[YCMI_CBDS_Summer_Course]_Solutions_Machine_Learning_and_Bioinformatics_Laboratory_Session_Disease_Classification.ipynb

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    "colab": {
      "name": "[YCMI_CBDS Summer Course] Solutions Machine Learning and Bioinformatics - Laboratory Session - Disease Classification",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J_huQfn3yzGF",
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      "source": [
        "# Lab Assignment: Disease Classification"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sICbvmbLyzGM",
        "colab_type": "text"
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      "source": [
        "In this exercise, you will learn to use the structure inherent in gene expression data to help classify samples by disease status. The following exercise is based on the results of Best et al. __2015__, \"RNA-Seq of Tumor-Educated Platelets Enables Blood-Based Pan-Cancer, Multiclass, and Molecular Pathway Cancer Diagnostics\", _Cancer Cell_, 28, Pgs. 666–676. In summary, the paper uses quantifications of the mRNA content of blood platelets using RNA-seq experiments, in order to determine whether some blood-based biomarkers can be obtained for 6 different cancer types. The concept is based on the idea that platelets can be \"educated\" by tumors, either by direct incorporation of tumor-derived biomolecules, or through specific splicing events induced by tumor-driven signaling. These signatures of tumor-educated platelets (TEP) can be evaluated through analysis of the transcript expression patterns in different individuals.\n",
        "\n",
        "The lab assignment aims to explore pre-processing and possible technical biases of the data, followed by unsupervised clustering methods, supervised machine learning models, and finally, the quality of these models. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qZG_BF4ryzGQ",
        "colab_type": "text"
      },
      "source": [
        "## Preprocessing and unsupervised clustering of the data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vRZr7f6xyzGU",
        "colab_type": "text"
      },
      "source": [
        "### Import the necessary libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YYUC-JR8yzGY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "from sklearn import preprocessing,decomposition\n",
        "import plotnine as p9\n",
        "from sklearn import cluster\n",
        "from scipy.cluster.hierarchy import dendrogram, linkage\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn import svm\n",
        "from sklearn.metrics import roc_curve, auc\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "669cfd07-4afa-415b-ad38-01701ed9135c",
        "id": "p5AI5cAh2t_1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "source": [
        "#google drive connection\n",
        "from google.colab import drive\n",
        "drive.mount(\"/content/gdrive\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dw7ANhOIyzGl",
        "colab_type": "text"
      },
      "source": [
        "### Normalization of the data\n",
        "\n",
        "In this step, you will normalize the data by the transformation \\begin{equation*} x_{i} \\to \\frac{x_{i} - \\mu}{\\sigma}, \\end{equation*} where $\\mu$ is the mean and $\\sigma$ is the standard deviation of the data. This is done to ensure that the scales of all the expression values are about the same, and thus prevents biases in the weight attached to a given variable, simply because its magnitude happens to be larger or smaller on average. These shifted and rescaled values are the so-called Z-scores of the data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "19mXtqoFyzGo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#### In the space below, you will \n",
        "### 1. Use the read.csv command from pandas to read the gene expression matrix from the file \n",
        "#### \"TEP_RNAseq_geneRPKM_filtered.tsv\"\n",
        "\n",
        "#link from mounted drive file\n",
        "gene_expr = pd.read_csv(\n",
        "    \"/content/gdrive/My Drive/[YCMI_CBDS Summer Course] Data/bioinformatics/TEP_RNAseq_geneRPKM_filtered.txt\",\n",
        "                        sep=\"\\t\",\n",
        "                        header=None)\n",
        "\n",
        "#### 2. Create an object of the class \"StandardScaler\" available in the \"preprocessing\" library\n",
        "scaler_obj = preprocessing.StandardScaler()\n",
        "\n",
        "#### 3. \"fit\" the gene expression matrix, i.e. use the function \"fit\" to find the mean and standard deviation\n",
        "scaler_obj.fit(gene_expr)\n",
        "\n",
        "#### 4. Use these fit values to \"transform\" the matrix\n",
        "gene_expr_norm = scaler_obj.transform(gene_expr)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BdkOwFM0yzGv",
        "colab_type": "text"
      },
      "source": [
        "### Principal Component Analyis (PCA) of the data\n",
        "\n",
