Overlap_between_MEFs_and_EB_for_HDACs_HATs_Inflammatory_and_NFkB_genes.ipynb

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  "metadata": {
    "kernelspec": {
      "display_name": "R",
      "language": "R",
      "name": "ir"
    },
    "language_info": {
      "codemirror_mode": "r",
      "file_extension": ".r",
      "mimetype": "text/x-r-source",
      "name": "R",
      "pygments_lexer": "r",
      "version": "3.3.1"
    },
    "colab": {
      "name": "Overlap between MEFs and EB for HDACs HATs Inflammatory and NFkB genes.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "machine_shape": "hm",
      "include_colab_link": true
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/gongx030/Etv2_pioneer/blob/master/Overlap_between_MEFs_and_EB_for_HDACs_HATs_Inflammatory_and_NFkB_genes.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RJ9MLPdm3F9c"
      },
      "source": [
        "By Nikita Dsouza"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BksfiAIOgKiY"
      },
      "source": [
        "The goal of this notebook id to examine the expression pattern of HATs, HDACs, Inflammatory related and NFkB related pathways in both ES/EB and MEF systems to see if the genes are upregulated or downregulated using a Treatment and control"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cs5vybio3JZR"
      },
      "source": [
        "![Screen Shot 2021-09-21 at 2.36.36 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)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6OhqEEeZHroa"
      },
      "source": [
        "start_time0 <- Sys.time()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wFuTF2MUEiAk"
      },
      "source": [
        "### Install R packages\n",
        "A large fraction of the running time of this notebook is in installing the Seurat R package, since it has lots of dependencies and many of them use Rcpp which results in the need to compile lots of C++ code."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8EZ8Zm7zagc0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4de7d30b-bcbe-406a-ea1a-d083b5ced3f2"
      },
      "source": [
        "if (!requireNamespace(\"BiocManager\", quietly = TRUE))\n",
        "  install.packages(\"BiocManager\")\n",
        "BiocManager::install(c(\n",
        "  'SummarizedExperiment', 'rhdf5', 'SingleCellExperiment', 'limma', 'goseq', \n",
        "  'stringr', 'ggplot2', 'TxDb.Mmusculus.UCSC.mm10.knownGene', 'org.Mm.eg.db',\n",
        "  'wordcloud', 'plotrix', 'DESeq2','clusterProfiler','VennDiagram','fgsea','cowplot'\n",
        "), Ncpus = 2)\n",
        "install.packages(c('farver', \"Seurat\"), Ncpus = 2)\n",
        "install.packages('devtools')\n",
        "devtools::install_github('rstudio/reticulate')\n",
        "devtools::install_github(\"GSEA-MSigDB/GSEA_R\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Installing package into ‘/usr/local/lib/R/site-library’\n",
            "(as ‘lib’ is unspecified)\n",
            "\n",
            "'getOption(\"repos\")' replaces Bioconductor standard repositories, see\n",
            "'?repositories' for details\n",
            "\n",
            "replacement repositories:\n",
            "    CRAN: https://cran.rstudio.com\n",
            "\n",
            "\n",
            "Bioconductor version 3.13 (BiocManager 1.30.16), R 4.1.1 (2021-08-10)\n",
            "\n",
            "Warning message:\n",
            "“package(s) not installed when version(s) same as current; use `force = TRUE` to\n",
            "  re-install: 'stringr' 'ggplot2'”\n",
            "Installing package(s) 'BiocVersion', 'SummarizedExperiment', 'rhdf5',\n",
            "  'SingleCellExperiment', 'limma', 'goseq',\n",
            "  'TxDb.Mmusculus.UCSC.mm10.knownGene', 'org.Mm.eg.db', 'wordcloud', 'plotrix',\n",
            "  'DESeq2', 'clusterProfiler', 'VennDiagram', 'fgsea', 'cowplot'\n",
            "\n",
            "also installing the dependencies ‘gridGraphics’, ‘Rhtslib’, ‘rjson’, ‘filelock’, ‘tweenr’, ‘polyclip’, ‘RcppEigen’, ‘ggplotify’, ‘patchwork’, ‘lazyeval’, ‘zlibbioc’, ‘bitops’, ‘XML’, ‘Rsamtools’, ‘GenomicAlignments’, ‘restfulr’, ‘plogr’, ‘png’, ‘BiocFileCache’, ‘xtable’, ‘ggforce’, ‘ggrepel’, ‘viridis’, ‘tidygraph’, ‘graphlayouts’, ‘ggfun’, ‘ape’, ‘aplot’, ‘tidytree’, ‘treeio’, ‘formatR’, ‘matrixStats’, ‘XVector’, ‘RCurl’, ‘GenomeInfoDbData’, ‘rtracklayer’, ‘RSQLite’, ‘KEGGREST’, ‘Biostrings’, ‘BiocIO’, ‘biomaRt’, ‘snow’, ‘annotate’, ‘DO.db’, ‘reshape2’, ‘ggraph’, ‘igraph’, ‘scatterpie’, ‘shadowtext’, ‘ggtree’, ‘lambda.r’, ‘futile.options’, ‘MatrixGenerics’, ‘GenomicRanges’, ‘Biobase’, ‘BiocGenerics’, ‘S4Vectors’, ‘IRanges’, ‘GenomeInfoDb’, ‘DelayedArray’, ‘Rhdf5lib’, ‘rhdf5filters’, ‘BiasedUrn’, ‘geneLenDataBase’, ‘AnnotationDbi’, ‘GO.db’, ‘GenomicFeatures’, ‘BiocParallel’, ‘genefilter’, ‘locfit’, ‘geneplotter’, ‘RcppArmadillo’, ‘downloader’, ‘DOSE’, ‘enrichplot’, ‘GOSemSim’, ‘plyr’, ‘qvalue’, ‘yulab.utils’, ‘futile.logger’, ‘gridExtra’, ‘fastmatch’, ‘BH’\n",
            "\n",
            "\n",
            "Old packages: 'gert', 'openssl', 'rmarkdown', 'vroom', 'waldo', 'nlme'\n",
            "\n",
            "Installing packages into ‘/usr/local/lib/R/site-library’\n",
            "(as ‘lib’ is unspecified)\n",
            "\n",
            "also installing the dependencies ‘gtools’, ‘caTools’, ‘sass’, ‘jquerylib’, ‘sitmo’, ‘globals’, ‘listenv’, ‘parallelly’, ‘zoo’, ‘htmlwidgets’, ‘crosstalk’, ‘promises’, ‘here’, ‘gplots’, ‘httpuv’, ‘sourcetools’, ‘later’, ‘bslib’, ‘spatstat.data’, ‘spatstat.utils’, ‘spatstat.sparse’, ‘abind’, ‘tensor’, ‘goftest’, ‘deldir’, ‘FNN’, ‘RSpectra’, ‘dqrng’, ‘fitdistrplus’, ‘future’, ‘future.apply’, ‘ggridges’, ‘ica’, ‘irlba’, ‘leiden’, ‘lmtest’, ‘miniUI’, ‘pbapply’, ‘plotly’, ‘RANN’, ‘RcppAnnoy’, ‘reticulate’, ‘ROCR’, ‘Rtsne’, ‘scattermore’, ‘sctransform’, ‘SeuratObject’, ‘shiny’, ‘spatstat.core’, ‘spatstat.geom’, ‘uwot’, ‘RcppProgress’\n",
            "\n",
            "\n",
            "Installing package into ‘/usr/local/lib/R/site-library’\n",
            "(as ‘lib’ is unspecified)\n",
            "\n",
            "Downloading GitHub repo rstudio/reticulate@HEAD\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\u001b[32m✔\u001b[39m  \u001b[90mchecking for file ‘/tmp/Rtmpr2j7zN/remotes4d5eb7d347/rstudio-reticulate-277f410/DESCRIPTION’\u001b[39m\u001b[36m\u001b[39m\n",
            "\u001b[90m─\u001b[39m\u001b[90m  \u001b[39m\u001b[90mpreparing ‘reticulate’:\u001b[39m\u001b[36m\u001b[39m\n",
            "\u001b[32m✔\u001b[39m  \u001b[90mchecking DESCRIPTION meta-information\u001b[39m\u001b[36m\u001b[39m\n",
            "\u001b[90m─\u001b[39m\u001b[90m  \u001b[39m\u001b[90mcleaning src\u001b[39m\u001b[36m\u001b[39m\n",
            "\u001b[90m─\u001b[39m\u001b[90m  \u001b[39m\u001b[90mchecking for LF line-endings in source and make files and shell scripts\u001b[39m\u001b[36m\u001b[39m\n",
            "\u001b[90m─\u001b[39m\u001b[90m  \u001b[39m\u001b[90mchecking for empty or unneeded directories\u001b[39m\u001b[36m\u001b[39m\n",
            "\u001b[90m─\u001b[39m\u001b[90m  \u001b[39m\u001b[90mbuilding ‘reticulate_1.22-9000.tar.gz’\u001b[39m\u001b[36m\u001b[39m\n",
            "   \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Installing package into ‘/usr/local/lib/R/site-library’\n",
            "(as ‘lib’ is unspecified)\n",
            "\n",
            "Downloading GitHub repo GSEA-MSigDB/GSEA_R@HEAD\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\u001b[32m✔\u001b[39m  \u001b[90mchecking for file ‘/tmp/Rtmpr2j7zN/remotes4d6f2363d1/GSEA-MSigDB-GSEA_R-8fbd326/DESCRIPTION’\u001b[39m\u001b[36m\u001b[39m\n",
            "\u001b[90m─\u001b[39m\u001b[90m  \u001b[39m\u001b[90mpreparing ‘GSEA’:\u001b[39m\u001b[36m\u001b[39m\n",
            "\u001b[32m✔\u001b[39m  \u001b[90mchecking DESCRIPTION meta-information\u001b[39m\u001b[36m\u001b[39m\n",
            "\u001b[90m─\u001b[39m\u001b[90m  \u001b[39m\u001b[90mchecking for LF line-endings in source and make files and shell scripts\u001b[39m\u001b[36m\u001b[39m\n",
            "\u001b[90m─\u001b[39m\u001b[90m  \u001b[39m\u001b[90mchecking for empty or unneeded directories\u001b[39m\u001b[36m\u001b[39m\n",
            "\u001b[90m─\u001b[39m\u001b[90m  \u001b[39m\u001b[90mbuilding ‘GSEA_1.2.tar.gz’\u001b[39m\u001b[36m\u001b[39m\n",
            "   \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Installing package into ‘/usr/local/lib/R/site-library’\n",
            "(as ‘lib’ is unspecified)\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y1qTsMNEtcBP"
      },
      "source": [
        "# Loading R libraries for the analysis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nQFQk04TzjFj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "71c3ca51-7ff7-4b51-86c7-7f14897d7076"
      },
      "source": [
        "library(SummarizedExperiment)\n",
        "library(RColorBrewer)\n",
        "library(plyr)\n",
        "library(dplyr)\n",
        "library(parallel)\n",
        "library(ggplot2)\n",
        "library(goseq)\n",
        "library(stringr)\n",
        "library(TxDb.Mmusculus.UCSC.mm10.knownGene)\n",
        "library(org.Mm.eg.db)\n",
        "library(wordcloud)\n",
        "library(plotrix)\n",
        "library(DESeq2)\n",
        "library(GSEA)\n",
        "library(fgsea)\n",
        "library(VennDiagram)\n",
        "library(cowplot)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Loading required package: MatrixGenerics\n",
            "\n",
            "Loading required package: matrixStats\n",
            "\n",
            "\n",
            "Attaching package: ‘MatrixGenerics’\n",
            "\n",
            "\n",
            "The following objects are masked from ‘package:matrixStats’:\n",
            "\n",
            "    colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse,\n",
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            "    colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds,\n",
