CCS_Alignment.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Deep learning the collisional cross sections of the peptide universe from a million experimental values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Florian Meier, Niklas D. Köhler, Andreas-David Brunner, Jean-Marc H. Wanka, Eugenia Voytik, Maximilian T. Strauss, Fabian J. Theis, Matthias Mann"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pre-print: https://doi.org/10.1101/2020.05.19.102285  \n",
    "Publication: pending"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "revised 09/2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import glob\n",
    "\n",
    "from numba import njit, prange\n",
    "import time\n",
    "\n",
    "from scipy import stats\n",
    "import scipy.cluster.hierarchy as shc\n",
    "import scipy.spatial.distance as ssd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "@njit\n",
    "def mean_dist_pairwise(matrix, shape):\n",
    "    dist = np.zeros((shape,shape))\n",
    "    \n",
    "    for i in prange(shape):\n",
    "        for j in prange(shape):\n",
    "            dist[j,i] = np.nanmean(matrix[:,i] - matrix[:,j])\n",
    "    \n",
    "    return dist  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Import raw data from MaxQuant output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Sequence</th>\n",
       "      <th>Length</th>\n",
       "      <th>Modifications</th>\n",
       "      <th>Modified sequence</th>\n",
       "      <th>Oxidation (M) Probabilities</th>\n",
       "      <th>Oxidation (M) Score Diffs</th>\n",
       "      <th>Acetyl (Protein N-term)</th>\n",
       "      <th>Oxidation (M)</th>\n",
       "      <th>Missed cleavages</th>\n",
       "      <th>Proteins</th>\n",
       "      <th>...</th>\n",
       "      <th>Mod. peptide ID</th>\n",
       "      <th>MS/MS IDs</th>\n",
       "      <th>Best MS/MS</th>\n",
       "      <th>AIF MS/MS IDs</th>\n",
       "      <th>Oxidation (M) site IDs</th>\n",
       "      <th>Missed cleavages (Trypsin/P)</th>\n",
       "      <th>Missed cleavages (LysN)</th>\n",
       "      <th>Missed cleavages (LysC/P)</th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>1/K0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AAAAELLLNLLFFEK</td>\n",
       "      <td>15</td>\n",
       "      <td>Unmodified</td>\n",
       "      <td>_AAAAELLLNLLFFEK_</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>G5EG62</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AAAAELLLNLLFFEK</td>\n",
       "      <td>15</td>\n",
       "      <td>Unmodified</td>\n",
       "      <td>_AAAAELLLNLLFFEK_</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>G5EG62</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AAAAIIDSTTETNKGDDYR</td>\n",
       "      <td>19</td>\n",
       "      <td>Unmodified</td>\n",
       "      <td>_AAAAIIDSTTETNKGDDYR_</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>G5EC97</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AAAANLNYIR</td>\n",
       "      <td>10</td>\n",
       "      <td>Unmodified</td>\n",
       "      <td>_AAAANLNYIR_</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>P53588</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AAAAPTISQSISTLTSGMR</td>\n",
       "      <td>19</td>\n",
       "      <td>Oxidation (M)</td>\n",
       "      <td>_AAAAPTISQSISTLTSGM(ox)R_</td>\n",
       "      <td>AAAAPTISQSISTLTSGM(1)R</td>\n",
       "      <td>AAAAPTISQSISTLTSGM(70.6)R</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>O02109</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2035</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 76 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Sequence  Length  Modifications          Modified sequence  \\\n",
       "0      AAAAELLLNLLFFEK      15     Unmodified          _AAAAELLLNLLFFEK_   \n",
