alphatims snippets

plotting_example_dda.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hello there!\n",
    "\n",
    "I will start from the scratch here and I will go into how to plot timstof data\n",
    "using alphatims... hope it is handy!\n",
    "\n",
    "In brief I will search a timstof file using the latest version of sage (that can read\n",
    "the .d directly), then I will select a couple of peptide and plot their spectra.\n",
    "\n",
    "We are going to start from the top installing everything we will need."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.3\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "! pip install \"spectrum_utils[iplot]==0.4.2\" alphatims matplotlib pandas > /dev/null"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now I will run sage\n",
    "\n",
    "For this I will need:\n",
    "1. A config file\n",
    "2. A fasta\n",
    "3. Your mass spec data (in my case a file called `supersecretfile.d`)\n",
    "\n",
    "I will use a config file that I have already created (I made this file for this example, I do not claim this is the config you should be using), this is how it looks like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "    \"database\": {\n",
      "        \"bucket_size\": 16384,\n",
      "        \"fragment_min_mz\": 150.0,\n",
      "        \"fragment_max_mz\": 1500.0,\n",
      "        \"enzyme\": {\n",
      "            \"missed_cleavages\": 2,\n",
      "            \"cleave_at\": \"KR\"\n",
      "        },\n",
      "        \"static_mods\": {\n",
      "            \"C\": 57.0216\n",
      "        },\n",
      "        \"variable_mods\": {\n",
      "            \"M\": [15.9949]\n",
      "        },\n",
      "        \"decoy_tag\": \"rev_\",\n",
      "        \"generate_decoys\": true,\n",
      "        \"fasta\": \"UP000005640_9606.fasta\"\n",
      "    },\n",
      "    \"deisotope\": false,\n",
      "    \"min_matched_peaks\": 3,\n",
      "    \"wide_window\": false,\n",
      "    \"chimera\": false,\n",
      "    \"max_fragment_charge\": 2,\n",
      "    \"report_psms\": 1,\n",
      "    \"fragment_tol\": {\n",
      "        \"da\": [\n",
      "            -0.02,\n",
      "            0.02\n",
      "        ]\n",
      "    },\n",
      "    \"precursor_tol\": {\n",
      "        \"ppm\": [\n",
      "            -25,\n",
      "            25\n",
      "        ]\n",
      "    },\n",
      "    \"isotope_errors\": [\n",
      "        0,\n",
      "        0\n",
      "      ],\n",
      "    \"precursor_charge\": [2, 5],\n",
      "    \"predict_rt\": true,\n",
      "    \"min_peaks\": 10,\n",
      "    \"max_peaks\": 150\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "! cat sageconfig.json\n",
    "# Note how I have defined the fasta file inside the config file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we call sage ... and wait for exhausting <30 seconds for it to complete."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import subprocess\n",
    "\n",
    "_ = subprocess.run(\n",
    "    [\"./target/release/sage\", \"sageconfig.json\", \"supersecretfile.d\"],\n",
    "    capture_output=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This generates a file called `results.sage.tsv`, that we will read using pandas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\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>peptide</th>\n",
       "      <th>proteins</th>\n",
       "      <th>num_proteins</th>\n",
       "      <th>filename</th>\n",
       "      <th>scannr</th>\n",
       "      <th>rank</th>\n",
       "      <th>label</th>\n",
       "      <th>expmass</th>\n",
       "      <th>calcmass</th>\n",
       "      <th>charge</th>\n",
       "      <th>...</th>\n",
       "      <th>matched_intensity_pct</th>\n",
       "      <th>scored_candidates</th>\n",
       "      <th>poisson</th>\n",
       "      <th>sage_discriminant_score</th>\n",
       "      <th>posterior_error</th>\n",
       "      <th>spectrum_q</th>\n",
       "      <th>peptide_q</th>\n",
       "      <th>protein_q</th>\n",
       "      <th>ms1_intensity</th>\n",
       "      <th>ms2_intensity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DAVTYTEHAK</td>\n",
       "      <td>sp|P62805|H4_HUMAN</td>\n",
       "      <td>1</td>\n",
       "      <td>supersecretfile.d</td>\n",
       "      <td>29354</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1133.53250</td>\n",
       "      <td>1133.53530</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>49.899574</td>\n",
       "      <td>555</td>\n",
       "      <td>-7.677502</td>\n",
       "      <td>0.058102</td>\n",
       "      <td>-10.513044</td>\n",
       "      <td>0.000048</td>\n",
       "      <td>0.000093</td>\n",
       "      <td>0.000421</td>\n",
       "      <td>354527.0</td>\n",