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            "    rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks,\n",
            "    rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars,\n",
            "    rowWeightedMads, rowWeightedMeans, rowWeightedMedians,\n",
            "    rowWeightedSds, rowWeightedVars\n",
            "\n",
            "\n",
            "Loading required package: GenomicRanges\n",
            "\n",
            "Loading required package: stats4\n",
            "\n",
            "Loading required package: BiocGenerics\n",
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            "Loading required package: parallel\n",
            "\n",
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            "Attaching package: ‘BiocGenerics’\n",
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            "The following objects are masked from ‘package:parallel’:\n",
            "\n",
            "    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,\n",
            "    clusterExport, clusterMap, parApply, parCapply, parLapply,\n",
            "    parLapplyLB, parRapply, parSapply, parSapplyLB\n",
            "\n",
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            "The following objects are masked from ‘package:stats’:\n",
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            "    IQR, mad, sd, var, xtabs\n",
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            "The following objects are masked from ‘package:base’:\n",
            "\n",
            "    anyDuplicated, append, as.data.frame, basename, cbind, colnames,\n",
            "    dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep,\n",
            "    grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget,\n",
            "    order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,\n",
            "    rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,\n",
            "    union, unique, unsplit, which.max, which.min\n",
            "\n",
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            "Attaching package: ‘S4Vectors’\n",
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            "\n",
            "    expand.grid, I, unname\n",
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            "Loading required package: IRanges\n",
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            "Loading required package: GenomeInfoDb\n",
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            "\n",
            "Welcome to Bioconductor\n",
            "\n",
            "    Vignettes contain introductory material; view with\n",
            "    'browseVignettes()'. To cite Bioconductor, see\n",
            "    'citation(\"Biobase\")', and for packages 'citation(\"pkgname\")'.\n",
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            "Attaching package: ‘dplyr’\n",
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            "The following objects are masked from ‘package:plyr’:\n",
            "\n",
            "    arrange, count, desc, failwith, id, mutate, rename, summarise,\n",
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            "    count\n",
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            "    filter, lag\n",
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            "The following objects are masked from ‘package:base’:\n",
            "\n",
            "    intersect, setdiff, setequal, union\n",
            "\n",
            "\n",
            "Loading required package: BiasedUrn\n",
            "\n",
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            "\n",
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            "Attaching package: ‘geneLenDataBase’\n",
            "\n",
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            "The following object is masked from ‘package:S4Vectors’:\n",
            "\n",
            "    unfactor\n",
            "\n",
            "\n",
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            "\n",
            "Loading required package: GenomicFeatures\n",
            "\n",
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            "\n",
            "\n",
            "Attaching package: ‘AnnotationDbi’\n",
            "\n",
            "\n",
            "The following object is masked from ‘package:dplyr’:\n",
            "\n",
            "    select\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "Loading required package: grid\n",
            "\n",
            "Loading required package: futile.logger\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fi0rMk_2YawG"
      },
      "source": [
        "# Loading the gene list obtained from GO containg 4 categories (Hats, HDACs, Inflammatory related and NFkB pathwway related)\n",
        "https://www.ebi.ac.uk/QuickGO/annotations?goUsage=descendants&goUsageRelationships=is_a,part_of,occurs_in&goId=GO:0004402&taxonId=10090&taxonUsage=descendants"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rT4YQXp_khqd"
      },
      "source": [
        "gene_list <- c(\n",
        "  \"nfkb\" = \"https://s3.msi.umn.edu/dsouz072/datasets/dataset=etv2_pioneer=20210404a/Nf_kB.tsv\",\n",
        "  \"hdac\" = \"https://s3.msi.umn.edu/dsouz072/datasets/dataset=etv2_pioneer=20210404a/HDAC_complex.tsv\",\n",
        "  \"hat\" = \"https://s3.msi.umn.edu/dsouz072/datasets/dataset=etv2_pioneer=20210404a/Hat.tsv\",\n",
        "  \"inflammatory\" = \"https://s3.msi.umn.edu/dsouz072/datasets/dataset=etv2_pioneer=20210404a/Inflammatory_go.tsv\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LioNw9g2lN7L",
        "outputId": "28b91ddb-4bd5-43da-f24b-3a44b589a5dc"
      },
      "source": [
        "for (i in 1:length(gene_list)){\n",
        "  sprintf('wget -x -c -nH %s', gene_list[i]) %>% system()\n",
        "}\n",
        "gene_list <- gsub('https://s3.msi.umn.edu/', './', gene_list)\n",
        "message(sprintf('all gene_list exist: %s', all(file.exists(gene_list))))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "all gene_list exist: TRUE\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fKbzPrlXto2i"
      },
      "source": [
        "## Gene list for HDACs, HATs, Inflammatory and NFkB"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2Xu8nT8LldOq"
      },
      "source": [
        "genes <- lapply(gene_list, function(g){read.table(g, header = TRUE, sep = '\\t')})"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "nNkNjetNmKV9",
        "outputId": "a97c13f7-b7aa-4325-ee9c-4e1f5167492f"
      },
      "source": [
        "unique(genes[[\"hdac\"]]$GO.NAME)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "[1] \"Swr1 complex\"                \"Sin3 complex\"               \n",
              "[3] \"histone deacetylase complex\" \"NuRD complex\"               \n",
              "[5] \"Sin3-type complex\"          "
            ],
            "text/latex": "\\begin{enumerate*}\n\\item 'Swr1 complex'\n\\item 'Sin3 complex'\n\\item 'histone deacetylase complex'\n\\item 'NuRD complex'\n\\item 'Sin3-type complex'\n\\end{enumerate*}\n",
            "text/markdown": "1. 'Swr1 complex'\n2. 'Sin3 complex'\n3. 'histone deacetylase complex'\n4. 'NuRD complex'\n5. 'Sin3-type complex'\n\n\n",
            "text/html": [
              "<style>\n",
              ".list-inline {list-style: none; margin:0; padding: 0}\n",
              ".list-inline>li {display: inline-block}\n",
              ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
              "</style>\n",
              "<ol class=list-inline><li>'Swr1 complex'</li><li>'Sin3 complex'</li><li>'histone deacetylase complex'</li><li>'NuRD complex'</li><li>'Sin3-type complex'</li></ol>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vf3zMR-_mZsB"
      },
      "source": [
        "genes[[\"hdac_sub\"]] <- genes[[\"hdac\"]][grepl('histone', genes[[\"hdac\"]]$GO.NAME),] #Subsetting Histone Deactylase complex"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wL3K4ZLCtz2C",
        "outputId": "fb140e4e-17a5-4709-99cb-270ec1d24680"
      },
      "source": [
        "str(genes)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "List of 5\n",
            " $ nfkb        :'data.frame':\t55 obs. of  14 variables:\n",
            "  ..$ GENE.PRODUCT.DB     : chr [1:55] \"UniProtKB\" \"UniProtKB\" \"UniProtKB\" \"UniProtKB\" ...\n",
            "  ..$ GENE.PRODUCT.ID     : chr [1:55] \"B1AUW6\" \"E9PUV9\" \"E9QNL4\" \"F8VQ54\" ...\n",
            "  ..$ SYMBOL              : chr [1:55] \"Irak1\" \"Card10\" \"Chuk\" \"Dicer1\" ...\n",
            "  ..$ QUALIFIER           : chr [1:55] \"involved_in\" \"involved_in\" \"involved_in\" \"involved_in\" ...\n",
            "  ..$ GO.TERM             : chr [1:55] \"GO:0007250\" \"GO:0007250\" \"GO:0038061\" \"GO:0038061\" ...\n",
            "  ..$ GO.NAME             : chr [1:55] \"activation of NF-kappaB-inducing kinase activity\" \"activation of NF-kappaB-inducing kinase activity\" \"NIK/NF-kappaB signaling\" \"NIK/NF-kappaB signaling\" ...\n",
            "  ..$ ECO.ID              : chr [1:55] \"ECO:0000265\" \"ECO:0000265\" \"ECO:0000265\" \"ECO:0000265\" ...\n",
            "  ..$ GO.EVIDENCE.CODE    : chr [1:55] \"IEA\" \"IEA\" \"IEA\" \"IEA\" ...\n",
            "  ..$ REFERENCE           : chr [1:55] \"GO_REF:0000107\" \"GO_REF:0000107\" \"GO_REF:0000107\" \"GO_REF:0000107\" ...\n",
            "  ..$ WITH.FROM           : chr [1:55] \"UniProtKB:P51617|ensembl:ENSP00000358997\" \"UniProtKB:Q9BWT7|ensembl:ENSP00000251973\" \"UniProtKB:O15111|ensembl:ENSP00000359424\" \"UniProtKB:Q9UPY3|ensembl:ENSP00000343745\" ...\n",
            "  ..$ TAXON.ID            : int [1:55] 10090 10090 10090 10090 10090 10090 10090 10090 10090 10090 ...\n",
