VCTest/VCtest.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Check-version\" data-toc-modified-id=\"Check-version-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Check version</a></span></li><li><span><a href=\"#Install-packages-when-first-use\" data-toc-modified-id=\"Install-packages-when-first-use-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Install packages when first use</a></span></li><li><span><a href=\"#Use-packages\" data-toc-modified-id=\"Use-packages-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>Use packages</a></span></li><li><span><a href=\"#Reproducibility\" data-toc-modified-id=\"Reproducibility-4\"><span class=\"toc-item-num\">4&nbsp;&nbsp;</span>Reproducibility</a></span></li><li><span><a href=\"#Load-and-filter-missing-value-of-the-definition-file\" data-toc-modified-id=\"Load-and-filter-missing-value-of-the-definition-file-5\"><span class=\"toc-item-num\">5&nbsp;&nbsp;</span>Load and filter missing value of the definition file</a></span></li><li><span><a href=\"#Read-in-plink-format-genotype-files\" data-toc-modified-id=\"Read-in-plink-format-genotype-files-6\"><span class=\"toc-item-num\">6&nbsp;&nbsp;</span>Read in plink format genotype files</a></span></li><li><span><a href=\"#Trait-Simulation\" data-toc-modified-id=\"Trait-Simulation-7\"><span class=\"toc-item-num\">7&nbsp;&nbsp;</span>Trait Simulation</a></span><ul class=\"toc-item\"><li><span><a href=\"#Generalized-Linear-Model\" data-toc-modified-id=\"Generalized-Linear-Model-7.1\"><span class=\"toc-item-num\">7.1&nbsp;&nbsp;</span>Generalized Linear Model</a></span></li></ul></li><li><span><a href=\"#Extract-phenotype-and-covariates\" data-toc-modified-id=\"Extract-phenotype-and-covariates-8\"><span class=\"toc-item-num\">8&nbsp;&nbsp;</span>Extract phenotype and covariates</a></span></li><li><span><a href=\"#Loop-over-genes\" data-toc-modified-id=\"Loop-over-genes-9\"><span class=\"toc-item-num\">9&nbsp;&nbsp;</span>Loop over genes</a></span></li><li><span><a href=\"#Output-results\" data-toc-modified-id=\"Output-results-10\"><span class=\"toc-item-num\">10&nbsp;&nbsp;</span>Output results</a></span><ul class=\"toc-item\"><li><ul class=\"toc-item\"><li><span><a href=\"#eRLRT\" data-toc-modified-id=\"eRLRT-10.0.1\"><span class=\"toc-item-num\">10.0.1&nbsp;&nbsp;</span>eRLRT</a></span></li><li><span><a href=\"#eLRT\" data-toc-modified-id=\"eLRT-10.0.2\"><span class=\"toc-item-num\">10.0.2&nbsp;&nbsp;</span>eLRT</a></span></li><li><span><a href=\"#eScore\" data-toc-modified-id=\"eScore-10.0.3\"><span class=\"toc-item-num\">10.0.3&nbsp;&nbsp;</span>eScore</a></span></li></ul></li></ul></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# New variance component test for SNP set analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the oringinal tutorial, this one changes a new dataset for SNP 24 and focuses on genes with 3 or more SNPs. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check version "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This tutorial has been checked with Julia version 1.4.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Julia Version 1.4.1\n",
      "Commit 381693d3df* (2020-04-14 17:20 UTC)\n",
      "Platform Info:\n",
      "  OS: macOS (x86_64-apple-darwin18.7.0)\n",
      "  CPU: Intel(R) Core(TM) i5-6267U CPU @ 2.90GHz\n",
      "  WORD_SIZE: 64\n",
      "  LIBM: libopenlibm\n",
      "  LLVM: libLLVM-8.0.1 (ORCJIT, skylake)\n"
     ]
    }
   ],
   "source": [
    "versioninfo()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Install packages when first use"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: For this tutorial, you need to have the statistial software R and R pakage \"pracma\" installed.\n",
    "\n",
    "Here we activate the project environment in the current working directory and list packages that is local to this tutorial."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m\u001b[1m Activating\u001b[22m\u001b[39m environment at `~/Box Sync/workspace/Tutorials/VCTest/VCTestnewtutorialfiles/Project.toml`\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32m\u001b[1mStatus\u001b[22m\u001b[39m `~/Box Sync/workspace/Tutorials/VCTest/VCTestnewtutorialfiles/Project.toml`\n",
      " \u001b[90m [336ed68f]\u001b[39m\u001b[37m CSV v0.6.2\u001b[39m\n",
      " \u001b[90m [a93c6f00]\u001b[39m\u001b[37m DataFrames v0.21.4\u001b[39m\n",
      " \u001b[90m [31c24e10]\u001b[39m\u001b[37m Distributions v0.23.5\u001b[39m\n",
      " \u001b[90m [38e38edf]\u001b[39m\u001b[37m GLM v1.3.9\u001b[39m\n",
      " \u001b[90m [7ab2ea78]\u001b[39m\u001b[37m MendelPlots v0.1.3 #master (https://github.com/OpenMendel/MendelPlots.jl.git)\u001b[39m\n",
      " \u001b[90m [b51826c6]\u001b[39m\u001b[37m OrdinalMultinomialModels v0.3.3 #master (https://github.com/OpenMendel/OrdinalMultinomialModels.jl)\u001b[39m\n",
      " \u001b[90m [1a8c2f83]\u001b[39m\u001b[37m Query v0.12.2\u001b[39m\n",
      " \u001b[90m [4e780e97]\u001b[39m\u001b[37m SnpArrays v0.3.2 #master (https://github.com/OpenMendel/SnpArrays.jl)\u001b[39m\n",
      " \u001b[90m [2913bbd2]\u001b[39m\u001b[37m StatsBase v0.33.0\u001b[39m\n",
      " \u001b[90m [3eaba693]\u001b[39m\u001b[37m StatsModels v0.6.12\u001b[39m\n",
      " \u001b[90m [dec3038e]\u001b[39m\u001b[37m TraitSimulation v0.1.0 #master (https://github.com/OpenMendel/TraitSimulation.jl)\u001b[39m\n",
      " \u001b[90m [813005db]\u001b[39m\u001b[37m VarianceComponentModels v0.1.0 #master (https://github.com/OpenMendel/VarianceComponentModels.jl.git)\u001b[39m\n",
      " \u001b[90m [1b8b39c6]\u001b[39m\u001b[37m VarianceComponentTest v0.1.0 #master (https://github.com/jinjinzhou/VarianceComponentTest.jl.git)\u001b[39m\n",
      " \u001b[90m [8bb1440f]\u001b[39m\u001b[37m DelimitedFiles \u001b[39m\n",
      " \u001b[90m [37e2e46d]\u001b[39m\u001b[37m LinearAlgebra \u001b[39m\n",
      " \u001b[90m [9a3f8284]\u001b[39m\u001b[37m Random \u001b[39m\n"
     ]
    }
   ],
   "source": [
    "using Pkg\n",
    "Pkg.activate()\n",
    "Pkg.status()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can install OpenMendel packages using the following command: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "# using Pkg\n",
    "# Pkg.add(PackageSpec(url=\"https://github.com/OpenMendel/SnpArrays.jl.git\"))\n",
    "# Pkg.add(PackageSpec(url=\"https://github.com/OpenMendel/MendelPlots.jl.git\"))\n",
    "# Pkg.add(PackageSpec(url=\"https://github.com/OpenMendel/TraitSimulation.jl\"))\n",
    "# Pkg.add(PackageSpec(url=\"https://github.com/jinjinzhou/VarianceComponentTest.jl.git\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Use packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "using SnpArrays\n",
    "using CSV\n",
    "using DataFrames, Query\n",
    "using Distributions, DelimitedFiles, LinearAlgebra\n",
    "using VarianceComponentTest\n",
    "using StatsModels, Random, StatsBase, TraitSimulation\n",
    "using Random\n",
    "using GLM "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reproducibility"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We set a random seed using the Random.jl package for reproducibility. If you wish to end up with different data, you will need to comment out these commands or use another value in Random.seed!()."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "Random.seed!(100);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load and filter missing value of the definition file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The gene annotation is contained in the definition file in the last (sixth) column. This data is from Option 24 of the Mendel data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "df= (CSV.read(\"SNP_def24a.in\", header=false))[:, [1, 2, 3, 6]]\n",
    "chromosomes = df[:, 2]\n",
    "AdjBasepairs = df[:, 3]\n",
    "genes = df[:, end];"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set missing value as -1 and count the number of unique genes because we are only interested in genes with 3 or more snps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4824"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genes[ismissing.(genes)] .= -1 # missing value as -1\n",
