EsmailELBoBDev2/BioExam.ipynb

## BioExam.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting gtfparse\n",
      "  Downloading https://files.pythonhosted.org/packages/91/3d/c67f23990c778989a8c4d4ca9eb5397997f6020bd5f52393311f84816690/gtfparse-1.2.1.tar.gz\n",
      "Requirement already satisfied: numpy>=1.7 in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (from gtfparse) (1.19.2)\n",
      "Requirement already satisfied: pandas>=0.15 in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (from gtfparse) (1.1.3)\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (from pandas>=0.15->gtfparse) (2.8.1)\n",
      "Requirement already satisfied: pytz>=2017.2 in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (from pandas>=0.15->gtfparse) (2020.1)\n",
      "Requirement already satisfied: six>=1.5 in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (from python-dateutil>=2.7.3->pandas>=0.15->gtfparse) (1.15.0)\n",
      "Building wheels for collected packages: gtfparse\n",
      "  Building wheel for gtfparse (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25h  Stored in directory: /home/jupyterlab/.cache/pip/wheels/e2/74/a0/92c0c82fe374f6bfc3e4224c1fd4be6ae5529e11366dd9e874\n",
      "Successfully built gtfparse\n",
      "Installing collected packages: gtfparse\n",
      "Successfully installed gtfparse-1.2.1\n"
     ]
    }
   ],
   "source": [
    "## https://github.com/openvax/gtfparse\n",
    "!pip install gtfparse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Extracted GTF attributes: ['gene_id', 'transcript_id', 'cov', 'FPKM', 'TPM']\n"
     ]
    }
   ],
   "source": [
    "from gtfparse import read_gtf\n",
    "\n",
    "df = read_gtf(\n",
    "    \"Transcripts.gtf\",\n",
    "    column_converters={\"FPKM\": float})\n",
    "\n",
    "gene_fpkms = {\n",
    "    gene_name: fpkm\n",
    "    for (gene_name, fpkm, feature)\n",
    "    in zip(df[\"seqname\"], df[\"FPKM\"], df[\"feature\"])\n",
    "    if feature == \"gene\"\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seqname</th>\n",
       "      <th>source</th>\n",
       "      <th>feature</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>score</th>\n",
       "      <th>strand</th>\n",
       "      <th>frame</th>\n",
       "      <th>gene_id</th>\n",
       "      <th>transcript_id</th>\n",
       "      <th>cov</th>\n",
       "      <th>FPKM</th>\n",
       "      <th>TPM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>VFFH01002606.1</td>\n",
       "      <td>StringTie</td>\n",
       "      <td>transcript</td>\n",
       "      <td>246006</td>\n",
       "      <td>246411</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>nan</td>\n",
       "      <td>0</td>\n",
       "      <td>STRG.1</td>\n",
       "      <td>STRG.1.1</td>\n",
       "      <td>6.849754</td>\n",
       "      <td>0.898377</td>\n",
       "      <td>1.198143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>VFFH01002606.1</td>\n",
       "      <td>StringTie</td>\n",
       "      <td>transcript</td>\n",
       "      <td>291762</td>\n",
       "      <td>292127</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>nan</td>\n",
       "      <td>0</td>\n",
       "      <td>STRG.2</td>\n",
       "      <td>STRG.2.1</td>\n",
       "      <td>8.683060</td>\n",
       "      <td>1.138823</td>\n",
       "      <td>1.518820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>VFFH01002606.1</td>\n",
       "      <td>StringTie</td>\n",
       "      <td>transcript</td>\n",
       "      <td>251083</td>\n",
       "      <td>271097</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>-</td>\n",
       "      <td>0</td>\n",
       "      <td>STRG.3</td>\n",
       "      <td>STRG.3.1</td>\n",
       "      <td>2.606259</td>\n",
       "      <td>0.341823</td>\n",
       "      <td>0.455881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>VFFH01002073.1</td>\n",
       "      <td>StringTie</td>\n",
       "      <td>transcript</td>\n",
       "      <td>44696</td>\n",
       "      <td>45005</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>nan</td>\n",
       "      <td>0</td>\n",
       "      <td>STRG.4</td>\n",
       "      <td>STRG.4.1</td>\n",
       "      <td>5.922581</td>\n",
       "      <td>0.776774</td>\n",
       "      <td>1.035964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>VFFH01002073.1</td>\n",
       "      <td>StringTie</td>\n",
       "      <td>transcript</td>\n",
       "      <td>17183</td>\n",
       "      <td>34094</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>+</td>\n",
       "      <td>0</td>\n",
       "      <td>STRG.5</td>\n",
       "      <td>STRG.5.1</td>\n",
       "      <td>3.375132</td>\n",
       "      <td>0.442664</td>\n",
       "      <td>0.590370</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          seqname     source     feature   start     end   score strand frame  \\\n",
       "0  VFFH01002606.1  StringTie  transcript  246006  246411  1000.0    nan     0   \n",
       "1  VFFH01002606.1  StringTie  transcript  291762  292127  1000.0    nan     0   \n",
       "2  VFFH01002606.1  StringTie  transcript  251083  271097  1000.0      -     0   \n",
       "3  VFFH01002073.1  StringTie  transcript   44696   45005  1000.0    nan     0   \n",
       "4  VFFH01002073.1  StringTie  transcript   17183   34094  1000.0      +     0   \n",
       "\n",
       "  gene_id transcript_id       cov      FPKM       TPM  \n",
       "0  STRG.1      STRG.1.1  6.849754  0.898377  1.198143  \n",
       "1  STRG.2      STRG.2.1  8.683060  1.138823  1.518820  \n",
       "2  STRG.3      STRG.3.1  2.606259  0.341823  0.455881  \n",
       "3  STRG.4      STRG.4.1  5.922581  0.776774  1.035964  \n",
       "4  STRG.5      STRG.5.1  3.375132  0.442664  0.590370  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 21,
     "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.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 22,
     "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.plot(y= \"FPKM\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gene_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>STRG.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>STRG.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>STRG.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>STRG.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>STRG.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  gene_id\n",
       "0  STRG.1\n",
       "1  STRG.2\n",
       "2  STRG.3\n",
       "3  STRG.4\n",
       "4  STRG.5"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df.loc[df['start'] > 1, ['gene_id']]\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python",
   "language": "python",
   "name": "conda-env-python-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}