chromVAR_Brg1_KD_ATAC_D7.ipynb

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      "name": "chromVAR_Brg1_KD_ATAC_D7.ipynb",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/gongx030/etv2_pioneer/blob/master/chromVAR_Brg1_KD_ATAC_D7.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4TLq2MbDD3zq"
      },
      "source": [
        "# chromVAR analysis of Brg1 KD ATAC-seq data at D7\n",
        "\n",
        "by Wuming Gong\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6OhqEEeZHroa"
      },
      "source": [
        "start_time0 <- Sys.time()"
      ],
      "execution_count": 113,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "obcf6pAcw6TQ"
      },
      "source": [
        "### Install external packages"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "99vS8MZUxApi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "080f6705-25e9-4bfe-e5ff-abbcf07e4316"
      },
      "source": [
        "system('apt-get -q install libgsl-dev', intern = TRUE)"
      ],
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "[1] \"Reading package lists...\"                                       \n",
              "[2] \"Building dependency tree...\"                                    \n",
              "[3] \"Reading state information...\"                                   \n",
              "[4] \"libgsl-dev is already the newest version (2.4+dfsg-6).\"         \n",
              "[5] \"0 upgraded, 0 newly installed, 0 to remove and 16 not upgraded.\""
            ],
            "text/latex": "\\begin{enumerate*}\n\\item 'Reading package lists...'\n\\item 'Building dependency tree...'\n\\item 'Reading state information...'\n\\item 'libgsl-dev is already the newest version (2.4+dfsg-6).'\n\\item '0 upgraded, 0 newly installed, 0 to remove and 16 not upgraded.'\n\\end{enumerate*}\n",
            "text/markdown": "1. 'Reading package lists...'\n2. 'Building dependency tree...'\n3. 'Reading state information...'\n4. 'libgsl-dev is already the newest version (2.4+dfsg-6).'\n5. '0 upgraded, 0 newly installed, 0 to remove and 16 not upgraded.'\n\n\n",
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              "<ol class=list-inline><li>'Reading package lists...'</li><li>'Building dependency tree...'</li><li>'Reading state information...'</li><li>'libgsl-dev is already the newest version (2.4+dfsg-6).'</li><li>'0 upgraded, 0 newly installed, 0 to remove and 16 not upgraded.'</li></ol>\n"
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          "metadata": {
            "tags": []
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    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "id": "nGBlrxdZEtzm",
        "outputId": "8d1c486d-f85e-451c-88b7-cc7fc9d96d77"
      },
      "source": [
        "system('apt-get install libcairo2-dev', intern = TRUE) # for Cairo package that is required by ComplexHeatmap package"
      ],
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "[1] \"Reading package lists...\"                                         \n",
              "[2] \"Building dependency tree...\"                                      \n",
              "[3] \"Reading state information...\"                                     \n",
              "[4] \"libcairo2-dev is already the newest version (1.15.10-2ubuntu0.1).\"\n",
              "[5] \"0 upgraded, 0 newly installed, 0 to remove and 16 not upgraded.\"  "
            ],
            "text/latex": "\\begin{enumerate*}\n\\item 'Reading package lists...'\n\\item 'Building dependency tree...'\n\\item 'Reading state information...'\n\\item 'libcairo2-dev is already the newest version (1.15.10-2ubuntu0.1).'\n\\item '0 upgraded, 0 newly installed, 0 to remove and 16 not upgraded.'\n\\end{enumerate*}\n",
            "text/markdown": "1. 'Reading package lists...'\n2. 'Building dependency tree...'\n3. 'Reading state information...'\n4. 'libcairo2-dev is already the newest version (1.15.10-2ubuntu0.1).'\n5. '0 upgraded, 0 newly installed, 0 to remove and 16 not upgraded.'\n\n\n",
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              "<ol class=list-inline><li>'Reading package lists...'</li><li>'Building dependency tree...'</li><li>'Reading state information...'</li><li>'libcairo2-dev is already the newest version (1.15.10-2ubuntu0.1).'</li><li>'0 upgraded, 0 newly installed, 0 to remove and 16 not upgraded.'</li></ol>\n"
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wFuTF2MUEiAk"
      },
      "source": [
        "### Install R packages"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2AQqz-pyE0b7",
        "outputId": "045aff5d-3de4-41d4-f34a-82a51d237622"
      },
      "source": [
        "install.packages('BiocManager')"
      ],
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Installing package into '/usr/local/lib/R/site-library'\n",
            "(as 'lib' is unspecified)\n",
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hv8nq9sjEzhx"
      },
      "source": [
        "required_packages <- c(\n",
        "  'devtools', 'circlize', 'dplyr', 'gplots',\n",
        "  'SummarizedExperiment', 'BSgenome.Mmusculus.UCSC.mm10', 'BiocParallel', 'ComplexHeatmap', \n",
        "  'rtracklayer', 'EnrichedHeatmap', 'ChIPpeakAnno', 'ChIPseeker', 'TxDb.Mmusculus.UCSC.mm10.knownGene', \n",
        "  'org.Mm.eg.db', 'clusterProfiler', 'ReactomePA',\n",
        "  'gsl', 'DirichletMultinomial', 'chromVAR', 'motifmatchr', 'PWMEnrich'\n",
        ")"
      ],
      "execution_count": 117,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FDu62M5JFJZt"
      },
