Exploratory Data Analysis in Python.ipynb

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    "# Exploratory Data Analysis in Python\n",
    "\n",
    "In this course I'm going to share with you my techniques and thought processes for exploratory data analysis.  I'll demonstrate everything with analysis of a real data set.  It is important to emphasize that there isn't a single right way to do exploratory data analysis.  The way you explore your data depends on the questions you're trying to answer and the data itself.  As such, you shouldn't try to replicate this analysis exactly, rather let the data and your curiousity guide you.  \n",
    "\n",
    "There are a few core libraries you want to become familiar with.  This includes:\n",
    "\n",
    "* NumPy: provides the core array class for efficient storage and vectorized computation.\n",
    "* Pandas: wraps NumPy arrays with a series and dataframe object providing lots of convenient methods.\n",
    "* SciPy: a wide variety of scientific computation tools, including statistics, optimization and signal processing.\n",
    "* Scikit-learn: a wide variety of machine learning methods, as well as data processing helpers.\n",
    "* Matplotlib: The most full-featured Python plotting library.  Generates static images.\n",
    "* Bokeh: A new plotting library.  Able to render interactive plots in notebooks.\n",
    "\n"
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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "import hdbscan\n",
    "\n",
    "from collections import Counter\n",
    "from bokeh.io import output_notebook, show\n",
    "from bokeh.models import Range1d\n",
    "from scipy.stats import gaussian_kde, anderson, skew, kurtosis, gamma, entropy\n",
    "from itertools import zip_longest, count, cycle\n",
    "from helpers import block_heatmap, scatter_with_hover, plot_histogram, counter_histogram, bar_color\n",
    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
    "from sklearn.decomposition import FastICA, PCA\n",
    "from sklearn.manifold import MDS, TSNE\n",
    "from statsmodels.distributions.empirical_distribution import ECDF\n",
    "from ipywidgets import interact\n",
    "from matplotlib.colors import ListedColormap\n",
    "\n",
    "%matplotlib inline\n",
    "warnings.filterwarnings('ignore')\n",
    "plt.rcParams['figure.figsize'] = (12, 5)\n",
    "output_notebook()"
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    "## The dataset\n",
    "\n",
    "This dataset is basically a table of samples with nuclear protein abundance data.  These samples are from either normal mice or mice with a condition similar to downs syndrome.  Some of these mice were given a drug called memantine, which they hypothesized could rescue cognitive function in affected mice.  There are 38 normal mice and 34 affected mice, and each mouse contributed 15 samples to the data set, for a total of 1080 samples.  The abundance ratio of 77 proteins are measured for each sample.\n",
    "  \n",
    "This dataset is available from [here](https://archive.ics.uci.edu/ml/datasets/Mice+Protein+Expression)."
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    "df = pd.read_excel(\"Data_Cortex_Nuclear.xls\", index_col=0)\n",
    "\n",
    "df = df.fillna(df.mean())\n",
    "df_numeric = df.filter(like=\"_N\", axis=1)"
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    "## Generating additional summary statistics\n",
    "\n",
    "Pandas dataframes has a handy method called describe.  Describe gives you a variety of summary statistics for each variable.  The summary statistics returned by this method are also a dataframe, so you can amend it easily to include other statistics.  We'll do exactly that, adding the variable's coefficient of variation.\n",
    "\n",
    "The coefficient of variation is just the ratio of the standard deviation to the mean of the distribution.  Distributions with a large coefficient of variation are more likely to signal a change in the underlying data generation process, as opposed to random variation."
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       "      <th></th>\n",
       "      <th>ERBB4_N</th>\n",
       "      <th>P3525_N</th>\n",
       "      <th>BAX_N</th>\n",
       "      <th>GFAP_N</th>\n",
       "      <th>pGSK3B_Tyr216_N</th>\n",
       "      <th>PSD95_N</th>\n",
       "      <th>ARC_N</th>\n",
       "      <th>pS6_N</th>\n",
       "      <th>pGSK3B_N</th>\n",
       "      <th>CDK5_N</th>\n",
       "      <th>...</th>\n",
       "      <th>Tau_N</th>\n",
       "      <th>pPKCG_N</th>\n",
       "      <th>pCAMKII_N</th>\n",
       "      <th>pP70S6_N</th>\n",
       "      <th>ITSN1_N</th>\n",
       "      <th>SOD1_N</th>\n",
       "      <th>BRAF_N</th>\n",
       "      <th>DYRK1A_N</th>\n",
       "      <th>pERK_N</th>\n",
       "      <th>AcetylH3K9_N</th>\n",
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       "      <th>count</th>\n",
       "      <td>1080.000000</td>\n",
       "      <td>1080.000000</td>\n",
       "      <td>1080.000000</td>\n",
       "      <td>1080.000000</td>\n",
       "      <td>1080.000000</td>\n",
       "      <td>1080.000000</td>\n",
       "      <td>1080.000000</td>\n",
       "      <td>1080.000000</td>\n",
       "      <td>1080.000000</td>\n",
       "      <td>1080.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1080.000000</td>\n",
       "      <td>1080.000000</td>\n",
       "      <td>1080.000000</td>\n",
       "      <td>1080.000000</td>\n",
       "      <td>1080.000000</td>\n",
       "      <td>1080.000000</td>\n",
       "      <td>1080.000000</td>\n",
       "      <td>1080.000000</td>\n",
       "      <td>1080.000000</td>\n",
       "      <td>1080.000000</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.156478</td>\n",
       "      <td>0.291276</td>\n",
       "      <td>0.179307</td>\n",
       "      <td>0.120889</td>\n",
       "      <td>0.848767</td>\n",
       "      <td>2.235236</td>\n",
       "      <td>0.121521</td>\n",
       "      <td>0.121521</td>\n",
       "      <td>0.161211</td>\n",
       "      <td>0.292434</td>\n",
       "      <td>...</td>\n",
       "      <td>0.210489</td>\n",
       "      <td>1.706599</td>\n",
       "      <td>3.537109</td>\n",
       "      <td>0.394475</td>\n",
       "      <td>0.617102</td>\n",
       "      <td>0.542635</td>\n",
       "      <td>0.378519</td>\n",
       "      <td>0.425810</td>\n",
       "      <td>0.545904</td>\n",
       "      <td>0.216482</td>\n",
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       "      <th>std</th>\n",
       "      <td>0.015071</td>\n",
       "      <td>0.030015</td>\n",
       "      <td>0.018826</td>\n",
       "      <td>0.013233</td>\n",
       "      <td>0.094311</td>\n",
       "      <td>0.254395</td>\n",
       "      <td>0.014276</td>\n",
       "      <td>0.014276</td>\n",
       "      <td>0.019308</td>\n",
       "      <td>0.037380</td>\n",
       "      <td>...</td>\n",
       "      <td>0.069010</td>\n",
       "      <td>0.578442</td>\n",
       "      <td>1.293368</td>\n",
       "      <td>0.155953</td>\n",
       "      <td>0.251290</td>\n",
       "      <td>0.280178</td>\n",
       "      <td>0.216087</td>\n",
       "      <td>0.249015</td>\n",
       "      <td>0.344829</td>\n",
       "      <td>0.185309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.100217</td>\n",
       "      <td>0.207438</td>\n",
       "      <td>0.072326</td>\n",
       "      <td>0.086114</td>\n",
       "      <td>0.577397</td>\n",
       "      <td>1.206098</td>\n",
       "      <td>0.067254</td>\n",
       "      <td>0.067254</td>\n",
       "      <td>0.099976</td>\n",
       "      <td>0.181157</td>\n",
       "      <td>...</td>\n",
       "      <td>0.096233</td>\n",
       "      <td>0.598767</td>\n",
       "      <td>1.343998</td>\n",
       "      <td>0.131120</td>\n",
       "      <td>0.245359</td>\n",
