Tianhui Ma_Healthcare Final Project_V2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tianhui Ma Healthcare Project: \n",
    "### Prediction of Cancer Occurrence based on Regression Analysis of DNA, RNA and Protein of c-Myc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 610,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!/usr/bin/env python\n",
    "# encoding: utf-8\n",
    "# -*- coding: utf-8 -*\n",
    "import os\n",
    "import pandas as pd\n",
    "import plotly\n",
    "import plotly.graph_objs as go\n",
    "import numpy as np\n",
    "import io\n",
    "plotly.tools.set_credentials_file(username='tianhuima', api_key='PJNM7WxVRU45oKWlSb4K') # set API key"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Data Extraction & Data Cleanup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 611,
   "metadata": {},
   "outputs": [
    {
     "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>Gene</th>\n",
       "      <th>RNA</th>\n",
       "      <th>DNA</th>\n",
       "      <th>PROTEIN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DMS53_LUNG</td>\n",
       "      <td>5.787634</td>\n",
       "      <td>0.4555</td>\n",
       "      <td>0.114065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SW1116_LARGE_INTESTINE</td>\n",
       "      <td>6.467195</td>\n",
       "      <td>0.1829</td>\n",
       "      <td>0.067394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NCIH1694_LUNG</td>\n",
       "      <td>0.532113</td>\n",
       "      <td>0.2812</td>\n",
       "      <td>-1.137268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>P3HR1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE</td>\n",
       "      <td>8.072169</td>\n",
       "      <td>-0.0255</td>\n",
       "      <td>1.036026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>HUT78_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE</td>\n",
       "      <td>6.905030</td>\n",
       "      <td>0.3993</td>\n",
       "      <td>1.320054</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       Gene       RNA     DNA   PROTEIN\n",
       "0                                DMS53_LUNG  5.787634  0.4555  0.114065\n",
       "1                    SW1116_LARGE_INTESTINE  6.467195  0.1829  0.067394\n",
       "2                             NCIH1694_LUNG  0.532113  0.2812 -1.137268\n",
       "3  P3HR1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE  8.072169 -0.0255  1.036026\n",
       "4  HUT78_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE  6.905030  0.3993  1.320054"
      ]
     },
     "execution_count": 611,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "############################################## Data extraction\n",
    "urlrna = \"https://raw.githubusercontent.com/TianhuiMa/Gene/master/RNA.txt\"\n",
    "s1=requests.get(urlrna).content\n",
    "RNA = pd.read_csv(io.StringIO(s1.decode('utf-8')), sep = \"\\t\") # insert RNA data\n",
    "\n",
    "urldna = \"https://raw.githubusercontent.com/TianhuiMa/Gene/master/DNA.txt\"\n",
    "s2=requests.get(urldna).content\n",
    "DNA = pd.read_csv(io.StringIO(s2.decode('utf-8')),sep = \"\\t\") # insert DNA data\n",
    "\n",
    "urlPROTEIN = \"https://raw.githubusercontent.com/TianhuiMa/Gene/master/PROTEIN(1).txt\"\n",
    "s3=requests.get(urlPROTEIN).content\n",
    "PROTEIN = pd.read_csv(io.StringIO(s3.decode('utf-8')), sep = \"\\t\") # insert PROTEIN data\n",
    "\n",
    "\n",
    "############################################## Data Cleanup\n",
    "##Data Merge\n",
    "del DNA['MYC_TRUE_CN'] # Delete no need columns\n",
    "data = RNA.merge(DNA, how = 'outer',on = \"Gene\") # Data Merge\n",
    "data = data.merge(PROTEIN, how = 'outer',on = \"Gene\") # Data Merge\n",
    "data.columns = ['Gene', 'RNA', 'DNA', 'PROTEIN'] # Rename columns\n",
    "\n",
    "## Data Clean\n",
    "data = data.dropna(axis = 0, how = 'any')\n",
    "data = pd.DataFrame(data)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "###### Research Question 1：Visualize the relationship among DNA, RNA and Protein levels of all tumor cell lines and construct a generalized linear regression model for DNA-Protein, DNA-RNA, and RNA-Protein. This question will provide prediction power for tumor occurrence based on DNA or RNA level. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 670,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'file:///Users/tianhuima/RNA.html'"
      ]
     },
     "execution_count": 670,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "############################################## Data visualization \n",
    "\n",
    "# the relationship between RNA and Protein \n",
    "trace1 = go.Scatter(\n",
    "    x = data[\"RNA\"],\n",
    "    y = data[\"PROTEIN\"],\n",
    "    mode='markers',\n",
    "    marker=dict(\n",
    "        size=12,\n",
    "        color = np.random.random(data.iloc[:,0].size), #set color equal to a variable\n",
    "        colorscale=[[0.0, 'rgb(165,0,38)'], [0.1111111111111111, 'rgb(215,48,39)'], [0.2222222222222222, 'rgb(244,109,67)'], [0.3333333333333333, 'rgb(253,174,97)'], [0.4444444444444444, 'rgb(254,224,144)'], [0.5555555555555556, 'rgb(224,243,248)'], [0.6666666666666666, 'rgb(171,217,233)'], [0.7777777777777778, 'rgb(116,173,209)'], [0.8888888888888888, 'rgb(69,117,180)'], [1.0, 'rgb(49,54,149)']],\n",
    "        showscale=False,\n",
    "        opacity=0.8\n",
    "    )\n",
    ") # define draw data\n",
    "## set x axis name\n",
    "axis_templatex=dict(\n",
    "    title='RNA'\n",
    ")\n",
    "## set y axis name\n",
    "axis_templatey=dict(\n",
    "    title='PROTEIN'\n",
    ")\n",
    "layout=go.Layout(\n",
    "    xaxis=axis_templatex,\n",
    "    yaxis=axis_templatey\n",
    ")\n",
    "plotdata = [trace1]\n",
    "fig = go.Figure(data=plotdata, layout=layout)\n",
    "plotly.offline.plot(fig, filename='RNA.html') # plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 613,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>PROTEIN</td>     <th>  R-squared:         </th> <td>   0.294</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.293</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   364.6</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 27 Jun 2018</td> <th>  Prob (F-statistic):</th> <td>3.05e-68</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>23:39:37</td>     <th>  Log-Likelihood:    </th> <td> -636.87</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   879</td>      <th>  AIC:               </th> <td>   1278.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   877</td>      <th>  BIC:               </th> <td>   1287.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>   -0.8848</td> <td>    0.050</td> <td>  -17.674</td> <td> 0.000</td> <td>   -0.983</td> <td>   -0.787</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>RNA</th>   <td>    0.1699</td> <td>    0.009</td> <td>   19.095</td> <td> 0.000</td> <td>    0.152</td> <td>    0.187</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>30.883</td> <th>  Durbin-Watson:     </th> <td>   1.961</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>  33.513</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.446</td> <th>  Prob(JB):          </th> <td>5.28e-08</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.348</td> <th>  Cond. No.          </th> <td>    17.2</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                PROTEIN   R-squared:                       0.294\n",
       "Model:                            OLS   Adj. R-squared:                  0.293\n",
       "Method:                 Least Squares   F-statistic:                     364.6\n",
       "Date:                Wed, 27 Jun 2018   Prob (F-statistic):           3.05e-68\n",
       "Time:                        23:39:37   Log-Likelihood:                -636.87\n",
       "No. Observations:                 879   AIC:                             1278.\n",
       "Df Residuals:                     877   BIC:                             1287.\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const         -0.8848      0.050    -17.674      0.000      -0.983      -0.787\n",
       "RNA            0.1699      0.009     19.095      0.000       0.152       0.187\n",
       "==============================================================================\n",
       "Omnibus:                       30.883   Durbin-Watson:                   1.961\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               33.513\n",
       "Skew:                           0.446   Prob(JB):                     5.28e-08\n",
       "Kurtosis:                       3.348   Cond. No.                         17.2\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 613,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "x = data[\"RNA\"]\n",
    "x = sm.add_constant(x)\n",
    "y = data[\"PROTEIN\"]\n",
    "md2 = sm.OLS(y,x).fit()\n",
    "md2.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 671,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'file:///Users/tianhuima/DNA.html'"
      ]
     },
     "execution_count": 671,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the relationship between DNA and Protein\n",
    "trace2 = go.Scatter(\n",
    "    x = data[\"DNA\"],\n",
    "    y = data[\"PROTEIN\"],\n",
    "    mode='markers',\n",
    "    marker=dict(\n",
    "        size=12,\n",
    "        color = np.random.random(data.iloc[:,0].size), #set color equal to a variable\n",
    "        colorscale=[[0.0, 'rgb(165,0,38)'], [0.1111111111111111, 'rgb(215,48,39)'], [0.2222222222222222, 'rgb(244,109,67)'], [0.3333333333333333, 'rgb(253,174,97)'], [0.4444444444444444, 'rgb(254,224,144)'], [0.5555555555555556, 'rgb(224,243,248)'], [0.6666666666666666, 'rgb(171,217,233)'], [0.7777777777777778, 'rgb(116,173,209)'], [0.8888888888888888, 'rgb(69,117,180)'], [1.0, 'rgb(49,54,149)']],\n",
    "        showscale=False,\n",
    "        opacity=0.8\n",
    "    )\n",
    ")# define draw data\n",
    "## set X axis name\n",
    "axis_templatex=dict(\n",
    "    title='DNA'\n",
    ")\n",
    "## set y axis name\n",
    "axis_templatey=dict(\n",
    "    title='PROTEIN'\n",
    ")\n",
    "layout=go.Layout(\n",
    "    xaxis=axis_templatex,\n",
    "    yaxis=axis_templatey\n",
    ")\n",
    "plotdata = [trace2]\n",
    "fig = go.Figure(data=plotdata, layout=layout)\n",
    "plotly.offline.plot(fig, filename='DNA.html') # plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 615,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>PROTEIN</td>     <th>  R-squared:         </th> <td>   0.081</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.079</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   76.78</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 27 Jun 2018</td> <th>  Prob (F-statistic):</th> <td>9.74e-18</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>23:39:40</td>     <th>  Log-Likelihood:    </th> <td> -752.79</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   879</td>      <th>  AIC:               </th> <td>   1510.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   877</td>      <th>  BIC:               </th> <td>   1519.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>   -0.1115</td> <td>    0.024</td> <td>   -4.632</td> <td> 0.000</td> <td>   -0.159</td> <td>   -0.064</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>DNA</th>   <td>    0.2641</td> <td>    0.030</td> <td>    8.763</td> <td> 0.000</td> <td>    0.205</td> <td>    0.323</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 6.451</td> <th>  Durbin-Watson:     </th> <td>   1.930</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.040</td> <th>  Jarque-Bera (JB):  </th> <td>   6.428</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.209</td> <th>  Prob(JB):          </th> <td>  0.0402</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.024</td> <th>  Cond. No.          </th> <td>    2.09</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                PROTEIN   R-squared:                       0.081\n",
       "Model:                            OLS   Adj. R-squared:                  0.079\n",
       "Method:                 Least Squares   F-statistic:                     76.78\n",
       "Date:                Wed, 27 Jun 2018   Prob (F-statistic):           9.74e-18\n",
       "Time:                        23:39:40   Log-Likelihood:                -752.79\n",
       "No. Observations:                 879   AIC:                             1510.\n",
       "Df Residuals:                     877   BIC:                             1519.\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const         -0.1115      0.024     -4.632      0.000      -0.159      -0.064\n",
       "DNA            0.2641      0.030      8.763      0.000       0.205       0.323\n",
       "==============================================================================\n",
       "Omnibus:                        6.451   Durbin-Watson:                   1.930\n",
       "Prob(Omnibus):                  0.040   Jarque-Bera (JB):                6.428\n",
       "Skew:                           0.209   Prob(JB):                       0.0402\n",
       "Kurtosis:                       3.024   Cond. No.                         2.09\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 615,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "x = data[\"DNA\"]\n",
    "x = sm.add_constant(x)\n",
    "y = data[\"PROTEIN\"]\n",
    "md2 = sm.OLS(y,x).fit()\n",
    "md2.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 616,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'file:///Users/tianhuima/DNA_AND_RNA.html'"
      ]
     },
