PW1_PrincipalComponentAnalysis_GeneticMap.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "HNcbJVN49tol"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import pandas\n",
        "import zipfile\n",
        "import urllib.request\n",
        "import plotnine\n",
        "from subprocess import Popen"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "9_EU6XvW9tom"
      },
      "outputs": [],
      "source": [
        "data_dir = \"data\"\n",
        "os.makedirs(data_dir, exist_ok=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bTmWkLUA9tom"
      },
      "source": [
        "## Скачаем данные"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q5vw1QWp9ton"
      },
      "source": [
        "### Скачаем архив"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3sM74Bbp9too",
        "outputId": "75179dac-7c3b-44f9-ca7e-0d4099158b7d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('data/plink_data.zip', <http.client.HTTPMessage at 0x7efd2b8ec790>)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "data_url = \"http://www.russiangenome.ru/biengi.zip\"\n",
        "data_zip = os.path.join(data_dir, \"plink_data.zip\")\n",
        "\n",
        "urllib.request.urlretrieve(data_url, data_zip)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fo9q1tyK9top"
      },
      "source": [
        "### Распакуем архив"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "-1HXaKCf9top"
      },
      "outputs": [],
      "source": [
        "with zipfile.ZipFile(data_zip, 'r') as zipped_datafile:\n",
        "    zipped_datafile.extractall(data_dir)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7CzJVsTD9top"
      },
      "source": [
        "### Получим путь к данным"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "S6b_dp979top"
      },
      "outputs": [],
      "source": [
        "data_prefix = [os.path.splitext(os.path.join(data_dir, file))[0] for file in os.listdir(data_dir) if file.endswith('.bed')][0]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "plSIvwhL9toq"
      },
      "source": [
        "## Скачаем plink"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "DyC_ONoi9toq"
      },
      "outputs": [],
      "source": [
        "%%bash\n",
        "URL64BIT=\"http://s3.amazonaws.com/plink1-assets/plink_linux_x86_64_20200121.zip\"\n",
        "URL32BIT=\"http://s3.amazonaws.com/plink1-assets/plink_linux_i686_20200121.zip\"\n",
        "\n",
        "# Измените версию если нужно\n",
        "URL=$URL64BIT\n",
        "\n",
        "wget -q $URL -O plink.zip\n",
        "unzip -q -n plink.zip plink"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nwV67Go79toq"
      },
      "source": [
        "## Определим функции для работы с plink"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "IrCiH92P9toq"
      },
      "outputs": [],
      "source": [
        "def prune_data(plink_prefix, out_prefix): \n",
        "    cmd = ['/content/plink', '--bfile', plink_prefix, #указываем файл, который идёт на вход\n",
        "           '--indep', '50', '5', '2', '--out', out_prefix #указываем, что хотим получить независимые позиции\n",
        "    ]\n",
        "    prune_result = out_prefix + '.prune.in' \n",
        "    assert Popen(cmd).wait() == 0\n",
        "\n",
        "    cmd = ['/content/plink', '--bfile', plink_prefix, '--extract', prune_result, #оставляем только отфильтрованные позиции\n",
        "           '--make-bed', '--out', out_prefix #сохраняем\n",
        "    ]\n",
        "    assert Popen(cmd).wait() == 0\n",
        "\n",
        "    return \n",
        "\n",
        "def pca_plink(plink_prefix, out_prefix, n_components=20): #получаем pca с помощью plink\n",
        "    cmd = ['/content/plink', '--pca', str(n_components), '--autosome'] #считаем только по аутосомам\n",
        "    cmd += ['--bfile', plink_prefix, '--out', out_prefix]\n",
        "\n",
        "    assert Popen(cmd).wait() == 0\n",
        "\n",
        "    return out_prefix + '.eigenvec', out_prefix + '.eigenval'\n",
        "\n",
        "def plot_pca(df, dim=2):  #просто отрисовываем\n",
        "    size=5\n",
        "    symbol='cicle',\n",
        "    opacity=1.\n",
        "    if dim == 2:\n",
        "        scatter = go.Scatter(\n",
        "            visible=True,\n",
        "            name=name,\n",
