figure_s1_01_sc-rna-seq_qc.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "97647c55",
   "metadata": {},
   "source": [
    "# load library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "dd4796e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "source(\"r/jupyter_common.R\")\n",
    "source(\"r/identify_cell_types.R\")\n",
    "source(\"r/plot_sc_clusters.R\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "53bd5099",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "suppressPackageStartupMessages(library(Seurat))\n",
    "suppressPackageStartupMessages(library(ArchR))\n",
    "\n",
    "suppressPackageStartupMessages(library(RColorBrewer))\n",
    "suppressPackageStartupMessages(library(ggplot2))\n",
    "suppressPackageStartupMessages(library(IRdisplay))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "383e4b8c",
   "metadata": {},
   "source": [
    "# patameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e824fe80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'male-bc'"
      ],
      "text/latex": [
       "'male-bc'"
      ],
      "text/markdown": [
       "'male-bc'"
      ],
      "text/plain": [
       "[1] \"male-bc\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "'RNA_harmony_th.0'"
      ],
      "text/latex": [
       "'RNA\\_harmony\\_th.0'"
      ],
      "text/markdown": [
       "'RNA_harmony_th.0'"
      ],
      "text/plain": [
       "[1] \"RNA_harmony_th.0\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "'umapharmony'"
      ],
      "text/latex": [
       "'umapharmony'"
      ],
      "text/markdown": [
       "'umapharmony'"
      ],
      "text/plain": [
       "[1] \"umapharmony\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "'cluster.type.harmony'"
      ],
      "text/latex": [
       "'cluster.type.harmony'"
      ],
      "text/markdown": [
       "'cluster.type.harmony'"
      ],
      "text/plain": [
       "[1] \"cluster.type.harmony\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cancer_type <- \"male-bc\"\n",
    "\n",
    "args <- list()\n",
    "args$cancer_type <- cancer_type\n",
    "args$method_to_identify_cell_types <- \"singler_blueprint_encode\"\n",
    "args$harmony_theta <- 0\n",
    "\n",
    "str_column_of_meta_data_harmony <- sprintf(\"RNA_harmony_th.%s\", paste(args$harmony_theta, collapse=\",\"))\n",
    "\n",
    "str_reduction <- \"pca\"\n",
    "str_umap_reduction <- \"umap\"  \n",
    "col_cluster_types <- \"cluster.type\"\n",
    "\n",
    "if (args$harmony_theta >= 0) {\n",
    "    str_column_of_meta_data_cluster <- str_column_of_meta_data_harmony\n",
    "    str_reduction <- \"harmony\"\n",
    "    str_umap_reduction <- \"umapharmony\"\n",
    "    col_cluster_types <- \"cluster.type.harmony\"\n",
    "}\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"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e85f69c",
   "metadata": {},
   "source": [
    "# read scRNA-seq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8d558c4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "dir_rna <- \"/home/hkim77/francolab.w/sc-rna-seq/male-bc/run-20220725\"\n",
    "fname_rna <- sprintf(\"%s/output_male-bc/rds_%s/male-bc_sc-rna-seq_merged_seurat_obj.rds\",\n",
    "                     dir_rna, cancer_type)\n",
    "\n",
    "rna <- readRDS(fname_rna)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f54b7a4b",
   "metadata": {},
   "source": [
    "## list_sort"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5886c1f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 2 × 20</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Sample</th><th scope=col>NA</th><th scope=col>Adipocytes</th><th scope=col>CD4+ T-cells</th><th scope=col>CD8+ T-cells</th><th scope=col>Chondrocytes</th><th scope=col>DCs</th><th scope=col>Endothelial cells</th><th scope=col>Epi. Non-tumor</th><th scope=col>Epi. Tumor</th><th scope=col>Epi. Unassigned</th><th scope=col>Fibroblasts</th><th scope=col>HSC</th><th scope=col>Macrophages</th><th scope=col>Mast cells</th><th scope=col>Mesangial cells</th><th scope=col>Monocytes</th><th scope=col>Myocytes</th><th scope=col>Pericytes</th><th scope=col>Smooth muscle</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>446B7L</td><td> 7</td><td>30</td><td>NA</td><td>1</td><td> 1</td><td> 4</td><td>1306</td><td>164</td><td>1647</td><td>1865</td><td>502</td><td>NA</td><td>41</td><td>NA</td><td>2</td><td>4</td><td> 9</td><td> 1</td><td>NA</td></tr>\n",
