Cluster_tSNE.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 345,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.manifold import TSNE\n",
    "from numpy import reshape\n",
    "import seaborn as sns\n",
    "import pandas as pd \n",
    "\n",
    "pd.options.display.max_rows = 999\n",
    "# https://www.datatechnotes.com/2020/11/tsne-visualization-example-in-python.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "metadata": {},
   "outputs": [],
   "source": [
    "tsne = TSNE(n_components=2, verbose=1, random_state=123)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.000110</td>\n",
       "      <td>0.000390</td>\n",
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       "      <td>0.000296</td>\n",
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       "      <td>0.000165</td>\n",
       "      <td>0.000274</td>\n",
       "      <td>0.000468</td>\n",
       "      <td>0.000132</td>\n",
       "      <td>0.000177</td>\n",
       "      <td>0.000424</td>\n",
       "      <td>0.000202</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>0.000509</td>\n",
       "      <td>0.000347</td>\n",
       "      <td>0.000229</td>\n",
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       "      <td>0.000300</td>\n",
       "      <td>0.000577</td>\n",
       "      <td>0.000697</td>\n",
       "      <td>0.000665</td>\n",
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       "<p>5 rows × 256 columns</p>\n",
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      "text/plain": [
       "       AAAA      AAAC      AAAG      AAAT      AACA      AACC      AACG  \\\n",
       "0  0.000291  0.000566  0.000578  0.000459  0.000513  0.000375  0.000247   \n",
       "1  0.000166  0.000113  0.000462  0.000401  0.000073  0.000075  0.000247   \n",
       "2  0.000665  0.000396  0.000520  0.000287  0.000293  0.000751  0.000330   \n",
       "3  0.000624  0.000509  0.000347  0.000229  0.000293  0.000300  0.000577   \n",
       "4  0.000166  0.000396  0.000289  0.000229  0.000220  0.000601  0.000165   \n",
       "\n",
       "       AACT      AAGA      AAGC  ...      TTCG      TTCT      TTGA      TTGC  \\\n",
       "0  0.000784  0.000517  0.000220  ...  0.000184  0.000628  0.000568  0.000110   \n",
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       "4  0.000436  0.000665  0.000385  ...  0.000368  0.000393  0.000207  0.000274   \n",
       "\n",
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       "0  0.000110  0.000390  0.000528  0.000296  0.000518  0.000283  \n",
       "1  0.000549  0.000078  0.000264  0.000532  0.000377  0.000606  \n",
       "2  0.000274  0.000468  0.000132  0.000177  0.000424  0.000202  \n",
       "3  0.000219  0.000546  0.000396  0.000414  0.000330  0.000162  \n",
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       "\n",
       "[5 rows x 256 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(2936, 256)"
      ]
     },
     "execution_count": 347,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"tetraFrequency.csv\")\n",
    "display(df.head())\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.00029113, 0.00056564, 0.00057807, ..., 0.00029556, 0.00051809,\n",
       "        0.00028297],\n",
       "       [0.00016636, 0.00011313, 0.00046245, ..., 0.00053201, 0.00037679,\n",
       "        0.00060635],\n",
       "       [0.00066545, 0.00039595, 0.00052026, ..., 0.00017734, 0.00042389,\n",
       "        0.00020212],\n",
       "       ...,\n",
       "       [0.0004159 , 0.00033939, 0.00017342, ..., 0.00041378, 0.0001413 ,\n",
       "        0.00044466],\n",
       "       [0.00012477, 0.00039595, 0.00040465, ..., 0.00023645, 0.00028259,\n",
       "        0.00040424],\n",
       "       [0.00016636, 0.00039595, 0.00040465, ..., 0.00029556, 0.00023549,\n",
       "        0.00028297]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(2936, 256)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "2936"
      ]
     },
     "execution_count": 348,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df.to_numpy()\n",
    "display(X)\n",
    "display(X.shape)\n",
    "\n",
    "y = [num for num in range(0,2936)]\n",
    "len(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[t-SNE] Computing 91 nearest neighbors...\n",
      "[t-SNE] Indexed 2936 samples in 0.294s...\n",
      "[t-SNE] Computed neighbors for 2936 samples in 6.487s...\n",
      "[t-SNE] Computed conditional probabilities for sample 1000 / 2936\n",
      "[t-SNE] Computed conditional probabilities for sample 2000 / 2936\n",
      "[t-SNE] Computed conditional probabilities for sample 2936 / 2936\n",
      "[t-SNE] Mean sigma: 0.000806\n",
      "[t-SNE] KL divergence after 250 iterations with early exaggeration: 84.560463\n",
      "[t-SNE] KL divergence after 1000 iterations: 2.721181\n"
     ]
    }
   ],
   "source": [
    "z = tsne.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(-55, 15, 'Toxins/Trash')"
      ]
     },
     "execution_count": 357,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "df = pd.DataFrame()\n",
    "df[\"y\"] = y\n",
    "df[\"comp-1\"] = z[:,0]\n",
    "df[\"comp-2\"] = z[:,1]\n",
    "\n",
    "##########################\n",
    "\n",
    "bottom_left = (z[:,0] < -33) & (z[:,1] < -11)\n",
    "far_right = z[:,0] > 35\n",
    "middle_left = (z[:,1] < 10) & (z[:,1] > -10) & (z[:,0] < -25)\n",
    "\n",
    "alltogether = bottom_left | far_right | middle_left\n",
    "##########################\n",
    "\n",
    "\n",
    "df['color'] = alltogether\n",
    "df['color'] = df['color'].astype('int')\n",
    "df.head()\n",
    "\n",
    "sns.scatterplot(x=\"comp-1\", y=\"comp-2\",\n",
    "                       data=df).set(title=\"Vibrio Cholera Dimension Reduction\", \n",
    "                             xticks=[x for x in range(-40,40, 10)],\n",
    "                             yticks=[x for x in range(-40,40, 10)]) \n",
    "'''\n",
    "\n",
    "plot = sns.scatterplot(x=\"comp-1\", y=\"comp-2\",\n",
