{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DreaMS Atlas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The DreaMS Atlas is a large-scale molecular network containing 201 million MS/MS spectra from the MassIVE GNPS repository, constructed using DreaMS embeddings. Each node in the network corresponds to a mass spectrum derived from a specific biological or environmental sample (e.g., human skin or blood, plant extracts, marine environments, food, and many others). Each edge represents a DreaMS similarity, linking a node to its three nearest neighbors across the entire MassIVE GNPS. This tutorial demonstrates various methods for exploring and analyzing the DreaMS Atlas through a user-friendly API.\n",
    "\n",
    "<p align=\"center\">\n",
    "  <img src=\"https://raw.githubusercontent.com/pluskal-lab/DreaMS/b577db12bc8ec0c2010d10854f528007fcc2db2d/assets/fig_atlas_mini.png\" width=\"100%\"/>\n",
    "</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initialization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import all the necessary packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "from rdkit import Chem\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import dreams.utils.plots as plots\n",
    "import dreams.utils.spectra as su\n",
    "from dreams.api import DreaMSAtlas\n",
    "from dreams.utils.misc import networkx_to_dataframe\n",
    "from dreams.definitions import *\n",
    "%reload_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initialize the DreaMS Atlas. Please note that the first initialization involves downloading over 400 GB of data files. However, once the files are downloaded, they accessed directly from the disk, so there’s no need to load all the data into memory, eliminating the requirement for a RAM-intensive machine to work with the Atlas.\n",
    "\n",
    "<div class=\"alert alert-success\">\n",
    "ℹ️ In future updates, we plan to develop a web server that will allow access to the DreaMS Atlas from a remote server, removing the need to host all the data locally. Future release will also include the ability to extend the Atlas with new nodes and to query the Atlas using a spectrum of interest by DreaMS similarity.\n",
    "</div>\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initializing DreaMS Atlas data structures...\n",
      "Loaded spectral library (79,300 spectra).\n",
      "Loaded GeMS-C1 dataset (75,520,646 spectra).\n",
      "Loaded DreaMS Atlas edges (134,524,452 edges).\n",
      "Loaded DreaMS Atlas nodes representing DreaMS k-NN clusters of GeMS-C1 (33,631,113 nodes).\n",
      "Loaded LSH clusters of DreaMS Atlas nodes representing GeMS-C (201,223,336 spectra).\n"
     ]
    }
   ],
   "source": [
    "atlas = DreaMSAtlas()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Accesing data from the Atlas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's pick one of the 76 million spectra in GeMS-C1 dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "{37552437: {'DreaMS_embedding': array([-0.8768827 , -0.41617227,  0.02967745, ..., -1.2626797 ,\n",
       "          1.6408856 , -0.64122283], dtype=float32),\n",
       "  'RT': 550.4616,\n",
       "  'charge': 1,\n",
       "  'instrument accuracy est.': 0.00013683233,\n",
       "  'lsh': -450688754114588762,\n",
       "  'name': '20160906_pgk965_SloanSurfaceProject_Metabolomics_2-21',\n",
       "  'precursor_mz': 404.1232,\n",
       "  'msv_id': 'MSV000086209',\n",
       "  'msv_species': 'NCBITaxon:2;NCBITaxon:4751',\n",
       "  'msv_species_resolved': 'Bacteria (NCBITaxon:2)',\n",
       "  'msv_instrument': nan,\n",
       "  'msv_instrument_resolved': nan,\n",
       "  'msv_title': 'GNPS - Microbial and metabolic succession on common building materials under high humidity conditions',\n",
       "  'msv_description': 'Despite considerable efforts to characterize the microbial ecology of the built environment, the metabolic mechanisms underpinning microbial colonization and successional dynamics remain unclear, particularly at high moisture conditions. Here, we applied bacterial/viral particle counting, qPCR, amplicon sequencing of the genes encoding 16S and ITS rRNA, and metabolomics to longitudinally characterize the ecological dynamics of four common building materials maintained at high humidity. We varied the natural inoculum provided to each material and wet half of the samples to simulate a potable water leak. Wetted materials had higher growth rates and lower alpha diversity compared to non-wetted materials, and wetting described the majority of the variance in bacterial, fungal, and metabolite structure. Inoculation location was weakly associated with bacterial and fungal beta diversity. Material type influenced bacterial and viral particle abundance and bacterial and metabolic (but not fungal) diversity. Metabolites indicative of microbial activity were identified, and they too differed by material.',\n",
