KB_stats.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0b5c13ab-2e0c-4ed5-b41b-6125fdf2eb10",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/trose/.conda/envs/metabolomics2/lib/python3.10/site-packages/linex2/parser.py:22: RuntimeWarning: Package 'lynx' (LipidLynxX) not available. Lipid name conversions will not be possible.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "from tqdm import tqdm\n",
    "import pickle\n",
    "import random\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy\n",
    "from sklearn.metrics.pairwise import pairwise_kernels\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import image\n",
    "import seaborn as sns\n",
    "from collections import defaultdict, Counter\n",
    "\n",
    "from metaspace import SMInstance\n",
    "from anndata import AnnData\n",
    "from metaspace_converter import metaspace_to_anndata\n",
    "from datetime import date, datetime \n",
    "import time\n",
    "\n",
    "import utils\n",
    "from coloc_utils import *\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1077a31f-fcfb-4023-8263-8da7b2e0e773",
   "metadata": {},
   "source": [
    "# Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fef93720-f6b9-498f-9950-9a638373d64b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from config import date_key, data_dir, store_dir, figuredata_dir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e08184f6-ea2d-4a85-93e7-e11f76cd362c",
   "metadata": {},
   "outputs": [],
   "source": [
    "dss = pickle.load(open(os.path.join(store_dir, 'all_datasets.pickle'), \"rb\" ) )\n",
    "results = pickle.load(open(os.path.join(store_dir, 'hmdb4_results.pickle'), \"rb\" ) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bf98e8e1-45ad-4366-a811-77705260b525",
   "metadata": {},
   "outputs": [],
   "source": [
    "md = utils.make_metadata_dict(dss, results, fdrcutoff=0.05)\n",
    "mdt = utils.clean_metadata_table(utils.metadata_dict_totable(md))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04e3d5d6-df72-42ff-ae68-a2ee844c9a89",
   "metadata": {},
   "source": [
    "# Helper functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e07dcef1-0604-4402-98db-cc335f4ebb0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def toYearFraction(date):\n",
    "    def sinceEpoch(date): # returns seconds since epoch\n",
    "        return time.mktime(date.timetuple())\n",
    "    s = sinceEpoch\n",
    "\n",
    "    year = date.year\n",
    "    startOfThisYear = datetime(year=year, month=1, day=1)\n",
    "    startOfNextYear = datetime(year=year+1, month=1, day=1)\n",
    "\n",
    "    yearElapsed = s(date) - s(startOfThisYear)\n",
    "    yearDuration = s(startOfNextYear) - s(startOfThisYear)\n",
    "    fraction = yearElapsed/yearDuration\n",
    "\n",
    "    return date.year + fraction\n",
    "\n",
    "def apd(results, fdr_cutoff=0.05):\n",
    "    n_anno = []\n",
    "    for tab in results.values():\n",
    "        if tab[tab['fdr'] <= fdr_cutoff].shape[0] >0:\n",
    "            n_anno.append(tab[tab['fdr'] <= fdr_cutoff].shape[0])\n",
    "            \n",
    "    return n_anno\n",
    "\n",
    "def dpa(results, fdr_cutoff=0.05):\n",
    "    dpad = defaultdict(int)\n",
    "    for tab in results.values():\n",
    "        tmp = list(tab[tab['fdr'] <= fdr_cutoff].index)\n",
    "        for t in tmp:\n",
    "            dpad[t] += 1\n",
    "    return dpad"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a0d5607-64b0-4176-b4fc-18787cb36a6d",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Knowledge base statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c82d3ca5-ce89-4a27-9a6c-384c454b3472",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rc('font', size=SMALL_SIZE)          # controls default text sizes\n",
    "plt.rc('axes', titlesize=SMALL_SIZE)     # fontsize of the axes title\n",
    "plt.rc('axes', labelsize=SMALL_SIZE)    # fontsize of the x and y labels\n",
    "plt.rc('xtick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
    "plt.rc('ytick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
    "plt.rc('legend', fontsize=SMALLSMALL_SIZE, title_fontsize=SMALL_SIZE)    # legend fontsize\n",
    "plt.rc('figure', titlesize=BIGGER_SIZE)  # fontsize of the figure title"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2106be58-ddd6-40eb-94c3-1f8b04d53864",
   "metadata": {},
   "outputs": [],
   "source": [
    "# img = image.imread('drawings/metaspace_sketch.png')\n",
    "# msc1 = image.imread('drawings/metaspace_screenshot1.png')\n",
    "# msc2 = image.imread('drawings/metaspace_screenshot2.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2338aa76-a722-47a9-a9bd-5e4fb8d8710f",
   "metadata": {},
   "source": [
    "## Submissions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4c98c098-4603-4331-a77c-43a5dab663f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# All dataset IDs are not public and therefore not available on GitHub\n",
    "# Comment out the whole cell if table is not available\n",
    "all_ds_ids = pd.read_csv(os.path.join(data_dir, 'all_dataset_ids-2024-04-04.csv'))\n",
    "all_ds_ids['dataset_id'] = all_ds_ids['dataset_id'].apply(lambda x: toYearFraction(date.fromisoformat(x.split('_')[0])))\n",
    "all_ds_ids['Datasets'] = 'All'\n",
    "all_ds_ids = all_ds_ids.rename(columns={'dataset_id':'Date'})\n",
    "all_ds_ids = all_ds_ids.sort_values('Date')\n",
    "all_ds_ids['Submissions'] = np.arange(all_ds_ids.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5a5bb6f8-0cea-4057-89d3-434e1dad61aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "sm = SMInstance()\n",
    "new_datasets = sm.datasets()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "92618826-6bd3-43e6-b1f1-2f62ceae4ebb",
   "metadata": {},
   "outputs": [],
   "source": [