    "                       data=df, hue=df.color.tolist(),\n",
    "                       palette=sns.color_palette(\"hls\", 2)).set(title=\"Vibrio Cholera Dimension Reduction\", \n",
    "                             xticks=[x for x in range(-60,60, 5)],\n",
    "                             yticks=[x for x in range(-100,100, 10)]) \n",
    "'''\n",
    "\n",
    "plt.text(40,0,'rRNA')\n",
    "plt.text(-40, -30, 'rRNA')\n",
    "plt.text(-55, 15, \"Toxins/Trash\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.savefig(\"Vibrio_Cholera_tSNE.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1</th>\n",
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       "      <th>3</th>\n",
       "      <td>3</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   y     comp-1     comp-2  color\n",
       "0  0  10.845245 -12.159692      0\n",
       "1  1  22.130840 -23.940817      0\n",
       "2  2 -10.197120  -3.744754      0\n",
       "3  3  -1.063221 -10.309160      0\n",
       "4  4  28.109728 -28.594606      0"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['color'] = middle_left\n",
    "df['color'] = df['color'].astype('int')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>112</th>\n",
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       "      <td>-2.010358</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>121</th>\n",
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       "    <tr>\n",
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       "      <th>126</th>\n",
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       "      <td>-30.515560</td>\n",
       "      <td>-0.978630</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>127</td>\n",
       "      <td>-31.167408</td>\n",
       "      <td>-2.451567</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>128</td>\n",
       "      <td>-31.653685</td>\n",
       "      <td>-3.600238</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>129</td>\n",
       "      <td>-34.948811</td>\n",
       "      <td>-3.118284</td>\n",
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       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>130</td>\n",
       "      <td>-39.101974</td>\n",
       "      <td>-1.013883</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>131</td>\n",
       "      <td>-37.407280</td>\n",
       "      <td>-2.154957</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>132</td>\n",
       "      <td>-34.659943</td>\n",
       "      <td>0.127962</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>133</td>\n",
       "      <td>-38.600021</td>\n",
       "      <td>-2.104628</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>134</td>\n",
       "      <td>-34.668488</td>\n",
       "      <td>-3.447419</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>139</td>\n",
       "      <td>-31.825474</td>\n",
       "      <td>-3.539508</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>140</td>\n",
       "      <td>-30.509792</td>\n",
       "      <td>-2.086132</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>141</td>\n",
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       "      <td>-3.333163</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>142</td>\n",
       "      <td>-37.527210</td>\n",
       "      <td>2.713999</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>144</td>\n",
       "      <td>-29.002762</td>\n",
       "      <td>1.975947</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>153</td>\n",
       "      <td>-28.233423</td>\n",
       "      <td>-6.580640</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>315</th>\n",
       "      <td>315</td>\n",
       "      <td>-28.798429</td>\n",
       "      <td>-6.395526</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>638</th>\n",
       "      <td>638</td>\n",
       "      <td>-30.104898</td>\n",
       "      <td>-7.624944</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>715</th>\n",
       "      <td>715</td>\n",
       "      <td>-37.403191</td>\n",
       "      <td>0.831462</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>716</th>\n",
       "      <td>716</td>\n",
       "      <td>-37.620300</td>\n",
       "      <td>2.971840</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>722</th>\n",
       "      <td>722</td>\n",
       "      <td>-34.594101</td>\n",
       "      <td>1.574919</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>724</th>\n",
       "      <td>724</td>\n",
       "      <td>-33.373142</td>\n",
       "      <td>3.964368</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>725</th>\n",
       "      <td>725</td>\n",
       "      <td>-39.770748</td>\n",
       "      <td>2.835627</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>726</th>\n",
       "      <td>726</td>\n",
       "      <td>-37.764774</td>\n",
       "      <td>-3.064705</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>727</th>\n",
       "      <td>727</td>\n",
       "      <td>-39.836254</td>\n",
       "      <td>-3.596825</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>728</th>\n",
       "      <td>728</td>\n",
       "      <td>-39.506962</td>\n",
       "      <td>-3.442415</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>729</th>\n",
       "      <td>729</td>\n",
       "      <td>-39.724979</td>\n",
       "      <td>-2.973763</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>730</th>\n",
       "      <td>730</td>\n",
       "      <td>-40.521198</td>\n",
       "      <td>-3.057629</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>731</th>\n",
       "      <td>731</td>\n",
       "      <td>-38.646458</td>\n",
       "      <td>-2.312860</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>732</th>\n",
       "      <td>732</td>\n",
       "      <td>-39.047459</td>\n",
       "      <td>-3.631675</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>733</th>\n",
       "      <td>733</td>\n",
       "      <td>-39.657345</td>\n",
