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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J4GainthEcrJ"
      },
      "source": [
        "# ISB-CGC Community Notebooks\n",
        "\n",
        "Check out more notebooks at our [Community Notebooks Repository](https://github.com/isb-cgc/Community-Notebooks)!\n",
        "\n",
        "```\n",
        "Title:   Comparing gene expression in tumor against gene expression in normal tissue\n",
        "Author:  Fabian Seidl\n",
        "Created: 2021-07-09\n",
        "URL:     https://github.com/isb-cgc/Community-Notebooks/blob/master/Notebooks/How_to_analyze_differential_expression_between_paired_tumor_and_normal_samples.ipynb\n",
        "Purpose: To perform differential expression analysis comparing tumor to normal tissue\n",
        "Notes:\n",
        "```"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eVZb079pWn04"
      },
      "source": [
        "# Overview\n",
        "This notebook is intended to demonstrate how to run a differential gene expression analysis between tumor and normal samples using GDC cancer data made available in BigQuery. We will be using the TCGA gene expression dataset in `isb-cgc-bq`, specifically the table `isb-cgc-bq.TCGA_versioned.RNAseq_hg38_gdc_r35`. This notebook is modeled to somewhat mirror the differential expression analysis found in Peng, et al. *Scientific Reports* 2015 (https://doi.org/10.1038/srep13413).\n",
        "\n",
        "First we need to set up the environment and authenticate our session. This notebook will use both python to construct and run our SQL query and the Bioconductor R libraries `edgeR` and `DESeq2`."
      ]
    },
    {
      "cell_type": "code",
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7-zs3J1B4SNs",
        "outputId": "14c7a320-abe1-44ec-ef56-17c4a80c987c"
      },
      "source": [
        "from google.colab import auth\n",
        "import pandas_gbq\n",
        "import pandas as pd\n",
        "import numpy\n",
        "import seaborn\n",
        "from google.cloud import bigquery\n",
        "!pip install rpy2==3.5.1\n",
        "%load_ext rpy2.ipython\n",
        "\n",
        "bq_project='isb-project-zero'\n",
        "auth.authenticate_user()\n",
        "client = bigquery.Client(project=bq_project)\n",
        "print('Authenticated')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: rpy2==3.5.1 in /usr/local/lib/python3.10/dist-packages (3.5.1)\n",
            "Requirement already satisfied: cffi>=1.10.0 in /usr/local/lib/python3.10/dist-packages (from rpy2==3.5.1) (1.15.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from rpy2==3.5.1) (3.1.2)\n",
            "Requirement already satisfied: pytz in /usr/local/lib/python3.10/dist-packages (from rpy2==3.5.1) (2022.7.1)\n",
            "Requirement already satisfied: tzlocal in /usr/local/lib/python3.10/dist-packages (from rpy2==3.5.1) (5.0.1)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.10/dist-packages (from cffi>=1.10.0->rpy2==3.5.1) (2.21)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->rpy2==3.5.1) (2.1.3)\n",
            "Authenticated\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lQmssXUg4f-G"
      },
      "source": [
        "# Finding all cases with data for both normal and tumor aliquots\n",
        "Our first step is to join sample_type data to our RNA seq table and identify the cases in the RNA seq BigQuery table of choice that have data for both tumor and normal aliquots. To perform this operation we can write an SQL query which groups rows by case barcode and then use the `array_agg()` function to concatenate the `sample_type_name` field for each case.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "9oh4aK5BWnPj",
        "outputId": "3b848ffd-ab73-495b-ea2f-6e827d744504"
      },
      "source": [
        "sub_project = \"TCGA-BRCA\"\n",
        "rna_table = \"isb-cgc-bq.TCGA_versioned.RNAseq_hg38_gdc_r35\"\n",
        "\n",
        "case_query = \"\"\"\n",
        "WITH rna as (\n",
        "    SELECT\n",
        "       case_barcode,\n",
        "       sample_barcode,\n",
        "       aliquot_barcode,\n",
