{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cell Line Exp-PTM Distance Comparisons\n",
    "There are 37 cell lines that are measured both in both CST (post-translational-modification, PTM) and CCLE (gene expression, Exp) datasets. We expect that the cell lines should be arranged similarly in PTM-space and gene-expression-space - in other words cell line distances measured based on PTM or gene-expression data should be similar. \n",
    "\n",
    "Since the CCLE gene expression data is not missing any data (measurements) and has already been pre-normalized, we trust gene expression data more than PTM data. Furthermore, we can use cell-line distances in gene-expression-space as a measure for how well we have processed the PTM data. For instance, we would expect that if a normalization procedure is improving the the PTM data then cell line distances in PTM-space should become more similar to cell line distances in gene-expression-space. \n",
    "\n",
    "We can use the Mantel test to measure the correlation between the two distance matrices, PTM and gene-expression distance matrices, and determine if our normalization procedures are improving the PTM data. \n",
    "\n",
    "### Python Scripts\n",
    "First, I made a version of the PTM ratio data (including all PTM types) that was comparable to the CCLE data. This involved only keeping the 37 cell lines found in the CCLE data and averaging over repeat measurements of the same cell line in the CST data: `make_CCLE_comparable_PTM_matrix.py`.\n",
    "\n",
    "I used the following python scripts to perform these calculations. `precalc_PTM_norm.py` was used to pre-calculate different versions of the PTM and gene-expression data where different normalization procetures were performed (e.g. column quantile normalization). \n",
    "\n",
    "Finally, `compare_cl_distances.py` was used to run the Mantel test and calculate the correlations between the distance matrices after a set of different normalization procedures were run.\n",
    "\n",
    "### Imports and Function Definitions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline \n",
    "import matplotlib\n",
    "matplotlib.style.use('ggplot')\n",
    "\n",
    "def load_mantel_data(dist_metric):\n",
    "    import pandas as pd\n",
    "\n",
    "    # load non-PC: Pairwise complete data\n",
    "    data_type = 'cl_exp_vs_ptm_'+dist_metric+'_pairwise-False.txt'\n",
    "    filename = '../lung_cellline_3_1_16/lung_cl_all_ptm/compare_cl_dist/' + data_type\n",
    "    \n",
    "    f = open(filename, 'r')\n",
    "    lines_norm = f.readlines()\n",
    "    f.close()\n",
    "\n",
    "    # load PC: Pairwise complete data\n",
    "    data_type = 'cl_exp_vs_ptm_'+dist_metric+'_pairwise-True.txt'\n",
    "    filename = '../lung_cellline_3_1_16/lung_cl_all_ptm/compare_cl_dist/' + data_type    \n",
    "    f = open(filename, 'r')\n",
    "    lines_pc = f.readlines()\n",
    "    f.close()    \n",
    "    \n",
    "    names = []\n",
    "    corr = []\n",
    "    pval = []\n",
    "    zscore = []\n",
    "    \n",
    "    info = {}\n",
    "    \n",
    "    # normal (non-pairwise-complete)\n",
    "    for i in range(len(lines_norm)):\n",