        "Now, you will perform a PCA on the data to visualize any possible structure. You will plot the projections of the data points onto pairs of principal components of the data, and color the data points by the diagnosis and by the sequencing batch. This step is mainly to serve as a check on any systematic biases associated with the data due to technical variation. It also allows you to quickly check on how separable the data is, and thus how likely any classification methods are likely to be."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H2NcCuTQyzGy",
        "colab_type": "code",
        "outputId": "d84a0349-2a55-4cf6-9b07-ed6c54801181",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        }
      },
      "source": [
        "#### Create a PCA object with 5 components (an object with no data at first)\n",
        "\n",
        "n_components = 5\n",
        "\n",
        "pca = decomposition.PCA(n_components=n_components)\n",
        "\n",
        "#### Now use \"fit_transform\" to run a PCA on the normalized gene expression data and project it onto the first\n",
        "#### 5 components; this is the reduced description\n",
        "gene_reduced = pca.fit_transform(gene_expr_norm)\n",
        "\n",
        "# create a list of PCA labels (pca column dimensions)\n",
        "pca_labels = []\n",
        "for i in range(n_components):\n",
        "  # start label at PC1 rather than PC0\n",
        "  pca_labels.append(f\"\"\"PC{i + 1}\"\"\")\n",
        "\n",
        "#lazy load\n",
        "gene_reduced_df = pd.DataFrame(gene_reduced, columns=pca_labels)\n",
        "\n",
        "#read in SampleIDs_RPKM\n",
        "ids = pd.read_csv(\"/content/gdrive/My Drive/[YCMI_CBDS Summer Course] Data/bioinformatics/SampleIDs_RPKM_filtered.txt\",\n",
        "                  sep=\"\\t\").reset_index()\n",
        "\n",
        "#plot the data, facet y=PCY, x=PCX\n",
        "plot_data = []\n",
        "for pcx in pca_labels:\n",
        "  for pcy in pca_labels:\n",
        "    if pcx < pcy:\n",
        "      plot_data.append(pd.DataFrame(\n",
        "          {\n",
        "              'CancerStatus': ids['CancerStatus'],\n",
        "              'Batch': ids['Batch'],\n",
        "              'PCX': pcx, \n",
        "              'PCY': pcy, \n",
        "              'X': gene_reduced_df[pcx],\n",
        "              'Y': gene_reduced_df[pcy]\n",
        "          }\n",
        "      ))\n",
        "\n",
        "(\n",
        "    p9.ggplot(pd.concat(plot_data))\n",
        "      + p9.geom_point(p9.aes(x=\"X\", y=\"Y\", color=\"CancerStatus\"))\n",
        "      + p9.facet_grid(\"PCY~PCX\",scales=\"free\")\n",
        "      + p9.labs(x=\"\", y=\"\")\n",
        "      + p9.labels.ggtitle(\"PCs colored by Diagnosis\")\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 640x480 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<ggplot: (8764509992417)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MB9ey0vfHAte",
        "colab_type": "code",
        "outputId": "66b509f1-c911-4c2f-ceeb-b6516036c2ca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        }
      },
      "source": [
        "#we can visualize a single dimension as well\n",
        "(\n",
        "    p9.ggplot(pd.DataFrame({\n",
        "      'CancerStatus': ids['CancerStatus'],\n",
        "      'Batch': ids['Batch'],\n",
        "      'X': gene_reduced_df['PC2'],\n",
        "      'Y': gene_reduced_df['PC3']\n",
        "    }))\n",
        "    + p9.geom_point(p9.aes(x=\"X\", y=\"Y\", color=\"CancerStatus\"))\n",
        "    + p9.labs(x=\"PC2\", y=\"PC3\")\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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UObOrmjAqERGRhtFiJhuKSMtTVmQTKEv80cuOwo8rtBuXiIi0fOqRFpGkcXtr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\nUgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<ggplot: (-9223363277338393400)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rjahf1HVpAM1",
        "colab_type": "code",
        "outputId": "aa3d8b98-ffa1-42ee-b937-4392cabe0842",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        }
      },
      "source": [
        "(\n",
        "    p9.ggplot(pd.concat(plot_data))\n",
        "      + p9.geom_point(p9.aes(x=\"X\", y=\"Y\", color=\"Batch\"))\n",
        "      + p9.facet_grid(\"PCY~PCX\",scales=\"free\")\n",
        "      + p9.labs(x=\"\", y=\"\")\n",
        "      + p9.labels.ggtitle(\"PCs colored by Sequencing Batch\")\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<ggplot: (-9223363272349266309)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F-co9FqNyzG6",
        "colab_type": "text"
      },
      "source": [
        "### Upload prefiltered differentially expressed (DEX) transcript dataset\n",
        "\n",
        "We now consider two comparisons of interest: firstly, we seek a model that distinguishes between healthy samples and all cancer samples; secondly, we seek a model that is able to discriminate healthy samples from those derived from GBM (Glioblastoma Multiforme) patients.\n",
        "\n",
