{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Clustering MS/MS spectra with LSH\n",
    "\n",
    "The algorithm of LSH (locality-sensitive hashing) with random projections allows for the fast identification of highly similar or near-duplicate MS/MS spectra. This notebook first showcases how to apply it to a collection of 200 thousand mass spectra and then discusses the results. If you are interested in how the algorithm works, please refer to [our paper](https://chemrxiv.org/engage/chemrxiv/article-details/6626775021291e5d1d61967f) or [this tutorial on LSH](https://www.pinecone.io/learn/series/faiss/locality-sensitive-hashing-random-projection/).\n",
    "\n",
    "<p align=\"center\">\n",
    "  <img src=\"https://raw.githubusercontent.com/pluskal-lab/DreaMS/b577db12bc8ec0c2010d10854f528007fcc2db2d/assets/lsh.png\" width=\"50%\"/>\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import all necessary libraries\n",
    "import time\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from pathlib import Path\n",
    "from tqdm import tqdm\n",
    "import dreams.utils.mols as mu\n",
    "from dreams.utils.plots import init_plotting\n",
    "from dreams.utils.spectra import PeakListModifiedCosine\n",
    "from dreams.utils.data import MSData\n",
    "from dreams.algorithms.lsh import BatchedPeakListRandomProjection\n",
    "%reload_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As an example, we will use the [MassSpecGym dataset](https://huggingface.co/datasets/roman-bushuiev/MassSpecGym/blob/main/data/auxiliary/MassSpecGym.mgf) in the `.mgf` format. However, other data formats such as `.mzML` are also supported. Please see the last section of this tutorial or refer to the `data_import.ipynb` tutorial for details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading dataset MassSpecGym into memory (231104 spectra)...\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(231104, 2, 128)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "in_pth = Path('../data/MassSpecGym.mgf')\n",
    "msdata = MSData.from_mgf(in_pth, mol_col='SMILES', adduct_col='ADDUCT')\n",
    "msdata.get_spectra().shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Computing LSH hashes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's initialize the LSH algorithm. The algorithm has two main parameters: `bin_step` and `n_hyperplanes`. `bin_step` defines the width of the m/z range binning window when binning input spectra. `n_hyperplanes` specifies the number of hyperplanes used to split the space of binned spectra into disjoint regions, with each region forming a unique hash. The narrower the binning window and the more hyperplanes used, the more accurate the LSH clustering becomes. For example, if the goal of clustering is to only group near-duplicate spectra, `n_hyperplanes=128` is a good choice. Conversely, if the goal is to cluster structural analogs, `n_hyperplanes=20` may be a better option. Here, we will use `n_hyperplanes=50`, which appears to be a sweet spot (see our paper or [this notebook](https://github.com/pluskal-lab/DreaMS/blob/main/experiments/clustering/clustering_evaluation.ipynb) for details). The second parameter, `bin_step`, does not significantly influence the clustering."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "lsh_projector = BatchedPeakListRandomProjection(bin_step=1, n_hyperplanes=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now compute the LSH hashes. As input, the LSH projector takes MS/MS spectra in the form of a tensor with the shape (num_spectra, 2, num_peaks), where 2 represents the m/z and intensity values. As output, it produces an array of hashes with a length equal to num_spectra."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Computing LSHs: 100%|██████████| 231104/231104 [00:34<00:00, 6619.11it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(231104,)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lshs = lsh_projector.compute(msdata.get_spectra())\n",