        "       Ensembl_gene_id_v,\n",
        "       gene_name,\n",
        "       unstranded,\n",
        "       fpkm_uq_unstranded,\n",
        "       sample_type_name,\n",
        "    FROM `isb-cgc-bq.TCGA_versioned.RNAseq_hg38_gdc_r35`\n",
        "    WHERE gene_type = 'protein_coding'\n",
        "    AND project_short_name = '{0}'\n",
        "),\n",
        "cases as (\n",
        "  SELECT case_barcode, agg FROM\n",
        "      (SELECT\n",
        "        case_barcode,\n",
        "        array_agg(distinct sample_type_name) agg\n",
        "      FROM rna\n",
        "      GROUP BY case_barcode)\n",
        "  WHERE array_length(agg) > 1\n",
        "  AND (\"Solid Tissue Normal\" in UNNEST(agg))\n",
        "  )\"\"\".format(sub_project)\n",
        "\n",
        "select_cases = \"\"\"\n",
        "SELECT * FROM cases\n",
        "\"\"\"\n",
        "\n",
        "query_job = client.query( case_query + select_cases )\n",
        "# print( case_query + select_cases )\n",
        "joined_data = query_job.result().to_dataframe()\n",
        "joined_data.head(5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   case_barcode                                   agg\n",
              "0  TCGA-GI-A2C9  [Solid Tissue Normal, Primary Tumor]\n",
              "1  TCGA-BH-A0DH  [Primary Tumor, Solid Tissue Normal]\n",
              "2  TCGA-BH-A0DL  [Primary Tumor, Solid Tissue Normal]\n",
              "3  TCGA-BH-A1EV  [Primary Tumor, Solid Tissue Normal]\n",
              "4  TCGA-BH-A0BS  [Solid Tissue Normal, Primary Tumor]"
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              "    <tr>\n",
              "      <th>4</th>\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8QUYgFmG0xHB"
      },
      "source": [
        "# Subsetting the gene set\n",
        "Performing a comparison across all genes is outside of the scope of this notebook, so we will reduce our gene set to a more manageable size. A common metric to subset by is the variance of expression level. We can calculate this metric in an SQL query by grouping by the `Ensembl_gene_id_v` field and calculating the `VARIANCE()` of the counts field. We then order our results by this value and select the top 2000 genes."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "xud56wZI0wcw",
        "outputId": "361ac303-d443-4cd4-cb74-1f0e47aa73f7"
      },
      "source": [
        "expression_query = \"\"\",\n",
        "mean_expr as (\n",
        "  SELECT * FROM (\n",
        "    SELECT\n",
        "      rna.Ensembl_gene_id_v,\n",
        "      VARIANCE(rna.fpkm_uq_unstranded) var_fpkm\n",
        "    FROM rna\n",
        "    JOIN cases ON rna.case_barcode = cases.case_barcode\n",
        "    WHERE rna.sample_type_name = 'Solid Tissue Normal'\n",
        "    GROUP BY rna.Ensembl_gene_id_v)\n",
        "  ORDER BY var_fpkm DESC\n",
        "  LIMIT 2000)\"\"\".format(rna_table)\n",
        "\n",
        "select_genes = \"\"\"\n",
        "SELECT * FROM mean_expr\n",
        "\"\"\"\n",
        "\n",
        "query_job = client.query( case_query + expression_query + select_genes )\n",
        "# print( case_query + expression_query + select_genes )\n",
        "joined_data = query_job.result().to_dataframe()\n",
        "joined_data.head(5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Ensembl_gene_id_v      var_fpkm\n",
              "0   ENSG00000198886.2  4.219112e+07\n",
              "1   ENSG00000198938.2  3.523055e+07\n",
              "2   ENSG00000198804.2  2.799827e+07\n",
              "3  ENSG00000143632.14  2.615222e+07\n",
              "4   ENSG00000198840.2  2.575547e+07"
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "umuxlYPaHdxz"
      },
      "source": [
        "# Joins are used to generate the table for analysis\n",
        "To generate the final table we join the full RNA expression table back to the tables housing the list of cases as well as the list of genes. As both `edgeR` and `DESeq2` require counts as input the important fields we need are `case_barcode`, `aliquot_barcode`, `gene_name`, `sample_type_name`, and `HTSeq__Counts`."
