BQMachineLearning.ipynb

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    "colab": {
      "name": "BigQueryMachineLearning.ipynb",
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      "name": "R"
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        "<a href=\"https://colab.research.google.com/github/isb-cgc/ISB-CGC-Demos/blob/jhp-dev/BigQueryMachineLearning.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "# Introduction to BigQuery Machine Learning\n",
        "\n",
        "This notebook demonstrates how to build a BigQuery Machine Learning (ML) model to predict breast cancer tumor status (i.e., tumor vs. normal) using RNA-seq data. For a more general overview of machine learning, see this [review](https://homes.cs.washington.edu/~pedrod/papers/cacm12.pdf). \n",
        "\n",
        "This notebook steps through the following process, which is also illustrated in the figure below:\n",
        "\n",
        "1. Retrieve the data from the ISB-CGC BigQuery table and split it into training and testing data. \n",
        "2. Optimize model parameters, including feature size, normalization method, classifier type, and classifier parameters. In this tutorial, we will make assumptions about the optimal parameters. \n",
        "3. Normalize the data. \n",
        "4. Select features, i.e., differentially expressed genes to be used in the prediction model.\n",
        "5. Train the prediction model.\n",
        "6. Evaluate the prediction model's performance.\n",
        "7. Use the model to predict the labels of future samples.\n",
        "\n",
        "![train-test-norm-Page-1.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rYhVE5CDYBfr"
      },
      "source": [
        "\n",
        "\n",
        "This notebook queries and creates data tables and models in BigQuery and may incur some costs. Though the costs for this notebook's operations are minimal, we urge you to be cautious when running BigQuery commands. Please see [Google's BigQuery Pricing](https://cloud.google.com/bigquery/pricing) page for more information. \n",
        "\n",
        "## Contact Us\n",
        "\n",
        "Please contact us to learn more about BigQuery, to discuss cost considerations when working with BigQuery projects, or to discuss any projects you feel may benefit from the ISB-CGC Platform. \n",
        "\n",
        "- Email us: [feedback@isb-cgc.org](mailto:feedback@isb-cgc.org)\n",
        "- Check out our website: [https://isb-cgc.org](https://isb-cgc.org)\n",
        "- Visit our [office hours](https://isb-cancer-genomics-cloud.readthedocs.io/en/latest/sections/office_hours.html)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M6CmV_OB0uSQ"
      },
      "source": [
        "## Glossary of Terms\n",
        "\n",
        "Refer to the following tables of terms as you work through the notebook."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SbMqYTXh0-qP"
      },
      "source": [
        "### BigQuery Projects and Tables\n",
        "\n",
        "This notebook will read and/or create the following BigQuery tables or ML models. \n",
        "\n",
        "| Term | Description |\n",
        "| :- | :- |\n",
        "| isb-cgc-bq | Name of the BigQuery project containing public ISB-CGC data |\n",
        "| TCGA_versioned | Name of the BigQuery dataset containing versioned TCGA data |\n",
        "| RNAseq_hg38_gdc_r28 | Name of the BigQuery table containing TCGA gene expression data |\n",
        "| tcga_brca_ml\\_table\\_* | Name of the temporary BigQuery table created for BigQuery ML |\n",
        "| tcga_brca_ml\\_model\\_* | Name of the BigQuery ML model created by this notebook |"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3DalW8Y71HbI"
      },
      "source": [
        "\n",
        "### BigQuery Table Fields of Interest\n",
        "\n",
        "The following are RNAseq_hg38_gdc_r28 table fields used in this notebook. See the [ISB-CGC BigQuery Table Search](https://isb-cgc.appspot.com/bq_meta_search/) for more details. \n",
        "\n",
        "| Term | Description |\n",
        "| :- | :- |\n",
        "| case_barcode | Original case barcode |\n",
        "| gene_name | Gene name e.g., TTN, DDR1, etc. |\n",
        "| HTSeq__Counts | Number of mapped reads to each gene as calculated by the Python package HTSeq. (See [documentation](https://docs.gdc.cancer.gov/Encyclopedia/pages/HTSeq-Counts/) for more information)  |\n",
        "| HTSeq__FPKM_UQ | FPKM is implemented at the GDC on gene-level read counts that are produced by HTSeq1 and generated using custom scripts. (See [documentation](https://docs.gdc.cancer.gov/Encyclopedia/pages/HTSeq-FPKM/) for more information) |\n",
        "| sample_type_name | The longer name of the sample type; e.g. Primary Tumor, Solid Normal Tissue, Metastatic, etc. |\n",
        "| gene_type | The type of genetic element to which the reads mapped; e.g. protein_coding, ribozyme, etc. |\n",
        "| project_short_name | Project name abbreviation; the program name appended with a project name abbreviation; e.g., TCGA-BRCA, etc. |"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U0Y9L6Hv1MI_"
      },
      "source": [
        "### Important BigQuery Functions\n",
        "\n",
        "More information about BigQuery SQL syntax can be found [here](https://cloud.google.com/bigquery/docs/reference/standard-sql/query-syntax). \n",
        "\n",
        "| Term | Description |\n",
        "| :- | :- |\n",
        "| CREATE OR REPLACE MODEL | Create a BigQuery ML model |\n",
        "| ML.EVALUATE | Evaluate the performance of a BigQuery ML model |\n",
        "| ML.PREDICT | Predict the labels and class probabilities of a BigQuery ML Model |\n",
        "| FARM_FINGERPRINT | Generates a \"random\" integer from a string, useful for data splitting |\n",
        "| UNION ALL | Combine the results of multiple queries |\n",
        "| PIVOT | Convert table rows to columns |"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k2mD1f5Oze58"
      },
      "source": [
        "# Initialize Notebook Environment\n",
        "\n",
        "Before running the analysis, install and load dependencies, authenticate to BigQuery, and customize notebook parameters. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YpHycYStU5c5"
      },
      "source": [
        "## Install and Load Dependencies\n",
        "Load all R package dependencies, installing if necessary. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UXVqtXrgCI82"
      },
      "source": [
        "# BigQuery library\n",
        "if (!require(bigrquery)) {\n",
        "  print('Installing bigrquery package')\n",
        "  install.packages('bigrquery')\n",
        "  if (!require(bigrquery)) {\n",
        "    print('Cannot install bigrquery package')\n",
        "  }\n",
        "}\n",
        "\n",
        "# Tidyr package\n",
        "if (!require('tidyr')) {\n",
        "  print('Installing tidyr package')\n",
        "  install.packages('tidyr')\n",
        "  if (!require('tidyr')) {\n",
        "    print('Cannot install tidyr')\n",
        "  }\n",
        "}\n",
        "\n",
        "# Scales package to support visualization\n",
        "if (!require('scales')) {\n",
        "  print('Installing scales package')\n",
        "  install.packages('scales')\n",
        "  if (!require('scales')) {\n",
        "    print('Cannot install scales')\n",
        "  }\n",
        "}\n",
        "\n",
        "# Resample package for basic statistical functions\n",
        "if (!require('resample')) {\n",
        "  print('Installing resample package')\n",
        "  install.packages('resample')\n",
        "  if (!require('resample')) {\n",
        "    print('Cannot install resample')\n",
        "  }\n",
        "}\n",
        "\n",
        "# Digest package for generating random strings\n",
        "if (!require('digest')) {\n",
        "  print('Installing digest package')\n",
        "  install.packages('digest')\n",
        "  if (!require('digest')) {\n",
        "    print('Cannot install digest')\n",
        "  }\n",
        "}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gwk3SIpWvvZP"
      },
      "source": [
        "## Authenticate to Access BigQuery\n",
        "\n",
        "Before using BigQuery, we need to get authorization for access to BigQuery and the Google Cloud. For more information see ['Quick Start Guide to ISB-CGC'](https://isb-cancer-genomics-cloud.readthedocs.io/en/latest/sections/HowToGetStartedonISB-CGC.html). "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S8L9t8xzxtTO"
      },
      "source": [
        "# Authenticate to BQ\n",
        "bq_auth(path = \"~/key-file\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ayz7JytvYOBk"
      },
      "source": [
        "## Set Parameters\n",
        "Set or update notebook parameters in this section."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WWeICtjTMRl2",
        "outputId": "057772e6-2409-427c-bb08-d36449436161"
      },
      "source": [
        "# Generate a random string so that your tables and models are unique\n",
        "my_name <- \"My Name\" ### important, change this to your name\n",
        "current_date <- as.character(as.numeric(Sys.time()))\n",
        "\n",
        "# create a random string\n",
        "hash <- substr(\n",
        "  digest(paste(my_name, current_date, sep = ''), algo = \"sha256\"), 1, 8\n",
        ")\n",
        "\n",
        "print(sprintf('Your random hash: %s', hash))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1] \"Your random hash: 1e25d19b\"\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ahf2HPSpYQci"
      },
      "source": [
        "# set the google project that will be billed for this notebook's computations\n",
        "google_project <- 'isb-cgc-training-001'\n",
        "\n",
        "# Set the number of feature to use for the ML model. This corresponds to the\n",
        "# number of genes to use in the dataset used to train/test the ML model.\n",
        "num_ml_features <- 30\n",
        "\n",
        "# BQ project for storing tmp table and ML model\n",
        "bq_project <- 'isb-cgc-training-001'\n",
        "\n",
        "# BQ dataset for storing tmp table and ML model\n",
        "bq_dataset <- 'nih_library_2021_10'\n",
        "\n",
        "# BQ tmp table name for training and testing data\n",
        "bq_tmp_table <- paste('tcga_brca_ml_table_', hash, sep = '') \n",
        "\n",
        "# Name of BQ ML model\n",
        "bq_ml_model <- paste('tcga_brca_ml_model_', hash, sep = '')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7hhC7IiFYweL"
      },
      "source": [
        "# Prepare Gene Expression Data for Prediction Modeling\n",
        "\n",
        "In the first step, we construct a query to extract data from a BigQuery table, then prepare it for feature selection and prediction modeling by splitting it into training and testing sets. More information about data splitting can be found [here](https://developers.google.com/machine-learning/crash-course/training-and-test-sets/splitting-data). \n",
        "\n",
        "![train-test-norm-Page-2.png](data:image/png;base64,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)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tluR-IuxwrcY"
      },
      "source": [
        "## Query Gene Expression Data\n",
        "\n",
        "The first component of the query extracts expression values of all TCGA-BRCA protein-coding genes:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3tVcNb3ZjECL"
      },
      "source": [
        "rna_brca_query <- sprintf(\"\n",
        "  -- This table contains RNAseq expression of protein-coding genes for  \n",
        "  -- TCGA BRCA normal and cancer samples. \n",
        "  SELECT \n",
        "    case_barcode,\n",
        "    gene_name,\n",
        "    HTSeq__Counts,\n",
        "    HTSeq__FPKM_UQ,\n",
        "    sample_type_name\n",
        "\n",
        "  FROM `isb-cgc-bq.TCGA_versioned.RNAseq_hg38_gdc_r28` AS tcga_rna\n",
        "\n",
        "  WHERE\n",
        "    gene_type = 'protein_coding'\n",
        "    AND project_short_name = 'TCGA-BRCA'\n",
        "\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u5OpFoDUkBmk"
      },
      "source": [
        "## Filter Samples Using Metadata\n",
        "\n",
        "Using the table created by the previous query, we filter the cases to include only cases that have BOTH tumor and normal samples and no metastatic samples. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ALR5AyHDk2ZW"
      },
      "source": [
        "cases_query <- sprintf(\"\n",
        "  -- This table filters for cases that include both normal and tumor samples\n",
        "  SELECT\n",
        "    case_barcode,\n",
        "    agg_sample_type_name\n",
        "\n",
        "  FROM (\n",
        "    SELECT\n",
        "      case_barcode,\n",
        "      -- combine all sample types for each case into a single array\n",
        "      ARRAY_AGG(DISTINCT sample_type_name) AS agg_sample_type_name\n",
        "\n",
        "    FROM rna_brca\n",
        "    \n",
        "    GROUP BY case_barcode\n",
        "  )\n",
        "  \n",
        "  WHERE \n",
        "    'Solid Tissue Normal' IN UNNEST(agg_sample_type_name)\n",
        "    AND 'Primary Tumor' IN UNNEST(agg_sample_type_name)\n",
        "    AND 'Metastatic' NOT IN UNNEST(agg_sample_type_name)\n",
        "\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IZnz5vMnlkxx"
      },
      "source": [
        "## Label Samples and Split into Training & Testing Sets\n",
        "\n",
        "Now we join the \"rna_brca\" and \"cases\" tables to produce the final query. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uTBJ0EFGltIJ",
        "outputId": "3fb1a5bc-d4dd-4001-c8b5-6f0b1c71e9c9"
      },
      "source": [
        "# Construct the final query\n",
        "ge_query <- sprintf(\"\n",
        "WITH rna_brca AS (%s),\n",
        "\n",
        "cases AS (%s)\n",
        "\n",
        "SELECT \n",
        "  rna_brca.case_barcode,\n",
        "  gene_name,\n",
        "  \n",
        "  -- In situations where there are multiple records for each case, take the \n",
        "  -- highest RNAseq count value as the expression for that gene\n",
        "  MAX(HTSeq__Counts) AS gene_expression,\n",
        "  sample_type_name,\n",
        "\n",
        "  CASE\n",
        "    -- Assign a numeric label to each case depending on sample type\n",
        "    WHEN sample_type_name = 'Primary Tumor' THEN 1\n",
        "    WHEN sample_type_name = 'Solid Tissue Normal' THEN 0\n",
        "  END AS label,\n",
        "\n",
        "  CASE\n",
        "    -- Randomly assign each case to the training or testing dataset\n",
        "    WHEN MOD(ABS(FARM_FINGERPRINT(rna_brca.case_barcode)), 10) < 5 THEN 'training'\n",
        "    WHEN MOD(ABS(FARM_FINGERPRINT(rna_brca.case_barcode)), 10) >= 5 THEN 'testing'\n",
        "  END AS data_partition\n",
        "\n",
        "FROM rna_brca\n",
        "\n",
        "INNER JOIN cases \n",
        "  ON rna_brca.case_barcode = cases.case_barcode\n",
        "\n",
        "GROUP BY case_barcode, gene_name, sample_type_name\n",
        "ORDER BY gene_name, case_barcode\n",
        "\", rna_brca_query, cases_query)\n",
        "\n",
        "# Display the final query\n",
        "cat(ge_query)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "WITH rna_brca AS (\n",
            "  -- This table contains RNAseq expression of protein-coding genes for  \n",
            "  -- TCGA BRCA normal and cancer samples. \n",
            "  SELECT \n",
            "    case_barcode,\n",
            "    gene_name,\n",
            "    HTSeq__Counts,\n",
            "    HTSeq__FPKM_UQ,\n",
            "    sample_type_name\n",
            "\n",
            "  FROM `isb-cgc-bq.TCGA_versioned.RNAseq_hg38_gdc_r28` AS tcga_rna\n",
            "\n",
            "  WHERE\n",
            "    gene_type = 'protein_coding'\n",
            "    AND project_short_name = 'TCGA-BRCA'\n",
            "),\n",
            "\n",
            "cases AS (\n",
            "  -- This table filters for cases that include both normal and tumor samples\n",
            "  SELECT\n",
            "    case_barcode,\n",
            "    agg_sample_type_name\n",
            "\n",
            "  FROM (\n",
            "    SELECT\n",
            "      case_barcode,\n",
            "      -- combine all sample types for each case into a single array\n",
            "      ARRAY_AGG(DISTINCT sample_type_name) AS agg_sample_type_name\n",
            "\n",
            "    FROM rna_brca\n",
            "    \n",
            "    GROUP BY case_barcode\n",
            "  )\n",
            "  \n",
            "  WHERE \n",
            "    'Solid Tissue Normal' IN UNNEST(agg_sample_type_name)\n",
            "    AND 'Primary Tumor' IN UNNEST(agg_sample_type_name)\n",
            "    AND 'Metastatic' NOT IN UNNEST(agg_sample_type_name)\n",
            ")\n",
            "\n",
            "SELECT \n",
            "  rna_brca.case_barcode,\n",
            "  gene_name,\n",
            "  \n",