       "      <td>0.217120</td>\n",
       "      <td>0.143894</td>\n",
       "      <td>0.145327</td>\n",
       "      <td>0.149155</td>\n",
       "      <td>0.052528</td>\n",
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       "      <th>25%</th>\n",
       "      <td>0.146969</td>\n",
       "      <td>0.270059</td>\n",
       "      <td>0.168172</td>\n",
       "      <td>0.112765</td>\n",
       "      <td>0.793739</td>\n",
       "      <td>2.079338</td>\n",
       "      <td>0.110839</td>\n",
       "      <td>0.110839</td>\n",
       "      <td>0.149253</td>\n",
       "      <td>0.272594</td>\n",
       "      <td>...</td>\n",
       "      <td>0.167991</td>\n",
       "      <td>1.296830</td>\n",
       "      <td>2.479861</td>\n",
       "      <td>0.282131</td>\n",
       "      <td>0.473669</td>\n",
       "      <td>0.319712</td>\n",
       "      <td>0.264356</td>\n",
       "      <td>0.288163</td>\n",
       "      <td>0.337486</td>\n",
       "      <td>0.103571</td>\n",
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       "      <th>50%</th>\n",
       "      <td>0.156371</td>\n",
       "      <td>0.290646</td>\n",
       "      <td>0.180745</td>\n",
       "      <td>0.120459</td>\n",
       "      <td>0.849858</td>\n",
       "      <td>2.242197</td>\n",
       "      <td>0.121626</td>\n",
       "      <td>0.121626</td>\n",
       "      <td>0.160212</td>\n",
       "      <td>0.293820</td>\n",
       "      <td>...</td>\n",
       "      <td>0.188630</td>\n",
       "      <td>1.664560</td>\n",
       "      <td>3.329624</td>\n",
       "      <td>0.378673</td>\n",
       "      <td>0.566365</td>\n",
       "      <td>0.446027</td>\n",
       "      <td>0.326730</td>\n",
       "      <td>0.366540</td>\n",
       "      <td>0.444223</td>\n",
       "      <td>0.150417</td>\n",
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       "      <th>75%</th>\n",
       "      <td>0.165421</td>\n",
       "      <td>0.311602</td>\n",
       "      <td>0.191577</td>\n",
       "      <td>0.127724</td>\n",
       "      <td>0.916173</td>\n",
       "      <td>2.420226</td>\n",
       "      <td>0.131955</td>\n",
       "      <td>0.131955</td>\n",
       "      <td>0.171739</td>\n",
       "      <td>0.312484</td>\n",
       "      <td>...</td>\n",
       "      <td>0.233943</td>\n",
       "      <td>2.112973</td>\n",
       "      <td>4.480652</td>\n",
       "      <td>0.480704</td>\n",
       "      <td>0.697500</td>\n",
       "      <td>0.695318</td>\n",
       "      <td>0.412716</td>\n",
       "      <td>0.487574</td>\n",
       "      <td>0.663173</td>\n",
       "      <td>0.269653</td>\n",
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       "      <th>max</th>\n",
       "      <td>0.208698</td>\n",
       "      <td>0.443735</td>\n",
       "      <td>0.241141</td>\n",
       "      <td>0.213621</td>\n",
       "      <td>1.204598</td>\n",
       "      <td>2.877873</td>\n",
       "      <td>0.158748</td>\n",
       "      <td>0.158748</td>\n",
       "      <td>0.253210</td>\n",
       "      <td>0.817402</td>\n",
       "      <td>...</td>\n",
       "      <td>0.602768</td>\n",
       "      <td>3.381976</td>\n",
       "      <td>7.464070</td>\n",
       "      <td>1.129171</td>\n",
       "      <td>2.602662</td>\n",
       "      <td>1.872899</td>\n",
       "      <td>2.133416</td>\n",
       "      <td>2.516367</td>\n",
       "      <td>3.566685</td>\n",
       "      <td>1.459387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cova</th>\n",
       "      <td>0.096313</td>\n",
       "      <td>0.103047</td>\n",
       "      <td>0.104995</td>\n",
       "      <td>0.109467</td>\n",
       "      <td>0.111116</td>\n",
       "      <td>0.113811</td>\n",
       "      <td>0.117482</td>\n",
       "      <td>0.117482</td>\n",
       "      <td>0.119768</td>\n",
       "      <td>0.127823</td>\n",
       "      <td>...</td>\n",
       "      <td>0.327855</td>\n",
       "      <td>0.338945</td>\n",
       "      <td>0.365657</td>\n",
       "      <td>0.395343</td>\n",
       "      <td>0.407210</td>\n",
       "      <td>0.516329</td>\n",
       "      <td>0.570875</td>\n",
       "      <td>0.584803</td>\n",
       "      <td>0.631665</td>\n",
       "      <td>0.856000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9 rows × 77 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           ERBB4_N      P3525_N        BAX_N       GFAP_N  pGSK3B_Tyr216_N  \\\n",
       "count  1080.000000  1080.000000  1080.000000  1080.000000      1080.000000   \n",
       "mean      0.156478     0.291276     0.179307     0.120889         0.848767   \n",
       "std       0.015071     0.030015     0.018826     0.013233         0.094311   \n",
       "min       0.100217     0.207438     0.072326     0.086114         0.577397   \n",
       "25%       0.146969     0.270059     0.168172     0.112765         0.793739   \n",
       "50%       0.156371     0.290646     0.180745     0.120459         0.849858   \n",
       "75%       0.165421     0.311602     0.191577     0.127724         0.916173   \n",
       "max       0.208698     0.443735     0.241141     0.213621         1.204598   \n",
       "cova      0.096313     0.103047     0.104995     0.109467         0.111116   \n",
       "\n",
       "           PSD95_N        ARC_N        pS6_N     pGSK3B_N       CDK5_N  \\\n",
       "count  1080.000000  1080.000000  1080.000000  1080.000000  1080.000000   \n",
       "mean      2.235236     0.121521     0.121521     0.161211     0.292434   \n",
       "std       0.254395     0.014276     0.014276     0.019308     0.037380   \n",
       "min       1.206098     0.067254     0.067254     0.099976     0.181157   \n",
       "25%       2.079338     0.110839     0.110839     0.149253     0.272594   \n",
       "50%       2.242197     0.121626     0.121626     0.160212     0.293820   \n",
       "75%       2.420226     0.131955     0.131955     0.171739     0.312484   \n",
       "max       2.877873     0.158748     0.158748     0.253210     0.817402   \n",
       "cova      0.113811     0.117482     0.117482     0.119768     0.127823   \n",
       "\n",
       "           ...             Tau_N      pPKCG_N    pCAMKII_N     pP70S6_N  \\\n",
       "count      ...       1080.000000  1080.000000  1080.000000  1080.000000   \n",
       "mean       ...          0.210489     1.706599     3.537109     0.394475   \n",
       "std        ...          0.069010     0.578442     1.293368     0.155953   \n",
       "min        ...          0.096233     0.598767     1.343998     0.131120   \n",
       "25%        ...          0.167991     1.296830     2.479861     0.282131   \n",
       "50%        ...          0.188630     1.664560     3.329624     0.378673   \n",
       "75%        ...          0.233943     2.112973     4.480652     0.480704   \n",
       "max        ...          0.602768     3.381976     7.464070     1.129171   \n",
       "cova       ...          0.327855     0.338945     0.365657     0.395343   \n",
       "\n",
       "           ITSN1_N       SOD1_N       BRAF_N     DYRK1A_N       pERK_N  \\\n",
       "count  1080.000000  1080.000000  1080.000000  1080.000000  1080.000000   \n",
       "mean      0.617102     0.542635     0.378519     0.425810     0.545904   \n",
       "std       0.251290     0.280178     0.216087     0.249015     0.344829   \n",
       "min       0.245359     0.217120     0.143894     0.145327     0.149155   \n",
       "25%       0.473669     0.319712     0.264356     0.288163     0.337486   \n",
       "50%       0.566365     0.446027     0.326730     0.366540     0.444223   \n",
       "75%       0.697500     0.695318     0.412716     0.487574     0.663173   \n",
       "max       2.602662     1.872899     2.133416     2.516367     3.566685   \n",
       "cova      0.407210     0.516329     0.570875     0.584803     0.631665   \n",
