ML__for_genomic_data.ipynb

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      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/vksuvam/bioinformatics/blob/main/ML__for_genomic_data.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "crC79g2TTTov",
        "outputId": "c6751700-3230-401c-9906-fa48a67c9758"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting genomic-benchmarks\n",
            "  Downloading genomic_benchmarks-0.0.9.tar.gz (21 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting biopython>=1.79 (from genomic-benchmarks)\n",
            "  Downloading biopython-1.81-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.1 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.1/3.1 MB\u001b[0m \u001b[31m9.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: requests>=2.23.0 in /usr/local/lib/python3.10/dist-packages (from genomic-benchmarks) (2.31.0)\n",
            "Requirement already satisfied: pip>=20.0.1 in /usr/local/lib/python3.10/dist-packages (from genomic-benchmarks) (23.1.2)\n",
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            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.1.4->genomic-benchmarks) (2023.3.post1)\n",
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            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.23.0->genomic-benchmarks) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.23.0->genomic-benchmarks) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.23.0->genomic-benchmarks) (2023.7.22)\n",
            "Requirement already satisfied: multidict>=4.0 in /usr/local/lib/python3.10/dist-packages (from yarl->genomic-benchmarks) (6.0.4)\n",
            "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4->gdown>=4.2.0->genomic-benchmarks) (2.5)\n",
            "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /usr/local/lib/python3.10/dist-packages (from requests>=2.23.0->genomic-benchmarks) (1.7.1)\n",
            "Building wheels for collected packages: genomic-benchmarks\n",
            "  Building wheel for genomic-benchmarks (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for genomic-benchmarks: filename=genomic_benchmarks-0.0.9-py3-none-any.whl size=22506 sha256=0bbcd7a6036b49b254721f6e1dac5af671059dbebe26a877571d29184a6867a0\n",
            "  Stored in directory: /root/.cache/pip/wheels/86/78/72/de9d75be800b6d679b8814b9663b3f610c34188b443b8b9b77\n",
            "Successfully built genomic-benchmarks\n",
            "Installing collected packages: biopython, genomic-benchmarks\n",
            "Successfully installed biopython-1.81 genomic-benchmarks-0.0.9\n"
          ]
        }
      ],
      "source": [
        "!pip install genomic-benchmarks"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# List available datasets\n",
        "from genomic_benchmarks.data_check import list_datasets\n",
        "\n",
        "list_datasets()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tyH1OrJjTj8c",
        "outputId": "e000b0b0-de81-468d-ac6d-57f2e728c51b"
      },
      "execution_count": 2,
      "outputs": [
        {
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          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/genomic_benchmarks/utils/datasets.py:11: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n",
            "  from tqdm.autonotebook import tqdm\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['dummy_mouse_enhancers_ensembl',\n",
              " 'drosophila_enhancers_stark',\n",
              " 'demo_human_or_worm',\n",
              " 'human_ensembl_regulatory',\n",
              " 'human_nontata_promoters',\n",
              " 'human_ocr_ensembl',\n",
              " 'human_enhancers_cohn',\n",
              " 'demo_coding_vs_intergenomic_seqs',\n",
              " 'human_enhancers_ensembl']"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Display information about the \"demo_coding_vs_intergenomic_seqs\" dataset with version 0\n",
        "from genomic_benchmarks.data_check import info\n",
        "\n",
        "info(\"demo_coding_vs_intergenomic_seqs\", version=0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 199
        },
        "id": "tsLBc8IKTmwV",
        "outputId": "e92963cc-59f8-40ec-bde0-bd7e36a2c76a"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset `demo_coding_vs_intergenomic_seqs` has 2 classes: coding_seqs, intergenomic_seqs.\n",
            "\n",
            "All lengths of genomic intervals equals 200.\n",
            "\n",
            "Totally 100000 sequences have been found, 75000 for training and 25000 for testing.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   train   test\n",
              "coding_seqs        37500  12500\n",
              "intergenomic_seqs  37500  12500"
            ],
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          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from genomic_benchmarks.dataset_getters.pytorch_datasets import DemoCodingVsIntergenomicSeqs\n",
        "\n",
        "train = DemoCodingVsIntergenomicSeqs(split='train', version=0)\n",
        "test = DemoCodingVsIntergenomicSeqs(split='test', version=0)\n",
        "\n",
        "# Access a specific example from the training dataset (e.g., the 3000th sample)\n",
        "train[3000]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JWl3n--mVagM",
        "outputId": "1a5fd870-f3a9-4e83-94b8-230a807128d5"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1cpXg0ULuTGF7h1_HTYvc6p8M-ee43t-v\n",
            "To: /root/.genomic_benchmarks/demo_coding_vs_intergenomic_seqs.zip\n",
            "100%|██████████| 33.9M/33.9M [00:00<00:00, 72.8MB/s]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('ACCCAAATGCCCGAAAGAATGGGCAAATTTTTAAAATAGAGCTTGGCCAAAAAAGCCAGTCTCAATTCTGTAACTAAAGAGAAGCCATTTATATCGAAGTTTAAAAACAAAAAATACATAGTGATGCATGTATACATGTATAATACAAAATTAAGATGGTTACATCTTTGAAGCAGAGCTGAGAATACAATCATGAACAT',\n",