    "public_ids = pd.DataFrame({'Date': [toYearFraction(date.fromisoformat(x.id.split('_')[0])) for x in new_datasets]})\n",
    "public_ids = public_ids[public_ids['Date'] <= all_ds_ids['Date'].max()]\n",
    "public_ids['Datasets'] = 'Public'\n",
    "public_ids = public_ids.sort_values('Date')\n",
    "public_ids['Submissions'] = np.arange(public_ids.shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "352db7b1-e91a-4cd7-a7ac-6826f6b0f3a6",
   "metadata": {},
   "source": [
    "## Contributors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b5767f05-36c9-4975-8276-6dc28602150d",
   "metadata": {},
   "outputs": [],
   "source": [
    "grp = []\n",
    "sbmttr = []\n",
    "\n",
    "submitter_scaling = 50\n",
    "\n",
    "for ds in dss:\n",
    "    if not (ds.group is None or ds.submitter is None):\n",
    "        grp.append(ds.group['shortName'])\n",
    "        sbmttr.append(ds.submitter['id'])\n",
    "\n",
    "labs = pd.DataFrame({'Lab': grp, 'Submitter': sbmttr})\n",
    "#labs.groupby('Lab').nunique()\n",
    "labs = labs.groupby('Lab').agg('count').rename(columns={'Submitter': 'Datasets'}).join(labs.groupby('Lab').nunique())\n",
    "labs['Submitter'] = - labs['Submitter'] * submitter_scaling\n",
    "labs = labs.sort_values('Datasets', ascending=False).reset_index()\n",
    "labs = labs[:15]\n",
    "labs['Lab'] = labs['Lab'].replace({'♡EMBL♡': 'EMBL'})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eaca4e48-d02d-4f67-9ff7-e87179a7ff50",
   "metadata": {},
   "source": [
    "## Organisms/Organs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "73c15a07-236d-4195-ad3f-2abb1105e98e",
   "metadata": {},
   "outputs": [],
   "source": [
    "org_org = mdt[['Organism', 'Organism_Part']].rename(columns={'Organism_Part': 'Organ'}).reset_index()  # .groupby(['Organism', 'Organ']).agg('count')\n",
    "most_organs = list(org_org.groupby('Organ').agg('count').sort_values('Organism', ascending=False).index[:10])\n",
    "most_organisms = list(org_org.groupby('Organism').agg('count').sort_values('Organ', ascending=False).index[:3])\n",
    "\n",
    "most_organisms = [x for x in most_organisms if x not in ['N/A', 'Multiple']]\n",
    "\n",
    "org_org = org_org[org_org['Organism'].isin(most_organisms)]\n",
    "org_org = org_org[org_org['Organ'].isin(most_organs)]\n",
    "\n",
    "\n",
    "org_org2 = org_org.groupby(['Organism', 'Organ']).agg('count').reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5db0e971-b4e1-4114-ab5f-a7a1f6b32f51",
   "metadata": {},
   "source": [
    "## Figure assembly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "57da3eec-deb0-49bf-94b7-f9b5261b6cc1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Polarity</th>\n",
       "      <th>maldi_matrix</th>\n",
       "      <th>Organism</th>\n",
       "      <th>Condition</th>\n",
       "      <th>Organism_Part</th>\n",
       "      <th>Group</th>\n",
       "      <th>mzmin</th>\n",
       "      <th>mzmax</th>\n",
       "      <th>Analyzer</th>\n",
       "      <th>Ionisation_Source</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Positive</td>\n",
       "      <td>DHB</td>\n",
       "      <td>2648</td>\n",
       "      <td>2648</td>\n",
       "      <td>2648</td>\n",
       "      <td>2648</td>\n",
       "      <td>2586</td>\n",
       "      <td>2586</td>\n",
       "      <td>2648</td>\n",
       "      <td>2648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Negative</td>\n",
       "      <td>DAN</td>\n",
       "      <td>1076</td>\n",
       "      <td>1076</td>\n",
       "      <td>1076</td>\n",
       "      <td>1076</td>\n",
       "      <td>1033</td>\n",
       "      <td>1033</td>\n",
       "      <td>1076</td>\n",
       "      <td>1076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Negative</td>\n",
       "      <td>Other</td>\n",
       "      <td>1056</td>\n",
       "      <td>1056</td>\n",
       "      <td>1056</td>\n",
       "      <td>1056</td>\n",
       "      <td>1030</td>\n",
       "      <td>1030</td>\n",
       "      <td>1056</td>\n",
       "      <td>1056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Positive</td>\n",
       "      <td>Other</td>\n",
       "      <td>718</td>\n",
       "      <td>718</td>\n",
       "      <td>718</td>\n",
       "      <td>718</td>\n",
       "      <td>662</td>\n",
       "      <td>662</td>\n",
       "      <td>718</td>\n",
       "      <td>718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Negative</td>\n",
       "      <td>NEDC</td>\n",
       "      <td>534</td>\n",
       "      <td>534</td>\n",
       "      <td>534</td>\n",
       "      <td>534</td>\n",
       "      <td>527</td>\n",
       "      <td>527</td>\n",
       "      <td>534</td>\n",
       "      <td>534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Positive</td>\n",
       "      <td>CHCA</td>\n",
       "      <td>503</td>\n",
       "      <td>503</td>\n",
       "      <td>503</td>\n",
       "      <td>503</td>\n",
       "      <td>452</td>\n",
       "      <td>452</td>\n",
       "      <td>503</td>\n",
       "      <td>503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Negative</td>\n",
       "      <td>9AA</td>\n",
       "      <td>191</td>\n",
       "      <td>191</td>\n",
       "      <td>191</td>\n",
       "      <td>191</td>\n",
       "      <td>163</td>\n",
       "      <td>163</td>\n",
       "      <td>191</td>\n",
       "      <td>191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Positive</td>\n",
       "      <td>DHA</td>\n",
       "      <td>172</td>\n",
       "      <td>172</td>\n",
       "      <td>172</td>\n",
       "      <td>172</td>\n",
       "      <td>170</td>\n",
       "      <td>170</td>\n",
       "      <td>172</td>\n",
       "      <td>172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Positive</td>\n",
       "      <td>DAN</td>\n",
