notebooks/figure_generation/GenFig1_3.ipynb

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  "metadata": {
    "colab": {
      "name": "GenFig1_3.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/pachterlab/GRNP_2020/blob/master/notebooks/figure_generation/GenFig1_3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dhydD96df06z"
      },
      "source": [
        "**Generates subplots in figure 1 and 3**\n",
        "\n",
        "This notebook generates 6 subfigures: The 2 histograms in fig 1, the two histograms in fig 3A, and the plots in fig 3B and 3C.\n",
        "\n",
        "Steps:\n",
        "1. Download the code and processed data\n",
        "2. Setup the R environment\n",
        "3. Generate the figures\n",
        "\n",
        "The data for this figure is produced by the following notebooks:\n",
        "\n",
        "Processing of FASTQ files with kallisto and bustools:\n",
        "\n",
        "https://github.com/pachterlab/GRNP_2020/blob/master/notebooks/FASTQ_processing/ProcessEVAL.ipynb\n",
        "\n",
        "Preprocessing of BUG files:\n",
        "\n",
        "https://github.com/pachterlab/GRNP_2020/blob/master/notebooks/R_processing/ProcessR_EVAL.ipynb\n",
        "\n",
        "Precalculate figure data: \n",
        "\n",
        "https://github.com/pachterlab/GRNP_2020/blob/master/notebooks/figure_generation/GenFig1_3Data.ipynb\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h8RnKVMXgbzr"
      },
      "source": [
        "**1. Download the code and processed data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "doUAtCxIyOiI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f9f48b14-90cd-4293-e4ab-21d3f1fe3f71"
      },
      "source": [
        "#download the R code\n",
        "![ -d \"GRNP_2020\" ] && rm -r GRNP_2020\n",
        "\n",
        "!git clone https://github.com/pachterlab/GRNP_2020.git\n"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'GRNP_2020'...\n",
            "remote: Enumerating objects: 294, done.\u001b[K\n",
            "remote: Counting objects: 100% (294/294), done.\u001b[K\n",
            "remote: Compressing objects: 100% (237/237), done.\u001b[K\n",
            "remote: Total 2012 (delta 214), reused 83 (delta 57), pack-reused 1718\u001b[K\n",
            "Receiving objects: 100% (2012/2012), 10.86 MiB | 19.01 MiB/s, done.\n",
            "Resolving deltas: 100% (1399/1399), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dUNSQ1qBZb2g",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8ccd8d55-a586-40f4-b594-980848e8d59d"
      },
      "source": [
        "#download processed data from Zenodo\n",
        "![ -d \"figureData\" ] && rm -r figureData\n",
        "!mkdir figureData\n",
        "!cd figureData && wget https://zenodo.org/record/4661263/files/FigureData.zip?download=1 && unzip 'FigureData.zip?download=1' && rm 'FigureData.zip?download=1'\n",
        "\n",
        "!cd figureData && wget https://zenodo.org/record/4661263/files/EVAL.zip?download=1 && unzip 'EVAL.zip?download=1' && rm 'EVAL.zip?download=1'\n"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2021-04-04 17:46:24--  https://zenodo.org/record/4661263/files/FigureData.zip?download=1\n",
            "Resolving zenodo.org (zenodo.org)... 137.138.76.77\n",
            "Connecting to zenodo.org (zenodo.org)|137.138.76.77|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 7490400 (7.1M) [application/octet-stream]\n",
            "Saving to: ‘FigureData.zip?download=1’\n",
            "\n",
            "FigureData.zip?down 100%[===================>]   7.14M  2.90MB/s    in 2.5s    \n",
            "\n",
            "2021-04-04 17:46:28 (2.90 MB/s) - ‘FigureData.zip?download=1’ saved [7490400/7490400]\n",
