Data_Preprocessing_Clustering_Flow_Cytometry.ipynb

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    "# Data Preprocessing Fundamentals\n",
    "\n",
    "### Author: Rhondene Wint (PhD Student- UC Merced rwint@ucmerced.edu)\n",
    "\n",
    "### Objective- You are going to learn how to \n",
    "1. To find and ways of handing missing data  and outliers (simple data exploration and cleanup)\n",
    "2. To visualise data such as plotting 5-summary statistics and distribution of your data\n",
    "2. To combine data from different files (data integration) \n",
    "2. To transform data \n",
    "3. Perform dimensionality reduction- Principal Component Analysis (PCA) \n",
    "\n",
    "### Overview:\n",
    "\n",
    "We are going to perform data preprocessing and some data exploration on flow cytometry dataset representing the intensities of fluorescent labelled antibodies on blood cell markers obtain from <a href ='https://www.cs.cornell.edu/~arb/data/flow-cytometry/index.html'> here </a>. However, I have modified the data for the purposes of this demonstration.   \n",
    "<b> Scenario </b>:     \n",
    "You are going on a dinner date later, which is an accomplised feat while in grad school, so you decided to split your sample and perform the experiment in 2 batches thus you have two data files -'flowcyt_exp_1.csv ' and 'flowcyt_exp_2.csv'. You would like see if you can identity the different populations of blood cells based on the variations in fluorescent intensities using state-of-the-art data science tools in Python programming language. \n",
    "\n",
    "#### <i> Advice: If you are still new to programming and data analysis in Python I find the best method for learning is to type the code for myself. You can copy and paste this code or re-run the notebook, but typing it over helps more, albeit it is painstaking. No Pain No Gain :-/</i>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "## import these packages which will do bulk of work for us\n",
    "import pandas as pd  #work with data in tables\n",
    "import matplotlib.pyplot as plt   #plot graphs\n",
    "import seaborn  #visualization \n",
    "import numpy as np  #numeric computation "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 1. Load and View Your data \n",
    "\n",
    "Your data is stored in a file somewhere so we need to load it into python as a <i> dataframe</i> which is simply a table. Tables are preferred format for working  with data when modelling. We will load the data from a .csv file into a dataframe using <i><b> pandas </b> </i> which we have shortened to 'pd'. \n",
    "\n",
    "<p> Pandas is one of the most widely used libraries for working with data in tables - and like most libraries in python it is straightfoward to use.  </p> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "##load the two experiment data into a pandas dataframe or table\n",
    "exp_1 = pd.read_table('flowcyt_exp_1.csv', sep=',')\n",
    "exp_2 = pd.read_table('flowcyt_exp_2.csv', sep=',')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Get an idea about the structure of your data with <i> .head(), .info()</i>, <i>.shape</i>, and <i>.describe()</i>\n",
    "<b>.info()</b> is a useful method for quickly gaining insight about your data. This is probably the first thing you do after loading your data into a dataframe.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1500 entries, 0 to 1499\n",
      "Data columns (total 5 columns):\n",
      "FITC-CD4    1500 non-null float64\n",
      "PE-CD8      1498 non-null float64\n",
      "ECD-CD19    1500 non-null float64\n",
      "PC5-CD45    1499 non-null float64\n",
      "PC7-CD3     1500 non-null float64\n",
      "dtypes: float64(5)\n",
      "memory usage: 58.7 KB\n"
     ]
    }
   ],
   "source": [
    "exp_1.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2000 entries, 0 to 1999\n",
      "Data columns (total 5 columns):\n",
      "FITC-CD4    2000 non-null float64\n",
      "PE-CD8      2000 non-null float64\n",
      "ECD-CD19    2000 non-null float64\n",
      "PC5-CD45    2000 non-null float64\n",
      "PC7-CD3     2000 non-null float64\n",
      "dtypes: float64(5)\n",
      "memory usage: 78.2 KB\n"
     ]
    }
   ],
   "source": [
    "exp_2.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1500, 5)\n",
      "(2000, 5)\n"
     ]
    }
   ],
   "source": [
    "#calling shape also gives the diminensions of the data (rows vs columns)\n",
    "print(exp_1.shape)\n",
    "print(exp_2.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "      <th>ECD-CD19</th>\n",
       "      <th>PC5-CD45</th>\n",
       "      <th>PC7-CD3</th>\n",
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       "      <th>0</th>\n",
       "      <td>6.319052</td>\n",
       "      <td>10.247240</td>\n",
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       "      <td>348.236416</td>\n",
