MitelmanDB/Exploring_and_comparing_MitelmanDB_CytoConverter_and_TCGA_datasets.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/isb-cgc/Community-Notebooks/blob/master/MitelmanDB/Exploring_and_comparing_MitelmanDB_CytoConverter_and_TCGA_datasets.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qgwv4lK5V8Zc"
      },
      "source": [
        "# Exploring and comparing the Mitelman DB, CytoConverter, and TCGA datasets\n",
        "Check out other notebooks at our [Community Notebooks Repository](https://github.com/isb-cgc/Community-Notebooks)!\n",
        "\n",
        "- **Title:** Exploring the Mitelman DB and CytoConverter datasets\n",
        "- **Author:** Boris Aguilar, John Phan, Elaine Lee\n",
        "- **Created:** 04-23-2022\n",
        "- **Purpose:** Demonstrate basic usage of the Mitelman DB and CytoConverter datasets \n",
        "\n",
        "This notebook demonstrates basic usage of the Mitelman DB and CytoConverter datasets to explore chromosomal aberrations and compare then with the TCGA data for three well know chromosomal deletions: chromosome 1 in breast cancer, chromosome 3 in kidney adenocarcinoma, and chromosome 5 in acute myeloid leukemia.  \n",
        "\n",
        "The Mitelman DB is hosted by ISB-CGC and can be accessed at this URL: https://mitelmandatabase.isb-cgc.org/. This notebook replicates some of the analyses from the paper by Denomy et al: https://cancerres.aacrjournals.org/content/79/20/5181. Note, however that results are not replicated exactly as some of the underlying data has changed since publication.  \n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1zVfEL8i8__O"
      },
      "source": [
        "# Initialize Notebook Environment\n",
        "\n",
        "Before running the analysis, we need to load dependencies, authenticate to BigQuery, and customize notebook parameters."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ujUW1wF23d93"
      },
      "source": [
        "### Import Dependencies"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LNlADvO-gken"
      },
      "outputs": [],
      "source": [
        "# GCP Libraries\n",
        "from google.cloud import bigquery\n",
        "from google.colab import auth\n",
        "\n",
        "# Data Analytics\n",
        "import numpy as np\n",
        "from scipy import stats\n",
        "\n",
        "# Data Visualization\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.ticker as ticker"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LBHnu8bZDEcE"
      },
      "source": [
        "### Authenticate\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). Alternative authentication methods can be found [here](https://googleapis.dev/python/google-api-core/latest/auth.html)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "M6BEFHj2dd5a"
      },
      "outputs": [],
      "source": [
        "# If you're using Google Colab, authenticate to gcloud with the following\n",
        "auth.authenticate_user()\n",
        "\n",
        "# alternatively, use the gcloud SDK\n",
        "#!gcloud auth application-default login"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RWuECMsRptWJ"
      },
      "source": [
        "### Google project ID\n",
        "\n",
        "To run this notebook, you will need to have your Google Cloud Account set up. If you need to set up a Google Cloud Account, follow the \"Obtain a Google identity\" and \"Set up a Google Cloud Project\" steps on our [Quick-Start Guide documentation](https://isb-cancer-genomics-cloud.readthedocs.io/en/latest/sections/HowToGetStartedonISB-CGC.html) page."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "veP6gPatu2iW"
      },
      "outputs": [],
      "source": [
        "# set the google project that will be billed for this notebook's computations\n",
        "google_project = 'my_google_project_id' ## change me"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F6zTODbDq3g9"
      },
      "source": [
        "## BigQuery Client\n",
        "\n",
        "Create the BigQuery client."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HNkfSXedwzbC"
      },
      "outputs": [],
      "source": [
        "# Create a client to access the data within BigQuery\n",
        "client = bigquery.Client(google_project)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **1. Chromosomal gains and losses of breast cancer samples from Mitelman DB and TCGA data sets**"
      ],
      "metadata": {
        "id": "BOrJ3lH9Yjen"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7kVZ88dU85Vs"
      },
      "source": [
        "## Calculate Frequency of Gains and Losses in MitelmanDB Samples\n",
        "\n",
        "We can use CytoConverter genomic coordinates to calculate the frequency of chromosomal gains and losses across a cohort of samples, e.g., across all breast cancer samples. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VWaAJ2J5FEUv"
      },
      "outputs": [],
      "source": [
        "# Set parameters for this query\n",
        "cancer_type = 'TCGA-BRCA' # Cancer type for TCGA\n",
        "bq_project = 'mitelman-db'  # project name of Mitelman-DB BigQuery table\n",
        "bq_dataset = 'prod' # Name of the dataset containing Mitelman-DB BigQuery tables\n",
        "morphology = '3111' # Breast cancer\n",
        "topology = '0401' # Adenocarcinoma"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T7OM3vxke2sg"
      },
      "source": [
        "First, we identify all MitelmanDB cases related to the morphology and topology of interest. \n",
        "\n",
        "This query was copied from the new feature of the MitelmanDB interface: View Overall Gain/Loss in chromosome. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NL7fhUTUL2W2"
      },
      "outputs": [],
      "source": [
        "case_query = \"\"\"\n",
        "WITH cyto_cases AS (\n",
        " SELECT DISTINCT\n",
        " c.Refno,\n",
        " c.CaseNo,\n",
        " c.InvNo,\n",
        "FROM\n",
        " `{bq_project}.{bq_dataset}.CytogenInvValid` c,\n",
        " `{bq_project}.{bq_dataset}.Reference` Reference,\n",
        " `{bq_project}.{bq_dataset}.Cytogen` Cytogen\n",
        "LEFT JOIN `{bq_project}.{bq_dataset}.Koder` KoderM\n",
        "ON\n",
        " (Cytogen.Morph = KoderM.Kod AND KoderM.KodTyp = 'MORPH')\n",
        "LEFT JOIN `{bq_project}.{bq_dataset}.Koder` KoderT\n",
        "ON\n",
        " (Cytogen.Topo = KoderT.Kod AND KoderT.KodTyp = 'TOP')\n",
        "WHERE\n",
        " Cytogen.RefNo = c.RefNo\n",
        " AND Cytogen.CaseNo = c.CaseNo\n",
        " AND c.Refno = Reference.Refno\n",
        " AND Cytogen.Morph IN ('{morphology}')\n",
        " AND Cytogen.Topo IN ('{topology}')\n",
        "),\n",
        "SampleCount AS(\n",
        "\n",
        " SELECT COUNT(*) AS sCount\n",
        " FROM cyto_cases\n",
        "),\n",
        "Case_CC_Kary_Result AS (\n",
        " SELECT cc_result.*\n",
        " FROM cyto_cases\n",
        " LEFT JOIN `{bq_project}.{bq_dataset}.CytoConverted` AS cc_result\n",
        " ON cc_result.RefNo = cyto_cases.RefNo\n",
        " AND cc_result.caseNo = cyto_cases.caseNo\n",
        " AND cc_result.invNo = cyto_cases.invNo\n",
        "),\n",
        "Clone_imbal_sums AS (\n",
        " SELECT cytoBands.chromosome,\n",
        " cytoBands.cytoband_name,\n",
        " cytoBands.hg38_start,\n",
        " cytoBands.hg38_stop,\n",
        " Case_CC_Kary_Result.RefNo,\n",
        " Case_CC_Kary_Result.CaseNo,\n",
        " Case_CC_Kary_Result.InvNo,\n",
        " Case_CC_Kary_Result.Clone,\n",
        " SUM( CASE WHEN type = 'Gain' THEN 1 ELSE 0 END ) AS totalGain,\n",
        " SUM( CASE WHEN type = 'Loss' THEN 1 ELSE 0 END ) AS totalLoss\n",
        " FROM `{bq_project}.{bq_dataset}.CytoBands_hg38` AS cytoBands\n",
        " INNER JOIN Case_CC_Kary_Result\n",
        "  ON cytoBands.chromosome = Case_CC_Kary_Result.Chr\n",
        " WHERE cytoBands.hg38_start >= Case_CC_Kary_Result.Start\n",
        " AND cytoBands.hg38_stop <= Case_CC_Kary_Result.End\n",
        " GROUP BY\n",
        " cytoBands.chromosome,\n",
        " cytoBands.cytoband_name,\n",
        " cytoBands.hg38_start,\n",
        " cytoBands.hg38_stop,\n",
        " Case_CC_Kary_Result.RefNo,\n",
        " Case_CC_Kary_Result.CaseNo,\n",
        " Case_CC_Kary_Result.InvNo,\n",
        " Case_CC_Kary_Result.Clone\n",
        "),\n",
        "AMP_DEL_counts AS (\n",
        " SELECT Clone_imbal_sums.chromosome, Clone_imbal_sums.cytoband_name, Clone_imbal_sums.hg38_start, Clone_imbal_sums.hg38_stop,\n",
        " Clone_imbal_sums.RefNo, Clone_imbal_sums.CaseNo, Clone_imbal_sums.InvNo, Clone_imbal_sums.Clone,\n",
        " CASE WHEN Clone_imbal_sums.totalGain > 1 THEN Clone_imbal_sums.totalGain ELSE 0 END AS amplified,\n",
        " CASE WHEN Clone_imbal_sums.totalLoss > 1 THEN Clone_imbal_sums.totalLoss ELSE 0 END AS hozy_deleted,\n",
        " CASE WHEN Clone_imbal_sums.totalGain > 1 THEN 1 ELSE 0 END AS amp_count,\n",
        " CASE WHEN Clone_imbal_sums.totalLoss > 1 THEN 1 ELSE 0 END AS hozy_del_count,\n",
        " FROM Clone_imbal_sums\n",
        "),\n",
        "Singular_imbal AS (\n",
        " SELECT Clone_imbal_sums.chromosome,\n",
        " Clone_imbal_sums.cytoband_name,\n",
        " Clone_imbal_sums.hg38_start,\n",
        " Clone_imbal_sums.hg38_stop,\n",
        " Clone_imbal_sums.RefNo,\n",
        " Clone_imbal_sums.CaseNo,\n",
        " Clone_imbal_sums.InvNo,\n",
        " Clone_imbal_sums.Clone,\n",
        " Clone_imbal_sums.totalGain - AMP_DEL_counts.amplified AS Singular_gain,\n",
        " Clone_imbal_sums.totalLoss - AMP_DEL_counts.hozy_deleted AS Singular_loss,\n",
        " AMP_DEL_counts.amp_count,\n",
        " AMP_DEL_counts.hozy_del_count\n",
        " FROM Clone_imbal_sums\n",
        " INNER JOIN AMP_DEL_counts\n",
        " ON Clone_imbal_sums.chromosome = AMP_DEL_counts.chromosome\n",
        " AND Clone_imbal_sums.cytoband_name = AMP_DEL_counts.cytoband_name\n",
        " AND Clone_imbal_sums.hg38_start= AMP_DEL_counts.hg38_start\n",
        " AND Clone_imbal_sums.hg38_stop = AMP_DEL_counts.hg38_stop\n",
        " AND Clone_imbal_sums.RefNo= AMP_DEL_counts.RefNo\n",
        " AND Clone_imbal_sums.CaseNo = AMP_DEL_counts.CaseNo\n",
        " AND Clone_imbal_sums.InvNo = AMP_DEL_counts.InvNo\n",
        " AND Clone_imbal_sums.Clone = AMP_DEL_counts.Clone\n",
        "),\n",
        "Sample_dist_count AS (\n",
        " SELECT Singular_imbal.chromosome,\n",
        " Singular_imbal.cytoband_name,\n",
        " Singular_imbal.hg38_start,\n",
        " Singular_imbal.hg38_stop,\n",
        " Singular_imbal.RefNo,\n",
        " Singular_imbal.CaseNo,\n",
        " Singular_imbal.InvNo,\n",
        " CASE WHEN SUM(Singular_imbal.Singular_gain)> 0 THEN 1 ELSE 0 END AS Sample_dist_singular_gain,\n",
        " CASE WHEN SUM(Singular_imbal.Singular_loss)> 0 THEN 1 ELSE 0 END AS Sample_dist_singular_loss,\n",
        " CASE WHEN SUM(Singular_imbal.amp_count)>0 THEN 1 ELSE 0 END AS Sample_dist_amp,\n",
        " CASE WHEN SUM(Singular_imbal.hozy_del_count)>0 THEN 1 ELSE 0 END AS Sample_dist_del,\n",
        " FROM Singular_imbal\n",
        " GROUP BY\n",
        " Singular_imbal.chromosome,\n",
        " Singular_imbal.cytoband_name,\n",
        " Singular_imbal.hg38_start,\n",
        " Singular_imbal.hg38_stop,\n",
        " Singular_imbal.RefNo,\n",
        " Singular_imbal.CaseNo,\n",
        " Singular_imbal.InvNo\n",
        ")\n",
        "SELECT Sample_dist_count.chromosome,\n",
        " CASE WHEN SUBSTRING(Sample_dist_count.chromosome, 4) = 'X' THEN 23\n",
        "      WHEN SUBSTRING(Sample_dist_count.chromosome, 4) = 'Y' THEN 24\n",
        "      ELSE CAST(SUBSTRING(Sample_dist_count.chromosome, 4) AS INT64)\n",
        " END AS chr_ord,\n",
        " Sample_dist_count.cytoband_name,\n",
        " Sample_dist_count.hg38_start,\n",
        " Sample_dist_count.hg38_stop,\n",
        " SampleCount.sCount,\n",
        " SUM(Sample_dist_count.Sample_dist_singular_gain) AS total_gain,\n",
        " SUM(Sample_dist_count.Sample_dist_singular_loss) AS total_loss,\n",
        " SUM(Sample_dist_count.Sample_dist_amp) AS total_amp,\n",
        " SUM(Sample_dist_count.Sample_dist_del) AS total_del,\n",
        " ROUND(SUM(Sample_dist_count.Sample_dist_singular_gain)/SampleCount.sCount*100, 2) AS gain_freq,\n",
        " ROUND(SUM(Sample_dist_count.Sample_dist_singular_loss)/SampleCount.sCount*100, 2) AS loss_freq,\n",
        " ROUND(SUM(Sample_dist_count.Sample_dist_amp)/SampleCount.sCount*100, 2) AS amp_freq,\n",
        " ROUND(SUM(Sample_dist_count.Sample_dist_del)/SampleCount.sCount*100, 2) AS del_freq\n",
        " FROM Sample_dist_count, SampleCount\n",
        " GROUP BY\n",
        " Sample_dist_count.chromosome,\n",
        " Sample_dist_count.cytoband_name,\n",
        " Sample_dist_count.hg38_start,\n",
        " Sample_dist_count.hg38_stop,\n",
        " SampleCount.sCount\n",
        " ORDER BY chr_ord, Sample_dist_count.hg38_start\n",
        "\"\"\".format(\n",
        "  bq_project=bq_project,\n",
        "  bq_dataset=bq_dataset,\n",
        "  morphology=morphology,\n",
        "  topology=topology\n",
        ")\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pHENARIgT93a"
      },
      "outputs": [],
      "source": [
        "# Display the query\n",
        "# print(case_query)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-zSgO4jOSP0n",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "7573c909-f42c-45b2-98b5-ddd322d31762"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    chromosome  chr_ord cytoband_name  hg38_start  hg38_stop  sCount  \\\n",
              "0         chr1        1          1p36           0   27600000     787   \n",
              "1         chr1        1          1p35    27600000   34300000     787   \n",
              "2         chr1        1          1p34    34300000   46300000     787   \n",
              "3         chr1        1          1p33    46300000   50200000     787   \n",
              "4         chr1        1          1p32    50200000   60800000     787   \n",
