Explained_variance_fits.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "58b06f91",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import seaborn as sns\n",
    "sns.set(font_scale=1.5, style='ticks')\n",
    "import pandas as pd\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline\n",
    "\n",
    "hits_label = ['jGCaMP8f', 'jGCaMP8m', 'jGCaMP8s', 'jGCaMP7f', 'XCaMP-Gf', 'GCaMP6s', 'GCaMP6f', 'TG-GCaMP6s', 'TG-GCaMP6f']\n",
    "hits_colors = ['#0000ff',  '#ff0000', '#666666', '#00ff00',  '#0099ff',  '#FFD670',  '#FFD670',  '#FFD670',   '#FFD670']\n",
    "\n",
    "sensor6_df = pd.read_csv('GCaMP8_exported_ROIs_s2f_full/GCaMP6s_ev.csv')\n",
    "sensor8_df = pd.read_csv('GCaMP8_exported_ROIs_s2f_full/GCaMP8s_ev.csv')\n",
    "sensor_df = pd.concat([sensor8_df, sensor6_df])\n",
    "sensor8_df_sub = sensor8_df[sensor8_df['Cell'].str.contains('jGCaMP8')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "08fad612-3a30-4bd4-9fc0-761cc03fe70a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>EV_s</th>\n",
       "      <th>EV_l</th>\n",
       "      <th>EV_diff</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cell</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>GCaMP6f</th>\n",
       "      <td>0.851359</td>\n",
       "      <td>0.629933</td>\n",
       "      <td>0.221425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GCaMP6s</th>\n",
       "      <td>0.923782</td>\n",
       "      <td>0.822381</td>\n",
       "      <td>0.101401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TG-GCaMP6f</th>\n",
       "      <td>0.773050</td>\n",
       "      <td>0.649590</td>\n",
       "      <td>0.129884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TG-GCaMP6s</th>\n",
       "      <td>0.716795</td>\n",
       "      <td>0.678078</td>\n",
       "      <td>0.079626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XCaMP-Gf</th>\n",
       "      <td>0.806587</td>\n",
       "      <td>0.774985</td>\n",
       "      <td>0.031602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jGCaMP7f</th>\n",
       "      <td>0.833249</td>\n",
       "      <td>0.762958</td>\n",
       "      <td>0.070291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jGCaMP8f</th>\n",
       "      <td>0.790561</td>\n",
       "      <td>0.732378</td>\n",
       "      <td>0.058183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jGCaMP8m</th>\n",
       "      <td>0.865567</td>\n",
       "      <td>0.849915</td>\n",
       "      <td>0.015652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jGCaMP8s</th>\n",
       "      <td>0.798567</td>\n",
       "      <td>0.783303</td>\n",
       "      <td>0.015264</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                EV_s      EV_l   EV_diff\n",
       "Cell                                    \n",
       "GCaMP6f     0.851359  0.629933  0.221425\n",
       "GCaMP6s     0.923782  0.822381  0.101401\n",
       "TG-GCaMP6f  0.773050  0.649590  0.129884\n",
       "TG-GCaMP6s  0.716795  0.678078  0.079626\n",
       "XCaMP-Gf    0.806587  0.774985  0.031602\n",
       "jGCaMP7f    0.833249  0.762958  0.070291\n",
       "jGCaMP8f    0.790561  0.732378  0.058183\n",
       "jGCaMP8m    0.865567  0.849915  0.015652\n",
       "jGCaMP8s    0.798567  0.783303  0.015264"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sensor_df.groupby('Cell').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "364c2eba-53f5-430a-91f7-73b418bb54f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>EV_s</th>\n",
       "      <th>EV_l</th>\n",
       "      <th>EV_diff</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cell</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>GCaMP6f</th>\n",
       "      <td>0.158858</td>\n",
       "      <td>0.218959</td>\n",
       "      <td>0.116284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GCaMP6s</th>\n",
       "      <td>0.072502</td>\n",
       "      <td>0.085215</td>\n",
       "      <td>0.047652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TG-GCaMP6f</th>\n",
       "      <td>0.133178</td>\n",
       "      <td>0.130459</td>\n",
       "      <td>0.103242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TG-GCaMP6s</th>\n",
       "      <td>0.217331</td>\n",
       "      <td>0.145288</td>\n",
       "      <td>0.071503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XCaMP-Gf</th>\n",
       "      <td>0.188200</td>\n",
       "      <td>0.175970</td>\n",
       "      <td>0.024614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jGCaMP7f</th>\n",
       "      <td>0.147217</td>\n",
       "      <td>0.133016</td>\n",
       "      <td>0.053728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jGCaMP8f</th>\n",