       "      <td>32.775910</td>\n",
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       "      <th>1</th>\n",
       "      <td>17.596262</td>\n",
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       "      <td>161.834871</td>\n",
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       "      <th>2</th>\n",
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       "      <td>2.288821</td>\n",
       "      <td>19.927998</td>\n",
       "      <td>9.303102</td>\n",
       "      <td>231.140357</td>\n",
       "      <td>95.825287</td>\n",
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       "      <th>4</th>\n",
       "      <td>0.346184</td>\n",
       "      <td>1.513483</td>\n",
       "      <td>0.964166</td>\n",
       "      <td>53.802542</td>\n",
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       "    FITC-CD4     PE-CD8  ECD-CD19    PC5-CD45     PC7-CD3\n",
       "0   6.319052  10.247240  5.823141  348.236416   32.775910\n",
       "1  17.596262  12.079250  6.377226  161.834871  105.072122\n",
       "2   8.035667   7.312782  4.571919  143.615860   15.172019\n",
       "3   2.288821  19.927998  9.303102  231.140357   95.825287\n",
       "4   0.346184   1.513483  0.964166   53.802542    4.992490"
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     "execution_count": 9,
     "metadata": {},
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   ],
   "source": [
    "##view each the first 5 instances of the table\n",
    "exp_1.head(5)"
   ]
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       "      <th>FITC-CD4</th>\n",
       "      <th>PE-CD8</th>\n",
       "      <th>ECD-CD19</th>\n",
       "      <th>PC5-CD45</th>\n",
       "      <th>PC7-CD3</th>\n",
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       "      <th>0</th>\n",
       "      <td>0.837327</td>\n",
       "      <td>1.183511</td>\n",
       "      <td>0.845910</td>\n",
       "      <td>57.769830</td>\n",
       "      <td>5.396848</td>\n",
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       "      <th>1</th>\n",
       "      <td>1.323701</td>\n",
       "      <td>2.126696</td>\n",
       "      <td>1.357080</td>\n",
       "      <td>40.802041</td>\n",
       "      <td>5.512243</td>\n",
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       "      <td>4.044537</td>\n",
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       "   FITC-CD4    PE-CD8  ECD-CD19   PC5-CD45    PC7-CD3\n",
       "0  0.837327  1.183511  0.845910  57.769830   5.396848\n",
       "1  1.323701  2.126696  1.357080  40.802041   5.512243\n",
       "2  3.643993  3.363613  3.243449  67.657535  10.886203\n",
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     "execution_count": 10,
     "metadata": {},
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   ],
   "source": [
    "##view each the first 5 instances of the table\n",
    "exp_2.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Data science terminology </b>  \n",
    "For this dataset , each row represents a single blood cell and each column is the intensity of the fluoroscent labelled antibody marker found on that blood cell. In data science, Rows are sometimes called <b>instances or examples</b>  and columns are called <b>features or attributes or variables</b>\n",
    "\n",
    "<p>\n",
    "Note two things:   \n",
    "- Both tables have the same number and types of columns. This means we can successfully combine or <i> integrate </i> both data. <b> To combine tables, the number of columns must at least be the same.</b>\n",
    "- We also see missing values.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Data Integration\n",
    "\n",
    "Use pd.concat([table1, table2..], axis=) to combine multiple tables. We can combine tables by stacking them  row-wise (axis=0) or sideways along their column-wise (axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 3500 entries, 0 to 1999\n",
      "Data columns (total 5 columns):\n",
      "FITC-CD4    3500 non-null float64\n",
      "PE-CD8      3498 non-null float64\n",
      "ECD-CD19    3500 non-null float64\n",
      "PC5-CD45    3499 non-null float64\n",
      "PC7-CD3     3500 non-null float64\n",
      "dtypes: float64(5)\n",
      "memory usage: 164.1 KB\n"
     ]
    }
   ],
   "source": [
    "#combine both tables into a single table.\n",
    "combined_exp = pd.concat([exp_1,exp_2],axis=0)#axis=0 means we stack the tables along their rows\n",
    "#check the dimensions\n",
    "combined_exp.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 864x720 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## a quick look at the distribution of each feature column in our dataset with histogram\n",
    "combined_exp.hist(bins=50, figsize=(12,10), color='skyblue')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Data Clean-up: Missing Values and Outliers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3a :  Dealing with Missing Values\n",
