ColabHDStIM.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/github/rohitfarmer/ColabHDStIM/blob/main/ColabHDStIM.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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      "cell_type": "markdown",
      "source": [
        "# HDStIM: High Dimensional Stimulation Immune Mapping ('HDStIM')\n",
        "\n",
        "<img src=\"https://raw.githubusercontent.com/niaid/HDStIM/master/man/figures/sticker.png\" height=\"200\" align=\"right\" style=\"height:200px\">\n",
        "\n",
        "A method for identifying responses to experimental stimulation in mass or flow cytometry that uses high dimensional analysis of measured parameters and can be performed with an end-to-end unsupervised approach.\n",
        "\n",
        "**Main HDStIM resources:** \n",
        "Documentation website: [https://niaid.github.io/HDStIM/](https://niaid.github.io/HDStIM/) \n",
        "Source code: [https://github.com/niaid/HDStIM/](https://github.com/niaid/HDStIM/) \n",
        "CRAN page: [https://cran.r-project.org/package=HDStIM](https://cran.r-project.org/package=HDStIM)\n",
        "\n",
        "Note: This notebook is an attempt to make it easier to use HDStIM. However, since it runs on Google Colab, it has its limitations. For example, uploading a large dataset is very slow and may not be straightforward. Also, the marker ranking function that utilizes multiple cores may be orders of magnitude slower than on a local machine with more than two cores. See the more information section at the bottom of the notebook on how to run this notebook locally (recommended).\n",
        "\n",
        "To report bugs related to ColabHDstIM: [https://github.com/rohitfarmer/ColabHDStIM/issues](https://github.com/rohitfarmer/ColabHDStIM/issues)"
      ],
      "metadata": {
        "id": "RDGvrPQThusl"
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    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "OeBFuKJaI-4T",
        "cellView": "form"
      },
      "outputs": [],
      "source": [
        "#@title 1 Install and load R packages\n",
        "if(!require(HDStIM)) suppressMessages(install.packages(\"HDStIM\")) \n",
        "if(!require(tidyverse)) suppressMessages(install.packages(\"tidyverse\"))   \n",
        "if(!require(googledrive)) suppressMessages(install.packages(\"googledrive\"))    \n",
        "if(!require(DT)) suppressMessages(install.packages(\"DT\"))\n",
        "suppressMessages(library(tidyverse))\n",
        "suppressMessages(library(googledrive))\n",
        "suppressMessages(library(HDStIM))\n",
        "suppressMessages(library(IRdisplay))\n",
        "suppressMessages(library(DT))"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2 Example data\n",
        "HDStIM package comes with an example dataset used for unit testing during CRAN submission. It can also be used as an example of how to format the main single-cell marker expression data frame, and other parameters passed to the main HDStIM function."
      ],
      "metadata": {
        "id": "TB5cKNX0h1k6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@title 2.1 An example input data frame/tibble for HDStIM\n",
        "saveRDS(chi11$expr_data, file.path(\"hdstim-example-data.rds\"))\n",
        "head(chi11$expr_data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "-h01aBm0Ldo1",
        "outputId": "e00e3475-3c61-42d3-9790-634708aa13ce",
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      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table class=\"dataframe\">\n",
              "<caption>A tibble: 6 × 37</caption>\n",
              "<thead>\n",
              "\t<tr><th scope=col>cluster_id</th><th scope=col>sample_id</th><th scope=col>condition</th><th scope=col>patient_id</th><th scope=col>stim_type</th><th scope=col>merging1</th><th scope=col>CD45</th><th scope=col>CD7</th><th scope=col>CD19</th><th scope=col>pPLCg2</th><th scope=col>⋯</th><th scope=col>CD16</th><th scope=col>CD38</th><th scope=col>CD8</th><th scope=col>CD45RA</th><th scope=col>CD3</th><th scope=col>pERK1_2</th><th scope=col>HLA_DR</th><th scope=col>pS6</th><th scope=col>CD127</th><th scope=col>ncount</th></tr>\n",
              "\t<tr><th scope=col>&lt;dbl&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;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;dbl&gt;</th><th scope=col>⋯</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><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><td>79</td><td>CHI-011_1_2_A </td><td>CHI</td><td>CHI-011</td><td>A</td><td>CD11c CD14 CD38</td><td>3.110460</td><td>2.601485</td><td>0.05114502</td><td>0.7158504</td><td>⋯</td><td>0.09500555</td><td>2.3173963</td><td>0.2970178</td><td>0.1930449</td><td>1.78627827</td><td>0.04236087</td><td>2.7674600</td><td>0.0000000</td><td>0.3288540</td><td>1659</td></tr>\n",
              "\t<tr><td>70</td><td>CHI-011_1_1_A </td><td>CHI</td><td>CHI-011</td><td>A</td><td>CD11c CD14 CD38</td><td>3.756624</td><td>1.960488</td><td>0.00000000</td><td>1.5146819</td><td>⋯</td><td>0.00000000</td><td>0.9883232</td><td>0.1512892</td><td>1.1411799</td><td>1.79931812</td><td>0.03323656</td><td>3.8737299</td><td>0.0000000</td><td>0.2692525</td><td>1659</td></tr>\n",
              "\t<tr><td>69</td><td>CHI-011_3_10_A</td><td>CHI</td><td>CHI-011</td><td>A</td><td>CD11c CD14 CD38</td><td>2.911722</td><td>1.310564</td><td>0.00000000</td><td>0.0000000</td><td>⋯</td><td>0.00000000</td><td>1.7305496</td><td>0.3464784</td><td>0.0000000</td><td>1.59753981</td><td>0.00000000</td><td>1.9176080</td><td>0.0000000</td><td>0.0000000</td><td>1659</td></tr>\n",
              "\t<tr><td>69</td><td>CHI-011_3_10_A</td><td>CHI</td><td>CHI-011</td><td>A</td><td>CD11c CD14 CD38</td><td>2.919472</td><td>1.376643</td><td>0.00000000</td><td>0.0000000</td><td>⋯</td><td>0.00000000</td><td>2.5056298</td><td>0.0000000</td><td>0.0000000</td><td>0.02306957</td><td>0.00000000</td><td>0.5110521</td><td>0.5063124</td><td>0.0000000</td><td>1659</td></tr>\n",
              "\t<tr><td>79</td><td>CHI-011_4_12_A</td><td>CHI</td><td>CHI-011</td><td>A</td><td>CD11c CD14 CD38</td><td>2.862431</td><td>1.731462</td><td>0.00000000</td><td>0.6692741</td><td>⋯</td><td>0.93988490</td><td>0.4948810</td><td>0.0000000</td><td>0.0967478</td><td>0.44313860</td><td>0.00000000</td><td>3.3273032</td><td>0.0000000</td><td>0.6059084</td><td>1659</td></tr>\n",
              "\t<tr><td>70</td><td>CHI-011_2_6_A </td><td>CHI</td><td>CHI-011</td><td>A</td><td>CD11c CD14 CD38</td><td>3.554719</td><td>2.645969</td><td>0.93413488</td><td>2.0074880</td><td>⋯</td><td>0.00000000</td><td>0.7568753</td><td>0.0000000</td><td>0.0639216</td><td>1.42091355</td><td>0.54579525</td><td>4.5526857</td><td>0.2791606</td><td>0.2860811</td><td>1659</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ],
            "text/markdown": "\nA tibble: 6 × 37\n\n| cluster_id &lt;dbl&gt; | sample_id &lt;chr&gt; | condition &lt;chr&gt; | patient_id &lt;chr&gt; | stim_type &lt;chr&gt; | merging1 &lt;chr&gt; | CD45 &lt;dbl&gt; | CD7 &lt;dbl&gt; | CD19 &lt;dbl&gt; | pPLCg2 &lt;dbl&gt; | ⋯ ⋯ | CD16 &lt;dbl&gt; | CD38 &lt;dbl&gt; | CD8 &lt;dbl&gt; | CD45RA &lt;dbl&gt; | CD3 &lt;dbl&gt; | pERK1_2 &lt;dbl&gt; | HLA_DR &lt;dbl&gt; | pS6 &lt;dbl&gt; | CD127 &lt;dbl&gt; | ncount &lt;int&gt; |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 79 | CHI-011_1_2_A  | CHI | CHI-011 | A | CD11c CD14 CD38 | 3.110460 | 2.601485 | 0.05114502 | 0.7158504 | ⋯ | 0.09500555 | 2.3173963 | 0.2970178 | 0.1930449 | 1.78627827 | 0.04236087 | 2.7674600 | 0.0000000 | 0.3288540 | 1659 |\n| 70 | CHI-011_1_1_A  | CHI | CHI-011 | A | CD11c CD14 CD38 | 3.756624 | 1.960488 | 0.00000000 | 1.5146819 | ⋯ | 0.00000000 | 0.9883232 | 0.1512892 | 1.1411799 | 1.79931812 | 0.03323656 | 3.8737299 | 0.0000000 | 0.2692525 | 1659 |\n| 69 | CHI-011_3_10_A | CHI | CHI-011 | A | CD11c CD14 CD38 | 2.911722 | 1.310564 | 0.00000000 | 0.0000000 | ⋯ | 0.00000000 | 1.7305496 | 0.3464784 | 0.0000000 | 1.59753981 | 0.00000000 | 1.9176080 | 0.0000000 | 0.0000000 | 1659 |\n| 69 | CHI-011_3_10_A | CHI | CHI-011 | A | CD11c CD14 CD38 | 2.919472 | 1.376643 | 0.00000000 | 0.0000000 | ⋯ | 0.00000000 | 2.5056298 | 0.0000000 | 0.0000000 | 0.02306957 | 0.00000000 | 0.5110521 | 0.5063124 | 0.0000000 | 1659 |\n| 79 | CHI-011_4_12_A | CHI | CHI-011 | A | CD11c CD14 CD38 | 2.862431 | 1.731462 | 0.00000000 | 0.6692741 | ⋯ | 0.93988490 | 0.4948810 | 0.0000000 | 0.0967478 | 0.44313860 | 0.00000000 | 3.3273032 | 0.0000000 | 0.6059084 | 1659 |\n| 70 | CHI-011_2_6_A  | CHI | CHI-011 | A | CD11c CD14 CD38 | 3.554719 | 2.645969 | 0.93413488 | 2.0074880 | ⋯ | 0.00000000 | 0.7568753 | 0.0000000 | 0.0639216 | 1.42091355 | 0.54579525 | 4.5526857 | 0.2791606 | 0.2860811 | 1659 |\n\n",
