{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy\n",
    "from sourmash_lib import fig\n",
    "import pylab"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Large yeast data set / mRNAseq\n",
    "\n",
    "See https://github.com/bartongroup/profDGE48/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# running sourmash subcommand: compare\n",
      "loading ERR458510.sig\n",
      "loading ERR458523.sig\n",
      "loading ERR458665.sig\n",
      "loading ERR458681.sig\n",
      "loading ERR458685.sig\n",
      "loading ERR458747.sig\n",
      "loading ERR458753.sig\n",
      "loading ERR458755.sig\n",
      "loading ERR458884.sig\n",
      "loading ERR458937.sig\n",
      "loading ERR458966.sig\n",
      "loading ERR459001.sig\n",
      "loading ERR459005.sig\n",
      "loading ERR459013.sig\n",
      "loading ERR459049.sig\n",
      "loading ERR459068.sig\n",
      "loading ERR459102.sig\n",
      "loading ERR459124.sig\n",
      "loading ERR459128.sig\n",
      "loading ERR459140.sig\n",
      "loading mut-1a.sig\n",
      "loading mut-1b.sig\n",
      "loading wt-1a.sig\n",
      "loading wt-1b.sig\n",
      "0-           ERR458510\t[ 1.     0.829  0.564  0.836  0.559  0.864  0.816  0.687  0.62   0.601\n",
      "  0.611  0.547  0.214  0.607  0.427  0.641  0.707  0.796  0.641  0.473\n",
      "  0.834  0.835  0.51   0.54 ]\n",
      "1-           ERR458523\t[ 0.829  1.     0.603  0.895  0.531  0.847  0.768  0.655  0.571  0.558\n",
      "  0.563  0.506  0.198  0.605  0.396  0.594  0.697  0.781  0.594  0.445\n",
      "  0.907  0.91   0.47   0.497]\n",
      "2-           ERR458665\t[ 0.564  0.603  1.     0.583  0.446  0.548  0.598  0.528  0.489  0.503\n",
      "  0.462  0.453  0.318  0.418  0.51   0.494  0.562  0.572  0.462  0.365\n",
      "  0.595  0.6    0.384  0.407]\n",
      "3-           ERR458681\t[ 0.836  0.895  0.583  1.     0.53   0.868  0.762  0.653  0.571  0.553\n",
      "  0.565  0.516  0.209  0.613  0.411  0.597  0.714  0.793  0.601  0.447\n",
      "  0.879  0.883  0.473  0.503]\n",
      "4-           ERR458685\t[ 0.559  0.531  0.446  0.53   1.     0.536  0.549  0.64   0.622  0.661\n",
      "  0.617  0.785  0.331  0.421  0.449  0.634  0.51   0.529  0.624  0.719\n",
      "  0.534  0.534  0.715  0.8  ]\n",
      "5-           ERR458747\t[ 0.864  0.847  0.548  0.868  0.536  1.     0.775  0.661  0.572  0.558\n",
      "  0.568  0.518  0.193  0.632  0.393  0.605  0.699  0.818  0.615  0.454\n",
      "  0.847  0.845  0.485  0.511]\n",
      "6-           ERR458753\t[ 0.816  0.768  0.598  0.762  0.549  0.775  1.     0.729  0.6    0.596\n",
      "  0.58   0.531  0.217  0.619  0.427  0.624  0.67   0.776  0.62   0.463\n",
      "  0.786  0.79   0.485  0.514]\n",
      "7-           ERR458755\t[ 0.687  0.655  0.528  0.653  0.64   0.661  0.729  1.     0.73   0.725\n",
      "  0.717  0.623  0.238  0.551  0.482  0.757  0.585  0.659  0.752  0.547\n",
      "  0.668  0.669  0.554  0.59 ]\n",
      "8-           ERR458884\t[ 0.62   0.571  0.489  0.571  0.622  0.572  0.6    0.73   1.     0.8    0.886\n",
      "  0.677  0.271  0.433  0.535  0.887  0.596  0.599  0.796  0.583  0.577\n",
      "  0.577  0.611  0.655]\n",
      "9-           ERR458937\t[ 0.601  0.558  0.503  0.553  0.661  0.558  0.596  0.725  0.8    1.     0.78\n",
      "  0.73   0.3    0.438  0.523  0.803  0.564  0.593  0.76   0.619  0.567\n",
      "  0.566  0.647  0.697]\n",
      "10-           ERR458966\t[ 0.611  0.563  0.462  0.565  0.617  0.568  0.58   0.717  0.886  0.78   1.\n",
      "  0.674  0.256  0.448  0.515  0.86   0.588  0.586  0.802  0.577  0.565\n",
      "  0.564  0.621  0.665]\n",
      "11-           ERR459001\t[ 0.547  0.506  0.453  0.516  0.785  0.518  0.531  0.623  0.677  0.73\n",