    "There are values in <i> PE-CD8</i> and <i>PC5-CD45</i> columns.    \n",
    "We handle missing data points with two Apporaches:  \n",
    "- drop an entire row with a missing value or \n",
    "- <b> impute </b> the missing values with the mean or mode or median.\n",
    "\n",
    "The dataframe will be copied to demonstrate both ways. Whichever route you take depends on how much missing data is present. Always keep in mind that you want to retain as much data as possble."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 3500 entries, 0 to 1999\n",
      "Data columns (total 5 columns):\n",
      "FITC-CD4    3500 non-null float64\n",
      "PE-CD8      3498 non-null float64\n",
      "ECD-CD19    3500 non-null float64\n",
      "PC5-CD45    3499 non-null float64\n",
      "PC7-CD3     3500 non-null float64\n",
      "dtypes: float64(5)\n",
      "memory usage: 164.1 KB\n"
     ]
    }
   ],
   "source": [
    "#copy the original data \n",
    "df = combined_exp.copy()\n",
    "#before dropping rows with missing values \n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 3497 entries, 0 to 1999\n",
      "Data columns (total 5 columns):\n",
      "FITC-CD4    3497 non-null float64\n",
      "PE-CD8      3497 non-null float64\n",
      "ECD-CD19    3497 non-null float64\n",
      "PC5-CD45    3497 non-null float64\n",
      "PC7-CD3     3497 non-null float64\n",
      "dtypes: float64(5)\n",
      "memory usage: 163.9 KB\n"
     ]
    }
   ],
   "source": [
    "##after dropping rows with empty values \n",
    "df.dropna(axis=0, inplace=True) \n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 3500 entries, 0 to 1999\n",
      "Data columns (total 5 columns):\n",
      "FITC-CD4    3500 non-null float64\n",
      "PE-CD8      3498 non-null float64\n",
      "ECD-CD19    3500 non-null float64\n",
      "PC5-CD45    3499 non-null float64\n",
      "PC7-CD3     3500 non-null float64\n",
      "dtypes: float64(5)\n",
      "memory usage: 164.1 KB\n"
     ]
    }
   ],
   "source": [
    "###impute missing values with the mean oe each column\n",
    "df = combined_exp.copy()\n",
    "##before imputing the data\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 3500 entries, 0 to 1999\n",
      "Data columns (total 5 columns):\n",
      "FITC-CD4    3500 non-null float64\n",
      "PE-CD8      3500 non-null float64\n",
      "ECD-CD19    3500 non-null float64\n",
      "PC5-CD45    3500 non-null float64\n",
      "PC7-CD3     3500 non-null float64\n",
      "dtypes: float64(5)\n",
      "memory usage: 164.1 KB\n"
     ]
    }
   ],
   "source": [
    "###impute missing values with the mean oe each column\n",
    "m1 = df['PE-CD8'].mean()\n",
    "df['PE-CD8'].fillna(m1, inplace=True) ##fill missing values with the mean\n",
    "m2 = df['PC5-CD45'].mean()\n",
    "df['PC5-CD45'].fillna(m2,inplace=True)##fill missing values with the mean\n",
    "##after filling empty rows with mean\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Now that the demo is over, I choose to drop the rows with missing values in the original data since they aren't that many "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 3497 entries, 0 to 1999\n",
      "Data columns (total 5 columns):\n",
      "FITC-CD4    3497 non-null float64\n",
      "PE-CD8      3497 non-null float64\n",
      "ECD-CD19    3497 non-null float64\n",
      "PC5-CD45    3497 non-null float64\n",
      "PC7-CD3     3497 non-null float64\n",
      "dtypes: float64(5)\n",
      "memory usage: 163.9 KB\n"
     ]
    }
   ],
   "source": [
    "combined_exp.dropna(axis=0, inplace=True)\n",
    "combined_exp.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3b. Finding Outliers and Doing Summary Statistics\n",
    "\n",
    "We can obtain quick summary statistics of each column (feature) using the <b> .describe() </b> method to gain insight about the distrubition of the data. This shows mean, max, min, count (non-null values), percentiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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>FITC-CD4</th>\n",
       "      <th>PE-CD8</th>\n",
       "      <th>ECD-CD19</th>\n",
       "      <th>PC5-CD45</th>\n",
       "      <th>PC7-CD3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3497.000000</td>\n",
       "      <td>3497.000000</td>\n",
       "      <td>3497.000000</td>\n",
       "      <td>3497.000000</td>\n",
       "      <td>3497.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8.259508</td>\n",
       "      <td>14.034130</td>\n",
       "      <td>6.331480</td>\n",
       "      <td>98.286916</td>\n",
       "      <td>31.857014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>40.406166</td>\n",
       "      <td>339.698436</td>\n",
       "      <td>27.940002</td>\n",