      "source": [
        "missing_packages <- required_packages[!required_packages %in% rownames(installed.packages())]\n",
        "if (length(missing_packages) > 0){\n",
        "    BiocManager::install(missing_packages)\n",
        "}"
      ],
      "execution_count": 118,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "67T7odcIksTR"
      },
      "source": [
        "### Install `chromVAR` and `chromVARmotifs`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "syUICLgnfESQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "28cf3923-f7e8-4973-ce08-dba0eb6a4462"
      },
      "source": [
        "devtools::install_github(\"GreenleafLab/chromVARmotifs\")"
      ],
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Skipping install of 'chromVARmotifs' from a github remote, the SHA1 (38bed559) has not changed since last install.\n",
            "  Use `force = TRUE` to force installation\n",
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XiWD5bRSpT8k"
      },
      "source": [
        "### Load R packages"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nQFQk04TzjFj"
      },
      "source": [
        "library(chromVARmotifs)\n",
        "library(chromVAR)\n",
        "library(BSgenome.Mmusculus.UCSC.mm10)\n",
        "library(motifmatchr)\n",
        "library(BiocParallel)\n",
        "register(MulticoreParam(2))\n",
        "library(SummarizedExperiment)\n",
        "library(gplots)\n",
        "library(circlize)\n",
        "library(dplyr)\n",
        "library(ComplexHeatmap)\n",
        "library(EnrichedHeatmap)\n",
        "library(rtracklayer)\n",
        "library(parallel)\n",
        "library(ChIPpeakAnno)\n",
        "library(ChIPseeker)\n",
        "library(TxDb.Mmusculus.UCSC.mm10.knownGene)\n",
        "library(org.Mm.eg.db)\n",
        "library(clusterProfiler)\n",
        "library(ReactomePA)"
      ],
      "execution_count": 120,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KTlR7IjcYeAI"
      },
      "source": [
        "## Load the motif PWMs"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DWJ7LNk3bCts"
      },
      "source": [
        "data(\"homer_pwms\") "
      ],
      "execution_count": 121,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 935
        },
        "id": "TexwmghzYirV",
        "outputId": "1a2c1a50-146e-42e7-ea7e-60631a6051b8"
      },
      "source": [
        "Etv2_canonical <- homer_pwms[['Etv2(ETS)/ES-ER71-ChIP-Seq(GSE59402)/Homer(0.967)']]\n",
        "Etv2_canonical\n",
        "Etv2_partial <- reverseComplement(Etv2_canonical)\n",
        "Etv2_partial@profileMatrix <- Etv2_partial@profileMatrix[, 1:8]\n",
        "Etv2_partial@ID <- 'Etv2(ETS)/Partial'\n",
        "Etv2_partial@name <- 'Etv2(ETS)/Partial'\n",
        "Etv2_partial"
      ],
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "An object of class PWMatrix\n",
              "ID: Etv2(ETS)/ES-ER71-ChIP-Seq(GSE59402)/Homer(0.967)\n",
              "Name: Etv2(ETS)\n",
              "Matrix Class: Unknown\n",
              "strand: +\n",
              "Pseudocounts: \n",
              "Tags: \n",
              "$evidence\n",
              "[1] \"ES-ER71-ChIP-Seq(GSE59402)\"\n",
              "\n",
              "$source\n",
              "[1] \"Homer(0.967)\"\n",
              "\n",
              "Background: \n",
              "   A    C    G    T \n",
              "0.25 0.25 0.25 0.25 \n",
              "Matrix: \n",
              "         [,1]       [,2]        [,3]       [,4]        [,5]      [,6]      [,7]\n",
              "A -0.16016875 -0.3856625  0.60758929 -2.3859667  0.08801088 -5.521461 -5.521461\n",
              "C  0.18564935  0.1988509 -0.60880603  0.6418539 -5.52146092 -5.521461  1.383290\n",
              "G  0.02371653 -0.0618754  0.06203539 -0.9571127 -2.74887220 -5.521461 -5.521461\n",
              "T -0.08338161  0.1484200 -0.58698698  0.4848922  1.04380405  1.383290 -5.521461\n",
              "       [,8]       [,9]       [,10]      [,11]       [,12]\n",
              "A -5.521461 -2.4304185 -1.53247687 -0.1544844 -0.46621461\n",
              "C  1.383290 -2.4304185 -0.03666398  0.1071607  0.09066769\n",
              "G -5.521461  0.4134333  0.69912925 -0.3787969  0.09431068\n",
              "T -5.521461  0.8381130 -0.21319322  0.2966507  0.16451494"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "An object of class PWMatrix\n",
              "ID: Etv2(ETS)/Partial\n",
              "Name: Etv2(ETS)/Partial\n",
              "Matrix Class: Unknown\n",
              "strand: -\n",
              "Pseudocounts: \n",
              "Tags: \n",
              "$evidence\n",
              "[1] \"ES-ER71-ChIP-Seq(GSE59402)\"\n",
              "\n",
              "$source\n",
              "[1] \"Homer(0.967)\"\n",
              "\n",
              "Background: \n",
              "   A    C    G    T \n",
              "0.25 0.25 0.25 0.25 \n",
              "Matrix: \n",
              "         [,1]       [,2]        [,3]       [,4]      [,5]      [,6]      [,7]\n",
              "A  0.16451494  0.2966507 -0.21319322  0.8381130 -5.521461 -5.521461  1.383290\n",
              "C  0.09431068 -0.3787969  0.69912925  0.4134333 -5.521461 -5.521461 -5.521461\n",
              "G  0.09066769  0.1071607 -0.03666398 -2.4304185  1.383290  1.383290 -5.521461\n",
              "T -0.46621461 -0.1544844 -1.53247687 -2.4304185 -5.521461 -5.521461 -5.521461\n",
              "         [,8]\n",
              "A  1.04380405\n",
              "C -2.74887220\n",
              "G -5.52146092\n",