    "lshs.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Understanding LSH hashes and clusters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For 213548 input spectra, LSH projector computed 213548 hashes. Each hash is represented as a string, as we can see by looking at 5 first hashes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([b'6a7214a6297b5c622c127f1c0247f96d88a261093e09ce6f2a856847a8be1d2c',\n",
       "       b'fa4508829a1e08bf616f4b96fd15bc14a41cb4df76d25e2329d0fe47ec560957',\n",
       "       b'0767dca9252e0688644428074053494ab1327b1438cd47cab32253e3a5b02ca5',\n",
       "       b'bc64c196e6e02f6eb326b1bd9a88818c5698ff62d785c5f3179b0f063a45e95b',\n",
       "       b'0e57ca912a42fb9261617968703a106ebe332a3c09d1e63558fea74fca76f306'],\n",
       "      dtype='|S64')"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lshs[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Under the hood, each string is a hashed boolean vector of size `n_hyperplanes`. Each hyperplane divides the space of binned mass spectra into two complementary regions. Consequently, each bit in this vector indicates which region of the space the binned mass spectrum falls into with respect to a specific random hyperplane. To get the boolean vectors instead of the hashed string, one can set `as_str=False`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ True,  True,  True,  True, False, False, False,  True,  True,\n",
       "        False,  True,  True, False, False,  True, False, False,  True,\n",
       "        False,  True, False,  True, False, False, False, False,  True,\n",
       "         True,  True, False, False,  True, False,  True, False, False,\n",
       "         True,  True, False, False,  True, False, False,  True, False,\n",
       "        False,  True,  True,  True,  True],\n",
       "       [ True,  True,  True,  True,  True, False, False,  True,  True,\n",
       "         True,  True,  True, False,  True, False, False, False,  True,\n",
       "         True,  True, False,  True,  True, False, False, False,  True,\n",
       "         True, False, False,  True,  True, False,  True, False, False,\n",
       "        False,  True, False, False,  True, False, False,  True, False,\n",
       "        False,  True,  True,  True,  True],\n",
       "       [ True, False,  True,  True, False,  True, False,  True,  True,\n",
       "        False,  True,  True, False, False,  True,  True, False,  True,\n",
       "        False,  True, False,  True, False, False, False, False, False,\n",
       "         True,  True,  True, False,  True, False,  True, False,  True,\n",
       "         True,  True, False, False,  True, False, False, False, False,\n",
       "        False,  True, False,  True,  True]])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lsh_projector.compute(msdata.get_spectra()[:3], as_str=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(132725,)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(lshs).shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Given 213,548 spectra, we obtained 164,288 unique LSHs. According to the principle of LSH, similar spectra produce identical hashes. In other words, we efficiently clustered the 213,548 spectra into 164,288 distinct clusters reflecting their spectral similarity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/73/x57b4d9x09qbcqjjd5ccz_6r0000gn/T/ipykernel_48126/3293225505.py:1: FutureWarning: The behavior of value_counts with object-dtype is deprecated. In a future version, this will *not* perform dtype inference on the resulting index. To retain the old behavior, use `result.index = result.index.infer_objects()`\n",