            "text/latex": "A tibble: 6 × 37\n\\begin{tabular}{lllllllllllllllllllll}\n cluster\\_id & sample\\_id & condition & patient\\_id & stim\\_type & merging1 & CD45 & CD7 & CD19 & pPLCg2 & ⋯ & CD16 & CD38 & CD8 & CD45RA & CD3 & pERK1\\_2 & HLA\\_DR & pS6 & CD127 & ncount\\\\\n <dbl> & <chr> & <chr> & <chr> & <chr> & <chr> & <dbl> & <dbl> & <dbl> & <dbl> & ⋯ & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <int>\\\\\n\\hline\n\t 79 & CHI-011\\_1\\_2\\_A  & CHI & CHI-011 & A & CD11c CD14 CD38 & 3.110460 & 2.601485 & 0.05114502 & 0.7158504 & ⋯ & 0.09500555 & 2.3173963 & 0.2970178 & 0.1930449 & 1.78627827 & 0.04236087 & 2.7674600 & 0.0000000 & 0.3288540 & 1659\\\\\n\t 70 & CHI-011\\_1\\_1\\_A  & CHI & CHI-011 & A & CD11c CD14 CD38 & 3.756624 & 1.960488 & 0.00000000 & 1.5146819 & ⋯ & 0.00000000 & 0.9883232 & 0.1512892 & 1.1411799 & 1.79931812 & 0.03323656 & 3.8737299 & 0.0000000 & 0.2692525 & 1659\\\\\n\t 69 & CHI-011\\_3\\_10\\_A & CHI & CHI-011 & A & CD11c CD14 CD38 & 2.911722 & 1.310564 & 0.00000000 & 0.0000000 & ⋯ & 0.00000000 & 1.7305496 & 0.3464784 & 0.0000000 & 1.59753981 & 0.00000000 & 1.9176080 & 0.0000000 & 0.0000000 & 1659\\\\\n\t 69 & CHI-011\\_3\\_10\\_A & CHI & CHI-011 & A & CD11c CD14 CD38 & 2.919472 & 1.376643 & 0.00000000 & 0.0000000 & ⋯ & 0.00000000 & 2.5056298 & 0.0000000 & 0.0000000 & 0.02306957 & 0.00000000 & 0.5110521 & 0.5063124 & 0.0000000 & 1659\\\\\n\t 79 & CHI-011\\_4\\_12\\_A & CHI & CHI-011 & A & CD11c CD14 CD38 & 2.862431 & 1.731462 & 0.00000000 & 0.6692741 & ⋯ & 0.93988490 & 0.4948810 & 0.0000000 & 0.0967478 & 0.44313860 & 0.00000000 & 3.3273032 & 0.0000000 & 0.6059084 & 1659\\\\\n\t 70 & CHI-011\\_2\\_6\\_A  & CHI & CHI-011 & A & CD11c CD14 CD38 & 3.554719 & 2.645969 & 0.93413488 & 2.0074880 & ⋯ & 0.00000000 & 0.7568753 & 0.0000000 & 0.0639216 & 1.42091355 & 0.54579525 & 4.5526857 & 0.2791606 & 0.2860811 & 1659\\\\\n\\end{tabular}\n",
            "text/plain": [
              "  cluster_id sample_id      condition patient_id stim_type merging1       \n",
              "1 79         CHI-011_1_2_A  CHI       CHI-011    A         CD11c CD14 CD38\n",
              "2 70         CHI-011_1_1_A  CHI       CHI-011    A         CD11c CD14 CD38\n",
              "3 69         CHI-011_3_10_A CHI       CHI-011    A         CD11c CD14 CD38\n",
              "4 69         CHI-011_3_10_A CHI       CHI-011    A         CD11c CD14 CD38\n",
              "5 79         CHI-011_4_12_A CHI       CHI-011    A         CD11c CD14 CD38\n",
              "6 70         CHI-011_2_6_A  CHI       CHI-011    A         CD11c CD14 CD38\n",
              "  CD45     CD7      CD19       pPLCg2    ⋯ CD16       CD38      CD8      \n",
              "1 3.110460 2.601485 0.05114502 0.7158504 ⋯ 0.09500555 2.3173963 0.2970178\n",
              "2 3.756624 1.960488 0.00000000 1.5146819 ⋯ 0.00000000 0.9883232 0.1512892\n",
              "3 2.911722 1.310564 0.00000000 0.0000000 ⋯ 0.00000000 1.7305496 0.3464784\n",
              "4 2.919472 1.376643 0.00000000 0.0000000 ⋯ 0.00000000 2.5056298 0.0000000\n",
              "5 2.862431 1.731462 0.00000000 0.6692741 ⋯ 0.93988490 0.4948810 0.0000000\n",
              "6 3.554719 2.645969 0.93413488 2.0074880 ⋯ 0.00000000 0.7568753 0.0000000\n",
              "  CD45RA    CD3        pERK1_2    HLA_DR    pS6       CD127     ncount\n",
              "1 0.1930449 1.78627827 0.04236087 2.7674600 0.0000000 0.3288540 1659  \n",
              "2 1.1411799 1.79931812 0.03323656 3.8737299 0.0000000 0.2692525 1659  \n",
              "3 0.0000000 1.59753981 0.00000000 1.9176080 0.0000000 0.0000000 1659  \n",
              "4 0.0000000 0.02306957 0.00000000 0.5110521 0.5063124 0.0000000 1659  \n",
              "5 0.0967478 0.44313860 0.00000000 3.3273032 0.0000000 0.6059084 1659  \n",
              "6 0.0639216 1.42091355 0.54579525 4.5526857 0.2791606 0.2860811 1659  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title 2.2 Examples of other input parameters for HDStIM\n",
        "cat(\"State markers:\\n\")\n",
        "cat(paste0(chi11$state_markers, colapse = \",\"))\n",
        "cat(\"\\n\\nCluster col:\\n\")\n",
        "cat(chi11$cluster_col)\n",
        "cat(\"\\n\\nStim labels:\\n\")\n",
        "cat(paste0(chi11$stim_label, colapse = \",\"))\n",
        "cat(\"\\n\\nUnstim labels:\\n\")\n",
        "cat(chi11$unstim_label)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "cellView": "form",
        "id": "oh5pWxAbiBPZ",
        "outputId": "0adadf1e-e87f-4553-ff29-35f9d6d8de0c"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "State markers:\n",
            "pPLCg2, pSTAT5, AKT, pSTAT1, pP38, pSTAT3, IkBa, pCREB, pERK1_2, pS6,\n",
            "\n",
            "Cluster col:\n",
            "merging1\n",
            "\n",
            "Stim labels:\n",
            "A, T, L, G,\n",
            "\n",
            "Unstim labels:\n",
            "U"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "fVkojPnViA6S"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3 Upload and read data for the analysis\n",
        "There are multiple ways through which you can upload data into the Colab environment to use with HDStIM.\n",
        "\n",
        "1. **Preferred:** uploading data to your/temporary Google Drive and downloading the data using the file id in the share link (see more information section at the bottom).\n",
        "2. **Very very slow:** Using the files tab on the left-hand side and the built-in colab file upload option. However, it is very slow and might only be *useful for small datasets (< 1GB)*.\n",
        "\n",
        "*Note: if you are using a local runtime, you can read the data directly from your local path.*\n",
        " \n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "fl71p0yyiHw2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@title 3.1 Upload data from a Google Drive\n",
        "file_id <- \"NULL\" #@param {type:\"string\"}\n",
        "overwrite <- \"FALSE\" #@param {type:\"string\"}\n",
        "if(file_id != \"NULL\"){\n",
        "  drive_deauth()\n",
        "  drive_user()\n",
        "  public_file <-  drive_get(as_id(file_id))\n",
        "  drive_download(public_file, overwrite = overwrite)\n",
        "}\n"
      ],
      "metadata": {
        "cellView": "form",
        "id": "xs773qFjPmAP"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title 3.2 Read the uploaded data for HDStIM\n",
        "\n",
        "file_type = \"RDS\" #@param [\"TSV\", \"RDS\"]\n",
        "path_to_file = \"hdstim-example-data.rds\" #@param {type:\"string\"}\n",
        "if(file_type == \"TSV\"){\n",
        "  data_in <- read_tsv(file.path(path_to_file), \n",
        "                      show_col_types = FALSE)\n",
        "} else if(file_type == \"RDS\"){\n",
        "    data_in <- suppressMessages(readRDS(file.path(path_to_file)) %>%\n",
        "    as_tibble())\n",
        "}"
      ],
      "metadata": {
        "id": "DYkuX77rRLl0",
        "cellView": "form"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title 3.4 Preview input data (optional)\n",
        "head(data_in)"
      ],
      "metadata": {
        "cellView": "form",
        "id": "7SeahrWERP2w",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "outputId": "6182759b-d5cf-4eb8-8d0c-522ddf7b46aa"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table class=\"dataframe\">\n",
              "<caption>A tibble: 6 × 37</caption>\n",
              "<thead>\n",
              "\t<tr><th scope=col>cluster_id</th><th scope=col>sample_id</th><th scope=col>condition</th><th scope=col>patient_id</th><th scope=col>stim_type</th><th scope=col>merging1</th><th scope=col>CD45</th><th scope=col>CD7</th><th scope=col>CD19</th><th scope=col>pPLCg2</th><th scope=col>⋯</th><th scope=col>CD16</th><th scope=col>CD38</th><th scope=col>CD8</th><th scope=col>CD45RA</th><th scope=col>CD3</th><th scope=col>pERK1_2</th><th scope=col>HLA_DR</th><th scope=col>pS6</th><th scope=col>CD127</th><th scope=col>ncount</th></tr>\n",
              "\t<tr><th scope=col>&lt;dbl&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;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;dbl&gt;</th><th scope=col>⋯</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><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><td>79</td><td>CHI-011_1_2_A </td><td>CHI</td><td>CHI-011</td><td>A</td><td>CD11c CD14 CD38</td><td>3.110460</td><td>2.601485</td><td>0.05114502</td><td>0.7158504</td><td>⋯</td><td>0.09500555</td><td>2.3173963</td><td>0.2970178</td><td>0.1930449</td><td>1.78627827</td><td>0.04236087</td><td>2.7674600</td><td>0.0000000</td><td>0.3288540</td><td>1659</td></tr>\n",
              "\t<tr><td>70</td><td>CHI-011_1_1_A </td><td>CHI</td><td>CHI-011</td><td>A</td><td>CD11c CD14 CD38</td><td>3.756624</td><td>1.960488</td><td>0.00000000</td><td>1.5146819</td><td>⋯</td><td>0.00000000</td><td>0.9883232</td><td>0.1512892</td><td>1.1411799</td><td>1.79931812</td><td>0.03323656</td><td>3.8737299</td><td>0.0000000</td><td>0.2692525</td><td>1659</td></tr>\n",
              "\t<tr><td>69</td><td>CHI-011_3_10_A</td><td>CHI</td><td>CHI-011</td><td>A</td><td>CD11c CD14 CD38</td><td>2.911722</td><td>1.310564</td><td>0.00000000</td><td>0.0000000</td><td>⋯</td><td>0.00000000</td><td>1.7305496</td><td>0.3464784</td><td>0.0000000</td><td>1.59753981</td><td>0.00000000</td><td>1.9176080</td><td>0.0000000</td><td>0.0000000</td><td>1659</td></tr>\n",