        "            x=df.loc[:, 0],\n",
        "            y=df.loc[:, 1],\n",
        "            text=[(name, indiv) for _, indiv in zip(\n",
        "                range(df.loc[:, cols[0]].shape[0]), point_names)],\n",
        "            textposition='middle right',\n",
        "            mode='markers',\n",
        "            marker=dict(size=size, opacity=opacity, symbol=symbol))\n",
        "    elif dim == 3:\n",
        "        scatter = go.Scatter3d(\n",
        "            visible=True,\n",
        "            name=name,\n",
        "            x=df.loc[:, 0],\n",
        "            y=df.loc[:, 1],\n",
        "            z=df.loc[:, 2],\n",
        "            text=[(name, indiv) for _, indiv in zip(\n",
        "                range(df.loc[:, cols[0]].shape[0]), point_names)],\n",
        "            textposition='middle right',\n",
        "            mode='markers',\n",
        "            marker=dict(size=size, opacity=opacity, symbol=symbol))\n",
        "    return scatter\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jNwmNOM99tor"
      },
      "source": [
        "# Посторим PCA"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4TxXEdhH9tor"
      },
      "source": [
        "## Запустим прунинг"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "rt0Ph6ZD9tor"
      },
      "outputs": [],
      "source": [
        "pruned_data = data_prefix + '_pruned'\n",
        "prune_data(data_prefix, pruned_data)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bQLteH0B9tor"
      },
      "source": [
        "## Запустим PCA"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "fDXDmfry9tor"
      },
      "outputs": [],
      "source": [
        "pca_prefix = pruned_data + '_pruned'\n",
        "pca_eigenvec, pca_eigenval = pca_plink(pruned_data, pca_prefix)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WYc9UOGf9tos"
      },
      "source": [
        "## Нарисуем PCA"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ytZNq_F59tos"
      },
      "source": [
        "### Прочитаем популяции"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "PP0hiSM59tos",
        "outputId": "95082b05-b50b-402e-9006-9b78a945d634",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 FID                IID Population\n",
              "0  4461875510_R01C01  4461875510_R01C01      Karel\n",
              "1  4461875510_R01C02  4461875510_R01C02      Karel\n",
              "2  4461875510_R02C01  4461875510_R02C01      Karel\n",
              "3  4461875513_R02C02  4461875513_R02C02      Karel\n",
              "4  4461875513_R01C02  4461875513_R01C02      Karel"
            ],
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              "      <th>0</th>\n",
              "      <td>4461875510_R01C01</td>\n",
              "      <td>4461875510_R01C01</td>\n",
              "      <td>Karel</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4461875510_R01C02</td>\n",
              "      <td>4461875510_R01C02</td>\n",
              "      <td>Karel</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4461875510_R02C01</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4461875513_R02C02</td>\n",
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              "      <td>Karel</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4461875513_R01C02</td>\n",
              "      <td>4461875513_R01C02</td>\n",
              "      <td>Karel</td>\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "pop_filename = data_prefix + \".pedind\"\n",
        "pop_df = pandas.read_csv(pop_filename, sep=\"\\s+\", header=None, usecols = [0, 1, 5],\n",
        "                        names=['FID', 'IID', 'Population'])\n",
        "\n",
        "pop_df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PlNJRqY_9tos"
      },
      "source": [
        "### Прочитаем результаты PCA"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "xOkV57PH9tos",
        "outputId": "bde15b04-d737-42df-ae75-82b52c31ffe3",
        "colab": {
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          "height": 236
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  0                  1         2         3         4   \\\n",
              "0  4461875510_R01C01  4461875510_R01C01  0.023656  0.044685  0.024901   \n",