       "      <td>92.890647</td>\n",
       "      <td>100.211465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.225878</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.301767</td>\n",
       "      <td>2.037050</td>\n",
       "      <td>1.598360</td>\n",
       "      <td>33.935579</td>\n",
       "      <td>5.271917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.004625</td>\n",
       "      <td>3.058436</td>\n",
       "      <td>2.522471</td>\n",
       "      <td>49.835253</td>\n",
       "      <td>8.206375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.675464</td>\n",
       "      <td>9.310731</td>\n",
       "      <td>5.290990</td>\n",
       "      <td>165.527502</td>\n",
       "      <td>19.714374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>999.970436</td>\n",
       "      <td>20000.000000</td>\n",
       "      <td>999.970436</td>\n",
       "      <td>999.970436</td>\n",
       "      <td>5000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          FITC-CD4        PE-CD8     ECD-CD19     PC5-CD45      PC7-CD3\n",
       "count  3497.000000   3497.000000  3497.000000  3497.000000  3497.000000\n",
       "mean      8.259508     14.034130     6.331480    98.286916    31.857014\n",
       "std      40.406166    339.698436    27.940002    92.890647   100.211465\n",
       "min       0.000000      0.000000     0.000000     2.225878     0.000000\n",
       "25%       1.301767      2.037050     1.598360    33.935579     5.271917\n",
       "50%       2.004625      3.058436     2.522471    49.835253     8.206375\n",
       "75%       3.675464      9.310731     5.290990   165.527502    19.714374\n",
       "max     999.970436  20000.000000   999.970436   999.970436  5000.000000"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_exp.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Observations and Inferences for Each Column</b>:    \n",
    "<li>If mean is larger than the median this indicates that data is skewed to large values (right)</li>\n",
    "<li> If the 1<sup>st</sup> quartile (25th) is closer to median than the 2<sup>nd</sup> adds support that distribution  is skewed to large values </li>\n",
    "<li> if  the standard deviation is much smaller than the range (max-min) this indicates long tail distribution </li>\n",
    "\n",
    "What other inferences about the distribution of the data can you make?\n",
    "\n",
    "<b> Let's Visualise the Distribution for the PE-CD8 marker</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "font_title = {'family': 'sans serif', 'color':  'black', 'weight': 'bold', 'size': 14}\n",
    "font_ax =  {'family': 'sans serif', 'color':  'brown', 'weight': 'normal', 'size': 14}\n",
    "\n",
    "##Write to function to easily generate summary stats plots for any column in our dataframe\n",
    "def plotstats(df,col):\n",
    "    #plot two charts, one over the other\n",
    "    fig,ax = plt.subplots(2,1, figsize=(12,8))\n",
    "    plt.title(\"Distribution of {} Fluoresence Intensities\".format(col), fontdict=font_title)\n",
    "    #Boxplot\n",
    "    df.dropna().boxplot(col,ax=ax[0], vert=False, return_type='dict') #excludes null values in col\n",
    "    #plot a histogram \n",
    "    temp = df[col].as_matrix()  #converts df into numpy matrix array\n",
    "    ax[1].hist(temp, bins=80, alpha=0.7)\n",
    "    plt.ylabel('Number of Blood Cells')\n",
    "    plt.xlabel(col)\n",
    "    return [col]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['PE-CD8']"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## call our function \n",
    "plotstats(combined_exp, 'PE-CD8')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Note</b> The box plot and histogram are on the same scale. However the boxplot gives us a better picture of outliers. Please note that outliers are not always due to noise so they may be meaningful. However I have deliberately modified the data with outliers (the maximum intensitiy should not above 1000 units)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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>FITC-CD4</th>\n",
       "      <th>PE-CD8</th>\n",
       "      <th>ECD-CD19</th>\n",
       "      <th>PC5-CD45</th>\n",
       "      <th>PC7-CD3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3495.000000</td>\n",
       "      <td>3495.000000</td>\n",
       "      <td>3495.000000</td>\n",
       "      <td>3495.000000</td>\n",
       "      <td>3495.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8.259575</td>\n",
       "      <td>8.316992</td>\n",
       "      <td>6.333020</td>\n",
       "      <td>98.274781</td>\n",
       "      <td>30.442148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>40.417432</td>\n",
       "      <td>33.271905</td>\n",
       "      <td>27.947881</td>\n",
       "      <td>92.909349</td>\n",