              "..         ...      ...           ...         ...        ...     ...   \n",
              "315       chrX       23          Xq27   138900000  148000000     787   \n",
              "316       chrX       23          Xq28   148000000  156040895     787   \n",
              "317       chrY       24          Yp11           0   10400000     787   \n",
              "318       chrY       24          Yq11    10400000   26600000     787   \n",
              "319       chrY       24          Yq12    26600000   57227415     787   \n",
              "\n",
              "     total_gain  total_loss  total_amp  total_del  gain_freq  loss_freq  \\\n",
              "0            66          93         46         20       8.39      11.82   \n",
              "1            71          88         48         18       9.02      11.18   \n",
              "2            72          89         47         17       9.15      11.31   \n",
              "3            72          86         49         17       9.15      10.93   \n",
              "4            72          84         49         17       9.15      10.67   \n",
              "..          ...         ...        ...        ...        ...        ...   \n",
              "315          72          94         46          3       9.15      11.94   \n",
              "316          73          93         45          4       9.28      11.82   \n",
              "317           0           8          0          0       0.00       1.02   \n",
              "318           0           8          0          0       0.00       1.02   \n",
              "319           0           8          0          0       0.00       1.02   \n",
              "\n",
              "     amp_freq  del_freq  \n",
              "0        5.84      2.54  \n",
              "1        6.10      2.29  \n",
              "2        5.97      2.16  \n",
              "3        6.23      2.16  \n",
              "4        6.23      2.16  \n",
              "..        ...       ...  \n",
              "315      5.84      0.38  \n",
              "316      5.72      0.51  \n",
              "317      0.00      0.00  \n",
              "318      0.00      0.00  \n",
              "319      0.00      0.00  \n",
              "\n",
              "[320 rows x 14 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-18dbf4ea-6acc-4765-a77e-8fee3d721b12\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>chromosome</th>\n",
              "      <th>chr_ord</th>\n",
              "      <th>cytoband_name</th>\n",
              "      <th>hg38_start</th>\n",
              "      <th>hg38_stop</th>\n",
              "      <th>sCount</th>\n",
              "      <th>total_gain</th>\n",
              "      <th>total_loss</th>\n",
              "      <th>total_amp</th>\n",
              "      <th>total_del</th>\n",
              "      <th>gain_freq</th>\n",
              "      <th>loss_freq</th>\n",
              "      <th>amp_freq</th>\n",
              "      <th>del_freq</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>chr1</td>\n",
              "      <td>1</td>\n",
              "      <td>1p36</td>\n",
              "      <td>0</td>\n",
              "      <td>27600000</td>\n",
              "      <td>787</td>\n",
              "      <td>66</td>\n",
              "      <td>93</td>\n",
              "      <td>46</td>\n",
              "      <td>20</td>\n",
              "      <td>8.39</td>\n",
              "      <td>11.82</td>\n",
              "      <td>5.84</td>\n",
              "      <td>2.54</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>chr1</td>\n",
              "      <td>1</td>\n",
              "      <td>1p35</td>\n",
              "      <td>27600000</td>\n",
              "      <td>34300000</td>\n",
              "      <td>787</td>\n",
              "      <td>71</td>\n",
              "      <td>88</td>\n",
              "      <td>48</td>\n",
              "      <td>18</td>\n",
              "      <td>9.02</td>\n",
              "      <td>11.18</td>\n",
              "      <td>6.10</td>\n",
              "      <td>2.29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>chr1</td>\n",
              "      <td>1</td>\n",
              "      <td>1p34</td>\n",
              "      <td>34300000</td>\n",
              "      <td>46300000</td>\n",
              "      <td>787</td>\n",
              "      <td>72</td>\n",
              "      <td>89</td>\n",
              "      <td>47</td>\n",
              "      <td>17</td>\n",
              "      <td>9.15</td>\n",
              "      <td>11.31</td>\n",
              "      <td>5.97</td>\n",
              "      <td>2.16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>chr1</td>\n",
              "      <td>1</td>\n",
              "      <td>1p33</td>\n",
              "      <td>46300000</td>\n",
              "      <td>50200000</td>\n",
              "      <td>787</td>\n",
              "      <td>72</td>\n",
              "      <td>86</td>\n",
              "      <td>49</td>\n",
              "      <td>17</td>\n",
              "      <td>9.15</td>\n",
              "      <td>10.93</td>\n",
              "      <td>6.23</td>\n",
              "      <td>2.16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>chr1</td>\n",
              "      <td>1</td>\n",
              "      <td>1p32</td>\n",
              "      <td>50200000</td>\n",
              "      <td>60800000</td>\n",
              "      <td>787</td>\n",
              "      <td>72</td>\n",
              "      <td>84</td>\n",
              "      <td>49</td>\n",
              "      <td>17</td>\n",
              "      <td>9.15</td>\n",
              "      <td>10.67</td>\n",
              "      <td>6.23</td>\n",
              "      <td>2.16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>315</th>\n",
              "      <td>chrX</td>\n",
              "      <td>23</td>\n",
              "      <td>Xq27</td>\n",
              "      <td>138900000</td>\n",
              "      <td>148000000</td>\n",
              "      <td>787</td>\n",
              "      <td>72</td>\n",
              "      <td>94</td>\n",
              "      <td>46</td>\n",
              "      <td>3</td>\n",
              "      <td>9.15</td>\n",
              "      <td>11.94</td>\n",
              "      <td>5.84</td>\n",
              "      <td>0.38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>316</th>\n",
              "      <td>chrX</td>\n",
              "      <td>23</td>\n",
              "      <td>Xq28</td>\n",
              "      <td>148000000</td>\n",
              "      <td>156040895</td>\n",
              "      <td>787</td>\n",
              "      <td>73</td>\n",
              "      <td>93</td>\n",
              "      <td>45</td>\n",
              "      <td>4</td>\n",
              "      <td>9.28</td>\n",
              "      <td>11.82</td>\n",
              "      <td>5.72</td>\n",
              "      <td>0.51</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>317</th>\n",
              "      <td>chrY</td>\n",
              "      <td>24</td>\n",
              "      <td>Yp11</td>\n",
              "      <td>0</td>\n",
              "      <td>10400000</td>\n",
              "      <td>787</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.02</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>318</th>\n",
              "      <td>chrY</td>\n",
              "      <td>24</td>\n",
              "      <td>Yq11</td>\n",
              "      <td>10400000</td>\n",
              "      <td>26600000</td>\n",
              "      <td>787</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.02</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>319</th>\n",
              "      <td>chrY</td>\n",
              "      <td>24</td>\n",
              "      <td>Yq12</td>\n",
              "      <td>26600000</td>\n",
              "      <td>57227415</td>\n",
              "      <td>787</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.02</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>320 rows × 14 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-18dbf4ea-6acc-4765-a77e-8fee3d721b12')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      display: none;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-18dbf4ea-6acc-4765-a77e-8fee3d721b12 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-18dbf4ea-6acc-4765-a77e-8fee3d721b12');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "# Run the query and put results in a data frame\n",
        "final = client.query(case_query).result().to_dataframe()\n",
        "\n",
        "# Display the table of cases\n",
        "final"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "stsbJQuzkDCP"
      },
      "source": [
        "### Visualize Chromosomal Gains and Losses\n",
        "We can now visualize the number of gains and losses for a specific chromosomal region across all samples in the Mitelman DB. \n",
        "\n",
        "We will visualize the number of amplifications (**amp:** gains of more than 1 copy), the number of gains (**gain:** gains of 1 extra copy), the number of losses (**loss:** loss of 1 copy) and the number of homozygous deletions (**del:** loss of the 2 copies). The x-axis represent the coordinate of the chromosome (in this case, chromosome 1). The y-axis represents the number of samples exhibiting an amplification. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 264
        },
        "id": "3cEHIeULfp-_",
        "outputId": "dfcb7fc4-30be-40ca-9e51-5863573d20c6"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 252x208.8 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "ch1df = final[final.chromosome=='chr1'] \n",
        "\n",
        "fig, axes = plt.subplots(nrows=2, ncols=1) \n",
        "fig.set_size_inches(3.5,2.9)\n",
        "\n",
        "positions = [0, 5, 10, 15, 20]\n",
        "\n",
        "ax1 = ch1df.plot(ax = axes[0], kind='bar', stacked=True, \n",
        "           x=\"hg38_start\", y=[\"gain_freq\",\"amp_freq\"] , color=['palegreen','darkseagreen'],\n",
        "           width=0.9, rot=30, fontsize =10 )\n",
        "ax1.set_ylabel('Frequency (%)',  fontsize = 10)\n",
        "ax1.set_xlabel('')\n",
        "\n",
        "ax1.set_xticks(positions)\n",
        "ax1.xaxis.set_ticklabels([])\n",
        "\n",
        "#ax1.set_xticks([])\n",
        "ax1.legend([\"1 Extra Copy\", \">1 Extra copy\"] , fontsize = 9)\n",
        "\n",
        "ax2 = ch1df.plot(ax = axes[1], kind='bar', stacked=True, \n",
        "           x=\"hg38_start\", y=[\"loss_freq\",\"del_freq\"] , color=['lightcoral','firebrick'],\n",
        "           width=0.9, rot=30, fontsize =10 )\n",
        "ax2.set_xlabel('Start Pos (hg38)',  fontsize=10)\n",
        "ax2.set_ylabel('Frequency (%)',  fontsize = 10)\n",
        "ax2.set_xticks(positions)\n",
        "ax2.legend([\"Loss of 1 copy\", \"Homozygous Deletions\"] , fontsize = 9)\n",
        "\n",
        "fig.suptitle('MitelmanDB', fontsize=11)\n",
        "\n",
        "fig.savefig('Frequencies_MitelmanDB_Kidney_chr3.png', dpi=300, bbox_inches = 'tight')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0VK1uPXclDDG"
      },
      "source": [
        "### Calculate Frequency of TCGA Copy Number Gains and Losses\n",
        "\n",
        "As a comparison to MitelmanDB gain and loss frequency, we can calculate similar frequencies using TCGA Copy Number data. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ipxIMEzR97gF"
      },
      "outputs": [],
      "source": [
        "cnv_query = \"\"\"\n",
        "WITH copy AS (\n",
        "  SELECT case_barcode,\t#sample_barcode,\taliquot_barcode, \n",
        "    chromosome,\tstart_pos,\tend_pos,\tMAX(copy_number) as copy_number\n",
        "  FROM `isb-cgc-bq.TCGA_versioned.copy_number_segment_allelic_hg38_gdc_r23` \n",
        "  WHERE  project_short_name = '{tumor_type}'\n",
        "  GROUP BY case_barcode, chromosome,\tstart_pos,\tend_pos\n",
        "),\n",
        "total_cases AS (\n",
        "  SELECT COUNT( DISTINCT case_barcode) as total\n",
        "  FROM copy \n",
        "),\n",
        "cytob AS (\n",
        "  SELECT chromosome, cytoband_name, hg38_start, hg38_stop,\n",
        "  FROM mitelman-db.prod.CytoBands_hg38\n",
        "),\n",
        "joined AS (\n",
        "  SELECT cytob.chromosome, cytoband_name, hg38_start, hg38_stop,\n",
        "    case_barcode,\n",
        "    ( ABS(hg38_stop - hg38_start) + ABS(end_pos - start_pos) \n",
        "      - ABS(hg38_stop - end_pos) - ABS(hg38_start - start_pos) )/2.0  AS overlap ,\n",
        "    copy_number  \n",
        "  FROM copy\n",
        "  LEFT JOIN cytob\n",
        "  ON cytob.chromosome = copy.chromosome \n",
        "  WHERE \n",
        "    #cytob.hg38_start >= copy.start_pos AND cytob.hg38_start <= copy.end_pos  \n",
        "    ( cytob.hg38_start >= copy.start_pos AND copy.end_pos >= cytob.hg38_start )\n",
        "    OR ( copy.start_pos >= cytob.hg38_start  AND  copy.start_pos <= cytob.hg38_stop )\n",
        "),\n",
        "cbands AS(\n",
        "SELECT chromosome, cytoband_name, hg38_start, hg38_stop, case_barcode,\n",
        "    #ROUND( SUM(overlap*copy_number) / SUM(overlap) ) as copy_number\n",
        "    #ARRAY_AGG( copy_number ORDER BY overlap DESC )[OFFSET(0)] as copy_number\n",
        "    #ANY_VALUE(copy_number) as copy_number\n",
        "    MAX(copy_number) as copy_number\n",
        "    #MIN(copy_number) as copy_number\n",
        "FROM joined\n",
        "GROUP BY \n",
        "   chromosome, cytoband_name, hg38_start, hg38_stop, case_barcode\n",
        "),\n",
        "aberrations AS (\n",
        "  SELECT\n",
        "    chromosome,\n",
        "    hg38_start,\n",
        "    hg38_stop,\n",
        "    -- Amplifications: more than two copies for diploid > 4\n",
        "    SUM( IF (copy_number > 3 , 1 , 0) ) AS total_amp,\n",
        "    -- Gains: at most two extra copies\n",
        "    SUM( IF( copy_number = 3 ,1, 0) ) AS total_gain,\n",
