PCC_coloc_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 1,
=======
   "execution_count": 3,
>>>>>>> e5564df64c1d51e3698474b5413d7d22b2e2f988
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
<<<<<<< HEAD
      "test1.tif , format = (3, 512, 512)\n",
      "Pearson coef, pvalue: (0.76199767756664705, 0.0)\n",
      "test2.tif , format = (3, 512, 512)\n",
      "Pearson coef, pvalue: (0.74781335454502318, 0.0)\n",
      "test3.tif , format = (3, 512, 512)\n",
      "Pearson coef, pvalue: (0.72028028623932339, 0.0)\n",
      "test4.tif , format = (3, 512, 512)\n",
      "Pearson coef, pvalue: (0.17330807423529637, 0.0)\n",
      "test.tif , format = (3, 512, 512)\n",
      "Pearson coef, pvalue: (0.75068466561840519, 0.0)\n"
=======
      "20170714_SypGFP_msp300_r11_HRP_DAPI_p2s5r_highMag_n2_crop.tif , format = (39, 4, 204, 953)\n",
      "Pearson coef, pvalue: (0.0011667984769677588, 0.60692782808274492)\n"
>>>>>>> e5564df64c1d51e3698474b5413d7d22b2e2f988
     ]
    },
    {
     "data": {
<<<<<<< HEAD
      "image/png": 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PPB7HbDYzNTWlG7Hf+c53aG9vx+/360ax1m1NM5RDoRDnzp1jeHiYvr4+rFYr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\nVWaZ4X5eeF/w9Bnkck/QA7IrWfn9fsePB1VXKvc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RJ4lfh+c6yLNmzcprZrllWSxZsoTz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11060d1d0>"
=======
      "image/png": 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Xu5jQBJvLw3WEpuSFifPGw3FYX2A2ocysJ1wgXROHVQG/jcfZQsKNMjd0c57biGXuLMLN\nGK8QyshD7MKjPu6+2d0XF2lNup7Q97yF0O/0wJ7IQ55ZwCBgWWI/fKeTdXiVEKDPIOyfBkJFoqNl\nLCf0OT9LOA8mn5Msev4zsy8kygWEJuxrLfRjl/roSDtWemuelMLM7iQ0N16bSJsAfC/21aWKhYdT\nPx2bl2Q/YmZHES44Dupq//W+wMyeBL7j7p3dfJHqPOyu3jr/7U0PQO7z4sF+FvCB3s2JiHSVmZ1M\nqMFvJNz9dwKhBWW/ykNa6N2SCVb4lWC5z7s7mfYrhGbK/3T3NT2T446Z2cNF1uULnU+999qd/dTF\n5bzDwmvBdsTPm4nlPGtmDRZepzUwMc01ZrbawqvfPp5IPzFOs9rMbs3158X+jB/E9CfjBVKuqabQ\n+j2cn889yczOK5KPkm8a25O6uSyMJDwS8Qqhu2Gauzd3e6Z3IQ97ojxYuJ+h4LbrrpXpTWqWFCki\nBqBD3P01C894/pJwt+NZwGZ3n21mVxPeSHGVmY0idKyPJdy09DNghLvvMLOlwExC/+RCwrNMD5vZ\npcAJ7v5ZC69EOtPdz+3xlRVJGdXcRIqINx/lrmIPih8nvJ0hd4duLeGmGGJ6nYcHVdcQbrIYa+Fl\nxGXuviTeZDAvb5rcvOYDVblanYjsOgU3kQ6Y2YFm9jThjt1FHh7WH5JorlpPeP4Twp2WyYdbG2Na\nJn7PT28zTbwpYwvhzjYR2Q0KbiIdcPcd7v5+oHzChAlXPvfcc96/f//+hBqcu/vOAQMG9Ad8xowZ\nM+6+++67c8Muuuiii+6///77ly1btqyqqqoql/74448/ftppp50G+OjRo0evW7duXW5YRUVFxUsv\nvfRS7nfuM2fOHK+srPTKyko3s+d6YVOI7FMU3ERK4O6vnHLKKdTX1zNkyBCam0PFrbm5mcGDBwOQ\nyWQIcSpobGwkk8mQyWRobGxsl54/zfbt29myZQuDBrWvuNXU1LB8+XKWL18O4aFnEemAgptIERbe\ntjAgfn/nokWLOO6445gyZQq1taGbrLa2lqlTw/+nnDJlCnV1dbS0tLBmzRoaGhoYO3Ysw4YNo6ys\njCVLluDuzJs3r800uXnNnz+fiRMnoi43kd2n59xEihsG1Fr49/YHTJo0idNPP50PfehDnHPOOcyd\nO5cjjzyS++67D4DRo0dzzjnnMGrUKPr06cNtt93GgQceCMDtt9/OhRdeyLZt25g8eTKTJ4d/Zn/x\nxRdz/vnnk81mOeyww6irq+ulVRVJFz0KIFK6veJgMbMV7l7Z2/kQ2ZupWVJERFJHwU1ERFJHwU1E\nRFJHwU1ERFJHwU1ERFJHwU1ERFJHwU1ERFJHwU1ERFJHwU1ERFJHwU1ERFJHwU1ERFJHwU1ERFJH\nwU1ERFJHwU1ERFJHwU1ERFJHwU1ERFJHwU1ERFJHwU1ERFJHwU1ERFJHwU1ERFJHwU1ERFJHwU1E\nRFJHwU1ERFJHwU1ERFJHwU1ERFJHwU1ERFJHwU2kCDMbbmY/N7NVZrbyG9/4BgCbN29m0qRJHHvs\nsUyaNImXX365dZobb7yRbDbLyJEjeeSRR1rTV6xYwfHHH082m2XmzJm4OwAtLS2ce+65ZLNZxo0b\nx9q1a3t0HUXSSsFNpLjtwOfdfRQw/rbbbmPVqlXMnj2bqqoqGhoaqKqqYvbs2QCsWrWKuro6Vq5c\nSX19PZdeeik7duwA4JJLLuGOO+6goaGBhoYG6uvrAZg7dy4DBw5k9erVXH755Vx11VW9tKoi6aLg\nJlKEuze7+2/i91ff85730NTUxIIFC6iurgagurqaBx98EIAFCxYwffp0+vbty9FHH002m2Xp0qU0\nNzezdetWxo8fj5lxwQUXtJkmN69p06axePHi1lqdiOw6BTeREpjZUU899RTjxo1jw4YNDBs2DICh\nQ4eyYcMGAJqamhg+fHjrNOXl5TQ1NdHU1ER5eXm79Pxp+vTpQ//+/dm0aVO75c+ZM4fKykoqKysB\nDt9DqymSGn16OwMiezsz6wf88JZbbqGsrCx/GGa2x/NQU1NDTU1Nbpkb9/gCRfZxqrmJdMDMDgJ+\nCNxz1llnATBkyBCam5sBaG5uZvDgwQBkMhnWrVvXOm1jYyOZTIZMJkNjY2O79Pxptm/fzpYtWxg0\naFAPrJlIuim4iRRhoUo2F/g/d/9aLn3KlCnU1tYCUFtby9SpU1vT6+rqaGlpYc2aNTQ0NDB27FiG\nDRtGWVkZS5Yswd2ZN29em2ly85o/fz4TJ07skZqgSOq5uz766FPgA3wUcOC3wNPve9/7/Kc//alv\n3LjRJ06c6Nls1quqqnzTpk2eM2vWLK+oqPARI0b4woULW9OXLVvmo0eP9oqKCp8xY4bv3LnT3d23\nbdvm06ZN82OOOcbHjBnjzz//vHcGWN7b20Yfffb2j7nrziyREu0VB4uZrXD3yt7Oh8jeTM2SIiKS\nOgpuIiKSOgpuIiKSOgpuIiKSOgpuIiKSOgpuklpmdqCZfcbMvmJmH8kbdm1v5UtE9jwFN0mz/wJO\nBjYBt5rZ1xLDzuqdLIlIT1BwkzQb6+5/7+63AOOAfmb2gJn1BfQaEJEUU3CTNDs498Xdt7t7DfA0\n8CjQr9dyJSJ7nIKbpNlyMzs1meDuXwb+GziqV3IkIj1Cr98SKd1ecbDo9VsindP/c5P9gpl9mFBb\nay3z7j6v1zIkInuUgpuknpndDRxD6G/bEZMdUHATSSkFN9kfVAKjXG3wIvsN3VAi+4PngKG9nQkR\n6TmquUlqmdlPCM2PhwKrzGwp0JIb7u5TeitvIrJnKbhJmv0YGAI8kZd+EtDc89kRkZ6i4CZpNhW4\nxt2fTSaa2WbgBmBur+RKRPY49blJmg3JD2wAMe2ons+OiPQUBTdJswEdDHtnj+VCRHqcgpuk2XIz\n+8f8RDP7NLCiF/IjIj1Er9+S1DKzIcCPgDd5O5hVEl6ofKa7r+/iLPeKg0Wv3xLpnIKbpJ6ZnQK8\nN/5c6e6P7uKs9oqDRcFNpHMKbiKl2ysOFgU3kc6pz01ERFJHwU2kCDO708xeNLPncmmbN29m0qRJ\nHHvssUyaNImXX365dfwbb7yRbDbLyJEjeeSRR1rTV6xYwfHHH082m2XmzJnkWktaWlo499xzyWaz\njBs3jrVr1/bcyomknIKbSHF3AW3+2ens2bOpqqqioaGBqqoqZs+eDcCqVauoq6tj5cqV1NfXc+ml