    "chromosomes[ismissing.(genes)] .= -1\n",
    "uniquegenes = unique(genes)\n",
    "genes_number = length(uniquegenes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read in plink format genotype files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read three files containing information about the names of snps, locations and phenotype messages. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "plinkFile = \"SNP_24\"\n",
    "## parse data from files\n",
    "plinkBedfile = string(plinkFile, \".bed\");\n",
    "plinkBimfile = string(plinkFile, \".bim\");\n",
    "plinkFamfile = string(plinkFile, \".fam\");\n",
    "SNP_24 = SnpData(\"SNP_24\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "# BIM file: chr, rs#, morgan, bp position, allele1, allele2\n",
    "bimdata = readdlm(plinkBimfile, String);\n",
    "chrID = bimdata[:, 1];\n",
    "snpID = bimdata[:, 2];\n",
    "snpPos = bimdata[:, 4];\n",
    "nSNP = length(snpID);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "# FAM file: fam ID, ind ID, father ID, mother ID, sex, phenotype\n",
    "famdata = readdlm(plinkFamfile)\n",
    "nPer = length(famdata[:, 1])\n",
    "Fam_Person_id = DataFrame(FamID = famdata[:, 1], PID =famdata[:, 2]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Trait Simulation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use TraitSimulation.jl package to simulate traits from genotype data for unrelateds by Generalized Linear Models (GLMs). We will simulate our continuous trait treating sex and three snps as fixed effects in the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2200×10000 SnpArray:\n",
       " 0x02  0x03  0x03  0x00  0x02  0x03  …  0x02  0x03  0x03  0x03  0x02  0x03\n",
       " 0x02  0x02  0x03  0x02  0x02  0x03     0x03  0x02  0x02  0x03  0x02  0x02\n",
       " 0x03  0x02  0x03  0x02  0x02  0x03     0x03  0x02  0x02  0x02  0x02  0x03\n",
       " 0x03  0x02  0x03  0x02  0x03  0x03     0x03  0x00  0x03  0x03  0x03  0x02\n",
       " 0x03  0x03  0x03  0x00  0x02  0x03     0x03  0x03  0x03  0x03  0x02  0x03\n",
       " 0x03  0x02  0x03  0x02  0x03  0x03  …  0x03  0x03  0x03  0x03  0x02  0x03\n",
       " 0x03  0x02  0x02  0x02  0x03  0x03     0x02  0x00  0x02  0x03  0x02  0x02\n",
       " 0x03  0x00  0x02  0x03  0x00  0x03     0x03  0x02  0x02  0x03  0x02  0x03\n",
       " 0x03  0x02  0x00  0x02  0x02  0x03     0x03  0x02  0x03  0x03  0x02  0x03\n",
       " 0x03  0x02  0x03  0x02  0x03  0x03     0x02  0x03  0x03  0x03  0x03  0x03\n",
       " 0x02  0x02  0x02  0x00  0x03  0x03  …  0x03  0x03  0x03  0x03  0x02  0x02\n",
       " 0x03  0x03  0x03  0x00  0x03  0x03     0x02  0x00  0x00  0x02  0x03  0x02\n",
       " 0x02  0x02  0x03  0x02  0x02  0x03     0x03  0x03  0x02  0x03  0x03  0x03\n",
       "    ⋮                             ⋮  ⋱           ⋮                    \n",
       " 0x03  0x03  0x02  0x03  0x03  0x03     0x02  0x03  0x02  0x03  0x02  0x02\n",
       " 0x03  0x02  0x03  0x03  0x03  0x03     0x02  0x02  0x02  0x03  0x02  0x02\n",
       " 0x03  0x02  0x00  0x02  0x02  0x03  …  0x03  0x02  0x03  0x03  0x03  0x02\n",
       " 0x03  0x02  0x03  0x00  0x02  0x03     0x03  0x03  0x03  0x03  0x03  0x02\n",
       " 0x00  0x03  0x02  0x03  0x03  0x03     0x03  0x03  0x03  0x03  0x03  0x03\n",
       " 0x02  0x03  0x03  0x02  0x02  0x03     0x02  0x00  0x00  0x02  0x02  0x03\n",
       " 0x02  0x02  0x03  0x00  0x03  0x03     0x02  0x03  0x03  0x03  0x02  0x02\n",
       " 0x02  0x02  0x02  0x02  0x02  0x03  …  0x00  0x03  0x03  0x03  0x02  0x03\n",
       " 0x03  0x02  0x03  0x03  0x03  0x03     0x03  0x00  0x00  0x03  0x02  0x02\n",
       " 0x03  0x02  0x03  0x03  0x03  0x02     0x03  0x02  0x02  0x03  0x03  0x02\n",
       " 0x03  0x03  0x02  0x00  0x00  0x02     0x02  0x03  0x03  0x03  0x00  0x03\n",
       " 0x03  0x03  0x03  0x00  0x03  0x03     0x03  0x03  0x00  0x03  0x03  0x02"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "snpdata = SnpArray(\"Snp_24.bed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10000-element Array{SubString{String},1}:\n",
       " \"rs3934834\"\n",
       " \"rs6603793\"\n",
       " \"rs2843153\"\n",
       " \"rs7535528\"\n",
       " \"rs12046158\"\n",
       " \"rs2817134\"\n",
       " \"rs4494089\"\n",
       " \"rs4648505\"\n",
       " \"rs1181883\"\n",
       " \"rs10915433\"\n",
       " \"rs4609397\"\n",
       " \"rs600933\"\n",
       " \"rs350187\"\n",
       " ⋮\n",
       " \"rs135720\"\n",
       " \"rs138407\"\n",
       " \"rs2413552\"\n",
       " \"rs139426\"\n",
       " \"rs137636\"\n",
       " \"rs132572\"\n",
       " \"rs137948\"\n",
       " \"rs739182\"\n",
       " \"rs12159200\"\n",
       " \"rs9611502\"\n",
       " \"rs6519287\"\n",
       " \"rs11090076\""
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "snpdef24 = readdlm(\"SNP_24.bim\"; header = false)\n",
    "snpid = map(x -> strip(string(x)), snpdef24[:, 2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We focus on genes with 3 or more snps. Although it can be altered, we use rs649296, rs11122146, rs1875238 and rs12489977 in this tutorial.  \n",
    "These four snps are from four different genes and chromosomes. Among them, rs649296 is from gene 24175 (on chromosome 8) which has 20 snps; rs11122146 is from gene 158 (on chromosome 1) which has 5 snps; rs1875238 is from gene 25064 (on chromosome 9); rs12489977 is from gene 17442 (on chromosome 3).  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1834"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ind_rs649296 = findall(x -> x == \"rs649296\", snpid)[1]\n",
    "ind_rs11122146 = findall(x -> x == \"rs11122146\", snpid)[1]\n",
    "ind_rs1875238 = findall(x -> x == \"rs1875238\", snpid)[1]\n",
    "ind_rs12489977 = findall(x -> x == \"rs12489977\", snpid)[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2200×6 Array{Float64,2}:\n",
       " 1.0  1.0  2.0  2.0  2.0  2.0\n",
       " 1.0  0.0  1.0  2.0  2.0  2.0\n",
       " 1.0  1.0  2.0  2.0  2.0  1.0\n",
       " 1.0  1.0  2.0  2.0  2.0  1.0\n",
       " 1.0  1.0  2.0  2.0  1.0  2.0\n",
       " 1.0  0.0  2.0  2.0  2.0  1.0\n",
       " 1.0  0.0  1.0  2.0  2.0  1.0\n",
       " 1.0  1.0  2.0  2.0  2.0  1.0\n",
       " 1.0  1.0  2.0  2.0  2.0  1.0\n",
       " 1.0  0.0  2.0  1.0  2.0  0.0\n",
       " 1.0  1.0  2.0  2.0  1.0  2.0\n",
       " 1.0  0.0  2.0  2.0  2.0  1.0\n",
       " 1.0  1.0  2.0  2.0  1.0  2.0\n",
       " ⋮                        ⋮\n",
       " 1.0  0.0  2.0  2.0  2.0  1.0\n",
       " 1.0  0.0  2.0  2.0  2.0  1.0\n",
       " 1.0  0.0  2.0  2.0  2.0  1.0\n",
       " 1.0  0.0  1.0  2.0  2.0  1.0\n",
       " 1.0  0.0  2.0  2.0  2.0  2.0\n",
       " 1.0  0.0  2.0  1.0  2.0  2.0\n",
       " 1.0  0.0  2.0  2.0  2.0  0.0\n",
       " 1.0  0.0  2.0  2.0  2.0  1.0\n",
       " 1.0  1.0  2.0  1.0  1.0  1.0\n",
       " 1.0  1.0  2.0  2.0  1.0  2.0\n",
       " 1.0  1.0  2.0  2.0  2.0  1.0\n",
       " 1.0  1.0  1.0  2.0  2.0  0.0"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "snp1 = convert(Vector{Float64}, @view(snpdata[:, ind_rs649296]))\n",
    "snp2 = convert(Vector{Float64}, @view(snpdata[:, ind_rs11122146]))\n",
    "snp3 = convert(Vector{Float64}, @view(snpdata[:, ind_rs1875238]))\n",
    "snp4 = convert(Vector{Float64}, @view(snpdata[:, ind_rs12489977]))\n",
    "sex = Float64.(famdata[:, 5])\n",
    "df_X = DataFrame(sex = sex, snp1 = snp1, snp2 = snp2,snp3 = snp3, snp4 = snp4)\n",
    "X = [ones(size(df_X, 1)) sex snp1 snp2 snp3 snp4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generalized Linear Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The parameters of snps can be changed by yourself and please make the value between a proper range or the final pvalue will not be significant. And the following ones just are examples for 3 or 4 snps.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```\n",