      ]
    },
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          "height": 363
        },
        "id": "Zw1y-8ABQO9n",
        "outputId": "d2f5cdc9-8fda-4d49-c0a0-a2f867854ee7"
      },
      "source": [
        "final_join = \"\"\"\n",
        "SELECT rna.case_barcode,\n",
        "       rna.sample_barcode,\n",
        "       rna.aliquot_barcode,\n",
        "       rna.Ensembl_gene_id_v,\n",
        "       rna.gene_name,\n",
        "       rna.unstranded,\n",
        "       rna.sample_type_name,\n",
        "FROM rna\n",
        "  JOIN cases ON rna.case_barcode = cases.case_barcode\n",
        "  JOIN mean_expr ON rna.Ensembl_gene_id_v = mean_expr.Ensembl_gene_id_v\n",
        "WHERE rna.sample_type_name = 'Solid Tissue Normal'\n",
        "  OR rna.sample_type_name = 'Primary Tumor'\n",
        "ORDER BY Ensembl_gene_id_v, case_barcode, aliquot_barcode\n",
        "\"\"\".format(rna_table)\n",
        "\n",
        "query_job = client.query( case_query + expression_query + final_join )\n",
        "# print( case_query + expression_query + final_join )\n",
        "joined_data = query_job.result().to_dataframe()\n",
        "joined_data.head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   case_barcode    sample_barcode               aliquot_barcode  \\\n",
              "0  TCGA-A7-A0CE  TCGA-A7-A0CE-01A  TCGA-A7-A0CE-01A-11R-A00Z-07   \n",
              "1  TCGA-A7-A0CE  TCGA-A7-A0CE-11A  TCGA-A7-A0CE-11A-21R-A089-07   \n",
              "2  TCGA-A7-A0CH  TCGA-A7-A0CH-01A  TCGA-A7-A0CH-01A-21R-A00Z-07   \n",
              "3  TCGA-A7-A0CH  TCGA-A7-A0CH-11A  TCGA-A7-A0CH-11A-32R-A089-07   \n",
              "4  TCGA-A7-A0D9  TCGA-A7-A0D9-01A  TCGA-A7-A0D9-01A-31R-A056-07   \n",
              "5  TCGA-A7-A0D9  TCGA-A7-A0D9-11A  TCGA-A7-A0D9-11A-53R-A089-07   \n",
              "6  TCGA-A7-A0DB  TCGA-A7-A0DB-01A  TCGA-A7-A0DB-01A-11R-A00Z-07   \n",
              "7  TCGA-A7-A0DB  TCGA-A7-A0DB-01A  TCGA-A7-A0DB-01A-11R-A277-07   \n",
              "8  TCGA-A7-A0DB  TCGA-A7-A0DB-01C  TCGA-A7-A0DB-01C-02R-A277-07   \n",
              "9  TCGA-A7-A0DB  TCGA-A7-A0DB-11A  TCGA-A7-A0DB-11A-33R-A089-07   \n",
              "\n",
              "   Ensembl_gene_id_v gene_name  unstranded     sample_type_name  \n",
              "0  ENSG00000000005.6      TNMD          54        Primary Tumor  \n",
              "1  ENSG00000000005.6      TNMD         319  Solid Tissue Normal  \n",
              "2  ENSG00000000005.6      TNMD           0        Primary Tumor  \n",
              "3  ENSG00000000005.6      TNMD        2114  Solid Tissue Normal  \n",
              "4  ENSG00000000005.6      TNMD          72        Primary Tumor  \n",
              "5  ENSG00000000005.6      TNMD        3612  Solid Tissue Normal  \n",
              "6  ENSG00000000005.6      TNMD          68        Primary Tumor  \n",
              "7  ENSG00000000005.6      TNMD          47        Primary Tumor  \n",
              "8  ENSG00000000005.6      TNMD          29        Primary Tumor  \n",
              "9  ENSG00000000005.6      TNMD         303  Solid Tissue Normal  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-faec0d78-5f34-4dd8-9634-c815b0e52067\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>case_barcode</th>\n",
              "      <th>sample_barcode</th>\n",
              "      <th>aliquot_barcode</th>\n",
              "      <th>Ensembl_gene_id_v</th>\n",
