scimap-tutorial-getting-started.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Getting Started with Scimap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nCreated on Fri Jun 26 23:11:32 2020\\n@author: Ajit Johnson Nirmal\\nScimap Getting Started tutorial\\n'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#!/usr/bin/env python3\n",
    "# -*- coding: utf-8 -*-\n",
    "\"\"\"\n",
    "Created on Fri Jun 26 23:11:32 2020\n",
    "@author: Ajit Johnson Nirmal\n",
    "Scimap Getting Started tutorial\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Before you start make sure you have installed the following packages\n",
    "# pip install scimap\n",
    "# pip install scanpy\n",
    "# pip install leidenalg\n",
    "# pip install PyQt5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tutorial material"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can download the material for this tutorial from the following [link:](https://www.dropbox.com/s/rra13zir52o9hio/getting_started%20and%20phenotyping.zip?dl=0)  \n",
    "The presentation files are available [here:](https://github.com/ajitjohnson/Jupyter-Notebooks/blob/master/tutorials/scimap_tutorial/getting_started%20and%20phenotyping/scimap_tutorial.pdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tutorial video"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe width=\"450\" height=\"250\" src=\"https://www.youtube.com/embed/knh5elRksUk\" frameborder=\"0\" allow=\"accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import HTML\n",
    "HTML('<iframe width=\"450\" height=\"250\" src=\"https://www.youtube.com/embed/knh5elRksUk\" frameborder=\"0\" allow=\"accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe>')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load necessary libraries\n",
    "import sys\n",
    "import os\n",
    "import anndata as ad\n",
    "import pandas as pd\n",
    "import scanpy as sc\n",
    "import seaborn as sns; sns.set(color_codes=True)\n",
    "\n",
    "# Import Scimap\n",
    "import scimap as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set the working directory\n",
    "os.chdir (\"/Users/aj/Desktop/scimap_tutorial/\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load data using AnnData"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load data\n",
    "data = pd.read_csv ('counts_table.csv') # Counts matrix\n",
    "meta = pd.read_csv ('meta_data.csv') # Meta data like x and y coordinates \n",
    "\n",
    "# combine the data and metadata file to generate the AnnData object\n",
    "adata = ad.AnnData (data)\n",
    "adata.obs = meta"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print adata to check for it's content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 4825 × 48\n",
       "    obs: 'X_centroid', 'Y_centroid', 'Area', 'MajorAxisLength', 'MinorAxisLength', 'Eccentricity', 'Solidity', 'Extent', 'Orientation'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X_centroid</th>\n",
       "      <th>Y_centroid</th>\n",
       "      <th>Area</th>\n",
       "      <th>MajorAxisLength</th>\n",
       "      <th>MinorAxisLength</th>\n",
       "      <th>Eccentricity</th>\n",
       "      <th>Solidity</th>\n",
       "      <th>Extent</th>\n",
       "      <th>Orientation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>511.555556</td>\n",
       "      <td>9.846154</td>\n",
       "      <td>117</td>\n",
       "      <td>14.532270</td>\n",
       "      <td>10.273628</td>\n",
       "      <td>0.707261</td>\n",
       "      <td>0.959016</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>-0.695369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>579.330097</td>\n",
       "      <td>9.398058</td>\n",
       "      <td>103</td>\n",
       "      <td>16.056286</td>\n",
       "      <td>8.776323</td>\n",
       "      <td>0.837396</td>\n",
       "      <td>0.903509</td>\n",
       "      <td>0.613095</td>\n",
       "      <td>1.115707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>630.958333</td>\n",
       "      <td>12.883333</td>\n",
       "      <td>120</td>\n",
       "      <td>15.222005</td>\n",
       "      <td>10.310756</td>\n",
       "      <td>0.735653</td>\n",
       "      <td>0.975610</td>\n",
       "      <td>0.681818</td>\n",
       "      <td>0.151616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>745.194631</td>\n",
       "      <td>16.275168</td>\n",
       "      <td>149</td>\n",
