01_expression_clustering.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "01_expression_clustering.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyOutEVTP2EayYREvQr2uzWk",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/moqri/gene-regulatory-network/blob/master/01_expression_clustering.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UJUOssTVl4o_",
        "colab_type": "text"
      },
      "source": [
        "## Expression Correlation\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iOioRyG3lTpO",
        "colab_type": "text"
      },
      "source": [
        "Figure 1.\n",
        "\n",
        "![alt text](https://www.pnas.org/content/pnas/96/12/6745/F1.medium.gif\n",
        ")\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ujiyrrF9iygU",
        "colab_type": "code",
        "outputId": "df62f1e0-74b5-432e-8d6f-bc9e6db12ddd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        }
      },
      "source": [
        "# Replication using data from https://www.pnas.org/content/95/25/14863\n",
        "!wget -nc -O array.xls https://www.pnas.org/highwire/filestream/584765/field_highwire_adjunct_files/1/3917data.xls "
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-05-28 17:24:43--  https://www.pnas.org/highwire/filestream/584765/field_highwire_adjunct_files/1/3917data.xls\n",
            "Resolving www.pnas.org (www.pnas.org)... 104.18.8.175, 104.18.9.175\n",
            "Connecting to www.pnas.org (www.pnas.org)|104.18.8.175|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://www.pnas.org/content/pnas/suppl/1998/12/08/95.25.14863.DC1/3917data.xls [following]\n",
            "--2020-05-28 17:24:44--  https://www.pnas.org/content/pnas/suppl/1998/12/08/95.25.14863.DC1/3917data.xls\n",
            "Reusing existing connection to www.pnas.org:443.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: unspecified [application/vnd.ms-excel]\n",
            "Saving to: ‘array.xls’\n",
            "\n",
            "array.xls               [            <=>     ]   2.80M   790KB/s    in 3.6s    \n",
            "\n",
            "2020-05-28 17:24:49 (790 KB/s) - ‘array.xls’ saved [2940358]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wlPL8EzCjtxx",
        "colab_type": "code",
        "outputId": "aaf6c7a4-db30-4718-fc53-2e985dac18f7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        }
      },
      "source": [
        "import pandas as pd\n",
        "array=pd.read_excel('array.xls',index_col=0,skiprows=2)\n",
        "array.iloc[:5,:5]"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>NAME</th>\n",
              "      <th>alpha 0</th>\n",
              "      <th>alpha 7</th>\n",
              "      <th>alpha 14</th>\n",
              "      <th>alpha 21</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ORF</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>YBR166C</th>\n",
              "      <td>TYR1   TYROSINE BIOSYNTHESIS    PREPHENATE DEH...</td>\n",
              "      <td>0.33</td>\n",
              "      <td>-0.17</td>\n",
              "      <td>0.04</td>\n",
              "      <td>-0.07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>YOR357C</th>\n",
              "      <td>GRD19  SECRETION        GOLGI PROTEIN RETENTION</td>\n",
              "      <td>-0.64</td>\n",
              "      <td>-0.38</td>\n",
              "      <td>-0.32</td>\n",
              "      <td>-0.29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>YLR292C</th>\n",
              "      <td>SEC72  SECRETION        ER PROTEIN TRANSLOCATI...</td>\n",
              "      <td>-0.23</td>\n",
              "      <td>0.19</td>\n",
              "      <td>-0.36</td>\n",
              "      <td>0.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>YGL112C</th>\n",
              "      <td>TAF60  TRANSCRIPTION       TFIID 60 KD SUBUNIT</td>\n",
              "      <td>-0.69</td>\n",
              "      <td>-0.89</td>\n",
              "      <td>-0.74</td>\n",
              "      <td>-0.56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>YIL118W</th>\n",
              "      <td>RHO3   CYTOSKELETON        GTP-BINDING PROTEIN...</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.01</td>\n",
              "      <td>-0.81</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                      NAME  ...  alpha 21\n",
              "ORF                                                         ...          \n",
              "YBR166C  TYR1   TYROSINE BIOSYNTHESIS    PREPHENATE DEH...  ...     -0.07\n",
              "YOR357C    GRD19  SECRETION        GOLGI PROTEIN RETENTION  ...     -0.29\n",
              "YLR292C  SEC72  SECRETION        ER PROTEIN TRANSLOCATI...  ...      0.14\n",
              "YGL112C     TAF60  TRANSCRIPTION       TFIID 60 KD SUBUNIT  ...     -0.56\n",
              "YIL118W  RHO3   CYTOSKELETON        GTP-BINDING PROTEIN...  ...       NaN\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h39LXcC1pdF_",
        "colab_type": "code",
        "outputId": "6ef34eb1-f016-4277-d94a-5e9f3ab3b5ee",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "import re\n",
        "cleaned_names=[re.sub('  ', '_',name).split('_')[-1].strip() for name in array.NAME ]\n",
        "array.index=cleaned_names\n",
        "df=array.drop('NAME',1).transpose()\n",
        "df.plot.scatter('RIBOSOMAL PROTEIN L22A','RIBOSOMAL PROTEIN L3')"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0db1455940>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CqSkTWHHtu5x",
        "colab_type": "text"
      },
      "source": [
        "Find highest pair-wise correlations:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m119MalQtzJt",
        "colab_type": "code",
        "outputId": "89baeae9-5ef2-4269-fdfe-679f6eac695e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "import numpy as np\n",
        "corr_matrix = df.corr()\n",
        "\n",
        "#the matrix is symmetric so we need to extract upper triangle matrix without diagonal (k = 1)\n",
        "sol = (corr_matrix.abs().where(np.triu(np.ones(corr_matrix.shape), k=1).astype(np.bool))\n",
        "                 .stack()\n",
        "                 .sort_values(ascending=False))\n",
        "sol[:2]"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "METALLOTHIONEIN        METALLOTHIONEIN          0.989404\n",
              "RIBOSOMAL PROTEIN S1B  RIBOSOMAL PROTEIN S1A    0.977169\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J_qvC2uuuhCI",
        "colab_type": "code",
        "outputId": "d5247e7f-edfc-46ed-ef82-95ff40e4013e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "df.plot.scatter('RIBOSOMAL PROTEIN S1A','RIBOSOMAL PROTEIN S1B')"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0db14f4470>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}