              "      <th>gene_name</th>\n",
              "      <th>unstranded</th>\n",
              "      <th>sample_type_name</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>TCGA-A7-A0CE</td>\n",
              "      <td>TCGA-A7-A0CE-01A</td>\n",
              "      <td>TCGA-A7-A0CE-01A-11R-A00Z-07</td>\n",
              "      <td>ENSG00000000005.6</td>\n",
              "      <td>TNMD</td>\n",
              "      <td>54</td>\n",
              "      <td>Primary Tumor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>TCGA-A7-A0CE</td>\n",
              "      <td>TCGA-A7-A0CE-11A</td>\n",
              "      <td>TCGA-A7-A0CE-11A-21R-A089-07</td>\n",
              "      <td>ENSG00000000005.6</td>\n",
              "      <td>TNMD</td>\n",
              "      <td>319</td>\n",
              "      <td>Solid Tissue Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>TCGA-A7-A0CH</td>\n",
              "      <td>TCGA-A7-A0CH-01A</td>\n",
              "      <td>TCGA-A7-A0CH-01A-21R-A00Z-07</td>\n",
              "      <td>ENSG00000000005.6</td>\n",
              "      <td>TNMD</td>\n",
              "      <td>0</td>\n",
              "      <td>Primary Tumor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>TCGA-A7-A0CH</td>\n",
              "      <td>TCGA-A7-A0CH-11A</td>\n",
              "      <td>TCGA-A7-A0CH-11A-32R-A089-07</td>\n",
              "      <td>ENSG00000000005.6</td>\n",
              "      <td>TNMD</td>\n",
              "      <td>2114</td>\n",
              "      <td>Solid Tissue Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>TCGA-A7-A0D9</td>\n",
              "      <td>TCGA-A7-A0D9-01A</td>\n",
              "      <td>TCGA-A7-A0D9-01A-31R-A056-07</td>\n",
              "      <td>ENSG00000000005.6</td>\n",
              "      <td>TNMD</td>\n",
              "      <td>72</td>\n",
              "      <td>Primary Tumor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>TCGA-A7-A0D9</td>\n",
              "      <td>TCGA-A7-A0D9-11A</td>\n",
              "      <td>TCGA-A7-A0D9-11A-53R-A089-07</td>\n",
              "      <td>ENSG00000000005.6</td>\n",
              "      <td>TNMD</td>\n",
              "      <td>3612</td>\n",
              "      <td>Solid Tissue Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>TCGA-A7-A0DB</td>\n",
              "      <td>TCGA-A7-A0DB-01A</td>\n",
              "      <td>TCGA-A7-A0DB-01A-11R-A00Z-07</td>\n",
              "      <td>ENSG00000000005.6</td>\n",
              "      <td>TNMD</td>\n",
              "      <td>68</td>\n",
              "      <td>Primary Tumor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>TCGA-A7-A0DB</td>\n",
              "      <td>TCGA-A7-A0DB-01A</td>\n",
              "      <td>TCGA-A7-A0DB-01A-11R-A277-07</td>\n",
              "      <td>ENSG00000000005.6</td>\n",
              "      <td>TNMD</td>\n",
              "      <td>47</td>\n",
              "      <td>Primary Tumor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>TCGA-A7-A0DB</td>\n",
              "      <td>TCGA-A7-A0DB-01C</td>\n",
              "      <td>TCGA-A7-A0DB-01C-02R-A277-07</td>\n",
              "      <td>ENSG00000000005.6</td>\n",
              "      <td>TNMD</td>\n",
              "      <td>29</td>\n",
              "      <td>Primary Tumor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>TCGA-A7-A0DB</td>\n",