       "1      AAAAELLLNLLFFEK      15     Unmodified          _AAAAELLLNLLFFEK_   \n",
       "2  AAAAIIDSTTETNKGDDYR      19     Unmodified      _AAAAIIDSTTETNKGDDYR_   \n",
       "3           AAAANLNYIR      10     Unmodified               _AAAANLNYIR_   \n",
       "4  AAAAPTISQSISTLTSGMR      19  Oxidation (M)  _AAAAPTISQSISTLTSGM(ox)R_   \n",
       "\n",
       "  Oxidation (M) Probabilities  Oxidation (M) Score Diffs  \\\n",
       "0                         NaN                        NaN   \n",
       "1                         NaN                        NaN   \n",
       "2                         NaN                        NaN   \n",
       "3                         NaN                        NaN   \n",
       "4      AAAAPTISQSISTLTSGM(1)R  AAAAPTISQSISTLTSGM(70.6)R   \n",
       "\n",
       "   Acetyl (Protein N-term)  Oxidation (M)  Missed cleavages Proteins  ...  \\\n",
       "0                        0              0               0.0   G5EG62  ...   \n",
       "1                        0              0               0.0   G5EG62  ...   \n",
       "2                        0              0               1.0   G5EC97  ...   \n",
       "3                        0              0               0.0   P53588  ...   \n",
       "4                        0              1               0.0   O02109  ...   \n",
       "\n",
       "  Mod. peptide ID MS/MS IDs Best MS/MS AIF MS/MS IDs Oxidation (M) site IDs  \\\n",
       "0               0         0          0           NaN                    NaN   \n",
       "1               0         1          1           NaN                    NaN   \n",
       "2               1         2          2           NaN                    NaN   \n",
       "3               2         3          3           NaN                    NaN   \n",
       "4               3         4          4           NaN                   2035   \n",
       "\n",
       "  Missed cleavages (Trypsin/P)  Missed cleavages (LysN)  \\\n",
       "0                          NaN                      NaN   \n",
       "1                          NaN                      NaN   \n",
       "2                          NaN                      NaN   \n",
       "3                          NaN                      NaN   \n",
       "4                          NaN                      NaN   \n",
       "\n",
       "  Missed cleavages (LysC/P)  Unnamed: 0  1/K0  \n",
       "0                       NaN         NaN   NaN  \n",
       "1                       NaN         NaN   NaN  \n",
       "2                       NaN         NaN   NaN  \n",
       "3                       NaN         NaN   NaN  \n",
       "4                       NaN         NaN   NaN  \n",
       "\n",
       "[5 rows x 76 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load evidence.txt files from folder\n",
    "filenames = glob.glob(\"data/evidence*.txt\")\n",
    "evidences = [pd.read_csv(filename, sep='\\t', engine='python', header=0) for filename in filenames]\n",
    "\n",
    "# Combine all evidences in one dataframe\n",
    "evidence_all = pd.concat(evidences, sort=False, ignore_index=True)\n",
    "\n",
    "# Clean up \n",
    "del evidences\n",
    "\n",
    "evidence_all.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2786717"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop reverse hits\n",
    "# Drop features with no intensity value\n",
    "# Drop charge 1 features\n",
    "\n",
    "evidence_all = evidence_all.loc[(evidence_all['Reverse'] != '+') & \\\n",
    "                                (evidence_all['Intensity'] > 0) & \\\n",
    "                                (evidence_all['Charge'] != 1)]\n",
    "len(evidence_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "718917"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Keep only one evidence per raw file\n",
