genepairsincelegans.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mejian1/ExopherGeneExpressionProfiling/blob/main/genepairsincelegans.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Import necessary libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# Load the dataset\n",
        "file_path = '/content/NHRGeneFamilyAGP.csv'\n",
        "data = pd.read_csv(file_path)\n",
        "\n",
        "# Display the first few rows of the dataset\n",
        "print(data.head())\n",
        "\n",
        "# Ensure genomic coordinates are numeric for sorting and calculating distances\n",
        "data['genomic_coordinates'] = pd.to_numeric(data['genomic_coordinates'], errors='coerce')\n",
        "\n",
        "# Drop rows with NaN values in 'genomic_coordinates' or 'chromosome_number'\n",
        "data.dropna(subset=['genomic_coordinates', 'Chromosomes'], inplace=True)\n",
        "\n",
        "# Sort the dataset by chromosome number and genomic coordinates\n",
        "data.sort_values(by=['Chromosomes', 'genomic_coordinates'], inplace=True)\n",
        "\n",
        "# Calculate the distances between consecutive genes on the same chromosome\n",
        "data['distance_to_next'] = data.groupby('Chromosomes')['genomic_coordinates'].diff().shift(-1)\n",
        "\n",
        "# Display the results\n",
        "print(data)\n",
        "\n",
        "# Function to visualize gene proximity\n",
        "def plot_gene_proximity(df):\n",
        "    plt.figure(figsize=(14, 7))\n",
        "    for chromosome in df['Chromosomes'].unique():\n",
        "        chr_data = df[df['Chromosomes'] == chromosome]\n",
        "        plt.scatter(chr_data['genomic_coordinates'], [chromosome]*len(chr_data), label=f'Chr {chromosome}', alpha=0.6)\n",
        "    plt.xlabel('Genomic Coordinates')\n",
        "    plt.ylabel('Chromosome')\n",
        "    plt.title('Gene Proximity on Chromosomes')\n",
        "    plt.legend(title='Chromosomes')\n",
        "    plt.show()\n",
        "\n",
        "# Plot gene proximity\n",
        "plot_gene_proximity(data)\n",
        "\n",
        "# Define the filename for the output file\n",
        "output_file_name = 'gene_proximity_analysis.csv'\n",
        "\n",
        "# Export the dataframe to a CSV file\n",
        "data.to_csv(output_file_name, index=False)\n",
        "\n",
        "# Provide a download link for the generated CSV file\n",
        "from google.colab import files\n",
        "files.download(output_file_name)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "ZPudQ8jzKH_H",
        "outputId": "3efb9f23-cea2-488a-f0b4-ac794275985b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   NCBI GeneID   Symbol                                     Description  \\\n",
            "0       190581  nhr-276                 Nuclear Hormone Receptor family   \n",
            "1       191732   nhr-85   Nuclear hormone receptor family member nhr-85   \n",
            "2       183768  nhr-169  Nuclear hormone receptor family member nhr-169   \n",
            "3       184026   nhr-89   Nuclear hormone receptor family member nhr-89   \n",
            "4       184021  nhr-174  Nuclear hormone receptor family member nhr-174   \n",
            "\n",
            "        Gene Type  Transcripts Chromosomes Nomenclature ID  \\\n",
            "0  PROTEIN_CODING            1           I  WBGene00013512   \n",
            "1  PROTEIN_CODING            2           I  WBGene00003675   \n",
            "2  PROTEIN_CODING            1           I  WBGene00008289   \n",
            "3  PROTEIN_CODING            1           I  WBGene00003679   \n",
            "4  PROTEIN_CODING            1           I  WBGene00008474   \n",
            "\n",
            "  Annotation Genomic Range Accession   genomic_coordinates  \\\n",
            "0                         NC_003279.8             14041627   \n",
            "1                         NC_003279.8             13055201   \n",
            "2                         NC_003279.8             12442299   \n",
            "3                         NC_003279.8             12439936   \n",
            "4                         NC_003279.8             12417386   \n",
            "\n",
            "   genomic_coordinates.1 Orientation  Proteins  \n",
            "0               14043478        plus         1  \n",
            "1               13062122       minus         2  \n",
            "2               12443780        plus         1  \n",
            "3               12441253       minus         1  \n",