              "      <td>TCGA-A7-A0DB-11A</td>\n",
              "      <td>TCGA-A7-A0DB-11A-33R-A089-07</td>\n",
              "      <td>ENSG00000000005.6</td>\n",
              "      <td>TNMD</td>\n",
              "      <td>303</td>\n",
              "      <td>Solid Tissue Normal</td>\n",
              "    </tr>\n",
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          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Tx_JFBNDJBce"
      },
      "source": [
        "# Moving the analysis to R\n",
        "At this juncture we will move the analysis to R to take advantage of the standard Bioconductor differential expression packages. During the initial setup we loaded `rpy2` which allows us to execute cells in R using cell magic.\n",
        "\n",
        "We will first need to install our R packages. Be aware that unfortunately this step can take upwards of 10 minutes and will request user input on whether to update dependencies, enter \"a\" for updating all. This behaviour can be common working in the cloud, which can be dependent on server availability and speed of interconnections. Executing this code in a Google Virtual Machine, especially one with the libraries pre-installed, is substantially faster."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3Gn5tkZrw4Sx"
      },
      "source": [
        "%%R\n",
        "if (!requireNamespace(\"BiocManager\", quietly = TRUE))\n",
        "    install.packages(\"BiocManager\")\n",
        "BiocManager::install(c(\"edgeR\", \"DESeq2\"))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ar0qyISyJy5e"
      },
      "source": [
        "We can import the data frame into the R environment by with the `-i` flag in our `%%R` cell magic call and load the libraries we just installed."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wa6q_a5Q7u-L"
      },
      "source": [
        "%%R -i joined_data\n",
        "library(DESeq2)\n",
        "library(edgeR)\n",
        "library(scales)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fUj_0p4SKFIi"
      },
      "source": [
        "# Converting our longform data table into a count matrix\n",
        "Both `edgeR` and `DESeq2` expect a data matrix of counts as an input where each column corresponds to a sample and each row to a gene. In our query we sorted our data frame by the `gene_name`, `case_barcode`, and `aliquot_barcode`. This allows us to split our data frame on gene_name and then `rbind()` the resulting count vectors into a matrix."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ShkRzBTqGRNt",
        "outputId": "917209bd-39b5-47f4-a93d-630726b65eed"
      },
      "source": [
        "%%R\n",
        "split_df <- split(joined_data, f=joined_data$gene_name)\n",
        "str(split_df[c(1,2)]) # showing two data frames from our list output\n",
        "expression_list <- lapply(split_df, function(df){df$unstranded})\n",
        "exp_matrix <- as.data.frame(Reduce(rbind, expression_list))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "List of 2\n",
            " $ A2M :'data.frame':\t231 obs. of  7 variables:\n",
            "  ..$ case_barcode     : chr [1:231] \"TCGA-A7-A0CE\" \"TCGA-A7-A0CE\" \"TCGA-A7-A0CH\" \"TCGA-A7-A0CH\" ...\n",
            "  ..$ sample_barcode   : chr [1:231] \"TCGA-A7-A0CE-01A\" \"TCGA-A7-A0CE-11A\" \"TCGA-A7-A0CH-01A\" \"TCGA-A7-A0CH-11A\" ...\n",