        "    -- Homozygous deletions, or complete deletions\n",
        "    SUM( IF( copy_number = 0, 1, 0) ) AS total_homodel,\n",
        "    -- Heterozygous deletions, 1 copy lost\n",
        "    SUM( IF( copy_number = 1, 1, 0) ) AS total_heterodel,\n",
        "    -- Normal for Diploid = 2\n",
        "    SUM( IF( copy_number = 2, 1, 0) )  AS total_normal\n",
        "\n",
        "  FROM cbands\n",
        "  GROUP BY chromosome, cytoband_name, hg38_start, hg38_stop\n",
        ")\n",
        "SELECT chromosome, hg38_start, hg38_stop,\n",
        "  total,  \n",
        "  100 * total_amp / total as freq_amp, \n",
        "  100 * total_gain / total as freq_gain,\n",
        "  100 * total_homodel/ total as freq_homodel, \n",
        "  100 * total_heterodel / total as freq_heterodel, \n",
        "  100 * total_normal / total as freq_normal  \n",
        "FROM aberrations, total_cases\n",
        "ORDER BY chromosome, hg38_start, hg38_stop\n",
        "\"\"\".format( tumor_type = cancer_type)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2M-vUFIzRPY0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "945ed604-b878-49ae-9494-99f176a95506"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    chromosome  hg38_start  hg38_stop  total   freq_amp  freq_gain  \\\n",
              "0         chr1           0   27600000   1067  23.055295  23.149016   \n",
              "1         chr1    27600000   34300000   1067  18.462980  22.305530   \n",
              "2         chr1    34300000   46300000   1067  22.586692  21.930647   \n",
              "3         chr1    46300000   50200000   1067  20.337395  21.555764   \n",
              "4         chr1    50200000   60800000   1067  24.367385  20.149953   \n",
              "..         ...         ...        ...    ...        ...        ...   \n",
              "300       chrX   138900000  148000000   1067  30.084349  14.058107   \n",
              "301       chrX   148000000  156040895   1067  27.085286  16.869728   \n",
              "302       chrY           0   10400000   1067   0.374883   0.281162   \n",
              "303       chrY    10400000   26600000   1067   0.468604   0.281162   \n",
              "304       chrY    26600000   57227415   1067  29.428304   7.872540   \n",
              "\n",
              "     freq_homodel  freq_heterodel  freq_normal  \n",
              "0        0.000000        7.966261    45.829428  \n",
              "1        0.000000        7.310216    51.921275  \n",
              "2        0.000000        5.342081    50.140581  \n",
              "3        0.000000        5.716963    52.389878  \n",
              "4        0.000000        5.529522    49.953140  \n",
              "..            ...             ...          ...  \n",
              "300      0.187441        9.278351    46.391753  \n",
              "301      0.093721        9.278351    46.672915  \n",
              "302     96.251172        2.811621     0.281162  \n",
              "303     95.970009        2.999063     0.281162  \n",
              "304     27.928772       19.962512    14.807873  \n",
              "\n",
              "[305 rows x 9 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-55f6d0d3-c23b-4f75-9cec-7a952199edbb\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
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              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>chromosome</th>\n",
              "      <th>hg38_start</th>\n",
              "      <th>hg38_stop</th>\n",
              "      <th>total</th>\n",
              "      <th>freq_amp</th>\n",
              "      <th>freq_gain</th>\n",
              "      <th>freq_homodel</th>\n",
              "      <th>freq_heterodel</th>\n",
              "      <th>freq_normal</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>chr1</td>\n",
              "      <td>0</td>\n",
              "      <td>27600000</td>\n",
              "      <td>1067</td>\n",
              "      <td>23.055295</td>\n",
              "      <td>23.149016</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7.966261</td>\n",
              "      <td>45.829428</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>chr1</td>\n",
              "      <td>27600000</td>\n",
              "      <td>34300000</td>\n",
              "      <td>1067</td>\n",
              "      <td>18.462980</td>\n",
              "      <td>22.305530</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7.310216</td>\n",
              "      <td>51.921275</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>chr1</td>\n",
              "      <td>34300000</td>\n",
              "      <td>46300000</td>\n",
              "      <td>1067</td>\n",
              "      <td>22.586692</td>\n",
              "      <td>21.930647</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.342081</td>\n",
              "      <td>50.140581</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>chr1</td>\n",
              "      <td>46300000</td>\n",
              "      <td>50200000</td>\n",
              "      <td>1067</td>\n",
              "      <td>20.337395</td>\n",
              "      <td>21.555764</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.716963</td>\n",
              "      <td>52.389878</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>chr1</td>\n",
              "      <td>50200000</td>\n",
              "      <td>60800000</td>\n",
              "      <td>1067</td>\n",
              "      <td>24.367385</td>\n",
              "      <td>20.149953</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.529522</td>\n",
              "      <td>49.953140</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>300</th>\n",
              "      <td>chrX</td>\n",
              "      <td>138900000</td>\n",
              "      <td>148000000</td>\n",
              "      <td>1067</td>\n",
              "      <td>30.084349</td>\n",
              "      <td>14.058107</td>\n",
              "      <td>0.187441</td>\n",
              "      <td>9.278351</td>\n",
              "      <td>46.391753</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>301</th>\n",
              "      <td>chrX</td>\n",
              "      <td>148000000</td>\n",
              "      <td>156040895</td>\n",
              "      <td>1067</td>\n",
              "      <td>27.085286</td>\n",
              "      <td>16.869728</td>\n",
              "      <td>0.093721</td>\n",
              "      <td>9.278351</td>\n",
              "      <td>46.672915</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>302</th>\n",
              "      <td>chrY</td>\n",
              "      <td>0</td>\n",
              "      <td>10400000</td>\n",
              "      <td>1067</td>\n",
              "      <td>0.374883</td>\n",
              "      <td>0.281162</td>\n",
              "      <td>96.251172</td>\n",
              "      <td>2.811621</td>\n",
              "      <td>0.281162</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>303</th>\n",
              "      <td>chrY</td>\n",
              "      <td>10400000</td>\n",
              "      <td>26600000</td>\n",
              "      <td>1067</td>\n",
              "      <td>0.468604</td>\n",
              "      <td>0.281162</td>\n",
              "      <td>95.970009</td>\n",
              "      <td>2.999063</td>\n",
              "      <td>0.281162</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>304</th>\n",
              "      <td>chrY</td>\n",
              "      <td>26600000</td>\n",
              "      <td>57227415</td>\n",
              "      <td>1067</td>\n",
              "      <td>29.428304</td>\n",
              "      <td>7.872540</td>\n",
              "      <td>27.928772</td>\n",
              "      <td>19.962512</td>\n",
              "      <td>14.807873</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>305 rows × 9 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-55f6d0d3-c23b-4f75-9cec-7a952199edbb')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-55f6d0d3-c23b-4f75-9cec-7a952199edbb button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "# Execute query and put results into a data frame\n",
        "cnv = client.query(cnv_query).result().to_dataframe()\n",
        "\n",
        "# Display table\n",
        "cnv"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dxdBcA6eK6Wf"
      },
      "source": [
        "### Plot frequencies from TCGA copy numbers "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 264
        },
        "id": "FcBoJNngOQyr",
        "outputId": "18a73e15-f874-40d4-c908-4bb43bf6dc77"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 252x208.8 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "ch1df = cnv[cnv.chromosome=='chr1'] \n",
        "\n",
        "\n",
        "fig, axes = plt.subplots(nrows=2, ncols=1) \n",
        "fig.set_size_inches(3.5,2.9)\n",
        "\n",
        "positions = [0, 5, 10, 15, 20]\n",
        "\n",
        "ax1 = ch1df.plot(ax = axes[0], kind='bar', stacked=True, \n",
        "           x=\"hg38_start\", y=[\"freq_gain\",\"freq_amp\"] , color=['palegreen','darkseagreen'],\n",
        "           width=0.9, rot=30, fontsize =10 )\n",
        "ax1.set_xlabel('Start Pos (hg38)',  fontsize=10)\n",
        "ax1.set_ylabel('Frequency (%)',  fontsize = 10)\n",
        "ax1.set_xlabel('')\n",
        "\n",
        "ax1.set_xticks(positions)\n",
        "ax1.xaxis.set_ticklabels([])\n",
        "ax1.legend([\"1 Extra Copy\", \">1 Extra copy\"] , fontsize = 9)\n",
        "\n",
        "ax2 = ch1df.plot(ax = axes[1], kind='bar', stacked=True, \n",
        "           x=\"hg38_start\", y=[\"freq_heterodel\",\"freq_homodel\"] , color=['lightcoral','firebrick'],\n",
        "           width=0.9, rot=30, fontsize =10 )\n",
        "ax2.set_xlabel('Start Pos (hg38)',  fontsize=10)\n",
        "ax2.set_ylabel('Frequency (%)',  fontsize = 10)\n",
        "ax2.set_xticks(positions)\n",
        "ax2.legend([\"Loss of 1 copy\", \"Homozygous Deletions\"] , fontsize = 9)\n",
        "\n",
        "fig.suptitle('TCGA', fontsize=11)\n",
        "\n",
        "fig.savefig('Frequencies_TCGA_Kidney_chr3.png', dpi=300, bbox_inches = 'tight')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **2. Chromosomal gains and losses of kidney adenocarcinoma samples from Mitelman DB and TCGA datasets**"
      ],
      "metadata": {
        "id": "Y7gbIRA4Ww7a"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The steps to analyze kidney adenocarcinoma are similar to the previous example (breast cancer):"
      ],
      "metadata": {
        "id": "4xFDvxTw3iDB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Calculate Frequency of Gains and Losses in Mitelman DB Samples\n"
      ],
      "metadata": {
        "id": "gSDA_BnXXNm8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Set parameters for this query\n",
        "cancer_type = 'TCGA-KIRC' # Cancer type for TCGA\n",
        "bq_project = 'mitelman-db'  # project name of Mitelman-DB BigQuery table\n",
        "bq_dataset = 'prod' # Name of the dataset containing Mitelman-DB BigQuery tables\n",
        "morphology = '3111' # Kidney\n",
        "topology = '0301' # Adenocarcinoma"
      ],
      "metadata": {
        "id": "IZwuS_ts4jcY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Identify all Mitelman DB cases related to the morphology and topology of interest."
      ],
      "metadata": {
        "id": "ekPr4rBn4oDu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "case_query = \"\"\"\n",
        "WITH cyto_cases AS (\n",
        " SELECT DISTINCT\n",
        " c.Refno,\n",
        " c.CaseNo,\n",
        " c.InvNo,\n",
        "FROM\n",
        " `{bq_project}.{bq_dataset}.CytogenInvValid` c,\n",
        " `{bq_project}.{bq_dataset}.Reference` Reference,\n",
        " `{bq_project}.{bq_dataset}.Cytogen` Cytogen\n",
        "LEFT JOIN `{bq_project}.{bq_dataset}.Koder` KoderM\n",
        "ON\n",
        " (Cytogen.Morph = KoderM.Kod AND KoderM.KodTyp = 'MORPH')\n",
        "LEFT JOIN `{bq_project}.{bq_dataset}.Koder` KoderT\n",
        "ON\n",
        " (Cytogen.Topo = KoderT.Kod AND KoderT.KodTyp = 'TOP')\n",
        "WHERE\n",
        " Cytogen.RefNo = c.RefNo\n",
        " AND Cytogen.CaseNo = c.CaseNo\n",
        " AND c.Refno = Reference.Refno\n",
        " AND Cytogen.Morph IN ('{morphology}')\n",
        " AND Cytogen.Topo IN ('{topology}')\n",
        "),\n",
        "SampleCount AS(\n",
        "\n",
        " SELECT COUNT(*) AS sCount\n",
        " FROM cyto_cases\n",
        "),\n",
        "Case_CC_Kary_Result AS (\n",
        " SELECT cc_result.*\n",
        " FROM cyto_cases\n",
        " LEFT JOIN `{bq_project}.{bq_dataset}.CytoConverted` AS cc_result\n",
        " ON cc_result.RefNo = cyto_cases.RefNo\n",
        " AND cc_result.caseNo = cyto_cases.caseNo\n",
        " AND cc_result.invNo = cyto_cases.invNo\n",
        "),\n",
        "Clone_imbal_sums AS (\n",
        " SELECT cytoBands.chromosome,\n",
        " cytoBands.cytoband_name,\n",
        " cytoBands.hg38_start,\n",
        " cytoBands.hg38_stop,\n",
        " Case_CC_Kary_Result.RefNo,\n",
        " Case_CC_Kary_Result.CaseNo,\n",
        " Case_CC_Kary_Result.InvNo,\n",
        " Case_CC_Kary_Result.Clone,\n",
        " SUM( CASE WHEN type = 'Gain' THEN 1 ELSE 0 END ) AS totalGain,\n",
        " SUM( CASE WHEN type = 'Loss' THEN 1 ELSE 0 END ) AS totalLoss\n",
        " FROM `{bq_project}.{bq_dataset}.CytoBands_hg38` AS cytoBands\n",
        " INNER JOIN Case_CC_Kary_Result\n",
        "  ON cytoBands.chromosome = Case_CC_Kary_Result.Chr\n",
        " WHERE cytoBands.hg38_start >= Case_CC_Kary_Result.Start\n",
        " AND cytoBands.hg38_stop <= Case_CC_Kary_Result.End\n",
        " GROUP BY\n",
        " cytoBands.chromosome,\n",
        " cytoBands.cytoband_name,\n",
        " cytoBands.hg38_start,\n",
        " cytoBands.hg38_stop,\n",
        " Case_CC_Kary_Result.RefNo,\n",
        " Case_CC_Kary_Result.CaseNo,\n",
        " Case_CC_Kary_Result.InvNo,\n",