            "\n",
            "Archive:  FigureData.zip?download=1\n",
            " extracting: Fig3C_r1.RDS            \n",
            " extracting: Fig3C_r2.RDS            \n",
            " extracting: Fig3_h1.RDS             \n",
            " extracting: Fig3_h2.RDS             \n",
            "  inflating: Fig4AC_ldata.RDS        \n",
            "  inflating: Fig4AC_ldata2.RDS       \n",
            "  inflating: Fig4_DE.RDS             \n",
            "  inflating: gc.RDS                  \n",
            "  inflating: simDepthData.RDS        \n",
            "  inflating: simFcData.RDS           \n",
            "  inflating: simGexData.RDS          \n",
            " extracting: simMuData.RDS           \n",
            "--2021-04-04 17:46:29--  https://zenodo.org/record/4661263/files/EVAL.zip?download=1\n",
            "Resolving zenodo.org (zenodo.org)... 137.138.76.77\n",
            "Connecting to zenodo.org (zenodo.org)|137.138.76.77|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 206479312 (197M) [application/octet-stream]\n",
            "Saving to: ‘EVAL.zip?download=1’\n",
            "\n",
            "EVAL.zip?download=1 100%[===================>] 196.91M  4.35MB/s    in 55s     \n",
            "\n",
            "2021-04-04 17:47:25 (3.59 MB/s) - ‘EVAL.zip?download=1’ saved [206479312/206479312]\n",
            "\n",
            "Archive:  EVAL.zip?download=1\n",
            "   creating: EVAL/\n",
            "  inflating: EVAL/Bug_10.RData       \n",
            "  inflating: EVAL/Bug_100.RData      \n",
            "  inflating: EVAL/Bug_20.RData       \n",
            "  inflating: EVAL/Bug_25.RData       \n",
            "  inflating: EVAL/Bug_40.RData       \n",
            "  inflating: EVAL/Bug_5.RData        \n",
            "  inflating: EVAL/Bug_60.RData       \n",
            "  inflating: EVAL/Bug_80.RData       \n",
            "  inflating: EVAL/ds_summary.txt     \n",
            "  inflating: EVAL/PredEvalData.RDS   \n",
            "  inflating: EVAL/Stats.RData        \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oesgTqLO0Qje",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d8a0572e-6f71-4bc6-bbbd-c3f92fc645a1"
      },
      "source": [
        "#Check that download worked\n",
        "!cd figureData && ls -l && cd EVAL && ls -l"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total 7944\n",
            "drwxr-xr-x 2 root root    4096 Jul  1  2020 EVAL\n",
            "-rw-r--r-- 1 root root     239 Jun 30  2020 Fig3C_r1.RDS\n",
            "-rw-r--r-- 1 root root     233 Jun 30  2020 Fig3C_r2.RDS\n",
            "-rw-r--r-- 1 root root     683 Jun 30  2020 Fig3_h1.RDS\n",
            "-rw-r--r-- 1 root root     830 Jun 30  2020 Fig3_h2.RDS\n",
            "-rw-r--r-- 1 root root  372141 Apr  3 16:59 Fig4AC_ldata2.RDS\n",
            "-rw-r--r-- 1 root root  480126 Apr  3 16:59 Fig4AC_ldata.RDS\n",
            "-rw-r--r-- 1 root root 3949960 Feb 25 18:38 Fig4_DE.RDS\n",
            "-rw-r--r-- 1 root root  304548 Feb 26 13:30 gc.RDS\n",
            "-rw-r--r-- 1 root root 1128027 Feb 15 11:34 simDepthData.RDS\n",
            "-rw-r--r-- 1 root root  621646 Feb 15 13:22 simFcData.RDS\n",
            "-rw-r--r-- 1 root root 1237109 Feb 15 15:46 simGexData.RDS\n",
            "-rw-r--r-- 1 root root     284 Feb 15 01:28 simMuData.RDS\n",
            "total 212788\n",
            "-rw-r--r-- 1 root root 37523336 Jun 30  2020 Bug_100.RData\n",
            "-rw-r--r-- 1 root root 17301493 Jun 30  2020 Bug_10.RData\n",
            "-rw-r--r-- 1 root root 23443334 Jun 30  2020 Bug_20.RData\n",
            "-rw-r--r-- 1 root root 25288320 Jun 30  2020 Bug_25.RData\n",