       "      <td>161</td>\n",
       "      <td>161</td>\n",
       "      <td>161</td>\n",
       "      <td>161</td>\n",
       "      <td>156</td>\n",
       "      <td>156</td>\n",
       "      <td>161</td>\n",
       "      <td>161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Negative</td>\n",
       "      <td>Norharmane</td>\n",
       "      <td>97</td>\n",
       "      <td>97</td>\n",
       "      <td>97</td>\n",
       "      <td>97</td>\n",
       "      <td>85</td>\n",
       "      <td>85</td>\n",
       "      <td>97</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Positive</td>\n",
       "      <td>Mix</td>\n",
       "      <td>78</td>\n",
       "      <td>78</td>\n",
       "      <td>78</td>\n",
       "      <td>78</td>\n",
       "      <td>77</td>\n",
       "      <td>77</td>\n",
       "      <td>78</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Negative</td>\n",
       "      <td>DHB</td>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "      <td>68</td>\n",
       "      <td>68</td>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Negative</td>\n",
       "      <td>DMAN</td>\n",
       "      <td>67</td>\n",
       "      <td>67</td>\n",
       "      <td>67</td>\n",
       "      <td>67</td>\n",
       "      <td>67</td>\n",
       "      <td>67</td>\n",
       "      <td>67</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Positive</td>\n",
       "      <td>HCCA</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>41</td>\n",
       "      <td>41</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Negative</td>\n",
       "      <td>BPYN</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Positive</td>\n",
       "      <td>DHAP</td>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "      <td>38</td>\n",
       "      <td>38</td>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Positive</td>\n",
       "      <td>Norharmane</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Negative</td>\n",
       "      <td>DCTB</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Positive</td>\n",
       "      <td>glycerol</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Negative</td>\n",
       "      <td>DHAP</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Negative</td>\n",
       "      <td>Mix</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Positive</td>\n",
       "      <td>BPYN</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Negative</td>\n",
       "      <td>CHCA</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Polarity maldi_matrix  Organism  Condition  Organism_Part  Group  mzmin  \\\n",
       "24  Positive          DHB      2648       2648           2648   2648   2586   \n",
       "4   Negative          DAN      1076       1076           1076   1076   1033   \n",
       "15  Negative        Other      1056       1056           1056   1056   1030   \n",
       "31  Positive        Other       718        718            718    718    662   \n",
       "13  Negative         NEDC       534        534            534    534    527   \n",
       "19  Positive         CHCA       503        503            503    503    452   \n",
       "0   Negative          9AA       191        191            191    191    163   \n",
       "22  Positive          DHA       172        172            172    172    170   \n",
       "21  Positive          DAN       161        161            161    161    156   \n",
       "14  Negative   Norharmane        97         97             97     97     85   \n",
       "28  Positive          Mix        78         78             78     78     77   \n",
       "8   Negative          DHB        75         75             75     75     68   \n",
       "9   Negative         DMAN        67         67             67     67     67   \n",
       "26  Positive         HCCA        47         47             47     47     41   \n",
       "1   Negative         BPYN        47         47             47     47     44   \n",
       "23  Positive         DHAP        43         43             43     43     38   \n",
       "30  Positive   Norharmane        29         29             29     29     28   \n",
       "5   Negative         DCTB        16         16             16     16     11   \n",
       "33  Positive     glycerol        15         15             15     15      0   \n",
       "7   Negative         DHAP        14         14             14     14     13   \n",
       "12  Negative          Mix        12         12             12     12     11   \n",
       "18  Positive         BPYN        11         11             11     11     11   \n",
       "2   Negative         CHCA        11         11             11     11      9   \n",
       "\n",
       "    mzmax  Analyzer  Ionisation_Source  \n",
       "24   2586      2648               2648  \n",
       "4    1033      1076               1076  \n",
       "15   1030      1056               1056  \n",
       "31    662       718                718  \n",
       "13    527       534                534  \n",
       "19    452       503                503  \n",
       "0     163       191                191  \n",
       "22    170       172                172  \n",
       "21    156       161                161  \n",
       "14     85        97                 97  \n",
       "28     77        78                 78  \n",
       "8      68        75                 75  \n",
       "9      67        67                 67  \n",
       "26     41        47                 47  \n",
       "1      44        47                 47  \n",
       "23     38        43                 43  \n",
       "30     28        29                 29  \n",
       "5      11        16                 16  \n",
       "33      0        15                 15  \n",
       "7      13        14                 14  \n",
