ExploratoryDataAnalysis_gene_expr.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "851ed1bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "630f2435",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\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>samples</th>\n",
       "      <th>FPKM</th>\n",
       "      <th>title</th>\n",
       "      <th>tissue</th>\n",
       "      <th>metastasis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TSPAN6</td>\n",
       "      <td>CA.102548</td>\n",
       "      <td>0.93</td>\n",
       "      <td>tumor rep1</td>\n",
       "      <td>breast tumor</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TNMD</td>\n",
       "      <td>CA.102548</td>\n",
       "      <td>0.00</td>\n",
       "      <td>tumor rep1</td>\n",
       "      <td>breast tumor</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DPM1</td>\n",
       "      <td>CA.102548</td>\n",
       "      <td>0.00</td>\n",
       "      <td>tumor rep1</td>\n",
       "      <td>breast tumor</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SCYL3</td>\n",
       "      <td>CA.102548</td>\n",
       "      <td>5.78</td>\n",
       "      <td>tumor rep1</td>\n",
       "      <td>breast tumor</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>C1orf112</td>\n",
       "      <td>CA.102548</td>\n",
       "      <td>2.83</td>\n",
       "      <td>tumor rep1</td>\n",
       "      <td>breast tumor</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       gene    samples  FPKM       title        tissue metastasis\n",
       "0    TSPAN6  CA.102548  0.93  tumor rep1  breast tumor        yes\n",
       "1      TNMD  CA.102548  0.00  tumor rep1  breast tumor        yes\n",
       "2      DPM1  CA.102548  0.00  tumor rep1  breast tumor        yes\n",
       "3     SCYL3  CA.102548  5.78  tumor rep1  breast tumor        yes\n",
       "4  C1orf112  CA.102548  2.83  tumor rep1  breast tumor        yes"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_long = pd.read_csv('gene_samples_FPKM.csv')\n",
    "#data_long.drop('Unnamed: 0')\n",
    "data_long.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fb026f8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\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>samples</th>\n",
       "      <th>FPKM</th>\n",
       "      <th>title</th>\n",
       "      <th>tissue</th>\n",
       "      <th>metastasis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>310</th>\n",
       "      <td>BRCA1</td>\n",
       "      <td>CA.102548</td>\n",
       "      <td>30.37</td>\n",
       "      <td>tumor rep1</td>\n",
       "      <td>breast tumor</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20556</th>\n",
       "      <td>BRCA1</td>\n",
       "      <td>CA.104338</td>\n",
       "      <td>24.43</td>\n",
       "      <td>tumor rep2</td>\n",
       "      <td>breast tumor</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40802</th>\n",
       "      <td>BRCA1</td>\n",
       "      <td>CA.105094</td>\n",
       "      <td>43.58</td>\n",
       "      <td>tumor rep3</td>\n",
       "      <td>breast tumor</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61048</th>\n",
       "      <td>BRCA1</td>\n",
       "      <td>CA.109745</td>\n",
       "      <td>11.05</td>\n",
       "      <td>tumor rep4</td>\n",
       "      <td>breast tumor</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81294</th>\n",
       "      <td>BRCA1</td>\n",
       "      <td>CA.1906415</td>\n",
       "      <td>3.38</td>\n",
       "      <td>tumor rep5</td>\n",
       "      <td>breast tumor</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        gene     samples   FPKM       title        tissue metastasis\n",
       "310    BRCA1   CA.102548  30.37  tumor rep1  breast tumor        yes\n",
       "20556  BRCA1   CA.104338  24.43  tumor rep2  breast tumor        yes\n",
       "40802  BRCA1   CA.105094  43.58  tumor rep3  breast tumor        yes\n",
       "61048  BRCA1   CA.109745  11.05  tumor rep4  breast tumor         no\n",
       "81294  BRCA1  CA.1906415   3.38  tumor rep5  breast tumor         no"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 1. barplot\n",
    "data_filtered = data_long[data_long['gene'] == 'BRCA1']\n",
    "\n",
    "# fig, ax = plt.subplots()\n",
    "# dat_filtered.plot(x='samples', y='FPKM', kind='bar', ax=ax, color='tissue')\n",
    "# plt.show()\n",
    "data_filtered.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ca809cc8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='samples', ylabel='FPKM'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=data_filtered, x='samples', y='FPKM', hue='tissue')\n",