            "  ..$ aliquot_barcode  : chr [1:231] \"TCGA-A7-A0CE-01A-11R-A00Z-07\" \"TCGA-A7-A0CE-11A-21R-A089-07\" \"TCGA-A7-A0CH-01A-21R-A00Z-07\" \"TCGA-A7-A0CH-11A-32R-A089-07\" ...\n",
            "  ..$ Ensembl_gene_id_v: chr [1:231] \"ENSG00000175899.15\" \"ENSG00000175899.15\" \"ENSG00000175899.15\" \"ENSG00000175899.15\" ...\n",
            "  ..$ gene_name        : chr [1:231] \"A2M\" \"A2M\" \"A2M\" \"A2M\" ...\n",
            "  ..$ unstranded       : int [1:231] 28180 122749 12099 85733 28978 93660 31811 22960 2606 84430 ...\n",
            "  ..$ sample_type_name : chr [1:231] \"Primary Tumor\" \"Solid Tissue Normal\" \"Primary Tumor\" \"Solid Tissue Normal\" ...\n",
            " $ AAMP:'data.frame':\t231 obs. of  7 variables:\n",
            "  ..$ case_barcode     : chr [1:231] \"TCGA-A7-A0CE\" \"TCGA-A7-A0CE\" \"TCGA-A7-A0CH\" \"TCGA-A7-A0CH\" ...\n",
            "  ..$ sample_barcode   : chr [1:231] \"TCGA-A7-A0CE-01A\" \"TCGA-A7-A0CE-11A\" \"TCGA-A7-A0CH-01A\" \"TCGA-A7-A0CH-11A\" ...\n",
            "  ..$ aliquot_barcode  : chr [1:231] \"TCGA-A7-A0CE-01A-11R-A00Z-07\" \"TCGA-A7-A0CE-11A-21R-A089-07\" \"TCGA-A7-A0CH-01A-21R-A00Z-07\" \"TCGA-A7-A0CH-11A-32R-A089-07\" ...\n",
            "  ..$ Ensembl_gene_id_v: chr [1:231] \"ENSG00000127837.10\" \"ENSG00000127837.10\" \"ENSG00000127837.10\" \"ENSG00000127837.10\" ...\n",
            "  ..$ gene_name        : chr [1:231] \"AAMP\" \"AAMP\" \"AAMP\" \"AAMP\" ...\n",
            "  ..$ unstranded       : int [1:231] 3062 9829 6161 7860 7086 4722 7269 2941 214 4417 ...\n",
            "  ..$ sample_type_name : chr [1:231] \"Primary Tumor\" \"Solid Tissue Normal\" \"Primary Tumor\" \"Solid Tissue Normal\" ...\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "91s9mJRFcja9"
      },
      "source": [
        "We load our genes as rownames and the aliquot_barcodes as column names"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LaUykx-YOZX2",
        "outputId": "8917bcc4-bbe5-4c58-9a96-48cdb045d73b"
      },
      "source": [
        "%%R\n",
        "colnames(exp_matrix) <- split_df[[1]]$aliquot_barcode\n",
        "genes <- names(split_df)\n",
        "rownames(exp_matrix) <- genes\n",
        "str(exp_matrix, list.len=5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "'data.frame':\t2000 obs. of  231 variables:\n",
            " $ TCGA-A7-A0CE-01A-11R-A00Z-07: int  28180 3062 1306 8 1660 1029 168 16859 299 1 ...\n",
            " $ TCGA-A7-A0CE-11A-21R-A089-07: int  122749 9829 2846 1676 6656 1275 4077 12451 4387 6 ...\n",
            " $ TCGA-A7-A0CH-01A-21R-A00Z-07: int  12099 6161 32 2992 3193 978 343 5454 2849 0 ...\n",
            " $ TCGA-A7-A0CH-11A-32R-A089-07: int  85733 7860 5980 8475 8724 2671 6010 17694 6122 6 ...\n",
            " $ TCGA-A7-A0D9-01A-31R-A056-07: int  28978 7086 455 211 3870 1412 463 3870 3917 1 ...\n",
            "  [list output truncated]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MpoJ_5lvLcsN"
      },
      "source": [
        "We also need to generate vectors of which case each aliquot corresponds to as well as whether the aliquot is a tumor or normal sample. We can easily retrieve this information from our split data frame.\n",
        "\n",
        "As a quick sanity check we can generate a heatmap to look at how our aliquots cluster and generate a histogram to compare the distributions of normal and tumor expression values."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 534