              "\t<tr><td>69</td><td>CHI-011_3_10_A</td><td>CHI</td><td>CHI-011</td><td>A</td><td>CD11c CD14 CD38</td><td>2.919472</td><td>1.376643</td><td>0.00000000</td><td>0.0000000</td><td>⋯</td><td>0.00000000</td><td>2.5056298</td><td>0.0000000</td><td>0.0000000</td><td>0.02306957</td><td>0.00000000</td><td>0.5110521</td><td>0.5063124</td><td>0.0000000</td><td>1659</td></tr>\n",
              "\t<tr><td>79</td><td>CHI-011_4_12_A</td><td>CHI</td><td>CHI-011</td><td>A</td><td>CD11c CD14 CD38</td><td>2.862431</td><td>1.731462</td><td>0.00000000</td><td>0.6692741</td><td>⋯</td><td>0.93988490</td><td>0.4948810</td><td>0.0000000</td><td>0.0967478</td><td>0.44313860</td><td>0.00000000</td><td>3.3273032</td><td>0.0000000</td><td>0.6059084</td><td>1659</td></tr>\n",
              "\t<tr><td>70</td><td>CHI-011_2_6_A </td><td>CHI</td><td>CHI-011</td><td>A</td><td>CD11c CD14 CD38</td><td>3.554719</td><td>2.645969</td><td>0.93413488</td><td>2.0074880</td><td>⋯</td><td>0.00000000</td><td>0.7568753</td><td>0.0000000</td><td>0.0639216</td><td>1.42091355</td><td>0.54579525</td><td>4.5526857</td><td>0.2791606</td><td>0.2860811</td><td>1659</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ],
            "text/markdown": "\nA tibble: 6 × 37\n\n| cluster_id &lt;dbl&gt; | sample_id &lt;chr&gt; | condition &lt;chr&gt; | patient_id &lt;chr&gt; | stim_type &lt;chr&gt; | merging1 &lt;chr&gt; | CD45 &lt;dbl&gt; | CD7 &lt;dbl&gt; | CD19 &lt;dbl&gt; | pPLCg2 &lt;dbl&gt; | ⋯ ⋯ | CD16 &lt;dbl&gt; | CD38 &lt;dbl&gt; | CD8 &lt;dbl&gt; | CD45RA &lt;dbl&gt; | CD3 &lt;dbl&gt; | pERK1_2 &lt;dbl&gt; | HLA_DR &lt;dbl&gt; | pS6 &lt;dbl&gt; | CD127 &lt;dbl&gt; | ncount &lt;int&gt; |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 79 | CHI-011_1_2_A  | CHI | CHI-011 | A | CD11c CD14 CD38 | 3.110460 | 2.601485 | 0.05114502 | 0.7158504 | ⋯ | 0.09500555 | 2.3173963 | 0.2970178 | 0.1930449 | 1.78627827 | 0.04236087 | 2.7674600 | 0.0000000 | 0.3288540 | 1659 |\n| 70 | CHI-011_1_1_A  | CHI | CHI-011 | A | CD11c CD14 CD38 | 3.756624 | 1.960488 | 0.00000000 | 1.5146819 | ⋯ | 0.00000000 | 0.9883232 | 0.1512892 | 1.1411799 | 1.79931812 | 0.03323656 | 3.8737299 | 0.0000000 | 0.2692525 | 1659 |\n| 69 | CHI-011_3_10_A | CHI | CHI-011 | A | CD11c CD14 CD38 | 2.911722 | 1.310564 | 0.00000000 | 0.0000000 | ⋯ | 0.00000000 | 1.7305496 | 0.3464784 | 0.0000000 | 1.59753981 | 0.00000000 | 1.9176080 | 0.0000000 | 0.0000000 | 1659 |\n| 69 | CHI-011_3_10_A | CHI | CHI-011 | A | CD11c CD14 CD38 | 2.919472 | 1.376643 | 0.00000000 | 0.0000000 | ⋯ | 0.00000000 | 2.5056298 | 0.0000000 | 0.0000000 | 0.02306957 | 0.00000000 | 0.5110521 | 0.5063124 | 0.0000000 | 1659 |\n| 79 | CHI-011_4_12_A | CHI | CHI-011 | A | CD11c CD14 CD38 | 2.862431 | 1.731462 | 0.00000000 | 0.6692741 | ⋯ | 0.93988490 | 0.4948810 | 0.0000000 | 0.0967478 | 0.44313860 | 0.00000000 | 3.3273032 | 0.0000000 | 0.6059084 | 1659 |\n| 70 | CHI-011_2_6_A  | CHI | CHI-011 | A | CD11c CD14 CD38 | 3.554719 | 2.645969 | 0.93413488 | 2.0074880 | ⋯ | 0.00000000 | 0.7568753 | 0.0000000 | 0.0639216 | 1.42091355 | 0.54579525 | 4.5526857 | 0.2791606 | 0.2860811 | 1659 |\n\n",
            "text/latex": "A tibble: 6 × 37\n\\begin{tabular}{lllllllllllllllllllll}\n cluster\\_id & sample\\_id & condition & patient\\_id & stim\\_type & merging1 & CD45 & CD7 & CD19 & pPLCg2 & ⋯ & CD16 & CD38 & CD8 & CD45RA & CD3 & pERK1\\_2 & HLA\\_DR & pS6 & CD127 & ncount\\\\\n <dbl> & <chr> & <chr> & <chr> & <chr> & <chr> & <dbl> & <dbl> & <dbl> & <dbl> & ⋯ & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <int>\\\\\n\\hline\n\t 79 & CHI-011\\_1\\_2\\_A  & CHI & CHI-011 & A & CD11c CD14 CD38 & 3.110460 & 2.601485 & 0.05114502 & 0.7158504 & ⋯ & 0.09500555 & 2.3173963 & 0.2970178 & 0.1930449 & 1.78627827 & 0.04236087 & 2.7674600 & 0.0000000 & 0.3288540 & 1659\\\\\n\t 70 & CHI-011\\_1\\_1\\_A  & CHI & CHI-011 & A & CD11c CD14 CD38 & 3.756624 & 1.960488 & 0.00000000 & 1.5146819 & ⋯ & 0.00000000 & 0.9883232 & 0.1512892 & 1.1411799 & 1.79931812 & 0.03323656 & 3.8737299 & 0.0000000 & 0.2692525 & 1659\\\\\n\t 69 & CHI-011\\_3\\_10\\_A & CHI & CHI-011 & A & CD11c CD14 CD38 & 2.911722 & 1.310564 & 0.00000000 & 0.0000000 & ⋯ & 0.00000000 & 1.7305496 & 0.3464784 & 0.0000000 & 1.59753981 & 0.00000000 & 1.9176080 & 0.0000000 & 0.0000000 & 1659\\\\\n\t 69 & CHI-011\\_3\\_10\\_A & CHI & CHI-011 & A & CD11c CD14 CD38 & 2.919472 & 1.376643 & 0.00000000 & 0.0000000 & ⋯ & 0.00000000 & 2.5056298 & 0.0000000 & 0.0000000 & 0.02306957 & 0.00000000 & 0.5110521 & 0.5063124 & 0.0000000 & 1659\\\\\n\t 79 & CHI-011\\_4\\_12\\_A & CHI & CHI-011 & A & CD11c CD14 CD38 & 2.862431 & 1.731462 & 0.00000000 & 0.6692741 & ⋯ & 0.93988490 & 0.4948810 & 0.0000000 & 0.0967478 & 0.44313860 & 0.00000000 & 3.3273032 & 0.0000000 & 0.6059084 & 1659\\\\\n\t 70 & CHI-011\\_2\\_6\\_A  & CHI & CHI-011 & A & CD11c CD14 CD38 & 3.554719 & 2.645969 & 0.93413488 & 2.0074880 & ⋯ & 0.00000000 & 0.7568753 & 0.0000000 & 0.0639216 & 1.42091355 & 0.54579525 & 4.5526857 & 0.2791606 & 0.2860811 & 1659\\\\\n\\end{tabular}\n",
            "text/plain": [
              "  cluster_id sample_id      condition patient_id stim_type merging1       \n",
              "1 79         CHI-011_1_2_A  CHI       CHI-011    A         CD11c CD14 CD38\n",
              "2 70         CHI-011_1_1_A  CHI       CHI-011    A         CD11c CD14 CD38\n",
              "3 69         CHI-011_3_10_A CHI       CHI-011    A         CD11c CD14 CD38\n",
              "4 69         CHI-011_3_10_A CHI       CHI-011    A         CD11c CD14 CD38\n",
              "5 79         CHI-011_4_12_A CHI       CHI-011    A         CD11c CD14 CD38\n",
              "6 70         CHI-011_2_6_A  CHI       CHI-011    A         CD11c CD14 CD38\n",
              "  CD45     CD7      CD19       pPLCg2    ⋯ CD16       CD38      CD8      \n",
              "1 3.110460 2.601485 0.05114502 0.7158504 ⋯ 0.09500555 2.3173963 0.2970178\n",
              "2 3.756624 1.960488 0.00000000 1.5146819 ⋯ 0.00000000 0.9883232 0.1512892\n",
              "3 2.911722 1.310564 0.00000000 0.0000000 ⋯ 0.00000000 1.7305496 0.3464784\n",
              "4 2.919472 1.376643 0.00000000 0.0000000 ⋯ 0.00000000 2.5056298 0.0000000\n",
              "5 2.862431 1.731462 0.00000000 0.6692741 ⋯ 0.93988490 0.4948810 0.0000000\n",
              "6 3.554719 2.645969 0.93413488 2.0074880 ⋯ 0.00000000 0.7568753 0.0000000\n",
              "  CD45RA    CD3        pERK1_2    HLA_DR    pS6       CD127     ncount\n",
              "1 0.1930449 1.78627827 0.04236087 2.7674600 0.0000000 0.3288540 1659  \n",
              "2 1.1411799 1.79931812 0.03323656 3.8737299 0.0000000 0.2692525 1659  \n",
              "3 0.0000000 1.59753981 0.00000000 1.9176080 0.0000000 0.0000000 1659  \n",
              "4 0.0000000 0.02306957 0.00000000 0.5110521 0.5063124 0.0000000 1659  \n",
              "5 0.0967478 0.44313860 0.00000000 3.3273032 0.0000000 0.6059084 1659  \n",
              "6 0.0639216 1.42091355 0.54579525 4.5526857 0.2791606 0.2860811 1659  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 4 HDSTiM\n",
        "\n",
        "The main HDStIM function requires the following data and parameters.\n",
        "\n",
        "1. `dat:` A tibble with the single cell data. Cells on rows and markers on columns. Mandatory columns include `condition` (contrast conditions), `stim_type` (labels for stimulants), and a column with cell population names. In addition to marker expression and mandatory columns, other covariates can also be included.  \n",
        "2. `state_markers:` A character vector with the labels of state markers from the stimulation panel.\n",
        "3. `cellpop_col:` Column in the tibble with the cell population IDs.\n",
        "4. `stim_lab:` A character vector of stim label(s). This list should be the same as the stimuli labels in the `stim_type` column.\n",
        "5. `unstim_lab:` A character vector of unstim label(s). This list should be the same as in the `stim_type` column.\n",
        "6. `umap:` Boolean (TRUE/FALSE) to carry out UMAP on the selected cells.\n",
        "7. `umap_cells:` An integer; for calculating UMAPs, take a minimum of `umap_cells` per cluster or the total number of cells if the cluster size is smaller than umap_cells.\n"
      ],
      "metadata": {
        "id": "Uc2Fu7XskUDx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@title 4.1 Input parameters for the main HDStIM function\n",
        "#expr_data <- data_in #@param {type:\"raw\"}\n",
        "state_markers <- \"pPLCg2, pSTAT5, AKT, pSTAT1, pP38, pSTAT3, IkBa, pCREB, pERK1_2, pS6\" #@param {type:\"string\"}\n",
        "state_markers <- stringr::str_replace_all(stringr::str_split(state_markers, \",\")[[1]], \" \", \"\")\n",
        "cluster_col <- \"merging1\" #@param {type:\"string\"}\n",
        "stim_label <- \"A, T, L, G\" #@param {type:\"string\"}\n",
        "stim_label <- stringr::str_replace_all(stringr::str_split(stim_label,\",\")[[1]], \" \", \"\")\n",
        "unstim_label <- \"U\" #@param {type:\"string\"}\n",
        "unstim_label <- stringr::str_replace_all(stringr::str_split(unstim_label,\",\")[[1]], \" \", \"\")\n",
        "umap <- \"TRUE\" #@param {type:\"string\"}\n",
        "umap_cells <- 1000 #@param {type:\"number\"}"
      ],
      "metadata": {
        "cellView": "form",
        "id": "2zq-eQ1GJFIv"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title 4.2 Run HDStIM\n",