    "#using ggplot % ggplot(., aes(x = samples, y = FPKM, fill = tissue)) +\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "efb88b7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='FPKM', ylabel='Density'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# density plot\n",
    "sns.kdeplot(data=data_filtered, x='FPKM', hue='tissue', fill=True, alpha=0.3)\n",
    "#ggplot(., aes(x = FPKM, fill = tissue)) geom_density(alpha = 0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b9e3e2d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='metastasis', ylabel='FPKM'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(data=data_filtered, x='metastasis', y='FPKM')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "9e446537",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_filtered_1_2 = data_long[(data_long['gene'] == 'BRCA1') | (data_long['gene'] == 'BRCA2')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "b419c0f4",
   "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>gene</th>\n",
       "      <th>BRCA1</th>\n",
       "      <th>BRCA2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>samples</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CA.102548</th>\n",
       "      <td>30.37</td>\n",
       "      <td>0.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CA.104338</th>\n",
       "      <td>24.43</td>\n",
       "      <td>0.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CA.105094</th>\n",
       "      <td>43.58</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CA.109745</th>\n",
       "      <td>11.05</td>\n",
       "      <td>1.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CA.1906415</th>\n",
       "      <td>3.38</td>\n",
       "      <td>0.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CA.1912627</th>\n",
       "      <td>5.96</td>\n",
       "      <td>3.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CA.1924346</th>\n",
       "      <td>4.26</td>\n",
       "      <td>1.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CA.1926760</th>\n",
       "      <td>11.52</td>\n",
       "      <td>4.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CA.1927842</th>\n",
       "      <td>9.87</td>\n",
       "      <td>3.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CA.1933414</th>\n",
       "      <td>3.47</td>\n",
       "      <td>3.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CA.1940640</th>\n",
       "      <td>11.28</td>\n",
       "      <td>1.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CA.2004407</th>\n",
       "      <td>6.63</td>\n",
       "      <td>1.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CA.2005288</th>\n",
       "      <td>5.46</td>\n",
       "      <td>1.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CA.2006047</th>\n",
       "      <td>5.40</td>\n",
       "      <td>2.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CA.2008260</th>\n",
       "      <td>14.64</td>\n",
       "      <td>3.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CA.2009329</th>\n",
       "      <td>6.91</td>\n",
       "      <td>0.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CA.2009381</th>\n",
       "      <td>9.49</td>\n",
       "      <td>2.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CA.2009850</th>\n",
       "      <td>10.00</td>\n",
       "      <td>4.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CA.2017611</th>\n",
       "      <td>3.85</td>\n",
       "      <td>1.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CA.2039179</th>\n",
       "      <td>4.11</td>\n",
       "      <td>0.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CA.2040686</th>\n",
       "      <td>4.37</td>\n",
       "      <td>1.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CA.2045012</th>\n",
       "      <td>6.68</td>\n",
       "      <td>1.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CA.2046297</th>\n",
       "      <td>5.96</td>\n",
       "      <td>2.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CA.348981</th>\n",
       "      <td>8.87</td>\n",
       "      <td>1.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CA.354300</th>\n",
       "      <td>6.04</td>\n",
       "      <td>0.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CA.359448</th>\n",
       "      <td>5.20</td>\n",
       "      <td>1.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CA.94377</th>\n",
       "      <td>8.64</td>\n",
       "      <td>8.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CA.98389</th>\n",
       "      <td>7.02</td>\n",
       "      <td>1.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CA.98475</th>\n",