            "  ..$ ASSIGNED.BY         : chr [1:55] \"Ensembl\" \"Ensembl\" \"Ensembl\" \"Ensembl\" ...\n",
            "  ..$ ANNOTATION.EXTENSION: chr [1:55] \"\" \"\" \"\" \"\" ...\n",
            "  ..$ GO.ASPECT           : chr [1:55] \"P\" \"P\" \"P\" \"P\" ...\n",
            " $ hdac        :'data.frame':\t290 obs. of  14 variables:\n",
            "  ..$ GENE.PRODUCT.DB     : chr [1:290] \"UniProtKB\" \"UniProtKB\" \"UniProtKB\" \"UniProtKB\" ...\n",
            "  ..$ GENE.PRODUCT.ID     : chr [1:290] \"A0A087WNL7\" \"A0A087WNX7\" \"A0A087WP63\" \"A0A087WQ34\" ...\n",
            "  ..$ SYMBOL              : chr [1:290] \"Srcap\" \"Srcap\" \"Gm42715\" \"Morf4l1\" ...\n",
            "  ..$ QUALIFIER           : chr [1:290] \"part_of\" \"part_of\" \"part_of\" \"part_of\" ...\n",
            "  ..$ GO.TERM             : chr [1:290] \"GO:0000812\" \"GO:0000812\" \"GO:0000812\" \"GO:0016580\" ...\n",
            "  ..$ GO.NAME             : chr [1:290] \"Swr1 complex\" \"Swr1 complex\" \"Swr1 complex\" \"Sin3 complex\" ...\n",
            "  ..$ ECO.ID              : chr [1:290] \"ECO:0000318\" \"ECO:0000318\" \"ECO:0000318\" \"ECO:0000256\" ...\n",
            "  ..$ GO.EVIDENCE.CODE    : chr [1:290] \"IBA\" \"IBA\" \"IBA\" \"IEA\" ...\n",
            "  ..$ REFERENCE           : chr [1:290] \"PMID:21873635\" \"PMID:21873635\" \"PMID:21873635\" \"GO_REF:0000002\" ...\n",
            "  ..$ WITH.FROM           : chr [1:290] \"PANTHER:PTN000084261|PomBase:SPAC11E3.01c|SGD:S000002742\" \"PANTHER:PTN000084261|PomBase:SPAC11E3.01c|SGD:S000002742\" \"PANTHER:PTN000084261|PomBase:SPAC11E3.01c|SGD:S000002742\" \"InterPro:IPR038011\" ...\n",
            "  ..$ TAXON.ID            : int [1:290] 10090 10090 10090 10090 10090 10090 10090 10090 10090 10090 ...\n",
            "  ..$ ASSIGNED.BY         : chr [1:290] \"GO_Central\" \"GO_Central\" \"GO_Central\" \"InterPro\" ...\n",
            "  ..$ ANNOTATION.EXTENSION: chr [1:290] \"\" \"\" \"\" \"\" ...\n",
            "  ..$ GO.ASPECT           : chr [1:290] \"C\" \"C\" \"C\" \"C\" ...\n",
            " $ hat         :'data.frame':\t280 obs. of  14 variables:\n",
            "  ..$ GENE.PRODUCT.DB     : chr [1:280] \"UniProtKB\" \"UniProtKB\" \"UniProtKB\" \"UniProtKB\" ...\n",
            "  ..$ GENE.PRODUCT.ID     : chr [1:280] \"A0A0U1RNW7\" \"A0A0U1RNW7\" \"A0A0U1RP23\" \"A0A0U1RP23\" ...\n",
            "  ..$ SYMBOL              : chr [1:280] \"Kat8\" \"Kat8\" \"Crebbp\" \"Crebbp\" ...\n",
            "  ..$ QUALIFIER           : chr [1:280] \"enables\" \"enables\" \"enables\" \"enables\" ...\n",
            "  ..$ GO.TERM             : chr [1:280] \"GO:0004402\" \"GO:0004402\" \"GO:0004402\" \"GO:0004402\" ...\n",
            "  ..$ GO.NAME             : chr [1:280] \"histone acetyltransferase activity\" \"histone acetyltransferase activity\" \"histone acetyltransferase activity\" \"histone acetyltransferase activity\" ...\n",
            "  ..$ ECO.ID              : chr [1:280] \"ECO:0000256\" \"ECO:0000501\" \"ECO:0000256\" \"ECO:0000501\" ...\n",
            "  ..$ GO.EVIDENCE.CODE    : chr [1:280] \"IEA\" \"IEA\" \"IEA\" \"IEA\" ...\n",
            "  ..$ REFERENCE           : chr [1:280] \"GO_REF:0000002\" \"GO_REF:0000003\" \"GO_REF:0000002\" \"GO_REF:0000003\" ...\n",
            "  ..$ WITH.FROM           : chr [1:280] \"InterPro:IPR037906\" \"EC:2.3.1.48\" \"InterPro:IPR013178|InterPro:IPR031162\" \"EC:2.3.1.48\" ...\n",
            "  ..$ TAXON.ID            : int [1:280] 10090 10090 10090 10090 10090 10090 10090 10090 10090 10090 ...\n",
            "  ..$ ASSIGNED.BY         : chr [1:280] \"InterPro\" \"UniProt\" \"InterPro\" \"UniProt\" ...\n",
            "  ..$ ANNOTATION.EXTENSION: logi [1:280] NA NA NA NA NA NA ...\n",
            "  ..$ GO.ASPECT           : logi [1:280] FALSE FALSE FALSE FALSE FALSE FALSE ...\n",
            " $ inflammatory:'data.frame':\t1238 obs. of  14 variables:\n",
            "  ..$ GENE.PRODUCT.DB     : chr [1:1238] \"UniProtKB\" \"UniProtKB\" \"UniProtKB\" \"UniProtKB\" ...\n",
            "  ..$ GENE.PRODUCT.ID     : chr [1:1238] \"A0A087WPM9\" \"A0A087WPT2\" \"A0A087WQB4\" \"A0A087WR50\" ...\n",
            "  ..$ SYMBOL              : chr [1:1238] \"Ms4a2\" \"Ptgs2\" \"Il17f\" \"Fn1\" ...\n",
            "  ..$ QUALIFIER           : chr [1:1238] \"involved_in\" \"involved_in\" \"involved_in\" \"involved_in\" ...\n",
            "  ..$ GO.TERM             : chr [1:1238] \"GO:0006954\" \"GO:0006954\" \"GO:0006954\" \"GO:0006953\" ...\n",
            "  ..$ GO.NAME             : chr [1:1238] \"inflammatory response\" \"inflammatory response\" \"inflammatory response\" \"acute-phase response\" ...\n",
            "  ..$ ECO.ID              : chr [1:1238] \"ECO:0000256\" \"ECO:0000256\" \"ECO:0000256\" \"ECO:0000323\" ...\n",
            "  ..$ GO.EVIDENCE.CODE    : chr [1:1238] \"IEA\" \"IEA\" \"IEA\" \"IEA\" ...\n",
            "  ..$ REFERENCE           : chr [1:1238] \"GO_REF:0000002\" \"GO_REF:0000002\" \"GO_REF:0000002\" \"GO_REF:0000043\" ...\n",
            "  ..$ WITH.FROM           : chr [1:1238] \"InterPro:IPR030420\" \"InterPro:IPR029576\" \"InterPro:IPR020440\" \"UniProtKB-KW:KW-0011\" ...\n",
            "  ..$ TAXON.ID            : int [1:1238] 10090 10090 10090 10090 10090 10090 10090 10090 10090 10090 ...\n",
            "  ..$ ASSIGNED.BY         : chr [1:1238] \"InterPro\" \"InterPro\" \"InterPro\" \"UniProt\" ...\n",
            "  ..$ ANNOTATION.EXTENSION: chr [1:1238] \"\" \"\" \"\" \"\" ...\n",
            "  ..$ GO.ASPECT           : chr [1:1238] \"P\" \"P\" \"P\" \"P\" ...\n",
            " $ hdac_sub    :'data.frame':\t126 obs. of  14 variables:\n",
            "  ..$ GENE.PRODUCT.DB     : chr [1:126] \"UniProtKB\" \"UniProtKB\" \"UniProtKB\" \"UniProtKB\" ...\n",
            "  ..$ GENE.PRODUCT.ID     : chr [1:126] \"A0A087WSF0\" \"A0A0R4J1F3\" \"A0A1B0GR65\" \"A0A1B0GR65\" ...\n",
            "  ..$ SYMBOL              : chr [1:126] \"Hdac4\" \"Hdac9\" \"Hdac9\" \"Hdac9\" ...\n",
            "  ..$ QUALIFIER           : chr [1:126] \"part_of\" \"part_of\" \"part_of\" \"part_of\" ...\n",
            "  ..$ GO.TERM             : chr [1:126] \"GO:0000118\" \"GO:0000118\" \"GO:0000118\" \"GO:0000118\" ...\n",
            "  ..$ GO.NAME             : chr [1:126] \"histone deacetylase complex\" \"histone deacetylase complex\" \"histone deacetylase complex\" \"histone deacetylase complex\" ...\n",
            "  ..$ ECO.ID              : chr [1:126] \"ECO:0000256\" \"ECO:0000256\" \"ECO:0000256\" \"ECO:0000256\" ...\n",
            "  ..$ GO.EVIDENCE.CODE    : chr [1:126] \"IEA\" \"IEA\" \"IEA\" \"IEA\" ...\n",
            "  ..$ REFERENCE           : chr [1:126] \"GO_REF:0000002\" \"GO_REF:0000002\" \"GO_REF:0000002\" \"GO_REF:0000104\" ...\n",
            "  ..$ WITH.FROM           : chr [1:126] \"InterPro:IPR017320\" \"InterPro:IPR017320\" \"InterPro:IPR017320\" \"UniRule:UR000135806\" ...\n",
            "  ..$ TAXON.ID            : int [1:126] 10090 10090 10090 10090 10090 10090 10090 10090 10090 10090 ...\n",
            "  ..$ ASSIGNED.BY         : chr [1:126] \"InterPro\" \"InterPro\" \"InterPro\" \"UniProt\" ...\n",
            "  ..$ ANNOTATION.EXTENSION: chr [1:126] \"\" \"\" \"\" \"\" ...\n",
            "  ..$ GO.ASPECT           : chr [1:126] \"C\" \"C\" \"C\" \"C\" ...\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IlBpUisUufMR"
      },
      "source": [
        "# scRNA seq"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X7jl3EBdW22C"
      },
      "source": [
        "## Load processed scRNA-seq data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YjAWC2nhp7MU"
      },
      "source": [
        "se_file <- c(\n",
        "  \"se\" = \"https://s3.msi.umn.edu/gongx030/etv2_pioneer/data/processed_Etv2_scRNAseq.rds\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p0xaI-WnqFm5",
        "outputId": "5730cb3d-74ea-44f7-a1cc-ed479c6ed878"
      },
      "source": [
        "for (i in 1:length(se_file)){\n",
        "  sprintf('wget -x -c -nH %s', se_file[i]) %>% system()\n",
        "}\n",
        "se_file <- gsub('https://s3.msi.umn.edu/', './', se_file)\n",
        "message(sprintf('all se_file exist: %s', all(file.exists(se_file))))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "all se_file exist: TRUE\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JwQyfRx1qXeg"
      },
      "source": [
        "se <- se_file %>%\n",
        "  readRDS()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 164
        },
        "id": "JzSj56egq5B3",
        "outputId": "16fa5239-4e64-412e-a393-a220fa0e5c6b"
      },
      "source": [
        "se"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "class: SummarizedExperiment \n",
              "dim: 17010 15475 \n",
              "metadata(1): curves\n",
              "assays(3): counts normalized_counts scaled_counts\n",
              "rownames: NULL\n",
              "rowData names(6): id name ... is_hvg clusters\n",
              "colnames: NULL\n",
              "colData names(7): barcodes group ... umap cluster"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dB3mu5ATpb3S"
      },
      "source": [
        "## Calculating Diferential expression for Treatment (MEF_Dox_D7b) vs Control (MEF_NoDox)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZwU0EAfAq7sZ",
        "outputId": "648f4ba6-0ef8-4320-ec28-33cb062b51e6"
      },
      "source": [
        "X <- assays(se)$scaled_counts\n",
        "clust <- colData(se)$cluster\n",
        "control <- colData(se)$group == 'MEF_NoDox'\n",
        "treatment <- colData(se)$group == 'MEF_Dox_D7b'\n",
        "pvalues_tc <- mclapply(\n",
        "  1:nrow(X), function(i) wilcox.test(X[i, treatment], X[i, control], alternative = 'two.sided')$p.value,\n",
        "  mc.cores = 4) %>% unlist()\n",
        "fc_tc <- rowMeans(assays(se)$normalized_counts[, treatment]+1)/rowMeans(assays(se)$normalized_counts[, control]+1)\n",
