sensor_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# Sensor analysis\n",
    "*Frederik Rogge, 2022*\n",
    "\n",
    "Here, we analyze\n",
    "- correlations between raw fluorescence and neuropil,\n",
    "- signal-to-noise ratio, and\n",
    "- signal intensity\n",
    "\n",
    "for all the different calcium indicators."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import pylab as pl\n",
    "import pandas as pd"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [],
   "source": [
    "base_path = ''\n",
    "\n",
    "# Load data\n",
    "file_dict = {\n",
    "    'local Ribo8s': [\n",
    "        'path/to/file'\n",
    "    ],\n",
    "    'local RiboL18s': [],\n",
    "    'local soma8s': [],\n",
    "    'local 8s': [],\n",
    "    'local 6f': [],\n",
    "    'RO 8s': [],\n",
    "    'RO soma8m': [],\n",
    "    'RO soma8s': [],\n",
    "    'RO RiboL18s': []\n",
    "}\n",
    "\n",
    "data = {}\n",
    "for category in file_dict.keys():\n",
    "    data[category] = {}\n",
    "    for n, path in enumerate(file_dict[category]):\n",
    "        data[category][path] = {}\n",
    "        found = False\n",
    "        for root, dirs, files in os.walk(os.path.join(base_path, path)):\n",
    "            if 'iscell.npy' in files:\n",
    "                data[category][path]['iscell'] = np.load(os.path.join(root, 'iscell.npy'), allow_pickle=True)\n",
    "                data[category][path]['f'] = np.load(os.path.join(root, 'F.npy'), allow_pickle=True)\n",
    "                data[category][path]['fneu'] = np.load(os.path.join(root, 'Fneu.npy'), allow_pickle=True)\n",
    "                data[category][path]['ops'] = np.load(os.path.join(root, 'ops.npy'), allow_pickle=True).item()\n",
    "                data[category][path]['stat'] = np.load(os.path.join(root, 'stat.npy'), allow_pickle=True)\n",
    "                found = True\n",
    "                continue\n",
    "        if not found:\n",
    "            print(f'No files found in {path}')"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['local Ribo8s', 'local RiboL18s', 'local soma8s', 'local 8s', 'local 6f', 'RO 8s', 'RO soma8m', 'RO soma8s', 'RO RiboL18s'])\n"
     ]
    }
   ],
   "source": [
    "print(data.keys())"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Correlation between raw F trace and neuropil Fneu"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [],
   "source": [
    "corr_data = []\n",
    "for i, (category, category_data) in enumerate(data.items()):\n",
    "\n",
    "    corrs = []\n",
    "\n",
    "    for k, v in category_data.items():\n",
    "\n",
    "        # Get data\n",
    "        iscell = v['iscell']\n",
    "        f, fneu = v['f'][np.where(iscell[:, 0] > 0.5, True, False)], v['fneu'][np.where(iscell[:, 0] > 0.5, True, False)]\n",
    "        n_cells = f.shape[0]\n",
    "\n",
    "        # Calculate correlation matrix\n",
    "        corr = np.corrcoef(f, fneu)\n",
    "\n",
    "        # Select values on diagonal of upper right corner\n",
    "        mask = np.concatenate([np.zeros((n_cells, n_cells)), np.eye(n_cells)], axis=1)\n",
    "        mask = np.concatenate([mask, np.zeros((n_cells, corr.shape[0]))]).astype(np.bool_)\n",
    "        corr = corr[mask]\n",
    "\n",
    "        corrs.append(corr)\n",
    "\n",
    "    corr_data.append(np.concatenate(corrs, axis=0))"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Boxplot"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1000x500 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 5))\n",
    "ax.boxplot(corr_data)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.set_xticklabels(list(data.keys()), rotation=45, ha='right')\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Export as dataframe"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9\n",
      "     local Ribo8s  local RiboL18s  local soma8s  local 8s  local 6f     RO 8s  \\\n",
      "0        0.129906        0.139440      0.374689  0.351071  0.532588  0.752728   \n",
      "1        0.219040        0.068841     -0.026433  0.331850  0.633531  0.714407   \n",
      "2        0.200365        0.165378      0.369828  0.203927  0.663480  0.585131   \n",