        },
        "id": "RCXQc1Nqv3nW",
        "outputId": "a0c7788b-0a1c-4e39-d774-3cfc1e124bff"
      },
      "source": [
        "%%R\n",
        "colnames(exp_matrix) <- split_df[[1]]$id\n",
        "cases <- gsub('-', '.', split_df[[1]]$case_barcode)\n",
        "str(cases)\n",
        "sample_type <- gsub(' ', '.', split_df[[1]]$sample_type_name)\n",
        "str(sample_type)\n",
        "heatmap(as.matrix(exp_matrix), labCol=sample_type, rowv=NA, scale=\"row\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " chr [1:231] \"TCGA.A7.A0CE\" \"TCGA.A7.A0CE\" \"TCGA.A7.A0CH\" \"TCGA.A7.A0CH\" ...\n",
            " chr [1:231] \"Primary.Tumor\" \"Solid.Tissue.Normal\" \"Primary.Tumor\" ...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        },
        "id": "I_DFbn4g4ct1",
        "outputId": "7724512c-28bc-4cac-8b49-35b739ac202c"
      },
      "source": [
        "%%R\n",
        "norm <- log10(unlist(exp_matrix[,which(sample_type == 'Solid.Tissue.Normal')]))\n",
        "tumor <- log10(unlist(exp_matrix[,which(sample_type == 'Primary.Tumor')]))\n",
        "\n",
        "p_norm <- hist(norm, breaks=seq(0,8,0.1), plot=0)\n",
        "p_tumor <- hist(tumor, breaks=seq(0,8,0.1), plot=0)\n",
        "plot( p_norm, col=rgb(0,0,1,1/8), xlim=c(0,8), xlab='log10(count)', main='')\n",
        "plot( p_tumor, col=rgb(1,0,0,1/8), xlim=c(0,8), add=T)\n",
        "legend(\"topright\", legend=c(\"Normal\", \"Tumor\"), fill=c(rgb(0,0,1,1/8), rgb(1,0,0,1/8)), bty=\"n\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pju8tPYdM3Ra"
      },
      "source": [
        "# Calculating differential expression using edgeR\n",
        "We can now feed these data to edgeR, generate significance values of differential expression, and visualize the results using a scatterplot. Genes that fall below a 0.01 significance threshold are color coded red."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YTATTXaU0ttu",
        "outputId": "319735da-dc71-4d73-d9a9-5a7de116b292"
      },
      "source": [
        "%%R\n",
        "dge_obj <- DGEList(\n",
        "             counts=exp_matrix,\n",
        "             lib.size=colSums(exp_matrix),\n",
        "             samples=cases,\n",
        "             group=sample_type)\n",
        "disp <- estimateDisp(dge_obj, method=\"pooled\")\n",
        "edgeR_test <- exactTest(disp)\n",
        "results_df <- topTags(edgeR_test, nrow(exp_matrix))@.Data[[1]]\n",
        "str(results_df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:rpy2.rinterface_lib.callbacks:R[write to console]: Using classic mode.\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "'data.frame':\t2000 obs. of  4 variables:\n",
            " $ logFC : num  2.89 3.38 -3.46 2.34 3.4 ...\n",
            " $ logCPM: num  9.64 6.54 12.97 11.38 6.31 ...\n",
            " $ PValue: num  4.08e-121 3.55e-115 4.38e-115 1.47e-108 3.14e-108 ...\n",
            " $ FDR   : num  8.17e-118 2.92e-112 2.92e-112 7.37e-106 1.26e-105 ...\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 515
        },
        "id": "D6injlNmZYjH",
        "outputId": "b2b1aa21-a7e3-41b0-bfa9-e60bdbaeeb02"
      },
      "source": [
        "%%R\n",
        "colors <- c(alpha('red', 0.3), alpha('black', 0.6))[(results_df$FDR > 0.01)+1]\n",
        "plot(results_df$logCPM, results_df$logFC,\n",
        "     pch=19, cex=1.7, col=colors,\n",