       "\n",
       "       AcetylH3K9_N  \n",
       "count   1080.000000  \n",
       "mean       0.216482  \n",
       "std        0.185309  \n",
       "min        0.052528  \n",
       "25%        0.103571  \n",
       "50%        0.150417  \n",
       "75%        0.269653  \n",
       "max        1.459387  \n",
       "cova       0.856000  \n",
       "\n",
       "[9 rows x 77 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "description = df.describe()\n",
    "coef_variation = description.loc[\"std\"] / description.loc[\"mean\"]\n",
    "description.loc[\"cova\"] = coef_variation\n",
    "description.sort_values(by=\"cova\", axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This data set contains 15 samples for each individual mouse.  Let's update the data frame to include which mouse each sample came from in an easily usable format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mouse_id = pd.Series([i.split(\"_\")[0] for i in df.index], index=df.index, name='mouse_id')\n",
    "df = df.join(mouse_id)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Normalization\n",
    "\n",
    "One important initial step before any real analysis is performed is to normalize the data.  This is necessary in order to prevent variables with large values from having an undue influence on results. There are a few different methods of normalizing your data:\n",
    " \n",
    "* The most common method is to divide a dimension’s values by the largest value it contains.  This method is easy to interpret, and it is robust to outliers when the coefficient of variation on a dimension is low.  \n",
    "* Another very common method is to subtract the dimension’s mean and divide by its standard deviation, a process sometimes called standardizing.  This method provides more information about the relative similarity of values when the coefficient of variation on a dimension is high.  It performs poorly on dimensions with a low coefficient of variation and large outliers.\n",
    "* An underutilized option is to use the cumulative distribution function of the dimension’s empirical distribution to get a percentile ranking.  This method works well in most cases and is robust to outliers.  Its usefulness degrades somewhat when a dimension’s coefficient of variation gets very small.\n",
    " \n",
    "The normalization method you use depends on your data and the aspects of it that you want to highlight.  You can use a different normalization method for each dimension of your data, though I don’t advise you do this unless absolutely necessary.  In general my preference is for the empirical distribution method.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true,
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [],
   "source": [
    "minmax_normalized_df = pd.DataFrame(MinMaxScaler().fit_transform(df_numeric),\n",
    "                                    columns=df_numeric.columns, index=df_numeric.index)\n",
    "\n",
    "standardized_df = pd.DataFrame(StandardScaler().fit_transform(df_numeric), columns=df_numeric.columns,\n",
    "                               index=df_numeric.index)\n",
    "\n",
    "ecdf_normalized_df = df_numeric.apply(lambda c: pd.Series(ECDF(c)(c), index=c.index))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing distributions\n",
    "\n",
    "The best way to understand a data set is to look at the distribution of its properties.  The best way to examine a distribution is by looking at its density function.  The graph of the density function is a complete visual description of the distribution.  Unfortunately, the density function of real data is unknown.\n",
    "\n",
    "Histograms naturally approximate the density function for discrete data.  In the case of continuous data, histograms are extremely sensitive to the number of bins and their edges.  They can still work well, but it requires a lot of data and usually some hand tuning.\n",
    "\n",
    "An alternative technique to histograms is to use a density estimator.  Density estimators work well with continuous variables, and they produce reasonable results without a lot of data.  Density estimation is basically a form of smoothing or interpolation between observed data points.  The density estimator I prefer is called a gaussian kernel density estimator.  This is basically a linear interpolater where the distance between points is transformed by a function called the kernel.  It has a single parameter, the width of the kernel, which can be reasonably estimated from the data.\n",
    "\n",
    "The best option is to take advantage of both histograms and density estimators.  All my plots in this presentation will follow this convention.  I also include the cumulative distribution function (CDF).  This lets you see the relative proportion of the distribution at or below a given value."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring Data\n",
    "\n",
    "My strategy for exploring data is to look for things that are out of the ordinary.  If you had a data set that was just uncorrelated gaussian random variables, you couldn't learn anything from it, it would literally be white noise.  The majority of the information content in most data set is are its variations from normality.\n",
    "\n",
    "The distribution of the coefficient of variation tells you about the relative variability of that data.  High coefficients of variation for a variable indicate a likely candidate for experimental effect."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "data": {
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stwHrJ8BrBx37CJgV2XIiZN0WzL5yQqMyIS1Zz7uKAOMwHKgO8PE/thGT4OWK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\nILKOqRo3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe5cc310c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_histogram(description.loc[\"cova\"], title=\"Coefficient of variation\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on this distribution, 0.3 appears to be a good cut-off value.  Let's go ahead and take a look at those proteins."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AcetylH3K9_N    0.856000\n",
       "pERK_N          0.631665\n",
       "DYRK1A_N        0.584803\n",
       "BRAF_N          0.570875\n",
       "SOD1_N          0.516329\n",
       "ITSN1_N         0.407210\n",
       "pP70S6_N        0.395343\n",
       "pCAMKII_N       0.365657\n",
       "pPKCG_N         0.338945\n",
       "Tau_N           0.327855\n",
       "pELK_N          0.326353\n",
       "S6_N            0.320205\n",
       "H3AcK18_N       0.319686\n",
       "pPKCAB_N        0.315412\n",
       "ADARB1_N        0.302102\n",
       "Name: cova, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "high_cova = description.loc[\"cova\"].where(lambda x: x > 0.30).dropna().sort_values(ascending=False)\n",
    "high_cova"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another good step is to perform normality tests on the variables.  Variables that deviate highly from normality are more likely to be informative.  Either the Anderson-Darling test or the Shapiro-Wilks test works well for this purpose."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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r2rlzZ59zu91uXX755ZKkNWvWaNu2bfFjQ6GQUlNTdeONN8paq/vuu0/l5eV9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\n8v8BHv1RpIW+ScQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe585cb8470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "norm = pd.Series([anderson(df_numeric[c])[0] for c in df_numeric], index=df_numeric.columns)\n",
    "maximum_finite = max(filter(lambda x: not x == np.infty, norm))\n",
    "finite_norm = norm.apply(lambda x: min(maximum_finite, x))\n",
    "\n",
    "plot_histogram(finite_norm, title=\"Anderson-Darling test statistic\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on this distribution, 20 seems like a good cut-off value for a list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BRAF_N          85.892736\n",