            "4               12418800        plus         1  \n",
            "     NCBI GeneID   Symbol                                        Description  \\\n",
            "15        171884   nhr-64      Nuclear hormone receptor family member nhr-64   \n",
            "14        172251    nhr-2       Nuclear hormone receptor family member nhr-2   \n",
            "13        172460   nhr-23      Nuclear hormone receptor family member nhr-23   \n",
            "12        192082   unc-55                    Nuclear hormone receptor unc-55   \n",
            "11        172836   nhr-62  NR LBD domain-containing protein;Nuclear hormo...   \n",
            "..           ...      ...                                                ...   \n",
            "191       181621   nhr-91      Nuclear hormone receptor family member nhr-91   \n",
            "190       181661   nhr-48  Nuclear hormone receptor family member nhr-48;...   \n",
            "189       191392  nhr-255                    Nuclear Hormone Receptor family   \n",
            "188       181705   nhr-28      Nuclear hormone receptor family member nhr-28   \n",
            "187       181783    nhr-1  Nuclear hormone receptor family member nhr-1;N...   \n",
            "\n",
            "          Gene Type  Transcripts Chromosomes Nomenclature ID  \\\n",
            "15   PROTEIN_CODING            3           I  WBGene00003654   \n",
            "14   PROTEIN_CODING            1           I  WBGene00003601   \n",
            "13   PROTEIN_CODING            6           I  WBGene00003622   \n",
            "12   PROTEIN_CODING            2           I  WBGene00006790   \n",
            "11   PROTEIN_CODING            4           I  WBGene00003652   \n",
            "..              ...          ...         ...             ...   \n",
            "191  PROTEIN_CODING            2           X  WBGene00003681   \n",
            "190  PROTEIN_CODING            4           X  WBGene00003638   \n",
            "189  PROTEIN_CODING            1           X  WBGene00014068   \n",
            "188  PROTEIN_CODING            2           X  WBGene00003624   \n",
            "187  PROTEIN_CODING            3           X  WBGene00003600   \n",
            "\n",
            "    Annotation Genomic Range Accession   genomic_coordinates  \\\n",
            "15                          NC_003279.8              3101983   \n",
            "14                          NC_003279.8              5818474   \n",
            "13                          NC_003279.8              7214745   \n",
            "12                          NC_003279.8              7898434   \n",
            "11                          NC_003279.8              9839598   \n",
            "..                                  ...                  ...   \n",
            "191                         NC_003284.9             15309375   \n",
            "190                         NC_003284.9             15708213   \n",
            "189                         NC_003284.9             15742610   \n",
            "188                         NC_003284.9             16354976   \n",
            "187                         NC_003284.9             17519735   \n",
            "\n",
            "     genomic_coordinates.1 Orientation  Proteins  distance_to_next  \n",
            "15                 3109530        plus         3         2716491.0  \n",
            "14                 5821134       minus         1         1396271.0  \n",
            "13                 7221299        plus         6          683689.0  \n",
            "12                 7903334       minus         2         1941164.0  \n",
            "11                 9844809        plus         4           30120.0  \n",
            "..                     ...         ...       ...               ...  \n",
            "191               15314898        plus         2          398838.0  \n",
            "190               15716875        plus         4           34397.0  \n",
            "189               15744298        plus         1          612366.0  \n",
            "188               16359650        plus         2         1164759.0  \n",
            "187               17528197        plus         3               NaN  \n",
            "\n",
            "[210 rows x 13 columns]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_77596ffc-985b-44bb-8b4a-75c617f907ca\", \"gene_proximity_analysis.csv\", 28705)"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Import necessary libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from google.colab import files\n",
        "\n",