       "      <td>13.29</td>\n",
       "      <td>2.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CA.99145</th>\n",
       "      <td>9.54</td>\n",
       "      <td>1.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAP.102548</th>\n",
       "      <td>15.91</td>\n",
       "      <td>9.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAP.104338</th>\n",
       "      <td>7.86</td>\n",
       "      <td>1.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAP.105094</th>\n",
       "      <td>8.58</td>\n",
       "      <td>2.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAP.109745</th>\n",
       "      <td>7.23</td>\n",
       "      <td>1.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAP.1906415</th>\n",
       "      <td>5.43</td>\n",
       "      <td>0.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAP.1912627</th>\n",
       "      <td>16.01</td>\n",
       "      <td>10.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAP.1924346</th>\n",
       "      <td>3.29</td>\n",
       "      <td>0.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAP.1926760</th>\n",
       "      <td>6.94</td>\n",
       "      <td>1.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAP.1927842</th>\n",
       "      <td>9.86</td>\n",
       "      <td>6.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAP.1933414</th>\n",
       "      <td>4.71</td>\n",
       "      <td>1.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAP.1940640</th>\n",
       "      <td>17.85</td>\n",
       "      <td>3.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAP.2004407</th>\n",
       "      <td>5.52</td>\n",
       "      <td>0.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAP.2005288</th>\n",
       "      <td>6.32</td>\n",
       "      <td>0.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAP.2006047</th>\n",
       "      <td>11.78</td>\n",
       "      <td>0.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAP.2008260</th>\n",
       "      <td>15.59</td>\n",
       "      <td>9.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAP.2009329</th>\n",
       "      <td>2.54</td>\n",
       "      <td>1.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAP.2009381</th>\n",
       "      <td>6.19</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAP.2009850</th>\n",
       "      <td>4.59</td>\n",
       "      <td>1.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAP.2017611</th>\n",
       "      <td>18.49</td>\n",
       "      <td>25.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAP.2039179</th>\n",
       "      <td>6.86</td>\n",
       "      <td>0.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAP.2040686</th>\n",
       "      <td>4.80</td>\n",
       "      <td>1.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAP.2046297</th>\n",
       "      <td>6.27</td>\n",
       "      <td>0.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAP.2046641</th>\n",
       "      <td>6.73</td>\n",
       "      <td>2.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAP.348981</th>\n",
       "      <td>1.87</td>\n",
       "      <td>1.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAP.354300</th>\n",
       "      <td>3.46</td>\n",
       "      <td>0.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAP.359448</th>\n",
       "      <td>4.14</td>\n",
       "      <td>1.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAP.94377</th>\n",
       "      <td>6.58</td>\n",
       "      <td>0.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAP.98389</th>\n",
       "      <td>9.77</td>\n",
       "      <td>1.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAP.98475</th>\n",
       "      <td>4.62</td>\n",
       "      <td>1.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAP.99145</th>\n",
       "      <td>1.17</td>\n",
       "      <td>0.84</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "gene         BRCA1  BRCA2\n",
       "samples                  \n",
       "CA.102548    30.37   0.38\n",
       "CA.104338    24.43   0.23\n",
       "CA.105094    43.58   0.00\n",
       "CA.109745    11.05   1.31\n",
       "CA.1906415    3.38   0.53\n",
       "CA.1912627    5.96   3.37\n",
       "CA.1924346    4.26   1.21\n",
       "CA.1926760   11.52   4.39\n",
       "CA.1927842    9.87   3.91\n",
       "CA.1933414    3.47   3.51\n",
       "CA.1940640   11.28   1.69\n",
       "CA.2004407    6.63   1.33\n",
       "CA.2005288    5.46   1.51\n",
       "CA.2006047    5.40   2.36\n",
       "CA.2008260   14.64   3.23\n",
       "CA.2009329    6.91   0.96\n",
       "CA.2009381    9.49   2.05\n",
       "CA.2009850   10.00   4.29\n",