        "remove_input_data_from_memory <- \"TRUE\" #@param {type:\"string\"}\n",
        "mapped_data <-  HDStIM(data_in, state_markers, cluster_col, stim_label,\n",
        "                       unstim_label, seed_val = 123, \n",
        "                       umap = umap, umap_cells = umap_cells, \n",
        "                       verbose = FALSE)\n",
        "\n",
        "if(remove_input_data_from_memory == \"TRUE\") rm(data_in); ggc <- gc()"
      ],
      "metadata": {
        "cellView": "form",
        "id": "BHhiK8lDOsVj"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title 4.3 Responding populations mapped by HDStIM\n",
        "sig_spc <- dplyr::filter(mapped_data$all_fisher_p_val, p.value < 0.05)\n",
        "n_sig_spc <- nrow(sig_spc)\n",
        "resp_add_idx <- sig_spc %>% dplyr::arrange(by = stim_type) %>%\n",
        "  dplyr::arrange(by = cell_population) %>%\n",
        "  dplyr::mutate(\"index\" = seq(1, nrow(sig_spc))) %>%\n",
        "  dplyr::select(index, stim_type, cell_population, p.value)\n",
        "View(resp_add_idx)\n",
        "\n"
      ],
      "metadata": {
        "cellView": "form",
        "id": "PWjcSsUNRupg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 412
        },
        "outputId": "bc3d12cb-d137-42a2-b9ef-16d809df11c8"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   index stim_type cell_population              p.value     \n",
              "1   1    A         CD11c CD14 CD38              3.052288e-26\n",
              "2   2    G         CD11c CD14 CD38              6.434257e-34\n",
              "3   3    L         CD11c CD14 CD38              9.604947e-40\n",
              "4   4    T         CD11c CD14 CD38              4.839550e-07\n",
              "5   5    A         CD19 CD20 CD45RA HLA-DR CD24 5.555794e-18\n",
              "6   6    T         CD19 CD20 CD45RA HLA-DR CD24 7.602217e-03\n",
              "7   7    T         CD3 CD4 HLA-DR               3.154513e-04\n",
              "8   8    A         CD3 CD8 CD127 CD45RA         1.709727e-24\n",
              "9   9    T         CD3 CD8 CD127 CD45RA         9.715599e-32\n",
              "10 10    A         CD3 CD8 CD27 CD127           3.829911e-23"
            ],
            "text/html": [
              "<table class=\"dataframe\">\n",
              "<caption>A data.frame: 10 × 4</caption>\n",
              "<thead>\n",
              "\t<tr><th scope=col>index</th><th scope=col>stim_type</th><th scope=col>cell_population</th><th scope=col>p.value</th></tr>\n",
              "\t<tr><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><td> 1</td><td>A</td><td>CD11c CD14 CD38             </td><td>3.052288e-26</td></tr>\n",
              "\t<tr><td> 2</td><td>G</td><td>CD11c CD14 CD38             </td><td>6.434257e-34</td></tr>\n",
              "\t<tr><td> 3</td><td>L</td><td>CD11c CD14 CD38             </td><td>9.604947e-40</td></tr>\n",
              "\t<tr><td> 4</td><td>T</td><td>CD11c CD14 CD38             </td><td>4.839550e-07</td></tr>\n",
              "\t<tr><td> 5</td><td>A</td><td>CD19 CD20 CD45RA HLA-DR CD24</td><td>5.555794e-18</td></tr>\n",
              "\t<tr><td> 6</td><td>T</td><td>CD19 CD20 CD45RA HLA-DR CD24</td><td>7.602217e-03</td></tr>\n",
              "\t<tr><td> 7</td><td>T</td><td>CD3 CD4 HLA-DR              </td><td>3.154513e-04</td></tr>\n",
              "\t<tr><td> 8</td><td>A</td><td>CD3 CD8 CD127 CD45RA        </td><td>1.709727e-24</td></tr>\n",
              "\t<tr><td> 9</td><td>T</td><td>CD3 CD8 CD127 CD45RA        </td><td>9.715599e-32</td></tr>\n",
              "\t<tr><td>10</td><td>A</td><td>CD3 CD8 CD27 CD127          </td><td>3.829911e-23</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ],
            "text/markdown": "\nA data.frame: 10 × 4\n\n| index &lt;int&gt; | stim_type &lt;chr&gt; | cell_population &lt;chr&gt; | p.value &lt;dbl&gt; |\n|---|---|---|---|\n|  1 | A | CD11c CD14 CD38              | 3.052288e-26 |\n|  2 | G | CD11c CD14 CD38              | 6.434257e-34 |\n|  3 | L | CD11c CD14 CD38              | 9.604947e-40 |\n|  4 | T | CD11c CD14 CD38              | 4.839550e-07 |\n|  5 | A | CD19 CD20 CD45RA HLA-DR CD24 | 5.555794e-18 |\n|  6 | T | CD19 CD20 CD45RA HLA-DR CD24 | 7.602217e-03 |\n|  7 | T | CD3 CD4 HLA-DR               | 3.154513e-04 |\n|  8 | A | CD3 CD8 CD127 CD45RA         | 1.709727e-24 |\n|  9 | T | CD3 CD8 CD127 CD45RA         | 9.715599e-32 |\n| 10 | A | CD3 CD8 CD27 CD127           | 3.829911e-23 |\n\n",
            "text/latex": "A data.frame: 10 × 4\n\\begin{tabular}{llll}\n index & stim\\_type & cell\\_population & p.value\\\\\n <int> & <chr> & <chr> & <dbl>\\\\\n\\hline\n\t  1 & A & CD11c CD14 CD38              & 3.052288e-26\\\\\n\t  2 & G & CD11c CD14 CD38              & 6.434257e-34\\\\\n\t  3 & L & CD11c CD14 CD38              & 9.604947e-40\\\\\n\t  4 & T & CD11c CD14 CD38              & 4.839550e-07\\\\\n\t  5 & A & CD19 CD20 CD45RA HLA-DR CD24 & 5.555794e-18\\\\\n\t  6 & T & CD19 CD20 CD45RA HLA-DR CD24 & 7.602217e-03\\\\\n\t  7 & T & CD3 CD4 HLA-DR               & 3.154513e-04\\\\\n\t  8 & A & CD3 CD8 CD127 CD45RA         & 1.709727e-24\\\\\n\t  9 & T & CD3 CD8 CD127 CD45RA         & 9.715599e-32\\\\\n\t 10 & A & CD3 CD8 CD27 CD127           & 3.829911e-23\\\\\n\\end{tabular}\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 5 HDStIM diagnostic plots"
      ],
      "metadata": {
        "id": "C-AhaD-rkmHl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@title 5.1 Generate and cache all the plots\n",
        "cat(\"Generating plots for K-means and Fisher's exact test results.\\n\")\n",
        "k_plots <- plot_K_Fisher(mapped_data, path = NULL, verbose = FALSE)\n",
        "cat(\"Generating plots for pre and post-HDStIM response marker distribution (very slow).\\n\")\n",
        "e_plots <- plot_exprs(mapped_data, path = NULL,verbose = FALSE)\n",
        "if(\"umap\" %in% names(mapped_data)){\n",
        "  cat(\"Generate UMAPs to show responding and non responding cells.\")\n",
        "  u_plots <- plot_umap(mapped_data, path = NULL, verbose = FALSE)\n",
        "}\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "cellView": "form",
        "id": "IsjaXWKW4wER",
        "outputId": "8ee6f5ed-ef08-4ee6-a4b5-7bab89aa10cf"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Generating plots for K-means and Fisher's exact test results.\n",
            "Generating plots for pre and post HDStIM response marker distribution (very slow).\n",
            "Generate UMAPs to show responding and non responding cells."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title 5.1 Plots to show K-means and Fisher's exact test results\n",
        "theme_set(theme_bw())\n",
        "if(n_sig_spc ==0){\n",
        "    print(\"Nothing to plot here.\")\n",
        "} else if(n_sig_spc > 0){\n",
        "  plot_index_no = 1 #@param {type:\"integer\"}\n",
        "  if(plot_index_no <= n_sig_spc & plot_index_no > 0){\n",
        "    k_plots[[plot_index_no]]        \n",
        "  } else {\n",
        "      print(\"The selected value is not in the index.\")\n",
        "  }\n",
        "}\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "id": "TSTsixOHKP17",
        "outputId": "d0713b54-1ae3-4be9-faf9-e7d5cb616bc9",
        "cellView": "form"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "plot without title"
            ],
            "image/png": 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Nh+TsY2d0t3QSkdAggggAACKRfgOUgpP0AUDwEEEEgioEcIaPjqFi1aJFmcZRBA\nAAEEEEAghwABUg4YJpcmoL4weq6KHQyhtAxYCwEEShYgOCqZjhURQAABBBAIBeiDFFLwBgEE\nEEAAAQQQQAABBOpdgD5I9X4GsP8IIIAAAggggAACCCAQChAghRS8QQABBBBAAAEEEEAAgXoX\nIEAq8Qw45ZRTjH3KesG/HXfcMdzC5Zdf7pa/++67w2lJ32iEKm1vjz32SLpKWZe74oor3PZH\njRpV1nyfeuqpWMPNNtvMyG7//fc39qGY7rkuZd1wIzI777zzXJntA30bkUvlVr3zzjvNnnvu\naQYNGmTsA3+NfVimHhBdtgLYhwMb+7Bk89vf/tbYBwibxRdf3G3r6KOPNk888UTO7Wg9Pa8n\nmhrzHYnm0xTvX3nlFTNs2DDTr18/96wvPe9rrbXWMsOHDzezZs1qsMlqnSdxrg0KV+UJ9sG4\nzk7PXCIhgAACCCCQOgF7oUQqQWDw4MHuCev2gOZ91VPvfcp+yryfnuTVXvS47ehp99VIf/jD\nH9z2d91117Ju/l//+ldeP+/bpk2bwAaWZd12qZnZIMOV2QYapWZRsfWOP/54V9aWLVsG9uG+\nge3E7z4feOCBwc8//9zocrz55pvBqquu6vJU3jZACnr16hXoeOnYabsXXXRRg+3YC+RgmWWW\nCWwAlTGvMd+RjIzK+EFOI0eODOyDUN0+tW/fPrBBUtClSxf3WftpBycJ/vGPf2RstRrnSS7X\njIJV+cOrr74a2IdoO7upU6dWuTRsHgEEEEAAgYYC1CDZq5vGpL/97W/mgw8+yPmnu84+bbPN\nNuaMM84wa6+9tp9Uc6/2IrFJyty/f/8Ghu+99555/PHHXW3ETz/9ZPbaay/z/vvvN8n2F8ZM\nn376aaNajPXWW898/PHHxgYz5tNPPzUbb7yxGTdunDnrrLMavdvbb7+9mTZtmjnmmGPMJ598\nYj766CPzzjvvmO+//97cdNNNbsjpoUOHmptvvjljW3qgaVytSxq/I0cddZQ588wzzRJLLOHc\nvvrqK2Mv8l35VQOqmrIff/zR1dI99thjGftZ6Q+5XCtdjlzb0/dZNY16oDQJAQQQQACBtAow\nzHcjj0zHjh1Njx49EuXim+QlWjhlC6nplFL37t2bpGS2xiHWsWfPnuaRRx4xq622mlFznLvu\nustdjDdJIRaiTBcsWGAOPvhgo1c1W1tqqaXc3tkaTaMmd7b2xlx66aXG1tgYW8tT0p4rMHr7\n7bddAKYHlEZT8+bNze9+9zujgOGyyy4zt99+uwtwo8vEvU/bd0Tnm5qXKin42GmnncJiax/X\nX39996cbB7amzJx88slun8OFeOMEFBCNGDHCXHLJJUZWaob59ddfo4MAAggggEAqBUq7Mkrl\nrqS/UFOmTDEvvvii2WSTTYxtlhQW+IUXXjBqkz9jxgzTtWtX07dvX6M787qYzZUeffRRoz4w\nqhlYZ511zBZbbGFWWGGF2MXVJ0H5a9vfffedWXPNNd1d3OWWWy5jefUXUY2X+jnpgZO2yZBb\nVn2B1O9CafXVV89Yp5SyZ2SQ4IOe7SIPBUiqFVFtRb700EMPuQv3bGe