              "T  0.08801088"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sDTuQ3MeYnIS"
      },
      "source": [
        "Etv2_partial <- as(list(Etv2_partial), 'SimpleList')\n",
        "names(Etv2_partial) <- 'Etv2_partial'"
      ],
      "execution_count": 123,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qJ4vyG7PYveJ"
      },
      "source": [
        "homer_pwms <- c(Etv2_partial, homer_pwms)"
      ],
      "execution_count": 124,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BSjJwXNzspFx"
      },
      "source": [
        "## Read and process the ATAC-seq read counts data \n",
        "Read data for each sample and count the motifs at each interval"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ojuU1ngisM0z",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        },
        "outputId": "27393f5d-7b1f-4e88-8877-5b53ada0e99f"
      },
      "source": [
        "se <- readRDS(gzcon(url('https://s3.msi.umn.edu/gongx030/datasets/dataset=Etv2ATAC_version=20190228a/all_ATAC.rds')))\n",
        "se"
      ],
      "execution_count": 125,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "class: RangedSummarizedExperiment \n",
              "dim: 152282 30 \n",
              "metadata(0):\n",
              "assays(1): counts\n",
              "rownames(152282): chr1 chr1 ... chrY chrY\n",
              "rowData names(3): score source group\n",
              "colnames(30): EB_Dox_D25_rep1 EB_Dox_D25_rep2 ...\n",
              "  MEF_Brg1_KD_NoDox_D7_rep1 MEF_Brg1_KD_NoDox_D7_rep2\n",
              "colData names(3): bw_file name group"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SFH8BAbfs-aq"
      },
      "source": [
        "# remove intervals that have reads in at zero or one condition\n",
        "se <- se[rowSums(assays(se)$counts > 0) >= 2] \n",
        "se <- addGCBias(se, genome = BSgenome.Mmusculus.UCSC.mm10)\n",
        "colData(se)$is_MEF <- grepl('^MEF_', colData(se)$group)\n",
        "colData(se)$is_EB <- grepl('^EB_', colData(se)$group)\n",
        "colData(se)$is_flk1pos <- grepl('Flk1pos', colData(se)$group)\n",
        "colData(se)$is_dox <- grepl('_Dox_', colData(se)$group)\n",
        "colData(se)$is_Brg1_KD <- grepl('_Brg1_', colData(se)$group)\n",
        "time <- gsub('_Flk1pos', '', gsub('.+_(D.+)$', '\\\\1', colData(se)$group))\n",
        "time[time == 'MEF_NoDox'] <- 'D0'\n",
        "colData(se)$time <- time"
      ],
      "execution_count": 126,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OiLxB-hstYKw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 938
        },
        "outputId": "54733adc-304f-4ea1-b7f4-d89357745d5c"
      },
      "source": [
        "MEF_cols <- colorpanel(4, low = 'black', high = 'green') # time\n",
        "EB_col <- 'red'\n",
        "group2col <- c(\n",
        "  'MEF_NoDox' = MEF_cols[1],\n",
        "  'MEF_Dox_D1' = MEF_cols[2],\n",
        "  'MEF_Dox_D2' = MEF_cols[3],\n",
        "  'MEF_Dox_D7' = MEF_cols[4],\n",
        "  'MEF_Dox_D7_Flk1pos' = MEF_cols[4],\n",
        "\n",
        "  'EB_NoDox_D25' = EB_col,\n",
        "  'EB_Dox_D25' = EB_col,\n",
        "  'EB_Dox_D25_Flk1pos' = EB_col,\n",
        "  \n",
        "  'MEF_Brg1_KD_D0' = MEF_cols[1],\n",
        "  'MEF_Brg1_KD_Dox_D1' = MEF_cols[2],\n",
        "  'MEF_Brg1_KD_Dox_D2' = MEF_cols[3],\n",
        "  'MEF_Brg1_KD_Dox_D7' = MEF_cols[4],\n",
        "  \n",
        "  'MEF_Brg1_KD_NoDox_D1' = MEF_cols[2],\n",
        "  'MEF_Brg1_KD_NoDox_D2' = MEF_cols[3],\n",
        "  'MEF_Brg1_KD_NoDox_D7' = MEF_cols[4]\n",
        ")\n",
        "pch <- rep(21, ncol(se))\n",
        "pch[colData(se)$is_Brg1_KD] <- 24\n",
        "colData(se)$pch <- pch\n",
        "colData(se)$bg <- group2col[as.character(colData(se)$group)]\n",
        "colData(se)$col <- group2col[as.character(colData(se)$group)]\n",
        "colData(se)                             "
      ],
      "execution_count": 127,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "DataFrame with 30 rows and 12 columns\n",
              "                                                                                                                                                        bw_file\n",
              "                                                                                                                                                       <factor>\n",
              "EB_Dox_D25_rep1                             https://s3.msi.umn.edu/gongx030/datasets/dataset=Etv2ATAC_version=20190228b/EB_Dox_D25_rep1_treat_pileup.bw        \n",
              "EB_Dox_D25_rep2                             https://s3.msi.umn.edu/gongx030/datasets/dataset=Etv2ATAC_version=20190228b/EB_Dox_D25_rep2_treat_pileup.bw        \n",
              "EB_NoDox_D25_rep1                           https://s3.msi.umn.edu/gongx030/datasets/dataset=Etv2ATAC_version=20190228b/EB_NoDox_D25_rep1_treat_pileup.bw      \n",
              "EB_NoDox_D25_rep2                           https://s3.msi.umn.edu/gongx030/datasets/dataset=Etv2ATAC_version=20190228b/EB_NoDox_D25_rep2_treat_pileup.bw      \n",
              "EB_Dox_D25_Flk1pos_rep1                     https://s3.msi.umn.edu/gongx030/datasets/dataset=Etv2ATAC_version=20190228b/EB_Dox_D25_Flk1pos_rep1_treat_pileup.bw\n",
              "...                                                                                                                                                         ...\n",
              "MEF_Brg1_KD_NoDox_D1_rep2 https://s3.msi.umn.edu/gongx030/datasets/dataset=Brg1KO_MEF_reprogramming_version=20200817a/MEF_Brg1_KD_NoDox_D1_rep2_treat_pileup.bw\n",
