\begin{tabular}{r|llllll}\n",
       "  & row & col & size & circularity & colonysize & ncolonysize\\\\\n",
       "  & <int> & <int> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t1 & 1 & 1 & 3861 & 0.8612960 & 3861 & 1.0046823\\\\\n",
       "\t2 & 1 & 2 & 3060 & 0.8894521 & 3060 & 0.9251287\\\\\n",
       "\t3 & 1 & 3 & 2962 & 0.9150902 & 2962 & 1.0517116\\\\\n",
       "\t4 & 1 & 4 & 2721 & 0.9170527 & 2721 & 1.0196817\\\\\n",
       "\t5 & 1 & 5 & 2557 & 0.9042184 & 2557 & 0.9883676\\\\\n",
       "\t6 & 1 & 6 & 3119 & 0.9169053 & 3119 & 1.2351216\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 6\n",
       "\n",
       "| <!--/--> | row &lt;int&gt; | col &lt;int&gt; | size &lt;dbl&gt; | circularity &lt;dbl&gt; | colonysize &lt;dbl&gt; | ncolonysize &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|\n",
       "| 1 | 1 | 1 | 3861 | 0.8612960 | 3861 | 1.0046823 |\n",
       "| 2 | 1 | 2 | 3060 | 0.8894521 | 3060 | 0.9251287 |\n",
       "| 3 | 1 | 3 | 2962 | 0.9150902 | 2962 | 1.0517116 |\n",
       "| 4 | 1 | 4 | 2721 | 0.9170527 | 2721 | 1.0196817 |\n",
       "| 5 | 1 | 5 | 2557 | 0.9042184 | 2557 | 0.9883676 |\n",
       "| 6 | 1 | 6 | 3119 | 0.9169053 | 3119 | 1.2351216 |\n",
       "\n"
      ],
      "text/plain": [
       "  row col size circularity colonysize ncolonysize\n",
       "1 1   1   3861 0.8612960   3861       1.0046823  \n",
       "2 1   2   3060 0.8894521   3060       0.9251287  \n",
       "3 1   3   2962 0.9150902   2962       1.0517116  \n",
       "4 1   4   2721 0.9170527   2721       1.0196817  \n",
       "5 1   5   2557 0.9042184   2557       0.9883676  \n",
       "6 1   6   3119 0.9169053   3119       1.2351216  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "write.table(\n",
    "    df1,\n",
    "    file=output_path,\n",
    "    sep=\"\\t\", row.names = FALSE,\n",
    ")\n",
    "head(df1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a895975c-a83f-4703-bd45-16e9506eb4df",
   "metadata": {},
   "source": [
    "### Plots for the normalized data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5fc8d6eb-262b-47e4-ba85-672b074a211f",
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "hist(df1$size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e6edcd32-9491-4a58-96e4-2141de7d3b77",
   "metadata": {
    "tags": [],
    "vscode": {
     "languageId": "r"
    }
   },
   "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": [
    "plot(df1$colonysize,df1$ncolonysize)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c860d022-465c-4294-9e7a-40cbdc2ecf0f",
   "metadata": {
    "tags": [],
    "vscode": {
     "languageId": "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>'R version 4.3.2 (2023-10-31)'</li><li>'Platform: x86_64-conda-linux-gnu (64-bit)'</li><li>'Running under: Ubuntu 22.04.1 LTS'</li><li>''</li><li>'Locale:'</li><li><span style=white-space:pre-wrap>'  LC_CTYPE=C.UTF-8       LC_NUMERIC=C           LC_TIME=C.UTF-8       '</span></li><li><span style=white-space:pre-wrap>'  LC_COLLATE=C.UTF-8     LC_MONETARY=C.UTF-8    LC_MESSAGES=C.UTF-8   '</span></li><li><span style=white-space:pre-wrap>'  LC_PAPER=C.UTF-8       LC_NAME=C              LC_ADDRESS=C          '</span></li><li><span style=white-space:pre-wrap>'  LC_TELEPHONE=C         LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C   '</span></li><li>''</li><li>'Package version:'</li><li><span