\nl7JjR/gHBJdccgl33HEHDQ0NNDQ0UF9fD8DcuXMZOHAgq1ev5vLLL+eqq67q0ZUTSTMFN5Ei3P1x\nYHMybcGCBVRXVwNQXV3Ngw8+2Jo+ffp0+vbty9FHH002m2Xp0qU0NzezdetWxo8fj5lxwQUXtJkm\nN69p06axePFi1Acu0j0U3ES6YMOGDQwbNgyAoUOHsmHDBgCampoYPnx463jl5eU0NTXR1NREeXl5\nu/T8afr06UP//v3ZtGlTweXOmTOHyspKKisrAQ7fA6smkip6t6TILjIzzKxHllVTU0NNTU1uuRt7\nZKEi+zDV3ES6YMiQITQ3h38o0NzczODBgwHIZDKsW7eudbzGxkYymQyZTIbGxsZ26fnTbN++nS1b\ntjBo0KCeWhWRVFNwE+mCKVOmUFtbC0BtbS1Tp05tTa+rq6OlpYU1a9bQ0NDA2LFjGTZsGGVlZSxZ\nsgR3Z968eW2myc1r/vz5TJw4scdqgiKp5+766KNPgQ9wL+H/vr0FNH73u9/1jRs3+sSJEz2bzXpV\nVZVv2rTJc2bNmuUVFRU+YsQIX7hwYWv6smXLfPTo0V5RUeEzZszwnTt3urv7tm3bfNq0aX7MMcf4\nmDFj/Pnnn/dSAMt7e9voo8/e/tEbSkRKt1ccLHpDiUjn1CwpIiKpo+AmIiKpo+AmIiKpo+AmIiKp\no+AmIiKpo+AmIiKpo+AmIiKpo+AmIiKpo+AmIiKpo+AmIiKpo+AmIiKpo+AmIiKpo+AmIiKpo+Am\nIiKpo+AmIiKpo+AmIiKpo+AmIiKpo+AmIiKpo+AmIiKpo+AmIiKpo+AmIiKpo+AmIiKpo+AmIiKp\no+AmIiKpo+Amsg+4+eabMTPMDODE3s6PyN5OwU2kl9XX1zNy5Eiy2SyzZ89uN/zmm2/miiuu6IWc\niey7zN17Ow8i+4puP1h27NjBiBEjWLRoEeXl5YwZM4Z7772XUaNGtY4Ta2ttM+LePlFEWqnmJtKL\nli5dSjabpaKigoMPPpjp06ezYMGC3s6WyD5PwU2kFzU1NTF8+PDW3+Xl5TQ1NfVijkTSQc2SIiUy\ns+eAN7p5tgOBMuBP8fdhQD/gz4lx2t1AomZJkY716e0MiOxD3nD3yu6coZl9CPiSu388/r4GwN1v\n7GCa5d2ZB5E0UrOkSO9aBhxrZkeb2cHAdODHvZwnkX2eam4ivcjdt5vZZcAjwIHAne6+spezJbLP\nU3ATKd2cPTFTd18ILOztfIikiW4oERGR1FGfm4iIpI6Cm+xXzOxOM3sx3tafSzvMzBaZWUP8OzAx\n7BozW21mvzezjyfSTzSzZ+OwWy2+RsTM+prZD2L6k2Z2VDfm/dSYj9VmdnV3zVckjRTcZH9zF3Bq\nXtrVwGJ3PxZYHH9jZqMIdy+OjtPcbmYHxmm+DfwjcGz85OZ5MfCyu2eBrwP/0R2Zjsu9DZgMjAI+\nGfMnIgUouMl+xd0fBzbnJU8FauP3WuATifQ6d29x9zXAamCsmQ0Dytx9iYdO63l50+TmNR+oytXq\ndtNYYLW7/9Hd3wTq4rJEpAAFNxEY4u7N8ft6YEj8ngHWJcZrjGmZ+D0/vc007r4d2AIM6oY8FsuL\niBSg4CaSEGtiuoVYZB+n4CYCG2JTI/HvizG9CRieGK88pjXF7/npbaYxsz5Af2BTN+SxWF5EpAAF\nN5Hwuqvq+L0aWJBInx7vgDyacOPI0tiEudXMxsf+tAvypsnNaxrwqHfPw6R6TZdIF+gNJbJfMbN7\ngQnA4WbWCHwRmA3cZ2YXE97Ofw6Au680s/uAVcB2YIa774izupRw5+U7gYfjB2AucLeZrSbcuDK9\nO/Kt13SJdI3eUCIiIqmjZkkREUkdBTcREUkdBTcREUkdBTcREUkdBTcREUkdBTeRbmRmQ82szsye\nN7MVZrbQzGrM7KEi418W3/LvZnZ4T+dXJK0U3ES6SXyg+0fAY+5+jLufCFzD2++qLORXwMcIz9eJ\nSDdRcBPpPqcAb7n7d3IJ7v4M8ATQz8zmm9nvzOye3H8KcPen3H1t72RXJL0U3ES6z3uBFUWGfQD4\nHOF/sVUAH+mpTInsjxTcRHrGUndvdPedwNPAUb2cH5FUU3AT6T4rgROLDGtJfN+B3usqskcpuIl0\nn0eBvmZWk0swsxOAk3ovSyL7JwU3kW4S/7XNmcDH4qMAK4EbCf/duyAzmxn/O0E58Fsz+27P5FYk\n3fRfAUREJHVUcxMRkdRRcBMRkdRRcBMRkdRRcBMRkdRRcBMRkdRRcBMRkdRRcBMRkdRRcBMRkdT5\nfxi5MAsBeku6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11450d590>"