      "  pd.Series(lshs).value_counts()[:5]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "b'1837d421d9ac64694af4f61c2fe33b9c80a407f4727e9026bf0d885d78aecbb1'    2918\n",
       "b'9a7cb3b4370931ce14afcad68915e2a0bdaa3668f205bbfabe80cb1e584feba2'    1430\n",
       "b'f6d2b0a44144a234791af76b9a24b38da00e8e469efb7a361b7b1f35856f8c1b'     546\n",
       "b'2a39baf79bebdf9c08877e414e84445b9765535f1b61a859eeab98b0f0c13898'     482\n",
       "b'31d08efc262f7696439913f80265b5668023cc01857b817734c052b71d598b93'     451\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(lshs).value_counts()[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The largest cluster contains 6,337 spectra and is represented by the hash bfae93ce054b4edc7aa794552cf5223a8c160080e7c8a7b02b6fe3fe8db2f8ee. Let’s ensure that clusters are formed by similar spectra by inspecting some cluster of 5 spectra."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/73/x57b4d9x09qbcqjjd5ccz_6r0000gn/T/ipykernel_48126/1304460116.py:2: FutureWarning: The behavior of value_counts with object-dtype is deprecated. In a future version, this will *not* perform dtype inference on the resulting index. To retain the old behavior, use `result.index = result.index.infer_objects()`\n",
      "  lsh_counts = pd.Series(lshs).value_counts()\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 0x38b073bc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'COLLISION_ENERGY': 20.0, 'FOLD': 'train', 'FORMULA': 'C8H10ClN5O3S', 'IDENTIFIER': 'MassSpecGymID0026098', 'INCHIKEY': 'NWWZPOKUUAIXIW', 'INSTRUMENT_TYPE': 'QTOF', 'PARENT_MASS': 291.019324, 'PRECURSOR_FORMULA': 'C8H11ClN5O3S', 'SIMULATION_CHALLENGE': 'True', 'adduct': '[M+H]+', 'precursor_mz': 292.0266, 'smiles': 'CN\\\\1COCN(/C1=N\\\\[N+](=O)[O-])CC2=CN=C(S2)Cl'}\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 0x38b073220>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'COLLISION_ENERGY': 8.760798, 'FOLD': 'train', 'FORMULA': 'C8H10ClN5O3S', 'IDENTIFIER': 'MassSpecGymID0026108', 'INCHIKEY': 'NWWZPOKUUAIXIW', 'INSTRUMENT_TYPE': 'Orbitrap', 'PARENT_MASS': 291.019324, 'PRECURSOR_FORMULA': 'C8H11ClN5O3S', 'SIMULATION_CHALLENGE': 'True', 'adduct': '[M+H]+', 'precursor_mz': 292.0266, 'smiles': 'CN\\\\1COCN(/C1=N\\\\[N+](=O)[O-])CC2=CN=C(S2)Cl'}\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 0x38b073bc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'COLLISION_ENERGY': 8.760798, 'FOLD': 'train', 'FORMULA': 'C8H10ClN5O3S', 'IDENTIFIER': 'MassSpecGymID0026113', 'INCHIKEY': 'NWWZPOKUUAIXIW', 'INSTRUMENT_TYPE': 'Orbitrap', 'PARENT_MASS': 291.019324, 'PRECURSOR_FORMULA': 'C8H11ClN5O3S', 'SIMULATION_CHALLENGE': 'True', 'adduct': '[M+H]+', 'precursor_mz': 292.0266, 'smiles': 'CN\\\\1COCN(/C1=N\\\\[N+](=O)[O-])CC2=CN=C(S2)Cl'}\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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TUlL4aeRacfEiGzSIAczJiWVlaeuoDyCXy3ft2hUVFSUWiwHY2tq6urru2rVLh00+yK5du+zt7QEMHTq0pKSEMaZWq8+cOfPRRx+Fhoaam5vfjVQHB4cZM2akZqdebbmq5yJJ11GMmjS1Wr1y5UqR6J6FvgYPHrxmzRodnXLW1bHISAYwsZh9/rn2j3/8+PGFCxfyUzUBSKXS8PDwwYMH878WCxYs0NHk3Puo1erExET+H3b27NkP/Clqamo6cOBAfHz88OHD+WpH/W8UTsHrnFfs1diMqow6ZV0HmzvdePpc8zmtfgPSCRSjhH300Ufm5ubW1tb8ybuum1Mq2Tvv8GtQ7f/73+O0cstWWVlZUlLSkCFD7v4Y+Pn5JSYm3rp1izGmUqmSk5Otra0BuLu7f//9911v8RFu377N3wMqFosTExM78pGSkpIvv/xyzvk5dmfscAr8H4s8iz/99qePKz7Ob7rn+nWDqmFJ2ZLg88H9C/q/fOXlW4pbi68vXnFjhW6+DXk8ilHCvv76awDz5s3TZ6NpaU0uLq4Axo8fX1VV9WQHubscgfjOAtHu7u5xcXGnT5/+/c4XL168+yDlqKio27dvd+0bPNivv/7q5eXFL4OSmZnZ2Y8r1Irs+ux3y94dUTRCdEp0N1I9znrMvzJ/W/U2xtjE3yaG/xZe2FxY1lq2vHz59urtFKPCohgl7M033wTQwX6TFh0/ftzNzQ2At7d3QUFBpz6bm5sbFxfn5OTEx6K5uXlERERGRsajJ8O275a6ubm1H1vTioyMLfzBAwICrt1dePVJVSoqM6oyYq/G9j7bmw/T0edHFzUXWeVZyRSy9ntSjAqLYpSwCRMm4M7qwu+//76fn9+WLVv00/T169f5kXRbW9uODAFdv349MTGRf64nb/jw4UlJSTKZ7LGfvevSpUv8c0T4bqlWVrFTq5XXr8dnZ/9BJOLmzp3b1MFZtR08OFOfaTqzumJ12u20HdU7ni169r4dKEaFRTFKmIeHBwB+KHn69OkA9BajjLHm5mZ+lRYzM7OH9YgftpbgmTNnnqxRlUqVlJRkZWUlEom2bHmuunpnF74BUyhk58+H5Obi1CnpiRObunKox/qu6rvA4sD73qQYFRbFqKmrqanhOM7Kyoq/wZ+f5Kjn5YjuG9du35W7by1Bc3NzLa4lePHixTVr5ufmIjcXJSVzFIon6ZY2Nubl5/fLzcWZMz3r6n7uelWP9kvjL06/OrWoW9q/STEqLIpRU8cvnDFixAjGmFwuF4vFYrGYv29Sz/bs2WNnZwdg1KhRu3fvTkhIuPsggLsn7zpYRl4tkyWfPm2Tm4szZ1yqqrZ16sO3b397+rRVbi6Ki4NaW/VxV5JSrfQ95/tu2btqpmaMNaga6pR1FKPCMqQH4BIhFBQUABg0aBCA4uJipVI5cODA9jPD9Wby5MnZ2dmRkZHHjx+fMmUK/2afPn2io6Ojo6P5brIOcM7OsXZ2E65cWVBff7Ck5AVHxyhPz7Vicc9Hf4wxZXn50oqK1QCcnWM9PT/nOKluKryHGWf2vff3MVdivq36tpekV2lr6canNuqhXfIIFKOmrn2Mtn8tiKFDh+bk5ISGhl64cKF///6ffPJJSEjIfXcH6IJU+pSv74HKyq+uX19cXb21vv5Q795rnJyiH7a/UikrKXmxvj6L48w9Pf/j7LxA1xW2N9BiYM6AnAZ1Q62q1l3iLoJotM1oDpw+ayDtUYyaut/HqJ+fn4D1uLi45OfnC9Ey3y0Nu3p1QV1d5pUrMdXVW/v2TZZIegFQKmUtLVckEneptHdT0+lLl55vbb0qkXh4e/+ftXWgENXCRmRjI7LhX5tzApw9kLsoRk1d+xgtLCyEoL1RwUmlfX18fqqs/Or69Xdqa/cUFg4dMOB4dfVWmewLK6sRra1XXF3/UVu7u7X1qq3tWC+vDLHYReiSifAoRk1adTU8PH7197/Ir5sn+Em9YeCcnWPt7adcu7ZQpaqXSHqVly8fPLhEKu0DAGD29hGWlv6uru9wHP33IQDAMcaEroEI5sgRjBmDkSNx8iTkcrmNjY1IJGpoaLi7Sp5pYypVPceZnTnTo1+/TY6Os4SuhxgonV+8J4asoAAA+N5ncbHU27th6tRiytA7ODMzO5HIum/fr65d+3th4TCZbB1A3Q5yP4pRk8bHKD+kVFAgunDBQiTyErYkA+TkFD1kSJmHx4c3b35y48ZKocshBodi1KS17422f03uw3ESe/sIV9e3Gxqyha6FGBy6Rm7SCgsBitFHUqnqKyu/srObAKiqqtJtbccJXRExOBSjpqu6GhUVsLFBnz4AxehDMKZQqWquX3+HMYWd3QQ3t3eErogYHIpR03XuHAAMGgSOQ3MzrlyBRIJ2d7ETABCLe/Tq9b7QVRCDRtdGTVf77mdREVQq9O8PiUTYogjpfihGTReNLxGiFRSjpotilBCtoGujpmviRDg6wt8foBglpAvoZlCTs20bzMwwfbpmMyUFISFoaEBeHkJC4OYmaHGEdEMUoyYnIgKZmcjLw8CBABAQgI8/xp0HDxNCOo2ujZqiqVPx2mugH1BCtIJi1BTNno3WVmzaJHQdhBgFilFTxHFYuxZLlqCqSuhSCOn+KEZN1DPPICoKCQlC10FI90cTnkzXBx9g8GAoFACgUsHMTOiCCOmeqDdquuztsWoVbt6EWo3nn8frr0OtFromQroh6o2anEWLMGCA5vWcOVCr0diIffugUODyZaSlwdZWvwVVVuL773HxInr3Rng4LY5Cuh3qjZqc8HA89ZTmNcchOhoREcjKgosLdu9GQAAuXNBjNZcuYcgQnD4Nf39UVmLkSPz8sx6bJ0QLaPo90SgpwdSpKCiAkxMyMhASopdWZ82CpyfWrNFspqdj1SoI85x6Qp4Q9UaJhpcXTpxAZCRu30ZYGD7/XDfNMIbsbMyfj8uXAeDkSbzwQtvfvvACCgtx+7Zu2iZEJyhGSRsbG+zYgYQEqFSIi8Orr2rG8bWjtBSrV8PXF2PHYsMGzez/W7fg7Ny2j1QKe3tUVGivVUJ0jmKU3IPj8N57SE+HpSXWr0doKGSyLh2wvh4ntlzBuHHo2xdLluDiRfTpg3ffxZw5ANC7N8rL2/ZubERNDTw9u9QkIfpF10bJg+XlITISpaXw9sbOnZ1eQ0+txrFjSE1FejrESnmVuTvXIseUKZg3DxMnQnxnikhsLFpbsXGjZvOzz/Dtt8jJ0eIXIUTXKEbJQ5WXY9o0/PILbG3x7beYOrVDnyopwcaN2LQJV68CAMdh9Gikvnasb4R/21yqs2excSMkErz9Np57Dt7e+OMfUVyMPXuwbx+eeUZXX4kQHaAYJY8ilyM2Fqmp4DgsX4733nv8R5KS8OabANC7N+bMwYIF7WaCymRIT0dKCvLyAMDWFjduQCxGVhYuXEDv3ggNhYODjr4LITpCMUoegzF89BH+9S+o1Zg9G//9LywtH7W/TIZ//AMxMRg7FhwHAK2t2LsXIbvftEtdqxm06tEDs2cjJgYBAfr4DoToEg0xkcfgOMTHY9s22Njgp5/w3Xfw8Wmbop+aivh4zes1ayCToWdPbNiAcePAcSgowJIl6NMH06fjZElPqNUYPx4pKSgtxX/+QxlKjAPdDEo6ZPp0HD2K2lpUVKCyEn/9KzIzAaC2VjOU/803yMyEQoHQUPj7Y906bNzYNo/e3x+3ZryGzfPpKSXE+FBvlHTUkCEYMwYAxo6FXI60tHv+dvJkDBiAyZPh5weJBImJyM+HoyNiY3H4MM6exZxFjpShxChRb5R0Gsfh008xdSomTWp709UVYWHw8tKMxicmokcPTJoEqVSoMgnRE4pR8iSGD8e0aVi6VPNcPF77VJ0/X/9FESIMilHyhFaswKBBUKmEroMQodG1UfKEHBywahW++UboOggRGsUoeXLz5iE4WOgiCBEaTb8nndbUhIoKPPUURPQrTAj1RskTOHwY3t6YOFHoOggxDBSjpNMKCgDA11foOggxDBSjpNP4GO3s0nmEGCuKUdJpFKOEtEdDTKRzGIOjo+ZW+vaP/yDEZFFvlHROaSlqa+HmRhlKiAbFKOkcOqMn5D4Uo6Rz+Bj18xO6DkIMBt1TTzpnWFXWm8O4kUMCABuhayHEINAQE+mkwEDk5ODwYYweLXQphBgEilHSGYzB3h719bh9Gz16CF0NIQaBro2Szrh2DfX1cHenDCXkLopR0hk0Tk/I71CMks6gGCXkdyhGSWdQjBLyOxSjpDMoRgn5HRqpJx3GGOzs0NCAqio4OgpdDSGGgqbfkw5racGyZbh+nTKUkPYoRknHHDqEkyehVGLiRDAGjhO6IEIMBV0bJR3wxhtYuBCWlujRA8uX48UXAaC2Fj/+iB9/xOXLQtdHiJCoN0oe5/RpbNyIkhLNlPs//xn9++OnnxAQALkcAD2rnpg4ilHyOCdOYPz4ttuWLCzw/PP4+WdMmIDISEErI8Qg0Ek9eZyKivuXaHZxQUWFQNUQYnAoRsnj9O6NGzfueae8HH36CFQNIQaHYpQ8zpgxOHgQZWWazfp6bN2KceOELIkQQ0LXRsnjDByIJUsQFIQFCyCRICUFzz+PkBChyyLEUNBdTKRjzp7FiROoq8OoUQgOFroaQgwIxSghhHQJXRslhJAuoRglhJAuoRglhJAuoRglhJAuoRglhJAuoRglhJAu+X9syKIB9H+jygAAAYZ6VFh0cmRraXRQS0wgcmRraXQgMjAyMy4wOS41AAB4nHu/b+09BiDgZ0AAISAWBuIGRjaGBCDNyMzOoAGkmZkhfCYmDgYFEA3jIqTBNAuMz66gBdbOwgERYOIAC/xnZoTpZHOA6GBzyADrYGR3gBoFlRCA0EyCYBsZuRkYGRiZGJiYgVIMLKwMrGwqLGzsWkxMTKwc7BwqzBycGkwcXAqs3AzcPAw8vBlMvHwJfPwZTPwCCfyCDGyMKiwCPAkiTECj2BjZWFmYmVh5eQT4+cQfgQyH+3/N22kHtrivtQNxbN8EH1j3M8YexPblcjmw/7WoA4idZHZlv0mjOZgtZsGwf82lL2A11/bb7xd9cBesV/A+54FAvd17QWzGg5IHenhP7gexV6fP379knuIBEDt1i7KdPicDmN2tvX//H9UGMHvTywt2efmsYPVLJF7YJ6WlWYPYlXt5HbyKdoLtal6W6zBh30Ew+8/cdod21nu2IHaJiIuDjE4wWO+hu1sdbrFP2wd2JwBQ1FakQW+3PwAAAfN6VFh0TU9MIHJka2l0IDIwMjMuMDkuNQAAeJx9VEtuGzEM3fsUvIAFfiVqkYU9DpKijQ00bu5QoMveHyHHsEcphGpMQZQePyKfvIMcP0/ff/+Fx+DTbgeA//n13uFDEHH3BrmA4/PLtzMs18PxvrNcfp2v70AO1MMmvq/Yw/Xydt8hWGCvpVE1EsAi4ioeC1zHZspwhr0UIeKuce5VyG0ClPQoBeOcO3ARZSOdABUusKdSrXUK/+GxcaUJ0NIjFkN0q0ClE9fWJ8CaOWJp2pHTkYqI4QTY0iMXMmXFNMFO0tsE6emSi2IALdO1hpHmBNkTSYXVxChtjE1wLCUsry9PBB+HH0/6qD9GFTAy6V49rQJpdZYz0a1e2sN/zaYhapX2b4D9LQI97DguG7VAR5G1jkwsPIuQraPiJGy38zXSBKgB5MKktYdWVN1xmrNFUSTYJezBxhIcqzxjV4RZQAtLJJmkInTFWYupwTus7GrqeRtn9T69jcPyB6xUF+G1ydwFeeb0+Xz68ihuz+R4OZ+2Z5Ifb48hFJCN8hSiG7EpxDb6UkjdSEohbaMih/jGNwrpG6k4hHCgC+UGDaxId8RDu2mdZGir5kQ69E9zIhsalWr0YeiI5kRtKD2tOz6UmPICD72nTRtiZ2XHOqZ+/6uK9e4T1xTvyNKZrioAAAEJelRYdFNNSUxFUyByZGtpdCAyMDIzLjA5LjUAAHicHY8xcsQwCEWvktKeyArwAaHRbOXePsBmK7eZNGn38EHupKevx2c/eD/3Y9kvuX6vZf9Z/2T92vlxfD+Pz9fyONfnub0+3sumtbEbF6pAKKKMDRXM0hOFg8NuRHlEkQqVTI+Nq1vrnCS8iSehakRhXrh2Fo8bNe0k064A0pQHCgLKjAsLpAyuwRDLEBGMzZNJFVbv6VKNVCFrQubdGI4ytAqCND8xhVIvw6oH0pdm6aAMj7t2s4lCNHqKN6lsKjoZdUbPIRMqiXLJtaxRw72gqGXlfDMx8OzO1iMXS0RE5ndKOyyHaiJ1Lev7H2CSTjeTh45qAAAAAElFTkSuQmCC",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x38b073840>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'COLLISION_ENERGY': 8.760798, 'FOLD': 'train', 'FORMULA': 'C8H10ClN5O3S', 'IDENTIFIER': 'MassSpecGymID0026120', 'INCHIKEY': 'NWWZPOKUUAIXIW', 'INSTRUMENT_TYPE': 'Orbitrap', 'PARENT_MASS': 291.019324, 'PRECURSOR_FORMULA': 'C8H11ClN5O3S', 'SIMULATION_CHALLENGE': 'True', 'adduct': '[M+H]+', 'precursor_mz': 292.0266, 'smiles': 'CN\\\\1COCN(/C1=N\\\\[N+](=O)[O-])CC2=CN=C(S2)Cl'}\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 0x38b073bc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'COLLISION_ENERGY': 8.760798, 'FOLD': 'train', 'FORMULA': 'C8H10ClN5O3S', 'IDENTIFIER': 'MassSpecGymID0026126', 'INCHIKEY': 'NWWZPOKUUAIXIW', 'INSTRUMENT_TYPE': 'Orbitrap', 'PARENT_MASS': 291.019324, 'PRECURSOR_FORMULA': 'C8H11ClN5O3S', 'SIMULATION_CHALLENGE': 'True', 'adduct': '[M+H]+', 'precursor_mz': 292.0266, 'smiles': 'CN\\\\1COCN(/C1=N\\\\[N+](=O)[O-])CC2=CN=C(S2)Cl'}\n"
     ]
    }
   ],
   "source": [
    "# Pick some LSH forming a cluster of 5 spectra\n",
    "lsh_counts = pd.Series(lshs).value_counts()\n",
    "lsh_i = lsh_counts[lsh_counts == 5].index[0]\n",
    "idx = np.where(lshs == lsh_i)[0]\n",
    "\n",
    "# Display clustered spectra\n",
    "for i in idx:\n",
    "    print(msdata.at(int(i), plot_mol=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, all spectra correspond to the same compound measured under different MS conditions (e.g., QTOF and Orbitrap). Notice that the LSH algorithm is robust to noise, which is evident from the signals at > 800 Da in some spectra. We can further verify that all pairs of spectra have high modified cosine similarities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.        , 0.98942941, 0.99080761, 0.98932561, 0.99061096],\n",