     "execution_count": 616,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#the relationship between DNA and RNA\n",
    "trace2 = go.Scatter(\n",
    "    x = data[\"DNA\"],\n",
    "    y = data[\"RNA\"],\n",
    "    mode='markers',\n",
    "    marker=dict(\n",
    "        size=12,\n",
    "        color = np.random.random(data.iloc[:,0].size), #set color equal to a variable\n",
    "        colorscale=[[0.0, 'rgb(165,0,38)'], [0.1111111111111111, 'rgb(215,48,39)'], [0.2222222222222222, 'rgb(244,109,67)'], [0.3333333333333333, 'rgb(253,174,97)'], [0.4444444444444444, 'rgb(254,224,144)'], [0.5555555555555556, 'rgb(224,243,248)'], [0.6666666666666666, 'rgb(171,217,233)'], [0.7777777777777778, 'rgb(116,173,209)'], [0.8888888888888888, 'rgb(69,117,180)'], [1.0, 'rgb(49,54,149)']],\n",
    "        showscale=False,\n",
    "        opacity=0.8\n",
    "    )\n",
    ")# define draw data\n",
    "## set x axis name\n",
    "axis_templatex=dict(\n",
    "    title='DNA'\n",
    ")\n",
    "## set y axis name\n",
    "axis_templatey=dict(\n",
    "    title='RNA'\n",
    ")\n",
    "layout=go.Layout(\n",
    "    xaxis=axis_templatex,\n",
    "    yaxis=axis_templatey\n",
    ")\n",
    "plotdata = [trace2]\n",
    "fig = go.Figure(data=plotdata, layout=layout)\n",
    "plotly.offline.plot(fig, filename='DNA_AND_RNA.html') # plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 617,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>RNA</td>       <th>  R-squared:         </th> <td>   0.125</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.124</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   125.8</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 27 Jun 2018</td> <th>  Prob (F-statistic):</th> <td>2.20e-27</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>23:39:44</td>     <th>  Log-Likelihood:    </th> <td> -1750.3</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   879</td>      <th>  AIC:               </th> <td>   3505.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   877</td>      <th>  BIC:               </th> <td>   3514.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>    4.7924</td> <td>    0.075</td> <td>   64.004</td> <td> 0.000</td> <td>    4.645</td> <td>    4.939</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>DNA</th>   <td>    1.0519</td> <td>    0.094</td> <td>   11.218</td> <td> 0.000</td> <td>    0.868</td> <td>    1.236</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>261.785</td> <th>  Durbin-Watson:     </th> <td>   1.830</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td> 843.773</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-1.439</td>  <th>  Prob(JB):          </th> <td>5.98e-184</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 6.841</td>  <th>  Cond. No.          </th> <td>    2.09</td> \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                    RNA   R-squared:                       0.125\n",
       "Model:                            OLS   Adj. R-squared:                  0.124\n",
       "Method:                 Least Squares   F-statistic:                     125.8\n",
       "Date:                Wed, 27 Jun 2018   Prob (F-statistic):           2.20e-27\n",
       "Time:                        23:39:44   Log-Likelihood:                -1750.3\n",
       "No. Observations:                 879   AIC:                             3505.\n",
       "Df Residuals:                     877   BIC:                             3514.\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const          4.7924      0.075     64.004      0.000       4.645       4.939\n",
       "DNA            1.0519      0.094     11.218      0.000       0.868       1.236\n",
       "==============================================================================\n",
       "Omnibus:                      261.785   Durbin-Watson:                   1.830\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              843.773\n",
       "Skew:                          -1.439   Prob(JB):                    5.98e-184\n",
       "Kurtosis:                       6.841   Cond. No.                         2.09\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 617,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "x = data[\"DNA\"]\n",
    "x = sm.add_constant(x)\n",
    "y = data[\"RNA\"]\n",
    "md2 = sm.OLS(y,x).fit()\n",
    "md2.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "###### Research Question 2: Visualize the relationship among DNA, RNA and Protein levels of cell lines of specific tumor type( for example: Lung cancer, STOMACH cancer and SKIN cancer) and construct a generalized linear regression model for DNA-Protein, DNA-RNA, and RNA-Protein. This question will provide prediction power for occurrence of specific tumor type based on DNA or RNA level. \n",
    "\n",
    "## LUNG Cancer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 618,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import statsmodels.api as sm\n",
    "import statsmodels.formula.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 619,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "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>Gene</th>\n",
       "      <th>RNA</th>\n",
       "      <th>DNA</th>\n",
       "      <th>PROTEIN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DMS53_LUNG</td>\n",
       "      <td>5.787634</td>\n",
       "      <td>0.4555</td>\n",
       "      <td>0.114065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NCIH1694_LUNG</td>\n",
       "      <td>0.532113</td>\n",
       "      <td>0.2812</td>\n",
       "      <td>-1.137268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>NCIH1184_LUNG</td>\n",
       "      <td>0.342109</td>\n",
       "      <td>-0.3427</td>\n",
       "      <td>-0.127913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>NCIH2227_LUNG</td>\n",
       "      <td>-1.681285</td>\n",
       "      <td>-0.2863</td>\n",
       "      <td>-0.686956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>RERFLCAD2_LUNG</td>\n",
       "      <td>5.538319</td>\n",
       "      <td>0.3963</td>\n",
       "      <td>-0.112106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>NCIH2347_LUNG</td>\n",
       "      <td>5.479658</td>\n",
       "      <td>0.6188</td>\n",
       "      <td>-0.267261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>NCIH2087_LUNG</td>\n",
       "      <td>6.932414</td>\n",
       "      <td>1.6433</td>\n",
       "      <td>0.907519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>NCIH2085_LUNG</td>\n",
       "      <td>5.274371</td>\n",
       "      <td>-0.1127</td>\n",
       "      <td>-0.438074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>NCIH2066_LUNG</td>\n",
       "      <td>5.633139</td>\n",
       "      <td>0.4493</td>\n",
       "      <td>0.141166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>NCIH1341_LUNG</td>\n",
       "      <td>7.547795</td>\n",
       "      <td>1.3065</td>\n",
       "      <td>0.240771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>NCIH2029_LUNG</td>\n",
       "      <td>-1.349043</td>\n",
       "      <td>0.5785</td>\n",
       "      <td>-1.292468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>LU65_LUNG</td>\n",
       "      <td>6.116482</td>\n",
       "      <td>2.5304</td>\n",
       "      <td>1.038455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>NCIH1573_LUNG</td>\n",
       "      <td>4.550179</td>\n",
       "      <td>0.3588</td>\n",
       "      <td>-0.784912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>NCIH1651_LUNG</td>\n",
       "      <td>4.259441</td>\n",
       "      <td>0.7022</td>\n",
       "      <td>-0.717587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>NCIH226_LUNG</td>\n",
       "      <td>5.338989</td>\n",
       "      <td>0.5509</td>\n",
       "      <td>-0.778210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>NCIH1666_LUNG</td>\n",
       "      <td>7.075447</td>\n",
       "      <td>0.4297</td>\n",
       "      <td>0.989522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>NCIH1734_LUNG</td>\n",
       "      <td>4.506209</td>\n",
       "      <td>0.2523</td>\n",
       "      <td>0.454407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>NCIH2342_LUNG</td>\n",
       "      <td>4.578252</td>\n",
       "      <td>1.1580</td>\n",
       "      <td>-0.101334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>EBC1_LUNG</td>\n",
       "      <td>5.290536</td>\n",
       "      <td>0.5819</td>\n",
       "      <td>0.223340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>NCIH322_LUNG</td>\n",
       "      <td>4.370289</td>\n",
       "      <td>0.7652</td>\n",
       "      <td>-0.778183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>HS618T_LUNG</td>\n",
       "      <td>4.063108</td>\n",
       "      <td>0.0315</td>\n",
       "      <td>0.378635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>NCIH2291_LUNG</td>\n",
       "      <td>6.072888</td>\n",
       "      <td>0.4318</td>\n",
       "      <td>0.548433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>HCC33_LUNG</td>\n",
       "      <td>-0.894313</td>\n",
       "      <td>0.1048</td>\n",
       "      <td>0.052386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>CORL105_LUNG</td>\n",
       "      <td>5.998980</td>\n",
       "      <td>0.3074</td>\n",
       "      <td>-0.328300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>COLO668_LUNG</td>\n",
       "      <td>1.124759</td>\n",
       "      <td>0.0475</td>\n",
       "      <td>-0.813312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>NCIH1915_LUNG</td>\n",
       "      <td>5.397272</td>\n",
       "      <td>-0.0049</td>\n",
       "      <td>-0.387299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>HCC2279_LUNG</td>\n",
       "      <td>6.086341</td>\n",
       "      <td>1.1304</td>\n",
       "      <td>-0.435354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>NCIH838_LUNG</td>\n",
       "      <td>2.341240</td>\n",
       "      <td>0.4334</td>\n",
       "      <td>-0.316659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>HCC1171_LUNG</td>\n",
       "      <td>5.252473</td>\n",
       "      <td>0.5289</td>\n",
       "      <td>-0.386418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>NCIH2228_LUNG</td>\n",
       "      <td>5.823995</td>\n",
       "      <td>0.1516</td>\n",
       "      <td>-0.391004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>758</th>\n",
       "      <td>DMS79_LUNG</td>\n",
       "      <td>1.499070</td>\n",
       "      <td>0.7379</td>\n",
       "      <td>-0.982106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>759</th>\n",
       "      <td>NCIH2106_LUNG</td>\n",
       "      <td>-1.106599</td>\n",
       "      <td>0.1071</td>\n",
       "      <td>-1.487617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>762</th>\n",
       "      <td>NCIH526_LUNG</td>\n",
       "      <td>-0.340084</td>\n",
       "      <td>0.3292</td>\n",
       "      <td>-1.418323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>765</th>\n",
       "      <td>NCIH2196_LUNG</td>\n",
       "      <td>1.514282</td>\n",
       "      <td>0.2475</td>\n",
       "      <td>0.589279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>770</th>\n",
       "      <td>NCIH446_LUNG</td>\n",
       "      <td>6.718704</td>\n",
       "      <td>2.5547</td>\n",
       "      <td>1.101105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>793</th>\n",
       "      <td>CALU1_LUNG</td>\n",
       "      <td>4.848258</td>\n",
       "      <td>-0.4536</td>\n",
       "      <td>0.071922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>795</th>\n",
       "      <td>NCIH2170_LUNG</td>\n",
       "      <td>7.499360</td>\n",
       "      <td>3.5924</td>\n",
       "      <td>0.218639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>798</th>\n",
       "      <td>NCIH2444_LUNG</td>\n",
       "      <td>4.449961</td>\n",
       "      <td>0.4400</td>\n",
       "      <td>-0.388407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>807</th>\n",
       "      <td>NCIH1299_LUNG</td>\n",
       "      <td>5.680487</td>\n",
       "      <td>0.6839</td>\n",
       "      <td>1.044130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>869</th>\n",
       "      <td>NCIH2126_LUNG</td>\n",
       "      <td>4.513190</td>\n",
       "      <td>0.5158</td>\n",
       "      <td>-0.574728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>872</th>\n",
       "      <td>HCC827_LUNG</td>\n",
       "      <td>7.071396</td>\n",
       "      <td>1.6884</td>\n",
       "      <td>0.308300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>926</th>\n",
       "      <td>LXF289_LUNG</td>\n",
       "      <td>6.465195</td>\n",
       "      <td>0.6540</td>\n",
       "      <td>0.368839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>948</th>\n",
       "      <td>SCLC21H_LUNG</td>\n",
       "      <td>8.394348</td>\n",
       "      <td>3.5303</td>\n",
       "      <td>1.310908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>949</th>\n",
       "      <td>CALU3_LUNG</td>\n",
       "      <td>5.297756</td>\n",
       "      <td>0.7147</td>\n",
       "      <td>0.299457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>950</th>\n",
       "      <td>NCIH1435_LUNG</td>\n",
       "      <td>4.643561</td>\n",
       "      <td>0.9360</td>\n",
       "      <td>-0.222264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>951</th>\n",
       "      <td>NCIH2009_LUNG</td>\n",
       "      <td>5.186233</td>\n",
       "      <td>0.4440</td>\n",
       "      <td>0.546570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>952</th>\n",