       "      <td>54.604878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.225878</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.301767</td>\n",
       "      <td>2.036573</td>\n",
       "      <td>1.598360</td>\n",
       "      <td>33.920320</td>\n",
       "      <td>5.271440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.004625</td>\n",
       "      <td>3.052714</td>\n",
       "      <td>2.522471</td>\n",
       "      <td>49.804735</td>\n",
       "      <td>8.202561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.674987</td>\n",
       "      <td>9.258279</td>\n",
       "      <td>5.285268</td>\n",
       "      <td>165.588537</td>\n",
       "      <td>19.690532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>999.970436</td>\n",
       "      <td>999.970436</td>\n",
       "      <td>999.970436</td>\n",
       "      <td>999.970436</td>\n",
       "      <td>999.970436</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          FITC-CD4       PE-CD8     ECD-CD19     PC5-CD45      PC7-CD3\n",
       "count  3495.000000  3495.000000  3495.000000  3495.000000  3495.000000\n",
       "mean      8.259575     8.316992     6.333020    98.274781    30.442148\n",
       "std      40.417432    33.271905    27.947881    92.909349    54.604878\n",
       "min       0.000000     0.000000     0.000000     2.225878     0.000000\n",
       "25%       1.301767     2.036573     1.598360    33.920320     5.271440\n",
       "50%       2.004625     3.052714     2.522471    49.804735     8.202561\n",
       "75%       3.674987     9.258279     5.285268   165.588537    19.690532\n",
       "max     999.970436   999.970436   999.970436   999.970436   999.970436"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Remove outlier values by dropping rows in PE-CD8 and PC5-CD45 with values beyond 10000\n",
    "combined_exp = combined_exp[combined_exp['PE-CD8']<10000]\n",
    "combined_exp = combined_exp[combined_exp['PC7-CD3']<5000]\n",
    "#check\n",
    "combined_exp.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['PE-CD8']"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##plot the distribution of PC-CD8 after\n",
    "plotstats(combined_exp, 'PE-CD8')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Play around with the function to explore the distribution of other feature columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Data Transformation \n",
    "\n",
    "<b> Data Transformation </b> is often a necessary step before performing any modelling on your data, especially if you do not know the distribution of your data  or you want rescale your data to a Gaussian (normal) or before doing dimensionality reduction like PCA.   \n",
    "Two popular types of data transformation is <i> normalisation </i>  and <i> standardisation</i>. In data science and machine learning these  methods are sometimes referred to as <b> <i> feature scaling </i> </b>. In both cases, the goal is get the values of each feature variable on the same scale or in a restricted range. \n",
    "\n",
    "- <b> Normalization aka Min-Max Scaling </b>: rescales the column feature values between 0 and 1. The highest value will = 1 and the lowest value =0.\n",
    "- <b> Standardization aka Z-normalization </b> : rescales the feature values to have a mean of  and standard deviation of 1 so that they are now standard normal distibuted.\n",
    "\n",
    "You can easily transform your data with python's sklearn library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df_stand = StandardScaler().fit_transform(combined_exp)  ##Z-score normalization\n",
    "df_minmax = MinMaxScaler().fit_transform(combined_exp)  ## Min-max scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unscaled Means:  [ 8.25957457  8.31699237  6.33302018 98.27478146 30.44214792]\n",
      "Z-score standardization means:  [ 4.47265814e-17 -1.82972378e-17 -9.14861892e-18 -2.84623700e-17\n",
      " -6.09907928e-17]\n",
      " Min-Max rescaled means:  [0.00825982 0.00831724 0.00633321 0.09626603 0.03044305]\n"
     ]
    }
   ],
   "source": [
    "##compare means of the rescaled data to the original data\n",
    "orig_means = combined_exp.describe().loc['mean'].values\n",
    "\n",
    "stand_means = []\n",
    "for i in range(5): #5 features\n",
    "    stand_means.append(df_stand[:,i].mean())\n",
    "\n",
    "minmax_means = []\n",
    "for i in range(5): #5 features\n",
    "    minmax_means.append(df_minmax[:,i].mean())\n",
    "\n",
    "print('Unscaled Means: ', orig_means)\n",
    "print('Z-score standardization means: ', np.asarray(stand_means))  #means will be very small close to zero\n",
    "print(' Min-Max rescaled means: ', np.asarray(minmax_means))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Data/Dimensionality Reduction  \n",
    "\n",
    "Data reduction aims to reduce the complexity of high dimensional data. It is important for:\n",
