intro.ipynb

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   "source": [
    "# Strojové učení v hmotnostní spektrometrii\n",
    "## Cesta tam a zase zpátky\n",
    "### Aleš Křenek, Filip Jozefov, Adam Hájek"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52415ad3",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Hra na MS"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63f62357",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
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   "source": [
    "## Referenční data (2 týmy)\n",
    "- \"ionizace\" -- rozložení zvířátka na 3-8 částí\n",
    "  - částečně respektujte, jak se rozpadá samo, ale ne vždy stejně, občas užijte mírné násilí\n",
    "- \"měření spektra\"\n",
    "  - hmotnosti fragmentů určete podle klíče na dalším slajdu\n",
    "  - u každé unikátní hmotnosti spočtěte počet výskytů \n",
    "  - nadiktujte mi seznam dvojic [_hmotnost, počet fragmentů_]\n",
    "  \n",
    "## Experimentální data\n",
    "- dtto, udělal jsem za vás, abyste tak snadno nepoznali, co bylo na vstupu"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7888a461",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
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   "source": [
    "## Příklady hmotností\n",
    "<table> \n",
    "    <tr>\n",
    "        <td><img src=\"23.jpeg\" width=\"300\"/></td><td>0.67&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</td>\n",
    "        <td><img src=\"1.jpeg\" width=\"300\"/></td><td>1.00&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</td>\n",
    "        <td><img src=\"2.jpeg\" width=\"300\"/></td><td>2.00</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td><img src=\"123.jpeg\" width=\"300\"/></td><td>1.67&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</td>\n",
    "        <td><img src=\"3.jpeg\" width=\"300\"/></td><td>3.00&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</td>\n",
    "        <td><img src=\"523.jpeg\" width=\"300\"/></td><td>5.67</td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61b60efc",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Referenční data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "c6416e69",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "zelva_ = np.array([ [1.0, 21], [1.67,2], [ 2.0,3], [3.0,2], [4.0,2], [4.67,2],\n",
    "                  [5.67,1], [6.0,2], [7.0,1], [7.67,1], [8.0,1], [10.0,1]])\n",
    "pes_ = np.array([ [ 0.67, 2 ], [1.0, 5], [2.0, 7], [2.67, 2], [3.0, 1],\n",
    "                 [3.67, 1], [4.0, 4], [4.67, 8], [5.0,2], [5.67,3], [6.0, 1], [6.67, 2]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "a2c5f110",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matchms as ms\n",
    "zelva = ms.Spectrum(mz=zelva_[:,0]*10.,intensities=zelva_[:,1],metadata={ 'Compound Name':'želva','precursor_mz':'100'})\n",
    "pes = ms.Spectrum(mz=pes_[:,0]*10.,intensities=pes_[:,1],metadata={'Compound Name':'pes', 'precursor_mz':'100'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "aab03d07",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
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   },
   "outputs": [
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      "text/plain": [
       "<Figure size 2000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "zelva.plot_against(pes,figsize=(10,4))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "563f151d",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Experimentální data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "b131b763",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "pokus1_ = np.array([ [0.67,3], [1.0,7], [3.67,1], [4.0,8], [4.67,5], \n",
    "                    [5.0,1], [5.67,1], [6.67,8]])\n",
    "pokus2_ = np.array([ [0.67,1], [1.0,18], [2.0,5], [3.0,3], [4.0,3],[4.67,1],\n",
    "                   [5.0,1], [6.0,1], [6.67,1], [7.67,2], [8.0,1], [8.67,1]])\n",
    "pokus1 = ms.Spectrum(mz=pokus1_[:,0]*10.,intensities=pokus1_[:,1],metadata={ 'Compound Name':'Pokus 1','precursor_mz':'100'})\n",
    "pokus2 = ms.Spectrum(mz=pokus2_[:,0]*10.,intensities=pokus2_[:,1],metadata={'Compound Name':'Pokus 2','precursor_mz':'100'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "c7341da3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pokus1.plot_against(pokus2,figsize=(10,4))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37cdc677",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Kouknu a vidím"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "b8da8857",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pokus1.plot_against(zelva,figsize=(10,4))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "9903118f",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pokus2.plot_against(zelva,figsize=(10,4))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "486b2851",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Zobecněný skalární součin\n",
    "\n",
    "$$ \\textit{DP}_{a,b}(S_p,S_r) = \\frac{\\sum_k (m_k^aI_{pk}^b \\; m_k^a I_{rk}^b)}{\\sqrt{\\sum_k (m_k^a I_{pk}^b)^2 \\sum_k (m_k^a I_{rk}^b)^2 } }$$\n",
    "\n",
    "- skalární součin intenzit na stejných hodnotách $m$ (rozsah indexu $k$)\n",