       "      <td>NCIH1869_LUNG</td>\n",
       "      <td>6.196604</td>\n",
       "      <td>0.8324</td>\n",
       "      <td>0.630111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>958</th>\n",
       "      <td>HCC4006_LUNG</td>\n",
       "      <td>3.251396</td>\n",
       "      <td>0.1694</td>\n",
       "      <td>-0.456971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>961</th>\n",
       "      <td>SHP77_LUNG</td>\n",
       "      <td>4.641811</td>\n",
       "      <td>0.4460</td>\n",
       "      <td>0.020762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>968</th>\n",
       "      <td>NCIH841_LUNG</td>\n",
       "      <td>4.534590</td>\n",
       "      <td>-0.3065</td>\n",
       "      <td>0.109864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>970</th>\n",
       "      <td>NCIH1944_LUNG</td>\n",
       "      <td>5.222311</td>\n",
       "      <td>0.7070</td>\n",
       "      <td>-0.528057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>971</th>\n",
       "      <td>NCIH2405_LUNG</td>\n",
       "      <td>5.334465</td>\n",
       "      <td>0.3354</td>\n",
       "      <td>-0.901009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>983</th>\n",
       "      <td>NCIH727_LUNG</td>\n",
       "      <td>4.488922</td>\n",
       "      <td>0.4548</td>\n",
       "      <td>-0.026026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>985</th>\n",
       "      <td>HCC2935_LUNG</td>\n",
       "      <td>2.924681</td>\n",
       "      <td>-0.9044</td>\n",
       "      <td>0.037752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>LCLC103H_LUNG</td>\n",
       "      <td>3.751041</td>\n",
       "      <td>0.2774</td>\n",
       "      <td>0.199502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1005</th>\n",
       "      <td>LCLC97TM1_LUNG</td>\n",
       "      <td>7.404797</td>\n",
       "      <td>0.4852</td>\n",
       "      <td>0.468395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1049</th>\n",
       "      <td>NCIH2073_LUNG</td>\n",
       "      <td>4.247340</td>\n",
       "      <td>0.8741</td>\n",
       "      <td>0.028575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1060</th>\n",
       "      <td>HOP62_LUNG</td>\n",
       "      <td>4.585131</td>\n",
       "      <td>0.4826</td>\n",
       "      <td>0.253346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1061</th>\n",
       "      <td>EKVX_LUNG</td>\n",
       "      <td>4.599648</td>\n",
       "      <td>0.1015</td>\n",
       "      <td>-0.296200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1068</th>\n",
       "      <td>HOP92_LUNG</td>\n",
       "      <td>6.212163</td>\n",
       "      <td>0.4185</td>\n",
       "      <td>0.037123</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>162 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Gene       RNA     DNA   PROTEIN\n",
       "0         DMS53_LUNG  5.787634  0.4555  0.114065\n",
       "2      NCIH1694_LUNG  0.532113  0.2812 -1.137268\n",
       "10     NCIH1184_LUNG  0.342109 -0.3427 -0.127913\n",
       "13     NCIH2227_LUNG -1.681285 -0.2863 -0.686956\n",
       "29    RERFLCAD2_LUNG  5.538319  0.3963 -0.112106\n",
       "34     NCIH2347_LUNG  5.479658  0.6188 -0.267261\n",
       "41     NCIH2087_LUNG  6.932414  1.6433  0.907519\n",
       "43     NCIH2085_LUNG  5.274371 -0.1127 -0.438074\n",
       "47     NCIH2066_LUNG  5.633139  0.4493  0.141166\n",
       "48     NCIH1341_LUNG  7.547795  1.3065  0.240771\n",
       "49     NCIH2029_LUNG -1.349043  0.5785 -1.292468\n",
       "53         LU65_LUNG  6.116482  2.5304  1.038455\n",
       "66     NCIH1573_LUNG  4.550179  0.3588 -0.784912\n",
       "68     NCIH1651_LUNG  4.259441  0.7022 -0.717587\n",
       "73      NCIH226_LUNG  5.338989  0.5509 -0.778210\n",
       "74     NCIH1666_LUNG  7.075447  0.4297  0.989522\n",
       "77     NCIH1734_LUNG  4.506209  0.2523  0.454407\n",
       "78     NCIH2342_LUNG  4.578252  1.1580 -0.101334\n",
       "79         EBC1_LUNG  5.290536  0.5819  0.223340\n",
       "84      NCIH322_LUNG  4.370289  0.7652 -0.778183\n",
       "85       HS618T_LUNG  4.063108  0.0315  0.378635\n",
       "89     NCIH2291_LUNG  6.072888  0.4318  0.548433\n",
       "109       HCC33_LUNG -0.894313  0.1048  0.052386\n",
       "115     CORL105_LUNG  5.998980  0.3074 -0.328300\n",
       "118     COLO668_LUNG  1.124759  0.0475 -0.813312\n",
       "139    NCIH1915_LUNG  5.397272 -0.0049 -0.387299\n",
       "142     HCC2279_LUNG  6.086341  1.1304 -0.435354\n",
       "158     NCIH838_LUNG  2.341240  0.4334 -0.316659\n",
       "164     HCC1171_LUNG  5.252473  0.5289 -0.386418\n",
       "187    NCIH2228_LUNG  5.823995  0.1516 -0.391004\n",
       "...              ...       ...     ...       ...\n",
       "758       DMS79_LUNG  1.499070  0.7379 -0.982106\n",
       "759    NCIH2106_LUNG -1.106599  0.1071 -1.487617\n",
       "762     NCIH526_LUNG -0.340084  0.3292 -1.418323\n",
       "765    NCIH2196_LUNG  1.514282  0.2475  0.589279\n",
       "770     NCIH446_LUNG  6.718704  2.5547  1.101105\n",
       "793       CALU1_LUNG  4.848258 -0.4536  0.071922\n",
       "795    NCIH2170_LUNG  7.499360  3.5924  0.218639\n",
       "798    NCIH2444_LUNG  4.449961  0.4400 -0.388407\n",
       "807    NCIH1299_LUNG  5.680487  0.6839  1.044130\n",
       "869    NCIH2126_LUNG  4.513190  0.5158 -0.574728\n",
       "872      HCC827_LUNG  7.071396  1.6884  0.308300\n",
       "926      LXF289_LUNG  6.465195  0.6540  0.368839\n",
       "948     SCLC21H_LUNG  8.394348  3.5303  1.310908\n",
       "949       CALU3_LUNG  5.297756  0.7147  0.299457\n",
       "950    NCIH1435_LUNG  4.643561  0.9360 -0.222264\n",
       "951    NCIH2009_LUNG  5.186233  0.4440  0.546570\n",
       "952    NCIH1869_LUNG  6.196604  0.8324  0.630111\n",
       "958     HCC4006_LUNG  3.251396  0.1694 -0.456971\n",
       "961       SHP77_LUNG  4.641811  0.4460  0.020762\n",
       "968     NCIH841_LUNG  4.534590 -0.3065  0.109864\n",
       "970    NCIH1944_LUNG  5.222311  0.7070 -0.528057\n",
       "971    NCIH2405_LUNG  5.334465  0.3354 -0.901009\n",
       "983     NCIH727_LUNG  4.488922  0.4548 -0.026026\n",
       "985     HCC2935_LUNG  2.924681 -0.9044  0.037752\n",
       "997    LCLC103H_LUNG  3.751041  0.2774  0.199502\n",
       "1005  LCLC97TM1_LUNG  7.404797  0.4852  0.468395\n",
       "1049   NCIH2073_LUNG  4.247340  0.8741  0.028575\n",
       "1060      HOP62_LUNG  4.585131  0.4826  0.253346\n",
       "1061       EKVX_LUNG  4.599648  0.1015 -0.296200\n",
       "1068      HOP92_LUNG  6.212163  0.4185  0.037123\n",
       "\n",
       "[162 rows x 4 columns]"
      ]
     },
     "execution_count": 619,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"Gene\"] = data[\"Gene\"].astype(str)\n",
    "y=data[data[\"Gene\"].str.contains(\"LUNG\")]\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 620,
   "metadata": {},
   "outputs": [
    {
     "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>Gene</th>\n",
       "      <th>RNA</th>\n",
       "      <th>DNA</th>\n",
       "      <th>PROTEIN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DMS53_LUNG</td>\n",
       "      <td>5.787634</td>\n",
       "      <td>0.4555</td>\n",
       "      <td>0.114065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NCIH1694_LUNG</td>\n",
       "      <td>0.532113</td>\n",
       "      <td>0.2812</td>\n",
       "      <td>-1.137268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>NCIH1184_LUNG</td>\n",
       "      <td>0.342109</td>\n",
       "      <td>-0.3427</td>\n",
       "      <td>-0.127913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>NCIH2227_LUNG</td>\n",
       "      <td>-1.681285</td>\n",
       "      <td>-0.2863</td>\n",
       "      <td>-0.686956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>RERFLCAD2_LUNG</td>\n",
       "      <td>5.538319</td>\n",
       "      <td>0.3963</td>\n",
       "      <td>-0.112106</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Gene       RNA     DNA   PROTEIN\n",
       "0       DMS53_LUNG  5.787634  0.4555  0.114065\n",
       "2    NCIH1694_LUNG  0.532113  0.2812 -1.137268\n",
       "10   NCIH1184_LUNG  0.342109 -0.3427 -0.127913\n",
       "13   NCIH2227_LUNG -1.681285 -0.2863 -0.686956\n",
       "29  RERFLCAD2_LUNG  5.538319  0.3963 -0.112106"
      ]
     },
     "execution_count": 620,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = y\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 621,
   "metadata": {},
   "outputs": [
    {
     "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>RNA</th>\n",
       "      <th>DNA</th>\n",
       "      <th>PROTEIN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>RNA</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.511292</td>\n",
       "      <td>0.663106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DNA</th>\n",
       "      <td>0.511292</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.525217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PROTEIN</th>\n",
       "      <td>0.663106</td>\n",
       "      <td>0.525217</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              RNA       DNA   PROTEIN\n",
       "RNA      1.000000  0.511292  0.663106\n",
       "DNA      0.511292  1.000000  0.525217\n",
       "PROTEIN  0.663106  0.525217  1.000000"
      ]
     },
     "execution_count": 621,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 622,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 623,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.34648987256778996, array([0.420484]))"
      ]
     },
     "execution_count": 623,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = LinearRegression()\n",
    "X = df[['DNA']]\n",
    "y = df['PROTEIN']\n",
    "lr.fit(X,y)\n",
    "# print the result\n",
    "lr.intercept_,lr.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 624,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.33434871693778706"
      ]
     },
     "execution_count": 624,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get y-hat\n",
    "yhat = lr.predict(X = df[['DNA']])\n",
    "# MSE\n",
    "mean_squared_error(df['PROTEIN'],yhat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 625,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115f5f278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# lm plot\n",
    "plt.scatter(df['DNA'],df['PROTEIN'])\n",
    "plt.plot(df['DNA'],yhat)\n",
    "#set the name of X axis\n",
    "plt.xlabel('DNA')\n",
    "#set the name of y axis\n",
    "plt.ylabel('PROTEIN')\n",
    "#set the title of the plot\n",
    "plt.title('LUNG_The Relationship between DNA and PROTEIN')\n",
    "#set the grid color, linestyle and linewidth for the plot\n",
    "plt.grid(color='#95a5a6',linestyle='--', linewidth=1,axis='both',alpha=0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 626,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>PROTEIN</td>     <th>  R-squared:         </th> <td>   0.128</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.122</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   23.58</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 27 Jun 2018</td> <th>  Prob (F-statistic):</th> <td>2.82e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>23:39:49</td>     <th>  Log-Likelihood:    </th> <td> -156.57</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   162</td>      <th>  AIC:               </th> <td>   315.1</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   161</td>      <th>  BIC:               </th> <td>   318.2</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "   <td></td>      <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>DNA</th> <td>    0.2193</td> <td>    0.045</td> <td>    4.856</td> <td> 0.000</td> <td>    0.130</td> <td>    0.309</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 0.281</td> <th>  Durbin-Watson:     </th> <td>   1.432</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.869</td> <th>  Jarque-Bera (JB):  </th> <td>   0.439</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.053</td> <th>  Prob(JB):          </th> <td>   0.803</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.768</td> <th>  Cond. No.          </th> <td>    1.00</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                PROTEIN   R-squared:                       0.128\n",
       "Model:                            OLS   Adj. R-squared:                  0.122\n",
       "Method:                 Least Squares   F-statistic:                     23.58\n",
       "Date:                Wed, 27 Jun 2018   Prob (F-statistic):           2.82e-06\n",
       "Time:                        23:39:49   Log-Likelihood:                -156.57\n",
       "No. Observations:                 162   AIC:                             315.1\n",
       "Df Residuals:                     161   BIC:                             318.2\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "DNA            0.2193      0.045      4.856      0.000       0.130       0.309\n",
       "==============================================================================\n",
       "Omnibus:                        0.281   Durbin-Watson:                   1.432\n",
       "Prob(Omnibus):                  0.869   Jarque-Bera (JB):                0.439\n",
       "Skew:                           0.053   Prob(JB):                        0.803\n",
       "Kurtosis:                       2.768   Cond. No.                         1.00\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 626,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear_model = sm.OLS(y,X)\n",
    "\n",
    "results = linear_model.fit()\n",