        "# Step 1: Upload the file\n",
        "file_path = ('/content/gene_proximity_analysis.csv')\n",
        "#\n",
        "\n",
        "# Load the dataset\n",
        "data = pd.read_csv(file_path)\n",
        "\n",
        "# Step 2: Load the dataset\n",
        "# file_name = list()\n",
        "# data = pd.read_csv(file_name)\n",
        "\n",
        "# Display the first few rows of the dataset\n",
        "print(\"Data loaded successfully. Here are the first few rows:\")\n",
        "print(data.head())\n",
        "\n",
        "# Step 3: Data preprocessing\n",
        "# Ensure genomic coordinates are numeric for sorting and calculating distances\n",
        "data['genomic_coordinates'] = pd.to_numeric(data['genomic_coordinates'], errors='coerce')\n",
        "\n",
        "# Drop rows with NaN values in 'genomic_coordinates' or 'chromosome_number'\n",
        "data.dropna(subset=['genomic_coordinates', 'Chromosomes'], inplace=True)\n",
        "\n",
        "# Sort the dataset by chromosome number and genomic coordinates\n",
        "data.sort_values(by=['Chromosomes', 'genomic_coordinates'], inplace=True)\n",
        "\n",
        "# Step 4: Calculate the distances between consecutive genes on the same chromosome\n",
        "data['distance_to_next'] = data.groupby('Chromosomes')['genomic_coordinates'].diff().shift(-1)\n",
        "\n",
        "# Step 5: Label genes as paired (P) or singleton (S)\n",
        "data['proximity_label'] = 'S'  # Default to singleton\n",
        "\n",
        "for i in range(len(data) - 1):\n",
        "    if data.iloc[i]['Chromosomes'] == data.iloc[i + 1]['Chromosomes']:\n",
        "        data.at[i, 'proximity_label'] = 'P'\n",
        "        data.at[i + 1, 'proximity_label'] = 'P'\n",
        "\n",
        "# Ensure the last gene in the list is labeled correctly if paired\n",
        "if len(data) > 1 and data.iloc[-1]['Chromosomes'] == data.iloc[-2]['Chromosomes']:\n",
        "    data.at[len(data) - 1, 'proximity_label'] = 'P'\n",
        "\n",
        "print(\"\\nData with proximity labels:\")\n",
        "print(data[['Symbol', 'Chromosomes', 'genomic_coordinates', 'distance_to_next', 'proximity_label']])\n",
        "\n",
        "# Step 6: Visualize gene proximity\n",
        "def plot_gene_proximity(df):\n",
        "    plt.figure(figsize=(14, 7))\n",
        "    for chromosome in df['Chromosomes'].unique():\n",
        "        chr_data = df[df['Chromosomes'] == chromosome]\n",
        "        plt.scatter(chr_data['genomic_coordinates'], [chromosome]*len(chr_data), label=f'Chr {chromosome}', alpha=0.6)\n",
        "    plt.xlabel('Genomic Coordinates')\n",
        "    plt.ylabel('Chromosome')\n",
        "    plt.title('Gene Proximity on Chromosomes')\n",
        "    plt.legend(title='Chromosomes')\n",
        "    plt.grid(True)\n",
        "    plt.show()\n",
        "\n",
        "# Plot gene proximity\n",
        "plot_gene_proximity(data)\n",
        "\n",
        "# Step 7: Export the results\n",
        "output_file_name = 'gene_proximity_analysis_with_labels.csv'\n",
        "data.to_csv(output_file_name, index=False)\n",
        "\n",
        "# Provide a download link for the generated CSV file\n",
        "print(f\"\\nThe processed data with proximity labels has been saved to {output_file_name}. Downloading now...\")\n",
        "files.download(output_file_name)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "FV-XiPgwPFLs",
        "outputId": "90bed656-454e-4b48-b98c-f0addcf8cce6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Data loaded successfully. Here are the first few rows:\n",
            "   NCBI GeneID  Symbol                                        Description  \\\n",
            "0       171884  nhr-64      Nuclear hormone receptor family member nhr-64   \n",
            "1       172251   nhr-2       Nuclear hormone receptor family member nhr-2   \n",
            "2       172460  nhr-23      Nuclear hormone receptor family member nhr-23   \n",
            "3       192082  unc-55                    Nuclear hormone receptor unc-55   \n",
            "4       172836  nhr-62  NR LBD domain-containing protein;Nuclear hormo...   \n",
            "\n",
            "        Gene Type  Transcripts Chromosomes Nomenclature ID  \\\n",
            "0  PROTEIN_CODING            3           I  WBGene00003654   \n",
            "1  PROTEIN_CODING            1           I  WBGene00003601   \n",
            "2  PROTEIN_CODING            6           I  WBGene00003622   \n",
            "3  PROTEIN_CODING            2           I  WBGene00006790   \n",