       "  'msv_create_time': '2020-09-29 08:10:16.0',\n",
       "  'msv_user': nan,\n",
       "  'msv_keywords': nan}}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "i = 37552437\n",
    "atlas.get_data(i, plot=True, return_spec=False, msv_metadata=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The displayed spectrum represents a single node in the Atlas. In addition to mass spectrometry attributes such as MS/MS peaks, precursor m/z, and retention time, the spectrum is associated with a DreaMS embedding and MassIVE GNPS metadata, which includes, for example, the species studied or the study description.\n",
    "\n",
    "According to the construction of the DreaMS Atlas, each node represents a cluster of MS/MS spectra obtained using DreaMS and LSH. Let us explore the cluster corresponding to the selected node."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Node 37552437 represents a cluster of 6 spectra with high DreaMS similarity.\n",
      "Spectrum with index 32730250 further represents an LSH cluster of 1 spectra.\n",
      "Showing first spectrum:\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x120 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Spectrum with index 32730265 further represents an LSH cluster of 2 spectra.\n",
      "Showing first spectrum:\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x120 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Spectrum with index 32730269 further represents an LSH cluster of 42 spectra.\n",
      "Showing first spectrum:\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x120 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Spectrum with index 37552435 further represents an LSH cluster of 3 spectra.\n",
      "Showing first spectrum:\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x120 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Spectrum with index 37552437 further represents an LSH cluster of 5 spectra.\n",
      "Showing first spectrum:\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x120 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Spectrum with index 37552438 further represents an LSH cluster of 2 spectra.\n",
      "Showing first spectrum:\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x120 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dreams_cluster = atlas.get_node_cluster(i, lsh=True)\n",
    "print(f'Node {i} represents a cluster of {len(dreams_cluster)} spectra with high DreaMS similarity.')\n",
    "for spec_i, lsh_cluster in dreams_cluster.items():\n",
    "    print(f'Spectrum with index {spec_i} further represents an LSH cluster of {len(lsh_cluster)} spectra.')\n",
    "    print('Showing first spectrum:')\n",
    "    su.plot_spectrum(lsh_cluster[0][SPECTRUM], prec_mz=lsh_cluster[0][PRECURSOR_MZ], figsize=(3, 1.2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring local structure of the Atlas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The DreaMS Atlas API allows for the visualization of a neighborhood of a given node as an interactive graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "i = 37542683\n",
    "g_nbhd = atlas.get_neighbors(i, n_hops=3, msv_metadata=True)\n",
    "\n",
    "plots.plot_nx_graph(\n",
    "    g_nbhd,\n",
    "    node_attrs=[PRECURSOR_MZ, NAME, SMILES, 'msv_id'],\n",
    "    node_color_attr='msv_id',\n",
    "    special_node=i,\n",
    "    special_nodes=[n[0] for n in g_nbhd.nodes(data=True) if SMILES in n[1]],\n",
    "    node_size=12\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://raw.githubusercontent.com/pluskal-lab/DreaMS/3e58c74b9809a46bc41ba1d0d21d680b2aa9a797/assets/atlas_example.gif\" width=\"1000\" align=\"center\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let’s examine the similarities between three spectra from the neighborhood and our query spectrum of interest. Note that one of the nodes represents an entry from the MoNA spectral library and is therefore annotated with a molecular structure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Node 37496414\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Node 34207\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x7f2f10a3fd80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Node 37552437\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for n in list(g_nbhd.nodes(data=True))[-3:]:\n",