       "AcetylH3K9_N    85.783742\n",
       "DYRK1A_N        74.352495\n",
       "pERK_N          66.770354\n",
       "pELK_N          58.045646\n",
       "Tau_N           52.473828\n",
       "ITSN1_N         48.509978\n",
       "SOD1_N          46.589849\n",
       "RRP1_N          40.618962\n",
       "BCL2_N          35.070336\n",
       "H3AcK18_N       29.416407\n",
       "H3MeK4_N        29.278540\n",
       "ELK_N           26.198888\n",
       "pPKCAB_N        25.716155\n",
       "EGR1_N          24.663063\n",
       "BAD_N           20.816607\n",
       "Name: anderson, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "description.loc['anderson'] = finite_norm\n",
    "\n",
    "high_anderson = description.loc['anderson'].where(lambda x: x > 20).dropna().sort_values(ascending=False)\n",
    "high_anderson"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There seems to be a significant overlap between variables with a high coefficient of variation, and variables that are highly non-normal.\n",
    "\n",
    "If we create sets from the proteins above the cutoff level in each case, we can see which proteins are present in both sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'BRAF_N', 'pELK_N', 'Tau_N', 'SOD1_N', 'pERK_N', 'H3AcK18_N', 'ITSN1_N', 'AcetylH3K9_N', 'DYRK1A_N', 'pPKCAB_N'}\n"
     ]
    }
   ],
   "source": [
    "high_anderson_and_cova = set(high_anderson.index) & set(high_cova.index)\n",
    "print(high_anderson_and_cova)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That leaves ten variables that have been selected for through multiple mechanisms.  Let's go ahead and visualize them all at the same time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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FF1xAYmIihmGQnh77Ju0azdnI6EQquw5Yw2ziOJb40KFD7C3fis3hJkfmMGfO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YsZS3NZclMRvtghzTZuXMRUlZ4tsKqKiGBZFrZtY9t2ZHk3l8sVSQ8Gg5F9dK/j9XpxuVx0dXXh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\niBTB/wd8DckC3TNG6AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe585c57b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "protein_data = [df[protein] for protein in high_anderson_and_cova]\n",
    "\n",
    "plot_histogram(*protein_data, title=high_anderson_and_cova);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Other interesting metrics to look at include the skew and kurtosis of distributions.  Skew measures the symmetry of a distribution about its mean, while kurtosis measures the portion of the data in the tails of the distribution.\n",
    "\n",
    "Skew and kurtosis can be combined into a nice scatter plot that tells you a lot about the structure of your data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <div class=\"bk-plotdiv\" id=\"1be8e52e-fc22-40c1-b9f9-633f5212c722\"></div>\n",
       "    </div>\n",
       "<script type=\"text/javascript\">\n",
       "  \n",
       "  (function(global) {\n",
       "    function now() {\n",
       "      return new Date();\n",
       "    }\n",
       "  \n",
       "    var force = false;\n",
       "  \n",
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     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "skews = df_numeric.apply(lambda x: skew(x))\n",
    "skews.name = \"skew\"\n",
    "kurts = df_numeric.apply(lambda x: kurtosis(x))\n",
    "kurts.name = \"kurtosis\"\n",
    "\n",
    "proteins = pd.Series([i.split(\"_\")[0] for i in kurts.index], index=kurts.index, name=\"protein\")\n",
    "sk_df = pd.concat([skews, kurts, proteins], axis=1)\n",
    "fig = scatter_with_hover(sk_df, \"skew\", \"kurtosis\", name=\"skew_kurt\", cols=[\"protein\"])\n",
    "show(fig, notebook_handle=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In all likelyhood, the normality tests have already picked up those outliers, but lets take a look at them just for completeness."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>skew</th>\n",
       "      <th>kurtosis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DYRK1A_N</th>\n",
       "      <td>4.356940</td>\n",
       "      <td>27.302549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ITSN1_N</th>\n",
       "      <td>3.622487</td>\n",
       "      <td>21.481122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pELK_N</th>\n",
       "      <td>3.994880</td>\n",
       "      <td>25.609647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pERK_N</th>\n",
       "      <td>3.835335</td>\n",
       "      <td>22.367986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BRAF_N</th>\n",
       "      <td>4.547660</td>\n",
       "      <td>28.176251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CDK5_N</th>\n",
       "      <td>2.481156</td>\n",
       "      <td>35.276894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AcetylH3K9_N</th>\n",
       "      <td>2.678724</td>\n",
       "      <td>8.917021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RRP1_N</th>\n",
       "      <td>3.998651</td>\n",
       "      <td>45.898794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tau_N</th>\n",
       "      <td>1.864722</td>\n",
       "      <td>4.548578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GluR4_N</th>\n",
       "      <td>4.732316</td>\n",
       "      <td>62.253754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BCL2_N</th>\n",
       "      <td>1.415548</td>\n",
       "      <td>3.732147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H3AcK18_N</th>\n",
       "      <td>1.921434</td>\n",
       "      <td>6.456818</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  skew   kurtosis\n",
       "DYRK1A_N      4.356940  27.302549\n",
       "ITSN1_N       3.622487  21.481122\n",
       "pELK_N        3.994880  25.609647\n",
       "pERK_N        3.835335  22.367986\n",
       "BRAF_N        4.547660  28.176251\n",
       "CDK5_N        2.481156  35.276894\n",
       "AcetylH3K9_N  2.678724   8.917021\n",
       "RRP1_N        3.998651  45.898794\n",
       "Tau_N         1.864722   4.548578\n",
       "GluR4_N       4.732316  62.253754\n",
       "BCL2_N        1.415548   3.732147\n",
       "H3AcK18_N     1.921434   6.456818"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "high_skew_kurt = sk_df.loc[((skews * kurts).abs() >= 5),[\"skew\", \"kurtosis\"]]\n",
    "high_skew_kurt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These proteins should be starting to get familiar by now.\n",
    "\n",
    "### Identifying structure\n",
    "\n",
    "In addition to looking for deviations, it is important to identify the latent structure in the data.  There are a number of techniques for accomplishing this.  Clustering is a good way to expose structure in the data.  Hierarchical clustering algorithms are good because you don't need to specify a cluster count.  Hierarchical clustering algorithms also gives your variables a partial order, which you can take advantage of.  My preferred form of clustering is a technique called HDBSCAN.  It takes the minimum cluster size as the only parameter, and it produces both discrete and hierarchical clustering."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "data": {
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KJUmSdNimTZvGgAEDOO200w70XXPNNTQ1NdHU1MTQoUNpamoCYO/evUyZMoXTTz+dESNG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\nSZJKDMmSJElSyf8HTwUafFAcUYEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe583b13cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fit = hdbscan.HDBSCAN(min_cluster_size=12).fit(ecdf_normalized_df)\n",
    "base_sample_clusters = pd.Series(fit.labels_, index=ecdf_normalized_df.index)\n",
    "counter_histogram(base_sample_clusters)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the initial clustering produced a large number of unclustered samples (-1).  Ideally, there would be relatively few unclustered samples.  To try and get that number down, I'm going to try varying the data set used to cluster the samples.\n",
