figure2_02_dge.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f42aca45",
   "metadata": {},
   "source": [
    "# load library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3923e930",
   "metadata": {},
   "outputs": [],
   "source": [
    "source(\"r/jupyter_common.R\")\n",
    "\n",
    "source(\"r/enrichment_analysis.R\")\n",
    "source(\"r/find_markers.R\")\n",
    "source(\"r/identify_cell_types.R\")\n",
    "source(\"r/plot_sc_clusters.R\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "076042f0",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# data structure\n",
    "suppressPackageStartupMessages(library(stringi))\n",
    "suppressPackageStartupMessages(library(forcats))\n",
    "\n",
    "# utils\n",
    "suppressPackageStartupMessages(library(amap)) # for Dist()\n",
    "suppressPackageStartupMessages(library(scales))\n",
    "\n",
    "# scRNA-seq\n",
    "suppressPackageStartupMessages(library(Seurat))\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bfa7459",
   "metadata": {},
   "source": [
    "## parallel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bdaa516f",
   "metadata": {},
   "outputs": [],
   "source": [
    "suppressPackageStartupMessages(library(future))\n",
    "plan(\"multicore\", workers = 8)\n",
    "\n",
    "options(future.globals.maxSize = 5 * 1024^3) # 5GB\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3076641a",
   "metadata": {},
   "source": [
    "# parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7204f433",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'er+bc-cell-line'"
      ],
      "text/latex": [
       "'er+bc-cell-line'"
      ],
      "text/markdown": [
       "'er+bc-cell-line'"
      ],
      "text/plain": [
       "[1] \"er+bc-cell-line\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "'RNA_snn_res.0.8'"
      ],
      "text/latex": [
       "'RNA\\_snn\\_res.0.8'"
      ],
      "text/markdown": [
       "'RNA_snn_res.0.8'"
      ],
      "text/plain": [
       "[1] \"RNA_snn_res.0.8\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "'umap'"
      ],
      "text/latex": [
       "'umap'"
      ],
      "text/markdown": [
       "'umap'"
      ],
      "text/plain": [
       "[1] \"umap\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "'cluster.type'"
      ],
      "text/latex": [
       "'cluster.type'"
      ],
      "text/markdown": [
       "'cluster.type'"
      ],
      "text/plain": [
       "[1] \"cluster.type\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cancer_type <- \"er+bc-cell-line\"\n",
    "\n",
    "\n",
    "args <- list()\n",
    "args$cancer_type <- cancer_type\n",
    "args$method_to_identify_cell_types <- \"singler_blueprint_encode\"\n",
    "args$seurat_resolution <- 0.8\n",
    "args$harmony_theta <- 0\n",
    "\n",
    "str_column_of_meta_data_cluster <- sprintf(\"RNA_snn_res.%g\", args$seurat_resolution)\n",
    "str_column_of_meta_data_harmony <- sprintf(\"RNA_harmony_th.%s\", paste(args$harmony_theta, collapse=\",\"))\n",
    "\n",
    "str_reduction <- \"pca\"\n",
    "str_umap_reduction <- \"umap\"  \n",
    "col_cluster_types <- \"cluster.type\"\n",
    "\n",
    "str_condition <- cancer_type\n",
    "\n",
    "str_condition\n",
    "str_column_of_meta_data_cluster\n",
    "str_umap_reduction\n",
    "col_cluster_types\n",
    "\n",
    "figure_format <- \"pdf\"\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4121f0a",
   "metadata": {},
   "source": [
    "## set.seed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "60200569",
   "metadata": {},
   "outputs": [],
   "source": [
    "# set seed for reproducibility\n",
    "set.seed(51)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bb58886",
   "metadata": {},
   "source": [
    "## display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9dcd2205",
   "metadata": {},
   "outputs": [],
   "source": [
    "options(repr.matrix.max.cols=150, repr.matrix.max.rows=50)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d44885a",
   "metadata": {},
   "source": [
    "## nv_cell_type_conversion_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "056c4bc9",
   "metadata": {},
   "outputs": [],
   "source": [
    "nv_cell_type_conversion_table <- c(\n",
    "            \"Epithelial cells\"=\"Epi. CNA-\",\n",
    "            \"Normal-like\"=\"Normal-like\",\n",
    "            \"Basal\"=\"Basal\",\n",
    "            \"Her2\"=\"Her2\",\n",
    "            \"LumA\"=\"grpA\",\n",
    "            \"LumB\"=\"grpB\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "589fe403",
   "metadata": {},
   "source": [
    "# read scRNA-seq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d5f0426d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'/home/hkim77/spanheimer_lab.w/sc-rna-seq/tr-bc/run-20220121/output/rds_er+bc-cell-line/er+bc-cell-line_sc-rna-seq_merged_seurat_obj.rds'"
      ],
      "text/latex": [
       "'/home/hkim77/spanheimer\\_lab.w/sc-rna-seq/tr-bc/run-20220121/output/rds\\_er+bc-cell-line/er+bc-cell-line\\_sc-rna-seq\\_merged\\_seurat\\_obj.rds'"
      ],
      "text/markdown": [
       "'/home/hkim77/spanheimer_lab.w/sc-rna-seq/tr-bc/run-20220121/output/rds_er+bc-cell-line/er+bc-cell-line_sc-rna-seq_merged_seurat_obj.rds'"
      ],
      "text/plain": [
       "[1] \"/home/hkim77/spanheimer_lab.w/sc-rna-seq/tr-bc/run-20220121/output/rds_er+bc-cell-line/er+bc-cell-line_sc-rna-seq_merged_seurat_obj.rds\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "dir_rna <- \"/home/hkim77/spanheimer_lab.w/sc-rna-seq/tr-bc/run-20220121\"\n",
    "fname_rna <- sprintf(\"%s/output/rds_%s/%s_sc-rna-seq_merged_seurat_obj.rds\",\n",
    "                     dir_rna, cancer_type, cancer_type)\n",
    "fname_rna\n",
    "\n",
    "rna <- readRDS(fname_rna)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd5eb854",
   "metadata": {},
   "source": [
    "https://mojaveazure.github.io/seurat-object/reference/Seurat-methods.html\n",
    "\n",
    "dim: The number of features (nrow) and cells (ncol) for the default assay; note: while the number of features changes depending on the active assay, the number of cells remains the same across all assays\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "91b8afd8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Seurat'"
      ],
      "text/latex": [
       "'Seurat'"
      ],
      "text/markdown": [
       "'Seurat'"
      ],
      "text/plain": [
       "[1] \"Seurat\"\n",
       "attr(,\"package\")\n",
       "[1] \"SeuratObject\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "An object of class Seurat \n",
       "20470 features across 4280 samples within 1 assay \n",
       "Active assay: RNA (20470 features, 2000 variable features)\n",
       " 2 dimensional reductions calculated: pca, umap"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>'RNA'</li><li>'pca'</li><li>'umap'</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'RNA'\n",
       "\\item 'pca'\n",
       "\\item 'umap'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'RNA'\n",
       "2. 'pca'\n",
       "3. 'umap'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] \"RNA\"  \"pca\"  \"umap\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "'RNA'"
      ],
      "text/latex": [
       "'RNA'"
      ],
      "text/markdown": [
       "'RNA'"
      ],
      "text/plain": [
       "[1] \"RNA\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "class(rna)\n",
    "rna\n",
    "\n",
    "names(rna)\n",
    "DefaultAssay(rna)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b09af4be",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>20470</li><li>4280</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 20470\n",
       "\\item 4280\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 20470\n",
       "2. 4280\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 20470  4280"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(rna)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f877f3b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>'T47D_Control#AAACCCACAGTCAGTT-1'</li><li>'T47D_Control#AAACCCAGTAGTTAGA-1'</li><li>'T47D_Control#AAACGAAAGTAGCCAG-1'</li><li>'T47D_Control#AAACGCTTCGGAGCAA-1'</li><li>'T47D_Control#AAAGAACGTTCACGAT-1'</li><li>'T47D_Control#AAAGAACTCCACGTAA-1'</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'T47D\\_Control\\#AAACCCACAGTCAGTT-1'\n",
       "\\item 'T47D\\_Control\\#AAACCCAGTAGTTAGA-1'\n",
       "\\item 'T47D\\_Control\\#AAACGAAAGTAGCCAG-1'\n",
       "\\item 'T47D\\_Control\\#AAACGCTTCGGAGCAA-1'\n",
       "\\item 'T47D\\_Control\\#AAAGAACGTTCACGAT-1'\n",
       "\\item 'T47D\\_Control\\#AAAGAACTCCACGTAA-1'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'T47D_Control#AAACCCACAGTCAGTT-1'\n",
       "2. 'T47D_Control#AAACCCAGTAGTTAGA-1'\n",
       "3. 'T47D_Control#AAACGAAAGTAGCCAG-1'\n",
       "4. 'T47D_Control#AAACGCTTCGGAGCAA-1'\n",
       "5. 'T47D_Control#AAAGAACGTTCACGAT-1'\n",
       "6. 'T47D_Control#AAAGAACTCCACGTAA-1'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] \"T47D_Control#AAACCCACAGTCAGTT-1\" \"T47D_Control#AAACCCAGTAGTTAGA-1\"\n",
       "[3] \"T47D_Control#AAACGAAAGTAGCCAG-1\" \"T47D_Control#AAACGCTTCGGAGCAA-1\"\n",
       "[5] \"T47D_Control#AAAGAACGTTCACGAT-1\" \"T47D_Control#AAAGAACTCCACGTAA-1\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(Cells(rna))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b47e694",
   "metadata": {},
   "source": [
    "## idents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "cba3c4da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".dl-inline {width: auto; margin:0; padding: 0}\n",
       ".dl-inline>dt, .dl-inline>dd {float: none; width: auto; display: inline-block}\n",
       ".dl-inline>dt::after {content: \":\\0020\"; padding-right: .5ex}\n",
       ".dl-inline>dt:not(:first-of-type) {padding-left: .5ex}\n",
       "</style><dl class=dl-inline><dt>T47D_Control#AAACCCACAGTCAGTT-1</dt><dd>6-LumA</dd><dt>T47D_Control#AAACCCAGTAGTTAGA-1</dt><dd>1-LumA</dd><dt>T47D_Control#AAACGAAAGTAGCCAG-1</dt><dd>4-LumA</dd><dt>T47D_Control#AAACGCTTCGGAGCAA-1</dt><dd>2-LumB</dd><dt>T47D_Control#AAAGAACGTTCACGAT-1</dt><dd>5-LumB</dd><dt>T47D_Control#AAAGAACTCCACGTAA-1</dt><dd>2-LumB</dd></dl>\n",
       "\n",
       "<details>\n",
       "\t<summary style=display:list-item;cursor:pointer>\n",
       "\t\t<strong>Levels</strong>:\n",
       "\t</summary>\n",
       "\t<style>\n",
       "\t.list-inline {list-style: none; margin:0; padding: 0}\n",
       "\t.list-inline>li {display: inline-block}\n",
       "\t.list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "\t</style>\n",
       "\t<ol class=list-inline><li>'6-LumA'</li><li>'1-LumA'</li><li>'4-LumA'</li><li>'2-LumB'</li><li>'5-LumB'</li><li>'0-LumA'</li><li>'3-LumB'</li><li>'7-LumB'</li><li>'9-LumB'</li><li>'8-LumB'</li><li>'10-LumB'</li></ol>\n",
       "</details>"
      ],
      "text/latex": [
       "\\begin{description*}\n",
       "\\item[T47D\\textbackslash{}\\_Control\\textbackslash{}\\#AAACCCACAGTCAGTT-1] 6-LumA\n",
       "\\item[T47D\\textbackslash{}\\_Control\\textbackslash{}\\#AAACCCAGTAGTTAGA-1] 1-LumA\n",
       "\\item[T47D\\textbackslash{}\\_Control\\textbackslash{}\\#AAACGAAAGTAGCCAG-1] 4-LumA\n",
       "\\item[T47D\\textbackslash{}\\_Control\\textbackslash{}\\#AAACGCTTCGGAGCAA-1] 2-LumB\n",
       "\\item[T47D\\textbackslash{}\\_Control\\textbackslash{}\\#AAAGAACGTTCACGAT-1] 5-LumB\n",
       "\\item[T47D\\textbackslash{}\\_Control\\textbackslash{}\\#AAAGAACTCCACGTAA-1] 2-LumB\n",
       "\\end{description*}\n",
       "\n",
       "\\emph{Levels}: \\begin{enumerate*}\n",
       "\\item '6-LumA'\n",
       "\\item '1-LumA'\n",
       "\\item '4-LumA'\n",
       "\\item '2-LumB'\n",
       "\\item '5-LumB'\n",
       "\\item '0-LumA'\n",
       "\\item '3-LumB'\n",
       "\\item '7-LumB'\n",
       "\\item '9-LumB'\n",
       "\\item '8-LumB'\n",
       "\\item '10-LumB'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "T47D_Control#AAACCCACAGTCAGTT-1\n",
       ":   6-LumAT47D_Control#AAACCCAGTAGTTAGA-1\n",
       ":   1-LumAT47D_Control#AAACGAAAGTAGCCAG-1\n",
       ":   4-LumAT47D_Control#AAACGCTTCGGAGCAA-1\n",
       ":   2-LumBT47D_Control#AAAGAACGTTCACGAT-1\n",
       ":   5-LumBT47D_Control#AAAGAACTCCACGTAA-1\n",
       ":   2-LumB\n",
       "\n",
       "\n",
       "**Levels**: 1. '6-LumA'\n",
       "2. '1-LumA'\n",
       "3. '4-LumA'\n",
       "4. '2-LumB'\n",
       "5. '5-LumB'\n",
       "6. '0-LumA'\n",
       "7. '3-LumB'\n",
       "8. '7-LumB'\n",
       "9. '9-LumB'\n",
       "10. '8-LumB'\n",
       "11. '10-LumB'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "T47D_Control#AAACCCACAGTCAGTT-1 T47D_Control#AAACCCAGTAGTTAGA-1 \n",
       "                         6-LumA                          1-LumA \n",
       "T47D_Control#AAACGAAAGTAGCCAG-1 T47D_Control#AAACGCTTCGGAGCAA-1 \n",