        " Case_CC_Kary_Result.Clone\n",
        "),\n",
        "AMP_DEL_counts AS (\n",
        " SELECT Clone_imbal_sums.chromosome, Clone_imbal_sums.cytoband_name, Clone_imbal_sums.hg38_start, Clone_imbal_sums.hg38_stop,\n",
        " Clone_imbal_sums.RefNo, Clone_imbal_sums.CaseNo, Clone_imbal_sums.InvNo, Clone_imbal_sums.Clone,\n",
        " CASE WHEN Clone_imbal_sums.totalGain > 1 THEN Clone_imbal_sums.totalGain ELSE 0 END AS amplified,\n",
        " CASE WHEN Clone_imbal_sums.totalLoss > 1 THEN Clone_imbal_sums.totalLoss ELSE 0 END AS hozy_deleted,\n",
        " CASE WHEN Clone_imbal_sums.totalGain > 1 THEN 1 ELSE 0 END AS amp_count,\n",
        " CASE WHEN Clone_imbal_sums.totalLoss > 1 THEN 1 ELSE 0 END AS hozy_del_count,\n",
        " FROM Clone_imbal_sums\n",
        "),\n",
        "Singular_imbal AS (\n",
        " SELECT Clone_imbal_sums.chromosome,\n",
        " Clone_imbal_sums.cytoband_name,\n",
        " Clone_imbal_sums.hg38_start,\n",
        " Clone_imbal_sums.hg38_stop,\n",
        " Clone_imbal_sums.RefNo,\n",
        " Clone_imbal_sums.CaseNo,\n",
        " Clone_imbal_sums.InvNo,\n",
        " Clone_imbal_sums.Clone,\n",
        " Clone_imbal_sums.totalGain - AMP_DEL_counts.amplified AS Singular_gain,\n",
        " Clone_imbal_sums.totalLoss - AMP_DEL_counts.hozy_deleted AS Singular_loss,\n",
        " AMP_DEL_counts.amp_count,\n",
        " AMP_DEL_counts.hozy_del_count\n",
        " FROM Clone_imbal_sums\n",
        " INNER JOIN AMP_DEL_counts\n",
        " ON Clone_imbal_sums.chromosome = AMP_DEL_counts.chromosome\n",
        " AND Clone_imbal_sums.cytoband_name = AMP_DEL_counts.cytoband_name\n",
        " AND Clone_imbal_sums.hg38_start= AMP_DEL_counts.hg38_start\n",
        " AND Clone_imbal_sums.hg38_stop = AMP_DEL_counts.hg38_stop\n",
        " AND Clone_imbal_sums.RefNo= AMP_DEL_counts.RefNo\n",
        " AND Clone_imbal_sums.CaseNo = AMP_DEL_counts.CaseNo\n",
        " AND Clone_imbal_sums.InvNo = AMP_DEL_counts.InvNo\n",
        " AND Clone_imbal_sums.Clone = AMP_DEL_counts.Clone\n",
        "),\n",
        "Sample_dist_count AS (\n",
        " SELECT Singular_imbal.chromosome,\n",
        " Singular_imbal.cytoband_name,\n",
        " Singular_imbal.hg38_start,\n",
        " Singular_imbal.hg38_stop,\n",
        " Singular_imbal.RefNo,\n",
        " Singular_imbal.CaseNo,\n",
        " Singular_imbal.InvNo,\n",
        " CASE WHEN SUM(Singular_imbal.Singular_gain)> 0 THEN 1 ELSE 0 END AS Sample_dist_singular_gain,\n",
        " CASE WHEN SUM(Singular_imbal.Singular_loss)> 0 THEN 1 ELSE 0 END AS Sample_dist_singular_loss,\n",
        " CASE WHEN SUM(Singular_imbal.amp_count)>0 THEN 1 ELSE 0 END AS Sample_dist_amp,\n",
        " CASE WHEN SUM(Singular_imbal.hozy_del_count)>0 THEN 1 ELSE 0 END AS Sample_dist_del,\n",
        " FROM Singular_imbal\n",
        " GROUP BY\n",
        " Singular_imbal.chromosome,\n",
        " Singular_imbal.cytoband_name,\n",
        " Singular_imbal.hg38_start,\n",
        " Singular_imbal.hg38_stop,\n",
        " Singular_imbal.RefNo,\n",
        " Singular_imbal.CaseNo,\n",
        " Singular_imbal.InvNo\n",
        ")\n",
        "SELECT Sample_dist_count.chromosome,\n",
        " CASE WHEN SUBSTRING(Sample_dist_count.chromosome, 4) = 'X' THEN 23\n",
        "      WHEN SUBSTRING(Sample_dist_count.chromosome, 4) = 'Y' THEN 24\n",
        "      ELSE CAST(SUBSTRING(Sample_dist_count.chromosome, 4) AS INT64)\n",
        " END AS chr_ord,\n",
        " Sample_dist_count.cytoband_name,\n",
        " Sample_dist_count.hg38_start,\n",
        " Sample_dist_count.hg38_stop,\n",
        " SampleCount.sCount,\n",
        " SUM(Sample_dist_count.Sample_dist_singular_gain) AS total_gain,\n",
        " SUM(Sample_dist_count.Sample_dist_singular_loss) AS total_loss,\n",
        " SUM(Sample_dist_count.Sample_dist_amp) AS total_amp,\n",
        " SUM(Sample_dist_count.Sample_dist_del) AS total_del,\n",
        " ROUND(SUM(Sample_dist_count.Sample_dist_singular_gain)/SampleCount.sCount*100, 2) AS gain_freq,\n",
        " ROUND(SUM(Sample_dist_count.Sample_dist_singular_loss)/SampleCount.sCount*100, 2) AS loss_freq,\n",
        " ROUND(SUM(Sample_dist_count.Sample_dist_amp)/SampleCount.sCount*100, 2) AS amp_freq,\n",
        " ROUND(SUM(Sample_dist_count.Sample_dist_del)/SampleCount.sCount*100, 2) AS del_freq\n",
        " FROM Sample_dist_count, SampleCount\n",
        " GROUP BY\n",
        " Sample_dist_count.chromosome,\n",
        " Sample_dist_count.cytoband_name,\n",
        " Sample_dist_count.hg38_start,\n",
        " Sample_dist_count.hg38_stop,\n",
        " SampleCount.sCount\n",
        " ORDER BY chr_ord, Sample_dist_count.hg38_start\n",
        "\"\"\".format(\n",
        "  bq_project=bq_project,\n",
        "  bq_dataset=bq_dataset,\n",
        "  morphology=morphology,\n",
        "  topology=topology\n",
        ")\n"
      ],
      "metadata": {
        "id": "-6AJv-cX4q-e"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Run the query and put results in a dataframe\n",
        "final = client.query(case_query).result().to_dataframe()\n",
        "final"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "XAL39MUW5I4O",
        "outputId": "c2e5b0d2-65ba-4822-8f58-9fc26f96d9d7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    chromosome  chr_ord cytoband_name  hg38_start  hg38_stop  sCount  \\\n",
              "0         chr1        1          1p36           0   27600000    1235   \n",
              "1         chr1        1          1p35    27600000   34300000    1235   \n",
              "2         chr1        1          1p34    34300000   46300000    1235   \n",
              "3         chr1        1          1p33    46300000   50200000    1235   \n",
              "4         chr1        1          1p32    50200000   60800000    1235   \n",
              "..         ...      ...           ...         ...        ...     ...   \n",
              "315       chrX       23          Xq27   138900000  148000000    1235   \n",
              "316       chrX       23          Xq28   148000000  156040895    1235   \n",
              "317       chrY       24          Yp11           0   10400000    1235   \n",
              "318       chrY       24          Yq11    10400000   26600000    1235   \n",
              "319       chrY       24          Yq12    26600000   57227415    1235   \n",
              "\n",
              "     total_gain  total_loss  total_amp  total_del  gain_freq  loss_freq  \\\n",
              "0            86          90         64          3       6.96       7.29   \n",
              "1            87          75         67          3       7.04       6.07   \n",
              "2            87          74         67          3       7.04       5.99   \n",
              "3            86          69         67          3       6.96       5.59   \n",
              "4            87          69         67          3       7.04       5.59   \n",
              "..          ...         ...        ...        ...        ...        ...   \n",
              "315         113          75         37          3       9.15       6.07   \n",
              "316         113          75         37          3       9.15       6.07   \n",
              "317          43         349          1         22       3.48      28.26   \n",
              "318          43         348          1         22       3.48      28.18   \n",
              "319          43         350          1         22       3.48      28.34   \n",
              "\n",
              "     amp_freq  del_freq  \n",
              "0        5.18      0.24  \n",
              "1        5.43      0.24  \n",
              "2        5.43      0.24  \n",
              "3        5.43      0.24  \n",
              "4        5.43      0.24  \n",
              "..        ...       ...  \n",
              "315      3.00      0.24  \n",
              "316      3.00      0.24  \n",
              "317      0.08      1.78  \n",
              "318      0.08      1.78  \n",
              "319      0.08      1.78  \n",
              "\n",
              "[320 rows x 14 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4ca31946-60a7-46ae-b734-23a8bb01138d\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>chromosome</th>\n",
              "      <th>chr_ord</th>\n",
              "      <th>cytoband_name</th>\n",
              "      <th>hg38_start</th>\n",
              "      <th>hg38_stop</th>\n",
              "      <th>sCount</th>\n",
              "      <th>total_gain</th>\n",
              "      <th>total_loss</th>\n",
              "      <th>total_amp</th>\n",
              "      <th>total_del</th>\n",
              "      <th>gain_freq</th>\n",
              "      <th>loss_freq</th>\n",
              "      <th>amp_freq</th>\n",
              "      <th>del_freq</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>chr1</td>\n",
              "      <td>1</td>\n",
              "      <td>1p36</td>\n",
              "      <td>0</td>\n",
              "      <td>27600000</td>\n",
              "      <td>1235</td>\n",
              "      <td>86</td>\n",
              "      <td>90</td>\n",
              "      <td>64</td>\n",
              "      <td>3</td>\n",
              "      <td>6.96</td>\n",
              "      <td>7.29</td>\n",
              "      <td>5.18</td>\n",
              "      <td>0.24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>chr1</td>\n",
              "      <td>1</td>\n",
              "      <td>1p35</td>\n",
              "      <td>27600000</td>\n",
              "      <td>34300000</td>\n",
              "      <td>1235</td>\n",
              "      <td>87</td>\n",
              "      <td>75</td>\n",
              "      <td>67</td>\n",
              "      <td>3</td>\n",
              "      <td>7.04</td>\n",
              "      <td>6.07</td>\n",
              "      <td>5.43</td>\n",
              "      <td>0.24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>chr1</td>\n",
              "      <td>1</td>\n",
              "      <td>1p34</td>\n",
              "      <td>34300000</td>\n",
              "      <td>46300000</td>\n",
              "      <td>1235</td>\n",
              "      <td>87</td>\n",
              "      <td>74</td>\n",
              "      <td>67</td>\n",
              "      <td>3</td>\n",
              "      <td>7.04</td>\n",
              "      <td>5.99</td>\n",
              "      <td>5.43</td>\n",
              "      <td>0.24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>chr1</td>\n",
              "      <td>1</td>\n",
              "      <td>1p33</td>\n",
              "      <td>46300000</td>\n",
              "      <td>50200000</td>\n",
              "      <td>1235</td>\n",
              "      <td>86</td>\n",
              "      <td>69</td>\n",
              "      <td>67</td>\n",
              "      <td>3</td>\n",
              "      <td>6.96</td>\n",
              "      <td>5.59</td>\n",
              "      <td>5.43</td>\n",
              "      <td>0.24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>chr1</td>\n",
              "      <td>1</td>\n",
              "      <td>1p32</td>\n",
              "      <td>50200000</td>\n",
              "      <td>60800000</td>\n",
              "      <td>1235</td>\n",
              "      <td>87</td>\n",
              "      <td>69</td>\n",
              "      <td>67</td>\n",
              "      <td>3</td>\n",
              "      <td>7.04</td>\n",
              "      <td>5.59</td>\n",
              "      <td>5.43</td>\n",
              "      <td>0.24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>315</th>\n",
              "      <td>chrX</td>\n",
              "      <td>23</td>\n",
              "      <td>Xq27</td>\n",
              "      <td>138900000</td>\n",
              "      <td>148000000</td>\n",
              "      <td>1235</td>\n",
              "      <td>113</td>\n",
              "      <td>75</td>\n",
              "      <td>37</td>\n",
              "      <td>3</td>\n",
              "      <td>9.15</td>\n",
              "      <td>6.07</td>\n",
              "      <td>3.00</td>\n",
              "      <td>0.24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>316</th>\n",
              "      <td>chrX</td>\n",
              "      <td>23</td>\n",
              "      <td>Xq28</td>\n",
              "      <td>148000000</td>\n",
              "      <td>156040895</td>\n",
              "      <td>1235</td>\n",
              "      <td>113</td>\n",
              "      <td>75</td>\n",
              "      <td>37</td>\n",
              "      <td>3</td>\n",
              "      <td>9.15</td>\n",
              "      <td>6.07</td>\n",
              "      <td>3.00</td>\n",
              "      <td>0.24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>317</th>\n",
              "      <td>chrY</td>\n",
              "      <td>24</td>\n",
              "      <td>Yp11</td>\n",
              "      <td>0</td>\n",
              "      <td>10400000</td>\n",
              "      <td>1235</td>\n",
              "      <td>43</td>\n",
              "      <td>349</td>\n",
              "      <td>1</td>\n",
              "      <td>22</td>\n",
              "      <td>3.48</td>\n",
              "      <td>28.26</td>\n",
              "      <td>0.08</td>\n",
              "      <td>1.78</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>318</th>\n",
              "      <td>chrY</td>\n",
              "      <td>24</td>\n",
              "      <td>Yq11</td>\n",
              "      <td>10400000</td>\n",
              "      <td>26600000</td>\n",
              "      <td>1235</td>\n",
              "      <td>43</td>\n",
              "      <td>348</td>\n",
              "      <td>1</td>\n",
              "      <td>22</td>\n",
              "      <td>3.48</td>\n",
              "      <td>28.18</td>\n",
              "      <td>0.08</td>\n",
              "      <td>1.78</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>319</th>\n",