              "1  4461875510_R01C02  4461875510_R01C02  0.029982  0.033608  0.032103   \n",
              "2  4461875510_R02C01  4461875510_R02C01  0.025548  0.043980  0.023810   \n",
              "3  4461875513_R02C02  4461875513_R02C02  0.022953  0.045839  0.024924   \n",
              "4  4461875513_R01C02  4461875513_R01C02  0.024415  0.043405  0.018632   \n",
              "\n",
              "         5         6         7         8         9   ...        12        13  \\\n",
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              "4 -0.004825 -0.007012  0.008693 -0.011580  0.011086  ...  0.013590  0.035081   \n",
              "\n",
              "         14        15        16        17        18        19        20  \\\n",
              "0 -0.010690 -0.035015 -0.095092  0.014845 -0.033535 -0.013700  0.036779   \n",
              "1 -0.029270 -0.019349 -0.020024 -0.011725  0.016307  0.008480 -0.009798   \n",
              "2 -0.025415 -0.024280 -0.135829  0.018732 -0.032434 -0.039587  0.028321   \n",
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              "4 -0.020707 -0.024508 -0.139401  0.012883 -0.033515 -0.008684  0.042671   \n",
              "\n",
              "         21  \n",
              "0  0.001613  \n",
              "1  0.005745  \n",
              "2  0.034078  \n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4461875510_R01C02</td>\n",
              "      <td>4461875510_R01C02</td>\n",
              "      <td>0.029982</td>\n",
              "      <td>0.033608</td>\n",
              "      <td>0.032103</td>\n",
              "      <td>-0.014175</td>\n",
              "      <td>-0.004904</td>\n",
              "      <td>0.022838</td>\n",
              "      <td>-0.003047</td>\n",
              "      <td>0.004947</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.004695</td>\n",
              "      <td>-0.001433</td>\n",
              "      <td>-0.029270</td>\n",
              "      <td>-0.019349</td>\n",
              "      <td>-0.020024</td>\n",
              "      <td>-0.011725</td>\n",
              "      <td>0.016307</td>\n",
              "      <td>0.008480</td>\n",
              "      <td>-0.009798</td>\n",
              "      <td>0.005745</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4461875510_R02C01</td>\n",
              "      <td>4461875510_R02C01</td>\n",
              "      <td>0.025548</td>\n",
              "      <td>0.043980</td>\n",
              "      <td>0.023810</td>\n",
              "      <td>-0.012104</td>\n",
              "      <td>-0.004047</td>\n",
              "      <td>0.016872</td>\n",
              "      <td>-0.007012</td>\n",
              "      <td>0.017078</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.000110</td>\n",
              "      <td>0.040661</td>\n",
              "      <td>-0.025415</td>\n",
              "      <td>-0.024280</td>\n",
              "      <td>-0.135829</td>\n",
              "      <td>0.018732</td>\n",
              "      <td>-0.032434</td>\n",
              "      <td>-0.039587</td>\n",
              "      <td>0.028321</td>\n",
              "      <td>0.034078</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4461875513_R02C02</td>\n",
              "      <td>4461875513_R02C02</td>\n",
              "      <td>0.022953</td>\n",
              "      <td>0.045839</td>\n",
              "      <td>0.024924</td>\n",
              "      <td>-0.015718</td>\n",
              "      <td>-0.011845</td>\n",
              "      <td>0.007434</td>\n",
              "      <td>-0.018396</td>\n",
              "      <td>0.024504</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.013387</td>\n",
              "      <td>0.026770</td>\n",
              "      <td>-0.036955</td>\n",
              "      <td>-0.028485</td>\n",
              "      <td>-0.125899</td>\n",
              "      <td>0.049872</td>\n",
              "      <td>-0.016697</td>\n",
              "      <td>-0.007108</td>\n",
              "      <td>0.016528</td>\n",
              "      <td>0.005287</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4461875513_R01C02</td>\n",
              "      <td>4461875513_R01C02</td>\n",
              "      <td>0.024415</td>\n",