    "# Select feature with maximum intensity\n",
    "\n",
    "selection = ['Modified sequence', 'Sequence', 'Charge', 'Mass', 'm/z', 'CCS', 'Experiment', \n",
    "             'id', 'Intensity', 'Score', 'Length', 'Raw file']\n",
    "evidence_agg = evidence_all.loc[evidence_all.groupby(\n",
    "    ['Modified sequence', 'Charge', 'Raw file'])['Intensity'].idxmax()][selection]\n",
    "\n",
    "evidence_pivot_long = evidence_agg.pivot_table(index = ['Modified sequence', 'Charge'], \n",
    "                                               columns = 'Raw file', \n",
    "                                               values = 'CCS')\n",
    "del evidence_agg \n",
    "\n",
    "evidence_pivot_long = evidence_pivot_long.astype(np.float32)\n",
    "\n",
    "len(evidence_pivot_long)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(718917, 374862)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter peptides with only one occurence to speed up and save memory (747 runs - 1)\n",
    "\n",
    "evidence_pivot = evidence_pivot_long.loc[evidence_pivot_long.isnull().sum(axis=1) < (len(set(evidence_all['Raw file'])) - 1)]\n",
    "evidence_pivot = evidence_pivot.astype(np.float32)\n",
    "\n",
    "len(evidence_pivot_long), len(evidence_pivot)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Align CCS values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.063884325822195\n"
     ]
    }
   ],
   "source": [
    "# calculate pair-wise distances\n",
    "\n",
    "start = time.time()\n",
    "evidence_distance = pd.DataFrame(mean_dist_pairwise(np.array(evidence_pivot), evidence_pivot.shape[1]))\n",
    "end = time.time()\n",
    "\n",
    "print((end - start)/60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(747, 747)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evidence_distance.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-180.5926513671875, 541.7779541015625)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fill NA distances with arbitrary large number\n",
    "evidence_distance_cluster = evidence_distance.fillna(3*evidence_distance.max().max())\n",
    "evidence_distance_cluster.min().min(), evidence_distance_cluster.max().max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File exported.\n"
     ]
    }
   ],
   "source": [
    "# save result matrix\n",
    "evidence_distance.to_csv('output/evidence_distance_float32.csv', index=False)\n",
    "print('File exported.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load result matrix\n",
    "evidence_distance = pd.read_csv('output/evidence_distance_float32.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.21554856,  1.02286336,  1.52692547, ..., -0.21146111,\n",
       "       -0.43880397, -0.89470349])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dist_matrix = ssd.squareform(evidence_distance_cluster, checks=False)\n",
    "dist_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Perform hierarchical clustering to sort runs by distance\n",
    "\n",
    "Z = shc.linkage(abs(dist_matrix), 'ward')\n",
    "plt.figure(figsize=(25, 10))\n",
    "plt.title('Hierarchical Clustering Dendrogram')\n",
    "plt.xlabel('sample index or (cluster size)')\n",
    "plt.ylabel('distance')\n",
    "\n",
    "shc.dendrogram(\n",
    "    Z,\n",
    "    truncate_mode='lastp',  # show only the last p merged clusters\n",
    "    p=250,  # show only the last p merged clusters\n",
    "    leaf_rotation=90.,\n",
    "    leaf_font_size=12.,\n",
    "    show_contracted=True,  # to get a distribution impression in truncated branches\n",
    ")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "nruns = len(evidence_pivot_long.columns) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\voytik\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:15: RuntimeWarning: Mean of empty slice\n",
      "  from ipykernel import kernelapp as app\n",