       "CA.2017611    3.85   1.95\n",
       "CA.2039179    4.11   0.83\n",
       "CA.2040686    4.37   1.40\n",
       "CA.2045012    6.68   1.70\n",
       "CA.2046297    5.96   2.02\n",
       "CA.348981     8.87   1.16\n",
       "CA.354300     6.04   0.62\n",
       "CA.359448     5.20   1.70\n",
       "CA.94377      8.64   8.35\n",
       "CA.98389      7.02   1.27\n",
       "CA.98475     13.29   2.61\n",
       "CA.99145      9.54   1.45\n",
       "CAP.102548   15.91   9.28\n",
       "CAP.104338    7.86   1.25\n",
       "CAP.105094    8.58   2.61\n",
       "CAP.109745    7.23   1.25\n",
       "CAP.1906415   5.43   0.31\n",
       "CAP.1912627  16.01  10.13\n",
       "CAP.1924346   3.29   0.54\n",
       "CAP.1926760   6.94   1.26\n",
       "CAP.1927842   9.86   6.39\n",
       "CAP.1933414   4.71   1.68\n",
       "CAP.1940640  17.85   3.63\n",
       "CAP.2004407   5.52   0.28\n",
       "CAP.2005288   6.32   0.27\n",
       "CAP.2006047  11.78   0.68\n",
       "CAP.2008260  15.59   9.82\n",
       "CAP.2009329   2.54   1.05\n",
       "CAP.2009381   6.19   0.79\n",
       "CAP.2009850   4.59   1.30\n",
       "CAP.2017611  18.49  25.96\n",
       "CAP.2039179   6.86   0.64\n",
       "CAP.2040686   4.80   1.20\n",
       "CAP.2046297   6.27   0.57\n",
       "CAP.2046641   6.73   2.11\n",
       "CAP.348981    1.87   1.91\n",
       "CAP.354300    3.46   0.63\n",
       "CAP.359448    4.14   1.66\n",
       "CAP.94377     6.58   0.47\n",
       "CAP.98389     9.77   1.81\n",
       "CAP.98475     4.62   1.10\n",
       "CAP.99145     1.17   0.84"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#data_spread = data_filtered_1_2.pivot(index='samples', columns='gene', values='FPKM')\n",
    "data_spread = data_filtered_1_2.pivot(index='gene', columns='samples', values='FPKM')\n",
    "data_spread = data_spread.transpose()\n",
    "data_spread"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "e4803efd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x27c5dddcca0>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#sns.scatterplot(data=data_spread, x='BRCA1', y='BRCA2')\n",
    "sns.lmplot(data=data_spread, x='BRCA1', y='BRCA2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "33b40e3d",
   "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>samples</th>\n",
       "      <th>CA.102548</th>\n",
       "      <th>CA.104338</th>\n",
       "      <th>CA.105094</th>\n",
       "      <th>CA.109745</th>\n",
       "      <th>CA.1906415</th>\n",
       "      <th>CA.1912627</th>\n",
       "      <th>CA.1924346</th>\n",
       "      <th>CA.1926760</th>\n",
       "      <th>CA.1927842</th>\n",
       "      <th>CA.1933414</th>\n",
       "      <th>...</th>\n",
       "      <th>CAP.2040686</th>\n",
       "      <th>CAP.2046297</th>\n",
       "      <th>CAP.2046641</th>\n",
       "      <th>CAP.348981</th>\n",
       "      <th>CAP.354300</th>\n",
       "      <th>CAP.359448</th>\n",
       "      <th>CAP.94377</th>\n",
       "      <th>CAP.98389</th>\n",
       "      <th>CAP.98475</th>\n",
       "      <th>CAP.99145</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gene</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ALK</th>\n",
       "      <td>0.22</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.98</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.22</td>\n",
       "      <td>1.27</td>\n",
       "      <td>1.50</td>\n",
       "      <td>3.76</td>\n",
       "      <td>...</td>\n",
       "      <td>1.85</td>\n",
       "      <td>1.82</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.16</td>\n",
       "      <td>5.38</td>\n",
       "      <td>1.43</td>\n",
       "      <td>0.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BRCA1</th>\n",
       "      <td>30.37</td>\n",
       "      <td>24.43</td>\n",
       "      <td>43.58</td>\n",
       "      <td>11.05</td>\n",
       "      <td>3.38</td>\n",
       "      <td>5.96</td>\n",
       "      <td>4.26</td>\n",
       "      <td>11.52</td>\n",
       "      <td>9.87</td>\n",
       "      <td>3.47</td>\n",
       "      <td>...</td>\n",
       "      <td>4.80</td>\n",
       "      <td>6.27</td>\n",
       "      <td>6.73</td>\n",
       "      <td>1.87</td>\n",
       "      <td>3.46</td>\n",
       "      <td>4.14</td>\n",
       "      <td>6.58</td>\n",
       "      <td>9.77</td>\n",
       "      <td>4.62</td>\n",
       "      <td>1.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BRCA2</th>\n",
       "      <td>0.38</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.31</td>\n",
       "      <td>0.53</td>\n",
       "      <td>3.37</td>\n",
       "      <td>1.21</td>\n",
       "      <td>4.39</td>\n",