    "        inst_line = lines_norm[i].strip().split('\\t')\n",
    "\n",
    "        if i > 0:\n",
    "            inst_name = inst_line[0]\n",
    "            info[inst_name] = {}\n",
    "            info[inst_name]['corr'] = float(inst_line[1])\n",
    "            info[inst_name]['pval'] = float(inst_line[2])\n",
    "            info[inst_name]['zscore'] = float(inst_line[3])\n",
    "                        \n",
    "    # add pairwise complete data\n",
    "    for i in range(len(lines_pc)):\n",
    "        inst_line = lines_pc[i].strip().split('\\t')\n",
    "\n",
    "        if i > 0:\n",
    "            inst_name = inst_line[0] + '-PC'\n",
    "            info[inst_name] = {}\n",
    "            info[inst_name]['corr'] = float(inst_line[1])\n",
    "            info[inst_name]['pval'] = float(inst_line[2])\n",
    "            info[inst_name]['zscore'] = float(inst_line[3])\n",
    "              \n",
    "    inst_vect = []\n",
    "    all_names = ['none', 'none-PC', 'col-qn row-zscore', 'row-zscore', 'col-qn', \n",
    "                'filter none', 'filter none-PC', 'col-qn row-zscore filter']\n",
    "    \n",
    "    for inst_name in all_names:\n",
    "        inst_vect.append( info[inst_name]['corr'] )\n",
    "    \n",
    "    inst_vect = np.asarray(inst_vect)\n",
    "    \n",
    "    data = pd.Series(data=inst_vect, index=all_names)\n",
    "    \n",
    "    \n",
    "    fig = data.plot(kind='bar', figsize=(10,5))\n",
    "    full_title = 'Correlation of Cell-Line PTM and Exp Dist-Mats: ' + dist_metric\n",
    "    fig.set_title(full_title)\n",
    "            \n",
    "    return data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comparison of Cell-Line PTM and Expression Distances\n",
    "The following plots show the Pearson correlation of the PTM and Exp cell-line distance matrices. Each bar shows the correlation value after a different normalization procedure for the PTM data or a different method of calculating distnaces (e.g. pairwise-complete (PC)). When pairwise-complete distance calculations are not used, missing log2-ratio values are interpolated with zeros. \n",
    "\n",
    "The bars correspond to:\n",
    "* none: No PTM normalization or filtering\n",
    "* none-PC: No PTM normalization or filtering and PC (pairwise-complete) distance calculation \n",
    "* col-qn row-zscore: Quantile normalization of the columns followed by row Z-score\n",
    "* row-zscore: Row Z-score only\n",
    "* col-qn: Column quantile normalization only\n",
    "* filter none: Filter out all PTMs with missing values and no other normalizations\n",
    "* filter none-PC: Filter out all PTMs with missing values, no other normalizations, and PC distance calculation\n",
    "* col-qn row-zscore filter: Quantile normalization of the columns followed by row Z-score followed by filtering out all PTMs with missing values\n",
    "\n",
    "The greater the correlation, the better agreement between Exp and PTM data. With this correlation we can determine which procedures are improving the agreement between CCLE gene expression data and CST PTM data. \n",
    "\n",