    "\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 627,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.9216440457523634, array([0.18596929]))"
      ]
     },
     "execution_count": 627,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = LinearRegression()\n",
    "X = df[['RNA']]\n",
    "y = df['PROTEIN']\n",
    "lr.fit(X,y)\n",
    "# print the result\n",
    "lr.intercept_,lr.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 628,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.25869388485369893"
      ]
     },
     "execution_count": 628,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get y-hat\n",
    "yhat = lr.predict(X = df[['RNA']])\n",
    "# MSE\n",
    "mean_squared_error(df['PROTEIN'],yhat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 629,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115c70320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# lm plot\n",
    "plt.scatter(df['RNA'],df['PROTEIN'])\n",
    "plt.plot(df['RNA'],yhat)\n",
    "#set the name of X axis\n",
    "plt.xlabel('RNA')\n",
    "#set the name of y axis\n",
    "plt.ylabel('PROTEIN')\n",
    "#set the title of the plot\n",
    "plt.title('LUNG_The Relationship between RNA and PROTEIN')\n",
    "#set the grid color, linestyle and linewidth for the plot\n",
    "plt.grid(color='#95a5a6',linestyle='--', linewidth=1,axis='both',alpha=0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 630,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>PROTEIN</td>     <th>  R-squared:         </th> <td>   0.059</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.053</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   10.09</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 27 Jun 2018</td> <th>  Prob (F-statistic):</th>  <td>0.00179</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>23:39:49</td>     <th>  Log-Likelihood:    </th> <td> -162.72</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   162</td>      <th>  AIC:               </th> <td>   327.4</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   161</td>      <th>  BIC:               </th> <td>   330.5</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "   <td></td>      <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>RNA</th> <td>    0.0312</td> <td>    0.010</td> <td>    3.176</td> <td> 0.002</td> <td>    0.012</td> <td>    0.051</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 6.730</td> <th>  Durbin-Watson:     </th> <td>   1.524</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.035</td> <th>  Jarque-Bera (JB):  </th> <td>   6.357</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.455</td> <th>  Prob(JB):          </th> <td>  0.0417</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.338</td> <th>  Cond. No.          </th> <td>    1.00</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                PROTEIN   R-squared:                       0.059\n",
       "Model:                            OLS   Adj. R-squared:                  0.053\n",
       "Method:                 Least Squares   F-statistic:                     10.09\n",
       "Date:                Wed, 27 Jun 2018   Prob (F-statistic):            0.00179\n",
       "Time:                        23:39:49   Log-Likelihood:                -162.72\n",
       "No. Observations:                 162   AIC:                             327.4\n",
       "Df Residuals:                     161   BIC:                             330.5\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "RNA            0.0312      0.010      3.176      0.002       0.012       0.051\n",
       "==============================================================================\n",
       "Omnibus:                        6.730   Durbin-Watson:                   1.524\n",
       "Prob(Omnibus):                  0.035   Jarque-Bera (JB):                6.357\n",
       "Skew:                           0.455   Prob(JB):                       0.0417\n",
       "Kurtosis:                       3.338   Cond. No.                         1.00\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 630,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear_model = sm.OLS(y,X)\n",
    "\n",
    "results = linear_model.fit()\n",
    "\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 631,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.12847178746678878, array([0.17910858]))"
      ]
     },
     "execution_count": 631,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = LinearRegression()\n",
    "X = df[['RNA']]\n",
    "y = df['DNA']\n",
    "lr.fit(X,y)\n",
    "# print the result\n",
    "lr.intercept_,lr.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 632,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5320452957326156"
      ]
     },
     "execution_count": 632,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get y-hat\n",
    "yhat = lr.predict(X = df[['RNA']])\n",
    "# MSE\n",
    "mean_squared_error(df['DNA'],yhat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 633,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c1dbecb70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# lm plot\n",
    "plt.scatter(df['RNA'],df['DNA'])\n",
    "plt.plot(df['RNA'],yhat)\n",
    "#set the name of X axis\n",
    "plt.xlabel('RNA')\n",
    "#set the name of y axis\n",
    "plt.ylabel('DNA')\n",
    "#set the title of the plot\n",
    "plt.title('LUNG_The Relationship between RNA and DNA')\n",
    "#set the grid color, linestyle and linewidth for the plot\n",
    "plt.grid(color='#95a5a6',linestyle='--', linewidth=1,axis='both',alpha=0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 634,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>DNA</td>       <th>  R-squared:         </th> <td>   0.565</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.563</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   209.3</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 27 Jun 2018</td> <th>  Prob (F-statistic):</th> <td>6.33e-31</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>23:39:50</td>     <th>  Log-Likelihood:    </th> <td> -179.28</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   162</td>      <th>  AIC:               </th> <td>   360.6</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   161</td>      <th>  BIC:               </th> <td>   363.6</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "   <td></td>      <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>RNA</th> <td>    0.1575</td> <td>    0.011</td> <td>   14.467</td> <td> 0.000</td> <td>    0.136</td> <td>    0.179</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>35.360</td> <th>  Durbin-Watson:     </th> <td>   1.989</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>  51.540</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 1.206</td> <th>  Prob(JB):          </th> <td>6.43e-12</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 4.349</td> <th>  Cond. No.          </th> <td>    1.00</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                    DNA   R-squared:                       0.565\n",
       "Model:                            OLS   Adj. R-squared:                  0.563\n",
       "Method:                 Least Squares   F-statistic:                     209.3\n",
       "Date:                Wed, 27 Jun 2018   Prob (F-statistic):           6.33e-31\n",
       "Time:                        23:39:50   Log-Likelihood:                -179.28\n",
       "No. Observations:                 162   AIC:                             360.6\n",
       "Df Residuals:                     161   BIC:                             363.6\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "RNA            0.1575      0.011     14.467      0.000       0.136       0.179\n",
       "==============================================================================\n",
       "Omnibus:                       35.360   Durbin-Watson:                   1.989\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               51.540\n",
       "Skew:                           1.206   Prob(JB):                     6.43e-12\n",
       "Kurtosis:                       4.349   Cond. No.                         1.00\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 634,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear_model = sm.OLS(y,X)\n",
    "\n",
    "results = linear_model.fit()\n",
    "\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### STOMACH Cancer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 635,
   "metadata": {},
   "outputs": [
    {
     "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>Gene</th>\n",
       "      <th>RNA</th>\n",
       "      <th>DNA</th>\n",
       "      <th>PROTEIN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>RERFGC1B_STOMACH</td>\n",
       "      <td>4.163422</td>\n",
       "      <td>0.2182</td>\n",
       "      <td>-0.466282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>HS746T_STOMACH</td>\n",
       "      <td>5.466250</td>\n",
       "      <td>0.6573</td>\n",
       "      <td>-0.140819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>LMSU_STOMACH</td>\n",
       "      <td>5.772277</td>\n",
       "      <td>1.4295</td>\n",
       "      <td>0.381425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>ECC10_STOMACH</td>\n",
       "      <td>6.835379</td>\n",
       "      <td>0.7480</td>\n",
       "      <td>0.942912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>TGBC11TKB_STOMACH</td>\n",
       "      <td>6.351509</td>\n",
       "      <td>0.0065</td>\n",
       "      <td>0.347877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>SNU520_STOMACH</td>\n",
       "      <td>6.069303</td>\n",
       "      <td>0.4150</td>\n",
       "      <td>0.412011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>GSS_STOMACH</td>\n",
       "      <td>6.540482</td>\n",
       "      <td>0.1945</td>\n",
       "      <td>0.993752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>SNU620_STOMACH</td>\n",
       "      <td>6.152818</td>\n",
       "      <td>0.6756</td>\n",
       "      <td>0.026765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>ECC12_STOMACH</td>\n",
       "      <td>5.673451</td>\n",
       "      <td>0.8568</td>\n",
       "      <td>-0.000147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>GSU_STOMACH</td>\n",
       "      <td>6.726958</td>\n",
       "      <td>0.1696</td>\n",
       "      <td>0.316572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>FU97_STOMACH</td>\n",
       "      <td>4.432397</td>\n",
       "      <td>-0.1215</td>\n",
       "      <td>0.258136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>GCIY_STOMACH</td>\n",
       "      <td>7.232425</td>\n",
       "      <td>2.8949</td>\n",
       "      <td>1.135549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222</th>\n",
       "      <td>SH10TC_STOMACH</td>\n",
       "      <td>4.677929</td>\n",
       "      <td>0.7512</td>\n",
       "      <td>-0.463676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>MKN1_STOMACH</td>\n",
       "      <td>2.602767</td>\n",
       "      <td>0.9634</td>\n",
       "      <td>0.243181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>MKN74_STOMACH</td>\n",
       "      <td>5.756319</td>\n",
       "      <td>1.1913</td>\n",
       "      <td>-1.110995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267</th>\n",
       "      <td>KE39_STOMACH</td>\n",
       "      <td>6.038875</td>\n",
       "      <td>1.1361</td>\n",
       "      <td>-1.169672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>HGC27_STOMACH</td>\n",
       "      <td>6.524637</td>\n",
       "      <td>2.6416</td>\n",
       "      <td>1.076213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>273</th>\n",
       "      <td>HUG1N_STOMACH</td>\n",
       "      <td>5.335091</td>\n",
       "      <td>0.5939</td>\n",
       "      <td>0.437281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275</th>\n",
       "      <td>NUGC4_STOMACH</td>\n",
       "      <td>5.954491</td>\n",
       "      <td>-0.1477</td>\n",
       "      <td>-0.646606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>SNU16_STOMACH</td>\n",
       "      <td>8.067766</td>\n",
       "      <td>3.3196</td>\n",
       "      <td>1.086650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>322</th>\n",
       "      <td>NCIN87_STOMACH</td>\n",
       "      <td>6.457551</td>\n",
       "      <td>0.8180</td>\n",
       "      <td>-0.115791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331</th>\n",
       "      <td>OCUM1_STOMACH</td>\n",
       "      <td>6.574569</td>\n",
       "      <td>0.3605</td>\n",
       "      <td>0.301457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>366</th>\n",
       "      <td>SNU5_STOMACH</td>\n",
       "      <td>6.426951</td>\n",
       "      <td>0.5573</td>\n",
       "      <td>0.984817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>456</th>\n",
       "      <td>SNU601_STOMACH</td>\n",
       "      <td>5.725546</td>\n",
       "      <td>1.2583</td>\n",
       "      <td>0.541755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458</th>\n",
       "      <td>SNU668_STOMACH</td>\n",
       "      <td>5.819285</td>\n",
       "      <td>1.0217</td>\n",
       "      <td>0.345569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>470</th>\n",
       "      <td>NCCSTCK140_STOMACH</td>\n",
       "      <td>6.189719</td>\n",
       "      <td>0.6616</td>\n",
       "      <td>-0.650689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>472</th>\n",
       "      <td>SNU719_STOMACH</td>\n",
       "      <td>5.809927</td>\n",
       "      <td>-0.1381</td>\n",