    "mean_formula = \"20 + 3(sex) - 0.36(snp1) + 0.45(snp2) - 0.44(snp3) + 0.46(snp4)\" # 4diff   \n",
    "             = \"20 + 3(sex) + 0.35(snp1) - 0.56(snp2) + 0.60(snp3)\" # 3diff  \n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2200-element Array{Float64,1}:\n",
       " 22.56186394879911\n",
       " 20.963753180716484\n",
       " 22.158579006283265\n",
       " 22.54848333683822\n",
       " 25.238739240482467\n",
       " 21.792563919325847\n",
       " 20.516070523314003\n",
       " 21.88929714913077\n",
       " 22.054318998066133\n",
       " 20.48771094116441\n",
       " 23.35578685114142\n",
       " 19.54600815844331\n",
       " 24.546949927556458\n",
       "  ⋮\n",
       " 20.513081412495513\n",
       " 19.97498935533755\n",
       " 20.012863282650194\n",
       " 18.89981260984406\n",
       " 20.439382647573503\n",
       " 20.810539404126388\n",
       " 20.76426228744117\n",
       " 18.207387858338755\n",
       " 19.82900394065494\n",
       " 24.146275918482964\n",
       " 23.530204830639423\n",
       " 21.65355552220941"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_formula = \"20 + 3(sex) - 0.36(snp1) + 0.45(snp2) - 0.44(snp3) + 0.46(snp4)\"\n",
    "β = [20; 3; -0.36; 0.45; -0.44;  0.46]\n",
    "dist = Normal(2)\n",
    "link = IdentityLink()\n",
    "GLM_trait_model = GLMTrait(X, β, dist, link)\n",
    "Simulated_GLM_trait = simulate(GLM_trait_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary Stats:\n",
      "Length:         2200\n",
      "Missing Count:  0\n",
      "Mean:           21.580462\n",
      "Minimum:        16.535669\n",
      "1st Quartile:   20.039228\n",
      "Median:         21.769778\n",
      "3rd Quartile:   23.066879\n",
      "Maximum:        26.607824\n",
      "Type:           Float64\n"
     ]
    }
   ],
   "source": [
    "describe(Simulated_GLM_trait)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"data-frame\"><thead><tr><th></th><th>FamID</th><th>PID</th><th>x1</th><th>sex</th><th>snp1</th><th>snp2</th><th>snp3</th><th>snp4</th></tr><tr><th></th><th>Float64</th><th>Float64</th><th>Float64</th><th>Float64</th><th>Float64</th><th>Float64</th><th>Float64</th><th>Float64</th></tr></thead><tbody><p>2,200 rows × 8 columns</p><tr><th>1</th><td>1.0</td><td>1.0</td><td>22.5619</td><td>1.0</td><td>2.0</td><td>2.0</td><td>2.0</td><td>2.0</td></tr><tr><th>2</th><td>2.0</td><td>1.0</td><td>20.9638</td><td>0.0</td><td>1.0</td><td>2.0</td><td>2.0</td><td>2.0</td></tr><tr><th>3</th><td>3.0</td><td>1.0</td><td>22.1586</td><td>1.0</td><td>2.0</td><td>2.0</td><td>2.0</td><td>1.0</td></tr><tr><th>4</th><td>4.0</td><td>1.0</td><td>22.5485</td><td>1.0</td><td>2.0</td><td>2.0</td><td>2.0</td><td>1.0</td></tr><tr><th>5</th><td>5.0</td><td>1.0</td><td>25.2387</td><td>1.0</td><td>2.0</td><td>2.0</td><td>1.0</td><td>2.0</td></tr><tr><th>6</th><td>6.0</td><td>1.0</td><td>21.7926</td><td>0.0</td><td>2.0</td><td>2.0</td><td>2.0</td><td>1.0</td></tr><tr><th>7</th><td>7.0</td><td>1.0</td><td>20.5161</td><td>0.0</td><td>1.0</td><td>2.0</td><td>2.0</td><td>1.0</td></tr><tr><th>8</th><td>8.0</td><td>1.0</td><td>21.8893</td><td>1.0</td><td>2.0</td><td>2.0</td><td>2.0</td><td>1.0</td></tr><tr><th>9</th><td>9.0</td><td>1.0</td><td>22.0543</td><td>1.0</td><td>2.0</td><td>2.0</td><td>2.0</td><td>1.0</td></tr><tr><th>10</th><td>10.0</td><td>1.0</td><td>20.4877</td><td>0.0</td><td>2.0</td><td>1.0</td><td>2.0</td><td>0.0</td></tr><tr><th>11</th><td>11.0</td><td>1.0</td><td>23.3558</td><td>1.0</td><td>2.0</td><td>2.0</td><td>1.0</td><td>2.0</td></tr><tr><th>12</th><td>12.0</td><td>1.0</td><td>19.546</td><td>0.0</td><td>2.0</td><td>2.0</td><td>2.0</td><td>1.0</td></tr><tr><th>13</th><td>13.0</td><td>1.0</td><td>24.5469</td><td>1.0</td><td>2.0</td><td>2.0</td><td>1.0</td><td>2.0</td></tr><tr><th>14</th><td>14.0</td><td>1.0</td><td>24.7602</td><td>1.0</td><td>2.0</td><td>2.0</td><td>2.0</td><td>2.0</td></tr><tr><th>15</th><td>15.0</td><td>1.0</td><td>19.1655</td><td>0.0</td><td>2.0</td><td>2.0</td><td>2.0</td><td>0.0</td></tr><tr><th>16</th><td>16.0</td><td>1.0</td><td>23.1325</td><td>1.0</td><td>2.0</td><td>2.0</td><td>2.0</td><td>2.0</td></tr><tr><th>17</th><td>17.0</td><td>1.0</td><td>19.3258</td><td>0.0</td><td>2.0</td><td>2.0</td><td>1.0</td><td>0.0</td></tr><tr><th>18</th><td>18.0</td><td>1.0</td><td>21.465</td><td>1.0</td><td>2.0</td><td>0.0</td><td>2.0</td><td>1.0</td></tr><tr><th>19</th><td>19.0</td><td>1.0</td><td>24.5101</td><td>1.0</td><td>2.0</td><td>2.0</td><td>1.0</td><td>1.0</td></tr><tr><th>20</th><td>20.0</td><td>1.0</td><td>23.59</td><td>1.0</td><td>2.0</td><td>0.0</td><td>2.0</td><td>1.0</td></tr><tr><th>21</th><td>21.0</td><td>1.0</td><td>22.4442</td><td>1.0</td><td>1.0</td><td>2.0</td><td>2.0</td><td>0.0</td></tr><tr><th>22</th><td>22.0</td><td>1.0</td><td>22.2441</td><td>1.0</td><td>2.0</td><td>2.0</td><td>2.0</td><td>1.0</td></tr><tr><th>23</th><td>23.0</td><td>1.0</td><td>20.3553</td><td>0.0</td><td>2.0</td><td>2.0</td><td>1.0</td><td>1.0</td></tr><tr><th>24</th><td>24.0</td><td>1.0</td><td>19.5897</td><td>0.0</td><td>2.0</td><td>2.0</td><td>2.0</td><td>2.0</td></tr><tr><th>25</th><td>25.0</td><td>1.0</td><td>19.8247</td><td>0.0</td><td>2.0</td><td>2.0</td><td>1.0</td><td>1.0</td></tr><tr><th>26</th><td>26.0</td><td>1.0</td><td>20.2169</td><td>0.0</td><td>2.0</td><td>2.0</td><td>2.0</td><td>2.0</td></tr><tr><th>27</th><td>27.0</td><td>1.0</td><td>20.9021</td><td>0.0</td><td>2.0</td><td>1.0</td><td>2.0</td><td>2.0</td></tr><tr><th>28</th><td>28.0</td><td>1.0</td><td>18.778</td><td>0.0</td><td>2.0</td><td>1.0</td><td>2.0</td><td>0.0</td></tr><tr><th>29</th><td>29.0</td><td>1.0</td><td>23.4007</td><td>1.0</td><td>2.0</td><td>2.0</td><td>2.0</td><td>2.0</td></tr><tr><th>30</th><td>30.0</td><td>1.0</td><td>23.2944</td><td>1.0</td><td>2.0</td><td>1.0</td><td>2.0</td><td>2.0</td></tr><tr><th>&vellip;</th><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td></tr></tbody></table>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|cccccccc}\n",
       "\t& FamID & PID & x1 & sex & snp1 & snp2 & snp3 & snp4\\\\\n",
       "\t\\hline\n",
       "\t& Float64 & Float64 & Float64 & Float64 & Float64 & Float64 & Float64 & Float64\\\\\n",
       "\t\\hline\n",
       "\t1 & 1.0 & 1.0 & 22.5619 & 1.0 & 2.0 & 2.0 & 2.0 & 2.0 \\\\\n",
       "\t2 & 2.0 & 1.0 & 20.9638 & 0.0 & 1.0 & 2.0 & 2.0 & 2.0 \\\\\n",
       "\t3 & 3.0 & 1.0 & 22.1586 & 1.0 & 2.0 & 2.0 & 2.0 & 1.0 \\\\\n",
       "\t4 & 4.0 & 1.0 & 22.5485 & 1.0 & 2.0 & 2.0 & 2.0 & 1.0 \\\\\n",
       "\t5 & 5.0 & 1.0 & 25.2387 & 1.0 & 2.0 & 2.0 & 1.0 & 2.0 \\\\\n",
       "\t6 & 6.0 & 1.0 & 21.7926 & 0.0 & 2.0 & 2.0 & 2.0 & 1.0 \\\\\n",
       "\t7 & 7.0 & 1.0 & 20.5161 & 0.0 & 1.0 & 2.0 & 2.0 & 1.0 \\\\\n",
       "\t8 & 8.0 & 1.0 & 21.8893 & 1.0 & 2.0 & 2.0 & 2.0 & 1.0 \\\\\n",
       "\t9 & 9.0 & 1.0 & 22.0543 & 1.0 & 2.0 & 2.0 & 2.0 & 1.0 \\\\\n",
       "\t10 & 10.0 & 1.0 & 20.4877 & 0.0 & 2.0 & 1.0 & 2.0 & 0.0 \\\\\n",
       "\t11 & 11.0 & 1.0 & 23.3558 & 1.0 & 2.0 & 2.0 & 1.0 & 2.0 \\\\\n",
       "\t12 & 12.0 & 1.0 & 19.546 & 0.0 & 2.0 & 2.0 & 2.0 & 1.0 \\\\\n",
       "\t13 & 13.0 & 1.0 & 24.5469 & 1.0 & 2.0 & 2.0 & 1.0 & 2.0 \\\\\n",
       "\t14 & 14.0 & 1.0 & 24.7602 & 1.0 & 2.0 & 2.0 & 2.0 & 2.0 \\\\\n",
       "\t15 & 15.0 & 1.0 & 19.1655 & 0.0 & 2.0 & 2.0 & 2.0 & 0.0 \\\\\n",
       "\t16 & 16.0 & 1.0 & 23.1325 & 1.0 & 2.0 & 2.0 & 2.0 & 2.0 \\\\\n",
       "\t17 & 17.0 & 1.0 & 19.3258 & 0.0 & 2.0 & 2.0 & 1.0 & 0.0 \\\\\n",
       "\t18 & 18.0 & 1.0 & 21.465 & 1.0 & 2.0 & 0.0 & 2.0 & 1.0 \\\\\n",
       "\t19 & 19.0 & 1.0 & 24.5101 & 1.0 & 2.0 & 2.0 & 1.0 & 1.0 \\\\\n",
       "\t20 & 20.0 & 1.0 & 23.59 & 1.0 & 2.0 & 0.0 & 2.0 & 1.0 \\\\\n",
       "\t21 & 21.0 & 1.0 & 22.4442 & 1.0 & 1.0 & 2.0 & 2.0 & 0.0 \\\\\n",
       "\t22 & 22.0 & 1.0 & 22.2441 & 1.0 & 2.0 & 2.0 & 2.0 & 1.0 \\\\\n",
       "\t23 & 23.0 & 1.0 & 20.3553 & 0.0 & 2.0 & 2.0 & 1.0 & 1.0 \\\\\n",
       "\t24 & 24.0 & 1.0 & 19.5897 & 0.0 & 2.0 & 2.0 & 2.0 & 2.0 \\\\\n",