>>>>>>> e5564df64c1d51e3698474b5413d7d22b2e2f988
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
<<<<<<< HEAD
    "# Pearson correlation coefficient for quantifying colocalisation in images\n",
=======
    "# Pearson correlation coefficient for quantifying colocalisation of diffuse signal in images\n",
>>>>>>> e5564df64c1d51e3698474b5413d7d22b2e2f988
    "\n",
    "import os\n",
    "import tifffile\n",
    "import pandas as pd\n",
    "from scipy import stats\n",
<<<<<<< HEAD
=======
    "import random\n",
>>>>>>> e5564df64c1d51e3698474b5413d7d22b2e2f988
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline \n",
    "\n",
    "# specify directory of images\n",
<<<<<<< HEAD
    "indir = ('/Users/joshtitlow/tmp/coloc/')\n",
=======
    "indir = ('/Users/joshtitlow/tmp/coloc/syp_coloc/')\n",
>>>>>>> e5564df64c1d51e3698474b5413d7d22b2e2f988
    "infiles = os.listdir(indir)\n",
    "\n",
    "# create a figure and start counting\n",
    "fig = plt.figure(figsize = (10,10))\n",
    "n = 1\n",
    "\n",
    "# create a df and a list to store filename and PCC values\n",
    "df = pd.DataFrame()\n",
    "filename = []\n",
    "PCC = []\n",
    "\n",
    "\n",
<<<<<<< HEAD
    "# Convert .ome.tiff to .tiff \n",
=======
    "# analyse each file in the directory\n",
>>>>>>> e5564df64c1d51e3698474b5413d7d22b2e2f988
    "for file in infiles: \n",
    "    if file.endswith('.tif'):\n",
    "        if not file.startswith('._'):\n",
    "            \n",
    "            # read image file and show data structure\n",
    "            im = tifffile.imread(os.path.join(indir, file))\n",
    "            print file, ', format =', im.shape\n",
    "            filename.append(file)\n",
    "            \n",
<<<<<<< HEAD
    "            # load channels into a flattened array\n",
    "            ch1 = im[0].flatten()\n",
    "            ch2 = im[1].flatten()\n",
    "\n",
=======
    "            # choose focal plane and load channels into a flattened array\n",
    "            focus = im.shape[0]/2\n",
    "            ch1 = im[focus, 1].flatten()\n",
    "            ch2 = im[focus, 2].flatten()\n",
    "            ch1_random = random.shuffle(ch1)\n",
    "            \n",
>>>>>>> e5564df64c1d51e3698474b5413d7d22b2e2f988
    "            # Perform pearson correlation analysis (returns coefficient and p-value)\n",
    "            pearson = stats.pearsonr(ch1, ch2)\n",