            "-rw-r--r-- 1 root root 29057075 Jun 30  2020 Bug_40.RData\n",
            "-rw-r--r-- 1 root root 11226736 Jun 30  2020 Bug_5.RData\n",
            "-rw-r--r-- 1 root root 32629892 Jun 30  2020 Bug_60.RData\n",
            "-rw-r--r-- 1 root root 35477251 Jun 30  2020 Bug_80.RData\n",
            "-rw-r--r-- 1 root root     1025 Jul  1  2020 ds_summary.txt\n",
            "-rw-r--r-- 1 root root  4167784 Jul  1  2020 PredEvalData.RDS\n",
            "-rw-r--r-- 1 root root  1761192 Jun 30  2020 Stats.RData\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sCmhNVdYgkWH"
      },
      "source": [
        "**2. Prepare the R environment**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5Gt6rQkSXriM"
      },
      "source": [
        "#switch to R mode\n",
        "%reload_ext rpy2.ipython\n"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jJ3rQJCdgeJa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "130b00f1-37f1-4b23-92bf-717550595643"
      },
      "source": [
        "#install the R packages and setup paths\n",
        "%%R\n",
        "install.packages(\"dplyr\")\n",
        "install.packages(\"ggplot2\")\n",
        "install.packages(\"ggpubr\")\n",
        "install.packages(\"preseqR\")\n",
        "\n"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "R[write to console]: Installing package into ‘/usr/local/lib/R/site-library’\n",
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            "R[write to console]: also installing the dependencies ‘matrixStats’, ‘RcppArmadillo’, ‘numDeriv’, ‘SparseM’, ‘MatrixModels’, ‘conquer’, ‘sp’, ‘data.table’, ‘openxlsx’, ‘minqa’, ‘nloptr’, ‘statmod’, ‘RcppEigen’, ‘carData’, ‘abind’, ‘pbkrtest’, ‘quantreg’, ‘maptools’, ‘rio’, ‘lme4’, ‘corrplot’, ‘car’, ‘ggrepel’, ‘ggsci’, ‘cowplot’, ‘ggsignif’, ‘gridExtra’, ‘polynom’, ‘rstatix’\n",
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x56fjfCSicrp"
      },
      "source": [
        "**3. Generate the figures**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V37XLBAO68oR"
      },
      "source": [
        "#First set some path variables\n",
        "%%R\n",
        "source(\"GRNP_2020/RCode/pathsGoogleColab.R\")\n"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R6kuhOmzZL_X",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1606530c-5b57-4655-a49b-25d6c704b886"
      },
      "source": [
        "#Import helpers (available in other notebooks)\n",
        "%%R\n",
        "source(paste0(sourcePath,\"ButterflyHelpers.R\"))\n",
        "source(paste0(sourcePath,\"ggplotHelpers.R\"))\n",
        "source(paste0(sourcePath,\"modZTNB.R\"))\n",
        "\n",
        "\n",
        "\n"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "R[write to console]: \n",
            "Attaching package: ‘dplyr’\n",
            "\n",
            "\n",
            "R[write to console]: The following objects are masked from ‘package:stats’:\n",
            "\n",
            "    filter, lag\n",
            "\n",
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            "R[write to console]: The following objects are masked from ‘package:base’:\n",
            "\n",
            "    intersect, setdiff, setequal, union\n",
            "\n",
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kqZPO7XjtPPa"
      },
      "source": [
        "#create figure directory\n",
        "![ -d \"figures\" ] && rm -r figures\n",
        "!mkdir figures"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tkQeT362BM7V",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 977
        },
        "outputId": "91d9d09e-a4b6-43f0-80aa-76c4ee436c3a"
      },
      "source": [
        "#Create and save the figures\n",
        "%%R\n",
        "library(ggplot2)\n",
        "library(ggpubr)\n",