       "      <td>-2.781207</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>734</th>\n",
       "      <td>734</td>\n",
       "      <td>-33.056118</td>\n",
       "      <td>-2.113420</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>735</th>\n",
       "      <td>735</td>\n",
       "      <td>-34.722462</td>\n",
       "      <td>0.804815</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>738</th>\n",
       "      <td>738</td>\n",
       "      <td>-38.392483</td>\n",
       "      <td>-1.489860</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>740</th>\n",
       "      <td>740</td>\n",
       "      <td>-30.269894</td>\n",
       "      <td>-4.923888</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>741</th>\n",
       "      <td>741</td>\n",
       "      <td>-32.610390</td>\n",
       "      <td>-1.627540</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>742</th>\n",
       "      <td>742</td>\n",
       "      <td>-36.886322</td>\n",
       "      <td>-3.993470</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>743</th>\n",
       "      <td>743</td>\n",
       "      <td>-35.278812</td>\n",
       "      <td>-1.877730</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>744</th>\n",
       "      <td>744</td>\n",
       "      <td>-39.252159</td>\n",
       "      <td>-2.383076</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>745</th>\n",
       "      <td>745</td>\n",
       "      <td>-37.760025</td>\n",
       "      <td>-3.422823</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>746</th>\n",
       "      <td>746</td>\n",
       "      <td>-35.595894</td>\n",
       "      <td>-4.608791</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>747</th>\n",
       "      <td>747</td>\n",
       "      <td>-34.197392</td>\n",
       "      <td>-1.203192</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>748</th>\n",
       "      <td>748</td>\n",
       "      <td>-36.380112</td>\n",
       "      <td>-1.080737</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>749</th>\n",
       "      <td>749</td>\n",
       "      <td>-40.963982</td>\n",
       "      <td>-2.817035</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>750</th>\n",
       "      <td>750</td>\n",
       "      <td>-37.337170</td>\n",
       "      <td>-0.035145</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>751</th>\n",
       "      <td>751</td>\n",
       "      <td>-37.873920</td>\n",
       "      <td>-1.117691</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>752</th>\n",
       "      <td>752</td>\n",
       "      <td>-35.994530</td>\n",
       "      <td>-3.348745</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>753</th>\n",
       "      <td>753</td>\n",
       "      <td>-34.762474</td>\n",
       "      <td>-0.298655</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>754</th>\n",
       "      <td>754</td>\n",
       "      <td>-36.040901</td>\n",
       "      <td>-0.274014</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>755</th>\n",
       "      <td>755</td>\n",
       "      <td>-39.271259</td>\n",
       "      <td>0.363993</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>756</th>\n",
       "      <td>756</td>\n",
       "      <td>-36.745934</td>\n",
       "      <td>2.261780</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>757</th>\n",
       "      <td>757</td>\n",
       "      <td>-35.688442</td>\n",
       "      <td>0.197605</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>758</th>\n",
       "      <td>758</td>\n",
       "      <td>-36.341354</td>\n",
       "      <td>-2.373695</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>760</th>\n",
       "      <td>760</td>\n",
       "      <td>-40.516941</td>\n",
       "      <td>-2.085906</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>761</th>\n",
       "      <td>761</td>\n",
       "      <td>-34.847702</td>\n",
       "      <td>1.473469</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>775</th>\n",
       "      <td>775</td>\n",
       "      <td>-34.161804</td>\n",
       "      <td>-4.934171</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>793</th>\n",
       "      <td>793</td>\n",
       "      <td>-25.269650</td>\n",
       "      <td>4.095628</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>828</th>\n",
       "      <td>828</td>\n",
       "      <td>-29.941692</td>\n",
       "      <td>-6.075901</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>840</th>\n",
       "      <td>840</td>\n",
       "      <td>-33.060459</td>\n",
       "      <td>-1.515880</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>841</th>\n",
       "      <td>841</td>\n",
       "      <td>-38.852413</td>\n",
       "      <td>-0.069216</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>842</th>\n",
       "      <td>842</td>\n",
       "      <td>-35.610867</td>\n",
       "      <td>4.188738</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>848</th>\n",
       "      <td>848</td>\n",
       "      <td>-37.284630</td>\n",
       "      <td>0.830060</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>936</th>\n",
       "      <td>936</td>\n",
       "      <td>-25.318068</td>\n",
       "      <td>3.027726</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>988</th>\n",
       "      <td>988</td>\n",
       "      <td>-32.032215</td>\n",
       "      <td>-8.774473</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1048</th>\n",
       "      <td>1048</td>\n",
       "      <td>-33.195610</td>\n",
       "      <td>1.514256</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1066</th>\n",
       "      <td>1066</td>\n",
       "      <td>-25.291010</td>\n",
       "      <td>3.011070</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1206</th>\n",
       "      <td>1206</td>\n",
       "      <td>-34.226185</td>\n",
       "      <td>-1.194811</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1262</th>\n",
       "      <td>1262</td>\n",