      "C:\\Users\\voytik\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:8: RuntimeWarning: Mean of empty slice\n",
      "  \n",
      "C:\\Users\\voytik\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:21: RuntimeWarning: Mean of empty slice\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Clusters merged.\n"
     ]
    }
   ],
   "source": [
    "col = nruns\n",
    "nancount = 0\n",
    "\n",
    "for cluster in Z:\n",
    "    # loop through clusters and merge them pairwise      \n",
    "    \n",
    "    # align first\n",
    "    delta = np.nanmean(evidence_pivot_long.iloc[:, int(cluster[1])] - evidence_pivot_long.iloc[:, int(cluster[0])])   \n",
    "    \n",
    "    if(~np.isnan(delta)):\n",
    "        # Merge neighboring runs \n",
    "        # Calculate mean\n",
    "\n",
    "        evidence_pivot_long[col] = np.nanmean([evidence_pivot_long.iloc[:, int(cluster[1])] - 0.5*delta, \n",
    "                                               evidence_pivot_long.iloc[:, int(cluster[0])] + 0.5*delta], axis = 0)\n",
    "    else:\n",
    "        # NaN difference in a cluster to be merged   \n",
    "        # Merge runs but keep external calibration\n",
    "        \n",
    "        evidence_pivot_long[col] = np.nanmean([evidence_pivot_long.iloc[:, int(cluster[1])], \n",
    "                                               evidence_pivot_long.iloc[:, int(cluster[0])]], axis = 0)\n",
    "\n",
    "        \n",
    "    col += 1\n",
    "    \n",
    "print('Clusters merged.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1493, 747)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Store experimental values in separate dataframe\n",
    "evidence_pivot_tmp = evidence_pivot_long.copy()\n",
    "evidence_pivot_long = evidence_pivot_long.iloc[:, 0:nruns]\n",
    "\n",
    "len(evidence_pivot_tmp.columns), len(evidence_pivot_long.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate deviation from mean to use as correction factors\n",
    "\n",
    "evidence_pivot_deviation = evidence_pivot_long.subtract(evidence_pivot_tmp.iloc[:, -1], axis = 0)\n",
    "evidence_pivot_deviation.mean(axis = 0, skipna = True).plot(figsize = (30,8))\n",
    "\n",
    "plt.xlabel('Raw file')\n",
    "plt.ylabel('Mean deviation from the median CCS')\n",
    "plt.xticks(size=8, rotation=10);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Subtract run correction factor from each peptide CCS value in each run\n",
    "evidence_pivot_aligned = evidence_pivot_long.subtract(evidence_pivot_deviation.mean(axis = 0, skipna = True), axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Aligned pivot dataset exported.\n"
     ]
    }
   ],
   "source": [
    "# Export aligned dataset\n",
    "evidence_pivot_aligned.to_csv('output/evidence_pivot_aligned.csv', index=False)\n",
    "print('Aligned pivot dataset exported.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Whole-proteome digests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#evidence_pivot_aligned = pd.read_csv('output/evidence_pivot_aligned.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(747, 360)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Exclude Proteome Tools data\n",
    "proteometools = set(evidence_all.loc[evidence_all['Experiment'].isin(\n",
    "    ['Proteotypic', 'SRMATLAS', 'MissingGeneSet'])]['Raw file'])\n",
    "evidence_pivot_aligned_endo = evidence_pivot_aligned.drop(proteometools, axis = 1)\n",
    "\n",
    "len(evidence_pivot_aligned.columns), len(evidence_pivot_aligned_endo.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate mean CCS value from aligned data\n",
    "\n",
    "evidence_short_aligned = evidence_pivot_aligned_endo.drop(columns = evidence_pivot_aligned_endo.columns.tolist())\n",
    "evidence_short_aligned['CCS'] = evidence_pivot_aligned_endo.mean(axis = 1, skipna = True)\n",