       "\t25 & 25.0 & 1.0 & 19.8247 & 0.0 & 2.0 & 2.0 & 1.0 & 1.0 \\\\\n",
       "\t26 & 26.0 & 1.0 & 20.2169 & 0.0 & 2.0 & 2.0 & 2.0 & 2.0 \\\\\n",
       "\t27 & 27.0 & 1.0 & 20.9021 & 0.0 & 2.0 & 1.0 & 2.0 & 2.0 \\\\\n",
       "\t28 & 28.0 & 1.0 & 18.778 & 0.0 & 2.0 & 1.0 & 2.0 & 0.0 \\\\\n",
       "\t29 & 29.0 & 1.0 & 23.4007 & 1.0 & 2.0 & 2.0 & 2.0 & 2.0 \\\\\n",
       "\t30 & 30.0 & 1.0 & 23.2944 & 1.0 & 2.0 & 1.0 & 2.0 & 2.0 \\\\\n",
       "\t$\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "2200×8 DataFrame. Omitted printing of 1 columns\n",
       "│ Row  │ FamID   │ PID     │ x1      │ sex     │ snp1    │ snp2    │ snp3    │\n",
       "│      │ \u001b[90mFloat64\u001b[39m │ \u001b[90mFloat64\u001b[39m │ \u001b[90mFloat64\u001b[39m │ \u001b[90mFloat64\u001b[39m │ \u001b[90mFloat64\u001b[39m │ \u001b[90mFloat64\u001b[39m │ \u001b[90mFloat64\u001b[39m │\n",
       "├──────┼─────────┼─────────┼─────────┼─────────┼─────────┼─────────┼─────────┤\n",
       "│ 1    │ 1.0     │ 1.0     │ 22.5619 │ 1.0     │ 2.0     │ 2.0     │ 2.0     │\n",
       "│ 2    │ 2.0     │ 1.0     │ 20.9638 │ 0.0     │ 1.0     │ 2.0     │ 2.0     │\n",
       "│ 3    │ 3.0     │ 1.0     │ 22.1586 │ 1.0     │ 2.0     │ 2.0     │ 2.0     │\n",
       "│ 4    │ 4.0     │ 1.0     │ 22.5485 │ 1.0     │ 2.0     │ 2.0     │ 2.0     │\n",
       "│ 5    │ 5.0     │ 1.0     │ 25.2387 │ 1.0     │ 2.0     │ 2.0     │ 1.0     │\n",
       "│ 6    │ 6.0     │ 1.0     │ 21.7926 │ 0.0     │ 2.0     │ 2.0     │ 2.0     │\n",
       "│ 7    │ 7.0     │ 1.0     │ 20.5161 │ 0.0     │ 1.0     │ 2.0     │ 2.0     │\n",
       "│ 8    │ 8.0     │ 1.0     │ 21.8893 │ 1.0     │ 2.0     │ 2.0     │ 2.0     │\n",
       "│ 9    │ 9.0     │ 1.0     │ 22.0543 │ 1.0     │ 2.0     │ 2.0     │ 2.0     │\n",
       "│ 10   │ 10.0    │ 1.0     │ 20.4877 │ 0.0     │ 2.0     │ 1.0     │ 2.0     │\n",
       "⋮\n",
       "│ 2190 │ 2190.0  │ 1.0     │ 19.975  │ 0.0     │ 2.0     │ 2.0     │ 2.0     │\n",
       "│ 2191 │ 2191.0  │ 1.0     │ 20.0129 │ 0.0     │ 2.0     │ 2.0     │ 2.0     │\n",
       "│ 2192 │ 2192.0  │ 1.0     │ 18.8998 │ 0.0     │ 1.0     │ 2.0     │ 2.0     │\n",
       "│ 2193 │ 2193.0  │ 1.0     │ 20.4394 │ 0.0     │ 2.0     │ 2.0     │ 2.0     │\n",
       "│ 2194 │ 2194.0  │ 1.0     │ 20.8105 │ 0.0     │ 2.0     │ 1.0     │ 2.0     │\n",
       "│ 2195 │ 2195.0  │ 1.0     │ 20.7643 │ 0.0     │ 2.0     │ 2.0     │ 2.0     │\n",
       "│ 2196 │ 2196.0  │ 1.0     │ 18.2074 │ 0.0     │ 2.0     │ 2.0     │ 2.0     │\n",
       "│ 2197 │ 2197.0  │ 1.0     │ 19.829  │ 1.0     │ 2.0     │ 1.0     │ 1.0     │\n",
       "│ 2198 │ 2198.0  │ 1.0     │ 24.1463 │ 1.0     │ 2.0     │ 2.0     │ 1.0     │\n",
       "│ 2199 │ 2199.0  │ 1.0     │ 23.5302 │ 1.0     │ 2.0     │ 2.0     │ 2.0     │\n",
       "│ 2200 │ 2200.0  │ 1.0     │ 21.6536 │ 1.0     │ 1.0     │ 2.0     │ 2.0     │"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Trait1_GLM = hcat(Fam_Person_id, Simulated_GLM_trait, df_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"Trait1_GLM.csv\""
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CSV.write(\"Trait1_GLM.csv\", Trait1_GLM)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extract phenotype and covariates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2200-element Array{Float64,1}:\n",
       " 22.56186394879911\n",
       " 20.963753180716484\n",
       " 22.158579006283265\n",
       " 22.54848333683822\n",
       " 25.238739240482467\n",
       " 21.792563919325847\n",
       " 20.516070523314003\n",
       " 21.88929714913077\n",
       " 22.054318998066133\n",
       " 20.48771094116441\n",
       " 23.35578685114142\n",
       " 19.54600815844331\n",
       " 24.546949927556458\n",
       "  ⋮\n",
       " 20.513081412495513\n",
       " 19.97498935533755\n",
       " 20.012863282650194\n",
       " 18.89981260984406\n",
       " 20.439382647573503\n",
       " 20.810539404126388\n",
       " 20.76426228744117\n",
       " 18.207387858338755\n",
       " 19.82900394065494\n",
       " 24.146275918482964\n",
       " 23.530204830639423\n",
       " 21.65355552220941"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ytrait = readdlm(\"Trait1_GLM.csv\", ','; header = false, skipstart=1)\n",
    "y = convert(Vector{Float64}, ytrait[:, 3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2200×2 Array{Float64,2}:\n",
       " 1.0  1.0\n",
       " 1.0  0.0\n",
       " 1.0  1.0\n",
       " 1.0  1.0\n",
       " 1.0  1.0\n",
       " 1.0  0.0\n",
       " 1.0  0.0\n",
       " 1.0  1.0\n",
       " 1.0  1.0\n",
       " 1.0  0.0\n",
       " 1.0  1.0\n",
       " 1.0  0.0\n",
       " 1.0  1.0\n",
       " ⋮    \n",
       " 1.0  0.0\n",
       " 1.0  0.0\n",
       " 1.0  0.0\n",
       " 1.0  0.0\n",
       " 1.0  0.0\n",
       " 1.0  0.0\n",
       " 1.0  0.0\n",
       " 1.0  0.0\n",
       " 1.0  1.0\n",
       " 1.0  1.0\n",
       " 1.0  1.0\n",
       " 1.0  1.0"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# covariates\n",
    "#2 change to 1.0; 1 change to 0.0\n",
    "X = [ones(size(sex,1)) Float64.(sex)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loop over genes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will conduct gene set analysis for valid genes with 3 or more snps. The time will not be too long due to the filtered principle but may take a few minutes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "190.740716 seconds (199.57 M allocations: 196.265 GiB, 26.88% gc time)\n"
     ]
    }
   ],
   "source": [
    "nNullSimPts = 1000000;\n",
    "nPtsChi2 = 500;\n",
    "pvalELRT = Array{Float64}(undef, genes_number)\n",
    "pvalERLRT = Array{Float64}(undef, genes_number)\n",
    "pvalEscore = Array{Float64}(undef, genes_number)\n",
    "@time for i in 1:length(uniquegenes)\n",
    "    if uniquegenes[i] == -1 #missing values for genes were coded as -1\n",
    "        pvalELRT[i] = NaN\n",
    "        pvalERLRT[i] = NaN\n",
    "        pvalEscore[i] = NaN\n",
    "        continue\n",
    "    end\n",
    "    snpset_index = genes.== uniquegenes[i]\n",
    "    if (sum((snpset_index)) < 3 )\n",
    "        pvalELRT[i] = NaN\n",
    "        pvalERLRT[i] = NaN\n",
    "        pvalEscore[i] = NaN\n",
    "        continue\n",
    "    else\n",
    "        G = convert(Matrix{Float64}, SNP_24.snparray[:, snpset_index])\n",
    "        # get rid of mono-snp\n",
    "        maf = sum(G,dims=1)/(2*nPer);\n",
    "        G = G[:, map(x -> maf[x] > 0, 1:size(G,2))];\n",
    "        windowSize = size(G,2);\n",
    "    \n",
    "        if(windowSize > 2 && windowSize < 50)\n",
    "            (b, vc0, vc1, pvalELRT[i]) =\n",
    "                vctest(y, X, G, \n",
    "                   tests = \"eLRT\", \n",
    "                   Vform = \"half\", \n",
    "                    nPtsChi2 = nPtsChi2,\n",
    "                    windowSize = windowSize,\n",
    "                    pvalueComputings = \"chi2\",\n",
    "#                     pvalueComputings = \"MonteCarlo\", \n",
    "                    nNullSimPts = nNullSimPts);  \n",
    "            (b, vc0, vc1, pvalERLRT[i]) =\n",
    "                vctest(y, X, G, \n",
    "                tests = \"eRLRT\", \n",
    "                Vform = \"half\",\n",
    "                tolX = 1e-6,\n",
    "                nPtsChi2 = nPtsChi2,\n",
    "                windowSize = windowSize,\n",
    "                pvalueComputings = \"chi2\",\n",
    "#                 pvalueComputings = \"MonteCarlo\", \n",
    "                nNullSimPts = nNullSimPts);\n",
    "### \n",
    "## eScore uses integration to calculate p-value -currently Julia's package not updated used R package \n",
    "## used R package pracma, need to first instll: install.packages(\"pracma\")\n",
    "            (b, vc0, vc1, pvalEscore[i]) =\n",
    "                vctest(y, X, G, tests = \"eScore\", \n",
    "                    Vform = \"half\",\n",