/jmqh7r//fmObgpkt\nt9zST3Y1H5MnT3bb+fzzz82KK67o9lf7X6jmTIHAa6+95vJTvtH04YcfusCud+/e7jhF5yU9\nNlrn2WefNbZJkjvOqmnJl1QW/a2xxhruGEaX7dy5s9l6662N+sLJYbvttnOzbVMyc9VVV7n3\n6rOkGpN8Sfkr6QGluZJtmmmeeeYZ1+fJVmC7mqUbbrjBTJ8+3a0yceJE8/LLL7tzTjcbsr8j\n8+bNc4GJAmWVWcGyznvV4uj4apqeuaUaxnvvvdfN13Y0b+edd85VrMTTzz33XLesapGiwVF2\nBqeddpoZP368Cza/+OIL06lTp+xF3OdSz5NC3zN9r/O5+sLoWOi3Q8Gtba7rfjtUoxM9v2Wp\nGkadx6rtVoCo74NtVuxqc5WXgm6l3XbbLee+ugV++UeBuAJy3UxSzeLpp59u9D0lIYAAAggg\nkEqBhq3umJJEwPdBsheUSRZ3y8T1r7CDPYT9GFq1ahW+V18OezEY5u37IKnfhg0QwuXsSeXe\n2zuygb2ADpf3b2yTlsBeXLpl7EVQ0Lp1a/e+Q4cOwfXXX+8Xc68+3zFjxoT9LZT/hRdeGMye\nPTuwtTiBDbLCdZKWPVwh5o3vg2TvxMfM/XXSiSee6Mp92GGH/Toxxzt7ceeWtTUYsUvYJl9u\nvr3wc/NtLUugffb9ZmzNQMb+b7TRRoG9Ax7mFde3JG6aX8EGIW57Nujwk9xrMcdGK5x66qku\nHxuwZeQT90HnpY7dIYccEjc7sINtuPn2QjWcr33055O9gA6n53pjm+uFTjbQybVYxnTbHDXc\nht+WXtUvRSn7O+LP+yFDhgS2KVuDde3FfaBl7A2CBvOOPPLIjG0X+8E2AQx0Lqh86mtVKOk8\niqa4cyJuml8n13mS5HtWyNUO8hLovNdvgPbHn+t6b4Nt9/325bABnlvGBoSBvpdaRn/25k1g\nA1bXd81Ps4GbXy3vq61Zc98xHSulzTff3OVJH6S8bMxEAAEEEKiSAH2Q7P/0jUlqwjR//vzY\nP83Ll1Qj8Ne//tXYzvOur429eHD9cNRMRzVD6tuQndRvQ3dv1ZxHfZ/UB0J3cdVcJXuEOy27\n7bbburzOP/98M2fOHKNt3Hfffe6u+3777WeefPLJ7E0YjVSnpk5qDqNmWur7o5ot1S6p9kmp\nlLI32FDCCdq3W2+91S09cODAgmvJw3YCN6qdyO7roBoJjeKmWg/vpZoHNf9RMzTVqmhkNdld\nc8017i67atZ0R72cqZRjo+aNuqOvmqhCSfkr5arJ8LWTM2fODLNSkzHlr798tUJ+BXmpf5GS\nDWDcuSFH1Qxo1MW4pKaaKttWW23lZtsA2X3u06dP3OLhNB0XHY9rr73Wnc86H1QDcs8997jv\nj85t1S7q2KkmRc0G1bRP/aFKTaqx1HfY3lRwtSmF8onWwhRaNun8pN+zQq42uHa/GRtuuKGr\nsdPx0W+MDaDd74F+C7KTvhevv/660br2BoJRLZlctZ/+PLEDVmSvFvtZv3OqTdT3koQAAggg\ngEDqBaoUmNX8Zn0Nkj3A4R3W7PfZtSLZd8dt0OLWtQM3ZHjYgCvYfffdg3333TewF3xunu68\n+vyza60+++wzd6dbd4ejNR0HHHCAW8cOJJGRvz7YvhVunr3QCef5GiSNdqY7zvlSMWXPl4+v\nQdJIaPbCOvx78MEHA9tvJZCNbQroyqpy2c7w+bIL5x100EFuHXuxHE7TG7+9vffeO5wuZ9ll\nL6sF7PDVLh873Hi4fFwtQNw0v0JczUCxx8bnlfT1hBNOcOWOO/bK48Ybb3TzbfCbNMucy519\n9tmBRnbz56deVVNpg+zABimu1iF7Zf/9KTSKXfS8twFSRjb++6Rt22aMGfM02qLKYftbZUwv\n5oNtCubyUM1pKSnunIib5vOOO0+K/Z7Fub711luBaqdtgB18+eWXfnPhq34DZKXfBCVfg6Rp\ntt9VuFw531CDVE5N8kIAAQQQKLcAfZDsVUBj0tJLL+06HMflsfzyy8dNDqf5/jwaacwOGeye\n+6P+IbqT72tMwoUjb3S3Ppq0zrrrruv6b9iLIaMR4ZTUwVx3e22wEF3cvdcdfPX5eO6551x/\njmh/E/XLscMWN1gnOqHUskfziL5Xnwh70RSdlPFefUpUI6DBHJIk7bNqHGwgYPbZZ59wFdus\n0L23w1xnTFOtXPY+2y+b6++jBVXzVs5U6rFJWgbVaipl75Nf309Xv6PGJtv80dimj+58e+CB\nB1wNkkazU18h/WkEu9tuu82d443ZlvrKRNMGG2zgPqpWM3vwEN+/TqP2lZp0/JX8a6n5NGa9\ncnzPdExUc7rLLrvE9itTTap+B/7973+H/dF8mW3A5d/yigACCCCAQN0IECA18lCPHj3aNUMr\nJRsNt6zma7qAVFMXXWSqCZkuBNVMzA+MEM1bTaPimk2pCZySb1KmTuzqWK0BDnxzpmg+eq+L\nJiWNRKaBHnxaaaWV/Nucr6WUPWdmdoYCxOxO8GrCpSBTza+0vWhScx0NA56d1PRQFrbfkGt6\nZWui3PDTmiaTSZMmuTz1INpoUpMyNTdUMy01K1KgqUEI1OFeyQcc0XVKfd+YY5N0mxqlTsnW\nGMSu4qfbvmix84udqFHJbK2c+9O68tMgHxoeWwNfaLQ3NXdT8F9K0kAM/hz362uaUnZwpGm+\n6Vdjghvd/FDS90OBZKmj/blMSvynHN8zlV9Jx0PNRbOTD/79cn6+bpro5gsJAQQQQACBehMg\nQKriEdcFl0Z00ghtqumwgzK4vj3qd/DnP//ZDB8+3Og5SwpyfPIXhf5zrlc9k0ZJ/SdyXZT6\nkfT0zJpoigvAovP1vpSyZ+cR/awR41RDlDQp8NFIe9lJfSX8hbRtxuaGXVYA+vvf/94NNa0R\numxzuYxRu2SlO+Ua4UtJ3uoXpkBVgdvf//737M0U9Tm7L1pjjk3SDfth2dUnJy756dGaw7jl\n8k1THxbV0Ky11loNFpOfgljV3ql2UyMH6rzWM5lKSb7PVNy6uc7vuGWLmabvh2pg586d64Ik\n/33JlYeC78UWW8wFg76GLteycdOzzxMtU47vmT/fVPsaZ6XgVjdIVKMcTUl+B6LL8x4BBBBA\nAIGFRYAAqcpHUhdgel6M/vSwSQ3Bq47rGm5ZTe/Uqdr2pyi6lGpipLx1UeQv/IvOpMAKTVX2\nApt1sy+++OLYZm8+MNBC6njuh19WgKQgVGVW4BRN8pWRhru2fXf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k7jooHHUnYz8BAgQIECBAgAABAkUU6FNA\nKmKHtZkAAQIECBAgQIAAAQLdCQhI3cnYT4AAAQIECBAgQIBA6QQEpNINuQ4TIECAAAECBAgQ\nINCdgIDUnYz9BAgQIECAAAECBAiUTkBAKt2Q6zABAgQIECBAgMDCELj//vvTqaee2rCqX331\n1YbU9eSTT6bTTjstnX766Ske2+Yv0OMy388991zabbfd5l9LF6/GfZJsBAgQIECAAAECBMoi\ncN999+WAdNRRR/W5y9ddd12aOHFiuv322/tU1yc+8Yk0ZcqUtOuuu6ZHHnkk37LnyiuvTB/6\n0If6VG8rH9xjQHr77bfTv//971Y20DcCBAgQIECAAAECTSXw8MMP9/k7eMxoXXXVVempp55K\nq666au7fpz71qfTFL35RQJrPaPcYkFZZZZX0xz/+cT5VeIkAAQIECBAgQIBAeQTuueee9POf\n/zz99a9/Tdtvv33aY4890oorrtgB4M0330xHHHFEOvbYY9Naa62VX5s+fXr6xje+kU455ZS0\n9NJLpz/84Q/phz/8YXr66afTmDFj0oEHHpi23HLLNHXq1BSzPFE+9n3nO99JyyyzTIpT7uLx\nvffem5Zffvl0wAEHpG233TbXfdttt6Vbbrkl13PxxRenffbZJy2xxBJpwoQJbeEoCm699da5\n7kqlkgYMGJCP9Z+OAq5B6ujhGQECBAgQIECAAIFuBe66664cSp544om0++67p1/96lfpYx/7\n2DzlZ82alc4999z0/PPPt7324osv5n1vvPFG3v/BD34wDRs2LIegCCvvfe9707Rp09IKK6yQ\nQ82IESNyYBo6dGieTdpiiy3S5MmT8+lygwcPTh/+8IfTz372s1z/X/7yl3yd0QknnJDDVwS0\nnXbaKR1//PFt7x8PLrvssvSud71LOOqg0vFJjzNIrkHqCOYZAQIECBAgQIBAeQWOPvrotN9+\n+6UzzjgjI+yyyy55BimCTW+2OIUuglLMKEUg2nPPPdP666+fYmYnfm611VZ5hipmkGI76aST\n0rPPPptnj5Zaaql06KGHpnXWWSd9+ctfzrNFUeaf//xnDlCbb755PJ1ni4Uabr755mStgHlo\nOuzoMSC5BqmDlycECBAgQIAAAQIlFYjw8sADD6QISbVtueWWS7///e/z0zjFrd4tAlCcevfO\nd74z7bDDDnm2J4LXO97xji6ruPvuu9NKK62Ug1KtwD/+8Y88ExWn4sW22GKLpU033bT2coef\nEcS+/e1v5xmvmImydS/QY0ByDVL3eF4hQIAAAQIECBAoj0AsXPb666+nxRdfvO5OR6iqbbNn\nz649TCNHjkwReuJ6oWuvvTYddthh6cgjj0yTJk3K1wm1FfzfBzNmzEhxyt3Agf93hczo0aPT\ncccd17Yvrmtq/3ocGpMdMdsUp9bF6Xkf+MAHOlfteSeBHgNSp/KeEiBAgAABAgQIECilQISa\nOB3uz3/+c9puu+2yQQSQWKTh85//fAeTuG4otghUtS1Wk6ttcc1QLPYQwSj+xAIMMZMUp+7F\nQgqdF1BYe+21029/+9u89HctBD3++OMpFmeIBRu62/bdd998Wl2U23DDDbsrZn87gf+LoO12\n1vvwoYceSldccUVeWz2O+dvf/lbvocoRIECAAAECBAgQKJxAXBP03e9+N4eVWIghbr566623\n5oUP2ndm+PDheaGF8847L7322mspFnWI+xq132IVuvguPXfu3ByQYpaotuLdsssum685iiA1\nZ86cdPDBB+dV7eJUuSgX1yPtvffe6Zprrkm1MNa+7nh84YUX5tX2vva1r+Vj4hTA2p94T1vX\nAgsUkB599NH0/ve/P22yySYpbj51wQUX5NrjeQzAzJkzu343ewkQIECAAAECBAgUWCBWiYuZ\nnrjRaiyWcPnll+fvwnH6W+ftxz/+cbrhhhvyEt3jxo3rcO1SLLAQQeurX/1qrmfNNddMG220\nUX4e9cR37UGDBqV11103L8wQK89dcskl6ayzzkorr7xyWm+99dKoUaNyQOv8vrXnsYR4nOJ3\n0EEH5fqiztqfWOXO1rXAgCra/50Y2XWZDntj+m/jjTdOcQ7lXnvtle/uu9pqq+UBixT7i1/8\nIn3mM59JkZZbcbv00kvzbwEixTfj9uRpw5uxWdpEgEAXAmseuWj+cdrpkuldvLtdBAg0o8Dk\nvf//m3k2W9vixqL7779/2myzzRZp0xbV59eCuMeEwMsvvzzP/Y86A8UpeLGYQoSZ2qlxncvE\nqtFx/VAs+d15i/eI19pvUV+cVtfdzFH7sh73XqDX1yCdffbZ6ZVXXkkPPvhgimAU51zGFgk3\nLv6KwY+0Gn96cwFb75vuCAIECBAgQIAAAQL9IxArxnW+OWxXLYlQFIspzG+L1em62zqHoygX\n37dtC0+g16fYxR1/46ZWEY662j75yU/m8yTbX4TWVTn7CBAgQIAAAQIECBAg0GwCvQ5IcX5l\nXIPU3RY3vIqtuzXcuzvOfgIECBAgQIAAAQIECPS3QK8D0pZbbpliNY2rrrpqnrbH9Umxskbc\nO2l+U4XzHGgHAQIECBAgQIAAAQIEmkCg19cgffrTn05xHdJHP/rR9O53vzsvSRjLGMYCDRGa\nYkWMWKjBRoAAAQIECBAgQIAAgaIJ9DogDR48OF133XXp2GOPTT/96U/z3Xmj0/fee29ecjDC\nU23hhqJhaC8BAgQIECBAgAABAuUW6HVACq5YVjCW8f7e976XHnvssfTCCy+kMWPG5D9Dhgwp\nt6jeEyBAgAABAgQIECBQWIFeBaS4ZdLzzz/fdn1RLDsYN62aPn16evzxx/ONrAoroeEECBAg\nQIAAAQJNJbAg9ydqqg5oTCEF6l6kYerUqWmDDTZIX/jCF+bp6K9//eu89HfcQDYWcLARIECA\nAAECBAgQIECgiAJ1BaSbb7457bjjjmnatGlp2WWXnaef73nPe9IBBxyQHnnkkRSPYzbJRoAA\nAQIECBAgQIAAgaIJ1BWQjjzyyLT44ouna665Jv34xz+ep4+bbbZZuuCCC/LrL7/8cjr++OPn\nKWMHAQIECBAgQIAAAQIEml2gx4D05z//Od1///1pn332SR/+8Ifn25+ddtopzyRdeeWVadas\nWfMt60UCBAgQIECAAAECBAg0m0CPAal2utzOO+9cV9u33XbbNHfuXKfZ1aWlEAECBAgQIECA\nAAECzSTQY0BaaaWVcnvjBrD1bLVyAwYMqKe4MgQIECBAgAABAgQIEGgagR4D0qabbppiOe/J\nkyfX1egpU6akCEejR4+uq7xCBAgQIECAAAECBAgQaBaBHgPSwIED0/jx49OkSZPSE088Md92\n33777TlIbbfddnlRh/kW9iIBAgQIECBAgAABAgSaTKDHgBTtnTBhQpo9e3aK1eouvfTS/Lh9\nP2Lluljdbuutt05DhgxJEydObP+yxwQIECBAgAABAgQIECiEQF0BKU6zu+mmm9KIESPS3nvv\nnX/GTWMjEI0aNSots8wy6bDDDkvrrLNOuueee9KWW25ZiM5rJAECBAgQIECAAAECBNoLDG7/\nZH6Px44dm+644450+eWX55933nlnvnHsWmutlfbYY480bty4dOihh6aRI0fOrxqvESBAgAAB\nAgQIECBAoGkF6g5I0YM11lgjHXPMMW2diRXrhg8f3vbcAwIECBAgQIAAAQIECBRZoK5T7Lrr\noHDUnYz9BAgQIECAAAECBAgUUaBPAamIHdZmAgQIECBAgAABAgQIdCcgIHUnYz8BAgQIECBA\ngAABAqUTEJBKN+Q6TIAAAQIECBAgQIBAdwICUncy9hMgQIAAAQIECBAgUDoBAal0Q67DBAgQ\nIECAAAECBAh0JyAgdSdjPwECBAgQIECAAAECpRMQkEo35DpMgAABAgQIECBAgEB3AgJSdzL2\nEyBAgAABAgQIECBQOgEBqXRDrsMECBAgQIAAAQIECHQnICB1J2M/AQIECBAgQIAAAQKlExCQ\nSjfkOkyAAAECBAgQIECAQHcCAlJ3MvYTIECAAAECBAgQIFA6AQGpdEOuwwQIECBAgAABAgQI\ndCcgIHUnYz8BAgQIECBAgAABAqUTEJBKN+Q6TIAAAQIECBAgQIBAdwICUncy9hMgQIAAAQIE\nCBAgUDoBAal0Q67DBAgQIECAAAECBAh0JyAgdSdjPwECBAgQIECAAAECpRMQkEo35DpMgAAB\nAgQIECBAgEB3AgJSdzL2EyBAgAABAgQIECBQOgEBqXRDrsMECBAgQIAAAQIECHQnICB1J2M/\nAQIECBAgQIAAAQKlExCQSjfkOkyAAAECBAgQIECAQHcCAlJ3MvYTIECAAAECBAgQIFA6AQGp\ndEOuwwQIECBAgAABAgQIdCcgIHUnYz8BAgQIECBAgAABAqUTGFy6Hvexw5VKJc2ZMyfNnDmz\njzU5nACBsgv4HCn73wD9JzCvQLN+LsydOzfFdyAbgTIICEi9HOX4cJg9e3Z68803e3mk4gQI\nEOgo4HOko4dnBAikpv1+EQHJRqAsAgJSL0d64MCBafjw4WnppZfu5ZGLpviMRfM23oUAgQYI\nLLrPkdcb0FpVECCwKAQW3edC73ozdOjQNGDAgN4dpDSBggq4BqmgA6fZBAgQIECAAAECBAg0\nXkBAarypGgkQIECAAAECBAgQKKiAgFTQgdNsAgQIECBAgAABAgQaLyAgNd5UjQQIECBAgAAB\nAgQIFFRAQCrowGk2AQIECBAgQIAAAQKNFxCQGm+qRgIECBAgQIAAAQIECiogIBV04DSbAAEC\nBAgQIECAAIHGCwhIjTdVIwECBAgQIECAAAECBRUQkAo6cJpNgAABAgQIECBAgEDjBQSkxpuq\nkQABAgQIECBAgACBggoISAUdOM0mQIAAAQIECBAgQKDxAgJS403VSIAAAQIECBAgQIBAQQUE\npIIOnGYTIECAAAECBAgQINB4AQGp8aZqJECAAAECBAgQIECgoAICUkEHTrMJECBAgAABAgQI\nEGi8gIDUeFM1EiBAgAABAgQIECBQUAEBqaADp9kECBAgQIAAAQIECDReQEBqvKkaCRAgQIAA\nAQIECBAoqICAVNCB02wCBAgQIECAAAECBBovICA13lSNBAgQIECAAAECBAgUVEBAKujAaTYB\nAgQIECBAgAABAo0XEJAab6pGAgQIECBAgAABAgQKKiAgFXTgNJsAAQIECBAgQIAAgcYLCEiN\nN1UjAQIECBAgQIAAAQIFFRCQCjpwmk2AAAECBAgQIECAQOMFBKTGm6qRAAECBAgQIECAAIGC\nCghIBR04zSZAgAABAgQIECBAoPECAlLjTdVIgAABAgQIECBAgEBBBQSkgg6cZhMgQIAAAQIE\nCBAg0HgBAanxpmokQIAAAQIECBAgQKCgAgJSQQdOswkQIECAAAECBAgQaLyAgNR4UzUSIECA\nAAECBAgQIFBQAQGpoAOn2QQIECBAgAABAgQINF5AQGq8qRoJECBAgAABAgQIECiogIBU0IHT\nbAIECBAgQIAAAQIEGi8gIDXeVI0ECBAgQIAAAQIECBRUQEAq6MBpNgECBAgQIECAAAECjRcQ\nkBpvqkYCBAgQIECAAAECBAoqICAVdOA0mwABAgQIECBAgACBxgsISI03VSMBAgQIECBAgAAB\nAgUVEJAKOnCaTYAAAQIECBAgQIBA4wUEpMabqpEAAQIECBAgQIAAgYIKCEgFHTjNJkCAAAEC\nBAgQIECg8QICUuNN1UiAAAECBAgQIECAQEEFBKSCDpxmEyBAgAABAgQIECDQeAEBqfGmaiRA\ngAABAgQIECBAoKACAlJBB06zCRAgQIAAAQIECBBovICA1HhTNRIgQIAAAQIECBAgUFABAamg\nA6fZBAgQIECAAAECBAg0XkBAarypGgkQIECAAAECBAgQKKiAgFTQgdNsAgQIECBAgAABAgQa\nLyAgNd5UjQQIECBAgAABAgQIFFRAQCrowGk2AQIECBAgQIAAAQKNFxCQGm+qRgIECBAgQIAA\nAQIECiogIBV04DSbAAECBAgQIECAAIHGCwhIjTdVIwECBAgQIECAAAECBRUQkAo6cJpNgAAB\nAgQIECBAgEDjBQSkxpuqkQABAgQIECBAgACBggoISAUdOM0mQIAAAQIECBAgQKDxAgJS403V\nSIAAAQIECBAgQIBAQQUEpIIOnGYTIECAAAECBAgQINB4AQGp8aZqJECAAAECBAgQIECgoAIC\nUkEHTrMJECBAgAABAgQIEGi8gIDUeFM1EiBAgAABAgQIECBQUAEBqaADp9kECBAgQIAAAQIE\nCDReQEBqvKkaCRAgQIAAAQIECBAoqICAVNCB9/D9WAAALTpJREFU02wCBAgQIECAAAECBBov\nICA13lSNBAgQIECAAAECBAgUVEBAKujAaTYBAgQIECBAgAABAo0XEJAab6pGAgQIECBAgAAB\nAgQKKiAgFXTgNJsAAQIECBAgQIAAgcYLCEiNN1UjAQIECBAgQIAAAQIFFRCQCjpwmk2AAAEC\nBAgQIECAQOMFBKTGm6qRAAECBAgQIECAAIGCCghIBR04zSZAgAABAgQIECBAoPECgxtfZeNr\nfOONN9Ltt9+ennnmmbThhhumzTffvO1NXnvttXTHHXe0Pa892HrrrdOQIUPSrFmz0nXXXZde\neeWV9JGPfCQts8wytSLp4YcfTvfdd1/af//92/Z5QIAAAQIECBAgQIBAeQWaPiBdf/316ZRT\nTkkbbbRRGjFiRDr//PPTzjvvnI4++ug8ag8++GD61re+lZZbbrkOo/jud787B6QTTzwxLbvs\nsmn06NHp8MMPT5dccklbuTPPPFM4atPwgAABAgQIECBAgACBpg5Ib7/9drrwwgvTIYcckj7x\niU/k0br55pvTCSeckHbbbbe09tprp8ceeyyNHTs2/ehHP5pnNGfOnJnuvvvudOmll6YVVlgh\n/3z66afTaqutlu68885cfquttprnODsIECBAgAABAgQIECinQFNfg/TSSy+ld73rXWn77bdv\nG53NNtssP47T7WKLgLTuuuvmx53/U6lUUvwZPnx4filOuXv99dfzvnPOOScdeOCBnQ/xnAAB\nAgQIECBAgACBEgs09QxSnDZ31FFHdRieG264IQ0aNKgtFEVAWmyxxdKxxx6bpk2bltZff/18\nKt2oUaPSsGHD8vVKcZpezDbFjFS8PnXq1LTkkkumWtjq8AadnkyaNCkfV9v997//PZ/qF9dF\n2QgQINAXAZ8jfdFzLIHWFGjWz4U5c+bkXzC3prpeEego0NQBqWNTU3r88cfTT37yk7T33nun\nFVdcMcUCDc8991xaaaWV0l577ZXe9773pSuuuCJ97nOfSxdffHEaOXJkOuKII/LjBx54IE2c\nODH/z33eeeel4447LlcfH0QRsCJ0dbXF6XzxoVDbtthiixysYtEHGwECBPoi4HOkL3qOJdCa\nAs36uTB79mwBqTX/yulVFwKFCUgPPfRQniXaZptt0mc/+9nclQhAv/zlL/MiDEOHDs37Nthg\ng7zwQsw07brrrnlxhloYigJTpkxJMbsU5SJExezSwIED8+l248ePz3W0/8/Xv/71Dh8ITzzx\nRJ6ZWmqppdoXa5rHbzVNSzSEAIGeBBbd58hLPTXF6wQINInAovtc6F2H4zKFAQMG9O4gpQkU\nVKAQAenWW29NEVT22GOPdPDBB7dRx/+oMXvUfhszZkxafvnl07PPPtt+d34cM0EXXHBBnkmK\n2ad4fPXVV+frmM4444zUVUCK92y/xYIPcWysqGcjQIBAXwQW3eeIgNSXcXIsgUUpsOg+F3rX\nq8GDBwtIvSNTusACTb1IQ7jeeOON6Wtf+1o+Va59OIrXnnrqqTxbFNcF1bYIRv/617/yLFFt\nX+1n3A9pnXXWydcjPfLII7lMfBDFIg9x/VIs4GAjQIAAAQIECBAgQKC8Ak0dkF588cV08skn\npw9+8INpjTXWSHHPo9qfWOEu9sVCDGeddVaaMWNGnjWKexvFzWC33XbbDqMaN4y96KKL2k7P\ni1AUQSoWbpg+fXoOS3HKno0AAQIECBAgQIAAgfIKNPUpdpMnT06xiML//M//5D/thylWrfvw\nhz+cjjzyyDRhwoS0++6755fjFLs4Xa7zFHWsRher1q2++uq5XISoWHDhmGOOyeEq7qtkI0CA\nAAECBAgQIECg3AJNHZD22WefFH/mt6233nr5BrAvvPBCigsIu7u4cZVVVkmxwEP7LVa1i1Pt\nYjnxWBXPRoAAAQIECBAgQIBAuQWaOiD1Zmgi5MxviyXAu9rGjh3b1W77CBAgQIAAAQIECBAo\noUBTX4NUwvHQZQIECBAgQIAAAQIE+lFAQOpHfG9NgAABAgQIECBAgEBzCQhIzTUeWkOAAAEC\nBAgQIECAQD8KCEj9iO+tCRAgQIAAAQIECBBoLgEBqbnGQ2sIECBAgAABAgQIEOhHAQGpH/G9\nNQECBAgQIECAAAECzSUgIDXXeGgNAQIECBAgQIAAAQL9KCAg9SO+tyZAgAABAgQIECBAoLkE\nBKTmGg+tIUCAAAECBAgQIECgHwUEpH7E99YECBAgQIAAAQIECDSXgIDUXOOhNQQIECBAgAAB\nAgQI9KOAgNSP+N6aAAECBAgQIECAAIHmEhCQmms8tIYAAQIECBAgQIAAgX4UEJD6Ed9bEyBA\ngAABAgQIECDQXAICUnONh9YQIECAAAECBAgQINCPAgJSP+J7awIECBAgQIAAAQIEmktAQGqu\n8dAaAgQIECBAgAABAgT6UUBA6kd8b02AAAECBAgQIECAQHMJCEjNNR5aQ4AAAQIECBAgQIBA\nPwoISP2I760JECBAgAABAgQIEGguAQGpucZDawgQIECAAAECBAgQ6EcBAakf8b01AQIECBAg\nQIAAAQLNJSAgNdd4aA0BAgQIECBAgAABAv0oICD1I763JkCAAAECBAgQIECguQQEpOYaD60h\nQIAAAQIECBAgQKAfBQSkfsT31gQIECBAgAABAgQINJeAgNRc46E1BAgQIECAAAECBAj0o4CA\n1I/43poAAQIECBAgQIAAgeYSEJCaazy0hgABAgQIECBAgACBfhQQkPoR31sTIECAAAECBAgQ\nINBcAgJSc42H1hAgQIAAAQIECBAg0I8CAlI/4ntrAgQIECBAgAABAgSaS0BAaq7x0BoCBAgQ\nIECAAAECBPpRQEDqR3xvTYAAAQIECBAgQIBAcwkISM01HlpDgAABAgQIECBAgEA/CghI/Yjv\nrQkQIECAAAECBAgQaC4BAam5xkNrCBAgQIAAAQIECBDoRwEBqR/xvTUBAgQIECBAgAABAs0l\nICA113hoDQECBAgQIECAAAEC/SggIPUjvrcmQIAAAQIECBAgQKC5BASk5hoPrSFAgAABAgQI\nECBAoB8FBKR+xPfWBAgQIECAAAECBAg0l4CA1FzjoTUECBAgQIAAAQIECPSjgIDUj/jemgAB\nAgQIECBAgACB5hIQkJprPLSGAAECBAgQIECAAIF+FBjcj+/trReCwGErPLYQalUlAQILQ2Dy\nwqhUnQQIECBAgECfBMwg9YnPwQQIECBAgAABAgQItJKAgNRKo6kvBAgQIECAAAECBAj0SUBA\n6hOfgwkQIECAAAECBAgQaCUBAamVRlNfCBAgQIAAAQIECBDok4CA1Cc+BxMgQIAAAQIECBAg\n0EoCAlIrjaa+ECBAgAABAgQIECDQJwEBqU98DiZAgAABAgQIECBAoJUEBKRWGk19IUCAAAEC\nBAgQIECgTwICUp/4HEyAAAECBAgQIECAQCsJCEitNJr6QoAAAQIECBAgQIBAnwQEpD7xOZgA\nAQIECBAgQIAAgVYSEJBaaTT1hQABAgQIECBAgACBPgkISH3iczABAgQIECBAgAABAq0kICC1\n0mjqCwECBAgQIECAAAECfRIQkPrE52ACBAgQIECAAAECBFpJQEBqpdHUFwIECBAgQIAAAQIE\n+iQgIPWJz8EECBAgQIAAAQIECLSSgIDUSqOpLwQIECBAgAABAgQI9ElAQOoTn4MJECBAgAAB\nAgQIEGglAQGplUZTXwgQIECAAAECBAgQ6JOAgNQnPgcTIECAAAECBAgQINBKAgJSK42mvhAg\nQIAAAQIECBAg0CcBAalPfA4mQIAAAQIECBAgQKCVBASkVhpNfSFAgAABAgQIECBAoE8CAlKf\n+BxMgAABAgQIECBAgEArCQhIrTSa+kKAAAECBAgQIECAQJ8EBKQ+8TmYAAECBAgQIECAAIFW\nEhCQWmk09YUAAQIECBAgQIAAgT4JDO7T0SU8+O23305vvfVWeuWVV0rYe10mQKCRAj5HGqmp\nLgKtIdCsnwuzZ89OlUqlNZD1gkAPAgJSD0CdXx4wYEAaNGhQWmyxxTq/1CTPX2uSdmgGAQI9\nCSy6zxGfCz2NhdcJNIvAovtc6F2PBw500lHvxJQusoCA1MvRi4A0ZMiQNGzYsF4eqTgBAgQ6\nCvgc6ejhGQECqWm/X8Qvh+M7kI1AGQT8OqAMo6yPBAgQIECAAAECBAjUJSAg1cWkEAECBAgQ\nIECAAAECZRAQkMowyvpIgAABAgQIECBAgEBdAgJSXUwKESBAgAABAgQIECBQBgEBqQyjrI8E\nCBAgQIAAAQIECNQlICDVxaQQAQIECBAgQIAAAQJlEBCQyjDK+kiAAAECBAgQIECAQF0CAlJd\nTAoRIECAAAECBAgQIFAGAQGpDKOsjwQIECBAgAABAgQI1CUgINXFpBABAgQIECBAgAABAmUQ\nEJDKMMr6SIAAAQIECBAgQIBAXQICUl1MChEgQIAAAQIECBAgUAYBAakMo6yPBAgQIECAAAEC\nBAjUJSAg1cWkEAECBAgQIECAAAECZRAQkMowyvpIgAABAgQIECBAgEBdAgJSXUwKESBAgAAB\nAgQIECBQBgEBqQyjrI8ECBAgQIAAAQIECNQlICDVxaQQAQIECBAgQIAAAQJlEBCQyjDK+kiA\nAAECBAgQIECAQF0CAlJdTAoRIECAAAECBAgQIFAGAQGpDKOsjwQIECBAgAABAgQI1CUgINXF\npBABAgQIECBAgAABAmUQEJDKMMr6SIAAAQIECBAgQIBAXQICUl1MChEgQIAAAQIECBAgUAYB\nAakMo6yPBAgQIECAAAECBAjUJSAg1cWkEAECBAgQIECAAAECZRAQkMowyvpIgAABAgQIECBA\ngEBdAgJSXUwKESBAgAABAgQIECBQBgEBqQyjrI8ECBAgQIAAAQIECNQlICDVxaQQAQIECBAg\nQIAAAQJlEBCQyjDK+kiAAAECBAgQIECAQF0CAlJdTAoRIECAAAECBAgQIFAGAQGpDKOsjwQI\nECBAgAABAgQI1CUgINXFpBABAgQIECBAgAABAmUQEJDKMMr6SIAAAQIECBAgQIBAXQICUl1M\nChEgQIAAAQIECBAgUAYBAakMo6yPBAgQIECAAAECBAjUJSAg1cWkEAECBAgQIECAAAECZRAQ\nkMowyvpIgAABAgQIECBAgEBdAgJSXUwKESBAgAABAgQIECBQBgEBqQyjrI8ECBAgQIAAAQIE\nCNQlICDVxaQQAQIECBAgQIAAAQJlEBCQyjDK+kiAAAECBAgQIECAQF0CAlJdTAoRIECAAAEC\nBAgQIFAGAQGpDKOsjwQIECBAgAABAgQI1CUgINXFpBABAgQIECBAgAABAmUQEJDKMMr6SIAA\nAQIECBAgQIBAXQICUl1MChEgQIAAAQIECBAgUAYBAakMo6yPBAgQIECAAAECBAjUJSAg1cWk\nEAECBAgQIECAAAECZRAQkMowyvpIgAABAgQIECBAgEBdAgJSXUwKESBAgAABAgQIECBQBgEB\nqQyjrI8ECBAgQIAAAQIECNQlICDVxaQQAQIECBAgQIAAAQJlEBCQyjDK+kiAAAECBAgQIECA\nQF0CAlJdTAoRIECAAAECBAgQIFAGAQGpDKOsjwQIECBAgAABAgQI1CUgINXFpBABAgQIECBA\ngAABAmUQEJDKMMr6SIAAAQIECBAgQIBAXQICUl1MChEgQIAAAQIECBAgUAYBAakMo6yPBAgQ\nIECAAAECBAjUJSAg1cWkEAECBAgQIECAAAECZRAQkMowyvpIgAABAgQIECBAgEBdAgJSXUwK\nESBAgAABAgQIECBQBgEBqQyjrI8ECBAgQIAAAQIECNQlICDVxaQQAQIECBAgQIAAAQJlEBCQ\nyjDK+kiAAAECBAgQIECAQF0CAlJdTAoRIECAAAECBAgQIFAGAQGpDKOsjwQIECBAgAABAgQI\n1CUgINXFpBABAgQIECBAgAABAmUQEJDKMMr6SIAAAQIECBAgQIBAXQICUl1MChEgQIAAAQIE\nCBAgUAYBAakMo6yPBAgQIECAAAECBAjUJSAg1cWkEAECBAgQIECAAAECZRAQkMowyvpIgAAB\nAgQIECBAgEBdAi0RkF577bV0/fXXp1/+8pfp6aef7tDxWbNmpUmTJqULL7wwzZgxo8NrDz/8\ncN7fYacnBAgQIECAAAECBAiUVqDwAenJJ59Mu+66a7riiitSBJ7PfOYz6c4772wb0BNPPDH9\n9a9/TcOGDUuHH3542/54cOaZZ6b11luvwz5PCBAgQIAAAQIECBAor0DhA9JJJ52UPvKRj6Rz\nzjknfeMb30j77rtvOu2001KlUkkzZ85Md999d9pvv/3SnnvumV5//fW2GaZaiNpqq63KO/p6\nToAAAQIECBAgQIBAB4FCB6QXX3wx/elPf8ozSAMGDMgd23nnndMzzzyTHn300RySIigNHz48\nvzZkyJAckmJfBKoDDzywA4YnBAgQIECAAAECBAiUW2Bwkbv/3HPP5eavssoqbd14xzvekYYO\nHZr++c9/prFjx6bNN988X5+09tprp7fffjutv/76aerUqWnJJZdMm222Wdtx3T343Oc+l+bO\nndv2coSt1VZbLb300ktt+zwgQIDAggj4HFkQNccQaG2BZv1ciGu64xfMNgJlECh0QHr22WfT\nYostlv+0H6wllliibUGGI444Il188cXpgQceSBMnTsz/c5933nnpuOOOy4e88cYb+fhBgwa1\nr6Lt8U033ZTmzJnT9nyLLbZIEcji9L1m3M7fput+NGNbtYlA2QUW1eeIz4Wy/03T/yIJLKrP\nhd6axC+LBaTeqilfVIFCB6Q4Za59eKkNQvxPPGLEiPx09OjRbWEodkyZMiWNGjUqbbDBBjk4\nxep3AwcOzKfbjR8/vlZF289bb721wwfCb37zm/Tmm2+mFVdcsa2MBwQWlkD8YxSzofF3fdll\nl11Yb6NeAgQKJhCfC/Fv13LLLVewlmtuUQVisav4O2cjUAaBQgek+IchwlDMAtUCUQzaq6++\nmlZeeeV5xi/C1AUXXJBnkmJp8Hh89dVXp8ceeyydccYZqauAtMwyy3SoJ07fi9/u+JDowOLJ\nQhKo/bYurrHzd24hIauWQIEFfC4UePAK1vTatd4Fa7bmElgggUL/KmDVVVdNgwcPTo888khb\n52PRhrjWqP11SbUXr7vuurTOOuukuB4pjomZpAhW6667bpo2bVpewKFW1k8CBAgQIECAAAEC\nBMonUOiAtNRSS6UddtghzwTFEt5vvfVWOvfcc9OOO+6Yll9++Q6jGRcXXnTRRemzn/1s3h+h\n6F//+lcOU9OnT89haeTIkR2O8YQAAQIECBAgQIAAgXIJFPoUuxiqQw45JN//aJdddsmLLWyy\nySbp85///DyjOGnSpLxq3eqrr55fi1PnYsGFY445Ji/osNtuu81zjB0ECBAgQIAAAQIECJRL\noPABKYLO97///XzdUaxEt/jii3c5gnHK3TbbbNPhtVjVLk61i2uZLLrQgcYTAgQIECBAgAAB\nAqUUKHxAqo1a3Ndoftv73ve+Ll+OeyXZCBAgQIAAAQIECBAgEAKFvgbJEBIgQIAAAQIECBAg\nQKCRAgJSIzXVRYAAAQIECBAgQIBAoQUEpEIPn8YTIECAAAECBAgQINBIAQGpkZrqIkCAAAEC\nBAgQIECg0AICUqGHT+MJECBAgAABAgQIEGikgIDUSE11ESBAgAABAgQIECBQaAEBqdDDp/EE\nCBAgQIAAAQIECDRSQEBqpKa6CBAgQIAAAQIECBAotICAVOjh03gCBAgQIECAAAECBBopICA1\nUlNdBAgQIECAAAECBAgUWkBAKvTwaTwBAgQIECBAgAABAo0UEJAaqakuAgQIECBAgAABAgQK\nLSAgFXr4NJ4AAQIECBAgQIAAgUYKCEiN1FQXAQIECBAgQIAAAQKFFhCQCj18Gk+AAAECBAgQ\nIECAQCMFBKRGaqqLAAECBAgQIECAAIFCCwhIhR4+jSdAgAABAgQIECBAoJECAlIjNdVFgAAB\nAgQIECBAgEChBQSkQg+fxhMgQIAAAQIECBAg0EgBAamRmuoiQIAAAQIECBAgQKDQAgJSoYdP\n4wkQIECAAAECBAgQaKSAgNRITXURIECAAAECBAgQIFBoAQGp0MOn8QQIECBAgAABAgQINFJA\nQGqkproIECBAgAABAgQIECi0wOBCt76fGn/ZZZelqVOn9tO7e9syCVQqlTR79uw0YMCANGTI\