case_study_dna.ipynb

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      "provenance": []
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    "kernelspec": {
      "name": "python3",
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    "language_info": {
      "name": "python"
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Case Study\n",
        "\n",
        "#### By Leila Khaertdinova, B21-DS02\n",
        "\n",
        "---\n"
      ],
      "metadata": {
        "id": "pitwGAqtxNHK"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uPrUEEhZvuc3"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from scipy.stats import chi2_contingency"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ds = pd.read_csv('hcmv.data.txt')"
      ],
      "metadata": {
        "id": "fn4v5Yt6BpFx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ds.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "EICPIhFDUbT5",
        "outputId": "aa34e8d4-e4ca-45ae-9384-3b8e905d75d0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            location\n",
              "count     296.000000\n",
              "mean   116960.104730\n",
              "std     64732.033538\n",
              "min       177.000000\n",
              "25%     63714.000000\n",
              "50%    117826.000000\n",
              "75%    171143.500000\n",
              "max    228953.000000"
            ],
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              "\n",
              "  <div id=\"df-90aa4e78-f5ff-4691-8a47-d799be0d26bb\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "    }\n",
              "\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>location</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>296.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>116960.104730</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>64732.033538</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>177.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>63714.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>117826.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>171143.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>228953.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "          document.querySelector('#df-90aa4e78-f5ff-4691-8a47-d799be0d26bb button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Random scatter**\n",
        "\n"
      ],
      "metadata": {
        "id": "N-N4gqV_F_0p"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let us first simulate several random scatters:"
      ],
      "metadata": {
        "id": "y2ThYNJSjrID"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "locations = ds['location']\n",
        "count = np.arange(1, ds.shape[0] + 1)\n",
        "\n",
        "random_scatters = []\n",
        "random_size = 3\n",
        "\n",
        "for _ in range(random_size):\n",
        "    random_scatters.append(np.random.randint(min(locations), max(locations), size=len(locations)))"
      ],
      "metadata": {
        "id": "Lls2j4GD--JM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(24, 18))\n",
        "for i in range(random_size):\n",
        "    plt.subplot(331 + i)\n",
        "    plt.scatter(count, random_scatters[i] , color='mediumpurple')\n",
        "    plt.xlabel('Palindrome sites')\n",
        "    plt.ylabel('Locations')\n",
        "\n",
        "plt.suptitle('Randomly simulated palindrome locations', y=0.915)\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 395
        },
        "id": "3Nhd4oieAYJr",
        "outputId": "87b617e1-1577-4391-8cdb-6dd52b4b6d60"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1728x1296 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let us now visualize real data:"
      ],
      "metadata": {
        "id": "OHK1Et81kAYn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(9, 7))\n",
        "\n",
        "plt.scatter(count, locations , color='darkblue')\n",
        "\n",
        "plt.title('Real CMV palindrome locations')\n",
        "plt.xlabel('Palindrome sites')\n",
        "plt.ylabel('Locations')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "3WI-B1EXwHsL",
        "outputId": "47c6e493-361f-4cec-ded3-c2b1e3971ea9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 648x504 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Locations and spacings**"
      ],
      "metadata": {
        "id": "NCeMYXSZHvOL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let us now examine the spacings between consecutive palindroms, sums of consecutive pairs and triplet palindroms spacings for the real data:"
      ],
      "metadata": {
        "id": "DoB_hNNnkLw7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "spacings = []\n",
        "for i in range(len(count) - 1):\n",