       "\t<tr><td>4CC61L</td><td>13</td><td> 2</td><td> 1</td><td>4</td><td>22</td><td>26</td><td>   2</td><td> NA</td><td>2648</td><td>  31</td><td>503</td><td> 1</td><td>95</td><td> 2</td><td>1</td><td>5</td><td>NA</td><td>NA</td><td> 1</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 2 × 20\n",
       "\\begin{tabular}{llllllllllllllllllll}\n",
       " Sample & NA & Adipocytes & CD4+ T-cells & CD8+ T-cells & Chondrocytes & DCs & Endothelial cells & Epi. Non-tumor & Epi. Tumor & Epi. Unassigned & Fibroblasts & HSC & Macrophages & Mast cells & Mesangial cells & Monocytes & Myocytes & Pericytes & Smooth muscle\\\\\n",
       " <chr> & <int> & <int> & <int> & <int> & <int> & <int> & <int> & <int> & <int> & <int> & <int> & <int> & <int> & <int> & <int> & <int> & <int> & <int> & <int>\\\\\n",
       "\\hline\n",
       "\t 446B7L &  7 & 30 & NA & 1 &  1 &  4 & 1306 & 164 & 1647 & 1865 & 502 & NA & 41 & NA & 2 & 4 &  9 &  1 & NA\\\\\n",
       "\t 4CC61L & 13 &  2 &  1 & 4 & 22 & 26 &    2 &  NA & 2648 &   31 & 503 &  1 & 95 &  2 & 1 & 5 & NA & NA &  1\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 2 × 20\n",
       "\n",
       "| Sample &lt;chr&gt; | NA &lt;int&gt; | Adipocytes &lt;int&gt; | CD4+ T-cells &lt;int&gt; | CD8+ T-cells &lt;int&gt; | Chondrocytes &lt;int&gt; | DCs &lt;int&gt; | Endothelial cells &lt;int&gt; | Epi. Non-tumor &lt;int&gt; | Epi. Tumor &lt;int&gt; | Epi. Unassigned &lt;int&gt; | Fibroblasts &lt;int&gt; | HSC &lt;int&gt; | Macrophages &lt;int&gt; | Mast cells &lt;int&gt; | Mesangial cells &lt;int&gt; | Monocytes &lt;int&gt; | Myocytes &lt;int&gt; | Pericytes &lt;int&gt; | Smooth muscle &lt;int&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 446B7L |  7 | 30 | NA | 1 |  1 |  4 | 1306 | 164 | 1647 | 1865 | 502 | NA | 41 | NA | 2 | 4 |  9 |  1 | NA |\n",
       "| 4CC61L | 13 |  2 |  1 | 4 | 22 | 26 |    2 |  NA | 2648 |   31 | 503 |  1 | 95 |  2 | 1 | 5 | NA | NA |  1 |\n",
       "\n"
      ],
      "text/plain": [
       "  Sample NA Adipocytes CD4+ T-cells CD8+ T-cells Chondrocytes DCs\n",
       "1 446B7L  7 30         NA           1             1            4 \n",
       "2 4CC61L 13  2          1           4            22           26 \n",
       "  Endothelial cells Epi. Non-tumor Epi. Tumor Epi. Unassigned Fibroblasts HSC\n",
       "1 1306              164            1647       1865            502         NA \n",
       "2    2               NA            2648         31            503          1 \n",
       "  Macrophages Mast cells Mesangial cells Monocytes Myocytes Pericytes\n",
       "1 41          NA         2               4          9        1       \n",
       "2 95           2         1               5         NA       NA       \n",
       "  Smooth muscle\n",
       "1 NA           \n",
       "2  1           "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 2 × 8</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Sample</th><th scope=col>NA</th><th scope=col>Chondrocytes</th><th scope=col>HSC</th><th scope=col>Mesangial cells</th><th scope=col>Myocytes</th><th scope=col>Pericytes</th><th scope=col>Smooth muscle</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>446B7L</td><td> 7</td><td> 1</td><td>NA</td><td>2</td><td> 9</td><td> 1</td><td>NA</td></tr>\n",
       "\t<tr><td>4CC61L</td><td>13</td><td>22</td><td> 1</td><td>1</td><td>NA</td><td>NA</td><td> 1</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 2 × 8\n",
       "\\begin{tabular}{llllllll}\n",
       " Sample & NA & Chondrocytes & HSC & Mesangial cells & Myocytes & Pericytes & Smooth muscle\\\\\n",
       " <chr> & <int> & <int> & <int> & <int> & <int> & <int> & <int>\\\\\n",
       "\\hline\n",
       "\t 446B7L &  7 &  1 & NA & 2 &  9 &  1 & NA\\\\\n",
       "\t 4CC61L & 13 & 22 &  1 & 1 & NA & NA &  1\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 2 × 8\n",
       "\n",
       "| Sample &lt;chr&gt; | NA &lt;int&gt; | Chondrocytes &lt;int&gt; | HSC &lt;int&gt; | Mesangial cells &lt;int&gt; | Myocytes &lt;int&gt; | Pericytes &lt;int&gt; | Smooth muscle &lt;int&gt; |\n",
       "|---|---|---|---|---|---|---|---|\n",
       "| 446B7L |  7 |  1 | NA | 2 |  9 |  1 | NA |\n",
       "| 4CC61L | 13 | 22 |  1 | 1 | NA | NA |  1 |\n",
       "\n"
      ],
      "text/plain": [
       "  Sample NA Chondrocytes HSC Mesangial cells Myocytes Pericytes Smooth muscle\n",
       "1 446B7L  7  1           NA  2                9        1        NA           \n",