    "## Cell-line distances measured using Euclidean distance\n",
    "The bar graph below shows how the cell-line distance matrices (PTM and Exp) correlation changes after different normalization procedures or by using different distance measures (e.g. pairwise-complete). \n",
    "\n",
    "The correlation between expression (Exp) and unmodified PTM data is 0.34. Using pairwise-complete distance calculation slightly reduces the correlation to 0.31. The correlation increases to 0.67 when the columns are quantile normalized and the rows are Z-scored. \n",
    "\n",
    "We expect that the correlation will increase when we remove PTMs with missing data and we can see that filtering out PTMs with missing values (filter none) increases the correlation to 0.45 (I'm not sure why the filtered pairwise-complete calculation is not exactly the same, but it is very close). We also improve on the filtered correlation by performing column QN and row Zscore. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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8vDzvPSf33Xeftm/frpEjR+rSSy/1lv/uu++0ZMkSlZaWeu/ZKfO73/0uqOfo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\nISIiIiIi2WGhQ0REREREsvP/AH5nhW+qAW9EAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1102951d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_euclidean = load_mantel_data('euclidean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "none                        0.282250\n",
       "none-PC                     0.316570\n",
       "col-qn row-zscore           0.695508\n",
       "row-zscore                  0.334966\n",
       "col-qn                      0.446664\n",
       "filter none                 0.162769\n",
       "filter none-PC              0.458089\n",
       "col-qn row-zscore filter    0.707248\n",
       "dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_euclidean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusions\n",
    "These results indicate that column quantile normalization as well as row zscoring improve the agreement between CCLE gene expression data and CST PTM data. Furthermore, we see a greater improvement using these normalizations than we see with either pairwise-complete distance calculation or filtering out PTMs with missing data.\n",
    "\n",
    "We see largely the same results using different distance metrics to calculate cell-line distance. \n",
    "\n",
    "## Cell-line distances measured using Cosine distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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u8qx/+PBhffTRRyosLPTcQ1TkscceC+g5Q/Xq1dPcuXN12223qXv37urZs6cS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\nRGj79u169dVXFR4erh49eki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SYQgAAACALYUGuwMAAACBCMs/If2eV2XtH8vNVlhhYZW1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\nIiIikhKLISIiIiIiktJ/A6C2d45GzCNKAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110c3eb10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_cosine = load_mantel_data('cosine')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "none                        0.229308\n",
       "none-PC                     0.291881\n",
       "col-qn row-zscore           0.387605\n",