            "4  PROTEIN_CODING            4           I  WBGene00003652   \n",
            "\n",
            "  Annotation Genomic Range Accession   genomic_coordinates  \\\n",
            "0                         NC_003279.8              3101983   \n",
            "1                         NC_003279.8              5818474   \n",
            "2                         NC_003279.8              7214745   \n",
            "3                         NC_003279.8              7898434   \n",
            "4                         NC_003279.8              9839598   \n",
            "\n",
            "   genomic_coordinates.1 Orientation  Proteins  distance_to_next  \n",
            "0                3109530        plus         3         2716491.0  \n",
            "1                5821134       minus         1         1396271.0  \n",
            "2                7221299        plus         6          683689.0  \n",
            "3                7903334       minus         2         1941164.0  \n",
            "4                9844809        plus         4           30120.0  \n",
            "\n",
            "Data with proximity labels:\n",
            "      Symbol Chromosomes  genomic_coordinates  distance_to_next  \\\n",
            "0     nhr-64           I              3101983         2716491.0   \n",
            "1      nhr-2           I              5818474         1396271.0   \n",
            "2     nhr-23           I              7214745          683689.0   \n",
            "3     unc-55           I              7898434         1941164.0   \n",
            "4     nhr-62           I              9839598           30120.0   \n",
            "..       ...         ...                  ...               ...   \n",
            "205   nhr-91           X             15309375          398838.0   \n",
            "206   nhr-48           X             15708213           34397.0   \n",
            "207  nhr-255           X             15742610          612366.0   \n",
            "208   nhr-28           X             16354976         1164759.0   \n",
            "209    nhr-1           X             17519735               NaN   \n",
            "\n",
            "    proximity_label  \n",
            "0                 P  \n",
            "1                 P  \n",
            "2                 P  \n",
            "3                 P  \n",
            "4                 P  \n",
            "..              ...  \n",
            "205               P  \n",
            "206               P  \n",
            "207               P  \n",
            "208               P  \n",
            "209               P  \n",
            "\n",
            "[210 rows x 5 columns]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x700 with 1 Axes>"
            ],
            "image/png": 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aAAAAAAB0LXdXB3C4smxLWyq3qDpUrXRPuo7IPEKm0XF5t82bN2vixInKzs7WfffdpzFjxigUCmnx4sW65pprtHbt2nat17ZtRSIRud10FQAAAAAAuju+3XeANXvX6IUNL2hDxQYFw0F53V4VZhXqnMJzNKLHiA4p8+qrr5ZhGFq+fLnS0tJi00eNGqXLLrssbtk9e/bo/PPP1+LFi9WvXz/df//9OueccyRJS5cu1SmnnKKXX35ZP/3pT7Vq1SotWbJEJ598cofEDQAAAAAAOg+XhiXYmr1r9MjKR/TZ3s+U5c3SEZlHKMubpc/2fqZHVj6iNXvXJLzM0tJSLVq0SNdcc01cEqhBdnZ23Os5c+booosu0ieffKKpU6dqxowZKi0tjVvm1ltv1T333KM1a9Zo7NixCY8ZAAAAAAB0PhJBCWTZll7Y8ILKAmUqyCpQujddLtOldG+6CrIKVBYo0783/FuWbSW03PXr18u2bQ0fPrxVy8+aNUvTp0/X4MGDdffdd6u6ulrLly+PW+bOO+/U5MmTVVhYqNzc3ITGCwAAAAAAugaJoATaUrlFGyo2KC81T4ZhxM0zDEN5qXlaX7FeWyq3JLRc27bbtHzjET5paWnKzMxUSUlJ3DITJkxISGwAAAAAACB5kAhKoOpQtYLhoPxuf7Pz/W6/guGgqkPVCS13yJAhMgyj1TeE9ng8ca8Nw5BlxY9Sau4SMwAAAAAA0L2RCEqgdE+6vG6v6sJ1zc6vC9fJ6/Yq3ZOe0HJzc3M1ZcoUzZ07VzU1NU3ml5eXJ7Q8AAAAAADQPZEISqAjMo9QYVahdtXuanK5lm3b2lW7S4OzBuuIzCMSXvbcuXMViUR03HHH6dlnn9W6deu0Zs0a/f73v9cJJ5yQ8PIAAAAAAED3w+PjE8g0TJ1TeI52VO/QxoqNykvNk9/tV124TrtqdynHl6NphdNkGonPvxUUFGjFihW66667dNNNN6m4uFi9evXS+PHj9fDDDye8PAAAAAAA0P2QCEqwET1G6HvjvqcXNrygDRUbtLt2t7xur0b3GK1phdM0oseIDis7Pz9fDz30kB566KEDLtPcjaUbXzp28sknt/rm082NejpYWQAAAAAAoOuQCOoAI3qM0LDcYdpSuUXVoWqle9J1ROYRHTISCAAAAAAAoLVIBHUQ0zA1KGtQV4cBAAAAAAAQwxAVAAAAAAAAhyARBAAAAAAA4BAkggAAAAAAAByCRBAAAAAAAIBDkAgCAAAAAABwCBJBAAAAAAAADkEiCAAAAAAAwCFIBDmMYRhauHBhV4cBAAAAAAC6AImgw8jOnTt17bXXqqCgQD6fTwMGDNC0adP0+uuvd0h5DUmlXbt2yePx6Omnn252udmzZ+uYY47pkBgAAAAAAEDrkQjqILZlKbBpk+pWrVJg0ybZltWh5W3evFnjx4/XG2+8ofvuu0+rVq3SokWLdMopp+iaa65p93pt21Y4HG5xmby8PJ111ln6y1/+0mReTU2N/vGPf2j27NntjgEAAAAAACQGiaAOUL96tXbdc692/b+7tOveX0X/vede1a9e3WFlXn311TIMQ8uXL9cFF1ygoUOHatSoUbrxxhv1wQcfxC27Z88enX/++UpNTdWQIUP0wgsvxOYtXbpUhmHolVde0fjx4+Xz+fTuu+8etPzZs2fr9ddf15YtW+KmP/PMMwqHw5oxY0ZiNhQAAAAAALQbiaAEq1+9Wrvn/kH1n30qMytL3oEDZWZlqf6zT6PTOyAZVFpaqkWLFumaa65RWlpak/nZ2dlxr+fMmaOLLrpIn3zyiaZOnaoZM2aotLQ0bplbb71V99xzj9asWaOxY8ceNIapU6cqLy9P8+fPj5s+b948feMb32gSAwAAAAAA6HwkghLItiyVL3xekfIyeQsK5UpPl+FyyZWeLm9BoSLlZSp//vmEXya2fv162bat4cOHt2r5WbNmafr06Ro8eLDuvvtuVVdXa/ny5XHL3HnnnZo8ebIKCwuVm5t70HW6XC5deumlmj9/vmzbliRt2LBB77zzji677LK2bxQAAAAAAEg4EkEJFCwqUnDDBrnz+sgwjLh5hmHInddHwfUbFCwqSmi5DYmX1mo8wictLU2ZmZkqKSmJW2bChAltjuOyyy7Tpk2b9Oabb0qKjgYaNGiQTj311DavCwAAAAAAJB6JoASyqqtlBQIy/f5m55t+v6xAQFZ1dULLHTJkiAzD0Nq1a1u1vMfjiXttGIas/UYpNXeJWWvimDRpkubNmyfLsvTEE0/oO9/5TpOkGAAAAAAA6BokghLITE+X6fPJqqtrdr5VVyfT55OZnp7QcnNzczVlyhTNnTtXNTU1TeaXl5cntLyWzJ49W88++6yeffZZbd++XbNmzeq0sgEAAAAAQMtIBCWQd+BAeQsLFd61s8nlWrZtK7xrp7yDC+UdODDhZc+dO1eRSETHHXec/n97dx4XVb3/D/x1ZmdfRGFQXNhUFPfkmpkrUvozLR9umUtq9XApTdS8dbtKpdktb+WV67Vugm2iXlz6pimGouWSe2KiVwi3BFGRZViHmc/vDy+TE4sMDDAwr+fjMY+ccz5zzvuc9+ccTu855zPx8fG4fPkyUlJSsGbNGvTr18/q66vKuHHjoFQq8dJLL2H48OHw8/NrsHUTERERERERUfVYCLIiSSaD+5jRkLt7oPTXNBh0OgiDAQadDqW/pkHu4QH30aMhyay/2/39/XH69GkMHjwYkZGR6Nq1K8LDw5GYmIh169ZZfX1VcXR0xMSJE3Hv3j0OEk1ERERERERkYxSNHUBzowkJQcu5c5CzYydK09JQlpUFmVoNTdeucB89GpqQkHpbt1arxdq1a7F27doq21Q2sPSDj44NGjSoxoNPV9Vu/fr1WL9+fY2WQUREREREREQNh4WgeqAJCYF3p04ovXoVRp0OMmdnqNq1q5c7gYiIiIiIiIiIaoqFoHoiyWRQd+jQ2GEQEREREREREZnwFhUiIiIiIiIiIjvBQhARERERERERkZ1gIYiIiIiIiIiIyE6wEEREREREREREZCdYCCIiIiIiIiIishMsBBERERERERER2QkWgoiIiIiIiIiI7AQLQXZGkiTs2LGjscMgIiIiIiIiokbAQlAzkpmZiZdffhn+/v5Qq9Xw8/PDqFGjkJiYaNX13Lp1C0qlEnFxcZXOnzlzJnr16mXVdRIRERERERFR3bEQVE+EUSDnViFuXclDzq1CCKOo1/VduXIFvXv3xv79+/H+++8jOTkZe/bsweDBgzF37txaL1cIgbKyMrNp3t7eGDlyJDZs2FChfUFBAbZs2YKZM2fWep1EREREREREVD9YCKoHt6/l48f/XMahuP/iyH9ScSjuv/jxP5dx+1p+va1zzpw5kCQJx48fx9ixYxEcHIwuXbpg4cKFOHbsmFnbO3fu4Omnn4ajoyOCgoLwzTffmOYlJSVBkiR899136N27N9RqNX788ccK65s5cyYSExNx7do1s+lbt25FWVkZJk+eXD8bSkRERERERES1xkKQld2+lo8Tu9KRdS0faicFXL0doHZSIOt/0+ujGJSdnY09e/Zg7ty5cHJyqjDf3d3d7H1UVBTGjx+Pc+fOYcSIEZg8eTKys7PN2ixduhSrVq1CSkoKunXrVmGZI0aMgLe3N2JjY82mx8TE4JlnnqmwTiIiIiIiIiJqfCwEWZEwClw8loGiAj08fByhclBAJpOgclDAw8cRRQV6XPwpw+qPiaWmpkIIgU6dOtWo/fTp0zFp0iQEBgZi5cqV0Ol0OH78uFmbt956C+Hh4QgICICnp2eFZcjlckybNg2xsbEQ4v72pKWl4YcffsCMGTPqvlFEREREREREZHUsBFlR7u0i3MsohLO7GpIkmc2TJAnO7mrcu1mI3NtFVl1veSGmph68w8fJyQmurq7Iysoya9OnT5+HLmfGjBlIT0/HgQMHANy/G6h9+/YYMmSIRfEQERERERERUcNgIciKSorKYNAboVDLK52vUMth0BtRUlRW6fzaCgoKgiRJuHjxYo3aK5VKs/eSJMFoNJpNq+wRs8rWO2DAAMTExMBoNOLzzz/H888/X6EIRkRERERERES2gYUgK1I7KCBXylBWYqh0flmJAXKlDGoHhVXX6+npiYiICERHR6OgoKDC/JycHKuu70EzZ85EfHw84uPj8dtvv2H69On1ti4iIiIiIiIiqhsWgqzIraUDPLSO0OWUVHhcSwgBXU4JPHwd4dbSwerrjo6OhsFgQN++fREfH4/Lly8jJSUFa9asQb9+/ay+vnLjxo2DUqnESy+9hOHDh8PPz6/e1kVEREREREREdcNCkBVJMgmd/qSFg5MS9zILUVpUBqNRoLSoDPcyC+HgpESnMC0kmfUfnfL398fp06cxePBgREZGomvXrggPD0diYiLWrVtn9fWVc3R0xMSJE3Hv3j0OEk1ERERERERk46z7jBKhZVsXPDKyAy4ey8C9jEIU5pRCrpShVTsXdArTomVbl3pbt1arxdq1a7F27doq21Q2sPSDj44NGjTI4sGn169fj/Xr11v0GSIiIiIiIiJqeCwE1YOWbV3g1cYZubeLUFJUBrWDAm4tHerlTiAiIiIiIiIioppiIaieSDIJ7t6OjR0GEREREREREZEJxwgiIiIiIiIiIrITLAQREREREREREdkJFoKIiIiIiIiIiOwExwgiIgCA0Shw5W4B8ovL4KJRoH0LJ8g4wDlVgn2lera0f2wpFvo9H/cKS5F6SwdJArxdNegf4AWFgt/NWcrW+3dl8QHAlbsFyC3SI69ID2eNAjmFpbhytxAlZQaEaF0R1ta9UWO0pX1oiea0LURE9c3mC0HTp09HTk4OduzY0dihEDVb53/LRfzpG0jN0qFEb4RaKUNgK2eM7dUGXVu7NXZ4ZEPYV6pnS/vHlmKh3/NxJPUOrmUXQm8QkCRAo5DDz9MBLw0MwOgerRs7zCbD1vt3ZfF5OCoBSLieXYibOUUo0BtQoi9DmeH+ZyQJUMhl6OCpxpyAxonRlvahJZrTthARNQSbLwQRUf06/1su1iReRnZBKbRuDnBwk6Oo1IDkG7n47V4RXhkaxIsoAsC+8jC2tH9sKRb6PR+Xs/LxW04xjEYBhVyC0ShQajAg/U4B3vn2AgBgRJdWjRyt7bP1/l1ZfJm5xTj03zsoMwqoFTIYjEYUl5ahzHj/MxIACMBoNOJ6dhEQAOxOzsDoXm0bLEZb2oeWaE7bQkTUUHgfMpEdMxoF4k/fQHZBKQJbOcNZo4BcJsFZo0BgK2dkF5Ri2+nfYDSKxg6VGhn7SvVsaf/YUiz0ez7u5BfjXoEeRqOARimDSi6DRimHBAlKmYSiUgM+OfgrysorA1QpW+/flcUnk4DMvCLIZUCZwQBdiR5lBiMeTLUk3X/JJAny/z3NtOHH9HrpD7a+Dy3RnLaFiKghNfk7gkpKSlBSUmJ6n5eXBwDQ6/XQ6/VmbfV6PYQQMBqNMBpt80JLCGH6b33EKJfLER8fjzFjxlh92fXFaDRCCAG9Xg+5XN7Y4dS78n77x/5bH67cKcCV23lo46aGQhIAHrhQkoA2bmqk385F2q1ctPdyqvd4qGHzbwn2lepZc//UtQ8wV7alPB8OSgmGsjK4qCTcHw7ofl6USgkGo4Crgxx38gtxLDULgO2dA2yFrffvyuLLK9ajuEQPZ5UMZXrAIAQgjFDLTWEDAOQyCQICTsr7U+7kFeLH/2aif1DLeo/RxAb2oSWa07aUs9XrAGo47AP2rSb5t0bfkER55cFGPWyMoOXLlyMqKqrC9K+//hqOjo5m0xQKBXx8fODn5weVSlUf4TaqW7duYfXq1UhISEBGRga8vLwQGhqK2bNnY+DAgQAADw8PfPnllxg5cmSd1pWcnIyhQ4ciNjYWI0aMME3/5ptv8OKLL2L//v0ICQmp0zrKlZaW4vr168jMzERZWZlVlklERERERETU1BQWFuLZZ59Fbm4uXF1da7WMJl8IquyOID8/P9y5c6fCTikuLsb169fRvn17aDSa+gwbwmhEzq0MlBQWQu3oCHdvLSTZw5/EE0IgPz8fLi4ukKSa/9LBlStXMGDAALi7u2P58uUIDQ2FXq9HQkICPv30U1y4cH/sAUvvCBJCwGAwQKGoePPYihUrEB0djeTkZLRo0QJZWVkIDQ1FZGQklixZUuPYH6a4uBhXrlyBn59fvefNFuj1euzbtw/h4eFQKpX1uq4rdwqw8rsUuGlUcNZUzLGuuAy5xaV4/cnOTeabtKauIfNvCfaV6llz/9S1DzBXtqU8H6VlRpy7nguFXILigV8yMgrAYBRw1sihNwisGh2CvF9P29w5wFbYev+uLL68Ij3OXs+BJAE5BaX37wiCgP7B8YFQfkcQ4KqU8OfuevwtWYUVz3SvlzuCbHkfWqI5bUs5W70OoIbDPmDfapL/vLw8eHl51akQ1OQfDVOr1VCr1RWmK5XKCjvOYDBAkiTIZDLIalCUqa1b6Wn45WAisn+7jrLSUihUKni29kOXgUPh3aH6n4EofxysPM6amjdvHiRJwvHjx+Hk9PsfutDQUMycOdNsWdnZ2Rg7diz27t2L1q1bY/Xq1XjqqacAAElJSRg8eDB2796Nv/zlL0hOTkZCQgIGDRpUYZ2vv/46/u///g8vv/wy4uLiMHv2bAQFBWHx4sVW3b8ymQySJFWa0+asIbY3wNsN7Vu6IvlGLgLVSrPioxACN3JL0K2NOwK83fgTrA3M1vo7+0r16mP/1LYPMFe2pTwfP1+7B7lCgfySMmiUEmT/y0tpmREquYScIiPatXDCnwJbIeFX2zsH2Apb79+VxeeoUUGjVuKurgQlRkBAglImQ4nh9yEAZBIgGe8Xgwr097+j9XJ1xGPBPlAorHvNauv70BLNaVv+iOcAYh+wb9Xl3xr9goNFW9mt9DQci9+ErPRUaJxd4KFtDY2zC7LSU3EsfhNupadZfZ3Z2dnYs2cP5s6da1YEKufu7m72PioqCuPHj8e5c+cwYsQITJ48GdnZ2WZtli5dilWrViElJQXdunWrdL1yuRwbN27Ezp078eyzz2Lv3r2IjY21i3F8mguZTMLYXm3g6aRCapYOuuIyGIwCuuIypGbp4OmkwjO9Wje5iyeyPvaV6tnS/rGlWOj3fHi5aODhpIRMJqFYb0SpwYhivQECAnqjgINKjhcH+lv9f/qbG1vv35XFZxSAj6sDDEZAIZfDWa2EQi7Dg6kW4v7LKAQM/7tXf8ZjHeqlP9j6PrREc9oWIqKGxKsNKxJGI345mIii/Dx4+vpB7eAImUwGtYMjPH39UJSfhwuH9kNYeRDo1NRUCCHQqVOnGrWfPn06Jk2ahMDAQKxcuRI6nQ7Hjx83a/PWW28hPDwcAQEB8PT0rHJZnTt3xoIFC7Bp0yYsX74cwcHBddoWanhdW7vhlaFBCG3jhpyiUly5U4CcolJ0a+POn1wlM+wr1bOl/WNLsdDv+RjUsRU6tHCEUi6hzCAgAKjkcvh7OeMv/y8Eo3u0buxQmwRb79+VxScgMDC4JYZ0aoW2no5QK+TQqBRQyiXTo2GQ7t8F3dbz/hiXI0K1DRqjLe1DSzSnbSEiaihN/tEwW3IvMwPZv12Hs6dXhfF9JEmCs6cX7t64hnuZGfD0td7FnqXDPD14h4+TkxNcXV2RlZVl1qZPnz41WpZOp8PmzZvh6OiIH374wapjA1HD6draDSFaV1y5W4D84jK4aBRo38KJ36BRBewr1bOl/WNLsZB5Pu4VliL1lg6SBHi7atA/wIt3AlnI1vt3VfEBwJW7Bcgt0iOvSA9njQI5haW4crcQJWUGhGhdEdbWHQkJexotRlvZh5ZoTttCRNQQbL4QFBsb29gh1FhpYQHKSkuhVFc+oLFSpYau9C5KCwusut6goCBIkoSLFy/WqP0fnymUJKnCT9VX9ohZZRYvXgyNRoMjR47gT3/6Ez7//HNMnTq1ZoGTTZHJJPi3dG7sMKgJYF+pni3tH1uKhczz0btd1XfbUs3Yev+uKr6HxdyQPxlt6/vQEs1pW4iI6hu/frIilaMTFCoV9CXFlc7Xl5ZAoVJB5WjdXy3w9PREREQEoqOjUVBQsciUk5Nj1fWV27dvH/79739j48aN6N69O9555x0sWLAAGRkZ9bI+IiIiIiIiIqobFoKsyMNHC8/WftBl36nwuJYQArrsO2jRpi08fKz/zHd0dDQMBgP69u2L+Ph4XL58GSkpKVizZg369etn9fXl5eVh5syZWLx4MR555BEAwKuvvoqQkBC8+OKLVl8fEREREREREdUdC0FWJMlk6DJwKBxcXJF98zpKigphNBhQUlSI7JvX4eDiipDHh0Cqh5+u9/f3x+nTpzF48GBERkaia9euCA8PR2JiItatW2f19S1YsABubm5Yvny5aZpMJkNMTAz279+Pzz//3OrrJCIiIiIiIqK6sfkxgpoa7w4B+NPYSfjlYCKyf7sOXeldKFQqePsHIeTxIfDuEFBv69ZqtVi7di3Wrl1bZZvKBpZ+8NGxQYMG1Wjw6Q0bNlQ6PSgoqNLH04iIiIiIiIio8bEQVA+8OwSgVbsOuJeZgdLCAqgcneDho62XO4GIiIiIiIiIiGqKhaB6IslkVv2JeCIiIiIiIiKiuuItKkREREREREREdoKFICIiIiIiIiIiO2GXhaCaDIZMtoP5IiIiIiIiIrIOuyoEyeVyAEBpaWkjR0KWKCwsBAAolcpGjoSIiIiIiIioabOrwaIVCgUcHR1x+/ZtKJVKyGzwV7yMRiNKS0tRXFxsk/E1JCEECgsLkZWVBXd3d1Mhj4iIiIiIiIhqx64KQZIkQavVIj09HVevXm3scColhEBRUREcHBwgSVJjh2MT3N3d4ePj09hhEBERERERETV5dlUIAgCVSoWgoCCbfTxMr9fj0KFDePzxx/koFO4/DsY7gYiIiIiIiIisw+4KQQAgk8mg0WgaO4xKyeVylJWVQaPRsBBERERERERERFZl34PQEBERERERERHZERaCiIiIiIiIiIjsBAtBRERERERERER2otmNESSEAADk5eU1ciS1o9frUVhYiLy8PI4RZKfYB+wb80/sA/aN+Sf2AfvG/BP7gH2rSf7Lax3ltY/aaHaFoPz8fACAn59fI0dCRERERERERGR9+fn5cHNzq9VnJVGXMpINMhqNuHnzJlxcXCBJUmOHY7G8vDz4+fnh+vXrcHV1bexwqBGwD9g35p/YB+wb80/sA/aN+Sf2AftWk/wLIZCfnw9fX1/IZLUb7afZ3REkk8nQpk2bxg6jzlxdXXng2zn2AfvG/BP7gH1j/ol9wL4x/8Q+YN8elv/a3glUjoNFExERERERERHZCRaCiIiIiIiIiIjsBAtBNkatVmPZsmVQq9WNHQo1EvYB+8b8E/uAfWP+iX3AvjH/xD5g3xoq/81usGgiIiIiIiIiIqoc7wgiIiIiIiIiIrITLAQREREREREREdkJFoKIiIiIiIiIiOwEC0FERERERERERHaChaAGEB0djfbt20Oj0SAsLAzHjx+vtv3WrVvRqVMnaDQahIaGYvfu3WbzhRD461//Cq1WCwcHBwwbNgyXL1+uz02gOrKkD3z66acYMGAAPDw84OHhgWHDhlVoP336dEiSZPZ64okn6nszqJYsyX9sbGyF3Go0GrM2PAc0LZbkf9CgQRXyL0kSRo4caWrD479pOXToEEaNGgVfX19IkoQdO3Y89DNJSUno1asX1Go1AgMDERsbW6GNpdcW1Dgszf+2bdsQHh6Oli1bwtXVFf369cPevXvN2ixfvrzCOaBTp071uBVUW5bmPykpqdK/AZmZmWbtePw3HZb2gcr+xkuShC5dupja8BzQdLz77rt45JFH4OLiglatWmHMmDG4dOnSQz/XEPUAFoLq2ebNm7Fw4UIsW7YMp0+fRvfu3REREYGsrKxK2x85cgSTJk3CzJkzcebMGYwZMwZjxozB+fPnTW3+9re/Yc2aNfjXv/6Fn376CU5OToiIiEBxcXFDbRZZwNI+kJSUhEmTJuHAgQM4evQo/Pz8MHz4cPz2229m7Z544glkZGSYXps2bWqIzSELWZp/AHB1dTXL7dWrV83m8xzQdFia/23btpnl/vz585DL5Rg3bpxZOx7/TUdBQQG6d++O6OjoGrVPT0/HyJEjMXjwYJw9exYLFizArFmzzIoBtTmvUOOwNP+HDh1CeHg4du/ejVOnTmHw4MEYNWoUzpw5Y9auS5cuZueAH3/8sT7CpzqyNP/lLl26ZJbfVq1amebx+G9aLO0DH3/8sVnur1+/Dk9PzwrXATwHNA0HDx7E3LlzcezYMezbtw96vR7Dhw9HQUFBlZ9psHqAoHrVt29fMXfuXNN7g8EgfH19xbvvvltp+/Hjx4uRI0eaTQsLCxMvvfSSEEIIo9EofHx8xPvvv2+an5OTI9Rqtdi0aVM9bAHVlaV94I/KysqEi4uL2Lhxo2natGnTxOjRo60dKtUDS/MfExMj3NzcqlwezwFNS12P/w8//FC4uLgInU5nmsbjv+kCILZv315tmyVLloguXbqYTZswYYKIiIgwva9rv6LGUZP8VyYkJERERUWZ3i9btkx0797deoFRg6hJ/g8cOCAAiHv37lXZhsd/01Wbc8D27duFJEniypUrpmk8BzRdWVlZAoA4ePBglW0aqh7AO4LqUWlpKU6dOoVhw4aZpslkMgwbNgxHjx6t9DNHjx41aw8AERERpvbp6enIzMw0a+Pm5oawsLAql0mNpzZ94I8KCwuh1+vh6elpNj0pKQmtWrVCx44dMXv2bNy9e9eqsVPd1Tb/Op0O7dq1g5+fH0aPHo1ffvnFNI/ngKbDGsf/Z599hokTJ8LJyclsOo//5uth1wHW6FfUdBiNRuTn51e4Brh8+TJ8fX3h7++PyZMn49q1a40UIdWHHj16QKvVIjw8HIcPHzZN5/Fvfz777DMMGzYM7dq1M5vOc0DTlJubCwAVzukPaqh6AAtB9ejOnTswGAzw9vY2m+7t7V3hWd9ymZmZ1bYv/68ly6TGU5s+8EevvfYafH19zQ72J554Ap9//jkSExPx3nvv4eDBg3jyySdhMBisGj/VTW3y37FjR2zYsAE7d+7El19+CaPRiEcffRQ3btwAwHNAU1LX4//48eM4f/48Zs2aZTadx3/zVtV1QF5eHoqKiqzyd4Wajg8++AA6nQ7jx483TQsLC0NsbCz27NmDdevWIT09HQMGDEB+fn4jRkrWoNVq8a9//Qvx8fGIj4+Hn58fBg0ahNOnTwOwznUlNR03b97Ed999V+E6gOeApsloNGLBggXo378/unbtWmW7hqoHKGrckoga3KpVqxAXF4ekpCSzAYMnTpxo+ndoaCi6deuGgIAAJCUlYejQoY0RKllJv3790K9fP9P7Rx99FJ07d8b69evx9ttvN2Jk1NA+++wzhIaGom/fvmbTefwT2Yevv/4aUVFR2Llzp9kYMU8++aTp3926dUNYWBjatWuHLVu2YObMmY0RKllJx44d0bFjR9P7Rx99FGlpafjwww/xxRdfNGJk1Bg2btwId3d3jBkzxmw6zwFN09y5c3H+/HmbGc+JdwTVIy8vL8jlcty6dcts+q1bt+Dj41PpZ3x8fKptX/5fS5ZJjac2faDcBx98gFWrViEhIQHdunWrtq2/vz+8vLyQmppa55jJeuqS/3JKpRI9e/Y05ZbngKajLvkvKChAXFxcjS7oePw3L1VdB7i6usLBwcEq5xWyfXFxcZg1axa2bNlS4RGBP3J3d0dwcDDPAc1U3759Tbnl8W8/hBDYsGEDpkyZApVKVW1bngNs37x58/Dtt9/iwIEDaNOmTbVtG6oewEJQPVKpVOjduzcSExNN04xGIxITE82+8X9Qv379zNoDwL59+0ztO3ToAB8fH7M2eXl5+Omnn6pcJjWe2vQB4P5I8G+//Tb27NmDPn36PHQ9N27cwN27d6HVaq0SN1lHbfP/IIPBgOTkZFNueQ5oOuqS/61bt6KkpATPPffcQ9fD4795edh1gDXOK2TbNm3ahOeffx6bNm3CyJEjH9pep9MhLS2N54Bm6uzZs6bc8vi3HwcPHkRqamqNvhDiOcB2CSEwb948bN++Hfv370eHDh0e+pkGqwdYNMw1WSwuLk6o1WoRGxsrLly4IF588UXh7u4uMjMzhRBCTJkyRSxdutTU/vDhw0KhUIgPPvhApKSkiGXLlgmlUimSk5NNbVatWiXc3d3Fzp07xblz58To0aNFhw4dRFFRUYNvHz2cpX1g1apVQqVSif/85z8iIyPD9MrPzxdCCJGfny8WLVokjh49KtLT08X3338vevXqJYKCgkRxcXGjbCNVzdL8R0VFib1794q0tDRx6tQpMXHiRKHRaMQvv/xiasNzQNNhaf7LPfbYY2LChAkVpvP4b3ry8/PFmTNnxJkzZwQA8fe//12cOXNGXL16VQghxNKlS8WUKVNM7X/99Vfh6OgoFi9eLFJSUkR0dLSQy+Viz549pjYP61dkOyzN/1dffSUUCoWIjo42uwbIyckxtYmMjBRJSUkiPT1dHD58WAwbNkx4eXmJrKysBt8+qp6l+f/www/Fjh07xOXLl0VycrKYP3++kMlk4vvvvze14fHftFjaB8o999xzIiwsrNJl8hzQdMyePVu4ubmJpKQks3N6YWGhqU1j1QNYCGoA//jHP0Tbtm2FSqUSffv2FceOHTPNGzhwoJg2bZpZ+y1btojg4GChUqlEly5dxK5du8zmG41G8eabbwpvb2+hVqvF0KFDxaVLlxpiU6iWLOkD7dq1EwAqvJYtWyaEEKKwsFAMHz5ctGzZUiiVStGuXTvxwgsv8ALAhlmS/wULFpjaent7ixEjRojTp0+bLY/ngKbF0r8BFy9eFABEQkJChWXx+G96yn8O+o+v8rxPmzZNDBw4sMJnevToIVQqlfD39xcxMTEVlltdvyLbYWn+Bw4cWG17IYSYMGGC0Gq1QqVSidatW4sJEyaI1NTUht0wqhFL8//ee++JgIAAodFohKenpxg0aJDYv39/heXy+G86avM3ICcnRzg4OIhPPvmk0mXyHNB0VJZ7AGZ/1xurHiD9L0AiIiIiIiIiImrmOEYQEREREREREZGdYCGIiIiIiIiIiMhOsBBERERERERERGQnWAgiIiIiIiIiIrITLAQREREREREREdkJFoKIiIiIiIiIiOwEC0FERERERERERHaChSAiIiIiIiIiIis4dOgQRo0aBV9fX0iShB07dlj0+eXLl0OSpAovJycnq8XIQhARERE1e4MGDcKCBQsaOwyrmj59OsaMGWN63xy3kYiIqKkpKChA9+7dER0dXavPL1q0CBkZGWavkJAQjBs3zmoxshBERERE1crMzMT8+fMRGBgIjUYDb29v9O/fH+vWrUNhYWFjh1cj27Ztw9tvv12nZaSmpuL5559HmzZtoFar0aFDB0yaNAknT560UpR1Y41t/KPly5ejR48eVl0mERFRc/bkk0/inXfewdNPP13p/JKSEixatAitW7eGk5MTwsLCkJSUZJrv7OwMHx8f0+vWrVu4cOECZs6cabUYFVZbEhERETU7v/76K/r37w93d3esXLkSoaGhUKvVSE5OxieffILWrVvjqaeeauwwH8rT07NOnz958iSGDh2Krl27Yv369ejUqRPy8/Oxc+dOREZG4uDBg1aKtCK9Xg+lUvnQdnXdRiIiIqp/8+bNw4ULFxAXFwdfX19s374dTzzxBJKTkxEUFFSh/b///W8EBwdjwIABVouBdwQRERFRlebMmQOFQoGTJ09i/Pjx6Ny5M/z9/TF69Gjs2rULo0aNMrXNycnBrFmz0LJlS7i6umLIkCH4+eefTfPL7y754osv0L59e7i5uWHixInIz883tSkpKcErr7yCVq1aQaPR4LHHHsOJEydM85OSkiBJEvbu3YuePXvCwcEBQ4YMQVZWFr777jt07twZrq6uePbZZ83uVvrjY1MlJSV47bXX4OfnB7VajcDAQHz22WeV7gMhBKZPn46goCD88MMPGDlyJAICAtCjRw8sW7YMO3fuNLVNTk7GkCFD4ODggBYtWuDFF1+ETqczzTcajXjrrbdMdxX16NEDe/bsMc2/cuUKJEnC5s2bMXDgQGg0Gnz11VcwGAxYuHAh3N3d0aJFCyxZsgRCCLM4/7iN7du3x8qVKzFjxgy4uLigbdu2+OSTT8w+89prryE4OBiOjo7w9/fHm2++Cb1eDwCIjY1FVFQUfv75Z9P4BLGxsTXK9c8//4zBgwfDxcUFrq6u6N27t83cOUVERNRYrl27hpiYGGzduhUDBgxAQEAAFi1ahMceewwxMTEV2hcXF+Orr76y6t1AAAtBREREVIW7d+8iISEBc+fOrXKAQkmSTP8eN26cqSBz6tQp9OrVC0OHDkV2drapTVpaGnbs2IFvv/0W3377LQ4ePIhVq1aZ5i9ZsgTx8fHYuHEjTp8+jcDAQERERJgtA7hfVFq7di2OHDmC69evY/z48fjoo4/w9ddfY9euXUhISMA//vGPKrdt6tSp2LRpE9asWYOUlBSsX78ezs7OlbY9e/YsfvnlF0RGRkImq3jp5O7uDuD+mAARERHw8PDAiRMnsHXrVnz//feYN2+eqe3HH3+M1atX44MPPsC5c+cQERGBp556CpcvXzZb5tKlSzF//nykpKQgIiICq1evRmxsLDZs2IAff/wR2dnZ2L59e5XbV2716tXo06cPzpw5gzlz5mD27Nm4dOmSab6LiwtiY2Nx4cIFfPzxx/j000/x4YcfAgAmTJiAyMhIdOnSxTRGwYQJEwA8PNeTJ09GmzZtcOLECZw6dQpLly6t0V1NREREzVlycjIMBgOCg4Ph7Oxseh08eBBpaWkV2m/fvh35+fmYNm2adQMRRERERJU4duyYACC2bdtmNr1FixbCyclJODk5iSVLlgghhPjhhx+Eq6urKC4uNmsbEBAg1q9fL4QQYtmyZcLR0VHk5eWZ5i9evFiEhYUJIYTQ6XRCqVSKr776yjS/tLRU+Pr6ir/97W9CCCEOHDggAIjvv//e1Obdd98VAERaWppp2ksvvSQiIiJM7wcOHCjmz58vhBDi0qVLAoDYt29fjfbD5s2bBQBx+vTpatt98sknwsPDQ+h0OtO0Xbt2CZlMJjIzM4UQQvj6+ooVK1aYfe6RRx4Rc+bMEUIIkZ6eLgCIjz76yKyNVqs17QMhhNDr9aJNmzZi9OjRlW6jEEK0a9dOPPfcc6b3RqNRtGrVSqxbt67KbXj//fdF7969Te+XLVsmunfvbtamJrl2cXERsbGxVa6HiIjIHgAQ27dvN72Pi4sTcrlcXLx4UVy+fNnslZGRUeHzQ4YMEWPGjLF6XBwjiIiIiCxy/PhxGI1GTJ48