       "      <td>124220.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ISGLIYEETR</td>\n",
       "      <td>sp|P62805|H4_HUMAN</td>\n",
       "      <td>1</td>\n",
       "      <td>supersecretfile.d</td>\n",
       "      <td>110082</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1179.61160</td>\n",
       "      <td>1179.61350</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>65.916970</td>\n",
       "      <td>468</td>\n",
       "      <td>-7.990956</td>\n",
       "      <td>0.123981</td>\n",
       "      <td>-22.260176</td>\n",
       "      <td>0.000048</td>\n",
       "      <td>0.000093</td>\n",
       "      <td>0.000421</td>\n",
       "      <td>420286.0</td>\n",
       "      <td>112749.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TVTAMDVVYALK</td>\n",
       "      <td>sp|P62805|H4_HUMAN</td>\n",
       "      <td>1</td>\n",
       "      <td>supersecretfile.d</td>\n",
       "      <td>187355</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1309.69810</td>\n",
       "      <td>1309.69520</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>31.199205</td>\n",
       "      <td>408</td>\n",
       "      <td>-8.350520</td>\n",
       "      <td>0.068153</td>\n",
       "      <td>-11.938371</td>\n",
       "      <td>0.000048</td>\n",
       "      <td>0.000093</td>\n",
       "      <td>0.000421</td>\n",
       "      <td>217890.0</td>\n",
       "      <td>28868.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>VFLENVIR</td>\n",
       "      <td>sp|P62805|H4_HUMAN</td>\n",
       "      <td>1</td>\n",
       "      <td>supersecretfile.d</td>\n",
       "      <td>156075</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>988.56396</td>\n",
       "      <td>988.57056</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>53.481020</td>\n",
       "      <td>271</td>\n",
       "      <td>-5.503287</td>\n",
       "      <td>-0.007880</td>\n",
       "      <td>-4.499355</td>\n",
       "      <td>0.000048</td>\n",
       "      <td>0.000093</td>\n",
       "      <td>0.000421</td>\n",
       "      <td>56537.0</td>\n",
       "      <td>16754.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DNIQGITKPAIR</td>\n",
       "      <td>sp|P62805|H4_HUMAN</td>\n",
       "      <td>1</td>\n",
       "      <td>supersecretfile.d</td>\n",
       "      <td>77292</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1324.74940</td>\n",
       "      <td>1324.74620</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>17.522009</td>\n",
       "      <td>209</td>\n",
       "      <td>-7.549754</td>\n",
       "      <td>0.025110</td>\n",
       "      <td>-6.793108</td>\n",
       "      <td>0.000048</td>\n",
       "      <td>0.000093</td>\n",
       "      <td>0.000421</td>\n",
       "      <td>77707.0</td>\n",
       "      <td>10171.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        peptide            proteins  num_proteins           filename  scannr  \\\n",
       "0    DAVTYTEHAK  sp|P62805|H4_HUMAN             1  supersecretfile.d   29354   \n",
       "1    ISGLIYEETR  sp|P62805|H4_HUMAN             1  supersecretfile.d  110082   \n",
       "2  TVTAMDVVYALK  sp|P62805|H4_HUMAN             1  supersecretfile.d  187355   \n",
       "3      VFLENVIR  sp|P62805|H4_HUMAN             1  supersecretfile.d  156075   \n",
       "4  DNIQGITKPAIR  sp|P62805|H4_HUMAN             1  supersecretfile.d   77292   \n",
       "\n",
       "   rank  label     expmass    calcmass  charge  ...  matched_intensity_pct  \\\n",
       "0     1      1  1133.53250  1133.53530       2  ...              49.899574   \n",
       "1     1      1  1179.61160  1179.61350       2  ...              65.916970   \n",
       "2     1      1  1309.69810  1309.69520       2  ...              31.199205   \n",
       "3     1      1   988.56396   988.57056       2  ...              53.481020   \n",
       "4     1      1  1324.74940  1324.74620       2  ...              17.522009   \n",
       "\n",
       "   scored_candidates   poisson  sage_discriminant_score  posterior_error  \\\n",
       "0                555 -7.677502                 0.058102       -10.513044   \n",
       "1                468 -7.990956                 0.123981       -22.260176   \n",
       "2                408 -8.350520                 0.068153       -11.938371   \n",
       "3                271 -5.503287                -0.007880        -4.499355   \n",
       "4                209 -7.549754                 0.025110        -6.793108   \n",
       "\n",
       "   spectrum_q  peptide_q  protein_q  ms1_intensity  ms2_intensity  \n",
       "0    0.000048   0.000093   0.000421       354527.0       124220.0  \n",
       "1    0.000048   0.000093   0.000421       420286.0       112749.0  \n",
       "2    0.000048   0.000093   0.000421       217890.0        28868.0  \n",