style=white-space:pre-wrap>'  base64enc_0.1-3     bootstrap_2019.6    cli_3.6.1          '</span></li><li><span style=white-space:pre-wrap>'  compiler_4.3.2      crayon_1.5.2        digest_0.6.33      '</span></li><li><span style=white-space:pre-wrap>'  ellipsis_0.3.2      evaluate_0.23       fansi_1.0.5        '</span></li><li><span style=white-space:pre-wrap>'  fastmap_1.1.1       glue_1.6.2          graphics_4.3.2     '</span></li><li><span style=white-space:pre-wrap>'  grDevices_4.3.2     htmltools_0.5.7     IRdisplay_1.1      '</span></li><li><span style=white-space:pre-wrap>'  IRkernel_1.3.2.9000 jsonlite_1.8.7      lifecycle_1.0.4    '</span></li><li><span style=white-space:pre-wrap>'  logging_0.10-108    magrittr_2.0.3      methods_4.3.2      '</span></li><li><span style=white-space:pre-wrap>'  pbdZMQ_0.3-10       pillar_1.9.0        repr_1.1.6         '</span></li><li><span style=white-space:pre-wrap>'  rlang_1.1.2         stats_4.3.2         stringi_1.8.1      '</span></li><li><span style=white-space:pre-wrap>'  stringr_1.5.1       tools_4.3.2         utf8_1.2.4         '</span></li><li><span style=white-space:pre-wrap>'  utils_4.3.2         uuid_1.1-1          vctrs_0.6.4        '</span></li><li><span style=white-space:pre-wrap>'  xfun_0.41          '</span></li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'R version 4.3.2 (2023-10-31)'\n",
       "\\item 'Platform: x86\\_64-conda-linux-gnu (64-bit)'\n",
       "\\item 'Running under: Ubuntu 22.04.1 LTS'\n",
       "\\item ''\n",
       "\\item 'Locale:'\n",
       "\\item '  LC\\_CTYPE=C.UTF-8       LC\\_NUMERIC=C           LC\\_TIME=C.UTF-8       '\n",
       "\\item '  LC\\_COLLATE=C.UTF-8     LC\\_MONETARY=C.UTF-8    LC\\_MESSAGES=C.UTF-8   '\n",
       "\\item '  LC\\_PAPER=C.UTF-8       LC\\_NAME=C              LC\\_ADDRESS=C          '\n",
       "\\item '  LC\\_TELEPHONE=C         LC\\_MEASUREMENT=C.UTF-8 LC\\_IDENTIFICATION=C   '\n",
       "\\item ''\n",
       "\\item 'Package version:'\n",
       "\\item '  base64enc\\_0.1-3     bootstrap\\_2019.6    cli\\_3.6.1          '\n",
       "\\item '  compiler\\_4.3.2      crayon\\_1.5.2        digest\\_0.6.33      '\n",
       "\\item '  ellipsis\\_0.3.2      evaluate\\_0.23       fansi\\_1.0.5        '\n",
       "\\item '  fastmap\\_1.1.1       glue\\_1.6.2          graphics\\_4.3.2     '\n",
       "\\item '  grDevices\\_4.3.2     htmltools\\_0.5.7     IRdisplay\\_1.1      '\n",
       "\\item '  IRkernel\\_1.3.2.9000 jsonlite\\_1.8.7      lifecycle\\_1.0.4    '\n",
       "\\item '  logging\\_0.10-108    magrittr\\_2.0.3      methods\\_4.3.2      '\n",
       "\\item '  pbdZMQ\\_0.3-10       pillar\\_1.9.0        repr\\_1.1.6         '\n",
       "\\item '  rlang\\_1.1.2         stats\\_4.3.2         stringi\\_1.8.1      '\n",
       "\\item '  stringr\\_1.5.1       tools\\_4.3.2         utf8\\_1.2.4         '\n",
       "\\item '  utils\\_4.3.2         uuid\\_1.1-1          vctrs\\_0.6.4        '\n",
       "\\item '  xfun\\_0.41          '\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'R version 4.3.2 (2023-10-31)'\n",
       "2. 'Platform: x86_64-conda-linux-gnu (64-bit)'\n",
       "3. 'Running under: Ubuntu 22.04.1 LTS'\n",
       "4. ''\n",
       "5. 'Locale:'\n",
       "6. <span style=white-space:pre-wrap>'  LC_CTYPE=C.UTF-8       LC_NUMERIC=C           LC_TIME=C.UTF-8       '</span>\n",