       "      <td>0.329702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>483</th>\n",
       "      <td>SNU216_STOMACH</td>\n",
       "      <td>5.173963</td>\n",
       "      <td>1.1370</td>\n",
       "      <td>-0.615307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>525</th>\n",
       "      <td>NUGC2_STOMACH</td>\n",
       "      <td>5.189261</td>\n",
       "      <td>0.7619</td>\n",
       "      <td>0.340684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>553</th>\n",
       "      <td>SNU1_STOMACH</td>\n",
       "      <td>5.662684</td>\n",
       "      <td>-0.0096</td>\n",
       "      <td>-0.068939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>725</th>\n",
       "      <td>AGS_STOMACH</td>\n",
       "      <td>4.988408</td>\n",
       "      <td>-0.0886</td>\n",
       "      <td>-0.051981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>752</th>\n",
       "      <td>KATOIII_STOMACH</td>\n",
       "      <td>5.872451</td>\n",
       "      <td>0.2872</td>\n",
       "      <td>0.128943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>780</th>\n",
       "      <td>NUGC3_STOMACH</td>\n",
       "      <td>5.353466</td>\n",
       "      <td>0.2856</td>\n",
       "      <td>-0.148575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>786</th>\n",
       "      <td>IM95_STOMACH</td>\n",
       "      <td>6.670583</td>\n",
       "      <td>0.5489</td>\n",
       "      <td>0.420261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>863</th>\n",
       "      <td>MKN7_STOMACH</td>\n",
       "      <td>5.764927</td>\n",
       "      <td>1.6623</td>\n",
       "      <td>0.031588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>987</th>\n",
       "      <td>MKN45_STOMACH</td>\n",
       "      <td>4.242567</td>\n",
       "      <td>0.1993</td>\n",
       "      <td>0.896227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1006</th>\n",
       "      <td>2313287_STOMACH</td>\n",
       "      <td>5.814019</td>\n",
       "      <td>1.1390</td>\n",
       "      <td>-0.527479</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Gene       RNA     DNA   PROTEIN\n",
       "39      RERFGC1B_STOMACH  4.163422  0.2182 -0.466282\n",
       "52        HS746T_STOMACH  5.466250  0.6573 -0.140819\n",
       "116         LMSU_STOMACH  5.772277  1.4295  0.381425\n",
       "131        ECC10_STOMACH  6.835379  0.7480  0.942912\n",
       "147    TGBC11TKB_STOMACH  6.351509  0.0065  0.347877\n",
       "155       SNU520_STOMACH  6.069303  0.4150  0.412011\n",
       "162          GSS_STOMACH  6.540482  0.1945  0.993752\n",
       "167       SNU620_STOMACH  6.152818  0.6756  0.026765\n",
       "168        ECC12_STOMACH  5.673451  0.8568 -0.000147\n",
       "169          GSU_STOMACH  6.726958  0.1696  0.316572\n",
       "192         FU97_STOMACH  4.432397 -0.1215  0.258136\n",
       "221         GCIY_STOMACH  7.232425  2.8949  1.135549\n",
       "222       SH10TC_STOMACH  4.677929  0.7512 -0.463676\n",
       "235         MKN1_STOMACH  2.602767  0.9634  0.243181\n",
       "236        MKN74_STOMACH  5.756319  1.1913 -1.110995\n",
       "267         KE39_STOMACH  6.038875  1.1361 -1.169672\n",
       "271        HGC27_STOMACH  6.524637  2.6416  1.076213\n",
       "273        HUG1N_STOMACH  5.335091  0.5939  0.437281\n",
       "275        NUGC4_STOMACH  5.954491 -0.1477 -0.646606\n",
       "320        SNU16_STOMACH  8.067766  3.3196  1.086650\n",
       "322       NCIN87_STOMACH  6.457551  0.8180 -0.115791\n",
       "331        OCUM1_STOMACH  6.574569  0.3605  0.301457\n",
       "366         SNU5_STOMACH  6.426951  0.5573  0.984817\n",
       "456       SNU601_STOMACH  5.725546  1.2583  0.541755\n",
       "458       SNU668_STOMACH  5.819285  1.0217  0.345569\n",
       "470   NCCSTCK140_STOMACH  6.189719  0.6616 -0.650689\n",
       "472       SNU719_STOMACH  5.809927 -0.1381  0.329702\n",
       "483       SNU216_STOMACH  5.173963  1.1370 -0.615307\n",
       "525        NUGC2_STOMACH  5.189261  0.7619  0.340684\n",
       "553         SNU1_STOMACH  5.662684 -0.0096 -0.068939\n",
       "725          AGS_STOMACH  4.988408 -0.0886 -0.051981\n",
       "752      KATOIII_STOMACH  5.872451  0.2872  0.128943\n",
       "780        NUGC3_STOMACH  5.353466  0.2856 -0.148575\n",
       "786         IM95_STOMACH  6.670583  0.5489  0.420261\n",
       "863         MKN7_STOMACH  5.764927  1.6623  0.031588\n",
       "987        MKN45_STOMACH  4.242567  0.1993  0.896227\n",
       "1006     2313287_STOMACH  5.814019  1.1390 -0.527479"
      ]
     },
     "execution_count": 635,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"Gene\"] = data[\"Gene\"].astype(str)\n",
    "x=data[data[\"Gene\"].str.contains(\"STOMACH\")]\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 636,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 637,
   "metadata": {},
   "outputs": [
    {
     "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>Gene</th>\n",
       "      <th>RNA</th>\n",
       "      <th>DNA</th>\n",
       "      <th>PROTEIN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>RERFGC1B_STOMACH</td>\n",
       "      <td>4.163422</td>\n",
       "      <td>0.2182</td>\n",
       "      <td>-0.466282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>HS746T_STOMACH</td>\n",
       "      <td>5.466250</td>\n",
       "      <td>0.6573</td>\n",
       "      <td>-0.140819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>LMSU_STOMACH</td>\n",
       "      <td>5.772277</td>\n",
       "      <td>1.4295</td>\n",
       "      <td>0.381425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>ECC10_STOMACH</td>\n",
       "      <td>6.835379</td>\n",
       "      <td>0.7480</td>\n",
       "      <td>0.942912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>TGBC11TKB_STOMACH</td>\n",
       "      <td>6.351509</td>\n",
       "      <td>0.0065</td>\n",
       "      <td>0.347877</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Gene       RNA     DNA   PROTEIN\n",
       "39    RERFGC1B_STOMACH  4.163422  0.2182 -0.466282\n",
       "52      HS746T_STOMACH  5.466250  0.6573 -0.140819\n",
       "116       LMSU_STOMACH  5.772277  1.4295  0.381425\n",
       "131      ECC10_STOMACH  6.835379  0.7480  0.942912\n",
       "147  TGBC11TKB_STOMACH  6.351509  0.0065  0.347877"
      ]
     },
     "execution_count": 637,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = x\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 638,
   "metadata": {},
   "outputs": [
    {
     "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>RNA</th>\n",
       "      <th>DNA</th>\n",
       "      <th>PROTEIN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>RNA</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.39706</td>\n",
       "      <td>0.322885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DNA</th>\n",
       "      <td>0.397060</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>0.277110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PROTEIN</th>\n",
       "      <td>0.322885</td>\n",
       "      <td>0.27711</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              RNA      DNA   PROTEIN\n",
       "RNA      1.000000  0.39706  0.322885\n",
       "DNA      0.397060  1.00000  0.277110\n",
       "PROTEIN  0.322885  0.27711  1.000000"
      ]
     },
     "execution_count": 638,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 639,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.004085373691450234, array([0.20489782]))"
      ]
     },
     "execution_count": 639,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = LinearRegression()\n",
    "X = df[['DNA']]\n",
    "y = df['PROTEIN']\n",
    "lr.fit(X,y)\n",
    "# print the result\n",
    "lr.intercept_,lr.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 640,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3179143428839849"
      ]
     },
     "execution_count": 640,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get y-hat\n",
    "yhat = lr.predict(X = df[['DNA']])\n",
    "# MSE\n",
    "mean_squared_error(df['PROTEIN'],yhat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 641,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c1dbec128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# lm plot\n",
    "plt.scatter(df['DNA'],df['PROTEIN'])\n",
    "plt.plot(df['DNA'],yhat)\n",
    "#set the name of X axis\n",
    "plt.xlabel('DNA')\n",
    "#set the name of y axis\n",
    "plt.ylabel('PROTEIN')\n",
    "#set the title of the plot\n",
    "plt.title('STOMACH_The Relationship between DNA and PROTEIN')\n",
    "#set the grid color, linestyle and linewidth for the plot\n",
    "plt.grid(color='#95a5a6',linestyle='--', linewidth=1,axis='both',alpha=0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 642,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>PROTEIN</td>     <th>  R-squared:         </th> <td>   0.138</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.114</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   5.778</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 27 Jun 2018</td> <th>  Prob (F-statistic):</th>  <td>0.0215</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>23:39:52</td>     <th>  Log-Likelihood:    </th> <td> -31.301</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    37</td>      <th>  AIC:               </th> <td>   64.60</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    36</td>      <th>  BIC:               </th> <td>   66.21</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "   <td></td>      <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>DNA</th> <td>    0.2023</td> <td>    0.084</td> <td>    2.404</td> <td> 0.022</td> <td>    0.032</td> <td>    0.373</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 2.984</td> <th>  Durbin-Watson:     </th> <td>   1.967</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.225</td> <th>  Jarque-Bera (JB):  </th> <td>   2.271</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.606</td> <th>  Prob(JB):          </th> <td>   0.321</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.041</td> <th>  Cond. No.          </th> <td>    1.00</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                PROTEIN   R-squared:                       0.138\n",
       "Model:                            OLS   Adj. R-squared:                  0.114\n",
       "Method:                 Least Squares   F-statistic:                     5.778\n",
       "Date:                Wed, 27 Jun 2018   Prob (F-statistic):             0.0215\n",
       "Time:                        23:39:52   Log-Likelihood:                -31.301\n",
       "No. Observations:                  37   AIC:                             64.60\n",
       "Df Residuals:                      36   BIC:                             66.21\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "DNA            0.2023      0.084      2.404      0.022       0.032       0.373\n",
       "==============================================================================\n",
       "Omnibus:                        2.984   Durbin-Watson:                   1.967\n",
       "Prob(Omnibus):                  0.225   Jarque-Bera (JB):                2.271\n",
       "Skew:                          -0.606   Prob(JB):                        0.321\n",
       "Kurtosis:                       3.041   Cond. No.                         1.00\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 642,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear_model = sm.OLS(y,X)\n",
    "\n",
    "results = linear_model.fit()\n",
    "\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 643,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.9943413413853258, array([0.19893007]))"
      ]
     },
     "execution_count": 643,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = LinearRegression()\n",
    "X = df[['RNA']]\n",
    "y = df['PROTEIN']\n",
    "lr.fit(X,y)\n",
    "# print the result\n",
    "lr.intercept_,lr.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 644,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3084567171304645"
      ]
     },
     "execution_count": 644,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get y-hat\n",
    "yhat = lr.predict(X = df[['RNA']])\n",
    "# MSE\n",
    "mean_squared_error(df['PROTEIN'],yhat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 645,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f6b6f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# lm plot\n",
    "plt.scatter(df['RNA'],df['PROTEIN'])\n",
    "plt.plot(df['RNA'],yhat)\n",
    "#set the name of X axis\n",
    "plt.xlabel('RNA')\n",
    "#set the name of y axis\n",
    "plt.ylabel('PROTEIN')\n",
    "#set the title of the plot\n",
    "plt.title('STOMACH_The Relationship between RNA and PROTEIN')\n",
    "#set the grid color, linestyle and linewidth for the plot\n",