              "MEF_Brg1_KD_NoDox_D2_rep1 https://s3.msi.umn.edu/gongx030/datasets/dataset=Brg1KO_MEF_reprogramming_version=20200817a/MEF_Brg1_KD_NoDox_D2_rep1_treat_pileup.bw\n",
              "MEF_Brg1_KD_NoDox_D2_rep2 https://s3.msi.umn.edu/gongx030/datasets/dataset=Brg1KO_MEF_reprogramming_version=20200817a/MEF_Brg1_KD_NoDox_D2_rep2_treat_pileup.bw\n",
              "MEF_Brg1_KD_NoDox_D7_rep1 https://s3.msi.umn.edu/gongx030/datasets/dataset=Brg1KO_MEF_reprogramming_version=20200817a/MEF_Brg1_KD_NoDox_D7_rep1_treat_pileup.bw\n",
              "MEF_Brg1_KD_NoDox_D7_rep2 https://s3.msi.umn.edu/gongx030/datasets/dataset=Brg1KO_MEF_reprogramming_version=20200817a/MEF_Brg1_KD_NoDox_D7_rep2_treat_pileup.bw\n",
              "                                               name                group\n",
              "                                           <factor>             <factor>\n",
              "EB_Dox_D25_rep1             EB_Dox_D25_rep1           EB_Dox_D25        \n",
              "EB_Dox_D25_rep2             EB_Dox_D25_rep2           EB_Dox_D25        \n",
              "EB_NoDox_D25_rep1           EB_NoDox_D25_rep1         EB_NoDox_D25      \n",
              "EB_NoDox_D25_rep2           EB_NoDox_D25_rep2         EB_NoDox_D25      \n",
              "EB_Dox_D25_Flk1pos_rep1     EB_Dox_D25_Flk1pos_rep1   EB_Dox_D25_Flk1pos\n",
              "...                                             ...                  ...\n",
              "MEF_Brg1_KD_NoDox_D1_rep2 MEF_Brg1_KD_NoDox_D1_rep2 MEF_Brg1_KD_NoDox_D1\n",
              "MEF_Brg1_KD_NoDox_D2_rep1 MEF_Brg1_KD_NoDox_D2_rep1 MEF_Brg1_KD_NoDox_D2\n",
              "MEF_Brg1_KD_NoDox_D2_rep2 MEF_Brg1_KD_NoDox_D2_rep2 MEF_Brg1_KD_NoDox_D2\n",
              "MEF_Brg1_KD_NoDox_D7_rep1 MEF_Brg1_KD_NoDox_D7_rep1 MEF_Brg1_KD_NoDox_D7\n",
              "MEF_Brg1_KD_NoDox_D7_rep2 MEF_Brg1_KD_NoDox_D7_rep2 MEF_Brg1_KD_NoDox_D7\n",
              "                             is_MEF     is_EB is_flk1pos    is_dox is_Brg1_KD\n",
              "                          <logical> <logical>  <logical> <logical>  <logical>\n",
              "EB_Dox_D25_rep1               FALSE      TRUE      FALSE      TRUE      FALSE\n",
              "EB_Dox_D25_rep2               FALSE      TRUE      FALSE      TRUE      FALSE\n",
              "EB_NoDox_D25_rep1             FALSE      TRUE      FALSE     FALSE      FALSE\n",
              "EB_NoDox_D25_rep2             FALSE      TRUE      FALSE     FALSE      FALSE\n",
              "EB_Dox_D25_Flk1pos_rep1       FALSE      TRUE       TRUE      TRUE      FALSE\n",
              "...                             ...       ...        ...       ...        ...\n",
              "MEF_Brg1_KD_NoDox_D1_rep2      TRUE     FALSE      FALSE     FALSE       TRUE\n",
              "MEF_Brg1_KD_NoDox_D2_rep1      TRUE     FALSE      FALSE     FALSE       TRUE\n",
              "MEF_Brg1_KD_NoDox_D2_rep2      TRUE     FALSE      FALSE     FALSE       TRUE\n",
              "MEF_Brg1_KD_NoDox_D7_rep1      TRUE     FALSE      FALSE     FALSE       TRUE\n",
              "MEF_Brg1_KD_NoDox_D7_rep2      TRUE     FALSE      FALSE     FALSE       TRUE\n",
              "                                 time       pch          bg         col\n",
              "                          <character> <numeric> <character> <character>\n",
              "EB_Dox_D25_rep1                   D25        21         red         red\n",
              "EB_Dox_D25_rep2                   D25        21         red         red\n",
              "EB_NoDox_D25_rep1                 D25        21         red         red\n",
              "EB_NoDox_D25_rep2                 D25        21         red         red\n",
              "EB_Dox_D25_Flk1pos_rep1           D25        21         red         red\n",
              "...                               ...       ...         ...         ...\n",
              "MEF_Brg1_KD_NoDox_D1_rep2          D1        24     #005500     #005500\n",
              "MEF_Brg1_KD_NoDox_D2_rep1          D2        24     #00AA00     #00AA00\n",
              "MEF_Brg1_KD_NoDox_D2_rep2          D2        24     #00AA00     #00AA00\n",
              "MEF_Brg1_KD_NoDox_D7_rep1          D7        24     #00FF00     #00FF00\n",
              "MEF_Brg1_KD_NoDox_D7_rep2          D7        24     #00FF00     #00FF00"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F-7DtNt1ub1T"
      },
      "source": [
        "## chromVAR analysis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iGplSK6GsYPk"
      },
      "source": [
        "set.seed(1)\n",
        "include <- colData(se)$is_MEF & colData(se)$time == 'D7'\n",
        "se_D7 <- se[, include]\n",
        "se_D7 <- se_D7[rowSums(assays(se_D7)$counts > 0) >= 2]\n",
        "motif_ix <- matchMotifs(homer_pwms, se_D7, genome = 'mm10')\n",
        "dev_D7 <- computeDeviations(object = se_D7, annotations = motif_ix)\n",
        "v_D7 <- computeVariability(dev_D7)"
      ],
      "execution_count": 128,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ritXnnZxuloO"
      },
      "source": [
        "PCA of the motif deiviations"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UEbPJo2MuaIv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "outputId": "62add4e5-0f76-40cc-8096-9143d85e6de9"
      },
      "source": [
        "m <- v_D7$p_value_adj < 1e-10\n",
        "cat(sprintf('# of significant motifs: %d(%d)\\n', sum(m), length(m)))\n",
        "s <- prcomp(assays(dev_D7)$z[m, ])\n",