    "    print('Node', n[0])\n",
    "    su.plot_spectrum(spec=n[1][SPECTRUM], mirror_spec=g_nbhd.nodes[i][SPECTRUM], prec_mz=n[1][PRECURSOR_MZ], mirror_prec_mz=g_nbhd.nodes[i][PRECURSOR_MZ])\n",
    "    if SMILES in n[1]:\n",
    "        display(Chem.MolFromSmiles(n[1][SMILES]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One can also explore the neighborhood as a `pandas` data frame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>node_id</th>\n",
       "      <th>RT</th>\n",
       "      <th>msv_keywords</th>\n",
       "      <th>msv_instrument_resolved</th>\n",
       "      <th>msv_create_time</th>\n",
       "      <th>name</th>\n",
       "      <th>msv_species_resolved</th>\n",
       "      <th>id</th>\n",
       "      <th>DreaMS_embedding</th>\n",
       "      <th>msv_instrument</th>\n",
       "      <th>...</th>\n",
       "      <th>msv_species</th>\n",
       "      <th>charge</th>\n",
       "      <th>lsh</th>\n",
       "      <th>msv_description</th>\n",
       "      <th>spectrum</th>\n",
       "      <th>instrument accuracy est.</th>\n",
       "      <th>msv_id</th>\n",
       "      <th>msv_title</th>\n",
       "      <th>neighbors</th>\n",
       "      <th>edge_weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>37542683</td>\n",
       "      <td>309.739990</td>\n",
       "      <td>Skin</td>\n",
       "      <td>maXis</td>\n",
       "      <td>2018-07-31 14:41:27.0</td>\n",
       "      <td>BD4_V14_S2-015_arm_psoriasis_BD4_01_14378</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>None</td>\n",
       "      <td>[-0.10430071, 0.32463387, 1.2688519, 1.7766136...</td>\n",
       "      <td>MS:1001541</td>\n",
       "      <td>...</td>\n",
       "      <td>NCBITaxon:9606</td>\n",
       "      <td>0</td>\n",
       "      <td>-4.529384e+17</td>\n",
       "      <td>GNPS - Skin psoriasis molecular cartography st...</td>\n",
       "      <td>[[106.06563568115234, 106.07821655273438, 119....</td>\n",
       "      <td>0.000712</td>\n",
       "      <td>MSV000082674</td>\n",
       "      <td>GNPS - Skin psoriasis molecular cartography st...</td>\n",
       "      <td>[37542661, 37542656, 37648590]</td>\n",
       "      <td>[0.8408746152701184, 0.829488577059148, 0.7978...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37542661</td>\n",
       "      <td>306.300995</td>\n",
       "      <td>Facial cleanser, skin, temporal</td>\n",
       "      <td>maXis</td>\n",
       "      <td>2018-06-01 15:00:35.0</td>\n",
       "      <td>5A9_%20V1_%20Nose_%20D14H0_%20W7_BA9_01_11583</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>None</td>\n",
       "      <td>[0.19762689, 0.17532083, 1.2078881, 1.549347, ...</td>\n",
       "      <td>MS:1001541</td>\n",
       "      <td>...</td>\n",
       "      <td>NCBITaxon:9606</td>\n",
       "      <td>0</td>\n",
       "      <td>-4.529386e+17</td>\n",
       "      <td>MS/MS spectra were collected from face of 6 in...</td>\n",
       "      <td>[[121.03773498535156, 151.0904998779297, 156.0...</td>\n",
       "      <td>0.000614</td>\n",
       "      <td>MSV000082432</td>\n",
       "      <td>GNPS - Colgate facial cleanser longitudinal st...</td>\n",
       "      <td>[37542653, 32640049, 37542632]</td>\n",
       "      <td>[0.8550434399274379, 0.8537323876318667, 0.851...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37542656</td>\n",
       "      <td>309.917999</td>\n",
       "      <td>Skin</td>\n",
       "      <td>maXis</td>\n",
       "      <td>2018-07-31 14:41:27.0</td>\n",
       "      <td>BD3_V14_S2-014_arm_healthy_BD3_01_14337</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>None</td>\n",
       "      <td>[0.38015515, -0.5561968, 1.060871, 0.93926096,...</td>\n",
       "      <td>MS:1001541</td>\n",
       "      <td>...</td>\n",
       "      <td>NCBITaxon:9606</td>\n",
       "      <td>0</td>\n",
       "      <td>-4.529386e+17</td>\n",
       "      <td>GNPS - Skin psoriasis molecular cartography st...</td>\n",
       "      <td>[[119.0505599975586, 134.0594482421875, 145.02...</td>\n",