    "evidence_short_aligned.reset_index(inplace = True)\n",
    "evidence_short_aligned = evidence_short_aligned.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2465262"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select evidences\n",
    "\n",
    "group = ['CElegans_Tryp',\n",
    " 'Drosophila_LysC',\n",
    " 'Drosophila_LysN',\n",
    " 'Drosophila_Trp',\n",
    " 'Ecoli_LysC',\n",
    " 'Ecoli_LysN',\n",
    " 'Ecoli_trypsin',\n",
    " 'HeLa_LysC',\n",
    " 'HeLa_LysN',\n",
    " 'HeLa_Trp_2',\n",
    " 'HeLa_Trypsin_1',\n",
    " 'Yeast_LysC',\n",
    " 'Yeast_LysN',\n",
    " 'Yeast_Trypsin']    \n",
    "\n",
    "evidence_tmp = evidence_all.loc[evidence_all['Experiment'].isin(group)]\n",
    "len(evidence_tmp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select evidence columns of interest\n",
    "selection = ['Modified sequence', 'Sequence', 'Charge', 'Mass', 'm/z', 'Experiment', 'id', \n",
    "             'Intensity', 'Score', 'Length', 'Retention time']\n",
    "evidence_unique = evidence_tmp.loc[evidence_tmp.groupby(['Modified sequence', 'Charge'])['Intensity'].idxmax()][selection]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(559979, 559979)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(evidence_short_aligned['CCS']), len(evidence_unique)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>Modified sequence</th>\n",
       "      <th>Sequence</th>\n",
       "      <th>Charge</th>\n",
       "      <th>Mass</th>\n",
       "      <th>m/z</th>\n",
       "      <th>Experiment</th>\n",
       "      <th>id</th>\n",
       "      <th>Intensity</th>\n",
       "      <th>Score</th>\n",
       "      <th>Length</th>\n",
       "      <th>Retention time</th>\n",
       "      <th>CCS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1095336</th>\n",
       "      <td>_(ac)AAAAAAAAAAGAAGGR_</td>\n",
       "      <td>AAAAAAAAAAGAAGGR</td>\n",
       "      <td>2</td>\n",
       "      <td>1239.63200</td>\n",
       "      <td>620.823275</td>\n",
       "      <td>HeLa_Trp_2</td>\n",
       "      <td>9</td>\n",
       "      <td>149810.0</td>\n",
       "      <td>162.800</td>\n",
       "      <td>16</td>\n",
       "      <td>70.140</td>\n",
       "      <td>409.092438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1447561</th>\n",
       "      <td>_(ac)AAAAAAAGAAGSAAPAAAAGAPGSGGAPSGSQGVLIGDR_</td>\n",
       "      <td>AAAAAAAGAAGSAAPAAAAGAPGSGGAPSGSQGVLIGDR</td>\n",
       "      <td>3</td>\n",
       "      <td>3144.55482</td>\n",
       "      <td>1049.192220</td>\n",
       "      <td>HeLa_Trypsin_1</td>\n",
       "      <td>124</td>\n",
       "      <td>194000.0</td>\n",
       "      <td>71.438</td>\n",
       "      <td>39</td>\n",
       "      <td>3947.700</td>\n",
       "      <td>772.098022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1095353</th>\n",
       "      <td>_(ac)AAAAAAAGDSDSWDADAFSVEDPVRK_</td>\n",
       "      <td>AAAAAAAGDSDSWDADAFSVEDPVRK</td>\n",
       "      <td>2</td>\n",
       "      <td>2634.18340</td>\n",
       "      <td>1318.098980</td>\n",
       "      <td>HeLa_Trp_2</td>\n",
       "      <td>26</td>\n",
       "      <td>6416400.0</td>\n",
       "      <td>111.750</td>\n",
       "      <td>26</td>\n",
       "      <td>94.079</td>\n",
       "      <td>573.213257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1095357</th>\n",
       "      <td>_(ac)AAAAAAAGDSDSWDADAFSVEDPVRK_</td>\n",
       "      <td>AAAAAAAGDSDSWDADAFSVEDPVRK</td>\n",
       "      <td>3</td>\n",
       "      <td>2634.18340</td>\n",
       "      <td>879.068411</td>\n",
       "      <td>HeLa_Trp_2</td>\n",
       "      <td>30</td>\n",
       "      <td>5400600.0</td>\n",
       "      <td>42.218</td>\n",
       "      <td>26</td>\n",
       "      <td>94.841</td>\n",
       "      <td>635.000610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1095350</th>\n",