       "12     11        12                 12  \n",
       "18     11        11                 11  \n",
       "2       9        11                 11  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp = mdt.groupby(['Polarity', 'maldi_matrix']).agg('count').reset_index().sort_values('Organism', ascending=False)\n",
    "tmp = tmp[tmp['Organism']>=10]\n",
    "tmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "a8ffa495-58e0-4829-afc4-14b55f8fefb2",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "pd.concat([all_ds_ids, public_ids]).to_csv(os.path.join(figuredata_dir, 'submissions.csv'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "4c11baa6-0734-400a-9bf2-dce5beaaf7db",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_485/2581366282.py:19: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_xticklabels(ax.get_xticklabels(), rotation = 45, ha=\"right\", rotation_mode=\"anchor\")\n",
      "/tmp/ipykernel_485/2581366282.py:27: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_xticklabels(ax.get_xticklabels(), rotation = 45, ha=\"right\", fontsize=SMALLSMALL_SIZE, rotation_mode=\"anchor\")\n",
      "/tmp/ipykernel_485/2581366282.py:35: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_yticklabels(ylabs)\n",
      "/tmp/ipykernel_485/2581366282.py:36: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_xticklabels(ax.get_xticklabels(), rotation = 45, rotation_mode=\"anchor\", ha=\"right\")\n",
      "/tmp/ipykernel_485/2581366282.py:46: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_xticklabels(ax.get_xticklabels(), rotation = 45, rotation_mode=\"anchor\", ha=\"right\")\n",
      "/tmp/ipykernel_485/2581366282.py:57: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_xticklabels(ax.get_xticklabels(), rotation = 45, ha=\"right\", fontsize=SMALLSMALL_SIZE, rotation_mode=\"anchor\")\n",
      "/tmp/ipykernel_485/2581366282.py:87: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax_joint.set_xticklabels(ax_joint.get_xticklabels(), rotation = 45, ha=\"right\", fontsize=SMALL_SIZE, rotation_mode=\"anchor\")\n",
      "/tmp/ipykernel_485/2581366282.py:118: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_xticklabels(ax.get_xticklabels(), rotation = 45, ha=\"right\")\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1181.1x1889.76 with 22 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(30*cm, 48*cm))\n",
    "\n",
    "(part1fig, part2fig, part3fig) = fig.subfigures(3, 1, height_ratios=[1.1, 1., 3.5])\n",
    "\n",
    "# ax = sketchfig.subplots(1,)\n",
    "# sketchfig.subplots_adjust(hspace=0.0, wspace=0.0, left=.0, right=.9)\n",
    "# ax.imshow(img, aspect='equal')\n",
    "# ax.axis('off')\n",
    "# ax.text(0, 1., 'A', transform=ax.transAxes, size=BIGGER_SIZE, weight='bold')\n",
    "\n",
    "axs = part1fig.subplots(1, 4)\n",
    "part1fig.subplots_adjust(hspace=0.5, wspace=0.4, left=.0, right=1, bottom=.5, top=1.)\n",
    "    #left=0.1, bottom=0.1, right=0.9, top=0.9, wspace=0.4, hspace=0.4\n",
    "\n",
    "ax = axs[0]\n",
    "# Replace `pd.concat([all_ds_ids, public_ids])` with `public_ids` if private table with all dataset IDs is not available\n",
    "sns.lineplot(data=pd.concat([all_ds_ids, public_ids]), x='Date', y='Submissions', hue='Datasets', palette={'Public': '#1187ee', 'All': '#c3e2f9ff'}, lw=3, ax=ax)\n",
    "sns.despine(offset=5, trim=False, ax=ax)\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation = 45, ha=\"right\", rotation_mode=\"anchor\")\n",
    "ax.text(-0.25, 1., 'A', transform=ax.transAxes, size=BIGGER_SIZE, weight='bold')\n",
    "\n",
    "ax = axs[1]\n",
    "sns.despine(offset=5, trim=False, ax=ax)\n",
    "ax.text(-0.4, 1., 'B', transform=ax.transAxes, size=BIGGER_SIZE, weight='bold')\n",
    "sns.barplot(data=labs, x='Lab', y='Datasets', color='#1187ee', ax=ax, saturation=1)\n",
    "sns.barplot(data=labs, x='Lab', y='Submitter', color='#c3e2f9ff', ax=ax, saturation=1)\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation = 45, ha=\"right\", fontsize=SMALLSMALL_SIZE, rotation_mode=\"anchor\")\n",
    "\n",
    "# Replace negative values for to number of submitters\n",
    "ylabs = ax.get_yticklabels()\n",
    "for yticklabel in ylabs:\n",
    "    label = int(yticklabel.get_text().replace('−', '-'))\n",
    "    if label < 0:\n",
    "        yticklabel.set_text(int(label/(-submitter_scaling)))\n",
    "ax.set_yticklabels(ylabs)\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation = 45, rotation_mode=\"anchor\", ha=\"right\")\n",
    "ax.set_ylabel('Submitters ←   → Datasets')\n",
    "ax.set_xlabel('Top contributing institutions')\n",
    "ax.yaxis.set_label_coords(-0.25, .32)\n",
    "\n",
    "ax = axs[2]\n",
    "sns.despine(offset=5, trim=False, ax=ax)\n",
    "ax.text(-0.4, 1., 'C', transform=ax.transAxes, size=BIGGER_SIZE, weight='bold')\n",
    "tmp = mdt.groupby(['Polarity', 'Analyzer']).agg('count').reset_index()\n",
    "sns.barplot(data=tmp, x='Analyzer', y='Condition', hue='Polarity', palette={'Negative': '#1187ee', 'Positive': '#c3e2f9ff'}, ax=ax, saturation=1)\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation = 45, rotation_mode=\"anchor\", ha=\"right\")\n",
    "ax.set_ylabel('Datasets')\n",
    "\n",
    "ax = axs[3]\n",
    "sns.despine(offset=5, trim=False, ax=ax)\n",
    "ax.text(-0.4, 1., 'D', transform=ax.transAxes, size=BIGGER_SIZE, weight='bold')\n",
    "tmp = mdt.groupby(['Polarity', 'maldi_matrix']).agg('count').reset_index().sort_values('Organism', ascending=False)\n",
    "tmp = tmp[tmp['Organism']>=10]\n",
    "sns.barplot(data=tmp, x='maldi_matrix', y='Condition', hue='Polarity', palette={'Negative': '#1187ee', 'Positive': '#c3e2f9ff'}, ax=ax, saturation=1)\n",
    "ax.set_ylabel('Datasets')\n",
    "ax.set_xlabel('MALDI matrix')\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation = 45, ha=\"right\", fontsize=SMALLSMALL_SIZE, rotation_mode=\"anchor\")\n",