       "                         4-LumA                          2-LumB \n",
       "T47D_Control#AAAGAACGTTCACGAT-1 T47D_Control#AAAGAACTCCACGTAA-1 \n",
       "                         5-LumB                          2-LumB \n",
       "11 Levels: 6-LumA 1-LumA 4-LumA 2-LumB 5-LumB 0-LumA 3-LumB 7-LumB ... 10-LumB"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(Idents(rna))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "83d2c97c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>'6-LumA'</li><li>'1-LumA'</li><li>'4-LumA'</li><li>'2-LumB'</li><li>'5-LumB'</li><li>'0-LumA'</li><li>'3-LumB'</li><li>'7-LumB'</li><li>'9-LumB'</li><li>'8-LumB'</li><li>'10-LumB'</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item '6-LumA'\n",
       "\\item '1-LumA'\n",
       "\\item '4-LumA'\n",
       "\\item '2-LumB'\n",
       "\\item '5-LumB'\n",
       "\\item '0-LumA'\n",
       "\\item '3-LumB'\n",
       "\\item '7-LumB'\n",
       "\\item '9-LumB'\n",
       "\\item '8-LumB'\n",
       "\\item '10-LumB'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. '6-LumA'\n",
       "2. '1-LumA'\n",
       "3. '4-LumA'\n",
       "4. '2-LumB'\n",
       "5. '5-LumB'\n",
       "6. '0-LumA'\n",
       "7. '3-LumB'\n",
       "8. '7-LumB'\n",
       "9. '9-LumB'\n",
       "10. '8-LumB'\n",
       "11. '10-LumB'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] \"6-LumA\"  \"1-LumA\"  \"4-LumA\"  \"2-LumB\"  \"5-LumB\"  \"0-LumA\"  \"3-LumB\" \n",
       " [8] \"7-LumB\"  \"9-LumB\"  \"8-LumB\"  \"10-LumB\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "levels(rna)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f72d42a",
   "metadata": {},
   "source": [
    "## n_samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b8c91128",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>'T47D_Control'</li><li>'T47D_Tamoxifen'</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'T47D\\_Control'\n",
       "\\item 'T47D\\_Tamoxifen'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'T47D_Control'\n",
       "2. 'T47D_Tamoxifen'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] \"T47D_Control\"   \"T47D_Tamoxifen\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "2"
      ],
      "text/latex": [
       "2"
      ],
      "text/markdown": [
       "2"
      ],
      "text/plain": [
       "[1] 2"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "samples <- unique(rna@meta.data$Sample)\n",
    "samples\n",
    "\n",
    "n_samples <- length(samples)\n",
    "n_samples\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "309d3ac2",
   "metadata": {},
   "source": [
    "## list_samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2712efff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<strong>$T47D</strong> = <dl>\n",
       "\t<dt>$control</dt>\n",
       "\t\t<dd>'T47D_Control'</dd>\n",
       "\t<dt>$treatment</dt>\n",
       "\t\t<dd>'T47D_Tamoxifen'</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\textbf{\\$T47D} = \\begin{description}\n",
       "\\item[\\$control] 'T47D\\_Control'\n",
       "\\item[\\$treatment] 'T47D\\_Tamoxifen'\n",
       "\\end{description}\n"
      ],
      "text/markdown": [
       "**$T47D** = $control\n",
       ":   'T47D_Control'\n",
       "$treatment\n",
       ":   'T47D_Tamoxifen'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "$T47D\n",
       "$T47D$control\n",
       "[1] \"T47D_Control\"\n",
       "\n",
       "$T47D$treatment\n",
       "[1] \"T47D_Tamoxifen\"\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "1"
      ],
      "text/latex": [
       "1"
      ],
      "text/markdown": [
       "1"
      ],
      "text/plain": [
       "[1] 1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "list_samples <- list()\n",
    "\n",
    "idx <- grep(\"Control\", samples)\n",
    "u_samples <- gsub(\"_Control\", \"\", samples[idx])\n",
    "for (sample in u_samples) {\n",
    "    list_samples[[sample]]$control <- sprintf(\"%s_Control\", sample)\n",
    "    list_samples[[sample]]$treatment <- sprintf(\"%s_Tamoxifen\", sample)\n",
    "}\n",
    "\n",
    "list_samples\n",
    "length(list_samples)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f725871",
   "metadata": {},
   "source": [
    "## mtx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c372000e",
   "metadata": {},
   "outputs": [],
   "source": [
    "if (DefaultAssay(rna) == \"integrated\") {\n",
    "    mtx <- GetAssayData(object = rna, assay=NULL, slot = \"data\")\n",
    "} else {\n",
    "    mtx <- GetAssayData(object = rna, assay=\"RNA\", slot = \"data\")\n",
    "}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4b1ca83",
   "metadata": {},
   "source": [
    "## list_sort"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "123d3539",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "\n",
    "list_sort <- sort_cluster_members(rna, args,\n",
    "                    col_cluster_types = col_cluster_types,\n",
    "                    str_umap_reduction = str_umap_reduction,\n",
    "                    f_merge_immune_cell = FALSE)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65e4d65f",
   "metadata": {},
   "source": [
    "## colors"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf96dc41",
   "metadata": {},
   "source": [
    "### samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d82bd376",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>'#A6CEE3'</li><li>'#FB9A99'</li><li>'#1F78B4'</li><li>'#E31A1C'</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item '\\#A6CEE3'\n",
       "\\item '\\#FB9A99'\n",
       "\\item '\\#1F78B4'\n",
       "\\item '\\#E31A1C'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. '#A6CEE3'\n",
       "2. '#FB9A99'\n",
       "3. '#1F78B4'\n",
       "4. '#E31A1C'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] \"#A6CEE3\" \"#FB9A99\" \"#1F78B4\" \"#E31A1C\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "4"
      ],
      "text/latex": [
       "4"
      ],
      "text/markdown": [
       "4"
      ],
      "text/plain": [
       "[1] 4"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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zb969zhPQa9fuTIke+8806nTp1EjwJg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\nGhoAAILR0AAAEIyGBgCAYDQ0AAAE8/8AyFIoJ48XOYsAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420.6,
       "width": 480.6
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# make patient sample metadata and color assignments \n",
    "# http://applied-r.com/rcolorbrewer-palettes/\n",
    "\n",
    "if (n_samples <= 4) {\n",
    "    sampleColors <- brewer.pal(12, \"Paired\")\n",
    "    sampleColors <- sampleColors[c(1,5,2,6)]\n",
    "} else if (n_samples <= 12){\n",
    "    sampleColors <- brewer.pal(n_samples, \"Paired\")\n",
    "} else {\n",
    "    vec_color1 <- colorRampPalette(brewer.pal(9, \"Set1\"))(n_samples/2)\n",
    "    vec_color2 <- colorspace::lighten(vec_color1, amount=0.4) \n",
    "    sampleColors <- c(rbind(vec_color1, vec_color2))\n",
    "}\n",
    "\n",
    "sampleColors\n",
    "length(sampleColors)\n",
    "\n",
    "options(repr.plot.width=8.01, repr.plot.height=7.01)\n",
    "pie(rep(1,n_samples), col=sampleColors) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbc545c6",
   "metadata": {},
   "source": [
    "### epi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a6d4152d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>'Epithelial cells'</li><li>'Epi. Non-tumor'</li><li>'LEp_prog'</li><li>'LEp_secretory'</li><li>'LEp'</li><li>'LEp_hormone'</li><li>'BEp'</li><li>'BEp_MaSCs'</li><li>'BEp_myo'</li><li>'Epi. Unassigned'</li><li>'Epi. Tumor'</li><li>'Normal-like'</li><li>'NBL'</li><li>'Basal'</li><li>'CLow'</li><li>'Her2E'</li><li>'LumA'</li><li>'LumB'</li><li>'Keratinocytes'</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'Epithelial cells'\n",
       "\\item 'Epi. Non-tumor'\n",
       "\\item 'LEp\\_prog'\n",
       "\\item 'LEp\\_secretory'\n",
       "\\item 'LEp'\n",
       "\\item 'LEp\\_hormone'\n",
       "\\item 'BEp'\n",
       "\\item 'BEp\\_MaSCs'\n",
       "\\item 'BEp\\_myo'\n",
       "\\item 'Epi. Unassigned'\n",
       "\\item 'Epi. Tumor'\n",
       "\\item 'Normal-like'\n",
       "\\item 'NBL'\n",
       "\\item 'Basal'\n",
       "\\item 'CLow'\n",
       "\\item 'Her2E'\n",
       "\\item 'LumA'\n",
       "\\item 'LumB'\n",
       "\\item 'Keratinocytes'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'Epithelial cells'\n",
       "2. 'Epi. Non-tumor'\n",
       "3. 'LEp_prog'\n",
       "4. 'LEp_secretory'\n",
       "5. 'LEp'\n",
       "6. 'LEp_hormone'\n",
       "7. 'BEp'\n",
       "8. 'BEp_MaSCs'\n",
       "9. 'BEp_myo'\n",
       "10. 'Epi. Unassigned'\n",
       "11. 'Epi. Tumor'\n",
       "12. 'Normal-like'\n",
       "13. 'NBL'\n",
       "14. 'Basal'\n",
       "15. 'CLow'\n",
       "16. 'Her2E'\n",
       "17. 'LumA'\n",
       "18. 'LumB'\n",
       "19. 'Keratinocytes'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] \"Epithelial cells\" \"Epi. Non-tumor\"   \"LEp_prog\"         \"LEp_secretory\"   \n",
       " [5] \"LEp\"              \"LEp_hormone\"      \"BEp\"              \"BEp_MaSCs\"       \n",
       " [9] \"BEp_myo\"          \"Epi. Unassigned\"  \"Epi. Tumor\"       \"Normal-like\"     \n",
       "[13] \"NBL\"              \"Basal\"            \"CLow\"             \"Her2E\"           \n",
       "[17] \"LumA\"             \"LumB\"             \"Keratinocytes\"   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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4S0XBclkUieLrYihJDks6NbOo4/mNFt\nw/knielJr67v8OQETu/QYvDRxP/K+e5yrYaO68En7/7ZfbnUm4PsM0fOZBHDYaPa1dcHFKCm\nkQAAQIWJxeJPnz4xXUU1MTAwsLW1ZboKhpwbJU8Icd9f+ItjJBKJRHJlogYhhGW/KuLrvqLr\nPvqEcLrvTalwTdd+a0CI/rQ7JXblnhmlpaI//FiqRCKRSET351kQItNq/Rvx1yExmxxlCct6\n5YvP2/fnmhGi9dvl/C8jxI8XNeYJGw4+kiaRSCSS29P0CREotZh2O/vLkAqduWSGLv4er07W\nJUTB43N5xbICPFUJUR93qUDyo8vlnR+uRojioFM5Xw9LP9ibR4jN8hcSgJoB96EBAGjYtGmT\njo5OQkJC+UNrv6SkJNyHLt8PG+N02FW6K6Bdv/6GX7fYrd1c+KTg2rXQCl9JVlaWkITbwc++\n3lPmddv1Pvnd3/2Lm9A8OHTwBWE5jxxhXGJde11PLxdK/Nj/5JviMUeOvCJ81y7OnC8jqCaL\nn+ZmxB8coPj1qCydriMc+F82K3Lm74X6+ccSjlt/95I9cuS79u7IIUkBATdL7Cx9OW7HccMM\nSPqJ3f5fZphknDx8No/dZtRw85+9QgDVCBkaAIAGdXX1vLy8y5cvM11IlcvLy8vKykKGLt8P\n70MHjy7VXpFtampYcpuloaFOSEZiYm5Fr9TSe1Uf7aJbc2x0G3f0mrFmz5k7sdkl5wxnP336\njhAtKyuVUoepWltrExIREUEIIUWvX0cRoqOvX97ffxMTY3pn/l7aixcJhOgaG3NK7ZY1NNQl\n5MPLlyXfYpS6HKEcxo6xpvLP7j74+d1q6skjF/N5nUcP0S6nbIBqgwwNAECDq6srISQkJITp\nQqrcx48fCSENGjRgupA6gy8vT5XaIRAICCE01rChDIceux+2f/7ApqJ7R9bPHtWjhV4DA1fv\n/U8/p/DMzExCSPzmVt+2iV8fR0h6fHw2ISQ1JUXyeYL2T3EVFLhftypy5u9lZWWRz/OdS5GT\nkyOEZGeXOKr05QghpiPHtZMturp731tCCPl0/HBwgbDPqP7K5ZQNUH2QoQEAaGjYsKGxsfGV\nK1eYLqTKobGdtOXl5ZXekZGRQQhRUVEpc3jZ2A3sPZftv/wyKSX6zpntCzzNs0K2eDkPO/aJ\nEEKKW6joTQ0v87Z48extVQ0NGUJSU1N/fp1vVOTM3xMKhYSQnJycb/YX7yn+6g9peI5zlycP\n/9n3mJCPfof/LWowcFT3sq8DwAh0hQcAoMfFxWXnzp3R0dH6+vrVc8Xs7OzY2NiwsDChUJie\nnp6ampqWlpaWllb8H6mpqXl5eTk5OXn5eUVFRcV3DDMzMkWiMvqmURSlqKRY/B9KSkryfHk+\nn6+goCAQCOTk5IRCoVAobNCggbq6ekxMTPEhWVlZxTdM4dcURkbGEaLzZbsgPj6JEFVtbe5P\nDvoRlkDPvttY+24jumqYtdoQ4PdvkccAGX6TJobkXGxkZB5pzvvRgQYGeoTERkRkEYeK/79a\nkTN/T9HKSocEx7x+nU9sSnyT+W/exBKibWn586aJCr3HDVT3333o0MNRGnuvFBlOG+WChhxQ\nkyBDAwDQ065du507d169etXLy0uKp83Ozo6IiIiIiIiOjo6Pj09MTIyLi0tMTIyNi83OKvuj\nckKIDFdGToHH5snIysmy5dhsGZaStiIhREPYgGJRZR5SkF0gLhITCcnNzM3IyRClFOVnFxRk\nF4iKysjcc+fOnTt3LpfHVVdX19fT1/uPvr5+o0aN9PT0yrmZCCWEnTgR/7v3fxN6RSHnLuUR\nfg9XhwoeLr6/roPHBs7Ue+d/+zopWFZZWZ4QWR6PRQghNgMGmq1deW73voQBY7W+jIn7q7vT\nDpmpe/wn28oQYtOnr9HadcHHTqYMGfrfHfDErS4GMx85rXlyceIP5htX5MzFzzySEm/emg8c\nZLRp3aWjJ1I8Bn25254e4HexkOh6eLQq5xvmtB83wnj32iNLflcOl1gtH9Wy7H/QAAxBhgYA\noMfFxYUQEhISUukMnZOT8/z58zdv3kRERLx9+zYiIuLN2zcfEj98M0xeUU6oJq/dpIGCujyR\nkLuBT7Uaazp5O3KFXDkFHleBy1PgyXCl9mtcXCQuyCnIzyrI/pSdk5qT+SEr9k6svCo/Nz0v\nNzU3Ozn70auHN2/e/OYoJWUlI0MjMzMzS0tLMzMzc3NzU1PT8mfb1kdCgw9bPWZqb5noatGg\n6HXA/Cn/JPFsl8zvU9G7waymnTooL5k3u7e3cOPvXW10hJKU19e2T9n8lGM5Z2InFiGEUHZz\ndk4P7LT+d7dxvH8W9W2inB935/iKidOC0u3XtbMq/qdCtZzz14RT3f+a3GuJ1t4pTloFkVe3\nTFoYUqQ5bsqAHz+wV5EzE0UTE3Vy/s2tkNhcHWGBSEmR33L+jsmBXbf49F+msGlUWyN+2tub\n/8zw9s81nXBkYWt2ed8x1XzsGNt1swICotltNqIhB9Q0lASL/wIA0GRubp6bmxsdHV2RwWKx\nOCIi4vHjx0+ePHn69OmjR48iIyPFYvGXAUIVeRVdJTUdJVU9ZVVdJZWGiooNhApq8iXzcVJ0\nypqeO7VtGnod9pT+91NhRflFGQkZGQmZ6e8zMhIy0uPS0hMy02LTMhK+LoTHZrMbGTSytLC0\nsLCwtbW1s7MzNjZmsWrb4zfnRwu67C77/j97wMmiI71+PuazlhtjQ3/XISGTNF3+FE06e8Px\n6twNfteexWYQRX377uNXrpvSRo3O3dXMR/tXrtsdeO15bFJaAVdVS9fErvPw2XNG2peYU53x\n9NDalduOhzyKSsqh5DWMm7Xv/9u86X1NSzzZJ0m+u3PZsh2nbr54n81R1zO16zJ6zuyRrTSL\nU23o9EatNiQOC8zb27301cs/c9yJCR7eB+99KOSpDd0ft6MnmxCScm/X8uU7Tt14HpdWxFHR\na+LUa8ycOSOafXk68IeXI4SQpO1uOuODSdc98UEj1Gi8UABVDxkaAIC2CRMmbNu2LSoqqlGj\nRt9/VSwWv3r1Kjw8PCws7O7du8+ePc3J+dw4gc1mqemraBqraZk2aNBIRVVXSVVXiSdf/oTY\n3Iy8BU6bVRqpjDs3Wrrfi1QU5BQkR6akRKV8iviUHJWSEpmSEp0qKvz8mb5AKLC1sbX7j7m5\nuYxM/foUNGSSpsuf+eODU/9qz3QptUzM/1obTovwuvBuT0c5pmsBKK1+/RYDAJCKdu3abdu2\n7cqVKyNGjCje8+HDh/Dw8OLcHBYelpH++b6sgppAp6lmQ7MGmsZqWibqGoZqMpxyP8IuA0/I\nY8mw8rPypfY9SBWHz9Gy0tSy0vyyRywSp0anfnjxMfH5h8TnH+4+vnv9+vXiL/HkeM3tmzs6\nOrZp06Z169bKyvWkXxnuWdGWcX7phtusFmtnI0BDDYQMDQBAm4uLC0VR586dk5OTu3LlSkjI\nldevPy/VxuHJalto2DYx12uipd+koZLWz3sPVBRFEYESPy+3QCpnqwYsNkvVUFXVUNWym0Xx\nnrTYtA8vPiY+T0x89iH8QXhxpGaxWBaWFk6OTm3atHFycqq2VidQo4mKCkXZ7y5vHjt0d3rr\ntf9OMWW6IIAyIEMDANDw8ePHq1evhoSEcDgcPz8/Pz8/QoiqjnJz9yZ6TbT0rbU1jdVY7CqZ\n+ytQ5WdHVnhJu5pHSVdJSVfJrKMpIUQikiS9SYq9Fxf3ID72Xuy2bdu2bdtGCBEqCAcNHOTm\n5ta+fft6c3+akABPmd4Hy+iKUpLquCuftrWrlnKYd368ivvfBQK9lgN3ha4fZY6oAjUS5kMD\nAJRDJBKFhoYGBQWdPXv28ePHxb82Bcp8JS0Fx0HNjJrrKUvpZvPPbR939G149OxnM6rhWtUs\n/X3Gi3Mvrqy/ypZhiYrEhBA2m928eXM3N7eOHTs6ODhU6fxpsVgcFRVlZGRUdZcAgLoHb+4A\nAMqWkpJy4cKFM2fOnL9wPiU5hRAiUJG362pp1ELPyF5PVUepmusRKPMlYklBVgFHUNc6xyk2\nVOAp8Aghzl4tnYbYv7oZ+fJm5OPQR6GhocuWLRMqCN06uLm7u3fv3p3eon4Vs2HDhlmzZr15\n8wYxGgAqDvehAeqc7/ptsWTlFNV1TW2de42Z7u1eog9VuZ253PcXBnjWt7faUVFRfn5+p0+f\nDg0NFYlEFEVpW2hYOBpatDXSbaz1o4VLqkHAmuAbh+4N9/Mq+ehenXFqeuDzoBfzz//WwEC1\neI9YJIl58v7lzYiXNyLfPYoXi8QyMjJt27Z1d3d3d3eX1szpwsJCQ0NDiUQSFRVVvEQIAEBF\n1Lc/jgD1xtf4KynISIp5+u9fsybO6XXo9Opb12ZZy5Q9sl6LjIz09/f38/O7e/cuIYQnz7Vs\nZ2ThZGTuZKigViNWuhYo8wkhqTFpdTJDx4THynJlvwRoQgiLTTWy0W5ko915Ytvs1Jwnl18/\nCX5149b1y5cv+/j42NnZ9urVu0+fPo0bN/6V6/r5+cXFxa1evRoBGgBowd9NgDqP4ig0MG49\naIN/SrjmpBurNl+ZtduN6Zpqjm+iM19Brrl7E2s3M1OHRmzZyjShqzoCFT4hJD0ujelCpC85\nKiUrKUvH4ofvDeSV+Q59bRz62hTkFr64HvHk31fPQl4tXLhw4cKF1tbWQ4YMGThwoJ6eXiUu\n7evry+fzR4+uiV23AaAmQ4YGqDc0jI0F5EZGcnIBITTn054dqdDtb62FoafNj8/a4Hfj5ftM\noqBn22X00jXTXbRqVtCsoJSUlEOHDu3bt+/OnTukZkfnL4ozdEZiJtOFSF9MWAwhxKJt+dOR\nOXKyTTuaN+1oLhaJ396JeXDu2cPzL2bNmjVnzhxHR8fBgwf379+/4nOmb968GRYWNmHCBFVV\n1fJHAwCUgAwNUF9IXt29n0XYzRya038gjcPhEJJ4ZFgPxU7Ltof8bdmg6E3AgsHDZ3e6m3Lj\n4ZoWtecJt6KiovPnz+/du/fMmcD8/AKegNvcvUnTjuYmLfVrbHT+Ql6ZTwjJSspiuhDpiw6P\nIYS06mdT8UNYbJapQyNTh0b95nd+cT3i7pmnoZdvX7t2zdt7cqdOnYcOHeru7s7hlPNPc/Pm\nzRRFTZ48+ZeqB4B6CRkaoO4T5aW8C/Vf8fvqJw1c1233rkTrARkZGUKS3wrnvNk8wJAQQoj1\noD+3hZ132rxp2eHZgcNqQR/f58+f79u3b+8/ez8kfqBYlL61tn2PxrZdLbn8WvMOoHg+dE5y\nDtOFSJuERIfFcORk1fQq03ODLcu2cjW1cjXNzyl4fOnl3cCnZ88FBQYGqjdQHz5s+OjRo01N\ny16hIyYm5uTJk126dLGwsPi1bwAA6iNkaIA66tRQWWpoyR18k94rd68Z24xf7sj/tN+ZGjy6\nRAM3u379Db9usVu7ufA37712LZQM6yKtqqUuLy/vyJEjW7duLZ6zoa6n0nlSW/vujaW1fGB1\nKr4PnZuex3QhUvYp4lNOSo5uY61fPA+Xz2nubt3c3TozOftOwOPbfg/XrVu3fv36tm3bjhkz\npm/fvjwer+T4LVu2FBUV+fj4/OJ1AaB+QoYGqKNKdNuQFKQnxry4fnTdvL6Nt7quOnF8mj2/\n7JE/xTY1NSy5zdLQUCckOjExlxA5aZYuHZGRkdu2bdu9Z3dKcgqXz2nZp2lzdyv9pjoUY73p\nfpWcAo/FZuVl1rUMHR0WQwhp7GwsrRMKVeVdR7VyGdkq8m7MrWP3b124dfXq1cnek72Geo0b\nN674rnNOTs6ePXsaN27s5oYnbAGgMpChAeo+iqOoZezgMc/PluNoMXPG4KVOL1e3oL8aNV9e\nvnT8FAgEhJC0tLSalKHFYvHly5d37Nhx4sQJkUikrq/SzdO5ZV8bviKv/INrNooi8kpy+TmF\nTBciZcWToR3620n3tBRFjJrrGTXX67sg707A49t+DzZv3uzr69uxY8cpU6a8fv06JSVl9erV\nVO19UwUAjEKGBqg/WCbdu5jNvP387Pm3q1uUPUX0Z/LyvrkBmpGRQQipioXjKiUrK2v27NkH\nDhxIT09nsVmWzsZtBtgat2xUlzKSQIWf8y6F6SqkSkJiwmO5fI5Kw6qaXXBA6QkAACAASURB\nVMNX4Dl7tXD2ahH1IC7kn7DgS5cuXLjA4XDk5eX79OlTRRcFgDoPGRqgPimeEJqfn1+Zgwsj\nI+MI0fmyXRAfn0SIqrY2V1rlVdbHjx99fX3/2vpXSmoKIaSRrY7n6h5KmrVvxnO5BCr8xIhP\nTFchTR9fJ+Wm5epba1fDtQxsdQxsdTKSsm4cuntx+82CggJDI8Phw4ZPmzatcr2lAaA+o/9x\nLgDUVpLIgNNPCNFq375ybQjCTpyI/7olCjl3KY/wXV0dpFReZbx582b8+PH6+vorVqwQyihO\n6zGHzZL5GJlcJwM0IURemS8RSwpyCpguRGo+T4Z2Mam2KyqoC7r6tFt7b2b/RV3kVDi+vr4m\nJsYjRox49epVtdUAAHUAMjRAPSDKTY1/ev6PEe7zbrBMx+xa4lqpT6CEBh+2esw8fj86NTc3\n6dHh36b8k8SznTW/DzPrYN+5c6dfv37m5ubbt2831TDf4PXnyRnnh7Yd2bFpl5z03CfBrxmp\nqqp9Xu77XSrThUhNdHgMoeh1hpYKjpys02D7uWfHj98xSN9We+/evZaWlgMHDnz8+HE1VwIA\ntRQyNEAddWqoLPUfjrKBfc/ph9Ld1p17dm9HV/Ufj/yWw6a4L+O4nTad8mYdGu9qqqao09r7\nitrgDcEXF1hX+4ywsLCwLl26tGjR4sSJE45mznt+O7xvkl/7Jh1ZFIsQ4uk0nBBy1vdqdZdV\nLeRV+ISQ1Jg6kqElYkns3Vgun6PYQMhIARSLsnQ2nrxv6MyTY5p2ND927FjTpk3d3NxCQ0MZ\nqQcAahHMhwaoczrvypLskvJIQggpKuKYDVh9fMDqyhb2y+7evbt48eKgoCA2i93Nzn2k6zgj\njW/nADTWtbZpZPco+sGHiE8aRmqM1Fl1BMpyhJC0+AymC5GOj68+5qXnGdjqlD+0iulYao7Y\n3DfueeLFbTf+vfRvcHBwx44d58+f7+TkxHRpAFBD4T40AFSQRCJh7NpPnjzx8PBo0aLFubPn\n3Ky7+E87u2LQ+u8DdLEhTiMkEsnJNcHVXGQ1KF5mJSOhjmTo4snQVq70m8RUDR1LzZG+/eYE\njrPv0eTfy8Ft27bt3Lnz/fv3ma4LAGoiZGgAqNGePHnSp0+fpk2bHvc/7mbdxX9a0LqhvgYN\nDH9ySHsrN02lhlF3Y/OzKtWApAYrng+d+SGL6UKkIzqseDK0LdOFlKJprO61vtfcsxOa92xy\n8dJFe3v7AQMGvH5dN2fYA0ClIUMDQJV4/vy5SCT6lTPEx8ePHDnSxsYmICCgvVXHo1NOr/Xc\nbKhR/mp2LBZ7sKOXSCQO3BjyKwXUQAIVPiEkJyWb6UKkQCKSxN6N48lzi7+pmkZdX2Xoul5z\nAsfZdLLw8/OzsLDw8PB49+4d03UBQE2BDA0A5Wv3R6JEkratQ0XHP3nypHHjxosWLarc5TIz\nMxcsWGBqYvr33383N3I47BOw3usPEy2zip+hd4v+chy5+2eeVa6AGqv4PnRuWl1Y7jvxeWJ+\nVr62uQbThfyMprH6iM19fQ4NM2im6+fnZ2FpMWPGjJSUurXMDQBUCjI0AEifhYWFnZ3dmjVr\nHjx4QOtAsVi8b98+UxPT5cuXawi11g313T72H3NtS7oFCOUUejXvX5BXeOPwPbrH1mRyinIs\nNqtuzFGJDo8lhDRpX1MmQ/+EoZ2uzwGv8TsHqTVSWr9+vYmpydatW4uKipiuCwCYhAwNANIn\nIyPz999/UxQ1fPjwwsLCCh515swZKyurYcOGifLEC/utOD4tyM26S6VrGOQ4lKKoy7vrVJMy\niiJ8RV5hTkVf0posJjyGoiiHvjVrMvRPWLY1nnFitNf6XhLZookTJ9rY2vz7779MFwUAjEGG\nBoAqYW1tPXv27MePH69Zs6bcwW/fvu3WrVuPHj3eRUaP7+h9emZwn5YeLBb7VwrQU2vU1sIl\nMyk74m7sr5ynphGoyBfl1/o7oGKROPZeHE/A5SvymK6FBopF2fdoMu/8bz2nt4+IfNuhQ4ce\nPXpEREQwXRcAMAAZGgCqyvz5862srJYtW/bs2Q/nJefm5i5atKiJVZNz5851b9YrcGbweLfJ\nchw5qRQwxGm4hEhOr69TNwsFynJikZjpKn5VwtPEguwCbYsaPRn6Rzhysh3GtJ57bnxzd+ug\noCALCwsfH5+MjDrScBAAKggZGgCqCofD2b17t0gkGjVqVJk9OgIDAxtbNl66dKmmYsNtY/Yu\nH7hOTUH9+2GV1sK4lamWecLLpLTEupNv5JX5ErGkKK9234qOCYshhDR1M2e6kMpT0lQYutbd\ne7+Xhomar6+vhaXFiRMnmC4KAKoPMjQAVKEWLVr4+PiEhYX5+vqW3B8XF+fh4dGzZ8/EhA/j\n3Sb7TT3T0qR1VRQw2GmYWCIOWFN3bkUL6sRy39HhMRRFtezdlOlCfpVRc70Zx0cNXNotLSu1\nb9++7u7usbF1au4QAPwIMjQAVK1ly5aZmJjMnz//7du3hJCioqJVq1aZmpj6+/t3tesZODN4\nfEdvWbZsFV29i20PZXmVF9ciiopq/fyHYsVLFSZH1eIMLS4Sx92LlxNyeUIu07VIAcWiWg+w\nW3hpYusBdoGBgWbmZmvWrPnF5ugAUPMhQwNA1eLz+Tt37szNzR09evTDhw9btmw5d+5cLcWG\nO8ftXzlog3Qnb3yPK8P1aD1YVCS6+Of1Kr1QtSm+D50en8Z0IZX3/nFCYV6hbmMtpguRJr6i\n3MCl3SbtG6qgIT979uxm9s3Cw8OZLqq086MFFEX1OvCzaUDFY37i54cD1CfI0ABQ5ZydnceM\nGXP16tXmzZs/evR4hMvYw7+fsjdqWT1XH9DakyPDue33sHouV9WKl1nJSKjFM7yjiydDd7Jg\nuhDpM2mhPzNgjNvYNk+fPm3VqpWLi0tubi7TRdHnvr9Q8gMBnjJMVwdQQyBDA0CVCw0NvXbt\nGiFEyFPYP+mYT9cZXJnq+xBfRaDayaZ7bmbe3cC6sGxh8VyOrI+1eLnv4snQLdybMF1IlZDl\nyfSY5jrmLw+xWBxyNcTOzi4sLIzpogBA+pChAaAK5eTkTJ061dHRMfJt5KTOUy7Nv2mpw0By\n8nQaTgi5tP1G9V9a6gQqcoSQ7OTamqFFhaL4B/F8RR6Hz2G6lip08/A9QkizjlaR7yIcHR3n\nzZtXUFDAdFHSdHakAkWZLQp7dXhmL3sDNQGXK1A3cfJacyWB3kTwipwneLwaRTVZ8TIuwNu5\nkbKc0qig4v2ST+Hbp/ZxMNFQ4MpyhWqGzbr9tvHK+9LXTw79c1yHxppCHk/YwKzt0NUX4x4u\ntqIouRFBv/wSQL2HDA0AVeX+/fv2zew3btxopWN9dMrp0e1/k2Ez8zmwWUMLe6OWKXHp719/\nZKQAKSq+D52bnsd0IZUU//B9UX6RrlVDpgupQuEBj59cfq1l1GDJ2amb7y42tNVduXJls2Z2\n9+/fZ7o0qeFwOIQkHhnWY2P+oO0hb5LS4m75tk89OrtT+7nhdN4sVOQ8fD6fkJy43d7T7ltN\nXOW7ZoAZIYQknx3d0nH8wYxuG84/SUxPenV9hycncHqHFoOPJv5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Ujm5x8sp8iqLyMvOZLqQcnydD967d03b9lpzLzczrO6NLA70a1EJO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5bNunvAojW32/pNWwgMZKVXFN9Kkn2+yOrkM37cUAACAASURBVDDaEbg+LPs5S0aV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\n3nll55RUV4sk764I0vGhDI0gLaGhoQEAZayKT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cKEX3755e+//87Pz+fz+enp6VWz6gMHDpSXl9d0UMmDtdER8bf4gNrM18fxc8+Eh4cP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\nAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420.6,
       "width": 480.6
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "idx_epi <- grep(pattern_epi, names(nv_color))\n",
    "names(nv_color)[idx_epi]\n",
    "# http://applied-r.com/rcolorbrewer-palettes/\n",
    "colors_epi <- RColorBrewer::brewer.pal(9, \"Set1\")\n",
    "colors_epi <- colorspace::lighten(colors_epi, amount=0.4)\n",
    "\n",
    "nv_color[\"Epi. Tumor\"] <- \"forestgreen\"\n",
    "\n",
    "nv_color[\"LEp_prog\"] <- \"#b4fd9e\"\n",
    "nv_color[\"LEp\"] <- colors_epi[3]\n",
    "nv_color[\"BEp\"] <- colors_epi[4]\n",
    "\n",
    "nv_color[\"Basal\"] <- colors_epi[1]\n",
    "nv_color[\"Her2E\"] <- colors_epi[6]\n",
    "nv_color[\"Her2\"] <- colors_epi[6]\n",
    "nv_color[\"LumA\"] <- colors_epi[7]\n",
    "nv_color[\"LumB\"] <- colors_epi[5]\n",
    "nv_color[\"Normal-like\"] <- colorspace::darken(colors_epi[3], amount=0.4)\n",
    "\n",
    "pie(rep(1, length(idx_epi)), col=nv_color[idx_epi],\n",
    "       labels=names(nv_color)[idx_epi]) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "40eba03d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3z88cd5TAdgMhoasBELFy7ctGnT2I7hzz7YXvQWABbtuX7tJ3aJ2rFjx9tvvy16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\n3ygOyAAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420.6,
       "width": 480.6
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "list_sort <- update_nv_color_cell_type(list_sort)\n",
    "colors_epi <- list_sort$nv_color_cell_type\n",
    "\n",
    "pie(rep(1, length(colors_epi)), col=colors_epi,\n",
    "       labels=names(colors_epi)) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e245872c",
   "metadata": {},
   "source": [
    "### cell.type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "57d6d598",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Tu85owkSb3f6ig6iA5qNfN76/vRfg0958yZ07Nnz8zMJ883nDt37po1ayIjI7/+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FdxUgJhMaR+++4akJgkwBQ1ItzFeI8qqF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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420.6,
       "width": 480.6
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=8.01, repr.plot.height=7.01)\n",
    "pie(rep(1, length(list_sort$nv_color_cluster_type_ordered)),\n",
    "    col=list_sort$nv_color_cluster_type_ordered) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e280c46",
   "metadata": {},
   "source": [
    "# DGE"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1972e284",
   "metadata": {},
   "source": [
    "## gmt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e758e27d",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "list_gmt <- list()\n",
    "list_gmt[[\"h\"]] <- read.gmt(\"reference/gmt/h.all.v6.1.symbols.gmt\")\n",
    "list_gmt[[\"unc_lccc\"]] <- read.gmt(\"reference/gmt/pubmed21214954_human_gene_signatures.gmt\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e32d75c",
   "metadata": {},
   "source": [
    "## type_condition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "14129e39",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "method_dge <- \"seurat_findmarkers_enricher\"\n",
    "dir_xlsx <- \"xlsx\"\n",
    "\n",
    "list_genes_dn <- list()\n",
    "\n",
    "# additional conditions \n",
    "genes <- NULL\n",
    "str_cond1 <- NULL\n",
    "str_cond_ref <- NULL\n",
    "type_condition <- \"none\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be70970d",
   "metadata": {},
   "source": [
    "## cell line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5b0d4bc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "sample1 <- \"T47D_Tamoxifen\"\n",
    "sample_ref <- \"T47D_Control\"\n",
    "\n",
    "th_log2fc_ = 0.25\n",
    "th_padj_ = 0.01\n",
    "\n",
    "min.pct_ = 0.25\n",
    "min.diff.pct_ = -Inf\n",
    "max.cells.per.ident_ = Inf\n",
    "f_featureplot = FALSE\n",
    "n_sampling = -1\n",
    "# n_sampling=300\n",
    "cluster_rows = FALSE\n",
    "\n",
    "drug_name <- \"tamoxifen\"\n",
    "pattern_sample1 <- NULL\n",
    "pattern_sample_ref <- NULL\n",
    "\n",
    "group_name1 <- sample1\n",
    "group_name_ref <- sample_ref\n",
    "\n",
    "col_cluster_types_ <- NULL\n",
    "col_cell_types_ <- \"cell.type\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56c8e5f0",
   "metadata": {},
   "source": [
    "## find markers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "77033119",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sample1=T47D_Tamoxifen\n",
      "sample_ref=T47D_Control\n",
      "drug_name=tamoxifen\n",
      "\t\tgroup_name1=T47D_Tamoxifen\n",
      "\t\tgroup_name_ref=T47D_Control\n",
      "\t\tcol_cell_types_=cell.type\n",
      "\t\tth_log2fc=0.25\n",
      "\t\tth_padj=0.01\n",
      "\t\tmin.pct=0.25\n",
      "\t\tmin.diff.pct=-Inf\n",
      "\t\tmax.cells.per.ident=Inf\n",
      "\t\tmethod_dge=seurat_findmarkers_enricher\n",
      "\t\ttype_condition=none\n",
      "\t\tgenes=\n"
     ]
    }
   ],
   "source": [
    "cat(sprintf(\"sample1=%s\\n\", sample1))\n",
    "cat(sprintf(\"sample_ref=%s\\n\", sample_ref))\n",
    "\n",
    "cat(sprintf(\"drug_name=%s\\n\", drug_name))\n",
    "cat(sprintf(\"pattern_sample1=%s\\n\", pattern_sample1))\n",
    "cat(sprintf(\"pattern_sample_ref=%s\\n\", pattern_sample_ref))\n",
    "\n",
    "cat(sprintf(\"\\t\\tgroup_name1=%s\\n\", group_name1))\n",
    "cat(sprintf(\"\\t\\tgroup_name_ref=%s\\n\", group_name_ref))\n",
    "cat(sprintf(\"\\t\\tcol_cluster_types_=%s\\n\", col_cluster_types_))\n",
    "cat(sprintf(\"\\t\\tcol_cell_types_=%s\\n\", col_cell_types_))\n",
    "\n",
    "cat(sprintf(\"\\t\\tth_log2fc=%g\\n\", th_log2fc_))\n",