    "                    windowSize = windowSize)\n",
    "            println(\"finished $i\")\n",
    "            \n",
    "        else\n",
    "            pvalELRT[i] = NaN\n",
    "            pvalERLRT[i] = NaN\n",
    "            pvalEscore[i] = NaN\n",
    "        end\n",
    "    end    \n",
    "end"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Output results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because of the CSV package problem, the number format of pvalue in .txt files is not convenient to read so you can read them in Julia or other languages. You can remove the commented code to read the files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "chromosomes = Array{Int64}(undef, genes_number)\n",
    "for i in 1:length(uniquegenes)\n",
    "    chromosomes[i] = df[findfirst(genes .== uniquegenes[i]), 2]\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "AdjBasepairs = Array{Int64}(undef, genes_number)\n",
    "for j in 1:length(uniquegenes)\n",
    "    AdjBasepairs[j] = df[findfirst(genes .== uniquegenes[j]),3]\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"exactVC_eLRT_pVal_4diffsnps.txt\""
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index = .!isnan.(pvalELRT)\n",
    "plot_ELRT = DataFrame(GeneID = uniquegenes[index],\n",
    "    Chromosome = chromosomes[index], \n",
    "    AdjBasepair = AdjBasepairs[index],\n",
    "    Pvalue = pvalELRT[index])\n",
    "CSV.write(\"exactVC_eLRT_pVal_4diffsnps.txt\", plot_ELRT)\n",
    "\n",
    "#= read all the pvalues\n",
    "df1 = CSV.read(\"exactVC_eLRT_pVal_3diffsnps.txt\")[4]\n",
    "for i in 1:length(df1)\n",
    "    println(df1[i])\n",
    "end\n",
    "=#\n",
    "\n",
    "#= read pvalues which are not equal to 1\n",
    "df1 = CSV.read(\"exactVC_eLRT_pVal_4diffsnps.txt\")[4]\n",
    "for i in 1:length(df1)\n",
    "    if df1[i] != 1\n",
    "        println(df1[i])\n",
    "    end\n",
    "end\n",
    "=#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"exactVC_eRLRT_pVal_4diffsnps.txt\""
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index = .!isnan.(pvalERLRT)\n",
    "plot_ERLRT = DataFrame(GeneID = uniquegenes[index],\n",
    "    Chromosome = chromosomes[index], \n",
    "    AdjBasepair = AdjBasepairs[index],\n",
    "    Pvalue = pvalERLRT[index])\n",
    "CSV.write(\"exactVC_eRLRT_pVal_4diffsnps.txt\", plot_ERLRT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"exactVC_Escore_pVal_4diffsnps.txt\""
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index = .!isnan.(pvalEscore)\n",
    "plot_Escore = DataFrame(GeneID = uniquegenes[index],\n",
    "    Chromosome = chromosomes[index], \n",
    "    AdjBasepair = AdjBasepairs[index],\n",
    "    Pvalue = pvalEscore[index])\n",
    "CSV.write(\"exactVC_Escore_pVal_4diffsnps.txt\", plot_Escore)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Manhattan Plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following code produces the Manhattan plots for the three different methods and saves them as `.png` files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "using MendelPlots\n",
    "\n",
    "manhattan(plot_ELRT; pvalvar = \"Pvalue\", chrvar = \"Chromosome\", \n",
    "    posvar = \"AdjBasepair\", outfile = \"exactVC_eLRT_manhattan.png\", \n",
    "    fontsize = 18pt, linecolor = \"red\", ymax = 15.0)\n",
    "\n",
    "manhattan(plot_ERLRT; pvalvar = \"Pvalue\", chrvar = \"Chromosome\", \n",
    "    posvar = \"AdjBasepair\", outfile = \"exactVC_eRLRT_manhattan.png\", \n",
    "    fontsize = 18pt, linecolor = \"red\", ymax = 10.0)\n",
    "\n",
    "manhattan(plot_Escore; pvalvar = \"Pvalue\", chrvar = \"Chromosome\", \n",
    "    posvar = \"AdjBasepair\", outfile = \"exactVC_eScore_manhattan.png\", \n",
    "    fontsize = 18pt, linecolor = \"red\", ymax = 10.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### eRLRT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1TbM++/OMlnH+cjGaVpKk1HJJSh1qiXlekv+xGPkBAFjZnNAAAADAcmndWC9tx1SvAaWm3+PN+1I75WsLOV4fGd/73on3LkhjSqnl6S0h++uxte1J4Dk7sO7A+9N2PH7qsxciV6dXP74Q46T9Pruje2338/NNMjgw2Np006ntJxeshjW+4rorbktyXx/pzj+uHLun2dAc23eBi2A1zHm/1up9lXvz5LSdwlHqeN+FzdK9g/del5a/z0vqjy1WfgAAVjYNDQAAACyLXm3fWK81JyxFLYezA1PTC9rQUHpL3NBQO59eiGGas5uTanLmjEElE91u996FyHcw3z4e/4MtYaduPnvz6fPN1Rlc99fzHSNJasn+lpD5vwpkgayyNe73vv3pclS+ODI08roLLrjg4X1es2BW2Zy3Wov3VZKkl2e2hZTewKI1rezZs+dbKfnfM8XUmscvVn4AAFY2DQ0AAAAsj9LHqw/KGm9oqPmnbz/tvXA6uWdBx2tRB+qCNDSUUn4iLefil9QPLUSumQupX2wLWTe4br4b01/euWfnHfMco193LVGeVqtpjUutl/SfLw9PqW9Yd//grSPDzQdHNo788raN2344re+BmL/VNOd9WJP3VZKU5FltMYNlcHGbMGra/i74V4uaHwCAFav1hX4AAACwKDq5M9OtUYMXn3XxMZded2nb07CrUi1Zqs23xVJzT25ekJE6LU+RJyklf7cguWZSO7cmM58CX2s9aV4pkoVtYplJKYfPxusqWuNvZv+u43Psm2ry6FlkHkjy7NT67Jr6lpGh5msp+XAp9YPpDHyod2Tvk91ut/1PzdlYRXPemmKt3lcPeGJbwIHBAw9vzmsW7ZSQMp27W96fcdpi5QYAYGXT0AAAAMCyWD+1/muTpeW16Ukmj5w8IWk93nu1+sZyFzBPt3T3dRfmRIjSOzF15gfWpyfLpxYk10xllN5ttaWO1Mxr47Uk35zP9bNSl/bEjhmtojWemJi4f/vwlq01nY8kOWJuheThSc6vtZyf6V7K/tw1MtT8eU0+kF4+0L22+/k5jfsdOVbPnLfmWKv31QNaGxXK9MyvhJivlmaGJDl+MfMDALByeeUEAAAAy+K9E+/9ZpKptrjeZO+xS1DO4WqlNzR8a6EGKiknzhhQ87Xutd3215jMU62dW/sIm9fGa5Zw47WU2luqXG1W2xpfNr77EzX5uSST868qSfKwlFxQSt5VBvK5keHmC9s2Nv9l+8atT88cX0+x2ua8xZq8r77tuOUuAAAA5kpDAwAAAMvp620BtdQfW4pCDkedpXyi+DBX2zZey9K8s77T6bUeW19Lfdh8cpTk7vlcv1KtxjXujncvTS3PSfKVeRf23R5Ta36hV8tHtw03X9g23Px2c25zxmwGWI1zfihr9b4CAICVzisnAAAAWE5fTXLyTAElh19Dw/aztn9Pb930W2eKqTU3dMe7b5lPnprSeoLF2lFn3HitS9T80aud0nZ4emeeT2ev3XVfnWs8NjH20Qs3Xfij09OTv5la/m2SgfnW+FA1eXSS/1B6edXI0MgltdTf6o53v9rHlatyzg9m7d5XAACwsjmhAQAAgGVU/qI1IuWwa2iYHpg+NzVbZ/oqJRuWu87VpNbM+HR2Kdm/NHXU01tj0mk9eYTvtprXeOdVO28d27vrJSWdx5fkHUkW6zUOR6TUl5fkb5tzm6e0Ba/mOQcAAFYHDQ0AAAAsm1J6+9pianLmRUMXHb8E5fSvZLg1ppZPLkEla0ZJJmcMqFm/RKW0Hulfam9JjulfbdbCGo+Oj352dLz7yqO/dcwZqeWClPrfknx5LmO1OLX0sm/b0NazZgpaC3MOAACsbF45AQAAwLKZnOp9cHCg9fT1cqAc2Jzk3YtfUbuhoaEjSvLctrhaq4aGhdX2dPYJS1FEJzlj5oPxk14tNl7nZs2s8bv3vfu+JFd++yvNcPMjJXUoKc9J8owkR89n/G87qpbyngsuuOAJV1xxxdcOEbNm5pyZlZJ3Lmf+WuvMzTUAAKxZGhoAAABYNpdfe/lXRoabf0hyZkvov81h0tBwfI75ydrHZmNvsKehYUGVryczbnkuycZrLTlj5jKSzkDHxuucrN017o53/zbJ3yb57Zc8+SXr7jrlrqek9J5TS9lQap6e5Kg5Dn3quvvXvTXJiw7+8dqdc77DZ0f3dl++3EUAAMDBeOUEAAAAy6rUfLA1JnlWc3bzuKWop1Up7a+bSPZ3jux8btFrWUtK7myJOOXisy4+ZrHLqDWPbIuZnJy8bbHrWJWscZLkkhsvmRybGPvo2PiuN3b3dp93d73nxJTy3JT6WzX51OxHrBeef/75DzvoR+Z8rWg7/eCxGzZs8OAbAACHJQ0NAAAALKtayr5+4spgffEil9LqoqGLjq/Jxa2BtezpdrvTS1DSmlFT247G79y3/r5/vdh1lOSxLSFfvfzay7+y2HWsRtb44CYmJu4f2zv252N7d/3H7nj3SZ3ewGNrya+V5Et9DrFu/eT65x/sA3O+ZrQ1rqw7+YiTH7MUhQAAwGxpaAAAAGBZTU1P7esrsJafv+CsC05Z3GpmNlkmX53kpLa42qk7l6CcNaXTq//YGjSdH1nMGrYPb3lqktNnDKq5YTFrWM2scX8uu+ayL3b3dt9067duf1wpeWdfF9U892DfNudrQ01a17mU8gNLUQsAAMyWhgYAAACW1eXXXv6V1FzVR+iJ6wbXvWXRCzqE5uzmpKS+qo/Qu3J0rln0gtaYXq/8RVtMKeU5i1lDTefC1piOjde5ssazs2/fvqnRvd2Xl6SPPxfrQV/pYM7XhlLLR1pjBnoaGgAAOCx5NxoAAADLrgx2Xl+ne5vaI+vPjAyP/NHY+NiHFr+q71Q69VeTcnxrXLJ7rNs9sBQ1rSXda7ufHxlubksywykd9dxmQ3Nsd1/3noXOv2PHjs7NN9y0rS2u06sfX+jca8VqWOORjSMvTK1/eKjPS8mfjO7tvnyuNR7M6Hj3V0c2Nhek5nEzhJ12sG+uhjmnXen0PlxreeVMMbVXnp3k7YtVw8hw84kkP3ioz0vqK0fHd/33xcoPAMDK5YQGAAAAlt3ontG/rsnV/cTWUt/VNM1Ri13Tg23buO2HU0o/m5C9Tq/3e4te0NrV9jT5UZ2j6vMXI/FnPvbp5+QQm8IPcu+96w785WLkX0NW9BrXWgeTHHOor1rz1AUq9yGJy76WiIfN8NmKnnPaHZicbj2hISXP37Zx2w8tRv6RoZFnJHlyZrw3OncsRm4AAFY+DQ0AAAAcFgbSe30/caXmCeWeXNE0zfrFrilJNp+9+fRae3uTHN0aXOulO6/Z/anFr2qNKuWjbSG1lFclKQudunZK67H4Jdl11VVX3b3QudeUFb7GpZQvtQzxxA0bNizGial/2/L5/kN+ssLnnHZXXHfFbUn+oSWsk9p7zaIUUHova4mok0dOfnhRcgMAsOJpaAAAAOCwcNn47k+kZLyv4JKzyz3pLtLG4L+4+KyLjxkcGNiT5FF9hN/XmR583WLWs9bVUncmmWoJe/LI8Mj2hcx78VkXH1OTLW1xNcVx6fO00td4OtNtDQ1HnnzsyT8+q+L6UEud6QSGpOaLM1y7ouecftU/bY1ILmzOax67kFkfOJ2htXHlr6644oqvLWReAABWDw0NAAAAHDZqSV+nNCRJSjadcvQp7zvvvPPaT06YgxdsfMGJ9w/ctzsPHJPdT0G/d9l1l/3jYtTCA7pXd7+cWna1R9Y3bdq06biFyntg3X1vycxH9qcknxsbH/vQQuVcq1b6Gu/eu/sLSW6dMVkvr511gS1Kcs7MAfULh/popc85/anT5V2Z6aSOBwx2pvIfFirnRUMXHZ9SL00yMFNcKfWtC5UTAIDVR0MDAAAAh43u1d0bUuvv9H9FbY6ePvLmbcNbF/T97s1wc85Ub/LTKTm7z0v+7oipI96wkDVwcCXlHX2EPfaIqSNGm6aZcROtH81w89O15ufb4mqt/3W+uXjACl/jmpQZT5opybnNuc1T+i6wxbahbT+RmmfOmLPmL2euaUXPOX3oXtu9M6l/1