GSUlJQDuPwqk0+nQokULs7ZFRUVm33C1b98eLi4upvdarRZZWVkA7t8tpNfr0b9/f9N8pVKJvn37IiUlxWy53bp1M/3b29vb9GjTg9OOHz9eafxnz56FXC7HwIEDa7S94g+PYFUlJSUF3bt3N7t7qn///jAajbh06RIcHBxw8+ZNs+0rb/PgY1UA0KdPH9O/c3NzkZGRgbCwMNM0hUKBPn36PDS2B/eTJEnw8fEx7W8A2Lx5M9asWYO0tDTodDqUlZXB1dW12mXWJNcLFy7ErFmz8MUXX2DYsGEYN24cAgICql0uERFRc9ezZ08YDAZkZWU9dMyf9PR0HDhwAN98843V42AhiIiIiCoVGBgISZLMHiUCYCq4ODg4mKbpdDpotVqzX70oV/7oFIAKjwdJkgSj0WhxbA8uR5Iki5b7YNw1ERwcDAC4ePEievbsaWGktVPVo3iWqm6/HD16FJMnT0ZUVBQiIiLg5uaGuLg4rF69utpl1iTXy5cvx7PPPotdu3bhu+++w7JlyxAXF1flL6gQERE1FzqdDqmpqab36enpOHv2LDw9PREcHIzJkydj6tSpWL16NXr27Inbt28jMTER3bp1w8iRI02f27BhA7RaLZ588kmrx8gxgoiIiKhSLVq0QHh4ONauXYuCgoJq2/bq1QuZmZlQKBQIDAw0e3l5edVofQEBAVCpVDh8+LBpml6vx4kTJxASElKnbXlQaGgojEZjjX/pq0ePHggJCcHq1asrLS7l5OQAADp37oyff/7ZbF8dPnwYMpkMHTt2hKurK3x9fc22r7xNddvn5uYGrVaLn376yTStrKwMp06dqlH8VTly5AjatWuHN954A3369EFQUBCuXr1q1kalUsFgMJhNq2mug4OD8eqrryIhIQHPPPNMpYNgEhERNTcnT55Ez549TV8eLVy4ED179sRf//pXAEBMTAymTp2KyMhIdOzYEWPGjMGJEyfQtm1b0zKMRiNiY2Mxffp0yOVyq8fIQhARERFV6Z///CfKysrQp08fbN68GSkpKbh06RK+/PJLXLx40XRxMmzYMPTr1w9jxoxBQkICrly5giNHjuCNN96o8a9FOTk5Yfbs2Vi8eDH27NmDCxcu4IUXXkBhYaFVfy2jffv2mDZtGmbMmIEdO3YgPT0dSUlJ2LJlS6XtJUlCTEwM/vvf/2LAgAHYvXs3fv31V5w7dw4rVqzA6NGjAdwfIFmj0WDatGk4f/48Dhw4gJdffhlTpkyBt7c3AGDx4sV47733sHnzZly6dAlLly7F2bNnMX/+/Gpjnj9/PlatWoUdO3bg4sWLmDNnjqkAVVtBQUG4du0a4uLikJaWhjVr1lQYgLp9+/ambzLv3LmDkpKSh+a6qKgI8+bNQ1JSEq5evYrDhw/jxIkT6Ny5c53iJSIiagoGDRoEIUSFV/kvbyqVSkRFRSE9PR2lpaW4efMmtm3bhtDQUNMyZDIZrl+/jhUrVtRLjHw0jIiIiKoUEBCAM2fOYOXKlfjzn/+MGzduQK1WIyQkBIsWLcKcOXMA3C+W7N69G2+88Qaef/553L59Gz4+Pnj88cdNRZCaWLVqFYxGI6ZMmYL8/Hz06dMHe/fuhYeHh1W3a926dXj99dcxZ84c3L17F23btsXrr79eZfu+ffvi5MmTWLFiBV544QXcuXMHWq0Wjz76KD766CMAgKOjI/bu3Yv58+fjkUcegaOjI8aOHYu///3vpuW88soryM3NRWRkJLKyshASEoJvvvkGQUFB1cYbGRmJjIwMTJs2DTKZDDNmzMDTTz+N3NzcWu+Dp556Cq+++irmzZuHkpISjBw5Em+++SaWL19uajN27Fhs27YNgwcPRk5ODmJiYjB9+vRqcy2Xy3H37l1MnToVt27dgpeXF5555hlERUXVOlYiIiKyHknUdAREIiIiIiIiIiJq0vhoGBERERERERGRnWAhiIiIiIiIiIjITrAQRERERERERERkJ1gIIiIiIiIiIiKyEywEERERERERERHZCRaCiIiIiIiIiIjsBAtBRERERERERER2goUgIiIiIiIiIiI7wUIQEREREREREZGdYCGIiIiIiIiIiMhOsBBERERERERERGQn/j/UKkyVbmw+HAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "The processed data with proximity labels has been saved to gene_proximity_analysis_with_labels.csv. Downloading now...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_b2a4e913-1602-4cc9-941d-9cbe306df81b\", \"gene_proximity_analysis_with_labels.csv\", 29141)"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from scipy.cluster.hierarchy import linkage, fcluster\n",
        "\n",
        "# Load the data\n",
        "file_path = '/content/mdt15_RNAiAvgExpressionValue.xlsx'  # replace with your file path\n",
        "expression_data = pd.read_excel(file_path)\n",
        "\n",
        "# Extract relevant data\n",
        "gene_expression = expression_data[['Gene Name', 'Avg.Expression', 'Pfam ID']]\n",
        "\n",
        "# Pivot the data\n",
        "expression_pivot = gene_expression.pivot_table(index='Gene Name', values='Avg.Expression')\n",
        "\n",
        "# Standardize the data\n",
        "scaler = StandardScaler()\n",
        "expression_pivot_scaled = scaler.fit_transform(expression_pivot)\n",
        "\n",
        "# Perform hierarchical clustering\n",
        "linked = linkage(expression_pivot_scaled, method='ward')\n",
        "clusters = fcluster(linked, t=4, criterion='maxclust')\n",
        "\n",
        "# Add clusters to the original data\n",
        "gene_expression['Cluster'] = clusters\n",
        "\n",
        "# Sort by cluster\n",
        "gene_expression_sorted = gene_expression.sort_values(by='Cluster')\n",
        "\n",
        "# Generate a heatmap\n",
        "plt.figure(figsize=(12, 10))\n",
        "sns.heatmap(expression_pivot.loc[gene_expression_sorted['Gene Name']], cmap='viridis', linewidths=.5, annot=True,\n",
        "            cbar_kws={'label': 'Expression Level'})\n",
        "plt.title('Gene Expression Heatmap with Hierarchical Clustering')\n",
        "plt.xlabel('Cluster')\n",
        "plt.ylabel('Gene Name')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 979
        },
        "id": "4mZ8zMjiursq",
        "outputId": "6ab18707-7e66-42f3-bb73-121bbe0af890"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-1-4c6f282bc6f5>:26: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  gene_expression['Cluster'] = clusters\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1000 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNZtuhM97IaaDKXDQiEdh/Z",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}