    "cat(sprintf(\"\\t\\tth_padj=%g\\n\", th_padj_))\n",
    "cat(sprintf(\"\\t\\tmin.pct=%g\\n\", min.pct_))\n",
    "cat(sprintf(\"\\t\\tmin.diff.pct=%g\\n\", min.diff.pct_))\n",
    "cat(sprintf(\"\\t\\tmax.cells.per.ident=%g\\n\", max.cells.per.ident_))\n",
    "cat(sprintf(\"\\t\\tmethod_dge=%s\\n\", method_dge))\n",
    "\n",
    "cat(sprintf(\"\\t\\ttype_condition=%s\\n\", type_condition))\n",
    "cat(sprintf(\"\\t\\tgenes=%s\\n\", paste(genes, collapse = \", \")))\n",
    "cat(sprintf(\"\\t\\tstr_cond1=%s\\n\", str_cond1))\n",
    "cat(sprintf(\"\\t\\tstr_cond_ref=%s\\n\", str_cond_ref))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "217ec52d",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "seurat_findmarkers_enricher<br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;col_log2fc=avg_log2FC<br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;col_pvalue=p_val<br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;col_padj=p_val_adj<br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;th_log2fc=0.25<br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;th_padj=0.01<br>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "df_up"
      ]
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 8</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>p_val</th><th scope=col>avg_log2FC</th><th scope=col>pct.1</th><th scope=col>pct.2</th><th scope=col>p_val_adj</th><th scope=col>expr_min</th><th scope=col>expr_mean</th><th scope=col>expr_max</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>RPS29</th><td> 0.000000e+00</td><td>0.5033383</td><td>1</td><td>1.000</td><td> 0.000000e+00</td><td>0.000000</td><td>3.479241</td><td>4.702337</td></tr>\n",
       "\t<tr><th scope=row>RPS27</th><td>6.902129e-296</td><td>0.5279653</td><td>1</td><td>1.000</td><td>1.412866e-291</td><td>0.000000</td><td>3.952087</td><td>5.383424</td></tr>\n",
       "\t<tr><th scope=row>RPS28</th><td>1.096364e-277</td><td>0.5432672</td><td>1</td><td>1.000</td><td>2.244256e-273</td><td>0.000000</td><td>3.560273</td><td>4.942524</td></tr>\n",
       "\t<tr><th scope=row>RPL34</th><td>2.387078e-246</td><td>0.5604793</td><td>1</td><td>0.999</td><td>4.886350e-242</td><td>0.000000</td><td>3.170508</td><td>4.672081</td></tr>\n",
       "\t<tr><th scope=row>RPL37A</th><td>5.812193e-245</td><td>0.4580176</td><td>1</td><td>0.998</td><td>1.189756e-240</td><td>0.000000</td><td>3.213740</td><td>4.951160</td></tr>\n",
       "\t<tr><th scope=row>RPL37</th><td>6.547629e-244</td><td>0.4065432</td><td>1</td><td>1.000</td><td>1.340300e-239</td><td>2.296541</td><td>3.851047</td><td>5.188366</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 8\n",
       "\\begin{tabular}{r|llllllll}\n",
       "  & p\\_val & avg\\_log2FC & pct.1 & pct.2 & p\\_val\\_adj & expr\\_min & expr\\_mean & expr\\_max\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tRPS29 &  0.000000e+00 & 0.5033383 & 1 & 1.000 &  0.000000e+00 & 0.000000 & 3.479241 & 4.702337\\\\\n",
       "\tRPS27 & 6.902129e-296 & 0.5279653 & 1 & 1.000 & 1.412866e-291 & 0.000000 & 3.952087 & 5.383424\\\\\n",
       "\tRPS28 & 1.096364e-277 & 0.5432672 & 1 & 1.000 & 2.244256e-273 & 0.000000 & 3.560273 & 4.942524\\\\\n",
       "\tRPL34 & 2.387078e-246 & 0.5604793 & 1 & 0.999 & 4.886350e-242 & 0.000000 & 3.170508 & 4.672081\\\\\n",
       "\tRPL37A & 5.812193e-245 & 0.4580176 & 1 & 0.998 & 1.189756e-240 & 0.000000 & 3.213740 & 4.951160\\\\\n",
       "\tRPL37 & 6.547629e-244 & 0.4065432 & 1 & 1.000 & 1.340300e-239 & 2.296541 & 3.851047 & 5.188366\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 8\n",
       "\n",
       "| <!--/--> | p_val &lt;dbl&gt; | avg_log2FC &lt;dbl&gt; | pct.1 &lt;dbl&gt; | pct.2 &lt;dbl&gt; | p_val_adj &lt;dbl&gt; | expr_min &lt;dbl&gt; | expr_mean &lt;dbl&gt; | expr_max &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|\n",
       "| RPS29 |  0.000000e+00 | 0.5033383 | 1 | 1.000 |  0.000000e+00 | 0.000000 | 3.479241 | 4.702337 |\n",
       "| RPS27 | 6.902129e-296 | 0.5279653 | 1 | 1.000 | 1.412866e-291 | 0.000000 | 3.952087 | 5.383424 |\n",
       "| RPS28 | 1.096364e-277 | 0.5432672 | 1 | 1.000 | 2.244256e-273 | 0.000000 | 3.560273 | 4.942524 |\n",
       "| RPL34 | 2.387078e-246 | 0.5604793 | 1 | 0.999 | 4.886350e-242 | 0.000000 | 3.170508 | 4.672081 |\n",
       "| RPL37A | 5.812193e-245 | 0.4580176 | 1 | 0.998 | 1.189756e-240 | 0.000000 | 3.213740 | 4.951160 |\n",
       "| RPL37 | 6.547629e-244 | 0.4065432 | 1 | 1.000 | 1.340300e-239 | 2.296541 | 3.851047 | 5.188366 |\n",
       "\n"
      ],
      "text/plain": [
       "       p_val         avg_log2FC pct.1 pct.2 p_val_adj     expr_min expr_mean\n",
       "RPS29   0.000000e+00 0.5033383  1     1.000  0.000000e+00 0.000000 3.479241 \n",
       "RPS27  6.902129e-296 0.5279653  1     1.000 1.412866e-291 0.000000 3.952087 \n",
       "RPS28  1.096364e-277 0.5432672  1     1.000 2.244256e-273 0.000000 3.560273 \n",
       "RPL34  2.387078e-246 0.5604793  1     0.999 4.886350e-242 0.000000 3.170508 \n",
       "RPL37A 5.812193e-245 0.4580176  1     0.998 1.189756e-240 0.000000 3.213740 \n",
       "RPL37  6.547629e-244 0.4065432  1     1.000 1.340300e-239 2.296541 3.851047 \n",
       "       expr_max\n",
       "RPS29  4.702337\n",
       "RPS27  5.383424\n",
       "RPS28  4.942524\n",
       "RPL34  4.672081\n",
       "RPL37A 4.951160\n",
       "RPL37  5.188366"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>90</li><li>8</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 90\n",
       "\\item 8\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 90\n",
       "2. 8\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 90  8"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/html": [
       "df_dn"
      ]
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 8</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>p_val</th><th scope=col>avg_log2FC</th><th scope=col>pct.1</th><th scope=col>pct.2</th><th scope=col>p_val_adj</th><th scope=col>expr_min</th><th scope=col>expr_mean</th><th scope=col>expr_max</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>MT-ATP6</th><td>3.682745e-164</td><td>-0.7229942</td><td>0.999</td><td>0.999</td><td>7.538578e-160</td><td>0</td><td>3.9814516</td><td>5.805141</td></tr>\n",
       "\t<tr><th scope=row>FMN1</th><td>3.977448e-141</td><td>-0.4199418</td><td>0.704</td><td>0.931</td><td>8.141837e-137</td><td>0</td><td>0.3480963</td><td>2.098238</td></tr>\n",
       "\t<tr><th scope=row>NDFIP1</th><td>3.021772e-131</td><td>-0.3356485</td><td>0.948</td><td>0.988</td><td>6.185568e-127</td><td>0</td><td>0.6991458</td><td>2.343052</td></tr>\n",
       "\t<tr><th scope=row>SRP9</th><td>1.547235e-130</td><td>-0.3570587</td><td>0.988</td><td>0.998</td><td>3.167191e-126</td><td>0</td><td>1.1191913</td><td>2.278952</td></tr>\n",
       "\t<tr><th scope=row>IL6ST</th><td>3.016367e-129</td><td>-0.4440205</td><td>0.850</td><td>0.961</td><td>6.174503e-125</td><td>0</td><td>0.5394353</td><td>1.981163</td></tr>\n",
       "\t<tr><th scope=row>MRFAP1</th><td>9.310181e-127</td><td>-0.3181005</td><td>0.994</td><td>0.998</td><td>1.905794e-122</td><td>0</td><td>1.1701247</td><td>2.952899</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 8\n",
       "\\begin{tabular}{r|llllllll}\n",
       "  & p\\_val & avg\\_log2FC & pct.1 & pct.2 & p\\_val\\_adj & expr\\_min & expr\\_mean & expr\\_max\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tMT-ATP6 & 3.682745e-164 & -0.7229942 & 0.999 & 0.999 & 7.538578e-160 & 0 & 3.9814516 & 5.805141\\\\\n",
       "\tFMN1 & 3.977448e-141 & -0.4199418 & 0.704 & 0.931 & 8.141837e-137 & 0 & 0.3480963 & 2.098238\\\\\n",
       "\tNDFIP1 & 3.021772e-131 & -0.3356485 & 0.948 & 0.988 & 6.185568e-127 & 0 & 0.6991458 & 2.343052\\\\\n",
       "\tSRP9 & 1.547235e-130 & -0.3570587 & 0.988 & 0.998 & 3.167191e-126 & 0 & 1.1191913 & 2.278952\\\\\n",
       "\tIL6ST & 3.016367e-129 & -0.4440205 & 0.850 & 0.961 & 6.174503e-125 & 0 & 0.5394353 & 1.981163\\\\\n",
       "\tMRFAP1 & 9.310181e-127 & -0.3181005 & 0.994 & 0.998 & 1.905794e-122 & 0 & 1.1701247 & 2.952899\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 8\n",
       "\n",
       "| <!--/--> | p_val &lt;dbl&gt; | avg_log2FC &lt;dbl&gt; | pct.1 &lt;dbl&gt; | pct.2 &lt;dbl&gt; | p_val_adj &lt;dbl&gt; | expr_min &lt;dbl&gt; | expr_mean &lt;dbl&gt; | expr_max &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|\n",
       "| MT-ATP6 | 3.682745e-164 | -0.7229942 | 0.999 | 0.999 | 7.538578e-160 | 0 | 3.9814516 | 5.805141 |\n",
       "| FMN1 | 3.977448e-141 | -0.4199418 | 0.704 | 0.931 | 8.141837e-137 | 0 | 0.3480963 | 2.098238 |\n",
       "| NDFIP1 | 3.021772e-131 | -0.3356485 | 0.948 | 0.988 | 6.185568e-127 | 0 | 0.6991458 | 2.343052 |\n",
       "| SRP9 | 1.547235e-130 | -0.3570587 | 0.988 | 0.998 | 3.167191e-126 | 0 | 1.1191913 | 2.278952 |\n",
       "| IL6ST | 3.016367e-129 | -0.4440205 | 0.850 | 0.961 | 6.174503e-125 | 0 | 0.5394353 | 1.981163 |\n",
       "| MRFAP1 | 9.310181e-127 | -0.3181005 | 0.994 | 0.998 | 1.905794e-122 | 0 | 1.1701247 | 2.952899 |\n",
       "\n"
      ],
      "text/plain": [
       "        p_val         avg_log2FC pct.1 pct.2 p_val_adj     expr_min expr_mean\n",
       "MT-ATP6 3.682745e-164 -0.7229942 0.999 0.999 7.538578e-160 0        3.9814516\n",
       "FMN1    3.977448e-141 -0.4199418 0.704 0.931 8.141837e-137 0        0.3480963\n",
       "NDFIP1  3.021772e-131 -0.3356485 0.948 0.988 6.185568e-127 0        0.6991458\n",
       "SRP9    1.547235e-130 -0.3570587 0.988 0.998 3.167191e-126 0        1.1191913\n",
       "IL6ST   3.016367e-129 -0.4440205 0.850 0.961 6.174503e-125 0        0.5394353\n",
       "MRFAP1  9.310181e-127 -0.3181005 0.994 0.998 1.905794e-122 0        1.1701247\n",
       "        expr_max\n",
       "MT-ATP6 5.805141\n",
       "FMN1    2.098238\n",
       "NDFIP1  2.343052\n",
       "SRP9    2.278952\n",
       "IL6ST   1.981163\n",
       "MRFAP1  2.952899"
      ]
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       "</style>\n",
       "<ol class=list-inline><li>110</li><li>8</li></ol>\n"
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       "\\begin{enumerate*}\n",
       "\\item 110\n",
       "\\item 8\n",
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       "1. 110\n",
       "2. 8\n",
       "\n",
       "\n"
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       "[1] 110   8"
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       "up h"
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    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 5 × 9</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>ID</th><th scope=col>Description</th><th scope=col>GeneRatio</th><th scope=col>BgRatio</th><th scope=col>pvalue</th><th scope=col>p.adjust</th><th scope=col>qvalue</th><th scope=col>geneID</th><th scope=col>Count</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</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><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>HALLMARK_CHOLESTEROL_HOMEOSTASIS</th><td>HALLMARK_CHOLESTEROL_HOMEOSTASIS  </td><td>HALLMARK_CHOLESTEROL_HOMEOSTASIS  </td><td>11/38</td><td>74/4386 </td><td>1.228406e-11</td><td>4.053740e-10</td><td>3.491259e-10</td><td>SQLE/MVD/DHCR7/HMGCS1/ACTG1/IDI1/SCD/FDFT1/TMEM97/HMGCR/FDPS                          </td><td>11</td></tr>\n",
       "\t<tr><th scope=row>HALLMARK_MTORC1_SIGNALING</th><td>HALLMARK_MTORC1_SIGNALING         </td><td>HALLMARK_MTORC1_SIGNALING         </td><td>14/38</td><td>200/4386</td><td>3.937846e-10</td><td>6.497445e-09</td><td>5.595886e-09</td><td>SQLE/INSIG1/DHCR7/SQSTM1/DDIT3/HMGCS1/NUPR1/IDI1/SCD/HSPA5/TMEM97/HMGCR/BHLHE40/MTHFD2</td><td>14</td></tr>\n",
       "\t<tr><th scope=row>HALLMARK_ANDROGEN_RESPONSE</th><td>HALLMARK_ANDROGEN_RESPONSE        </td><td>HALLMARK_ANDROGEN_RESPONSE        </td><td>7/38 </td><td>101/4386</td><td>1.957838e-05</td><td>2.153622e-04</td><td>1.854794e-04</td><td>INSIG1/SAT1/HMGCS1/IDI1/SCD/HMGCR/DNAJB9                                              </td><td> 7</td></tr>\n",
       "\t<tr><th scope=row>HALLMARK_UNFOLDED_PROTEIN_RESPONSE</th><td>HALLMARK_UNFOLDED_PROTEIN_RESPONSE</td><td>HALLMARK_UNFOLDED_PROTEIN_RESPONSE</td><td>7/38 </td><td>113/4386</td><td>4.077355e-05</td><td>3.363818e-04</td><td>2.897068e-04</td><td>RPS14/HSPA5/HERPUD1/HYOU1/EIF4EBP1/MTHFD2/DNAJB9                                      </td><td> 7</td></tr>\n",
       "\t<tr><th scope=row>HALLMARK_P53_PATHWAY</th><td>HALLMARK_P53_PATHWAY              </td><td>HALLMARK_P53_PATHWAY              </td><td>6/38 </td><td>200/4386</td><td>6.782903e-03</td><td>4.476716e-02</td><td>3.855545e-02</td><td>RPL36/SAT1/DDIT3/NUPR1/RPS12/MKNK2                                                    </td><td> 6</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 5 × 9\n",
       "\\begin{tabular}{r|lllllllll}\n",