       "      <td>14.380200</td>\n",
       "      <td>13.404759</td>\n",
       "      <td>0.362027</td>\n",
       "      <td>0.967532</td>\n",
       "      <td>0.662222</td>\n",
       "      <td>-0.270451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>657.173653</td>\n",
       "      <td>18.035928</td>\n",
       "      <td>167</td>\n",
       "      <td>17.675831</td>\n",
       "      <td>12.110106</td>\n",
       "      <td>0.728428</td>\n",
       "      <td>0.943503</td>\n",
       "      <td>0.695833</td>\n",
       "      <td>-0.810890</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4820</th>\n",
       "      <td>559.597403</td>\n",
       "      <td>1091.577922</td>\n",
       "      <td>154</td>\n",
       "      <td>18.150307</td>\n",
       "      <td>11.683288</td>\n",
       "      <td>0.765281</td>\n",
       "      <td>0.900585</td>\n",
       "      <td>0.570370</td>\n",
       "      <td>-0.342315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4821</th>\n",
       "      <td>619.983871</td>\n",
       "      <td>1092.959677</td>\n",
       "      <td>248</td>\n",
       "      <td>21.734414</td>\n",
       "      <td>15.565820</td>\n",
       "      <td>0.697912</td>\n",
       "      <td>0.864111</td>\n",
       "      <td>0.551111</td>\n",
       "      <td>1.432242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4822</th>\n",
       "      <td>583.317073</td>\n",
       "      <td>1093.573171</td>\n",
       "      <td>82</td>\n",
       "      <td>12.060039</td>\n",
       "      <td>9.539789</td>\n",
       "      <td>0.611784</td>\n",
       "      <td>0.964706</td>\n",
       "      <td>0.630769</td>\n",
       "      <td>0.203023</td>\n",
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       "    <tr>\n",
       "      <th>4823</th>\n",
       "      <td>607.064394</td>\n",
       "      <td>1101.583333</td>\n",
       "      <td>264</td>\n",
       "      <td>22.549494</td>\n",
       "      <td>15.905321</td>\n",
       "      <td>0.708858</td>\n",
       "      <td>0.882943</td>\n",
       "      <td>0.661654</td>\n",
       "      <td>0.691838</td>\n",
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       "    <tr>\n",
       "      <th>4824</th>\n",
       "      <td>641.592486</td>\n",
       "      <td>1100.132948</td>\n",
       "      <td>346</td>\n",
       "      <td>23.149806</td>\n",
       "      <td>19.375564</td>\n",
       "      <td>0.547257</td>\n",
       "      <td>0.945355</td>\n",
       "      <td>0.791762</td>\n",
       "      <td>-1.390516</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4825 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      X_centroid   Y_centroid  Area  MajorAxisLength  MinorAxisLength  \\\n",
       "0     511.555556     9.846154   117        14.532270        10.273628   \n",
       "1     579.330097     9.398058   103        16.056286         8.776323   \n",
       "2     630.958333    12.883333   120        15.222005        10.310756   \n",
       "3     745.194631    16.275168   149        14.380200        13.404759   \n",
       "4     657.173653    18.035928   167        17.675831        12.110106   \n",
       "...          ...          ...   ...              ...              ...   \n",
       "4820  559.597403  1091.577922   154        18.150307        11.683288   \n",
       "4821  619.983871  1092.959677   248        21.734414        15.565820   \n",
       "4822  583.317073  1093.573171    82        12.060039         9.539789   \n",
       "4823  607.064394  1101.583333   264        22.549494        15.905321   \n",
       "4824  641.592486  1100.132948   346        23.149806        19.375564   \n",
       "\n",
       "      Eccentricity  Solidity    Extent  Orientation  \n",
       "0         0.707261  0.959016  0.750000    -0.695369  \n",
       "1         0.837396  0.903509  0.613095     1.115707  \n",
       "2         0.735653  0.975610  0.681818     0.151616  \n",
       "3         0.362027  0.967532  0.662222    -0.270451  \n",
       "4         0.728428  0.943503  0.695833    -0.810890  \n",
       "...            ...       ...       ...          ...  \n",
       "4820      0.765281  0.900585  0.570370    -0.342315  \n",
       "4821      0.697912  0.864111  0.551111     1.432242  \n",
       "4822      0.611784  0.964706  0.630769     0.203023  \n",
       "4823      0.708858  0.882943  0.661654     0.691838  \n",
       "4824      0.547257  0.945355  0.791762    -1.390516  \n",