        "In this part, you will extract a subset of genes that are (1) DEX between all cancer samples and healthy samples; (2) DEX between GBM samples and healthy samples. The calculation of DEX genes involves Bayesian methods beyond the scope of this course, but it is important to note that the calculation accounts for possible biases due to batch effects.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y5Exzo7LyzG9",
        "colab_type": "text"
      },
      "source": [
        "### Hierachical Clustering\n",
        "Here you will perform agglomerative clustering (as detailed during the lecture) on gene expression data in order to visualize the extent to which healthy samples and glioblastoma samples segregate using average and then complete linkage. You may also want to explore the extent to which the resultant trees change when using alternative types of linkage, such as single linkage. Here, Euclidean distance will be used as a means of measuring differences between samples."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hMq3JDVdyzHB",
        "colab_type": "code",
        "outputId": "650301d5-449e-42db-f024-1e7026c57335",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 884
        }
      },
      "source": [
        "'''\n",
        "Use gene expression data and agglomerative hierarchical \n",
        "clustering to predict healthy vs. glioblastoma samples.\n",
        "'''\n",
        "\n",
        "# Read in the file containing the gene expression data on all samples\n",
        "GBM_RNAseq_geneRPKM_filt_data = pd.read_csv(\"/content/gdrive/My Drive/[YCMI_CBDS Summer Course] Data/bioinformatics/GBM_all.txt\",\n",
        "                                            sep=\"\\t\")\n",
        "\n",
        "# Now explore the dataset by first checking its dimension.\n",
        "print(f\"\"\"Dimension of input data file:\n",
        "{GBM_RNAseq_geneRPKM_filt_data.shape}\n",
        "\n",
        "Sample contents of input data file:\n",
        "{GBM_RNAseq_geneRPKM_filt_data}\n",
        "\"\"\")\n",
        "\n",
        "\n",
        "# This list of binary_class_labels just contains 1's and 0's. We'd like to replace these by\n",
        "# their true meaning (i.e., \"Gliobl\" and \"HC\" for \"Glioblastoma\" and \"Healthy Control\", respectively)\n",
        "class_labels = GBM_RNAseq_geneRPKM_filt_data['CancerStatus'].replace({0: 'HC', 1: 'Gliobl'})\n",
        "\n",
        "# As a check -- view class labels to confirm that you've stored them correctly\n",
        "print(f\"\"\"Class labels:\n",
        "{class_labels}\n",
        "\n",
        "\"\"\")\n",
        "\n",
        "# Now extract the gene expression values\n",
        "GBM_RNAseq_geneRPKM_filt_data = GBM_RNAseq_geneRPKM_filt_data.drop('CancerStatus', axis=1)\n",
        "\n",
        "# As a check on the gene expression values -- view the data to confirm that you've stored them correctly:\n",
        "print(f\"\"\"Sample of gene expression values:\n",
        "{GBM_RNAseq_geneRPKM_filt_data}\n",
        "\n",
        "\"\"\")\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Dimension of input data file:\n",
            "(100, 55)\n",
            "\n",
            "Sample contents of input data file:\n",
            "    CancerStatus          V1         V2  ...        V52         V53       V54\n",
            "0              1  254.208422   7.796399  ...  10.128376   19.411640  5.953111\n",
            "1              1  204.448659  10.533216  ...  20.127452  172.831206  4.629499\n",
            "2              1  151.457796   6.038692  ...  41.249995  517.553473  4.130951\n",
            "3              1  156.848450   8.732049  ...  12.250968  222.412703  5.891798\n",
            "4              1  165.219830   5.009685  ...  11.536463  270.661571  5.691659\n",
            "..           ...         ...        ...  ...        ...         ...       ...\n",
            "95             0  297.762892  11.719477  ...   9.140295   14.886144  6.982408\n",
            "96             0   83.253434   1.804089  ...  10.305149    3.424009  2.181221\n",
            "97             0   91.063943   1.361060  ...   7.814813   14.026984  2.667057\n",
            "98             0   98.384441   2.128391  ...   9.448362   41.848065  3.298417\n",
            "99             0   49.667984   2.644970  ...   8.266088    8.094623  2.874109\n",
            "\n",
            "[100 rows x 55 columns]\n",
            "\n",
            "Class labels:\n",
            "0     Gliobl\n",
            "1     Gliobl\n",
            "2     Gliobl\n",
            "3     Gliobl\n",
            "4     Gliobl\n",
            "       ...  \n",
            "95        HC\n",
            "96        HC\n",
            "97        HC\n",
            "98        HC\n",
            "99        HC\n",
            "Name: CancerStatus, Length: 100, dtype: object\n",
            "\n",
            "\n",
            "Sample of gene expression values:\n",
            "            V1         V2        V3  ...        V52         V53       V54\n",