              " 0)"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "# Define a mapping of DNA bases to one-hot encoding\n",
        "base_to_index = {'A': 0, 'C': 1, 'G': 2, 'T': 3}\n",
        "\n",
        "# Function to one-hot encode a DNA sequence, treating 'N' as missing data\n",
        "def one_hot_encode(sequence, seq_length):\n",
        "    encoded_sequence = np.zeros((seq_length, 4), dtype=int)\n",
        "    for i, base in enumerate(sequence):\n",
        "        if base in base_to_index:\n",
        "            # Set the corresponding index to 1 for valid bases (A, C, G, T)\n",
        "            encoded_sequence[i, base_to_index[base]] = 1\n",
        "        else:\n",
        "            # Treat 'N' as missing data (all zeros)\n",
        "            encoded_sequence[i, :] = 0\n",
        "    return encoded_sequence\n",
        "\n",
        "# Apply one-hot encoding to the entire training and testing datasets\n",
        "train_encoded = [one_hot_encode(item[0], len(item[0])) for item in train]\n",
        "test_encoded = [one_hot_encode(item[0], len(item[0])) for item in test]\n",
        "\n",
        "# Access the one-hot encoded sequence of the first sample in the training dataset\n",
        "train_encoded[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qQ4Y1Tv0aNfV",
        "outputId": "8ec3461f-295f-4d0a-ba47-b2ec4dca0172"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "       [0, 1, 0, 0]])"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import accuracy_score, classification_report\n",
        "\n",
        "# Extract labels from the training and testing datasets\n",
        "train_labels = [item[1] for item in train]\n",
        "test_labels = [item[1] for item in test]\n",
        "\n",
        "# Reshape the one-hot encoded sequences into a two-dimensional format\n",
        "train_encoded = np.array(train_encoded).reshape(len(train_encoded), -1)\n",
        "test_encoded = np.array(test_encoded).reshape(len(test_encoded), -1)\n",
        "\n",
        "# 2. Choose an Algorithm (Random Forest)\n",
        "rf_classifier = RandomForestClassifier(n_estimators=100, random_state=42)\n",
        "\n",
        "# 3. Training the Model\n",
        "rf_classifier.fit(train_encoded, train_labels)\n",
        "\n",
        "# 4. Model Evaluation\n",
        "predictions = rf_classifier.predict(test_encoded)\n",
        "accuracy = accuracy_score(test_labels, predictions)\n",
        "report = classification_report(test_labels, predictions)\n",
        "\n",
        "print(f\"Accuracy: {accuracy}\")\n",
        "print(\"Classification Report:\\n\", report)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eE-Ptt4mat-d",
        "outputId": "c1618477-5497-4a78-cec5-ad57ebcd883a"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.76736\n",
            "Classification Report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.75      0.81      0.78     12500\n",
            "           1       0.79      0.73      0.76     12500\n",
            "\n",
            "    accuracy                           0.77     25000\n",
            "   macro avg       0.77      0.77      0.77     25000\n",
            "weighted avg       0.77      0.77      0.77     25000\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import confusion_matrix\n",
        "import seaborn as sns\n",
        "\n",
        "# Create a confusion matrix for the Random Forest classifier\n",
        "rf_cm = confusion_matrix(test_labels, predictions)\n",
        "\n",
        "# Plot the confusion matrix\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(rf_cm, annot=True, fmt='d', cmap='Blues', xticklabels=['Negative', 'Positive'], yticklabels=['Negative', 'Positive'])\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('True')\n",
        "plt.title('Random Forest Confusion Matrix')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "4Q1_K6taWJBy",
        "outputId": "cff54a78-1209-4d7f-cf1f-06085d466cf4"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import roc_curve, roc_auc_score\n",
        "\n",
        "# Calculate ROC curve for Random Forest\n",
        "rf_probs = rf_classifier.predict_proba(test_encoded)[:, 1]\n",
        "rf_fpr, rf_tpr, _ = roc_curve(test_labels, rf_probs)\n",
        "\n",
        "# Plot ROC curve\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.plot(rf_fpr, rf_tpr, marker='.')\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('Random Forest ROC Curve')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "NEh7m0nvWRHR",
        "outputId": "158c1cc5-f550-44ec-863c-b3dda54f5cfc"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# from genomic_benchmarks.loc2seq import download_dataset\n",
        "\n",
        "# download_dataset(\"human_nontata_promoters\", version=0)\n",
        "\n",
        "# from pathlib import Path\n",
        "# import tensorflow as tf\n",
        "\n",
        "# BATCH_SIZE = 64\n",
        "# SEQ_TRAIN_PATH = Path.home() / '.genomic_benchmarks' / 'human_nontata_promoters' / 'train'\n",
        "# SEQ_TEST_PATH = Path.home() / '.genomic_benchmarks' / 'human_nontata_promoters' / 'test'\n",
        "# CLASSES = ['negative', 'positive']\n",
        "\n",
        "# train_dset = tf.keras.preprocessing.text_dataset_from_directory(\n",
        "#     directory=SEQ_TRAIN_PATH,\n",
        "#     batch_size=BATCH_SIZE,\n",
        "#     class_names=CLASSES)\n",
        "\n",
        "# test_dset = tf.keras.preprocessing.text_dataset_from_directory(\n",
        "#     directory=SEQ_TEST_PATH,\n",
        "#     batch_size=BATCH_SIZE,\n",
        "#     class_names=CLASSES)"
      ],
      "metadata": {
        "id": "12MucM1ITpdc"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}