notebooks/04_biochemical-properties-analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PO-4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Section 4: Are the phospho-peptides: Acidic-, basic-,proline-,asparagine-, or hydrophobic-directed?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Introduction <a class=\"anchor\" id=\"introduction\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.1 Table of Contents <a class=\"anchor\" id=\"table-of-contents\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-16T15:32:25.662741Z",
     "start_time": "2020-04-16T15:32:25.655434Z"
    }
   },
   "source": [
    "* [1. Introduction](#introduction)\n",
    "    * [1.1 Table of Contents](#table-of-contents)\n",
    "    * [1.2 Project Description](#project-description)\n",
    "    * [1.3 Packages](#packages)\n",
    "        * [1.3.1 Python and 3rd party libraries](#python-and-third-party-libraries)\n",
    "        * [1.3.2 Custom packages](#custom-packages)\n",
    "        * [1.3.3 Magic functions](#magic-functions)\n",
    "        * [1.3.4 Reproducibility](#reproducibility)\n",
    "* [2. Data Loading](#data-loading)\n",
    "* [3. Data Exploration](#data-exploration)\n",
    "* [4. Data Preprocessing](#data-preprocessing)\n",
    "* [5. Modeling](#modeling)\n",
    "* [6. Functions](#functions)\n",
    "* [7. References](#references)\n",
    "* [8. Project contribution](#project-contribution)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.2 Project Description <a class=\"anchor\" id=\"project-description\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Brief description of what are you using in this notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.3 Packages <a class=\"anchor\" id=\"packages\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3.1 Python and 3rd party libraries <a class=\"anchor\" id=\"python-and-third-party-libraries\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-16T15:57:28.080865Z",
     "start_time": "2020-04-16T15:57:28.076022Z"
    }
   },
   "outputs": [],
   "source": [
    "import glob, os, sys\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import rc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  1.3.2 Custom packages <a class=\"anchor\" id=\"custom-packages\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-16T15:57:28.449870Z",
     "start_time": "2020-04-16T15:57:28.446211Z"
    }
   },
   "outputs": [],
   "source": [
    "sys.path.append('./')\n",
    "# from . import ., ."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3.3 Magic functions <a class=\"anchor\" id=\"magic-functions\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-16T15:57:28.845725Z",
     "start_time": "2020-04-16T15:57:28.824836Z"
    }
   },
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3.4 Reproducibility <a class=\"anchor\" id=\"reproducibility\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-16T15:57:29.393127Z",
     "start_time": "2020-04-16T15:57:29.149945Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fernando Pozo \n",
      "last updated: Mon Sep 14 2020 11:44:41 CEST \n",
      "\n",
      "pandas 1.0.4\n",
      "numpy 1.18.5\n",
      "Git hash: cefe14675e7c07123ef09f36d71551826a082e93\n",
      "/local/fpozoc/projects/dev/ph-proteomics/notebooks\n"
     ]
    }
   ],
   "source": [
    "#### !pip install watermark\n",
    "#### rasbt watermark https://github.com/rasbt/watermark\n",
    "%load_ext watermark\n",
    "%watermark -a 'Fernando Pozo' -u -n -t -z -g -p pandas,numpy\n",
    "\n",
    "!pwd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Data Loading <a class=\"anchor\" id=\"data-loading\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Treeck \n",
    "\n",
    "D) Phosphorylation motif analysis. Phosphorylation motif analysis was performed using the\n",
    "motif-x algorithm. All identified phosphorylation motifs were classified as one of six\n",
    "phosphorylation motif categories (acidic-, basic-, proline-, asparagine- or hydrophobicdirected or “other”). Motifs were assigned using binary decision tree: P in the +1 position\n",
    "(proline-directed), [R/K] in the -3 position (basic), [D/E] in the +1- +3 position, >2 [D/E] in\n",
    "the +1-+6 position (acidic), [F/I/L/M] in the +1 position (hydrophobic), or [N] in any\n",
    "specific position over background (asparagine). See also supplementary Figure S2 and Table\n",
    "S2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_exptypes = pd.read_csv('../data/interim/preprocessed_peptides.tsv.gz', compression='gzip', sep='\\t')\n",
    "df_exptypes['res_id'] = df_exptypes.apply(lambda row: row['proteinName'] + '-' + row['res-ptm'] + str(row['proteinSeq-loc']) + '-p', axis=1)\n",
    "df_bioc = df_exptypes[['type', 'res_id', 'kmer']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_motifs_categories(df:list)->list:\n",
    "    df[['k-drop1', 'k-6','k-5','k-4','k-3','k-2','k-1','k-ptm','k+1','k+2','k+3','k+4','k+5','k+6', 'k-drop2']] = df['kmer'].str.split('', expand=True)\n",
    "    df = df.drop(['k-drop1', 'k-drop2'], axis=1)\n",
    "\n",
    "    df.loc[df['k+1'].str.contains('P'), 'Proline-directed'] = 1\n",
    "\n",
    "    df.loc[df['k-3'].str.contains('R|K'), 'Basic'] = 1\n",
    "\n",
    "    for column in df[df.columns[6:9]]:\n",
    "        df.loc[df[column].str.contains('D|E'), 'Acidic'] = 1\n",
    "\n",
    "    for column in df[df.columns[10:16]]:\n",
    "        df.loc[df[column].str.contains('D|E'), 'Acidic'] = 1\n",
    "\n",
    "    df.loc[df['k+1'].str.contains('F|I|L|M'), 'Hydrophobic'] = 1\n",
    "\n",
    "    for column in df[df.columns[3:16]]:\n",
    "        df.loc[df[column].str.contains('N'), 'Asparagine'] = 1\n",
