Code_Problem_2_Sai_Thatigotla.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.decomposition import PCA\n",
    "from mpl_toolkits.mplot3d import Axes3D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = pd.read_csv('ovarian_cancerY.csv', header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = pd.read_csv('ovarian_cancer_grp.csv', header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = x.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.031998</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.065221</td>\n",
       "      <td>0.108920</td>\n",
       "      <td>0.153680</td>\n",
       "      <td>0.196610</td>\n",
       "      <td>0.205470</td>\n",
       "      <td>0.228110</td>\n",
       "      <td>0.217770</td>\n",
       "      <td>0.202740</td>\n",
       "      <td>0.160390</td>\n",
       "      <td>0.141550</td>\n",
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       "      <td>0.039307</td>\n",
       "      <td>0.048933</td>\n",
       "      <td>0.056367</td>\n",
       "      <td>0.039243</td>\n",
       "      <td>0.006035</td>\n",
       "      <td>0.003967</td>\n",
       "      <td>0.030555</td>\n",
       "      <td>0.056087</td>\n",
       "      <td>0.050519</td>\n",
       "      <td>0.038994</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.080745</td>\n",
       "      <td>0.105080</td>\n",
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       "      <td>0.165640</td>\n",
       "      <td>0.159810</td>\n",
       "      <td>0.179360</td>\n",
       "      <td>0.188540</td>\n",
       "      <td>0.184040</td>\n",
       "      <td>0.187080</td>\n",
       "      <td>0.175410</td>\n",
       "      <td>...</td>\n",
       "      <td>0.033520</td>\n",
       "      <td>0.040522</td>\n",
       "      <td>0.048022</td>\n",
       "      <td>0.033026</td>\n",
       "      <td>0.020556</td>\n",
       "      <td>0.021642</td>\n",
       "      <td>0.017161</td>\n",
       "      <td>0.004715</td>\n",
       "      <td>0.013811</td>\n",
       "      <td>0.025288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.115690</td>\n",
       "      <td>0.116620</td>\n",
       "      <td>0.133380</td>\n",
       "      <td>0.195540</td>\n",
       "      <td>0.192280</td>\n",
       "      <td>0.180400</td>\n",
       "      <td>0.196040</td>\n",
       "      <td>0.198720</td>\n",
       "      <td>0.173530</td>\n",
       "      <td>0.144080</td>\n",
       "      <td>...</td>\n",
       "      <td>0.034289</td>\n",
       "      <td>0.050247</td>\n",
       "      <td>0.052444</td>\n",
       "      <td>0.027980</td>\n",
       "      <td>0.015724</td>\n",
       "      <td>0.042677</td>\n",
       "      <td>0.072540</td>\n",
       "      <td>0.086192</td>\n",
       "      <td>0.073603</td>\n",
       "      <td>0.060128</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.092124</td>\n",
       "      <td>0.100540</td>\n",
       "      <td>0.112570</td>\n",
       "      <td>0.119400</td>\n",
       "      <td>0.113380</td>\n",
       "      <td>0.103230</td>\n",
       "      <td>0.093086</td>\n",
       "      <td>0.101040</td>\n",
       "      <td>0.151610</td>\n",
       "      <td>0.162770</td>\n",
       "      <td>...</td>\n",
       "      <td>0.011757</td>\n",
       "      <td>0.016846</td>\n",
       "      <td>0.028450</td>\n",
       "      <td>0.032914</td>\n",
       "      <td>0.029125</td>\n",
       "      <td>0.053310</td>\n",
       "      <td>0.063456</td>\n",
       "      <td>0.051392</td>\n",
       "      <td>0.024522</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <th>211</th>\n",
       "      <td>0.080488</td>\n",
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       "      <td>0.057791</td>\n",
       "      <td>0.073588</td>\n",
       "      <td>0.080470</td>\n",
       "      <td>0.084023</td>\n",
       "      <td>0.088232</td>\n",
       "      <td>0.097565</td>\n",
       "      <td>0.086802</td>\n",
       "      <td>0.072722</td>\n",
       "      <td>...</td>\n",
       "      <td>0.026808</td>\n",
       "      <td>0.022818</td>\n",
       "      <td>0.014763</td>\n",
       "      <td>0.011817</td>\n",
       "      <td>0.010543</td>\n",
       "      <td>0.001452</td>\n",
       "      <td>-0.001171</td>\n",
       "      <td>0.007329</td>\n",
       "      <td>0.021296</td>\n",
       "      <td>0.035113</td>\n",
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       "    <tr>\n",
       "      <th>212</th>\n",
       "      <td>0.051982</td>\n",
       "      <td>0.068565</td>\n",
       "      <td>0.086436</td>\n",
       "      <td>0.098018</td>\n",
       "      <td>0.113010</td>\n",
       "      <td>0.113130</td>\n",
       "      <td>0.100540</td>\n",