       "      <td>3.91</td>\n",
       "      <td>3.51</td>\n",
       "      <td>...</td>\n",
       "      <td>1.20</td>\n",
       "      <td>0.57</td>\n",
       "      <td>2.11</td>\n",
       "      <td>1.91</td>\n",
       "      <td>0.63</td>\n",
       "      <td>1.66</td>\n",
       "      <td>0.47</td>\n",
       "      <td>1.81</td>\n",
       "      <td>1.10</td>\n",
       "      <td>0.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MYCN</th>\n",
       "      <td>0.38</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.48</td>\n",
       "      <td>4.22</td>\n",
       "      <td>...</td>\n",
       "      <td>2.14</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.59</td>\n",
       "      <td>1.20</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TP53</th>\n",
       "      <td>38.27</td>\n",
       "      <td>29.34</td>\n",
       "      <td>16.09</td>\n",
       "      <td>22.91</td>\n",
       "      <td>20.56</td>\n",
       "      <td>27.81</td>\n",
       "      <td>14.35</td>\n",
       "      <td>87.82</td>\n",
       "      <td>117.87</td>\n",
       "      <td>34.07</td>\n",
       "      <td>...</td>\n",
       "      <td>31.10</td>\n",
       "      <td>39.32</td>\n",
       "      <td>38.47</td>\n",
       "      <td>42.37</td>\n",
       "      <td>25.08</td>\n",
       "      <td>32.68</td>\n",
       "      <td>6.54</td>\n",
       "      <td>34.37</td>\n",
       "      <td>40.71</td>\n",
       "      <td>30.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 60 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "samples  CA.102548  CA.104338  CA.105094  CA.109745  CA.1906415  CA.1912627  \\\n",
       "gene                                                                          \n",
       "ALK           0.22       0.05       0.00       1.98        0.74        0.66   \n",
       "BRCA1        30.37      24.43      43.58      11.05        3.38        5.96   \n",
       "BRCA2         0.38       0.23       0.00       1.31        0.53        3.37   \n",
       "MYCN          0.38       0.00       0.00       0.94        0.37        0.55   \n",
       "TP53         38.27      29.34      16.09      22.91       20.56       27.81   \n",
       "\n",
       "samples  CA.1924346  CA.1926760  CA.1927842  CA.1933414  ...  CAP.2040686  \\\n",
       "gene                                                     ...                \n",
       "ALK            0.22        1.27        1.50        3.76  ...         1.85   \n",
       "BRCA1          4.26       11.52        9.87        3.47  ...         4.80   \n",
       "BRCA2          1.21        4.39        3.91        3.51  ...         1.20   \n",
       "MYCN           0.81        0.46        0.48        4.22  ...         2.14   \n",
       "TP53          14.35       87.82      117.87       34.07  ...        31.10   \n",
       "\n",
       "samples  CAP.2046297  CAP.2046641  CAP.348981  CAP.354300  CAP.359448  \\\n",
       "gene                                                                    \n",
       "ALK             1.82         0.29        0.12        0.00        0.63   \n",
       "BRCA1           6.27         6.73        1.87        3.46        4.14   \n",
       "BRCA2           0.57         2.11        1.91        0.63        1.66   \n",
       "MYCN            0.87         0.59        1.20        0.48        0.28   \n",
       "TP53           39.32        38.47       42.37       25.08       32.68   \n",
       "\n",
       "samples  CAP.94377  CAP.98389  CAP.98475  CAP.99145  \n",
       "gene                                                 \n",
       "ALK           0.16       5.38       1.43       0.49  \n",
       "BRCA1         6.58       9.77       4.62       1.17  \n",
       "BRCA2         0.47       1.81       1.10       0.84  \n",
       "MYCN          0.26       0.75       0.13       0.46  \n",
       "TP53          6.54      34.37      40.71      30.50  \n",
       "\n",
       "[5 rows x 60 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genes_of_interest = ['BRCA1', 'BRCA2', 'TP53', 'ALK', 'MYCN']\n",
    "\n",
    "data_filtered = data_long.loc[data_long['gene'].isin(genes_of_interest)]\n",
    "data_pivot = data_filtered.pivot(index='gene', columns='samples', values='FPKM')\n",
    "data_pivot.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "fecb419f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='samples', ylabel='gene'>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 8))\n",
    "sns.heatmap(data=data_pivot, cmap='Reds', ax=ax, cbar_kws={'label': 'FPKM'})\n",
    "##ggplot(., aes(x = samples, y = gene, fill = FPKM)) +"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}