       "      <td>_(ac)AAAAAAAGDSDSWDADAFSVEDPVR_</td>\n",
       "      <td>AAAAAAAGDSDSWDADAFSVEDPVR</td>\n",
       "      <td>2</td>\n",
       "      <td>2506.08844</td>\n",
       "      <td>1254.051500</td>\n",
       "      <td>HeLa_Trp_2</td>\n",
       "      <td>23</td>\n",
       "      <td>245720.0</td>\n",
       "      <td>135.890</td>\n",
       "      <td>25</td>\n",
       "      <td>104.620</td>\n",
       "      <td>547.559082</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     Modified sequence  \\\n",
       "1095336                         _(ac)AAAAAAAAAAGAAGGR_   \n",
       "1447561  _(ac)AAAAAAAGAAGSAAPAAAAGAPGSGGAPSGSQGVLIGDR_   \n",
       "1095353               _(ac)AAAAAAAGDSDSWDADAFSVEDPVRK_   \n",
       "1095357               _(ac)AAAAAAAGDSDSWDADAFSVEDPVRK_   \n",
       "1095350                _(ac)AAAAAAAGDSDSWDADAFSVEDPVR_   \n",
       "\n",
       "                                        Sequence  Charge        Mass  \\\n",
       "1095336                         AAAAAAAAAAGAAGGR       2  1239.63200   \n",
       "1447561  AAAAAAAGAAGSAAPAAAAGAPGSGGAPSGSQGVLIGDR       3  3144.55482   \n",
       "1095353               AAAAAAAGDSDSWDADAFSVEDPVRK       2  2634.18340   \n",
       "1095357               AAAAAAAGDSDSWDADAFSVEDPVRK       3  2634.18340   \n",
       "1095350                AAAAAAAGDSDSWDADAFSVEDPVR       2  2506.08844   \n",
       "\n",
       "                 m/z      Experiment   id  Intensity    Score  Length  \\\n",
       "1095336   620.823275      HeLa_Trp_2    9   149810.0  162.800      16   \n",
       "1447561  1049.192220  HeLa_Trypsin_1  124   194000.0   71.438      39   \n",
       "1095353  1318.098980      HeLa_Trp_2   26  6416400.0  111.750      26   \n",
       "1095357   879.068411      HeLa_Trp_2   30  5400600.0   42.218      26   \n",
       "1095350  1254.051500      HeLa_Trp_2   23   245720.0  135.890      25   \n",
       "\n",
       "         Retention time         CCS  \n",
       "1095336          70.140  409.092438  \n",
       "1447561        3947.700  772.098022  \n",
       "1095353          94.079  573.213257  \n",
       "1095357          94.841  635.000610  \n",
       "1095350         104.620  547.559082  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evidence_unique['CCS'] = evidence_short_aligned['CCS'].values\n",
    "evidence_unique.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Aligned dataset exported.\n"
     ]
    }
   ],
   "source": [
    "# Export aligned dataset for deep learning\n",
    "evidence_unique.to_csv('output/evidence_aligned.csv', index=False)\n",
    "print('Aligned dataset exported.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ProteomeTools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(747, 387)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Proteome Tools data\n",
    "proteometools = set(evidence_all.loc[~evidence_all['Experiment'].isin(\n",
    "    ['Proteotypic', 'SRMATLAS', 'MissingGeneSet'])]['Raw file'])\n",
    "evidence_pivot_aligned_PT = evidence_pivot_aligned.drop(proteometools, axis = 1)\n",
    "\n",
    "len(evidence_pivot_aligned.columns), len(evidence_pivot_aligned_PT.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate mean CCS value from aligned data\n",
    "\n",
    "evidence_short_aligned_PT = evidence_pivot_aligned_PT.drop(columns = evidence_pivot_aligned_PT.columns.tolist())\n",
    "evidence_short_aligned_PT['CCS'] = evidence_pivot_aligned_PT.mean(axis = 1, skipna = True)\n",
    "evidence_short_aligned_PT.reset_index(inplace = True)\n",
    "evidence_short_aligned_PT = evidence_short_aligned_PT.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "321455"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select evidences\n",
    "\n",
    "group = ['Proteotypic', 'SRMATLAS', 'MissingGeneSet']    \n",