    "\n",
    "On the plus side, the total number of different experimental conditions in the data set is eight, so the fact that eight clusters were detected is good."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ODQAA02fSkVxV+yT5apIPtdZ+NfFaa60labvzwq21la215a215fPnz9+dTwUAgGk1qUiu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\niGQAAOj8P50mNDsEfGduAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe58347c898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "interesting_proteins = list(set(high_anderson.index) | set(high_cova.index) | set(high_skew_kurt.index))\n",
    "interesting_df = ecdf_normalized_df[interesting_proteins]\n",
    "fit = hdbscan.HDBSCAN(min_cluster_size=12).fit(interesting_df)\n",
    "interesting_sample_clusters = pd.Series(fit.labels_, index=interesting_df.index, name=\"cluster\")\n",
    "\n",
    "counter_histogram(interesting_sample_clusters)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While using only interesting proteins for clustering reduced the number of unlcustered points, the number of clusters is too small given our knowledge of the data.  Additionally, cluster 0 is larger than any known segment of the data set.  Let's take a look at the breakdown of that cluster to see what it's made of."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Genotype</th>\n",
       "      <th>Treatment</th>\n",
       "      <th>Behavior</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Control</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">Memantine</th>\n",
       "      <th>C/S</th>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S/C</th>\n",
       "      <td>103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Saline</th>\n",
       "      <th>C/S</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S/C</th>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Ts65Dn</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">Memantine</th>\n",
       "      <th>C/S</th>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S/C</th>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Saline</th>\n",
       "      <th>C/S</th>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S/C</th>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             cluster\n",
       "Genotype Treatment Behavior         \n",
       "Control  Memantine C/S            34\n",
       "                   S/C           103\n",
       "         Saline    C/S            11\n",
       "                   S/C            65\n",
       "Ts65Dn   Memantine C/S            50\n",
       "                   S/C            47\n",
       "         Saline    C/S            48\n",
       "                   S/C            86"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask = interesting_sample_clusters == 0\n",
    "groups = df[mask].join(interesting_sample_clusters).groupby([\"Genotype\", \"Treatment\", \"Behavior\"])\n",
    "groups.count()[[\"cluster\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That cluster seems to be weakly biased towards S/C mice, but overall it displays very poor separation of groups."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another technique we can try to improve our ability to resolve the samples into a good clustering is to perform dimensionality reduction on the variables.  This effectively eliminates colinearity between the variables that might interfere with achieving good clustering results.  There are a number of dimensionality reduction techniques, but it's usually a good idea to start with principle component analysis (PCA) as it provides you with information on how much of the data's original variance is captured by each dimension.  This is handy because it lets you put an upper bound on the number of dimensions to specify for other techniques such as independent component analysis and autoencoders."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AAJAxZTB296SZ3SvpUWWWa3vI3fea2f2Sdrn7dkm3SvoLM3NlplJ8ehZrBibV2jWoR/ee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\nrNMcAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe581b42cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pc = PCA()\n",
    "reduced_df = pd.DataFrame(pc.fit_transform(ecdf_normalized_df), index=ecdf_normalized_df)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "total_var = np.sum(pc.explained_variance_)\n",
    "cumulative_var = np.cumsum(pc.explained_variance_)\n",
    "ax.plot(cumulative_var / total_var);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From this plot we can see that the sweet spot is near 10 components.  One thing to keep in mind is that PCA works best when your data distribution is multivariate normal.  Since we know from the distribution of the Anderson Darling test statistics that the data deviates strongly from normality, other methods of dimensionality reduction are likely to perform better than PCA.\n",
    "\n",
    "Independent component analysis is a good technique to use with non-normal data.  Let's try it with 15 components and see what sort of results we get."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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V8jSXu8Cxxx7L3LlzOeyww3j66ad54403XK6gbmNJrmNb4hNme0Vt0qRJ3Hzz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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe581b42748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ic = FastICA(n_components=15)\n",
    "reduced_df = pd.DataFrame(ic.fit_transform(ecdf_normalized_df), index=ecdf_normalized_df.index)\n",
    "\n",
    "fit = hdbscan.HDBSCAN(min_cluster_size=12).fit(reduced_df)\n",
    "reduced_sample_clusters = pd.Series(fit.labels_, index=interesting_df.index, name=\"cluster\")\n",
    "\n",
    "counter_histogram(reduced_sample_clusters)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unfortunately, applying dimensionality reduction in this case didn't seem to help much.  It is possible that the results would be better with a different number of components, or different clustering parameters.  So far I've been guessing parameter values and intuitively evaluating the results.  This approach is time consuming and unlikely to produce optimal results.  What we need to do is perform a grid search over the parameter space to determine the optimal values.\n",
    "\n",
    "In order to perform a grid search, I need to define an objective measure of the *quality* of a given clustering. In this specific case, there are several factors to consider.\n",
    "\n",
    "The first is the number of resulting clusters.  Given that there are 8 different experimental conditions in the data set, it is reasonable to expect that there would be 8 clusters, but this is not necessarily the case.  Some experimental conditions may have little effect (producing fewer clusters), or there may be phenotypic variation in the mice within a given experimental condition (producing more clusters).  Thus, the solution should prefer clusterings with around 8 clusters, but that should be a loose constraint.  To model this prior belief about the number of clusters, I will scale the objective by the density of a gamma distribution with a mode of 8."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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XjMj2OkrMGpqdxjXj+vL7t/dQ0xDwOo6IiEiHUGGWuBQKOV7dfJDZo3NISfR7HSemff7S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\nEhl5jm6W6OsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe581920780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dist = gamma(8, 1)\n",
    "X = np.linspace(0, 30, 100)\n",
    "Y = [dist.pdf(x) for x in X]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(X, Y);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After the number of clusters, the next important factor is the quality of the individual clusters.  Since we're trying to recover the experimental conditions from the data, the most obvious criterion is the entropy of the set of experimental conditions for the cluster.\n",