       "2 4CC61L 13 22            1  1               NA       NA         1           "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 2 × 14</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Sample</th><th scope=col>Adipocytes</th><th scope=col>CD4+ T-cells</th><th scope=col>CD8+ T-cells</th><th scope=col>DCs</th><th scope=col>Endothelial cells</th><th scope=col>Epi. Non-tumor</th><th scope=col>Epi. Tumor</th><th scope=col>Epi. Unassigned</th><th scope=col>Fibroblasts</th><th scope=col>Macrophages</th><th scope=col>Mast cells</th><th scope=col>Monocytes</th><th scope=col>Others</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>446B7L</td><td>30</td><td>NA</td><td>1</td><td> 4</td><td>1306</td><td>164</td><td>1647</td><td>1865</td><td>502</td><td>41</td><td>NA</td><td>4</td><td>20</td></tr>\n",
       "\t<tr><td>4CC61L</td><td> 2</td><td> 1</td><td>4</td><td>26</td><td>   2</td><td> NA</td><td>2648</td><td>  31</td><td>503</td><td>95</td><td> 2</td><td>5</td><td>38</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 2 × 14\n",
       "\\begin{tabular}{llllllllllllll}\n",
       " Sample & Adipocytes & CD4+ T-cells & CD8+ T-cells & DCs & Endothelial cells & Epi. Non-tumor & Epi. Tumor & Epi. Unassigned & Fibroblasts & Macrophages & Mast cells & Monocytes & Others\\\\\n",
       " <chr> & <int> & <int> & <int> & <int> & <int> & <int> & <int> & <int> & <int> & <int> & <int> & <int> & <int>\\\\\n",
       "\\hline\n",
       "\t 446B7L & 30 & NA & 1 &  4 & 1306 & 164 & 1647 & 1865 & 502 & 41 & NA & 4 & 20\\\\\n",
       "\t 4CC61L &  2 &  1 & 4 & 26 &    2 &  NA & 2648 &   31 & 503 & 95 &  2 & 5 & 38\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 2 × 14\n",
       "\n",
       "| Sample &lt;chr&gt; | Adipocytes &lt;int&gt; | CD4+ T-cells &lt;int&gt; | CD8+ T-cells &lt;int&gt; | DCs &lt;int&gt; | Endothelial cells &lt;int&gt; | Epi. Non-tumor &lt;int&gt; | Epi. Tumor &lt;int&gt; | Epi. Unassigned &lt;int&gt; | Fibroblasts &lt;int&gt; | Macrophages &lt;int&gt; | Mast cells &lt;int&gt; | Monocytes &lt;int&gt; | Others &lt;int&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 446B7L | 30 | NA | 1 |  4 | 1306 | 164 | 1647 | 1865 | 502 | 41 | NA | 4 | 20 |\n",
       "| 4CC61L |  2 |  1 | 4 | 26 |    2 |  NA | 2648 |   31 | 503 | 95 |  2 | 5 | 38 |\n",
       "\n"
      ],
      "text/plain": [
       "  Sample Adipocytes CD4+ T-cells CD8+ T-cells DCs Endothelial cells\n",
       "1 446B7L 30         NA           1             4  1306             \n",
       "2 4CC61L  2          1           4            26     2             \n",
       "  Epi. Non-tumor Epi. Tumor Epi. Unassigned Fibroblasts Macrophages Mast cells\n",
       "1 164            1647       1865            502         41          NA        \n",
       "2  NA            2648         31            503         95           2        \n",
       "  Monocytes Others\n",
       "1 4         20    \n",
       "2 5         38    "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "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": "f5efb3d5",
   "metadata": {},
   "source": [
    "## nv_colors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "27027f28",
   "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>1-Epi. Unassigned</dt><dd>'#A0E989'</dd><dt>8-Epi. Unassigned</dt><dd>'#87CB72'</dd><dt>0-Epi. Tumor</dt><dd>'#6EAE5C'</dd><dt>2-Epi. Tumor</dt><dd>'#559145'</dd><dt>5-Epi. Tumor</dt><dd>'#3C742F'</dd><dt>6-Epi. Tumor</dt><dd>'#245719'</dd><dt>4-Fibroblasts</dt><dd>'#FABFD2'</dd><dt>3-Endothelial cells</dt><dd>'#93CEFF'</dd><dt>7-Macrophages</dt><dd>'#EEC900'</dd></dl>\n"
      ],
      "text/latex": [
       "\\begin{description*}\n",
       "\\item[1-Epi. Unassigned] '\\#A0E989'\n",
       "\\item[8-Epi. Unassigned] '\\#87CB72'\n",
       "\\item[0-Epi. Tumor] '\\#6EAE5C'\n",
       "\\item[2-Epi. Tumor] '\\#559145'\n",
       "\\item[5-Epi. Tumor] '\\#3C742F'\n",
       "\\item[6-Epi. Tumor] '\\#245719'\n",
       "\\item[4-Fibroblasts] '\\#FABFD2'\n",
       "\\item[3-Endothelial cells] '\\#93CEFF'\n",
       "\\item[7-Macrophages] '\\#EEC900'\n",
       "\\end{description*}\n"
      ],
      "text/markdown": [
       "1-Epi. Unassigned\n",
       ":   '#A0E989'8-Epi. Unassigned\n",
       ":   '#87CB72'0-Epi. Tumor\n",
       ":   '#6EAE5C'2-Epi. Tumor\n",
       ":   '#559145'5-Epi. Tumor\n",
       ":   '#3C742F'6-Epi. Tumor\n",
       ":   '#245719'4-Fibroblasts\n",
       ":   '#FABFD2'3-Endothelial cells\n",
       ":   '#93CEFF'7-Macrophages\n",
       ":   '#EEC900'\n",
       "\n"
      ],
      "text/plain": [
       "  1-Epi. Unassigned   8-Epi. Unassigned        0-Epi. Tumor        2-Epi. Tumor \n",
       "          \"#A0E989\"           \"#87CB72\"           \"#6EAE5C\"           \"#559145\" \n",
       "       5-Epi. Tumor        6-Epi. Tumor       4-Fibroblasts 3-Endothelial cells \n",