    "\n",
    "    df.loc[df['Proline-directed'] == 1, df.columns[17:]] = 0\n",
    "    df.loc[df['Basic'] == 1, df.columns[18:]] = 0\n",
    "    df.loc[df['Acidic'] == 1, df.columns[19:]] = 0\n",
    "    df.loc[df['Hydrophobic'] == 1, df.columns[20:]] = 0\n",
    "    df = df.fillna(0)\n",
    "    df_results = df[df.columns[16:]].sum(axis=0).reset_index().rename(columns={'index':'Category', 0:'n'})\n",
    "    df_results.loc[5] = ['Unclassified', df[df.columns[16:]].sum(axis=1).value_counts()[0]]\n",
    "    df_results['p'] = (df_results['n']/df_results['n'].sum())*100\n",
    "    return df_results.drop('n', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_motcat_wt = generate_motifs_categories(df_bioc.loc[df_bioc['type'] == 'wt']).rename(columns={'p': 'Motif category proportion (WT)'})\n",
    "df_motcat_ko = generate_motifs_categories(df_bioc.loc[df_bioc['type'] == 'ko']).rename(columns={'p': 'Motif category proportion (KO)'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_motcats = pd.merge(df_motcat_wt, df_motcat_ko, on='Category').set_index('Category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 432x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_motcats.plot.bar(rot=45, figsize=(6,3), colormap='Set2')\n",
    "plt.xlabel('')\n",
    "plt.ylabel('[%]')\n",
    "plt.title('Phosphorilated motif (13-mers) categories')\n",
    "plt.savefig('../reports/figures/figure3A.png', bbox_inches='tight', dpi=360)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Motif category proportion (WT)</th>\n",
       "      <th>Motif category proportion (KO)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Category</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Proline-directed</th>\n",
       "      <td>49.549116</td>\n",
       "      <td>39.639924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Basic</th>\n",
       "      <td>22.784658</td>\n",
       "      <td>26.405559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Acidic</th>\n",
       "      <td>20.355898</td>\n",
       "      <td>25.963361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hydrophobic</th>\n",
       "      <td>1.863653</td>\n",
       "      <td>2.874289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asparagine</th>\n",
       "      <td>1.659252</td>\n",
       "      <td>1.295009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unclassified</th>\n",
       "      <td>3.787423</td>\n",
       "      <td>3.821857</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Motif category proportion (WT)  \\\n",
       "Category                                           \n",
       "Proline-directed                       49.549116   \n",
       "Basic                                  22.784658   \n",
       "Acidic                                 20.355898   \n",
       "Hydrophobic                             1.863653   \n",
       "Asparagine                              1.659252   \n",
       "Unclassified                            3.787423   \n",
       "\n",
       "                  Motif category proportion (KO)  \n",
       "Category                                          \n",
       "Proline-directed                       39.639924  \n",
       "Basic                                  26.405559  \n",
       "Acidic                                 25.963361  \n",
       "Hydrophobic                             2.874289  \n",
       "Asparagine                              1.295009  \n",
       "Unclassified                            3.821857  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_motcats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Brief data source explanation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Data Exploration <a class=\"anchor\" id=\"data-exploration\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Descriptive statistics, plots, and figures."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Data Preprocessing <a class=\"anchor\" id=\"data-preprocessing\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once the data has been right understood follow some preprocessing guidelines."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Modeling <a class=\"anchor\" id=\"modeling\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Apply a model in necessary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. Functions <a class=\"anchor\" id=\"functions\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-16T15:57:31.638986Z",
     "start_time": "2020-04-16T15:57:31.621882Z"
    },
    "cell_style": "center"
   },
   "outputs": [],
   "source": [
    "def foo(arg1, arg2, arg3):\n",
    "    return list1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7. References <a class=\"anchor\" id=\"references\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's the initial reference for this notebook: https://github.com/dair-ai/data_science_writing_primer/blob/master/Writing_Primer_for_Data_Scientists.ipynb\n",
    "\n",
    "Below, give the reader references to follow properly your work.\n",
    "- Reference 1.\n",
    "- Reference 2."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_style": "center"
   },
   "source": [
    "# 8. Project contribution <a class=\"anchor\" id=\"project-contribution\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Author information**:\n",
    "Fernando Pozo ([@fpozoc](https://gitlab.com/fpozoc))\n",
    "\n",
    "You can find the data driven project jupyter notebook template [here](https://gitlab.com/fpozoc/data-driven-project-template/-/blob/master/notebooks/1.0-nb_template.ipynb)."
   ]
  }
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