    "\n",
    "Assuming that cluster entropy is the value that I want to minimize, I have to determine what exactly I'm going to take the entropy of.  The simplest solution is to take the entropy of all experimental conditions simultaneously.  The problem with this is that variance in one of the experimental conditions increases the entropy by the same amount as variance in all of them.  Intuitively, it is preferable if we calculate the entropy for each experimental condition separately, then add them together.\n",
    "\n",
    "An additional point of consideration here is that the data set has multiple samples from the same organism.  Theoretically a good clustering should put samples from the same organism in the same cluster.  Since the experimental conditions do not vary from sample to sample for a given organism, this is partially captured by the entropy of the experimental conditions, but I'm going to specifically include it to try and produce the best clusters possible.\n",
    "\n",
    "Finally, since each experimental condition has a roughly equivalent number of samples, I would prefer clusterings that evenly distribute samples among the various clusters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clusters_prior = gamma(8, 1)\n",
    "\n",
    "def ent(data):\n",
    "    p_data = data.value_counts()/len(data)\n",
    "    e = entropy(p_data)\n",
    "    return e\n",
    "\n",
    "def objective(df, labels):\n",
    "    e = ent(labels)\n",
    "    cluster_count_factor = clusters_prior.pdf(labels.max() + 1)\n",
    "    clusters = df.join(labels).groupby(\"cluster\")\n",
    "    for (i, c) in clusters:\n",
    "        e -= ent(c[\"Genotype\"]) - ent(c[\"Treatment\"]) - ent(c[\"Behavior\"]) - ent(c[\"mouse_id\"])\n",
    "    return e * cluster_count_factor "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import tempfile\n",
    "\n",
    "tmp_dir = tempfile.mkdtemp()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computation error, ndim: 18, method: leaf, min_samples: 13, min_cluster_size: 29\n",
      "Computation error, ndim: 18, method: leaf, min_samples: 14, min_cluster_size: 22\n",
      "Computation error, ndim: 18, method: leaf, min_samples: 14, min_cluster_size: 23\n",
      "Computation error, ndim: 18, method: leaf, min_samples: 14, min_cluster_size: 24\n",
      "Computation error, ndim: 18, method: leaf, min_samples: 14, min_cluster_size: 25\n",
      "Computation error, ndim: 18, method: leaf, min_samples: 14, min_cluster_size: 26\n",
      "Computation error, ndim: 18, method: leaf, min_samples: 14, min_cluster_size: 27\n",
      "Computation error, ndim: 18, method: leaf, min_samples: 14, min_cluster_size: 28\n",
      "Computation error, ndim: 18, method: leaf, min_samples: 14, min_cluster_size: 29\n",
      "Computation error, ndim: 19, method: leaf, min_samples: 13, min_cluster_size: 25\n",
      "Computation error, ndim: 19, method: leaf, min_samples: 13, min_cluster_size: 26\n",
      "Computation error, ndim: 19, method: leaf, min_samples: 13, min_cluster_size: 27\n",
      "Computation error, ndim: 19, method: leaf, min_samples: 13, min_cluster_size: 28\n",
      "Computation error, ndim: 19, method: leaf, min_samples: 13, min_cluster_size: 29\n",
      "Computation error, ndim: 19, method: leaf, min_samples: 14, min_cluster_size: 24\n",
      "Computation error, ndim: 19, method: leaf, min_samples: 14, min_cluster_size: 25\n",
      "Computation error, ndim: 19, method: leaf, min_samples: 14, min_cluster_size: 26\n",
      "Computation error, ndim: 19, method: leaf, min_samples: 14, min_cluster_size: 27\n",
      "Computation error, ndim: 19, method: leaf, min_samples: 14, min_cluster_size: 28\n",
      "Computation error, ndim: 19, method: leaf, min_samples: 14, min_cluster_size: 29\n"
     ]
    }
   ],
   "source": [
    "def search_parameters():\n",
    "    for ndim in range(8, 20):\n",
    "        ica = FastICA(n_components=ndim)\n",
    "        df_ = pd.DataFrame(ica.fit_transform(ecdf_normalized_df), index=ecdf_normalized_df.index)\n",
    "        for method in [\"eom\", \"leaf\"]:\n",
    "            for min_samples in range(5,15):\n",
    "                for min_cluster_size in range(15, 30):\n",
    "                    try:\n",
    "                        fit = hdbscan.HDBSCAN(min_cluster_size=min_cluster_size, min_samples=min_samples,\n",
    "                                              cluster_selection_method=method, memory=tmp_dir).fit(df_)\n",
    "                        clusters = pd.Series(fit.labels_, index=df.index, name=\"cluster\")\n",
    "                        yield [objective(df, clusters), ndim, method, min_samples, min_cluster_size]\n",
    "                    except ValueError as err:\n",
    "                        print(\"Computation error, ndim: %s, method: %s, min_samples: %s, min_cluster_size: %s\" %\n",
    "                             (ndim, method, min_samples, min_cluster_size))\n",
    "\n",
    "results = list(search_parameters())                 \n",
    "columns = [\"objective\", \"ndim\", \"method\", \"min_samples\", \"min_cluster_size\"]\n",
    "results = pd.DataFrame(results, columns=columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>objective</th>\n",
       "      <th>ndim</th>\n",
       "      <th>method</th>\n",
       "      <th>min_samples</th>\n",
       "      <th>min_cluster_size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1721</th>\n",
       "      <td>3.276155</td>\n",
       "      <td>13</td>\n",
       "      <td>leaf</td>\n",
       "      <td>9</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1723</th>\n",
       "      <td>3.276155</td>\n",
       "      <td>13</td>\n",
       "      <td>leaf</td>\n",
       "      <td>9</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1724</th>\n",
       "      <td>3.276155</td>\n",
       "      <td>13</td>\n",
       "      <td>leaf</td>\n",
       "      <td>9</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1719</th>\n",
       "      <td>3.276155</td>\n",
       "      <td>13</td>\n",
       "      <td>leaf</td>\n",
       "      <td>9</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1722</th>\n",
       "      <td>3.276155</td>\n",
       "      <td>13</td>\n",
       "      <td>leaf</td>\n",
       "      <td>9</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1720</th>\n",
       "      <td>3.276155</td>\n",
       "      <td>13</td>\n",
       "      <td>leaf</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1570</th>\n",
       "      <td>3.257672</td>\n",
       "      <td>13</td>\n",
       "      <td>eom</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1574</th>\n",
       "      <td>3.257672</td>\n",
       "      <td>13</td>\n",
       "      <td>eom</td>\n",
       "      <td>9</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1573</th>\n",
       "      <td>3.257672</td>\n",
       "      <td>13</td>\n",
       "      <td>eom</td>\n",
       "      <td>9</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1572</th>\n",
       "      <td>3.257672</td>\n",
       "      <td>13</td>\n",
       "      <td>eom</td>\n",
       "      <td>9</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1571</th>\n",
       "      <td>3.257672</td>\n",
       "      <td>13</td>\n",
       "      <td>eom</td>\n",
       "      <td>9</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1569</th>\n",
       "      <td>3.257672</td>\n",
       "      <td>13</td>\n",
       "      <td>eom</td>\n",
       "      <td>9</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2795</th>\n",
       "      <td>3.221017</td>\n",
       "      <td>17</td>\n",
       "      <td>eom</td>\n",
       "      <td>11</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2796</th>\n",