        "\n",
        "loadStats(\"EVAL\")\n",
        "\n",
        "################################\n",
        "#Fig 2A - histograms per gene \n",
        "################################\n",
        "\n",
        "colors = gg_color_hue(2)\n",
        "\n",
        "#read precalculated histograms\n",
        "h1 = readRDS(paste0(figure_data_path, \"Fig3_h1.RDS\"))\n",
        "h2 = readRDS(paste0(figure_data_path, \"Fig3_h2.RDS\"))\n",
        "\n",
        "df1 = data.frame(x = 1:20, y = h1$density[1:20])#30 for full\n",
        "fig3A_1 = ggplot(df1,aes(x=x,y=y)) +\n",
        "  geom_bar(stat=\"identity\", fill = colors[1]) +\n",
        "  labs(y=\"Density\", x=\"Counts per UMI\", title=\"\") +\n",
        "  theme(panel.background = element_rect(\"white\", \"white\", 0, \n",
        "                                          0, \"white\"))\n",
        "print(fig3A_1)\n",
        "\n",
        "df2 = data.frame(x = 1:20, y = h2$density[1:20])#30 for full\n",
        "fig3A_2 = ggplot(df2,aes(x=x,y=y)) +\n",
        "  geom_bar(stat=\"identity\", fill = colors[2]) +\n",
        "  labs(y=\"Density\", x=\"Counts per UMI\", title=\"\") +\n",
        "  theme(panel.background = element_rect(\"white\", \"white\", 0, \n",
        "                                        0, \"white\"))\n",
        "print(fig3A_2)\n",
        "\n",
        "ggsave(\n",
        "  paste0(figure_path, \"fig3A_1.png\"),\n",
        "  plot = fig3A_1, device = \"png\",\n",
        "  width = 3, height = 2.4, dpi = 300)\n",
        "\n",
        "ggsave(\n",
        "  paste0(figure_path, \"fig3A_2.png\"),\n",
        "  plot = fig3A_2, device = \"png\",\n",
        "  width = 3, height = 2.4, dpi = 300)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n"
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n"
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kbl2tuKBgVzn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "f2303c3f-466e-41fe-9bca-45d1e5bbfef7"
      },
      "source": [
        "%%R\n",
        "###########################\n",
        "# CU histograms for Fig 1\n",
        "###########################\n",
        "\n",
        "h3 = c(3,1,0,0,0)\n",
        "df1_1 = data.frame(x = 1:5, y = h3)\n",
        "fig1_1 = ggplot(df1_1,aes(x=x,y=y)) +\n",
        "  geom_bar(stat=\"identity\", fill = colors[1]) +\n",
        "  scale_y_continuous(breaks = c(0,1,2,3)) +\n",
        "  labs(y=\"UMIs\", x=\"Counts per UMI\", title=\"\") +\n",
        "  theme(panel.background = element_rect(\"white\", \"white\", 0, \n",
        "                                        0, \"white\"))\n",
        "print(fig1_1)\n",
        "\n",
        "ggsave(\n",
        "  paste0(figure_path, \"fig1_1.png\"),\n",
        "  plot = fig1_1, device = \"png\",\n",
        "  width = 2, height = 2, dpi = 300)\n",
        "\n",
        "\n",
        "h4 = c(0,2,2,1,0)\n",
        "df1_2 = data.frame(x = 1:5, y = h4)\n",
        "fig1_2 = ggplot(df1_2, aes(x=x,y=y)) +\n",
        "  geom_bar(stat=\"identity\", fill = colors[2]) +\n",
        "  scale_y_continuous(breaks = c(0,1,2)) +\n",
        "  labs(y=\"UMIs\", x=\"Counts per UMI\", title=\"\") +\n",
        "  theme(panel.background = element_rect(\"white\", \"white\", 0, \n",
        "                                        0, \"white\"))\n",
        "print(fig1_2)\n",
        "\n",
        "ggsave(\n",
        "  paste0(figure_path, \"fig1_2.png\"),\n",
        "  plot = fig1_2, device = \"png\",\n",
        "  width = 2, height = 2, dpi = 300)\n",
        "\n",
        "#predict h3 and h4\n",
        "dd1_1 = as.matrix(data.frame(1:5,h3));\n",
        "rSAC1_1 = mod.ztnb.rSAC(dd1_1, incTol = 1e-5, iterIncTol = 200);\n",