              "      <td>0.043405</td>\n",
              "      <td>0.018632</td>\n",
              "      <td>-0.004825</td>\n",
              "      <td>-0.007012</td>\n",
              "      <td>0.008693</td>\n",
              "      <td>-0.011580</td>\n",
              "      <td>0.011086</td>\n",
              "      <td>...</td>\n",
              "      <td>0.013590</td>\n",
              "      <td>0.035081</td>\n",
              "      <td>-0.020707</td>\n",
              "      <td>-0.024508</td>\n",
              "      <td>-0.139401</td>\n",
              "      <td>0.012883</td>\n",
              "      <td>-0.033515</td>\n",
              "      <td>-0.008684</td>\n",
              "      <td>0.042671</td>\n",
              "      <td>-0.008286</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 22 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b6b576bf-8e8b-4a95-a2c1-ea8b201f1fe6')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  </svg>\n",
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              "      \n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      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",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b6b576bf-8e8b-4a95-a2c1-ea8b201f1fe6 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-b6b576bf-8e8b-4a95-a2c1-ea8b201f1fe6');\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": 23
        }
      ],
      "source": [
        "eigenvec_df = pandas.read_csv(pca_eigenvec, sep=\"\\s+\", header=None)\n",
        "eigenvec_df.head()\n",
        "#первые две колонки - названия образцов, а дальше координаты по каждой из осей"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "id": "bGstnA1T9tot",
        "outputId": "35dac669-e530-4e27-feec-c3be51345cbc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 236
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        pc1       pc2       pc3       pc4       pc5       pc6       pc7  \\\n",
              "0  0.023656  0.044685  0.024901 -0.012835 -0.009770  0.006716 -0.010370   \n",
              "1  0.029982  0.033608  0.032103 -0.014175 -0.004904  0.022838 -0.003047   \n",
              "2  0.025548  0.043980  0.023810 -0.012104 -0.004047  0.016872 -0.007012   \n",
              "3  0.022953  0.045839  0.024924 -0.015718 -0.011845  0.007434 -0.018396   \n",
              "4  0.024415  0.043405  0.018632 -0.004825 -0.007012  0.008693 -0.011580   \n",
              "\n",
              "        pc8       pc9      pc10  ...      pc14      pc15      pc16      pc17  \\\n",
              "0  0.006021 -0.073206  0.037070  ... -0.035015 -0.095092  0.014845 -0.033535   \n",
              "1  0.004947 -0.014171 -0.014982  ... -0.019349 -0.020024 -0.011725  0.016307   \n",
              "2  0.017078 -0.101528  0.038745  ... -0.024280 -0.135829  0.018732 -0.032434   \n",
              "3  0.024504 -0.090599  0.039748  ... -0.028485 -0.125899  0.049872 -0.016697   \n",
              "4  0.011086 -0.083010  0.035734  ... -0.024508 -0.139401  0.012883 -0.033515   \n",
              "\n",
              "       pc18      pc19      pc20                FID                IID  \\\n",
              "0 -0.013700  0.036779  0.001613  4461875510_R01C01  4461875510_R01C01   \n",
              "1  0.008480 -0.009798  0.005745  4461875510_R01C02  4461875510_R01C02   \n",
              "2 -0.039587  0.028321  0.034078  4461875510_R02C01  4461875510_R02C01   \n",
              "3 -0.007108  0.016528  0.005287  4461875513_R02C02  4461875513_R02C02   \n",
              "4 -0.008684  0.042671 -0.008286  4461875513_R01C02  4461875513_R01C02   \n",
              "\n",
              "   Population  \n",
              "0       Karel  \n",
              "1       Karel  \n",
              "2       Karel  \n",
              "3       Karel  \n",
              "4       Karel  \n",
              "\n",
              "[5 rows x 23 columns]"
            ],
            "text/html": [
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              "      <th></th>\n",
              "      <th>pc1</th>\n",
              "      <th>pc2</th>\n",
              "      <th>pc3</th>\n",
              "      <th>pc4</th>\n",
              "      <th>pc5</th>\n",
              "      <th>pc6</th>\n",
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              "      <th>pc8</th>\n",