    "\n",
    "import matplotlib.patches as mpatches\n",
    "neg_patch = mpatches.Patch(color='#1187ee', label='Negative')\n",
    "pos_patch = mpatches.Patch(color='#c3e2f9ff', label='Positive')\n",
    "ax.legend(handles=[neg_patch, pos_patch], title='Polarity')\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "(subjoint, subrow2) = part2fig.subfigures(1, 2, wspace=.08, width_ratios = (1, 3))\n",
    "\n",
    "\n",
    "\n",
    "# Using code from the Seaborn JointGrid plot\n",
    "ratio=2\n",
    "gs = plt.GridSpec(ratio + 1, ratio + 2)\n",
    "ax_joint = subjoint.add_subplot(gs[1:, :-1])\n",
    "ax_marg_x = subjoint.add_subplot(gs[0, :-1], sharex=ax_joint)\n",
    "ax_marg_y = subjoint.add_subplot(gs[1:, -1], sharey=ax_joint)\n",
    "\n",
    "\n",
    "subjoint.subplots_adjust(hspace=0.5, wspace=0.4, left=.0, right=.8, bottom=.5, top=1.)\n",
    "ax_marg_x.text(-0.25, 1., 'E', transform=ax_marg_x.transAxes, size=BIGGER_SIZE, weight='bold')\n",
    "\n",
    "sns.despine(offset=5, trim=False, ax=ax_joint)\n",
    "sns.scatterplot(data=org_org2, y='Organism', x='Organ', size='index', color='#1187ee', legend=False, ax=ax_joint, size_norm=(20, 1510), sizes=(50, 200))\n",
    "ax_joint.set_ylim((2.4, -.4))\n",
    "tmp = ax_joint.get_xticklabels()\n",
    "ax_joint.set_xticklabels(ax_joint.get_xticklabels(), rotation = 45, ha=\"right\", fontsize=SMALL_SIZE, rotation_mode=\"anchor\")\n",
    "xdict = {x.get_text(): x.get_position()[0] for x in ax_joint.get_xticklabels()}\n",
    "ydict = {y.get_text(): y.get_position()[1] for y in ax_joint.get_yticklabels()}\n",
    "for i in range(org_org2.shape[0]):\n",
    "            ax_joint.text(xdict[org_org2.loc[i, :]['Organ']], ydict[org_org2.loc[i, :]['Organism']]-.4, org_org2.loc[i, :]['index'], \n",
    "                          ha=\"center\", va=\"center\", fontdict={'fontsize': XSMALL_SIZE})\n",
    "        \n",
    "sns.despine(offset=5, trim=False, ax=ax_marg_y)\n",
    "sns.histplot(data=org_org, y='Organism', color='#1187ee', ax=ax_marg_y, binwidth=.1, alpha=1, edgecolor=None)\n",
    "ax_marg_y.set_ylabel('')\n",
    "\n",
    "sns.despine(offset=5, trim=False, ax=ax_marg_x)\n",
    "sns.histplot(data=org_org, x='Organ', color='#1187ee', ax=ax_marg_x, binwidth=.1, alpha=1, edgecolor=None)\n",
    "ax_marg_x.set_xlabel('')\n",
    "\n",
    "plt.setp(ax_marg_x.get_xticklabels(), visible=False)\n",
    "plt.setp(ax_marg_y.get_yticklabels(), visible=False)\n",
    "plt.setp(ax_marg_x.get_xticklabels(minor=True), visible=False)\n",
    "plt.setp(ax_marg_y.get_yticklabels(minor=True), visible=False)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "axs = subrow2.subplots(1, 3)\n",
    "subrow2.subplots_adjust(hspace=0.5, wspace=0.4, left=.05, right=1, bottom=.5, top=1.)\n",
    "\n",
    "ax = axs[0]\n",
    "sns.despine(offset=5, trim=False, ax=ax)\n",
    "ax.text(-0.4, 1., 'F', transform=ax.transAxes, size=BIGGER_SIZE, weight='bold')\n",
    "tmp = pd.DataFrame({'Database counts': pd.DataFrame({'Database': flatten([x.databases for x in dss])}).value_counts()}).reset_index()[:10]\n",
    "sns.barplot(data=tmp, x='Database', y='Database counts', facecolor='#1187ee', saturation=1, ax=ax)\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation = 45, ha=\"right\")\n",
    "\n",
    "ax = axs[1]\n",
    "sns.despine(offset=5, trim=False, ax=ax)\n",
    "ax.text(-0.4, 1., 'G', transform=ax.transAxes, size=BIGGER_SIZE, weight='bold')\n",
    "ax.hist(np.log10(mdt['mzmin']), bins=20, color='#1187ee')\n",
    "ax.set_xlabel(r'$log_{10}$ minimum m/z')\n",
    "ax.set_xlim((1, 3.5))\n",
    "\n",
    "ax = axs[2]\n",
    "sns.despine(offset=5, trim=False, ax=ax)\n",
    "ax.text(-0.4, 1., 'H', transform=ax.transAxes, size=BIGGER_SIZE, weight='bold')\n",
    "ax.hist(np.log10(mdt['mzmax']), bins=20, color='#1187ee')\n",
    "ax.set_xlabel(r'$log_{10}$ maximum m/z')\n",
    "ax.set_xlim((1, 3.5))\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "axs12 = part3fig.subplots(3, 4)\n",
    "part3fig.subplots_adjust(left=.0, right=1., hspace=.65, top=1.)\n",
    "\n",
    "ax = axs12[0][0]\n",
    "fdr = 0.05\n",
    "sns.despine(offset=5, trim=False, ax=ax)\n",
    "ax.text(-0.2, 1., 'I', transform=ax.transAxes, size=BIGGER_SIZE, weight='bold')\n",
    "ax.hist(np.log10(apd(results, fdr)), bins=15, color='#1187ee')\n",
    "ax.set_title(f\"FDR $\\\\leq$ {fdr:.0%}%\")\n",
    "ax.set_xlabel(r'$log_{10}$ Annotations per dataset')\n",
    "ax.set_ylabel('Count')\n",
    "\n",
    "\n",
    "ax = axs12[0][1]\n",
    "fdr = 0.1\n",
    "sns.despine(offset=5, trim=False, ax=ax)\n",
    "ax.hist(np.log10(apd(results, fdr)), bins=15, color='#1187ee')\n",
    "ax.set_title(f\"FDR $\\\\leq$ {fdr:.0%}\")\n",
    "ax.set_xlabel(r'$log_{10}$ Annotations per dataset')\n",
    "\n",
    "ax = axs12[0][2]\n",
    "fdr = 0.2\n",
    "sns.despine(offset=5, trim=False, ax=ax)\n",
    "ax.hist(np.log10(apd(results, fdr)), bins=15, color='#1187ee')\n",
    "ax.set_title(f\"FDR $\\\\leq$ {fdr:.0%}\")\n",
    "ax.set_xlabel(r'$log_{10}$ Annotations per dataset')\n",
    "\n",
    "ax = axs12[0][3]\n",
    "fdr = 0.5\n",
    "sns.despine(offset=5, trim=False, ax=ax)\n",
    "ax.hist(np.log10(apd(results, fdr)), bins=15, color='#1187ee')\n",
    "ax.set_title(f\"FDR $\\\\leq$ {fdr:.0%}\")\n",
    "ax.set_xlabel(r'$log_{10}$ Annotations per dataset')\n",
    "\n",
    "ax = axs12[1][0]\n",
    "fdr = 0.05\n",
    "sns.despine(offset=5, trim=False, ax=ax)\n",
    "ax.text(-0.2, 1., 'J', transform=ax.transAxes, size=BIGGER_SIZE, weight='bold')\n",
    "ax.hist(np.log10(list(dpa(results, fdr).values())), bins=15, color='#1187ee')\n",
    "ax.set_title(f\"FDR $\\\\leq$ {fdr:.0%}\")\n",
    "ax.set_xlabel(r'$log_{10}$ Datasets per annotation')\n",
    "ax.set_ylabel('Count')\n",
    "\n",