       "3    0.000048   0.000093   0.000421        56537.0        16754.0  \n",
       "4    0.000048   0.000093   0.000421        77707.0        10171.0  \n",
       "\n",
       "[5 rows x 39 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv(\"results.sage.tsv\", sep=\"\\t\")\n",
    "# I will keep only the psms that match my favourite protein ... Histone h4\n",
    "# Which is labelled as UBB_HUMAN in the fasta file\n",
    "df = df[df[\"proteins\"].str.match(f\".*\\|H4.*HUMAN.*\")]\n",
    "\n",
    "# Now I will filter for only psms whose peptide has a q value < 0.01\n",
    "df = df[df[\"peptide_q\"] < 0.01]\n",
    "\n",
    "# Now I will remove all the decoys\n",
    "df = df[df[\"label\"] == 1]\n",
    "\n",
    "# Now I will keep only the best psm for each peptide/modification combination\n",
    "df = (\n",
    "    df.sort_values(\"sage_discriminant_score\", ascending=False)\n",
    "    .groupby([\"peptide\"])\n",
    "    .head(1)\n",
    "    .sort_values(\"ms2_intensity\", ascending=False)\n",
    ")\n",
    "\n",
    "# AND FINALLY we reset the indices.\n",
    "df = df.reset_index(drop=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will get the data we need from the scan that we care about!\n",
    "I will just use the second psm for this example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scan number: 110082 for peptide ISGLIYEETR/2 of mass 1179.6135 at 1400.2285 seconds\n"
     ]
    }
   ],
   "source": [
    "scan_number = df[\"scannr\"][1]\n",
    "peptide_seq = df[\"peptide\"][1]\n",
    "retention_time = df[\"rt\"][1]\n",
    "neutral_mass = df[\"calcmass\"][1]\n",
    "charge = df[\"charge\"][1]\n",
    "\n",
    "print(\n",
    "    f\"Scan number: {scan_number} for peptide {peptide_seq}/{charge} \"\n",
    "    f\"of mass {neutral_mass} at {retention_time} seconds\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I will load my file using alphatims."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:WARNING: No Bruker libraries are available for this operating system. Mobility and m/z values need to be estimated. While this estimation often returns acceptable results with errors < 0.02 Th, huge errors (e.g. offsets of 6 Th) have already been observed for some samples!\n",
      "100%|██████████| 16873/16873 [00:15<00:00, 1116.83it/s]\n"
     ]
    }
   ],
   "source": [
    "from alphatims.bruker import TimsTOF\n",
    "\n",
    "myfile = TimsTOF(\"supersecretfile.d\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then I will exrtact the data from the \"scan\" I care about.\n",
    "\n",
    "Another day I can explain better the weird relationship of \"scans\" and \"frames\" ...\n",
    "and \"pushes\" and \"retention time\" and \"ion mobility\"... BUT NOT TODAY!\n",
    "\n",
    "TODAY what matter is that the scan number reported by sage is actually\n",
    "the precursor index in alphatims."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "      <td>12687267</td>\n",
       "      <td>128560</td>\n",
       "      <td>1401.009733</td>\n",
       "      <td>23.350162</td>\n",
       "      <td>0.923860</td>\n",
       "      <td>590.10105</td>\n",
       "      <td>592.10105</td>\n",
       "      <td>402.557083</td>\n",
       "      <td>61</td>\n",
       "      <td>61</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1278</th>\n",
       "      <td>1610787001</td>\n",
       "      <td>8953</td>\n",
       "      <td>890</td>\n",
       "      <td>110082</td>\n",
       "      <td>12687291</td>\n",
       "      <td>289696</td>\n",
       "      <td>1401.009733</td>\n",
       "      <td>23.350162</td>\n",
       "      <td>0.911496</td>\n",
       "      <td>590.10105</td>\n",
       "      <td>592.10105</td>\n",
       "      <td>1067.834519</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1279</th>\n",
       "      <td>1610787002</td>\n",
       "      <td>8953</td>\n",
       "      <td>890</td>\n",
       "      <td>110082</td>\n",
       "      <td>12687291</td>\n",
       "      <td>289699</td>\n",
       "      <td>1401.009733</td>\n",
       "      <td>23.350162</td>\n",
       "      <td>0.911496</td>\n",
       "      <td>590.10105</td>\n",
       "      <td>592.10105</td>\n",
       "      <td>1067.849867</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1280</th>\n",
       "      <td>1610787003</td>\n",
       "      <td>8953</td>\n",
       "      <td>890</td>\n",
       "      <td>110082</td>\n",
       "      <td>12687291</td>\n",
       "      <td>289739</td>\n",
       "      <td>1401.009733</td>\n",
       "      <td>23.350162</td>\n",
       "      <td>0.911496</td>\n",
       "      <td>590.10105</td>\n",
       "      <td>592.10105</td>\n",
       "      <td>1068.054523</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1281</th>\n",
       "      <td>1610787004</td>\n",