       "      <td>0.000678</td>\n",
       "      <td>MSV000082674</td>\n",
       "      <td>GNPS - Skin psoriasis molecular cartography st...</td>\n",
       "      <td>[37542683, 37542627, 37542661]</td>\n",
       "      <td>[0.829488577059148, 0.7041841665583883, 0.6607...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>37648590</td>\n",
       "      <td>541.183533</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-09-29 08:10:16.0</td>\n",
       "      <td>20160906_pgk965_SloanSurfaceProject_Metabolomi...</td>\n",
       "      <td>Bacteria (NCBITaxon:2)</td>\n",
       "      <td>None</td>\n",
       "      <td>[-0.18013018, 0.5833932, 1.3610793, 2.4781635,...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NCBITaxon:2;NCBITaxon:4751</td>\n",
       "      <td>1</td>\n",
       "      <td>-4.326744e+17</td>\n",
       "      <td>Despite considerable efforts to characterize t...</td>\n",
       "      <td>[[89.41886901855469, 89.42453002929688, 96.847...</td>\n",
       "      <td>0.000139</td>\n",
       "      <td>MSV000086209</td>\n",
       "      <td>GNPS - Microbial and metabolic succession on c...</td>\n",
       "      <td>[32730262, 37542653, 32640049]</td>\n",
       "      <td>[0.8631632498896021, 0.8612309271933151, 0.842...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>37542653</td>\n",
       "      <td>349.489990</td>\n",
       "      <td>citrus</td>\n",
       "      <td>maXis 4G</td>\n",
       "      <td>2020-05-15 11:47:12.0</td>\n",
       "      <td>Plate_2_1_820_RE7_01_41664</td>\n",
       "      <td>Citrus sinensis (NCBITaxon:2711)</td>\n",
       "      <td>None</td>\n",
       "      <td>[0.024766939, -0.28584233, 1.1211307, 1.363762...</td>\n",
       "      <td>MS:1002279</td>\n",
       "      <td>...</td>\n",
       "      <td>NCBITaxon:2711</td>\n",
       "      <td>0</td>\n",
       "      <td>-4.529386e+17</td>\n",
       "      <td>Leaf tissues of orange trees extracted with et...</td>\n",
       "      <td>[[130.02940368652344, 133.05169677734375, 134....</td>\n",
       "      <td>0.000519</td>\n",
       "      <td>MSV000085416</td>\n",
       "      <td>GNPS_UCR_citrus_survivor_study_orchard_samples...</td>\n",
       "      <td>[32640049, 37496366, 37542658]</td>\n",
       "      <td>[0.8816033077004346, 0.8651546656827798, 0.864...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    node_id          RT                     msv_keywords  \\\n",
       "0  37542683  309.739990                             Skin   \n",
       "1  37542661  306.300995  Facial cleanser, skin, temporal   \n",
       "2  37542656  309.917999                             Skin   \n",
       "3  37648590  541.183533                              NaN   \n",
       "4  37542653  349.489990                           citrus   \n",
       "\n",
       "  msv_instrument_resolved        msv_create_time  \\\n",
       "0                   maXis  2018-07-31 14:41:27.0   \n",
       "1                   maXis  2018-06-01 15:00:35.0   \n",
       "2                   maXis  2018-07-31 14:41:27.0   \n",
       "3                     NaN  2020-09-29 08:10:16.0   \n",
       "4                maXis 4G  2020-05-15 11:47:12.0   \n",
       "\n",
       "                                                name  \\\n",
       "0          BD4_V14_S2-015_arm_psoriasis_BD4_01_14378   \n",
       "1      5A9_%20V1_%20Nose_%20D14H0_%20W7_BA9_01_11583   \n",
       "2            BD3_V14_S2-014_arm_healthy_BD3_01_14337   \n",
       "3  20160906_pgk965_SloanSurfaceProject_Metabolomi...   \n",
       "4                         Plate_2_1_820_RE7_01_41664   \n",
       "\n",
       "               msv_species_resolved    id  \\\n",
       "0                      Homo sapiens  None   \n",
       "1                      Homo sapiens  None   \n",
       "2                      Homo sapiens  None   \n",
       "3            Bacteria (NCBITaxon:2)  None   \n",
       "4  Citrus sinensis (NCBITaxon:2711)  None   \n",
       "\n",
       "                                    DreaMS_embedding msv_instrument  ...  \\\n",
       "0  [-0.10430071, 0.32463387, 1.2688519, 1.7766136...     MS:1001541  ...   \n",
       "1  [0.19762689, 0.17532083, 1.2078881, 1.549347, ...     MS:1001541  ...   \n",
       "2  [0.38015515, -0.5561968, 1.060871, 0.93926096,...     MS:1001541  ...   \n",
       "3  [-0.18013018, 0.5833932, 1.3610793, 2.4781635,...            NaN  ...   \n",