    "\n",
    "evidence_tmp = evidence_all.loc[evidence_all['Experiment'].isin(group)]\n",
    "len(evidence_tmp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select evidence columns of interest\n",
    "selection = ['Modified sequence', 'Sequence', 'Charge', 'Mass', 'm/z', 'Experiment', \n",
    "             'id', 'Intensity', 'Score', 'Length', 'Retention time']\n",
    "evidence_unique_PT = evidence_tmp.loc[evidence_tmp.groupby(['Modified sequence', 'Charge'])['Intensity'].idxmax()][selection]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(213852, 213852)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(evidence_short_aligned_PT['CCS']), len(evidence_unique_PT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>Modified sequence</th>\n",
       "      <th>Sequence</th>\n",
       "      <th>Charge</th>\n",
       "      <th>Mass</th>\n",
       "      <th>m/z</th>\n",
       "      <th>Experiment</th>\n",
       "      <th>id</th>\n",
       "      <th>Intensity</th>\n",
       "      <th>Score</th>\n",
       "      <th>Length</th>\n",
       "      <th>Retention time</th>\n",
       "      <th>CCS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2127089</th>\n",
       "      <td>_(ac)AAAAAAAAEQQSSNGPVKK_</td>\n",
       "      <td>AAAAAAAAEQQSSNGPVKK</td>\n",
       "      <td>2</td>\n",
       "      <td>1810.91734</td>\n",
       "      <td>906.465946</td>\n",
       "      <td>Proteotypic</td>\n",
       "      <td>11</td>\n",
       "      <td>21349.0</td>\n",
       "      <td>89.382</td>\n",
       "      <td>19</td>\n",
       "      <td>19.645</td>\n",
       "      <td>481.229279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2127135</th>\n",
       "      <td>_(ac)AAAAAATAAAAASIR_</td>\n",
       "      <td>AAAAAATAAAAASIR</td>\n",
       "      <td>2</td>\n",
       "      <td>1298.69426</td>\n",
       "      <td>650.354408</td>\n",
       "      <td>Proteotypic</td>\n",
       "      <td>57</td>\n",
       "      <td>29524.0</td>\n",
       "      <td>112.930</td>\n",
       "      <td>15</td>\n",
       "      <td>37.452</td>\n",
       "      <td>417.830170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2127143</th>\n",
       "      <td>_(ac)AAAAAAVGPGAGGAGSAVPGGAGPCATVSVFPGAR_</td>\n",
       "      <td>AAAAAAVGPGAGGAGSAVPGGAGPCATVSVFPGAR</td>\n",
       "      <td>3</td>\n",
       "      <td>2948.45629</td>\n",
       "      <td>983.826039</td>\n",
       "      <td>Proteotypic</td>\n",
       "      <td>65</td>\n",
       "      <td>27953.0</td>\n",
       "      <td>52.195</td>\n",
       "      <td>35</td>\n",
       "      <td>41.412</td>\n",
       "      <td>782.935242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2127146</th>\n",
       "      <td>_(ac)AAAAAETPEVLRECGCK_</td>\n",
       "      <td>AAAAAETPEVLRECGCK</td>\n",
       "      <td>2</td>\n",
       "      <td>1873.86623</td>\n",
       "      <td>937.940392</td>\n",
       "      <td>Proteotypic</td>\n",
       "      <td>68</td>\n",
       "      <td>27993.0</td>\n",
       "      <td>75.012</td>\n",
       "      <td>17</td>\n",
       "      <td>26.756</td>\n",
       "      <td>480.424042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2127150</th>\n",
       "      <td>_(ac)AAAAAGLGGGGAGPGPEAGDFLAR_</td>\n",
       "      <td>AAAAAGLGGGGAGPGPEAGDFLAR</td>\n",
       "      <td>2</td>\n",
       "      <td>2052.00247</td>\n",
       "      <td>1027.008510</td>\n",
       "      <td>Proteotypic</td>\n",
       "      <td>72</td>\n",
       "      <td>64770.0</td>\n",
       "      <td>123.920</td>\n",
       "      <td>24</td>\n",
       "      <td>39.708</td>\n",
       "      <td>515.897827</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Modified sequence  \\\n",
       "2127089                  _(ac)AAAAAAAAEQQSSNGPVKK_   \n",