        "names(pvalues_tc) <- names(fc_tc) <- rowData(se)$name"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "dimnames(.) <- NULL:  translated to \n",
            "dimnames(.) <- list(NULL,NULL)  <==>  unname(.)\n",
            "\n",
            "dimnames(.) <- NULL:  translated to \n",
            "dimnames(.) <- list(NULL,NULL)  <==>  unname(.)\n",
            "\n",
            "dimnames(.) <- NULL:  translated to \n",
            "dimnames(.) <- list(NULL,NULL)  <==>  unname(.)\n",
            "\n",
            "dimnames(.) <- NULL:  translated to \n",
            "dimnames(.) <- list(NULL,NULL)  <==>  unname(.)\n",
            "\n",
            "dimnames(.) <- NULL:  translated to \n",
            "dimnames(.) <- list(NULL,NULL)  <==>  unname(.)\n",
            "\n",
            "dimnames(.) <- NULL:  translated to \n",
            "dimnames(.) <- list(NULL,NULL)  <==>  unname(.)\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WnEU_LWst5sK"
      },
      "source": [
        "## Dataset containing Fold change and p values MEF D7 Flk1+ vs MEFs"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3Ts_u2b_tYIw"
      },
      "source": [
        "res_scrna <- data.frame(\n",
        "  name = rowData(se)$name, \n",
        "  fc = fc_tc, \n",
        "  pvalue = pvalues_tc\n",
        ")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RJftcazbtnbO"
      },
      "source": [
        "res_scrna$log2fc <- log2(fc_tc)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "jT257ER6ROJW",
        "outputId": "a32eb735-5644-40b6-dce5-05e831f94089"
      },
      "source": [
        "head(res_scrna)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  name    fc        pvalue       log2fc      \n",
              "1 Mrpl15  0.8561015 1.588210e-40 -0.224146290\n",
              "2 Lypla1  0.9869158 1.476162e-09 -0.019001027\n",
              "3 Gm37988 0.9997660 4.958114e-01 -0.000337565\n",
              "4 Tcea1   0.8246107 3.610119e-43 -0.278214927\n",
              "5 Rgs20   0.9858808 2.354869e-05 -0.020514837\n",
              "6 Atp6v1h 1.1638874 4.307872e-07  0.218951431"
            ],
            "text/latex": "A data.frame: 6 × 4\n\\begin{tabular}{r|llll}\n  & name & fc & pvalue & log2fc\\\\\n  & <chr> & <dbl> & <dbl> & <dbl>\\\\\n\\hline\n\t1 & Mrpl15  & 0.8561015 & 1.588210e-40 & -0.224146290\\\\\n\t2 & Lypla1  & 0.9869158 & 1.476162e-09 & -0.019001027\\\\\n\t3 & Gm37988 & 0.9997660 & 4.958114e-01 & -0.000337565\\\\\n\t4 & Tcea1   & 0.8246107 & 3.610119e-43 & -0.278214927\\\\\n\t5 & Rgs20   & 0.9858808 & 2.354869e-05 & -0.020514837\\\\\n\t6 & Atp6v1h & 1.1638874 & 4.307872e-07 &  0.218951431\\\\\n\\end{tabular}\n",
            "text/markdown": "\nA data.frame: 6 × 4\n\n| <!--/--> | name &lt;chr&gt; | fc &lt;dbl&gt; | pvalue &lt;dbl&gt; | log2fc &lt;dbl&gt; |\n|---|---|---|---|---|\n| 1 | Mrpl15  | 0.8561015 | 1.588210e-40 | -0.224146290 |\n| 2 | Lypla1  | 0.9869158 | 1.476162e-09 | -0.019001027 |\n| 3 | Gm37988 | 0.9997660 | 4.958114e-01 | -0.000337565 |\n| 4 | Tcea1   | 0.8246107 | 3.610119e-43 | -0.278214927 |\n| 5 | Rgs20   | 0.9858808 | 2.354869e-05 | -0.020514837 |\n| 6 | Atp6v1h | 1.1638874 | 4.307872e-07 |  0.218951431 |\n\n",
            "text/html": [
              "<table class=\"dataframe\">\n",
              "<caption>A data.frame: 6 × 4</caption>\n",
              "<thead>\n",
              "\t<tr><th></th><th scope=col>name</th><th scope=col>fc</th><th scope=col>pvalue</th><th scope=col>log2fc</th></tr>\n",
              "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><th scope=row>1</th><td>Mrpl15 </td><td>0.8561015</td><td>1.588210e-40</td><td>-0.224146290</td></tr>\n",
              "\t<tr><th scope=row>2</th><td>Lypla1 </td><td>0.9869158</td><td>1.476162e-09</td><td>-0.019001027</td></tr>\n",
              "\t<tr><th scope=row>3</th><td>Gm37988</td><td>0.9997660</td><td>4.958114e-01</td><td>-0.000337565</td></tr>\n",
              "\t<tr><th scope=row>4</th><td>Tcea1  </td><td>0.8246107</td><td>3.610119e-43</td><td>-0.278214927</td></tr>\n",
              "\t<tr><th scope=row>5</th><td>Rgs20  </td><td>0.9858808</td><td>2.354869e-05</td><td>-0.020514837</td></tr>\n",
              "\t<tr><th scope=row>6</th><td>Atp6v1h</td><td>1.1638874</td><td>4.307872e-07</td><td> 0.218951431</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RyFGr6teVNpM"
      },
      "source": [
        "## Subsetting the genes as up-regulated, down-regulated for the treatment"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FTQ9z1detqcj"
      },
      "source": [
        "up <- res_scrna %>%\n",
        "  filter(!is.na(pvalue) & log2fc > 0 & pvalue <0.05)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f1TcVbronv-D"
      },
      "source": [
        "down <- res_scrna %>%\n",
        "  filter(!is.na(pvalue) & log2fc < 0 & pvalue < 0.05)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "id": "sZsAO6Oannyl",
        "outputId": "6180e28a-f0a4-44df-bd1c-454827c78b74"
      },
      "source": [
        "dim(up)\n",
        "dim(down)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "[1] 3829    4"
            ],
            "text/latex": "\\begin{enumerate*}\n\\item 3829\n\\item 4\n\\end{enumerate*}\n",
            "text/markdown": "1. 3829\n2. 4\n\n\n",
            "text/html": [
              "<style>\n",
              ".list-inline {list-style: none; margin:0; padding: 0}\n",
              ".list-inline>li {display: inline-block}\n",
              ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
              "</style>\n",
              "<ol class=list-inline><li>3829</li><li>4</li></ol>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "[1] 6944    4"
            ],
            "text/latex": "\\begin{enumerate*}\n\\item 6944\n\\item 4\n\\end{enumerate*}\n",
            "text/markdown": "1. 6944\n2. 4\n\n\n",
            "text/html": [
              "<style>\n",
              ".list-inline {list-style: none; margin:0; padding: 0}\n",
              ".list-inline>li {display: inline-block}\n",
              ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
              "</style>\n",
              "<ol class=list-inline><li>6944</li><li>4</li></ol>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xxmfYXvRqzWr"
      },
      "source": [
        "## Subsetting genes for each of the 5 categories (Hats, HDACs, HDACs complex, Inflammatory related and NFkB pathwway related) in the up regulated and downregulated genes (treatment)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gLCRRgOLamLz"
      },
      "source": [
        "UP"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LObr5mVyqDiV"
      },
      "source": [
        "up_genes <- lapply(1:length(genes),function(x){up[up$name %in% genes[[x]]$SYMBOL,]})"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "32PCb3GnqbXE"
      },
      "source": [
        "names(up_genes) <- names(genes)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NboR2JLdqoRZ",
        "outputId": "cf9e9d20-6da9-4315-bb74-4919e39cdcd9"
      },
      "source": [
        "str(up_genes)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "List of 5\n",
            " $ nfkb        :'data.frame':\t6 obs. of  4 variables:\n",
            "  ..$ name  : chr [1:6] \"Ikbke\" \"Traf2\" \"Tlr4\" \"Relb\" ...\n",
            "  ..$ fc    : num [1:6] 1.04 1 1.06 1 1.03 ...\n",
            "  ..$ pvalue: num [1:6] 2.20e-05 7.88e-04 4.46e-11 2.42e-02 3.90e-04 ...\n",
            "  ..$ log2fc: num [1:6] 0.053 0.00593 0.08558 0.00684 0.04017 ...\n",
            " $ hdac        :'data.frame':\t24 obs. of  4 variables:\n",
            "  ..$ name  : chr [1:24] \"Satb2\" \"Dnttip1\" \"Zfp217\" \"Hdac6\" ...\n",
            "  ..$ fc    : num [1:24] 1.01 1.02 1.11 1.01 1 ...\n",
            "  ..$ pvalue: num [1:24] 1.31e-02 2.05e-04 5.68e-08 6.51e-03 1.14e-02 ...\n",
            "  ..$ log2fc: num [1:24] 0.01685 0.02411 0.14849 0.01297 0.00481 ...\n",
            " $ hat         :'data.frame':\t13 obs. of  4 variables:\n",
            "  ..$ name  : chr [1:13] \"Atf2\" \"Ncoa3\" \"Gtf2b\" \"Supt7l\" ...\n",
            "  ..$ fc    : num [1:13] 1.04 1.03 1.05 1.01 1.02 ...\n",
            "  ..$ pvalue: num [1:13] 0.017125 0.017757 0.012629 0.008127 0.000104 ...\n",
            "  ..$ log2fc: num [1:13] 0.0546 0.0445 0.0714 0.0127 0.0265 ...\n",
            " $ inflammatory:'data.frame':\t105 obs. of  4 variables:\n",
            "  ..$ name  : chr [1:105] \"Ly96\" \"Il1r1\" \"Il1rl2\" \"Il18r1\" ...\n",
            "  ..$ fc    : num [1:105] 1.22 1.26 1.02 1 1.08 ...\n",
            "  ..$ pvalue: num [1:105] 4.40e-21 5.86e-34 1.93e-05 3.80e-02 8.31e-08 ...\n",
            "  ..$ log2fc: num [1:105] 0.28171 0.33325 0.02566 0.00201 0.10891 ...\n",
            " $ hdac_sub    :'data.frame':\t15 obs. of  4 variables:\n",
            "  ..$ name  : chr [1:15] \"Satb2\" \"Dnttip1\" \"Zfp217\" \"Hdac6\" ...\n",
            "  ..$ fc    : num [1:15] 1.01 1.02 1.11 1.01 1 ...\n",
            "  ..$ pvalue: num [1:15] 1.31e-02 2.05e-04 5.68e-08 6.51e-03 1.14e-02 ...\n",
            "  ..$ log2fc: num [1:15] 0.01685 0.02411 0.14849 0.01297 0.00481 ...\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kTyxMY1qanP0"
      },
      "source": [
        "DOWN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2NvAbT9iq3vN"
      },
      "source": [
        "down_genes <- lapply(1:length(genes),function(x){down[down$name %in% genes[[x]]$SYMBOL,]})"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mcml_T-tq8wY"
      },
      "source": [
        "names(down_genes) <- names(genes)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FNa1e62Eq-Kv",
        "outputId": "35a368c0-137b-40e7-c7f3-9c4204102c67"
      },
      "source": [
        "str(down_genes)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "List of 5\n",
            " $ nfkb        :'data.frame':\t16 obs. of  4 variables:\n",