       "       [0.98942941, 1.        , 0.99805446, 0.99999409, 0.99802881],\n",
       "       [0.99080761, 0.99805446, 1.        , 0.9980001 , 0.99999553],\n",
       "       [0.98932561, 0.99999409, 0.9980001 , 1.        , 0.99797991],\n",
       "       [0.99061096, 0.99802881, 0.99999553, 0.99797991, 1.        ]])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cos_sim = PeakListModifiedCosine()\n",
    "cos_sim.compute_pairwise(specs=msdata['spectrum'][idx], prec_mzs=msdata['precursor_mz'][idx], avg=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that LSH clustering of mass spectra is designed to be a high-precision, low-recall algorithm. This means that while the clustering allows for the fast detection of near-duplicate spectra, it is conceptually too simple to cluster all different spectra of the same compound or to perfectly approximate modified cosine similarity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70,)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idx_inchi = np.where(np.array(msdata['INCHIKEY']) == 'NWWZPOKUUAIXIW')[0]\n",
    "idx_inchi.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The compound representing the selected cluster is present 70 times in the dataset. Let's look into all spectra of this compound."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Sort idx_inchi by their lshs\n",
    "idx_inchi = idx_inchi[np.argsort(lshs[idx_inchi])]\n",
    "idx_inchi\n",
    "\n",
    "# Plot heatmap of pairwise modified cosine similarities\n",
    "init_plotting(figsize=(6, 5))\n",
    "sns.heatmap(cos_sim.compute_pairwise(specs=msdata['spectrum'][idx_inchi], prec_mzs=msdata['precursor_mz'][idx_inchi], avg=False), linewidth=0)\n",
    "\n",
    "# Draw LSH boundaries\n",
    "for i, (a, b) in enumerate(zip(lshs[idx_inchi], lshs[idx_inchi][1:])):\n",
    "    if a != b:\n",
    "        plt.axvline(i + 1, color='black', linewidth=1)\n",
    "        plt.axhline(i + 1, color='black', linewidth=1)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each cell (framed with black lines) of the heatmap represents a separate LSH cluster corresponding to the same compound, with colors indicating modified cosine similarities. We observe that the spectra of the same compound are fragmented into different clusters, and not all pairs are connected, indicating low recall. However, all intra-cluster similarities are very high, demonstrating high precision.\n",
    "\n",
    "Ultimately, we clustered 213,548 spectra in one minute (on a MacBook M1), avoiding pairwise similarity calculations and achieving no false positives with respect to modified cosine similarity (a more systematic evaluation is in the next section). This is the main advantage of LSH compared to other methods. For a more systematic evaluation, please refer to our paper or to [this notebook](https://github.com/pluskal-lab/DreaMS/blob/main/experiments/clustering/clustering_evaluation.ipynb)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clustering spectra from a collection of .mzML files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's consider a practical example of deduplicating or clustering MS/MS spectra in a large collection of .mzML files. As an example, we will use files representing food samples from [MSV000084900](https://massive.ucsd.edu/ProteoSAFe/dataset.jsp?task=ce3254fe529d43f48077d7ad55b7da09). Although we will work with only three files here, the procedure can be scaled to a larger number of files since only one file is loaded into memory at a time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define path to the directory with mzML files\n",