BZXS17cDDc/Pd98TdOsn8yBdyeZ8cSHkvzv3tHl8vnmAwBg9dLQAAAAwGFlbGLXr9ak782Nmjy6plw5Mtxc3ZzTnDmf3M1wc9q2oea/lmQiJY/s87K76nQ2X3rdpW1PvrIARidGP5bkhra4kgyVb9U/mM/ma3NO88yStB7TnuSzd2f/O+eah++00te4lN7VrTG9dLecu+X7+xlvJs1wc1otvdGWsPt7k+X9MwWs9DmnP1PpvTXtrxdZV5Irm3OaGZtkZnLR0EXHl/2ZSMkFbbG95De73e70XHMBALD6aWgAAADgcFNzTF6YPjbXHmJj6eQzI8PNp0aGR36z2dg8OUlpu6h5XnPCtuGtLx4Zbj5QkltqyUtmU2tJvbh7bffzs6yVeSgDnZ9PMtkaWMvPZX8+1gw3T5xtjma4eUXp5M+TnNJeUPl/JyYm7p9tDg5tJa9x7+hydZL/0xL2qIFe50PNUPPcfsY8mGa4Oackf5PkUS2hV3ev736jbbyVPOf05/Lxy79US36zj9CjSycfGBke2fGzG372yNnk2LZx6+bJzoEbk/Tze/vPu+Pdd89mfAAA1p7B5S4AAAAAHqrb7d574aYLN01PTX0syWNmefkTk/rEUvNrI8PNbTX5PyW5NSm3Jb27k6wvKcf0Sh5Zah6Z5HtryhFzqbMmrx0b39X6NDYLa3TP6F83w82vl+S322JL8mNJPrFtuBntley6p3fPtYfaJG3Obc4otbwwtb4oyQ/1VUwtl42Nj10/qx+AVit5jbvd7oGRoZE3ptRLWkJPKyX/c2S4+bOavPmees+H2jbwX7DxBSdO1amLk/pzSX64j3ImS+m8rp+6V/Kc078n/PgT3njTDTc9oyRntYQemdTfuPfo/RePDI28uwyUq0avHv3kwQI3D29+9LrS2Vh75YW15ml9lnLfdKf30tlVDwDAWqShAQAAgMPSzqt23toMNRtLyV8kOWGOw5xS/uVJ4Jp/PrChJil1XuVN1ZKXdfd2W99HzuJ4wlOe8J9vuuGm4ZI8q4/w9TW5uNRcfFw59u6R4eazSb5ak05JuSelnl5qHll7eUxSZ3Oa5S1TvalXz+0noM1KXuOH3fawd3/j1K//Sk36ea3Ec0vy3OPKsfc1Q83HSql/U0v5Wif167V2SpIzUvKo1Po9U3XyKUn6fmK+JP9ldO/o3/cbv5LnnP7s2LGj1ww1L0zJ3yQ5oy2+Jt+bUt9Qe/UNI8PN7Um+XJPbSslUah6R5LQkj6r/96/Y/pTyst1X7/7c3H4KAADWEg0NAAAAHLa6E92bm6Hm6aVkNP09e38FcQAACHFJREFUjbwU7i61bh0b33Xdcheylu3YsaO3/aztF/XWTf9Zah43i0uPS/Lk5EHtLQ/8mq2vlnR+6vJru1+Z/aX0YyWv8SU3XjK57bxt2zPd+4sk/Z4Ac2Qp2ZCUDaUmNSX/UvXcGrBuXD915K/P5oKVPOf0rzvRvb05t9lcerkmyYmzuPTkJCeXZK6/J5MkteTXunvH/sfcRwAAYC2ZTXc0AAAALLnuRPfmekyeUmrajm9fCrcM9HrPHJ3QzHA4uOy6y/5xYGDwmUkOegz6Irq91vzU6PjoZ5c475qzktd4dM/oX9eSX1zIombhiwODgxsvve7S/bO9cCXPOf3rXt29YXqg9/QkX1jazOW3u3u7b1ranAAArGQaGgAAADjsdbvde0cnui9NyrYkdy1DCQdK8o6BgcEf3XnN7k8tQ34OYedVO2+tB/KTKfnIEqX8Yul0fro70b15ifKteSt5jbt7u5ck9XULUVS/aslNdTo/vfOqnbfOdYyVPOf0b/ee3Z+ZnJr6iSQ3LEG6+2vyM2PjY69dglwAAKwiGhoAAABYMcbGx8YOrJ98bC35tSS3L0HKXkku7fQGzhwd775y556ddyxBTmape333G3f37nleUl+b5O5FSnNfUl5fj8njR68eXeon19e8lbzGY+O73lhqfWFK7lmoMQ+t/PccnR/vXtv9/HxHWslzTv+uuO6K2+ox2VBLfiPJrE/06NM1A9O9J3fHu5cu0vgAAKxig8tdAAAAAMzGlVdeeVeSN5133nlvO7p3xL+rtfxykkctcJo7S3JNL3nz2Hj37xZ47P+r5J6W95Av+AZo6ZV7apnHy8+/Y7Clr/9QJiYm7k/y281w8yel1Dellp/NwjzI0UvN++tgfqm7Z2xhjmZfynmrM49Ve3VhNjAncyDrcyDJ+kOXMr9cK2qNH2J0Ytd7L9x04fXTU9OvT+q/y8L/n9yflVp/Z3SiO7GQg66oOXdfzVm32703yRs2n735vw0Odt64gOt8Y6n1tV7TBADAfJTlLgAAAADmqTTDzZOSPLckz03y7CTHzXKMXpJPlJprks5E79jex7vd7vSCV8qS2X7O9sfUganzai3nJfnJzLAheBDfTMq1pfau7qVMdCe6S3EaCLO0Utd4+6btj6zTU1t6tTQleUbmtnFck3wmKX9eSvmD0b2jn17gMg9qpc45s7P9nO2Pme5MP78kz0/yrPTfgDOdmk+m1F3Tnbpr99W7P7eIZQIAsEZoaAAAAGBV2bBhw+BpRz38ib06cHopvUfU5ORSyiNq6hFJkpJ7S3J7erm9V8ptSW5fV9Z96X173/f15a2cxbJp06bjjppc/7Ta6ZyeXu/UUsqpNfWUJAN54NUlt9dSbiu9clunTH/l+Fsf/teX3HjJ5DKXzSys1DVumuaogf29J9SUJyblB1JyQk05PrUen9R7U8sJKeWbKb27Si131VrvqKX8TQ7khu713W8sZ+0rdc6ZnRdsfMGJk3Xyxzq1nl6T01JyWkm5v5ayrqR3S69Xbumkc8tkJm+58947v7pv376p5a4ZAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAW1/8P1auK7T0ApncAAAAASUVORK5CYII="
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(\"image/png\", read(\"exactVC_eRLRT_manhattan.png\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### eLRT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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GeEdVX2zfvn0+wJIlS66wLCtLVVsC1VR1q2VZhaqaqap1RGQ38Kkx5sUOHToc6ouWL1/eQlUHqWp7Va1jWdYOVV0KPN+uXbtvY/42qtbSpUuvEZH+QFugtqoGRMSISBGwFViuqq9Nqf9mekicXgKtgPqoWKDFCOuBFWrkxcnNH1kZq7xkGbL23m6H2tLwuDsU7q6pgljVRdU2ooUibFAjy8B6YfLJD3/hVn72ujsbOpo2SES7qNGmKmSAiCAFonpARQ2IIgRQNgDzHYqfn9rsn3Hb8eHr7u9VrPqooE1F1UaEEEHWBU7SlYFznDzNzEdkIyLzMLyIs/tbgjVuAOmOang8qKxGZQEWx4NeBjQB2Yvo1yAv06DVokTrNio/fFaLjMDNKN2AE1FJQ7BRHEQKEbMGY70D+S/SqHXkaIyqwpavu4P0AU4HzQRZBzKPgP08dU/fVyb9T1+c1nJH4A9GuDxkcdzxeZbV+ifL6bPK3n7GdvstQVYZ0evF6NlYUjX8CqAiRjCiCEUKqy1l/Pbj8l9oldOqyOtjb79rTWtVBipWVxEaqaqtUADyNcJb6crz+Wpq2GINBi4B6oPuFvQTgz1L1HTH4joM9QEL0RBi5YkqBgICjgibVPlcjObWferkdwRJydxo2+1rTkVkkBE6CFIHdCfoUuMEnp/ZdmSBY5lBqF4M1FXYJehnBoEyAAAgAElEQVSnopKBRRdVGoJYYPYK8j5W8R9G91sYd/6Xap751xVXGqwsRM9ApRroehXmp9uh50dkLdhzMN24cRcEC6vVy0L0WgtaG7RW2JJk7QGzUoQ3g3t/fnnEiE+L45U5btwFwaLqdfsiXINyCkJ1lOOA6hbYtiLHKea8Iik4NWTtAfkSdAOYaljWKSiZKOsRnY8pep7sBXuZ0KMnSG9ETwMyEH5AmUt68TS5aX7CNhSdcFUXxOqHciaHbRALSEubKjfP3HE43dWdQPtDKVuFsJBg2pTS6crIHt+jPRY3oJxLeB67CXQRAabK4DlbyqSd0qkKocwbgO4ozRFCwGqEmdSo+rpkzXASfUYA3n73LJSbQNoSHltvBz4EpnJ117VJyT5Ibm4aVWv356CNDQzCd6BvUf+4GWzecyWW9kY5nfBvuA5lLnn2NLI672fOwuY4ejtCT9B6IIKyC2ExYp6kR7dwvzhn0aUYcz/oBSDVAAd0I5Y+z/7gM2R13u9V9T6Pa0a1TG4QpTsSthsorBbh9RM3MTMnR8ywCVrfGAar0AWlkcAelM/VYvqUbFkSSe6N0zQzmMeNCFcBZwtUU0AhTyy2YDjYD6Yr/CAwr7rFq/sMvbXERoWU2KhgxpThvI24aN/WaE0yGAR0w3Aigg3UBjJRglgUoHyHxSM0lBle66sCP2o7hBsI2+FqApsRFlHEVJrKZgC2an0cBqOEx8Gwp3H+2i3Z3zygXTblNgBqoWxWi8VWEVM73sam0kV8MI6GljJIlS4IDVH2IHxmwfQOt7A06WdwwYfj6SoOfR040xIaAwEFx4LdCqtR3t5XzPTut1PoRa4+Oe1EbGsQKpeC1gd2o3yCpS/I6Bv/XSZtTm4atYsHIFwNegqQBkiJtchB2IbyEQF+wmhHjLRAyCBsCwM0CBSDrkasN/m50auS0znktS46Le4U2JNXc6SlJltFThQ0iMp+hU+NCfzBUPyzHWCQqFwsSj2FXQrLUfPCZz1nRxxPnjPr6sa22IOAzoI2MMLuDNK+vWTv2QWnFTRpoaqNw7ojhOcvFuG27CPLsqYOGjRoTTy9c3Nz0/bv31+2nYLvgDc3bNgwIycnp0xdvPjiizWKi4tvVtVuQFPggIh8C8wYPHjwnKgFrZ99HLYZBHI5wonAXpBvgJdpfPXhI8Q112ZTRm+UGxEuQKiGYkC2IDoLcSbT8Nq4tgedvvs4AmmDELkcOBF0H1AIIqA1Dj+nvEXdKrnSWTz/5j7JIfGT+Pj4+KSGUYNG/SDhTgsAEe0zZsqzr8bLd+uAW4+z06ydpa89PfWZhNqvo0GHUnwOnAN0Bt5LUtYvwyJtg/AS0MxDru0ow+gibyVc7kI9CZtclIuipNiC4WYuS2yR4ZhlrtamCi+gdI+SYg/K7XSRaVFlLNJLEV4ETnBbrAhmlD0x/5YqkzMj3oelRkN9z7rok41ff9LuTuAhwgb8WGxQkRqiWitOuhDI49/80Pj3WVGME1+tuLCJJYFXFNrFkfWx45h+57Rd/mOcdK74+pO2HUD+RXihImEE+dQIfc9qvSTmpEIV+Xpl+z8J+gDhyZhb9incedaFSydHurnq0w4Xq9HpoE286H2QzC0ncsKHVxPIP/R6bAG5Wcb0KvN96h1vnoZxZhA2bkVANqBmgDx9fYXFqrAjhPUoYt1B5UQgm0sobYA8d/WuVAjT0a9dg8hEUuMcpwjTKQiOkPE9Ixrt9baZg0CfJGw0jEchWAvAdAOC5W86As+ctJd7Tt9BsfuaDoE+wb4THyArKzkjYiqY92gmxpoA9I+aRjGI/qOMznOeqoGEpoBeFyXXfuAervrtuJTrXJ45j/VGZBxhg1oE5D3Qn4ABRJjzBdQyNxW2s24oahNpQrhTlVsu65ecUS4nR61LzuB/gXuI8C4BjipP19zDva1HSFyj/i/Fwpe1jwhjiVbXwkIT4oauA8S1M8viV/V0dZgBnBktjajhxPVzabb2zRJfGwzx27d9oHfJuImT3OoSDQUhe9gDiPwRSHeZzYA8x85dd8uMGTEX63TEsN+ATATqREnxAcWmv0yeXMYIq7dkj0D1MaBa5HzyOmnpg2XMmKjOs3rLLY1R5xWgQwwNiWUuKa5ale87XsyeRo2ji0ic9ao6oH379hGN+uX56KOPTrMs61URifo+lUdEjKpGe59+VNXbReR2oJNLkQpMdxxnpG3b/wP8lsgbaBzgqYKCgt917lzR6PvJJ5+cHAqFcoEL3BS6LW0ni2qtYL8V9QRBFXhhTyB0y4wmT+S7exR3DFv7+/pGiv8l0MVjVqMwMTNUcPuYU8fENNQPXfu70Yj+nbBzeHQqvq47VBgxuemjr0VK3mdNds0aVs2lwBmxRH4SuJCvAi1LXZK88MK/J+bi2AM54Yz4TtVu2LyqN2iM/q8MmxC5sYJTxcbPGxMMvIJGawNkO2KG0OCs2WiuXW1Dq8fzA3qbY1VsFGwjZH9q8+B7aQRcuh+L6E8Ocl3DJ05e4Sb9xrs2ZqRp8VjgRmLbcfMEghq5z02Ej2wJ9qvzRJOfEhWgfdTe2mjd/4roPURoExSja+q/r5+f+JplxLX/tqry7M7v2t6ek5PjOlOiPPHCFQ0DNi+DXBJRGfhZkOG3DXjnjadevuIcy8grCqfFFCp8L6J9R/eb9+9oSZ6Z3v1cI5oLeqobPWsboXe+FWsSuBNhB0o0eVtQBkn22/PclHcQfb7X8RQVvQhcGSXJblRGYxW/gwZegBi2CuR2GT77kK1CJ11THeNMBnpHyXMAuFeGv/0cgE68qgNqxZp/f4Xl9JGhc707WufmplH1+H+CjiDyeKwI1b9xddc/u1ooj8ZbCy7E4mXg5MgJJB80I0rurYgsRLUvYcf4CCiIlYsxxyNyWQxN9qJWf3p2ib7QWI6hY/USFaYTxZYk8LlRZopwL1HHcbxWWMiQ6bcf3gQ1bKxeboTngYZudSkh1th5qYboO2WUbIyae5NejzKOchvjYvAtITpwkni3GWzX6hQzCaVPlBQHgN+F56g8SpT6u3TTazzw5RAyiw9VX57C/ZfcwhiAD8cxCuVhoo8rciXIsI5D2RflflJ8MJm6UsR04PK4iYX1RhhwaTZxx8OqKjzzr9+hmgNUiZQE4XmK0kbK3Vn5+vS0Nqjl1ZYdj28QK0tGD1jlNsMFb3c/S7EXgkS3C6kYJOKY3YBO2CuhO77v/s6h8eT5s3veiUhUm2uLghPovPdcghp1n3uxiPx9/fr1OdH62ClTplyoqjHaKb4Vkb6DBw/+CmDSpEnXiMgkos77+FBV+w0dOrTMvI+fZvUFniPsPBaJBVj2ANTUwWguEn1ujaoBGcOWhvfSunVE24Pm5sUrrzz/wTJ9pXe1L12m98IBwr9hYyjrlPR/Hd+hwcfH54gxetCo3YQ9TMOIXPb0lKfj7tjIycmxfl63vZhSA9FEnQmOBh1KcWw7NCzU9lgsILwb1xuKAfrTRXI9531fm2BYTvzJTAjoRWeZ7bmMY5F3tSYBPia8Qy02wkg6yXMVri/Sy7CYg7pcDC9nPL0lbQqj0yZES/0jMAO4y5Vs70w788KlN5e/+PmSdo2DaSxXxeWKg2y0xG7TsvWHm5NR5psV7S8xovNxvwgUG2Wb7WibM9otWxctyaoV7Z9V0VsTLkK586yLlj5Z+trXKzt0Rs1cvDlIVCBtfy2azRlIoOBQcxHC0EueuW42gI56vQUWS4lvnC7EaLfSERMUFW5/40VUBySjY3zkazKs9vLIb5Ka3OvtM7NQfYnUO158yPGhrpKTVWYRUW+bORp0TIrL4rWGB+hz7jbUW0/4IlfefVNSRr5kmfNUOlK8AOjoMkdY59njMwjsfx9oHTeH6H1cec8jyagZkzn/uAlhKimYy11fdD6jCjpFuqUog7r0i+EAF4eFL+uUkugDMVF4tUsWWfJLvhdRWPyK3qwwhfh1/b0J0rbrdfGjIC3K1dNQluBuMY6Gmz/m9G+fd5P0MKp3yfiJ//SWqZyI7OGPIwn32W/SsPF1EsUIpiOG9S1x+IvXDq5D7DYyduy2sE7D7kXEzbf1KSqXyPjxFVa5deTIBjjFy0nS2RBARfjvZV3ZfUJC/n7xKLQs6/I2bdp8GCvR8uXLWxhj3PSfXont0REFEdmqqvVdJH2lbdu2/Ut/90uXLm0KLAPc5D/EATuft2q/xwE7pr/Ce/aOPd3Gtx6fEuepm3584PhgqHhZSRSPBJF39jb9oecMieyQO3TtvQ8i/L/E5aOietPEkx97sfTF7E05VZ3CA2txWc+f2+fwafDc+Alj8w3pBe2p3XpP/KQx2PTVjYg8j7d3sxilJ43ODC/Sbv+mISGzHIj34RqQfmkO1xfZ2jdeIb2+sXludrqXeAaFakmnBo83WxYr0aqcVWl1d2fMU/fORalmgwSlTb1Hm22Jn7QiW+9cMwVkULx0G2t/xtLmE/EUEEL1ldtumNcvEb3c8ty0bvVCQWsZGnfRywD/I/B7heouxR8wai6944b5n5a/8dRLV50nqh8QfdE3IlWBm/JsAomPqBxEesmw2bPcJNYp19YiVLyEGM5RhxA2oTRyIXakDH/7OR3XsyqW8x7IhS40eQC1lyJmHvHn3ztRbSfZc9xH+cjNtcmsPROlZ9y0omPpcXlic/K3F7VBzSLib/44UhhUetPzspnxEg4dq5epMIck7QYlfLLvAJfOuFvyB43V7pbwJpUTZfxHArSZPFQqLhZu0oEo0/A+FtpOGs2pK+5tBhs1A5vFKG08lhWR03evZMySS6niHB4Gi/BHA44oD7kQsdzOo3P7u0mpI+iHz3Jcic0ntsNXWQrFcHnHkcQcD+vTLz6Ccm9cacpCTOgv2IG5JGLLjs8exOrgxqnhgrd/c5aiK0nym1HVOafkVblmRtYM57w5Pf8sKn+Ml6dh8fFcu6sDdlTfZgDGDxkyZET5i1OmTGmrqouIX397jTEdRKSliLixf613HKfN8OHDw5sEfpo9BHQi8b5B0XWo1ALibYYrSc+rNLw6q7xNSmfkD0Z1UtzyKrIPy3SsBKcG36EhCpWxg83Hx8cnGmV2hho1O6MlLE2JR6CnsKtHuQ7HPrO0Kha5JDoAFCyEySxU79vaDC/gzjM7ALzAYo3mAfrrIsCTuHFmAFCe5H0ta3R4V2sivOTamQEqDPPGFg3mPybqBpIT0EpzZgC46etP2t1U/mIgyPPunRkAtInRUFI7W1et6lTNiL5CqpwZAIR6TkCmq0YeXH/9Sfvrk3FmALCEf3z1aZuzD/793bI2NTDmZVJglCiqtpvN7cps9glg8YL+dlYdzcmxEF7B3WJMOpa8rCNzDxv3bps5uPKdGQD0TPJCjyUl4a7cxqhOoXLG4Bezw/5DmfJGv3EW6BOVUBbXb87k1g1u7bWHGMjcxys4Hh1RrOI/496ZAQ7qbO9/GDfODAAqf+Ptx92l9cq8R5shjCdFjumvpf2bpcGI0XEFYdz7uZrQrpXFr+hAN84M4YLovWgGtyRSTmWy4DU9WWEc7ur6FKuYZ+MlyslRC3Xd3gGwuWEHttaPFpAqCiKP6YgR53jLdBjNzu6RhDMDwG/YtGl0RNnDhp0AMhl37WBTjDMBQEeMaI3I312WfwGW+VvEO07RFLw4M8RYFBJVTvngfQKFnqLhuiXdcZxXvvjii6i74VXVMuF+OtXODJBgG+PSmQGg77Jly4aVu/YiHp0ZADKdDC7ZEzegQydzfK3/8So7GsGi0LjknBkA9KrqPzS7O9Kd4evua4/wl+TkIyoyYeja355U+qJTuP9lPNTzuc6XNDBJn6bUksIqT8ZPFoMtXzRDJJH+L4gwnfVfhnfbhcwU4jszAFiovuDGmQFgZkuHl872FHU43TImd+NdG2POp+vsznjwF3RmADjRFOvERDJuvXPtjW6cGQCa7DyP5tsv9laASN8xL11RqXOAUMCa4MKZAcAC/asHZwaATEus3JzcPmXmWjm5fdJEdQYenRkA8oD5aUkFrbBRfUHH9XRnQwkVj8GNMwPg0pkB4Emd0OMMLP2bO2cGAHkIMa/jbv5dG5GXNCfH/Xwso/YdrpwZAFRu4e13r3ct+yCLF1dBTS5HjzMDhI+meYE3F8TsM7LHaU0NR25NhTMDwIXVM/nrTU/r8VY44kNlHZl+AiGmVLi6WZuiTCCxsVBdCnnZUw6bh1LlzADwn1qtGX/6X8tcU+XPAv/rUkQbJyMph8rIWDyDN2cGgHQVXp43LXqkKh0z/QpXzgwAwmXYgVlUjjMDQE3UvKy5uVEipITptLhTwKTIAUhEuq+tWnDXeXN6Xioqf4ifAzYHd7Ai8z/xkmVPmTKlTMSQ3NzcDFV1uxZQw7bt10TErf3rJNu2wzvkNr55CuhzuPkGVZri1pkBQOnNT2+XsT1obsEpqI51VV5FqmOsV3Rx9JAXPqnFd2jw8fE5kpTx7hQR922Ql0XWo1+HY59qDAEvi8QRycTiHk85FumlwKUectQC7vBUxrHI+9oM5UYPOYIYyhp1A9xKCsLf/60gol02TOXHhSrjifzVinbtgVhhFKNx1apP2nlcPSpFfkE20CDh/NFp//XKNl0j39K4XtjxULDFWA8e/LvQllsQ6nkVEo29Tb6j4LgyR7vXoqjoDnae3RPBy9a/hgQC2eHiVFCSfnbXiAzVkW8kvhU3FLyHSjVSyd1625xSjnvm91SeAYgHvz8O2/sOsD+iHuM6pIqZT9RCud1zPpU/I1TYnRADC0tdGRM8Y6z7SKWzFDAtLepRqVUc5b5EZBqP36UoD+bkxN4icqSxivFa130W5GrLWAkuPYNrCEfn8sS6Zld7zWKDeTB+sihI8n0Kov+jOTkV2x9bvLWDwjWanX1+yfN4GLfLLTpsWBkDvA4ffiFItJDY0cqPSaCoiAbfuI4s661okYZ5eXnZ0e4vW7bsasJnLB+r/OHgsRdLliy5jJhHgMSmcVE96hXF9utQMb+9ccs9Xo9LqMDw9fe1Qoh29JAnRPT+PqtyKswxjZo/kJqRcxXEOtSOD1v7+/ogHhsU5Twn4hHNXrmRrV9GC03sQo3A74gcRtoNx5NmjWbzqouAK1znEm/97T/aF3uKXKVIkzSKBke7v23kqmpAjMnVkUGgx/a71iTiqOlpLNRy05WI19feENl5LQU8M737ucA17nNIIt/syccX7yszj69dvG8A0DwBWQCsDSj5ybUeNbHMnfES6birmhM+2izVBBH9f6BenF0tvDn3nU/jFe4cFFauDCI84EE2qPzJU3qA/WYQKYgeVQlkEojdDoVIjS2pHCMDNg/gZaEyMboNGatljyZVkulvQLiKTerut9ykdVBGJlxWFN5odiu70suYcoQom2MiIoxePC7q8QCe+XA8LYDEIuoIjTLzGR79vud5i5tjOJPhTLYVxnRq2pNXvY+ouj7mNx5GeECM9Sc8jB2/yFxDYZxTH1W1TFu2f//+QbhzCj2YvwXe7F89J0+efD6WfT+pc5CqiOiDaCmnNtX7kizvdH4ucOUA65M8R5XRyMfH51dPmTCFtqM1oyUsTZ8+fWwkZQsyR4MOvwau/UXkSELlpkrXoxeHnojHPl3oSa6W9hr+TSpU+dycSbGm5tjWBCJlNv9yZbuzDv5hJfa+AGCSyKtGKu2dE6ko+4vlFzUDzo6QPBG6r1x5QckPKN7fiTjTp31Nviuf4VpUvdeXlvw+d75xDsJJcVKnEhtbPa8sHsak5DuLQVW08HIAzVkcQOhRmYU1LLS5aLfntfVmzHs8Ve+rNzK0G4nsxhA9Ee/nUXcj9/HK2PmR8nfov/ZWtkePiuq5vAW52lIk6vnM0Wh06ekk7khWGYjnZxdLY+fRBMckeVUbkJfp2U+uu/bp49kwo0OGNAJc7oiMST22/tguwvUE3mFzPV4WIcOkYZc7p9uunP659oYNntKruh/hROr3S3Gsj3GbrFix4nyI+5yuaFoYx9dapVp6PlEcQ93jqPkNqXPTrV29yv5LSl8YuSqnGok55EahVN1KqD8J6N7A2UyaJn1ah4VjeVgcLo8m2f/ptaip1G/mxxrKV/U87o7X6O++plXtSvRzzo8oGkPPSGy+67tW4G0skFFUi+P2exzWi5z4zMs93EUI8IixKn3cHsYqV7dW8m376iTOnCghvg62XENlrSsYqzuVuZgF4Lbf2byrA9HPfY/GWcxe4M2BSxKYEx8plF4xb8cZ/yZIGkJWJcitgJS3/ST/PIK6Xry/khQ7qwMUSxrL6nWPnzA66bbizQk4BmK4hiTGTmoit0n6z3/VR4k03/hlidNnCpJSZzBBaiN6SfyUhwnhsDF9W7xkrSZOnHgoIlkqxuvxCJeR7JgvLo346fzDtgdJQXmJ2DV9EsJ3aPDx8TlyCGViVRrLcuVM0KhmozKTB4XEzy8/GnT4NeB9oSIaJzFHvQzeEyn31F9sN/CRIpHfQ6lBrVLhZoUWqVDFwWZbipzzE/nRLJVDz2ESe1/CZWsS73jqvo+KotWqINuyA6ksLzOzOFiyIiApeSdKU1RjV/lLp4Ik9l0DhLTS6joqkliZmj0uCFL5zhdWybe8Y2cDvIW9TYgWeQk4MGlq2hvPmCP6vlShupXaXVZvPlydyon+wk/27mi3Gsx5UT3tYrFMYm2g8gu9FxEoeWbPoe+J1+8k0T/kZXhWpyo1a3qPphUMnkKqFmtN2d+0xMHCezsocjYJ7ZQr379UThtQZe8eT+nFwyZeY6Kf5UUS45yjBcdxWgCIJDQWKENNJ350dqNW0u1MUmPESPLssjoVpu9vincnuhhowxInCVTM+YlIsIDq7D0oL3FVEm0Dt62qhrsjB2MV3oIUvGfxWFs7tkNDhNqLrpP+AuPcKHidV1ma2PykeoH3rledUKWMH1L9rUcvp+zvnIpyf7aSdmg4NdpRh4fQSvyeRCsrHPxh3NdzYs/p3b5y1HzvFRA5mRgh9CVFtqSKgitn3lMeLf0ubNFMcH08SgyhuOtvK7Gd2ZiZnGgrhe+kJi8rcn5xmnMkYsF6Jnb7qMrplVBozGMuIrHL3h9fqG2X/r4rvZ0qRFvh3YksAcL2Tp25qxapiTBz9LbhvzJ8hwYfH58jSZnoCCLG1WzVFJkyhk+B7dHSHiM6+Pj4HPUcnU4wJoJeIkenrpFIoaZhSVZCoV2TQxMss9FxwpGcbDv2MfNeHDG8rCIejQQzjgn9xXO86KOPYFpizxDX+H8sYMzR9wyJ1usx1D9GQ471dutI4iHyxdGMI6nvvwvsvLBMk4o2KhkR/vscoQKi18lR9P177tuPIt2PdrQS5ge/jtaw0nFX7/67/OundPtmpeh7PCremuSUMEfTvCZaH/RL2IPc8CuYg/xiHGlbQl7K3iH/Nz9C+A4NPj4+RwwxZZ0J1EgbN/nU0lPKXfIW1/Uo0+FXgbI2RZJ+pLsUVnK5axH5dc/pJaF62U+Qw/HFUvSb2hjq/oL+PqKsOfRvJIlnstbETxOVVH0fkaigl0S4ligC+abGvk3hvzTlz5G2r9wRmMpaEvqd9OAzp+zZPZBQmZKTVQT8mGJdIhHWL7R1C5BX6YVVDXnPJEl9X0lgjmS5RYQyNqZUYvfb9wI/p1RmCQ2dqAGrfu4+UPZGuxkJSxL8LhPNVwlcniV7SKSu4zyDJtHXZuR77lsLyMjY5LmgYCh1bX+5+pAZM4qAnxKQtAoo8pzLlGtrUjd+pfRyUWGNSj2KN+o7JZLMOOfoQETWAKgmP+bYGzgQN41lJd/OSAp0LY2lUkanYpO3HiWBzjUyCtsmn/5IOLqgJV8kJkPYLykJ+pRY/ddrtR8kuQlGeI5Q6d9M091ezawSvU70iI5bYqImhp6RMInpfiDd+8/s2Fop9aRHqo3VcvqnoNw6yfsmrhWJ4xeRmA3CJereRpRwES7f6US/Q+/fwNHbpxuznqwsJ0aKytL9SBmWDv9W9WQ/EDcOf1yEL12lS+nYtCyN8pJrGlMxZipFss8ZWZeQruVo9OEysetOhPJnsSaPePfsrenEP9XKlGrLjDGV3k4FHf0WqBDaNeVI+Lnkhlq7gJ0pkHjUjNl+7fgODT4+PkcMFflvmQtCB1f5KHsOlMLcY1mHXwXKrJTIEY9yrATK9VrGsYgwG++D+HfoLIeNpSn6TVvZ35ImSZ+xWwYPD7ah5YVLDhlqFZPwM4k4SeStxHdOpILsVhcs/R74NhXiFea3arWqKFpZyVJ9YznfMEtnoeq9HC3RrfYXn6HifcEuUQSD2m8nnj/1dVqOQoqD7wLI+BHFVHJftT3NsKyWV3uj/MiyPZ9XikLxCFaZTyKLoshmIJYRLxKL6Dki9Q4liXwvcWju1KV+9FMlPJd3SR++BtZ5zLZd6rHca1mViSqzveaROH2pJFCfEHZmyDzgsalT5suYMZ4XBOTZKRuBVHyjOzHWxxULSOQdltcRFnrM5GBZ75QVk/i4IIJOh/61q0mT1ImtSCydj/Ux7paNGzeuLPl30s+yIX1LzPsC+fnF+QuSLUdS25fvLSbj/dIXpp86Zq8KH6SqAEsP61tsh14iAcP/NqlHYfLH2Ssm5LldLUVy9W4xC63ccViDfcLZW7yZWTXGcwWrsAClIGnFUoCot7lRvSebfQWs95KnMLCPHdXWecmCwubb+85f5SmTSywn9WOuSAhWmXLEJN8enpq0S5SLb8WxErFBuGU+pM6xKyKWy9/XDn0ERD2bLTLyH3p287ZgeTTbrYS3Yt3WytE9JPB6JcitQAT9k38em5ddpQsyFyG1BjTANiHabn0nfsLoFAes1NkSxPI+ryqNRpljyV03bgb5NBnZlUKcb0KUV1Jc4h6BJV4y2FicWFgvZhpVXT106NBD6yiWZVV6O2VZ1iwSmId7ZDuNahy2PagmX17l2/p8SvAdGnx8fI4cFjOA0odKtrjt5tuax/pOo+YAACAASURBVMrSp08fW+DKMmIkiQ70aNDh10AaE5CkvZYLUB71lONSFoCnBY/9KE94KuNY5FL5Dsh1nV4xwF/LXDM8g9eJegTzxf3Bf3oS4Qa3+0sEeaj0TpKzLlz2HlBxMSU+77VqvfyjBPIBUGSnjwV2JJo/GoJ8euYFSyLOSkX1oRQUYVA5JEeK9FlS6BldbVMzMnaUOeVnPyHnCbaGXge+8SDqZ4pCzwFITo4Bk4pnd4fKdHn6mh8Szm/sx4BK3HEkT8tzVx/+zUT+Stk+L6X8vfluQl4D4AgPkZNTaTrFpOvoHag85zmf8Bdgioccilr/67kcV7oEHobUGr0GFkYNVlVslL97lSciKorX7/JvnUs72R0FWAEexoMxXWBWl36xdz9LPV7XBBzQTlo3x2sWxUqibRSSf39FHpbx4yu+qyEexVs7+K6MH7+8pK330uBMlrFjy0SDkLGTPgbe8yAjLk4wyOYzWqVSZGl+DgaDY6Pd3LBhw0y89Z9HG3/NKtnx2bZt27lAwgbpLcGf2ZwWeyOnok9NP3WMp4gzkZjQ7NHPUUncuTGsDACi+tjUZjkVFqwtScm4DqAopPrwwT+mNXniJ/XuHMS/A2enQBWdQeNz/hs/XRQcK4n+T/agRU/RqNVHIO/HT38IT+XdsSyI5aGVUtWtVlHexGj3az/cfA8WY7zoUDnIovpPneJpoUQQVY9jgW8azUPF4xBR5MG4kQQSZNTAuZ+gzHetSmKL+xuLgtWmlr6QWZQ/DZWEI4Ke6EBmctHO92OK49pQZMSs/wCvJVNQFAzKH4HJHvIoql7a91XUqOpusbx790LwaLdS9T6OCjiTgM2e81U+BViBf8RK4BQlYEuKg8IkJ8RDwP5Uyo3AB1Oyy/ULyiNJORkoi6nvMtJKfdkKRO0HEqXHxsnUKUxq38eEtsPZmip9OmazCpiZUGZle1oxUcfD3uctEj+kV1LIatIOxHRYKMhLf0mR5COBlKDwGPAXL3nOPNCMKhrbWdWyyto08vLyJlHuKO84bMDbvG/B4MGDl2E7f6cyndpU/4Z0LrXBL5BseWvRKtOT1svHFb5Dg4+PzxHj6clPb0J1UelrapnfxcpTL7PeAOCkUpfWjJkyJmHD3dGgw6+CjrIPwwCSWVRRRtFZ1nnKEz46YiDiKhyUogyls3gZbB3LjMZ9iKsH6Cxld192lR0IN+FlF3I5O8kNwRmcHYi8QUZgu8Ak17K983rL1ksmlL/oGOdG8RKqUNlm2TIoGUXOO++93aIMJLUD8N0h5AaRyIvTrS5aNl3QF5MqQeRPZ1605JNDMtsv3amGm8T7zvQKBPKq0ejjMn5hishQeTZri8zIcrBMf9wZKxyEG2V81p5DV+p8NdbVLqJkUV1LKHhHMiJKnCFGpkij8tJXEij+Q5krT/X6FJEHK6O0eXXzebLpnvgJyyjEGyzbO74y9HGN7fwP8G/X6Q/qrMHf4nrhUB+i+12JOFPF56o7/wtyZ8rEFZ/JpaEWkW8qd3btJ6sTkdupL5MUXnWZfO7PwlOJlFOZdO4t/xFwW9cbEbLjyuwsIVHctncA1Nu2koZbPK0jgcqfZOykFd4yHUbGTngN0cSNq8ICGjR6POKtiRN/AB3lUtJmHB0S1mnSx4jrBYJvSKtyT8Q7ATMIUmSgFWFth4spzshIibhyhFR1YOvWraM2tFlZWY6I9Af2VYYCCeLWEXJ227Ztnzn4h4io4zg3eMh/iAIp5P1acX0hVjhUy/EqOxq2VTQcSeIYKQGB962deyM6jU1s+tgi4JGE5ZegyB1Tmz9aZpdwYXHB9XhYeFplt2ST3ShJTeQHNM3tdx+ZE85YDZrIOMygZjANzw/PB1Ruxl04cQXJTnfcOa90+95m0GcB91oJIbXt/vWebRWzPygi+Ccg4fY8BWx11AxOJGP9J5tNRNwteG+utYrv67/ntYg5t/V7x4vDqWcCwcAQwNWqoBoeBk8RNQotGHB31oz80hcHD36vwFhOfxJwgq6icGWB7TVbaRRkmIyY525h3TYjAXfO3iLuHP5FHpDsOZ+TYe4FvnaVB/4G2g9389b9YA+QrBnu57gNaj2Ksih+QgBepGdX7wtbV1xxANUBJBRNrpIIOwsNp3vnmP3dtNGyQ9SjLSk2X0uAe6eOki0CQ6i8SCDbjeHmCldPkNUYbk9Q5m6U3p5yBLkPXB5R4YJm+1Yx8tt7y1wTeBS3jurClyGH+1Klz0Es5RbEW+QeIGRZDGx7O1EdlmT0DW9CDIeHsizFMr1J8QaBUuQhpr+MGBFT/qqsGUVqmatJzTfzvlW/wcOfdp81H9TVhr66oVq0O9AyXrJ/DRo0qIyNcdSoUfs13E65qb98EektIm7tX1sIf+/Q8NpvEH7rIZ+HyJg6l8YFZWwP0i/9W4S73csoQz7QX7Lk6Gm7f+X4Dg0+Pj5HFLWk7IKbMnTkoJFXRko76uZRlwmU20kpM6LJHj1o9F9GDxr53KH/Bo++4kjr8H+KLrIQw5V4884EYS8wkC7ixeP+MJ3le5QOhM9TjsYulN50EfdRC451OsvP2FwMfBgjVT4wmi4S2UDaSWahXIP3c8P15uDLhQ+kRx07fymO6dCy9dLhovyJWINfPfT/bbgblKqoPCdV9/aPtDvnnDYrfhDHdBD4KqYQANEvxKZDy/OXeJ1kVaDVRUvnqkiPkudIlv+IWh3PufDjmLvatu2vMhh4Au878guAu85svaTCQtFZbZbOBu3pySmkHBk76nPyOwMI5lc7eGkX0Fue6nXo+5Qne38J5hJiO+VsA+kuT11XJvSh5OQYji/uDUyuPFuHLsEOtC8T/SBBZMx1k4GBEH1SngBvYXO5PJGVX/6GPNXrb8AdhL9/NxqGgI+I8h6pwNQT9nHt+Vtx3G/+UmAce/f1/cWiMxzkinsPUGBdhhJny7solnVY5+637yWkl6K8GyNTIcjvuOqeP8RIkzxX3f0sIkOIuSiun2JFd/SxRLRf4YX8Nv/ySLf3A0O79JNnE1VRRLTmbgYAzxC9TVJgiuTRKytLUmUATSmd+8ozwFBiOyCscISOnbPcOVB26Sdf2IZL4p09LSgn/LiIlqsmgWrYTTM+BYjcLePH/z83usRkx55bgIfxamhTXiCj2jWSkxPVqU/GTZwEeiOxF+L/jeV0lIkTDxnRZezEP6L6O2Iv8ryLyqUyJvJOfHlm0npUOpDksRqh9HRWd+7CjqZNkxETja0i0qN9+/bz4iVs27btl8aYS8T7meux3qefgcGqujJGmooCVd9MT08/jfDcKZp8BSbu2rWrt0jZrdgdO3b8r4h0xEMUk52Bvcyq8wH77Oib7QRmFoQKLo8UCSFRxjf952bbsTqQ4EKzwkuFGdpjfOsIUUxKmNT00ftV+B8SWdwS3a8weHKzRyoY+KefOmZv0NHTCO+ai6Ujn9tnsyzYuvTlRKJMfUTA7kij07zOLyrS8KznIF7/V4YdiPSi0VmHd4U2arkeWzogsdoA2YNwAw1bTS3cw3XVi5gWLSCVKAz8IsCkN9JdR2dQZYcRq1vDx5sujpe2yRNN8gOOcznoG3EFixSB1xAHMVA+w3E6NHqyeULRAgTRegd29kd5lmjjSpQf6i7Vj08d7zo6Q0k1v5i2b/u1lRWd4SC3Zs3+iZDpKDFCmgvsE9Gbbhs49wGBLsBGF6J/UtHLRw2YGzEq4B395y/ByGXg3nGqpoEb8u1Yh8McAP0p+m12odpHhs92HQpdhryzHcvuSOyoiHnASJzis4hrq9DRMmz2IwAy8J29pKV1QjVWlIwilPsZ9vaDkv3OOyg9iL3reQ1iLpHhb3lbPG7duhjyewKxNhAYkCfItBNyAAKg5+XvYUw3YjvRxFpI3I3qnJKNQNFRnYfwScw0UIChP1d3dbVpYtItMsvEtyWtBv5J7L5kfgA6Tx4q+wAmjZAZCNeTWOTIWPXwpVE6TL01yiarE2QsymC8RYhYTxqtaCJuNmAdpq7sQ+lC7OMiixAeAO4jRv1duP1dxizpRGbx3sP5lAc7jOC+jrfwe4HfE3tcMTetmC6dR6U+MkaHW9kWsuiosNRllq0C3TuMcBEpZ8f3o0qiU8bYVCSvkVd4hYy6cS4Y77bs+PyARScZfaOrzROfXzXrk6AEOhLPLhSjnxN4KRQo6PFpyXjy391n/VZUYtpcmxY24Dc72xPQqM5vBnhyw4YNN0uEtmTo0KGLRaSbiMRyfFsPdB48ePAngwcPnqyq8exf/wY6DBky5HD/2ajnUyUbBmJF1FiG2hcicglxnetEgSkUVe+FZFWY30qfqmMQHR6nvPKsx9BFsqr+ko6n/+dIKgaVj4+Pj1d+N2RI9TyT8T1Q+qCmAwp/PMCBZ6dOnVow+sbRzSRgeqjKo0CVUum2FKUVnz5+/PiIO5VGDxq1GWhw6ILoQ09PebbCztTK1MEjnwPnAJ1JcfjbI8pirUbY2N8TaI5gMHyHsBGlLsLpQDXCg4t52IzjEkl4cbRUuQGE/kBv4HSUqoQXQ9+hkAlc6XES8WtBVVjMNVgMQDkLqEHYqLIAGEdniW8UeVdrEmA4Sg+Es1CqI9gcdIQUwoEgw84pHxHgjm/T2uwxYmcb4UqBk0HyQL8V1Rmr1jV5OavUDohVn7Y7BUO2qlwuljYzqgEBENmD8gWir0vGvmlmX7XjLMsaoUI3gRYKQQmPQh2xdKMqH1swqWXrZXEnDLm5fexWTTf2U+EGsC4UobqiiNF9RlhhiUxftfaEV7K87NRwwX8+6lDdqeIMVyM9EJoTnmB9B2wQoR6qLRGprwZBSFMFS1ADey1Yieqr+Vbx9NatP3XtQf7lynZn2UaHqcglItQ1yi4RXaqOfCeWng9yjgg1VPlRkIWINa5V649iGiq/W9amRlHAHq5qeoA0JzwJ3qegFmQiUkMcW+yitFpAulVUhfTdx1Nj/WmhmuvOOIBKHuh3wDsEQhPkiayI36fm5Kax074JleuAFkAAWIPwNlXsCfLIb2LuQtXbZ16AOn8A62Kgeomnyn7EbEUsxWgNRAyoA2qhYiO6HpXlIDXAXIlIXcAKv8N8gVqPydO/iXluaCLobbl1ITgC9AqUZgj7CTuOOCAnolofUQuVEMIGkBWoKsLZKA0QdqOsxJIX5KleccNH68g3mmCZEaBdEas5kAGaBlioGkR+Bn0Ly/mrPJm1Qe+ceS6OGYpKR6CuWuxaWbNwxx9O25027/i8ekAAZA3oB0A6cClwKkoQQRAc1PoRMR8jOokr7jn6zrac+0RX0DtRbYccfF/YBfouFk9E1XnOY1eBDEQ4F6gJ/ASyCGQcV9217ojp//YjDbDsEcDlQFNgH8oqhFdYvu81cnIMcx+/GNWbUdogHAdsQ3g/zUmbPO/A6NNU6YfSCqE6wnpR5iOMc7s474bFuXquKsOAjkAdhZ0CSzBM6dJfvBwj9YuxOFcboIxQoRvKSSj7EFaJ8PL73/B6To73BaTcXE2ra7gJYYhCK5QMULWdgsI6O1YdaLJxnlV974ZdoN8g5GLkc0SHAp1Bm4IEwp4OEgLWILIQwzgZPz7hMNWR0BEjWqJmGCKdUNMIERuwQaqEXZwwJYsH80Gmyrhxbo2U6ODBdUkLjACuAJqB7D/0vA0a50oUByi95ZamEBqBShegMbAH0c9Rpsu4ia7O59A+fWxq18wCyQI5A7QasBaRRUABql2AUxEyQTFip5lgWrCgRnV2NT4htO2Mlt8XpaWtBTJF5HygFgdHSFCkqgbYIiJfAP+yLMuo6gBVPYdwu7FJRLaralWgCRBQ1TUiMttxnIkdO3b0FHVh1apVaXv37r0JGA6cAVQp0WGviPysqkERyQC2qOpiy7KmGGPaAteJyGlAGrBGROYEAoEJrVu33qOq1rJly3oDfYGWQHXC0S3yS/59vKruFpGVqvpC+/btD/VFS5cuPZ/w/KADUIfwUVxLjDGTO3ToEHMRZeXKlcHi4uIBInKDql5AeB5hAWqMcSzL2qmqn4RE33ih/sygEbkWOFOhligiIsagmwSWq8i0yU0fibtonCg5mmP9+EPedYr2U6GVhdRQtBBQhKoYMhAEVSOW/KSwVB3r+cnNH3Z9tNmQ9fefLCaUjchlGDlJhIAKJUfImf0gIQXbgqAq6xDmiwbHTTz5r3EjkQxZ/7tsNfJnQetLScT8EDab7IYsD7QxeyUzFP6+5V1wnict4zOKioaD9gBKxoO6GliEUhuxOiOcGA79rl+h9ks0POPNuItrXtn6VX2MNQLRboTbjnC0/3CY/RDoGtA5FIQm0Oy8yNEoNNdma6u+YPqg1hkI1VBdi+o8SBtXwQFj05cXnLYj8JciWy/OS5PM2nnIRT9a2verwJ7zN1vvCvwXlatVtIWg6eEwHGJQFUQFlWIR1jtqTZJAlecaPNbAc8jrzXev62wZc7NAF4U6qoigIRFrtSpvgo5zxKptY4aBdgLqg7VblRVB0Tmh8O/WHTgOFQshpKrFCCJh87Aq7BD0M4Xc+rVOfkUS6N8ise3udeepMUOBjqB1QHaALjUik2deOLLAKENV9BKQuiC7VM1nYmk1VWkrUIewigdQWZFmBe4f0W+W+2hbKSAnJ8c6/rTlfUCz0HB7qPCDqM4PODru1pvmH1pEfzy3T0YwtG8w0As4D6Rq+I7JA+sLVTMzvUba5BE9Z8V13n88t09GmrN3iBq5BotTFaqJkiGQgahlq1DdiJ4TkuIzi6UAlW+xdCNQFaU5UB3hB5T5hIrHsbVoJydU7Q/SG+V0oGr4e2Eu4oyXYfMSsqGoIkzq+RvU9IdDNogNwAJCMk5unf3ToXQTu18DMuBwOtmIsAB1xsnwdyLaKnRi9ytRGYhyHkJN0E2ILEKtcTJ8VplFLJ10TXUcMxyhB2h4/i2yGszr1MicJlkzkttFO3the2AQ0A70OGA7ygdYOpEel8fcROGaefMyCdlDUXoCpxAeW3yHyhukhV6g2O5F2A52BlAVWIvoO9jOeK68cidvLjgbm3sJj1uPCw9PdD+wBPgHV3cN94uz518D1v0gZ4JmgBqErRheplge5rquno/RzB6nNUMwDOih0FzCdoP/ivC6rbwwfoQUD3pOm1oWI4DLgEbAHpTPsJg+OVsiHrF567N6XJFFtkJ3hFaE7bQKFInyswqOQpqE56VrVZhbmM+r6Rn0FuVK4GQgT4RvFWbsq8XLM9w4VG/V+oQYAXQDmqIIkAlkYGGjFAPrUf5JY8Yl3d9t0isx3IBwPlATZRPCIgKMP3SMxWZtimEEymVIuP4a5a3deuN3D+nVGyY3IDzO3IywyILxHUaU3TTy4bOcjEW2Kl2kJL/Av43y4iW3EteZNllUkY/H0UuhvwhnGqWBFe5/ILyY/F9V3rbSmNhxqLcoZDrmX6ejOhw52AeyG2QFmGly243vlUn7TG41TOFQVHpi0RwlDcQBtVEIH7OsH6HWVkTbAKcBGYTnCSCkgYRQVoedDndNldtv9+z42Wlxpyp78ms+IGpuVqSBpQTUIl+Urx34Y0Cdzeb/s3fv8XHVdf7H358zSUov3KEIqAgiKLoKsj9dUbG4CiQtSC+TFrA0xTXaJoXiiquul5GF1QW0YJPUzWqbgkCbKRdbmhRktYhXlFVQkItKUURays3SW5I5n98fvSUhyTlJZjKTmdfz8ejjwZz5Xj7nnJxhvnM+5/u1xL9IOl3SRMledIU/DwItf6ByTZ/fJ9+57tzjlVGtdiXZvcZcL43x8kf/6ZW3dLxt+3HHSXqtmXW6e0a7fnMrk/Scu98XhuG3P/7xj0cmfd1www3ju7q69twL2Ps55e53BEHQMnfu3B6JxEuXLj1cUrdxn17Rrhkw03/+859Xpvp78GXDuiNV3rnnN49jJG2R6XeSr9CR59y295p7om2MxmcuUqgaBXvG1nK5XpCpXWHQrNdNjvztwW/deqS69AmZ7erPtVWmbbt++rQDdu2nPSGF39PWcctsrmUtYbqXrdr1+X60Ys4UVSpIaAAw4ubXzD87kLXp1Z9BGblcpj7nanTp/MaWxhX9tRs3oSGXMQxScSQ0AAAAAAAAAAAAYDhIaOgHS04AGHFNLU3rzP1yvXrawUQ/iQRu5guymEhQEDEAAAAAAAAAAAAA6B8JDQDyYvHypq8rtCmKXONIj0o2bfGypoYYzfac2skHXuMzRzEAAAAAAAAAAAAAyAKWnACQV6lUKtj81OYPm/sZ7vZ6KTzczTbK9aRMP2tsaWyXlN01NwsnBpacAAAAAAAAAAAAAEtOAAAKzm+0K1FiUp7jAAAAAAAAAAAAQP5s1a57RkflO5BCw5ITAAAAAAAAAAAAAACg4JDQAAAAAAAAAAAAAAA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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(\"image/png\", read(\"exactVC_eLRT_manhattan.png\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### eScore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(\"image/png\", read(\"exactVC_eScore_manhattan.png\"))"
   ]
  }
 ],
 "metadata": {
  "@webio": {
   "lastCommId": null,
   "lastKernelId": null
  },
  "kernelspec": {
   "display_name": "Julia 1.4.1",
   "language": "julia",
   "name": "julia-1.4"
  },
  "language_info": {
   "file_extension": ".jl",
   "mimetype": "application/julia",
   "name": "julia",
   "version": "1.4.1"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": true,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": true,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}