        "     xlab='log(fold change)', ylab='log(counts per million)')\n",
        "sum(results_df$FDR <= 0.01)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1] 1587\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dcI2Z4qHNj2j"
      },
      "source": [
        "# Or perform the same calcuations using DESeq2\n",
        "As above we can perform comparisons using DESeq2 instead. We generate the expected input matrix and sample information data frame and run the model on our data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fjLrYs1lT4E0",
        "outputId": "d54d0452-40d2-4d8d-a961-ccedc3b44ba0"
      },
      "source": [
        "%%R\n",
        "col_info <- as.data.frame(cbind(cases, sample_type), stringAsFactors=TRUE)\n",
        "deseq_obj <- DESeqDataSetFromMatrix( countData = exp_matrix, colData= col_info, design= ~ cases + sample_type)\n",
        "deseq_obj <- estimateSizeFactors(deseq_obj)\n",
        "deseq_obj <- estimateDispersions(deseq_obj)\n",
        "deseq_obj <- nbinomWaldTest(deseq_obj)\n",
        "deseq_output <- results(deseq_obj)\n",
        "summary(deseq_output)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:rpy2.rinterface_lib.callbacks:R[write to console]: gene-wise dispersion estimates\n",
            "\n",
            "WARNING:rpy2.rinterface_lib.callbacks:R[write to console]: mean-dispersion relationship\n",
            "\n",
            "WARNING:rpy2.rinterface_lib.callbacks:R[write to console]: final dispersion estimates\n",
            "\n",
            "WARNING:rpy2.rinterface_lib.callbacks:R[write to console]: 20 rows did not converge in beta, labelled in mcols(object)$betaConv. Use larger maxit argument with nbinomWaldTest\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "out of 2000 with nonzero total read count\n",
            "adjusted p-value < 0.1\n",
            "LFC > 0 (up)       : 911, 46%\n",
            "LFC < 0 (down)     : 828, 41%\n",
            "outliers [1]       : 0, 0%\n",
            "low counts [2]     : 0, 0%\n",
            "(mean count < 28)\n",
            "[1] see 'cooksCutoff' argument of ?results\n",
            "[2] see 'independentFiltering' argument of ?results\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gTKl_vCmSE1G"
      },
      "source": [
        "We can once again visualize our results plotting mean expression against fold change."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 515
        },
        "id": "MLKrsI934P9u",
        "outputId": "f0e7a5cd-9440-4737-bc86-bc1f5f6d868b"
      },
      "source": [
        "%%R\n",
        "deseq_results <- deseq_output@listData\n",
        "colors <- c(alpha('red', 0.3), alpha('black', 0.6))[(deseq_results$padj > 0.01)+1]\n",
        "plot(log(deseq_results$baseMean), deseq_results$log2FoldChange,\n",
        "     pch=19, cex=1.7, col=colors,\n",
        "     xlab='log(mean expression)', ylab='log2(fold change)')\n",
        "sum(deseq_results$padj <= 0.01)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1] 1600\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MCSTcfaUhgCp"
      },
      "source": [
        "# Summary\n",
        "It is quite simple to retrieve Genomic Data Commons gene expression data through the ISB-CGC BQ ecosystem. These data can be subset and summarized easily via SQL queries and the results loaded into either R or Python for further analysis and visualization."
      ]
    }
  ]
}