            "  ..$ name  : chr [1:16] \"Cops8\" \"Traf6\" \"Irak1\" \"Ikbkg\" ...\n",
            "  ..$ fc    : num [1:16] 0.857 0.99 0.94 0.988 0.999 ...\n",
            "  ..$ pvalue: num [1:16] 1.11e-40 5.22e-08 2.31e-23 5.50e-09 4.40e-09 ...\n",
            "  ..$ log2fc: num [1:16] -0.22347 -0.01499 -0.08877 -0.01797 -0.00118 ...\n",
            " $ hdac        :'data.frame':\t36 obs. of  4 variables:\n",
            "  ..$ name  : chr [1:36] \"Hdac4\" \"Csnk2a1\" \"Tbl1x\" \"Anp32e\" ...\n",
            "  ..$ fc    : num [1:36] 0.992 0.889 0.878 0.749 0.963 ...\n",
            "  ..$ pvalue: num [1:36] 1.28e-08 5.03e-29 6.20e-28 1.11e-92 9.24e-13 ...\n",
            "  ..$ log2fc: num [1:36] -0.0118 -0.1692 -0.1869 -0.4169 -0.0542 ...\n",
            " $ hat         :'data.frame':\t21 obs. of  4 variables:\n",
            "  ..$ name  : chr [1:21] \"Gtf3c4\" \"Mettl8\" \"Hat1\" \"Kat14\" ...\n",
            "  ..$ fc    : num [1:21] 0.984 0.999 0.773 0.965 0.97 ...\n",
            "  ..$ pvalue: num [1:21] 2.62e-11 1.16e-04 2.86e-94 2.46e-16 3.07e-13 ...\n",
            "  ..$ log2fc: num [1:21] -0.02339 -0.00182 -0.37185 -0.0514 -0.04333 ...\n",
            " $ inflammatory:'data.frame':\t91 obs. of  4 variables:\n",
            "  ..$ name  : chr [1:91] \"Il18rap\" \"Cxcr2\" \"Mapkapk2\" \"Cfh\" ...\n",
            "  ..$ fc    : num [1:91] 0.988 0.998 0.903 0.971 0.648 ...\n",
            "  ..$ pvalue: num [1:91] 5.12e-08 5.00e-02 7.42e-40 9.12e-10 8.20e-113 ...\n",
            "  ..$ log2fc: num [1:91] -0.01715 -0.00295 -0.14751 -0.04246 -0.62572 ...\n",
            " $ hdac_sub    :'data.frame':\t16 obs. of  4 variables:\n",
            "  ..$ name  : chr [1:16] \"Hdac4\" \"Tbl1x\" \"Hdac1\" \"Ncor2\" ...\n",
            "  ..$ fc    : num [1:16] 0.992 0.878 0.963 0.979 0.975 ...\n",
            "  ..$ pvalue: num [1:16] 1.28e-08 6.20e-28 9.24e-13 1.19e-14 2.53e-02 ...\n",
            "  ..$ log2fc: num [1:16] -0.0118 -0.1869 -0.0542 -0.0302 -0.0373 ...\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yBo-CEucbixt"
      },
      "source": [
        "#RNAseq"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ym7Z3pKhruXL"
      },
      "source": [
        "## Load the bulk RNA-seq of Etv2 induction in ES/EB and calculating Differential expression of treatment (EB_Dox_D25_Flk1pos_Etv2) vs control (EB_NoDox_D25_Etv2)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5FATg-wBrrPp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 365
        },
        "outputId": "4b43cbfd-9c67-4154-b656-7f3f382a0495"
      },
      "source": [
        "se_rna <- readRDS(gzcon(url('https://s3.msi.umn.edu/gongx030/datasets/dataset=Etv2RNA-seq_version=20190909a/se.rds')))\n",
        "se_rna <- DESeqDataSet(se_rna, design = ~ group)\n",
        "se_rna <- estimateSizeFactors(se_rna)\n",
        "se_rna <- DESeq(se_rna)\n",
        "table(colData(se_rna)$group)\n",
        "res <- results(se_rna, contrast = c('group', 'EB_Dox_D25_Flk1pos_Etv2', 'EB_NoDox_D25_Etv2'))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Warning message in DESeqDataSet(se_rna, design = ~group):\n",
            "“some variables in design formula are characters, converting to factors”\n",
            "using pre-existing size factors\n",
            "\n",
            "estimating dispersions\n",
            "\n",
            "gene-wise dispersion estimates\n",
            "\n",
            "mean-dispersion relationship\n",
            "\n",
            "final dispersion estimates\n",
            "\n",
            "fitting model and testing\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\n",
              "         EB_Dox_D2_Etv2         EB_Dox_D25_Etv2 EB_Dox_D25_Flk1pos_Etv2 \n",
              "                      2                       2                       2 \n",
              "       EB_NoDox_D2_Etv2       EB_NoDox_D25_Etv2 \n",
              "                      2                       2 "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 365
        },
        "id": "UNj_jDEX2Iud",
        "outputId": "c258b8dd-98c1-4b0a-afc9-cf9594624d27"
      },
      "source": [
        "head(res)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "log2 fold change (MLE): group EB_Dox_D25_Flk1pos_Etv2 vs EB_NoDox_D25_Etv2 \n",
              "Wald test p-value: group EB_Dox_D25_Flk1pos_Etv2 vs EB_NoDox_D25_Etv2 \n",
              "DataFrame with 6 rows and 6 columns\n",
              "               baseMean log2FoldChange     lfcSE       stat      pvalue\n",
              "              <numeric>      <numeric> <numeric>  <numeric>   <numeric>\n",
              "0610005C13Rik   22.5079      2.2176660  0.749898  2.9572892 0.003103568\n",
              "0610007C21Rik  317.4460      0.3328445  0.226857  1.4671992 0.142321886\n",
              "0610007L01Rik 2692.5195     -0.3353582  0.101905 -3.2909007 0.000998672\n",
              "0610007N19Rik   78.4967     -0.0255568  0.346032 -0.0738567 0.941124427\n",
              "0610007P08Rik 1393.9053      0.0461691  0.121432  0.3802055 0.703792879\n",
              "0610007P14Rik 1402.6347     -0.0875989  0.137903 -0.6352198 0.525285091\n",
              "                    padj\n",
              "               <numeric>\n",
              "0610005C13Rik 0.00845671\n",
              "0610007C21Rik 0.23300793\n",
              "0610007L01Rik 0.00302910\n",
              "0610007N19Rik 0.97192676\n",
              "0610007P08Rik 0.79528900\n",
              "0610007P14Rik 0.64609835"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dQeByBTpbeA6"
      },
      "source": [
        "## Subsetting the genes as up-regulated, down-regulated for the treatment"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XhNXPBrDucDs"
      },
      "source": [
        "up_rna <- as.data.frame(res) %>%\n",
        "  filter(pvalue < 0.05 & log2FoldChange > 0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1Mb_p6GBuyI-"
      },
      "source": [
        "down_rna <- as.data.frame(res) %>%\n",
        "  filter(pvalue < 0.05 & log2FoldChange < 0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "id": "CvmB7hpCuqUW",
        "outputId": "578e9af7-255b-40ba-d917-2b5f0caf867b"
      },
      "source": [
        "dim(up_rna)\n",
        "dim(down_rna)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "[1] 4365    6"
            ],
            "text/latex": "\\begin{enumerate*}\n\\item 4365\n\\item 6\n\\end{enumerate*}\n",
            "text/markdown": "1. 4365\n2. 6\n\n\n",
            "text/html": [
              "<style>\n",
              ".list-inline {list-style: none; margin:0; padding: 0}\n",
              ".list-inline>li {display: inline-block}\n",
              ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
              "</style>\n",
              "<ol class=list-inline><li>4365</li><li>6</li></ol>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "[1] 4277    6"
            ],
            "text/latex": "\\begin{enumerate*}\n\\item 4277\n\\item 6\n\\end{enumerate*}\n",
            "text/markdown": "1. 4277\n2. 6\n\n\n",
            "text/html": [
              "<style>\n",
              ".list-inline {list-style: none; margin:0; padding: 0}\n",
              ".list-inline>li {display: inline-block}\n",
              ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
              "</style>\n",
              "<ol class=list-inline><li>4277</li><li>6</li></ol>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LbvG-JuSbA-i"
      },
      "source": [
        "## Subsetting genes for each of the 5 categories (Hats, HDACs, HDACs complex, Inflammatory related and NFkB pathwway related) in the up regulated and downregulated genes (treatment)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "97GiE0VXxNIS"
      },
      "source": [
        "up_rna_genes <- lapply(1:length(genes),function(x){up_rna[rownames(up_rna) %in% genes[[x]]$SYMBOL,]})\n",
        "names(up_rna_genes) <- names(genes)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xEQ9Bbcfxx5w",
        "outputId": "aa376521-3e53-4c3a-cc4e-5ad4a5f4a3d8"
      },
      "source": [
        "str(up_rna_genes)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "List of 5\n",
            " $ nfkb        :'data.frame':\t14 obs. of  6 variables:\n",
            "  ..$ baseMean      : num [1:14] 3045.9 985 1768.1 5287.3 18.4 ...\n",
            "  ..$ log2FoldChange: num [1:14] 0.714 1.06 0.241 0.273 2.632 ...\n",
            "  ..$ lfcSE         : num [1:14] 0.106 0.146 0.107 0.121 0.794 ...\n",
            "  ..$ stat          : num [1:14] 6.71 7.26 2.24 2.26 3.31 ...\n",
            "  ..$ pvalue        : num [1:14] 1.97e-11 3.92e-13 2.49e-02 2.36e-02 9.22e-04 ...\n",
            "  ..$ padj          : num [1:14] 1.74e-10 4.03e-12 5.34e-02 5.09e-02 2.82e-03 ...\n",
            " $ hdac        :'data.frame':\t17 obs. of  6 variables:\n",
            "  ..$ baseMean      : num [1:17] 5179 779 717 327 2715 ...\n",
            "  ..$ log2FoldChange: num [1:17] 0.387 0.564 0.407 1.021 0.291 ...\n",
            "  ..$ lfcSE         : num [1:17] 0.113 0.128 0.146 0.174 0.101 ...\n",
            "  ..$ stat          : num [1:17] 3.41 4.41 2.78 5.85 2.88 ...\n",
            "  ..$ pvalue        : num [1:17] 6.42e-04 1.06e-05 5.44e-03 4.79e-09 3.99e-03 ...\n",
            "  ..$ padj          : num [1:17] 2.02e-03 4.58e-05 1.39e-02 3.28e-08 1.06e-02 ...\n",
            " $ hat         :'data.frame':\t8 obs. of  6 variables:\n",
            "  ..$ baseMean      : num [1:8] 2285 693 1281 597 3256 ...\n",
            "  ..$ log2FoldChange: num [1:8] 0.327 1.208 0.326 0.379 0.223 ...\n",
            "  ..$ lfcSE         : num [1:8] 0.098 0.1616 0.1288 0.1476 0.0993 ...\n",
            "  ..$ stat          : num [1:8] 3.33 7.47 2.53 2.57 2.24 ...\n",
            "  ..$ pvalue        : num [1:8] 8.59e-04 7.74e-14 1.14e-02 1.03e-02 2.49e-02 ...\n",