       "      <td>0.101610</td>\n",
       "      <td>0.104090</td>\n",
       "      <td>0.097123</td>\n",
       "      <td>...</td>\n",
       "      <td>0.009567</td>\n",
       "      <td>0.023404</td>\n",
       "      <td>0.038047</td>\n",
       "      <td>0.044016</td>\n",
       "      <td>0.052350</td>\n",
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       "      <td>0.019540</td>\n",
       "      <td>0.022541</td>\n",
       "      <td>0.027594</td>\n",
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       "      <th>213</th>\n",
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       "      <td>0.076061</td>\n",
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       "      <td>0.064008</td>\n",
       "      <td>0.059959</td>\n",
       "      <td>0.073831</td>\n",
       "      <td>0.065935</td>\n",
       "      <td>0.048028</td>\n",
       "      <td>...</td>\n",
       "      <td>0.026115</td>\n",
       "      <td>0.026615</td>\n",
       "      <td>0.027872</td>\n",
       "      <td>0.030449</td>\n",
       "      <td>0.027307</td>\n",
       "      <td>0.013349</td>\n",
       "      <td>0.011723</td>\n",
       "      <td>0.018312</td>\n",
       "      <td>0.019686</td>\n",
       "      <td>0.016553</td>\n",
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       "      <td>0.127510</td>\n",
       "      <td>0.131810</td>\n",
       "      <td>0.127920</td>\n",
       "      <td>0.149410</td>\n",
       "      <td>0.148420</td>\n",
       "      <td>0.126160</td>\n",
       "      <td>0.110060</td>\n",
       "      <td>...</td>\n",
       "      <td>0.040197</td>\n",
       "      <td>0.045514</td>\n",
       "      <td>0.027957</td>\n",
       "      <td>0.019837</td>\n",
       "      <td>0.017701</td>\n",
       "      <td>0.019901</td>\n",
       "      <td>0.019325</td>\n",
       "      <td>0.037866</td>\n",
       "      <td>0.058761</td>\n",
       "      <td>0.078826</td>\n",
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       "      <th>215</th>\n",
       "      <td>0.062180</td>\n",
       "      <td>0.092555</td>\n",
       "      <td>0.124560</td>\n",
       "      <td>0.159250</td>\n",
       "      <td>0.154490</td>\n",
       "      <td>0.166410</td>\n",
       "      <td>0.172030</td>\n",
       "      <td>0.172800</td>\n",
       "      <td>0.134730</td>\n",
       "      <td>0.104280</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.002662</td>\n",
       "      <td>0.011649</td>\n",
       "      <td>0.016184</td>\n",
       "      <td>0.009600</td>\n",
       "      <td>0.002260</td>\n",
       "      <td>0.011427</td>\n",
       "      <td>0.013144</td>\n",
       "      <td>0.030164</td>\n",
       "      <td>0.041931</td>\n",
       "      <td>0.055006</td>\n",
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       "<p>216 rows × 15000 columns</p>\n",
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      "text/plain": [
       "        0         1         2         3         4         5         6      \\\n",
       "0    0.097044  0.109940  0.130760  0.170930  0.165680  0.170840  0.171300   \n",
       "1    0.065221  0.108920  0.153680  0.196610  0.205470  0.228110  0.217770   \n",
       "2    0.080745  0.105080  0.134210  0.165640  0.159810  0.179360  0.188540   \n",
       "3    0.115690  0.116620  0.133380  0.195540  0.192280  0.180400  0.196040   \n",
       "4    0.092124  0.100540  0.112570  0.119400  0.113380  0.103230  0.093086   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
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       "212  0.051982  0.068565  0.086436  0.098018  0.113010  0.113130  0.100540   \n",
       "213  0.023482  0.036036  0.053269  0.076061  0.071301  0.064008  0.059959   \n",
       "214  0.055729  0.073022  0.097375  0.127510  0.131810  0.127920  0.149410   \n",
       "215  0.062180  0.092555  0.124560  0.159250  0.154490  0.166410  0.172030   \n",
       "\n",
       "        7         8         9      ...     14990     14991     14992  \\\n",
       "0    0.151740  0.132200  0.159420  ...  0.001533  0.006324  0.003863   \n",
       "1    0.202740  0.160390  0.141550  ...  0.039307  0.048933  0.056367   \n",
       "2    0.184040  0.187080  0.175410  ...  0.033520  0.040522  0.048022   \n",
       "3    0.198720  0.173530  0.144080  ...  0.034289  0.050247  0.052444   \n",
       "4    0.101040  0.151610  0.162770  ...  0.011757  0.016846  0.028450   \n",
       "..        ...       ...       ...  ...       ...       ...       ...   \n",
       "211  0.097565  0.086802  0.072722  ...  0.026808  0.022818  0.014763   \n",
       "212  0.101610  0.104090  0.097123  ...  0.009567  0.023404  0.038047   \n",