    "- špičky bez protikusu ve druhém spektru se ignorují\n",
    "- váženo hmotností $m$ a modifikováno exponenty $a,b$\n",
    "- normalizace součiny sama se sebou"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "577a9dfb",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "# regular dot product\n",
    "dp = ms.similarity.ModifiedCosine(mz_power=1.,intensity_power=.5)\n",
    "\n",
    "# Stein dot product\n",
    "sdp = ms.similarity.ModifiedCosine(mz_power=3.,intensity_power=.6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "567fd526",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>pokus1</th>\n",
       "      <th>pokus2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>zelva</th>\n",
       "      <td>0.303316</td>\n",
       "      <td>0.646565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pes</th>\n",
       "      <td>0.829269</td>\n",
       "      <td>0.559601</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         pokus1    pokus2\n",
       "zelva  0.303316  0.646565\n",
       "pes    0.829269  0.559601"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "pd.DataFrame(np.vectorize(lambda x: x[0])(dp.matrix(references=[zelva,pes],queries=[pokus1,pokus2])),\n",
    "                index=['zelva','pes'], columns=['pokus1','pokus2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "b1378fb8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pokus1</th>\n",
       "      <th>pokus2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>zelva</th>\n",
       "      <td>0.065104</td>\n",
       "      <td>0.438502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pes</th>\n",
       "      <td>0.818457</td>\n",
       "      <td>0.300636</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         pokus1    pokus2\n",
       "zelva  0.065104  0.438502\n",
       "pes    0.818457  0.300636"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(np.vectorize(lambda x: x[0])(sdp.matrix(references=[zelva,pes],queries=[pokus1,pokus2])),\n",
    "                index=['zelva','pes'], columns=['pokus1','pokus2'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f59915c9",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# MS doopravdy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab44f790",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Typická zadání\n",
    "- Postupy aplikujeme na molekuly (v našem případě tzv. \"malé\")\n",
    "- Dostanu vzorek neznámého složení\n",
    "- A) Obsahuje nějakou konkrétní sloučeninu (např. stopy výbušnin při letištní kontrole)?\n",
    "- B) Co všechno ve vzorku bylo?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39b72677",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Fáze analýzy\n",
    "- chromatografická separace \n",
    "  - plynová či kapalinová, komplexní reakce molekul vzorku s rozpouštědlem a kolonou\n",
    "- ionizace složek a měření hmotnostního spektra\n",
    "  - pohyb nabitých částic v elektrickém poli, doba letu, rezonance, ...\n",
    "- výpočetní zpracování získaných dat\n",
    "  - odstranění šumu, dekonvoluce, korekce artefaktů měření, ... \n",
    "- určení jednotlivých složek z izolovaných spekter\n",
    "  - porovnání s databázemi\n",
    "\n",
    "_Spousta zajímavé fyziky, matematiky a programování, zájemcům popovídáme u večeře_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7de11976",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# A kde je to strojové učení?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee2365a7",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Chemický prostor vs. spektrální databáze \n",
    "\n",
    "- 350 000 položek v databázi NIST EI-MS (známá spektra)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96404022",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "- téměř $10^9$ položek v databázi ZINC (když zaplatím, někdo mi to uvaří)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb5aae73",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "- odhadovaných $10^{60}$ možných malých molekul"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1aff2aa",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Dopředné modely (Filip)\n",
    "- ze vzorce určit spektrum\n",
    "- model je natrénovaný na experimentální databázi (NIST)\n",
    "- např. množina (i rozsáhlá) \"podezřelých\" sloučenin, jejichž spektra neznám; srovnávám generovaná spektra s experimentem\n",
    "- generování trénovací sady pro zpětné modely"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0ca02a9",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "## Zpětné modely (Adam)\n",
    "- z naměřeného spektra chci přímo určit vzorec\n",
    "- model je výrazně větší, je třeba trénovat na mnohem větší sadě než NIST\n",
    "- plnohodnotná necílená analýza"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8909f432",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Cesta tam a zase zpátky\n",
    "\n",
    "<table>\n",
    "    <tr>\n",
    "        <td><img src='thc-formula.png' width='500'/></td>\n",
    "        <td>$\\leftrightarrow$</td>\n",
    "        <td><img src='thc-spec.png' width='500'/></td>\n",
    "    </tr>\n",
    "</table>\n",
    " "
   ]
  }
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