            "  ..$ padj          : num [1:8] 2.64e-03 8.43e-13 2.69e-02 2.46e-02 5.34e-02 ...\n",
            " $ inflammatory:'data.frame':\t98 obs. of  6 variables:\n",
            "  ..$ baseMean      : num [1:98] 1840.7 36.8 402.5 111.5 56.7 ...\n",
            "  ..$ log2FoldChange: num [1:98] 0.33 1.374 0.721 0.663 2.239 ...\n",
            "  ..$ lfcSE         : num [1:98] 0.114 0.501 0.167 0.285 0.477 ...\n",
            "  ..$ stat          : num [1:98] 2.88 2.74 4.31 2.33 4.7 ...\n",
            "  ..$ pvalue        : num [1:98] 3.94e-03 6.08e-03 1.60e-05 2.01e-02 2.63e-06 ...\n",
            "  ..$ padj          : num [1:98] 1.05e-02 1.54e-02 6.76e-05 4.42e-02 1.25e-05 ...\n",
            " $ hdac_sub    :'data.frame':\t11 obs. of  6 variables:\n",
            "  ..$ baseMean      : num [1:11] 327 2715 3746 156 4188 ...\n",
            "  ..$ log2FoldChange: num [1:11] 1.021 0.291 4.283 1.035 0.37 ...\n",
            "  ..$ lfcSE         : num [1:11] 0.174 0.101 0.119 0.249 0.155 ...\n",
            "  ..$ stat          : num [1:11] 5.85 2.88 36.03 4.15 2.38 ...\n",
            "  ..$ pvalue        : num [1:11] 4.79e-09 3.99e-03 2.87e-284 3.35e-05 1.73e-02 ...\n",
            "  ..$ padj          : num [1:11] 3.28e-08 1.06e-02 2.29e-281 1.34e-04 3.88e-02 ...\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QEyR-2BBxSjo"
      },
      "source": [
        "down_rna_genes <- lapply(1:length(genes),function(x){down_rna[rownames(down_rna) %in% genes[[x]]$SYMBOL,]})\n",
        "names(down_rna_genes) <- names(genes)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kAckc-I9bwve"
      },
      "source": [
        "# Common up and down regulated genes in treatment between ES/EB (blue) and MEF(yellow)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pxchHV7my8Lj"
      },
      "source": [
        "## HAT's"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "35eUwlavcYkm"
      },
      "source": [
        "UP_HATs"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "vzPGYesdxSe4",
        "outputId": "c02348d2-111e-4a49-8fdb-f1a9108655f6"
      },
      "source": [
        "options(repr.plot.width=7, repr.plot.height=7)\n",
        "draw.pairwise.venn(\n",
        "  area1 = length(up_genes[['hat']]$name),\n",
        "  area2 = length(rownames(up_rna_genes[['hat']])),\n",
        "  cross.area = length(intersect(rownames(up_rna_genes[['hat']]), up_genes[['hat']]$name)),\n",
        "  lty = \"blank\", \n",
        "  scaled = FALSE,\n",
        "  fill = c(\"paleturquoise\", \"khaki1\"),\n",
        "  cex = 2,\n",
        "  cat.cex = 1\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "(polygon[GRID.polygon.11], polygon[GRID.polygon.12], polygon[GRID.polygon.13], polygon[GRID.polygon.14], text[GRID.text.15], text[GRID.text.16], text[GRID.text.17], text[GRID.text.18], text[GRID.text.19]) "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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xuFnvV49HijGFIRetUEerxJDKkIPWoC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\nVH/5aygAAAAASUVORK5CYII=",
            "text/plain": [
              "plot without title"
            ]
          },
          "metadata": {
            "image/png": {
              "width": 420,
              "height": 420
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HdMLAUuyceGF"
      },
      "source": [
        "DOWN_HATs"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "id": "IjSgAlq3xScP",
        "outputId": "4f7f61ea-a811-45a9-dff2-46999f14545a"
      },
      "source": [
        "options(repr.plot.width=7, repr.plot.height=7)\n",
        "venn <- draw.pairwise.venn(\n",
        "  area1 = length(down_genes[['hat']]$name),\n",
        "  area2 = length(rownames(down_rna_genes[['hat']])),\n",
        "  cross.area = length(intersect(rownames(down_rna_genes[['hat']]), down_genes[['hat']]$name)), \n",
        "  lty = \"blank\", \n",
        "  fill = c(\"paleturquoise\", \"khaki1\"),\n",
        "  cex = 2,\n",
        "  scaled = FALSE,\n",
        "  cat.cex = 1\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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rebpHr3htQSXc4uertRqndfHtJIjzcK3c42erpRymdfH9JIjzcJHc82ertJ6lff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\nEqEAAAADSURBVKGBg3dZAmUAAAAASUVORK5CYII=",
            "text/plain": [
              "plot without title"
            ]
          },
          "metadata": {
            "image/png": {
              "width": 420,
              "height": 420
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rJP2otmX0JpL"
      },
      "source": [
        "## HDAC"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rrgR11xtciHw"
      },
      "source": [
        "UP_HDACs"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "gwjbjQ8R6OB0",
        "outputId": "f3dca6a1-e8b5-496a-d822-5c88f4241eee"
      },
      "source": [
        "options(repr.plot.width=7, repr.plot.height=7)\n",
        "draw.pairwise.venn(\n",
        "  area1 = length(up_genes[['hdac']]$name),\n",
        "  area2 = length(rownames(up_rna_genes[['hdac']])),\n",
        "  cross.area = length(intersect(rownames(up_rna_genes[['hdac']]), up_genes[['hdac']]$name)), \n",
        "  lty = \"blank\",\n",
        "  scaled = FALSE, \n",
        "  fill = c(\"paleturquoise\", \"khaki1\"),\n",
        "  cex = 2,\n",
        "  cat.cex = 1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "(polygon[GRID.polygon.29], polygon[GRID.polygon.30], polygon[GRID.polygon.31], polygon[GRID.polygon.32], text[GRID.text.33], text[GRID.text.34], text[GRID.text.35], text[GRID.text.36], text[GRID.text.37]) "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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xRDKkaveyR6vEkMqRg97pHo8SapCIne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\nQmCC",
            "text/plain": [
              "plot without title"
            ]
          },
          "metadata": {
            "image/png": {
              "width": 420,
              "height": 420
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eCSc61M5ckL8"
      },
      "source": [
        "UP_HDACs_only_histonedeacytelase complex"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "5AmOvaUb9Yvk",
        "outputId": "0005ad5e-b515-42b7-e5fe-a1bc01ca85e2"
      },
      "source": [
        "options(repr.plot.width=7, repr.plot.height=7)\n",
        "draw.pairwise.venn(\n",
        "  area1 = length(up_genes[['hdac_sub']]$name),\n",
        "  area2 = length(rownames(up_rna_genes[['hdac_sub']])),\n",
        "  cross.area = length(intersect(rownames(up_rna_genes[['hdac_sub']]), up_genes[['hdac_sub']]$name)), \n",
        "  lty = \"blank\", \n",
        "  scaled = FALSE,\n",
        "  fill = c(\"paleturquoise\", \"khaki1\"),\n",
        "  cex = 2,\n",
        "  cat.cex = 1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "(polygon[GRID.polygon.38], polygon[GRID.polygon.39], polygon[GRID.polygon.40], polygon[GRID.polygon.41], text[GRID.text.42], text[GRID.text.43], text[GRID.text.44], text[GRID.text.45], text[GRID.text.46]) "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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M5hoS3DCjunsQAAkxvOOWyy2AEAhCHsoDVOYwnJOSy0aIhh5zQWAGBLQzuHTRY7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\ngAAAAABJRU5ErkJggg==",
            "text/plain": [
              "plot without title"
            ]
          },
          "metadata": {
            "image/png": {
              "width": 420,
              "height": 420
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7PPnhCZbcwUC"
      },
      "source": [
        "DOWN_HDACs"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "id": "V_VlZXF3xSXS",
        "outputId": "1e00a95f-4f33-48d9-aa40-794be7292eb7"
      },
      "source": [
        "options(repr.plot.width=7, repr.plot.height=7)\n",
        "venn <- draw.pairwise.venn(\n",
        "  area1 = length(down_genes[['hdac']]$name),\n",
        "  area2 = length(rownames(down_rna_genes[['hdac']])),\n",
        "  cross.area = length(intersect(rownames(down_rna_genes[['hdac']]), down_genes[['hdac']]$name)), \n",
        "  lty = \"blank\", \n",
        "  scaled = FALSE,\n",
        "  fill = c(\"paleturquoise\", \"khaki1\"),\n",
        "  cex = 2,\n",
        "  cat.cex = 1\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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LRJfTpxYbEDAAhD2AEABCHsAACCEHYAAEEIOwCAIIQdAEAQwg6AWvk+MRgcYQeD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\nIewAAIL4//Ry+G7VOfaNAAAAAElFTkSuQmCC",
            "text/plain": [
              "plot without title"
            ]
          },
          "metadata": {
            "image/png": {
              "width": 420,
              "height": 420
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gwf7r_JwczWt"
      },
      "source": [
        "DOWN_HDACs_only_histonedeacytelase complex"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "Lu-IZJD39ff_",
        "outputId": "373523c2-9096-4032-9351-309ccb6e2c51"
      },
      "source": [
        "options(repr.plot.width=7, repr.plot.height=7)\n",
        "draw.pairwise.venn(\n",
        "  area1 = length(down_genes[['hdac_sub']]$name),\n",
        "  area2 = length(rownames(down_rna_genes[['hdac_sub']])),\n",
        "  cross.area = length(intersect(rownames(down_rna_genes[['hdac_sub']]), down_genes[['hdac_sub']]$name)), \n",
        "  lty = \"blank\", \n",
        "  scaled = FALSE,\n",
        "  fill =  c( \"khaki1\",\"paleturquoise\"),\n",
        "  cex = 2,\n",
        "  cat.cex = 1)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "(polygon[GRID.polygon.56], polygon[GRID.polygon.57], polygon[GRID.polygon.58], polygon[GRID.polygon.59], text[GRID.text.60], text[GRID.text.61], text[GRID.text.62], text[GRID.text.63], text[GRID.text.64]) "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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FFz2PmAHVRzDgvAenV/zM5iB0BTfMwO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\nAAAAAElFTkSuQmCC",