    "            print 'Pearson coef, pvalue:', pearson\n",
    "            PCC.append(pearson[0])\n",
    "        \n",
    "            # plot the data\n",
    "            #ax = plt.subplot(111)\n",
    "            ax = fig.add_subplot(5,5,n)\n",
    "            ax.scatter(ch1,ch2, c='black', alpha=0.5)\n",
    "            ax.set_title(file)\n",
    "            ax.set_xlabel('Ch1')\n",
    "            ax.set_ylabel('Ch2')\n",
    "            ax.spines['right'].set_visible(False)\n",
    "            ax.spines['top'].set_visible(False)\n",
<<<<<<< HEAD
    "            plt.xlim(0, 65000)\n",
    "            plt.ylim(0, 65000)\n",
=======
    "            plt.xlim(0, 10000)\n",
    "            plt.ylim(0, 30000)\n",
>>>>>>> e5564df64c1d51e3698474b5413d7d22b2e2f988
    "            \n",
    "            # iterate\n",
    "            n+=1\n",
    "\n",
    "# plot the data and save results\n",
    "plt.tight_layout()\n",
    "\n",
<<<<<<< HEAD
    "plt.savefig('PCC_figs.png')\n",
=======
    "#plt.savefig('PCC_figs.png')\n",
>>>>>>> e5564df64c1d51e3698474b5413d7d22b2e2f988
    "plt.show(fig)\n",
    "\n",
    "\n",
    "df['filename'] = filename\n",
    "df['PCC'] = PCC\n",
<<<<<<< HEAD
    "df.to_csv('PCC_values.csv', index=False)"
=======
    "#df.to_csv('PCC_values.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'im' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-afbdf33aed57>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'im' is not defined"
     ]
    }
   ],
   "source": [
    "im.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "focus = im.shape[0]/2\n",
    "focus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 24,  22,  24, ..., 111,  45,  53],\n",
       "       [ 18,  28,  20, ...,  32,  63,  57],\n",
       "       [ 20,  26,  23, ...,  87,  26,  42],\n",
       "       ..., \n",
       "       [ 21,  14,  17, ...,  74,  96,  98],\n",
       "       [ 25,  19,  30, ...,  63,  86,  49],\n",
       "       [ 22,  27,  23, ...,  55,  51,  61]], dtype=uint16)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "im[18, 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([344,  62, 225, ..., 146,  63, 103], dtype=uint16)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ch1"
>>>>>>> e5564df64c1d51e3698474b5413d7d22b2e2f988
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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