       "      <td>-35.974255</td>\n",
       "      <td>-0.817292</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1263</th>\n",
       "      <td>1263</td>\n",
       "      <td>-33.403236</td>\n",
       "      <td>0.123499</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1311</th>\n",
       "      <td>1311</td>\n",
       "      <td>-34.512642</td>\n",
       "      <td>-6.967713</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1345</th>\n",
       "      <td>1345</td>\n",
       "      <td>-35.245987</td>\n",
       "      <td>1.321923</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1362</th>\n",
       "      <td>1362</td>\n",
       "      <td>-30.257113</td>\n",
       "      <td>-1.877823</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1364</th>\n",
       "      <td>1364</td>\n",
       "      <td>-29.216057</td>\n",
       "      <td>-3.066440</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1365</th>\n",
       "      <td>1365</td>\n",
       "      <td>-35.749760</td>\n",
       "      <td>-2.713949</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1366</th>\n",
       "      <td>1366</td>\n",
       "      <td>-37.107964</td>\n",
       "      <td>-1.812024</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1390</th>\n",
       "      <td>1390</td>\n",
       "      <td>-25.018177</td>\n",
       "      <td>0.469749</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1398</th>\n",
       "      <td>1398</td>\n",
       "      <td>-35.550941</td>\n",
       "      <td>4.224648</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1413</th>\n",
       "      <td>1413</td>\n",
       "      <td>-30.819731</td>\n",
       "      <td>-5.227886</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1417</th>\n",
       "      <td>1417</td>\n",
       "      <td>-41.548637</td>\n",
       "      <td>0.020807</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1422</th>\n",
       "      <td>1422</td>\n",
       "      <td>-28.525965</td>\n",
       "      <td>7.865444</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1424</th>\n",
       "      <td>1424</td>\n",
       "      <td>-41.552696</td>\n",
       "      <td>0.023462</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1429</th>\n",
       "      <td>1429</td>\n",
       "      <td>-28.526632</td>\n",
       "      <td>7.866359</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1435</th>\n",
       "      <td>1435</td>\n",
       "      <td>-25.763586</td>\n",
       "      <td>-3.660509</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1441</th>\n",
       "      <td>1441</td>\n",
       "      <td>-25.740755</td>\n",
       "      <td>-3.679526</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1555</th>\n",
       "      <td>1555</td>\n",
       "      <td>-38.852985</td>\n",
       "      <td>1.559285</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1640</th>\n",
       "      <td>1640</td>\n",
       "      <td>-38.987518</td>\n",
       "      <td>5.502830</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1778</th>\n",
       "      <td>1778</td>\n",
       "      <td>-36.626392</td>\n",
       "      <td>7.038387</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1803</th>\n",
       "      <td>1803</td>\n",
       "      <td>-32.370449</td>\n",
       "      <td>-4.128669</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1806</th>\n",
       "      <td>1806</td>\n",
       "      <td>-38.942097</td>\n",
       "      <td>-4.774977</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1807</th>\n",
       "      <td>1807</td>\n",
       "      <td>-38.680286</td>\n",
       "      <td>-4.541138</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1953</th>\n",
       "      <td>1953</td>\n",
       "      <td>-40.140533</td>\n",
       "      <td>3.038202</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2036</th>\n",
       "      <td>2036</td>\n",
       "      <td>-38.008148</td>\n",
       "      <td>-5.567211</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2410</th>\n",
       "      <td>2410</td>\n",
       "      <td>-40.010025</td>\n",
       "      <td>-4.197345</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2411</th>\n",
       "      <td>2411</td>\n",
       "      <td>-38.063110</td>\n",
       "      <td>-3.688959</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2412</th>\n",
       "      <td>2412</td>\n",
       "      <td>-38.977295</td>\n",
       "      <td>-4.918305</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         y     comp-1    comp-2  color\n",
       "54      54 -37.367435  3.773123      1\n",
       "56      56 -36.811428 -1.631629      1\n",
       "57      57 -33.842182 -3.469730      1\n",
       "58      58 -27.278339 -3.271122      0\n",
       "59      59 -29.874750 -2.587281      0\n",
       "64      64 -37.603085  3.071225      1\n",
       "67      67 -35.882954  2.986134      1\n",
       "111    111 -35.478718 -4.727273      1\n",
       "112    112 -36.711697 -2.010358      1\n",
       "121    121 -39.146259  2.710641      1\n",
       "125    125 -32.959549 -0.487548      1\n",
       "126    126 -30.515560 -0.978630      1\n",
       "127    127 -31.167408 -2.451567      1\n",
       "128    128 -31.653685 -3.600238      1\n",
       "129    129 -34.948811 -3.118284      1\n",
       "130    130 -39.101974 -1.013883      1\n",
       "131    131 -37.407280 -2.154957      1\n",
       "132    132 -34.659943  0.127962      1\n",
       "133    133 -38.600021 -2.104628      1\n",
       "134    134 -34.668488 -3.447419      1\n",
       "139    139 -31.825474 -3.539508      1\n",
       "140    140 -30.509792 -2.086132      1\n",
       "141    141 -40.611782 -3.333163      1\n",
       "142    142 -37.527210  2.713999      1\n",
       "144    144 -29.002762  1.975947      0\n",
       "153    153 -28.233423 -6.580640      0\n",
       "315    315 -28.798429 -6.395526      0\n",