            "text/plain": [
              "plot without title"
            ]
          },
          "metadata": {
            "image/png": {
              "width": 420,
              "height": 420
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r4GIxW858qXn"
      },
      "source": [
        "## Inflammatory"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DOmAzRJlc2lb"
      },
      "source": [
        "UP_Inflammatory"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "tPpcCyVgxNFt",
        "outputId": "3ab8969a-2046-40e1-9c1a-1594633d48af"
      },
      "source": [
        "options(repr.plot.width=7, repr.plot.height=7)\n",
        "draw.pairwise.venn(\n",
        "  area1 = length(up_genes[['inflammatory']]$name),\n",
        "  area2 = length(rownames(up_rna_genes[['inflammatory']])),\n",
        "  cross.area = length(intersect(rownames(up_rna_genes[['inflammatory']]), up_genes[['inflammatory']]$name)), \n",
        "  lty = \"blank\", \n",
        "  fill = c(\"paleturquoise\", \"khaki1\"),\n",
        "  cex = 2,\n",
        "  scaled = FALSE,\n",
        "  cat.cex = 1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "(polygon[GRID.polygon.65], polygon[GRID.polygon.66], polygon[GRID.polygon.67], polygon[GRID.polygon.68], text[GRID.text.69], text[GRID.text.70], text[GRID.text.71], text[GRID.text.72], text[GRID.text.73]) "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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8bv3z0Ejho9+k07+zYYqmTHnd/4zCu/y77mS8ef980zLk4pbbX1JjfN+NEqv7rP\n/rv96vJpKaXf33pvnz++fPnym268s/vi4d4xFOHSn19/510PHnjA7hnXzHzgscaL0aNH7brL\nto3XG/X4DuW5z7wwucfX6/X0+BMrB++NX7f+MO+W5rLYlc7fTJ588Ic/3Hh92YUX9nnNvXfc\n0ThRHTFiRPfCl1IaO27cn2bN6rnJ9fabX/2q8eJdBxwwfkK/36oEIR30D+9ab/21U0oz73vk\niT8/3fuCm35753PPvphSWm/9tQ/6h3e1+/6gGcaPH/vArMd6PgbU2xVXrniX6nsOfHv30eru\nb91+1KiRjdcZT0Xccus9jRejRo182+5vasId0zzCrow+deKJa6y5Zkrp6ksuuWf69N4X/Oz7\n32+82Oegg1636abdf33s+PFdXV133NrvBwv9+vLLf3/jjSmlMWPHfvK445p721B+Y8au9un/\n/EhKqaur68xXv8u82/Lly0875QeN18d+9iNj+vqsByi/8ePHdHV13XzrH/u74OeX3XDDtBkp\npbFjVzvxiyu/vmjixAkfOnSfxuvzvvfLPn92yZKl3zp3xZeM/Z9/fvfE/r92mUIIuzJae911\nz/n5z8eMHbt8+fKjDznknhkzev7qReee+8sf/ziltP7UqcefdVbPX2o8DPHd00+/7YYbOjs7\ne/7SogULvve1r33msMNSSiNHjTrlO9/ZYpttWv6/BMrn8I8ddOB790gpXfSDq75/3srP9168\naMln/u2MP9x2b0rpve975+EfO7iwW4ThaXyw9le+euFvrp/e+FqwbgsWLDr1tAv/6SMnpJRG\njRp5wfeO2/aNm/a84NRTjn7dRuullG659Z4jP/HVnl/El1J6+unnDjrksw8+9HhKadNNpn7l\nv49u9f8WBst77Epqp7e//YJrrvnCkUc+Nnv2oXvuudPb377ZllsuWbLkjltvfWz27JTS9jvt\n9I2LL/6byZN7/tTYceNSSvdMn374gQeuMXHiVttvv+6UKcuWLn12zpx777ij8VUWm2655ZfO\nPXeH3bx5iJrq6Og49/zj1ljzrJ9edO3njj3zwgt+tcOOWy9etOSWm+7665PPpJQO+5eDvnTq\nJ4u+TRi68ePGppRu/8P9e+9/zKRJE960/RYbTJm8dGnn03Oemz5j5uLFS1NKW2258fnnfb73\nh9VNmTJ52m/O+chhJ99y6z3nfe+Xl/7i+nfsscPUqessW9b5yJ+evOnmuxtPTm4TEWMAABBx\nSURBVLzzHW/50QXHr7NO32/Co0AdGV+YmGFW/1+KQBMtXrTo15dd9uvLL589c+azc+aMHDVq\n8nrr/e0uu+x78MHvOuCA3tc/N3fubdOm3XvHHTPvvvu5uXPnvfTS/Jdf7uzsXHPixHWmTHnz\nLrvsuf/+e+y7b0dHR++fpbkmd83Iv4gWyH54oqfbbv7jTy685g+33/fUX58dPWrklA3W2e1t\nbzr0w/u/ZSdjdjtMHr3qF8/TLHPmPH/9tBnTZ8y8864H58x5/qWXX3nppfmdncsnTZqwwZTJ\nu+263QH7v/Xd+781458FXV1d026849JfXD99xgN/fvzpl19+ZcSIjokTJ2y6ydSddtz6A+/f\nK+Pji2ma0bsO4YeEHbSEsINswg5yDCnsvMcOACAIYQcAEISwAwAIQtgBAAQh7KAlnu3YMf8i\nqCtPTkCLCDsAgCCEHQBAEMIOACCIIYbdVuPGNfc+AABYYUifTpwsdgAAYQg7AIAghB0AQBDC\nDgAgCGEHABCEsAMACELYAdBWvk8MWkfYQav4ulgA2kzYAQAEMfSw8+UTAADNN9SvnUgWOwCA\nMIQdAEAQwg4AIAhhBwAQhLCDFvKJJ7AKH2IHLSXsAACCGFbY+cQTAIBmGsZnnSSLHQBAGMIO\nACAIYQcAEISwAwAIQthBa/nEE+jms06g1YQdAEA5DO+R2DT8sPOJJwAAJWGxAwAIQtgBAAQh\n7AAAghB20HIejIXkkVhoiyaEnecnAACGa9iPxCaLHQBAGMIOACAIYQcAEISwg3bw/AQ158kJ\naI/mhJ3nJwAAhq4ZT04kix0AQBjCDgAgCGEHbeJtdtSWN9hB2zQt7LzNDgBgKJr0BrtksQMA\nCEPYAQAEIewAAIIQdtA+np+ghjw5Ae3UzLDz/AQAwOA078mJZLEDAAhD2AHQKs5hoc2aHHZO\nYyGbt9kBsFJTz2GTxQ4AIAxhBwAQhLCDdnMaS014gx20X/PDztvsAADyNfsNdsliBwAQhrAD\nAAhC2EEBvM2O8LzBDgrRkrDzNjsAgCwteINdstgBAIQh7KAYTmMJzDksFKVVYec0FgCgb605\nh00WOwCAMIQdFMZpLCE5h4UCtTDsnMYCAKyqZeewyWIHABCGsIMiOY0lGOewUKzWhp3TWACA\nlVp5DpssdgAAYQg7KJjTWMJwDguFa3nYOY0FAEip5eewyWIHABCGsIPiOY0lAOewUAbtCDun\nsQBA3bX+HDZZ7AAAwhB2UApOY6k057BQEm0KO6exAEB9teUcNlnsAADCaF/YGe0gm9NYKso5\nLORo11yXLHYAAGEIOygRox2VY66DUmlr2DmNBQDqpY3nsMliBwAQRrvDzmgH2ZzGUiHOYSFH\ne+e6ZLEDAAhD2EHpGO2oBHMdlFABYec0FgCIr+3nsMliBwAQRjFhZ7SDbE5jKTnnsJCjiLku\nWewAAMIoLOyMdpDNaEdpmesgR0FzXbLYAQCEIeygvIx2lJC5DsqsyLBzGgsARFPcOWyy2EHJ\nGe0oFXMdlFzBYWe0AwDiKHSuS4WHHZDLaEdJmOug/IoPO6MdABBB0XNdKkPYAQDQFKUIO6Md\nZHMaS+Gcw0KOEsx1qSRhBwDA8JUl7Ix2kM1oR4HMdZCjHHNdKk/YAQAwTCUKO6MdZDPaUQhz\nHeQozVyXShV2AAAMR7nCzmgH2Yx2tJm5DnKUaa5LZQs7IJe2o21UHVRO6cLOaAcAVEPJ5rpU\nwrADchntaANzHVRRGcPOaAcAlF355rpUzrADchntaClzHVRUScPOaAcAlFcp57pU2rADchnt\naBFzHVRXecPOaAcAlFFZ57pU5rBL2g7yGO1oOnMd5Chx1aWShx0AAANX9rAz2kE2ox1NZK6D\nHOWe61L5ww7Ipe1oClUHAVQg7Ix2AEDxSj/XpUqEXdJ2kMdoxzCZ6yBHFaouVSXsAADIVZmw\nM9pBNqMdQ2augxwVmetShcIOyKXtGAJVB5FUKeyMdgBAu1VnrkvVCruk7SCP0Y5BMddBjkpV\nXapc2AEA0J/qhZ3RDrIZ7Rggcx3kqNpcl6oYdkAubUcuVQc5Klh1qaJhZ7QDAOitkmGXtB3k\nMdqRwVwHOao516Xqhh2QS9vRJ1UHgVU47Ix2AEDzVXauS5UOu6TtII/RjlWY6yBHlasuVT3s\ngFzajm6qDsKrfNgZ7QCA5qj4XJcChF3SdpDHaEcy10Gu6lddihF2SdtBHm1Xc6oOcoSouhQm\n7IBc2q62VB3UR5ywM9oBAEMRZa5LkcIuaTvIY7SrIXMd5AhUdSlY2AG5tF2tqDqom2hhZ7QD\nAAYq1lyX4oVd0naQx2hXE+Y6yBGu6lLIsEvaDvJou/BUHeSIWHUpatgBubRdYKoOaits2Bnt\nAIC+BZ3rUuCwS9oO8hjtQjLXQY64VZdih13SdpBH2wWj6iBH6KpL4cMuaTvIo+3CUHWQI3rV\npTqEHZBL2wWg6oBUk7Az2gFA3dVgrks1Cbuk7SCP0a7SzHWQox5Vl+oTdknbQR5tV1GqDnLU\npupSrcIuaTvIo+0qR9VBjjpVXapb2AG5tF2FqDpgFbULO6Md5NJ2laDqIF/N5rpUw7BL2g4A\n6qB+VZfqGXZJ20Eeo13JmesgRy2rLtU27JK2gzzarrRUHeSoa9WlOodd0naQR9uVkKqDHDWu\nulTzsEvaDvJou1JRdZCj3lWXhF3SdpBH25WEqoMcta+6JOyAgdB2hVN1wEAIu5SMdjAA2q5A\nqg7ymetSSsKum7aDXNquEKoO8qm6Vwm7lbQdAFSPqutB2L2GtoNsRrs2M9dBDlX3WsJuVdoO\nsmm7tlF1kEPV9SLs+qDtIJu2awNVBzlUXV+EXd+0HWTTdi2l6iCHquuHsOuXtoNs2q5FVB3k\nUHX9E3ZZtB1k03ZNp+ogh6rLJOxyaDvIpu2aSNVBDlWXR9jl03aQTds1haqDHKpuAITdgGg7\nyKbthknVQQ5VNzDCbqC0HWTTdkOm6iCHqhswYTcI2g6yabshUHWQQ9UNhrAbHG0H2bTdoKg6\nyKHqBqmjq6ur6HuonlkLFxZ9C1B2k7tmFH0LpSbpIJ+qGzyL3VDY7SCX6S6DqoN8qm5IhN0Q\naTvIpe36pOogn6obKkexw+VYFrI5k+1J1UEOSTc8FrvhMt1BNrtdN1UHOVTdsAm7JtB2kE3b\nJVUHuVRdMwi75tB2kK3mbafqIIeqaxJh1zTaDrLVtu1UHeRQdc3j4Ykm8ywF5KrP4xSSDvKp\nuqay2DWZ3Q5y1WS6U3WQT9U1m8WuVUx3kC32bqfqIIekaw2LXauY7iBb4N1O1UEOVdcywq6F\ntB1kC9l2qg5yqLpWchTbcs5kIVeMY1lJB/lUXYsJuzaRd5Ct6m2n6iCHpGsLR7Ft4lgWslX6\nWFbVQQ5V1y7Crn20HWSraNupOsih6trIUWy7OZOFXFU5lpV0kE/VtZewK4a8g2zlbztVBzkk\nXREcxRbDsSxkK/mxrKqDHKquIBa7ItntIFfZpjtJB/lUXXGEXfHkHWQrT9upOsgh6YrmKLZ4\njmUhW0mOZVUd5FB1JWCxKxHTHWQrarqTdJBD0pWGxa5ETHeQrZDpTtVBDlVXJha7MjLdQbb2\nTHeSDnJIuvKx2JWR6Q6ytWG6U3WQQ9WVksWu1Ex3kK0V052kgxySrsQsdqVmuoNsTZ/uVB3k\nUHXlZrGrBtMdZBv+dCfpIIekqwKLXTWY7iDbMKc7VQc5VF1FWOwqxnQH2QY73Uk6yCHpKkXY\nVY+2g1wDyTtJB/lUXdUIu6qSd5Atu+1UHeSQdNUk7KpN3kG23nkn6SCHpKsyYVd52g5yNfJO\n0kE+VVdxwi4IeQfZthr1x6JvAcpN0oUg7EKRd9Dbaz4taOnvi7sRKCtJF4iwC0jeQUO/HwAp\n76BB0oUj7GLSdtTcgD7TW95Rc6ouImEXmbyjhgb9NS3yjhqSdHEJu/jkHfUxxC/f03bUh6SL\nTtjVhbwjtiZ8n7K8IzZJVw/Crl7kHfE0Iel6knfEI+nqRNjVkbwjhiYnXU/yjhgkXf0Iu/qS\nd1RXC5OuJ3lHdUm6uhJ2dSfvqJY2JV1P8o5qkXT1JuxISd5RegX0XG8Kj5KTdAg7epJ3lFAp\nkq4neUcJSTpeJezog8KjDEqXdD3JO8pAz9GLsKNf8o6ilDrpepJ3FEXS0Q9hRw55R9tUpud6\nU3i0jaQjk7BjoBQerVPhpOtJ3tE6eo6BEXYMjryjiYL0XG8KjyaSdAyGsGOIFB7DETbpepJ3\nDIeeY0iEHcOl8Bi4WvRcbwqPgdNzDI+woznkHRlq2nO9KTwySDqaQdjRZAqPniRdH+QdPek5\nmkrY0SoKr8703IAovDrTc7SGsKPlFF596LkhUnj1oedoMWFH+yi8kMRck4m8kPQc7SLsKIbI\nqzo913IKr+rEHEUQdhRM4VWLniuAwqsWPUehhB0lIvLKScyViMgrJzFHaQg7SkrkFUvMVYDI\nK5aYo5SEHRUg8tpDzFWYyGsPMUfpCTuqR+c1i5ILS+c1i5KjaoQdlafzBk7J1ZTOGzglR8UJ\nO6LReT0pOfqg83pScsQi7IivJqmn4RiWmtSejCM6YUd9VTf4NBxtUt3aE3DUlbCDfhVVfrqN\nyiiq/HQb9EPYAQAEMaLoGwAAoDmEHQBAEMIOACAIYQcAEISwAwAIQtgBAAQh7AAAghB2AABB\nCDsAgCCEHQBAEMIOACAIYQcAEISwAwAIQtgBAAQh7AAAghB2AABBCDsAgCCEHQBAEMIOACAI\nYQcAEISwAwAIQtgBAAQh7AAAghB2AABBCDsAgCCEHQBAEMIOACAIYQcAEISwAwAIQtgBAAQh\n7AAAghB2AABBCDsAgCCEHQBAEMIOACAIYQcAEISwAwAIQtgBAAQh7AAAghB2AABBCDsAgCCE\nHQBAEMIOACAIYQcAEISwAwAIQtgBAAQh7AAAghB2AABBCDsAgCCEHQBAEMIOACAIYQcAEISw\nAwAIQtgBAAQh7AAAghB2AABBCDsAgCCEHQBAEMIOACAIYQcAEISwAwAIQtgBAAQh7AAAghB2\nAABBCDsAgCCEHQBAEMIOACAIYQcAEISwAwAIQtgBAAQh7AAAghB2AABBCDsAgCCEHQBAEMIO\nACAIYQcAEISwAwAIQtgBAAQh7AAAghB2AABBCDsAgCCEHQBAEMIOACAIYQcAEISwAwAIQtgB\nAAQh7AAAghB2AABBCDsAgCCEHQBAEMIOACAIYQcAEISwAwAIQtgBAAQh7AAAghB2AABBCDsA\ngCCEHQBAEMIOACAIYQcAEISwAwAIQtgBAAQh7AAAghB2AABBCDsAgCCEHQBAEMIOACAIYQcA\nEISwAwAIQtgBAAQh7AAAghB2AABBCDsAgCCEHQBAEMIOACAIYQcAEISwAwAIQtgBAAQh7AAA\nghB2AABBCDsAgCCEHQBAEMIOACAIYQcAEISwAwAIQtgBAAQh7AAAghB2AABBCDsAgCCEHQBA\nEMIOACAIYQcAEISwAwAIQtgBAAQh7AAAghB2AABBCDsAgCCEHQBAEMIOACAIYQcAEISwAwAI\nQtgBAAQh7AAAghB2AABBCDsAgCCEHQBAEMIOACAIYQcAEISwAwAIQtgBAAQh7AAAgvj/BZyr\niwGQF5AAAAAASUVORK5CYII=",
            "text/plain": [
              "plot without title"
            ]
          },
          "metadata": {
            "image/png": {
              "width": 420,
              "height": 420
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fNgE_9Tgc4qG"
      },
      "source": [
        "DOWN_Inflammatory"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "RYJfERF_85d8",
        "outputId": "a4b4668c-f4e6-4cac-aa05-41a1b2a467e8"
      },
      "source": [
        "options(repr.plot.width=7, repr.plot.height=7)\n",
        "draw.pairwise.venn(\n",
        "  area1 = length(down_genes[['inflammatory']]$name),\n",
        "  area2 = length(rownames(down_rna_genes[['inflammatory']])),\n",
        "  cross.area = length(intersect(rownames(down_rna_genes[['inflammatory']]), down_genes[['inflammatory']]$name)), \n",
        "  lty = \"blank\", \n",
        "  fill = c(\"paleturquoise\", \"khaki1\"),\n",
        "  cex = 2,\n",
        "  scaled = FALSE,\n",