              "      <td>chrY</td>\n",
              "      <td>24</td>\n",
              "      <td>Yq12</td>\n",
              "      <td>26600000</td>\n",
              "      <td>57227415</td>\n",
              "      <td>1235</td>\n",
              "      <td>43</td>\n",
              "      <td>350</td>\n",
              "      <td>1</td>\n",
              "      <td>22</td>\n",
              "      <td>3.48</td>\n",
              "      <td>28.34</td>\n",
              "      <td>0.08</td>\n",
              "      <td>1.78</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>320 rows × 14 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4ca31946-60a7-46ae-b734-23a8bb01138d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
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              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-4ca31946-60a7-46ae-b734-23a8bb01138d button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-4ca31946-60a7-46ae-b734-23a8bb01138d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Visualize Chromosomal Gains and Losses\n"
      ],
      "metadata": {
        "id": "zakbZJ8n5Owt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ch1df = final[final.chromosome=='chr3'] \n",
        "\n",
        "fig, axes = plt.subplots(nrows=2, ncols=1) \n",
        "fig.set_size_inches(3.5,2.9)\n",
        "\n",
        "positions = [0, 5, 10, 15, 20]\n",
        "\n",
        "ax1 = ch1df.plot(ax = axes[0], kind='bar', stacked=True, \n",
        "           x=\"hg38_start\", y=[\"gain_freq\",\"amp_freq\"] , color=['palegreen','darkseagreen'],\n",
        "           width=0.9, rot=30, fontsize =10 )\n",
        "ax1.set_ylabel('Frequency (%)',  fontsize = 10)\n",
        "ax1.set_xlabel('')\n",
        "\n",
        "ax1.set_xticks(positions)\n",
        "ax1.xaxis.set_ticklabels([])\n",
        "\n",
        "ax1.legend([\"1 Extra Copy\", \">1 Extra copy\"] , fontsize = 9)\n",
        "\n",
        "ax2 = ch1df.plot(ax = axes[1], kind='bar', stacked=True, \n",
        "           x=\"hg38_start\", y=[\"loss_freq\",\"del_freq\"] , color=['lightcoral','firebrick'],\n",
        "           width=0.9, rot=30, fontsize =10 )\n",
        "ax2.set_xlabel('Start Pos (hg38)',  fontsize=10)\n",
        "ax2.set_ylabel('Frequency (%)',  fontsize = 10)\n",
        "ax2.set_xticks(positions)\n",
        "ax2.legend([\"Loss of 1 copy\", \"Homozygous Deletions\"] , fontsize = 9)\n",
        "\n",
        "fig.suptitle('MitelmanDB', fontsize=11)\n",
        "\n",
        "fig.savefig('Frequencies_MitelmanDB_Kidney_chr3.png', dpi=300, bbox_inches = 'tight')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 264
        },
        "id": "LxYqZo945OXG",
        "outputId": "51886314-b134-48bc-9d36-81d59128d18f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 252x208.8 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Calculate Frequency of TCGA Copy Number Gains and Losses\n"
      ],
      "metadata": {
        "id": "jSTLrBet7M4u"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cnv_query = \"\"\"\n",
        "WITH copy AS (\n",
        "  SELECT case_barcode,\t#sample_barcode,\taliquot_barcode, \n",
        "    chromosome,\tstart_pos,\tend_pos,\tMAX(copy_number) as copy_number\n",
        "  FROM `isb-cgc-bq.TCGA_versioned.copy_number_segment_allelic_hg38_gdc_r23` \n",
        "  WHERE  project_short_name = '{tumor_type}'\n",
        "  GROUP BY case_barcode, chromosome,\tstart_pos,\tend_pos\n",
        "),\n",
        "total_cases AS (\n",
        "  SELECT COUNT( DISTINCT case_barcode) as total\n",
        "  FROM copy \n",
        "),\n",
        "cytob AS (\n",
        "  SELECT chromosome, cytoband_name, hg38_start, hg38_stop,\n",
        "  FROM mitelman-db.prod.CytoBands_hg38\n",
        "),\n",
        "joined AS (\n",
        "  SELECT cytob.chromosome, cytoband_name, hg38_start, hg38_stop,\n",
        "    case_barcode,\n",
        "    ( ABS(hg38_stop - hg38_start) + ABS(end_pos - start_pos) \n",
        "      - ABS(hg38_stop - end_pos) - ABS(hg38_start - start_pos) )/2.0  AS overlap ,\n",
        "    copy_number  \n",
        "  FROM copy\n",
        "  LEFT JOIN cytob\n",
        "  ON cytob.chromosome = copy.chromosome \n",
        "  WHERE \n",
        "    #cytob.hg38_start >= copy.start_pos AND cytob.hg38_start <= copy.end_pos  \n",
        "    ( cytob.hg38_start >= copy.start_pos AND copy.end_pos >= cytob.hg38_start )\n",
        "    OR ( copy.start_pos >= cytob.hg38_start  AND  copy.start_pos <= cytob.hg38_stop )\n",
        "),\n",
        "cbands AS(\n",
        "SELECT chromosome, cytoband_name, hg38_start, hg38_stop, case_barcode,\n",
        "    #ROUND( SUM(overlap*copy_number) / SUM(overlap) ) as copy_number\n",
        "    #ARRAY_AGG( copy_number ORDER BY overlap DESC )[OFFSET(0)] as copy_number\n",
        "    #ANY_VALUE(copy_number) as copy_number\n",
        "    MAX(copy_number) as copy_number\n",
        "    #MIN(copy_number) as copy_number\n",
        "FROM joined\n",
        "GROUP BY \n",
        "   chromosome, cytoband_name, hg38_start, hg38_stop, case_barcode\n",
        "),\n",
        "aberrations AS (\n",
        "  SELECT\n",
        "    chromosome,\n",
        "    hg38_start,\n",
        "    hg38_stop,\n",
        "    -- Amplifications: more than two copies for diploid > 4\n",
        "    SUM( IF (copy_number > 3 , 1 , 0) ) AS total_amp,\n",
        "    -- Gains: at most two extra copies\n",
        "    SUM( IF( copy_number = 3 ,1, 0) ) AS total_gain,\n",
        "    -- Homozygous deletions, or complete deletions\n",
        "    SUM( IF( copy_number = 0, 1, 0) ) AS total_homodel,\n",
        "    -- Heterozygous deletions, 1 copy lost\n",
        "    SUM( IF( copy_number = 1, 1, 0) ) AS total_heterodel,\n",
        "    -- Normal for Diploid = 2\n",
        "    SUM( IF( copy_number = 2, 1, 0) )  AS total_normal\n",
        "\n",
        "  FROM cbands\n",
        "  GROUP BY chromosome, cytoband_name, hg38_start, hg38_stop\n",
        ")\n",
        "SELECT chromosome, hg38_start, hg38_stop,\n",
        "  total,  \n",
        "  100 * total_amp / total as freq_amp, \n",
        "  100 * total_gain / total as freq_gain,\n",
        "  100 * total_homodel/ total as freq_homodel, \n",
        "  100 * total_heterodel / total as freq_heterodel, \n",
        "  100 * total_normal / total as freq_normal  \n",
        "FROM aberrations, total_cases\n",
        "ORDER BY chromosome, hg38_start, hg38_stop\n",
        "\"\"\".format( tumor_type = cancer_type)"
      ],
      "metadata": {
        "id": "l-AQDv5B7MDu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Execute query and put results into a data frame\n",
        "cnv = client.query(cnv_query).result().to_dataframe()\n",
        "\n",
        "# Display table\n",
        "cnv"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "1t1126Xt7hrN",
        "outputId": "8ee2f5a0-4e65-4176-e811-d4a9a6070a71"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    chromosome  hg38_start  hg38_stop  total   freq_amp  freq_gain  \\\n",
              "0         chr1           0   27600000    517  18.568665  10.444874   \n",
              "1         chr1    27600000   34300000    517  15.667311   8.123791   \n",
              "2         chr1    34300000   46300000    517  17.021277   7.350097   \n",
              "3         chr1    46300000   50200000    517  16.247582   7.156673   \n",
              "4         chr1    50200000   60800000    517  18.375242   8.317215   \n",
              "..         ...         ...        ...    ...        ...        ...   \n",
              "300       chrX   138900000  148000000    517   7.350097   3.094778   \n",
              "301       chrX   148000000  156040895    517   9.671180   6.576402   \n",
              "302       chrY           0   10400000    517   7.350097   5.996132   \n",
              "303       chrY    10400000   26600000    517   8.123791   5.609284   \n",
              "304       chrY    26600000   57227415    517   9.477756   8.704062   \n",
              "\n",
              "     freq_homodel  freq_heterodel  freq_normal  \n",
              "0        0.000000        6.576402    64.410058  \n",
              "1        0.000000        6.769826    69.439072  \n",
              "2        0.000000        5.415861    70.212766  \n",
              "3        0.000000        5.609284    70.986460  \n",
              "4        0.000000        5.029014    68.278530  \n",
              "..            ...             ...          ...  \n",
              "300     38.491296       24.564797    26.499033  \n",
              "301     17.794971       25.531915    40.425532  \n",
              "302     55.705996        7.350097    23.597679  \n",
              "303     55.899420        7.350097    23.017408  \n",
              "304     34.042553       19.729207    28.046422  \n",
              "\n",
              "[305 rows x 9 columns]"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>chromosome</th>\n",
              "      <th>hg38_start</th>\n",
              "      <th>hg38_stop</th>\n",
              "      <th>total</th>\n",
              "      <th>freq_amp</th>\n",
              "      <th>freq_gain</th>\n",
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              "      <th>0</th>\n",
              "      <td>chr1</td>\n",
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              "      <td>517</td>\n",
              "      <td>18.568665</td>\n",
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              "      <th>1</th>\n",
              "      <td>chr1</td>\n",
              "      <td>27600000</td>\n",
              "      <td>34300000</td>\n",
              "      <td>517</td>\n",
              "      <td>15.667311</td>\n",
              "      <td>8.123791</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>6.769826</td>\n",
              "      <td>69.439072</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>chr1</td>\n",
              "      <td>34300000</td>\n",
              "      <td>46300000</td>\n",
              "      <td>517</td>\n",
              "      <td>17.021277</td>\n",
              "      <td>7.350097</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.415861</td>\n",
              "      <td>70.212766</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>chr1</td>\n",
              "      <td>46300000</td>\n",
              "      <td>50200000</td>\n",
              "      <td>517</td>\n",
              "      <td>16.247582</td>\n",
              "      <td>7.156673</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.609284</td>\n",
              "      <td>70.986460</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>chr1</td>\n",
              "      <td>50200000</td>\n",
              "      <td>60800000</td>\n",
              "      <td>517</td>\n",
              "      <td>18.375242</td>\n",
              "      <td>8.317215</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.029014</td>\n",
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              "    <tr>\n",
              "      <th>300</th>\n",
              "      <td>chrX</td>\n",
              "      <td>138900000</td>\n",
              "      <td>148000000</td>\n",
              "      <td>517</td>\n",
              "      <td>7.350097</td>\n",
              "      <td>3.094778</td>\n",
              "      <td>38.491296</td>\n",
              "      <td>24.564797</td>\n",
              "      <td>26.499033</td>\n",
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              "    <tr>\n",
              "      <th>301</th>\n",
              "      <td>chrX</td>\n",
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              "      <td>517</td>\n",
              "      <td>9.671180</td>\n",
              "      <td>6.576402</td>\n",
              "      <td>17.794971</td>\n",
              "      <td>25.531915</td>\n",
              "      <td>40.425532</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>302</th>\n",
              "      <td>chrY</td>\n",
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              "      <td>10400000</td>\n",
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              "      <td>7.350097</td>\n",
              "      <td>5.996132</td>\n",
              "      <td>55.705996</td>\n",
              "      <td>7.350097</td>\n",
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              "      <td>19.729207</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Plot frequencies from TCGA copy numbers "
      ],
      "metadata": {
        "id": "0Pu9Oup677uU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Chr 5 frequency of amplifications\n",
        "ch1df = cnv[cnv.chromosome=='chr3'] \n",
        "\n",
        "\n",
        "fig, axes = plt.subplots(nrows=2, ncols=1) \n",
        "fig.set_size_inches(3.5,2.9)\n",
        "\n",
        "positions = [0, 5, 10, 15, 20]\n",
        "\n",
        "ax1 = ch1df.plot(ax = axes[0], kind='bar', stacked=True, \n",
        "           x=\"hg38_start\", y=[\"freq_gain\",\"freq_amp\"] , color=['palegreen','darkseagreen'],\n",
        "           width=0.9, rot=30, fontsize =10 )\n",
        "ax1.set_xlabel('Start Pos (hg38)',  fontsize=10)\n",
        "ax1.set_ylabel('Frequency (%)',  fontsize = 10)\n",
        "ax1.set_xlabel('')\n",
        "\n",
        "ax1.set_xticks(positions)\n",
        "ax1.xaxis.set_ticklabels([])\n",
        "ax1.legend([\"1 Extra Copy\", \">1 Extra copy\"] , fontsize = 9)\n",
        "\n",
        "ax2 = ch1df.plot(ax = axes[1], kind='bar', stacked=True, \n",
        "           x=\"hg38_start\", y=[\"freq_heterodel\",\"freq_homodel\"] , color=['lightcoral','firebrick'],\n",
        "           width=0.9, rot=30, fontsize =10 )\n",
        "ax2.set_xlabel('Start Pos (hg38)',  fontsize=10)\n",
        "ax2.set_ylabel('Frequency (%)',  fontsize = 10)\n",
        "ax2.set_xticks(positions)\n",
        "ax2.legend([\"Loss of 1 copy\", \"Homozygous Deletions\"] , fontsize = 9)\n",