       "      <td>3.221017</td>\n",
       "      <td>17</td>\n",
       "      <td>eom</td>\n",
       "      <td>11</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2793</th>\n",
       "      <td>3.221017</td>\n",
       "      <td>17</td>\n",
       "      <td>eom</td>\n",
       "      <td>11</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2794</th>\n",
       "      <td>3.221017</td>\n",
       "      <td>17</td>\n",
       "      <td>eom</td>\n",
       "      <td>11</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2791</th>\n",
       "      <td>3.190767</td>\n",
       "      <td>17</td>\n",
       "      <td>eom</td>\n",
       "      <td>11</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2792</th>\n",
       "      <td>3.190767</td>\n",
       "      <td>17</td>\n",
       "      <td>eom</td>\n",
       "      <td>11</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1544</th>\n",
       "      <td>3.166101</td>\n",
       "      <td>13</td>\n",
       "      <td>eom</td>\n",
       "      <td>7</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>3.163694</td>\n",
       "      <td>8</td>\n",
       "      <td>leaf</td>\n",
       "      <td>7</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      objective  ndim method  min_samples  min_cluster_size\n",
       "1721   3.276155    13   leaf            9                26\n",
       "1723   3.276155    13   leaf            9                28\n",
       "1724   3.276155    13   leaf            9                29\n",
       "1719   3.276155    13   leaf            9                24\n",
       "1722   3.276155    13   leaf            9                27\n",
       "1720   3.276155    13   leaf            9                25\n",
       "1570   3.257672    13    eom            9                25\n",
       "1574   3.257672    13    eom            9                29\n",
       "1573   3.257672    13    eom            9                28\n",
       "1572   3.257672    13    eom            9                27\n",
       "1571   3.257672    13    eom            9                26\n",
       "1569   3.257672    13    eom            9                24\n",
       "2795   3.221017    17    eom           11                20\n",
       "2796   3.221017    17    eom           11                21\n",
       "2793   3.221017    17    eom           11                18\n",
       "2794   3.221017    17    eom           11                19\n",
       "2791   3.190767    17    eom           11                16\n",
       "2792   3.190767    17    eom           11                17\n",
       "1544   3.166101    13    eom            7                29\n",
       "187    3.163694     8   leaf            7                22"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.sort_values(\"objective\", ascending=False).head(n=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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kSZIkKWNIliRJkjKGZEmSJCljSJYkSZIyhmRJkiQpY0iWJEmSMoZkSeqFCy+8kMbGRkaN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\nkiQpY0iWJEmSMv8PLXR1X1HudfgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe58195ca58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ic = FastICA(n_components=13)\n",
    "reduced_df = pd.DataFrame(ic.fit_transform(ecdf_normalized_df), index=ecdf_normalized_df.index)\n",
    "\n",
    "fit = hdbscan.HDBSCAN(min_cluster_size=28, cluster_selection_method='leaf', min_samples=9).fit(reduced_df)\n",
    "reduced_sample_clusters = pd.Series(fit.labels_, index=interesting_df.index, name=\"cluster\")\n",
    "\n",
    "counter_histogram(reduced_sample_clusters)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This clustering is **much** better than the ones produced by manual exploration.\n",
    "\n",
    "It is worth noting that ICA with 8 components seems to produce results that are nearly as good as the 13 component case.  Let's take a look at how proteins map into this latent feature space."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <div class=\"bk-plotdiv\" id=\"2c9f64eb-cca3-4c67-ba50-d93af3b2b78a\"></div>\n",
       "    </div>\n",
       "<script type=\"text/javascript\">\n",
       "  \n",
       "  (function(global) {\n",
       "    function now() {\n",
       "      return new Date();\n",
       "    }\n",
       "  \n",
       "    var force = false;\n",
       "  \n",
       "    if (typeof (window._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n",
       "      window._bokeh_onload_callbacks = [];\n",
       "      window._bokeh_is_loading = undefined;\n",
       "    }\n",
       "  \n",
       "  \n",
       "    \n",
       "    if (typeof (window._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "      window._bokeh_timeout = Date.now() + 0;\n",
       "      window._bokeh_failed_load = false;\n",
       "    }\n",
       "  \n",
       "    var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "       \"<div style='background-color: #fdd'>\\n\"+\n",
       "       \"<p>\\n\"+\n",
       "       \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "       \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "       \"</p>\\n\"+\n",
       "       \"<ul>\\n\"+\n",
       "       \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "       \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "       \"</ul>\\n\"+\n",
       "       \"<code>\\n\"+\n",
       "       \"from bokeh.resources import INLINE\\n\"+\n",
       "       \"output_notebook(resources=INLINE)\\n\"+\n",
       "       \"</code>\\n\"+\n",
       "       \"</div>\"}};\n",
       "  \n",
       "    function display_loaded() {\n",
       "      if (window.Bokeh !== undefined) {\n",
       "        var el = document.getElementById(\"2c9f64eb-cca3-4c67-ba50-d93af3b2b78a\");\n",
       "        el.textContent = \"BokehJS \" + Bokeh.version + \" successfully loaded.\";\n",
       "      } else if (Date.now() < window._bokeh_timeout) {\n",
       "        setTimeout(display_loaded, 100)\n",
       "      }\n",
       "    }if ((window.Jupyter !== undefined) && Jupyter.notebook.kernel) {\n",
       "      comm_manager = Jupyter.notebook.kernel.comm_manager\n",
       "      comm_manager.register_target(\"530fa96d-7144-4aee-9447-92eb19766381\", function () {});\n",
       "    }\n",
       "  \n",
       "    function run_callbacks() {\n",
       "      window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
       "      delete window._bokeh_onload_callbacks\n",
       "      console.info(\"Bokeh: all callbacks have finished\");\n",
       "    }\n",
       "  \n",
       "    function load_libs(js_urls, callback) {\n",
       "      window._bokeh_onload_callbacks.push(callback);\n",
       "      if (window._bokeh_is_loading > 0) {\n",
       "        console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "        return null;\n",
       "      }\n",
       "      if (js_urls == null || js_urls.length === 0) {\n",
       "        run_callbacks();\n",
       "        return null;\n",
       "      }\n",
       "      console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "      window._bokeh_is_loading = js_urls.length;\n",
       "      for (var i = 0; i < js_urls.length; i++) {\n",
       "        var url = js_urls[i];\n",
       "        var s = document.createElement('script');\n",
       "        s.src = url;\n",
       "        s.async = false;\n",
       "        s.onreadystatechange = s.onload = function() {\n",
       "          window._bokeh_is_loading--;\n",
       "          if (window._bokeh_is_loading === 0) {\n",
       "            console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
       "            run_callbacks()\n",
       "          }\n",
       "        };\n",
       "        s.onerror = function() {\n",
       "          console.warn(\"failed to load library \" + url);\n",
       "        };\n",