            "  -- In situations where there are multiple records for each case, take the \n",
            "  -- highest RNAseq count value as the expression for that gene\n",
            "  MAX(HTSeq__Counts) AS gene_expression,\n",
            "  sample_type_name,\n",
            "\n",
            "  CASE\n",
            "    -- Assign a numeric label to each case depending on sample type\n",
            "    WHEN sample_type_name = 'Primary Tumor' THEN 1\n",
            "    WHEN sample_type_name = 'Solid Tissue Normal' THEN 0\n",
            "  END AS label,\n",
            "\n",
            "  CASE\n",
            "    -- Randomly assign each case to the training or testing dataset\n",
            "    WHEN MOD(ABS(FARM_FINGERPRINT(rna_brca.case_barcode)), 10) < 5 THEN 'training'\n",
            "    WHEN MOD(ABS(FARM_FINGERPRINT(rna_brca.case_barcode)), 10) >= 5 THEN 'testing'\n",
            "  END AS data_partition\n",
            "\n",
            "FROM rna_brca\n",
            "\n",
            "INNER JOIN cases \n",
            "  ON rna_brca.case_barcode = cases.case_barcode\n",
            "\n",
            "GROUP BY case_barcode, gene_name, sample_type_name\n",
            "ORDER BY gene_name, case_barcode\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XgomQrWl2aVt"
      },
      "source": [
        "## Execute the Query to Retrieve Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "kimnGcREnpnn",
        "outputId": "e7745aaf-477d-4dac-c984-bbde324e89a6"
      },
      "source": [
        "# Run the query\n",
        "ge_table <- bq_project_query(google_project, ge_query, quiet = TRUE)\n",
        "\n",
        "# Download the query result\n",
        "ge_data <- bq_table_download(ge_table, quiet = TRUE)\n",
        "\n",
        "# Show the dataframe\n",
        "head(ge_data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  case_barcode gene_name gene_expression sample_type_name    label\n",
              "1 TCGA-A7-A0CE A1BG      28              Solid Tissue Normal 0    \n",
              "2 TCGA-A7-A0CE A1BG      11              Primary Tumor       1    \n",
              "3 TCGA-A7-A0CH A1BG      21              Primary Tumor       1    \n",
              "4 TCGA-A7-A0CH A1BG      26              Solid Tissue Normal 0    \n",
              "5 TCGA-A7-A0D9 A1BG      26              Primary Tumor       1    \n",
              "6 TCGA-A7-A0D9 A1BG       5              Solid Tissue Normal 0    \n",
              "  data_partition\n",
              "1 training      \n",
              "2 training      \n",
              "3 testing       \n",
              "4 testing       \n",
              "5 testing       \n",
              "6 testing       "
            ],
            "text/latex": "A tibble: 6 × 6\n\\begin{tabular}{llllll}\n case\\_barcode & gene\\_name & gene\\_expression & sample\\_type\\_name & label & data\\_partition\\\\\n <chr> & <chr> & <int> & <chr> & <int> & <chr>\\\\\n\\hline\n\t TCGA-A7-A0CE & A1BG & 28 & Solid Tissue Normal & 0 & training\\\\\n\t TCGA-A7-A0CE & A1BG & 11 & Primary Tumor       & 1 & training\\\\\n\t TCGA-A7-A0CH & A1BG & 21 & Primary Tumor       & 1 & testing \\\\\n\t TCGA-A7-A0CH & A1BG & 26 & Solid Tissue Normal & 0 & testing \\\\\n\t TCGA-A7-A0D9 & A1BG & 26 & Primary Tumor       & 1 & testing \\\\\n\t TCGA-A7-A0D9 & A1BG &  5 & Solid Tissue Normal & 0 & testing \\\\\n\\end{tabular}\n",
            "text/markdown": "\nA tibble: 6 × 6\n\n| case_barcode &lt;chr&gt; | gene_name &lt;chr&gt; | gene_expression &lt;int&gt; | sample_type_name &lt;chr&gt; | label &lt;int&gt; | data_partition &lt;chr&gt; |\n|---|---|---|---|---|---|\n| TCGA-A7-A0CE | A1BG | 28 | Solid Tissue Normal | 0 | training |\n| TCGA-A7-A0CE | A1BG | 11 | Primary Tumor       | 1 | training |\n| TCGA-A7-A0CH | A1BG | 21 | Primary Tumor       | 1 | testing  |\n| TCGA-A7-A0CH | A1BG | 26 | Solid Tissue Normal | 0 | testing  |\n| TCGA-A7-A0D9 | A1BG | 26 | Primary Tumor       | 1 | testing  |\n| TCGA-A7-A0D9 | A1BG |  5 | Solid Tissue Normal | 0 | testing  |\n\n",
            "text/html": [
              "<table class=\"dataframe\">\n",
              "<caption>A tibble: 6 × 6</caption>\n",
              "<thead>\n",
              "\t<tr><th scope=col>case_barcode</th><th scope=col>gene_name</th><th scope=col>gene_expression</th><th scope=col>sample_type_name</th><th scope=col>label</th><th scope=col>data_partition</th></tr>\n",
              "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><td>TCGA-A7-A0CE</td><td>A1BG</td><td>28</td><td>Solid Tissue Normal</td><td>0</td><td>training</td></tr>\n",
              "\t<tr><td>TCGA-A7-A0CE</td><td>A1BG</td><td>11</td><td>Primary Tumor      </td><td>1</td><td>training</td></tr>\n",
              "\t<tr><td>TCGA-A7-A0CH</td><td>A1BG</td><td>21</td><td>Primary Tumor      </td><td>1</td><td>testing </td></tr>\n",
              "\t<tr><td>TCGA-A7-A0CH</td><td>A1BG</td><td>26</td><td>Solid Tissue Normal</td><td>0</td><td>testing </td></tr>\n",
              "\t<tr><td>TCGA-A7-A0D9</td><td>A1BG</td><td>26</td><td>Primary Tumor      </td><td>1</td><td>testing </td></tr>\n",
              "\t<tr><td>TCGA-A7-A0D9</td><td>A1BG</td><td> 5</td><td>Solid Tissue Normal</td><td>0</td><td>testing </td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "avtkdrbsvPxC"
      },
      "source": [
        "The previous query labeled the data as either \"training\" or \"testing\" using the \"FARM_FINGERPRINT\" function. More information about that function can be found [here](https://cloud.google.com/bigquery/docs/reference/standard-sql/hash_functions). We use those labels to pull the training data into a data frame. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "NPPEtbqA22E0",
        "outputId": "be424c29-275a-468e-afb5-edc4e10b339d"
      },
      "source": [
        "# Pull the training data into a data frame\n",
        "ge_train_data <- ge_data[ge_data$data_partition == 'training',]\n",
        "\n",
        "# Display preview of data frame\n",
        "head(ge_train_data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  case_barcode gene_name gene_expression sample_type_name    label\n",
              "1 TCGA-A7-A0CE A1BG       28             Solid Tissue Normal 0    \n",
              "2 TCGA-A7-A0CE A1BG       11             Primary Tumor       1    \n",
              "3 TCGA-A7-A0DB A1BG        6             Solid Tissue Normal 0    \n",
              "4 TCGA-A7-A0DB A1BG       47             Primary Tumor       1    \n",
              "5 TCGA-AC-A2FM A1BG      259             Primary Tumor       1    \n",
              "6 TCGA-AC-A2FM A1BG       48             Solid Tissue Normal 0    \n",
              "  data_partition\n",
              "1 training      \n",
              "2 training      \n",
              "3 training      \n",
              "4 training      \n",
              "5 training      \n",
              "6 training      "
            ],
            "text/latex": "A tibble: 6 × 6\n\\begin{tabular}{llllll}\n case\\_barcode & gene\\_name & gene\\_expression & sample\\_type\\_name & label & data\\_partition\\\\\n <chr> & <chr> & <int> & <chr> & <int> & <chr>\\\\\n\\hline\n\t TCGA-A7-A0CE & A1BG &  28 & Solid Tissue Normal & 0 & training\\\\\n\t TCGA-A7-A0CE & A1BG &  11 & Primary Tumor       & 1 & training\\\\\n\t TCGA-A7-A0DB & A1BG &   6 & Solid Tissue Normal & 0 & training\\\\\n\t TCGA-A7-A0DB & A1BG &  47 & Primary Tumor       & 1 & training\\\\\n\t TCGA-AC-A2FM & A1BG & 259 & Primary Tumor       & 1 & training\\\\\n\t TCGA-AC-A2FM & A1BG &  48 & Solid Tissue Normal & 0 & training\\\\\n\\end{tabular}\n",
            "text/markdown": "\nA tibble: 6 × 6\n\n| case_barcode &lt;chr&gt; | gene_name &lt;chr&gt; | gene_expression &lt;int&gt; | sample_type_name &lt;chr&gt; | label &lt;int&gt; | data_partition &lt;chr&gt; |\n|---|---|---|---|---|---|\n| TCGA-A7-A0CE | A1BG |  28 | Solid Tissue Normal | 0 | training |\n| TCGA-A7-A0CE | A1BG |  11 | Primary Tumor       | 1 | training |\n| TCGA-A7-A0DB | A1BG |   6 | Solid Tissue Normal | 0 | training |\n| TCGA-A7-A0DB | A1BG |  47 | Primary Tumor       | 1 | training |\n| TCGA-AC-A2FM | A1BG | 259 | Primary Tumor       | 1 | training |\n| TCGA-AC-A2FM | A1BG |  48 | Solid Tissue Normal | 0 | training |\n\n",
            "text/html": [
              "<table class=\"dataframe\">\n",
              "<caption>A tibble: 6 × 6</caption>\n",
              "<thead>\n",
              "\t<tr><th scope=col>case_barcode</th><th scope=col>gene_name</th><th scope=col>gene_expression</th><th scope=col>sample_type_name</th><th scope=col>label</th><th scope=col>data_partition</th></tr>\n",
              "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><td>TCGA-A7-A0CE</td><td>A1BG</td><td> 28</td><td>Solid Tissue Normal</td><td>0</td><td>training</td></tr>\n",
              "\t<tr><td>TCGA-A7-A0CE</td><td>A1BG</td><td> 11</td><td>Primary Tumor      </td><td>1</td><td>training</td></tr>\n",
              "\t<tr><td>TCGA-A7-A0DB</td><td>A1BG</td><td>  6</td><td>Solid Tissue Normal</td><td>0</td><td>training</td></tr>\n",
              "\t<tr><td>TCGA-A7-A0DB</td><td>A1BG</td><td> 47</td><td>Primary Tumor      </td><td>1</td><td>training</td></tr>\n",
              "\t<tr><td>TCGA-AC-A2FM</td><td>A1BG</td><td>259</td><td>Primary Tumor      </td><td>1</td><td>training</td></tr>\n",
              "\t<tr><td>TCGA-AC-A2FM</td><td>A1BG</td><td> 48</td><td>Solid Tissue Normal</td><td>0</td><td>training</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "goV5lhvuu9oQ"
      },
      "source": [
        "## Pivot Data Frame\n",
        "\n",
        "Now we pivot the data frame so that each row corresponds to a gene or a sample label, and each column corresponds to a sample. This makes it easier to analyze downstream. We use the \"spread()\" function to do this:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "id": "6ZnjuSta4s8F",
        "outputId": "8b60d8db-7bb5-495a-8bc6-0ff3e69f8e62"
      },
      "source": [
        "# Create pivot table\n",
        "ge_train_data_pivot = spread(\n",
        "  ge_train_data,\n",
        "  key = 'gene_name',\n",
        "  value = 'gene_expression'\n",
        ")\n",
        "\n",
        "# Display preview of table\n",
        "head(ge_train_data_pivot)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  case_barcode sample_type_name    label data_partition A1BG A1CF A2M    A2ML1\n",
              "1 TCGA-A7-A0CE Primary Tumor       1     training        11  11    30166  89  \n",
              "2 TCGA-A7-A0CE Solid Tissue Normal 0     training        28  10   136313 114  \n",
              "3 TCGA-A7-A0DB Primary Tumor       1     training        47  33    35509  26  \n",
              "4 TCGA-A7-A0DB Solid Tissue Normal 0     training         6   0    90997  52  \n",
              "5 TCGA-AC-A2FM Primary Tumor       1     training       259   4    24126  12  \n",
              "6 TCGA-AC-A2FM Solid Tissue Normal 0     training        48   3    61379  17  \n",
              "  A3GALT2 A4GALT ⋯ ZWILCH ZWINT ZXDA ZXDB ZXDC ZYG11A ZYG11B ZYX   ZZEF1 ZZZ3\n",
              "1 5        405   ⋯ 3634   4032  413  1297 1659 189    2970   13762 4390  3752\n",
              "2 2       2279   ⋯  788    775  258  1314 2153 235    2544   22402 4525  3802\n",
              "3 4        514   ⋯  899   1536  405  1327 1879 196    3513   10182 3501  3236\n",
              "4 0       1593   ⋯  445    387  251   702 1281   6    3098    9682 3871  1591\n",
              "5 0       2894   ⋯ 1086   1823  376  2743 2153 143    1676    8745 7277  1806\n",
              "6 2       2053   ⋯  464    238  158   715 1336 128    1185   35824 2896  2001"
            ],
            "text/latex": "A tibble: 6 × 19716\n\\begin{tabular}{lllllllllllllllllllll}\n case\\_barcode & sample\\_type\\_name & label & data\\_partition & A1BG & A1CF & A2M & A2ML1 & A3GALT2 & A4GALT & ⋯ & ZWILCH & ZWINT & ZXDA & ZXDB & ZXDC & ZYG11A & ZYG11B & ZYX & ZZEF1 & ZZZ3\\\\\n <chr> & <chr> & <int> & <chr> & <int> & <int> & <int> & <int> & <int> & <int> & ⋯ & <int> & <int> & <int> & <int> & <int> & <int> & <int> & <int> & <int> & <int>\\\\\n\\hline\n\t TCGA-A7-A0CE & Primary Tumor       & 1 & training &  11 & 11 &  30166 &  89 & 5 &  405 & ⋯ & 3634 & 4032 & 413 & 1297 & 1659 & 189 & 2970 & 13762 & 4390 & 3752\\\\\n\t TCGA-A7-A0CE & Solid Tissue Normal & 0 & training &  28 & 10 & 136313 & 114 & 2 & 2279 & ⋯ &  788 &  775 & 258 & 1314 & 2153 & 235 & 2544 & 22402 & 4525 & 3802\\\\\n\t TCGA-A7-A0DB & Primary Tumor       & 1 & training &  47 & 33 &  35509 &  26 & 4 &  514 & ⋯ &  899 & 1536 & 405 & 1327 & 1879 & 196 & 3513 & 10182 & 3501 & 3236\\\\\n\t TCGA-A7-A0DB & Solid Tissue Normal & 0 & training &   6 &  0 &  90997 &  52 & 0 & 1593 & ⋯ &  445 &  387 & 251 &  702 & 1281 &   6 & 3098 &  9682 & 3871 & 1591\\\\\n\t TCGA-AC-A2FM & Primary Tumor       & 1 & training & 259 &  4 &  24126 &  12 & 0 & 2894 & ⋯ & 1086 & 1823 & 376 & 2743 & 2153 & 143 & 1676 &  8745 & 7277 & 1806\\\\\n\t TCGA-AC-A2FM & Solid Tissue Normal & 0 & training &  48 &  3 &  61379 &  17 & 2 & 2053 & ⋯ &  464 &  238 & 158 &  715 & 1336 & 128 & 1185 & 35824 & 2896 & 2001\\\\\n\\end{tabular}\n",
            "text/markdown": "\nA tibble: 6 × 19716\n\n| case_barcode &lt;chr&gt; | sample_type_name &lt;chr&gt; | label &lt;int&gt; | data_partition &lt;chr&gt; | A1BG &lt;int&gt; | A1CF &lt;int&gt; | A2M &lt;int&gt; | A2ML1 &lt;int&gt; | A3GALT2 &lt;int&gt; | A4GALT &lt;int&gt; | ⋯ ⋯ | ZWILCH &lt;int&gt; | ZWINT &lt;int&gt; | ZXDA &lt;int&gt; | ZXDB &lt;int&gt; | ZXDC &lt;int&gt; | ZYG11A &lt;int&gt; | ZYG11B &lt;int&gt; | ZYX &lt;int&gt; | ZZEF1 &lt;int&gt; | ZZZ3 &lt;int&gt; |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| TCGA-A7-A0CE | Primary Tumor       | 1 | training |  11 | 11 |  30166 |  89 | 5 |  405 | ⋯ | 3634 | 4032 | 413 | 1297 | 1659 | 189 | 2970 | 13762 | 4390 | 3752 |\n| TCGA-A7-A0CE | Solid Tissue Normal | 0 | training |  28 | 10 | 136313 | 114 | 2 | 2279 | ⋯ |  788 |  775 | 258 | 1314 | 2153 | 235 | 2544 | 22402 | 4525 | 3802 |\n| TCGA-A7-A0DB | Primary Tumor       | 1 | training |  47 | 33 |  35509 |  26 | 4 |  514 | ⋯ |  899 | 1536 | 405 | 1327 | 1879 | 196 | 3513 | 10182 | 3501 | 3236 |\n| TCGA-A7-A0DB | Solid Tissue Normal | 0 | training |   6 |  0 |  90997 |  52 | 0 | 1593 | ⋯ |  445 |  387 | 251 |  702 | 1281 |   6 | 3098 |  9682 | 3871 | 1591 |\n| TCGA-AC-A2FM | Primary Tumor       | 1 | training | 259 |  4 |  24126 |  12 | 0 | 2894 | ⋯ | 1086 | 1823 | 376 | 2743 | 2153 | 143 | 1676 |  8745 | 7277 | 1806 |\n| TCGA-AC-A2FM | Solid Tissue Normal | 0 | training |  48 |  3 |  61379 |  17 | 2 | 2053 | ⋯ |  464 |  238 | 158 |  715 | 1336 | 128 | 1185 | 35824 | 2896 | 2001 |\n\n",
            "text/html": [
              "<table class=\"dataframe\">\n",
              "<caption>A tibble: 6 × 19716</caption>\n",
              "<thead>\n",
              "\t<tr><th scope=col>case_barcode</th><th scope=col>sample_type_name</th><th scope=col>label</th><th scope=col>data_partition</th><th scope=col>A1BG</th><th scope=col>A1CF</th><th scope=col>A2M</th><th scope=col>A2ML1</th><th scope=col>A3GALT2</th><th scope=col>A4GALT</th><th scope=col>⋯</th><th scope=col>ZWILCH</th><th scope=col>ZWINT</th><th scope=col>ZXDA</th><th scope=col>ZXDB</th><th scope=col>ZXDC</th><th scope=col>ZYG11A</th><th scope=col>ZYG11B</th><th scope=col>ZYX</th><th scope=col>ZZEF1</th><th scope=col>ZZZ3</th></tr>\n",
              "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>⋯</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><td>TCGA-A7-A0CE</td><td>Primary Tumor      </td><td>1</td><td>training</td><td> 11</td><td>11</td><td> 30166</td><td> 89</td><td>5</td><td> 405</td><td>⋯</td><td>3634</td><td>4032</td><td>413</td><td>1297</td><td>1659</td><td>189</td><td>2970</td><td>13762</td><td>4390</td><td>3752</td></tr>\n",
              "\t<tr><td>TCGA-A7-A0CE</td><td>Solid Tissue Normal</td><td>0</td><td>training</td><td> 28</td><td>10</td><td>136313</td><td>114</td><td>2</td><td>2279</td><td>⋯</td><td> 788</td><td> 775</td><td>258</td><td>1314</td><td>2153</td><td>235</td><td>2544</td><td>22402</td><td>4525</td><td>3802</td></tr>\n",
              "\t<tr><td>TCGA-A7-A0DB</td><td>Primary Tumor      </td><td>1</td><td>training</td><td> 47</td><td>33</td><td> 35509</td><td> 26</td><td>4</td><td> 514</td><td>⋯</td><td> 899</td><td>1536</td><td>405</td><td>1327</td><td>1879</td><td>196</td><td>3513</td><td>10182</td><td>3501</td><td>3236</td></tr>\n",
              "\t<tr><td>TCGA-A7-A0DB</td><td>Solid Tissue Normal</td><td>0</td><td>training</td><td>  6</td><td> 0</td><td> 90997</td><td> 52</td><td>0</td><td>1593</td><td>⋯</td><td> 445</td><td> 387</td><td>251</td><td> 702</td><td>1281</td><td>  6</td><td>3098</td><td> 9682</td><td>3871</td><td>1591</td></tr>\n",
              "\t<tr><td>TCGA-AC-A2FM</td><td>Primary Tumor      </td><td>1</td><td>training</td><td>259</td><td> 4</td><td> 24126</td><td> 12</td><td>0</td><td>2894</td><td>⋯</td><td>1086</td><td>1823</td><td>376</td><td>2743</td><td>2153</td><td>143</td><td>1676</td><td> 8745</td><td>7277</td><td>1806</td></tr>\n",