       "row-zscore                  0.236043\n",
       "col-qn                      0.242099\n",
       "filter none                 0.160653\n",
       "filter none-PC              0.270716\n",
       "col-qn row-zscore filter    0.387638\n",
       "dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_cosine"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cell-line distances measured using Correlation distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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XiRMndOjQIW3btk27d+9WRESE/vCHP2jOnDlVLuedd97Rvffeq5SUFHXt2lUt\nW7bUwYMHtWzZMkVERPic43v06KF27drptddeU1RUlDp27CjLsjRu3Dh17NgxpPo9Qjnuzj//fKWm\npuqtt95SamqqUlNTdejQIa1du1Y9evRQ27Zt/dovJSVFjRo10lNPPaWCggLv/Sp33nmnPvnkE40e\nPVoXXHCBd/7Dhw9r2bJlKisr896z4/HAAw+E9B6d5s2ba9GiRbrmmms0ePBgDRs2TH369JHb7daO\nHTt08OBB77Fa3XN6VdeMisZHRkbqrbfe0uWXX64RI0YoJSVF/fr1U6NGjXTgwAF9+OGHysvL03ff\nfecX+E41btw4Pf7445o0aZI2bNigLl266IsvvtCqVat09dVX67XXXvOb55JLLtETTzyh7OxsjR49\nWk2aNFHTpk19wurpdY8dO1bLli3T4sWLdc4552jUqFGyLEtLly71voC7Ns/pOIucsee7wRZ2795t\n7rjjDtO7d2/jdDpNgwYNTNu2bc3w4cPNiy++aIqLi/3mee2110xaWppp0qSJiYmJMb179zaPPfaY\nz2MuPRITEyt9TGpV4405+ZjRiRMnmq5du5qYmBgTFxdnevbsacaNG2eWLVvmM63nvQanP6Zyy5Yt\nZsiQIaZp06amSZMmZuDAgWbZsmXm3XffDfh41LKyMjNlyhSTlJRkoqKi/B5PW1Hdx48fN4899pjp\n3bu3adiwoXE6nWbgwIHmtdde85s20KNdT5Wenl7pI64Deeedd8xll11mmjZtaqKjo03Xrl3N/fff\nH/Dxpzk5OSG9A+J027ZtM7/97W9Nt27dTGxsrImOjjYdO3Y0o0ePNkuWLAk4z6ZNm8yYMWNM27Zt\nTYMGDUzLli3Nueeea/7whz+Yjz76yGfaQPu4OjVPmzbNWJZlHA6HzyOCPf92OBzed9pMmzbNOByO\ngI+XrujxxBUdc6Fub0U8j0eu6vHBgXiO71AeL12Vhx56KOQ2yMnJMf379zeNGzc2CQkJZvTo0Wbn\nzp21ur+Li4vNjBkzTFJSkomOjjadO3c2Dz30kDlx4kS1Hi99+vFy+rHj2f6//OUvxrIsc+WVVwZc\n1ujRo43D4TBPPfWUd5jncbp5eXnmz3/+s+nRo4eJiYkx7du3N3fddVfAx99WxHN8nFpv48aNTYcO\nHcyll15qpk2bZr766quA8wY6B+3evdvcfffdZsCAASYhIcG7LzMzM83WrVv9lvHhhx+aoUOHmri4\nOONwOALXZm6NAAAgAElEQVS2Z0Vq47grLCw0EyZMMImJiSYmJsZ06dLFPPjgg+bYsWMVnqfXrl1r\nLrroIhMbG+vdd/v37zcHDx40U6ZMMampqaZ169YmOjradOjQwQwfPtysXbvWbzmhvkfH47PPPjPj\nxo0z7dq1Mw0aNDCtW7c26enpAV/dEMo5vaprRjDXlO+//9488MADpnfv3qZRo0YmNjbWdOvWzWRm\nZppXXnnFlJaWeqet6PO7a9cuM3LkSNOyZUvTuHFjM2DAAPPCCy+Y/Pz8Cs9lTz75pOnZs6eJjo72\nvnahqrrLy8vNX//6VzNgwADTqFEj77oqeiVFdc/jOLtYxgT5FTAAADjjsrKy9NJLLyk/P7/av3wA\nwC9RyPforF27Vrfffrt+85vf6MEHHwzY5/5UJSUlevXVV73z3H777WflU4/qWm3dRIvqow3CjzYI\nP9ogvNj/4UcbhB9tEH6/lDYIKehs2bJFL7/8sjIzMzVnzhx16tRJM2fOrPTGuv/5n//RZ599pttu\nu01z587VpEmT1LZt2xoXXt9U9iQdnBm0QfjRBuFHG4QX+z/8aIPwow3C75fSBiE9jGDlypUaOnSo\n982y2dnZ+vjjj5Wbm6srr7zSb/pPPvlEu3fv1jPPPON9GkqLFi1qXjUAAL8QlmXVytPmAOCXJuig\nU1paqry8PI0ePdo7zLIs9enTR3v37g04z/bt25WUlKSlS5fq/fffV3R0tAYMGKBrr71WDRo0qHn1\nAADY3IIFC7RgwYJwlwEA9U7QQcftdqu8vNzvrbpxcXH65ptvAs5z6NAh7dmzRw0aNNC9994rt9ut\n+fPn6+jRo5owYULNKgcAAACACtTpC0ONMXI4HLrzzjuVnJysc889VzfddJM2btyokpKSulz1Wcfz\n7H2ED20QfrRB+NEG4cX+Dz/aIPxog/D7pbRB0L/oOJ1OORwOFRUV+Qx3uVyKj48POE98fLyaNm3q\n87Ioz4MICgoK1Lp1a795Nm3a5HeDVM+ePTVy5MhgSz0rZWVlhbuEXzzaIPxog/CjDcKL/R9+tEH4\n0QbhZ5c2WL58uXbv3u0zLDU1VWlpaZJCCDqRkZFKSkryvoVZksrLy7Vz504NGzYs4Dw9evTQtm3b\ndPz4ccXExEiSvv32W1mWpebNmwecJy0tzVvc6Y4cOaLS0tJgSz6rOJ3OSp9Oh7pHG4QfbRB+tEF4\nsf/DjzYIP9og/Op7G0RGRqpp06YaOXJkpT+GhPTUtREjRmjevHlKTk5WcnKyVq9ereLiYg0ePFiS\ntGjRIhUWFmrixImSToaWN998U3/96