            "0   254.208422   7.796399  2.910379  ...  10.128376   19.411640  5.953111\n",
            "1   204.448659  10.533216  1.202617  ...  20.127452  172.831206  4.629499\n",
            "2   151.457796   6.038692  0.008586  ...  41.249995  517.553473  4.130951\n",
            "3   156.848450   8.732049  0.570622  ...  12.250968  222.412703  5.891798\n",
            "4   165.219830   5.009685  0.546328  ...  11.536463  270.661571  5.691659\n",
            "..         ...        ...       ...  ...        ...         ...       ...\n",
            "95  297.762892  11.719477  6.420371  ...   9.140295   14.886144  6.982408\n",
            "96   83.253434   1.804089  0.595534  ...  10.305149    3.424009  2.181221\n",
            "97   91.063943   1.361060  1.187523  ...   7.814813   14.026984  2.667057\n",
            "98   98.384441   2.128391  0.900678  ...   9.448362   41.848065  3.298417\n",
            "99   49.667984   2.644970  0.963624  ...   8.266088    8.094623  2.874109\n",
            "\n",
            "[100 rows x 54 columns]\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zFrfXfPR0HFx",
        "colab_type": "text"
      },
      "source": [
        "As a first step, perform hierarchical clustering and generate the associated dendrogram using average linkage. This step will be carried out using scipy's \"linkage\" and \"dendrogram\" programs (easier to visualize)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v6_DnO7j0GCl",
        "colab_type": "code",
        "outputId": "3b3fba01-88ff-499d-bb71-9bdf5b423f42",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 432
        }
      },
      "source": [
        "linked_average = linkage(GBM_RNAseq_geneRPKM_filt_data, 'average')\n",
        "plt.figure(figsize=(25,10))\n",
        "d = dendrogram(linked_average,\n",
        "    orientation='top',\n",
        "    labels=class_labels.tolist(),\n",
        "    distance_sort='descending',\n",
        "    leaf_font_size=9.5,\n",
        "    color_threshold=3100)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1800x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lzC0INcLvo8r",
        "colab_type": "text"
      },
      "source": [
        "Given that we'd like the healthy samples to constitute a different cluster from the GBM samples, we can investigate the extent to which our dendrogram with 2 classes splits up these two groups. To do this, we use a different implementation of the agglomerative clustering algorithm, this time from scikit-learn's \"cluster\" class."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q1EpNhkrxVot",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cluster_average = cluster.AgglomerativeClustering(n_clusters=2, affinity='euclidean', linkage='average')\n",
        "cluster_assignments = pd.Series(cluster_average.fit_predict(GBM_RNAseq_geneRPKM_filt_data)).replace({0: 'HC', 1: 'Gliobl'})\n",
        "print(f\"\"\"Cluster assignments:  \n",
        "{list(cluster_assignments)}\n",
        "\"\"\") "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "li5YCNLWx0Ud",
        "colab_type": "text"
      },
      "source": [
        "Evaluate how well hierarchical clustering performs by calculating the fraction of correct assignments. Use the \"sum\" function to find the total number of correct assignments."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_luJCaAUyQdA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "num_matched_assignments = sum(cluster_assignments == class_labels)\n",
        "matched_assignments_average = num_matched_assignments / len(cluster_assignments)\n",
        "print(f\"\"\"Fraction of matched class assignments using average linkage  =  {matched_assignments_average}\n",
        "\n",
        " \n",
        "\"\"\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W2FExRb9zXFv",
        "colab_type": "text"
      },
      "source": [
        "It seems as if average linkage does not work so well (as we may have guessed just by visualizing the dendrogram). Thus, as an alternative, let's see if using complete linkage (discussed in lecture) improves things (an example of yet another alternative would be single linkage, though, in practice, single linkage tends to result in overly extended clusters, as a result of successive fusions resulting from very small numbers of samples. (Just for reference -- the fraction of matched class assignments for single linkage was found to be ~44%). As a first step, let's repeat the procedure from above: first run scipy's \"linkage\" algorithm:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DizqAjrJXJUb",
        "colab_type": "code",
        "outputId": "3617e206-854a-49e6-8fb3-f4cb59eda849",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 432
        }
      },
      "source": [
        "linked_complete = linkage(GBM_RNAseq_geneRPKM_filt_data, 'complete')\n",
        "plt.figure(figsize=(25,10))\n",
        "d = dendrogram(linked_complete,\n",
        "    orientation='top',\n",
        "    labels=class_labels.tolist(),\n",
        "    distance_sort='descending',\n",