       "\n",
       "[4825 rows x 9 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.obs # prints the meta data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[16640.564  ,   719.6325 ,   527.7094 , ...,  1085.735  ,\n",
       "          218.54701,  3170.47   ],\n",
       "       [16938.3    ,   686.5534 ,   469.30096, ...,  1075.6407 ,\n",
       "          164.48544,  3116.767  ],\n",
       "       [16243.542  ,   819.4167 ,   604.39166, ...,  1164.3917 ,\n",
       "          227.74167,  3156.1084 ],\n",
       "       ...,\n",
       "       [28656.256  ,   878.2561 ,   585.3293 , ...,  1233.183  ,\n",
       "         1243.5488 ,  3194.195  ],\n",
       "       [22054.818  ,   685.8485 ,   424.85226, ...,  1031.2424 ,\n",
       "          313.32574,  3038.8105 ],\n",
       "       [23992.854  ,   850.25146,   529.89886, ...,  1000.5578 ,\n",
       "          285.98267,  3087.3005 ]], dtype=float32)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.X # prints the counts table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DNA1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BG1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BG2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BG3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DNA2</th>\n",
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: []\n",
       "Index: [DNA1, BG1, BG2, BG3, DNA2]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.var[0:5] # prints the first 5 channel or marker names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You would have noticed that\n",
    "- the data is not in log scale\n",
    "- All the DNA channels are there\n",
    "- The background channels are there\n",
    "If we diretly perform clustering or any other type of analysis, the above mentioned factors may affect the results and so it is recommended to remove them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load data using scimap's helper function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use this if the single-cell data was generated using **mcmicro pipeline**. With this function though many of the above limitations can be imediately addressed. By default it removes DNA channels and you can pass any channel name into `drop_markers` parameter inorder to not import them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading mcmicro_output.csv\n"
     ]
    }
   ],
   "source": [
    "image_path = ['/Users/aj/Desktop/scimap_tutorial/mcmicro_output.csv']\n",
    "adata = sm.pp.mcmicro_to_scimap (image_path, drop_markers = [\"PERK\", \"NOS2\",\"BG1\",\"BG2\",\"BG3\",\"ACTIN\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check adata contents now as we did previously"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 4825 × 30\n",
       "    obs: 'X_centroid', 'Y_centroid', 'Area', 'MajorAxisLength', 'MinorAxisLength', 'Eccentricity', 'Solidity', 'Extent', 'Orientation', 'imageid'\n",
       "    uns: 'all_markers'"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6.3674684, 6.4287267, 7.3826084, ..., 6.990933 , 5.3915663,\n",
       "        8.061951 ],\n",
       "       [6.340171 , 6.094227 , 7.339796 , ..., 6.981601 , 5.1088834,\n",
       "        8.044872 ],\n",
       "       [6.503502 , 6.3549495, 7.4734573, ..., 7.0608125, 5.4325933,\n",
       "        8.057412 ],\n",
       "       ...,\n",
       "       [6.5583014, 6.660794 , 7.4199724, ..., 7.1181645, 7.1265283,\n",
       "        8.069404 ],\n",
       "       [6.3370404, 6.281594 , 7.2397914, ..., 6.939489 , 5.7504296,\n",
       "        8.01955  ],\n",
       "       [6.3805585, 6.180567 , 7.2547846, ..., 6.909312 , 5.659422 ,\n",
       "        8.035377 ]], dtype=float32)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.X # Will now contain log normalized data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 581.5812 ,  618.38464, 1606.7778 , ..., 1085.735  ,  218.54701,\n",
       "        3170.47   ],\n",
       "       [ 565.8932 ,  442.29126, 1539.3981 , ..., 1075.6407 ,  164.48544,\n",
       "        3116.767  ],\n",
       "       [ 666.475  ,  574.3333 , 1759.6833 , ..., 1164.3917 ,  227.74167,\n",
       "        3156.1084 ],\n",
       "       ...,\n",
       "       [ 704.0732 ,  780.1707 , 1667.9878 , ..., 1233.183  , 1243.5488 ,\n",
       "        3194.195  ],\n",
       "       [ 564.1212 ,  533.64014, 1392.803  , ..., 1031.2424 ,  313.32574,\n",