              "      <th>pc9</th>\n",
              "      <th>pc10</th>\n",
              "      <th>...</th>\n",
              "      <th>pc14</th>\n",
              "      <th>pc15</th>\n",
              "      <th>pc16</th>\n",
              "      <th>pc17</th>\n",
              "      <th>pc18</th>\n",
              "      <th>pc19</th>\n",
              "      <th>pc20</th>\n",
              "      <th>FID</th>\n",
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              "      <th>Population</th>\n",
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              "      <th>0</th>\n",
              "      <td>0.023656</td>\n",
              "      <td>0.044685</td>\n",
              "      <td>0.024901</td>\n",
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              "      <td>0.006021</td>\n",
              "      <td>-0.073206</td>\n",
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              "      <td>...</td>\n",
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              "      <td>-0.095092</td>\n",
              "      <td>0.014845</td>\n",
              "      <td>-0.033535</td>\n",
              "      <td>-0.013700</td>\n",
              "      <td>0.036779</td>\n",
              "      <td>0.001613</td>\n",
              "      <td>4461875510_R01C01</td>\n",
              "      <td>4461875510_R01C01</td>\n",
              "      <td>Karel</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.029982</td>\n",
              "      <td>0.033608</td>\n",
              "      <td>0.032103</td>\n",
              "      <td>-0.014175</td>\n",
              "      <td>-0.004904</td>\n",
              "      <td>0.022838</td>\n",
              "      <td>-0.003047</td>\n",
              "      <td>0.004947</td>\n",
              "      <td>-0.014171</td>\n",
              "      <td>-0.014982</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.019349</td>\n",
              "      <td>-0.020024</td>\n",
              "      <td>-0.011725</td>\n",
              "      <td>0.016307</td>\n",
              "      <td>0.008480</td>\n",
              "      <td>-0.009798</td>\n",
              "      <td>0.005745</td>\n",
              "      <td>4461875510_R01C02</td>\n",
              "      <td>4461875510_R01C02</td>\n",
              "      <td>Karel</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.025548</td>\n",
              "      <td>0.043980</td>\n",
              "      <td>0.023810</td>\n",
              "      <td>-0.012104</td>\n",
              "      <td>-0.004047</td>\n",
              "      <td>0.016872</td>\n",
              "      <td>-0.007012</td>\n",
              "      <td>0.017078</td>\n",
              "      <td>-0.101528</td>\n",
              "      <td>0.038745</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.024280</td>\n",
              "      <td>-0.135829</td>\n",
              "      <td>0.018732</td>\n",
              "      <td>-0.032434</td>\n",
              "      <td>-0.039587</td>\n",
              "      <td>0.028321</td>\n",
              "      <td>0.034078</td>\n",
              "      <td>4461875510_R02C01</td>\n",
              "      <td>4461875510_R02C01</td>\n",
              "      <td>Karel</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.022953</td>\n",
              "      <td>0.045839</td>\n",
              "      <td>0.024924</td>\n",
              "      <td>-0.015718</td>\n",
              "      <td>-0.011845</td>\n",
              "      <td>0.007434</td>\n",
              "      <td>-0.018396</td>\n",
              "      <td>0.024504</td>\n",
              "      <td>-0.090599</td>\n",
              "      <td>0.039748</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.028485</td>\n",
              "      <td>-0.125899</td>\n",
              "      <td>0.049872</td>\n",
              "      <td>-0.016697</td>\n",
              "      <td>-0.007108</td>\n",
              "      <td>0.016528</td>\n",
              "      <td>0.005287</td>\n",
              "      <td>4461875513_R02C02</td>\n",
              "      <td>4461875513_R02C02</td>\n",
              "      <td>Karel</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.024415</td>\n",
              "      <td>0.043405</td>\n",