        "eigs <- s$sdev^2\n",
        "z <- s$rotation\n",
        "\n",
        "pc_x <- 1; pc_y <- 2\n",
        "cex <- 3\n",
        "\n",
        "par(mar = c(5, 5, 5, 5))\n",
        "options(repr.plot.width=6, repr.plot.height=6)\n",
        "plot(\n",
        "  z[, pc_x], z[, pc_y], \n",
        "  pch = colData(se_D7)$pch, \n",
        "  bg = colData(se_D7)$bg, \n",
        "  col = colData(se_D7)$col, \n",
        "  cex = cex, \n",
        "  xlab = sprintf('PC%d(%.3f%%)', pc_x, eigs[pc_x] / sum(eigs) * 100), \n",
        "  ylab = sprintf('PC%d(%.3f%%)', pc_y, eigs[pc_y] / sum(eigs) * 100),\n",
        "  xlim = c(-0.6, 0.5),\n",
        "  ylim = c(-0.9, 0.5)\n",
        ")\n",
        "points(\n",
        "  z[colData(se_D7)$is_dox, pc_x], z[colData(se_D7)$is_dox, pc_y], \n",
        "  pch = colData(se_D7)$pch[colData(se_D7)$is_dox], \n",
        "  col = 'blue', \n",
        "  cex = cex, \n",
        "  lwd = 3\n",
        ")\n",
        "points(\n",
        "  z[colData(se_D7)$is_flk1pos, pc_x], z[colData(se_D7)$is_flk1pos, pc_y], \n",
        "  pch = 3, \n",
        "  col = 'pink', \n",
        "  cex = cex, \n",
        "  lwd = 3\n",
        ")"
      ],
      "execution_count": 129,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "# of significant motifs: 230(333)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ckDR1FrpAPLhxA4YE8IHACQEcnPLUuyxiTjXCweytFosCcEDgDIiCJFLK9vFbbC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\nSUxX5ep0ma56IOiTzbY2r9ah39ixY201OgBkC7ESu0gWaTniPJm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DKp+u9H0pn+pKnsnd3Z2TZQAAUEF54BCRT5f/HerSfPCSrcN6bU12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\nRK5CYII=",
            "text/plain": [
              "plot without title"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 360,
              "height": 360
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mW6kvVeVvIis",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "456be588-a5c4-41ad-895b-bd02a2d470de"
      },
      "source": [
        "m <- v_D7$p_value_adj < 1e-200\n",
        "cat(sprintf('# of significant motifs: %d(%d)\\n', sum(m), length(m)))\n",
        "Y <- assays(dev_D7)$z\n",
        "rownames(Y) <- rowData(dev_D7)$name\n",
        "Y <- Y[m, ]\n",
        "col_fun <- colorRamp2(quantile(Y, c(0.1, 0.5, 0.9)), c(\"blue\", \"black\", \"red\"))"
      ],
      "execution_count": 130,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "# of significant motifs: 47(333)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iS6udKUBgotL"
      },
      "source": [
        "highlights <- c(\n",
        "  'Etv2(ETS)/Partial',\n",
        "  'AP-1(bZIP)',\n",
        "  'Atf3(bZIP)',\n",
        "  'Bach1(bZIP)',\n",
        "#  'Bach2(bZIP)',\n",
        "  'BATF(bZIP)',\n",
        "#  'EHF(ETS)',\n",
        "#  'ELF5(ETS)',\n",
        "  'Elk1(ETS)',\n",
        "#  'Elk4(ETS)',\n",
        "  'ERG(ETS)',\n",
        "  'ETS1(ETS)',\n",
        "#  'Ets1-distal(ETS)',\n",
        "#  'ETS:E-box(ETS,bHLH)',\n",
        "#  'ETS(ETS)',\n",
        "  'ETS:RUNX(ETS,Runt)',\n",
        "  'ETV1(ETS)',\n",
        "  'Etv2(ETS)',\n",
        "#  'EWS:ERG-fusion(ETS)',\n",
        "#  'EWS:FLI1-fusion(ETS)',\n",
        "  'Fli1(ETS)',\n",
        "#  'Fosl2(bZIP)',\n",
        "  'Foxo1(Forkhead)',\n",
        "  'Fra1(bZIP)',\n",
        "  'Fra2(bZIP)',\n",
        "  'GABPA(ETS)',\n",
        "  'Jun-AP1(bZIP)',\n",
        "#  'KLF14(Zf)',\n",
        "  'KLF3(Zf)',\n",
        "#  'Klf4(Zf)',\n",
        "#  'KLF5(Zf)',\n",
        "#  'Klf9(Zf)',\n",
        "  'Maz(Zf)',\n",
        "  'Meis1(Homeobox)',\n",
        "  'NF-E2(bZIP)',\n",
        "  'NFY(CCAAT)',\n",
        "  'Nrf2(bZIP)',\n",
        "  'PBX1(Homeobox)',\n",
        "  'PU.1(ETS)',\n",
        "  'RUNX(Runt)',\n",
        "  'Sp1(Zf)',\n",
        "  'SPDEF(ETS)',\n",
        "  'SpiB(ETS)',\n",
        "#  'TEAD2(TEA)',\n",
        "  'TEAD(TEA)',\n",
        "  'VDR(NR),DR3',\n",
        "  'YY1(Zf)'\n",
        ")"
      ],
      "execution_count": 131,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3jNpAAlImLIR"
      },
      "source": [
        "column_annotation <- HeatmapAnnotation(\n",
        "  flk1pos = colData(se_D7)$is_flk1pos,\n",
        "  dox = colData(se_D7)$is_dox,\n",
        "#  time = colData(se_D7)$time,\n",
        "  Brg1_KD = colData(se_D7)$is_Brg1_KD,\n",
        "  col = list(\n",
        "    dox = c('TRUE' = 'blue', 'FALSE' = 'grey'), \n",
        "    flk1pos = c('TRUE' = 'pink', 'FALSE' = 'gray'),\n",
        "    Brg1_KD  = c('TRUE' = 'yellow', 'FALSE' = 'gray'),\n",
        "    time = c('D0' = MEF_cols[1], 'D1' = MEF_cols[2],'D2' = MEF_cols[3],'D7' = MEF_cols[4])\n",
        "  ),\n",
        "  show_legend = TRUE,\n",
        "  annotation_name_side = \"left\"\n",
        ")"
      ],
      "execution_count": 132,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 701
        },
        "id": "yaX-rfeSeyoj",
        "outputId": "77263e7e-b7fe-45fd-be60-c750f91df3a6"
      },
      "source": [
        "options(repr.plot.width = 8, repr.plot.height = nrow(Y) * 0.2 + 2)\n",
        "Heatmap(\n",
        "  Y,\n",
        "  cluster_rows = TRUE,\n",
        "  cluster_columns = TRUE,\n",
        "  top_annotation = column_annotation,\n",
        "  show_row_names = TRUE,\n",
        "  show_column_names = FALSE,\n",
        "  show_column_dend = FALSE,\n",
        "  col = col_fun,\n",
        "  show_row_dend = FALSE,\n",
        "  row_names_gp = gpar(fontsize = 15)\n",