    "plt.grid(color='#95a5a6',linestyle='--', linewidth=1,axis='both',alpha=0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 646,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>PROTEIN</td>     <th>  R-squared:         </th> <td>   0.093</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.068</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   3.703</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 27 Jun 2018</td> <th>  Prob (F-statistic):</th>  <td>0.0622</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>23:39:53</td>     <th>  Log-Likelihood:    </th> <td> -32.243</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    37</td>      <th>  AIC:               </th> <td>   66.49</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    36</td>      <th>  BIC:               </th> <td>   68.10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "   <td></td>      <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>RNA</th> <td>    0.0316</td> <td>    0.016</td> <td>    1.924</td> <td> 0.062</td> <td>   -0.002</td> <td>    0.065</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 0.909</td> <th>  Durbin-Watson:     </th> <td>   2.173</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.635</td> <th>  Jarque-Bera (JB):  </th> <td>   0.885</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.339</td> <th>  Prob(JB):          </th> <td>   0.642</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.662</td> <th>  Cond. No.          </th> <td>    1.00</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                PROTEIN   R-squared:                       0.093\n",
       "Model:                            OLS   Adj. R-squared:                  0.068\n",
       "Method:                 Least Squares   F-statistic:                     3.703\n",
       "Date:                Wed, 27 Jun 2018   Prob (F-statistic):             0.0622\n",
       "Time:                        23:39:53   Log-Likelihood:                -32.243\n",
       "No. Observations:                  37   AIC:                             66.49\n",
       "Df Residuals:                      36   BIC:                             68.10\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "RNA            0.0316      0.016      1.924      0.062      -0.002       0.065\n",
       "==============================================================================\n",
       "Omnibus:                        0.909   Durbin-Watson:                   2.173\n",
       "Prob(Omnibus):                  0.635   Jarque-Bera (JB):                0.885\n",
       "Skew:                          -0.339   Prob(JB):                        0.642\n",
       "Kurtosis:                       2.662   Cond. No.                         1.00\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 646,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear_model = sm.OLS(y,X)\n",
    "\n",
    "results = linear_model.fit()\n",
    "\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 647,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-1.12922346966531, array([0.33084503]))"
      ]
     },
     "execution_count": 647,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = LinearRegression()\n",
    "X = df[['RNA']]\n",
    "y = df['DNA']\n",
    "lr.fit(X,y)\n",
    "# print the result\n",
    "lr.intercept_,lr.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 648,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5305541859917932"
      ]
     },
     "execution_count": 648,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get y-hat\n",
    "yhat = lr.predict(X = df[['RNA']])\n",
    "# MSE\n",
    "mean_squared_error(df['DNA'],yhat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 649,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c1cf75ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# lm plot\n",
    "plt.scatter(df['RNA'],df['DNA'])\n",
    "plt.plot(df['RNA'],yhat)\n",
    "#set the name of X axis\n",
    "plt.xlabel('RNA')\n",
    "#set the name of y axis\n",
    "plt.ylabel('DNA')\n",
    "#set the title of the plot\n",
    "plt.title('STOMACH_The Relationship between RNA and DNA')\n",
    "#set the grid color, linestyle and linewidth for the plot\n",
    "plt.grid(color='#95a5a6',linestyle='--', linewidth=1,axis='both',alpha=0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 650,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>DNA</td>       <th>  R-squared:         </th> <td>   0.547</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.535</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   43.54</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 27 Jun 2018</td> <th>  Prob (F-statistic):</th> <td>1.11e-07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>23:39:53</td>     <th>  Log-Likelihood:    </th> <td> -41.912</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    37</td>      <th>  AIC:               </th> <td>   85.82</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    36</td>      <th>  BIC:               </th> <td>   87.43</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "   <td></td>      <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>RNA</th> <td>    0.1409</td> <td>    0.021</td> <td>    6.599</td> <td> 0.000</td> <td>    0.098</td> <td>    0.184</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>12.849</td> <th>  Durbin-Watson:     </th> <td>   2.062</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.002</td> <th>  Jarque-Bera (JB):  </th> <td>  12.807</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 1.261</td> <th>  Prob(JB):          </th> <td> 0.00166</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 4.395</td> <th>  Cond. No.          </th> <td>    1.00</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                    DNA   R-squared:                       0.547\n",
       "Model:                            OLS   Adj. R-squared:                  0.535\n",
       "Method:                 Least Squares   F-statistic:                     43.54\n",
       "Date:                Wed, 27 Jun 2018   Prob (F-statistic):           1.11e-07\n",
       "Time:                        23:39:53   Log-Likelihood:                -41.912\n",
       "No. Observations:                  37   AIC:                             85.82\n",
       "Df Residuals:                      36   BIC:                             87.43\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "RNA            0.1409      0.021      6.599      0.000       0.098       0.184\n",
       "==============================================================================\n",
       "Omnibus:                       12.849   Durbin-Watson:                   2.062\n",
       "Prob(Omnibus):                  0.002   Jarque-Bera (JB):               12.807\n",
       "Skew:                           1.261   Prob(JB):                      0.00166\n",
       "Kurtosis:                       4.395   Cond. No.                         1.00\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 650,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear_model = sm.OLS(y,X)\n",
    "\n",
    "results = linear_model.fit()\n",
    "\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### SKIN Cancer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 651,
   "metadata": {},
   "outputs": [
    {
     "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>Gene</th>\n",
       "      <th>RNA</th>\n",
       "      <th>DNA</th>\n",
       "      <th>PROTEIN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>RVH421_SKIN</td>\n",
       "      <td>6.213419</td>\n",
       "      <td>0.3495</td>\n",
       "      <td>-0.275106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>HS895T_SKIN</td>\n",
       "      <td>3.644536</td>\n",
       "      <td>0.0040</td>\n",
       "      <td>-0.923141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>HS940T_SKIN</td>\n",
       "      <td>3.653476</td>\n",
       "      <td>0.2111</td>\n",
       "      <td>-0.601584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>SKMEL1_SKIN</td>\n",
       "      <td>5.465042</td>\n",
       "      <td>0.2107</td>\n",
       "      <td>-0.643500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>HS936T_SKIN</td>\n",
       "      <td>4.916971</td>\n",
       "      <td>-0.0069</td>\n",
       "      <td>-0.163685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>SKMEL28_SKIN</td>\n",
       "      <td>5.476062</td>\n",
       "      <td>0.0449</td>\n",
       "      <td>-0.476686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>SH4_SKIN</td>\n",
       "      <td>5.583495</td>\n",
       "      <td>0.6915</td>\n",
       "      <td>0.109079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>CJM_SKIN</td>\n",
       "      <td>6.654520</td>\n",
       "      <td>0.5774</td>\n",
       "      <td>0.134809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>WM115_SKIN</td>\n",
       "      <td>3.280969</td>\n",
       "      <td>0.0414</td>\n",
       "      <td>0.379686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>COLO800_SKIN</td>\n",
       "      <td>4.118405</td>\n",
       "      <td>-0.3022</td>\n",
       "      <td>-0.299500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>UACC62_SKIN</td>\n",
       "      <td>4.936992</td>\n",
       "      <td>0.2266</td>\n",
       "      <td>-0.420981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>373</th>\n",
       "      <td>RPMI7951_SKIN</td>\n",
       "      <td>3.636930</td>\n",
       "      <td>0.2157</td>\n",
       "      <td>-0.478558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>377</th>\n",
       "      <td>COLO783_SKIN</td>\n",
       "      <td>5.607130</td>\n",
       "      <td>0.7343</td>\n",
       "      <td>-0.204283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>381</th>\n",
       "      <td>COLO792_SKIN</td>\n",
       "      <td>5.505489</td>\n",
       "      <td>-0.1639</td>\n",
       "      <td>0.616084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>383</th>\n",
       "      <td>WM983B_SKIN</td>\n",
       "      <td>4.818431</td>\n",
       "      <td>0.7392</td>\n",
       "      <td>-0.512935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>WM793_SKIN</td>\n",
       "      <td>3.624815</td>\n",
       "      <td>0.2188</td>\n",
       "      <td>-0.112321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>MDAMB435S_SKIN</td>\n",
       "      <td>2.506424</td>\n",
       "      <td>0.6427</td>\n",
       "      <td>-0.441525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>WM1799_SKIN</td>\n",
       "      <td>4.279620</td>\n",
       "      <td>0.5511</td>\n",
       "      <td>-0.438606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>MEWO_SKIN</td>\n",
       "      <td>5.678934</td>\n",
       "      <td>0.0578</td>\n",
       "      <td>-0.019147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>401</th>\n",
       "      <td>WM88_SKIN</td>\n",
       "      <td>3.446192</td>\n",
       "      <td>-0.1693</td>\n",
       "      <td>0.353545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>407</th>\n",
       "      <td>IGR1_SKIN</td>\n",
       "      <td>4.783284</td>\n",
       "      <td>-0.0162</td>\n",
       "      <td>0.254929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>475</th>\n",
       "      <td>A2058_SKIN</td>\n",
       "      <td>3.542960</td>\n",
       "      <td>0.4028</td>\n",
       "      <td>-0.504215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>508</th>\n",
       "      <td>IGR39_SKIN</td>\n",
       "      <td>3.320123</td>\n",
       "      <td>0.3712</td>\n",
       "      <td>-1.037292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>540</th>\n",
       "      <td>HT144_SKIN</td>\n",
       "      <td>3.676550</td>\n",
       "      <td>0.1055</td>\n",
       "      <td>-0.658678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541</th>\n",
       "      <td>SKMEL31_SKIN</td>\n",
       "      <td>2.189876</td>\n",
       "      <td>0.3597</td>\n",
       "      <td>-0.725074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>542</th>\n",
       "      <td>HS839T_SKIN</td>\n",
       "      <td>3.206050</td>\n",
       "      <td>0.0104</td>\n",
       "      <td>-0.154066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>543</th>\n",
       "      <td>HS939T_SKIN</td>\n",
       "      <td>4.494627</td>\n",
       "      <td>0.0490</td>\n",
       "      <td>-0.212727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>574</th>\n",
       "      <td>HS688AT_SKIN</td>\n",
       "      <td>3.254078</td>\n",
       "      <td>0.0269</td>\n",
       "      <td>0.061248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>585</th>\n",
       "      <td>A101D_SKIN</td>\n",
       "      <td>2.831571</td>\n",
       "      <td>-0.2914</td>\n",
       "      <td>0.247979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>625</th>\n",
       "      <td>COLO829_SKIN</td>\n",
       "      <td>4.953190</td>\n",
       "      <td>0.1492</td>\n",
       "      <td>-0.288385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>628</th>\n",
       "      <td>IPC298_SKIN</td>\n",
       "      <td>6.732477</td>\n",
       "      <td>0.1582</td>\n",
       "      <td>0.561010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>640</th>\n",
       "      <td>SKMEL24_SKIN</td>\n",
       "      <td>3.091256</td>\n",
       "      <td>0.1002</td>\n",
       "      <td>-0.335922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>641</th>\n",
       "      <td>K029AX_SKIN</td>\n",
       "      <td>4.935388</td>\n",
       "      <td>0.1497</td>\n",
       "      <td>1.317395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>646</th>\n",
       "      <td>HS695T_SKIN</td>\n",
       "      <td>3.128827</td>\n",
       "      <td>0.2583</td>\n",
       "      <td>-0.643518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>651</th>\n",
       "      <td>SKMEL3_SKIN</td>\n",
       "      <td>5.494150</td>\n",
       "      <td>0.0327</td>\n",
       "      <td>0.226943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>657</th>\n",
       "      <td>HMCB_SKIN</td>\n",
       "      <td>5.592615</td>\n",
       "      <td>0.6020</td>\n",
       "      <td>0.183803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>659</th>\n",
       "      <td>MALME3M_SKIN</td>\n",
       "      <td>4.151681</td>\n",
       "      <td>0.0298</td>\n",