       "      <td>8953</td>\n",
       "      <td>890</td>\n",
       "      <td>110082</td>\n",
       "      <td>12687291</td>\n",
       "      <td>290183</td>\n",
       "      <td>1401.009733</td>\n",
       "      <td>23.350162</td>\n",
       "      <td>0.911496</td>\n",
       "      <td>590.10105</td>\n",
       "      <td>592.10105</td>\n",
       "      <td>1070.327514</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1282</th>\n",
       "      <td>1610787005</td>\n",
       "      <td>8953</td>\n",
       "      <td>890</td>\n",
       "      <td>110082</td>\n",
       "      <td>12687291</td>\n",
       "      <td>291584</td>\n",
       "      <td>1401.009733</td>\n",
       "      <td>23.350162</td>\n",
       "      <td>0.911496</td>\n",
       "      <td>590.10105</td>\n",
       "      <td>592.10105</td>\n",
       "      <td>1077.515563</td>\n",
       "      <td>57</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1283 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      raw_indices  frame_indices  scan_indices  precursor_indices  \\\n",
       "0      1610785723           8953           866             110082   \n",
       "1      1610785724           8953           866             110082   \n",
       "2      1610785725           8953           866             110082   \n",
       "3      1610785726           8953           866             110082   \n",
       "4      1610785727           8953           866             110082   \n",
       "...           ...            ...           ...                ...   \n",
       "1278   1610787001           8953           890             110082   \n",
       "1279   1610787002           8953           890             110082   \n",
       "1280   1610787003           8953           890             110082   \n",
       "1281   1610787004           8953           890             110082   \n",
       "1282   1610787005           8953           890             110082   \n",
       "\n",
       "      push_indices  tof_indices    rt_values  rt_values_min  mobility_values  \\\n",
       "0         12687267        64794  1401.009733      23.350162         0.923860   \n",
       "1         12687267        66053  1401.009733      23.350162         0.923860   \n",
       "2         12687267        93209  1401.009733      23.350162         0.923860   \n",
       "3         12687267       118384  1401.009733      23.350162         0.923860   \n",
       "4         12687267       128560  1401.009733      23.350162         0.923860   \n",
       "...            ...          ...          ...            ...              ...   \n",
       "1278      12687291       289696  1401.009733      23.350162         0.911496   \n",
       "1279      12687291       289699  1401.009733      23.350162         0.911496   \n",
       "1280      12687291       289739  1401.009733      23.350162         0.911496   \n",
       "1281      12687291       290183  1401.009733      23.350162         0.911496   \n",
       "1282      12687291       291584  1401.009733      23.350162         0.911496   \n",
       "\n",
       "      quad_low_mz_values  quad_high_mz_values    mz_values  intensity_values  \\\n",
       "0              590.10105            592.10105   227.169679               284   \n",
       "1              590.10105            592.10105   230.150293               144   \n",
       "2              590.10105            592.10105   299.169194               171   \n",
       "3              590.10105            592.10105   371.226391               202   \n",
       "4              590.10105            592.10105   402.557083                61   \n",
       "...                  ...                  ...          ...               ...   \n",
       "1278           590.10105            592.10105  1067.834519                10   \n",
       "1279           590.10105            592.10105  1067.849867                10   \n",
       "1280           590.10105            592.10105  1068.054523                58   \n",
       "1281           590.10105            592.10105  1070.327514                33   \n",
       "1282           590.10105            592.10105  1077.515563                57   \n",
       "\n",
       "      corrected_intensity_values  \n",
       "0                            284  \n",
       "1                            144  \n",
       "2                            171  \n",
       "3                            202  \n",
       "4                             61  \n",
       "...                          ...  \n",
       "1278                          10  \n",
       "1279                          10  \n",
       "1280                          58  \n",
       "1281                          33  \n",
       "1282                          57  \n",
       "\n",