              "      <td>0.018632</td>\n",
              "      <td>-0.004825</td>\n",
              "      <td>-0.007012</td>\n",
              "      <td>0.008693</td>\n",
              "      <td>-0.011580</td>\n",
              "      <td>0.011086</td>\n",
              "      <td>-0.083010</td>\n",
              "      <td>0.035734</td>\n",
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              "      <td>-0.033515</td>\n",
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              "      <td>0.042671</td>\n",
              "      <td>-0.008286</td>\n",
              "      <td>4461875513_R01C02</td>\n",
              "      <td>4461875513_R01C02</td>\n",
              "      <td>Karel</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 23 columns</p>\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-412a965c-baea-4cda-a522-240d61941cce 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-412a965c-baea-4cda-a522-240d61941cce');\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": 43
        }
      ],
      "source": [
        "merge_df = pandas.merge(eigenvec_df, pop_df, left_on=[0, 1], right_on=['FID', 'IID'])\n",
        "merge_df = merge_df.drop(columns=[0,1])\n",
        "merge_df.rename(columns={ merge_df.columns[0]: \"pc1\",\n",
        "                          merge_df.columns[1]: \"pc2\", \n",
        "                          merge_df.columns[2]: \"pc3\", \n",
        "                          merge_df.columns[3]: \"pc4\", \n",
        "                          merge_df.columns[4]: \"pc5\", \n",
        "                          merge_df.columns[5]: \"pc6\", \n",
        "                          merge_df.columns[6]: \"pc7\", \n",
        "                          merge_df.columns[7]: \"pc8\", \n",
        "                          merge_df.columns[8]: \"pc9\", \n",
        "                          merge_df.columns[9]: \"pc10\", \n",
        "                          merge_df.columns[10]: \"pc11\", \n",
        "                          merge_df.columns[11]: \"pc12\", \n",
        "                          merge_df.columns[12]: \"pc13\", \n",
        "                          merge_df.columns[13]: \"pc14\", \n",
        "                          merge_df.columns[14]: \"pc15\", \n",
        "                          merge_df.columns[15]: \"pc16\", \n",
        "                          merge_df.columns[16]: \"pc17\", \n",
        "                          merge_df.columns[17]: \"pc18\", \n",
        "                          merge_df.columns[18]: \"pc19\", \n",
        "                          merge_df.columns[19]: \"pc20\"}, inplace = True)\n",
        "merge_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "r9L-5t1J9tot",
        "outputId": "e579b2a7-eec9-4908-c16a-a838fe1c239f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 516
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/plotnine/utils.py:1246: FutureWarning: is_categorical is deprecated and will be removed in a future version.  Use is_categorical_dtype instead\n",
            "  if pdtypes.is_categorical(arr):\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<ggplot: (8726612422625)>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "plotnine.ggplot(merge_df) + plotnine.aes(x=\"pc1\", y=\"pc2\", color='Population') + plotnine.geom_point() #меняя компоненты, получаем разный взгляд на срез популяций"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plotnine.ggplot(merge_df) + plotnine.aes(x=\"pc18\", y=\"pc19\", color='Population') + plotnine.geom_point()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 516
        },
        "id": "C5emKXsrEQI1",
        "outputId": "508c37e3-b6c8-4468-8edc-e213b1894bf4"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/plotnine/utils.py:1246: FutureWarning: is_categorical is deprecated and will be removed in a future version.  Use is_categorical_dtype instead\n",
            "  if pdtypes.is_categorical(arr):\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<ggplot: (8726611938233)>"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.5.3"
    },
    "colab": {
      "name": "PW№_PrincipalComponentAnalysis_GeneticMap.ipynb",
      "provenance": [],
      "collapsed_sections": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}