        "    leaf_font_size=9.5,\n",
        "    color_threshold=5000)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1800x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jLaFZjX81Xb6",
        "colab_type": "text"
      },
      "source": [
        "Now, run the second step: quantification of the fraction of correct assignment's using the scikit-learn package's \"cluster.AgglomerativeClustering\" algorithm."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pHBfLlFCXM8F",
        "colab_type": "code",
        "outputId": "6ec96fa4-659f-4c32-de4b-efac2ef362a0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "source": [
        "\n",
        "cluster_complete = cluster.AgglomerativeClustering(n_clusters=2, affinity='euclidean', linkage='complete')\n",
        "cluster_assignments = pd.Series(cluster_complete.fit_predict(GBM_RNAseq_geneRPKM_filt_data)).replace({0: 'HC', 1: 'Gliobl'})\n",
        "print(f\"\"\"Cluster assignments:  \n",
        "{list(cluster_assignments)}\n",
        "\n",
        "\"\"\")\n",
        "\n",
        "\n",
        "# Evaluate how well hierarchical clustering performs by calculating the fraction of correct assignments\n",
        "num_matched_assignments = sum(cluster_assignments == class_labels)\n",
        "matched_assignments_complete = num_matched_assignments / len(cluster_assignments)\n",
        "print(f\"Fraction of matched class assignments using complete linkage  =  {matched_assignments_complete}\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cluster assignments:  \n",
            "['HC', 'Gliobl', 'HC', 'HC', 'HC', 'HC', 'HC', 'HC', 'HC', 'Gliobl', 'HC', 'HC', 'Gliobl', 'Gliobl', 'HC', 'Gliobl', 'HC', 'HC', 'HC', 'HC', 'HC', 'HC', 'HC', 'HC', 'Gliobl', 'Gliobl', 'Gliobl', 'Gliobl', 'Gliobl', 'Gliobl', 'Gliobl', 'Gliobl', 'HC', 'HC', 'HC', 'Gliobl', 'HC', 'HC', 'HC', 'HC', 'HC', 'Gliobl', 'HC', 'Gliobl', 'Gliobl', 'HC', 'Gliobl', 'Gliobl', 'HC', 'HC', 'HC', 'Gliobl', 'HC', 'Gliobl', 'Gliobl', 'HC', 'HC', 'HC', 'Gliobl', 'HC', 'HC', 'HC', 'Gliobl', 'Gliobl', 'HC', 'HC', 'HC', 'Gliobl', 'HC', 'HC', 'Gliobl', 'HC', 'HC', 'Gliobl', 'HC', 'HC', 'HC', 'Gliobl', 'Gliobl', 'Gliobl', 'HC', 'HC', 'HC', 'HC', 'Gliobl', 'HC', 'HC', 'HC', 'Gliobl', 'Gliobl', 'HC', 'HC', 'Gliobl', 'HC', 'HC', 'HC', 'Gliobl', 'HC', 'HC', 'Gliobl']\n",
            "\n",
            "\n",
            "Fraction of matched class assignments using complete linkage  =  0.51\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HeuouMYa1tZX",
        "colab_type": "text"
      },
      "source": [
        "**Conclusion of clustering analysis:** What is clear from these analyses is that simple agglomerative clustering is not sufficient to define clear clusters. Accordingly, the simple criteria that go into these unsupervised methods were unable to discriminate between the cases and controls. Next, we move on to **Supervised** methods to see if we can come up with better models."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N4lhWijZyzHH",
        "colab_type": "text"
      },
      "source": [
        "## Supervised Methods"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k_oVjks1yzHJ",
        "colab_type": "text"
      },
      "source": [
        "In the following section, you will construct an outer loop over different possible training/testing dataset parcellations, i.e. you will separate out a different fraction of the total number of samples as your training set for each iteration of the loop.\n",
        "\n",
        "In the body of the loop, you will first execute a logistic regression."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cJQBGiAxyzHL",
        "colab_type": "text"
      },
      "source": [
        "### Logistic Regression\n",
        "Use the 'cross_val_score' function to perform 5 fold cross-validation, while training a logistic classifier with an l2 penalty term. The penalty term should be varied over the range 10, 1, 0.1 ... 1e-8.  Plot the cross validation scores across this range, then train a classifier using all the training data with the best regularizer, and evaluate the final training and test set accuracy."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sLeAnBvJyzHO",
        "colab_type": "code",
        "outputId": "31fb0212-fb2a-4639-bda3-99edca7b57d2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 498
        }
      },
      "source": [
        "train = pd.read_csv(\"/content/gdrive/My Drive/[YCMI_CBDS Summer Course] Data/bioinformatics/Pancancer_train.txt\",sep=\"\\t\")\n",
        "test = pd.read_csv(\"/content/gdrive/My Drive/[YCMI_CBDS Summer Course] Data/bioinformatics/Pancancer_test.txt\",sep=\"\\t\")\n",
        "####Separate out a training set and a test set (remove the column called\n",
        "#### 'CancerStatus' from the training and test input dataframes)\n",
        "X = train.drop('CancerStatus', axis=1)\n",
        "y = train['CancerStatus']\n",
        "X2 = test.drop('CancerStatus', axis=1)\n",
        "y2 = test['CancerStatus']\n",
        "\n",