       "  & ID & Description & GeneRatio & BgRatio & pvalue & p.adjust & qvalue & geneID & Count\\\\\n",
       "  & <chr> & <chr> & <chr> & <chr> & <dbl> & <dbl> & <dbl> & <chr> & <int>\\\\\n",
       "\\hline\n",
       "\tHALLMARK\\_CHOLESTEROL\\_HOMEOSTASIS & HALLMARK\\_CHOLESTEROL\\_HOMEOSTASIS   & HALLMARK\\_CHOLESTEROL\\_HOMEOSTASIS   & 11/38 & 74/4386  & 1.228406e-11 & 4.053740e-10 & 3.491259e-10 & SQLE/MVD/DHCR7/HMGCS1/ACTG1/IDI1/SCD/FDFT1/TMEM97/HMGCR/FDPS                           & 11\\\\\n",
       "\tHALLMARK\\_MTORC1\\_SIGNALING & HALLMARK\\_MTORC1\\_SIGNALING          & HALLMARK\\_MTORC1\\_SIGNALING          & 14/38 & 200/4386 & 3.937846e-10 & 6.497445e-09 & 5.595886e-09 & SQLE/INSIG1/DHCR7/SQSTM1/DDIT3/HMGCS1/NUPR1/IDI1/SCD/HSPA5/TMEM97/HMGCR/BHLHE40/MTHFD2 & 14\\\\\n",
       "\tHALLMARK\\_ANDROGEN\\_RESPONSE & HALLMARK\\_ANDROGEN\\_RESPONSE         & HALLMARK\\_ANDROGEN\\_RESPONSE         & 7/38  & 101/4386 & 1.957838e-05 & 2.153622e-04 & 1.854794e-04 & INSIG1/SAT1/HMGCS1/IDI1/SCD/HMGCR/DNAJB9                                               &  7\\\\\n",
       "\tHALLMARK\\_UNFOLDED\\_PROTEIN\\_RESPONSE & HALLMARK\\_UNFOLDED\\_PROTEIN\\_RESPONSE & HALLMARK\\_UNFOLDED\\_PROTEIN\\_RESPONSE & 7/38  & 113/4386 & 4.077355e-05 & 3.363818e-04 & 2.897068e-04 & RPS14/HSPA5/HERPUD1/HYOU1/EIF4EBP1/MTHFD2/DNAJB9                                       &  7\\\\\n",
       "\tHALLMARK\\_P53\\_PATHWAY & HALLMARK\\_P53\\_PATHWAY               & HALLMARK\\_P53\\_PATHWAY               & 6/38  & 200/4386 & 6.782903e-03 & 4.476716e-02 & 3.855545e-02 & RPL36/SAT1/DDIT3/NUPR1/RPS12/MKNK2                                                     &  6\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 5 × 9\n",
       "\n",
       "| <!--/--> | ID &lt;chr&gt; | Description &lt;chr&gt; | GeneRatio &lt;chr&gt; | BgRatio &lt;chr&gt; | pvalue &lt;dbl&gt; | p.adjust &lt;dbl&gt; | qvalue &lt;dbl&gt; | geneID &lt;chr&gt; | Count &lt;int&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|\n",
       "| HALLMARK_CHOLESTEROL_HOMEOSTASIS | HALLMARK_CHOLESTEROL_HOMEOSTASIS   | HALLMARK_CHOLESTEROL_HOMEOSTASIS   | 11/38 | 74/4386  | 1.228406e-11 | 4.053740e-10 | 3.491259e-10 | SQLE/MVD/DHCR7/HMGCS1/ACTG1/IDI1/SCD/FDFT1/TMEM97/HMGCR/FDPS                           | 11 |\n",
       "| HALLMARK_MTORC1_SIGNALING | HALLMARK_MTORC1_SIGNALING          | HALLMARK_MTORC1_SIGNALING          | 14/38 | 200/4386 | 3.937846e-10 | 6.497445e-09 | 5.595886e-09 | SQLE/INSIG1/DHCR7/SQSTM1/DDIT3/HMGCS1/NUPR1/IDI1/SCD/HSPA5/TMEM97/HMGCR/BHLHE40/MTHFD2 | 14 |\n",
       "| HALLMARK_ANDROGEN_RESPONSE | HALLMARK_ANDROGEN_RESPONSE         | HALLMARK_ANDROGEN_RESPONSE         | 7/38  | 101/4386 | 1.957838e-05 | 2.153622e-04 | 1.854794e-04 | INSIG1/SAT1/HMGCS1/IDI1/SCD/HMGCR/DNAJB9                                               |  7 |\n",
       "| HALLMARK_UNFOLDED_PROTEIN_RESPONSE | HALLMARK_UNFOLDED_PROTEIN_RESPONSE | HALLMARK_UNFOLDED_PROTEIN_RESPONSE | 7/38  | 113/4386 | 4.077355e-05 | 3.363818e-04 | 2.897068e-04 | RPS14/HSPA5/HERPUD1/HYOU1/EIF4EBP1/MTHFD2/DNAJB9                                       |  7 |\n",
       "| HALLMARK_P53_PATHWAY | HALLMARK_P53_PATHWAY               | HALLMARK_P53_PATHWAY               | 6/38  | 200/4386 | 6.782903e-03 | 4.476716e-02 | 3.855545e-02 | RPL36/SAT1/DDIT3/NUPR1/RPS12/MKNK2                                                     |  6 |\n",
       "\n"
      ],
      "text/plain": [
       "                                   ID                                \n",
       "HALLMARK_CHOLESTEROL_HOMEOSTASIS   HALLMARK_CHOLESTEROL_HOMEOSTASIS  \n",
       "HALLMARK_MTORC1_SIGNALING          HALLMARK_MTORC1_SIGNALING         \n",
       "HALLMARK_ANDROGEN_RESPONSE         HALLMARK_ANDROGEN_RESPONSE        \n",
       "HALLMARK_UNFOLDED_PROTEIN_RESPONSE HALLMARK_UNFOLDED_PROTEIN_RESPONSE\n",
       "HALLMARK_P53_PATHWAY               HALLMARK_P53_PATHWAY              \n",
       "                                   Description                        GeneRatio\n",
       "HALLMARK_CHOLESTEROL_HOMEOSTASIS   HALLMARK_CHOLESTEROL_HOMEOSTASIS   11/38    \n",
       "HALLMARK_MTORC1_SIGNALING          HALLMARK_MTORC1_SIGNALING          14/38    \n",
       "HALLMARK_ANDROGEN_RESPONSE         HALLMARK_ANDROGEN_RESPONSE         7/38     \n",
       "HALLMARK_UNFOLDED_PROTEIN_RESPONSE HALLMARK_UNFOLDED_PROTEIN_RESPONSE 7/38     \n",
       "HALLMARK_P53_PATHWAY               HALLMARK_P53_PATHWAY               6/38     \n",
       "                                   BgRatio  pvalue       p.adjust    \n",
       "HALLMARK_CHOLESTEROL_HOMEOSTASIS   74/4386  1.228406e-11 4.053740e-10\n",
       "HALLMARK_MTORC1_SIGNALING          200/4386 3.937846e-10 6.497445e-09\n",
       "HALLMARK_ANDROGEN_RESPONSE         101/4386 1.957838e-05 2.153622e-04\n",
       "HALLMARK_UNFOLDED_PROTEIN_RESPONSE 113/4386 4.077355e-05 3.363818e-04\n",
       "HALLMARK_P53_PATHWAY               200/4386 6.782903e-03 4.476716e-02\n",
       "                                   qvalue      \n",
       "HALLMARK_CHOLESTEROL_HOMEOSTASIS   3.491259e-10\n",
       "HALLMARK_MTORC1_SIGNALING          5.595886e-09\n",
       "HALLMARK_ANDROGEN_RESPONSE         1.854794e-04\n",
       "HALLMARK_UNFOLDED_PROTEIN_RESPONSE 2.897068e-04\n",
       "HALLMARK_P53_PATHWAY               3.855545e-02\n",
       "                                   geneID                                                                                \n",
       "HALLMARK_CHOLESTEROL_HOMEOSTASIS   SQLE/MVD/DHCR7/HMGCS1/ACTG1/IDI1/SCD/FDFT1/TMEM97/HMGCR/FDPS                          \n",
       "HALLMARK_MTORC1_SIGNALING          SQLE/INSIG1/DHCR7/SQSTM1/DDIT3/HMGCS1/NUPR1/IDI1/SCD/HSPA5/TMEM97/HMGCR/BHLHE40/MTHFD2\n",
       "HALLMARK_ANDROGEN_RESPONSE         INSIG1/SAT1/HMGCS1/IDI1/SCD/HMGCR/DNAJB9                                              \n",
       "HALLMARK_UNFOLDED_PROTEIN_RESPONSE RPS14/HSPA5/HERPUD1/HYOU1/EIF4EBP1/MTHFD2/DNAJB9                                      \n",
       "HALLMARK_P53_PATHWAY               RPL36/SAT1/DDIT3/NUPR1/RPS12/MKNK2                                                    \n",
       "                                   Count\n",
       "HALLMARK_CHOLESTEROL_HOMEOSTASIS   11   \n",
       "HALLMARK_MTORC1_SIGNALING          14   \n",
       "HALLMARK_ANDROGEN_RESPONSE          7   \n",
       "HALLMARK_UNFOLDED_PROTEIN_RESPONSE  7   \n",
       "HALLMARK_P53_PATHWAY                6   "
      ]
     },
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     "output_type": "display_data"
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       "<ol class=list-inline><li>5</li><li>9</li></ol>\n"
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       "\\begin{enumerate*}\n",
       "\\item 5\n",
       "\\item 9\n",
       "\\end{enumerate*}\n"
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       "1. 5\n",
       "2. 9\n",
       "\n",
       "\n"
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       "[1] 5 9"
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      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 9</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>ID</th><th scope=col>Description</th><th scope=col>GeneRatio</th><th scope=col>BgRatio</th><th scope=col>pvalue</th><th scope=col>p.adjust</th><th scope=col>qvalue</th><th scope=col>geneID</th><th scope=col>Count</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</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><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Ribosomal_Cluster_BMC.Med.Genomics.2011_PMID.21214954</th><td>Ribosomal_Cluster_BMC.Med.Genomics.2011_PMID.21214954       </td><td>Ribosomal_Cluster_BMC.Med.Genomics.2011_PMID.21214954       </td><td>18/85</td><td>50/19982 </td><td>4.919926e-31</td><td>7.871882e-29</td><td>6.370010e-29</td><td>RPS29/RPS28/RPL37A/RPL37/RPL39/RPL36/RPL35A/RPS21/RPLP2/RPS8/RPL35/RPS15A/RPL12/RPL32/RPS24/RPS12/RPS13/RPL11</td><td>18</td></tr>\n",
       "\t<tr><th scope=row>MRibosomal_BMC.Med.Genomics.2011_PMID.21214954</th><td>MRibosomal_BMC.Med.Genomics.2011_PMID.21214954              </td><td>MRibosomal_BMC.Med.Genomics.2011_PMID.21214954              </td><td>9/85 </td><td>23/19982 </td><td>2.295479e-16</td><td>1.836383e-14</td><td>1.486020e-14</td><td>RPL39/RPL36/RPL41/RPL22L1/RPL35/RPS15/RPS24/RPS13/RPL10                                                      </td><td> 9</td></tr>\n",
       "\t<tr><th scope=row>Duke_Module07_glucosedepletion_Mike_PMID.20335537</th><td>Duke_Module07_glucosedepletion_Mike_PMID.20335537           </td><td>Duke_Module07_glucosedepletion_Mike_PMID.20335537           </td><td>5/85 </td><td>27/19982 </td><td>9.272832e-08</td><td>4.945510e-06</td><td>4.001959e-06</td><td>SAT1/HERPUD1/HYOU1/KLHL24/DNAJB9                                                                             </td><td> 5</td></tr>\n",
       "\t<tr><th scope=row>Duke_Module02_akt_Mike_PMID.20335537</th><td>Duke_Module02_akt_Mike_PMID.20335537                        </td><td>Duke_Module02_akt_Mike_PMID.20335537                        </td><td>6/85 </td><td>77/19982 </td><td>9.225306e-07</td><td>3.690122e-05</td><td>2.986086e-05</td><td>INSIG1/FTL/SCD/FDFT1/TMEM97/BHLHE40                                                                          </td><td> 6</td></tr>\n",
       "\t<tr><th scope=row>MCF7.E2.induced.genes_JCO.2006_PMID.16505416</th><td>MCF7.E2.induced.genes_JCO.2006_PMID.16505416                </td><td>MCF7.E2.induced.genes_JCO.2006_PMID.16505416                </td><td>9/85 </td><td>290/19982</td><td>3.959407e-06</td><td>1.267010e-04</td><td>1.025278e-04</td><td>SQLE/INSIG1/MSMO1/DHCR7/IDI1/SCD/FDFT1/SEC61B/HMGCR                                                          </td><td> 9</td></tr>\n",
       "\t<tr><th scope=row>JANES_Oscillation_JUND_KRT5_Nat.Cell.Biol.2014_PMID.24658685</th><td>JANES_Oscillation_JUND_KRT5_Nat.Cell.Biol.2014_PMID.24658685</td><td>JANES_Oscillation_JUND_KRT5_Nat.Cell.Biol.2014_PMID.24658685</td><td>4/85 </td><td>35/19982 </td><td>1.443734e-05</td><td>3.849959e-04</td><td>3.115427e-04</td><td>RPS29/TMEM258/RPS21/S100A6                                                                                   </td><td> 4</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 9\n",
       "\\begin{tabular}{r|lllllllll}\n",
       "  & ID & Description & GeneRatio & BgRatio & pvalue & p.adjust & qvalue & geneID & Count\\\\\n",
       "  & <chr> & <chr> & <chr> & <chr> & <dbl> & <dbl> & <dbl> & <chr> & <int>\\\\\n",
       "\\hline\n",
       "\tRibosomal\\_Cluster\\_BMC.Med.Genomics.2011\\_PMID.21214954 & Ribosomal\\_Cluster\\_BMC.Med.Genomics.2011\\_PMID.21214954        & Ribosomal\\_Cluster\\_BMC.Med.Genomics.2011\\_PMID.21214954        & 18/85 & 50/19982  & 4.919926e-31 & 7.871882e-29 & 6.370010e-29 & RPS29/RPS28/RPL37A/RPL37/RPL39/RPL36/RPL35A/RPS21/RPLP2/RPS8/RPL35/RPS15A/RPL12/RPL32/RPS24/RPS12/RPS13/RPL11 & 18\\\\\n",
       "\tMRibosomal\\_BMC.Med.Genomics.2011\\_PMID.21214954 & MRibosomal\\_BMC.Med.Genomics.2011\\_PMID.21214954               & MRibosomal\\_BMC.Med.Genomics.2011\\_PMID.21214954               & 9/85  & 23/19982  & 2.295479e-16 & 1.836383e-14 & 1.486020e-14 & RPL39/RPL36/RPL41/RPL22L1/RPL35/RPS15/RPS24/RPS13/RPL10                                                       &  9\\\\\n",
       "\tDuke\\_Module07\\_glucosedepletion\\_Mike\\_PMID.20335537 & Duke\\_Module07\\_glucosedepletion\\_Mike\\_PMID.20335537            & Duke\\_Module07\\_glucosedepletion\\_Mike\\_PMID.20335537            & 5/85  & 27/19982  & 9.272832e-08 & 4.945510e-06 & 4.001959e-06 & SAT1/HERPUD1/HYOU1/KLHL24/DNAJB9                                                                              &  5\\\\\n",
       "\tDuke\\_Module02\\_akt\\_Mike\\_PMID.20335537 & Duke\\_Module02\\_akt\\_Mike\\_PMID.20335537                         & Duke\\_Module02\\_akt\\_Mike\\_PMID.20335537                         & 6/85  & 77/19982  & 9.225306e-07 & 3.690122e-05 & 2.986086e-05 & INSIG1/FTL/SCD/FDFT1/TMEM97/BHLHE40                                                                           &  6\\\\\n",
       "\tMCF7.E2.induced.genes\\_JCO.2006\\_PMID.16505416 & MCF7.E2.induced.genes\\_JCO.2006\\_PMID.16505416                 & MCF7.E2.induced.genes\\_JCO.2006\\_PMID.16505416                 & 9/85  & 290/19982 & 3.959407e-06 & 1.267010e-04 & 1.025278e-04 & SQLE/INSIG1/MSMO1/DHCR7/IDI1/SCD/FDFT1/SEC61B/HMGCR                                                           &  9\\\\\n",
       "\tJANES\\_Oscillation\\_JUND\\_KRT5\\_Nat.Cell.Biol.2014\\_PMID.24658685 & JANES\\_Oscillation\\_JUND\\_KRT5\\_Nat.Cell.Biol.2014\\_PMID.24658685 & JANES\\_Oscillation\\_JUND\\_KRT5\\_Nat.Cell.Biol.2014\\_PMID.24658685 & 4/85  & 35/19982  & 1.443734e-05 & 3.849959e-04 & 3.115427e-04 & RPS29/TMEM258/RPS21/S100A6                                                                                    &  4\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 9\n",