       "      <td>-0.713741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>666</th>\n",
       "      <td>MELJUSO_SKIN</td>\n",
       "      <td>5.073033</td>\n",
       "      <td>0.4349</td>\n",
       "      <td>-0.062347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>677</th>\n",
       "      <td>COLO679_SKIN</td>\n",
       "      <td>5.191301</td>\n",
       "      <td>-0.0609</td>\n",
       "      <td>-0.079841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>685</th>\n",
       "      <td>MELHO_SKIN</td>\n",
       "      <td>4.749239</td>\n",
       "      <td>-0.0192</td>\n",
       "      <td>-0.161613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>687</th>\n",
       "      <td>G361_SKIN</td>\n",
       "      <td>6.970888</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.320016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>702</th>\n",
       "      <td>COLO741_SKIN</td>\n",
       "      <td>5.381931</td>\n",
       "      <td>-0.2359</td>\n",
       "      <td>-0.079811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>733</th>\n",
       "      <td>SKMEL30_SKIN</td>\n",
       "      <td>4.946529</td>\n",
       "      <td>0.1305</td>\n",
       "      <td>-0.112140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>736</th>\n",
       "      <td>C32_SKIN</td>\n",
       "      <td>5.404091</td>\n",
       "      <td>0.5146</td>\n",
       "      <td>0.708009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>768</th>\n",
       "      <td>A375_SKIN</td>\n",
       "      <td>2.278254</td>\n",
       "      <td>0.1492</td>\n",
       "      <td>-0.282528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>771</th>\n",
       "      <td>HS294T_SKIN</td>\n",
       "      <td>2.905796</td>\n",
       "      <td>-0.0579</td>\n",
       "      <td>-0.219517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>772</th>\n",
       "      <td>SKMEL5_SKIN</td>\n",
       "      <td>5.590554</td>\n",
       "      <td>0.3858</td>\n",
       "      <td>-0.066808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>773</th>\n",
       "      <td>HS944T_SKIN</td>\n",
       "      <td>3.025650</td>\n",
       "      <td>0.1078</td>\n",
       "      <td>-0.377217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>788</th>\n",
       "      <td>WM2664_SKIN</td>\n",
       "      <td>5.554644</td>\n",
       "      <td>-0.1838</td>\n",
       "      <td>0.061463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>794</th>\n",
       "      <td>LOXIMVI_SKIN</td>\n",
       "      <td>6.863870</td>\n",
       "      <td>0.5506</td>\n",
       "      <td>0.002552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>799</th>\n",
       "      <td>UACC257_SKIN</td>\n",
       "      <td>5.583763</td>\n",
       "      <td>0.2660</td>\n",
       "      <td>0.244601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>809</th>\n",
       "      <td>IGR37_SKIN</td>\n",
       "      <td>5.298646</td>\n",
       "      <td>0.2610</td>\n",
       "      <td>-0.102916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>953</th>\n",
       "      <td>HS852T_SKIN</td>\n",
       "      <td>3.113158</td>\n",
       "      <td>-0.0029</td>\n",
       "      <td>-0.722854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>957</th>\n",
       "      <td>HS934T_SKIN</td>\n",
       "      <td>3.926267</td>\n",
       "      <td>0.0040</td>\n",
       "      <td>-0.200868</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Gene       RNA     DNA   PROTEIN\n",
       "9       RVH421_SKIN  6.213419  0.3495 -0.275106\n",
       "22      HS895T_SKIN  3.644536  0.0040 -0.923141\n",
       "36      HS940T_SKIN  3.653476  0.2111 -0.601584\n",
       "59      SKMEL1_SKIN  5.465042  0.2107 -0.643500\n",
       "82      HS936T_SKIN  4.916971 -0.0069 -0.163685\n",
       "95     SKMEL28_SKIN  5.476062  0.0449 -0.476686\n",
       "99         SH4_SKIN  5.583495  0.6915  0.109079\n",
       "122        CJM_SKIN  6.654520  0.5774  0.134809\n",
       "181      WM115_SKIN  3.280969  0.0414  0.379686\n",
       "193    COLO800_SKIN  4.118405 -0.3022 -0.299500\n",
       "202     UACC62_SKIN  4.936992  0.2266 -0.420981\n",
       "373   RPMI7951_SKIN  3.636930  0.2157 -0.478558\n",
       "377    COLO783_SKIN  5.607130  0.7343 -0.204283\n",
       "381    COLO792_SKIN  5.505489 -0.1639  0.616084\n",
       "383     WM983B_SKIN  4.818431  0.7392 -0.512935\n",
       "396      WM793_SKIN  3.624815  0.2188 -0.112321\n",
       "398  MDAMB435S_SKIN  2.506424  0.6427 -0.441525\n",
       "399     WM1799_SKIN  4.279620  0.5511 -0.438606\n",
       "400       MEWO_SKIN  5.678934  0.0578 -0.019147\n",
       "401       WM88_SKIN  3.446192 -0.1693  0.353545\n",
       "407       IGR1_SKIN  4.783284 -0.0162  0.254929\n",
       "475      A2058_SKIN  3.542960  0.4028 -0.504215\n",
       "508      IGR39_SKIN  3.320123  0.3712 -1.037292\n",
       "540      HT144_SKIN  3.676550  0.1055 -0.658678\n",
       "541    SKMEL31_SKIN  2.189876  0.3597 -0.725074\n",
       "542     HS839T_SKIN  3.206050  0.0104 -0.154066\n",
       "543     HS939T_SKIN  4.494627  0.0490 -0.212727\n",
       "574    HS688AT_SKIN  3.254078  0.0269  0.061248\n",
       "585      A101D_SKIN  2.831571 -0.2914  0.247979\n",
       "625    COLO829_SKIN  4.953190  0.1492 -0.288385\n",
       "628     IPC298_SKIN  6.732477  0.1582  0.561010\n",
       "640    SKMEL24_SKIN  3.091256  0.1002 -0.335922\n",
       "641     K029AX_SKIN  4.935388  0.1497  1.317395\n",
       "646     HS695T_SKIN  3.128827  0.2583 -0.643518\n",
       "651     SKMEL3_SKIN  5.494150  0.0327  0.226943\n",
       "657       HMCB_SKIN  5.592615  0.6020  0.183803\n",
       "659    MALME3M_SKIN  4.151681  0.0298 -0.713741\n",
       "666    MELJUSO_SKIN  5.073033  0.4349 -0.062347\n",
       "677    COLO679_SKIN  5.191301 -0.0609 -0.079841\n",
       "685      MELHO_SKIN  4.749239 -0.0192 -0.161613\n",
       "687       G361_SKIN  6.970888  0.3613  0.320016\n",
       "702    COLO741_SKIN  5.381931 -0.2359 -0.079811\n",
       "733    SKMEL30_SKIN  4.946529  0.1305 -0.112140\n",
       "736        C32_SKIN  5.404091  0.5146  0.708009\n",
       "768       A375_SKIN  2.278254  0.1492 -0.282528\n",
       "771     HS294T_SKIN  2.905796 -0.0579 -0.219517\n",
       "772     SKMEL5_SKIN  5.590554  0.3858 -0.066808\n",
       "773     HS944T_SKIN  3.025650  0.1078 -0.377217\n",
       "788     WM2664_SKIN  5.554644 -0.1838  0.061463\n",
       "794    LOXIMVI_SKIN  6.863870  0.5506  0.002552\n",
       "799    UACC257_SKIN  5.583763  0.2660  0.244601\n",
       "809      IGR37_SKIN  5.298646  0.2610 -0.102916\n",
       "953     HS852T_SKIN  3.113158 -0.0029 -0.722854\n",
       "957     HS934T_SKIN  3.926267  0.0040 -0.200868"
      ]
     },
     "execution_count": 651,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"Gene\"] = data[\"Gene\"].astype(str)\n",
    "p=data[data[\"Gene\"].str.contains(\"SKIN\")]\n",
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 652,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 653,
   "metadata": {},
   "outputs": [
    {
     "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>Gene</th>\n",
       "      <th>RNA</th>\n",
       "      <th>DNA</th>\n",
       "      <th>PROTEIN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>RVH421_SKIN</td>\n",
       "      <td>6.213419</td>\n",
       "      <td>0.3495</td>\n",
       "      <td>-0.275106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>HS895T_SKIN</td>\n",
       "      <td>3.644536</td>\n",
       "      <td>0.0040</td>\n",
       "      <td>-0.923141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>HS940T_SKIN</td>\n",
       "      <td>3.653476</td>\n",
       "      <td>0.2111</td>\n",
       "      <td>-0.601584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>SKMEL1_SKIN</td>\n",
       "      <td>5.465042</td>\n",
       "      <td>0.2107</td>\n",
       "      <td>-0.643500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>HS936T_SKIN</td>\n",
       "      <td>4.916971</td>\n",
       "      <td>-0.0069</td>\n",
       "      <td>-0.163685</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Gene       RNA     DNA   PROTEIN\n",
       "9   RVH421_SKIN  6.213419  0.3495 -0.275106\n",
       "22  HS895T_SKIN  3.644536  0.0040 -0.923141\n",
       "36  HS940T_SKIN  3.653476  0.2111 -0.601584\n",
       "59  SKMEL1_SKIN  5.465042  0.2107 -0.643500\n",
       "82  HS936T_SKIN  4.916971 -0.0069 -0.163685"
      ]
     },
     "execution_count": 653,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = p\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 654,
   "metadata": {},
   "outputs": [
    {
     "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>RNA</th>\n",
       "      <th>DNA</th>\n",
       "      <th>PROTEIN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>RNA</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.252607</td>\n",
       "      <td>0.440594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DNA</th>\n",
       "      <td>0.252607</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.092364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PROTEIN</th>\n",
       "      <td>0.440594</td>\n",
       "      <td>-0.092364</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              RNA       DNA   PROTEIN\n",
       "RNA      1.000000  0.252607  0.440594\n",
       "DNA      0.252607  1.000000 -0.092364\n",
       "PROTEIN  0.440594 -0.092364  1.000000"
      ]
     },
     "execution_count": 654,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 655,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.11936505045208781, array([-0.15282097]))"
      ]
     },
     "execution_count": 655,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = LinearRegression()\n",
    "X = df[['DNA']]\n",
    "y = df['PROTEIN']\n",
    "lr.fit(X,y)\n",
    "# print the result\n",
    "lr.intercept_,lr.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 656,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.18316962781811336"
      ]
     },
     "execution_count": 656,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get y-hat\n",
    "yhat = lr.predict(X = df[['DNA']])\n",
    "# MSE\n",
    "mean_squared_error(df['PROTEIN'],yhat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 657,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c1e539128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# lm plot\n",
    "plt.scatter(df['DNA'],df['PROTEIN'])\n",
    "plt.plot(df['DNA'],yhat)\n",
    "#set the name of X axis\n",
    "plt.xlabel('DNA')\n",
    "#set the name of y axis\n",
    "plt.ylabel('PROTEIN')\n",
    "#set the title of the plot\n",
    "plt.title('SKIN_The Relationship between DNA and PROTEIN')\n",
    "#set the grid color, linestyle and linewidth for the plot\n",
    "plt.grid(color='#95a5a6',linestyle='--', linewidth=1,axis='both',alpha=0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 658,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>PROTEIN</td>     <th>  R-squared:         </th> <td>   0.067</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.050</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   3.825</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 27 Jun 2018</td> <th>  Prob (F-statistic):</th>  <td>0.0558</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>23:39:55</td>     <th>  Log-Likelihood:    </th> <td> -32.154</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    54</td>      <th>  AIC:               </th> <td>   66.31</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    53</td>      <th>  BIC:               </th> <td>   68.30</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "   <td></td>      <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>DNA</th> <td>   -0.3698</td> <td>    0.189</td> <td>   -1.956</td> <td> 0.056</td> <td>   -0.749</td> <td>    0.009</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 7.510</td> <th>  Durbin-Watson:     </th> <td>   1.899</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.023</td> <th>  Jarque-Bera (JB):  </th> <td>   6.886</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.667</td> <th>  Prob(JB):          </th> <td>  0.0320</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 4.131</td> <th>  Cond. No.          </th> <td>    1.00</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                PROTEIN   R-squared:                       0.067\n",
       "Model:                            OLS   Adj. R-squared:                  0.050\n",
       "Method:                 Least Squares   F-statistic:                     3.825\n",
       "Date:                Wed, 27 Jun 2018   Prob (F-statistic):             0.0558\n",
       "Time:                        23:39:55   Log-Likelihood:                -32.154\n",
       "No. Observations:                  54   AIC:                             66.31\n",
       "Df Residuals:                      53   BIC:                             68.30\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "DNA           -0.3698      0.189     -1.956      0.056      -0.749       0.009\n",
       "==============================================================================\n",
       "Omnibus:                        7.510   Durbin-Watson:                   1.899\n",
       "Prob(Omnibus):                  0.023   Jarque-Bera (JB):                6.886\n",
       "Skew:                           0.667   Prob(JB):                       0.0320\n",
       "Kurtosis:                       4.131   Cond. No.                         1.00\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 658,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear_model = sm.OLS(y,X)\n",
