principal_component_analysis.ipynb

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        "<a href=\"https://colab.research.google.com/github/AjmalSarwary/Preprocessing/blob/main/principal_component_analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Neuer Abschnitt"
      ],
      "metadata": {
        "id": "8hu2CnDVbN41"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import random as rd\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn import preprocessing\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "piJMFvRZoLzU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Generate Array of 100 Gene Names\n",
        "genes = ['gene' + str(i) for i in range(1,101)]\n",
        "\n",
        "#Create Arrays of sample names 5 x wilde type (wt) and 5 x knock out (ko)\n",
        "wt = ['wt' +str(i) for i in range(1,6)]\n",
        "ko = ['ko' + str(i) for i in range(1,6)]\n",
        "\n",
        "data = pd.DataFrame(columns=[*wt, *ko], index=genes)\n",
        "\n",
        "for gene in data.index:\n",
        "  data.loc[gene,'wt1':'wt5']=np.random.poisson(lam=rd.randrange(10,1000), size=5)\n",
        "  data.loc[gene,'ko1':'ko5']=np.random.poisson(lam=rd.randrange(10,1000), size=5)\n",
        "\n",
        "print(data.shape)\n",
        "data.head()"
      ],
      "metadata": {
        "colab": {
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          "height": 224
        },
        "id": "uKBMNZQ2ouTP",
        "outputId": "2b91fd21-167c-429f-db51-add59cdabfa9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(100, 10)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       wt1  wt2  wt3  wt4  wt5  ko1  ko2  ko3  ko4  ko5\n",
              "gene1  222  193  191  231  228  929  939  956  979  989\n",
              "gene2  827  841  849  803  900  654  641  678  617  671\n",
              "gene3  320  288  288  263  286  622  554  612  593  605\n",
              "gene4  912  917  935  977  885  906  830  866  892  907\n",
              "gene5  475  433  429  482  462  854  855  895  885  844"
            ],
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              "      <th></th>\n",
              "      <th>wt1</th>\n",
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              "      <td>222</td>\n",
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              "      <td>935</td>\n",
              "      <td>977</td>\n",
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              "      <td>906</td>\n",
              "      <td>830</td>\n",
              "      <td>866</td>\n",
              "      <td>892</td>\n",
              "      <td>907</td>\n",
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              "      <th>gene5</th>\n",
              "      <td>475</td>\n",
              "      <td>433</td>\n",
              "      <td>429</td>\n",
              "      <td>482</td>\n",
              "      <td>462</td>\n",
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              "      <td>895</td>\n",
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          },
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Center and Scale the data\n",
        "#after centering: the mean value for each gene will be 0\n",
        "#after scaling the stdev for the values for each gene will be 1\n",
        "#the scale function expects data to be in rows instead of columns\n",
        "\n",
        "scaled_data=preprocessing.scale(data.T)"
      ],
      "metadata": {
        "id": "ymgjqrGIr-8P"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pca = PCA()\n",
        "pca.fit(scaled_data)\n",
        "#next step creates coordinates for PCA graph based on loading scores and scaled data\n",
        "pca_data=pca.transform(scaled_data)"
      ],
      "metadata": {
        "id": "1ezW8DRtr3sK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#scree plot to determine how many PCs should go into the final plot\n",
        "#1st calculate percentage of variation that each PC accounts for\n",
        "per_var = np.round(pca.explained_variance_ratio_ * 100, decimals=1)\n",
        "#labels for scree plot\n",
        "labels = ['PC' + str(x) for x in range(1, len(per_var)+1)]\n",
        "\n",
        "plt.bar(x=range(1,len(per_var)+1), height=per_var, tick_label=labels)\n",
        "plt.ylabel('Percentage of Explained Variance')\n",
        "plt.xlabel('Principal Component')\n",
        "plt.title('Scree Plot')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "5omZKHueuHJu",
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            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#new coordinates matrix where rows have sample labels and columns have PC labels\n",
        "pca_df = pd.DataFrame(pca_data, index=[*wt, *ko], columns=labels)\n",
        "\n",
        "plt.scatter(pca_df.PC1, pca_df.PC2)\n",
        "plt.title('PCA Graph')\n",
        "plt.xlabel('PC1 - {0}%'.format(per_var[0]))\n",
        "plt.ylabel('PC2 - {0}%'.format(per_var[1]))\n",
        "\n",
        "#use loop to add sample names to the graph\n",
        "for sample in pca_df.index:\n",
        "  plt.annotate(sample, (pca_df.PC1.loc[sample], pca_df.PC2.loc[sample]))\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "Yt58fXH5vdn7",
        "outputId": "b38d21d7-638c-41ce-999e-6aa8be40cd28"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#investigate the loading scores for PC1 to determine which gene had the largest influence on separating the two clusters along the x-axis\n",
        "loading_scores = pd.Series(pca.components_[0], index=genes)\n",
        "#magnitues sorted\n",
        "sorted_loading_scores=loading_scores.abs().sort_values(ascending=False)\n",
        "\n",
        "top_10_genes = sorted_loading_scores[0:10].index.values\n",
        "\n",
        "#if values are similar, means a lot of genes played a role in separating the sample, rather than just one or two\n",
        "loading_scores[top_10_genes]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4zEB4tWVxenh",
        "outputId": "45499dd1-69da-408d-9143-ca3ce4ed22af"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "gene69   -0.104584\n",
              "gene80    0.104575\n",
              "gene31    0.104567\n",
              "gene20    0.104525\n",
              "gene1    -0.104495\n",
              "gene16   -0.104493\n",
              "gene23   -0.104484\n",
              "gene42    0.104467\n",
              "gene50   -0.104464\n",
              "gene53   -0.104427\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    }
  ]
}