    "    - Compressing data so it saves storage and processing time\n",
    "    - Pattern Discover or Clustering data\n",
    "    - Outlier detection \n",
    "You can think of each feature as a dimension or axis on graph. We can comfortably read a 2-dimensional graph. 3-dimensional plots are sometimes tolerable but are to be avoided if possible, but anything higher than 4-dimensions is illegible for the human visual system. High dimensionality is commonly generated in scientific experiments. For example, this demo flow cytometry data has 5 cell-markers or features (5-dimensions) but most experiments can have 10 20 0r 30 markers per cell.  \n",
    "\n",
    "## PCA\n",
    "\n",
    "<b> Principal Componenent Analysis </b> is to probably the most widely used dimensionality reduction, for visualing high-dimensional data in the life sciences.     \n",
    "Formally, PCA reduces high dimensional feature space to a lower dimension feature subsapce by choosing new axes, called <b> principal components </b> , that retains as much the variation that was present in the orignal data. Josh Starmer's <a href = 'https://www.youtube.com/watch?v=_UVHneBUBW0'> PCA explanation</a> is a must watch for a deeper understanding PCA.   \n",
    "\n",
    "<b> Practical Issues of PCA </b>\n",
    "\n",
    "1. PCA is sensitive to very large values. if a feature is too large then it will dominate and be a principal component, thus we <b>MUST</b>  do feature scaling or normalisation.  \n",
    "2. Assumes subspace is linear."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "## import PCA library\n",
    "from sklearn.decomposition import PCA \n",
    "pca = PCA(n_components=2) #instantiate PCA with the number of dimensions or principal componenets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "##Step.1 Z-score normalize the data\n",
    "data_normed = StandardScaler().fit_transform(combined_exp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Step 2. perform PCA on the normalized data \n",
    "data_reduce=pca.fit_transform(data_normed)\n",
    "\n",
    "#tell us how much of the information is retained after reduction, higher is better\n",
    "var_exp = pca.explained_variance_ratio_ \n",
    "\n",
    "## coverting pandas dataframe makes it easier to plot\n",
    "data_reduce = pd.DataFrame(data_reduce, columns=['PC1', 'PC2']) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cumulative variance explained by PC1 and PC2: 89.41 %\n"
     ]
    }
   ],
   "source": [
    "# Check how much variation is retained from the original higher dimensional data \n",
    "print('Cumulative variance explained by PC1 and PC2: {} %'.format(round(np.sum(var_exp)*100,2))) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### What happens to the variance explained (which we want to maximise) if we did not normalize our data? Try running PCA on the unnormalized data and find out the impact"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3495, 5)\n",
      "(3495, 2)\n"
     ]
    }
   ],
   "source": [
    "print(data_normed.shape) #note that we have 5 feature columns aka 5-dimensions\n",
    "print(data_reduce.shape) #now the data is reduced to 2-dimensions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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>PC1</th>\n",
       "      <th>PC2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.104903</td>\n",
       "      <td>-1.640013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.018392</td>\n",
       "      <td>-1.046503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.032542</td>\n",
       "      <td>-0.182891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.239840</td>\n",
       "      <td>-1.366764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.668707</td>\n",
       "      <td>0.338521</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        PC1       PC2\n",
       "0  1.104903 -1.640013\n",
       "1  1.018392 -1.046503\n",
       "2  0.032542 -0.182891\n",
       "3  1.239840 -1.366764\n",
       "4 -0.668707  0.338521"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_reduce.head() #each principal componenent is an axis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> PCA </b> is a popular way of visualizing and finding hidden patterns in data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_reduce.plot.scatter('PC1', 'PC2', figsize=(10,8))\n",
    "plt.title('Principal Component Analysis of Fluorescent Labelled Blood Cells', fontsize=14, fontweight='bold')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our graph looks squashed because we have a few 'outliers' beyond at PC1= 10 that is obscuring the rest of the data, let's remove that and see how the graph looks. Again those outliers could be a meaningful data point such as a rare blood cell, so don't be quick to throw away 'outliers'. However, temporarily ommitting extreme can give us a better visual of out data. I am going to replot the PCA data without extreme values to demonstrate this point."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## copy the PCA data, filtering out extreme values \n",