        ") + \n",
        "  rowAnnotation(\n",
        "    mark = anno_mark(\n",
        "      at = which(rownames(Y) %in% highlights), \n",
        "      labels = rownames(Y)[rownames(Y) %in% highlights], \n",
        "      labels_gp = gpar(fontsize = 15), \n",
        "      padding = unit(1, \"mm\"), \n",
        "      which = 'row'\n",
        "    ) \n",
        "  )"
      ],
      "execution_count": 133,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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p64NYKIbD221RWwlVaRSsQp8bFh/1zZvXFZ/y6NrZ17n3hUZJ/5ziCXCdOTIv69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\n0psGbWDb8/bbsA2zx7V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vHb8DomcmnW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mBICKQwENAPCei4qK6t+/v8ZdM2fOXLNm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84Mw3x6L72w5Mau5W5tGhqJ+CZ1HDx85j8o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\n11JiI3LsDpNHl8m+npVfM4AiFIeDbXFKoHJhSg3Xydu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v+SZ2894DSafOplmsdrfadRs3bd6hx98ee+ofIR3vKPnCg0EdR57Oc0zeEt+/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\ngg==",
            "text/plain": [
              "plot without title"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 480,
              "height": 684
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PrxPJ_8EvyKm"
      },
      "source": [
        "At D7, The Etv2 motif associated chromatin accessibility decreased in Brg1 KD cells, compared with the WT reprogramming. This suggest that Brg1 may help to retain the Etv2 bindings on the chromatin, not so much related to the initialization."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_jOe0_CZn7Wd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "27b0c5d0-1f95-47d7-9c8d-72717df98534"
      },
      "source": [
        "sessionInfo()"
      ],
      "execution_count": 134,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "R version 4.0.3 (2020-10-10)\n",
              "Platform: x86_64-pc-linux-gnu (64-bit)\n",
              "Running under: Ubuntu 18.04.5 LTS\n",
              "\n",
              "Matrix products: default\n",
              "BLAS:   /usr/lib/x86_64-linux-gnu/openblas/libblas.so.3\n",
              "LAPACK: /usr/lib/x86_64-linux-gnu/libopenblasp-r0.2.20.so\n",
              "\n",
              "locale:\n",
              " [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n",
              " [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n",
              " [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n",
              " [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n",
              " [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n",
              "[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n",
              "\n",
              "attached base packages:\n",
              " [1] grid      stats4    parallel  stats     graphics  grDevices utils    \n",
              " [8] datasets  methods   base     \n",
              "\n",
              "other attached packages:\n",
              " [1] GetoptLong_1.0.5                         \n",
              " [2] ReactomePA_1.34.0                        \n",
              " [3] clusterProfiler_3.18.0                   \n",
              " [4] org.Mm.eg.db_3.12.0                      \n",
              " [5] TxDb.Mmusculus.UCSC.mm10.knownGene_3.10.0\n",
              " [6] GenomicFeatures_1.42.1                   \n",
              " [7] AnnotationDbi_1.52.0                     \n",
              " [8] ChIPseeker_1.26.0                        \n",
              " [9] ChIPpeakAnno_3.24.1                      \n",
              "[10] EnrichedHeatmap_1.20.0                   \n",
              "[11] ComplexHeatmap_2.6.2                     \n",
              "[12] dplyr_1.0.2                              \n",
              "[13] circlize_0.4.12                          \n",
              "[14] gplots_3.1.1                             \n",
              "[15] SummarizedExperiment_1.20.0              \n",
              "[16] Biobase_2.50.0                           \n",
              "[17] MatrixGenerics_1.2.0                     \n",
              "[18] matrixStats_0.57.0                       \n",
              "[19] BiocParallel_1.24.1                      \n",
              "[20] motifmatchr_1.12.0                       \n",
              "[21] BSgenome.Mmusculus.UCSC.mm10_1.4.0       \n",
              "[22] BSgenome_1.58.0                          \n",
              "[23] rtracklayer_1.50.0                       \n",
              "[24] Biostrings_2.58.0                        \n",
              "[25] XVector_0.30.0                           \n",
              "[26] GenomicRanges_1.42.0                     \n",
              "[27] GenomeInfoDb_1.26.2                      \n",
              "[28] IRanges_2.24.1                           \n",
              "[29] S4Vectors_0.28.1                         \n",
              "[30] BiocGenerics_0.36.0                      \n",
              "[31] chromVAR_1.12.0                          \n",
              "[32] chromVARmotifs_0.2.0                     \n",
              "\n",
              "loaded via a namespace (and not attached):\n",
              "  [1] rappdirs_0.3.1                         \n",
              "  [2] pbdZMQ_0.3-4                           \n",
              "  [3] R.methodsS3_1.8.1                      \n",
              "  [4] nabor_0.5.0                            \n",