    "ax = axs12[1][1]\n",
    "fdr = 0.1\n",
    "sns.despine(offset=5, trim=False, ax=ax)\n",
    "ax.hist(np.log10(list(dpa(results, fdr).values())), bins=15, color='#1187ee')\n",
    "ax.set_title(f\"FDR $\\\\leq$ {fdr:.0%}\")\n",
    "ax.set_xlabel(r'$log_{10}$ Datasets per annotation')\n",
    "\n",
    "ax = axs12[1][2]\n",
    "fdr = 0.2\n",
    "sns.despine(offset=5, trim=False, ax=ax)\n",
    "ax.hist(np.log10(list(dpa(results, fdr).values())), bins=15, color='#1187ee')\n",
    "ax.set_title(f\"FDR $\\\\leq$ {fdr:.0%}\")\n",
    "ax.set_xlabel(r'$log_{10}$ Datasets per annotation')\n",
    "\n",
    "ax = axs12[1][3]\n",
    "fdr = 0.5\n",
    "sns.despine(offset=5, trim=False, ax=ax)\n",
    "ax.hist(np.log10(list(dpa(results, fdr).values())), bins=15, color='#1187ee')\n",
    "ax.set_title(f\"FDR $\\\\leq$ {fdr:.0%}\")\n",
    "ax.set_xlabel(r'$log_{10}$ Datasets per annotation')\n",
    "\n",
    "ax = axs12[2][0]\n",
    "sns.despine(offset=5, trim=False, ax=ax)\n",
    "ax.text(-0.2, 1., 'K', transform=ax.transAxes, size=BIGGER_SIZE, weight='bold')\n",
    "dynamic_range = np.array([results[x][results[x]['fdr']==0.05]['intensity'].max() / results[x][results[x]['fdr']==0.05]['intensity'].min() for x in results.keys()])\n",
    "dynamic_range = dynamic_range[dynamic_range != 1]\n",
    "ax.hist(np.log10(dynamic_range), bins=20, color='#1187ee')\n",
    "ax.set_xlabel(r'$log_{10}$ dynamic range')\n",
    "ax.set_ylabel('Count')\n",
    "\n",
    "axs12[2][1].axis('off')\n",
    "axs12[2][2].axis('off')\n",
    "axs12[2][3].axis('off')\n",
    "\n",
    "plt.savefig('plots/KB_stats.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "29f1df80-b9b8-43bf-a1b4-479f5ea38f3a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25% Quartile: 6.0\n",
      "Median: 26.0\n",
      "75% Quartile: 98.0\n"
     ]
    }
   ],
   "source": [
    "# Quantiles\n",
    "vals = apd(results, .1)\n",
    "print('25% Quartile: {}'.format(np.quantile(vals, .25)))\n",
    "print('Median: {}'.format(np.median(vals)))\n",
    "print('75% Quartile: {}'.format(np.quantile(vals, .75)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "940f247e-dbdb-4175-896f-975eefbd2611",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of ions: 27127\n",
      "Number of molecules: 9590\n"
     ]
    }
   ],
   "source": [
    "# Different species/ions\n",
    "fdr_cutoff=0.1\n",
    "mols = set()\n",
    "ions = set()\n",
    "\n",
    "for tab in results.values():\n",
    "    res = tab[tab['fdr'] <= fdr_cutoff]\n",
    "    if res.shape[0] > 0:\n",
    "            ions = ions | set(res['ion'])\n",
    "            mols = mols | set(res.reset_index()['formula'])\n",
    "print('Number of ions: {}'.format(len(ions)))\n",
    "print('Number of molecules: {}'.format(len(mols)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "4f59e941-ccf6-4824-9704-bcbaf539a9e5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage of top-4 institutions to all datasets: 56.0%\n"
     ]
    }
   ],
   "source": [
    "# Percentage of top contributions\n",
    "top_c = 4\n",
    "\n",
    "print('Percentage of top-{} institutions to all datasets: {:.1%}'.format(top_c, labs[:top_c]['Datasets'].sum() / len(dss)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "a9cb8bd3-bc1e-4bc6-9f5d-30d2fd84d2f0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\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></th>\n",
       "      <th>ionFormula</th>\n",
       "      <th>ion</th>\n",
       "      <th>fdr</th>\n",
       "      <th>mz</th>\n",
       "      <th>offSample</th>\n",
       "      <th>moleculeNames</th>\n",
       "      <th>intensity</th>\n",
       "      <th>moleculeIds</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>formula</th>\n",
       "      <th>adduct</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>C14H16O6</th>\n",
       "      <th>+Na</th>\n",
       "      <td>C14H16O6Na</td>\n",
       "      <td>C14H16O6+Na+</td>\n",
       "      <td>0.05</td>\n",
       "      <td>303.083870</td>\n",
       "      <td>True</td>\n",
       "      <td>[Gravolenic acid]</td>\n",
       "      <td>32945.972656</td>\n",
       "      <td>[HMDB0033884]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C44H86NO8P</th>\n",
       "      <th>+H</th>\n",
       "      <td>C44H87NO8P</td>\n",
       "      <td>C44H86NO8P+H+</td>\n",
       "      <td>0.05</td>\n",
       "      <td>788.616342</td>\n",
       "      <td>True</td>\n",
       "      <td>[PC(14:0/22:1(13Z)), PC(14:1(9Z)/22:0), PC(16:...</td>\n",
       "      <td>36115.445312</td>\n",
       "      <td>[HMDB0007887, HMDB0007919, HMDB0007978, HMDB00...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C35H52O5</th>\n",
       "      <th>+H</th>\n",
       "      <td>C35H53O5</td>\n",
       "      <td>C35H52O5+H+</td>\n",
       "      <td>0.10</td>\n",
       "      <td>553.388712</td>\n",
       "      <td>True</td>\n",
       "      <td>[Furohyperforin, 8-Hydroxyhyperforin 8,1-hemia...</td>\n",
       "      <td>20124.394531</td>\n",
       "      <td>[HMDB0033632, HMDB0035237, HMDB0035374]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C37H62O10</th>\n",
       "      <th>[M]+</th>\n",
       "      <td>C37H62O10</td>\n",
       "      <td>C37H62O10+</td>\n",
       "      <td>0.50</td>\n",
       "      <td>666.433710</td>\n",
       "      <td>True</td>\n",
       "      <td>[Notoginsenoside T2]</td>\n",
       "      <td>31729.603516</td>\n",
       "      <td>[HMDB0039050]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C37H47NO4</th>\n",
       "      <th>+H</th>\n",
       "      <td>C37H48NO4</td>\n",
       "      <td>C37H47NO4+H+</td>\n",
       "      <td>0.10</td>\n",
       "      <td>570.357746</td>\n",
       "      <td>True</td>\n",
       "      <td>[Janthitrem C]</td>\n",
       "      <td>11547.300781</td>\n",