    "no_outlier = data_reduce[data_reduce['PC1']<10]\n",
    "## Plot to see the effect \n",
    "no_outlier.plot.scatter('PC1', 'PC2', figsize=(10,8))\n",
    "plt.title('Principal Component Analysis of Fluorescent Labelled Blood Cells', fontsize=14, fontweight='bold')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## copy the PCA data, filtering out extreme values \n",
    "no_outlier = data_reduce[data_reduce['PC1']<2]\n",
    "## Plot to see the effect \n",
    "no_outlier.plot.scatter('PC1', 'PC2', figsize=(12,10))\n",
    "plt.title('Principal Component Analysis of Blood Cell Marker', fontsize=14, fontweight='bold')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Last Lap:  K-means Clustering\n",
    "\n",
    "Finally, The visual utility of PCA lies with the ability to find hidden patterns in our data by reducing the data to only relevant features. If we have the labels or categories for our data we can color our PCA plot by those labels. However, if we do not have labels for our instances PCA can be coupled with clustering algorithms such as <b>K-means</b> which makes for powerful data discovery.\n",
    "\n",
    "<b> K-means</b> is an unsupervised clustering algorithm that is widely used to assign data points to groups based on their similarity. In this case, we have unlabelled since we do not have information on the actual types of cells. K-means can be used with PCA to uncover hidden patterns in data.  \n",
    "3. Run Kmeans clustering algorithm to determine cell types   \n",
    "<b> Let's use kmeans to see if we can get distinct clusters of blood cells. We do not how many types or clusters of blood cells there are so I will show you a popular to determine the optimal number of clusters   </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import silhouette_score "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The mandatory parameters for Kmeans is the n_clusters (we do not know) and of course the data (which can be the PCA transformed data or the original data). I will use kmeans on the orginal normalized data.\n",
    "\n",
    "<i> Aside: Thanks to <i>Sebastien Raschka's Python Mchine Learning book for teaching me this techniqe</i></i> !!! \n",
    "\n",
    "### Elbow  Method + Silhouette Analysis to find optimal k clusters\n",
    "- How do we know the number of clusters to pass to KMeans?  We use an iterative method to quantify the quality of each cluster sizes, based on two approaches: \n",
    "       \n",
    "       1) measuring level of similarity of members in their assigned clusters (sum-squared error aka distortion) \n",
    "       2) measuring the level of dissimilarity between clusters.   \n",
    "        \n",
    "The Aim is to find the optimal number of cluster k that minimises the within-cluster error (sum of squres error) and maximises the between-cluster differences.  \n",
    "\n",
    "\n",
    "- <b> Elbow method</b> is to identify the value of k where there is a sharp decrease in distortion.\n",
    "- <b>Silhouette analysis</b> is a graphical way of measuring how tightly grouped the clusters are. The best value is 1 and the worst value is -1. Values near 0 indicate overlapping clusters\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Pass in sequential number of clusters and evaluate which number gives the best cohesiveness \n",
    "distortions= []\n",
    "sil_scores = [] #average silhoutte score\n",
    "for i in range(2,10,1): #start with 2 clusters, increase by 1 upto a maximum of 10 clusters\n",
    "    km = KMeans(n_clusters=i, init='k-means++', n_init=10, max_iter=100, random_state=0)\n",
    "    km.fit(data_normed)\n",
    "    distortions.append(km.inertia_) #store the cohesive quality of each cluster size\n",
    "    sil_scores.append(silhouette_score(data_normed, km.labels_, metric='euclidean'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Plot to determine the 'Elbow'\n",
    "plt.figure(figsize=(8,8))\n",
    "plt.plot(range(2,10,1), distortions, marker='o')\n",
    "plt.xlabel('Number of clusters', fontsize=14)\n",
    "plt.ylabel('Within-clustral Quality Score', fontsize=14)\n",
    "plt.title('Elbow Analysis to Find Optimal k Clusters', fontsize=14, fontweight='bold', color='maroon')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Visualise the 'Silhouette '\n",
    "plt.figure(figsize=(8,8))\n",
    "plt.plot(range(2,10,1), sil_scores, marker='o')\n",
    "plt.xlabel('Number of clusters', fontsize=14)\n",
    "plt.ylabel('Mean Silhouette Scores', fontsize=14)\n",