       "213  0.073831  0.065935  0.048028  ...  0.026115  0.026615  0.027872   \n",
       "214  0.148420  0.126160  0.110060  ...  0.040197  0.045514  0.027957   \n",
       "215  0.172800  0.134730  0.104280  ... -0.002662  0.011649  0.016184   \n",
       "\n",
       "        14993     14994     14995     14996     14997     14998     14999  \n",
       "0    0.015751  0.028215  0.026312  0.020241  0.031998  0.040492  0.047161  \n",
       "1    0.039243  0.006035  0.003967  0.030555  0.056087  0.050519  0.038994  \n",
       "2    0.033026  0.020556  0.021642  0.017161  0.004715  0.013811  0.025288  \n",
       "3    0.027980  0.015724  0.042677  0.072540  0.086192  0.073603  0.060128  \n",
       "4    0.032914  0.029125  0.053310  0.063456  0.051392  0.024522 -0.001786  \n",
       "..        ...       ...       ...       ...       ...       ...       ...  \n",
       "211  0.011817  0.010543  0.001452 -0.001171  0.007329  0.021296  0.035113  \n",
       "212  0.044016  0.052350  0.054626  0.038809  0.019540  0.022541  0.027594  \n",
       "213  0.030449  0.027307  0.013349  0.011723  0.018312  0.019686  0.016553  \n",
       "214  0.019837  0.017701  0.019901  0.019325  0.037866  0.058761  0.078826  \n",
       "215  0.009600  0.002260  0.011427  0.013144  0.030164  0.041931  0.055006  \n",
       "\n",
       "[216 rows x 15000 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
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    }
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   "source": [
    "x"
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  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Cancer</td>\n",
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       "      <td>Normal</td>\n",
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       "<p>216 rows × 1 columns</p>\n",
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       "          0\n",
       "0    Cancer\n",
       "1    Cancer\n",
       "2    Cancer\n",
       "3    Cancer\n",
       "4    Cancer\n",
       "..      ...\n",
       "211  Normal\n",
       "212  Normal\n",
       "213  Normal\n",
       "214  Normal\n",
       "215  Normal\n",
       "\n",
       "[216 rows x 1 columns]"
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     "execution_count": 17,
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    "labels"
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  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_np = x.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1941.3x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pca = PCA().fit(x_np)\n",
    "plt.figure(1, figsize=(20,10))\n",
    "xi = np.arange(1, x.shape[0]+1, step=1)\n",
    "y = np.cumsum(pca.explained_variance_ratio_)\n",
    "plt.ylim(0.0,1.1)\n",
    "plt.plot(xi[0:100], y[0:100], marker='o', linestyle='--')\n",
    "\n",
    "plt.xlabel('Number of Components')\n",
    "plt.xticks(np.arange(0, 101, step=1)) #change from 0-based array index to 1-based human-readable label\n",
    "plt.ylabel('Cumulative variance (%)')\n",
    "plt.title('PCA - number of components vs. cumulative variance')\n",
    "\n",
    "plt.grid()\n",
    "\n",
    "plt.gca().margins(x=0)\n",
    "plt.gcf().canvas.draw()\n",
    "tl = plt.gca().get_xticklabels()\n",
    "maxsize = max([t.get_window_extent().width for t in tl])\n",
    "m = 0.2 # inch margin\n",
    "#s = maxsize/plt.gcf().dpi*x_np.shape[0]+2*m\n",
    "s = maxsize/plt.gcf().dpi*100+2*m\n",
    "margin = m/plt.gcf().get_size_inches()[0]\n",
    "\n",
    "plt.gcf().subplots_adjust(left=margin, right=1.-margin)\n",
    "plt.gcf().set_size_inches(s, plt.gcf().get_size_inches()[1])\n",
    "plt.show()\n",
    "#plt.savefig('pca_plot_samples.svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pca_obj = PCA(n_components=3)\n",
    "pca = pca_obj.fit_transform(x_np)\n",
    "cdict = {'Cancer': 'red', 'Normal': 'blue'}\n",
    "group = labels.values\n",
    "\n",
    "ig = plt.subplots()\n",
    "ax = plt.axes(projection='3d')\n",
    "for g in np.unique(group):\n",
    "    ix = np.where(group == g)\n",
    "    ax.scatter3D(pca[ix,0], pca[ix,1], pca[ix,2], c = cdict[g], label = g)\n",
    "ax.legend()\n",
    "plt.title(\"pca on gene expression\")\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 4.26897064e-04  4.98497685e-04  6.63929523e-04 ...  3.19776784e-04\n",
      "   2.70441851e-04  2.09978130e-04]\n",
      " [-9.75979861e-04 -7.97127463e-04 -9.98423358e-04 ...  1.32701656e-04\n",
      "  -1.48395437e-04 -4.43598120e-04]\n",
      " [ 2.92307034e-03  2.13157959e-03  1.16331297e-03 ...  5.66330384e-05\n",
      "   1.36949620e-04  1.91064197e-04]]\n"
     ]
    }
   ],
   "source": [
    "print(pca_obj.components_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}