181ZswYud1u/eMf/9CQIUMUFRUV8kaVlpbW2y5vxph6W7td\n0AbhRxuEH20QXuz/8KMNwo82CL9fShuEFHRSUlLkdru1ePFiuVwuJSYmasqUKXI6nZJOdmMrKCjw\nTh8TE6OHH35YL774oiZPnqzY2FilpKTouuuuq92tAAAAAIBTWMYYE+4ignX48OF6mz6bNWumwsLC\ncJfxi0YbhB9tEH60QXix/8OPNgg/2iD86nsbREVFKSEhocrp6vSpawAAAAAQDgQdAAAAALZD0AEA\nAABgOwQdAAAAALZD0AEAAABgOwQdAAAAALZD0AEAAABgOwQdAAAAALZD0AEAAABgOwQdAAAAALZD\n0AEAAABgOwQdAAAAALZD0AEAAABgOwQdAAAAALZD0AEAAABgOwQdAAAAALZD0AEAAABgOwQdAAAA\nALZD0AEAAABgOwQdAAAAALZD0AEAAABgOwQdAAAAALZD0AEAAABgOwQdAAAAALZD0AEAAABgOwQd\nAAAAALZD0AEAAABgOwQdAAAAALZD0AEAAABgOwQdAAAAALZD0AEAAABgOwQdAAAAALZD0AEAAABg\nOwQdAAAAALZD0AEAAABgOwQdAAAAALZD0AEAAABgOwQdAAAAALZD0AEAAABgOwQdAAAAALZD0AEA\nAABgOwQdAAAAALZD0AEAAABgOwQdAAAAALZD0AEAAABgOwQdAAAAALZD0AEAAABgOwQdAAAAALZD\n0AEAAABgOwQdAAAAALZD0AEAAABgOwQdAAAAALZD0AEAAABgOwQdAAAAALYTGeoMa9eu1YoVK+Ry\nuZSYmKjx48erS5cuAaf97LPPNGPGDL/hzz//vOLi4kKvFgAAAACCEFLQ2bJli15++WVlZ2era9eu\nWrVqlWbOnKm5c+fK6XRWON/cuXPVsGFD778rmxYAAAAAaiqkoLNy5UoNHTpU6enpkqTs7Gx9/PHH\nys3N1ZVXXlnhfE6nU40aNapRoQAAAMAvQVTxcelHd50t/1hRgaLKyups+Yp1qqRBTN0tP0hBB53S\n0lLl5eVp9OjR3mGWZalPnz7au3dvpfPee++9Ki0tVYcOHZSZmanu3btXv2IAAADAzn506/j9t4S7\nimqLmT1falaPgo7b7VZ5ebnfvTVxcXH65ptvAs7TtGlTZWdnKzk5WSUlJVq/fr2mTZumxx57TJ07\nd65Z5QAAAABQgZAfRhCKtm3bqm3btt5/d+vWTYcOHdKqVas0ceLEgPNs2rRJmzdv9hnWqlUrZWVl\nyel0yhhTlyXXmaioKDVr1izcZfyi0QbhRxuEH20QXuz/8KMNwo82qNqxooJwl1AjERERalKHbWxZ\nliQpJydHhw4d8hmXmpqqtLQ0SSEEHafTKYfDoaKiIp/hLpdL8fHxQReWnJyszz//vMLxaWlp3uJO\n53a7VVJSEvS6zibNmjVTYWFhuMv4RaMNwo82CD/aILzY/+FHG4QfbVC1Or1/5gwoKyur0zaOiopS\nQkKCsrKyKp0u6PfoREZGKikpSTt27PAOKy8v186dO9WtW7egC8vPz1fTpk2Dnh4AAAAAQhVS17UR\nI0Zo3rx5Sk5OVnJyslavXq3i4mINHjxYkrRo0SIVFhZ6u6WtWrVKrVq1Uvv27VVcXKzc3Fzt2rVL\nDz74YO1vCQAAAAD8fyEFnZSUFLndbi1evNj7wtApU6Z434vjcrlUUPB/fQrLysr00ksvqbCwUNHR\n0erUqZMefvhh9erVq3a3AgAAAABOEfLDCDIyMpSRkRFw3IQJE3z+PXLkSI0cObJ6lQEAAABANQV9\njw4AAAAA1BcEHQAAAAC2Q9ABAAAAYDsEHQAAAAC2Q9ABAAAAYDsEHQAAAAC2Q9ABAAAAYDsEHQAA\nAAC2Q9ABAAAAYDsEHQAAAAC2Q9ABAAAAYDsEHQAAAAC2Q9ABAAAAYDsEHQAAAAC2Q9ABAAAAYDsE\nHQAAAAC2Q9ABAAAAYDsEHQAAAAC2Q9ABAAAAYDsEHQAAAAC2Q9ABAAAAYDsEHQAAAAC2Q9ABAAAA\nYDsEHQAAAAC2Q9ABAAAAYDsEHQAAAAC2Q9ABAAAAYDsEHQAAAAC2Q9ABAAAAYDsEHQAAAAC2Q9AB\nAAAAYDsEHQAAAAC2Q9ABAAAAYDsEHQAAAAC2Q9ABAAAAYDuR4S4A8IgqPi796K6z5R8rKlBUWVmd\nLV+xTpU0iKm75QMAcAZwPYZdEHRw9vjRreP33xLuKqotZvZ8qRknVgBAPcf1GDZB1zUAAAAAtkPQ\nAQAAAGA7BB0AAAAAtkPQAQA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AAABsh6ADAAAAwHYIOgAAAABsh6ADAAAAwHYIOgAAAABs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\nIpjaEZhVMwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110c9f210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_correlation = load_mantel_data('correlation')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "none                        0.214984\n",
       "none-PC                     0.437375\n",
       "col-qn row-zscore           0.417548\n",
       "row-zscore                  0.301038\n",
       "col-qn                      0.238731\n",
       "filter none                 0.145517\n",
       "filter none-PC              0.437337\n",
       "col-qn row-zscore filter    0.504569\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
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