       "          \"#3C742F\"           \"#245719\"           \"#FABFD2\"           \"#93CEFF\" \n",
       "      7-Macrophages \n",
       "          \"#EEC900\" "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nv_colors <- list_sort$nv_color_cluster_type_ordered\n",
    "names(nv_colors) <- convert_cluster.type(names(nv_colors))\n",
    "nv_colors\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9867637",
   "metadata": {},
   "source": [
    "# nCells"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "20ebdd8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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v4zVquTaAOHGD5EJpPJZDK1/Ps2CBx+DzzvV+fXMavzx/Dyto2EK7bhdK7aAAAA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\nRbcBXdzrhvvqETq7KPi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/DtG9Em71B8\nTma6IXRMr9bVp5OrRLijvtEnXTfvialCdv/OLEf/75OS0QumdNMSVd/sdXBfz8tJidlnko8V\n+A/t5+t549hKXpshUf5iuOcz12QsywrdBx6x19I3f/3N5ri8ckbhGvXG569H3urSL6Yk8fPZ\nK0rHLZs33FPU+xglV1GzWSwWpVLUXzWbRWLlECoSSPKKCe8fGfju2hnhDUfeyqQV0z7YY3xg\n3vpXeol4U6A/tWltTo+nogPs2JonXROZDAaVnR1lrH5u1rFBn6x+NrCm9cUNr35kmfJcm0sF\nrcIHhYp635P5ws6PPkvv3LFgn+PLKx5rX/dPTMGBxTP/l1DqFDX7q9f72hOZ8rZ+vFb/yBuP\nB4v4RJS7J+bFdK+sl/Ytm/ddUVjM1DdiKrJOa4ZH3DQ2lx7dsOOqd0ddyYkdm2JV0fNmi3ts\nll5Fd0P8m/5mkVg5hIpalLUg95xdB38nIk9PT9If2rRjUs/o1vU/8/a9Hn3Ib8/aokI9kb1A\n/Wyc5eLOz9dVjZ0RE6Ah0gWPfyGYyHxh54dL48P/O3u0r5pIbWdHRCU5OUVkqqpiiK5f8s9e\nLb2ma+fXdWiYoAU0hfVc/O7jWdnxWaToerL0sfaudf4mbx31+sycGXN+P7h135S+0a6kDnh4\n1lzBuso76R5SufPdKcic8dP7n2w+EHvoeL7LwBdnvSzquxRLsSIAuBf61JVvzPo+13v4QF+N\nU2vryb8SM05kWrsNDvOon5BUl45uyPaKmdi7jai+fjT2uPn6T7q+fj6HTT8xVe7WZUAopcem\nF5kKcot9H4hsX+8It7J1kPrU1mO5Lj0fjfAUqo8tRbKBoyl3p1C07jlqVNSg4Y8+OWlET297\nkT0XpSHbrqixM8JsmMTKIVRkk6zHflyS5D/zvUkBGiKy79hJk7ovKftkXDrbuV9o69ovG8zF\n7V+vLR087emuLqLKG40+br7hk66DXBS2/8TU2pu9XjyTlCHvMahr/Zu9VmT+vt8QPnFUV6db\nzkEipBo4mnh3CpnCztHZSau0gdXWtitq9Iww2yWxcggV2RrzhZ1LP/xD0cE5x9zz30OunxYg\ncw7uG1R1PD4tJ/XA4RymlZ9fWxd1Ze6uz5b8bH7knWkRriL6BnJdo7ftauRpDDb2xNRG7r1W\nU1Tq6ZTDJ61Bferc7DX2u9Upfs/8Z5Q0biZ6W1INHNK7O4VtV6TxDe+qNvqNn9zXTVnnjDBb\nJbFyCBXZGObsntXrdicePKV3Vrj0HHbjMSgazx6De7oWZp7MPJUa++dvmzZv3Lz3nM+kuW+O\n8RPr0dWmZg5beWLqre+9duNmryf270u9qrDXWIszd36xbLM1RtQ3e+WQVAOH9O5OYesVqTxC\nwju5Ks0Xdi6c+1NFSH8b39EtsXIIFf1/e/cdF8W19gH8bGGXjvTee++9CiooIpaIyTVF03Pv\ne2OaUUHsxhivNzHdaDTFJGqMsQsiICxLWzq79CIdliYdgd15/1hYl7KIXpGZ9fn+J2zIPJ+Z\nPec3Z86cQygkZWt/J+ny9IzKjvb6br3FfoYP5jJIqZj5LF/hZ2Wga2hu7xn20jtvRNir4rsz\nm2vmQCSqtJwcHcen8WFrrwkXe21qrSqqaBscHFX22bRlk682fkdrnigJChxTthOUgNUpJK+i\nmWeETfrAyAifQsHf0O/MHloOkriKiFUOksSKBKgq1n7OsuXM/LqqXDbmGDBpWQ0STUnb2NLG\nxtpMX0WGEKXNOXPg2lzWXhPO52jvva8c/MZbYaZykjT4NjsJCRwzbycoJauioam+SEb0IuV3\nsb7Z/wd1Y/QmW3k8n2bJqwghJGZGmBC/9c7RDw8xFDx8BOsV497s5SCJq4hw5SBJqgjrqbh1\n9tSv564zihqGFPSMNCa24WCV5jElYHnKh2YO3G+aOte114SVcrKJt+Hc/0QSAsfDthNECHVn\n/XmR0zPcUnjj+BcX7q+I3bHSEM9fTcmrSIT4uxasjXHql5Sa5l4NrxArJdyHJ4FZb8IkrCJC\nloMIWdFYU/yXx9kabsKuiN+afPjjT2+3kin9jSWcIlZifFanhpMLkbfhwAYa8tNSM/Irmvrp\nqlqLpMmzZg7Rna7xqjLxdEqluscaT51J4ZUkZ+yq356UwqlCluu8dCZt3kvITW4fH/EDxxy2\nEyTY6hSSVdG03RFlyeI6AJK8ua+ztm7AxuWmeG5WplWkpCGuPyNCRc/ACSJeRVMXqOBVn4n5\npmftp4e3RIWvWrXYlF9TWFxZksGoIOo2HDxuxvHYmC8vMfMK8nOzUm/eTK2nmjlaqdNJ4jIH\nrvdJ4XGr6ngqyrQ6xvVcTqdiULDllMckNB39kYxreXybyGDzibZapNL8+y7hLmp4H2J7Aggf\nOOaynaCiJl5Xp5iJJFUkZndEIwWymA6AqmxkjN9mBYmtyNzYUUzmwHdFz8wJIlhFkxeo8DWr\n+PXz7vADr9hLIyTYhmOxr14rK7O8Kjeba7TY24hY23BgnWlHtv3YH/rx/ph3N632t1QcrGGz\n2TnJGa163l4GspMyB963m0dIZPm1Fe6UR1t7TUrN3tdeakh37Uv+6ni7BucF4QPH3LYTbLv6\na65umI8OzqccIYQkqaLZdke0VKIQbkbY7BXZmYrLHLj1bJ0gYlUk2ulWkmV6u3TCImwfrAtF\nljf08tSuS0uvKC3sMArz0ZdRtvJzlm3oNF2/wQN3+WmynuSju5JNP/jkZUdFColMU9Kx9g42\naU9M4eouWb3USpmCEFG2mxcQWX7tMdZek1KzcbMmwqDUE0H4wCFJ2wkKSExFD9sdUY9OrBlh\nD6/IWFeiypG8E4TviqYujSrMHOz8io4+kmFYsJnowZLkjNzN+xlJxSWdKmFLLGUQRdnKPxDH\nzx3Gdd3+7juW0fp/B+iPj8zyuYxjh6/JvLB/5ypjGkIjIyMUCgX/j1HQTMuvEXDttaeK8IED\nKVksDl1sq0Hl/HbgTPviLe94qIz/gibdnsmx2rJ1iZV9xKubg/TxfOFOQvyKRhvjPzt4uZvH\nbdEKjbRTEv6comjs7aV1l5FRUVbYZRzqrUcjyoywOVckJ1nlSN4Jwm9FMy2NKnyFsmOkrarb\nONhbb9o2HKWXs6sJtQ1Hfdrvt0qlbMNDLKQRQnwu42j0ib5Ve3euMqahgcLfdkefG/RfYo3v\nh0ICMy+/RsC1154e4gcORKFSSQh1p5/7Pb/XKDDSfWKnRKyBeb5M4/kXltkZa+J3lsNMCFcR\n1s48eYqj6W6uSEIIId7d+ONnEvNY5R19mH5oiIXoE1jhbRmnS3BbhnB5K/O/VITDctDkip7x\nE4RwWZG4pVGFmaOxKqeE4hxgO3kbjoHSq8nDrhuW203bOBqvKE3pl3JLuRTPMEelDtG0gRCS\narrzY1ytbuAqZ5WH/RkcELv8GgHXXntKCBY4+N3Fl3/87uTZG9k1w2rm5urSwudhRN1OkPgV\nYe3MYzHfN7k+t9xm/M0uou+OKPEVSVg5SAIq6mzhUhWUNWdeGvXBkth5jCK+pafINhzMn07m\nGL68JQxHT4YeRkZtrOQGq6KEzR3mnP2r/0HaQAhhjRl/ZUgHvxxoQpDBAPHLrxFv7bWngUiB\nA+PeObz1P8wBOWp3LaeYlZhStcjZ23z8O0nI7QSJX5Gg6ecu37trzaRVTom7O+IzUpGElYOI\nXlH0pyy1EH8DOhKzNOqDzFFw507xPYocbayzJP7bI3+NReJ6Gw5+N+fKT9+f+uN6RvWwhoWF\nujQJIRlTc5nC5JyaytI2k1f2v+MhbNEw7u0TP1a7v/OOpzqeJ6fx+poqy2q5PDkVRToZIYqy\nRC2/Nr+IEziwhguxx9rCDx7aEhWxMsiwn80q4oh+Jwm2nSCSgIrENf0CRNwd8ZmqSMLKQQSv\naHuoliA2iFsaVbgNR2NzZUFZy9DQ2CKvl9/d7KeDlzZhrCn+i2/zVV2FPS6/Lfnw1iOMHhla\n711OcXYy866yk6eZEoWkZOllMZyfUdbZXJBRTdIyMtBUInWVJxz/9ExfROyWADXcxg0eN/vn\nT3YdPnU5Mfn2jbQeu2XumlT0IHMQaimUBUGcwFF6/vOagN1vusghhChy+m7+NiOFafklWSLf\nSaJsJziO2BWJb/onZpmL3pYRYXfEZ7AiCSsHScoJEvf6rpSava89Vsxgd/Rhlq9Ev+6jJY2f\n8c4B1smDvzJYwh4Xa/gz9ov2VZ8efnd9eESQwb3CrCJOJnNi61ct5yBX1a6yYk5JXurNi+fO\nnr+c1mayMfbjlca4fZiCdTA+23ac67bp/zZHeuqr2K190VdDeEFB5pgTfAYOrL2sfEhVTZaE\nEEKjjfGHD1waofRUqwSvsRfOP6dpOPlN76FxuZ3gpHKQZFQkrukfrjq3e8ct2RBffUG7QZTd\nEZ/ZiiSsHCQJFSE0a+bwsxkp51qsXeuCr0mvND1XJ9Eet+3i5zUBu193lkUIkeUMPAKnNG5S\nyqZeYeGB9sb6ptZuQZGb394Uaq2Mr3Zuku5bn+5h+x06+IKjlpq6vpWDucqU+CqaOQiw/NqC\nwGPg6Mo4Fr3vZGKzYNU5fkPSqd8TctLYzfcVbCP8DEVOspgeGmcml4MQInpFszb9e/alGv7r\n4zVGInde+N8d8RmvSMLKQYSv6MhPHdbeRrJiM4eGU0gwzl6xQQhN6nGZZXT54Ub14DX2wn3M\nZmrcSFIKmobmllbmRtpKdHzPrGy7+c2JgZAP11nO+FBu/LUiCnGWX1sYeAwcFH5HLiOnujwj\nq1XP28tQx9rfWaaCmd860Fg7aBvspik65CZ6EeNsGZ8Jk8sxkCWRFlkRuKLuzGM7jiT1WkS+\nttpWQbSJGG/63/3kbbdpu2DhendEqEjiykH4ruhh+Ukr6sUAHWkSIuD+7BOZI6uYVdoyIGOx\nKsBYpN8lxA2VGDV3fkmq0vJd5aw6LbvyWu988cltuUBXTQoiyvJrCwSXgWORhZ+rQjlT2Emb\n6AlePcqrYmdypj0eE1zEFR1WG6LwtIyP0LRyDGRJVBUrolZExXqKmDl1d/PSa1U9fE3HO4DZ\nmn4B/O6OCBUhhCSuHITbih4xP+F4aVShfk5y7piBvgKvMTW+Qtk9eImzbDkzr3WoubrXIthD\nhy7yUbzfUIlFamJeYRZ0qgcHmU5ZlwCr+vPAD6kd6n7hDotm/m/BBDwGDjRDJ22sO9uUHBpe\nxxjHzZQ5Zp9khN+KKEpmPm4qNRms6mrWeAcwMmvT31Nw4vBFvpu7Lg2nuyNCRRMV+Ti6SlI5\nOD1Bj56fcLo0qlB7/Oc7v7x8lzyUcuJSt02gt5HSxA1VTSmrhFg3VGLJaaPS6+n5uRy+Y6DD\npI3ZSKq86r9SBp3XhNrIL9jhEQROAwd65MyBd4+eOfBrcgcw2nHrdJb4pv/4zqNVTq9scBW0\nLDjdHREqmqhIwspBODxBj5yfEMLn0qhCcsa6PSlXkzLYQ/5bD0aZUxF62Fsb+L2hEk/a2IxW\nmJRTUcgsGjX3dHjwghCvJunMLX7YP9dYyC3oARIBfgMHmtpJ6/t6GesQs4cWmFaOgYwkZI6S\nchQSvWuV/vSnsT0Fx3cerfaJ2fuClcggJE53R4SKJiqSsHIQDit6hPxEBGMteXGJxZ1jY/1V\nRQ3qHj4mglEbojZuCCHE76vLY6bnVbYjZR3V8YVCyUpWnhZDeenF1cWp6TUkLRNjHUWp4QbG\nt4fPY+t3vOlC0LP3VC144MA6aup4yovoYn4t0kmz2FT3pbZqKrh+9ehRy1EmEfZlqolGM6e5\nszLvrsjgsMDMTT+uQUU4J0nlzCk/EQSvp7HH9KX/C+bnMos4men1BM8cvFbm97E7v76clpub\nnXbrZka7tpuboWDzKrqWc6CbckdJMackL/X6xYt//30hrkL7+diPVxoRoDAcWNjAgXWkH4ve\nfTJtwNLXRWu2TtpJquBOXiWnQ3t5oDEdt68ePV45CBH2ZaoZBocFHQChmn5RUBHOSVI5D8lP\nxEFRMrI2kJfRdvM1782ZLXMQ4IYK46Yc3nG6f+nWgzH/XOehyi3OYRcxMxq1vLyN5ckIISSl\nbOa9fIWflb6OvqmNx9KNb78V6Qgbs83VwgYOEtZbxWQUVXOYBYOzdtLKNnaU3Bu5ZVKWz/no\nk/H66tFjl4PwWtHDzdAB9BGv6RcFFeGcJJUjNj8Rk/RsmYMIN1RY86W9+0v9D+xbbylPpUir\nmRiO5N8q7OhvyMoUyRzjG7PZ2tmYG6jKEvmEPXUL/EiFpuHgZ8MrZD68k1bQuJ97mUV2WRto\ngt8rVsLKmR2fW1x0T1VLkTyp0azpv3v1bD0Bm35EsIqwduZ3XzIVvexn23iCUBU9nISVI0DU\nzMHrrmDEXU9g5FVyeUo62ko0wQyGyZnD3dtEkdTF/P3mfc81a0Jwf0OFsX/75Pz90H8/byuP\nEOK1Jn0We4H+2p5NCiVMdknGpMwBHsuCz+GYcyc9VnPnYrXB2vUu6k/5AB+NZJSD9dWzkhPu\nZBXf7eQpaGkqSE2dDsXnMo5GH/w5oUrd099EgSTSaNbTV+zCYdPP665Ivnz+0vVkVlWvnL6J\nxrT7EkJVJFg5qsX7xUhb8WtLEKoiNMItvnXhj3NXkvKqu+m6ptpyhD5BCCGsp6efLk2fZSLh\nzPmJCJmD18b8LmbXj2m1zTXFeXnZKTdv5fVpOTgZCDrjB5kjI4VVV5d+/ka348oQM1xtc40E\nX6Jdp1otfayEy4/151/+nVVLMghebCbbl398xzftkQd2hpkZuNpgWQnFzXVZzEqShkLrpRN3\n5H2dNQg752YBLXzgQHPrpHvS/jhd7/TGJicVnF220xG8HKwr9+ddMV8lVDY3sLPTGInXb2S0\n0E3sLNSEbSefyzgafZTB5csY+EcudxTctlCUzHzc1Plakf9aj7umvyvnVGzMN0nVHW21FWWc\nnMSkIrpToLXKgwaDUBXNvgfHOGJV1JVzcueu0wX3eMOtFYUVPMfwYJPJT/oJVQ4SnKPt0Wcb\njHzddWVm/IqLz0/sLoNlfobixkYXHtaRdmT7qYHw2EPbX41aF+GtN1ZbXFzOZqZWKLj7WyhN\nZI4AB3pzKaf87pBx1PaPVhjibWxjsOD4x4du1Tfnp5XJuPqNZw46vT0zm+K3OsgSYxyKuaq7\n5fAmWxmEEFnZhFp2qaCT2t9YkJbClgl9dZ2dCuSNx4HhRj/71/fXR0RErNtyoqB38q/47cn7\nNm76KndgYY7ssRC0nAH2j/98ceuZgs4xDMPG7lUk/rD1xVUREas27bt2dxjDMIzXlvrZa5ER\nERFRH54pxWMFUw0UnXznHx+cYnHHMIw/1Jj6/XtRERGRb5+uHBv/AKEq4nPTPn/jxR0Xq4dm\n+RChKsL4dX+9/48PfuH08jEM4/U2Nt+b8gFilYNhGIY1/P6viIiIiIg1b3+d1cWf9mtxFY3U\nJ/z8d+ngUzzQR9cZtyvqrR/YwyI/GmtJOfLG6oiIVa8ey+6f9GE+f3rx+MCv/uWtiHH/iPm7\n9r7gx/eHhvgYxmf/sDni9dNVIp9vPL/lvd8LihNvJBa1jS7IEUsEHI3cydm+uH93lJn0SM2V\nfdu+uF03NP7zwarrn0b/OLg29k0XfM9vnoyQ5WBV57+M09wUvdFRhYIQoiiZB7/x6Re7I82k\nO1nHt8Weq7qPhrvqW3r5MhYb9u7baIW/CqbiV5796prS8zs2u6lTECJJ6/q/deD9IGV+U2JS\nGSb4CHEqEje2wetrrS4vq27t4wn+TZyKEEIjmefP9wa/8aKNAgkhRFbQ1VZC2HBHDbuwqKJ1\ngI8IVo6ApqYWCSmoq1Ob4j+N/ja7GxP53YPRmmkVSekveXk1bkZrxpriv/kuuZU36YdNidfy\nlf2X2oqOwFC0Aj448K7HIqz99jdn2GMivyGRcDeCO45kEr7SloKQtL6h5mDRqZh9l+6OIIRo\n0tIkhLorKzvQyNAQX/hx7F53j6ySkV3w8mB7DXgn5bHh4pGKEE3Dwd9RuoqVV1GaFX8ztbC0\nOCPu9x8vVhu/tGtruBHt4X8AX4hXTvW1r/8a9H8j0kpkyTySrLZLgLNUWXpOSU5mrWrg2jVh\nzjYekUstCNH0l1z87+V73i9vcFAW/oimK9d6PYmNmYQtsZRBCEmpOfg5EaCi7qwvd3yWLL9+\nzxbfBw/ihu/e/v7A3v/+dDk+If7q5bi8eyp2jkYqmsSoCCGEUE3Cj9dHPF4OMR+PUIOV17/Y\ntffr8/FJSbeuXkksG9VzWxy6lECXHEIIUe5XJiSUWr2+y6szLZOVyuo2fvBsZagh7Uoce8gE\n7/mp89aRvWfT0jPqtTx9HkyVrEw8nVKp7rHGU2fSzSpJzthVvz0phVOFLNd56eA1ZoiSMZBr\nup5WPWC2apNJa2YOU+TZCrW78EpaeU0dzyFA8PiuK+WHk+0+r68ww0sYJCp8BQ6EkJSqddAy\nH305MhnxxpCMtv2yze++FW6rTNAHZgQrpznzfGKJgkukp/bkoS8pFRtfe35Bai47p1LWZ4Wv\nBRHW70EIIV757Z/TqxTtIv0NH9yWkPh1qdcKx2xCw2wVBT8tsN1GAAAX0klEQVShqWqp4L4i\nKq8tKzmvvJA9bBvorEFDCA2V/Raz++qY2+rnVi12MZTuri2v5GQwatX9A0wWEaEihBBC9wqv\nxefTHAXX3Ej1+Zjdt2SXvvzyuqVuRjRuObukIDWDa7Ik3FWHGOWMo7Zk/53dYRm17U1PXiFD\nNHP0dfFtw3wd8Z+fZE1dTHpymEWcDNHMgdUxrudyOhWDgi3lJscKmo7+SMa1PL5NZLA5Hu+m\npqHqqfcm32JX9dlu3bu4J4PxIHNQdHTJeUl5VUUp6ZV9A9zcC99f5kXFvGqPt3mvxIO7wIEQ\nItEWGdq4+QYuXhzo62ZrNLGwLFERqRzZnqJLqSyuYtASy6nbAlBVHdy17iYzCgq7DML9DQjS\n+pPJjWk3CkuapfyX2ykKfzpQGn+1kOa+bimebzCnoSyy8jTvy0pmsTLK5dwDLGVLfth5Uev9\nox+ucDQxNDK3914WaNSWlV5WUdihH4bnWYeTKQ5VXE5Mb1cPCTaVufvHrtP0d45+uMxKV1vX\nyNpziZ9+W04mpzj/nmmYty5BrjmEEELS/SV/3+Eou6z3d3L0sxFmDnupmwf33qYtWeerh/9q\nSNI6br6mUzKHogavKI7FKSzhOQQ6qk1+uCDVnPVnhfbqKHdNYnTMJFVdLP9aXnULzf/df4Vr\nNjzIHCqqNt62UvUcTnkFh101ZPFC9AfLidLm4RoeAwdYMDQd6ea4O6y8Cin3xTbKU5ORjKGD\nSk0cI2tAN5Iwy4coGSndazaOetFbTaQDbso8l1CmHbgxwFikwRweHqZScf50lq7l4q5em8rI\nz2Q1a0pV35F7PnqlgfA0kWT13a3uM24VlWPGa/2NcDqKNhVZW+leYnxqdq2Cr27JqUK79zY7\nCcMuWd7Awx7LjMstHtKLDDQmyDUn0JB2LW/YInSJpez4a2uMTFZyHGPQb8/eKML0XSRpHTdf\nk24Ws7hkInPIm1vSC5JyKoqYbMza215DOJjBb7r+w2/dgf982W4RMfIGQkjOQLb2OrO8jmcV\nucTHz1M0c2hq2wSujFyxJGzdC+uDrMS/fQ4exbzdbY81xX/5+fW6kfn6+0+ZhJUjlrTXptfd\n5O+X/brvv3e42LRfKwWs9FfEmpqaF+DQHpO83zs7oqwURH+E3bvXg2RVVEXmXfYUHN/67ulC\n/J9fkmbIR7EvmNM60r44wbJyt5/SDlLNQ4NNEK+///7CHN7joNpueM1febDo5O6vs3sXLVKe\n/FuK0coVDiReR/u9hTm6x6WhrUVBLc0tgn/J2Uauc1dCGMZryUtid0//ZuETNtjA/Osco09Z\nk46wjowvoj9LbuWR9SNjY1YbSw+Wnt353r7fM+r6eAjrq4o/euCC1D8+XGeItxFcrJ359bbt\nn59jVPXwpv1S1nvVEnXUlxbHHEAUreCtn2zxUesXziEl0ZU01BUI8XyIIOZrhKM37Zv9ZxhZ\nRXzPFY7KhMm7YklYObORMXYx6c1KLeRkZbZoe3gaTV5Yj4TVplzrst5ArIcRU/QV37iQw3Nd\nHTE+k3R8G45t7y/RJMKdJ1XV3sugLavK8PUtawzlpl6No9XJl+pMnoty1ViQg3ssdAMns/7s\nlLyGe7yuYc3gIFNZ0aqoraw/8xateMmHULubkceqk+LySdarg0ylUF/ByZ1f1QVu+6dtU+bU\nOaS4xWu+858dX3I0A8KX+Ltaq481V9W31GZl1Gt5+liauQa6KLeXFJWUFaTd/PuvixcuJtYb\nPB/78UrczYXvzjy248jtu9y7xenx1xLyW+5Lq+vrqUg/GBbU0OWxruVyelWWLbGUIcsbewvH\nOeQ9Ai2V8BafCG6+Agfd0NWOdt9o3T+8VKgYhuH37ai5kbByZkfXdvXQa83K4HDS0ypp1m42\nasJGBGtL/uX8/aC3V5lPneJBJL2cuCv5ZPe1YdbyhNz0CyFpPfdlod4mitNaQ6z11qk/ekP+\n70VrRUJdojRNF0/91qyMu/daCgq69d1c9SdWGsW67vx0qtb9rTdd1YjV9tPbWBdYLXp+EaZ1\nJ3ceLfOI2bfRwWji2UoqW8ZvGa7P0WjZT9s/a1x+eP/zTjpqatrGdr5L/bXbc1klpYJnKxYG\nFj7LV/hZGegamtt7hr30zhsR9njcw4yK9RQxc5qHEVlWjtbXUlmUHn/tdmHriIyGnq6yNBkh\nJG9Aq76eXtAq57fCTgmh8cxR2GG+fIUD3tdiJ5z5m8MhpWbjaqlMHW2MPxh7pt/Gx2IRke5P\nppGwcmZHkjPwCrAc4bAK2KykZHafrIa25iJqT2Xi8cPn7j+3/Q1nYuf+4crEy3lDtmHhdoiI\naQMhhBCZQhnvrbpLs+9StdRlyOh+E+Pbg2eG1+182514J4gkq+/lbzVSmsOpLklLZrWN0cj3\nO+tLUn/58s+hyJ3v+qkS7fsmNVB2PZlDluq9cbbcI2biDViahoOfDXlAO3y9lzqeO7Ph9JOH\n4pU2bIs0Ex4lRd7Iy9eQm8nklGUK5nMo0JW0jS1tbKzN9FXwOhdeuCVv06Ciw9oNi7VHW+rr\n6qqKmHHXEgtbR2U09HU1zdS6EhJz7lKdIlzUSQghsryx7/IQe1yfIIKa70mj/MakE78nF2em\nVyi6+hK/k5awcmZBktZ2CllsK91dV5Kbybh97a8/L1zN7DTfKAmztUkN6ReyqpUMjNk/fEHI\ntPEAr+6vPdFfnb14OT7p1l+/XSxSfi52ewTuhrXniCSt5RQSbKcw1FrNZmWmpaaksGrJzq9s\n3xKKu4Wx54LSlHklt7gBhe6avN4GTcPO3Qbvt86duZcuF3Qa+K2c9AiZJKPnbo/lJBQ31Wdl\n1Gt7+hgRYSuz8czBTM3Nr5UO3f7pe+GWqlLDHQ3VVWUFzLhrSSX9Rr7WA9lZhaOWEV46gkZd\nsgexFw4Jw+Z7AtNg2R97dv9RNqTk9NaBneGGUxpD/sgIRqMRqOeevRxEwIoeYrS3sbKqeVBK\nzcTCWGW2vaiIo+bXN9/7s40ka/n8HiKnDYQQGm7JS7hxp7hpWEbPxjc01EOX0NUI8QY62nsx\neXV1eRyO0s/VcOXFn/NtXooi4nwnfsE3L+2K57n++5tdS1Unf+lHC797bVdcD4YhlYDtxz7y\nVlqgQ3xEow3xh3Z+m3NPye3tg9HL9alotKsi43ZcfEIau20Yo0vT7w9jnh/8FBMkv9BHKsme\nwmuxUmr2vvaInZbDnjYwwG+9c/TDQwwFDx/ibPo7SzmImBU9BIWuqK6tq6OpLEOViLSBEFJW\nuJeaxgvfTfS0gRCiKmhbunj7B/p7O1vpKuL8tnnuyDRZeQU5GrG/QlRVazdbvA9liEHS1B7L\njs8ryeNgDoEOk9bboKgPlyTQl65VpZiuWedGmPrGxznS03LSmNWL3HzNleVUDW09QyJWBllr\n0Ia4jc3dfEW7wCWWig//W+BxPZ11OMR00lgb49QvKTXNvRpeIVZKxOnNxGcOolb0rFG2XxwW\n4KJD9LQBwHwhKVlb0wqScysL0zkkOx+7B8GCV5X8W5nWq++/tdyeMGlDYFrmUCIjRJJS0DZz\n8gtbtXzx4mBXbWJVRDhPPnBgvZUJZ38+8+e11MK6QTl9Y01ZMkIzd9IkeXNfZ23dgI3LTfF8\nmmeqSEzmIEhFgESjEXisHoAnit9dfPnH706evZFdM6xmbq4uTUIIkRdZe1kM5qYXVxWkZDTJ\nWdiZqdJJY9zM7w7/rb7x/4J1CTlTaMbMgRBCiCwtLweN9nx7woGD35p8+ONDCS0kcn9DKbuY\nlRSf2a7u5GqkQEYzZw6qspExrmOy+IrEZA7cVwQAAEIY987hrf9hDshRu2s5xazElKpFzt7m\niygIIbqWS6CTTF1+fnkp69a1m6mMhD/PMSiRsR8t0cLZJDWso64RW6Q0Swjic4uL7qlqKZLF\nZw4w755o4OBV/brjq+7Vh4+8FxUesSrIFKvOL6oqy2CUy7v5WSpR0OTM0WgQ4mcg/dA/uqAe\nUhHRygEAAFFYw4XYY23hBw9tiYpYGWTYz2YVcUTvoGiqVoHL/I3kEYaR6GrWy157/80gvO0D\ng7Uzj8XsOlVvstpbd+YgxOcyjkYf/DmhSt3T30SBJJI58njuKxxVIHI8JU8ycIxlnf68K/zA\nZgcZhBCiyOs6BnppteSwKqpzs7nGwd56dIQmMkdNp/lz63E/4WgOFRGpHAAAmKT0/Oc1Abvf\ndJFDCFHk9N38bUYK0/JLskRHbaUU9a3d/IKCg3xdLcYfkeMH1s48FvN98RjWw1Vwi3RRm/4J\nPpdxNPoog8uXMfCPXC7Yb17wbEWdrxm63kcD2u2n5skEjtHG+M8OXu7mcVu0QiPtHrwmRVE0\n9vbSusvIqCgr7DIO9dYTjHhJqdkHBeF7wtGjVESAcgAAQNRoY/zhA5dGKD3VKsFr7IVNHE3D\nyW+GzIFTgrTBXb53t3PjlZQW3aXLrKe8g/wgbVhs2Dt5QRSKkomjFbTbT9XjpVWsnXniq5uN\n/PF/8uozEvJLU05dKW0oLOqe8j/QCPpga4QWqTflclL3tD+EG5JXEQAAiBJt5fhNOXeKy1K/\n/J3FrasbnvQxObtX9u9eZ0bvKTi+8wCOd6wUpo1da0xUraz0UHVp6bRNC4e76lt6Z0gbYEE8\nRuAQnOZaPUvh3gYU0/X79r1gJYMQj332ZHrPlP9A2u75dU4UrLa27n892nkieRUBAICoKa0c\n2Wj1ngOvOspj2FDGmZ+LBiZ/WDRz/Jc5tf3DA9G0IY0QMrCyluUVsQqmxiNZq437D+4/CGkD\nHx71kcrU0zxhYvoku6k6h0N2DrBTEX3rkN5fcjV52DVquR3+FlWRvIoAAEDUjK0cVcXKz1m2\nnJlXxc7kUBz8bFVFHy8Inq1UdFhtiMLd7LQZyiEp3y+/nJKPmUZO21WYpqqlgrMCnlmPFDjE\n9c0CEz10QXkeo4hn4WGvKS1Y+QrrTvvpRJ7RK/9ejru3OCSvIgAAEDVLK0dRFmSObE5OGnum\nzBESjMPZabzKC99k2m2NnVQOVWOs/DIjn28e6auH52knz7a5Bw7xV+3IyAiFIpjNPN5D55cX\nJicV3KPI0XmdJfHfHrnIWx37UagOzpZakryKAABA1ENbudkzBy6RVZ2WBlsrT2l9pdRHyy8x\nC0b0l/sTdftCyTfHwCH+qh2uOrd7xy3ZEF994RsovvaInVbc1FpVVNE2ODiq7LNpyyZfvC0Z\nK3kVAQCAqDm2cgTMHDOhqfMrrqWxmqUDwuBJN07NZdLorFftnn2phm+/6S0n8lNZqxcEm3Dy\n2hv6jEPXBJkp4GxXEcmrCAAARD1SK0cziYzd/5qjwmDZmV2Hkzqf+sE+EQoB60M1sZq/f0nB\n4zRXgOYywtGdeWzHkaRei8jXVtsqiOaT8av23U/edpu2TdnEqEAuJ5tZKuPsZzV1+GshSV5F\nAAAg6tFbOcE4R0On6foNHvibtzEnZHUTmZIbzJzSbrMQb136Qh8OmObhgYOK9RQxc+ru5qXX\nqnr4mo5fu7P1zQLCHrqEhbMeWvIqAgAAUY/VylGUrfwDcThLdO5kjHQHGLfzivMbNQL8TWRh\nIBpnHh44xpedz2BVV7PGr92RWa/anoIThy/y3dx1aSI9dP59l3AXNXwsiit5FQEAgKj/oZVb\ngKN9cshqtmaDzMSispy8PssANy0Y5sCVOU0anXztjnbcOp0l/qo9vvNoldMrG1wFMVlKzd7X\nXmpId+1L/uo4ys2SVxEAAIj6H1o5IqOoOTgqlKSyKQ5LQwg9WiOJSBiGzfGjow3xh3Z+m9ON\nIfXwT79/y2b6mewpOL7zaLVPzN4XrGSe7GHOD8mrCAAARD2brdxgRwdJTU1iypEYj7Dw10Re\nzmnurMy7K/JcUICAV63kVQQAAKKezVZOSlYWxjZw6NGWNp/huaDg2iXsVSt5FQEAgCho5QBO\nPPL29DNcu33EvmolryIAABAFrRzAg0eYw8HnFhePWjvqUtGD54JIxX2lEze1mZhXreRVBAAA\noqCVA/gx1xEOPpdxNPrgzwlV6p7+JgokkbxcT1+xC4dXLa+7NOHP38/+dT21uFPW2EJHbup+\nPoSrCAAAhHjdpQl//nb2wnVG6T0lMwstmRne0YdWDuAKZc+e3e3Mb/8bR3Zy0ZEWs0wKn8s4\nGn2UweXLGPhHLncUvGlEUTLzcVPna0X+az3erlqsK+d07M5ThUPU0bYqDjsv5RZb3ifYUvFB\neUSrCAAAhLCunNOxMd8xmocHWmsryvLvJFaq+weayE1qwaGVA3hDwrpvxmz+tpgnZbBix8G3\nZnhF+8FVa7Fh776NVrILcZiPoq/w+Mf/qQnYHv28rRKJ3513ev8nl6ukQnad2eImGOUgXEUA\nACA0UvHrh/sLXbdsf8lNjTLWlvHDgf/E1SlEHPrpDVvhZ6CVAzhERg1378oYGKqO1d84FHM8\np2fajI7hrvqWXuJctcP5p//LMPtn7Au2SiSEEFnZ5dWPN5iSBtraBoQfIVZFAAAgxCv7+ch1\njc0xm9zUKAghqqb3O1vCNdHgwIBo4w2tHMAhygcRzmd7Ar+I9m/LyOAUpef2mvq6Tnq2IqXm\n4Odk4xG51IIIV+1Q2qmjKVpRHwQbCB9okuRHqq9WqvoZduQkZ5W0jSoYOvgtJk5FAAAgxMv7\n7cgN+sp/h5sJV+0mkerTrvFcVuu0ZzLyqrswRS11OWlCtdvgGUFtamqS0dZW1Xbb9gk6HH0s\n88ahGDT12YqcuaPZwh3iI+loa+Px71fWjPmaT+ys1p+dlD3AHT7+NW20u2cIQ7+d83334NbF\nZrANCgCAaPrauEOorbUNQwrjbTTWkpxSpdjf+fHHHaMYhiFEN1j23p5/+aoRp90GzwjK/611\nr1ReGWqrSFYw9vHUrBMzzkEUssOV11OKOEUtmk4uRkpUXkfeLweOFZlu3r1v66vPrw1zkGso\nyK8qzy6j+y2zUSRgfQCAZxqtv/TKnaLKbu2wAGM6Qmi44sz+L0v1wzb/e9tH77yw1F6+pSiv\ntDi7SjFwiYX8Qh8sAJNQvr4Y56CupiBPRgjNljmGBoekpPC/WCxF21SjOSOTU5J+82rc7Wvn\nzsXVGr31n+2henQSQmQZDStfJ6mCxILKZrpXhJPyQh8tAAA8EpKm+khph8uLUY4qUgihbsaP\nf2H/OPDRCksVOplEldO09nORyk/IK2tXDFxup7DQRwuAKMqevZ8J0obAzJmjp+D4jr05WqEe\n2tRZ/hYukGQNfUK8TdSUNY1svFyU6mosXv3AT+PBWAZlkaVKy7WUWm2/dR6aC3icAADwGEjK\n9sGBVirjd38yxr5L3XVlREZryUpm0jV/pzfrB6x2VVuYQwRgZjNMZKBoB2/7ZIvXxHsr98bX\n2//oNRf69A/jkpyhZ/jzL21cZ9Fb3orI5ClPTsgUKplmbW26MMcGAADzC0MYzczUYKEPA4Ap\nZp45KZo53n6LsOvtj46Oom7Gjax+kZ9hnUlXmAoro/zlFuywAABgvmCtCfGFGiuf84PXUwDe\niH1Vg6Id/PG2SE1ENYgkZtpACBm7uaqgrjuf7zqR0TaCEELDDXe+3PXz/ajtGy3xPx0FAAAe\nbrD4/Hd/ZDcPYwjx+2qTju07izZs22hFW+jjAmAq8Zu3ScTOxYMlv8fuO1s5iEjSKnrq5PY2\nssur0e+vMCHKwyEAAJjNSPG3b8fEdSBEkVWSHesdVvZ5Lfq9FcbQxAEcEhM4JCJtCIy2F966\nmlzUOqKgZ+u7bImzFnwTAQCSY5RbeOtmWnn7qKyOlXdIsKMmjG0AnJo5cAwW/3ai1PXNKMKn\nDQAAAADggfhHKgAAAAAATwis7w0AAACAeQeBAwAAAADzDgIHAAAAAOYdBA4AAAAAzDsIHAAA\nAACYdxA4AAAAADDvIHAAAAAAYN5B4AAAAADAvIPAAQAAAIB5B4EDAAAAAPMOAgcAAAAA5h0E\nDgAAAADMOwgcAAAAAJh3EDgAAAAAMO8gcAAAAABg3kHgAAAAAMC8g8ABAAAAgHkHgQMAAAAA\n8w4CBwAAAADmHQQOAAAAAMw7CBwAAAAAmHcQOAAAAAAw7yBwAAAAAGDeQeAAAAAAwLyDwAEA\nAACAeQeBAwAAAADzDgIHAAAAAOYdBA4AAAAAzDsIHAAAAACYd/8P03cDGv3KyNkAAAAASUVO\nRK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 240,
       "width": 360
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "str_title <- \"ncells_rna\"\n",
    "\n",
    "gg <- print_ggpubr(rna,\n",
    "            x = col_cluster_types, y = \"cnt\",\n",
    "            color = col_cluster_types,\n",
    "            fill = col_cluster_types,\n",