        "    spacings.append(locations[i + 1] - locations[i])\n",
        "\n",
        "plt.figure(figsize=(14, 9))\n",
        "plt.hist(spacings, bins=60, color='darkblue')\n",
        "\n",
        "plt.title('Spacings between CMV consecutive palindromes')\n",
        "plt.xlabel('Spacings')\n",
        "plt.ylabel('Frequency')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 567
        },
        "id": "CozYjDEM2VkS",
        "outputId": "792307a2-9022-4beb-9ddf-18b510a75cfd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1008x648 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pairs = []\n",
        "for i in range(0, len(count), 2):\n",
        "    pairs.append(locations[i] + locations[i + 1])\n",
        "\n",
        "pairs_spacings = []\n",
        "for i in range(len(pairs) - 1):\n",
        "     pairs_spacings.append(locations[i + 1] - locations[i])\n",
        "\n",
        "plt.figure(figsize=(14, 9))\n",
        "plt.hist(pairs_spacings, bins=60, color='darkblue')\n",
        "\n",
        "plt.title('Sums of CMV consecutive palindrome pairs spacings')\n",
        "plt.xlabel('Spacings')\n",
        "plt.ylabel('Frequency')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 567
        },
        "id": "L7zu46J38RnF",
        "outputId": "8b77dd2a-0596-432e-a81c-fe50ca9a1225"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1008x648 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "triplet_spacings = []\n",
        "for i in range(len(count) - 2):\n",
        "    triplet_spacings.append(locations[i + 2] - locations[i])\n",
        "\n",
        "plt.figure(figsize=(14, 9))\n",
        "plt.hist(triplet_spacings, bins=60, color='darkblue')\n",
        "\n",
        "plt.title('Spacings between CMV triplet palindromes')\n",
        "plt.xlabel('Spacings')\n",
        "plt.ylabel('Frequency')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 567
        },
        "id": "tpAMXHEn6UfF",
        "outputId": "39bfa5a2-c0ff-43c4-ea17-ff22ebdd86a8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1008x648 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's repeat the procedure for the random scatters and compare the results of real data and simulated random data:"
      ],
      "metadata": {
        "id": "Y9zozQD0kjsr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(24, 18))\n",
        "\n",
        "for i in range(random_size):\n",
        "    sorted_scatters = sorted(random_scatters[i])\n",
        "    random_spacings = [sorted_scatters[j + 1] - sorted_scatters[j] for j in range(len(count) - 1)]\n",
        "    plt.subplot(331 + i)\n",
        "    plt.hist(spacings, bins=60, color='darkblue')\n",
        "    plt.hist(random_spacings, bins=60, color='mediumpurple')\n",
        "    plt.xlabel('Spacings')\n",
        "    plt.ylabel('Frequency')\n",
        "    \n",
        "\n",
        "plt.suptitle('Spacings between real CMV palindromes and spacings between random palindromes ', y=0.915)\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 395
        },
        "id": "MmWktLgLI4TW",
        "outputId": "20e8db0a-65d3-4d4d-c13e-d2374307a2c3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1728x1296 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(24, 18))\n",
        "pairs_random = []\n",
        "\n",
        "for i in range(random_size):\n",
        "    sorted_scatters = sorted(random_scatters[i])\n",
        "    pair_random = [sorted_scatters[j] + sorted_scatters[j + 1] for j in range(0, len(count) - 1, 2)]\n",
        "    pairs_random.append(pair_random)\n",
        "\n",
        "for i in range(random_size):\n",
        "    random_spacings = [pairs_random[i][j + 1] - pairs_random[i][j] for j in range(len(pairs_random[i]) - 1)]\n",
        "    plt.subplot(331 + i)\n",
        "    plt.hist(pairs_spacings, bins=60, color='darkblue')\n",
        "    plt.hist(random_spacings, bins=60, color='mediumpurple')\n",
        "    plt.xlabel('Spacings')\n",
        "    plt.ylabel('Frequency')\n",
        "    \n",
        "plt.suptitle('Sums of real CMV consecutive palindrome pairs spacings and sums of random palindromes pairs spacings', y=0.915)\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 395
        },
        "id": "3e37qJJ-MWIY",
        "outputId": "0b8646f4-4125-4d3a-bb3b-942a8327cd49"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1728x1296 with 3 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(24, 18))\n",
        "\n",
        "for i in range(random_size):\n",
        "    sorted_scatters = sorted(random_scatters[i])\n",
        "    random_triplets = [sorted_scatters[j + 2] - sorted_scatters[j] for j in range(len(count) - 2)]\n",
        "    plt.subplot(331 + i)\n",
