Avg_PSM_count_change.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The purpose of this notebook is to calculate the average increase in PSMs each tool finds under a 0.01 cutoff after having been resored though mokapot. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import mokapot\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import sys\n",
    "sys.path.append(\"..\")\n",
    "sys.path\n",
    "import data_loader as dl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#read in the megaScript and reformat it\n",
    "def clean_meagScript(file):\n",
    "    df = pd.read_csv(file, low_memory=False,  header=[0,1])\n",
    "    df.drop(columns = {\"Unnamed: 0_level_0\"})\n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Slicing out the Peptide Prophet Probability values for MsFragger. There is no qvalue or PEP, so this is the row we are using. \n",
    "Counting how many are at or under the cutoff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_msf_prob_len(df, cutoff):\n",
    "    msf_before = df[\"MsFragger\"]['Updated_probability']\n",
    "    #msf_before =  msf_before.dropna()\n",
    "    before_under_cutoff = len(msf_before.loc[msf_before <= cutoff])\n",
    "    \n",
    "    msf_after = df[\"MsFragger\"]['mokapot q-value']\n",
    "    #msf_after =  msf_after.dropna()\n",
    "    after_under_cutoff = len(msf_after.loc[msf_after <= cutoff])\n",
    "    \n",
    "    return (after_under_cutoff - before_under_cutoff)/before_under_cutoff"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Slicing out the qvalues from MetaMorpheus Counting how many are at or under the cutoff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_mm_Qval_len(df, cutoff):\n",
    "    mm_before = df[\"MetaMorpheus\"][\"QValue\"] \n",
    "    #mm_before =  mm_before.dropna() \n",
    "    before_under_cutoff = len(mm_before.loc[mm_before <= cutoff])\n",
    "    \n",
    "    mm_after = df[\"MetaMorpheus\"]['mokapot q-value'] \n",
    "    #mm_after =  mm_after.dropna() \n",
    "    after_under_cutoff = len(mm_after.loc[mm_after <= cutoff])\n",
    "    \n",
    "    \n",
    "    return (after_under_cutoff - before_under_cutoff)/before_under_cutoff"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Slicing out the qvalues from MsgfPlus and counting how many are at or under the cutoff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_msg_Qval_len(df, cutoff):\n",
    "    msg_before = df[\"MsgfPlus\"][\"QValue\"] \n",
    "    #msg_before =  msg_before.dropna() \n",
    "    before_under_cutoff = len(msg_before.loc[msg_before <= cutoff])\n",
    "    \n",
    "    msg_after = df[\"MsgfPlus\"]['mokapot q-value'] \n",
    "    #msg_after =  msg_after.dropna() \n",
    "    after_under_cutoff = len(msg_after.loc[msg_after <= cutoff])\n",
    "    \n",
    "    return (after_under_cutoff - before_under_cutoff)/before_under_cutoff"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Slicing out the PEP from MaxQuant. Counting how many are at or under the cutoff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_mq_PEP_len(df, cutoff):\n",
    "    mq_before = df[\"MaxQuant\"][\"PEP\"] \n",
    "    #mq_before =  mq_before.dropna() \n",
    "    before_under_cutoff = len(mq_before.loc[mq_before <= cutoff])\n",
    "    \n",
    "    mq_after = df[\"MaxQuant\"]['mokapot q-value'] \n",
    "    #mq_after =  mq_after.dropna() \n",
    "    after_under_cutoff = len(mq_after.loc[mq_after <= cutoff])\n",
    "    \n",
    "    return (after_under_cutoff - before_under_cutoff)/before_under_cutoff"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This function gets the number of scan values that were at or below the cutoff for each tool and returns them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_file_values(file, cutoff):\n",
    "    df = clean_meagScript(file)\n",
    "    msf = get_msf_prob_len(df, cutoff)\n",
    "    MM_QVal = get_mm_Qval_len(df, cutoff)\n",
    "    msg_QVal = get_msg_Qval_len(df, cutoff)\n",
    "    MQ_PEP = get_mq_PEP_len(df, cutoff)\n",
    "    values_list = {\"msf\" : msf, \"MM_QVal\" : MM_QVal, \"msg_QVal\" : msg_QVal, \"MQ_PEP\" : MQ_PEP}\n",
    "    return values_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reading in the data and making the graph for the 2ng data at a certain cutoff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/daishavanderwatt/anaconda3/lib/python3.8/site-packages/pandas/core/generic.py:3936: PerformanceWarning: dropping on a non-lexsorted multi-index without a level parameter may impact performance.\n",
      "  obj = obj._drop_axis(labels, axis, level=level, errors=errors)\n"
     ]
    }
   ],
   "source": [
    "File1 = get_file_values(\"MokaPot/MegaScript_2ng_rep1.csv\", 0.01)\n",
    "File2 = get_file_values(\"MokaPot/MegaScript_2ng_rep2.csv\", 0.01)\n",
    "File3 = get_file_values(\"MokaPot/MegaScript_2ng_rep3.csv\", 0.01)\n",
    "File4 = get_file_values(\"MokaPot/MegaScript_2ng_rep4.csv\", 0.01)\n",