       "4  [0.024766939, -0.28584233, 1.1211307, 1.363762...     MS:1002279  ...   \n",
       "\n",
       "                  msv_species charge           lsh  \\\n",
       "0              NCBITaxon:9606      0 -4.529384e+17   \n",
       "1              NCBITaxon:9606      0 -4.529386e+17   \n",
       "2              NCBITaxon:9606      0 -4.529386e+17   \n",
       "3  NCBITaxon:2;NCBITaxon:4751      1 -4.326744e+17   \n",
       "4              NCBITaxon:2711      0 -4.529386e+17   \n",
       "\n",
       "                                     msv_description  \\\n",
       "0  GNPS - Skin psoriasis molecular cartography st...   \n",
       "1  MS/MS spectra were collected from face of 6 in...   \n",
       "2  GNPS - Skin psoriasis molecular cartography st...   \n",
       "3  Despite considerable efforts to characterize t...   \n",
       "4  Leaf tissues of orange trees extracted with et...   \n",
       "\n",
       "                                            spectrum  \\\n",
       "0  [[106.06563568115234, 106.07821655273438, 119....   \n",
       "1  [[121.03773498535156, 151.0904998779297, 156.0...   \n",
       "2  [[119.0505599975586, 134.0594482421875, 145.02...   \n",
       "3  [[89.41886901855469, 89.42453002929688, 96.847...   \n",
       "4  [[130.02940368652344, 133.05169677734375, 134....   \n",
       "\n",
       "   instrument accuracy est.        msv_id  \\\n",
       "0                  0.000712  MSV000082674   \n",
       "1                  0.000614  MSV000082432   \n",
       "2                  0.000678  MSV000082674   \n",
       "3                  0.000139  MSV000086209   \n",
       "4                  0.000519  MSV000085416   \n",
       "\n",
       "                                           msv_title  \\\n",
       "0  GNPS - Skin psoriasis molecular cartography st...   \n",
       "1  GNPS - Colgate facial cleanser longitudinal st...   \n",
       "2  GNPS - Skin psoriasis molecular cartography st...   \n",
       "3  GNPS - Microbial and metabolic succession on c...   \n",
       "4  GNPS_UCR_citrus_survivor_study_orchard_samples...   \n",
       "\n",
       "                        neighbors  \\\n",
       "0  [37542661, 37542656, 37648590]   \n",
       "1  [37542653, 32640049, 37542632]   \n",
       "2  [37542683, 37542627, 37542661]   \n",
       "3  [32730262, 37542653, 32640049]   \n",
       "4  [32640049, 37496366, 37542658]   \n",
       "\n",
       "                                         edge_weight  \n",
       "0  [0.8408746152701184, 0.829488577059148, 0.7978...  \n",
       "1  [0.8550434399274379, 0.8537323876318667, 0.851...  \n",
       "2  [0.829488577059148, 0.7041841665583883, 0.6607...  \n",
       "3  [0.8631632498896021, 0.8612309271933151, 0.842...  \n",
       "4  [0.8816033077004346, 0.8651546656827798, 0.864...  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "networkx_to_dataframe(g_nbhd).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring global structure of the Atlas\n",
    "\n",
    "The global structure of the DreaMS Atlas can be analyzed in two ways. First, one can efficiently analyze the graph by accessing its adjacency matrix, which is stored as a sparse array. For example, let’s plot the distribution of the DreaMS similarities representing the graph edges."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adjacency matrix shape: (33631113, 33631113)\n",
      "Number of edges: 101104558\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "A = atlas.csrknn.csr\n",
    "print('Adjacency matrix shape:', A.shape)\n",
    "edges = np.asarray(A[A > 0]).ravel()\n",
    "\n",
    "print('Number of edges:', len(edges))\n",
    "sns.histplot(edges)\n",
    "plt.title('Distribution of DreaMS similarities')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Second, one can work with the Atlas as an `igraph` graph object, leveraging the many graph analysis methods implemented within the `igraph` package. For example, let’s plot the distribution of node degrees across the entire Atlas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Retrieving graph edges: 100%|██████████| 33631113/33631113 [03:09<00:00, 177295.81it/s]\n"
     ]
    }
   ],
   "source": [
    "G = atlas.csrknn.to_graph()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "degrees = G.degree()\n",
    "sns.histplot(degrees, log_scale=True, shrink=10)\n",
    "plt.title('Distribution of node degrees')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}