      "3        0.074990        0.148322      0.366413 -0.231427  0.150578  0.711744   \n",
      "4        0.011309        0.008035      0.514456  0.373647  0.426230 -0.030654   \n",
      "..            ...             ...           ...       ...       ...       ...   \n",
      "895           NaN        0.074685           NaN       NaN       NaN       NaN   \n",
      "896           NaN        0.011644           NaN       NaN       NaN       NaN   \n",
      "897           NaN        0.076891           NaN       NaN       NaN       NaN   \n",
      "898           NaN        0.003863           NaN       NaN       NaN       NaN   \n",
      "899           NaN        0.122967           NaN       NaN       NaN       NaN   \n",
      "\n",
      "     RO soma8m  RO soma8s  RO RiboL18s  \n",
      "0     0.279966   0.590442     0.152500  \n",
      "1     0.162400   0.624404     0.147307  \n",
      "2     0.168760   0.499871     0.109913  \n",
      "3     0.384754   0.171208     0.111707  \n",
      "4     0.175530   0.637259     0.191328  \n",
      "..         ...        ...          ...  \n",
      "895        NaN        NaN          NaN  \n",
      "896        NaN        NaN          NaN  \n",
      "897        NaN        NaN          NaN  \n",
      "898        NaN        NaN          NaN  \n",
      "899        NaN        NaN          NaN  \n",
      "\n",
      "[900 rows x 9 columns]\n"
     ]
    }
   ],
   "source": [
    "corr_dict = {\n",
    "    list(data.keys())[i]: corr_data[i] for i in range(len(list(data.keys())))\n",
    "}\n",
    "\n",
    "df = pd.DataFrame.from_dict(corr_dict, orient='index').T\n",
    "print(df)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Signal-to-noise"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "outputs": [],
   "source": [
    "sig_to_noise_ratios = []\n",
    "for i, (category, category_data) in enumerate(data.items()):\n",
    "\n",
    "    ratios = []\n",
    "\n",
    "    for k, v in category_data.items():\n",
    "\n",
    "        # Get data\n",
    "        iscell = np.where(v['iscell'][:, 0] > 0.5, True, False)\n",
    "        f, fneu = v['f'][iscell], v['fneu'][iscell]\n",
    "        n_cells = f.shape[0]\n",
    "\n",
    "        # Determine signal-to-noise ratio\n",
    "        sig2noise = np.median(f, axis=1) / np.median(fneu, axis=1)\n",
    "\n",
    "        ratios.append(sig2noise)\n",
    "\n",
    "    sig_to_noise_ratios.append(np.concatenate(ratios, axis=0))"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 5))\n",
    "ax.boxplot(sig_to_noise_ratios)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.set_xticklabels(list(data.keys()), rotation=45, ha='right')\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Export as dataframe"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     local Ribo8s  local RiboL18s  local soma8s  local 8s  local 6f     RO 8s  \\\n",
      "0        2.649011        2.200032      2.308235  2.284223  1.908678  2.306697   \n",
      "1        3.479959        5.280091      1.203020  1.201949  5.897404  1.646643   \n",
      "2        3.802286        3.994961      1.504606  3.766989  1.597360  1.911572   \n",
      "3        2.863105        3.539126      1.367524  1.271068  1.537339  1.987660   \n",
      "4        3.522846        4.539333      1.212889  1.069159  1.136939  2.013856   \n",
      "..            ...             ...           ...       ...       ...       ...   \n",
      "895           NaN        5.696030           NaN       NaN       NaN       NaN   \n",
      "896           NaN        7.919357           NaN       NaN       NaN       NaN   \n",
      "897           NaN        3.914871           NaN       NaN       NaN       NaN   \n",
      "898           NaN        2.407279           NaN       NaN       NaN       NaN   \n",
      "899           NaN        4.845216           NaN       NaN       NaN       NaN   \n",
      "\n",
      "     RO soma8m  RO soma8s  RO RiboL18s  \n",
      "0     2.108525   3.682345     1.606347  \n",
      "1     3.003705   2.251680     2.201518  \n",
      "2     1.804916   2.153427     2.954877  \n",
      "3     2.307820   3.597930     1.706964  \n",
      "4     1.974598   1.948625     1.408464  \n",
      "..         ...        ...          ...  \n",