       "638    638 -30.104898 -7.624944      1\n",
       "715    715 -37.403191  0.831462      1\n",
       "716    716 -37.620300  2.971840      1\n",
       "722    722 -34.594101  1.574919      1\n",
       "724    724 -33.373142  3.964368      1\n",
       "725    725 -39.770748  2.835627      1\n",
       "726    726 -37.764774 -3.064705      1\n",
       "727    727 -39.836254 -3.596825      1\n",
       "728    728 -39.506962 -3.442415      1\n",
       "729    729 -39.724979 -2.973763      1\n",
       "730    730 -40.521198 -3.057629      1\n",
       "731    731 -38.646458 -2.312860      1\n",
       "732    732 -39.047459 -3.631675      1\n",
       "733    733 -39.657345 -2.781207      1\n",
       "734    734 -33.056118 -2.113420      1\n",
       "735    735 -34.722462  0.804815      1\n",
       "738    738 -38.392483 -1.489860      1\n",
       "740    740 -30.269894 -4.923888      1\n",
       "741    741 -32.610390 -1.627540      1\n",
       "742    742 -36.886322 -3.993470      1\n",
       "743    743 -35.278812 -1.877730      1\n",
       "744    744 -39.252159 -2.383076      1\n",
       "745    745 -37.760025 -3.422823      1\n",
       "746    746 -35.595894 -4.608791      1\n",
       "747    747 -34.197392 -1.203192      1\n",
       "748    748 -36.380112 -1.080737      1\n",
       "749    749 -40.963982 -2.817035      1\n",
       "750    750 -37.337170 -0.035145      1\n",
       "751    751 -37.873920 -1.117691      1\n",
       "752    752 -35.994530 -3.348745      1\n",
       "753    753 -34.762474 -0.298655      1\n",
       "754    754 -36.040901 -0.274014      1\n",
       "755    755 -39.271259  0.363993      1\n",
       "756    756 -36.745934  2.261780      1\n",
       "757    757 -35.688442  0.197605      1\n",
       "758    758 -36.341354 -2.373695      1\n",
       "760    760 -40.516941 -2.085906      1\n",
       "761    761 -34.847702  1.473469      1\n",
       "775    775 -34.161804 -4.934171      1\n",
       "793    793 -25.269650  4.095628      0\n",
       "828    828 -29.941692 -6.075901      0\n",
       "840    840 -33.060459 -1.515880      1\n",
       "841    841 -38.852413 -0.069216      1\n",
       "842    842 -35.610867  4.188738      1\n",
       "848    848 -37.284630  0.830060      1\n",
       "936    936 -25.318068  3.027726      0\n",
       "988    988 -32.032215 -8.774473      1\n",
       "1048  1048 -33.195610  1.514256      1\n",
       "1066  1066 -25.291010  3.011070      0\n",
       "1206  1206 -34.226185 -1.194811      1\n",
       "1262  1262 -35.974255 -0.817292      1\n",
       "1263  1263 -33.403236  0.123499      1\n",
       "1311  1311 -34.512642 -6.967713      1\n",
       "1345  1345 -35.245987  1.321923      1\n",
       "1362  1362 -30.257113 -1.877823      1\n",
       "1364  1364 -29.216057 -3.066440      0\n",
       "1365  1365 -35.749760 -2.713949      1\n",
       "1366  1366 -37.107964 -1.812024      1\n",
       "1390  1390 -25.018177  0.469749      0\n",
       "1398  1398 -35.550941  4.224648      1\n",
       "1413  1413 -30.819731 -5.227886      1\n",
       "1417  1417 -41.548637  0.020807      1\n",
       "1422  1422 -28.525965  7.865444      0\n",
       "1424  1424 -41.552696  0.023462      1\n",
       "1429  1429 -28.526632  7.866359      0\n",
       "1435  1435 -25.763586 -3.660509      0\n",
       "1441  1441 -25.740755 -3.679526      0\n",
       "1555  1555 -38.852985  1.559285      1\n",
       "1640  1640 -38.987518  5.502830      1\n",
       "1778  1778 -36.626392  7.038387      1\n",
       "1803  1803 -32.370449 -4.128669      1\n",
       "1806  1806 -38.942097 -4.774977      1\n",
       "1807  1807 -38.680286 -4.541138      1\n",
       "1953  1953 -40.140533  3.038202      1\n",
       "2036  2036 -38.008148 -5.567211      1\n",
       "2410  2410 -40.010025 -4.197345      1\n",
       "2411  2411 -38.063110 -3.688959      1\n",
       "2412  2412 -38.977295 -4.918305      1"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bottom_left = (z[:,0] < -33) & (z[:,1] < -11)\n",
    "far_right = z[:,0] > 35\n",
    "middle_left = (z[:,1] < 10) & (z[:,1] > -10) & (z[:,0] < -25)\n",
    "df[ middle_left ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lets grab the Toxins/Trash and TSNE that only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAAA</th>\n",
       "      <th>AAAC</th>\n",
       "      <th>AAAG</th>\n",
       "      <th>AAAT</th>\n",
       "      <th>AACA</th>\n",
       "      <th>AACC</th>\n",
       "      <th>AACG</th>\n",
       "      <th>AACT</th>\n",
       "      <th>AAGA</th>\n",
       "      <th>AAGC</th>\n",
       "      <th>...</th>\n",
       "      <th>TTCG</th>\n",
       "      <th>TTCT</th>\n",
       "      <th>TTGA</th>\n",
       "      <th>TTGC</th>\n",
       "      <th>TTGG</th>\n",
       "      <th>TTGT</th>\n",
       "      <th>TTTA</th>\n",
       "      <th>TTTC</th>\n",
       "      <th>TTTG</th>\n",
       "      <th>TTTT</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000291</td>\n",
       "      <td>0.000566</td>\n",
       "      <td>0.000578</td>\n",
       "      <td>0.000459</td>\n",
       "      <td>0.000513</td>\n",
       "      <td>0.000375</td>\n",
       "      <td>0.000247</td>\n",
       "      <td>0.000784</td>\n",
       "      <td>0.000517</td>\n",
       "      <td>0.000220</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000184</td>\n",
       "      <td>0.000628</td>\n",
       "      <td>0.000568</td>\n",
       "      <td>0.000110</td>\n",
       "      <td>0.000110</td>\n",
       "      <td>0.000390</td>\n",
       "      <td>0.000528</td>\n",
       "      <td>0.000296</td>\n",
       "      <td>0.000518</td>\n",
       "      <td>0.000283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000166</td>\n",
       "      <td>0.000113</td>\n",
       "      <td>0.000462</td>\n",
       "      <td>0.000401</td>\n",
       "      <td>0.000073</td>\n",
       "      <td>0.000075</td>\n",
       "      <td>0.000247</td>\n",