nkDJ1XV8JEJiPwKxZs/KrQ4cOnU8pLxFonMATTzyR9t9//8ZVqCYCTSwwoPoFrNLE7Wu6pr3x\nxhtpxowZTdcuDWpNgfgSFP8gjR07Nn31q19tzU7qFQECvRY48MAD0xJLLJFOPfXUXh/rAAIL\nKrDCCiv4Zd2C4jmuUAJmkHo5XCNGjEjxx0ZgUQi89dZb6fnnn09rrrlmGjVq1KJ4S+9BgEAB\nBF544YU0d+5cnwsFGCtNJECgeAKuQSremGkxAQIECBAgQIAAAQILSUBAWkiwqiVAgAABAgQI\nECBAoHgCTrEr3phpcYkEBg4cmLbZZpu07rrrlqjXukqAQE8C73//+9PIkSN7KuZ1AgQIEFgA\nAYs0LACaQwgQIECAAAECBAgQaE0Bp9i15rjqFQECBAgQIECAAAECCyAgIC0AmkMIECBAgAAB\nAgQIEGhNAQGpNcdVrwosEDeGnTlzZu7B448/ni655JIC90bTCRBYVAL//ve/81vF8t/nn39+\neuaZZxbVW3sfAgQItJSAgNRSw6kzRRd47bXX8o1h495HscWdyy+77LKid0v7CRBYyAJxw9gr\nrrgiv8vbb7+dLrroovTss88u5HdVPQECBFpTQEBqzXHVq4IKRED6+9//3tb67bffPl199dVt\nzz0gQIBAVwKPPvpo2+4hQ4akm266KW2xxRZt+zwgQIAAgfoFBp1Y3eovriQBAn0ViBB04YUX\nposvvjjdfvvt6fXXX0/rrLNOeuutt9IPfvCD9OSTT6YZM2bkJXxffvnl9POf/zz9x3/8R5oz\nZ0465ZRT0uqrr55Pu/vFL36Rf0O80UYbpTvvvDOdc8456aGHHkrLLbdcWnbZZfvaTMcTINBA\ngcsvvzzFzPCaa67ZVmv8P1upVNIqq6ySrrzyyhSfDQ8++GC64IIL0i233JKWWGKJ/FrtgMmT\nJ+dT566//vo8uxyfG4sttlieZY7Pkpdeeim98soraezYsfmzYvTo0WnppZfOdcfnzL333pvr\njvdYY4018mdPtOG6665LAwYM6NC22nv6SYAAgTIKmEEq46jrc78KfPOb30z3339/itmh9ddf\nP5155pk5LA0aNCitvfbauW1jxozJIecf//hHmjJlSt4X1xVcc8016Utf+lKK+yNtsMEGOSgd\nffTR6bzzzksbbrhhmj59ejr++OP7tX/enACBeQXuuOOO9PDDD3d44cYbb8y/EImdd999dw41\nEVZi5idOkzvqqKPaXo9QdPrpp6dNNtkkbbvttvkz5Itf/GKub7XVVkvDhg1Lyy+/fA4+Ebri\ns+Jf//pXfj3qPvnkk9Ntt92Wxo0bl3+R8pWvfCUdd9xxacSIESmO//rXv57+/Oc/5/L+Q4AA\ngbILuFFs2f8G6P8iF4gvSQcffHD60Ic+lN87vpzEl6GhQ4fmLz5nn312vjls7I9FGjpvcePY\nQw45JO+O0/F+97vf5d8Qr7DCCmm77bZLu+66az5urbXW6nyo5wQINLHA8OHDcwiKX4B89KMf\nTbvsskue9YlZpz/+8Y/5htF77rlnnu2JoBSzTLNmzUrvec978szSeuutl8aPH59ioZfOWwSo\nb33rW2nw4MFpxRVXTCeccEI6/PDDU9QX2wMPPJDrc1PqznKeEyBQRgEzSGUcdX3uV4Edd9wx\nfe9730uHHXZYvpA6Tq+JLzX1bvElqLbFsfHlKcJRbEsttVT+Gafa2AgQKJZAhJMIR7HFz5gR\nevPNN/PzmDWKU+M++clPptNOOy1fq/ixj30s/2IlF+jhPzE7HeEotvjciG2rrbbKP+M/cSqe\nz402Dg8IECi5gIBU8r8Aur/oBY444oh00kknpbg+IFad2m+//dJZZ51Vd0OWXHLJDmXjFJna\nFtcR2AgQaE6BOPWt/dZ5pidmkNpvtbAU+zbffPN8/VAEpbjWME6tjZnouG6pnq32y5P2ZRdf\nfPH2Tz0mQIAAgf8VEJD8VSCwCAViIYZrr702Xz8U5/9PmjQpffrTn84XWce9jwScRTgY3orA\nIhSIU2hrs0HxtrHoSu0aoXqacdddd6VXX301HXTQQTkoxXWHcQpuXF9kI0CAAIHGCghIjfVU\nG4H5CsSKU1dddVWeMYrf/EYoipXq4lSaeK02OxQXS8eqUzYCBFpDIGaMY6W5WEglbuj6ox/9\nKMXCK51nlbrr7V//+tc0ceLEfGpdHBOnw8Xxo0aNyofEDNFTTz2VXnjhhe6qsJ8AAQIE6hSw\nSEOdUIoRaIRAzBAdeeSROSDtvvvu+QvOyiuvnL/4RP1xyktcFzBhwoR88XQs42sjQKD4AnHt\nUJwat9dee+XrhmKRllhRrt5Z449//OPpscceywu0xMIMcVycrlu7JjGuY4zbBMR1SnELABsB\nAgQILLjAgOpvojqeFL3gdTmSAIFeCLzxxht5BmmZZZaZ56h4LWaUYulvGwECrSMQMz9x3WCs\nKrcgW6x4GafmxcIsncNVXNMUs0oLWveCtMcxBAgQaEUBAakVR1WfCBAgQIAAAQIECBBYIAHX\nIC0Qm4MIECBAgAABAgQIEGhFAQGpFUdVnwgQIECAAAECBAgQWCABAWmB2BxEgAABAgQIECBA\ngEArCghIrTiq+kSAAAECBAgQIECAwAIJCEgLxOYgAgQIECBAgAABAgRaUcB9kFpxVPWJQAsI\nxA0v454uQ4YMSRtvvHFaeuml6+rVc889l2Ip5FVWWaWu8o0oFMuyx9LLK664YkOWWO6PPrR3\nmDNnToqbFT/55JPpne98Z/4zcKDfp7U38pgAAQIEWlfAv3itO7Z6RqCQAo8//njafPPN0/LL\nL5+222679IEPfCDFvaJiX9wos/0W9335zne+k8NJbf9uu+2Wdthhh9rTRfJz8uTJaY011kg3\n3XRTr9+vWfpQa/hJJ52UllhiibThhhumXXbZJd+INJ5PnDgxdb5tXldtr9XT08++HNtT3V4n\nQIAAAQJ9ERCQ+qLnWAIEGirwt7/9LY0bNy7FDMoZZ5yRbr755vSrX/0qHXzwwenpp59OW265\nZXr44Yfb3vOUU05JX/nKV9KsWbPa9kWZ97znPW3Pm/1BM/XhqKOOSscff3zadddd0zXXXJNu\nu+22HEBjTP7f//t/6cADD+zA2VXbOxSYz5O+HDufar1EgAABAgT6LOAUuz4TqoAAgUYJRBh6\n+eWX009+8pO0xx57tFW7++6759Cz//77p4suuih/aY8X41SwztsPf/jDzrua+nmz9GHu3Lnp\nwgsvzDNHl156aaqdUhdhM4LTeuutl+2/+93vtp3u2FXb68Xuy7H1vodyBAgQIEBgQQQEpAVR\ncwwBAgtFoHYK3UYbbTRP/Z/61KfSDTfckJZaaqn82i9+8Yv0+9//Pj+OL+2bbbZZ2m+//dI5\n55yTZ5Q+97nP5dfOPvvstOyyy6b3ve99+Qv+/fffnzbZZJO0zz77pNGjR6c77rgj/fKXv0xv\nvfVWivd473vfmwYMGJCPffTRR1O8z7777pvWXnvtvC/+8/e//z2de+65KU7ni/ftbrvuuuvS\nLbfckk8NjGuoxo4dm2dhRo4cmQ+ptw9R+M0338zvee+996YIM9GHmNFpf21W+F188cXp85//\nfLrvvvtSvP8///nP9K53vSsdeuihafjw4d01NcU1Xy+99FI+rbEWjmqFBw0alENpBNh//OMf\n+T27a3scE9djRZCdNm1amjFjRlprrbXSzjvvnMaPH5+r7O7YH/3oR2no0KHzzFRFXdG+CGq1\nrTYGMUbxdyL+znz2s59NNdtaOT8JECBAgECvBarnlNsIECDQFAJXXnllpfohVql+ka7ceeed\nlepiC92262tf+1qlet1PLl+d5ah8+ctfzmW32mqrSjWItB1XDQeVauiprLnmmpUxY8ZUql+k\n8zFR5vzzz68MHjy4Ug05+fV478MPP7zt2CuuuCKXrV5j1LYvHtx66615fxwfW1flqmErl1ln\nnXUqH/3oRysrrbRSfl5d9KAyc+bMfFy9faiGktzXaniobL/99pVq2KgsvvjilVGjRlWqgSnX\nFf+59tpr83t85jOfyT833XTTyrrrrpsfV6/hqlSDVVvZrh6ETXj84Ac/qLz44otdFWnb113b\nw6Z6/VilGlRyW6vBtFINWJVq6Kycd955+fjujq1e91SJ8eu8Va9Fq6y22mptu6tBsPKOd7yj\nUg2HlQ996EP570u0uxrEKtUg1VbOAwIECBAgsCACcdGtjQABAk0hEIHouOOOy1/oI6zEF+1P\nfvKTlbPOOqsSIaHz9o1vfCOXnT59ettLXQWkqOvoo49uK/PVr341H1ddfKByzz335P3V65gq\nW2yxRQ4etYJdBZ94raeAVJ3ZyvUfc8wxtapy2KvO4uT9v/nNb9r219OHHXfcsVKd/ancdddd\nbcdVV5mrVFfNq0SoqC54kPfXAlLs/+Mf/9hW9qCDDsrvO2XKlLZ9XT3405/+1BY6q7NIOawc\ne+yxlerMXaV6Stw8h3TV9ve///2VcK1eR9ZWvjrbk4NXhLTa1tWx9QakCMMRFqPe2nb55Zfn\nPp5++um1XX4SIECAAIEFErBIQ/Wbk40AgeYQiFPbvvWtb6WpU6em//qv/8qnhF122WXpkEMO\nyafDVQNHPr2st62Ner/5zW+2HVaddciPq+ErLwoRT2I58Ti97t///neqzp60lV2QB9XZqhTX\n8Zxwwglth0cbqjNJ+XmcglbvVg1/6frrr8+nncUCFLWtOjOVF6iIRSvCq/0Wp9PFKnS17ROf\n+ER+GItgzG+L64ziFMRTTz01VYNO+sMf/pBOPvnktO2226bqTFRedn1+x1f/FcqLPFx99dV5\nyfNa2VVXXTVVg2uH1QZrry3IzzgFMBbmqAbVtr8P0cdnn302VWcAF6RKxxA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          },
          "metadata": {
            "image/png": {
              "width": 420,
              "height": 420
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title 5.2 Plots to show pre and post-HDStIM response marker distribution\n",
        "theme_set(theme_bw())\n",
        "if(n_sig_spc ==0){\n",
        "    print(\"Nothing to plot here.\")\n",
        "} else if(n_sig_spc > 0){\n",
        "plot_index_no = 1 #@param {type:\"integer\"}\n",
        "  if(plot_index_no <= n_sig_spc & plot_index_no > 0){\n",
        "    ep <- e_plots[[plot_index_no]] +\n",
        "        theme(text = element_text(size = 11))\n",
        "        suppressMessages(print(ep))\n",
        "  } else {\n",
        "      print(\"The selected value is not in the index.\")\n",
        "  }\n",
        "}"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "id": "7mPJcK9GKT1k",
        "outputId": "43fe195f-4350-449a-8cb8-a0a9d6dcab87",
        "cellView": "form"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "plot without title"
            ],
            "image/png": 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MKnRGpIevToQStWrJARuMSizV5eHQwf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lBCO7YK3yClMW40Vz+MB/8uSTTyrTjJ49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\nxDz99NMydOhQVbddXdigQQNVF1yFu5jzyLO5r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Q4GD8zumOeCCQOr/wuHbUaKeuBQpPw7MMoKN58PLxweWKcvKAYlrrGo85n28E0ZJmaY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\nM+bqHJCMD4wTTnLyoRwxX/ruu+9UXFHTuMtVZK4xVt86NvCC4bL1+S9k0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DvvvFNQ4NVXXy34bX9YBCwCzhGAgAGZOLBoJ6KDW+W73sH4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pSbPUavARnVz6xcBq8A033KAn7qmCQayuc9as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"
          },
          "metadata": {
            "image/png": {
              "width": 420,
              "height": 420
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title 5.3 Plots to show responding and non-responding cells on a UMAP\n",
        "theme_set(theme_bw())\n",
        "if(\"umap\" %in% names(mapped_data)){\n",
        "  if(n_sig_spc ==0){\n",
        "      print(\"Nothing to plot here.\")\n",
        "  } else if(n_sig_spc > 0){\n",
        "    plot_index_no = 1 #@param {type:\"integer\"}\n",
        "    if(plot_index_no <= n_sig_spc & plot_index_no > 0){\n",
        "      u_plots[[plot_index_no]]        \n",
        "    } else {\n",
        "        print(\"The selected value is not in the index.\")\n",
        "    }\n",
        "  }    \n",
        "}else{\n",
        "    print(\"Umaps were not generated.\")\n",
        "}\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "id": "PflK5_VEKeyj",
        "outputId": "baddbb42-6e38-4f6d-f30d-59c3f1a53568",
        "cellView": "form"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "plot without title"
            ],
            "image/png": 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ARIgARIgARIgARIIIcIcA9SDnU2m0oCJEACJEACJEACJEACJBCeAAWk8HwYSgIk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CLSJDrA/JthAQArVI4PiiHchSqaYZD6MD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\n42TG1BvqXGbOxuVSedi4JO2Kt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qQH19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"
          },
          "metadata": {
            "image/png": {
              "width": 420,
              "height": 420
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 6 Rank markers per their contribution to the response\n",
        "\n",
        "Ranking markers per their contribution to the response through Boruta (random forest) is a time-intensive step, especially when the number of cells in a cell population is large. If many markers are labeled tentative, increase `max_runs` and rerun the function.\n",
        "\n",
        "1. `n_cells:` Number of cells to downsample the data. The default is NULL to include all the cells.\n",
        "2. `max_runs:` Maximum number of runs for the random forest algorithm. The default is 100."