    "File5 = get_file_values(\"MokaPot/MegaScript_2ng_rep5.csv\", 0.01)\n",
    "File6 = get_file_values(\"MokaPot/MegaScript_2ng_rep6.csv\", 0.01)\n",
    "\n",
    "msf_prob_2ng = [File1['msf'], File2['msf'], File3['msf'], File4['msf'], File5['msf'], File6['msf']]\n",
    "MM_qval_2ng = [File1['MM_QVal'], File2['MM_QVal'], File3['MM_QVal'], File4['MM_QVal'], File5['MM_QVal'], File6['MM_QVal']]\n",
    "msg_qval_2ng = [File1['msg_QVal'], File2['msg_QVal'], File3['msg_QVal'], File4['msg_QVal'], File5['msg_QVal'], File6['msg_QVal']]\n",
    "mq_PEP_2ng = [File1['MQ_PEP'], File2['MQ_PEP'], File3['MQ_PEP'], File4['MQ_PEP'], File5['MQ_PEP'], File6['MQ_PEP']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": [
    "import statistics as stats\n",
    "#getting the average % increase of PSMs after MokaPot\n",
    "msf_avg = stats.mean(msf_prob_2ng)\n",
    "mm_avg = stats.mean(MM_qval_2ng)\n",
    "msg_avg = stats.mean(msg_qval_2ng)\n",
    "mq_avg = stats.mean(mq_PEP_2ng)\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_axes([0,0,1,1])\n",
    "increases = [msf_avg, mm_avg, msg_avg, mq_avg]\n",
    "tools = ['msf', 'mm', 'msg', 'mq']\n",
    "ax.bar(tools, increases)\n",
    "plt.ylabel('average percent')\n",
    "#plt.xlabel('Number of PSMs using native score and 0.01 cutoff')\n",
    "plt.title('Average % increase in # PSMs after MokaPot')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Here the average of the difference between the pre-mokapot and post mokapot results are taken for the 2ng data. These are the caluclations that are included in the paper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MM avg: 0.12007718851592447\n",
      "msg avg: 0.001099581776723775\n",
      "msf_avg: 0.18344271595220346\n",
      "mq_avg: 0.40799636992271604\n"
     ]
    }
   ],
   "source": [
    "print(\"MM avg: \" + str(mm_avg))\n",
    "print(\"msg avg: \" + str(msg_avg))\n",
    "print(\"msf_avg: \" + str(msf_avg))\n",
    "print(\"mq_avg: \" + str(mq_avg))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Doing calculations and graphs for the 0.2ng data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "File7 = get_file_values(\"MokaPot/MegaScript_0.2ng_rep1.csv\", 0.01)\n",
    "File8 = get_file_values(\"MokaPot/MegaScript_0.2ng_rep2.csv\", 0.01)\n",
    "File9 = get_file_values(\"MokaPot/MegaScript_0.2ng_rep3.csv\", 0.01)\n",
    "File10 = get_file_values(\"MokaPot/MegaScript_0.2ng_rep4.csv\", 0.01)\n",
    "File11 = get_file_values(\"MokaPot/MegaScript_0.2ng_rep5.csv\", 0.01)\n",
    "File12 = get_file_values(\"MokaPot/MegaScript_0.2ng_rep6.csv\", 0.01)\n",
    "\n",
    "msf_prob_02ng = [File7['msf'], File8['msf'], File9['msf'], File10['msf'], File11['msf'], File12['msf']]\n",
    "\n",
    "MM_qval_02ng = [File7['MM_QVal'], File8['MM_QVal'], File9['MM_QVal'], File10['MM_QVal'], File11['MM_QVal'], File12['MM_QVal']]\n",
    "\n",
    "msg_qval_02ng = [File7['msg_QVal'], File8['msg_QVal'], File9['msg_QVal'], File10['msg_QVal'], File11['msg_QVal'], File12['msg_QVal']]\n",
    "\n",
    "mq_PEP_02ng = [File7['MQ_PEP'], File8['MQ_PEP'], File9['MQ_PEP'], File10['MQ_PEP'], File11['MQ_PEP'], File12['MQ_PEP']]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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": [
    "import statistics as stats\n",
    "#getting the average % increase of PSMs after MokaPot\n",
    "msf_avg = stats.mean(msf_prob_02ng)\n",
    "mm_avg = stats.mean(MM_qval_02ng)\n",
    "msg_avg = stats.mean(msg_qval_02ng)\n",
    "mq_avg = stats.mean(mq_PEP_02ng)\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_axes([0,0,1,1])\n",
    "increases = [msf_avg, mm_avg, msg_avg, mq_avg]\n",
    "tools = ['msf', 'mm', 'msg', 'mq']\n",
    "ax.bar(tools, increases)\n",
    "plt.ylabel('average percent')\n",
    "#plt.xlabel('Number of PSMs using native score and 0.01 cutoff')\n",
    "plt.title('Average % increase in # PSMs after MokaPot')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Here the average of the difference between the pre-mokapot and post mokapot results are taken for the 0.2ng data. These are the caluclations that are included in the paper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MM avg: 0.07324559760303745\n",
      "msg avg: -0.0009413467196535334\n",
      "msf_avg: 0.1991745220549629\n",
      "mq_avg: 0.5397769656290559\n"
     ]
    }
   ],
   "source": [
    "print(\"MM avg: \" + str(mm_avg))\n",
    "print(\"msg avg: \" + str(msg_avg))\n",
    "print(\"msf_avg: \" + str(msf_avg))\n",
    "print(\"mq_avg: \" + str(mq_avg))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.00243656528314569,\n",
       " 0.0022220323049312023,\n",
       " -0.0010427528675703858,\n",
       " -0.00010613457864572278,\n",
       " 0.0030131034966014996,\n",
       " 7.467702188036741e-05]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "msg_qval_2ng"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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