       "      <td>0.000348</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000550</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000460</td>\n",
       "      <td>0.000471</td>\n",
       "      <td>0.000568</td>\n",
       "      <td>0.000329</td>\n",
       "      <td>0.000549</td>\n",
       "      <td>0.000078</td>\n",
       "      <td>0.000264</td>\n",
       "      <td>0.000532</td>\n",
       "      <td>0.000377</td>\n",
       "      <td>0.000606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000665</td>\n",
       "      <td>0.000396</td>\n",
       "      <td>0.000520</td>\n",
       "      <td>0.000287</td>\n",
       "      <td>0.000293</td>\n",
       "      <td>0.000751</td>\n",
       "      <td>0.000330</td>\n",
       "      <td>0.000348</td>\n",
       "      <td>0.000370</td>\n",
       "      <td>0.000440</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000184</td>\n",
       "      <td>0.000393</td>\n",
       "      <td>0.000362</td>\n",
       "      <td>0.000165</td>\n",
       "      <td>0.000274</td>\n",
       "      <td>0.000468</td>\n",
       "      <td>0.000132</td>\n",
       "      <td>0.000177</td>\n",
       "      <td>0.000424</td>\n",
       "      <td>0.000202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000624</td>\n",
       "      <td>0.000509</td>\n",
       "      <td>0.000347</td>\n",
       "      <td>0.000229</td>\n",
       "      <td>0.000293</td>\n",
       "      <td>0.000300</td>\n",
       "      <td>0.000577</td>\n",
       "      <td>0.000697</td>\n",
       "      <td>0.000665</td>\n",
       "      <td>0.000330</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000184</td>\n",
       "      <td>0.000393</td>\n",
       "      <td>0.000310</td>\n",
       "      <td>0.000220</td>\n",
       "      <td>0.000219</td>\n",
       "      <td>0.000546</td>\n",
       "      <td>0.000396</td>\n",
       "      <td>0.000414</td>\n",
       "      <td>0.000330</td>\n",
       "      <td>0.000162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000166</td>\n",
       "      <td>0.000396</td>\n",
       "      <td>0.000289</td>\n",
       "      <td>0.000229</td>\n",
       "      <td>0.000220</td>\n",
       "      <td>0.000601</td>\n",
       "      <td>0.000165</td>\n",
       "      <td>0.000436</td>\n",
       "      <td>0.000665</td>\n",
       "      <td>0.000385</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000368</td>\n",
       "      <td>0.000393</td>\n",
       "      <td>0.000207</td>\n",
       "      <td>0.000274</td>\n",
       "      <td>0.000274</td>\n",
       "      <td>0.000312</td>\n",
       "      <td>0.000198</td>\n",
       "      <td>0.000118</td>\n",
       "      <td>0.000188</td>\n",
       "      <td>0.000040</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 256 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       AAAA      AAAC      AAAG      AAAT      AACA      AACC      AACG  \\\n",
       "0  0.000291  0.000566  0.000578  0.000459  0.000513  0.000375  0.000247   \n",
       "1  0.000166  0.000113  0.000462  0.000401  0.000073  0.000075  0.000247   \n",
       "2  0.000665  0.000396  0.000520  0.000287  0.000293  0.000751  0.000330   \n",
       "3  0.000624  0.000509  0.000347  0.000229  0.000293  0.000300  0.000577   \n",
       "4  0.000166  0.000396  0.000289  0.000229  0.000220  0.000601  0.000165   \n",
       "\n",
       "       AACT      AAGA      AAGC  ...      TTCG      TTCT      TTGA      TTGC  \\\n",
       "0  0.000784  0.000517  0.000220  ...  0.000184  0.000628  0.000568  0.000110   \n",
       "1  0.000348  0.000000  0.000550  ...  0.000460  0.000471  0.000568  0.000329   \n",
       "2  0.000348  0.000370  0.000440  ...  0.000184  0.000393  0.000362  0.000165   \n",
       "3  0.000697  0.000665  0.000330  ...  0.000184  0.000393  0.000310  0.000220   \n",
       "4  0.000436  0.000665  0.000385  ...  0.000368  0.000393  0.000207  0.000274   \n",
       "\n",
       "       TTGG      TTGT      TTTA      TTTC      TTTG      TTTT  \n",
       "0  0.000110  0.000390  0.000528  0.000296  0.000518  0.000283  \n",
       "1  0.000549  0.000078  0.000264  0.000532  0.000377  0.000606  \n",
       "2  0.000274  0.000468  0.000132  0.000177  0.000424  0.000202  \n",
       "3  0.000219  0.000546  0.000396  0.000414  0.000330  0.000162  \n",
       "4  0.000274  0.000312  0.000198  0.000118  0.000188  0.000040  \n",
       "\n",
       "[5 rows x 256 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(2936, 256)"
      ]
     },
     "execution_count": 358,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"tetraFrequency.csv\")\n",
    "display(df.head())\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAAA</th>\n",
       "      <th>AAAC</th>\n",
       "      <th>AAAG</th>\n",
       "      <th>AAAT</th>\n",
       "      <th>AACA</th>\n",
       "      <th>AACC</th>\n",
       "      <th>AACG</th>\n",
       "      <th>AACT</th>\n",
       "      <th>AAGA</th>\n",
       "      <th>AAGC</th>\n",
       "      <th>...</th>\n",
       "      <th>TTCG</th>\n",
       "      <th>TTCT</th>\n",
       "      <th>TTGA</th>\n",
       "      <th>TTGC</th>\n",
       "      <th>TTGG</th>\n",
       "      <th>TTGT</th>\n",
       "      <th>TTTA</th>\n",
       "      <th>TTTC</th>\n",
       "      <th>TTTG</th>\n",
       "      <th>TTTT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>0.000624</td>\n",
       "      <td>0.000170</td>\n",
       "      <td>0.000231</td>\n",
       "      <td>0.000401</td>\n",
       "      <td>0.000220</td>\n",
       "      <td>0.000225</td>\n",
       "      <td>0.000247</td>\n",
       "      <td>0.000087</td>\n",
       "      <td>0.000148</td>\n",
       "      <td>0.000275</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000184</td>\n",
       "      <td>0.000864</td>\n",
       "      <td>0.000516</td>\n",
       "      <td>0.000439</td>\n",
       "      <td>0.000329</td>\n",
       "      <td>0.000780</td>\n",
       "      <td>0.000528</td>\n",