      ],
      "metadata": {
        "id": "AU8aoS4plBuW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@title 6.1 Run marker ranking\n",
        "n_cells <- \"10000\" #@param {type:\"string\"}\n",
        "if(n_cells == \"NULL\"){\n",
        "    n_cells <- NULL\n",
        "} else {\n",
        "    n_cells <- as.integer(n_cells)\n",
        "}\n",
        "max_runs <- 100 #@param {type:\"integer\"}\n",
        "\n",
        "m_ranks <- marker_ranking_boruta(mapped_data, path = NULL, n_cells = n_cells,\n",
        "                                 max_runs = max_runs, seed_val = 123,\n",
        "                                 verbose = 0)"
      ],
      "metadata": {
        "id": "yhaNdjVSKt1u",
        "cellView": "form"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title 6.2 Marker ranking stim type and cell population index\n",
        "m_unique <- m_ranks$attribute_stats %>%\n",
        "dplyr::select(stim_type, cell_population) %>%\n",
        "unique() \n",
        "View(m_unique %>% dplyr::mutate(\"index\" = seq(1,nrow(m_unique))) %>%\n",
        "  dplyr::select(index, stim_type, cell_population))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 412
        },
        "id": "D6nyl-MVKxrG",
        "outputId": "5e0aa66f-292d-4138-8cb4-47f6f7c05e68",
        "cellView": "form"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   index stim_type cell_population             \n",
              "1   1    A         CD11c CD14 CD38             \n",
              "2   2    A         CD19 CD20 CD45RA HLA-DR CD24\n",
              "3   3    A         CD3 CD8 CD127 CD45RA        \n",
              "4   4    A         CD3 CD8 CD27 CD127          \n",
              "5   5    T         CD11c CD14 CD38             \n",
              "6   6    T         CD19 CD20 CD45RA HLA-DR CD24\n",
              "7   7    T         CD3 CD4 HLA-DR              \n",
              "8   8    T         CD3 CD8 CD127 CD45RA        \n",
              "9   9    L         CD11c CD14 CD38             \n",
              "10 10    G         CD11c CD14 CD38             "
            ],
            "text/html": [
              "<table class=\"dataframe\">\n",
              "<caption>A tibble: 10 × 3</caption>\n",
              "<thead>\n",
              "\t<tr><th scope=col>index</th><th scope=col>stim_type</th><th scope=col>cell_population</th></tr>\n",
              "\t<tr><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><td> 1</td><td>A</td><td>CD11c CD14 CD38             </td></tr>\n",
              "\t<tr><td> 2</td><td>A</td><td>CD19 CD20 CD45RA HLA-DR CD24</td></tr>\n",
              "\t<tr><td> 3</td><td>A</td><td>CD3 CD8 CD127 CD45RA        </td></tr>\n",
              "\t<tr><td> 4</td><td>A</td><td>CD3 CD8 CD27 CD127          </td></tr>\n",
              "\t<tr><td> 5</td><td>T</td><td>CD11c CD14 CD38             </td></tr>\n",
              "\t<tr><td> 6</td><td>T</td><td>CD19 CD20 CD45RA HLA-DR CD24</td></tr>\n",
              "\t<tr><td> 7</td><td>T</td><td>CD3 CD4 HLA-DR              </td></tr>\n",
              "\t<tr><td> 8</td><td>T</td><td>CD3 CD8 CD127 CD45RA        </td></tr>\n",
              "\t<tr><td> 9</td><td>L</td><td>CD11c CD14 CD38             </td></tr>\n",
              "\t<tr><td>10</td><td>G</td><td>CD11c CD14 CD38             </td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ],
            "text/markdown": "\nA tibble: 10 × 3\n\n| index &lt;int&gt; | stim_type &lt;chr&gt; | cell_population &lt;chr&gt; |\n|---|---|---|\n|  1 | A | CD11c CD14 CD38              |\n|  2 | A | CD19 CD20 CD45RA HLA-DR CD24 |\n|  3 | A | CD3 CD8 CD127 CD45RA         |\n|  4 | A | CD3 CD8 CD27 CD127           |\n|  5 | T | CD11c CD14 CD38              |\n|  6 | T | CD19 CD20 CD45RA HLA-DR CD24 |\n|  7 | T | CD3 CD4 HLA-DR               |\n|  8 | T | CD3 CD8 CD127 CD45RA         |\n|  9 | L | CD11c CD14 CD38              |\n| 10 | G | CD11c CD14 CD38              |\n\n",
            "text/latex": "A tibble: 10 × 3\n\\begin{tabular}{lll}\n index & stim\\_type & cell\\_population\\\\\n <int> & <chr> & <chr>\\\\\n\\hline\n\t  1 & A & CD11c CD14 CD38             \\\\\n\t  2 & A & CD19 CD20 CD45RA HLA-DR CD24\\\\\n\t  3 & A & CD3 CD8 CD127 CD45RA        \\\\\n\t  4 & A & CD3 CD8 CD27 CD127          \\\\\n\t  5 & T & CD11c CD14 CD38             \\\\\n\t  6 & T & CD19 CD20 CD45RA HLA-DR CD24\\\\\n\t  7 & T & CD3 CD4 HLA-DR              \\\\\n\t  8 & T & CD3 CD8 CD127 CD45RA        \\\\\n\t  9 & L & CD11c CD14 CD38             \\\\\n\t 10 & G & CD11c CD14 CD38             \\\\\n\\end{tabular}\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title 6.3 Plots to show marker ranking results\n",
        "theme_set(theme_bw())\n",
        "if(n_sig_spc ==0){\n",
        "    print(\"Nothing to plot here.\")\n",
        "} else if(n_sig_spc > 0){\n",
        "  plot_index_no = 1 #@param {type:\"integer\"}\n",
        "  if(plot_index_no <= n_sig_spc & plot_index_no > 0){\n",
        "    m_ranks$plots[[plot_index_no]] +\n",
        "    theme(text = element_text(size = 12))\n",
        "  } else {\n",
        "      print(\"The selected value is not in the index.\")\n",
        "  }\n",
        "}\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "id": "UFHlYJ2aK-zF",
        "outputId": "5960a3ac-2833-40de-c5c4-2359c7195b78",
        "cellView": "form"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "plot without title"
            ],
            "image/png": 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WuQ/COAAAIIIIAAAggggEClCRAgVRol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\nR83vM3+s0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mvhTGu73vpVozDkEgEEEKiAQMYB0nPPPecCpNmzZ9uJ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DoFstP55BV57rVr19qQIUNsyZIltvnmm9vo0aNtr732sssvv9yys0PT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\nSIAESIAESIAE3AT+A3721hANJrwHAAAAAElFTkSuQmCC"
          },
          "metadata": {
            "image/png": {
              "width": 420,
              "height": 420
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 7 Download analysis results\n",
        "You can save all the analysis results in your Google Colab instance and then download them individually to your local machine. \n"
      ],
      "metadata": {
        "id": "32IKX2etpuvP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@title 7.1 Output folder\n",
        "path_to_output_folder = \"hdstim-example-out\" #@param {type:\"string\"}\n",
        "create_folder = \"TRUE\" #@param {type:\"string\"}\n",
        "if(file.exists(path_to_output_folder)){\n",
        "    cat(\"WARNING: Output folder exists. Results will be over written.\")\n",
        "} else{\n",
        "    if(create_folder == TRUE){\n",
        "      cat(\"WARNING: Output folder does not exists. Creating one.\")\n",
        "      dir.create(path_to_output_folder, recursive = TRUE, showWarnings = FALSE)\n",
        "    } else if((create_folder == FALSE)){\n",
        "      cat(\"WARNING: Output folder does not exists. Please one.\")\n",
        "    }\n",
        "}\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "cellView": "form",
        "id": "g_SQ6CiHpvT1",
        "outputId": "7eb0f804-521b-42ae-9a43-92c41ef73dfa"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "WARNING: Output folder does not exists. Creating one."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title 7.2 Save HDStIM output and diagnostic plots in the output folder\n",
        "saveRDS(mapped_data, file.path(path_to_output_folder, \n",
        "                               \"hdstim-main-out.rds\"))\n",
        "write_tsv(mapped_data$all_fisher_p_val, file.path(path_to_output_folder, \n",
        "                                                  \"all-fisher-data.tsv\"))\n",
        "\n",
        "# HDStIM diagnostic plots\n",
        "## K Fisher plots\n",
        "theme_set(theme_grey())\n",
        "k_plots_path <- file.path(path_to_output_folder, \"k-fisher-plots\")\n",
        "#dir.create(k_plots_path, recursive = TRUE, showWarnings = FALSE )\n",
        "k_plots <- plot_K_Fisher(mapped_data, path = k_plots_path, verbose = FALSE)\n",
        "### Create tar.gz and delete individual plot files\n",
        "tar(file.path(path_to_output_folder,\"k-fisher-plots.tar.gz\")\n",
        ", files=k_plots_path, compression=\"gzip\")\n",
        "### Delete original uncompressed files\n",
        "delete_uncompressed_files_k = \"TRUE\" #@param {type:\"string\"}\n",
        "if(delete_uncompressed_files_k == \"TRUE\") {\n",
        "    unlink(k_plots_path, recursive = TRUE)\n",
        "}\n",
        "\n",
        "## Pre and post HDStIM maker expression plots\n",
        "theme_set(theme_grey())\n",
        "e_plots_path <- file.path(path_to_output_folder, \"pre-post-hdstim-expr-plots\")\n",
        "#dir.create(e_plots_path, recursive = TRUE, showWarnings = FALSE )\n",
        "e_plots <- suppressMessages(plot_exprs(mapped_data, path = e_plots_path,verbose = FALSE))\n",
        "### Create tar.gz and delete individual plot files\n",
        "tar(file.path(path_to_output_folder,\"pre-post-hdstim-expr-plots.tar.gz\")\n",
        ", files=e_plots_path, compression=\"gzip\")\n",
        "### Delete original uncompressed files\n",
        "delete_uncompressed_files_e = \"TRUE\" #@param {type:\"string\"}\n",
        "if(delete_uncompressed_files_e == \"TRUE\") {\n",
        "    unlink(e_plots_path, recursive = TRUE)\n",
        "}\n",
        "\n",
        "## UMAP if UMAPs are generated during HDStIM\n",
        "if(\"umap\" %in% names(mapped_data)){\n",
        "    theme_set(theme_grey())\n",
        "    u_plots_path <- file.path(path_to_output_folder, \"umap-plots\")\n",
        "    #dir.create(u_plots_path, recursive = TRUE, showWarnings = FALSE )\n",
        "    u_plots <- plot_umap(mapped_data, path = u_plots_path, verbose = FALSE)\n",
        "    ### Create tar.gz and delete individual plot files\n",
        "    tar(file.path(path_to_output_folder,\"umap-plots.tar.gz\")\n",
        "    , files=u_plots_path, compression=\"gzip\")\n",
        "    ### Delete original uncompressed files\n",
        "    delete_uncompressed_files_u = \"TRUE\" #@param {type:\"string\"}\n",
        "    if(delete_uncompressed_files_u == \"TRUE\") {\n",
        "      unlink(u_plots_path, recursive = TRUE)\n",
        "  }\n",
        "}\n"
      ],
      "metadata": {
        "cellView": "form",
        "id": "qQmX9PWCpzGZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title 7.3 Save marker ranking output and plots (if marker ranking was executed)\n",
        "\n",
        "saveRDS(m_ranks, file.path(path_to_output_folder, \n",
        "                           \"marker-ranking-main-out.rds\"))\n",
        "write_tsv(m_ranks$attribute_stats, file.path(path_to_output_folder, \n",
        "                                             \"marker-ranking-attributes-table.tsv\"))\n",
        "\n",
        "# Marker ranking plots\n",
        "c_s_comb <- m_ranks$attribute_stats %>% dplyr::select(stim_type, cell_population) %>%\n",
        "dplyr::group_by(, stim_type, cell_population) %>%\n",
        "ungroup() %>%\n",
        "unique()\n",
        "\n",
        "theme_set(theme_grey())\n",
        "mr_plots_path <- file.path(path_to_output_folder, \"marker-ranking-plots\")\n",
        "dir.create(mr_plots_path, recursive = TRUE, showWarnings = FALSE )\n",
        "for(i in 1:nrow(c_s_comb)){\n",
        "    att_plot <- m_ranks$plots[[i]]\n",
        "    stim <- c_s_comb$stim_type[i]\n",
        "    clust <- c_s_comb$cell_population[i]\n",
        "    att_file <- paste0(\"imp_\", stim, \"_\", clust, \".png\")\n",
        "    ggsave(att_file, plot = att_plot, path = mr_plots_path,\n",
        "           device = \"png\", dpi = 300, width = 7, height = 6, units = \"in\")\n",
        "}\n",
        "\n",
        "# Create tar.gz and delete individual plot files\n",
        "tar(file.path(path_to_output_folder,\"marker-ranking-plots.tar.gz\")\n",
        ", files=mr_plots_path, compression=\"gzip\")\n",
        "\n",
        "# Delete original uncompressed files\n",
        "delete_uncompressed_files = \"TRUE\" #@param {type:\"string\"}\n",
        "if(delete_uncompressed_files == \"TRUE\") {\n",
        "    unlink(mr_plots_path, recursive = TRUE)\n",
        "}\n"
      ],
      "metadata": {
        "cellView": "form",
        "id": "msUgM56Hp57z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# More information\n",
        "\n",
        "## Benchmarks/limits\n",
        "The primary limitation of running HDStIM on Google Colab is the RAM size (12 GB for free). A lack of CPUs will still run the software, albeit slowly. However, if any function hits the RAM limit, the system will crash and restart. This notebook has been tested to run successfully for a dataset with:\n",
        "\n",
        "* ~ 8 million cells,\n",
        "* across 24 cell populations,\n",
        "* and 13 stimulated + 1 unstimulated FCS files for a single donor.\n",
        "\n",
        "If hitting the RAM limits, run the notebook in a local runtime (see below) or split the data for each stimulant + unstimulated. \n",
        "\n",
        "## Running this notebook locally\n",
        "Google Colab notebooks can run locally (local runtime) on your system while still being hosted on Google servers. Please follow this link on how to setup local runtimes. [https://research.google.com/colaboratory/local-runtimes.html](https://research.google.com/colaboratory/local-runtimes.html)\n",
        "\n",
        "Once the initial setup is done use the code below to trigger the jupyter notebook for a local runtime.\n",
        "```\n",
        "jupyter notebook \\\n",
        " --NotebookApp.allow_origin='https://colab.research.google.com' \\\n",
        " --port=8888 \\\n",
        " --NotebookApp.port_retries=0\n",
        "```\n",
        "\n",
        "## How to find file ID from a Google Drive share link\n",
        "Below is a share link from my Google Drive pointing to the `hdstim-example-data.rds` file.\n",
        "\n",
        "`https://drive.google.com/file/d/1vj607etanUVYzVFj_HXkznHTd0Ltv_Y4/view?usp=sharing`\n",
        "\n",
        "This string `1vj607etanUVYzVFj_HXkznHTd0Ltv_Y4` is the **file ID** that we can use to download."
      ],
      "metadata": {
        "id": "iq4kmyRRrAoW"
      }
    }
  ]
}