       "[1283 rows x 14 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_peak_data = myfile[{\"precursor_indices\": scan_number}]\n",
    "my_peak_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>tof_indices</th>\n",
       "      <th>scan_indices</th>\n",
       "      <th>corrected_intensity_values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>641</th>\n",
       "      <td>209679</td>\n",
       "      <td>880</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>863</th>\n",
       "      <td>235968</td>\n",
       "      <td>883</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>209640</td>\n",
       "      <td>872</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219</th>\n",
       "      <td>209645</td>\n",
       "      <td>872</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>857</th>\n",
       "      <td>209765</td>\n",
       "      <td>883</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>984</th>\n",
       "      <td>209587</td>\n",
       "      <td>885</td>\n",
       "      <td>2155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>788</th>\n",
       "      <td>209588</td>\n",
       "      <td>882</td>\n",
       "      <td>2165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>847</th>\n",
       "      <td>209586</td>\n",
       "      <td>883</td>\n",
       "      <td>2335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>573</th>\n",
       "      <td>209587</td>\n",
       "      <td>879</td>\n",
       "      <td>2393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>711</th>\n",
       "      <td>209588</td>\n",
       "      <td>881</td>\n",
       "      <td>2510</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1283 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     tof_indices  scan_indices  corrected_intensity_values\n",
       "641       209679           880                           9\n",
       "863       235968           883                           9\n",
       "218       209640           872                           9\n",
       "219       209645           872                           9\n",
       "857       209765           883                           9\n",
       "..           ...           ...                         ...\n",
       "984       209587           885                        2155\n",
       "788       209588           882                        2165\n",
       "847       209586           883                        2335\n",
       "573       209587           879                        2393\n",
       "711       209588           881                        2510\n",
       "\n",
       "[1283 rows x 3 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_peak_data.sort_values(\"corrected_intensity_values\")[\n",
    "    [\"tof_indices\", \"scan_indices\", \"corrected_intensity_values\"]\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "once we have the scan data we can plot it!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'mobility (1/K0)')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.scatter(\n",
    "    my_peak_data[\"mz_values\"],\n",
    "    my_peak_data[\"mobility_values\"],\n",
    "    s=my_peak_data[\"intensity_values\"] / 100,\n",
    ")\n",
    "plt.xlabel(\"m/z\")\n",
    "plt.ylabel(\"mobility (1/K0)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see here than a single \"scan\" is actually many \"scans\" along the mobility dimension!\n",
    "\n",
    "If we want a more traditional \"scan\" we will just collapse all equivalent mzs\n",
    "(I am aware there are better ways to do this, but this is just an example, I am going for the\n",
    "easy version to explain a concept)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'mobility (1/K0)')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "my_flat_data = (\n",
    "    my_peak_data.groupby([\"tof_indices\"])\n",
    "    .agg(\n",
    "        {\n",
    "            \"corrected_intensity_values\": \"sum\",\n",
    "            \"mobility_values\": \"mean\",\n",
    "            \"mz_values\": \"mean\",\n",
    "        }\n",
    "    )\n",
    "    .reset_index()\n",
    "    .sort_values(\"corrected_intensity_values\", ascending=True)\n",
    ")\n",
    "\n",
    "# And now we plot it again ...\n",
    "plt.scatter(\n",
    "    my_flat_data[\"mz_values\"],\n",
    "    my_flat_data[\"mobility_values\"],\n",
    "    s=my_flat_data[\"corrected_intensity_values\"] / 100,\n",
    "    c=my_flat_data[\"corrected_intensity_values\"],\n",
    "    cmap=\"viridis_r\",\n",
    ")\n",
    "plt.xlabel(\"m/z\")\n",
    "plt.ylabel(\"mobility (1/K0)\")\n",
    "# And it might not seem like a lot changed, but now we have only 1 value per\n",
    "# m/z value, instead of 1 value per m/z value per frame."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What if we want an EVEN MORE traditional \"scan\"?\n",
    "\n",