    "plt.title('Silhouette Analysis to Find Optimal k Clusters', fontsize=14, fontweight='bold', color='maroon')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Recall that The best silihouette value is 1 and the worst value is -1. Values near 0 indicate overlapping (impure) clusters.\n",
    "\n",
    "#### My approach is to sometimes choose the number of cluster which is a trade-off between the two methods. In this case, k=5 seems like a good candidate that satisfies both evaluation method. \n",
    "\n",
    "### Now that we have determined the optimal number of clusters, Let's run KMeans on the original data and then color our PCA plot according to each cluster to visualise the distinct blood cell types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "#fit Kmeans to the normalized high dimensional data\n",
    "kmeans = KMeans(n_clusters=5,init='k-means++', n_init=10, max_iter=100, random_state=0).fit(data_normed)  \n",
    "#extract cluster assigned to each instances (cells)\n",
    "clusters = kmeans.labels_ \n",
    "#append to our PCA data frame so we can color the pca by cluster type\n",
    "data_reduce['Cluster'] = clusters "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3495,)\n",
      "(3495, 3)\n"
     ]
    }
   ],
   "source": [
    "print(clusters.shape)\n",
    "print(data_reduce.shape)\n",
    "#Note that PCA only reduces the number of dimensions (columns) but not the number of samples!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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>PC1</th>\n",
       "      <th>PC2</th>\n",
       "      <th>Cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.104903</td>\n",
       "      <td>-1.640013</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.018392</td>\n",
       "      <td>-1.046503</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.032542</td>\n",
       "      <td>-0.182891</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.239840</td>\n",
       "      <td>-1.366764</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.668707</td>\n",
       "      <td>0.338521</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        PC1       PC2  Cluster\n",
       "0  1.104903 -1.640013        2\n",
       "1  1.018392 -1.046503        2\n",
       "2  0.032542 -0.182891        0\n",
       "3  1.239840 -1.366764        2\n",
       "4 -0.668707  0.338521        0"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_reduce.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 475.5x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Visualise! \n",
    "seaborn.pairplot( x_vars= 'PC1',y_vars='PC2', data=data_reduce, hue='Cluster', size=6,\n",
    "                plot_kws=dict(alpha=0.5, s=20)) #s is size of markers, alpha controls transparency\n",
    "\n",
    "plt.title('Kmeans-PCA of Flow Cytometry of Blood Cells', fontsize=14, fontweight='bold')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Yes! Kmeans-PCA was able to resolve the distinct blood cell types based on the variation in the cell-specific fluorescent antibody measurements"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  Let's visualise without extreme values to get a close-up view of the squashed clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 475.5x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## copy the PCA data, filtering out extreme values \n",
    "no_outlier = data_reduce[data_reduce['PC1']<10] \n",
    "\n",
    "seaborn.pairplot( x_vars= 'PC1',y_vars='PC2', data=no_outlier, hue='Cluster', size=6,\n",
    "                plot_kws=dict(alpha=0.5, s=30)) #s is size of markers, alpha controls transparency\n",
    "\n",
    "plt.title('Kmeans-PCA of Flow Cytometry of Blood Cells', fontsize=14, fontweight='bold')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observations \n",
    "\n",
    "We clearly see why it isn't a good idea to throw out extreme values, when select samples with a PC1 below 10 we discarded 2 entire clusters of blood cells!. However, sometimes it a good idea to temporarily ommit extreme values that are obscuring other data points. "
   ]
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    "## Final Thoughts\n",
    "If you were able to cover the entire material in this notebook, believe it or not you have covered at least a month's worth of data science and programming material! Much this code can be reused and tweaked to fit any similar once it is in a table/dataframe format. In my experience,  the workflow will be pretty much the same:\n",
    "    1. Load data into a dataframe and Explore your data\n",
    "    2. Clean-up your data - remove or impute missing values\n",
    "    3. Integrate data if necessary\n",
    "    4. Transform or Scale your data\n",
    "    5. Perform whatever analysis or modelling you intend!\n",
    "\n",
    "\n",
    "### Thank you following through this tutorial and I hope it was helpful. "
   ]
  }
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