              "\t<tr><td>TCGA-AC-A2FM</td><td>Solid Tissue Normal</td><td>0</td><td>training</td><td> 48</td><td> 3</td><td> 61379</td><td> 17</td><td>2</td><td>2053</td><td>⋯</td><td> 464</td><td> 238</td><td>158</td><td> 715</td><td>1336</td><td>128</td><td>1185</td><td>35824</td><td>2896</td><td>2001</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2kiE6h1rDfra"
      },
      "source": [
        "# Optimize Parameters and Normalize Data\n",
        "\n",
        "In practice, parameter optimization and data normalization is required before feature selection and classifier training.\n",
        "\n",
        "![train-test-norm-Page-3.png](data:image/png;base64,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)\n",
        "\n",
        "We make assumptions about optimal parameters to simplify this example. These assumptions are:\n",
        "\n",
        "1. The number of features for the prediction model is **30**.\n",
        "2. The classifier to be used for the prediction model is **Logistic Regression**.\n",
        "\n",
        "Optimization of these parameters would require a procedure such as cross validation to estimate prediction performance for each combination of parameters. See [Phan et al.](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4983434/) and [Tong et al.](https://www.nature.com/articles/s41598-020-74567-y) for more details.  \n",
        "\n",
        "Data normalization is also an essential step, but can depend on the feature selection method and classifier. Moreover, normalization may need to be applied at various stages throughout the analysis process. Caution must be taken to avoid leakage of information from the testing data or from future samples when normalizing the training data. \n",
        "\n",
        "In this example, we use the following normalization methods:\n",
        "\n",
        "1. We use RNA-seq raw counts (un-normalized data) when selecting features using DESeq. \n",
        "2. When training the classifier, we use log of FPKM upper quartile normalization. We use the same normalization method for testing data and future samples. This normalization method can be applied to each sample independently, so there is no danger of information leakage. See the [NCI GDC Documentation](https://docs.gdc.cancer.gov/Encyclopedia/pages/HTSeq-FPKM-UQ/) for more information. \n",
        "3. When visualizing the data use Principal Component Analysis (PCA), we mean center and scale the data to unit variance. The means and variances used for shifting and scaling data are calculated **only with the training data**, then applied to the testing and future data."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K0i12JGWwKAe"
      },
      "source": [
        "# Identify Differentially Expressed Genes\n",
        "\n",
        "Now that we have extracted the data and split it into training and testing sets, we can identify features to be used for prediction modeling. \n",
        "\n",
        "![train-test-norm-Page-4.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_SBYF8LZyEz5"
      },
      "source": [
        "We can use DESeq to identify genes differentially expressed between tumor and normal samples. It is important that this analysis is performed **using only the the training data** in order to ensure that information from the testing data does not \"leak\" into the machine learning training process. Such leakage can bias estimated prediction performance of the resulting machine learning model."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i0pkdwFS4poq"
      },
      "source": [
        "In this tutorial, we assume that DESeq has been applied to the training data, resulting in the following top 30 differentially expressed genes. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QcF23g1Zjpn7"
      },
      "source": [
        "gene_list <- \"'COL10A1', 'MMP11', 'ADAMTS5', 'CAPN11', 'FIGF', 'PAMR1', 'KLHL29', 'INHBA', 'PPAPDC1A', 'LYVE1', 'COL11A1', 'WISP1', 'MT1M', 'NRG2', 'HOXA4', 'CA4', 'MMP13', 'NEK2', 'MAMDC2', 'PPP1R14A', 'SLC24A2', 'PER1', 'SCARA5', 'COMP', 'SPRY2', 'TPX2', 'ROBO3', 'CXCL2', 'NDRG2', 'HSD17B6'\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R6WZ7zDi38_7"
      },
      "source": [
        "# Train a Prediction Model using BigQuery ML\n",
        "\n",
        "Now that differentally expressed genes have been identified to be used as features for the prediction model, we can train the Logistic Regression model using BigQuery ML.\n",
        "\n",
        "![train-test-norm-Page-5.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "li26r9N8AfJ_"
      },
      "source": [
        "## Prepare Query to Create Subset Table for BigQuery ML\n",
        "\n",
        "For this step, we need to re-run the previous query to pull RNA-seq gene expression data from the TCGA RNA-seq BigQuery table and join it with clinical labels. We modify this query as follows:\n",
        "\n",
        "1. Extract the normalized FPKM gene expression data rather than raw counts.\n",
        "2. Pivot the data for use with BigQuery ML.\n",
        "3. Store the data in a separate table to reduce BigQuery costs. \n",
        "\n",
        "Recall that the query labels the samples based on whether they are normal or tumor samples. Tumor samples are labeled as \"1\" while normal samples are labeled as \"0\". This is the same procedure that we used to label the samples for differential expression analysis. \n",
        "\n",
        "Creation of a new BQ table with only the data of interest reduces the size of the data passed to BQ ML and can significantly reduce the cost of running BQ ML queries. This query also randomly splits the dataset into \"training\" and \"testing\" sets using the \"FARM_FINGERPRINT\" hash function in BigQuery. \"FARM_FINGERPRINT\" generates an integer from the input string. More information can be found [here](https://cloud.google.com/bigquery/docs/reference/standard-sql/hash_functions)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "biPKnAoT6Y3i",
        "outputId": "0da12d53-ada6-4454-94ae-cf208a92a9cc"
      },
      "source": [
        "# Construct a query that extracts all RNAseq expression data for samples and \n",
        "# genes of interest, labels the samples by tumor/normal status, partitions\n",
        "# the dataset into training and testing sets, and pivots the table\n",
        "ge_pivot_query <- sprintf(\"\n",
        "WITH rna_brca AS (%s),\n",
        "\n",
        "cases AS (%s)\n",
        "\n",
        "SELECT * FROM (\n",
        "  SELECT \n",
        "    rna_brca.case_barcode,\n",
        "    gene_name,\n",
        "  \n",
        "    -- In situations where there are multiple records for each case, take the \n",
        "    -- highest RNAseq count value as the expression for that gene\n",
        "    MAX(LOG(HTSeq__FPKM_UQ+1)) AS gene_expression,\n",
        "    sample_type_name,\n",
        "\n",
        "    CASE\n",
        "      -- Assign a numeric label to each case depending on sample type\n",
        "      WHEN sample_type_name = 'Solid Tissue Normal' THEN 0\n",
        "      WHEN sample_type_name = 'Primary Tumor' THEN 1\n",
        "    END AS label,\n",
        "\n",
        "    CASE\n",
        "      -- Randomly assign each case to the training or testing dataset\n",
        "      WHEN MOD(ABS(FARM_FINGERPRINT(rna_brca.case_barcode)), 10) < 5 THEN 'training'\n",
        "      WHEN MOD(ABS(FARM_FINGERPRINT(rna_brca.case_barcode)), 10) >= 5 THEN 'testing'\n",
        "    END AS data_partition\n",
        "\n",
        "  FROM rna_brca\n",
        "\n",
        "  INNER JOIN cases \n",
        "    ON rna_brca.case_barcode = cases.case_barcode\n",
        "\n",
        "  GROUP BY case_barcode, gene_name, sample_type_name\n",
        "\n",
        "  UNION ALL -- add 'Metastatic' samples to simulate predicting future samples\n",
        "\n",
        "  SELECT \n",
        "    rna_brca.case_barcode,\n",
        "    gene_name,\n",
        "    MAX(LOG(HTSeq__FPKM_UQ+1)) AS gene_expression,\n",
        "    sample_type_name,\n",
        "\n",
        "    -- distinguish these samples from normal and tumor samples\n",
        "    2 AS label, \n",
        "    'future' AS data_partition\n",
        "\n",
        "  FROM rna_brca\n",
        "\n",
        "  WHERE sample_type_name = 'Metastatic'\n",
        "\n",
        "  GROUP BY case_barcode, gene_name, sample_type_name\n",
        ")\n",
        "\n",
        "PIVOT (\n",
        "  MAX(gene_expression) FOR gene_name IN (%s)\n",
        ")\n",
        "\n",
        "ORDER BY case_barcode, data_partition, label\n",
        "\", rna_brca_query, cases_query, gene_list)\n",
        "\n",
        "# Display the full query\n",
        "cat(ge_pivot_query)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "WITH rna_brca AS (\n",
            "  -- This table contains RNAseq expression of protein-coding genes for  \n",
            "  -- TCGA BRCA normal and cancer samples. \n",
            "  SELECT \n",
            "    case_barcode,\n",
            "    gene_name,\n",
            "    HTSeq__Counts,\n",
            "    HTSeq__FPKM_UQ,\n",
            "    sample_type_name\n",
            "\n",
            "  FROM `isb-cgc-bq.TCGA_versioned.RNAseq_hg38_gdc_r28` AS tcga_rna\n",
            "\n",
            "  WHERE\n",
            "    gene_type = 'protein_coding'\n",
            "    AND project_short_name = 'TCGA-BRCA'\n",
            "),\n",
            "\n",
            "cases AS (\n",
            "  -- This table filters for cases that include both normal and tumor samples\n",
            "  SELECT\n",
            "    case_barcode,\n",
            "    agg_sample_type_name\n",
            "\n",
            "  FROM (\n",
            "    SELECT\n",
            "      case_barcode,\n",
            "      -- combine all sample types for each case into a single array\n",
            "      ARRAY_AGG(DISTINCT sample_type_name) AS agg_sample_type_name\n",
            "\n",
            "    FROM rna_brca\n",
            "    \n",
            "    GROUP BY case_barcode\n",
            "  )\n",
            "  \n",
            "  WHERE \n",
            "    'Solid Tissue Normal' IN UNNEST(agg_sample_type_name)\n",
            "    AND 'Primary Tumor' IN UNNEST(agg_sample_type_name)\n",
            "    AND 'Metastatic' NOT IN UNNEST(agg_sample_type_name)\n",
            ")\n",
            "\n",
            "SELECT * FROM (\n",
            "  SELECT \n",
            "    rna_brca.case_barcode,\n",
            "    gene_name,\n",
            "  \n",
            "    -- In situations where there are multiple records for each case, take the \n",
            "    -- highest RNAseq count value as the expression for that gene\n",
            "    MAX(LOG(HTSeq__FPKM_UQ+1)) AS gene_expression,\n",
            "    sample_type_name,\n",
            "\n",
            "    CASE\n",
            "      -- Assign a numeric label to each case depending on sample type\n",
            "      WHEN sample_type_name = 'Solid Tissue Normal' THEN 0\n",
            "      WHEN sample_type_name = 'Primary Tumor' THEN 1\n",
            "    END AS label,\n",
            "\n",
            "    CASE\n",
            "      -- Randomly assign each case to the training or testing dataset\n",
            "      WHEN MOD(ABS(FARM_FINGERPRINT(rna_brca.case_barcode)), 10) < 5 THEN 'training'\n",
            "      WHEN MOD(ABS(FARM_FINGERPRINT(rna_brca.case_barcode)), 10) >= 5 THEN 'testing'\n",
            "    END AS data_partition\n",
            "\n",
            "  FROM rna_brca\n",
            "\n",
            "  INNER JOIN cases \n",
            "    ON rna_brca.case_barcode = cases.case_barcode\n",
            "\n",
            "  GROUP BY case_barcode, gene_name, sample_type_name\n",
            "\n",
            "  UNION ALL -- add 'Metastatic' samples to simulate predicting future samples\n",
            "\n",
            "  SELECT \n",
            "    rna_brca.case_barcode,\n",
            "    gene_name,\n",
            "    MAX(LOG(HTSeq__FPKM_UQ+1)) AS gene_expression,\n",
            "    sample_type_name,\n",
            "\n",
            "    -- distinguish these samples from normal and tumor samples\n",
            "    2 AS label, \n",
            "    'future' AS data_partition\n",
            "\n",
            "  FROM rna_brca\n",
            "\n",
            "  WHERE sample_type_name = 'Metastatic'\n",
            "\n",
            "  GROUP BY case_barcode, gene_name, sample_type_name\n",
            ")\n",
            "\n",
            "PIVOT (\n",
            "  MAX(gene_expression) FOR gene_name IN ('COL10A1', 'MMP11', 'ADAMTS5', 'CAPN11', 'FIGF', 'PAMR1', 'KLHL29', 'INHBA', 'PPAPDC1A', 'LYVE1', 'COL11A1', 'WISP1', 'MT1M', 'NRG2', 'HOXA4', 'CA4', 'MMP13', 'NEK2', 'MAMDC2', 'PPP1R14A', 'SLC24A2', 'PER1', 'SCARA5', 'COMP', 'SPRY2', 'TPX2', 'ROBO3', 'CXCL2', 'NDRG2', 'HSD17B6')\n",
            ")\n",
            "\n",
            "ORDER BY case_barcode, data_partition, label\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KTqDeS-VLrDM"
      },
      "source": [
        "## Create Subset Table for BigQuery ML\n",
        "\n",
        "Run the query to create the table. This table contains only the samples and genes that we need to train the machine learning model. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SK3iodXTGM27"
      },
      "source": [
        "# Delete the tmp table if it exists\n",
        "bq_tmp_table_fullname <- paste(bq_project, bq_dataset, bq_tmp_table, sep = '.')\n",
        "if (bq_table_exists(bq_tmp_table_fullname)) {\n",
        "  bq_table_delete(bq_tmp_table_fullname)\n",
        "}\n",
        "\n",
        "# Run the query to create the temporary or subset table\n",
        "ge_pivot_table <- bq_project_query(\n",
        "  google_project,\n",
        "  ge_pivot_query,\n",
        "  destination_table = bq_tmp_table_fullname,\n",
        "  quiet = TRUE\n",
        ")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xOWDg2kzEY2h"
      },
      "source": [
        "## Preview Subset Table\n",
        "\n",
        "Let's take a look at this subset table. The data has been pivoted such that each of the genes is available as a column that can be \"SELECTED\" in a query. In addition, the \"label\" and \"data_partition\" columns simplify data handling for classifier training and evaluation.  "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 376
        },
        "id": "0hx8gCopC3mh",
        "outputId": "77196899-fb6d-476a-a277-a61212f44769"
      },
      "source": [
        "tmp_table_query <- sprintf(\"\n",
        "  SELECT\n",
        "    * --usually not recommended to use *, but in this case, we want to see all genes\n",
        "  FROM `%s`\n",
        "\", bq_tmp_table_fullname)\n",
        "\n",
        "# Run the query\n",
        "tmp_table <- bq_project_query(google_project, tmp_table_query, quiet = TRUE)\n",
        "\n",
        "# Download the query result\n",
        "tmp_data <- bq_table_download(tmp_table, quiet = TRUE)\n",
        "\n",
        "# Show the dataframe\n",
        "head(tmp_data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  case_barcode sample_type_name    label data_partition COL10A1   MMP11    \n",
              "1 TCGA-A7-A0CE Solid Tissue Normal 0     training        8.084782 10.007771\n",
              "2 TCGA-A7-A0CE Primary Tumor       1     training       11.946598 11.708615\n",
              "3 TCGA-A7-A0CH Solid Tissue Normal 0     testing         8.797859 10.581771\n",
              "4 TCGA-A7-A0CH Primary Tumor       1     testing        14.359123 13.958606\n",
              "5 TCGA-A7-A0D9 Solid Tissue Normal 0     testing         8.430422  9.907031\n",
              "6 TCGA-A7-A0D9 Primary Tumor       1     testing        13.501683 14.257378\n",
              "  ADAMTS5   CAPN11    FIGF      PAMR1     ⋯ SLC24A2  PER1     SCARA5   \n",
              "1 12.001531 11.652749 12.509690 13.530618 ⋯ 4.780028 13.68064 12.514925\n",
              "2 10.262342  8.422632  9.283322  9.807365 ⋯ 7.357463 13.50760  7.316013\n",
              "3 12.342606 10.633858 12.971092 13.076344 ⋯ 5.368873 13.37971 13.289296\n",
              "4 10.110411  8.428334  7.290992  9.604073 ⋯ 9.899814 11.31264  4.631522\n",
              "5 12.693907 10.593507 12.863596 12.284105 ⋯ 3.918165 13.61539 14.402506\n",
              "6  9.463249  9.269666  9.714173  9.975251 ⋯ 9.418867 11.99268  8.776945\n",
              "  COMP     SPRY2    TPX2     ROBO3     CXCL2     NDRG2    HSD17B6  \n",
              "1 10.44850 12.91842 11.03459 10.611805 13.130767 13.81798  9.144254\n",
              "2 13.21607 11.95265 13.30444  8.054271 11.131740 12.56180 10.584979\n",
              "3 10.26660 12.96287 10.88753 10.703300 12.257179 13.78315  8.840313\n",
              "4 13.92135 10.72367 12.05078  8.169261  9.694843 11.86710 10.889042\n",
              "5 11.32945 13.28733 10.32815 10.421386 12.735412 13.65413  7.850524\n",
              "6 13.90754 11.00835 11.73164  8.448684  8.776381 11.65811 10.777710"
            ],