       "\n",
       "| <!--/--> | ID &lt;chr&gt; | Description &lt;chr&gt; | GeneRatio &lt;chr&gt; | BgRatio &lt;chr&gt; | pvalue &lt;dbl&gt; | p.adjust &lt;dbl&gt; | qvalue &lt;dbl&gt; | geneID &lt;chr&gt; | Count &lt;int&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|\n",
       "| Ribosomal_Cluster_BMC.Med.Genomics.2011_PMID.21214954 | Ribosomal_Cluster_BMC.Med.Genomics.2011_PMID.21214954        | Ribosomal_Cluster_BMC.Med.Genomics.2011_PMID.21214954        | 18/85 | 50/19982  | 4.919926e-31 | 7.871882e-29 | 6.370010e-29 | RPS29/RPS28/RPL37A/RPL37/RPL39/RPL36/RPL35A/RPS21/RPLP2/RPS8/RPL35/RPS15A/RPL12/RPL32/RPS24/RPS12/RPS13/RPL11 | 18 |\n",
       "| MRibosomal_BMC.Med.Genomics.2011_PMID.21214954 | MRibosomal_BMC.Med.Genomics.2011_PMID.21214954               | MRibosomal_BMC.Med.Genomics.2011_PMID.21214954               | 9/85  | 23/19982  | 2.295479e-16 | 1.836383e-14 | 1.486020e-14 | RPL39/RPL36/RPL41/RPL22L1/RPL35/RPS15/RPS24/RPS13/RPL10                                                       |  9 |\n",
       "| Duke_Module07_glucosedepletion_Mike_PMID.20335537 | Duke_Module07_glucosedepletion_Mike_PMID.20335537            | Duke_Module07_glucosedepletion_Mike_PMID.20335537            | 5/85  | 27/19982  | 9.272832e-08 | 4.945510e-06 | 4.001959e-06 | SAT1/HERPUD1/HYOU1/KLHL24/DNAJB9                                                                              |  5 |\n",
       "| Duke_Module02_akt_Mike_PMID.20335537 | Duke_Module02_akt_Mike_PMID.20335537                         | Duke_Module02_akt_Mike_PMID.20335537                         | 6/85  | 77/19982  | 9.225306e-07 | 3.690122e-05 | 2.986086e-05 | INSIG1/FTL/SCD/FDFT1/TMEM97/BHLHE40                                                                           |  6 |\n",
       "| MCF7.E2.induced.genes_JCO.2006_PMID.16505416 | MCF7.E2.induced.genes_JCO.2006_PMID.16505416                 | MCF7.E2.induced.genes_JCO.2006_PMID.16505416                 | 9/85  | 290/19982 | 3.959407e-06 | 1.267010e-04 | 1.025278e-04 | SQLE/INSIG1/MSMO1/DHCR7/IDI1/SCD/FDFT1/SEC61B/HMGCR                                                           |  9 |\n",
       "| JANES_Oscillation_JUND_KRT5_Nat.Cell.Biol.2014_PMID.24658685 | JANES_Oscillation_JUND_KRT5_Nat.Cell.Biol.2014_PMID.24658685 | JANES_Oscillation_JUND_KRT5_Nat.Cell.Biol.2014_PMID.24658685 | 4/85  | 35/19982  | 1.443734e-05 | 3.849959e-04 | 3.115427e-04 | RPS29/TMEM258/RPS21/S100A6                                                                                    |  4 |\n",
       "\n"
      ],
      "text/plain": [
       "                                                             ID                                                          \n",
       "Ribosomal_Cluster_BMC.Med.Genomics.2011_PMID.21214954        Ribosomal_Cluster_BMC.Med.Genomics.2011_PMID.21214954       \n",
       "MRibosomal_BMC.Med.Genomics.2011_PMID.21214954               MRibosomal_BMC.Med.Genomics.2011_PMID.21214954              \n",
       "Duke_Module07_glucosedepletion_Mike_PMID.20335537            Duke_Module07_glucosedepletion_Mike_PMID.20335537           \n",
       "Duke_Module02_akt_Mike_PMID.20335537                         Duke_Module02_akt_Mike_PMID.20335537                        \n",
       "MCF7.E2.induced.genes_JCO.2006_PMID.16505416                 MCF7.E2.induced.genes_JCO.2006_PMID.16505416                \n",
       "JANES_Oscillation_JUND_KRT5_Nat.Cell.Biol.2014_PMID.24658685 JANES_Oscillation_JUND_KRT5_Nat.Cell.Biol.2014_PMID.24658685\n",
       "                                                             Description                                                 \n",
       "Ribosomal_Cluster_BMC.Med.Genomics.2011_PMID.21214954        Ribosomal_Cluster_BMC.Med.Genomics.2011_PMID.21214954       \n",
       "MRibosomal_BMC.Med.Genomics.2011_PMID.21214954               MRibosomal_BMC.Med.Genomics.2011_PMID.21214954              \n",
       "Duke_Module07_glucosedepletion_Mike_PMID.20335537            Duke_Module07_glucosedepletion_Mike_PMID.20335537           \n",
       "Duke_Module02_akt_Mike_PMID.20335537                         Duke_Module02_akt_Mike_PMID.20335537                        \n",
       "MCF7.E2.induced.genes_JCO.2006_PMID.16505416                 MCF7.E2.induced.genes_JCO.2006_PMID.16505416                \n",
       "JANES_Oscillation_JUND_KRT5_Nat.Cell.Biol.2014_PMID.24658685 JANES_Oscillation_JUND_KRT5_Nat.Cell.Biol.2014_PMID.24658685\n",
       "                                                             GeneRatio\n",
       "Ribosomal_Cluster_BMC.Med.Genomics.2011_PMID.21214954        18/85    \n",
       "MRibosomal_BMC.Med.Genomics.2011_PMID.21214954               9/85     \n",
       "Duke_Module07_glucosedepletion_Mike_PMID.20335537            5/85     \n",
       "Duke_Module02_akt_Mike_PMID.20335537                         6/85     \n",
       "MCF7.E2.induced.genes_JCO.2006_PMID.16505416                 9/85     \n",
       "JANES_Oscillation_JUND_KRT5_Nat.Cell.Biol.2014_PMID.24658685 4/85     \n",
       "                                                             BgRatio  \n",
       "Ribosomal_Cluster_BMC.Med.Genomics.2011_PMID.21214954        50/19982 \n",
       "MRibosomal_BMC.Med.Genomics.2011_PMID.21214954               23/19982 \n",
       "Duke_Module07_glucosedepletion_Mike_PMID.20335537            27/19982 \n",
       "Duke_Module02_akt_Mike_PMID.20335537                         77/19982 \n",
       "MCF7.E2.induced.genes_JCO.2006_PMID.16505416                 290/19982\n",
       "JANES_Oscillation_JUND_KRT5_Nat.Cell.Biol.2014_PMID.24658685 35/19982 \n",
       "                                                             pvalue      \n",
       "Ribosomal_Cluster_BMC.Med.Genomics.2011_PMID.21214954        4.919926e-31\n",
       "MRibosomal_BMC.Med.Genomics.2011_PMID.21214954               2.295479e-16\n",
       "Duke_Module07_glucosedepletion_Mike_PMID.20335537            9.272832e-08\n",
       "Duke_Module02_akt_Mike_PMID.20335537                         9.225306e-07\n",
       "MCF7.E2.induced.genes_JCO.2006_PMID.16505416                 3.959407e-06\n",
       "JANES_Oscillation_JUND_KRT5_Nat.Cell.Biol.2014_PMID.24658685 1.443734e-05\n",
       "                                                             p.adjust    \n",
       "Ribosomal_Cluster_BMC.Med.Genomics.2011_PMID.21214954        7.871882e-29\n",
       "MRibosomal_BMC.Med.Genomics.2011_PMID.21214954               1.836383e-14\n",
       "Duke_Module07_glucosedepletion_Mike_PMID.20335537            4.945510e-06\n",
       "Duke_Module02_akt_Mike_PMID.20335537                         3.690122e-05\n",
       "MCF7.E2.induced.genes_JCO.2006_PMID.16505416                 1.267010e-04\n",
       "JANES_Oscillation_JUND_KRT5_Nat.Cell.Biol.2014_PMID.24658685 3.849959e-04\n",
       "                                                             qvalue      \n",
       "Ribosomal_Cluster_BMC.Med.Genomics.2011_PMID.21214954        6.370010e-29\n",
       "MRibosomal_BMC.Med.Genomics.2011_PMID.21214954               1.486020e-14\n",
       "Duke_Module07_glucosedepletion_Mike_PMID.20335537            4.001959e-06\n",
       "Duke_Module02_akt_Mike_PMID.20335537                         2.986086e-05\n",
       "MCF7.E2.induced.genes_JCO.2006_PMID.16505416                 1.025278e-04\n",
       "JANES_Oscillation_JUND_KRT5_Nat.Cell.Biol.2014_PMID.24658685 3.115427e-04\n",
       "                                                             geneID                                                                                                       \n",
       "Ribosomal_Cluster_BMC.Med.Genomics.2011_PMID.21214954        RPS29/RPS28/RPL37A/RPL37/RPL39/RPL36/RPL35A/RPS21/RPLP2/RPS8/RPL35/RPS15A/RPL12/RPL32/RPS24/RPS12/RPS13/RPL11\n",
       "MRibosomal_BMC.Med.Genomics.2011_PMID.21214954               RPL39/RPL36/RPL41/RPL22L1/RPL35/RPS15/RPS24/RPS13/RPL10                                                      \n",
       "Duke_Module07_glucosedepletion_Mike_PMID.20335537            SAT1/HERPUD1/HYOU1/KLHL24/DNAJB9                                                                             \n",
       "Duke_Module02_akt_Mike_PMID.20335537                         INSIG1/FTL/SCD/FDFT1/TMEM97/BHLHE40                                                                          \n",
       "MCF7.E2.induced.genes_JCO.2006_PMID.16505416                 SQLE/INSIG1/MSMO1/DHCR7/IDI1/SCD/FDFT1/SEC61B/HMGCR                                                          \n",
       "JANES_Oscillation_JUND_KRT5_Nat.Cell.Biol.2014_PMID.24658685 RPS29/TMEM258/RPS21/S100A6                                                                                   \n",
       "                                                             Count\n",
       "Ribosomal_Cluster_BMC.Med.Genomics.2011_PMID.21214954        18   \n",
       "MRibosomal_BMC.Med.Genomics.2011_PMID.21214954                9   \n",
       "Duke_Module07_glucosedepletion_Mike_PMID.20335537             5   \n",
       "Duke_Module02_akt_Mike_PMID.20335537                          6   \n",
       "MCF7.E2.induced.genes_JCO.2006_PMID.16505416                  9   \n",
       "JANES_Oscillation_JUND_KRT5_Nat.Cell.Biol.2014_PMID.24658685  4   "
      ]
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       "<style>\n",
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       "</style>\n",
       "<ol class=list-inline><li>19</li><li>9</li></ol>\n"
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       "\\begin{enumerate*}\n",
       "\\item 19\n",
       "\\item 9\n",
       "\\end{enumerate*}\n"
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       "1. 19\n",
       "2. 9\n",
       "\n",
       "\n"
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       "[1] 19  9"
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       "down h"
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       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 5 × 9</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>ID</th><th scope=col>Description</th><th scope=col>GeneRatio</th><th scope=col>BgRatio</th><th scope=col>pvalue</th><th scope=col>p.adjust</th><th scope=col>qvalue</th><th scope=col>geneID</th><th scope=col>Count</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</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><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>HALLMARK_ESTROGEN_RESPONSE_EARLY</th><td>HALLMARK_ESTROGEN_RESPONSE_EARLY</td><td>HALLMARK_ESTROGEN_RESPONSE_EARLY</td><td>16/67</td><td>200/4386</td><td>2.763141e-08</td><td>1.160519e-06</td><td>1.017999e-06</td><td>IL6ST/FKBP4/MYBL1/OLFM1/B4GALT1/KRT19/ARL3/IGFBP4/TPBG/KRT15/CA12/GREB1/FKBP5/PGR/XBP1/STC2</td><td>16</td></tr>\n",
       "\t<tr><th scope=row>HALLMARK_ESTROGEN_RESPONSE_LATE</th><td>HALLMARK_ESTROGEN_RESPONSE_LATE </td><td>HALLMARK_ESTROGEN_RESPONSE_LATE </td><td>14/67</td><td>200/4386</td><td>1.230287e-06</td><td>2.583603e-05</td><td>2.266318e-05</td><td>IL6ST/FKBP4/OLFM1/KRT19/ARL3/IGFBP4/TPBG/CA12/FKBP5/PGR/XBP1/PRLR/PDCD4/NMU                </td><td>14</td></tr>\n",
       "\t<tr><th scope=row>HALLMARK_E2F_TARGETS</th><td>HALLMARK_E2F_TARGETS            </td><td>HALLMARK_E2F_TARGETS            </td><td>11/67</td><td>200/4386</td><td>1.827412e-04</td><td>1.918783e-03</td><td>1.683143e-03</td><td>TFRC/PRDX4/SNRPB/RBBP7/CENPE/H2AFZ/STMN1/CCNB2/BIRC5/PTTG1/UBE2S                           </td><td>11</td></tr>\n",
       "\t<tr><th scope=row>HALLMARK_MYC_TARGETS_V1</th><td>HALLMARK_MYC_TARGETS_V1         </td><td>HALLMARK_MYC_TARGETS_V1         </td><td>11/67</td><td>200/4386</td><td>1.827412e-04</td><td>1.918783e-03</td><td>1.683143e-03</td><td>RPS10/PSMA2/ERH/LDHA/CCT3/SRSF7/PRDX4/HNRNPA2B1/VDAC3/CCT5/H2AFZ                           </td><td>11</td></tr>\n",
       "\t<tr><th scope=row>HALLMARK_G2M_CHECKPOINT</th><td>HALLMARK_G2M_CHECKPOINT         </td><td>HALLMARK_G2M_CHECKPOINT         </td><td>9/67 </td><td>200/4386</td><td>3.051633e-03</td><td>2.563372e-02</td><td>2.248572e-02</td><td>SFPQ/H2AFV/CENPE/H2AFZ/STMN1/CCNB2/BIRC5/PTTG1/UBE2S                                       </td><td> 9</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 5 × 9\n",
       "\\begin{tabular}{r|lllllllll}\n",
       "  & ID & Description & GeneRatio & BgRatio & pvalue & p.adjust & qvalue & geneID & Count\\\\\n",
       "  & <chr> & <chr> & <chr> & <chr> & <dbl> & <dbl> & <dbl> & <chr> & <int>\\\\\n",
       "\\hline\n",
       "\tHALLMARK\\_ESTROGEN\\_RESPONSE\\_EARLY & HALLMARK\\_ESTROGEN\\_RESPONSE\\_EARLY & HALLMARK\\_ESTROGEN\\_RESPONSE\\_EARLY & 16/67 & 200/4386 & 2.763141e-08 & 1.160519e-06 & 1.017999e-06 & IL6ST/FKBP4/MYBL1/OLFM1/B4GALT1/KRT19/ARL3/IGFBP4/TPBG/KRT15/CA12/GREB1/FKBP5/PGR/XBP1/STC2 & 16\\\\\n",
