std_sialo_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from commons import data_processing\n",
    "from commons import my_mzml\n",
    "import pandas as pd \n",
    "import altair as alt \n",
    "import re \n",
    "import ntpath\n",
    "from matplotlib import pyplot as plt\n",
    "from scipy import ndimage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following cells require access to mzXML versions of the .raw data provided in the Pride repository. Please use ProteoWizard to convert the data before proceeding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xmls = data_processing.get_files(r'F:\\Research\\MS Data\\QE\\20220524\\mzxml')\n",
    "len(xmls)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20220524_GD_SGP_005_ugul_30C_run1\n",
      "20220524_GD_SGP_005_ugul_30C_run1\n",
      "20220524_GD_SGP_005_ugul_30C_run2\n",
      "20220524_GD_SGP_005_ugul_30C_run2\n",
      "20220524_GD_SGP_005_ugul_30C_run3\n",
      "20220524_GD_SGP_005_ugul_30C_run3\n",
      "20220524_GD_SGP_005_ugul_45C_run1\n",
      "20220524_GD_SGP_005_ugul_45C_run1\n",
      "20220524_GD_SGP_005_ugul_45C_run2-2\n",
      "20220524_GD_SGP_005_ugul_45C_run2-2\n",
      "20220524_GD_SGP_005_ugul_45C_run2\n",
      "20220524_GD_SGP_005_ugul_45C_run2\n",
      "20220524_GD_SGP_005_ugul_45C_run3-2\n",
      "20220524_GD_SGP_005_ugul_45C_run3-2\n",
      "20220524_GD_SGP_005_ugul_45C_run3\n",
      "20220524_GD_SGP_005_ugul_45C_run3\n",
      "20220524_GD_SGP_005_ugul_60C_run1\n",
      "20220524_GD_SGP_005_ugul_60C_run1\n",
      "20220524_GD_SGP_005_ugul_60C_run2\n",
      "20220524_GD_SGP_005_ugul_60C_run2\n",
      "20220524_GD_SGP_005_ugul_60C_run3\n",
      "20220524_GD_SGP_005_ugul_60C_run3\n",
      "20220524_GD_SGP_02_ugul_30C_run1\n",
      "20220524_GD_SGP_02_ugul_30C_run1\n",
      "20220524_GD_SGP_02_ugul_30C_run2\n",
      "20220524_GD_SGP_02_ugul_30C_run2\n",
      "20220524_GD_SGP_02_ugul_30C_run3\n",
      "20220524_GD_SGP_02_ugul_30C_run3\n",
      "20220524_GD_SGP_02_ugul_45C_run1-2\n",
      "20220524_GD_SGP_02_ugul_45C_run1-2\n",
      "20220524_GD_SGP_02_ugul_45C_run1\n",
      "20220524_GD_SGP_02_ugul_45C_run1\n",
      "20220524_GD_SGP_02_ugul_45C_run2\n",
      "20220524_GD_SGP_02_ugul_45C_run2\n",
      "20220524_GD_SGP_02_ugul_45C_run3\n",
      "20220524_GD_SGP_02_ugul_45C_run3\n",
      "20220524_GD_SGP_02_ugul_60C_run1\n",
      "20220524_GD_SGP_02_ugul_60C_run1\n",
      "20220524_GD_SGP_02_ugul_60C_run2\n",
      "20220524_GD_SGP_02_ugul_60C_run2\n",
      "20220524_GD_SGP_02_ugul_60C_run3\n",
      "20220524_GD_SGP_02_ugul_60C_run3\n"
     ]
    }
   ],
   "source": [
    "mass1 = 849.70 # SGP with 1 sialic acid\n",
    "mass2 = 956.06 # SGP with 2 sialic acid\n",
    "\n",
    "# fig, axs = plt.subplots(21, 2, figsize=(20, 40))\n",
    "\n",
    "masses = [mass1, mass2]\n",
    "\n",
    "data = pd.DataFrame()\n",
    "\n",
    "for i, ax in enumerate(axs.flat):\n",
    "    mass = masses[i%2]\n",
    "    file = xmls[i//2]\n",
    "    base = ntpath.basename(file)\n",
    "    base, _ = ntpath.splitext(base)\n",
    "    print(base)\n",
    "\n",
    "    \n",
    "\n",
    "    spl = base.split('_')\n",
    "    temp, run = spl[-2:]\n",
    "    conc = spl[-4]\n",
    "    if conc == '005':\n",
    "        conc = 0.05\n",
    "    else:\n",
    "        conc = 0.2\n",
    "\n",
    "    m = my_mzml.mzXML(file)\n",
    "    x1, y1 = m.ms1_extract(mass, tolerance=5)\n",
    "    y1 = ndimage.gaussian_filter(y1, 2)\n",
    "    \n",
    "    df = pd.DataFrame({\n",
    "        'concentration':conc,\n",
    "        'temperature':temp,\n",
    "        'run':run,\n",
    "        'mass':mass,\n",
    "        'time':x1,\n",
    "        'abundance':y1\n",
    "    })\n",
    "\n",
    "    data = pd.concat([data, df])\n",
    "    data.reset_index(drop=True, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "small_data = data[(data.time>=12) & (data.time<=22)]\n",
    "\n",
    "my_colors = alt.Color(\n",
    "    \"temperature:N\",\n",
    "    scale=alt.Scale(\n",
    "        domain=[\"30C\", \"45C\", \"60C\"],\n",
    "        range=[\"#6E6581\", \"#B0B2BB\", \"#6B8A97\"]\n",
    "    ),\n",
    ")\n",
    "\n",
    "data_left = alt.Chart(\n",
    "    small_data[(small_data.concentration==0.05) & (small_data.mass==849.70)]\n",
    ").encode(\n",
    "    x=alt.X('time:Q', title='Time',\n",
    "        axis=alt.Axis(\n",
    "            labelFlush=False,\n",
    "            labelFontSize=14,\n",
    "            labelFontWeight='bold'\n",
    "        )),\n",
    "    y=alt.Y('abundance:Q', title='Relative Abundance',\n",
    "        axis=alt.Axis(\n",
    "            format='.2e',\n",
    "            labelFontSize=14,\n",
    "            labelFontWeight='bold'\n",
    "        )),\n",
    "    color=my_colors\n",
    ").properties(\n",
    "    width=200,\n",
    "    height=100\n",