            "text/latex": "A tibble: 6 × 34\n\\begin{tabular}{lllllllllllllllllllll}\n case\\_barcode & sample\\_type\\_name & label & data\\_partition & COL10A1 & MMP11 & ADAMTS5 & CAPN11 & FIGF & PAMR1 & ⋯ & SLC24A2 & PER1 & SCARA5 & COMP & SPRY2 & TPX2 & ROBO3 & CXCL2 & NDRG2 & HSD17B6\\\\\n <chr> & <chr> & <int> & <chr> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & ⋯ & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n\\hline\n\t TCGA-A7-A0CE & Solid Tissue Normal & 0 & training &  8.084782 & 10.007771 & 12.001531 & 11.652749 & 12.509690 & 13.530618 & ⋯ & 4.780028 & 13.68064 & 12.514925 & 10.44850 & 12.91842 & 11.03459 & 10.611805 & 13.130767 & 13.81798 &  9.144254\\\\\n\t TCGA-A7-A0CE & Primary Tumor       & 1 & training & 11.946598 & 11.708615 & 10.262342 &  8.422632 &  9.283322 &  9.807365 & ⋯ & 7.357463 & 13.50760 &  7.316013 & 13.21607 & 11.95265 & 13.30444 &  8.054271 & 11.131740 & 12.56180 & 10.584979\\\\\n\t TCGA-A7-A0CH & Solid Tissue Normal & 0 & testing  &  8.797859 & 10.581771 & 12.342606 & 10.633858 & 12.971092 & 13.076344 & ⋯ & 5.368873 & 13.37971 & 13.289296 & 10.26660 & 12.96287 & 10.88753 & 10.703300 & 12.257179 & 13.78315 &  8.840313\\\\\n\t TCGA-A7-A0CH & Primary Tumor       & 1 & testing  & 14.359123 & 13.958606 & 10.110411 &  8.428334 &  7.290992 &  9.604073 & ⋯ & 9.899814 & 11.31264 &  4.631522 & 13.92135 & 10.72367 & 12.05078 &  8.169261 &  9.694843 & 11.86710 & 10.889042\\\\\n\t TCGA-A7-A0D9 & Solid Tissue Normal & 0 & testing  &  8.430422 &  9.907031 & 12.693907 & 10.593507 & 12.863596 & 12.284105 & ⋯ & 3.918165 & 13.61539 & 14.402506 & 11.32945 & 13.28733 & 10.32815 & 10.421386 & 12.735412 & 13.65413 &  7.850524\\\\\n\t TCGA-A7-A0D9 & Primary Tumor       & 1 & testing  & 13.501683 & 14.257378 &  9.463249 &  9.269666 &  9.714173 &  9.975251 & ⋯ & 9.418867 & 11.99268 &  8.776945 & 13.90754 & 11.00835 & 11.73164 &  8.448684 &  8.776381 & 11.65811 & 10.777710\\\\\n\\end{tabular}\n",
            "text/markdown": "\nA tibble: 6 × 34\n\n| case_barcode &lt;chr&gt; | sample_type_name &lt;chr&gt; | label &lt;int&gt; | data_partition &lt;chr&gt; | COL10A1 &lt;dbl&gt; | MMP11 &lt;dbl&gt; | ADAMTS5 &lt;dbl&gt; | CAPN11 &lt;dbl&gt; | FIGF &lt;dbl&gt; | PAMR1 &lt;dbl&gt; | ⋯ ⋯ | SLC24A2 &lt;dbl&gt; | PER1 &lt;dbl&gt; | SCARA5 &lt;dbl&gt; | COMP &lt;dbl&gt; | SPRY2 &lt;dbl&gt; | TPX2 &lt;dbl&gt; | ROBO3 &lt;dbl&gt; | CXCL2 &lt;dbl&gt; | NDRG2 &lt;dbl&gt; | HSD17B6 &lt;dbl&gt; |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| TCGA-A7-A0CE | Solid Tissue Normal | 0 | training |  8.084782 | 10.007771 | 12.001531 | 11.652749 | 12.509690 | 13.530618 | ⋯ | 4.780028 | 13.68064 | 12.514925 | 10.44850 | 12.91842 | 11.03459 | 10.611805 | 13.130767 | 13.81798 |  9.144254 |\n| TCGA-A7-A0CE | Primary Tumor       | 1 | training | 11.946598 | 11.708615 | 10.262342 |  8.422632 |  9.283322 |  9.807365 | ⋯ | 7.357463 | 13.50760 |  7.316013 | 13.21607 | 11.95265 | 13.30444 |  8.054271 | 11.131740 | 12.56180 | 10.584979 |\n| TCGA-A7-A0CH | Solid Tissue Normal | 0 | testing  |  8.797859 | 10.581771 | 12.342606 | 10.633858 | 12.971092 | 13.076344 | ⋯ | 5.368873 | 13.37971 | 13.289296 | 10.26660 | 12.96287 | 10.88753 | 10.703300 | 12.257179 | 13.78315 |  8.840313 |\n| TCGA-A7-A0CH | Primary Tumor       | 1 | testing  | 14.359123 | 13.958606 | 10.110411 |  8.428334 |  7.290992 |  9.604073 | ⋯ | 9.899814 | 11.31264 |  4.631522 | 13.92135 | 10.72367 | 12.05078 |  8.169261 |  9.694843 | 11.86710 | 10.889042 |\n| TCGA-A7-A0D9 | Solid Tissue Normal | 0 | testing  |  8.430422 |  9.907031 | 12.693907 | 10.593507 | 12.863596 | 12.284105 | ⋯ | 3.918165 | 13.61539 | 14.402506 | 11.32945 | 13.28733 | 10.32815 | 10.421386 | 12.735412 | 13.65413 |  7.850524 |\n| TCGA-A7-A0D9 | Primary Tumor       | 1 | testing  | 13.501683 | 14.257378 |  9.463249 |  9.269666 |  9.714173 |  9.975251 | ⋯ | 9.418867 | 11.99268 |  8.776945 | 13.90754 | 11.00835 | 11.73164 |  8.448684 |  8.776381 | 11.65811 | 10.777710 |\n\n",
            "text/html": [
              "<table class=\"dataframe\">\n",
              "<caption>A tibble: 6 × 34</caption>\n",
              "<thead>\n",
              "\t<tr><th scope=col>case_barcode</th><th scope=col>sample_type_name</th><th scope=col>label</th><th scope=col>data_partition</th><th scope=col>COL10A1</th><th scope=col>MMP11</th><th scope=col>ADAMTS5</th><th scope=col>CAPN11</th><th scope=col>FIGF</th><th scope=col>PAMR1</th><th scope=col>⋯</th><th scope=col>SLC24A2</th><th scope=col>PER1</th><th scope=col>SCARA5</th><th scope=col>COMP</th><th scope=col>SPRY2</th><th scope=col>TPX2</th><th scope=col>ROBO3</th><th scope=col>CXCL2</th><th scope=col>NDRG2</th><th scope=col>HSD17B6</th></tr>\n",
              "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&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>&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;dbl&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><td>TCGA-A7-A0CE</td><td>Solid Tissue Normal</td><td>0</td><td>training</td><td> 8.084782</td><td>10.007771</td><td>12.001531</td><td>11.652749</td><td>12.509690</td><td>13.530618</td><td>⋯</td><td>4.780028</td><td>13.68064</td><td>12.514925</td><td>10.44850</td><td>12.91842</td><td>11.03459</td><td>10.611805</td><td>13.130767</td><td>13.81798</td><td> 9.144254</td></tr>\n",
              "\t<tr><td>TCGA-A7-A0CE</td><td>Primary Tumor      </td><td>1</td><td>training</td><td>11.946598</td><td>11.708615</td><td>10.262342</td><td> 8.422632</td><td> 9.283322</td><td> 9.807365</td><td>⋯</td><td>7.357463</td><td>13.50760</td><td> 7.316013</td><td>13.21607</td><td>11.95265</td><td>13.30444</td><td> 8.054271</td><td>11.131740</td><td>12.56180</td><td>10.584979</td></tr>\n",
              "\t<tr><td>TCGA-A7-A0CH</td><td>Solid Tissue Normal</td><td>0</td><td>testing </td><td> 8.797859</td><td>10.581771</td><td>12.342606</td><td>10.633858</td><td>12.971092</td><td>13.076344</td><td>⋯</td><td>5.368873</td><td>13.37971</td><td>13.289296</td><td>10.26660</td><td>12.96287</td><td>10.88753</td><td>10.703300</td><td>12.257179</td><td>13.78315</td><td> 8.840313</td></tr>\n",
              "\t<tr><td>TCGA-A7-A0CH</td><td>Primary Tumor      </td><td>1</td><td>testing </td><td>14.359123</td><td>13.958606</td><td>10.110411</td><td> 8.428334</td><td> 7.290992</td><td> 9.604073</td><td>⋯</td><td>9.899814</td><td>11.31264</td><td> 4.631522</td><td>13.92135</td><td>10.72367</td><td>12.05078</td><td> 8.169261</td><td> 9.694843</td><td>11.86710</td><td>10.889042</td></tr>\n",
              "\t<tr><td>TCGA-A7-A0D9</td><td>Solid Tissue Normal</td><td>0</td><td>testing </td><td> 8.430422</td><td> 9.907031</td><td>12.693907</td><td>10.593507</td><td>12.863596</td><td>12.284105</td><td>⋯</td><td>3.918165</td><td>13.61539</td><td>14.402506</td><td>11.32945</td><td>13.28733</td><td>10.32815</td><td>10.421386</td><td>12.735412</td><td>13.65413</td><td> 7.850524</td></tr>\n",
              "\t<tr><td>TCGA-A7-A0D9</td><td>Primary Tumor      </td><td>1</td><td>testing </td><td>13.501683</td><td>14.257378</td><td> 9.463249</td><td> 9.269666</td><td> 9.714173</td><td> 9.975251</td><td>⋯</td><td>9.418867</td><td>11.99268</td><td> 8.776945</td><td>13.90754</td><td>11.00835</td><td>11.73164</td><td> 8.448684</td><td> 8.776381</td><td>11.65811</td><td>10.777710</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "toml2clPGUqG"
      },
      "source": [
        "## Train the Machine Learning Model\n",
        "\n",
        "Now we can train a classifier using BigQuery ML with the data stored in the subset table. This model will be stored in the location specified by the \"bq_ml_model\" variable, and can be reused to predict samples in the future.\n",
        "\n",
        "We pass three options to the BQ ML model: model_type, auto_class_weights, and input_label_cols. Model_type specifies the classifier model type. In this case, we use \"LOGISTIC_REG\" to train a logistic regression classifier. Other classifier options are documented [here](https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-create). Auto_class_weights indicates whether samples should be weighted to balance the classes. For example, if the dataset happens to have more samples labeled as \"Complete Response\", those samples would be less weighted to ensure that the model is not biased towards predicting those samples. Input_label_cols tells BigQuery that the \"label\" column should be used to determine each sample's label. \n",
        "\n",
        "**Warning**: BigQuery ML models can be very time-consuming and expensive to train. Please check your data size before running BigQuery ML commands. Information about BigQuery ML costs can be found [here](https://cloud.google.com/bigquery-ml/pricing)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gDKChWKRG03r",
        "outputId": "e3d549b0-b8c6-4cdd-fe43-f0f953e7053b"
      },
      "source": [
        "# Full model name\n",
        "bq_ml_model_fullname <- paste(bq_project, bq_dataset, bq_ml_model, sep = '.')\n",
        "\n",
        "# Construct query\n",
        "ml_model_query <- sprintf(\"\n",
        "  CREATE OR REPLACE MODEL `%s`\n",
        "\n",
        "  OPTIONS\n",
        "    (\n",
        "      model_type='LOGISTIC_REG',\n",
        "      auto_class_weights=TRUE,\n",
        "      input_label_cols=['label']\n",
        "    ) AS\n",
        "\n",
        "  SELECT\n",
        "    -- when training, we only need the labels and feature columns\n",
        "    * EXCEPT(case_barcode, sample_type_name, data_partition)  \n",
        "\n",
        "  FROM `%s`\n",
        "\n",
        "  WHERE data_partition = 'training' -- using training data only\n",
        "\", bq_ml_model_fullname, bq_tmp_table_fullname)\n",
        "\n",
        "# Display query\n",
        "cat(ml_model_query)\n",
        "\n",
        "# Run the query to train the model\n",
        "ml_model_result <- bq_project_query(\n",
        "  google_project,\n",
        "  ml_model_query,\n",
        "  quiet = TRUE\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "  CREATE OR REPLACE MODEL `isb-cgc-training-001.nih_library_2021_10.tcga_brca_ml_model_30e1b3df`\n",
            "\n",
            "  OPTIONS\n",
            "    (\n",
            "      model_type='LOGISTIC_REG',\n",
            "      auto_class_weights=TRUE,\n",
            "      input_label_cols=['label']\n",
            "    ) AS\n",
            "\n",
            "  SELECT\n",
            "    -- when training, we only need the labels and feature columns\n",
            "    * EXCEPT(case_barcode, sample_type_name, data_partition)  \n",
            "\n",
            "  FROM `isb-cgc-training-001.nih_library_2021_10.tcga_brca_ml_table_30e1b3df`\n",
            "\n",
            "  WHERE data_partition = 'training' -- using training data only\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XIOXaE0YNeqp"
      },
      "source": [
        "Details about the BigQuery ML model and training iterations can be viewed from the Google Console, for example:\n",
        "\n",
        "![Screen Shot 2021-09-29 at 11.38.04 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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Tsfcg4UKlw0N"
      },
      "source": [
        "## Visualize the Prediction Model\n",
        "\n",
        "Even though the data is high-dimensional (i.e., 30 features), we can use principal component analysis (PCA) to visualize the data in a lower dimensional space. Visualization can help with interpreting and understanding the model's performance. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mMKf_GXrCPZ0"
      },
      "source": [
        "### Scale The Data\n",
        "\n",
        "Before using PCA, the data need to be scaled to zero mean and unit variance. The original means and standard deviations of the training data are stored so that we can use them to scale the testing and future data. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QKPygDMCywkw"
      },
      "source": [
        "# Get the numeric columns of the input data, splitting into\n",
        "# training and testing sets\n",
        "train_data <- tmp_data[tmp_data$data_partition == 'training',]\n",
        "test_data <- tmp_data[tmp_data$data_partition == 'testing',]\n",
        "\n",
        "# Calculate the column means and stdevs of the training data in order to\n",
        "# scale the data to zero mean and unit variance\n",
        "train_data_numeric <- subset(\n",
        "  train_data, select=-c(case_barcode, sample_type_name, label, data_partition)\n",
        ")\n",
        "test_data_numeric <- subset(\n",
        "  test_data, select=-c(case_barcode, sample_type_name, label, data_partition)\n",
        ")\n",
        "\n",
        "# Center and scale the training data\n",
        "train_means = colMeans(train_data_numeric)\n",
        "train_sds = sqrt(colVars(train_data_numeric))\n",
        "tm_mat <- matrix(1,nrow(train_data_numeric),1) %*% train_means\n",
        "ts_mat <- matrix(1,nrow(train_data_numeric),1) %*% train_sds\n",
        "train_data_numeric_norm <- (train_data_numeric - tm_mat) / ts_mat"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kPP1Sq-oCSJQ"
      },
      "source": [
        "### Calculate Principal Components\n",
        "\n",
        "Now calculate principal components using the training data. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3HtLVAp2OnWj"
      },
      "source": [
        "# Calculate principal components using the training data\n",
        "pca <- prcomp(train_data_numeric_norm)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bxwM0H1yCXlp"
      },
      "source": [
        "### Plot Data in Principal Component Space\n",
        "\n",
        "The samples can now be projected on to the principal components and plotted. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "id": "AV5H0Wt85xUY",
        "outputId": "5517574b-fb1e-40ba-d8f4-ff16a66259fd"
      },
      "source": [
        "# Get the eigenvectors of the first two principal components\n",
        "eigen_vectors <- pca$rotation[,c('PC1', 'PC2')]\n",
        "\n",
        "# Project the training data onto the first two principal components \n",
        "train_proj <- t(eigen_vectors) %*% t(train_data_numeric_norm)\n",
        "\n",
        "# Plot\n",
        "colors <- c('red', 'blue')\n",
        "plot(\n",
        "  t(train_proj),\n",
        "  col = colors[factor(train_data$sample_type_name)],\n",
        "  pch = 16, cex = 2,\n",
        "  main = 'BRCA Tumor and Normal Data (Training)',\n",
        "  xlab = 'Principal Component 1',\n",
        "  ylab = 'Principal Component 2',\n",
        "  xlim=c(-10, 10), ylim=c(-8, 5)\n",
        ")\n",
        "legend(\n",
        "  'bottomleft',\n",
        "  legend=levels(factor(train_data$sample_type_name)),\n",
        "  col = colors,\n",