              "  [5] tidyr_1.1.2                            \n",
              "  [6] ggplot2_3.3.3                          \n",
              "  [7] bit64_4.0.5                            \n",
              "  [8] DelayedArray_0.16.0                    \n",
              "  [9] R.utils_2.10.1                         \n",
              " [10] data.table_1.13.6                      \n",
              " [11] KEGGREST_1.30.1                        \n",
              " [12] TFBSTools_1.28.0                       \n",
              " [13] RCurl_1.98-1.2                         \n",
              " [14] AnnotationFilter_1.14.0                \n",
              " [15] generics_0.1.0                         \n",
              " [16] callr_3.5.1                            \n",
              " [17] cowplot_1.1.1                          \n",
              " [18] lambda.r_1.2.4                         \n",
              " [19] usethis_2.0.0                          \n",
              " [20] RSQLite_2.2.2                          \n",
              " [21] shadowtext_0.0.7                       \n",
              " [22] bit_4.0.4                              \n",
              " [23] enrichplot_1.10.1                      \n",
              " [24] xml2_1.3.2                             \n",
              " [25] httpuv_1.5.4                           \n",
              " [26] assertthat_0.2.1                       \n",
              " [27] DirichletMultinomial_1.32.0            \n",
              " [28] viridis_0.5.1                          \n",
              " [29] hms_0.5.3                              \n",
              " [30] evaluate_0.14                          \n",
              " [31] promises_1.1.1                         \n",
              " [32] fansi_0.4.1                            \n",
              " [33] progress_1.2.2                         \n",
              " [34] caTools_1.18.0                         \n",
              " [35] dbplyr_2.0.0                           \n",
              " [36] igraph_1.2.6                           \n",
              " [37] DBI_1.1.0                              \n",
              " [38] htmlwidgets_1.5.3                      \n",
              " [39] futile.logger_1.4.3                    \n",
              " [40] purrr_0.3.4                            \n",
              " [41] ellipsis_0.3.1                         \n",
              " [42] backports_1.2.1                        \n",
              " [43] annotate_1.68.0                        \n",
              " [44] biomaRt_2.46.0                         \n",
              " [45] vctrs_0.3.6                            \n",
              " [46] remotes_2.2.0                          \n",
              " [47] ensembldb_2.14.0                       \n",
              " [48] Cairo_1.5-12.2                         \n",
              " [49] withr_2.3.0                            \n",
              " [50] ggforce_0.3.2                          \n",
              " [51] checkmate_2.0.0                        \n",
              " [52] GenomicAlignments_1.26.0               \n",
              " [53] prettyunits_1.1.1                      \n",
              " [54] cluster_2.1.0                          \n",
              " [55] DOSE_3.16.0                            \n",
              " [56] IRdisplay_0.7.0                        \n",
              " [57] lazyeval_0.2.2                         \n",
              " [58] seqLogo_1.56.0                         \n",
              " [59] crayon_1.3.4                           \n",
              " [60] pkgconfig_2.0.3                        \n",
              " [61] tweenr_1.0.1                           \n",
              " [62] pkgload_1.1.0                          \n",
              " [63] ProtGenerics_1.22.0                    \n",
              " [64] devtools_2.3.2                         \n",
              " [65] rlang_0.4.10                           \n",
              " [66] lifecycle_0.2.0                        \n",
              " [67] miniUI_0.1.1.1                         \n",
              " [68] downloader_0.4                         \n",
              " [69] BiocFileCache_1.14.0                   \n",
              " [70] VennDiagram_1.6.20                     \n",
              " [71] rprojroot_2.0.2                        \n",
              " [72] polyclip_1.10-0                        \n",
              " [73] graph_1.68.0                           \n",
              " [74] Matrix_1.3-0                           \n",
              " [75] IRkernel_1.1.1                         \n",
              " [76] boot_1.3-25                            \n",
              " [77] base64enc_0.1-3                        \n",
              " [78] GlobalOptions_0.1.2                    \n",
              " [79] processx_3.4.5                         \n",
              " [80] png_0.1-7                              \n",
              " [81] viridisLite_0.3.0                      \n",
              " [82] rjson_0.2.20                           \n",
              " [83] bitops_1.0-6                           \n",
              " [84] R.oo_1.24.0                            \n",
              " [85] KernSmooth_2.23-18                     \n",
              " [86] blob_1.2.1                             \n",
              " [87] shape_1.4.5                            \n",
              " [88] stringr_1.4.0                          \n",
              " [89] qvalue_2.22.0                          \n",
              " [90] regioneR_1.22.0                        \n",