        "\n",
        "fig.suptitle('TCGA', fontsize=11)\n",
        "\n",
        "#fig.savefig('Frequencies_TCGA_Kidney_chr3.png', dpi=300, bbox_inches = 'tight')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 264
        },
        "id": "zENGf5vG8AZr",
        "outputId": "d7a5d3c1-c21c-4cbd-8797-ff850a92084c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 252x208.8 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **3. Chromosomal gains and losses of AML samples from Mitelman DB and TCGA datasets**"
      ],
      "metadata": {
        "id": "99YS1Q57__ZI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Set parameters for this query\n",
        "cancer_type = 'TCGA-LAML' # Cancer type for TCGA\n",
        "bq_project = 'mitelman-db'  # project name of Mitelman-DB BigQuery table\n",
        "bq_dataset = 'prod' # Name of the dataset containing Mitelman-DB BigQuery tables\n",
        "morphology = '11%' # Adenocarcinoma"
      ],
      "metadata": {
        "id": "a_RZPPi_BI4t"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "case_query = \"\"\"\n",
        "WITH cyto_cases AS (\n",
        " SELECT DISTINCT\n",
        " c.Refno,\n",
        " c.CaseNo,\n",
        " c.InvNo,\n",
        "FROM\n",
        " `{bq_project}.{bq_dataset}.CytogenInvValid` c,\n",
        " `{bq_project}.{bq_dataset}.Reference` Reference,\n",
        " `{bq_project}.{bq_dataset}.Cytogen` Cytogen\n",
        "LEFT JOIN `{bq_project}.{bq_dataset}.Koder` KoderM\n",
        "ON\n",
        " (Cytogen.Morph = KoderM.Kod AND KoderM.KodTyp = 'MORPH')\n",
        "LEFT JOIN `{bq_project}.{bq_dataset}.Koder` KoderT\n",
        "ON\n",
        " (Cytogen.Topo = KoderT.Kod AND KoderT.KodTyp = 'TOP')\n",
        "WHERE\n",
        " Cytogen.RefNo = c.RefNo\n",
        " AND Cytogen.CaseNo = c.CaseNo\n",
        " AND c.Refno = Reference.Refno\n",
        " AND Cytogen.Morph LIKE '{morphology}'\n",
        "),\n",
        "SampleCount AS(\n",
        "\n",
        " SELECT COUNT(*) AS sCount\n",
        " FROM cyto_cases\n",
        "),\n",
        "Case_CC_Kary_Result AS (\n",
        " SELECT cc_result.*\n",
        " FROM cyto_cases\n",
        " LEFT JOIN `{bq_project}.{bq_dataset}.CytoConverted` AS cc_result\n",
        " ON cc_result.RefNo = cyto_cases.RefNo\n",
        " AND cc_result.caseNo = cyto_cases.caseNo\n",
        " AND cc_result.invNo = cyto_cases.invNo\n",
        "),\n",
        "Clone_imbal_sums AS (\n",
        " SELECT cytoBands.chromosome,\n",
        " cytoBands.cytoband_name,\n",
        " cytoBands.hg38_start,\n",
        " cytoBands.hg38_stop,\n",
        " Case_CC_Kary_Result.RefNo,\n",
        " Case_CC_Kary_Result.CaseNo,\n",
        " Case_CC_Kary_Result.InvNo,\n",
        " Case_CC_Kary_Result.Clone,\n",
        " SUM( CASE WHEN type = 'Gain' THEN 1 ELSE 0 END ) AS totalGain,\n",
        " SUM( CASE WHEN type = 'Loss' THEN 1 ELSE 0 END ) AS totalLoss\n",
        " FROM `{bq_project}.{bq_dataset}.CytoBands_hg38` AS cytoBands\n",
        " INNER JOIN Case_CC_Kary_Result\n",
        "  ON cytoBands.chromosome = Case_CC_Kary_Result.Chr\n",
        " WHERE cytoBands.hg38_start >= Case_CC_Kary_Result.Start\n",
        " AND cytoBands.hg38_stop <= Case_CC_Kary_Result.End\n",
        " GROUP BY\n",
        " cytoBands.chromosome,\n",
        " cytoBands.cytoband_name,\n",
        " cytoBands.hg38_start,\n",
        " cytoBands.hg38_stop,\n",
        " Case_CC_Kary_Result.RefNo,\n",
        " Case_CC_Kary_Result.CaseNo,\n",
        " Case_CC_Kary_Result.InvNo,\n",
        " Case_CC_Kary_Result.Clone\n",
        "),\n",
        "AMP_DEL_counts AS (\n",
        " SELECT Clone_imbal_sums.chromosome, Clone_imbal_sums.cytoband_name, Clone_imbal_sums.hg38_start, Clone_imbal_sums.hg38_stop,\n",
        " Clone_imbal_sums.RefNo, Clone_imbal_sums.CaseNo, Clone_imbal_sums.InvNo, Clone_imbal_sums.Clone,\n",
        " CASE WHEN Clone_imbal_sums.totalGain > 1 THEN Clone_imbal_sums.totalGain ELSE 0 END AS amplified,\n",
        " CASE WHEN Clone_imbal_sums.totalLoss > 1 THEN Clone_imbal_sums.totalLoss ELSE 0 END AS hozy_deleted,\n",
        " CASE WHEN Clone_imbal_sums.totalGain > 1 THEN 1 ELSE 0 END AS amp_count,\n",
        " CASE WHEN Clone_imbal_sums.totalLoss > 1 THEN 1 ELSE 0 END AS hozy_del_count,\n",
        " FROM Clone_imbal_sums\n",
        "),\n",
        "Singular_imbal AS (\n",
        " SELECT Clone_imbal_sums.chromosome,\n",
        " Clone_imbal_sums.cytoband_name,\n",
        " Clone_imbal_sums.hg38_start,\n",
        " Clone_imbal_sums.hg38_stop,\n",
        " Clone_imbal_sums.RefNo,\n",
        " Clone_imbal_sums.CaseNo,\n",
        " Clone_imbal_sums.InvNo,\n",
        " Clone_imbal_sums.Clone,\n",
        " Clone_imbal_sums.totalGain - AMP_DEL_counts.amplified AS Singular_gain,\n",
        " Clone_imbal_sums.totalLoss - AMP_DEL_counts.hozy_deleted AS Singular_loss,\n",
        " AMP_DEL_counts.amp_count,\n",
        " AMP_DEL_counts.hozy_del_count\n",
        " FROM Clone_imbal_sums\n",
        " INNER JOIN AMP_DEL_counts\n",
        " ON Clone_imbal_sums.chromosome = AMP_DEL_counts.chromosome\n",
        " AND Clone_imbal_sums.cytoband_name = AMP_DEL_counts.cytoband_name\n",
        " AND Clone_imbal_sums.hg38_start= AMP_DEL_counts.hg38_start\n",
        " AND Clone_imbal_sums.hg38_stop = AMP_DEL_counts.hg38_stop\n",
        " AND Clone_imbal_sums.RefNo= AMP_DEL_counts.RefNo\n",
        " AND Clone_imbal_sums.CaseNo = AMP_DEL_counts.CaseNo\n",
        " AND Clone_imbal_sums.InvNo = AMP_DEL_counts.InvNo\n",
        " AND Clone_imbal_sums.Clone = AMP_DEL_counts.Clone\n",
        "),\n",
        "Sample_dist_count AS (\n",
        " SELECT Singular_imbal.chromosome,\n",
        " Singular_imbal.cytoband_name,\n",
        " Singular_imbal.hg38_start,\n",
        " Singular_imbal.hg38_stop,\n",
        " Singular_imbal.RefNo,\n",
        " Singular_imbal.CaseNo,\n",
        " Singular_imbal.InvNo,\n",
        " CASE WHEN SUM(Singular_imbal.Singular_gain)> 0 THEN 1 ELSE 0 END AS Sample_dist_singular_gain,\n",
        " CASE WHEN SUM(Singular_imbal.Singular_loss)> 0 THEN 1 ELSE 0 END AS Sample_dist_singular_loss,\n",
        " CASE WHEN SUM(Singular_imbal.amp_count)>0 THEN 1 ELSE 0 END AS Sample_dist_amp,\n",
        " CASE WHEN SUM(Singular_imbal.hozy_del_count)>0 THEN 1 ELSE 0 END AS Sample_dist_del,\n",
        " FROM Singular_imbal\n",
        " GROUP BY\n",
        " Singular_imbal.chromosome,\n",
        " Singular_imbal.cytoband_name,\n",
        " Singular_imbal.hg38_start,\n",
        " Singular_imbal.hg38_stop,\n",
        " Singular_imbal.RefNo,\n",
        " Singular_imbal.CaseNo,\n",
        " Singular_imbal.InvNo\n",
        ")\n",
        "SELECT Sample_dist_count.chromosome,\n",
        " CASE WHEN SUBSTRING(Sample_dist_count.chromosome, 4) = 'X' THEN 23\n",
        "      WHEN SUBSTRING(Sample_dist_count.chromosome, 4) = 'Y' THEN 24\n",
        "      ELSE CAST(SUBSTRING(Sample_dist_count.chromosome, 4) AS INT64)\n",
        " END AS chr_ord,\n",
        " Sample_dist_count.cytoband_name,\n",
        " Sample_dist_count.hg38_start,\n",
        " Sample_dist_count.hg38_stop,\n",
        " SampleCount.sCount,\n",
        " SUM(Sample_dist_count.Sample_dist_singular_gain) AS total_gain,\n",
        " SUM(Sample_dist_count.Sample_dist_singular_loss) AS total_loss,\n",
        " SUM(Sample_dist_count.Sample_dist_amp) AS total_amp,\n",
        " SUM(Sample_dist_count.Sample_dist_del) AS total_del,\n",
        " ROUND(SUM(Sample_dist_count.Sample_dist_singular_gain)/SampleCount.sCount*100, 2) AS gain_freq,\n",
        " ROUND(SUM(Sample_dist_count.Sample_dist_singular_loss)/SampleCount.sCount*100, 2) AS loss_freq,\n",
        " ROUND(SUM(Sample_dist_count.Sample_dist_amp)/SampleCount.sCount*100, 2) AS amp_freq,\n",
        " ROUND(SUM(Sample_dist_count.Sample_dist_del)/SampleCount.sCount*100, 2) AS del_freq\n",
        " FROM Sample_dist_count, SampleCount\n",
        " GROUP BY\n",
        " Sample_dist_count.chromosome,\n",
        " Sample_dist_count.cytoband_name,\n",
        " Sample_dist_count.hg38_start,\n",
        " Sample_dist_count.hg38_stop,\n",
        " SampleCount.sCount\n",
        " ORDER BY chr_ord, Sample_dist_count.hg38_start\n",
        "\"\"\".format(\n",
        "  bq_project=bq_project,\n",
        "  bq_dataset=bq_dataset,\n",
        "  morphology=morphology\n",
        ")"
      ],
      "metadata": {
        "id": "0e_sAMseBNr9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Run the query and put results in a data frame\n",
        "final = client.query(case_query).result().to_dataframe()\n",
        "\n",
        "# Display the table of cases\n",
        "final"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "OU69--3bCPiF",
        "outputId": "8c4b74f1-8571-4650-cfa6-1250580d648f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    chromosome  chr_ord cytoband_name  hg38_start  hg38_stop  sCount  \\\n",
              "0         chr1        1          1p36           0   27600000   21109   \n",
              "1         chr1        1          1p35    27600000   34300000   21109   \n",
              "2         chr1        1          1p34    34300000   46300000   21109   \n",
              "3         chr1        1          1p33    46300000   50200000   21109   \n",
              "4         chr1        1          1p32    50200000   60800000   21109   \n",
              "..         ...      ...           ...         ...        ...     ...   \n",
              "315       chrX       23          Xq27   138900000  148000000   21109   \n",
              "316       chrX       23          Xq28   148000000  156040895   21109   \n",
              "317       chrY       24          Yp11           0   10400000   21109   \n",
              "318       chrY       24          Yq11    10400000   26600000   21109   \n",
              "319       chrY       24          Yq12    26600000   57227415   21109   \n",
              "\n",
              "     total_gain  total_loss  total_amp  total_del  gain_freq  loss_freq  \\\n",
              "0           352         341        280          9       1.67       1.62   \n",
              "1           351         236        287          8       1.66       1.12   \n",
              "2           351         227        287          8       1.66       1.08   \n",
              "3           350         206        287          6       1.66       0.98   \n",
              "4           353         211        288          6       1.67       1.00   \n",
              "..          ...         ...        ...        ...        ...        ...   \n",
              "315         406         776         90          2       1.92       3.68   \n",
              "316         406         788         90          2       1.92       3.73   \n",
              "317         253        1077         10         15       1.20       5.10   \n",
              "318         255        1077         10         15       1.21       5.10   \n",
              "319         254        1093          9         15       1.20       5.18   \n",
              "\n",
              "     amp_freq  del_freq  \n",
              "0        1.33      0.04  \n",
              "1        1.36      0.04  \n",
              "2        1.36      0.04  \n",
              "3        1.36      0.03  \n",
              "4        1.36      0.03  \n",
              "..        ...       ...  \n",
              "315      0.43      0.01  \n",
              "316      0.43      0.01  \n",
              "317      0.05      0.07  \n",
              "318      0.05      0.07  \n",
              "319      0.04      0.07  \n",
              "\n",
              "[320 rows x 14 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3702a9a7-a767-43a0-b98b-98ac87dbff31\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>chromosome</th>\n",
              "      <th>chr_ord</th>\n",
              "      <th>cytoband_name</th>\n",
              "      <th>hg38_start</th>\n",
              "      <th>hg38_stop</th>\n",
              "      <th>sCount</th>\n",
              "      <th>total_gain</th>\n",
              "      <th>total_loss</th>\n",
              "      <th>total_amp</th>\n",
              "      <th>total_del</th>\n",
              "      <th>gain_freq</th>\n",
              "      <th>loss_freq</th>\n",
              "      <th>amp_freq</th>\n",
              "      <th>del_freq</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>chr1</td>\n",
              "      <td>1</td>\n",
              "      <td>1p36</td>\n",
              "      <td>0</td>\n",
              "      <td>27600000</td>\n",
              "      <td>21109</td>\n",
              "      <td>352</td>\n",
              "      <td>341</td>\n",
              "      <td>280</td>\n",
              "      <td>9</td>\n",
              "      <td>1.67</td>\n",
              "      <td>1.62</td>\n",
              "      <td>1.33</td>\n",
              "      <td>0.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>chr1</td>\n",
              "      <td>1</td>\n",
              "      <td>1p35</td>\n",
              "      <td>27600000</td>\n",
              "      <td>34300000</td>\n",
              "      <td>21109</td>\n",
              "      <td>351</td>\n",
              "      <td>236</td>\n",
              "      <td>287</td>\n",
              "      <td>8</td>\n",
              "      <td>1.66</td>\n",
              "      <td>1.12</td>\n",
              "      <td>1.36</td>\n",
              "      <td>0.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>chr1</td>\n",
              "      <td>1</td>\n",
              "      <td>1p34</td>\n",
              "      <td>34300000</td>\n",
              "      <td>46300000</td>\n",
              "      <td>21109</td>\n",
              "      <td>351</td>\n",
              "      <td>227</td>\n",
              "      <td>287</td>\n",
              "      <td>8</td>\n",
              "      <td>1.66</td>\n",
              "      <td>1.08</td>\n",
              "      <td>1.36</td>\n",
              "      <td>0.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>chr1</td>\n",