        "  pch = 16, cex = 1.2\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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lPVU7FDmpGeEiYRJ0zeJqAdgh0ABI76btgcppH/JfoA8APBDgACTQ5nLlvjPLfq\nhVUDHoBA4+IJAAga58sCVN5VlpvPAvAJwQ4AdIlsB8B7BDsACBWyGoAgI9gBgF4RBAF4iWAH\nACFBSgMQfAQ7AAAAkyDYAQAAmAT92AGAudgf86U/PCDMEOwAwCycT+PjhhZAmOFQLACEhA/R\nSv1TLBZVN7QAYHYEOwAwOO5mAeC/OBQLAKFis3mRrlQ213kV1ywWjskGhctPgUUNLRDsAADw\nlYdgLY0i3iG0OBQLACFks6na0wcvDXBANoDULEwWOEKLYAcAIecht6lMfhJCg4bUL3w+JoQQ\nh2IBQAscoTM0shr0ihY7AACCjCCIUCHYAQAAmATBDgAAb9D8Bh3jHDsAMCyvOsaTnwKvcO9d\nGArBDgAAJy4TM2110D0OxQKAkXnVhkSDk0oEOBiWqYLduXPn8vPzta4CAELLuHHNYnH9p3lV\ngGEZKdjt27evZ8+ejRo16tSp09y5cysqKhwmmDp1ampqqia1AYCWFLOdV/0eh4bnm3Fpla6C\n9Lp6W/gwL8OcY5eXl9elS5erV6/GxMScPHly8+bNS5cuzc3NrVmzptalAYAOeLiQQoepQuXN\nuHRYOaBvhmmxmzJlyrVr13Jzc4uLi4uKit58880tW7ZkZGSUlJRoXRoA6IPULOf8pze6vRkX\nzXUwPsMEu3379mVlZWVmZlosFqvVOnbs2C+++GLv3r39+vVzPiYLANCpcDuDjVSH0DJMsCss\nLGzcuLH9kM6dOy9YsGDNmjXjxo3zebY//fRT5cqVLR6NGDFCCGHjywkAoWfoIMiOAyFnmHPs\nEhMT9+zZ4zBw0KBBBw8enDJlSkpKyvjx432YbWpq6pdffllaWuphmtWrV8+cOdNi6I0LDEjn\np0tx+hN8YcoNqfxNkN8d3w1oxzDBrnfv3rNmzZo9e/Zjjz0WFRUlD3/11VdPnjz59NNPnzx5\n0odjshaL5Y477vA8zZEjR7wuF/CD532fhonKoTA65Ad+h68BdMAwwW7ChAnLly8fM2bMihUr\nvvrqK3m4xWJZuHBhjRo1ZsyYoWF5QKCovFhQhHwnotu4CWiJ9R46Y5hz7GrXrr1r166RI0e2\naNHCYZTFYpk5c+ayZcuaNGmiSW1AoOj2OJX6uAkA0JBhWuyEEHXq1JkzZ467sb179+7du3co\n6wG0FbJGMq/6pqD9AgbmoS9AwCAM02IHmJ4+dyj6rApGpf/gz713YXBGarED4DKPw3oAACAA\nSURBVECHLWQ6LAla8v+XgW7XJ90WhvBGsAMABIFxG3ulxOa5flId9IpDsQCAQAtUqtMwP7l7\naX3epQ34L1rsALhl3DYXaMkEqU4nBQDeo8UOgFvs16ANWsUAX9FiBxgY+z7ojg/NdXInI6zQ\ngN9osQP0gp0awhdNdECAEOwAo9LnflCfVUHXOJcTCBwOxQI6osN+73VYEqAX7r4b/L6Bdmix\nA/RF5R4hlDsO9a/F7gzhwmLx9IuHH0PQDsEO0B3PpxtpcjKSmlck1SFcqMltZDtohEOxgE7J\nOUknN+nycExWD+XBwIy1AqlPbDr56iLMEOwAvdPPrkE/lUC/OCvTHtkOIcehWACAdoyVe8is\n0D2CHQAgoLjcBtAOwQ4AEGhcbgNohGAHAAgCz7ktfFIdR28RWlw8AQCm4DJAaJuf7F89bO8G\nG4ZvGZoi2AGAwXnuKVcnwUInZQBmx6FYADAyxSN9nu+RAMBcCHYAYFgkthCj3RG6R7ADAGPy\nKtURATVBEETIEewAIDyQ7QKCXvqgbwQ7AAC8QS990DGCHQAYEM1v2rLZPEU3Uh20Q3cnAAD4\nhAAH/SHYAfh/7tqA2HkBgFFwKBaAQk9nHPQDAKMg2AHhTk1uI9vpDu2oAFwh2AFhTX1iI9sZ\nHlkQCAMEOwBqke30haAGwAnBDghfBLUwQgoEwgPBDoAXyIL6ojKukeqAsEGwAwAj89xTriDV\nAeGFfuwAwPik9ObQoEqkA8IPwQ4AzIIkB4Q9gh0AQDtyK6MOU6l9C6gOywNcIdgB8AJ7NwSG\n82U4ukp4Oi8PcI+LJ4DwxR4K2vB8cbW2l157vr+e0Lo8QAnBDoBaBEEEgJ7vYafydcl20DGC\nHRDW1Gc1Uh0CwDT3sNN5eQhjBDsg3KlJbKQ6BIC3YSjE4YmsBlPg4gkg6PTfuZjLTtAcxgL4\nHYuF7wZ0iGAHBJHLqCQN9GGP4PMTVWInhfBlrOY6/f9YhHYIdkCwqLm0Ts0G2WE+dK0FhCl3\n25Rg/+aDoXCOHRAU6q+us//zdj7GamUAvMYqLlNcFCwrCCEIdkAw+LyBdYh3eu4XAggFWqEk\n9MMC1Qh2QID5v2mV5mCafiEAhA6bg7DnV7A7d+5cfn5+gCoB8D9snIGQ0nPTIJsDeMNTsNu3\nb1/Pnj0bNWrUqVOnuXPnVlRUOEwwderU1NTUYJYHQBW2/DAAb8OTnsOW0HF5bA7Cm9urYvPy\n8rp06XL16tWYmJiTJ09u3rx56dKlubm5NWvWDGV9AACEgs3mRSQKWaojpcFLblvspkyZcu3a\ntdzc3OLi4qKiojfffHPLli0ZGRklJSWhrA8AYB46v4edbhvhANXcBrt9+/ZlZWVlZmZaLBar\n1Tp27Ngvvvhi7969/fr1cz4mCwCAKjq/h53iS9tsNNdBz9wGu8LCwsaNG9sP6dy584IFC9as\nWTNu3LjgFwYAMCkP2SiUsckdDwWEsjZSHXzi9hy7xMTEPXv2OAwcNGjQwYMHp0yZkpKSMn78\n+CDXBgAwL80DnAea10aqg6/cBrvevXvPmjVr9uzZjz32WFRUlDz81VdfPXny5NNPP33y5EmO\nyQLOvDoDO4C41RgAwG2wmzBhwvLly8eMGbNixYqvvvpKHm6xWBYuXFijRo0ZM2aEpEIAXuPW\nkYCRyD/LAvKl5Zsf3tyeY1e7du1du3aNHDmyRYsWDqMsFsvMmTOXLVvWpEmTIJcHGJJOtqsc\nzAkXDrcc9nDvYeiK8yfl/2enk60PtOO2xU4IUadOnTlz5rgb27t37969ewehJMAMtDog68Bi\nYTtvap5XMlpu9UwPGwiYkadgB8Af0v6UrTf+x2Ft8DNyqb8xPNlOb4KxXeBThhDCz3vFAvBM\nD6lODzXA9fG1kB0zZSXQFT4OBBPBDgCCSU1082FPTzgwKD44BBnBDggWNuDwQrBXF1ZHc+M4\nLP6Lc+wAIGiCFKdIaXAQ2A5TYGQEO8AX7nasbFTxP97GL65yML3QnE/JWhTelA/Fbt68+ezZ\nsy5Hbd++fdmyZYEuCdA7Dxtnug8DoDG2QeFNOdh16tRp48aNLkdt2rRp+PDhgS4J0DU120y9\nbVeJmwh34dZpc/i8Uzhxeyj28OHDhw8flh7v3r27cuXKDhNcvnx56dKlV69eDWJ1gM6o31rq\ncLtKb7UGwHG0wHLZxYxEk+Wsk47LYWpug11OTs5zzz0nPX755ZfdTdanT5/AFwXokjk2yA7J\nweWbIlroHflADcXbchhlRfetr3MDvUEElNtg9+yzzw4ZMmTHjh3333//oEGDmjdv7jBBRERE\n48aNe/XqFeQKAQSLuz0FbXuaCd5CD7ePU+U5E4ZYLIR4eMPTVbHJycm9evXq2bPnyJEj27Vr\nF7KaAB0y06aVO1GZAY12Hnh1zkSI13I+OASZcncnq1evDkEdAHSIbGcSYfUp6v+QJdkOwaQc\n7Gw2W05OzgcffHDixImysjLnCfbv3x+EwgAdCeeNMNnOd97uv71d0CpPveLz0yGyHYJGOdhN\nnz59/PjxQoiYmJioqKjglwToC5tf6JqHiECk0zPfLokAlCgHu5kzZ2ZkZMydO7dx48YhKAgA\nzEN9w4w/Icw0AS4M24eJdwg05WD366+/5uTkkOoQntjeirDc2waSmj13mC9f+4Vj/9iHxWLE\nb2yQag7zlSqMKQe7xMREm57WD5vNdvTo0Z9++qmoqEgIUaNGjbS0tPr162tdFwC45y7e6Wnr\nqoGAdzXHuWsIe8rBbsCAAYsXL9ZDdyfnzp179dVXFy9e/NtvvzmMatCgwbBhw5566qkqVapo\nUhsA4X43Hebp5X9YEPbM1NWc3rDQwphysJswYUKfPn0GDhw4ePDgBg0aOF8/0bRp0+DU9jsF\nBQUdOnQ4evRoWlraPffc07Bhw6pVqwohLl68eOTIkW+++WbChAnLli1bv359zZo1Q1APTMlh\nR8O20SsedtP0eIz/kbKanruaMzoWV3hTDnbVqlWTHixZssTlBKE5UPviiy+eOHFi6dKlffv2\ndR5bUVHxzjvvjB49etKkSTNmzAhBPTAZl3sZDulI1HzFaXyBJw7rh666mjPZSmmytwPvqToU\nGx0dHRmpPGVQff7554MGDXKZ6oQQERERI0eO3Lhx42effUawg7cCHuD0fKFbMM5BovEFnoT4\nm8BpdghvynHNXUNdiJ05c6ZJkyaep2nWrFlubq5Xsz158mS/fv2uXLniYZpTp06JUDVMIvSC\nlOr0yYfaFJ/CPhSeaLJ+hKaXGUCXvGiHKyoqOnbsWL169eLi4oJXkDt169bdu3ev52l2795d\nt25dr2Zbs2bN3r17u7yjhmzbtm3Hjh2zsPsyo+B9qjpcX+RdmLYtGjTahZEArmferjdq1nLz\nrYjme0fwnqpg98033zz55JO7du0SQvzzn//s3r27EKJXr16PP/54ly5dglvgf2VmZr799ttt\n2rQZM2aM1Wp1GFtSUvL666+vWLHimWee8Wq2VapUGTdunOdp3nnnHW8bAmEIwQg3+jkI63kL\nrzLbsZuA7zT/Gnj4NupqzebYMQJKOdht3769W7duVqs1IyPjX//6lzTw1KlTO3bsuOeee7Zs\n2XLLLbcEuUghhHjppZc2bdo0fvz4l19++bbbbqtfv35sbKzNZisuLv7555+3b99+6dKlTp06\nvfDCCyEoBnDHQNtnz3sTXe34AN8ZYlUOSLYzxDtF8CkHu5dffjkpKSkvLy8yMjI5OVkaGB8f\nv3fv3jZt2rzyyivLly8PcpFCCBEXF7d169Y5c+Z88MEHGzZsqKiokEdFRUXdcsstQ4cOHTp0\naERERAiKAcxB3hH43Nu/gYIsjI3UoohehSCEUBPsvv3226eeeiolJaWwsNB+eEJCwogRI954\n442g1eYoOjp67NixY8eOvXLlyvHjx6U7T1SvXr1BgwbR0dEhKwPhzKzHTNgXABoL1GkcnMQa\n9pSD3YULF9zdsCs5Obm4uDjQJSmrXLlyWlpa6F8XMGWq84dZky78xWrhG5fxTj+n7sIIlINd\nUlLSwYMHXY7auHGjt1ehAggBfrFDY4GN/OG2Qju/X68WJo124a2S4hT33HPP3Llzv/vuO/uB\n586d++tf/7pw4cKePXsGrTYAJsQeB95hjQG8oRzsJk2aFBsb27ZtWynDPffcc61bt05OTv7b\n3/7WoEGDCRMmBL9IAF4I8X6Q3S6CiNVL+HQQluO2YUw52CUlJe3cuXP48OE///yzEGLPnj17\n9uypVq3an//85x07diQmJga/SCAoDLfLUFOwJm9K/YsabplDS6wugPdUdVCckJAwd+7cOXPm\n/Pbbb0VFRdWqVSPPAaGk5qYR2u4Ew7Off3ji7Wl2rB9AIHhxSzGLxZKYmEikg6E5nFVsxIs6\ndbv7o8djOOKerUDIKQc7m82Wk5PzwQcfnDhxwuU9Vffv3x+EwoCAcdizyP9KuxIjZjvdst87\nc2UehDB+W67PnXcDGlEOdtOnTx8/frwQIiYmJioqKvglAYHkYZ8i99NOL1HBwE4Q/0+3JxB4\n5lyzw4/CkL0pfn3CG8rBbubMmRkZGXPnzm3cuHEICgICSM3GUG5YIt4BwaLnAOdMcSvgeQI9\n3NorsK/u8v0a6zMNJ8rB7tdff83JySHVwXDURzQOGgIIsMBuVrRqtFNzyAM6o9zdSWJioo1P\nDkbj8zaQlR0IawHMT1q1/wdqK6bykAd0RjnYDRgwYPHixSEoBdAQWycAut4QqIxroUx13k6J\nkFA+FDthwoQ+ffoMHDhw8ODBDRo0cL5+omnTpsGpDfCRnrczPhxRoRERMKqAH5AVIbxoA8ak\nHOyqVasmPViyZInLCThQC5MJ9tksXOKG/8ceGs4U1wr5QfDOcvN2C8V5ynqiHOwGDBgQHR0d\nGelFV8aA0QUpe/mw6WNraVqclq43mv/eUrzY1mGVYA2BK8pxzV1DHWBugc12Dj33+vAsmIpX\nPfEgHLBKIEC8aIc7ffr0oUOHSkpKqlWrlp6eHhcXF7yyAH8EKpMFZD6+RTpBqjMxeuKBA1YJ\nBI7yVbFCiM2bN7dr1y4+Pr59+/Z33313u3btatWq1bVrV24mBtOT7ksRkA2p+ntmBuoVoUea\nH++DO0b51rEKwSPlFrvt27d37dq1vLy8Y8eO6enpVapUKSkpOXDgwLp16zp06LB9+/b09PQQ\nFAoEleIm3bf7Uviwp+AHOX6HFcL0CGoIKOVgN3ny5Pj4+K+++ur666+3H7579+7u3btPmjSJ\nk/CgQ3q78tSrYtiVm5auVkqEQJC+yWwj4J7yodgtW7aMHDnSIdUJIVq3bj1y5Mh169YFpzDA\nX+q3ey6ntFj+9+fDPIVTpwSA3rlc6cMKaQnGpxzsLly4kJKS4nJUo0aNzp49G+iSgIBRs5V2\nnsZ5v2a/swtN9+9hu2OFBlyGubCNdwHvT9iIvK3cuO/UjJSDXUJCwsGDB12OOnDgQEJCQqBL\nAgLJwwbH+RoFxX2ZymzHVg6GoWaND9LrOvzpR0C+wEbfCvh5yAPaUQ523bp1mzVr1ooVK+zv\nMGGz2XJzc+fMmdOjR49glgcEgHydqcOfA5V7Fjnbediayfspve2wgN/R5C7v7r4Vuvq2ePiG\nK164bprL2n075AGtKV88MXHixDVr1mRmZiYlJTVv3rxq1arSVbGFhYXJyckTJ04MQZWArqg8\ncVk/Oynoha4u6tGk7zTFF9XVZQGeK3HoozKUZYfstTyvsfr5pGBHucWuUaNGO3fuHDJkyOXL\nl9etW7dq1ap169aVlpYOGzZs165d7k6/A/TGc3OAD3tb/eygYWbBu6wyqNP7MxMjfrX8+Zh0\nHo+cWyjpbFPfVN15on79+osWLbLZbIWFhSUlJbGxsUlJScGuDAgI5zPCJTq/uIFtpmnpqtEu\nlOjyJ1C0WjJ8IgbhxS3FCgsLCwsLz58/X7t27YiIiPj4+OCVBfhP/WUQ4bmfhWbUZzt2pWEi\nbOM+gkDVLcXmz5+fmppat27dm2++uXPnzi1btkxISGjWrFl2dnaw6wN8Y+hjPuzNzc+Ip6X7\n823R5zdNVwLVkZI+LzRGCCm32M2bN2/kyJFWq7Vr16716tWrWrXqhQsXDh06tGPHjgEDBpSW\nlg4ePDgEhQLqsUGDAXhopNFbpNNEGB6N9XwEQU2kczkk3BZj2FMOdjNmzMjIyPjkk09q1Khh\nP/zo0aPdunWbOnUqwQ6GprcUyEY4jBjrwzZWtcblcMsa/6/AJ96FGeVDsfn5+S+++KJDqhNC\npKamjh079siRI8EpDPCR3oKaelxnBuB/VG4RDH3eCYJAOdjVqFEjIiLC5aiIiIg6deoEuiQg\nHBHpAHjN22uNEQaUg9199923atUql6NWr17dt2/fQJcEhDVOekYo6P9moPzWAXyifI7d5MmT\nMzMz8/Pz+/fvn5aWFhMTI9154r333istLR01atSJEyfkiemvGAaleW8DQepvD9AFzb9gkITh\nJSnhRznY1a1bVwixffv2JUuWOI9NS0uz/9fGGgPD0ueuh/OeESx0p2d0OtxgQQeUg11mZqbV\nag1BKYDm9JntBD+zESRq1vhArXlefbtY3QFfKQe73NzcENQBaMjhLohCl7+EyXYICg9rfMBX\nOJXZjhUd8IMXtxQDDCEgrW72exYdhjwgwEKWpRS/n6Q6wD+qgl1FRcW2bdsKCgrKysqcx/bv\n3z/QVQE+8iGEaXi1n7fV0mgHM3DZRsiaHTJsR8xOOdjt2rWrT58++fn57iYg2EEn9JzqnF+I\nhkCENbKF/3w+PEG2MzXlYDd69Ojz588//vjj6enpUVFRIagJ8IGeUx0AvQu3+/aS7cxLOdh9\n//33H374YWZmZgiqAXyj81TH9hPQL8Ubrer5C6zbK/mhHeU7T8TGxjZo0CAEpQDmw+1fAV1T\nk4p0npx83sTo/H3BV8rBrl+/fjk5OSEoBdAn3zabipGOwAdoTH2y0XMG0nNt0ILyodjXXnut\nf//+/fr1u//+++vWret8ml3Hjh2DUxtgSCQ2wIQcjsnq5Jw8Uh2cKAe7/fv379mz5/jx459+\n+qnLCbiNGLTl82VhEjXrr07OY+GrBgSMP19pD88N5U0A/d8q6fwMQvhEOdiNGTPm1KlT/fr1\nS0tLi4ykQ2Pojp+pS+V2OBh95uskLwLwgsrT8oIdmAKy7SDVmZFyUNu3b9/8+fMffvjhEFQD\naEXNdthzDvP5VDzuww4Yhlen5fGlhRaUg13VqlVbtGgRglIAbanMdvLEDkN8Fsr7sAPwnX4a\n2PVTCfRH+arYBx54YPXq1SEoBTAQ6aLXQOUtD/OhwxTAqIhf0IJyi90bb7zRt2/fgoKCBx54\noF69es5XxTZt2jQ4tQFqBepkNQ0PnpDeAIQUGx2TUg52NWvWFEJ8/fXXc+fOdTkBV8VCD7gQ\nAQDUYsdtXsrBbsCAAdHR0VwPC/0j2wFQS/PthcOrk7QQIMpxbcmSJSGoAwgIzbfVAKDA5UYq\nZB3gkSDNzot2uNOnTx86dKikpKRatWrp6elxcXHBKwvwmZrO4QHAu96GArU18TyfwPar6fws\nhAHlq2KFEJs3b27Xrl18fHz79u3vvvvudu3a1apVq2vXrvv37w92fQAABIvKO8+onFKRyjQW\njJ+kpLqwodxit3379q5du5aXl3fs2DE9Pb1KlSolJSUHDhxYt25dhw4dtm/fnp6eHoJCAQAI\nPCnxBOPerw7PDWxc86rRjlQXTpSD3eTJk+Pj47/66qvrr7/efvju3bu7d+8+adIkTsIDABib\nfefj7mKQPwdkvX2i/zfDsZ8M4UT5UOyWLVtGjhzpkOqEEK1btx45cuS6deuCUxgQAN5u0NgA\nAuHO81ZA/TYiNFsT9XfLQdhQDnYXLlxISUlxOapRo0Znz54NdElAIOltOwzA2NSflucn9Rd2\nON+gJrD3xoGhKB+KTUhIOHjwoMtRBw4cSEhICHRJAADomOdjoNy+BppSbrHr1q3brFmzVqxY\nYX+HCZvNlpubO2fOnB49egSzPCAAFH+48ssWgHccmsTkf9mUQGvKLXYTJ05cs2ZNZmZmUlJS\n8+bNq1atKl0VW1hYmJycPHHixBBUCfjP5aVvbIQB+IWNCHRGucWuUaNGO3fuHDJkyOXLl9et\nW7dq1ap169aVlpYOGzZs165d7k6/A/TJ/nc1G2QA+sUWCj5RdeeJ+vXrL1q0yGazFRYWlpSU\nxMbGJiUlBbsyAAAAeEWhxe63337bunWr9NhisSQnJzdt2jQpKWnOnDnnz58PfnkAABgfzW8I\nFU/BbuPGjenp6RMmTHAYvm/fvtGjR7do0eKnn34KZm0AAJiFV9mOIAhfuQ12BQUFDz74YHFx\ncefOnR1G3XjjjW+//XZBQUH37t2vXLkS5AoBAAgnpDr4wW2wmz9//unTp+fNm/fcc885jLJY\nLGPGjJk+ffqhQ4fef//9IFcIAIApqLloi1QH/7gNditWrGjSpMnQoUPdTTB69OiUlJRFixYF\npS4AAEzJXbzjWn0Egttgd+zYsbZt21aq5HaCyMjIdu3a/fDDD8EpDAAA83Loe4lIhwBxm9su\nXrxYu3Ztz0+uXbv21atXA10SAAAAfOE22NWuXfvYsWOen/yf//wnPj4+0CX57ty5c/n5+VpX\nAQAwPovF8Q8wArfBrk2bNmvXrj1z5oy7CQ4fPrxp06Z27doFpzAX9u3b17Nnz0aNGnXq1Gnu\n3LkVFRUOE0ydOjU1NTVk9QAATMhdjCPewQjcBrtBgwYVFxcPHz68vLzceezFixcHDhxYXl7+\nyCOPBLE6O3l5ebfddtuaNWtOnTq1bdu2UaNGdenS5dy5c6F5dQBAWFCMbmQ76JvbYPfggw92\n7do1Nze3Xbt2ubm5RUVF0vBTp069++67N9100/bt2x944IF77703NIVOmTLl2rVrubm5xcXF\nRUVFb7755pYtWzIyMkpKSkJTAAAgKJwPemrVNqbyRcl20DG394q1WCyffvrpQw899M9//rN3\n794Wi6VGjRoVFRVywsvKylq4cGGo6hT79u3LysrKzMwUQlit1rFjx7Zs2bJHjx79+vVbuXJl\nREREyCoBAASG54RksYT0WlGv4lqIawNU83RLsbi4uDVr1qxZs2bAgAGpqallZWVCiPT09Ecf\nfXTjxo3Z2dlVqlQJVZ2isLCwcePG9kM6d+68YMGCNWvWjBs3LmRlAAACQ02Qom0M8JLbFjtZ\njx49evToEYJSPEtMTNyzZ4/DwEGDBh08eHDKlCkpKSnjx4/XpDAAgNfUJ7bQtI35kCBptIMu\nKQc7nejdu/esWbNmz5792GOPRUVFycNfffXVkydPPv300ydPnnS+ThYAYHhEKEA1i80g35Yz\nZ87cfPPNx44d69q161dffWU/ymazPfHEE2+//bb8r/rZXrhwYeLEiZcvX/YwzcGDBzdt2lRU\nVBQbG+tD5QCA3/GheSzYuyrfjvkaZAeKgCstLbVarXl5ee3bt9e6FkeGabGrXbv2rl27Jk6c\nGB0d7TDKYrHMnDnzzjvvfPrpp48cOeLVbMvKyk6dOiWdPujOpUuXhJd5EQAQSDTaAeoYpsVO\nQ++8886IESNosQOAwNBh85gOS4KO6bnFztNVsQAAADAQYwe7adOmdezYUesqAAAG50PbG811\n0CXX59idOHFC/SxSUlICVIzXDh8+nJeXp9WrAwAA6IrrYFe/fn31s+AsPQBAcIVgR2OzeXGm\nHTs+6JXrYJeVlRXiOgAA4cKrCBVKKgsj1UHHXAe77OxsNU8uKSmRbx0LAEBQhDJIKWY7Uh30\nza+LJ1asWHHzzTcHqhQfvPbaa8ePH9ewAACAL9THo9AHKZvN9YtKAy0Wxz9AT1R1UHz69Ons\n7Oz8/Pzy8nJ54JUrV1avXl1cXBy02pTFxcXFxcVpWAAAQJnL7oXVHPfUsHnM+aXdVSsNpyUP\n+qAc7PLz82+77bZTp065eHJk5IsvvhiEqgAABucQg+z/lTOQ3Abmkn6ikppmOe6NAX1QDnYv\nvPDClStXZs+e3axZsy5duixYsCAlJWXDhg2LFy9+9913MzIyQlAlAMBIPCchhwyk8zyk/mAr\n2Q46oBzsNm3aNGrUqFGjRl25ckUIccMNN7Rr1y4jIyMrK6tLly4rV67s0KFD8OsEABgE7VuA\ndpQvnigoKGjcuLEQolKlSkKI0tJSaXirVq1GjRo1ceLEoNYHADASr9q39M/bIg3xpmBqysGu\nWrVqv/76qxAiOjo6Njb2p59+kkc1b958586dQawOAGBixCAg0JSDXadOnf7+979v2LBBCHHj\njTfOmTNHvhJ23bp1Vqs1qPUBAAyDoCZYCNCYcrB7/vnnz5w589RTTwkhhg8fvnPnzubNm/fu\n3bt169bz58+/++67g18kAAAAlClfPHHbbbdt3rx5+/btQohHHnnk0KFDM2bMyM3NtVgsvXr1\nmjFjRvCLBAAAgDJVHRTfcsstt9xyixDCYrH87W9/mzBhQmFhYWJiYpUqVYJcHgDA1Mx3eazJ\n3g6MRlWwkxQUFBQWFp4/f7527drJycmkOgCAv4hBQECpulfs/PnzU1NT69ate/PNN3fu3Lll\ny5YJCQnNmjXLzs4Odn0AAABQSbnFbt68eSNHjrRarV27dq1Xr17VqlUvXLhw6NChHTt2DBgw\noLS0dPDgwSEoFACAUFNzQ1uH6QFNKQe7GTNmZGRkfPLJJzVq1LAffvTo0W7duk2dOpVgBwAQ\nwvsYZAimfFMwL+VDsfn5+S+++KJDqhNCpKamjh079siRI8EpDABgdkZp31JZp1HeDkxNOdjV\nqFEjIiLC5aiIiIg6deoEuiQAgGGpDzfGikGeq7XZDPZ2YF7Kwe6+++5btWqVy1GrV6/u27dv\noEsCABiZmohjxBjkMr0R6aAzyufYTZ48OTMzMz8/v3///mlpaTExMSUlGUTIvwAAIABJREFU\nJQcOHHjvvfdKS0tHjRp14sQJeeKUlJRgVgsAMAIp67g8Nc3oMcjo9cPslINd3bp1hRDbt29f\nsmSJ89i0tDT7f22s8QAACXsEIOSUg11mZqbVag1BKQAAAPCHcrDLzc0NQR0AAADwk+tgV1hY\naLVaa9asKT32PIukpKTA1wUAAAAvuQ52ycnJGRkZX3zxhfTY8yw4rw4AAEAPXAe7rKysVq1a\nyY9DWA8AAAB85DrYZWdnu3wMAAAA3VLuoFjyww8/nD592v7f3bt3B6ckAIAZWSwu/gAElHKw\nKysr++Mf/9iiRYv9+/fLA9evX3/zzTc/+uijFRUVwSwPAGB8HjIc8Q4IKOVgN2vWrPfee69n\nz54NGzaUB959991ZWVmLFi2aPXt2MMsDABicmtxGtgMCRDnYLVq06N577129enVqaqo8MD09\nPTs7+5577iHYAQDcUp/YyHZAICgHu8OHD991110uR/3hD3/4+eefA10SACAske0AvykHu+rV\nq+fn57sclZ+fX6tWrQBXBAAwB4IaEHLKwa5nz57vvvvumjVr7AeWlZXNnz//H//4R7du3YJW\nGwAAALygfK/YyZMn//Of/+zZs2eDBg3S09OtVuv58+cPHDhw9uzZ5OTkyZMnh6BKAEBYsFgE\ndzMC/KDcYpecnLx79+4RI0aUlJR89dVXq1ev3rx5c0RExPDhw3fs2NGgQYMQVAkAAABFyi12\nQojExMR58+bNnTu3oKDg8uXLSUlJVatWDXZlAAAA8IqqYCexWCx169YNXikAAADwh3Kws9ls\nOTk5H3zwwYkTJ8rKypwnsL8jBQAAvuMEO8A/ysFu+vTp48ePF0LExMRERUUFvyQAgCnYbPR4\nAoSYcrCbOXNmRkbG3LlzGzduHIKCAABhiuY6wG/Kwe7XX3/Nyckh1QEAvEajHRBayt2dJCYm\n2vgVBQDwjco9CDsaIBCUg92AAQMWL14cglIAAObkObTZbKQ6IFCUD8VOmDChT58+AwcOHDx4\ncIMGDZyvn2jatGlwagMAmIUc3eQjs4Q5IAiUg121atWkB0uWLHE5AQdqAQBqscsAgkk52A0Y\nMCA6Ojoy0ouujAEAABB6ynHNXUMdAAAAdMV1sCssLLRarTVr1pQee55FUlJS4OsCAACAl1wH\nu+Tk5IyMjC+++EJ67HkWnGMHAACgB66DXVZWVqtWreTHIawHAAAAPnId7LKzs10+BgAAgG4p\nd1C8cuXKH374IQSlAAAAwB/KwS4rK2v16tUhKAUAAAD+UA52HTt2/Oabb65duxaCagAAAOAz\n5X7sPvzww7Fjx/bs2XPw4MHXXXddjRo1HCbglmIAAAB6oBzs5G7qpN5PnNHdCQAAgB4oB7us\nrKzo6OioqCiLfOdmAAAA6I9ysKO7EwAAAENQCHZXr17du3fvpUuXrr/+em4dBgAAoGeerop9\n//33k5KS2rZte9ddd9WtW/ehhx4qKioKWWUAAADwitsWu40bNz766KMREREZGRm1a9f+9ttv\nP/7448uXL+fm5oayPgAAAKjkNthNmzbNYrGsW7euU6dOQojS0tL+/fvn5ubu37+/RYsWIawQ\nAAAAqrg9FPvtt99269ZNSnVCiOjo6JdeekkIsXHjxtBUBgAAAK+4DXZnzpy57rrr7IdI/545\ncyboRQEAAMB7boPdtWvXqlSpYj+kcuXKQoiKioqgFwUAAADvKd8rFgAAAIZAsAMAADAJTx0U\nb968Wbpgwt6GDRscBjpPAwAAgNCz2Gw21yNU3xnW3RxM45133hkxYkRRUVFsbKzWtQAAAI2V\nlpZarda8vLz27dtrXYsjty12ixcvDmUdAAAA8JPbYPfwww+Hsg4AAAD4yQwXT1y8ePHZZ5/9\n8ccftS4EAABASyYJdlOnTj18+LDWhQAAAGjJ01WxujJs2DB3oy5duiSEmDVr1vLly4UQCxYs\nCF1ZAAAAumGYYPfuu+96nuDLL7+UHhDsAABAeDLModixY8dGRES0atXqiy++OPd7P/zwgxAi\nOztb+lfrSgEAALRhmGD35ptvfvvtt0KIHj16PP/88xaLJe6/qlevLoSoWrWq9K/WlQIAAGjD\nMMFOCHHrrbfu2LFjypQpixYtat68+bJly7SuCAAAQEeMFOyEEJGRkc8888z333/frFmzPn36\n9OrV6/jx41oXBQAAoAuGuXjCXpMmTb7++utFixY9+eSTzZs3f/LJJ32eVVlZ2SeffCJdV+vO\npk2bfJ4/AABAyBgy2EkeeeSRe+655/HHH580aZLPMykoKHjllVfKy8s9THPx4kWf5w8AABAy\nBg52QoiEhISPP/548ODBa9eubdKkiQ9zaNCgwb///W/P07zzzjsjRozwqUAAAIDQMXawk/To\n0aNHjx5aVwEAAKAxg1084WDatGkdO3bUugoAAABdMHawO3z4cF5entZVAAAA6IKxgx0AAABk\nBDsAAACTINgBAACYhLGD3WuvvcadJwAAACTG7u4kLi4uLi5O6yoAAAB0wdgtdgAAAJAR7AAA\nAEyCYAcAAGASBDsAAACTINgBAACYBMEOAADAJAh2AAAAJkGwAwAAMAmCHQAAgEkQ7AAAAEyC\nYAcAAGASBDsAAACTINgBAACYBMEOAADAJAh2AAAAJkGwAwAAMAmCHQAAgEkQ7AAAAEyCYAcA\nAGASBDsAAACTINgBAACYBMEOAADAJAh2AAAAJkGwAwAAMAmCHQAAgEkQ7AAAAEyCYAcAAGAS\nBDsAAACTINgBAACYBMEOAADAJAh2AAAAJhGpdQEAAPjHYvn/BzabpnUA2iPYAQCMSc5zzkNI\neAhXHIoFABiQc6pTPxYwL4IdAMBo1OQ2sh3CEsEOAGAo6hMb2Q7hh2AHADAOb7Ma2Q5hhmAH\nAABgEgQ7AAAAkyDYAQAAmATBDgBgapxmh3BCsAMAmBqdFSOcEOwAAABMgmAHAABgEgQ7AIBx\neHtcleOwCDMEOwAAAJMg2AEADEV9IxzNdQg/BDsAgNGoSWykOoSlSK0LAADAe1Juc9lHHZEO\nYYxgBwAwLDIc8HscigUAADAJgh0AAIBJEOwAAABMgmAHAABgEgQ7AAAAkyDYAQAAmATBDgAA\nwCQIdgAAACZBsAMAADAJgh0AAIBJEOwAAABMgmAHAABgEgQ7AAAAkyDYAQAAmATBDgAAwCQI\ndgAAACZBsAMAADAJgh0AAIBJEOwAAABMIlLrAvxSUVFx4MCBoqKi+vXr169fX+tyAAAAtGSk\nFrstW7aMHj1a/vfDDz+sV6/eTTfd1KFDhwYNGrRq1Wrjxo0algcAAKAtw7TYbdiwISMjIzo6\netasWRaLJScnZ9CgQbGxsX379o2Pjz906NDatWu7deuWl5d3yy23aF0sAACABgwT7CZNmhQX\nF5eXl2exWIQQTz/9dMOGDbdu3ZqcnCxNsG3btrvuumvSpEkrV67UtFIAAABtGOZQ7HfffTd4\n8OCmTZsKIS5cuHD06NFx48bJqU4I0bZt24cffnjTpk3a1QgAAKAlwwS7ioqKKlWqSI8rV65s\nsVhSUlIcpklJSbly5UrISwMAANAFwwS7Vq1aZWdnX7p0SQhhtVpvv/32rVu32k9w9erVzz77\nLD09XaMCAQAANGaYYPfss88eOnSoU6dOX375ZXl5+axZsz766KMPPvjg0qVLZWVl27Ztu+ee\ne/bu3Tty5EitKwUAANCGYS6euPfee+fPn//EE09kZGRUqVIlNTU1Ojp6yJAhQ4cOFUJUVFRY\nLJZx48YNHz7c2zn//PPPFRUVHiY4ffq073UDAACEimGCnRBi2LBh99133+LFi7/++usff/zx\n7NmzVqs1Nja2UaNGHTp0GDJkyM033+ztPI8cOZKWlmaz2YJRMAAAQChZyDQXL1703GK3aNGi\ncePGFRUVxcbGhqwqAACgT6WlpVarNS8vr3379lrX4shILXZBUr16dc8TxMTEhKYSAAAAfxjm\n4gmXpk2b1rFjR62rAAAA0AVjB7vDhw/n5eVpXQUAAIAuGDvYAQAAQEawAwAAMAmCHQAAgEkY\nO9i99tprx48f17oKAAAAXTB2dydxcXFxcXFaVwEAAKALxm6xAwAAgIxgBwAAYBIEOwAAAJMg\n2AEAAJgEwQ4AAMAkCHYAAAAmQbADAAAwCYIdAACASRi7g+JQWrhwodVq1boKwPASExPvv/9+\nrasAAHMi2KnSqVOnt956y2KxaF0IYGxXrlwpKCgoLy+vVInDBQAQeAQ7tX788cfo6GitqwCM\nbePGjXfeeafWVQCAafGjGQAAwCQIdgAAACZBsAMAADAJgh0AAIBJEOwAAABMgmAHAABgEgQ7\nAAAAkyDYAQAAmATBDgAAwCQIdgAAACZBsMP/tXfncVFV/R/AvxeYAQaQTXaURZFUREUKVBDE\n3RQIAXEPxRIppFxKLZceDUsrM0VNEVR6bDIzDU3TB1cIQTFEFmURBMNcIUd2mN8f99X8pmEb\nYWDg+nm//IN7OJx77r1e+XjOvWcAAACAIxDsAAAAADgCwQ4AAACAIxDsAAAAADgCwQ4AAACA\nIxDsAAAAADgCwQ4AAACAIxDsAAAAADgCwQ4AAACAIxDsAAAAADgCwQ4AAACAIxDsup+qqiqm\nKVpaWv379w8PDy8oKJCnnb179zIMM3HixI7ucAfZsmVLk+ehsZKSEmV3FgAAoDOoKbsD0Hb9\n+/cXCATs1w0NDffu3cvJycnJyYmOjhYKhVOmTFFu9zqaoaGhvb29dElRUVFVVZWxsbG+vr50\nOY/H69yuAQAAKAeCXTd24MABZ2dn6ZK8vLzQ0NCzZ88GBQXl5+ebmJi08OPBwcGzZ89WVVXt\n4G52lODg4ODgYOkSV1fXK1eurFy5MiIiQlm9AgAAUCJMxXY8hpH902H69u175MgRc3Pz58+f\nx8TEtFxZVVVVQ0MDo1kAAACcgWDXwZqMcR0Z73r06DFmzBgiSk1NZUv27NnDMMzChQvz8/O9\nvLy0tLTYAS2ZZ+xiYmIYhlmwYEFlZeXy5cutrKw0NDT69OkTGRkpFouJKDo6esiQIQKBwMDA\nICgoqLS0VHq/DQ0N0dHRnp6ehoaGPB5PV1d3+PDhu3fvbmhokNRpsiejR49mGGbdunWNj2Xr\n1q0Mw7i7u7f/tLBN+fr6ypRv375durydJ4GIfv/998DAQAsLCz6fr6en5+rq+sUXX1RWVrZ8\nEtp/gAAAAISp2I7VkYNzLWBnYEUiEbupqalJRM+ePZs1a1ZhYaGbm5uFhUXjn9LQ0GB/avr0\n6devXx85cmRxcXFSUtKqVav4fH5FRUVkZOTYsWMtLS3Pnz8vFAoLCgpSUlIkP75gwYLY2Fg+\nn+/p6WliYvLgwYOEhITk5OTU1NS9e/e20JNXX331/Pnz+/fvX7t2LfPvMyYUColIZr61Q7Xz\nJOzatWvx4sVisXj48OGTJk168uTJpUuXli1bJhQKExIStLW1mzsJnXaAAADAbQh2ysMwJBZ3\nRMN37twhInNzc3aTz+cTUWJiYr9+/e7cucMGi8bU1NSI6LfffnN2ds7OzmZTyJo1a/7zn/98\n9tln6urqV65cGTRoEBGlp6cPHTo0NTU1IyODLcnOzo6NjVVRUUlMTJQ89nf9+nUXF5fo6OiI\niAgHB4fmelJVVfXuu+8WFhaeO3fOy8tL0p+ioqLk5GSBQBAQENARZ0nhJyEnJyc8PJyIjh49\nKhkCLCsrGzVqVGpq6rp167Zs2dLcSQAAAFAITMVyTVFR0cmTJ4lo/PjxbImKigoRlZSUbNy4\nsYUYwY6WlZeXb9u2jQ00RBQSEkJEDx8+XLp0KRtfiGjw4MFsesvMzGRLdHV1hUKhzMscQ4cO\ndXV1JaLExMQWeqKhoTFr1iwiknkokB2u8/f319HRafvpeEHtOQm7du2qra319fWVnvDV09Pb\nsGEDEe3bt6+uro7kvhwAAABtgGDHHeXl5SdPnnz99dcrKysHDx4sM9BlYGDg4uLSaiNmZmb9\n+/eXbEpmCdmIJlP+999/s5vm5uaBgYFsPhOJRHfu3MnLy8vLy2MzWVlZWcs9WbhwIREdOXJE\n0iD9E+zefPPNVvuscG07CRcvXiSiyZMny7Tm5eXFMMzTp09zcnIkhXJeDgAAgBeCqdhu7NVX\nX22yfMiQIb/88gs7qyhhamrKyPHMn6WlpfSmZDEUmefA2HLpFyOys7PXr19/+vRpmRhHROJ/\nzzg37omjo6Ozs/PVq1eFQiEb8nJzc9PS0qytrT09PVvts8K17SQUFhYSkY2NjUxr2trahoaG\njx49unv3LjslTXJfDgAAgBeCYKc87X7AzsHBQUtLS7KpqanZu3fvSZMmBQQENF6dTs4JzeZW\nP2l5VZT09HQ3NzeRSOTo6Lh06dLevXuzKydv3bpVMg/bck9CQkKuXr0aExPDBjvJcJ1S0k/b\nTkJFRQX9826EDPadDOl3YztzfhkAAF4eCHYdSSzu0BdjY2JiZBYoVpZVq1aJRCI/P7/Dhw+z\nz5Cx4uLi5Gxh5syZS5cu/f3333Nzc+3s7IRCIcMw8+bN65j+/r+amhpFNaWlpVVdXc3GOxls\noeShPQAAgA6CZ+w6WJPDcmJxB70PqyzJyclEFBoaKp3qiCgjI0POFnR0dNiHAoVC4Y0bN27e\nvOnp6Wltba2oHrIdq6+vlyln3yBWCFtbWyJq/EG95eXlT548oaZmaQEAABQLwa7jsTFO+g9H\nsROOEj/99BObcsTyHTL78un3339