    "mzml_dir = Path('../data/MSV00008490')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/builder/jenkins/ws/enms_ntly_pyoms_whl_Release3.0.0/OpenMS/src/openms/source/FORMAT/HANDLERS/XMLHandler.cpp(130): While loading '../data/MSV00008490/G73954_1x_BC8_01_17287.mzML': Required attribute 'softwareRef' not present!\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing ../data/MSV00008490/G73954_1x_BC8_01_17287.mzML...\n",
      "Loading dataset G73954_1x_BC8_01_17287 into memory (1930 spectra)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Computing LSHs: 100%|██████████| 1930/1930 [00:00<00:00, 3349.27it/s]\n",
      "/Users/builder/jenkins/ws/enms_ntly_pyoms_whl_Release3.0.0/OpenMS/src/openms/source/FORMAT/HANDLERS/XMLHandler.cpp(130): While loading '../data/MSV00008490/G87408_1x_BD5_01_26032.mzML': Required attribute 'softwareRef' not present!\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing ../data/MSV00008490/G87408_1x_BD5_01_26032.mzML...\n",
      "Loading dataset G87408_1x_BD5_01_26032 into memory (1353 spectra)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Computing LSHs: 100%|██████████| 1353/1353 [00:00<00:00, 3404.85it/s]\n",
      "/Users/builder/jenkins/ws/enms_ntly_pyoms_whl_Release3.0.0/OpenMS/src/openms/source/FORMAT/HANDLERS/XMLHandler.cpp(130): While loading '../data/MSV00008490/G72676_BB5_01_18927.mzML': Required attribute 'softwareRef' not present!\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing ../data/MSV00008490/G72676_BB5_01_18927.mzML...\n",
      "Loading dataset G72676_BB5_01_18927 into memory (2308 spectra)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Computing LSHs: 100%|██████████| 2308/2308 [00:00<00:00, 3379.47it/s]\n",
      "/Users/builder/jenkins/ws/enms_ntly_pyoms_whl_Release3.0.0/OpenMS/src/openms/source/FORMAT/HANDLERS/XMLHandler.cpp(130): While loading '../data/MSV00008490/G83331_1x_RF3_01_21829.mzML': Required attribute 'softwareRef' not present!\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing ../data/MSV00008490/G83331_1x_RF3_01_21829.mzML...\n",
      "Loading dataset G83331_1x_RF3_01_21829 into memory (1470 spectra)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Computing LSHs: 100%|██████████| 1470/1470 [00:00<00:00, 3386.33it/s]\n",
      "/Users/builder/jenkins/ws/enms_ntly_pyoms_whl_Release3.0.0/OpenMS/src/openms/source/FORMAT/HANDLERS/XMLHandler.cpp(130): While loading '../data/MSV00008490/G75653_5x_BH3_01_19360.mzML': Required attribute 'softwareRef' not present!\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing ../data/MSV00008490/G75653_5x_BH3_01_19360.mzML...\n",
      "Loading dataset G75653_5x_BH3_01_19360 into memory (1851 spectra)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Computing LSHs: 100%|██████████| 1851/1851 [00:00<00:00, 3409.90it/s]\n"
     ]
    },
    {
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       "    <tr>\n",
       "      <th>1846</th>\n",
       "      <td>G75653_5x_BH3_01_19360.mzML</td>\n",
       "      <td>3030</td>\n",
       "      <td>b'0c5187cb9da473d5fdb75f26ac7a1d400996cbab0e96...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1847</th>\n",
       "      <td>G75653_5x_BH3_01_19360.mzML</td>\n",
       "      <td>3033</td>\n",
       "      <td>b'23388206af220e02b007f1b20dfd7e6acf911459e24d...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1848</th>\n",
       "      <td>G75653_5x_BH3_01_19360.mzML</td>\n",
       "      <td>3034</td>\n",
       "      <td>b'82f2b27ac666438582383c0daf884e14b82ab740b71a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1849</th>\n",
       "      <td>G75653_5x_BH3_01_19360.mzML</td>\n",
       "      <td>3037</td>\n",
       "      <td>b'23388206af220e02b007f1b20dfd7e6acf911459e24d...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1850</th>\n",