        "#### Loop over all the possible values of the 'hyperparameter' C, and run the \n",
        "#### SVC and cross_val_score for each choice of C; store the mean values of the\n",
        "#### cross-validation scores in the 'scores' list\n",
        "scores = []\n",
        "for i in range(10):\n",
        "    clf = LogisticRegression(C=10**(1-i), penalty='l2', solver='liblinear')\n",
        "    sc = cross_val_score(clf, X, y, cv=5)\n",
        "    scores.append(sc.mean())\n",
        "  \n",
        "#Create pandas dataframe out of the scores list, using a dictionary construct\n",
        "scores_df = pd.DataFrame({'i': range(1, len(scores) + 1), 'score': scores})\n",
        "\n",
        "#Identify the index i_star corresponding to the maximum cross validation score above\n",
        "i_star = scores_df['score'].idxmax()\n",
        "\n",
        "#Run Logistic regression with i_star \n",
        "clf_lr = LogisticRegression(C=10**(1-i_star), penalty='l2', solver='liblinear')\n",
        "clf_lr.fit(X, y)\n",
        "train_acc = clf_lr.score(X, y)\n",
        "test_acc = clf_lr.score(X2, y2)\n",
        "print(f\"training accuracy: {train_acc}\")\n",
        "print(f\"testing accuracy: {test_acc}\")\n",
        "\n",
        "p9.ggplot(scores_df) + p9.geom_line(p9.aes(x=\"i\",y=\"score\"), group=\"1\") + p9.scale_x_continuous(breaks=range(11))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "training accuracy: 1.0\n",
            "testing accuracy: 0.9302325581395349\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<ggplot: (8747236890965)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2IxpyZ5SyzHV",
        "colab_type": "text"
      },
      "source": [
        "### Support Vector Machines\n",
        "Repeat the process above, but train a linear SVM classifier.  Use cross validation to set the penalty term 'C', letting it vary across the range 1000, 100, ... 1e-6."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rTYSUZLQOFBc",
        "colab_type": "code",
        "outputId": "5b9d7b8f-cba4-49a8-b219-9d38a2a20530",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 498
        }
      },
      "source": [
        "train = pd.read_csv(\"/content/gdrive/My Drive/[YCMI_CBDS Summer Course] Data/bioinformatics/Pancancer_train.txt\",sep=\"\\t\")\n",
        "test = pd.read_csv(\"/content/gdrive/My Drive/[YCMI_CBDS Summer Course] Data/bioinformatics/Pancancer_test.txt\",sep=\"\\t\")\n",
        "####Separate out a training set and a test set (remove the column called\n",
        "#### 'CancerStatus' from the training and test input dataframes)\n",
        "X = train.drop('CancerStatus', axis=1)\n",
        "y = train['CancerStatus']\n",
        "X2 = test.drop('CancerStatus', axis=1)\n",
        "y2 = test['CancerStatus']\n",
        "\n",
        "#### Loop over all the possible values of the 'hyperparameter' C, and run the \n",
        "#### SVC and cross_val_score for each choice of C; store the mean values of the\n",
        "#### cross-validation scores in the 'scores' list\n",
        "scores = []\n",
        "for i in range(10):\n",
        "    clf = svm.SVC(kernel='linear', C=1000**(1-i))\n",
        "    sc = cross_val_score(clf, X, y, cv=5)\n",
        "    scores.append(sc.mean())\n",
        "  \n",
        "#Create pandas dataframe out of the scores list, using a dictionary construct\n",
        "scores_df = pd.DataFrame({'i': range(1, len(scores) + 1), 'score': scores})\n",
        "\n",
        "#Identify the index i_star corresponding to the maximum cross validation score above\n",
        "i_star = scores_df['score'].idxmax()\n",
        "\n",
        "#Run SVM with i_star \n",
        "clf_svm = svm.SVC(C=1000**(1-i_star), kernel='linear')\n",
        "clf_svm.fit(X, y)\n",
        "train_acc = clf_svm.score(X, y)\n",
        "test_acc = clf_svm.score(X2, y2)\n",
        "print(f\"training accuracy: {train_acc}\")\n",
        "print(f\"testing accuracy: {test_acc}\")\n",
        "\n",
        "p9.ggplot(scores_df) + p9.geom_line(p9.aes(x=\"i\",y=\"score\"), group=\"1\") + p9.scale_x_continuous(breaks=range(11))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "training accuracy: 0.9752475247524752\n",
            "testing accuracy: 0.9418604651162791\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<ggplot: (8747236924031)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B1lI6bsoyzHe",
        "colab_type": "text"
      },
      "source": [
        "## Analysis of the quality of results"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Joa15IEsyzHg",
        "colab_type": "text"
      },
      "source": [
        "In this section, you will draw a so-called \"Receiver-Operator Characteristic\" Curve or ROC Curve. This curve provides a visual representation of the quality of your model: it consists of a plot of the true positive rate $(= \\frac{Number\\ of\\ True\\ Positive\\ Calls\\ by\\ the\\ Model}{Number\\ of\\ Actual\\ Positives} )$ on the y-axis and the false positive rate $(= \\frac{Number\\ of\\ False\\ Positive\\ Calls\\ by\\ the\\ Model}{Number\\ of\\ Actual\\ Negatives} )$ on the x-axis.\n",