/8OBBUvRrE+zU571796QLa2trT506pahdsI8DxsfHy5Sz\nuzA3N7ezs1PUvgAAAJqEYAcKMGDAACI6fvy4pOTatWvh4eGjRo0iopKSEnkaGTlyZP/+/TMz\nM/fs2aOjozNt2jQF9pBdpuT69euXL19mS2prayMiItixNIVYtGiRurp6fHz8sWPHJIX3799f\nvXo1EYWHh+NtCQAA6GgIdqAAK1asIKLNmzdPnDjx3XffnTBhgouLS0RExFtvvUVE+/btW7Ro\n0dWrV1ttZ8GCBURUXl4eEBAg/V5I+zk7O7OLlXh5eY0ZMyYwMNDe3v7YsWP/+c9/SO4xxZb1\n6dMnKiqKYZg33njD09Pzrbfe8vb2fuWVV/Lz86dOnbp06dL27wIAAKBlCHagAFOnTo2NjXV0\ndDx37tyhQ4dqamqOHj26bNmygICA6dOnq6qqHjly5NmzZ622M3fuXHaVlo74GLH4+Pj58+cb\nGhpeunQpKSlp9OjRycnJ7PRoVVWVQnYxf/78pKSkadOm3b59OzY29tKlS4MHD963b9/Ro0dl\nVp8BAADoCIxCxiq4bffu3d99993Zs2fZD4OCjpOVlTVw4EAHBwf537qA7uXixYseHh719fUy\n79kAAHQjNTU16urqiYmJI0aMUHZfZOHfVuhC2E/fevfdd5XdEQAAgG4JwQ66iqioqEOHDg0Y\nMKATlq8DAADgJDz3A0p2+/btdevW5eTkXL9+XUNDIyYmRl1dXdmdAgAA6JYwYgdKJhKJDh8+\nnJWV5ebmdu7cuddee03ZPQIAAOiuMGIHSubk5FRbW6vsXgAAAHABRuwAAAAAOALBDgAAAIAj\nEOwAAAAAOALBDgAAAIAjEOwAAAAAOALBDgAAAIAjEOwAAAAAOALBrruqra3dv3+/j4/PwIED\n9fX1eTyeoaGhq6vr6tWr792714YGY2NjGYYZO3Ysu7l3716GYSZOnPhCP9XYli1bGPmUlJTI\nudMuqKqqij2Kfv36VVdXN1ln9uzZDMPExsZ2btfartWLCwAAXQ0WKO6WHjx4MGnSpLS0NCKy\ns7Nzdnaur68vKSm5cuXKlStXtm/f/vPPP48ePVrZ3SQiMjQ0tLe3ly4pKiqqqqoyNjbW19eX\nLufxeJ3btQ6Rm5v72WefrVmzRtkdAQCAlxGCXbcUGhqalpY2ZMiQuLi4gQMHSspv3bq1YMGC\nxMTEmTNnFhYWtudDV4ODg2fPnq2qqtrOrgYHBwcHB0uXuLq6XrlyZeXKlRERER20U2XR1tau\nrKyMjIycNWtWnz59lN0dAAB46WAqtpMwDDGMYpqqqKg4duwYEe3cuVM61RGRvb39iRMnevTo\n8eTJkzNnzrRnL6qqqhoaGp08iqaUnSqQrq5ueHh4VVXVO++8o+y+AADAywjBrsNJRzr263Ym\nvLKysvr6eiKytbVt/F1dXd2srKznz59PmTJFuvz3338PDAy0sLDg8/l6enqurq5ffPFFZWVl\nc3tp/LhbTU3Nxo0b7e3tNTQ0jI2N/f39MzMz23Ukcuw0Ozv7zTfftLGx0dDQ0NHR6devX1hY\n2O3bt6V/qtU6W7duZRjG19dXZnfbt29vXJ6enj537tzevXurq6sbGhp6eHjs379fLBbL0/+K\nior169dbWFicOnXqxx9/lOdHWr0ue/bsYRhm4cKF+fn5Xl5eWlpa7EhnTEwMwzALFiyorKxc\nvny5lZWVhoZGnz59IiMj2d5GR0cPGTJEIBAYGBgEBQWVlpZK77ehoSE6OtrT09PQ0JDH4+nq\n6g4fPnz37t0NDQ3ydBsAALomTMV2rOYyHMOQfFGhCWZmZlpaWs+fP4+Ojl65cmXjChYWFjIl\nu3btWrx4sVgsHj58+KRJk548eXLp0qVly5YJhcKEhARtbW159jtr1qwff/xRIBD4+Phoa2un\np6e7uLiEhYW18TDkcPXqVQ8Pj4qKiiFDhowcOZJhmGvXrkVFRR06dOjcuXODBw+Ws478fvjh\nhzlz5tTU1Li4uEyYMKG0tPTcuXMXL148derUd999p6LSyn+EqqurdXR0vvzyy+nTp0dEREyY\nMEFHR6eF+vJcF01NTSJ69uzZrFmzCgsL3dzc2OuroaFBRCKRaPr06devXx85cmRxcXFSUtKq\nVav4fH5FRUVkZOTYsWMtLS3Pnz8vFAoLCgpSUlIku16wYEFsbCyfz/f09DQxMXnw4EFCQkJy\ncnJqaurevXtf6KQBAEAXIobW7Nq1y93dvbq6+kV/kKilP+2xYsUK9vL5+PicOHGisrKyhcrZ\n2dk8Ho9hmKNHj0oKnz59OmjQICJaunQpWxITE0NEY8aMYTf37NlDRBMmTGA3T58+TUQaGhp/\n/PGHpJHt27erqalJ/5Q8XFxciOirr75q/C2Znfr5+RHR6tWrpets2LCBiPz9/eWv89VXX7Hn\nSmZ333zzjXR5UVGRpqYmwzAHDhyQ1MnLy2Oflvv2229bOCh2jE1dXZ3dHD9+PBG999570nVm\nzZpFRDExMeymnNdFKBQSkaWlpZeXV0VFhaTmDz/8QER6enpjx4599uwZW/jxxx8TkZGRkaWl\n5Y0bN9jCP/74g2EYIpKUZGVlEZGKikpqaqqkwbS0NHYSPCMjgy2R+SuhEBcuXCCi+vp6BbYJ\nANDJ2NUPEhMTld2RJmAqVmnaMyG7YcOG0NBQhmGOHTv2+uuv6+rqjhw5cuXKlWfPnq2trZWp\nvGvXrtraWl9fX+k5Rz09PTb97Nu3r66urtU9/ve//yWiGTNmSI+BhYWFDRgwoO2H0ZrCwkIi\nGj58uHThihUrzpw589lnn8lfR07bt2+vrKz08/ObM2eOpLBPnz5sOzt27HihptTV1b/55psb\nN240V0fO68IOE5aUlGzcuJEdvWOxWa28vHzbtm2SMdeQkBAievjw4dKlS9mASESDBw92dnYm\nIsnUua6urlAoPHDgAFvOGjp0qKurKxElJibKf6QAANCldPtgV1tbm5mZee3ateYWD+MkHo8X\nFRV148aN8PBwGxubmpqapKSkTZs2jRs3ztjY+IMPPhCJRJLKFy9eJKLJkyfLNOLl5cUwzNOn\nT3Nyclrd49WrV4nI09NTppwdmuog/fv3J6J169ZJP8zH4/HGjh0reb5QnjpySkhIIKKpU6fK\nlE+cOFFFRSU9Pf3p06dyNmVnZ/fBBx/U1dWFhoaKm5l0f6HrYmBgwI50yjAzM2PPAEsyC89G\nNJnyv//+m900NzcPDAxkRxBFItGdO3fy8vLy8vLYieOysjI5DxMAALqa7hTsEhISRo8ebWNj\nM3ny5CtXrhDR6dOnra2tHRwcnJ2djY2No6KilN3HTuXg4PD1118XFBSUlJQcOnRo8eLFtra2\nZWVln3/+uaurq+S3ODumZWNjI/Pj2trahoaGRHT37t1W98UuemxpaSlT3rhZBdq8ebOdnd3V\nq1cdHBwcHByWLFly4sSJqqqqF60jJ/ZExcXFvflvYWFh7Bxlbm6u/K2tXLmyT58+SUlJ0dHR\nLexOzutiamrKNDXGK3NFJCvFyDxnyZZLvxiRnZ0dFBSkr6+vo6Nja2trZ2dnZ2d38uRJImou\niQIAQNfXbV6e+P333ydMmFBXV9ejR4/Tp09fvHjxzJkzgYGBurq6c+fOraqqOnPmTFhYmK2t\nbXf53AIF/va0sLAICgoKCgoSi8VHjx6dN29eZmbmhg0bPv/8cyKqqKigf57Bl8E+gN/Cu7ES\nbCNsfWntWSqvVWZmZmlpaTt37jxw4MDNmzczMzO3bdumq6v7/vvvf/TRR+wcpTx15MQOc549\ne7a5CpKsLA8NDY3t27dPmjTpww8/fOONN9isJu2FrktzL2E0tzRMy0vGpKenu7m5iUQiR0fH\npUuX9u7dWyAQENHWrVsxDwsA0K11mxG7yMjInj17pqenl5eX379//7XXXgsKCrKxsbl169b+\n/fuFQmF+fr61tfXXX3+t7J7+v84f+GAYxs/Pj31V9vz582yhlpYW/RMjZLCF8rwVy0aNxvPd\n0nO+HUFbW3v58uUZGRklJSX79+/39vYWiURr1659//33X6hOk2pqamT2RUQnT55s7onUF/1w\nrYkTJ06bNu3x48fsyy4yQ24KuS5ts2rVKpFI5Ofnd/369Y8++mju3Ln+/v7+/v49e/bsoD0C\nAEDn6DbBLikpKSwszNHRkYiMjIw+//zzu3fvvvfee5IBD319/ZCN0BR+AAAYVUlEQVSQEOkF\nHbqy9mS+hISEDRs2sG8XNol9tuzRo0fSmwUFBTLVysvLnzx5QvJNp5qZmRHRn3/+KVMus6Rc\nx7GwsJg7d+6xY8d++eUXItq5c2fj10Saq8OO27GL/0m7c+eO9Gbfvn2JqKioSIHd3rp1q7a2\ndkxMTGJioszopkKuS9skJycTUWhoqMyIZkZGRgftEQAAOke3CXbl5eVWVlaSTfYRIiMjI+k6\nZmZmLzRZ1gnE4iYyXDtH8qKioj7++OMVK1Y0974I+xKA5H1V9o2H+Ph4mWqnTp0iInNzczs7\nu1Z3OmTIECKSSZP19fXsU1kd4cmTJ3Fxcd9//71M+YQJEzQ0NGpqah4+fChPHfpnHpN9TFCi\ntraWPQMSXl5eRNS4terq6ri4uPv377fhKCwtLdetWycWi0NDQ2VmXRVyXdpDZmL9p59+YlMm\nnrEDAOi+uk2wMzQ0zM/Pl2yyA0V5eXnSdfLz8xs/ydQVsPFO8qedPv74Yz6fn5KSMnnyZJnP\nfigpKVm5ciW7wOySJUvYwkWLFqmrq8fHx7MfRMa6f//+6tWriSg8PLzJp/JlBAQEEFFcXNy1\na9f+OSjx+vXrHzx40N7jaUZVVdX8+fNDQkIuX74sXX748OGqqipTU1NTU1N56hARu/DH9evX\nJdVqa2sjIiLYgTGJt99+WyAQXLhwYcuWLZLC2trasLCwOXPmLFq0qG0HsmTJkkGDBmVkZBw+\nfFi6XCHXpW3Y0H/8+HFJybVr18LDw0eNGkVEJSUlHbRfAADocJ2zXF77BQUFGRgYJCQkVFdX\n37hxY9CgQf379+/du3dJSQlbISsrS19fX7ImrQK1eYHijnP8+PEePXqwV9DCwsLV1dXV1dXa\n2pqdWdPQ0Ni5c6d0/ejoaBUVFYZhPDw8Fi5cOHXqVF1dXSKaOnVqbW0tW6flBYobGhrGjBlD\nRHw+f9y4cd7e3tbW1jo6Ops2baIOW6CY/SgwInJ2dp4xY8bMmTOdnZ0ZhlFVVRUKhfLXEYvF\n7PIfPB7Py8srICDAxsbGwsKCXZrO29tbUk0oFLKvHQwZMuTNN9/09/c3MTEhor59+0r+pjVJ\nZoFiGZcvX5akNMkCxWL5rgsbB11cXGTaZMtHjhwpU87upbS0VLpw2rRpRCT5WyGJdBMmTHjn\nnXfGjx+vqqq6efPmuLg49u/P22+/nZqaigWKAQCa1JUXKO42wS47O1v6xUADA4OsrCwrKyuB\nQDB69Ojhw4erqampqqpeuXLlhZotKCgwMjLSb5FAIOhqwU4sFj98+PDTTz9lPw+Kz+fzeLye\nPXu6ubmtWbOmqKiocf3k5GR/f38zMzMej6enpzdq1Ch2CVxJhZaDnVgsFolEK1eutLW15fP5\nRkZGfn5+mZmZ//vf/5qMFy2QP9iJxeKzZ88GBgZaW1sLBAI+n9+7d+8ZM2akpKRI/5Q8dR49\nejR//nxTU1Mej2dhYTF//vzi4uLffvuNiMaPHy9dMyMjg/2sWD6fLxAIBg0atGrVqsePH7d8\nUC0HO7FYHBwc3DjYieW4Lh0R7MRicWxsrKOjI5/PNzQ09PT0PH78uFgsrq6unj59upaWVs+e\nPRMSEhDsAACa1JWDHSPuPs/T3Lx589NPPy0oKOjfv/+HH35ob29/8+bNBQsWsB+LZGtr++WX\nX/r4+LxQmw0NDRcvXmz5oxcyMzOPHDly9uxZPp/fviMAeNldvHjRw8Ojvr7+hVaiAQDoUmpq\natTV1RMTE0eMGKHsvsjqNuvYEZGDgwP7wVbSJVeuXBGJRJWVlTIvUshJRUWl8acpyBAIBEeO\nHGlD4wAAAACdqTsFu+Zoa2t33IpfAAAAAN1F954N2bJli5ubm7J7AQAAANAldO9gl5eXh09A\nAgAAAGB172AHAAAAABIIdgAAAAAcgWAHAAAAwBHdO9ht2rSpuLhY2b0AAAAA6BK693Inenp6\nenp6yu4FAAAAQJfQvUfsAAAAAEACwQ4AAACAIxDsAAAAADgCwQ4AAACAIxDsAAAAADgCwQ4A\nAACAIxDsAAAAADgCwQ4AAACAIxDsAAAAADgCwQ4AAACAIxDsAAAAADgCwQ4AAACAI9SU3YFu\ngM/nNzQ0jB07VtkdAeAIVVVVZXcBAKC9+Hy+srvQBEYsFiu7D91Aenp6XV2dQpr66KOPKioq\nFi5cqJDWoKvZs2cPEeH6chWuL7fh+nLbnj17BALBhg0bFNKampra4MGDFdKUYmHETi4KvHim\npqZENHv2bEU1CF3K//73P8L15S5cX27D9eU29voOGzZM2R3pWHjGDgAAAIAjEOwAAAAAOALB\nDgAAAIAjEOwAAAAAOALBDgAAAIAjEOwAAAAAOALBDgAAAIAjEOwAAAAAOALBDgAAAIAj8MkT\nna1rfrQcKAquL7fh+nIbri+3vSTXF58V29mePn1KRPr6+sruCHQIXF9uw/XlNlxfbntJri+C\nHQAAAABH4Bk7AAAAAI5AsAMAAADgCAQ7AAAAAI5AsAMAAADgCAQ7AAAAAI5AsAMAAADgCAQ7\nAAAAAI5AsAMAAADgCAQ7AAAAAI5AsAMAAADgCAQ7AAAAAI5AsAMAAADgCAQ7AAAAAI5AsAMA\nAADgCAQ7AAAAAI5AsOsktbW1K1euVFVVdXZ2bvzdsrKyiIgIa2trPp9vbm4eEhJSWlra+Z2E\n9ouNjWWasmHDBmV3DdoFNymH4bblnpf5d66asjvwUsjOzp49e3Zubm6T362pqRkzZkxaWtq0\nadOcnJzy8/MPHDiQkJBw7do1fX39Tu4qtFNZWRkRzZgxo3fv3tLlI0eOVFKPQAFwk3IbbluO\nedl/54qhg5WXl2tqajo7O+fm5qqrqw8bNkymwpdffklEn332maREKBQS0dKlSzu3p6AAa9eu\nJaLU1FRldwQUCTcpt+G25RL8zsVUbIerq6tbvHhxUlJS3759m6xw4MABHR2dJUuWSEoCAwP7\n9u178OBBsVjcWd0ExWD/66+np6fsjoAi4SblNty2XILfuQh2Hc7AwGDLli08Hq/J71ZVVWVk\nZLz22mvq6urS5W5ubg8ePLhz506n9BEURvIbor6+vqSk5NGjR8ruEbQXblLOw23LJfidi2Cn\nZMXFxfX19b169ZIpt7KyIqKCggJldArarry8nIi2bt1qZGTUq1cvIyMje3v7//73v8ruF7Qd\nblLOw2378ngZbme8PKFkz549IyItLS2Zcm1tbcl3oRth/+t/6NChFStWWFhYZGdn79ixY9as\nWc+ePXv77beV3TtoC9yknIfb9uXxMtzOCHYKU1ZW9uGHH0o2+/btu2zZMjl/lmEYmRJ2pr9x\nOXQRzV3ujz/++J133pk4caLkH47Zs2c7OTmtWrUqODiYz+crp7vQbrhJOQy37cuG27czgp3C\niESi3bt3SzZHjhwpT7Dr0aMHNfW/hL///puIdHR0FNpHUJjmLreXl5dMzQEDBkyePPno0aPp\n6emvvvpqp/YSFAE3Kefhtn15vAy3M4KdwlhaWrbhhZrevXurqakVFRXJlOfn5xORnZ2dYjoH\nivZCl9vY2JiIRCJRR/YIOgpu0pcTbltOehluZ7w8oWR8Pn/YsGEpKSkVFRWSwoaGhgsXLvTq\n1UtmtUzo4kQi0c6dOw8dOiRTnpmZSf88nAvdDm5SbsNt+1J5GW5nBDvlW7BgQUVFxebNmyUl\n33777Z9//hkSEqLEXkEbCASCjRs3vvXWWzk5OZLCY8eOXb58eejQoba2tkrsG7QHblIOw237\nsuH87cxwYzm+ruzChQu//vor+/WWLVuMjIzmzZvHbi5fvtzQ0LC+vn706NGXLl3y8fFxcnLK\nzs4WCoUODg7JyckCgUB5HYe2OH78uK+vr0AgCAoKMjc3v3nz5s8//6yjo3Pu3DknJydl9w7a\nCDcpt+G25RL8zsVHinW4yMjI5k5+bm4uW+fZs2fLli2zsrLi8XgWFhZhYWGPHz9WbrehzZKS\nkiZNmqSnp6empmZubj537lzJhYbuCzcpt+G25Qz8zsWIHQAAAABH4Bk7AAAAAI5AsAMAAADg\nCAQ7AAAAAI5AsAMAAADgCAQ7AAAAAI5AsAMAAADgCAQ7AAAAAI5AsAMAAADgCAQ7AAAAAI5A\nsAMAAADgCAQ7AAAAAI5AsAMAAADgCAQ7AAAAAI5AsAMAAADgCAQ7AAAAAI5AsAMAAADgCAQ7\nAAAAAI5AsAMAAADgCAQ7AAAAAI5AsAMAAADgCAQ7AAAAAI5AsAMAAADgCAQ7AAAAAI5AsAMA\nAADgCAQ7AAAAAI5AsAMAAADgCAQ7AAAAAI5AsAMAAADgCAQ7AAAAAI5AsAMAAADgCAQ7AAAA\nAI5AsAMAAADgCAQ7AGivoKAghmHu37/fpZqStFZSUqKQ1gAAuj4EOwAgIoqLi2P+TVVV1cTE\nxM/P7/Llyy3/7JAhQyZMmKCurt7+biiwKTmJxeIff/zR19fX3NxcXV3d2NjY2dl548aNf/31\nV6f1oSvbtGlTXl5eq9Vqa2tXrlypqqrq7OzcCb0CgOYwYrFY2X0AAOWLi4ubM2fOyJEj3dzc\n2JLKyspbt26dOXNGLBbHxsbOnTtXuT1sg6CgIKFQWFxcbGlp2WSFsrKygICAs2fPCgSCMWPG\nWFlZPX78OCUlJT8/38jI6MiRI+7u7p3c5y6ltLTU3Nz8119/nThxYgvVsrOzZ8+enZub+/z5\n86FDh169erXTeggAMtSU3QEA6ELGjh27bt066ZJLly55eXlFRERMnz69MwfSOsesWbPOnj3r\n4+OzZ88eIyMjtrChoeHbb7995513fHx8cnJyjI2NldtJJUpNTW21zt9//z1s2LCBAwempaU5\nODh0Qq8AoAWYigWAlri7u48ZM+bp06fp6en0z1NrDx48GDdunKam5vHjx+nfD8bNnDmTYRiR\nSPTBBx9YW1urq6v36tXrq6++kp4cuH//fkhIiIWFhZaW1uDBg7/++uu6ujr2W9JNvfHGGwzD\nlJaWhoSEmJiYqKurv/LKKzt37pTuXkpKyhtvvNGzZ08+n29tbT1nzpzCwkI5D+3UqVMnT550\ncnL68ccfJamOiFRUVBYtWvTJJ584OTnl5+ezhUVFRcHBwRYWFnw+v2fPnt7e3ikpKZIfYY+6\nrKzs7bffNjExEQgErq6uKSkpFRUVERERFhYW2traI0aMSEtLk/yIPEcnz05bPtV//fVXWFiY\nlZUVn883MjLy9fWVzmottzBlyhQfHx8imjRpEsMwzc3I19XVLV68OCkpqW/fvnKeeQDoOBix\nA4BWGBoaElFFRQUR8fl8Inrvvfd4PN6aNWtsbW1lKrMV/P39bWxsvv/++4aGhvXr17///vt6\nenrBwcFE9PDhQ2dnZ5FINHfuXCsrq/Pnz0dERGRkZOzdu1emKXaA0NfXd/To0UePHm1oaPjk\nk08WL17M4/FCQkKI6Nq1ax4eHgYGBkuWLDE1NS0oKNixY8dvv/2WlZXF9rllBw4cIKLVq1er\nqTXxL+GqVatWrVrFfl1cXPzaa69VVFSEhoYOHDjw3r17UVFRo0aNOnv2LDtzzR51QECAu7v7\nqVOnbty4sWjRooCAAEdHx4EDBx4/frywsDAkJGTy5MnFxcU8Hk+eo5Nzpy2fahcXl7KyskWL\nFjk4OBQXF0dFRbm7u58+fdrDw6PVFj766CMDA4ODBw+uWbNm6NChAwYMaPI0GhgYbNmypdWz\nDQCdRAwAIBYfPHiQiNauXStTXlNTY2try44ticXi+fPnE9H48ePr6+sldaZPn05EbIUFCxYQ\n0YwZMyTfZQe9pkyZwm6GhoYS0enTpyUVXn/9dSK6efOmTFPs19JNlZWVqaurW1tbs5tRUVFO\nTk7nzp2TVPjmm2+I6JtvvpHuWHFxcZOHzB5XeXl5qydn3rx5RPTTTz9JSrKyslRVVV1dXdlN\n9qhDQ0MlFQIDA4nI399fUrJkyRIiSkxMlO5bC0cn505bPtVqamqpqamSCnfv3tXR0XF2dpaz\nhcjISCL69ddfWz1FLHV19WHDhslZGQA6AqZiAaBpVVVVGRkZQUFBBQUFQUFBpqamRMQwDBHN\nmzdPRaWlfz3YUMKytbUVCATsmiNisfiHH37o1avXuHHjJBW2bduWkJBgYmLSZFNBQUGSr3V1\ndd3d3QsLC0tLS4koNDT02rVrnp6eRFRbW1tVVcWOKsk5G/vXX3/p6ur26NGj5Wpisfjnn382\nMTHx9fWVFPbv33/48OHJycmPHz+WFPr5+Um+trOzIyJ2KpNlb29PRGzPWz06+Xfawqk+fPiw\no6OjpaXl/X/weLwRI0ZcvXpVJBK12gIAdEcIdgDw/9avXy9Z7kRTU9PR0fGnn37y9vbevXu3\ndDU2o7Sgd+/e0ps8Hq+2tpaISktLHz9+/Morr7ABkWVrazt69OiePXs22VS/fv2kNy0sLIhI\nstDdwYMHPTw89PX1+Xy+pqbmmDFjiEjyxF7LVFRU6uvrW612//798vLygQMHSveZ/jkJt2/f\nlukbi53elS5hZ2DZ89Dq0cm/0+ZO9YMHDx49epSWlmb2b6dPnyaiu3fvttoCAHRHeMYOAP6f\nh4cHOwBGRCoqKoaGhm5uboMHD5appqur23I7bIhprLKykv55vExOAoFAelNLS4uIysrKiGjV\nqlWRkZHOzs5fffWVjY2Nurp6ZmYm+4CaPMzNzW/duvXo0aPmMiXr+fPnkv1K09TUlHyX1fio\nmzsPEs0dnY6OTpt3ynr27BkRDRkyhJ1OlWFubi5/JwGgG0GwA4D/5+npKbPciWKx87lsLJOT\ndIghovLyciIyNDSsqqraunVrr169zp07p62tLf1dOY0YMeLWrVu//PIL+6qBDLFYnJGR4ejo\nyDYu0w1JCZvA2qy5o2v/TiV1Wl6CDgA4BlOxANB5tLS0jIyMsrOzpSf7bt26tX379szMzCZ/\nJDs7W3ozNzeXiMzMzO7fv19ZWens7CxJdUR04cIF+TvD5rlPPvmEHdySERUVNXjw4B07dpia\nmhoYGGRnZ4v/vZx7VlYWwzCtzkq3rLmja/9OTUxMevbsmZOTIxOjHz582J4OA0AXh2AHAJ3K\nx8fn8ePH+/fvl5SsW7fu3Xffra6ubrL+vn37JF/fvn07NTXV3t7eyMjIxMSEYRjp9yT++OMP\ndgWTqqoqeXri7u4+ffr0wsLCcePGSdarI6K6urpt27YtWbLEzMxs5syZROTn51daWnrs2DHp\nfaWkpHh5eenp6cl54C90dArZaUBAQFVV1ebNmyUlDx8+dHR0nDp1qpzdU1VVpX8m0AGgW8BU\nLAB0qrVr18bHx4eGhqanp1tZWV24cCE+Pn7u3LlOTk5N1q+urp46deqUKVMaGho+//xzsVi8\nZs0aItLU1Hz99dfj4+MXLVrk6emZlZW1ffv27777ztvb+8SJE4cOHfL29m61M/v27auurv75\n559feeUVd3f3fv36lZWVJScnFxUV2dranjp1Sl9fn4jWr18fHx8/Z86c8PBwe3v7wsLCHTt2\naGtrf/nll+08G80dnUJ2um7duhMnTnz66aelpaUeHh5//vnnrl27Hj9+HB4eLmcL7DqFmzZt\nunPnjru7+6uvvtq4zoULF3799Vf267q6unv37n344Yfs5vLly+VZUBAAFElpC60AQFfS3Dp2\nMtiVz3Jzc6ULG69jJ1NBV1d34MCBks3CwsLZs2cbGxvzeDxbW9svvviirq6ucVPs17m5uRER\nEebm5nw+f8CAAbGxsZJ2Hjx4MHPmTCMjI11dXS8vr0uXLonF4vXr12tra5uampaWlra8jp3E\n8ePH/fz8zM3NeTyejo6Oi4tLVFRURUWFdJ27d+8GBwebmZmpqakZGxsHBQVlZWW1cFrWrl1L\nRGyXWHv27CGiQ4cOSR9pC0fXhp02PtWlpaWhoaG9evVSU1PT09Pz9va+cuWK/C3U1NRMmzZN\nU1NTX1//8OHDTZ69Jl/OYMm0DACdgBH/+wEOAIAuIigoSCgUFhcXW1paKrsvisftowMAZcEz\ndgAAAAAcgWAHAAAAwBEIdgAAAAAcgWfsAAAAADgCI3YAAAAAHIFgBwAAAMARCHYAAAAAHIFg\nBwAAAMARCHYAAAAAHIFgBwAAAMARCHYAAAAAHIFgBwAAAMARCHYAAAAAHIFgBwAAAMARCHYA\nAAAAHIFgBwAAAMARCHYAAAAAHIFgBwAAAMARCHYAAAAAHIFgBwAAAMARCHYAAAAAHIFgBwAA\nAMARCHYAAAAAHIFgBwAAAMARCHYAAAAAHIFgBwAAAMARCHYAAAAAHIFgBwAAAMARCHYAAAAA\nHIFgBwAAAMAR/wcQ89Iavh4HmwAAAABJRU5ErkJggg==",
            "text/plain": [
              "Plot with title “BRCA Tumor and Normal Data (Training)”"
            ]
          },
          "metadata": {
            "image/png": {
              "width": 420,
              "height": 420
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AQE9hMD6KB5b"
      },
      "source": [
        "# Evaluate the Machine Learning Model\n",
        "Once the model has been trained and stored, we can evaluate the model's performance using the \"testing\" dataset from our subset table.\n",
        "\n",
        "![train-test-norm-Page-6.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5R4q2X2UIvn6"
      },
      "source": [
        "## Calculate Performance Metrics with BigQuery ML\n",
        "\n",
        "Evaluating a BQ ML model is generally less expensive than training. In the following BQ ML query, we use the \"data_partition = 'testing'\" clause to ensure that we only evaluate the model with test samples from the subset table.  \n",
        "\n",
        "BigQuery's ML.EVALUATE function returns several performance metrics: precision, recall, accuracy, f1_score, log_loss, and roc_auc. More details about these performance metrics are available from [Google's ML Crash Course](https://developers.google.com/machine-learning/crash-course/classification/video-lecture). Specific topics can be found at the following URLs: [precision and recall](https://developers.google.com/machine-learning/crash-course/classification/precision-and-recall), [accuracy](https://developers.google.com/machine-learning/crash-course/classification/accuracy), [ROC and AUC](https://developers.google.com/machine-learning/crash-course/classification/roc-and-auc). "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "yyr2hH3QKNjm",
        "outputId": "6155c32f-1253-4a46-d3be-53f23ac7d3cb"
      },
      "source": [
        "# Construct query\n",
        "ml_eval_query <- sprintf(\"\n",
        "  SELECT * FROM ML.EVALUATE (MODEL `%s`, \n",
        "    (\n",
        "      SELECT * EXCEPT(case_barcode, sample_type_name, data_partition)\n",
        "      FROM `%s`\n",
        "      WHERE data_partition = 'testing'\n",
        "    )\n",
        "  )\n",
        "\", bq_ml_model_fullname, bq_tmp_table_fullname)\n",
        "\n",
        "# Display query\n",
        "cat(ml_eval_query)\n",
        "\n",
        "# Run the query to evaluate the model\n",
        "ml_eval_result <- bq_project_query(\n",
        "  google_project,\n",
        "  ml_eval_query,\n",
        "  quiet = TRUE\n",
        ")\n",
        "\n",
        "# Download the eval result\n",
        "eval_data <- bq_table_download(ml_eval_result, quiet = TRUE)\n",
        "\n",
        "# Display the result\n",
        "eval_data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "  SELECT * FROM ML.EVALUATE (MODEL `isb-cgc-training-001.nih_library_2021_10.tcga_brca_ml_model_30e1b3df`, \n",
            "    (\n",
            "      SELECT * EXCEPT(case_barcode, sample_type_name, data_partition)\n",
            "      FROM `isb-cgc-training-001.nih_library_2021_10.tcga_brca_ml_table_30e1b3df`\n",
            "      WHERE data_partition = 'testing'\n",
            "    )\n",
            "  )\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  precision recall accuracy f1_score log_loss     roc_auc\n",
              "1 1         1      1        1        1.941171e-05 1      "
            ],
            "text/latex": "A tibble: 1 × 6\n\\begin{tabular}{llllll}\n precision & recall & accuracy & f1\\_score & log\\_loss & roc\\_auc\\\\\n <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n\\hline\n\t 1 & 1 & 1 & 1 & 1.941171e-05 & 1\\\\\n\\end{tabular}\n",
            "text/markdown": "\nA tibble: 1 × 6\n\n| precision &lt;dbl&gt; | recall &lt;dbl&gt; | accuracy &lt;dbl&gt; | f1_score &lt;dbl&gt; | log_loss &lt;dbl&gt; | roc_auc &lt;dbl&gt; |\n|---|---|---|---|---|---|\n| 1 | 1 | 1 | 1 | 1.941171e-05 | 1 |\n\n",
            "text/html": [
              "<table class=\"dataframe\">\n",
              "<caption>A tibble: 1 × 6</caption>\n",
              "<thead>\n",
              "\t<tr><th scope=col>precision</th><th scope=col>recall</th><th scope=col>accuracy</th><th scope=col>f1_score</th><th scope=col>log_loss</th><th scope=col>roc_auc</th></tr>\n",