    ")\n",
    "\n",
    "data_right = alt.Chart(\n",
    "    small_data[(small_data.concentration==0.05) & (small_data.mass==956.06)]\n",
    ").encode(\n",
    "    x=alt.X('time:Q', title='Time',\n",
    "        axis=alt.Axis(\n",
    "            labelFlush=False,\n",
    "            labelFontSize=14,\n",
    "            labelFontWeight='bold'\n",
    "        )),\n",
    "    y=alt.Y('abundance:Q', title='Relative Abundance',\n",
    "        axis=alt.Axis(\n",
    "            format='.2e',\n",
    "            labelFontSize=14,\n",
    "            labelFontWeight='bold'\n",
    "        )),\n",
    "    color=my_colors\n",
    ").properties(\n",
    "    width=200,\n",
    "    height=100\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>concentration</th>\n",
       "      <th>temperature</th>\n",
       "      <th>run</th>\n",
       "      <th>mass</th>\n",
       "      <th>time</th>\n",
       "      <th>abundance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1811</th>\n",
       "      <td>0.05</td>\n",
       "      <td>30C</td>\n",
       "      <td>run1</td>\n",
       "      <td>849.70</td>\n",
       "      <td>12.006350</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1812</th>\n",
       "      <td>0.05</td>\n",
       "      <td>30C</td>\n",
       "      <td>run1</td>\n",
       "      <td>849.70</td>\n",
       "      <td>12.012233</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1813</th>\n",
       "      <td>0.05</td>\n",
       "      <td>30C</td>\n",
       "      <td>run1</td>\n",
       "      <td>849.70</td>\n",
       "      <td>12.016700</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1814</th>\n",
       "      <td>0.05</td>\n",
       "      <td>30C</td>\n",
       "      <td>run1</td>\n",
       "      <td>849.70</td>\n",
       "      <td>12.026050</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1815</th>\n",
       "      <td>0.05</td>\n",
       "      <td>30C</td>\n",
       "      <td>run1</td>\n",
       "      <td>849.70</td>\n",
       "      <td>12.031983</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200860</th>\n",
       "      <td>0.20</td>\n",
       "      <td>60C</td>\n",
       "      <td>run3</td>\n",
       "      <td>956.06</td>\n",
       "      <td>21.860167</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200861</th>\n",
       "      <td>0.20</td>\n",
       "      <td>60C</td>\n",
       "      <td>run3</td>\n",
       "      <td>956.06</td>\n",
       "      <td>21.894333</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200862</th>\n",
       "      <td>0.20</td>\n",
       "      <td>60C</td>\n",
       "      <td>run3</td>\n",
       "      <td>956.06</td>\n",
       "      <td>21.925667</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200863</th>\n",
       "      <td>0.20</td>\n",
       "      <td>60C</td>\n",
       "      <td>run3</td>\n",
       "      <td>956.06</td>\n",
       "      <td>21.959333</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200864</th>\n",
       "      <td>0.20</td>\n",
       "      <td>60C</td>\n",
       "      <td>run3</td>\n",
       "      <td>956.06</td>\n",
       "      <td>21.990833</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>24214 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        concentration temperature   run    mass       time  abundance\n",
       "1811             0.05         30C  run1  849.70  12.006350        0.0\n",
       "1812             0.05         30C  run1  849.70  12.012233        0.0\n",
       "1813             0.05         30C  run1  849.70  12.016700        0.0\n",
       "1814             0.05         30C  run1  849.70  12.026050        0.0\n",
       "1815             0.05         30C  run1  849.70  12.031983        0.0\n",
       "...               ...         ...   ...     ...        ...        ...\n",
       "200860           0.20         60C  run3  956.06  21.860167        0.0\n",
       "200861           0.20         60C  run3  956.06  21.894333        0.0\n",
       "200862           0.20         60C  run3  956.06  21.925667        0.0\n",
       "200863           0.20         60C  run3  956.06  21.959333        0.0\n",
       "200864           0.20         60C  run3  956.06  21.990833        0.0\n",
       "\n",
       "[24214 rows x 6 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "small_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "(data_left.mark_area(opacity=0.3) + data_left.mark_line()).facet(\n",
    "    row='run',\n",
    "    column='temperature'\n",
    ").save('./figures/sgp_light.svg')\n",
    "\n",
    "(data_right.mark_area(opacity=0.3) + data_right.mark_line()).facet(\n",
    "    row='run',\n",
    "    column='temperature'\n",
    ").save('./figures/sgp_heavy.svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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