       "\tHALLMARK\\_ESTROGEN\\_RESPONSE\\_LATE & HALLMARK\\_ESTROGEN\\_RESPONSE\\_LATE  & HALLMARK\\_ESTROGEN\\_RESPONSE\\_LATE  & 14/67 & 200/4386 & 1.230287e-06 & 2.583603e-05 & 2.266318e-05 & IL6ST/FKBP4/OLFM1/KRT19/ARL3/IGFBP4/TPBG/CA12/FKBP5/PGR/XBP1/PRLR/PDCD4/NMU                 & 14\\\\\n",
       "\tHALLMARK\\_E2F\\_TARGETS & HALLMARK\\_E2F\\_TARGETS             & HALLMARK\\_E2F\\_TARGETS             & 11/67 & 200/4386 & 1.827412e-04 & 1.918783e-03 & 1.683143e-03 & TFRC/PRDX4/SNRPB/RBBP7/CENPE/H2AFZ/STMN1/CCNB2/BIRC5/PTTG1/UBE2S                            & 11\\\\\n",
       "\tHALLMARK\\_MYC\\_TARGETS\\_V1 & HALLMARK\\_MYC\\_TARGETS\\_V1          & HALLMARK\\_MYC\\_TARGETS\\_V1          & 11/67 & 200/4386 & 1.827412e-04 & 1.918783e-03 & 1.683143e-03 & RPS10/PSMA2/ERH/LDHA/CCT3/SRSF7/PRDX4/HNRNPA2B1/VDAC3/CCT5/H2AFZ                            & 11\\\\\n",
       "\tHALLMARK\\_G2M\\_CHECKPOINT & HALLMARK\\_G2M\\_CHECKPOINT          & HALLMARK\\_G2M\\_CHECKPOINT          & 9/67  & 200/4386 & 3.051633e-03 & 2.563372e-02 & 2.248572e-02 & SFPQ/H2AFV/CENPE/H2AFZ/STMN1/CCNB2/BIRC5/PTTG1/UBE2S                                        &  9\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 5 × 9\n",
       "\n",
       "| <!--/--> | ID &lt;chr&gt; | Description &lt;chr&gt; | GeneRatio &lt;chr&gt; | BgRatio &lt;chr&gt; | pvalue &lt;dbl&gt; | p.adjust &lt;dbl&gt; | qvalue &lt;dbl&gt; | geneID &lt;chr&gt; | Count &lt;int&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|\n",
       "| HALLMARK_ESTROGEN_RESPONSE_EARLY | HALLMARK_ESTROGEN_RESPONSE_EARLY | HALLMARK_ESTROGEN_RESPONSE_EARLY | 16/67 | 200/4386 | 2.763141e-08 | 1.160519e-06 | 1.017999e-06 | IL6ST/FKBP4/MYBL1/OLFM1/B4GALT1/KRT19/ARL3/IGFBP4/TPBG/KRT15/CA12/GREB1/FKBP5/PGR/XBP1/STC2 | 16 |\n",
       "| HALLMARK_ESTROGEN_RESPONSE_LATE | HALLMARK_ESTROGEN_RESPONSE_LATE  | HALLMARK_ESTROGEN_RESPONSE_LATE  | 14/67 | 200/4386 | 1.230287e-06 | 2.583603e-05 | 2.266318e-05 | IL6ST/FKBP4/OLFM1/KRT19/ARL3/IGFBP4/TPBG/CA12/FKBP5/PGR/XBP1/PRLR/PDCD4/NMU                 | 14 |\n",
       "| HALLMARK_E2F_TARGETS | HALLMARK_E2F_TARGETS             | HALLMARK_E2F_TARGETS             | 11/67 | 200/4386 | 1.827412e-04 | 1.918783e-03 | 1.683143e-03 | TFRC/PRDX4/SNRPB/RBBP7/CENPE/H2AFZ/STMN1/CCNB2/BIRC5/PTTG1/UBE2S                            | 11 |\n",
       "| HALLMARK_MYC_TARGETS_V1 | HALLMARK_MYC_TARGETS_V1          | HALLMARK_MYC_TARGETS_V1          | 11/67 | 200/4386 | 1.827412e-04 | 1.918783e-03 | 1.683143e-03 | RPS10/PSMA2/ERH/LDHA/CCT3/SRSF7/PRDX4/HNRNPA2B1/VDAC3/CCT5/H2AFZ                            | 11 |\n",
       "| HALLMARK_G2M_CHECKPOINT | HALLMARK_G2M_CHECKPOINT          | HALLMARK_G2M_CHECKPOINT          | 9/67  | 200/4386 | 3.051633e-03 | 2.563372e-02 | 2.248572e-02 | SFPQ/H2AFV/CENPE/H2AFZ/STMN1/CCNB2/BIRC5/PTTG1/UBE2S                                        |  9 |\n",
       "\n"
      ],
      "text/plain": [
       "                                 ID                              \n",
       "HALLMARK_ESTROGEN_RESPONSE_EARLY HALLMARK_ESTROGEN_RESPONSE_EARLY\n",
       "HALLMARK_ESTROGEN_RESPONSE_LATE  HALLMARK_ESTROGEN_RESPONSE_LATE \n",
       "HALLMARK_E2F_TARGETS             HALLMARK_E2F_TARGETS            \n",
       "HALLMARK_MYC_TARGETS_V1          HALLMARK_MYC_TARGETS_V1         \n",
       "HALLMARK_G2M_CHECKPOINT          HALLMARK_G2M_CHECKPOINT         \n",
       "                                 Description                      GeneRatio\n",
       "HALLMARK_ESTROGEN_RESPONSE_EARLY HALLMARK_ESTROGEN_RESPONSE_EARLY 16/67    \n",
       "HALLMARK_ESTROGEN_RESPONSE_LATE  HALLMARK_ESTROGEN_RESPONSE_LATE  14/67    \n",
       "HALLMARK_E2F_TARGETS             HALLMARK_E2F_TARGETS             11/67    \n",
       "HALLMARK_MYC_TARGETS_V1          HALLMARK_MYC_TARGETS_V1          11/67    \n",
       "HALLMARK_G2M_CHECKPOINT          HALLMARK_G2M_CHECKPOINT          9/67     \n",
       "                                 BgRatio  pvalue       p.adjust    \n",
       "HALLMARK_ESTROGEN_RESPONSE_EARLY 200/4386 2.763141e-08 1.160519e-06\n",
       "HALLMARK_ESTROGEN_RESPONSE_LATE  200/4386 1.230287e-06 2.583603e-05\n",
       "HALLMARK_E2F_TARGETS             200/4386 1.827412e-04 1.918783e-03\n",
       "HALLMARK_MYC_TARGETS_V1          200/4386 1.827412e-04 1.918783e-03\n",
       "HALLMARK_G2M_CHECKPOINT          200/4386 3.051633e-03 2.563372e-02\n",
       "                                 qvalue      \n",
       "HALLMARK_ESTROGEN_RESPONSE_EARLY 1.017999e-06\n",
       "HALLMARK_ESTROGEN_RESPONSE_LATE  2.266318e-05\n",
       "HALLMARK_E2F_TARGETS             1.683143e-03\n",
       "HALLMARK_MYC_TARGETS_V1          1.683143e-03\n",
       "HALLMARK_G2M_CHECKPOINT          2.248572e-02\n",
       "                                 geneID                                                                                     \n",
       "HALLMARK_ESTROGEN_RESPONSE_EARLY IL6ST/FKBP4/MYBL1/OLFM1/B4GALT1/KRT19/ARL3/IGFBP4/TPBG/KRT15/CA12/GREB1/FKBP5/PGR/XBP1/STC2\n",
       "HALLMARK_ESTROGEN_RESPONSE_LATE  IL6ST/FKBP4/OLFM1/KRT19/ARL3/IGFBP4/TPBG/CA12/FKBP5/PGR/XBP1/PRLR/PDCD4/NMU                \n",
       "HALLMARK_E2F_TARGETS             TFRC/PRDX4/SNRPB/RBBP7/CENPE/H2AFZ/STMN1/CCNB2/BIRC5/PTTG1/UBE2S                           \n",
       "HALLMARK_MYC_TARGETS_V1          RPS10/PSMA2/ERH/LDHA/CCT3/SRSF7/PRDX4/HNRNPA2B1/VDAC3/CCT5/H2AFZ                           \n",
       "HALLMARK_G2M_CHECKPOINT          SFPQ/H2AFV/CENPE/H2AFZ/STMN1/CCNB2/BIRC5/PTTG1/UBE2S                                       \n",
       "                                 Count\n",
       "HALLMARK_ESTROGEN_RESPONSE_EARLY 16   \n",
       "HALLMARK_ESTROGEN_RESPONSE_LATE  14   \n",
       "HALLMARK_E2F_TARGETS             11   \n",
       "HALLMARK_MYC_TARGETS_V1          11   \n",
       "HALLMARK_G2M_CHECKPOINT           9   "
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       "<ol class=list-inline><li>5</li><li>9</li></ol>\n"
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       "\\begin{enumerate*}\n",
       "\\item 5\n",
       "\\item 9\n",
       "\\end{enumerate*}\n"
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       "1. 5\n",
       "2. 9\n",
       "\n",
       "\n"
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       "[1] 5 9"
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     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 9</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>ID</th><th scope=col>Description</th><th scope=col>GeneRatio</th><th scope=col>BgRatio</th><th scope=col>pvalue</th><th scope=col>p.adjust</th><th scope=col>qvalue</th><th scope=col>geneID</th><th scope=col>Count</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</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><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Wirapati_Proliferation_Breast.Cancer.Res.2008_PMID.18662380</th><td>Wirapati_Proliferation_Breast.Cancer.Res.2008_PMID.18662380</td><td>Wirapati_Proliferation_Breast.Cancer.Res.2008_PMID.18662380</td><td>17/104</td><td>354/19982</td><td>3.859248e-12</td><td>9.223603e-10</td><td>6.499786e-10</td><td>MRPL13/GNG11/HNRNPA2B1/CCT5/JPT1/H2AFV/ARL6IP1/CENPE/H2AFZ/KNSTRN/STMN1/ZWINT/ECT2/CCNB2/BIRC5/PTTG1/UBE2S</td><td>17</td></tr>\n",
       "\t<tr><th scope=row>UNC_HS_Green18_BMC.Med.Genomics.2011_PMID.21214954</th><td>UNC_HS_Green18_BMC.Med.Genomics.2011_PMID.21214954         </td><td>UNC_HS_Green18_BMC.Med.Genomics.2011_PMID.21214954         </td><td>12/104</td><td>158/19982</td><td>3.620344e-11</td><td>4.326311e-09</td><td>3.048710e-09</td><td>IL6ST/IGFBP4/TPBG/KRT15/SGK3/CA12/GREB1/TRPS1/PGR/PDCD4/GATA3/STC2                                        </td><td>12</td></tr>\n",
       "\t<tr><th scope=row>MCF7.E2.induced.genes_JCO.2006_PMID.16505416</th><td>MCF7.E2.induced.genes_JCO.2006_PMID.16505416               </td><td>MCF7.E2.induced.genes_JCO.2006_PMID.16505416               </td><td>14/104</td><td>290/19982</td><td>3.322132e-10</td><td>2.646632e-08</td><td>1.865056e-08</td><td>FMN1/TFRC/FKBP4/MYBL1/OLFM1/B4GALT1/IGFBP4/KRT15/SGK3/CA12/GREB1/PGR/XBP1/JPT1                            </td><td>14</td></tr>\n",
       "\t<tr><th scope=row>UNC_HS_Green8_BMC.Med.Genomics.2011_PMID.21214954</th><td>UNC_HS_Green8_BMC.Med.Genomics.2011_PMID.21214954          </td><td>UNC_HS_Green8_BMC.Med.Genomics.2011_PMID.21214954          </td><td>13/104</td><td>314/19982</td><td>9.448583e-09</td><td>5.645528e-07</td><td>3.978351e-07</td><td>IL6ST/KRT19/IGFBP4/TPBG/REEP5/CA12/GREB1/MAGED2/TMBIM4/PGR/XBP1/GATA3/STC2                                </td><td>13</td></tr>\n",
       "\t<tr><th scope=row>Proliferation_Cluster_BMC.Med.Genomics.2011_PMID.21214954</th><td>Proliferation_Cluster_BMC.Med.Genomics.2011_PMID.21214954  </td><td>Proliferation_Cluster_BMC.Med.Genomics.2011_PMID.21214954  </td><td>9/104 </td><td>121/19982</td><td>1.374069e-08</td><td>6.568051e-07</td><td>4.628444e-07</td><td>CCT5/CENPE/H2AFZ/STMN1/ZWINT/ECT2/CCNB2/PTTG1/UBE2S                                                       </td><td> 9</td></tr>\n",
       "\t<tr><th scope=row>Duke_Module17_pr_Mike_PMID.20335537</th><td>Duke_Module17_pr_Mike_PMID.20335537                        </td><td>Duke_Module17_pr_Mike_PMID.20335537                        </td><td>7/104 </td><td>62/19982 </td><td>3.279285e-08</td><td>1.306249e-06</td><td>9.205011e-07</td><td>IL6ST/FKBP4/ARL3/CA12/GREB1/GOLM1/GATA3                                                                   </td><td> 7</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 9\n",
       "\\begin{tabular}{r|lllllllll}\n",
       "  & ID & Description & GeneRatio & BgRatio & pvalue & p.adjust & qvalue & geneID & Count\\\\\n",
       "  & <chr> & <chr> & <chr> & <chr> & <dbl> & <dbl> & <dbl> & <chr> & <int>\\\\\n",
       "\\hline\n",
       "\tWirapati\\_Proliferation\\_Breast.Cancer.Res.2008\\_PMID.18662380 & Wirapati\\_Proliferation\\_Breast.Cancer.Res.2008\\_PMID.18662380 & Wirapati\\_Proliferation\\_Breast.Cancer.Res.2008\\_PMID.18662380 & 17/104 & 354/19982 & 3.859248e-12 & 9.223603e-10 & 6.499786e-10 & MRPL13/GNG11/HNRNPA2B1/CCT5/JPT1/H2AFV/ARL6IP1/CENPE/H2AFZ/KNSTRN/STMN1/ZWINT/ECT2/CCNB2/BIRC5/PTTG1/UBE2S & 17\\\\\n",
       "\tUNC\\_HS\\_Green18\\_BMC.Med.Genomics.2011\\_PMID.21214954 & UNC\\_HS\\_Green18\\_BMC.Med.Genomics.2011\\_PMID.21214954          & UNC\\_HS\\_Green18\\_BMC.Med.Genomics.2011\\_PMID.21214954          & 12/104 & 158/19982 & 3.620344e-11 & 4.326311e-09 & 3.048710e-09 & IL6ST/IGFBP4/TPBG/KRT15/SGK3/CA12/GREB1/TRPS1/PGR/PDCD4/GATA3/STC2                                         & 12\\\\\n",
       "\tMCF7.E2.induced.genes\\_JCO.2006\\_PMID.16505416 & MCF7.E2.induced.genes\\_JCO.2006\\_PMID.16505416                & MCF7.E2.induced.genes\\_JCO.2006\\_PMID.16505416                & 14/104 & 290/19982 & 3.322132e-10 & 2.646632e-08 & 1.865056e-08 & FMN1/TFRC/FKBP4/MYBL1/OLFM1/B4GALT1/IGFBP4/KRT15/SGK3/CA12/GREB1/PGR/XBP1/JPT1                             & 14\\\\\n",
       "\tUNC\\_HS\\_Green8\\_BMC.Med.Genomics.2011\\_PMID.21214954 & UNC\\_HS\\_Green8\\_BMC.Med.Genomics.2011\\_PMID.21214954           & UNC\\_HS\\_Green8\\_BMC.Med.Genomics.2011\\_PMID.21214954           & 13/104 & 314/19982 & 9.448583e-09 & 5.645528e-07 & 3.978351e-07 & IL6ST/KRT19/IGFBP4/TPBG/REEP5/CA12/GREB1/MAGED2/TMBIM4/PGR/XBP1/GATA3/STC2                                 & 13\\\\\n",
       "\tProliferation\\_Cluster\\_BMC.Med.Genomics.2011\\_PMID.21214954 & Proliferation\\_Cluster\\_BMC.Med.Genomics.2011\\_PMID.21214954   & Proliferation\\_Cluster\\_BMC.Med.Genomics.2011\\_PMID.21214954   & 9/104  & 121/19982 & 1.374069e-08 & 6.568051e-07 & 4.628444e-07 & CCT5/CENPE/H2AFZ/STMN1/ZWINT/ECT2/CCNB2/PTTG1/UBE2S                                                        &  9\\\\\n",
       "\tDuke\\_Module17\\_pr\\_Mike\\_PMID.20335537 & Duke\\_Module17\\_pr\\_Mike\\_PMID.20335537                         & Duke\\_Module17\\_pr\\_Mike\\_PMID.20335537                         & 7/104  & 62/19982  & 3.279285e-08 & 1.306249e-06 & 9.205011e-07 & IL6ST/FKBP4/ARL3/CA12/GREB1/GOLM1/GATA3                                                                    &  7\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 9\n",
       "\n",
       "| <!--/--> | ID &lt;chr&gt; | Description &lt;chr&gt; | GeneRatio &lt;chr&gt; | BgRatio &lt;chr&gt; | pvalue &lt;dbl&gt; | p.adjust &lt;dbl&gt; | qvalue &lt;dbl&gt; | geneID &lt;chr&gt; | Count &lt;int&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|\n",
       "| Wirapati_Proliferation_Breast.Cancer.Res.2008_PMID.18662380 | Wirapati_Proliferation_Breast.Cancer.Res.2008_PMID.18662380 | Wirapati_Proliferation_Breast.Cancer.Res.2008_PMID.18662380 | 17/104 | 354/19982 | 3.859248e-12 | 9.223603e-10 | 6.499786e-10 | MRPL13/GNG11/HNRNPA2B1/CCT5/JPT1/H2AFV/ARL6IP1/CENPE/H2AFZ/KNSTRN/STMN1/ZWINT/ECT2/CCNB2/BIRC5/PTTG1/UBE2S | 17 |\n",