       "2127135                      _(ac)AAAAAATAAAAASIR_   \n",
       "2127143  _(ac)AAAAAAVGPGAGGAGSAVPGGAGPCATVSVFPGAR_   \n",
       "2127146                    _(ac)AAAAAETPEVLRECGCK_   \n",
       "2127150             _(ac)AAAAAGLGGGGAGPGPEAGDFLAR_   \n",
       "\n",
       "                                    Sequence  Charge        Mass          m/z  \\\n",
       "2127089                  AAAAAAAAEQQSSNGPVKK       2  1810.91734   906.465946   \n",
       "2127135                      AAAAAATAAAAASIR       2  1298.69426   650.354408   \n",
       "2127143  AAAAAAVGPGAGGAGSAVPGGAGPCATVSVFPGAR       3  2948.45629   983.826039   \n",
       "2127146                    AAAAAETPEVLRECGCK       2  1873.86623   937.940392   \n",
       "2127150             AAAAAGLGGGGAGPGPEAGDFLAR       2  2052.00247  1027.008510   \n",
       "\n",
       "          Experiment  id  Intensity    Score  Length  Retention time  \\\n",
       "2127089  Proteotypic  11    21349.0   89.382      19          19.645   \n",
       "2127135  Proteotypic  57    29524.0  112.930      15          37.452   \n",
       "2127143  Proteotypic  65    27953.0   52.195      35          41.412   \n",
       "2127146  Proteotypic  68    27993.0   75.012      17          26.756   \n",
       "2127150  Proteotypic  72    64770.0  123.920      24          39.708   \n",
       "\n",
       "                CCS  \n",
       "2127089  481.229279  \n",
       "2127135  417.830170  \n",
       "2127143  782.935242  \n",
       "2127146  480.424042  \n",
       "2127150  515.897827  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evidence_unique_PT['CCS'] = evidence_short_aligned_PT['CCS'].values\n",
    "evidence_unique_PT.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Aligned dataset exported.\n"
     ]
    }
   ],
   "source": [
    "# Export aligned dataset for deep learning\n",
    "evidence_unique_PT.to_csv('output/evidence_aligned_PT.csv', index=False)\n",
    "print('Aligned dataset exported.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparison whole-proteome measurements vs. ProteomeTools (experimental)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "evidence_PT = evidence_pivot_aligned_PT.drop(columns = evidence_pivot_aligned_PT.columns.tolist())\n",
    "evidence_PT['CCS'] = evidence_pivot_aligned_PT.mean(axis = 1, skipna = True)\n",
    "evidence_PT.reset_index(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "evidence_endo = evidence_pivot_aligned_endo.drop(columns = evidence_pivot_aligned_endo.columns.tolist())\n",
    "evidence_endo['CCS'] = evidence_pivot_aligned_endo.mean(axis = 1, skipna = True)\n",
    "evidence_endo.reset_index(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(718917, 718917)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(evidence_PT), len(evidence_endo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(evidence_endo['CCS'], evidence_PT['CCS'], s = 0.1, alpha = 0.1)\n",
    "plt.xlabel('CCS ($\\AA^2$), whole proteome digest')\n",
    "plt.ylabel('CCS ($\\AA^2$), ProteomeTools');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pearson correlation: 0.995\n",
      "n = 54914\n"
     ]
    }
   ],
   "source": [
    "mask = ~np.logical_or(np.isnan(evidence_endo['CCS']), np.isnan(evidence_PT['CCS']))  \n",
    "pcorr = stats.pearsonr(evidence_endo['CCS'][mask], evidence_PT['CCS'][mask])[0]\n",
    "\n",
    "print('Pearson correlation: {:1.3f}'.format(pcorr))\n",
    "print('n = {0}'.format(mask.sum()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Median absolute deviation = 0.6%\n"
     ]
    }
   ],
   "source": [
    "print('Median absolute deviation = {:1.1f}%'.format(np.abs((evidence_endo['CCS'] - evidence_PT['CCS']) /\n",
    "                                                           evidence_endo['CCS'] * 100).median()))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}