    "            order_x = names(nv_colors),\n",
    "            facet.by = NULL, facet.ncol = 1,\n",
    "            plot_type = \"barplot\",\n",
    "            color_manual = nv_colors, fill_manual = nv_colors,\n",
    "            ylimits = c(0, 3000),\n",
    "            ybreaks = pretty(c(0, 3000), n = 3),\n",
    "            yexpand = c(0.1, 0.1),\n",
    "            yintercept = NULL,\n",
    "            xlim = NULL, ylim = NULL,\n",
    "            xlab = \"\", ylab = \"# of cells\",\n",
    "            angle = 45, font_size = 12,\n",
    "            width = 6, height = 4,\n",
    "            str_condition_plot = sprintf(\"cluster_type_%s\", str_title),\n",
    "            n_log = 0)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41e9cef7",
   "metadata": {},
   "source": [
    "# nCount_RNA"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51317059",
   "metadata": {},
   "source": [
    "## barplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fbe84553",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5gHzK3z+Fn3/Zy7lHbJRKFRUYq7mhXR0aF0IiMtnXu0h8iVfR6QDgAAADiGpeBQ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\nRH5oJctr1AAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 240,
       "width": 360
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "str_title <- \"mean_of_ncounts\"\n",
    "\n",
    "gg <- print_ggpubr(rna,\n",
    "            x = col_cluster_types, y = \"nCount_RNA\",\n",
    "            color = col_cluster_types, \n",
    "            fill = col_cluster_types, \n",
    "            order_x = names(nv_colors),\n",
    "            facet.by = NULL, facet.ncol = 1,\n",
    "            plot_type = \"barplot\",\n",
    "            color_manual = nv_colors, fill_manual = nv_colors,\n",
    "            ylimits = c(0, 22500),\n",
    "            ybreaks = pretty(c(0, 20000), n = 3),\n",
    "            yexpand = c(0.1, 0.1),\n",
    "            yintercept = 5000,\n",
    "            xlim = NULL, ylim = NULL,\n",
    "            xlab = \"\", ylab = \"mean of nCount_RNA\",\n",
    "            angle = 45, font_size = 12,\n",
    "            width = 6, height = 4,\n",
    "            str_condition_plot = sprintf(\"cluster_type_%s\", str_title),\n",
    "            n_log = 0)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b66020e2",
   "metadata": {},
   "source": [
    "# nFeature_RNA"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0aaab4b",
   "metadata": {},
   "source": [
    "## barplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "74e9f230",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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CAAdtxE9K9xfht+KPiB/VgAbHetKO1c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\nDggEAoFAILPO/wPdAJGgL1VA4QAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 240,
       "width": 360
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "str_title <- \"mean_of_nfeatures\"\n",
    "\n",
    "gg <- print_ggpubr(rna,\n",
    "            x = col_cluster_types, y = \"nFeature_RNA\",\n",
    "            color = col_cluster_types, \n",
    "            fill = col_cluster_types, \n",
    "            order_x = names(nv_colors),\n",
    "            facet.by = NULL, facet.ncol = 1,\n",
    "            plot_type = \"barplot\",\n",
    "            color_manual = nv_colors, fill_manual = nv_colors,\n",
    "            ylimits = c(0, 4250),\n",
    "            ybreaks = pretty(c(0, 4000), n = 3),\n",
    "            yexpand = c(0.1, 0.1),\n",
    "            yintercept = 2000,\n",
    "            xlim = NULL, ylim = NULL,\n",
    "            xlab = \"\", ylab = \"mean of nFeature_RNA\",\n",
    "            angle = 45, font_size = 12,\n",
    "            width = 6, height = 4,\n",
    "            str_condition_plot = sprintf(\"cluster_type_%s\", str_title),\n",
    "            n_log = 0)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4e8ce16d",
   "metadata": {},
   "outputs": [],
   "source": [
    "writeLines(capture.output(sessionInfo()), \"txt/sessionInfo.txt\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3527da0",
   "metadata": {},
   "source": [
    "# reference"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20fd74ca",
   "metadata": {},
   "source": [
    "https://satijalab.org/seurat/\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "95c8a0b2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R 4.1.2",
   "language": "R",
   "name": "r4.1.2"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "4.1.2"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}