        "\n",
        "Use 'roc_curve' and 'auc' functions from sklearn.metrics to plot ROC curves for the logistic regression and SVM classifiers learnt above, and print out the AUC values for each classifier. \n",
        "\n",
        "In the first two lines, we provide the code for using 'decision functions': a decision function is the score you get from using your calculated model (LR or SVC) on your testing dataset, and is a measure of where a sample is relative to the decision boundary.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YL6ZZ37ryzHj",
        "colab_type": "text"
      },
      "source": [
        "### ROC curve"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZWbRCK_6yzHl",
        "colab_type": "code",
        "outputId": "f31992f1-ed22-4dda-c934-d79c2fb0c3be",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 471
        }
      },
      "source": [
        "y_score_lr = clf_lr.decision_function(X2)\n",
        "y_score_svm = clf_svm.decision_function(X2)\n",
        "####Find roc_curve here\n",
        "fpr1, tpr1, _ = roc_curve(y2, y_score_lr)\n",
        "fpr2, tpr2, _ = roc_curve(y2, y_score_svm)\n",
        "\n",
        "# TODO: not this. Just plot two different lines\n",
        "roc_dict = {\"regression_type\":[\"Logistic Regression\"]*len(fpr1) + [\"SVM\"]*len(fpr2),\n",
        "            \"fpr\":list(fpr1) + list(fpr2),\n",
        "            \"tpr\":list(tpr1) + list(tpr2)\n",
        "}\n",
        "\n",
        "roc_df = pd.DataFrame(roc_dict)\n",
        "\n",
        "auc1 = auc(fpr1, tpr1)\n",
        "auc2 = auc(fpr2, tpr2)\n",
        "print(f\"LR AUC: {auc1}\")\n",
        "print(f\"SVM AUC: {auc2}\")\n",
        "\n",
        "(\n",
        "    p9.ggplot(roc_df)\n",
        "    + p9.geom_line(p9.aes(x=\"fpr\",y=\"tpr\",color=\"regression_type\",group=\"regression_type\"))\n",
        "    + p9.scale_color_manual({\"Logistic Regression\":\"blue\",\"SVM\":\"red\"})\n",
        "    + p9.geom_abline(intercept=0,slope=1,linetype='dashed',color='green')\n",
        ")\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "LR AUC: 0.9763071895424837\n",
            "SVM AUC: 0.994281045751634\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<ggplot: (-9223363289622166384)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mLCqyEiJyzHs",
        "colab_type": "text"
      },
      "source": [
        "### Genes ranked according to weights in models \n",
        "\n",
        "Find the most discriminative genes, by ordering the coefficients of the logistic regression and SVM classifiers (use the coef_ attribute) by their absolute values.  Write out the 10 genes with the highest values, and their associated (signed) coefficients. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nc3Z3-w3yzHu",
        "colab_type": "code",
        "outputId": "b83a9b1f-4ea7-455d-dd2f-2d0d7a28cdac",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 595
        }
      },
      "source": [
        "geneIds = pd.read_csv(\"/content/gdrive/My Drive/[YCMI_CBDS Summer Course] Data/bioinformatics/GeneNames_Pancancer_filtered.txt\",\n",
        "                      header=None,\n",
        "                      names=['gene'])\n",
        "\n",
        "\n",
        "idx = pd.DataFrame.abs(clf_lr.coef_[0]).argsort()\n",
        "idx = idx[::-1]\n",
        "\n",
        "print(f\"\"\"Pancancer, LR, top geneIds:\n",
        "{geneIds['gene'][idx[0:10]]}\n",
        "\n",
        "Cofficients:\n",
        "{clf_lr.coef_[0,idx[0:10]]}\n",
        "\n",
        "\"\"\")\n",
        "\n",
        "idx = pd.DataFrame.abs(clf_svm.coef_[0]).argsort()\n",
        "idx = idx[::-1]\n",
        "\n",
        "print(f\"\"\"Pancancer, SVM, top geneIds:\n",
        "{geneIds['gene'][idx[0:10]]}\n",
        "\n",
        "Coefficients:\n",
        "{clf_svm.coef_[0,idx[0:10]]}\"\"\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Pancancer, LR, top geneIds:\n",
            "925        DEFA3\n",
            "265      ARHGDIB\n",
            "780    HIST1H2BC\n",
            "122       FKBP1A\n",
            "324        PRDX6\n",
            "712        LIMS1\n",
            "389        GNG11\n",
            "400         SAT1\n",
            "539      TMEM140\n",
            "881      HLA-DRA\n",
            "Name: gene, dtype: object\n",
            "\n",
            "Cofficients:\n",
            "[ 0.00802941  0.00786739  0.00657518 -0.00599873 -0.00535249 -0.00520535\n",
            " -0.00433601 -0.0042517   0.0040353  -0.00402329]\n",
            "\n",
            "\n",
            "Pancancer, SVM, top geneIds:\n",
            "925                DEFA3\n",
            "362                F13A1\n",
            "265              ARHGDIB\n",
            "939    ENSG00000272053.1\n",
            "389                GNG11\n",
            "930               MALAT1\n",
            "400                 SAT1\n",
            "780            HIST1H2BC\n",
            "881              HLA-DRA\n",
            "164                TUBB1\n",