    "we can use spectrum utils to plot it!\n",
    "\n",
    "We will first do some more pre-processing, I will bin all the data to buckets of 0.02da"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>binned_mz</th>\n",
       "      <th>corrected_intensity_values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>352</th>\n",
       "      <td>697.32</td>\n",
       "      <td>34252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>451</th>\n",
       "      <td>810.40</td>\n",
       "      <td>24744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>602</th>\n",
       "      <td>1067.54</td>\n",
       "      <td>12286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>542</th>\n",
       "      <td>980.50</td>\n",
       "      <td>11490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>371.22</td>\n",
       "      <td>8072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372</th>\n",
       "      <td>697.98</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>569.36</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>465.68</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>240.16</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429</th>\n",
       "      <td>787.74</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>635 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     binned_mz  corrected_intensity_values\n",
       "352     697.32                       34252\n",
       "451     810.40                       24744\n",
       "602    1067.54                       12286\n",
       "542     980.50                       11490\n",
       "92      371.22                        8072\n",
       "..         ...                         ...\n",
       "372     697.98                           9\n",
       "260     569.36                           9\n",
       "162     465.68                           9\n",
       "16      240.16                           9\n",
       "429     787.74                           9\n",
       "\n",
       "[635 rows x 2 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_flat_data[\"binned_mz\"] = (my_flat_data[\"mz_values\"] / 0.02).round(0) * 0.02\n",
    "binned_data = (\n",
    "    my_flat_data.groupby(\"binned_mz\")\n",
    "    .agg({\"corrected_intensity_values\": \"sum\"})\n",
    "    .reset_index()\n",
    "    .sort_values(\"corrected_intensity_values\", ascending=False)\n",
    ")\n",
    "\n",
    "binned_data\n",
    "\n",
    "\n",
    "# my_flat_data[my_flat_data[\"binned_mz\"] > 800].groupby(\"binned_mz\").agg(\n",
    "#     {\"corrected_intensity_values\": \"sum\"}\n",
    "# ).reset_index().sort_values(\"corrected_intensity_values\").tail(10).sort_values(\n",
    "#     \"binned_mz\"\n",
    "# )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='m/z', ylabel='Intensity'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import spectrum_utils.plot as sup\n",
    "import spectrum_utils.spectrum as sus\n",
    "\n",
    "PROTON_MASS = 1.0072  # Close enough for now\n",
    "\n",
    "identifier = f\"supersecretfile.d:scan:{scan_number}\"\n",
    "spectrum = sus.MsmsSpectrum(\n",
    "    identifier=identifier,\n",
    "    precursor_mz=(neutral_mass + (charge * PROTON_MASS)) / charge,\n",
    "    precursor_charge=charge,\n",
    "    mz=binned_data[\"binned_mz\"],\n",
    "    intensity=binned_data[\"corrected_intensity_values\"],\n",
    "    retention_time=retention_time,\n",
    ")\n",
    "\n",
    "# Process the spectrum.\n",
    "fragment_tol_mass, fragment_tol_mode = 20, \"ppm\"\n",
    "spectrum = (\n",
    "    spectrum.set_mz_range(min_mz=100, max_mz=1400)\n",
    "    .remove_precursor_peak(fragment_tol_mass, fragment_tol_mode)\n",
    "    .filter_intensity(min_intensity=0.05, max_num_peaks=150)\n",
    "    # .scale_intensity(\"root\")\n",
    "    .annotate_proforma(\n",
    "        peptide_seq, fragment_tol_mass, fragment_tol_mode, ion_types=\"aby\"\n",
    "    )\n",
    ")\n",
    "\n",
    "# Plot the spectrum.\n",
    "sup.spectrum(spectrum, grid=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is worth noting that for the tallest matching ions there are several peaks!\n",
    "This happens because the same fragment ion is detected along the mobility dimension with slightly\n",
    "different m/z values!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 6151.,  3329.,  3839., 34252., 24744.,  2207., 11490.,  2121.,\n",
       "       12286.], dtype=float32)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spectrum.mz[[len(x.fragment_annotations) > 0 for x in spectrum.annotation]]\n",
    "spectrum.intensity[[len(x.fragment_annotations) > 0 for x in spectrum.annotation]]\n",
    "# spectrum.annotation[1].__dict__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "tdf2lance",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}

plotting_example_dia.ipynb