       "        3038.8105 ],\n",
       "       [ 589.2572 ,  482.2659 , 1413.8584 , ..., 1000.5578 ,  285.98267,\n",
       "        3087.3005 ]], dtype=float32)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.raw.X # contains the raw data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X_centroid</th>\n",
       "      <th>Y_centroid</th>\n",
       "      <th>Area</th>\n",
       "      <th>MajorAxisLength</th>\n",
       "      <th>MinorAxisLength</th>\n",
       "      <th>Eccentricity</th>\n",
       "      <th>Solidity</th>\n",
       "      <th>Extent</th>\n",
       "      <th>Orientation</th>\n",
       "      <th>imageid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mcmicro_output_1</th>\n",
       "      <td>511.555556</td>\n",
       "      <td>9.846154</td>\n",
       "      <td>117</td>\n",
       "      <td>14.532270</td>\n",
       "      <td>10.273628</td>\n",
       "      <td>0.707261</td>\n",
       "      <td>0.959016</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>-0.695369</td>\n",
       "      <td>mcmicro_output</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mcmicro_output_2</th>\n",
       "      <td>579.330097</td>\n",
       "      <td>9.398058</td>\n",
       "      <td>103</td>\n",
       "      <td>16.056286</td>\n",
       "      <td>8.776323</td>\n",
       "      <td>0.837396</td>\n",
       "      <td>0.903509</td>\n",
       "      <td>0.613095</td>\n",
       "      <td>1.115707</td>\n",
       "      <td>mcmicro_output</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mcmicro_output_3</th>\n",
       "      <td>630.958333</td>\n",
       "      <td>12.883333</td>\n",
       "      <td>120</td>\n",
       "      <td>15.222005</td>\n",
       "      <td>10.310756</td>\n",
       "      <td>0.735653</td>\n",
       "      <td>0.975610</td>\n",
       "      <td>0.681818</td>\n",
       "      <td>0.151616</td>\n",
       "      <td>mcmicro_output</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mcmicro_output_4</th>\n",
       "      <td>745.194631</td>\n",
       "      <td>16.275168</td>\n",
       "      <td>149</td>\n",
       "      <td>14.380200</td>\n",
       "      <td>13.404759</td>\n",
       "      <td>0.362027</td>\n",
       "      <td>0.967532</td>\n",
       "      <td>0.662222</td>\n",
       "      <td>-0.270451</td>\n",
       "      <td>mcmicro_output</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mcmicro_output_5</th>\n",
       "      <td>657.173653</td>\n",
       "      <td>18.035928</td>\n",
       "      <td>167</td>\n",
       "      <td>17.675831</td>\n",
       "      <td>12.110106</td>\n",
       "      <td>0.728428</td>\n",
       "      <td>0.943503</td>\n",
       "      <td>0.695833</td>\n",
       "      <td>-0.810890</td>\n",
       "      <td>mcmicro_output</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mcmicro_output_4821</th>\n",
       "      <td>559.597403</td>\n",
       "      <td>1091.577922</td>\n",
       "      <td>154</td>\n",
       "      <td>18.150307</td>\n",
       "      <td>11.683288</td>\n",
       "      <td>0.765281</td>\n",
       "      <td>0.900585</td>\n",
       "      <td>0.570370</td>\n",
       "      <td>-0.342315</td>\n",
       "      <td>mcmicro_output</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mcmicro_output_4822</th>\n",
       "      <td>619.983871</td>\n",
       "      <td>1092.959677</td>\n",
       "      <td>248</td>\n",
       "      <td>21.734414</td>\n",
       "      <td>15.565820</td>\n",
       "      <td>0.697912</td>\n",
       "      <td>0.864111</td>\n",
       "      <td>0.551111</td>\n",
       "      <td>1.432242</td>\n",
       "      <td>mcmicro_output</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mcmicro_output_4823</th>\n",
       "      <td>583.317073</td>\n",
       "      <td>1093.573171</td>\n",
       "      <td>82</td>\n",
       "      <td>12.060039</td>\n",
       "      <td>9.539789</td>\n",
       "      <td>0.611784</td>\n",
       "      <td>0.964706</td>\n",
       "      <td>0.630769</td>\n",
       "      <td>0.203023</td>\n",
       "      <td>mcmicro_output</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mcmicro_output_4824</th>\n",
       "      <td>607.064394</td>\n",
       "      <td>1101.583333</td>\n",
       "      <td>264</td>\n",
       "      <td>22.549494</td>\n",
       "      <td>15.905321</td>\n",
       "      <td>0.708858</td>\n",
       "      <td>0.882943</td>\n",