        "    plt.hist(triplet_spacings, bins=60, color='darkblue')\n",
        "    plt.hist(random_triplets, bins=60, color='mediumpurple')\n",
        "    plt.xlabel('Spacings')\n",
        "    plt.ylabel('Frequency')\n",
        "    \n",
        "plt.suptitle('Real CMV triplet palindromes spacings and random triplet palindromes spacings', y=0.915)\n",
        "plt.xlabel('Spacings')\n",
        "plt.ylabel('Frequency')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 395
        },
        "id": "orQ8-FT0SdY7",
        "outputId": "54971154-7034-4c94-ab72-cd8063c828a5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1728x1296 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Counts**"
      ],
      "metadata": {
        "id": "vf3xJ7SKcrRN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(14, 9))\n",
        "\n",
        "plt.hist(locations, bins=40, color='darkblue')\n",
        "\n",
        "plt.title('Frequencies of CMV palindrome locations')\n",
        "plt.xlabel('Locations')\n",
        "plt.ylabel('Frequency')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 567
        },
        "id": "e_5Jpx-qw601",
        "outputId": "fb04d0a3-b19c-4041-b0f3-be21e8e06fa3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1008x648 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(24, 18))\n",
        "\n",
        "for i in range(random_size):\n",
        "    plt.subplot(331 + i)\n",
        "    plt.hist(locations, bins=40, color='darkblue')\n",
        "    plt.hist(random_scatters[i], bins=40, color='mediumpurple')\n",
        "    plt.xlabel('Locations')\n",
        "    plt.ylabel('Frequency')\n",
        "    \n",
        "plt.suptitle('Real CMV palindrome locations and random palindrome locations', y=0.915)\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 395
        },
        "id": "5EohfutU7iNt",
        "outputId": "defe3b45-ae15-4c59-a835-6f23085fe03b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1728x1296 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As can be seen from the visualizations in this part, the original data appears to align with the pattern of random scatters. This result were obtained by dividing the bins into small intervals, each comprising 5500 palindrome locations. By specifying such intervals (5500), we can determine that the palindrome clusters appear to be located in the range of [83500, 94500]."
      ],
      "metadata": {
        "id": "evnmdM2kk-xS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "To test whether original data is uniformly distributed, the Chi-Square goodness of fit test can be performed:"
      ],
      "metadata": {
        "id": "dV2_IiZdp_oa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "observed_counts = np.histogram(locations, bins=40)[0]\n",
        "\n",
        "scatter = sorted(random_scatters[2])\n",
        "expected_counts = np.random.uniform(0, 15, 40)\n",
        "\n",
        "p_value = chi2_contingency([observed_counts, expected_counts])[1]\n",
        "\n",
        "print(f'p-value: {p_value:.10f}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "isJaDXGQqIEN",
        "outputId": "93891bea-2155-4041-f9ba-19a7e289eff2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "p-value: 0.0716651766\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The obtained p-value indicates a insignificant difference between the observed and expected unform scatter. As a result, we can determine that original data is uniformly distributed.\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "ZVNm3PjZUSC5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**The biggest cluster**"
      ],
      "metadata": {
        "id": "exaXYUWqoENv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let us again analyze the frequency histogram:"
      ],
      "metadata": {
        "id": "W1xqjIIY-UyI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(14, 9))\n",
        "\n",
        "plt.hist(locations, bins=40, color='darkblue')\n",
        "\n",
        "plt.title('Frequencies of CMV palindrome locations')\n",
        "plt.xlabel('Locations')\n",
        "plt.ylabel('Frequency')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 567
        },
        "id": "A7GSI_K7-QwW",
        "outputId": "878b8a42-95f7-47d3-fe12-e1a7c433adcd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1008x648 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Based on the analysis, it can be recommended for biologists to focus their investigation on the DNA region in the range of [83500, 94500] (based on the visualizations of original data above), as this is where the clusters of palindromes are most densely located and maybe could potentially indicate a replication site. "
      ],
      "metadata": {
        "id": "ShV4OZlvoWGh"
      }
    }
  ]
}