    "\n",
    "results = linear_model.fit()\n",
    "\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 659,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.8481453231300387, array([0.15485802]))"
      ]
     },
     "execution_count": 659,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = LinearRegression()\n",
    "X = df[['RNA']]\n",
    "y = df['PROTEIN']\n",
    "lr.fit(X,y)\n",
    "# print the result\n",
    "lr.intercept_,lr.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 660,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.14888223324909994"
      ]
     },
     "execution_count": 660,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get y-hat\n",
    "yhat = lr.predict(X = df[['RNA']])\n",
    "# MSE\n",
    "mean_squared_error(df['PROTEIN'],yhat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 661,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c1dc666d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# lm plot\n",
    "plt.scatter(df['RNA'],df['PROTEIN'])\n",
    "plt.plot(df['RNA'],yhat)\n",
    "#set the name of X axis\n",
    "plt.xlabel('RNA')\n",
    "#set the name of y axis\n",
    "plt.ylabel('PROTEIN')\n",
    "#set the title of the plot\n",
    "plt.title('SKIN_The Relationship between RNA and PROTEIN')\n",
    "#set the grid color, linestyle and linewidth for the plot\n",
    "plt.grid(color='#95a5a6',linestyle='--', linewidth=1,axis='both',alpha=0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 662,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>PROTEIN</td>     <th>  R-squared:         </th> <td>   0.042</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.024</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   2.320</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 27 Jun 2018</td> <th>  Prob (F-statistic):</th>  <td> 0.134</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>23:39:56</td>     <th>  Log-Likelihood:    </th> <td> -32.879</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    54</td>      <th>  AIC:               </th> <td>   67.76</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    53</td>      <th>  BIC:               </th> <td>   69.75</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "   <td></td>      <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>RNA</th> <td>   -0.0199</td> <td>    0.013</td> <td>   -1.523</td> <td> 0.134</td> <td>   -0.046</td> <td>    0.006</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 6.376</td> <th>  Durbin-Watson:     </th> <td>   1.914</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.041</td> <th>  Jarque-Bera (JB):  </th> <td>   5.499</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.607</td> <th>  Prob(JB):          </th> <td>  0.0639</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.986</td> <th>  Cond. No.          </th> <td>    1.00</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                PROTEIN   R-squared:                       0.042\n",
       "Model:                            OLS   Adj. R-squared:                  0.024\n",
       "Method:                 Least Squares   F-statistic:                     2.320\n",
       "Date:                Wed, 27 Jun 2018   Prob (F-statistic):              0.134\n",
       "Time:                        23:39:56   Log-Likelihood:                -32.879\n",
       "No. Observations:                  54   AIC:                             67.76\n",
       "Df Residuals:                      53   BIC:                             69.75\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "RNA           -0.0199      0.013     -1.523      0.134      -0.046       0.006\n",
       "==============================================================================\n",
       "Omnibus:                        6.376   Durbin-Watson:                   1.914\n",
       "Prob(Omnibus):                  0.041   Jarque-Bera (JB):                5.499\n",
       "Skew:                           0.607   Prob(JB):                       0.0639\n",
       "Kurtosis:                       3.986   Cond. No.                         1.00\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 662,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear_model = sm.OLS(y,X)\n",
    "\n",
    "results = linear_model.fit()\n",
    "\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 663,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4.304065195603789, array([1.18914036]))"
      ]
     },
     "execution_count": 663,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = LinearRegression()\n",
    "X = df[['DNA']]\n",
    "y = df['RNA']\n",
    "lr.fit(X,y)\n",
    "# print the result\n",
    "lr.intercept_,lr.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 664,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.4000665121343696"
      ]
     },
     "execution_count": 664,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get y-hat\n",
    "yhat = lr.predict(X = df[['DNA']])\n",
    "# MSE\n",
    "mean_squared_error(df['RNA'],yhat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 665,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c1ea70198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# lm plot\n",
    "plt.scatter(df['DNA'],df['RNA'])\n",
    "plt.plot(df['DNA'],yhat)\n",
    "#set the name of X axis\n",
    "plt.xlabel('DNA')\n",
    "#set the name of y axis\n",
    "plt.ylabel('RNA')\n",
    "#set the title of the plot\n",
    "plt.title('SKIN_The Relationship between DNA and RNA')\n",
    "#set the grid color, linestyle and linewidth for the plot\n",
    "plt.grid(color='#95a5a6',linestyle='--', linewidth=1,axis='both',alpha=0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 666,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>RNA</td>       <th>  R-squared:         </th> <td>   0.376</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.364</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   31.94</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 27 Jun 2018</td> <th>  Prob (F-statistic):</th> <td>6.44e-07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>23:39:56</td>     <th>  Log-Likelihood:    </th> <td> -147.30</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    54</td>      <th>  AIC:               </th> <td>   296.6</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    53</td>      <th>  BIC:               </th> <td>   298.6</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "   <td></td>      <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>DNA</th> <td>    9.0146</td> <td>    1.595</td> <td>    5.652</td> <td> 0.000</td> <td>    5.815</td> <td>   12.214</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 0.857</td> <th>  Durbin-Watson:     </th> <td>   0.807</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.651</td> <th>  Jarque-Bera (JB):  </th> <td>   0.801</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.282</td> <th>  Prob(JB):          </th> <td>   0.670</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.804</td> <th>  Cond. No.          </th> <td>    1.00</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                    RNA   R-squared:                       0.376\n",
       "Model:                            OLS   Adj. R-squared:                  0.364\n",
       "Method:                 Least Squares   F-statistic:                     31.94\n",
       "Date:                Wed, 27 Jun 2018   Prob (F-statistic):           6.44e-07\n",
       "Time:                        23:39:56   Log-Likelihood:                -147.30\n",
       "No. Observations:                  54   AIC:                             296.6\n",
       "Df Residuals:                      53   BIC:                             298.6\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "DNA            9.0146      1.595      5.652      0.000       5.815      12.214\n",
       "==============================================================================\n",
       "Omnibus:                        0.857   Durbin-Watson:                   0.807\n",
       "Prob(Omnibus):                  0.651   Jarque-Bera (JB):                0.801\n",
       "Skew:                          -0.282   Prob(JB):                        0.670\n",
       "Kurtosis:                       2.804   Cond. No.                         1.00\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 666,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear_model = sm.OLS(y,X)\n",
    "\n",
    "results = linear_model.fit()\n",
    "\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "\n",
    "\n",
    "###                                                                            Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "######### Describe the dataset： The dataset that I selected is “03-08-18-Data Summary for c-Myc Gene”. It is consisted of 3 tables and each table contains 1458 observations. In order to achieve “Data Merge” easily, I transfer the format to txt. The dataset is used to analyze the correlation of either c-Myc DNA (aka. Copy number, CN#) or c-Myc RNA with c-Myc Protein in different cell lines (such as A549 as one of lung cancer cell lines, HCT116 as one of colon cancer cell lines) of different tumor types (such as breast, colorectal, lung and prostate cancer). \n",
    "\n",
    "As an oncogene that can transform a cell into a tumor cell in certain circumstances, c-Myc has attracted enough attention from both researchers and drug hunters. If we can well predict c-Myc protein level in the patient’s cells, that will provide a great tool to evaluate the probability of tumor occurrence and alert the victim in time, which might help prevent or kill the tumor at the early stage, saving a lot of lives. c-Myc is an oncogene that is often overexpressed (Protein level) in tumor cells, so it can be used a dignostic biomarker for tumor occurrence. With the advancement of DNA and RNA sequencing technologies in the past decade, DNA and RNA levels now can be measured much more accurately and easier than Protein level. The reason for this is that the detection approaches for Protein (typically Immunohistochemistry or Mass Spectrometry) is still not that advanced and sensitive as those for DNA and RNA. Given this, in the clinical setting, if DNA or RNA level can well predict Protein level of c-Myc, it could solve this technical barrier for Protein detection and provide better prediction for tumor occurrence in individuals. \n",
    "\n",
    "######### Question #1: Overall, at the first glance, there is a better correlation between RNA and protein among all cell lines regardless of tumor types, suggesting that RNA level might provide stronger prediction power for c-Myc protein level. This is confirmed by linear regression analysis using LinearRegression( ) code, with much better adjusted R^2 for RNA-Protein analysis (0.293) than that for DNA-Protein analysis (0.079). \n",
    "DNA-Protein Linear Regression Model for all cell lines: Adjusted R^2=0.079\n",
    "RNA-Protein Linear Regression Model in Lung: Adjusted R^2=0.293\n",
    "\n",
    "######### Question #2: As discussed above, it is great to see a nice correlation between RNA and Protein of c-Myc in all cell lines for all tumor types. However, in the actual clinical setting, it is always important to find out whether c-Myc protein is a good biomarker for a specific tumor type, rather than all tumor types. Given this fact, I re-did the scattered plots and linear regression analysis for three different tumor types, Lung, Stomach and Skin, and determine DNA-Protein and RNA-Protein relationships for each. \n",
    "Overall, out of all 3 tumor types, there is a better DNA-Protein correlation than RNA-Protein correlation, suggesting that DNA level might provide stronger prediction power for c-Myc protein level. This is a different observation from the above for all cell lines of all tumor types.  In addition, DNA-Protein correlation seems to be the strongest among all 3 tumor types, although adjusted R^2 (0.122) is not that satisfactory.  \n",
    "DNA-Protein Linear Regression Model in Lung: Y=0.4205X-0.3465 (Adjusted R^2=0.122) \n",
    "RNA-Protein Linear Regression Model in Lung: Y=0.1860X-0.9216 (Adjusted R^2=0.053) \n",
    "DNA-Protein Linear Regression Model in Stomach: Y=0.2049X-0.0041 (Adjusted R^2=0.114) \n",
    "RNA-Protein Linear Regression Model in Stomach: Y=0.1989X-0.9943 (Adjusted R^2=0.068) \n",
    "DNA-Protein Linear Regression Model in Skin: Y=-0.1528X-0.1194 (Adjusted R^2=0.050) \n",
    "RNA-Protein Linear Regression Model in Stomach: Y=0.1548X-0.8481 (Adjusted R^2=0.024)\n",
    "\n",
    "In addition, DNA-RNA correlation analysis has also been performed here in parallel, and it is obvious that DNA-RNA correlation is much better than either DNA-Protein or RNA-Protein correlation. This is quite reasonable given the fact that translational and post-translational regulation (RNA to Protein) are a lot more complicated than transcriptional and post-transcriptional regulation (DNA to RNA). \n",
    "\n",
    "In summary, for all tumor types, c-Myc RNA level provides the best prediction power for c-Myc protein level, which will could act a strong diagnostic biomarker for tumor occurrence. On the contrary, out of 3 selected tumor types, as compared to c-Myc RNA level, c-Myc DNA level provides slightly better prediction power for c-Myc protein level, which is the most prominent in Lung. This means, based on what question we are trying to answer, we should be careful about selecting either c-Myc RNA (tumor occurrence) or DNA (occurrence of a specific tumor type) for c-Myc protein level prediction.  \n",
    "\n"
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