              "      <td>1</td>\n",
              "      <td>1p33</td>\n",
              "      <td>46300000</td>\n",
              "      <td>50200000</td>\n",
              "      <td>21109</td>\n",
              "      <td>350</td>\n",
              "      <td>206</td>\n",
              "      <td>287</td>\n",
              "      <td>6</td>\n",
              "      <td>1.66</td>\n",
              "      <td>0.98</td>\n",
              "      <td>1.36</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>chr1</td>\n",
              "      <td>1</td>\n",
              "      <td>1p32</td>\n",
              "      <td>50200000</td>\n",
              "      <td>60800000</td>\n",
              "      <td>21109</td>\n",
              "      <td>353</td>\n",
              "      <td>211</td>\n",
              "      <td>288</td>\n",
              "      <td>6</td>\n",
              "      <td>1.67</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.36</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>315</th>\n",
              "      <td>chrX</td>\n",
              "      <td>23</td>\n",
              "      <td>Xq27</td>\n",
              "      <td>138900000</td>\n",
              "      <td>148000000</td>\n",
              "      <td>21109</td>\n",
              "      <td>406</td>\n",
              "      <td>776</td>\n",
              "      <td>90</td>\n",
              "      <td>2</td>\n",
              "      <td>1.92</td>\n",
              "      <td>3.68</td>\n",
              "      <td>0.43</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>316</th>\n",
              "      <td>chrX</td>\n",
              "      <td>23</td>\n",
              "      <td>Xq28</td>\n",
              "      <td>148000000</td>\n",
              "      <td>156040895</td>\n",
              "      <td>21109</td>\n",
              "      <td>406</td>\n",
              "      <td>788</td>\n",
              "      <td>90</td>\n",
              "      <td>2</td>\n",
              "      <td>1.92</td>\n",
              "      <td>3.73</td>\n",
              "      <td>0.43</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>317</th>\n",
              "      <td>chrY</td>\n",
              "      <td>24</td>\n",
              "      <td>Yp11</td>\n",
              "      <td>0</td>\n",
              "      <td>10400000</td>\n",
              "      <td>21109</td>\n",
              "      <td>253</td>\n",
              "      <td>1077</td>\n",
              "      <td>10</td>\n",
              "      <td>15</td>\n",
              "      <td>1.20</td>\n",
              "      <td>5.10</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>318</th>\n",
              "      <td>chrY</td>\n",
              "      <td>24</td>\n",
              "      <td>Yq11</td>\n",
              "      <td>10400000</td>\n",
              "      <td>26600000</td>\n",
              "      <td>21109</td>\n",
              "      <td>255</td>\n",
              "      <td>1077</td>\n",
              "      <td>10</td>\n",
              "      <td>15</td>\n",
              "      <td>1.21</td>\n",
              "      <td>5.10</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>319</th>\n",
              "      <td>chrY</td>\n",
              "      <td>24</td>\n",
              "      <td>Yq12</td>\n",
              "      <td>26600000</td>\n",
              "      <td>57227415</td>\n",
              "      <td>21109</td>\n",
              "      <td>254</td>\n",
              "      <td>1093</td>\n",
              "      <td>9</td>\n",
              "      <td>15</td>\n",
              "      <td>1.20</td>\n",
              "      <td>5.18</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.07</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>320 rows × 14 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3702a9a7-a767-43a0-b98b-98ac87dbff31')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\n",
              "      <script>\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3702a9a7-a767-43a0-b98b-98ac87dbff31');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Visualize Chromosomal Gains and Losses\n"
      ],
      "metadata": {
        "id": "OPnCM-jXCWAX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ch1df = final[final.chromosome=='chr5'] \n",
        "\n",
        "fig, axes = plt.subplots(nrows=2, ncols=1) \n",
        "fig.set_size_inches(3.5,2.9)\n",
        "\n",
        "positions = [0, 5, 10, 15]\n",
        "\n",
        "ax1 = ch1df.plot(ax = axes[0], kind='bar', stacked=True, \n",
        "           x=\"hg38_start\", y=[\"gain_freq\",\"amp_freq\"] , color=['palegreen','darkseagreen'],\n",
        "           width=0.9, rot=45, fontsize =10 )\n",
        "ax1.set_ylabel('Frequency (%)',  fontsize = 10)\n",
        "ax1.set_xlabel('')\n",
        "\n",
        "ax1.set_xticks(positions)\n",
        "ax1.xaxis.set_ticklabels([])\n",
        "\n",
        "#ax1.set_xticks([])\n",
        "ax1.legend([\"1 Extra Copy\", \">1 Extra copy\"] , fontsize = 9)\n",
        "\n",
        "ax2 = ch1df.plot(ax = axes[1], kind='bar', stacked=True, \n",
        "           x=\"hg38_start\", y=[\"loss_freq\",\"del_freq\"] , color=['lightcoral','firebrick'],\n",
        "           width=0.9, rot=30, fontsize =10 )\n",
        "ax2.set_xlabel('Start Pos (hg38)',  fontsize=10)\n",
        "ax2.set_ylabel('Frequency (%)',  fontsize = 10)\n",
        "ax2.set_xticks(positions)\n",
        "ax2.legend([\"Loss of 1 copy\", \"Homozygous Deletions\"] , fontsize = 9)\n",
        "\n",
        "fig.suptitle('MitelmanDB', fontsize=11)\n",
        "\n",
        "fig.savefig('Frequencies_MitelmanDB_AML_chr5.png', dpi=300, bbox_inches = 'tight')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 264
        },
        "id": "Cv1rPoR-CbY-",
        "outputId": "342e6e7c-8e7e-4971-ae6d-e761d4d578c9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 252x208.8 with 2 Axes>"
            ],
            "image/png": 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s/oP7D9KpbSeuXr3Kex8a7yWFCxcmNFso91a412Pf2InhizKHOls6AbO9M6Zltli8pWefnm6vlQorxReLv0j0/v3798d7kdS6p1bsZ1flf+WVV27Ie2+9e2M/P/7U4zz+1OOx5wm9oIaPHh7v+dOmTUvwRdb+4fY3yBFDcFAwXXsk7c/ME3zpZl8SkaoxJyJSDWNwYbFYkiAyMpIWLVqQv0D+2G63r/jSMg8EvhCRoxgrqIIYW2aLxeLC5Pcmx0sLCQlh2bJlXg9HEsKXTSM/i8idQMwU324blibjkZAPLEvaoKrGGbWH+Go1VQMo7pRTVURQ1Tk+lmkJECKiI7gecZ2QrJk+DmCao6pcPH+Ri1EXk87s4MumkY+AUsA2IMYBvmIc3FsyAAcuHiDkUAgiwtnQsx47l/M0b3op0y/PV7gYdZFd/+yiEY08Ks+Xlrk6cJfGXYCzZBjOXD/Dmb/PAIER89pfZQbC8z3Bl9nsHZhJL4vFEgD40jLnA353rKViA/CoamufpXJDtnDP4wSnRt7Uer7FkhL4oswjvb1RRGYA/wFOqmoFH2SwWCwOXnezVfV74AAQ4nz+Gdjq4e2zAN83o1osllh8cRvUBxOiJiZwbxFMULkkUdW1wFlvn22xWOLjywTYkxiH9hfAOCoAbksJoSwWS/LxZcx8TVUjYnYHiUgWkrVfJXEyUxTI1JiAs6QPUvJ/6osyfy8iLwLZRKQJ8ATgSbB1j1DVqZg40FSvXt2uZScTO0PvP/z1PfmizIOB3sB2TJzmZRjPIxZLqvyg/V1moL/MfDG0iAamOUeyEJFPgQZAPhE5DIxQ1Q+9lcWSfvG3gmYkfNmbvZ8ExsiqWjKpe1XVe+fJFoslQXzdmx1DKNAeiO/rxWKxpAm+bBo543IcUdWJGCd/FovFD/jSza7qchqEaaltVEmLxU/4onzjXD5fx2zt7OCTNBaLxWt8mc1umJKCWCwW3/Clm/1MYtdVdby3ZVssluTj62x2DWCxc94K+An401ehLBZL8vFFmYsCVVX1HwARGQksVdWU8ehtsViShS9WUwWACJfzCCfNYrH4AV9a5jnATyISE3KvDTDbd5EsFos3+DKb/aqILAfqOUk9VfWXlBHLYrEkF1+62WAiP15Q1UnAYREpkQIyWSwWL/DFbdAI4AVgiJMUAsxNCaEsFkvy8aVlbgu0Bi4BqOpRIGdKCGWxWJKPL8oc4USzUAARyZEyIlksFm/wRZnnicgHQG7HU+cqvHBUYLFYUgavZrPFePH7HLgT452zLDBcVVemoGwWiyUZeKXMqqoiskxVKwJWgS2WAMCXbvZWEamRYpJYLBaf8GUHWC2gq4gcwMxoC6bRrpQSglksluSRbGUWkWKq+j+gaSrIY7FYvMSbbvYiAFU9CIxX1YOuhycFiEgzEdktIntFZLAXMlgsljh4o8zi8jlJt7rxbhYJBt4BmgN3AQ+LyF1eyGGxWFzwRpnVzWdPqQnsVdV9qhoBfAY84EU5FovFBTGbuJJxg0gU/054ZQMux1zCTIDdksT97YBmqvqoc94NqKWq/ePkiw0ch1nH3u2myHzA6WRVIvCxdUof+KNOd6hq/oQuJHsCTFWDfZfHo+fEBo5LDBHZrKrVk8qXnrB1Sh8EWp18NYH0hiPA7S7nRZ00i8XiA/5Q5p+B0iJSQkRuAjrxr1NAi8XiJWkegUJVr4tIf+AbIBiYoao7fSgyya54OsTWKX0QUHVK9gSYxWIJTPzRzbZYLKmAVWaLJYNgldliySBYZbZYMghWmS2WDIJVZoslg2CV2WLJIFhltlgyCGm+A8wb8uXLp8WLF/e3GBaL39myZcvpFLOa8gfFixdn8+bN/hbDYvE7IuLWm4/H3WwRyeF4CbFYLAGIW2UWkSAR6SwiS0XkJLALOCYiv4vImyISlnZiWiyWpEism/0dJuTMEGCHqkYDiEgeoCHwuogsVFUb+dESUCwpVcqjfK3++iuVJUlbElPmxqoaGTdRVc8CC4AFIhKSapIlQmRkJIcPH+bq1av+eLwlwCn77rvk3bqVLBER/hYlTXGrzHEVWURCga4Yv1+fqOqZhJQ9LTh8+DA5c+akePHimLBXlszAue3bk8yTq0IFTgYHcwYo8MMPqS9UAJGcdeZJQATwN47vbH9x9epV8ubNaxXZEg8RIU+OHETekqhfyQyJ25ZZRD4FhqlqzMAiD/CF89nvjuutImcMPGltAXJXrMjNefN6lDc5v42MNL5ObMw8FHhFRI4Bo4G3gIVAKDAy9UXznPMvv5wi5eQaMcLttQMHDvDoo4+yatWqFHmWJ/Tv35/ffvuN5557jtatW8em9+rVi+XLl9OyZUumT5+eZvKkBp4qqCVpEhsz7wM6i0hdTCzmpUBLVY1KK+EyO99++y179uyJlz569Gi6d+/O3Ll2IcFX6nfv7nFeT1pxf7bgia0z3yoiT2JCyLTHjJW/EZFWaSVcoHP58mXat29PeHg4DRs2ZO/evagqnTt3pl69ejRs2JC1a9eyc+dO7rnnHho2bEjz5s3jlfPDDz9Qp04d6taty+OPP46q8tRTT3Ho0CEaNGjA3r17b8hfpEiRROX6+++/eeihh2LlOn78OCdOnKB58+aEh4fTokULTp06BUBYWBhPP/004eHhdO3alejoaF544QUWLlwIwKVLl6hatSrWV1zgk1g3exHG+2B24CNVfUBE5gPPi0hfVc30Sj116lQqVqzI8OHDWbt2LYMGDWLatGkcPHiQ9evXIyJER0czceJEevbsSd++fYmOjo5XTv/+/Zk3bx4lS5akV69eLFmyhClTprB8+XLWrFmTbLnGjBnD/fffT79+/QCIjo7mmWee4eGHH6Z79+7MmTOHMWPGMH78eK5fv06HDh2YMGECffr0YfHixfTp04enn36atm3b8sUXX9ChQ4dkz1F4MhbOXbFisuvmT5LTivuDxGaz8wLzMZNeRQBU9YqqjuLfsDGZmt27d1OnTh0A6tSpw65du8ibNy99+vShW7du9O3bl6NHj9KzZ0/27NlDly5dePPNN+OVc/78eUqWLHlDOb6wY8cO7rvvvtjzoKCgBGUFM1lUs2ZNAGrVqsXu3bsJCwsjIiKCI0eOMGfOHHr06JFsGW7OmzfJw5KyJKbMI4AVGIW+YfZaVY8lVbCIzBCRkyKywyUtj4isFJE/nb+3eit4IFC2bFk2btwIwMaNGylbtiyRkZF07dqVuXPnUr9+fSZMmEDWrFl56623+Pjjj1m5ciXb47RauXLlYt++fTeU4wsVKlS4oUWPjo5OUFYAVY01Yvn5558pU6YMYCbZXnzxRXLnzk3BggUB09p6clj8Q2ITYAswO728ZRbwNjDHJW0wsFpVxzpxmQcDL/jwDCDxWeiU5JdffqFx48bmmblyMWfOHLp37079+vUREaZNm8bJkyfp1KkTwcHBREREMHnyZD799FNmzZqFiFCwYMF4yjp58mS6dOlCcHAw5cuXv2HmOiGGDRvG8uXLOX78OI0bN+arr74iR44csdeHDBlCr169mDt3LsHBwXzyyScMHjyYRx55hOnTp5M9e3bmzDH/lixZsrBgwQIGDRpEkSJFYp/dtm1bnnrqKWbOnBlbrm1NAxu3TvBFZBowSVV3JHAtB9ARuKaqH7stXKQ48LWqVnDOdwMNVPWYiBQC1qhqks1Q9erV1dUE8o8//qBcuXJJ3WbxgLCwsHgTbADXrl2jbt26/PDDDwQHG2O560ePelRmlsKFPcrraT5vytx98CBFv/020by5RozweFnT07yp3bCIyBZ3weoSmwB7BxguIhWBHcApzBpzaeAWYAbgVpHdUMCli34cKJDM+y0pTHRERLyu8fZdu3h+zBh6t2vHP7//DqS/yarMSGLd7G1ABxG5GagOFAKuAH+oqrtYyR6jqioibtc7XOMzFytWzNfHWdywJ4H9y/fcey/rv/7aD9JYfCFJTyOqehFYk0LPOyEihVy62ScTeW5sfObq1avbRU6LJQnS2m3QYuARYKzz96s0fn6mISOu81oSJ9WU2THUaADkE5HDmKWuscA8EekNHAQ6pNbzMzt25jnzkaQyi0hFVU324qGqPuzmUqPklmWxWJLGk5b5XRHJilk3/lhVz6euSMnHUzO2pEhsk3xCVlPulnUyEjlLlaJWlSpcUyVn1qy89Mwz3FM9wZURAEaNG0eZKlXo5LIDzZUDhw6x/Y8/aHX//QC88fbbNG/UiIp2qdFnknROoKr1gC7A7cAWEflERJqkumSWgKBIwYKsmj+fTZs2MWHUKPo++yynz571uryDhw6xxGX9d1D//laRUwiPxsyq+qeIDAM2A5OBKmJ23r+oql+mpoDpgRMnTtCjRw8uX75Mjhw5mD17Nvnz5ycsLIy2bduyfv16qlWrRqFChfjmm2+49dZbWbTIOGt57LHH2LlzZ6xBRrVq1WjUyIxE/vnnHwBeeeUVVqxYwaRJkwBo0qQJ06dP5+LFi/Ts2ZP8+fOTN29eSpYsyciRI1myZAmjR4wgSITm993H0KefZvbnn3Pk2DFeHDiQw0eP8siAAXy/cSMTp05l3uLFZA8NpXWzZgx49FG39SwbFkab5s35ds0aOj/4IEPHjGHT5s1EREQwZMAAWja58R0/f8kS3p4xA1WlSXg4w55+molTp7L5119p1K4d46dMYdLrr9Orc2fq1qzJ6PHj+ea774hWZdjAgbRo3JhR48bx5/79XLp+nYN//cXH771H2VKl6N6/P4eOHiVLcDAjnnuOerVrp8a/1iv85fDAkzFzJaAn0BJYCbRS1a0iUhjYBGQaZd6yZQsNGjSIlz5mzBi3FkndunXjzTffpFy5crz66qsMHTqUNm3asG3bNg4ePEhkZCTr169n3759dOrUiZ9++ok1a9YQERFB27Ztee655wgPD2fYsGFcu3aNo0ePkiVLFu644w5at27N5MmTqV27Nn369AH+tZDatHgxuW65hWadOvGfnTvd1unThQtZ9cUX5Lz55gQtuuJStHBhjh4/zjfffcff587xfwsWcPnKFeq2akULZ6srwN/nzjHhgw9Ys3AhISEhtOvdm+1//MHAvn35+MsvmfrWW2QpXDg2/7YdO1j/44+sW7yY8xcuUKdlS5o5XfX8efLw6fTpfPTOO8z45BNe6N+fg4cP8/2iRbGWaYGEv6yrPGmZpwDTMa3wlZhEVT3qtNaZhmrVqsUbM4Oxnurfvz9gLJI+++wzwOx7rlSpEmBskKtUqQJA0aJFOXv27A2WTCVLluTvv/8GjPFD79696d69Ow0bNgSgTZs2LFy4kN9//53evXsDsHfvXmrUqAEYi6fDhw9z6tQpChQoQO5cuUx61ars+euvG0wYXbfwjnv5ZQa+9BKR16/Tt1s36joWVO44fPQo5cqUYceuXaz74QcatWsHQEREBGcc+QH2HjjA/44codnDZh703Pnz/O/wYW522UPuyp6//qJW1aqICLlz5SJ/vnyx3fmqznd4e5EirFq7lrx58tC7c2ceGTCA7NmyMWzgQIq6vBgyK5449GuJ8cZ5BWKd42cHUNWPUlO49II7i6S4xFUo1/v27dtH7ty5ARg0aBBVqlShY8eOsfl79+7NjBkzWLZsGQ888AAApUqVusHiCSB//vycOHGCc+fPo6r8uHUrZUqVIk/u3Bw+ZnbSbnVZg65asSIfTpjAa0OG8Mzw4YnW8899+/hqxQrub9CAu8qUoXF4OKvnz2f1/PlsXbWKfHnyxOYtWawYpYoX55vPPmP1/Pn8/M03NLvvPkJCQrh+/Xq8skuXKsWPW7eiqpw7f55Tp0/Hlhf3e4uMjKTLQw8xZ8oU6tWqxaRp0xKVO7PgScu8CmgMXHTOswPfAnVSS6jk4m9na+4skpKidevWLF26lLp16xIVFcWUKVM4dOgQU6ZMoXbt2ixevJiwsDCmT59O4cKFyZYtGw0bNiQkxLgrf+211+jVqxf58uUjV65c3HHHHQQFBfHmm2/SvHNngkRodt993F2+PGVLlWLStGk0f/hhKleoECtDjwEDOH32LFevXePxBOyWjxw/TuN27YgAsmfJwrtvvEG+PHlo3qgRmzZvplG7dghQpFAhZl2MiDEAABPnSURBVE+ZEntf3jx5GPDoozTp0IHgoCBCQkKYOWkSFcuVY9/Bg3Ts04eRY8fG5q9SoQL3VK9O3VatiFbljeHDCQpKuK05efo0XZ54ItYybcLo0R5934FISroicms1FZtBZJuqVk4qLTWxVlOGBx98kLFjx8baHEdGRsYqdp8+fWjatCntnG6vv6yWkpM3o1pNpaYlVmJWU550sy+JSFWXwqphDC4saURkZCQtWrSgYMGCsYoMsH37durVq8c999zDxYsXadOmjR+ltPgbT7rZA4EvROQoIEBBjC2zJY0ICQlh2bJl8dKrVq3KunXr/CCRJRDxxGrqZxG5E4iZ1dntr7A0rqiqdYRvSZDM6knUU0OLGkBxJ39VEUFVPZvlSQVCQ0M5c+aMDVFjiYeqcvbSJUIuXPC3KGmOJ5tGPgJKAduAGAf4yo2+vdKUokWLxq6pWhIm+ty5JPMEnT/vUb7UyptaZQb/73/k3brVo3IzEp60zNWBuzSA+i4hISGUKFHC32IENP6aeU1O3lQrc948j8rMaHgym70DM+llsVgCGE9a5nzA7yLyE3AtJlFVE/cHa7FY0hRPlHlkagthsVh8x5Olqe9F5A6gtKqucvZlB6e+aJa4JGd8acl8JDlmFpE+mBA1HzhJRTBB5SwWSwDhSTf7SaAm8CPEOiq4zZeHisgB4B/MUtd1d3tNLRaL53iizNdUNSJmc4aIZMGsM/tKQ1U9nQLlpHsCPYi3JX3giTJ/LyIvAtkc319PAEtSV6zMRaDH/bWkDzxZZx6MiTO1HegHLAN89TCiwLcissUJQxMPEekrIptFZLPd6WWxJI0ns9nRwDTnSCnqquoRZ+y9UkR2qeraOM+14WkslmTgyd7s/SQwRlbVkt4+VFWPOH9PishCzATb2sTvslgsieHp3uwYQoH2QB43eZPEie0cpKr/OJ/vB0Z5cq9dZ7VY3ONJN/tMnKSJIrIFSNz7m3sKAAud2fEsGGeBK7wsyy3pLYi2xeIrnnSzq7qcBmFaaq8DzqnqPuBub+/3J/YFYQlkPFHKcS6frwMHsNEbLZaAw5NudsO0EMRisfiGJ93sZxK7rqrjU04ci8XiLZ7OZtcAFjvnrYCfgD9TSyiLxZJ8PFHmokBVVf0HQERGAktVtWtqCmaxWJKHJ9s5CwARLucRTprFYgkgPGmZ5wA/OTu1ANoAs1NPJIvF4g2ezGa/KiLLgXpOUk9V/SV1xbJYLMnFk242mMiPF1R1EnBYRKyfW4slwPDEbdAI4AVgiJMUAsxNTaEsFkvy8aRlbgu0Bi4BqOpRIGdqCmWxWJKPJ8oc4USzUIi1erJYLAGGJ8o8T0Q+AHI7njpXkbKOCiwWSwqQ6Gy2GDvFz4E7gQuYsK7DVXVlGshmsViSQaLKrKoqIstUtSJgFdhiCWA86WZvFZEaqS6JxWLxCU92gNUCujqO6y8Bgmm0K6WmYBaLJXm4VWYRKaaq/wOapqE8FovFSxLrZi8CUNWDwHhVPeh6+PJQEWkmIrtFZK+IDPalLIvFYkhMmcXls9dudeMVKhIMvAM0B+4CHhaRu1KqfIsls5KYMqubz75SE9irqvtUNQL4DHggBcu3WDIlYjZ3JXBBJIp/J7yyAZdjLmEmwG7x6oEi7YBmqvqoc94NqKWq/ePk6wvEhK4pC+x2U2Q+IKMFoLN1Sh/4o053qGr+hC64nQBTVb8GVHcNT5MYIrI5o4WEtXVKHwRanTw1gUxJjgC3u5wXddIsFosP+EOZfwZKi0gJEbkJ6MS/zgItFouXeB2ZwltU9bqI9Ae+AYKBGaq604cik+yKp0NsndIHAVUntxNgFoslfeGPbrbFYkkFrDJbLBmEdK3MIpLmY36LJVBJl8osIllE5C1gnIg09rc8gYzjYCJDICK1RCSfv+VISVKyTulOmZ0f52SgECbm1Qsi8qSIZPWvZIGBiHQVkfEi0h3MVj1/y+Qrzg/+EDAC+Nw5T9cvqQTqVNPXOqU7ZcZ4Bq0MPKaqHwNvAWWA9n6VKgAQkU7As8AG4FkR+a+I3OFcS5c/fkfu+4ARqtoCWIbZm9DGr4L5gEudhrvU6WEcGwVv/1fpTplV9QIm4HsPJ2kD8AtQR0QK+kmsQOE+4B1VXYDZ114MaCEikp5aaBHJLiL5RCTIkbsaZqcgmNBIfwD3p6cut5s6xeyEjKlTUxHJ5+3/Kt0ps8NCoLKIFFLVi8B24Bqm651pEJGBItJNRKo4SVuBio7y/uich2FC8qYLRORRzMv6HeB9J/lDoKyI5FHV05hdhP8ADfwhY3JJqzqlV2Vej7FW6QGgqlswP9hsfpQpzRCRW0RkLtACE5FznoiUwvxgIvk3LtgazC6/PM59Ad3VFpEiwIMYM9numG2/vTE/8oNAFyfrbsz/OsQfciYHb+vkzf8qXSqzqh4DvgKai0h7ESkOXAWu+1OuNESAgsCDqvoWMA8YBJzEuESuLyK3quoRJ29DCMzJMBHJ63J6DvPiyaaq14DXMEpwK2ays56I1FTVy5h6Fo1bXiCQEnXy5n+VLpUZQFU3AmMwHktWAItU9Sf/SpU6iEiIM2N/pxNRRDBv9RgPLSMwLXQxTGucH3jZuRYK/J62EieNiAQ7y4sbROQVEfmPql4CfgRKAzj+2U9j6rke+AF4X0SmYibBNvpH+oTxe51UNV0fmG5JFn/LkYr1qwnswPhk+wB410mfBXQDQp3zTsAq53N+zLzCOuBLIIe/65FAvbpgZnFvw6xEHAZyAc8DI4EwJ18V4FfgJue8FtAPyO/vOgRandJtyxyDqkaqakbuXhcCVqhqG2AwcLeI9MJMpLTF8c+mqp8Bt4pIfVU9hflhtVXVB9W0Dn5HRFwDDgYDP6nqSVX9AqMEI4AFmDrHDA1+wdi73+mc/6iqHzh19DuBVKd0r8wZDREp6HgvjdmqGoaZqUdV/wYGAGOBLZhJk0dEpLaI5MZMgO1y8l5WM0vqd0SkkIh8BHwsIm2coUIUUEBEYn6Dz2OijYZiYpk1FJGZIvI5kBXY5w/Z3RGIdbImkAGEiAzATGT9inlzT8VMinyH8f103ck3F9iLmUzpAXQEigALVHVo2kueOCLyKabLuQpohfnRP4dZjnlSVTc4+YYD5VW1o4jkAToDWVV1nH8kd09A1snf4wx7xI63smDWIe9yzh/FdM9uBWYAU13yNsCsU2ZzzksDef1dhzj1CXb+3gZ8BOR2OV8LNHJ+2EuAws612pheR4i/5U+PdbLdbD8iIiVFJGZ5RYFwIMbr6VeYFnoI8BRmx1Nz51oZ4ISqXgFQ1T9V9UzaSe4eEblXRGYDw5zlsZOYocIDAM75eOBlVf0EMys/Uow31knARVWN9JP4CZJu6uTvt11mPICbMLPRezBd6L5O+kDgY5d8lTAhdYth9iK/7+TfCTzg73okUK+SmBdQD5zeBGbbYjiwzSVfDmAuZnNLTuB+4BOgj7/rkJ7rZFtm/1AZuFlVywDDgHudt/hqIMSlBT6NGTPfrqqLMJNfbwCVVfUrP8idFDWBP1R1Fv+OH/tgxvc7ROQ1ADWz61eBSFX9R1W/VdXOqjrNT3LHQ4yzSTA7C9NFnawypxEiUiPOzqAwZw/1Bsyml9KYjfdfYJYzUNWjmPHYTc55hKou1wDphopIWxEZICIxvqN/AoqKCTp4FrMB4jzQDDOz21VEOokJhFAViPaL4IkgIq2d2ebaTtKPpJM6WWVOA0SkIeZH0cx54x/HbOiIibD5DSZ6SGk165MHRWSWiKzDdNn+8oPYCSKGrCLyLvA0JtLJRBFpDZzC7Grq4GTfhdnwUlLNFtzHgFLAM8CLGkA79kQkv4isxvR+ZqnqWufSFUydYkxsA7dO/h6TZIYD6AlsxrS45TDjq6HASziz0JhN+Aucz9mBCkAXf8vupj7ZMOPD4s55R+B/Tt3aADMxIYfAmGX+n79l9qBOlYFtQIOY/4HLtdbAnECvk22ZUwFn44frd3sUmA/cATRUM776DtPqdnPyLAJyi0guNRs+dqhxvhAQiMhjItJLRCpg5I45wPQsrmN6GusxJqkTRORmoDxwQESy+0HsRBGR/4hIBcdC6VfM0mAvEfkvsFhEJolIe1VdjLGZD+g62U0jKYiINMLswT0JnFLVx5z0UZgfw1/A4xg7478w5orvAl9j3v5fAS+palSaC+8Gx7RyJsZkbyPGnO8ezIaV/JhdaNWA/cB/gNqqek5EJmA2stwBdFdVd4H/0hwRaQC8jhn7XsUYO4zH1OcjzAaQwZjlp9eAe1X1qIhMBAoTgHUCbDc7pQ7M2u+PQDvMpNUyoIlzrSNmyaIk8CdwEbjfuVYe6AW083cd4tQnZoNEC+BVl/T3MXuQ82E2r7wOdHCuzQXaOJ8FyOnvesSp080Yw5wvgU5OWhNMi3ynI3OZOPcsBEY5n4MCrU6uh3VV6wMxXWlVjcaMuX5S1fkicgtmQusPJ2tlzJs+G2b7Xw4gwpnN3olZNw4IRCQYGA0Ei8gizI+8nDNj3Quj3E8A36vqGhFZq6rRzlbFIMzEHmp+/f/4pRIJICJPYiyTHgT+C5x1Lm3AvJAmq6qKyF6Xe7Jg7JG/hdj/c8DUKS52zOwlItITszd3tJP0G1BNRKZhxowFgDedrtkEHEN0VX0cs8G+AKYlCBhEJBxjwHErZhgwBjNzuwYYh+k2twLyAjHj+VAR6YOp3zHggjMGDQhcZMmJecE+q6qHVPWSs7KQBfN/jHZertEiksv5//6EGQpt8YvwycXfXYP0eGC6a4swb/itQFknPT9m7fFx5zwUOAPUiHN/Vn/XwU296gHdXM7fBkY7n0cBBVyu/erkD8HM1tfxt/yJ1CsI80LthtnB1cXl2t3AepfzLJj1/ldwZq/Ty2FbZi9Q40RwgKpOwnTBRjqXzmC6pTucfFcxW/oKwA3d8mtpLLKnbMH4Ewt2zjfgbFjB2OI2BxDjpmkvsEeNPflMNZ5fAg4x3jCjMbvpYlYRWopIMREJAUoA34pIkIi8D/RW03IPU+MUMd1gldlLVPV/zseJQCkRae78aPYCU0WkrIi8iGm9djr3BNyOJ1fULIld039n05tiNriAmdUdJCIzMbPu21T1hD/kTA4u33lFzBLaCozLnk2YrZrlMXMAPwMnVfUDf8iZEtilqRRARPoBXVW1nnP+FsazRBAwSFUP+VO+5OK0zAosBZ5S1b2OdVchTEu2KR3WaQim11QZsyQVgXlZvYeZC+itqsfdlxD4WGX2kZhunIjMx6wvX8Z4y9yujoliesOZNLoJmI5ZmumNqdtAVT3vT9m8RUSGAnWBsar6vYiMx1itfagBstfdV6wypwDOTqCY7tsoVZ3sZ5F8RkRqYzaJbARmquqHfhbJJ0QkW8zL1XlZ3ZYehgnJwSpzCiAiz2H8Hb8QwJNbycLpVncDxmeUOoFZO9YM6gDSKnMK4DJjarH4DavMFksGwS5NWSwZBKvMFksGwSqzxZJBsMpssWQQrDIHECIyVER2ishvIrJNRGo56QO98WohIj1EpLCba7NEZL/znK0ick8KyD9RROo7nw+ISL5k3BsqIj+JyK/Od/Cyy7VGjozbRGS9iIQ56f3FxN2ygLWaCpQD471jE45FFcb4PyYqwgEgXzLLC8aYLlZ3c30WjkMEjI/n33yUPy/wg8t5smTGmIPe7HwOwTh6qO2c7wHKOZ+fwDjcA+Mr7Rd//+8C5bAtc+BQCDitzgYNVT2txlXNAIyrmu9E5DsAEXlPRDYn0IIdEJHXRWQr8DBQHRPYbJuIZEvk2WsxLnIQkWdEZIdzDHTScojIUqfV3CEiHRMo4yHMLjhXnnJa1O0icqdTVn4RWenIPl1EDopIPjVcdO4LcY6YdVPl30gfuTA+1VAToPyAiNRM9JvNLPj7bWIPc2BspLdhWqF3gXCXawdwaeWAPM7fmNa3kku+QS751uBZy9we0xJWwzhWyOHIsxMTS/ghYJrLvbkSKG820CqOzE85n58Apjuf3waGOJ+bYRQ1n0t9tmHcKr3uUlY9jHnpYUzg+Ftcrg3FOBzw+//Q34dtmQMENa1SNaAvxv/05yLSw032Dk7r+wvGhO8ul2ufJ+Oxb4rINueZvTGGCAtV9ZIjz5cYRdoONHFa/XqasLFFIUduV750/m4Bijuf6wKfAajqCuDvmMyqGqWqlTFbY2uK8QQKxj93C1UtinEuON7lGScxPZdMj1XmAML5Ma9R1RFAf0yLeAMiUgITJqWRqlbCmCmGumRJTmD151W1sqo2UdUdici1BxOtYTvwipgwpXG5EkcOcOJKY7xdeuxvTlXPYZwINBOR/MDd+q+jgM+BOi7ZQ51nZ3qsMgcIjjOD0i5JlTHRBME4kYvxUR3jLPC8iBTA8f7hBtf7PGEd0EZEsosJHt4WWOfMiF9W1bnAmxjFjssfOOPuJNiAE/FCRO7H+BuLGUvndj5nw3jN3IVpuXOJSBnn/ib86ygRjFdUty+izIT1zhk43AxMcX7Q1zEeS/o616YCK0TkqKo2FJFfMD/0QxjlcMcs4H0RuQLco0nYV6vqVhGZhXFkB2ac+4uINMV0yaMxDu4eT+D2pRjvl9OTqOfLwKci0g0ze38c89IpC8x2HCMEAfNU9WsAMQ4DFzjP/xvjJTSGe/nXbVOmxhpaWFIMEVkP/MfpJrvLkxWIUtXrztr2e8442ZvnVQGeUdVuSWbOBNiW2ZKSPIuJJe1WmZ3r88Q4N4zAhEf1lnyYeF0WbMtssWQY7ASYxZJBsMpssWQQrDJbLBkEq8wWSwbBKrPFkkH4f5k0YGzqa9YJAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Calculate Frequency of TCGA Copy Number Gains and Losses"
      ],
      "metadata": {
        "id": "OyvVjH2tCehN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cnv_query = \"\"\"\n",
        "WITH copy AS (\n",
        "  SELECT case_barcode,\t#sample_barcode,\taliquot_barcode, \n",
        "    chromosome,\tstart_pos,\tend_pos,\tMAX(copy_number) as copy_number\n",
        "  FROM `isb-cgc-bq.TCGA_versioned.copy_number_segment_allelic_hg38_gdc_r23` \n",
        "  WHERE  project_short_name = '{cancer_type}'\n",
        "  GROUP BY case_barcode, chromosome,\tstart_pos,\tend_pos\n",
        "),\n",
        "total_cases AS (\n",
        "  SELECT COUNT( DISTINCT case_barcode) as total\n",
        "  FROM copy \n",
        "),\n",
        "cytob AS (\n",
        "  SELECT chromosome, cytoband_name, hg38_start, hg38_stop,\n",
        "  FROM mitelman-db.prod.CytoBands_hg38\n",
        "),\n",
        "joined AS (\n",
        "  SELECT cytob.chromosome, cytoband_name, hg38_start, hg38_stop,\n",
        "    case_barcode,\n",
        "    ( ABS(hg38_stop - hg38_start) + ABS(end_pos - start_pos) \n",
        "      - ABS(hg38_stop - end_pos) - ABS(hg38_start - start_pos) )/2.0  AS overlap ,\n",
        "    copy_number  \n",
        "  FROM copy\n",
        "  LEFT JOIN cytob\n",
        "  ON cytob.chromosome = copy.chromosome \n",
        "  WHERE \n",
        "    ( cytob.hg38_start >= copy.start_pos AND copy.end_pos >= cytob.hg38_start )\n",
        "    OR ( copy.start_pos >= cytob.hg38_start  AND  copy.start_pos <= cytob.hg38_stop )\n",
        "),\n",
        "cbands AS(\n",
        "SELECT chromosome, cytoband_name, hg38_start, hg38_stop, case_barcode,\n",
        "    ROUND( SUM(overlap*copy_number) / SUM(overlap) ) as copy_number\n",
        "FROM joined\n",
        "GROUP BY \n",
        "   chromosome, cytoband_name, hg38_start, hg38_stop, case_barcode\n",
        "),\n",
        "aberrations AS (\n",
        "  SELECT\n",
        "    chromosome,\n",
        "    hg38_start,\n",
        "    hg38_stop,\n",
        "    -- Amplifications: more than two copies for diploid > 4\n",
        "    SUM( IF (copy_number > 3 , 1 , 0) ) AS total_amp,\n",
        "    -- Gains: at most two extra copies\n",
        "    SUM( IF( copy_number = 3 ,1, 0) ) AS total_gain,\n",
        "    -- Homozygous deletions, or complete deletions\n",
        "    SUM( IF( copy_number = 0, 1, 0) ) AS total_homodel,\n",
        "    -- Heterozygous deletions, 1 copy lost\n",
        "    SUM( IF( copy_number = 1, 1, 0) ) AS total_heterodel,\n",
        "    -- Normal for Diploid = 2\n",
        "    SUM( IF( copy_number = 2, 1, 0) )  AS total_normal\n",
        "\n",
        "  FROM cbands\n",
        "  GROUP BY chromosome, cytoband_name, hg38_start, hg38_stop\n",
        ")\n",
        "SELECT chromosome, hg38_start, hg38_stop,\n",
        "  total,  \n",
        "  100 * total_amp / total as freq_amp, \n",
        "  100 * total_gain / total as freq_gain,\n",
        "  100 * total_homodel/ total as freq_homodel, \n",
        "  100 * total_heterodel / total as freq_heterodel, \n",
        "  100 * total_normal / total as freq_normal  \n",
        "FROM aberrations, total_cases\n",
        "ORDER BY chromosome, hg38_start, hg38_stop\n",
        "\"\"\".format( cancer_type = cancer_type)"
      ],
      "metadata": {
        "id": "i17K54S0Clys"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Execute query and put results into a data frame\n",
        "cnv = client.query(cnv_query).result().to_dataframe()\n",
        "\n",
        "# Display table\n",
        "cnv"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "2J6Pm8jHCpcU",
        "outputId": "32b8a6ec-2496-45ea-d759-55a7b9d3240a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    chromosome  hg38_start  hg38_stop  total  freq_amp  freq_gain  \\\n",
              "0         chr1           0   27600000    190  4.210526   2.631579   \n",
              "1         chr1    27600000   34300000    190  4.736842   2.105263   \n",
              "2         chr1    34300000   46300000    190  4.736842   2.105263   \n",
              "3         chr1    46300000   50200000    190  4.736842   2.105263   \n",
              "4         chr1    50200000   60800000    190  4.736842   2.105263   \n",
              "..         ...         ...        ...    ...       ...        ...   \n",
              "300       chrX   138900000  148000000    190  2.105263   0.000000   \n",
              "301       chrX   148000000  156040895    190  2.105263   0.000000   \n",
              "302       chrY           0   10400000    190  2.105263   3.684211   \n",
              "303       chrY    10400000   26600000    190  2.105263   3.684211   \n",
              "304       chrY    26600000   57227415    190  2.105263   3.684211   \n",
              "\n",
              "     freq_homodel  freq_heterodel  freq_normal  \n",
              "0        0.000000        0.000000    93.157895  \n",
              "1        0.000000        0.000000    93.157895  \n",
              "2        0.000000        0.000000    93.157895  \n",
              "3        0.000000        0.000000    93.157895  \n",
              "4        0.000000        0.000000    93.157895  \n",
              "..            ...             ...          ...  \n",
              "300     22.105263       33.684211    42.105263  \n",
              "301     21.578947       34.210526    42.105263  \n",
              "302     47.368421        0.526316    46.315789  \n",
              "303     47.894737        0.000000    46.315789  \n",
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              "      <th></th>\n",
              "      <th>chromosome</th>\n",
              "      <th>hg38_start</th>\n",
              "      <th>hg38_stop</th>\n",
              "      <th>total</th>\n",
              "      <th>freq_amp</th>\n",
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              "      <td>chr1</td>\n",
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              "      <td>0.000000</td>\n",
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              "      <th>4</th>\n",
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              "    <tr>\n",
              "      <th>300</th>\n",
              "      <td>chrX</td>\n",
              "      <td>138900000</td>\n",
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          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Plot frequencies from TCGA copy numbers "
      ],
      "metadata": {
        "id": "zzSlwIg0C5-s"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Chr 5 frequency of amplifications\n",
        "ch1df = cnv[cnv.chromosome=='chr5'] \n",
        "\n",
        "\n",
        "fig, axes = plt.subplots(nrows=2, ncols=1) \n",
        "fig.set_size_inches(3.5,2.9)\n",
        "\n",
        "positions = [0, 5, 10, 15]\n",
        "\n",
        "ax1 = ch1df.plot(ax = axes[0], kind='bar', stacked=True, \n",
        "           x=\"hg38_start\", y=[\"freq_gain\",\"freq_amp\"] , color=['palegreen','darkseagreen'],\n",
        "           width=0.9, rot=30, fontsize =10 )\n",
        "ax1.set_xlabel('Start Pos (hg38)',  fontsize=10)\n",
        "ax1.set_ylabel('Frequency (%)',  fontsize = 10)\n",
        "ax1.set_xlabel('')\n",
        "\n",
        "ax1.set_xticks(positions)\n",
        "ax1.xaxis.set_ticklabels([])\n",
        "ax1.legend([\"1 Extra Copy\", \">1 Extra copy\"] , fontsize = 9)\n",
        "\n",
        "ax2 = ch1df.plot(ax = axes[1], kind='bar', stacked=True, \n",
        "           x=\"hg38_start\", y=[\"freq_heterodel\",\"freq_homodel\"] , color=['lightcoral','firebrick'],\n",
        "           width=0.9, rot=30, fontsize =10 )\n",
        "ax2.set_xlabel('Start Pos (hg38)',  fontsize=10)\n",
        "ax2.set_ylabel('Frequency (%)',  fontsize = 10)\n",
        "ax2.set_xticks(positions)\n",
        "ax2.legend([\"Loss of 1 copy\", \"Homozygous Deletions\"] , fontsize = 9)\n",
        "\n",
        "fig.suptitle('TCGA', fontsize=11)\n",
        "\n",
        "fig.savefig('Frequencies_TCGA_AML_chr5.png', dpi=300, bbox_inches = 'tight')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 264
        },
        "id": "8wUBmmCiDA-s",
        "outputId": "21699b3f-bf85-47a6-cb32-21523b196941"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 252x208.8 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HBGSd1wS5GRK"
      },
      "source": [
        "# **4. Conclusion**\n",
        "\n",
        "This notebook demonstrated usage of the Mitelman DB in BigQuery, which includes CytoConverter chromosomal coordinate data, for basic exploration and analysis of chromosomal aberrations and gene fusions.\n",
        "\n",
        "We observed that the frequency of gains and losses across samples in the TCGA and Mitelman DB are correlated in three know chromosomal deleteion: chromosome 1 in breast cancer, chromosome 3 in kidney adenocarcinoma, and chromosome 5 in acute myeloid leukemia. Some of these results are concordant with those of the Denomy et al. "
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}