       "      <td>G75653_5x_BH3_01_19360.mzML</td>\n",
       "      <td>3043</td>\n",
       "      <td>b'7d2e69f2c372b3b3bca7a1ce9d54c155109bf5dad5e0...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8912 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        file_name  scan_number  \\\n",
       "0     G73954_1x_BC8_01_17287.mzML            2   \n",
       "1     G73954_1x_BC8_01_17287.mzML            3   \n",
       "2     G73954_1x_BC8_01_17287.mzML            4   \n",
       "3     G73954_1x_BC8_01_17287.mzML            8   \n",
       "4     G73954_1x_BC8_01_17287.mzML            9   \n",
       "...                           ...          ...   \n",
       "1846  G75653_5x_BH3_01_19360.mzML         3030   \n",
       "1847  G75653_5x_BH3_01_19360.mzML         3033   \n",
       "1848  G75653_5x_BH3_01_19360.mzML         3034   \n",
       "1849  G75653_5x_BH3_01_19360.mzML         3037   \n",
       "1850  G75653_5x_BH3_01_19360.mzML         3043   \n",
       "\n",
       "                                                    lsh  \n",
       "0     b'da1d05deaf9942f8ad5ff66bec1d274b9a2afc4cf508...  \n",
       "1     b'f82876086f9da8d10033330b031c51964b0a3d3aee85...  \n",
       "2     b'cd8c4b381a3fcb4e39339e5f82811f0c6fd333d9472b...  \n",
       "3     b'da1d05deaf9942f8ad5ff66bec1d274b9a2afc4cf508...  \n",
       "4     b'1d6d296e796d14296f3b69bcd98f0e855984f249dfe6...  \n",
       "...                                                 ...  \n",
       "1846  b'0c5187cb9da473d5fdb75f26ac7a1d400996cbab0e96...  \n",
       "1847  b'23388206af220e02b007f1b20dfd7e6acf911459e24d...  \n",
       "1848  b'82f2b27ac666438582383c0daf884e14b82ab740b71a...  \n",
       "1849  b'23388206af220e02b007f1b20dfd7e6acf911459e24d...  \n",
       "1850  b'7d2e69f2c372b3b3bca7a1ce9d54c155109bf5dad5e0...  \n",
       "\n",
       "[8912 rows x 3 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Initialize LSH projector and resulting DataFrame\n",
    "lsh_projector = BatchedPeakListRandomProjection(bin_step=0.5, n_hyperplanes=50)\n",
    "df = pd.DataFrame()\n",
    "\n",
    "# Iterate over all .mzML files in the directory\n",
    "for p in mzml_dir.glob('*.mzML'):\n",
    "    print(f'Processing {p}...')\n",
    "\n",
    "    # Read spectra from .mzML file\n",
    "    msdata = MSData.from_mzml(p)\n",
    "\n",
    "    # Compute LSHs and store them with corresponding file_name and scan_numbers\n",
    "    df = pd.concat([df, pd.DataFrame({\n",
    "        'file_name': msdata.get_values('file_name'),\n",
    "        'scan_number': msdata.get_values('scan_number'),\n",
    "        'lsh': lsh_projector.compute(msdata.get_spectra())\n",
    "    })])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The resulting DataFrame contains LSH hashes merged from all three files. It also includes `file_name` and `scan_number` columns, allowing for tracing the corresponding spectra. Note that the `MSData.from_mzml` method creates an .hdf5 file for each input .mzML file, so the spectra or information from .mzML files can also be retrieved from the converted files (please see details in `data_import.ipynb` tutorial). The spectra can now be clustered by simply deduplicating their LSHs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of spectra before LSH clustering: 8912\n",
      "Number of spectra after LSH clustering: 6095\n"
     ]
    }
   ],
   "source": [
    "print(f'Number of spectra before LSH clustering: {len(df)}')\n",
    "df = df.drop_duplicates(subset=['lsh'])\n",
    "print(f'Number of spectra after LSH clustering: {len(df)}')"
   ]
  }
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