       "      <td>0.661654</td>\n",
       "      <td>0.691838</td>\n",
       "      <td>mcmicro_output</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mcmicro_output_4825</th>\n",
       "      <td>641.592486</td>\n",
       "      <td>1100.132948</td>\n",
       "      <td>346</td>\n",
       "      <td>23.149806</td>\n",
       "      <td>19.375564</td>\n",
       "      <td>0.547257</td>\n",
       "      <td>0.945355</td>\n",
       "      <td>0.791762</td>\n",
       "      <td>-1.390516</td>\n",
       "      <td>mcmicro_output</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4825 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     X_centroid   Y_centroid  Area  MajorAxisLength  \\\n",
       "mcmicro_output_1     511.555556     9.846154   117        14.532270   \n",
       "mcmicro_output_2     579.330097     9.398058   103        16.056286   \n",
       "mcmicro_output_3     630.958333    12.883333   120        15.222005   \n",
       "mcmicro_output_4     745.194631    16.275168   149        14.380200   \n",
       "mcmicro_output_5     657.173653    18.035928   167        17.675831   \n",
       "...                         ...          ...   ...              ...   \n",
       "mcmicro_output_4821  559.597403  1091.577922   154        18.150307   \n",
       "mcmicro_output_4822  619.983871  1092.959677   248        21.734414   \n",
       "mcmicro_output_4823  583.317073  1093.573171    82        12.060039   \n",
       "mcmicro_output_4824  607.064394  1101.583333   264        22.549494   \n",
       "mcmicro_output_4825  641.592486  1100.132948   346        23.149806   \n",
       "\n",
       "                     MinorAxisLength  Eccentricity  Solidity    Extent  \\\n",
       "mcmicro_output_1           10.273628      0.707261  0.959016  0.750000   \n",
       "mcmicro_output_2            8.776323      0.837396  0.903509  0.613095   \n",
       "mcmicro_output_3           10.310756      0.735653  0.975610  0.681818   \n",
       "mcmicro_output_4           13.404759      0.362027  0.967532  0.662222   \n",
       "mcmicro_output_5           12.110106      0.728428  0.943503  0.695833   \n",
       "...                              ...           ...       ...       ...   \n",
       "mcmicro_output_4821        11.683288      0.765281  0.900585  0.570370   \n",
       "mcmicro_output_4822        15.565820      0.697912  0.864111  0.551111   \n",
       "mcmicro_output_4823         9.539789      0.611784  0.964706  0.630769   \n",
       "mcmicro_output_4824        15.905321      0.708858  0.882943  0.661654   \n",
       "mcmicro_output_4825        19.375564      0.547257  0.945355  0.791762   \n",
       "\n",
       "                     Orientation         imageid  \n",
       "mcmicro_output_1       -0.695369  mcmicro_output  \n",
       "mcmicro_output_2        1.115707  mcmicro_output  \n",
       "mcmicro_output_3        0.151616  mcmicro_output  \n",
       "mcmicro_output_4       -0.270451  mcmicro_output  \n",
       "mcmicro_output_5       -0.810890  mcmicro_output  \n",
       "...                          ...             ...  \n",
       "mcmicro_output_4821    -0.342315  mcmicro_output  \n",
       "mcmicro_output_4822     1.432242  mcmicro_output  \n",
       "mcmicro_output_4823     0.203023  mcmicro_output  \n",
       "mcmicro_output_4824     0.691838  mcmicro_output  \n",
       "mcmicro_output_4825    -1.390516  mcmicro_output  \n",
       "\n",
       "[4825 rows x 10 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.obs # prints the meta data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We can use scanpy package to explore the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sc.pl.highest_expr_genes(adata, n_top=20, ) # Most expressing proteins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sc.tl.pca(adata, svd_solver='arpack') # peform PCA\n",
    "sc.pl.pca(adata, color='KI67') # scatter plot in the PCA coordinates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sc.pl.pca_variance_ratio(adata) # PCs to the total variance in the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the results\n",
    "adata.write('tutorial_data.h5ad')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**This concludes the `getting started` tutorial, continue with the `phenotyping` tutorial.**"
   ]
  }
 ],
 "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": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}