       "      <td>0.000887</td>\n",
       "      <td>0.000612</td>\n",
       "      <td>0.000889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>0.000749</td>\n",
       "      <td>0.000226</td>\n",
       "      <td>0.000578</td>\n",
       "      <td>0.000688</td>\n",
       "      <td>0.000293</td>\n",
       "      <td>0.000150</td>\n",
       "      <td>0.000247</td>\n",
       "      <td>0.000348</td>\n",
       "      <td>0.000591</td>\n",
       "      <td>0.000220</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000368</td>\n",
       "      <td>0.000707</td>\n",
       "      <td>0.000516</td>\n",
       "      <td>0.000220</td>\n",
       "      <td>0.000219</td>\n",
       "      <td>0.000468</td>\n",
       "      <td>0.000594</td>\n",
       "      <td>0.000650</td>\n",
       "      <td>0.000424</td>\n",
       "      <td>0.000889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>0.000665</td>\n",
       "      <td>0.000453</td>\n",
       "      <td>0.000867</td>\n",
       "      <td>0.000573</td>\n",
       "      <td>0.000513</td>\n",
       "      <td>0.000300</td>\n",
       "      <td>0.000577</td>\n",
       "      <td>0.000523</td>\n",
       "      <td>0.000887</td>\n",
       "      <td>0.000440</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000184</td>\n",
       "      <td>0.000471</td>\n",
       "      <td>0.000258</td>\n",
       "      <td>0.000329</td>\n",
       "      <td>0.000055</td>\n",
       "      <td>0.000156</td>\n",
       "      <td>0.000330</td>\n",
       "      <td>0.000118</td>\n",
       "      <td>0.000188</td>\n",
       "      <td>0.000445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>0.000374</td>\n",
       "      <td>0.000509</td>\n",
       "      <td>0.000347</td>\n",
       "      <td>0.000229</td>\n",
       "      <td>0.000513</td>\n",
       "      <td>0.000225</td>\n",
       "      <td>0.000412</td>\n",
       "      <td>0.000348</td>\n",
       "      <td>0.000591</td>\n",
       "      <td>0.000275</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000368</td>\n",
       "      <td>0.000628</td>\n",
       "      <td>0.000207</td>\n",
       "      <td>0.000274</td>\n",
       "      <td>0.000604</td>\n",
       "      <td>0.000234</td>\n",
       "      <td>0.000396</td>\n",
       "      <td>0.000473</td>\n",
       "      <td>0.000424</td>\n",
       "      <td>0.000404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>0.000541</td>\n",
       "      <td>0.000396</td>\n",
       "      <td>0.000520</td>\n",
       "      <td>0.000630</td>\n",
       "      <td>0.000440</td>\n",
       "      <td>0.000225</td>\n",
       "      <td>0.000412</td>\n",
       "      <td>0.000174</td>\n",
       "      <td>0.000739</td>\n",
       "      <td>0.000660</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000184</td>\n",
       "      <td>0.000471</td>\n",
       "      <td>0.000620</td>\n",
       "      <td>0.000220</td>\n",
       "      <td>0.000384</td>\n",
       "      <td>0.000468</td>\n",
       "      <td>0.000396</td>\n",
       "      <td>0.000296</td>\n",
       "      <td>0.000424</td>\n",
       "      <td>0.000404</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 256 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        AAAA      AAAC      AAAG      AAAT      AACA      AACC      AACG  \\\n",
       "54  0.000624  0.000170  0.000231  0.000401  0.000220  0.000225  0.000247   \n",
       "56  0.000749  0.000226  0.000578  0.000688  0.000293  0.000150  0.000247   \n",
       "57  0.000665  0.000453  0.000867  0.000573  0.000513  0.000300  0.000577   \n",
       "58  0.000374  0.000509  0.000347  0.000229  0.000513  0.000225  0.000412   \n",
       "59  0.000541  0.000396  0.000520  0.000630  0.000440  0.000225  0.000412   \n",
       "\n",
       "        AACT      AAGA      AAGC  ...      TTCG      TTCT      TTGA      TTGC  \\\n",
       "54  0.000087  0.000148  0.000275  ...  0.000184  0.000864  0.000516  0.000439   \n",
       "56  0.000348  0.000591  0.000220  ...  0.000368  0.000707  0.000516  0.000220   \n",
       "57  0.000523  0.000887  0.000440  ...  0.000184  0.000471  0.000258  0.000329   \n",
       "58  0.000348  0.000591  0.000275  ...  0.000368  0.000628  0.000207  0.000274   \n",
       "59  0.000174  0.000739  0.000660  ...  0.000184  0.000471  0.000620  0.000220   \n",
       "\n",
       "        TTGG      TTGT      TTTA      TTTC      TTTG      TTTT  \n",
       "54  0.000329  0.000780  0.000528  0.000887  0.000612  0.000889  \n",
       "56  0.000219  0.000468  0.000594  0.000650  0.000424  0.000889  \n",
       "57  0.000055  0.000156  0.000330  0.000118  0.000188  0.000445  \n",
       "58  0.000604  0.000234  0.000396  0.000473  0.000424  0.000404  \n",
       "59  0.000384  0.000468  0.000396  0.000296  0.000424  0.000404  \n",
       "\n",
       "[5 rows x 256 columns]"
      ]
     },
     "execution_count": 359,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_toxins = df[middle_left]\n",
    "df_toxins.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.00062386, 0.00016969, 0.00023123, ..., 0.00088668, 0.00061228,\n",
       "        0.00088932],\n",
       "       [0.00074863, 0.00022626, 0.00057807, ..., 0.00065023, 0.00042389,\n",
       "        0.00088932],\n",
       "       [0.00066545, 0.00045251, 0.0008671 , ..., 0.00011822, 0.00018839,\n",
       "        0.00044466],\n",
       "       ...,\n",
       "       [0.00124771, 0.00033939, 0.00057807, ..., 0.00017734, 0.0001413 ,\n",
       "        0.00024254],\n",
       "       [0.00149726, 0.00022626, 0.00069368, ..., 0.0004729 , 0.00018839,\n",
       "        0.00064678],\n",
       "       [0.00166362, 0.00062221, 0.0008093 , ..., 0.00029556, 0.0001413 ,\n",
       "        0.00032339]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(105, 256)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "105"
      ]
     },
     "execution_count": 360,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df_toxins.to_numpy()\n",