      "  0.674  1.     0.416  0.418  0.542  0.689  0.535  0.541  0.675  0.704\n",
      "  0.513  0.513  0.726  0.817]\n",
      "12-           ERR459005\t[ 0.214  0.198  0.318  0.209  0.331  0.193  0.217  0.238  0.271  0.3    0.256\n",
      "  0.416  1.     0.151  0.549  0.269  0.293  0.204  0.25   0.252  0.19\n",
      "  0.192  0.287  0.303]\n",
      "13-           ERR459013\t[ 0.607  0.605  0.418  0.613  0.421  0.632  0.619  0.551  0.433  0.438\n",
      "  0.448  0.418  0.151  1.     0.312  0.464  0.491  0.617  0.504  0.364\n",
      "  0.616  0.614  0.397  0.415]\n",
      "14-           ERR459049\t[ 0.427  0.396  0.51   0.411  0.449  0.393  0.427  0.482  0.535  0.523\n",
      "  0.515  0.542  0.549  0.312  1.     0.536  0.511  0.414  0.507  0.385\n",
      "  0.387  0.39   0.418  0.442]\n",
      "15-           ERR459068\t[ 0.641  0.594  0.494  0.597  0.634  0.605  0.624  0.757  0.887  0.803\n",
      "  0.86   0.689  0.269  0.464  0.536  1.     0.613  0.636  0.856  0.597\n",
      "  0.605  0.604  0.624  0.666]\n",
      "16-           ERR459102\t[ 0.707  0.697  0.562  0.714  0.51   0.699  0.67   0.585  0.596  0.564\n",
      "  0.588  0.535  0.293  0.491  0.511  0.613  1.     0.702  0.581  0.439\n",
      "  0.677  0.674  0.481  0.51 ]\n",
      "17-           ERR459124\t[ 0.796  0.781  0.572  0.793  0.529  0.818  0.776  0.659  0.599  0.593\n",
      "  0.586  0.541  0.204  0.617  0.414  0.636  0.702  1.     0.651  0.478\n",
      "  0.804  0.803  0.504  0.532]\n",
      "18-           ERR459128\t[ 0.641  0.594  0.462  0.601  0.624  0.615  0.62   0.752  0.796  0.76\n",
      "  0.802  0.675  0.25   0.504  0.507  0.856  0.581  0.651  1.     0.596\n",
      "  0.604  0.603  0.619  0.658]\n",
      "19-           ERR459140\t[ 0.473  0.445  0.365  0.447  0.719  0.454  0.463  0.547  0.583  0.619\n",
      "  0.577  0.704  0.252  0.364  0.385  0.597  0.439  0.478  0.596  1.     0.454\n",
      "  0.453  0.838  0.742]\n",
      "20-              mut-1a\t[ 0.834  0.907  0.595  0.879  0.534  0.847  0.786  0.668  0.577  0.567\n",
      "  0.565  0.513  0.19   0.616  0.387  0.605  0.677  0.804  0.604  0.454  1.\n",
      "  0.94   0.477  0.504]\n",
      "21-              mut-1b\t[ 0.835  0.91   0.6    0.883  0.534  0.845  0.79   0.669  0.577  0.566\n",
      "  0.564  0.513  0.192  0.614  0.39   0.604  0.674  0.803  0.603  0.453\n",
      "  0.94   1.     0.475  0.504]\n",
      "22-               wt-1a\t[ 0.51   0.47   0.384  0.473  0.715  0.485  0.485  0.554  0.611  0.647\n",
      "  0.621  0.726  0.287  0.397  0.418  0.624  0.481  0.504  0.619  0.838\n",
      "  0.477  0.475  1.     0.78 ]\n",
      "23-               wt-1b\t[ 0.54   0.497  0.407  0.503  0.8    0.511  0.514  0.59   0.655  0.697\n",
      "  0.665  0.817  0.303  0.415  0.442  0.666  0.51   0.532  0.658  0.742\n",
      "  0.504  0.504  0.78   1.   ]\n",
      "min similarity in matrix: 0.151216829337\n",
      "saving labels to: yeast.labels.txt\n",
      "saving distance matrix to: yeast\n"
     ]
    }
   ],
   "source": [
    "!../sourmash compare -o yeast *.sig -k 21"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.15121682933743241, 1.0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Pxd4A3JkefxS4PSLagPWSFgJHRsS/S9oYEben435IVipBGmcmQEQ8IqlJ0t4R8WJPX6CZ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\nE2EzMzMz65acCJuZmZlZt/T/AfeF+x0NQifsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10615f898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "D, labels = fig.load_matrix_and_labels('yeast')\n",
    "D = (D + D.T) / 2.0\n",
    "_ = fig.plot_composite_matrix(D, labels, vmin=0.48)\n",
    "numpy.min(D), numpy.max(D)"
   ]
  }
 ],
 "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": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}