       "7. <span style=white-space:pre-wrap>'  LC_COLLATE=C.UTF-8     LC_MONETARY=C.UTF-8    LC_MESSAGES=C.UTF-8   '</span>\n",
       "8. <span style=white-space:pre-wrap>'  LC_PAPER=C.UTF-8       LC_NAME=C              LC_ADDRESS=C          '</span>\n",
       "9. <span style=white-space:pre-wrap>'  LC_TELEPHONE=C         LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C   '</span>\n",
       "10. ''\n",
       "11. 'Package version:'\n",
       "12. <span style=white-space:pre-wrap>'  base64enc_0.1-3     bootstrap_2019.6    cli_3.6.1          '</span>\n",
       "13. <span style=white-space:pre-wrap>'  compiler_4.3.2      crayon_1.5.2        digest_0.6.33      '</span>\n",
       "14. <span style=white-space:pre-wrap>'  ellipsis_0.3.2      evaluate_0.23       fansi_1.0.5        '</span>\n",
       "15. <span style=white-space:pre-wrap>'  fastmap_1.1.1       glue_1.6.2          graphics_4.3.2     '</span>\n",
       "16. <span style=white-space:pre-wrap>'  grDevices_4.3.2     htmltools_0.5.7     IRdisplay_1.1      '</span>\n",
       "17. <span style=white-space:pre-wrap>'  IRkernel_1.3.2.9000 jsonlite_1.8.7      lifecycle_1.0.4    '</span>\n",
       "18. <span style=white-space:pre-wrap>'  logging_0.10-108    magrittr_2.0.3      methods_4.3.2      '</span>\n",
       "19. <span style=white-space:pre-wrap>'  pbdZMQ_0.3-10       pillar_1.9.0        repr_1.1.6         '</span>\n",
       "20. <span style=white-space:pre-wrap>'  rlang_1.1.2         stats_4.3.2         stringi_1.8.1      '</span>\n",
       "21. <span style=white-space:pre-wrap>'  stringr_1.5.1       tools_4.3.2         utf8_1.2.4         '</span>\n",
       "22. <span style=white-space:pre-wrap>'  utils_4.3.2         uuid_1.1-1          vctrs_0.6.4        '</span>\n",
       "23. <span style=white-space:pre-wrap>'  xfun_0.41          '</span>\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "R version 4.3.2 (2023-10-31)\n",
       "Platform: x86_64-conda-linux-gnu (64-bit)\n",
       "Running under: Ubuntu 22.04.1 LTS\n",
       "\n",
       "Locale:\n",
       "  LC_CTYPE=C.UTF-8       LC_NUMERIC=C           LC_TIME=C.UTF-8       \n",
       "  LC_COLLATE=C.UTF-8     LC_MONETARY=C.UTF-8    LC_MESSAGES=C.UTF-8   \n",
       "  LC_PAPER=C.UTF-8       LC_NAME=C              LC_ADDRESS=C          \n",
       "  LC_TELEPHONE=C         LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C   \n",
       "\n",
       "Package version:\n",
       "  base64enc_0.1-3     bootstrap_2019.6    cli_3.6.1          \n",
       "  compiler_4.3.2      crayon_1.5.2        digest_0.6.33      \n",
       "  ellipsis_0.3.2      evaluate_0.23       fansi_1.0.5        \n",
       "  fastmap_1.1.1       glue_1.6.2          graphics_4.3.2     \n",
       "  grDevices_4.3.2     htmltools_0.5.7     IRdisplay_1.1      \n",
       "  IRkernel_1.3.2.9000 jsonlite_1.8.7      lifecycle_1.0.4    \n",
       "  logging_0.10-108    magrittr_2.0.3      methods_4.3.2      \n",
       "  pbdZMQ_0.3-10       pillar_1.9.0        repr_1.1.6         \n",
       "  rlang_1.1.2         stats_4.3.2         stringi_1.8.1      \n",
       "  stringr_1.5.1       tools_4.3.2         utf8_1.2.4         \n",
       "  utils_4.3.2         uuid_1.1-1          vctrs_0.6.4        \n",
       "  xfun_0.41          "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xfun::session_info()"
   ]
  }
 ],
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