       "      <td>[HMDB0040684]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C32H45NO4</th>\n",
       "      <th>+Na</th>\n",
       "      <td>C32H45NO4Na</td>\n",
       "      <td>C32H45NO4+Na+</td>\n",
       "      <td>0.50</td>\n",
       "      <td>530.324041</td>\n",
       "      <td>False</td>\n",
       "      <td>[Gymnodimine]</td>\n",
       "      <td>79.284477</td>\n",
       "      <td>[HMDB0041430]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C45H89O8P</th>\n",
       "      <th>+H</th>\n",
       "      <td>C45H90O8P</td>\n",
       "      <td>C45H89O8P+H+</td>\n",
       "      <td>0.50</td>\n",
       "      <td>789.636744</td>\n",
       "      <td>True</td>\n",
       "      <td>[PA(18:0/24:0), PA(20:0/22:0), PA(21:0/21:0), ...</td>\n",
       "      <td>3389.039795</td>\n",
       "      <td>[HMDB0114894, HMDB0115082, HMDB0115234, HMDB01...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C53H80O4</th>\n",
       "      <th>+NH4</th>\n",
       "      <td>C53H84NO4</td>\n",
       "      <td>C53H80O4+NH4+</td>\n",
       "      <td>0.50</td>\n",
       "      <td>798.639447</td>\n",
       "      <td>True</td>\n",
       "      <td>[3-Demethylubiquinone-9]</td>\n",
       "      <td>3843.960693</td>\n",
       "      <td>[HMDB0060370]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C32H36N4O5</th>\n",
       "      <th>+NH4</th>\n",
       "      <td>C32H40N5O5</td>\n",
       "      <td>C32H36N4O5+NH4+</td>\n",
       "      <td>0.50</td>\n",
       "      <td>574.302357</td>\n",
       "      <td>True</td>\n",
       "      <td>[Quinidine barbiturate]</td>\n",
       "      <td>85.684792</td>\n",
       "      <td>[HMDB0015436]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C19H14ClF5N4O2</th>\n",
       "      <th>+NH4</th>\n",
       "      <td>C19H18N5O2ClF5</td>\n",
       "      <td>C19H14ClF5N4O2+NH4+</td>\n",
       "      <td>0.50</td>\n",
       "      <td>478.106331</td>\n",
       "      <td>True</td>\n",
       "      <td>[Flupoxam]</td>\n",
       "      <td>73.059372</td>\n",
       "      <td>[HMDB0041341]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1167 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           ionFormula                  ion   fdr          mz  \\\n",
       "formula        adduct                                                          \n",
       "C14H16O6       +Na         C14H16O6Na         C14H16O6+Na+  0.05  303.083870   \n",
       "C44H86NO8P     +H          C44H87NO8P        C44H86NO8P+H+  0.05  788.616342   \n",
       "C35H52O5       +H            C35H53O5          C35H52O5+H+  0.10  553.388712   \n",
       "C37H62O10      [M]+         C37H62O10           C37H62O10+  0.50  666.433710   \n",
       "C37H47NO4      +H           C37H48NO4         C37H47NO4+H+  0.10  570.357746   \n",
       "...                               ...                  ...   ...         ...   \n",
       "C32H45NO4      +Na        C32H45NO4Na        C32H45NO4+Na+  0.50  530.324041   \n",
       "C45H89O8P      +H           C45H90O8P         C45H89O8P+H+  0.50  789.636744   \n",
       "C53H80O4       +NH4         C53H84NO4        C53H80O4+NH4+  0.50  798.639447   \n",
       "C32H36N4O5     +NH4        C32H40N5O5      C32H36N4O5+NH4+  0.50  574.302357   \n",
       "C19H14ClF5N4O2 +NH4    C19H18N5O2ClF5  C19H14ClF5N4O2+NH4+  0.50  478.106331   \n",
       "\n",
       "                       offSample  \\\n",
       "formula        adduct              \n",
       "C14H16O6       +Na          True   \n",
       "C44H86NO8P     +H           True   \n",
       "C35H52O5       +H           True   \n",
       "C37H62O10      [M]+         True   \n",
       "C37H47NO4      +H           True   \n",
       "...                          ...   \n",
       "C32H45NO4      +Na         False   \n",
       "C45H89O8P      +H           True   \n",
       "C53H80O4       +NH4         True   \n",
       "C32H36N4O5     +NH4         True   \n",
       "C19H14ClF5N4O2 +NH4         True   \n",
       "\n",
       "                                                           moleculeNames  \\\n",
       "formula        adduct                                                      \n",
       "C14H16O6       +Na                                     [Gravolenic acid]   \n",
       "C44H86NO8P     +H      [PC(14:0/22:1(13Z)), PC(14:1(9Z)/22:0), PC(16:...   \n",
       "C35H52O5       +H      [Furohyperforin, 8-Hydroxyhyperforin 8,1-hemia...   \n",
       "C37H62O10      [M]+                                 [Notoginsenoside T2]   \n",
       "C37H47NO4      +H                                         [Janthitrem C]   \n",
       "...                                                                  ...   \n",
       "C32H45NO4      +Na                                         [Gymnodimine]   \n",
       "C45H89O8P      +H      [PA(18:0/24:0), PA(20:0/22:0), PA(21:0/21:0), ...   \n",
       "C53H80O4       +NH4                             [3-Demethylubiquinone-9]   \n",
       "C32H36N4O5     +NH4                              [Quinidine barbiturate]   \n",
       "C19H14ClF5N4O2 +NH4                                           [Flupoxam]   \n",
       "\n",
       "                          intensity  \\\n",
       "formula        adduct                 \n",
       "C14H16O6       +Na     32945.972656   \n",
       "C44H86NO8P     +H      36115.445312   \n",
       "C35H52O5       +H      20124.394531   \n",
       "C37H62O10      [M]+    31729.603516   \n",
       "C37H47NO4      +H      11547.300781   \n",
       "...                             ...   \n",
       "C32H45NO4      +Na        79.284477   \n",
       "C45H89O8P      +H       3389.039795   \n",
       "C53H80O4       +NH4     3843.960693   \n",
       "C32H36N4O5     +NH4       85.684792   \n",
       "C19H14ClF5N4O2 +NH4       73.059372   \n",