       "      <td>0.124319</td>\n",
       "      <td>0.117809</td>\n",
       "      <td>0.034785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jGCaMP8m</th>\n",
       "      <td>0.121710</td>\n",
       "      <td>0.121622</td>\n",
       "      <td>0.014620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jGCaMP8s</th>\n",
       "      <td>0.189954</td>\n",
       "      <td>0.196775</td>\n",
       "      <td>0.018317</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                EV_s      EV_l   EV_diff\n",
       "Cell                                    \n",
       "GCaMP6f     0.158858  0.218959  0.116284\n",
       "GCaMP6s     0.072502  0.085215  0.047652\n",
       "TG-GCaMP6f  0.133178  0.130459  0.103242\n",
       "TG-GCaMP6s  0.217331  0.145288  0.071503\n",
       "XCaMP-Gf    0.188200  0.175970  0.024614\n",
       "jGCaMP7f    0.147217  0.133016  0.053728\n",
       "jGCaMP8f    0.124319  0.117809  0.034785\n",
       "jGCaMP8m    0.121710  0.121622  0.014620\n",
       "jGCaMP8s    0.189954  0.196775  0.018317"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sensor_df.groupby('Cell').std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "35498825-390a-4b5f-af80-e0dfeb94435b",
   "metadata": {},
   "outputs": [],
   "source": [
    "EV_s_8f = sensor_df[sensor_df['Cell'].str.contains('jGCaMP8')]\n",
    "EV_s_other = sensor_df[~sensor_df['Cell'].str.contains('jGCaMP8')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "8ae5f108-e572-4528-88b2-6e614be3b161",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import ranksums, mannwhitneyu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "1538d9aa-d3e5-4cf5-be0d-7212c9d39efb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MannwhitneyuResult(statistic=4253.0, pvalue=1.2643051516742e-06)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mannwhitneyu(EV_s_8f['EV_l'], EV_s_other['EV_l'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "01e72ef7-aa74-4cf8-8d3b-c22d2e9773ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.7137694432073651, 0.7907900070630913)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "EV_s_other['EV_l'].mean(), EV_s_8f['EV_l'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "7ca0e633-c4aa-4916-ac85-1ed11f0e646e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.015855690430444217, 0.013858814500660161)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "EV_s_other['EV_l'].std()/np.sqrt(len(EV_s_other)), EV_s_8f['EV_l'].std()/np.sqrt(len(EV_s_8f))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e48819a9-174c-43f1-ae03-656cde54751a",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Figure B: Explained variance -- linear vs sigmoid model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0be3742b-f84f-485b-ba5f-365fba14d81e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fd8fc207",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(4, 3))\n",
    "idx = sensor8_df_sub['Type']\n",
    "plt.scatter(sensor8_df_sub['EV_l'][idx], sensor8_df_sub['EV_s'][idx], c='k')\n",
    "plt.scatter(sensor8_df_sub['EV_l'][~idx], sensor8_df_sub['EV_s'][~idx], edgecolors='k', facecolors='none')\n",
    "plt.plot([0, 1], [0, 1], '--r')\n",
    "sns.despine()\n",
    "plt.xlim([0, 1])\n",
    "plt.ylim([0, 1])\n",
    "plt.xlabel('EV linear model')\n",
    "plt.ylabel('EV sigmoid model')\n",
    "sns.despine()\n",
    "plt.savefig('figures/EV_linear_sigmoid.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1765924f-fe3c-497d-ae45-0a7772452083",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Figure C: Variance explained (sigmoid - linear)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "741f20aa",
   "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": [
    "sns.swarmplot(data=sensor_df, \n",
    "              x='Cell', \n",
    "              y='EV_diff', \n",
    "              order = hits_label, \n",
    "              size=3,\n",
    "              alpha=0.8,\n",
    "              color='darkgray',\n",
    "              edgecolor = None,\n",
    "              linewidth=0)\n",
    "ax = sns.boxplot(data=sensor_df, \n",
    "                 x='Cell', \n",
    "                 y='EV_diff',\n",
    "                 order = hits_label, \n",
    "                 showfliers=False, \n",
    "                 linewidth=2,\n",
    "                 width=.5,\n",
    "                 palette = hits_colors)\n",
    "\n",
    "sns.despine()\n",
    "ax.set_xticklabels(ax.get_xticklabels(),rotation=45)\n",
    "plt.ylim([-0.03, 0.42])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9d07cf3c-4731-46a3-a5eb-d6f9396b4eb2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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