              "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><td>1</td><td>1</td><td>1</td><td>1</td><td>1.941171e-05</td><td>1</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cop49YDJLPVF"
      },
      "source": [
        "We can see from this result that prediction of tumor vs. normal breast cancer samples is very easy, with a prediction accuracy of 100% on the testing dataset. This is not surprising, since the gene expression of tumor samples is expected to be markedly different from that of normal tissue samples. Other prediction problems, such as prediction of tumor grade or therapeutic response, can be much more challenging. Prediction models for these problems would be expected to yield lower prediction accuracies. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tXqsURCGCDyv"
      },
      "source": [
        "## Calculate Prediction Labels and Probabilities for Test Samples\n",
        "\n",
        "We can also calculate the actual predicted labels and the probability that each sample belongs to a class using the \"ML.PREDICT\" BigQuery method."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 599
        },
        "id": "UBHg-TAgCK_q",
        "outputId": "a609b7ba-f272-480b-a5b5-e5028266663a"
      },
      "source": [
        "# Construct the query to predict each sample\n",
        "ml_predict_query <- sprintf(\"\n",
        "  SELECT\n",
        "    label,\n",
        "    predicted_label,\n",
        "    (SELECT prob\n",
        "     FROM UNNEST(predicted_label_probs)\n",
        "     WHERE label = 1) AS tumor_prob,\n",
        "    (SELECT prob\n",
        "     FROM UNNEST(predicted_label_probs)\n",
        "     WHERE label = 0) AS normal_prob\n",
        "\n",
        "  FROM ML.PREDICT (MODEL `%s`, \n",
        "    (\n",
        "      SELECT * EXCEPT(case_barcode, sample_type_name, data_partition)\n",
        "      FROM `%s`\n",
        "      WHERE data_partition = 'testing' -- Use the testing dataset\n",
        "    )\n",
        "  )\n",
        "\", bq_ml_model_fullname, bq_tmp_table_fullname)\n",
        "\n",
        "# Display the query\n",
        "cat(ml_predict_query)\n",
        "\n",
        "# Run the query to evaluate the model\n",
        "ml_predict_result <- bq_project_query(\n",
        "  google_project,\n",
        "  ml_predict_query,\n",
        "  quiet = TRUE\n",
        ")\n",
        "\n",
        "# Download the eval result\n",
        "ml_predict_data <- bq_table_download(ml_predict_result, quiet = TRUE)\n",
        "\n",
        "# Display the result\n",
        "head(ml_predict_data)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "  SELECT\n",
            "    label,\n",
            "    predicted_label,\n",
            "    (SELECT prob\n",
            "     FROM UNNEST(predicted_label_probs)\n",
            "     WHERE label = 1) AS tumor_prob,\n",
            "    (SELECT prob\n",
            "     FROM UNNEST(predicted_label_probs)\n",
            "     WHERE label = 0) AS normal_prob\n",
            "\n",
            "  FROM ML.PREDICT (MODEL `isb-cgc-training-001.nih_library_2021_10.tcga_brca_ml_model_30e1b3df`, \n",
            "    (\n",
            "      SELECT * EXCEPT(case_barcode, sample_type_name, data_partition)\n",
            "      FROM `isb-cgc-training-001.nih_library_2021_10.tcga_brca_ml_table_30e1b3df`\n",
            "      WHERE data_partition = 'testing' -- Use the testing dataset\n",
            "    )\n",
            "  )\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  label predicted_label tumor_prob   normal_prob \n",
              "1 0     0               4.345065e-11 1.000000e+00\n",
              "2 1     1               1.000000e+00 8.881784e-16\n",
              "3 0     0               9.755096e-13 1.000000e+00\n",
              "4 1     1               1.000000e+00 3.710365e-13\n",
              "5 0     0               1.527256e-09 1.000000e+00\n",
              "6 1     1               1.000000e+00 1.712096e-09"
            ],
            "text/latex": "A tibble: 6 × 4\n\\begin{tabular}{llll}\n label & predicted\\_label & tumor\\_prob & normal\\_prob\\\\\n <int> & <int> & <dbl> & <dbl>\\\\\n\\hline\n\t 0 & 0 & 4.345065e-11 & 1.000000e+00\\\\\n\t 1 & 1 & 1.000000e+00 & 8.881784e-16\\\\\n\t 0 & 0 & 9.755096e-13 & 1.000000e+00\\\\\n\t 1 & 1 & 1.000000e+00 & 3.710365e-13\\\\\n\t 0 & 0 & 1.527256e-09 & 1.000000e+00\\\\\n\t 1 & 1 & 1.000000e+00 & 1.712096e-09\\\\\n\\end{tabular}\n",
            "text/markdown": "\nA tibble: 6 × 4\n\n| label &lt;int&gt; | predicted_label &lt;int&gt; | tumor_prob &lt;dbl&gt; | normal_prob &lt;dbl&gt; |\n|---|---|---|---|\n| 0 | 0 | 4.345065e-11 | 1.000000e+00 |\n| 1 | 1 | 1.000000e+00 | 8.881784e-16 |\n| 0 | 0 | 9.755096e-13 | 1.000000e+00 |\n| 1 | 1 | 1.000000e+00 | 3.710365e-13 |\n| 0 | 0 | 1.527256e-09 | 1.000000e+00 |\n| 1 | 1 | 1.000000e+00 | 1.712096e-09 |\n\n",
            "text/html": [
              "<table class=\"dataframe\">\n",
              "<caption>A tibble: 6 × 4</caption>\n",
              "<thead>\n",
              "\t<tr><th scope=col>label</th><th scope=col>predicted_label</th><th scope=col>tumor_prob</th><th scope=col>normal_prob</th></tr>\n",
              "\t<tr><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><td>0</td><td>0</td><td>4.345065e-11</td><td>1.000000e+00</td></tr>\n",
              "\t<tr><td>1</td><td>1</td><td>1.000000e+00</td><td>8.881784e-16</td></tr>\n",
              "\t<tr><td>0</td><td>0</td><td>9.755096e-13</td><td>1.000000e+00</td></tr>\n",
              "\t<tr><td>1</td><td>1</td><td>1.000000e+00</td><td>3.710365e-13</td></tr>\n",
              "\t<tr><td>0</td><td>0</td><td>1.527256e-09</td><td>1.000000e+00</td></tr>\n",
              "\t<tr><td>1</td><td>1</td><td>1.000000e+00</td><td>1.712096e-09</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RNa35aGLH8EB"
      },
      "source": [
        "These results confirm that the probability of samples belonging to their predicted classes is close to 100%. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MzyPei8FEuP_"
      },
      "source": [
        "## Visualize Prediction Results\n",
        "\n",
        "To verify the prediction result, we can project the test samples onto the two principal components of the training data and plot the results against the training data. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "id": "jDooZ6QeIJPY",
        "outputId": "9d7aab82-4056-480c-cdb3-11b703a8f154"
      },
      "source": [
        "# Center and scale the testing data with means and stdevs of the training data\n",
        "tm_mat <- matrix(1,nrow(test_data_numeric),1) %*% train_means\n",
        "ts_mat <- matrix(1,nrow(test_data_numeric),1) %*% train_sds\n",
        "test_data_numeric_norm <- (test_data_numeric - tm_mat) / ts_mat\n",
        "\n",
        "# Project the test data onto the first two principal components \n",
        "test_proj <- t(eigen_vectors) %*% t(test_data_numeric_norm)\n",
        "\n",
        "# Prepare the factors to group and label samples appropriately\n",
        "train_colors <- c('red', 'blue')\n",
        "test_colors <- c(alpha('purple', 0.5), alpha('black', 0.5))\n",
        "train_factors <- train_data$sample_type_name\n",
        "train_factors[train_factors == 'Primary Tumor'] <- 'Primary Tumor (Train)'\n",
        "train_factors[train_factors == 'Solid Tissue Normal'] <- 'Solid Tissue Normal (Train)'\n",
        "test_factors <- test_data$sample_type_name\n",
        "test_factors[test_factors == 'Primary Tumor'] <- 'Primary Tumor (Test)'\n",
        "test_factors[test_factors == 'Solid Tissue Normal'] <- 'Solid Tissue Normal (Test)'\n",
        "\n",
        "# Use empty circles to denote mis-classified samples, and O's to denote\n",
        "# correctly classified samples (in this example, all samples are correctly \n",
        "# classified)\n",
        "train_pch <- c()\n",
        "train_pch[1:length(train_factors)] <- 16\n",
        "test_pch <- c()\n",
        "test_pch[1:length(test_factors)] <- 16\n",
        "test_pch[ml_predict_data$label == 1 & ml_predict_data$predicted_label == 0] <- 1\n",
        "test_pch[ml_predict_data$label == 0 & ml_predict_data$predicted_label == 1] <- 1\n",
        "\n",
        "# Plot both training and testing data on the the same axes\n",
        "plot(\n",
        "  t(cbind(train_proj, test_proj)),\n",
        "  col = cbind(\n",
        "    t(train_colors[factor(train_factors)]), t(test_colors[factor(test_factors)])\n",
        "  ),\n",
        "  pch = cbind(t(train_pch), t(test_pch)), cex = 2, lwd = 2,\n",
        "  main = 'BRCA Tumor and Normal Data',\n",
        "  xlab = 'Principal Component 1',\n",
        "  ylab = 'Principal Component 2',\n",
        "  xlim=c(-10, 10), ylim=c(-8, 5)\n",
        ")\n",
        "\n",
        "# Add a legend to the plot\n",
        "legend(\n",
        "  'bottomleft',\n",
        "  legend=cbind(\n",
        "    t(levels(factor(train_factors))), t(levels(factor(test_factors)))\n",
        "  ),\n",
        "  col = cbind(train_colors, test_colors),\n",
        "  pch = cbind(t(train_pch), t(test_pch)), cex = 1.2\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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E5KtCw3amvJb9q0SVt7NL0F5r/5zW/K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\nTkSuQmCC",
            "text/plain": [
              "Plot with title “BRCA Tumor and Normal Data”"
            ]
          },
          "metadata": {
            "image/png": {
              "width": 420,
              "height": 420
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tb1a4ZE9QgO9"
      },
      "source": [
        "# Predict Future Samples\n",
        "\n",
        "The BigQuery ML model can now be applied to future samples to predict whether they are normal or tumor samples. To simulate this, we use metastatic TCGA BRCA samples. These samples were previously extracted from the TCGA RNAseq BigQuery table, labeled as 'future' samples, and stored in the temporary BQ table alongside the training and testing samples. \n",
        "\n",
        "![train-test-norm-Page-7.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SzuXX1A7lDzY"
      },
      "source": [
        "## Calculate Prediction Labels and Probabilites for Future Samples\n",
        "\n",
        "We use 'ML.PREDICT' on these 'future' samples to predict their class labels and probabilities for each class."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 613
        },
        "id": "cxSpmx78ScVo",
        "outputId": "2519d0c7-0622-4415-b22d-29738fde8743"
      },
      "source": [
        "# Construct the query to predict each future sample\n",
        "ml_future_query <- sprintf(\"\n",
        "  SELECT\n",
        "    predicted_label,\n",
        "    (SELECT prob\n",
        "     FROM UNNEST(predicted_label_probs)\n",
        "     WHERE label = 1) AS tumor_prob,\n",
        "    (SELECT prob\n",
        "     FROM UNNEST(predicted_label_probs)\n",
        "     WHERE label = 0) AS normal_prob\n",
        "\n",
        "  FROM ML.PREDICT (MODEL `%s`, \n",
        "    (\n",
        "      SELECT * EXCEPT(case_barcode, sample_type_name, data_partition)\n",
        "      FROM `%s`\n",
        "      WHERE data_partition = 'future' -- Use future dataset\n",
        "    )\n",
        "  )\n",
        "\", bq_ml_model_fullname, bq_tmp_table_fullname)\n",
        "\n",
        "# Display the query\n",
        "cat(ml_future_query)\n",
        "\n",
        "# Run the query to evaluate the model\n",
        "ml_future_result <- bq_project_query(\n",
        "  google_project,\n",
        "  ml_future_query,\n",
        "  quiet = TRUE\n",
        ")\n",
        "\n",
        "# Download the eval result\n",
        "ml_future_data <- bq_table_download(ml_future_result, quiet = TRUE)\n",
        "\n",
        "# Display the result\n",
        "ml_future_data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "  SELECT\n",
            "    predicted_label,\n",
            "    (SELECT prob\n",
            "     FROM UNNEST(predicted_label_probs)\n",
            "     WHERE label = 1) AS tumor_prob,\n",
            "    (SELECT prob\n",
            "     FROM UNNEST(predicted_label_probs)\n",
            "     WHERE label = 0) AS normal_prob\n",
            "\n",
            "  FROM ML.PREDICT (MODEL `isb-cgc-training-001.nih_library_2021_10.tcga_brca_ml_model_30e1b3df`, \n",
            "    (\n",
            "      SELECT * EXCEPT(case_barcode, sample_type_name, data_partition)\n",
            "      FROM `isb-cgc-training-001.nih_library_2021_10.tcga_brca_ml_table_30e1b3df`\n",
            "      WHERE data_partition = 'future' -- Use future dataset\n",
            "    )\n",
            "  )\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  predicted_label tumor_prob   normal_prob \n",
              "1 1               9.997682e-01 2.317893e-04\n",
              "2 1               9.999991e-01 8.505155e-07\n",
              "3 1               9.999849e-01 1.514955e-05\n",
              "4 0               5.987667e-05 9.999401e-01\n",
              "5 1               1.000000e+00 3.108624e-15\n",
              "6 1               1.000000e+00 4.440892e-16\n",
              "7 1               1.000000e+00 2.562173e-12"
            ],
            "text/latex": "A tibble: 7 × 3\n\\begin{tabular}{lll}\n predicted\\_label & tumor\\_prob & normal\\_prob\\\\\n <int> & <dbl> & <dbl>\\\\\n\\hline\n\t 1 & 9.997682e-01 & 2.317893e-04\\\\\n\t 1 & 9.999991e-01 & 8.505155e-07\\\\\n\t 1 & 9.999849e-01 & 1.514955e-05\\\\\n\t 0 & 5.987667e-05 & 9.999401e-01\\\\\n\t 1 & 1.000000e+00 & 3.108624e-15\\\\\n\t 1 & 1.000000e+00 & 4.440892e-16\\\\\n\t 1 & 1.000000e+00 & 2.562173e-12\\\\\n\\end{tabular}\n",
            "text/markdown": "\nA tibble: 7 × 3\n\n| predicted_label &lt;int&gt; | tumor_prob &lt;dbl&gt; | normal_prob &lt;dbl&gt; |\n|---|---|---|\n| 1 | 9.997682e-01 | 2.317893e-04 |\n| 1 | 9.999991e-01 | 8.505155e-07 |\n| 1 | 9.999849e-01 | 1.514955e-05 |\n| 0 | 5.987667e-05 | 9.999401e-01 |\n| 1 | 1.000000e+00 | 3.108624e-15 |\n| 1 | 1.000000e+00 | 4.440892e-16 |\n| 1 | 1.000000e+00 | 2.562173e-12 |\n\n",
            "text/html": [
              "<table class=\"dataframe\">\n",
              "<caption>A tibble: 7 × 3</caption>\n",
              "<thead>\n",
              "\t<tr><th scope=col>predicted_label</th><th scope=col>tumor_prob</th><th scope=col>normal_prob</th></tr>\n",
              "\t<tr><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><td>1</td><td>9.997682e-01</td><td>2.317893e-04</td></tr>\n",
              "\t<tr><td>1</td><td>9.999991e-01</td><td>8.505155e-07</td></tr>\n",
              "\t<tr><td>1</td><td>9.999849e-01</td><td>1.514955e-05</td></tr>\n",
              "\t<tr><td>0</td><td>5.987667e-05</td><td>9.999401e-01</td></tr>\n",
              "\t<tr><td>1</td><td>1.000000e+00</td><td>3.108624e-15</td></tr>\n",
              "\t<tr><td>1</td><td>1.000000e+00</td><td>4.440892e-16</td></tr>\n",
              "\t<tr><td>1</td><td>1.000000e+00</td><td>2.562173e-12</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nEmXdcF3lWIx"
      },
      "source": [
        "In the data frame of predicted labels and probabilities above, we can see that all but one of the seven samples are predicted as tumor samples.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v47Ln9Zglz1n"
      },
      "source": [
        "## Scale 'Future' Metastatic Samples to Prepare for Visualization\n",
        "\n",
        "We can visualize the 'future' metastatic samples by overlaying them on the PCA plot along with training and testing samples. To do this, we first need to normalize the samples by mean-centering and scaling them using the means and standard deviations calculated from the training data. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rUn2l99TTMeC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        },
        "outputId": "ed732d97-2cdb-47dd-fb3b-54db04a2b62d"
      },
      "source": [
        "# Get the numeric columns of the future (metastatic) data\n",
        "future_data <- tmp_data[tmp_data$data_partition == 'future',]\n",
        "future_data_numeric <- subset(\n",
        "  future_data, select=-c(case_barcode, sample_type_name, label, data_partition)\n",
        ")\n",
        "\n",
        "# Center and scale the future data with means and stdevs of the training data\n",
        "tm_mat <- matrix(1,nrow(future_data_numeric),1) %*% train_means\n",
        "ts_mat <- matrix(1,nrow(future_data_numeric),1) %*% train_sds\n",
        "future_data_numeric_norm <- (future_data_numeric - tm_mat) / ts_mat\n",
        "\n",
        "# Display the normalized future data\n",
        "future_data_numeric_norm"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  COL10A1     MMP11      ADAMTS5    CAPN11     FIGF       PAMR1      KLHL29    \n",
              "1  0.07031358 -0.7097134 -1.5961621 -1.0488689 -0.4538187 -0.3025102 -0.6781042\n",
              "2  0.11263779 -0.5872873 -2.0032134 -1.4568780 -1.7431609 -1.5089907  1.3474274\n",
              "3  0.08794490  0.5646599 -1.2392824 -0.7133338 -0.8124305 -0.1136345 -1.3319750\n",