    "display(X)\n",
    "display(X.shape)\n",
    "\n",
    "y = [num for num in range(0,X.shape[0])]\n",
    "len(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "metadata": {},
   "outputs": [],
   "source": [
    "tsne = TSNE(n_components=2, verbose=1, random_state=123, perplexity=13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 378,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[t-SNE] Computing 40 nearest neighbors...\n",
      "[t-SNE] Indexed 105 samples in 0.000s...\n",
      "[t-SNE] Computed neighbors for 105 samples in 0.008s...\n",
      "[t-SNE] Computed conditional probabilities for sample 105 / 105\n",
      "[t-SNE] Mean sigma: 0.001079\n",
      "[t-SNE] KL divergence after 250 iterations with early exaggeration: 72.346703\n",
      "[t-SNE] KL divergence after 1000 iterations: 0.830487\n"
     ]
    }
   ],
   "source": [
    "z = tsne.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 379,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nsns.scatterplot(x=\"comp-1\", y=\"comp-2\",\\n                       data=df, hue=df.color.tolist(),\\n                       palette=sns.color_palette(\"hls\", 2)).set(title=\"Vibrio Cholera Dimension Reduction\", \\n                             xticks=[x for x in range(-200,200, 50)],\\n                             yticks=[x for x in range(-500,1000, 100)]) \\n'"
      ]
     },
     "execution_count": 379,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "df = pd.DataFrame()\n",
    "df[\"y\"] = y\n",
    "df[\"comp-1\"] = z[:,0]\n",
    "df[\"comp-2\"] = z[:,1]\n",
    "\n",
    "\n",
    "##########################\n",
    "outliers = (z[:,1] > 500) | (z[:,1] < -500)\n",
    "offsection = z[:,0] < -70\n",
    "\n",
    "alltogether = bottom_left | far_right | middle_left\n",
    "##########################\n",
    "\n",
    "\n",
    "df['color'] = offsection\n",
    "df['color'] = df['color'].astype('int')\n",
    "\n",
    "\n",
    "sns.scatterplot(x=\"comp-1\", y=\"comp-2\",\n",
    "                       data=df).set(title=\"Vibrio Cholera Dimension Reduction\", \n",
    "                             xticks=[x for x in range(-70,70, 10)],\n",
    "                             yticks=[x for x in range(-70,70, 10)]) \n",
    "\n",
    "'''\n",
    "sns.scatterplot(x=\"comp-1\", y=\"comp-2\",\n",
    "                       data=df, hue=df.color.tolist(),\n",
    "                       palette=sns.color_palette(\"hls\", 2)).set(title=\"Vibrio Cholera Dimension Reduction\", \n",
    "                             xticks=[x for x in range(-200,200, 50)],\n",
    "                             yticks=[x for x in range(-500,1000, 100)]) \n",
    "'''\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y</th>\n",
       "      <th>comp-1</th>\n",
       "      <th>comp-2</th>\n",
       "      <th>color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>-74.327675</td>\n",
       "      <td>-48.829285</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>-79.304726</td>\n",
       "      <td>-13.245505</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>32</td>\n",
       "      <td>-94.340759</td>\n",
       "      <td>-11.072059</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>59</td>\n",
       "      <td>-93.054878</td>\n",
       "      <td>20.106691</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>60</td>\n",
       "      <td>-71.872452</td>\n",
       "      <td>-35.152317</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>69</td>\n",
       "      <td>-78.799400</td>\n",
       "      <td>20.749004</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>70</td>\n",
       "      <td>-85.553711</td>\n",
       "      <td>-58.343628</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>86</td>\n",
       "      <td>-88.478806</td>\n",
       "      <td>-68.613617</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>88</td>\n",
       "      <td>-108.826218</td>\n",
       "      <td>58.301872</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>90</td>\n",
       "      <td>-110.768173</td>\n",
       "      <td>50.186352</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>94</td>\n",
       "      <td>-90.971687</td>\n",
       "      <td>-35.415771</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>100</td>\n",
       "      <td>-101.651970</td>\n",
       "      <td>-18.654528</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       y      comp-1     comp-2  color\n",
       "6      6  -74.327675 -48.829285      1\n",
       "9      9  -79.304726 -13.245505      1\n",
       "32    32  -94.340759 -11.072059      1\n",
       "59    59  -93.054878  20.106691      1\n",
       "60    60  -71.872452 -35.152317      1\n",
       "69    69  -78.799400  20.749004      1\n",
       "70    70  -85.553711 -58.343628      1\n",
       "86    86  -88.478806 -68.613617      1\n",
       "88    88 -108.826218  58.301872      1\n",
       "90    90 -110.768173  50.186352      1\n",
       "94    94  -90.971687 -35.415771      1\n",
       "100  100 -101.651970 -18.654528      1"
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[offsection]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.00062386, 0.00016969, 0.00023123, ..., 0.00088668, 0.00061228,\n",
       "        0.00088932],\n",
       "       [0.00074863, 0.00022626, 0.00057807, ..., 0.00065023, 0.00042389,\n",
       "        0.00088932],\n",
       "       [0.00066545, 0.00045251, 0.0008671 , ..., 0.00011822, 0.00018839,\n",
       "        0.00044466],\n",
       "       ...,\n",
       "       [0.00124771, 0.00033939, 0.00057807, ..., 0.00017734, 0.0001413 ,\n",
       "        0.00024254],\n",
       "       [0.00149726, 0.00022626, 0.00069368, ..., 0.0004729 , 0.00018839,\n",
       "        0.00064678],\n",
       "       [0.00166362, 0.00062221, 0.0008093 , ..., 0.00029556, 0.0001413 ,\n",
       "        0.00032339]])"
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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