        "\n",
        "print(paste0(\"Prediction Gene 1: \",rSAC1_1(2)))#6.514601\n",
        "\n",
        "\n",
        "dd1_2 = as.matrix(data.frame(1:5,h4));\n",
        "rSAC1_2 = mod.ztnb.rSAC(dd1_2, incTol = 1e-5, iterIncTol = 200);\n",
        "print(paste0(\"Prediction Gene 2: \",rSAC1_2(2)))#5.375271 "
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1] \"Prediction Gene 1: 6.51460104759028\"\n",
            "[1] \"Prediction Gene 2: 5.37527053810671\"\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n"
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n"
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-1T-lf5KhE3C",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        },
        "outputId": "9d803aa5-5638-443a-ac6b-d176a6d082ba"
      },
      "source": [
        "#Fig 3B:\n",
        "############\n",
        "%%R\n",
        "r1 = data.frame(statsEVAL)[statsEVAL$gene == \"Vmn1r13\",]\n",
        "r2 = data.frame(statsEVAL)[statsEVAL$gene == \"Ubb\",]\n",
        "\n",
        "data1 = c(r1$CPM_EVAL_d_5, r1$CPM_EVAL_d_10, r1$CPM_EVAL_d_20, r1$CPM_EVAL_d_25, r1$CPM_EVAL_d_40, r1$CPM_EVAL_d_60, r1$CPM_EVAL_d_80, r1$CPM_EVAL_d_100)\n",
        "data2 = c(r2$CPM_EVAL_d_5, r2$CPM_EVAL_d_10, r2$CPM_EVAL_d_20, r2$CPM_EVAL_d_25, r2$CPM_EVAL_d_40, r2$CPM_EVAL_d_60, r2$CPM_EVAL_d_80, r2$CPM_EVAL_d_100)\n",
        "\n",
        "data=c(data1,data2)\n",
        "logData = log2(data + 1)\n",
        "gene = factor(c(rep(0,8),rep(1,8)), c(0,1), c(\"Vmn1r13\", \"Ubb\"))\n",
        "x = c(0.05,0.1,0.2,0.25,0.4,0.6,0.8,1);\n",
        "df = data.frame(data=logData, Gene=gene, x=log2(x))\n",
        "\n",
        "dfline = data.frame(x=c(log2(0.25), log2(0.25)), data=c(7.8, 9.5) )\n",
        "dfmark = df[df$x == log2(0.25),]\n",
        "\n",
        "fig3B = ggplot(df, aes(x=x, y=data, colour = Gene)) + \n",
        "  geom_line() + geom_point() +\n",
        "  ylab(expression(Log[2]*\"(CPM + 1)\")) +\n",
        "  geom_line(data=dfline, color=\"black\", linetype=2) +\n",
        "  geom_point(data=dfmark, size=6, shape=1, color=\"black\") +\n",
        "  #scale_x_continuous(name=\"Number of reads\", breaks = c(log2(0.05), -3, -2, -1, 0), labels = c(\"1x\", \"2.5x\", \"5x\", \"10x\", \"20x\")) +\n",
        "  scale_x_continuous(name=\"Number of reads\", breaks = c(log2(0.0625), log2(0.125), log2(0.25), log2(0.5), log2(1)), labels = c(\"1/16 x\", \"1/8 x\", \"1/4 x\", \"1/2 x\", \"1 x\")) +\n",
        "  ylim(c(7,10.2)) +\n",
        "  theme(panel.background = element_rect(\"white\", \"white\", 0, \n",
        "                                        0, \"white\"), legend.position= \"none\")\n",
        "\n",
        "print(fig3B)\n",
        "\n",
        "ggsave(\n",
        "  paste0(figure_path, \"fig3B.png\"),\n",
        "  plot = fig3B, device = \"png\",\n",
        "  width = 3, height = 3, dpi = 300)\n"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n"
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a-Ye0YhjhNJk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        },
        "outputId": "bc7527a5-0f4a-4b42-b1f5-ff4eb9250b5b"
      },
      "source": [
        "#Fig 3C\n",
        "###############\n",
        "#create prediction data\n",
        "%%R\n",
        "#load precalculated prediction data\n",
        "data1 = unlist(readRDS(paste0(figure_data_path, \"Fig3C_r1.RDS\"))[2:9]) #skip the gene column\n",
        "data2 = unlist(readRDS(paste0(figure_data_path, \"Fig3C_r2.RDS\"))[2:9]) #skip the gene column\n",
        "\n",
        "data=c(data1,data2)\n",
        "logData = log2(data + 1)\n",
        "gene = factor(c(rep(0,8),rep(1,8)), c(0,1), c(\"Gene 1 - Vmn1r13\", \"Gene 2 - Ubb\"))\n",