              "4 -0.39240885 -0.2199042  0.1626603 -1.4160074 -1.0498845  0.4403747  0.4232644\n",
              "5  0.70912442  0.5469210 -2.3860626 -0.7495957 -1.2436025 -1.4567319 -0.8170757\n",
              "6  0.38619311  0.3629761 -3.4168080 -1.1702663 -1.2315522 -1.7198494 -1.5123724\n",
              "7 -0.69416868 -0.5027786 -1.9201644 -1.8860144 -1.7600510 -1.7398615 -2.2328127\n",
              "  INHBA       PPAPDC1A     LYVE1      ⋯ SLC24A2    PER1        SCARA5    \n",
              "1 -0.37577403 -0.691229017 -0.4514032 ⋯ -1.0959616 -1.14153550 -0.1368610\n",
              "2  0.02255649 -0.862141805 -0.6289281 ⋯ -1.3114651 -1.01879742 -0.1566930\n",
              "3 -0.10685600  0.355791197  0.1034732 ⋯ -0.4792869 -0.02165508 -1.2150584\n",
              "4  0.59054841 -1.042092106  1.0846413 ⋯ -0.7673889  1.69677986  0.2040097\n",
              "5  0.67127335  0.009797258 -1.1787528 ⋯ -0.9307181 -2.06109646 -1.1930635\n",
              "6 -0.17207635 -0.215091419 -1.7801310 ⋯ -0.4713685 -1.11471333 -3.0691268\n",
              "7 -0.88706043 -1.053149756 -1.4899097 ⋯ -0.1314914 -1.18578449 -1.7592670\n",
              "  COMP       SPRY2      TPX2       ROBO3      CXCL2      NDRG2       HSD17B6   \n",
              "1 -0.3608953 -0.8444978  0.1079138  0.5014133 -0.5265033  0.45867670 -0.8671599\n",
              "2  0.7357694  0.2081205  1.5838067 -0.5203551  0.6176362 -1.42290695 -0.1849144\n",
              "3  0.3021092 -0.6389912  0.5519812 -1.1937827  0.6699216 -0.75255149  0.9938082\n",
              "4 -2.2076127  1.0127801 -0.0425655  1.5440444  0.9102478  0.08184398  1.7545815\n",
              "5  1.4558260 -1.2261020  1.6705225 -0.8158202 -1.2909155 -1.59957484  0.5511753\n",
              "6  0.6494447 -2.0153850  0.2357412 -1.5372070 -1.7922310 -0.96683538 -0.1367400\n",
              "7 -0.1354670 -1.7066117  0.8499577 -2.0796274 -1.5785620 -0.84771896  0.4573721"
            ],
            "text/latex": "A data.frame: 7 × 30\n\\begin{tabular}{lllllllllllllllllllll}\n COL10A1 & MMP11 & ADAMTS5 & CAPN11 & FIGF & PAMR1 & KLHL29 & INHBA & PPAPDC1A & LYVE1 & ⋯ & SLC24A2 & PER1 & SCARA5 & COMP & SPRY2 & TPX2 & ROBO3 & CXCL2 & NDRG2 & HSD17B6\\\\\n <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & ⋯ & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n\\hline\n\t  0.07031358 & -0.7097134 & -1.5961621 & -1.0488689 & -0.4538187 & -0.3025102 & -0.6781042 & -0.37577403 & -0.691229017 & -0.4514032 & ⋯ & -1.0959616 & -1.14153550 & -0.1368610 & -0.3608953 & -0.8444978 &  0.1079138 &  0.5014133 & -0.5265033 &  0.45867670 & -0.8671599\\\\\n\t  0.11263779 & -0.5872873 & -2.0032134 & -1.4568780 & -1.7431609 & -1.5089907 &  1.3474274 &  0.02255649 & -0.862141805 & -0.6289281 & ⋯ & -1.3114651 & -1.01879742 & -0.1566930 &  0.7357694 &  0.2081205 &  1.5838067 & -0.5203551 &  0.6176362 & -1.42290695 & -0.1849144\\\\\n\t  0.08794490 &  0.5646599 & -1.2392824 & -0.7133338 & -0.8124305 & -0.1136345 & -1.3319750 & -0.10685600 &  0.355791197 &  0.1034732 & ⋯ & -0.4792869 & -0.02165508 & -1.2150584 &  0.3021092 & -0.6389912 &  0.5519812 & -1.1937827 &  0.6699216 & -0.75255149 &  0.9938082\\\\\n\t -0.39240885 & -0.2199042 &  0.1626603 & -1.4160074 & -1.0498845 &  0.4403747 &  0.4232644 &  0.59054841 & -1.042092106 &  1.0846413 & ⋯ & -0.7673889 &  1.69677986 &  0.2040097 & -2.2076127 &  1.0127801 & -0.0425655 &  1.5440444 &  0.9102478 &  0.08184398 &  1.7545815\\\\\n\t  0.70912442 &  0.5469210 & -2.3860626 & -0.7495957 & -1.2436025 & -1.4567319 & -0.8170757 &  0.67127335 &  0.009797258 & -1.1787528 & ⋯ & -0.9307181 & -2.06109646 & -1.1930635 &  1.4558260 & -1.2261020 &  1.6705225 & -0.8158202 & -1.2909155 & -1.59957484 &  0.5511753\\\\\n\t  0.38619311 &  0.3629761 & -3.4168080 & -1.1702663 & -1.2315522 & -1.7198494 & -1.5123724 & -0.17207635 & -0.215091419 & -1.7801310 & ⋯ & -0.4713685 & -1.11471333 & -3.0691268 &  0.6494447 & -2.0153850 &  0.2357412 & -1.5372070 & -1.7922310 & -0.96683538 & -0.1367400\\\\\n\t -0.69416868 & -0.5027786 & -1.9201644 & -1.8860144 & -1.7600510 & -1.7398615 & -2.2328127 & -0.88706043 & -1.053149756 & -1.4899097 & ⋯ & -0.1314914 & -1.18578449 & -1.7592670 & -0.1354670 & -1.7066117 &  0.8499577 & -2.0796274 & -1.5785620 & -0.84771896 &  0.4573721\\\\\n\\end{tabular}\n",
            "text/markdown": "\nA data.frame: 7 × 30\n\n| COL10A1 &lt;dbl&gt; | MMP11 &lt;dbl&gt; | ADAMTS5 &lt;dbl&gt; | CAPN11 &lt;dbl&gt; | FIGF &lt;dbl&gt; | PAMR1 &lt;dbl&gt; | KLHL29 &lt;dbl&gt; | INHBA &lt;dbl&gt; | PPAPDC1A &lt;dbl&gt; | LYVE1 &lt;dbl&gt; | ⋯ ⋯ | SLC24A2 &lt;dbl&gt; | PER1 &lt;dbl&gt; | SCARA5 &lt;dbl&gt; | COMP &lt;dbl&gt; | SPRY2 &lt;dbl&gt; | TPX2 &lt;dbl&gt; | ROBO3 &lt;dbl&gt; | CXCL2 &lt;dbl&gt; | NDRG2 &lt;dbl&gt; | HSD17B6 &lt;dbl&gt; |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n|  0.07031358 | -0.7097134 | -1.5961621 | -1.0488689 | -0.4538187 | -0.3025102 | -0.6781042 | -0.37577403 | -0.691229017 | -0.4514032 | ⋯ | -1.0959616 | -1.14153550 | -0.1368610 | -0.3608953 | -0.8444978 |  0.1079138 |  0.5014133 | -0.5265033 |  0.45867670 | -0.8671599 |\n|  0.11263779 | -0.5872873 | -2.0032134 | -1.4568780 | -1.7431609 | -1.5089907 |  1.3474274 |  0.02255649 | -0.862141805 | -0.6289281 | ⋯ | -1.3114651 | -1.01879742 | -0.1566930 |  0.7357694 |  0.2081205 |  1.5838067 | -0.5203551 |  0.6176362 | -1.42290695 | -0.1849144 |\n|  0.08794490 |  0.5646599 | -1.2392824 | -0.7133338 | -0.8124305 | -0.1136345 | -1.3319750 | -0.10685600 |  0.355791197 |  0.1034732 | ⋯ | -0.4792869 | -0.02165508 | -1.2150584 |  0.3021092 | -0.6389912 |  0.5519812 | -1.1937827 |  0.6699216 | -0.75255149 |  0.9938082 |\n| -0.39240885 | -0.2199042 |  0.1626603 | -1.4160074 | -1.0498845 |  0.4403747 |  0.4232644 |  0.59054841 | -1.042092106 |  1.0846413 | ⋯ | -0.7673889 |  1.69677986 |  0.2040097 | -2.2076127 |  1.0127801 | -0.0425655 |  1.5440444 |  0.9102478 |  0.08184398 |  1.7545815 |\n|  0.70912442 |  0.5469210 | -2.3860626 | -0.7495957 | -1.2436025 | -1.4567319 | -0.8170757 |  0.67127335 |  0.009797258 | -1.1787528 | ⋯ | -0.9307181 | -2.06109646 | -1.1930635 |  1.4558260 | -1.2261020 |  1.6705225 | -0.8158202 | -1.2909155 | -1.59957484 |  0.5511753 |\n|  0.38619311 |  0.3629761 | -3.4168080 | -1.1702663 | -1.2315522 | -1.7198494 | -1.5123724 | -0.17207635 | -0.215091419 | -1.7801310 | ⋯ | -0.4713685 | -1.11471333 | -3.0691268 |  0.6494447 | -2.0153850 |  0.2357412 | -1.5372070 | -1.7922310 | -0.96683538 | -0.1367400 |\n| -0.69416868 | -0.5027786 | -1.9201644 | -1.8860144 | -1.7600510 | -1.7398615 | -2.2328127 | -0.88706043 | -1.053149756 | -1.4899097 | ⋯ | -0.1314914 | -1.18578449 | -1.7592670 | -0.1354670 | -1.7066117 |  0.8499577 | -2.0796274 | -1.5785620 | -0.84771896 |  0.4573721 |\n\n",
            "text/html": [
              "<table class=\"dataframe\">\n",
              "<caption>A data.frame: 7 × 30</caption>\n",
              "<thead>\n",
              "\t<tr><th scope=col>COL10A1</th><th scope=col>MMP11</th><th scope=col>ADAMTS5</th><th scope=col>CAPN11</th><th scope=col>FIGF</th><th scope=col>PAMR1</th><th scope=col>KLHL29</th><th scope=col>INHBA</th><th scope=col>PPAPDC1A</th><th scope=col>LYVE1</th><th scope=col>⋯</th><th scope=col>SLC24A2</th><th scope=col>PER1</th><th scope=col>SCARA5</th><th scope=col>COMP</th><th scope=col>SPRY2</th><th scope=col>TPX2</th><th scope=col>ROBO3</th><th scope=col>CXCL2</th><th scope=col>NDRG2</th><th scope=col>HSD17B6</th></tr>\n",
              "\t<tr><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;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;dbl&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><td> 0.07031358</td><td>-0.7097134</td><td>-1.5961621</td><td>-1.0488689</td><td>-0.4538187</td><td>-0.3025102</td><td>-0.6781042</td><td>-0.37577403</td><td>-0.691229017</td><td>-0.4514032</td><td>⋯</td><td>-1.0959616</td><td>-1.14153550</td><td>-0.1368610</td><td>-0.3608953</td><td>-0.8444978</td><td> 0.1079138</td><td> 0.5014133</td><td>-0.5265033</td><td> 0.45867670</td><td>-0.8671599</td></tr>\n",
              "\t<tr><td> 0.11263779</td><td>-0.5872873</td><td>-2.0032134</td><td>-1.4568780</td><td>-1.7431609</td><td>-1.5089907</td><td> 1.3474274</td><td> 0.02255649</td><td>-0.862141805</td><td>-0.6289281</td><td>⋯</td><td>-1.3114651</td><td>-1.01879742</td><td>-0.1566930</td><td> 0.7357694</td><td> 0.2081205</td><td> 1.5838067</td><td>-0.5203551</td><td> 0.6176362</td><td>-1.42290695</td><td>-0.1849144</td></tr>\n",
              "\t<tr><td> 0.08794490</td><td> 0.5646599</td><td>-1.2392824</td><td>-0.7133338</td><td>-0.8124305</td><td>-0.1136345</td><td>-1.3319750</td><td>-0.10685600</td><td> 0.355791197</td><td> 0.1034732</td><td>⋯</td><td>-0.4792869</td><td>-0.02165508</td><td>-1.2150584</td><td> 0.3021092</td><td>-0.6389912</td><td> 0.5519812</td><td>-1.1937827</td><td> 0.6699216</td><td>-0.75255149</td><td> 0.9938082</td></tr>\n",
              "\t<tr><td>-0.39240885</td><td>-0.2199042</td><td> 0.1626603</td><td>-1.4160074</td><td>-1.0498845</td><td> 0.4403747</td><td> 0.4232644</td><td> 0.59054841</td><td>-1.042092106</td><td> 1.0846413</td><td>⋯</td><td>-0.7673889</td><td> 1.69677986</td><td> 0.2040097</td><td>-2.2076127</td><td> 1.0127801</td><td>-0.0425655</td><td> 1.5440444</td><td> 0.9102478</td><td> 0.08184398</td><td> 1.7545815</td></tr>\n",
              "\t<tr><td> 0.70912442</td><td> 0.5469210</td><td>-2.3860626</td><td>-0.7495957</td><td>-1.2436025</td><td>-1.4567319</td><td>-0.8170757</td><td> 0.67127335</td><td> 0.009797258</td><td>-1.1787528</td><td>⋯</td><td>-0.9307181</td><td>-2.06109646</td><td>-1.1930635</td><td> 1.4558260</td><td>-1.2261020</td><td> 1.6705225</td><td>-0.8158202</td><td>-1.2909155</td><td>-1.59957484</td><td> 0.5511753</td></tr>\n",
              "\t<tr><td> 0.38619311</td><td> 0.3629761</td><td>-3.4168080</td><td>-1.1702663</td><td>-1.2315522</td><td>-1.7198494</td><td>-1.5123724</td><td>-0.17207635</td><td>-0.215091419</td><td>-1.7801310</td><td>⋯</td><td>-0.4713685</td><td>-1.11471333</td><td>-3.0691268</td><td> 0.6494447</td><td>-2.0153850</td><td> 0.2357412</td><td>-1.5372070</td><td>-1.7922310</td><td>-0.96683538</td><td>-0.1367400</td></tr>\n",
              "\t<tr><td>-0.69416868</td><td>-0.5027786</td><td>-1.9201644</td><td>-1.8860144</td><td>-1.7600510</td><td>-1.7398615</td><td>-2.2328127</td><td>-0.88706043</td><td>-1.053149756</td><td>-1.4899097</td><td>⋯</td><td>-0.1314914</td><td>-1.18578449</td><td>-1.7592670</td><td>-0.1354670</td><td>-1.7066117</td><td> 0.8499577</td><td>-2.0796274</td><td>-1.5785620</td><td>-0.84771896</td><td> 0.4573721</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q3Zv-axrmThc"
      },
      "source": [
        "## Visualize Prediction Results of 'Future' Metastatic Samples\n",
        "\n",
        "Now we can project these samples onto the first two principal components (calculated from the training data) and plot them. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "id": "W98pdAdXUbP9",
        "outputId": "096633b2-f431-46c3-9c92-f42bccba5e2c"
      },
      "source": [
        "# Project the future data onto the first two principal components \n",
        "future_proj <- t(eigen_vectors) %*% t(future_data_numeric_norm)\n",
        "\n",
        "# Prepare the factors to group and label samples appropriately\n",
        "future_colors <- c('green3')\n",
        "future_factors <- c()\n",
        "future_factors[1:nrow(future_data_numeric_norm)] <- 'Metastatic (Future)'\n",
        "\n",
        "# Use empty triangles to denote future metastatic samples classified as normal,\n",
        "# and filled triangles to denote future metastatic samples classified as tumor\n",
        "future_pch <- c()\n",
        "future_pch[1:nrow(future_data_numeric_norm)] <- 17\n",
        "future_pch[ml_future_data$predicted_label == 0] <- 2\n",
        "\n",
        "# Plot training, testing, and future data on the the same axes\n",
        "plot(\n",
        "  t(cbind(train_proj, test_proj, future_proj)),\n",
        "  col = cbind(\n",
        "    t(train_colors[factor(train_factors)]),\n",
        "    t(test_colors[factor(test_factors)]),\n",
        "    t(future_colors[factor(future_factors)])\n",
        "  ),\n",
        "  pch = cbind(t(train_pch), t(test_pch), t(future_pch)),\n",
        "  cex = 2, lwd = 2,\n",
        "  main = 'BRCA Tumor, Normal, and Metastatic (Future) Data',\n",
        "  xlab = 'Principal Component 1',\n",
        "  ylab = 'Principal Component 2',\n",
        "  xlim=c(-10, 10), ylim=c(-8, 5)\n",
        ")\n",
        "\n",
        "# Add a legend to the plot\n",
        "legend(\n",
        "  'bottomleft',\n",
        "  legend=cbind(\n",
        "    t(levels(factor(train_factors))),\n",
        "    t(levels(factor(test_factors))),\n",
        "    t(levels(factor(future_factors)))\n",
        "  ),\n",
        "  col = cbind(train_colors, test_colors, future_colors),\n",
        "  pch = cbind(16, 16, 16, 16, 17), cex = 1.2\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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LADAAAAcAgEOwAAAACHQLADAAAAcAgEOwAAAACHQLADAAAAcAgEOwAAAACHQLAD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V1T19+nRpaanU/QQFBdGFMxTxGLH4+PiQkBAulys3YuUAACAASURBVJuUlJSSkjJw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",
            "text/plain": [
              "Plot with title “BRCA Tumor, Normal, and Metastatic (Future) Data”"
            ]
          },
          "metadata": {
            "image/png": {
              "width": 420,
              "height": 420
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FUh0BZR8mnHW"
      },
      "source": [
        "# Interpretation and Conclusion\n",
        "\n",
        "This tutorial introduced BigQuery ML concepts by demonstrating an end-to-end workflow for predicting the tumor status (tumor vs. normal) of TCGA breast cancer RNA-seq samples. We created a BigQuery ML model using training samples, evaluated the model using testing samples, then used the final model to predict the tumor status of 'future' samples. \n",
        "\n",
        "The PCA plot of 'future' metastatic samples verifies what we observed in the predicted labels and probabilities. In principal component space, all but one metastatic sample group with the tumor samples (red and purple circles). A single sample (empty green triangle) is closer to the normal samples (blue and gray circles), which explains why the ML model classifies it as normal. The following are comments/caveats for this analysis:\n",
        "\n",
        "1. The use of metastatic samples as 'future' data is purely for the purposes of simulation and demonstration of how this prediction model can be applied to future samples. This prediction model was trained with no information about metastatic samples. Thus, the resulting prediction labels may not be biologically sensible. \n",
        "\n",
        "2. A more appropriate analysis approach could be to split the normal and tumor data into 'training', 'testing', and 'validation' samples, then using the 'validation' set in place of the 'future' samples. \n",
        "\n",
        "3. Used for data exploration, these results may be interpreted in the following ways:\n",
        "\n",
        "  a. The single metastatic sample has a gene expression signature that is similar to that of normal tissue and may warrant further biological investigation. \n",
        "  \n",
        "  b. The single metastatic sample has been mis-classified or mis-diagnosed and should be further investigated. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CroUWOludq-G"
      },
      "source": [
        "# Follow-Up Exercises\n",
        "\n",
        "Practice your BigQuery ML skills by solving the following exercises on your own. Come to our office hours to see the solutions. \n",
        "\n",
        "1. We've seen that this machine learning model can be used to predict whether TCGA-BRCA RNA-seq samples are tumor or normal samples. What happens if we apply the model to a different cancer (e.g., TCGA-GBM)? What are the implications of training a model with one cancer type and applying it to other cancer types?\n",
        "\n",
        "2. Feature selection can have a big effect on downstream prediction performance. Demonstrate this effect by training the model using genes that are **not** differentially expressed. For example, you can use DESeq to select the bottom 30 differentially expressed genes, or you can randomly select a set of genes. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tSpt8pwZdkG4"
      },
      "source": [
        "# Contact Us\n",
        "\n",
        "Please contact us to learn more about BigQuery, to discuss cost considerations when working with BigQuery projects, or to discuss any projects you feel may benefit from the ISB-CGC Platform. \n",
        "\n",
        "- Email us: [feedback@isb-cgc.org](mailto:feedback@isb-cgc.org)\n",
        "- Check out our website: [https://isb-cgc.org](https://isb-cgc.org)\n",
        "- Visit our [office hours](https://isb-cancer-genomics-cloud.readthedocs.io/en/latest/sections/office_hours.html)"
      ]
    }
  ]
}