      "895        NaN        NaN          NaN  \n",
      "896        NaN        NaN          NaN  \n",
      "897        NaN        NaN          NaN  \n",
      "898        NaN        NaN          NaN  \n",
      "899        NaN        NaN          NaN  \n",
      "\n",
      "[900 rows x 9 columns]\n"
     ]
    }
   ],
   "source": [
    "signal_to_noise_dict = {\n",
    "    list(data.keys())[i]: sig_to_noise_ratios[i] for i in range(len(list(data.keys())))\n",
    "}\n",
    "\n",
    "df = pd.DataFrame.from_dict(signal_to_noise_dict, orient='index').T\n",
    "print(df)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Signal intensity"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "outputs": [],
   "source": [
    "signal_intensities = []\n",
    "for i, (category, category_data) in enumerate(data.items()):\n",
    "\n",
    "    signals = []\n",
    "\n",
    "    for k, v in category_data.items():\n",
    "\n",
    "        # Get data\n",
    "        iscell = v['iscell']\n",
    "        f = v['f'][np.where(iscell[:, 0] > 0.5, True, False)]\n",
    "        n_cells = f.shape[0]\n",
    "\n",
    "        # Determine signal\n",
    "        signal = np.median(f, axis=1)\n",
    "\n",
    "        signals.append(signal)\n",
    "\n",
    "    signal_intensities.append(np.concatenate(signals, axis=0))"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1000x500 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 5))\n",
    "for i, signals in enumerate(signal_intensities):\n",
    "    ax.scatter(np.ones(signals.shape[0]) * (i + 1), signals)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.set_xticks([i for i in range(len(signal_intensities) + 1)])\n",
    "ax.set_xticklabels([''] + list(data.keys()), rotation=45, ha='right')\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Export as dataframe"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     local Ribo8s  local RiboL18s  local soma8s      local 8s     local 6f  \\\n",
      "0     2621.649902      741.894287   6229.186523  10066.414062  3992.859131   \n",
      "1     3012.158691     1837.432129   3258.779297   3320.561768  4680.397461   \n",
      "2     3257.431152     1891.208130   4423.585938   3793.100830  1465.083740   \n",
      "3     3161.312012     1750.632812   4875.292480   3163.344971  1123.979248   \n",
      "4     1430.717285     2033.769287   3449.231445   2462.277588  3063.548340   \n",
      "..            ...             ...           ...           ...          ...   \n",
      "895           NaN     2124.678711           NaN           NaN          NaN   \n",
      "896           NaN     3654.007324           NaN           NaN          NaN   \n",
      "897           NaN     1127.000732           NaN           NaN          NaN   \n",
      "898           NaN      579.788635           NaN           NaN          NaN   \n",
      "899           NaN     1882.935425           NaN           NaN          NaN   \n",
      "\n",
      "           RO 8s    RO soma8m    RO soma8s  RO RiboL18s  \n",
      "0    4182.645508  3279.364258  4363.741211   277.992554  \n",
      "1    3274.027100  5171.225586  2341.667725   455.415833  \n",
      "2    2489.699219  2868.493164  2076.780762   480.903046  \n",
      "3    3002.262207  3490.296387  5167.886719   320.188660  \n",
      "4    3814.385742  2996.865723  2575.039551   274.825867  \n",
      "..           ...          ...          ...          ...  \n",
      "895          NaN          NaN          NaN          NaN  \n",
      "896          NaN          NaN          NaN          NaN  \n",
      "897          NaN          NaN          NaN          NaN  \n",
      "898          NaN          NaN          NaN          NaN  \n",
      "899          NaN          NaN          NaN          NaN  \n",
      "\n",
      "[900 rows x 9 columns]\n"
     ]
    }
   ],
   "source": [
    "signal_intensities_dict = {\n",
    "    list(data.keys())[i]: signal_intensities[i] for i in range(len(list(data.keys())))\n",
    "}\n",
    "\n",
    "df = pd.DataFrame.from_dict(signal_intensities_dict, orient='index').T\n",
    "print(df)"
   ],
   "metadata": {
    "collapsed": false
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}