       "| UNC_HS_Green18_BMC.Med.Genomics.2011_PMID.21214954 | UNC_HS_Green18_BMC.Med.Genomics.2011_PMID.21214954          | UNC_HS_Green18_BMC.Med.Genomics.2011_PMID.21214954          | 12/104 | 158/19982 | 3.620344e-11 | 4.326311e-09 | 3.048710e-09 | IL6ST/IGFBP4/TPBG/KRT15/SGK3/CA12/GREB1/TRPS1/PGR/PDCD4/GATA3/STC2                                         | 12 |\n",
       "| MCF7.E2.induced.genes_JCO.2006_PMID.16505416 | MCF7.E2.induced.genes_JCO.2006_PMID.16505416                | MCF7.E2.induced.genes_JCO.2006_PMID.16505416                | 14/104 | 290/19982 | 3.322132e-10 | 2.646632e-08 | 1.865056e-08 | FMN1/TFRC/FKBP4/MYBL1/OLFM1/B4GALT1/IGFBP4/KRT15/SGK3/CA12/GREB1/PGR/XBP1/JPT1                             | 14 |\n",
       "| UNC_HS_Green8_BMC.Med.Genomics.2011_PMID.21214954 | UNC_HS_Green8_BMC.Med.Genomics.2011_PMID.21214954           | UNC_HS_Green8_BMC.Med.Genomics.2011_PMID.21214954           | 13/104 | 314/19982 | 9.448583e-09 | 5.645528e-07 | 3.978351e-07 | IL6ST/KRT19/IGFBP4/TPBG/REEP5/CA12/GREB1/MAGED2/TMBIM4/PGR/XBP1/GATA3/STC2                                 | 13 |\n",
       "| Proliferation_Cluster_BMC.Med.Genomics.2011_PMID.21214954 | Proliferation_Cluster_BMC.Med.Genomics.2011_PMID.21214954   | Proliferation_Cluster_BMC.Med.Genomics.2011_PMID.21214954   | 9/104  | 121/19982 | 1.374069e-08 | 6.568051e-07 | 4.628444e-07 | CCT5/CENPE/H2AFZ/STMN1/ZWINT/ECT2/CCNB2/PTTG1/UBE2S                                                        |  9 |\n",
       "| Duke_Module17_pr_Mike_PMID.20335537 | Duke_Module17_pr_Mike_PMID.20335537                         | Duke_Module17_pr_Mike_PMID.20335537                         | 7/104  | 62/19982  | 3.279285e-08 | 1.306249e-06 | 9.205011e-07 | IL6ST/FKBP4/ARL3/CA12/GREB1/GOLM1/GATA3                                                                    |  7 |\n",
       "\n"
      ],
      "text/plain": [
       "                                                            ID                                                         \n",
       "Wirapati_Proliferation_Breast.Cancer.Res.2008_PMID.18662380 Wirapati_Proliferation_Breast.Cancer.Res.2008_PMID.18662380\n",
       "UNC_HS_Green18_BMC.Med.Genomics.2011_PMID.21214954          UNC_HS_Green18_BMC.Med.Genomics.2011_PMID.21214954         \n",
       "MCF7.E2.induced.genes_JCO.2006_PMID.16505416                MCF7.E2.induced.genes_JCO.2006_PMID.16505416               \n",
       "UNC_HS_Green8_BMC.Med.Genomics.2011_PMID.21214954           UNC_HS_Green8_BMC.Med.Genomics.2011_PMID.21214954          \n",
       "Proliferation_Cluster_BMC.Med.Genomics.2011_PMID.21214954   Proliferation_Cluster_BMC.Med.Genomics.2011_PMID.21214954  \n",
       "Duke_Module17_pr_Mike_PMID.20335537                         Duke_Module17_pr_Mike_PMID.20335537                        \n",
       "                                                            Description                                                \n",
       "Wirapati_Proliferation_Breast.Cancer.Res.2008_PMID.18662380 Wirapati_Proliferation_Breast.Cancer.Res.2008_PMID.18662380\n",
       "UNC_HS_Green18_BMC.Med.Genomics.2011_PMID.21214954          UNC_HS_Green18_BMC.Med.Genomics.2011_PMID.21214954         \n",
       "MCF7.E2.induced.genes_JCO.2006_PMID.16505416                MCF7.E2.induced.genes_JCO.2006_PMID.16505416               \n",
       "UNC_HS_Green8_BMC.Med.Genomics.2011_PMID.21214954           UNC_HS_Green8_BMC.Med.Genomics.2011_PMID.21214954          \n",
       "Proliferation_Cluster_BMC.Med.Genomics.2011_PMID.21214954   Proliferation_Cluster_BMC.Med.Genomics.2011_PMID.21214954  \n",
       "Duke_Module17_pr_Mike_PMID.20335537                         Duke_Module17_pr_Mike_PMID.20335537                        \n",
       "                                                            GeneRatio BgRatio  \n",
       "Wirapati_Proliferation_Breast.Cancer.Res.2008_PMID.18662380 17/104    354/19982\n",
       "UNC_HS_Green18_BMC.Med.Genomics.2011_PMID.21214954          12/104    158/19982\n",
       "MCF7.E2.induced.genes_JCO.2006_PMID.16505416                14/104    290/19982\n",
       "UNC_HS_Green8_BMC.Med.Genomics.2011_PMID.21214954           13/104    314/19982\n",
       "Proliferation_Cluster_BMC.Med.Genomics.2011_PMID.21214954   9/104     121/19982\n",
       "Duke_Module17_pr_Mike_PMID.20335537                         7/104     62/19982 \n",
       "                                                            pvalue      \n",
       "Wirapati_Proliferation_Breast.Cancer.Res.2008_PMID.18662380 3.859248e-12\n",
       "UNC_HS_Green18_BMC.Med.Genomics.2011_PMID.21214954          3.620344e-11\n",
       "MCF7.E2.induced.genes_JCO.2006_PMID.16505416                3.322132e-10\n",
       "UNC_HS_Green8_BMC.Med.Genomics.2011_PMID.21214954           9.448583e-09\n",
       "Proliferation_Cluster_BMC.Med.Genomics.2011_PMID.21214954   1.374069e-08\n",
       "Duke_Module17_pr_Mike_PMID.20335537                         3.279285e-08\n",
       "                                                            p.adjust    \n",
       "Wirapati_Proliferation_Breast.Cancer.Res.2008_PMID.18662380 9.223603e-10\n",
       "UNC_HS_Green18_BMC.Med.Genomics.2011_PMID.21214954          4.326311e-09\n",
       "MCF7.E2.induced.genes_JCO.2006_PMID.16505416                2.646632e-08\n",
       "UNC_HS_Green8_BMC.Med.Genomics.2011_PMID.21214954           5.645528e-07\n",
       "Proliferation_Cluster_BMC.Med.Genomics.2011_PMID.21214954   6.568051e-07\n",
       "Duke_Module17_pr_Mike_PMID.20335537                         1.306249e-06\n",
       "                                                            qvalue      \n",
       "Wirapati_Proliferation_Breast.Cancer.Res.2008_PMID.18662380 6.499786e-10\n",
       "UNC_HS_Green18_BMC.Med.Genomics.2011_PMID.21214954          3.048710e-09\n",
       "MCF7.E2.induced.genes_JCO.2006_PMID.16505416                1.865056e-08\n",
       "UNC_HS_Green8_BMC.Med.Genomics.2011_PMID.21214954           3.978351e-07\n",
       "Proliferation_Cluster_BMC.Med.Genomics.2011_PMID.21214954   4.628444e-07\n",
       "Duke_Module17_pr_Mike_PMID.20335537                         9.205011e-07\n",
       "                                                            geneID                                                                                                    \n",
       "Wirapati_Proliferation_Breast.Cancer.Res.2008_PMID.18662380 MRPL13/GNG11/HNRNPA2B1/CCT5/JPT1/H2AFV/ARL6IP1/CENPE/H2AFZ/KNSTRN/STMN1/ZWINT/ECT2/CCNB2/BIRC5/PTTG1/UBE2S\n",
       "UNC_HS_Green18_BMC.Med.Genomics.2011_PMID.21214954          IL6ST/IGFBP4/TPBG/KRT15/SGK3/CA12/GREB1/TRPS1/PGR/PDCD4/GATA3/STC2                                        \n",
       "MCF7.E2.induced.genes_JCO.2006_PMID.16505416                FMN1/TFRC/FKBP4/MYBL1/OLFM1/B4GALT1/IGFBP4/KRT15/SGK3/CA12/GREB1/PGR/XBP1/JPT1                            \n",
       "UNC_HS_Green8_BMC.Med.Genomics.2011_PMID.21214954           IL6ST/KRT19/IGFBP4/TPBG/REEP5/CA12/GREB1/MAGED2/TMBIM4/PGR/XBP1/GATA3/STC2                                \n",
       "Proliferation_Cluster_BMC.Med.Genomics.2011_PMID.21214954   CCT5/CENPE/H2AFZ/STMN1/ZWINT/ECT2/CCNB2/PTTG1/UBE2S                                                       \n",
       "Duke_Module17_pr_Mike_PMID.20335537                         IL6ST/FKBP4/ARL3/CA12/GREB1/GOLM1/GATA3                                                                   \n",
       "                                                            Count\n",
       "Wirapati_Proliferation_Breast.Cancer.Res.2008_PMID.18662380 17   \n",
       "UNC_HS_Green18_BMC.Med.Genomics.2011_PMID.21214954          12   \n",
       "MCF7.E2.induced.genes_JCO.2006_PMID.16505416                14   \n",
       "UNC_HS_Green8_BMC.Med.Genomics.2011_PMID.21214954           13   \n",
       "Proliferation_Cluster_BMC.Med.Genomics.2011_PMID.21214954    9   \n",
       "Duke_Module17_pr_Mike_PMID.20335537                          7   "
      ]
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       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
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       "<ol class=list-inline><li>54</li><li>9</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 54\n",
       "\\item 9\n",
       "\\end{enumerate*}\n"
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       "1. 54\n",
       "2. 9\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 54  9"
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      "text/html": [
       "filename_xlsx: xlsx/er+bc-cell-line_T47D_Control_vs_T47D_Tamoxifen_Tumor_cells.xlsx\n"
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       "filename_figure: heatmap_er+bc-cell-line_T47D_Control_vs_T47D_Tamoxifen_Tumor_cells_zscore\n"
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hCWUumplcWZFHWBUJSVVKJOa6dJSpKroxxiSZKwxWGykCWMWMZcKUTkUaWfMkQD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\n42J0ZmZ0+LoOT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lWWwJEzRq5pqooooV\nipDtm4frtrzmLm/TZ/bvzsvCDiyfHqhe07794lt1HaKmSiXm++umfJk0bOOG4Q4AAI7vbNrw\nety6yWvv1vzcbr3LlNQTlmWxJZxuFDVc2I2n9Z0sqaO7w5RX3JsoHoReMDibHWo4b0d+7Z0a\nvQpCLRZeQaMWCSuqoOYAEzRCCDEUfkmIEEIMhQkaIYQYChM0QggxFCZohBBiKEzQCCHEUJig\nEUKIoTBBI4QQQ2GCRgghhvp/o6WNYMK+oD0AAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 570,
       "width": 240
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "list_markers <- find_markers(rna, sample1 = sample1,\n",
    "                        sample_ref = sample_ref,\n",
    "                        pattern_sample1 = pattern_sample1,\n",
    "                        pattern_sample_ref = pattern_sample_ref,\n",
    "                             \n",
    "                        # pattern_tumor_epi     \n",
    "                        pattern_cell_type1 = pattern_tumor_epi,\n",
    "                        pattern_cell_type_ref = pattern_tumor_epi,\n",
    "                             \n",
    "                        genes = genes,\n",
    "                        str_cond1 = str_cond1,\n",
    "                        str_cond_ref = str_cond_ref,\n",
    "                             \n",
    "                        group_name1 = sprintf(\"%s Tumor cells\", group_name1),\n",
    "                        group_name_ref = sprintf(\"%s Tumor cells\", group_name_ref),\n",
    "                        col_cluster_types = col_cluster_types_,\n",
    "                        col_cell_types = col_cell_types_,\n",
    "                        th_log2fc = th_log2fc_,\n",
    "                        th_padj = th_padj_, \n",
    "                        min.pct = min.pct_,\n",
    "                        min.diff.pct = min.diff.pct_,\n",
    "                        max.cells.per.ident = max.cells.per.ident_,\n",
    "                        method_dge = method_dge)\n",
    "\n",
    "str_condition_tmp <- sprintf(\"%s_%s_vs_%s_Tumor cells\",\n",
    "                             str_condition,\n",
    "                             group_name_ref, group_name1)\n",
    "\n",
    "if (type_condition != \"none\") {\n",
    "          str_condition_tmp <- sprintf(\"%s_%s\", str_condition_tmp, type_condition)\n",
    "}\n",
    "\n",
    "list_ea <- execute_enrichment_analysis(list_markers$markers,\n",
    "                        str_condition_tmp,\n",
    "                        th_log2fc = th_log2fc_,\n",
    "                        th_padj = th_padj_,\n",
    "                        method_dge = method_dge,\n",
    "                        dir_xlsx = dir_xlsx,       \n",
    "                        n_log = 1)\n",
    "\n",
    "\n",
    "genes_up <- rownames(list_ea$df_up)\n",
    "genes_dn <- rownames(list_ea$df_dn)\n",
    "list_genes_dn[[\"Epi\"]] <- genes_dn\n",
    "\n",
    "if (f_featureplot) {\n",
    "    gg <- featureplot_enrichment_analysis(rna,\n",
    "                list_ea,\n",
    "                str_condition_tmp)\n",
    "}\n",
    "\n",
    "list_out_heatmap <- heatmap_enrichment_analysis(list_markers,\n",
    "                    list_ea,\n",
    "                    str_condition_tmp,\n",
    "                    pattern_gene_removal=\"^MT-|^RPS|^RPL\",\n",
    "                    n_sampling=n_sampling,\n",
    "                    cluster_rows=cluster_rows)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8ec5414",
   "metadata": {},
   "source": [
    "# session info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "04158981",
   "metadata": {},
   "outputs": [],
   "source": [
    "writeLines(capture.output(sessionInfo()), \"txt/sessionInfo.txt\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11a8c620",
   "metadata": {},
   "source": [
    "# reference"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "674ff0fb",
   "metadata": {},
   "source": [
    "https://satijalab.org/seurat/\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1dad838f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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