       "\n",
       "                                                             moleculeIds  \n",
       "formula        adduct                                                     \n",
       "C14H16O6       +Na                                         [HMDB0033884]  \n",
       "C44H86NO8P     +H      [HMDB0007887, HMDB0007919, HMDB0007978, HMDB00...  \n",
       "C35H52O5       +H                [HMDB0033632, HMDB0035237, HMDB0035374]  \n",
       "C37H62O10      [M]+                                        [HMDB0039050]  \n",
       "C37H47NO4      +H                                          [HMDB0040684]  \n",
       "...                                                                  ...  \n",
       "C32H45NO4      +Na                                         [HMDB0041430]  \n",
       "C45H89O8P      +H      [HMDB0114894, HMDB0115082, HMDB0115234, HMDB01...  \n",
       "C53H80O4       +NH4                                        [HMDB0060370]  \n",
       "C32H36N4O5     +NH4                                        [HMDB0015436]  \n",
       "C19H14ClF5N4O2 +NH4                                        [HMDB0041341]  \n",
       "\n",
       "[1167 rows x 8 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results['2023-02-01_06h56m45s']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22a93ae1-bd87-4a0a-9f17-ba3a491d7597",
   "metadata": {},
   "source": [
    "# Figure 1 Suppl. 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "79abdc72-25d8-404e-bd1c-d068d95e2b01",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure(figsize=(30*cm, 30*cm))\n",
    "(row12fig, row3fig) = fig.subfigures(2, 1, height_ratios=[2, 1])\n",
    "\n",
    "axs12 = row12fig.subplots(2, 4)\n",
    "row12fig.subplots_adjust(left=.0, right=1., hspace=.4)\n",
    "\n",
    "ax = axs12[0][0]\n",
    "fdr = 0.05\n",
    "sns.despine(offset=5, trim=False, ax=ax)\n",
    "ax.text(-0.2, 1., 'A', transform=ax.transAxes, size=BIGGER_SIZE, weight='bold')\n",
    "ax.hist(np.log10(apd(results, fdr)), bins=15, color='#1187ee')\n",
    "ax.set_title(f\"FDR $\\\\leq {fdr:.0%}$%\")\n",
    "ax.set_xlabel(r'$log_{10}$ Annotations per dataset')\n",
    "ax.set_ylabel('Count')\n",
    "\n",
    "\n",
    "ax = axs12[0][1]\n",
    "fdr = 0.1\n",
    "sns.despine(offset=5, trim=False, ax=ax)\n",
    "ax.hist(np.log10(apd(results, fdr)), bins=15, color='#1187ee')\n",
    "ax.set_title(f\"FDR $\\\\leq$ {fdr:.0%}\")\n",
    "ax.set_xlabel(r'$log_{10}$ Annotations per dataset')\n",
    "\n",
    "ax = axs12[0][2]\n",
    "fdr = 0.2\n",
    "sns.despine(offset=5, trim=False, ax=ax)\n",
    "ax.hist(np.log10(apd(results, fdr)), bins=15, color='#1187ee')\n",
    "ax.set_title(f\"FDR $\\\\leq$ {fdr:.0%}\")\n",
    "ax.set_xlabel(r'$log_{10}$ Annotations per dataset')\n",
    "\n",
    "ax = axs12[0][3]\n",
    "fdr = 0.5\n",
    "sns.despine(offset=5, trim=False, ax=ax)\n",
    "ax.hist(np.log10(apd(results, fdr)), bins=15, color='#1187ee')\n",
    "ax.set_title(f\"FDR $\\\\leq$ {fdr:.0%}\")\n",
    "ax.set_xlabel(r'$log_{10}$ Annotations per dataset')\n",
    "\n",
    "ax = axs12[1][0]\n",
    "fdr = 0.05\n",
    "sns.despine(offset=5, trim=False, ax=ax)\n",
    "ax.text(-0.2, 1., 'B', transform=ax.transAxes, size=BIGGER_SIZE, weight='bold')\n",
    "ax.hist(np.log10(list(dpa(results, fdr).values())), bins=15, color='#1187ee')\n",
    "ax.set_title(f\"FDR $\\\\leq$ {fdr:.0%}\")\n",
    "ax.set_xlabel(r'$log_{10}$ Datasets per annotation')\n",
    "ax.set_ylabel('Count')\n",
    "\n",
    "ax = axs12[1][1]\n",
    "fdr = 0.1\n",
    "sns.despine(offset=5, trim=False, ax=ax)\n",
    "ax.hist(np.log10(list(dpa(results, fdr).values())), bins=15, color='#1187ee')\n",
    "ax.set_title(f\"FDR $\\\\leq$ {fdr:.0%}\")\n",
    "ax.set_xlabel(r'$log_{10}$ Datasets per annotation')\n",
    "\n",
    "ax = axs12[1][2]\n",
    "fdr = 0.2\n",
    "sns.despine(offset=5, trim=False, ax=ax)\n",
    "ax.hist(np.log10(list(dpa(results, fdr).values())), bins=15, color='#1187ee')\n",
    "ax.set_title(f\"FDR $\\\\leq$ {fdr:.0%}\")\n",
    "ax.set_xlabel(r'$log_{10}$ Datasets per annotation')\n",
    "\n",
    "ax = axs12[1][3]\n",
    "fdr = 0.5\n",
    "sns.despine(offset=5, trim=False, ax=ax)\n",
    "ax.hist(np.log10(list(dpa(results, fdr).values())), bins=15, color='#1187ee')\n",
    "ax.set_title(f\"FDR $\\\\leq$ {fdr:.0%}\")\n",
    "ax.set_xlabel(r'$log_{10}$ Datasets per annotation')\n",
    "\n",
    "axs3 = row3fig.subplots(1, 3)\n",
    "\n",
    "ax = axs3[0]\n",
    "sns.despine(offset=5, trim=False, ax=ax)\n",
    "ax.text(-0.15, 1., 'C', transform=ax.transAxes, size=BIGGER_SIZE, weight='bold')\n",
    "dynamic_range = np.array([results[x][results[x]['fdr']==0.05]['intensity'].max() / results[x][results[x]['fdr']==0.05]['intensity'].min() for x in results.keys()])\n",
    "dynamic_range = dynamic_range[dynamic_range != 1]\n",
    "ax.hist(np.log10(dynamic_range), bins=20, color='#1187ee')\n",
    "ax.set_xlabel(r'$log_{10}$ dynamic range')\n",
    "ax.set_ylabel('Count')\n",
    "\n",
    "ax = axs3[1]\n",
    "sns.despine(offset=5, trim=False, ax=ax)\n",
    "ax.text(-0.15, 1., 'D', transform=ax.transAxes, size=BIGGER_SIZE, weight='bold')\n",
    "ax.hist(np.log10(mdt['mzmin']), bins=20, color='#1187ee')\n",
    "ax.set_xlabel(r'$log_{10}$ minimum m/z')\n",
    "ax.set_xlim((1, 3.5))\n",
    "\n",
    "ax = axs3[2]\n",
    "sns.despine(offset=5, trim=False, ax=ax)\n",
    "ax.text(-0.15, 1., 'E', transform=ax.transAxes, size=BIGGER_SIZE, weight='bold')\n",
    "ax.hist(np.log10(mdt['mzmax']), bins=20, color='#1187ee')\n",
    "ax.set_xlabel(r'$log_{10}$ maximum m/z')\n",
    "ax.set_xlim((1, 3.5))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ee27f010-4765-498c-837a-aa9d955b2551",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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