        "x = c(0.05,0.1,0.2,0.25,0.4,0.6,0.8,1);\n",
        "df = data.frame(data=logData, Gene=gene, x=log2(x))\n",
        "dfline = data.frame(x=c(log2(0.25), log2(0.25)), data=c(8.3, 10.13) )\n",
        "dfmark = df[df$x == log2(0.25),]\n",
        "\n",
        "fig3C = ggplot(df, aes(x=x, y=data, colour = Gene)) + \n",
        "  geom_line() + geom_point() +\n",
        "  ylab(expression(Log[2]*\"(CPM + 1)\")) +\n",
        "  geom_line(data=dfline, color=\"black\", linetype=2) +\n",
        "  geom_point(data=dfmark, size=6, shape=1, color=\"black\") +\n",
        "  ylim(c(7,10.2)) +\n",
        "  scale_x_continuous(name=\"Number of reads\", breaks = c(log2(0.0625), log2(0.125), log2(0.25), log2(0.5), log2(1)), labels = c(\"1/16 x\", \"1/8 x\", \"1/4 x\", \"1/2 x\", \"1 x\")) +\n",
        "  theme(panel.background = element_rect(\"white\", \"white\", 0, 0, \"white\"), legend.position= \"none\")\n",
        "\n",
        "print(fig3C)\n",
        "\n",
        "ggsave(\n",
        "  paste0(figure_path, \"fig3C.png\"),\n",
        "  plot = fig3C, device = \"png\",\n",
        "  width = 3, height = 3, dpi = 300)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n"
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g1SbjAplhUqF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        },
        "outputId": "8e96983c-d4a1-434e-81e2-93bda1f3a4ec"
      },
      "source": [
        "#create a plot with Legend - copy the legend from here...\n",
        "#uses data from previous plot\n",
        "%%R\n",
        "fig3_Legend = ggplot(df, aes(x=x, y=data, colour = Gene)) + \n",
        "  geom_line() +\n",
        "  theme(panel.background = element_rect(\"white\", \"white\", 0, \n",
        "        0, \"white\"),legend.position= \"bottom\", legend.direction = \"vertical\", legend.title = element_blank())\n",
        "\n",
        "print(fig3_Legend)\n",
        "\n",
        "ggsave(\n",
        "  paste0(figure_path, \"fig3_Legend.png\"),\n",
        "  plot = fig3_Legend, device = \"png\",\n",
        "  width = 3, height = 3, dpi = 300)\n"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n"
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8IqJrPHJidP5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7c7b506f-3771-44b6-e953-c84a109d3708"
      },
      "source": [
        "####################################\n",
        "#Produce some data for the main text\n",
        "####################################\n",
        "%%R\n",
        "\n",
        "dsExpr = statsEVAL$CPM_EVAL_d_5\n",
        "fullExpr = statsEVAL$CPM_EVAL_d_100\n",
        "gene1 = statsEVAL$gene == \"Vmn1r13\"\n",
        "gene2 = statsEVAL$gene == \"Ubb\"\n",
        "print(paste0(\"FC full dataset (gene 1/2):\", fullExpr[gene1]/fullExpr[gene2]))#2.404394\n",
        "print(paste0(\"FC downsampled (0.05) dataset (gene 2/1):\", dsExpr[gene2]/dsExpr[gene1]))#4.876712\n",
        "\n",
        "#get the full number of reads\n",
        "loadBug(\"EVAL\", 1)\n",
        "print(paste0(\"Total reads full dataset: \", sum(bug_EVAL_100$count)))#29913038\n",
        "rmBug(\"EVAL\", 1)\n",
        "\n",
        "#get the downsampled number of reads\n",
        "loadBug(\"EVAL\", 0.05)\n",
        "print(paste0(\"Total reads downsampled (0.05) dataset: \", sum(bug_EVAL_5$count)))#1495651\n",
        "rmBug(\"EVAL\", 0.05)\n"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1] \"FC full dataset (gene 1/2):2.40439381611066\"\n",
            "[1] \"FC downsampled (0.05) dataset (gene 2/1):4.87671232876712\"\n",
            "[1] \"Total reads full dataset: 29913038\"\n",
            "[1] \"Total reads downsampled (0.05) dataset: 1495651\"\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}