              " [91] readr_1.4.0                            \n",
              " [92] CNEr_1.26.0                            \n",
              " [93] reactome.db_1.74.0                     \n",
              " [94] scales_1.1.1                           \n",
              " [95] memoise_1.1.0                          \n",
              " [96] graphite_1.36.0                        \n",
              " [97] magrittr_2.0.1                         \n",
              " [98] plyr_1.8.6                             \n",
              " [99] zlibbioc_1.36.0                        \n",
              "[100] compiler_4.0.3                         \n",
              "[101] scatterpie_0.1.5                       \n",
              "[102] RColorBrewer_1.1-2                     \n",
              "[103] plotrix_3.7-8                          \n",
              "[104] clue_0.3-58                            \n",
              "[105] Rsamtools_2.6.0                        \n",
              "[106] cli_2.2.0                              \n",
              "[107] ps_1.5.0                               \n",
              "[108] formatR_1.7                            \n",
              "[109] MASS_7.3-53                            \n",
              "[110] tidyselect_1.1.0                       \n",
              "[111] stringi_1.5.3                          \n",
              "[112] GOSemSim_2.16.1                        \n",
              "[113] askpass_1.1                            \n",
              "[114] locfit_1.5-9.4                         \n",
              "[115] ggrepel_0.9.0                          \n",
              "[116] fastmatch_1.1-0                        \n",
              "[117] tools_4.0.3                            \n",
              "[118] uuid_0.1-4                             \n",
              "[119] TFMPvalue_0.0.8                        \n",
              "[120] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2\n",
              "[121] gridExtra_2.3                          \n",
              "[122] farver_2.0.3                           \n",
              "[123] ggraph_2.0.4                           \n",
              "[124] digest_0.6.27                          \n",
              "[125] rvcheck_0.1.8                          \n",
              "[126] BiocManager_1.30.10                    \n",
              "[127] shiny_1.5.0                            \n",
              "[128] pracma_2.2.9                           \n",
              "[129] Rcpp_1.0.5                             \n",
              "[130] later_1.1.0.1                          \n",
              "[131] httr_1.4.2                             \n",
              "[132] colorspace_2.0-0                       \n",
              "[133] XML_3.99-0.5                           \n",
              "[134] fs_1.5.0                               \n",
              "[135] splines_4.0.3                          \n",
              "[136] RBGL_1.66.0                            \n",
              "[137] graphlayouts_0.7.1                     \n",
              "[138] multtest_2.46.0                        \n",
              "[139] plotly_4.9.3                           \n",
              "[140] sessioninfo_1.1.1                      \n",
              "[141] xtable_1.8-4                           \n",
              "[142] jsonlite_1.7.2                         \n",
              "[143] futile.options_1.0.1                   \n",
              "[144] poweRlaw_0.70.6                        \n",
              "[145] tidygraph_1.2.0                        \n",
              "[146] testthat_3.0.1                         \n",
              "[147] R6_2.5.0                               \n",
              "[148] pillar_1.4.7                           \n",
              "[149] htmltools_0.5.0                        \n",
              "[150] mime_0.9                               \n",
              "[151] glue_1.4.2                             \n",
              "[152] fastmap_1.0.1                          \n",
              "[153] DT_0.16                                \n",
              "[154] codetools_0.2-18                       \n",
              "[155] fgsea_1.16.0                           \n",
              "[156] pkgbuild_1.2.0                         \n",
              "[157] lattice_0.20-41                        \n",
              "[158] tibble_3.0.4                           \n",
              "[159] curl_4.3                               \n",
              "[160] gtools_3.8.2                           \n",
              "[161] GO.db_3.12.1                           \n",
              "[162] openssl_1.4.3                          \n",
              "[163] survival_3.2-7                         \n",
              "[164] repr_1.1.0                             \n",
              "[165] desc_1.2.0                             \n",
              "[166] munsell_0.5.0                          \n",
              "[167] DO.db_2.9                              \n",
              "[168] GenomeInfoDbData_1.2.4                 \n",
              "[169] reshape2_1.4.4                         \n",
              "[170] gtable_0.3.0                           "
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jJBUZVhEH0nx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "07e35ed6-fe58-4f51-904c-dbe1f9dbbff6"
      },
      "source": [
        "Sys.time() - start_time0"
      ],
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Time difference of 1.462755 mins"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}