            "Name: gene, dtype: object\n",
            "\n",
            "Coefficients:\n",
            "[ 0.00180754  0.0012803   0.00118351  0.00117131 -0.00115381 -0.00110009\n",
            " -0.00092042  0.00080441 -0.00068527 -0.00068008]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ATlS5bmyyzH0",
        "colab_type": "text"
      },
      "source": [
        "### **Further practice:** Repeat SVM training and evaluation with GBM data\n",
        "Repeat all steps from the 'SVM classifier' section above to train and evaluate an SVM classifier using the GBM data, plot an ROC curve and find the most discriminative genes."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C-W3fcMRyzH1",
        "colab_type": "code",
        "outputId": "2a6713b6-da48-465f-ce4d-f255b3721a84",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 991
        }
      },
      "source": [
        "train = pd.read_csv(\"/content/gdrive/My Drive/[YCMI_CBDS Summer Course] Data/bioinformatics/GBM_train.txt\", sep=\"\\t\")\n",
        "test = pd.read_csv(\"/content/gdrive/My Drive/[YCMI_CBDS Summer Course] Data/bioinformatics/GBM_test.txt\", sep=\"\\t\")\n",
        "####Separate out a training set and a test set (remove the column called\n",
        "#### 'CancerStatus' from the training and test input dataframes)\n",
        "X = train.drop(\"CancerStatus\", axis=1)\n",
        "y = train['CancerStatus']\n",
        "X2 = test.drop(\"CancerStatus\", axis=1)\n",
        "y2 = test['CancerStatus']\n",
        "\n",
        "#### Loop over all the possible values of the 'hyperparameter' C, and run the \n",
        "#### SVC and cross_val_score for each choice of C; store the mean values of the\n",
        "#### cross-validation scores in the 'scores' list\n",
        "scores = []\n",
        "for i in range(10):\n",
        "    clf = svm.SVC(kernel='linear', C=1000**(1-i))\n",
        "    sc = cross_val_score(clf, X, y, cv=5)\n",
        "    scores.append(sc.mean())\n",
        "  \n",
        "#Create pandas dataframe out of the scores list, using a dictionary construct\n",
        "scores_df = pd.DataFrame({'i': range(1, len(scores) + 1), 'score': scores})\n",
        "\n",
        "#Identify the index i_star corresponding to the maximum cross validation score above\n",
        "# Check the score matrix and see if multiple values of C produce the same\n",
        "#cross-validation score\n",
        "i_star = scores_df['score'].idxmax()\n",
        "\n",
        "#run SVM with i_star \n",
        "clf_svm = svm.SVC(C=1000**(1-i_star), kernel='linear')\n",
        "clf_svm.fit(X, y)\n",
        "train_acc = clf_svm.score(X, y)\n",
        "test_acc = clf_svm.score(X2, y2)\n",
        "\n",
        "y_score_svm = clf_svm.decision_function(X2)\n",
        "\n",
        "idx = pd.DataFrame.abs(clf_svm.coef_[0]).argsort()\n",
        "idx = idx[::-1]\n",
        "geneIds = pd.read_csv(\"/content/gdrive/My Drive/[YCMI_CBDS Summer Course] Data/bioinformatics/GeneNames_GBM_filtered.txt\",\n",
        "                      header=None,\n",
        "                      names=['gene'])\n",
        "\n",
        "\n",
        "print(f\"\"\"GBM training accuracy: {train_acc}\n",
        "GBM testing accuracy: {test_acc}\n",
        "\n",
        "GBM, SVM, top geneIds:\n",
        "{geneIds['gene'][idx[:10]]}\n",
        "\n",
        "Coefficients:\n",
        "{clf_svm.coef_[0,idx[0:10]]}\n",
        "\"\"\")\n",
        "\n",
        "fpr, tpr, _ = roc_curve(y2, y_score_svm)\n",
        "\n",
        "(\n",
        "    p9.ggplot(pd.DataFrame({\"tpr\":tpr,\"fpr\":fpr}))\n",
        "    + p9.geom_line(p9.aes(x=\"fpr\",y=\"tpr\"), color='red',linetype='solid')\n",
        "    + p9.geom_abline(intercept=0,slope=1,linetype='dashed',color='green')\n",
        ")\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "scores_df:     i     score\n",
            "0   1  0.742857\n",
            "1   2  0.742857\n",
            "2   3  0.700000\n",
            "3   4  0.742857\n",
            "4   5  0.600000\n",
            "5   6  0.600000\n",
            "6   7  0.600000\n",
            "7   8  0.600000\n",
            "8   9  0.600000\n",
            "9  10  0.600000\n",
            "GBM training accuracy: 1.0\n",
            "GBM testing accuracy: 0.8333333333333334\n",
            "\n",
            "GBM, SVM, top geneIds:\n",
            "4      FKBP5\n",
            "45    TCEAL9\n",
            "40     CLIC4\n",
            "48     TRBC2\n",
            "17      MFN2\n",
            "29     SKAP1\n",
            "22     ABCC4\n",
            "15     IL1R2\n",
            "18       MPL\n",
            "3      IPO11\n",
            "Name: gene, dtype: object\n",
            "\n",
            "Coefficients:\n",
            "[ 0.11176923  0.09953155 -0.08404336 -0.07456989  0.07307769 -0.07187333\n",
            "  0.06245034  0.05988366  0.05897254  0.05451153]\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<ggplot: (8747232536240)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    }
  ]
}