        "  cat.cex = 1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "(polygon[GRID.polygon.74], polygon[GRID.polygon.75], polygon[GRID.polygon.76], polygon[GRID.polygon.77], text[GRID.text.78], text[GRID.text.79], text[GRID.text.80], text[GRID.text.81], text[GRID.text.82]) "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Ow88/S+39APyu5DHzzy8MPelFJasWLVaR87r/vPe4bdyy8v7+/L99v3Dd1v01x7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",
            "text/plain": [
              "plot without title"
            ]
          },
          "metadata": {
            "image/png": {
              "width": 420,
              "height": 420
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XZAen8Pk9IvQ"
      },
      "source": [
        "## NFkB"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xbUm3NEec8zu"
      },
      "source": [
        "UP_NFkB"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "v1cn2pwa9ISa",
        "outputId": "cde045f7-73be-4b4c-ed0c-2b2157d1cf1a"
      },
      "source": [
        "options(repr.plot.width=7, repr.plot.height=7)\n",
        "draw.pairwise.venn(\n",
        "  area1 = length(up_genes[['nfkb']]$name),\n",
        "  area2 = length(rownames(up_rna_genes[['nfkb']])),\n",
        "  cross.area = length(intersect(rownames(up_rna_genes[['nfkb']]), up_genes[['nfkb']]$name)), \n",
        "  lty = \"blank\", \n",
        "  fill = c( \"khaki1\",\"paleturquoise\"),\n",
        "  cex = 2,\n",
        "  scaled = FALSE,\n",
        "  cat.cex = 1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "(polygon[GRID.polygon.83], polygon[GRID.polygon.84], polygon[GRID.polygon.85], polygon[GRID.polygon.86], text[GRID.text.87], text[GRID.text.88], text[GRID.text.89], text[GRID.text.90], text[GRID.text.91]) "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ejxJjGkYvS4R6LHm6QiJHq7Uehxj0SP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\nH27OpLZCM09rAAAAAElFTkSuQmCC",
            "text/plain": [
              "plot without title"
            ]
          },
          "metadata": {
            "image/png": {
              "width": 420,
              "height": 420
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZhCHUFCtc_QV"
      },
      "source": [
        "DOWN_NFkB"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "h5jek5c-xNDM",
        "outputId": "7fcbd28d-e89b-4b6b-939d-852c44a40027"
      },
      "source": [
        "options(repr.plot.width=7, repr.plot.height=7)\n",
        "draw.pairwise.venn(\n",
        "  area1 = length(down_genes[['nfkb']]$name),\n",
        "  area2 = length(rownames(down_rna_genes[['nfkb']]))+2,\n",
        "  cross.area = length(intersect(rownames(down_rna_genes[['nfkb']]), down_genes[['nfkb']]$name)), \n",
        "  scaled = FALSE,\n",
        "  euler.d = FALSE,\n",
        "  lty = \"blank\",\n",
        "  rotation.degree = 180, \n",
        "  fill =  c( \"khaki1\",\"paleturquoise\"),\n",
        "  cex = 2,\n",
        "  cat.cex = 1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "(polygon[GRID.polygon.92], polygon[GRID.polygon.93], polygon[GRID.polygon.94], polygon[GRID.polygon.95], text[GRID.text.96], text[GRID.text.97], text[GRID.text.98], text[GRID.text.99], text[GRID.text.100]) "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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HiTGFIpetpj0eNNsj0kertR6GmPRY83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",
            "text/plain": [
              "plot without title"
            ]
          },
          "metadata": {
            "image/png": {
              "width": 420,
              "height": 420
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zbHBXqg1thER",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "4be3cecc-161e-485c-d708-5801317434e0"
      },
      "source": [
        "Sys.time() - start_time0"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Time difference of 31.50395 mins"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9GomLPkntWy0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "4ffb1b22-94ce-4ab7-f2d2-638a5e887ebb"
      },
      "source": [
        "sessionInfo()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "R version 4.1.1 (2021-08-10)\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      parallel  stats4    stats     graphics  grDevices utils    \n",
              " [8] datasets  methods   base     \n",
              "\n",
              "other attached packages:\n",
              " [1] cowplot_1.1.1                            \n",
              " [2] VennDiagram_1.6.20                       \n",
              " [3] futile.logger_1.4.3                      \n",
              " [4] fgsea_1.18.0                             \n",
              " [5] GSEA_1.2                                 \n",
              " [6] DESeq2_1.32.0                            \n",
              " [7] plotrix_3.8-2                            \n",
              " [8] wordcloud_2.6                            \n",
              " [9] org.Mm.eg.db_3.13.0                      \n",
              "[10] TxDb.Mmusculus.UCSC.mm10.knownGene_3.10.0\n",
              "[11] GenomicFeatures_1.44.2                   \n",
              "[12] AnnotationDbi_1.54.1                     \n",
              "[13] stringr_1.4.0                            \n",
              "[14] goseq_1.44.0                             \n",
              "[15] geneLenDataBase_1.28.0                   \n",
              "[16] BiasedUrn_1.07                           \n",
              "[17] ggplot2_3.3.5                            \n",
              "[18] dplyr_1.0.7                              \n",
              "[19] plyr_1.8.6                               \n",
              "[20] RColorBrewer_1.1-2                       \n",
              "[21] SummarizedExperiment_1.22.0              \n",
              "[22] Biobase_2.52.0                           \n",
              "[23] GenomicRanges_1.44.0                     \n",
              "[24] GenomeInfoDb_1.28.4                      \n",
              "[25] IRanges_2.26.0                           \n",
              "[26] S4Vectors_0.30.0                         \n",
              "[27] BiocGenerics_0.38.0                      \n",
              "[28] MatrixGenerics_1.4.3                     \n",
              "[29] matrixStats_0.61.0                       \n",
              "\n",
              "loaded via a namespace (and not attached):\n",
              "  [1] uuid_0.1-4               fastmatch_1.1-3          BiocFileCache_2.0.0     \n",
              "  [4] repr_1.1.3               splines_4.1.1            BiocParallel_1.26.2     \n",
              "  [7] usethis_2.0.1            digest_0.6.27            htmltools_0.5.2         \n",
              " [10] GO.db_3.13.0             fansi_0.5.0              magrittr_2.0.1          \n",
              " [13] memoise_2.0.0            remotes_2.4.0            Biostrings_2.60.2       \n",
              " [16] annotate_1.70.0          prettyunits_1.1.1        colorspace_2.0-2        \n",
              " [19] blob_1.2.2               rappdirs_0.3.3           callr_3.7.0             \n",
              " [22] crayon_1.4.1             RCurl_1.98-1.5           jsonlite_1.7.2          \n",
              " [25] genefilter_1.74.0        survival_3.2-13          glue_1.4.2              \n",
              " [28] gtable_0.3.0             zlibbioc_1.38.0          XVector_0.32.0          \n",
              " [31] DelayedArray_0.18.0      pkgbuild_1.2.0           scales_1.1.1            \n",
              " [34] futile.options_1.0.1     DBI_1.1.1                Rcpp_1.0.7              \n",
              " [37] xtable_1.8-4             progress_1.2.2           bit_4.0.4               \n",
              " [40] httr_1.4.2               ellipsis_0.3.2           pkgconfig_2.0.3         \n",
              " [43] XML_3.99-0.8             dbplyr_2.1.1             locfit_1.5-9.4          \n",
              " [46] utf8_1.2.2               tidyselect_1.1.1         rlang_0.4.11            \n",
              " [49] munsell_0.5.0            tools_4.1.1              cachem_1.0.6            \n",
              " [52] cli_3.0.1                generics_0.1.0           RSQLite_2.2.8           \n",
              " [55] devtools_2.4.2           evaluate_0.14            fastmap_1.1.0           \n",
              " [58] yaml_2.2.1               processx_3.5.2           bit64_4.0.5             \n",
              " [61] fs_1.5.0                 purrr_0.3.4              KEGGREST_1.32.0         \n",
              " [64] nlme_3.1-152             formatR_1.11             xml2_1.3.2              \n",
              " [67] biomaRt_2.48.3           compiler_4.1.1           filelock_1.0.2          \n",
              " [70] curl_4.3.2               png_0.1-7                testthat_3.0.4          \n",
              " [73] tibble_3.1.4             geneplotter_1.70.0       stringi_1.7.4           \n",
              " [76] ps_1.6.0                 desc_1.3.0               lattice_0.20-44         \n",
              " [79] IRdisplay_1.0            Matrix_1.3-4             vctrs_0.3.8             \n",
              " [82] pillar_1.6.2             lifecycle_1.0.0          BiocManager_1.30.16     \n",
              " [85] data.table_1.14.0        bitops_1.0-7             rtracklayer_1.52.1      \n",
              " [88] R6_2.5.1                 BiocIO_1.2.0             gridExtra_2.3           \n",
              " [91] sessioninfo_1.1.1        lambda.r_1.2.4           assertthat_0.2.1        \n",
              " [94] pkgload_1.2.2            rprojroot_2.0.2          rjson_0.2.20            \n",
              " [97] withr_2.4.2              GenomicAlignments_1.28.0 Rsamtools_2.8.0         \n",
              "[100] GenomeInfoDbData_1.2.6   mgcv_1.8-36              hms_1.1.0               \n",
              "[103] IRkernel_1.2             pbdZMQ_0.3-5             base64enc_0.1-3         \n",
              "[106] restfulr_0.0.13         "
            ]
          },
          "metadata": {}
        }
      ]
    }
  ]
}