       "        console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "      }\n",
       "    };var element = document.getElementById(\"2c9f64eb-cca3-4c67-ba50-d93af3b2b78a\");\n",
       "    if (element == null) {\n",
       "      console.log(\"Bokeh: ERROR: autoload.js configured with elementid '2c9f64eb-cca3-4c67-ba50-d93af3b2b78a' but no matching script tag was found. \")\n",
       "      return false;\n",
       "    }\n",
       "  \n",
       "    var js_urls = [];\n",
       "  \n",
       "    var inline_js = [\n",
       "      function(Bokeh) {\n",
       "        (function() {\n",
       "          var fn = function() {\n",
       "            var docs_json = 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       "            var render_items = [{\"docid\":\"7adddc34-df42-4e71-8814-c23818d5bd42\",\"elementid\":\"2c9f64eb-cca3-4c67-ba50-d93af3b2b78a\",\"modelid\":\"2da243d4-0858-43dd-a6b0-a8c04c509d32\",\"notebook_comms_target\":\"530fa96d-7144-4aee-9447-92eb19766381\"}];\n",
       "            \n",
       "            Bokeh.embed.embed_items(docs_json, render_items);\n",
       "          };\n",
       "          if (document.readyState != \"loading\") fn();\n",
       "          else document.addEventListener(\"DOMContentLoaded\", fn);\n",
       "        })();\n",
       "      },\n",
       "      function(Bokeh) {\n",
       "      }\n",
       "    ];\n",
       "  \n",
       "    function run_inline_js() {\n",
       "      \n",
       "      if ((window.Bokeh !== undefined) || (force === true)) {\n",
       "        for (var i = 0; i < inline_js.length; i++) {\n",
       "          inline_js[i](window.Bokeh);\n",
       "        }if (force === true) {\n",
       "          display_loaded();\n",
       "        }} else if (Date.now() < window._bokeh_timeout) {\n",
       "        setTimeout(run_inline_js, 100);\n",
       "      } else if (!window._bokeh_failed_load) {\n",
       "        console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "        window._bokeh_failed_load = true;\n",
       "      } else if (force !== true) {\n",
       "        var cell = $(document.getElementById(\"2c9f64eb-cca3-4c67-ba50-d93af3b2b78a\")).parents('.cell').data().cell;\n",
       "        cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "      }\n",
       "  \n",
       "    }\n",
       "  \n",
       "    if (window._bokeh_is_loading === 0) {\n",
       "      console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "      run_inline_js();\n",
       "    } else {\n",
       "      load_libs(js_urls, function() {\n",
       "        console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "        run_inline_js();\n",
       "      });\n",
       "    }\n",
       "  }(this));\n",
       "</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ic = FastICA(n_components=8)\n",
    "ic.fit(ecdf_normalized_df)\n",
    "\n",
    "component_df = pd.DataFrame(ic.components_.transpose(), index=ecdf_normalized_df.columns)\n",
    "\n",
    "component_top_proteins = [pd.Series(c).index[:5] for c in component_df]\n",
    "normalized_component_df = component_df.apply(lambda c: pd.Series(ECDF(c)(c), index=c.index))\n",
    "\n",
    "fig = block_heatmap(normalized_component_df)\n",
    "fig.x_range = Range1d(-0.5,7.5)\n",
    "show(fig, notebook_handle=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe5834c1940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def reorder_rows(fit, df):\n",
    "    clusters = fit.single_linkage_tree_.to_networkx()\n",
    "    leaf_nodes = set([n for n in clusters.nodes()\n",
    "                      if clusters.in_degree(n) != 0  and clusters.out_degree(n) == 0])\n",
    "    index_order = {i: v for i, v in enumerate(df.index)}\n",
    "    rows = [index_order[int(c)] for c in nx.topological_sort(clusters) if c in leaf_nodes]\n",
    "    return df.loc[rows]\n",
    "\n",
    "clustered_df = ecdf_normalized_df.assign(cluster=reduced_sample_clusters)\n",
    "sorted_df = reorder_rows(fit, clustered_df).sort_values(\"cluster\")\n",
    "transpose_df = sorted_df.drop(\"cluster\", 1).transpose()\n",
    "\n",
    "t_fit = hdbscan.HDBSCAN(min_cluster_size=5, min_samples=2, cluster_selection_method='leaf').fit(transpose_df)\n",
    "\n",
    "dimension_clusters = pd.Series(t_fit.labels_, index=transpose_df.index)\n",
    "\n",
    "t_clustered_df = transpose_df.assign(cluster=dimension_clusters)\n",
    "t_sorted_df = reorder_rows(t_fit, t_clustered_df).sort_values(\"cluster\")\n",
    "\n",
    "clustered_only = t_sorted_df[t_sorted_df[\"cluster\"] >= 0]\n",
    "counter_histogram(dimension_clusters)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we're going to visualize the distribution of the variables.  In order to do this we need to convert them from 1080 dimensions down to 2.  The best techniques to do this are multidimensional scaling and t-stochastic neighbor embedding.  Both are available as part of scikits-learn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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I\",\"CREB\",\"ELK\",\"ERK\",\"GSK3B\",\"JNK\",\"MEK\",\"TRKA\",\"RSK\",\"APP\",\"Bcatenin\",\"SOD1\",\"MTOR\",\"P38\",\"pMTOR\",\"DSCR1\",\"AMPKA\",\"NR2B\",\"pNUMB\",\"RAPTOR\",\"TIAM1\",\"pP70S6\",\"NUMB\",\"P70S6\",\"pGSK3B\",\"pPKCG\",\"CDK5\",\"S6\",\"ADARB1\",\"AcetylH3K9\",\"RRP1\",\"BAX\",\"ARC\",\"ERBB4\",\"nNOS\",\"Tau\",\"GFAP\",\"GluR3\",\"GluR4\",\"IL1B\",\"P3525\",\"pCASP9\",\"PSD95\",\"SNCA\",\"Ubiquitin\",\"pGSK3B\",\"SHH\",\"BAD\",\"BCL2\",\"pS6\",\"pCFOS\",\"SYP\",\"H3AcK18\",\"EGR1\",\"H3MeK4\",\"CaNA\"],\"x\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[77]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[77]}}},\"id\":\"054cd775-d3c7-4cf5-8ce7-8278213aab92\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"plot\":{\"id\":\"0681fe54-3263-4df2-a045-7bd2449a9da8\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"d6b540c6-e025-4542-ac7f-7ad5f36fc7ad\",\"type\":\"BasicTicker\"}},\"id\":\"dd423ef2-df84-4d84-ba9b-66b18a470ef5\",\"type\":\"Grid\"},{\"attributes\":{\"dimension\":1,\"plot\":{\"id\":\"0681fe54-3263-4df2-a045-7bd2449a9da8\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"a933bced-04d0-43e2-ab0a-3ca02a848f2e\",\"type\":\"BasicTicker\"}},\"id\":\"e09c75ec-2a7c-433c-ba7f-07b59dc168bb\",\"type\":\"Grid\"},{\"attributes\":{},\"id\":\"d6b540c6-e025-4542-ac7f-7ad5f36fc7ad\",\"type\":\"BasicTicker\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.1},\"fill_color\":{\"value\":\"#1f77b4\"},\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"#1f77b4\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"00a12b27-7bcb-4a18-8d2e-01b4ba751cb5\",\"type\":\"X\"},{\"attributes\":{},\"id\":\"a933bced-04d0-43e2-ab0a-3ca02a848f2e\",\"type\":\"BasicTicker\"},{\"attributes\":{\"bottom_units\":\"screen\",\"fill_alpha\":{\"value\":0.5},\"fill_color\":{\"value\":\"lightgrey\"},\"left_units\":\"screen\",\"level\":\"overlay\",\"line_alpha\":{\"value\":1.0},\"line_color\":{\"value\":\"black\"},\"line_dash\":[4,4],\"line_width\":{\"value\":2},\"plot\":null,\"render_mode\":\"css\",\"right_units\":\"screen\",\"top_units\":\"screen\"},\"id\":\"0d747ddc-f534-49c6-ae6b-03b1f9327f77\",\"type\":\"BoxAnnotation\"},{\"attributes\":{},\"id\":\"de2a223d-756a-4848-8b72-82a3cfc60978\",\"type\":\"ToolEvents\"},{\"attributes\":{\"callback\":null},\"id\":\"ae799bc4-9c0b-4844-b06f-dcb75651dd01\",\"type\":\"DataRange1d\"},{\"attributes\":{\"data_source\":{\"id\":\"054cd775-d3c7-4cf5-8ce7-8278213aab92\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"b13e9adb-01fb-45d6-8109-23f25f97727b\",\"type\":\"X\"},\"hover_glyph\":null,\"muted_glyph\":null,\"name\":\"main\",\"nonselection_glyph\":{\"id\":\"00a12b27-7bcb-4a18-8d2e-01b4ba751cb5\",\"type\":\"X\"},\"selection_glyph\":null},\"id\":\"fa3eac18-9862-4346-91f5-b3f668a44d7d\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"callback\":null,\"names\":[\"main\"],\"plot\":{\"id\":\"0681fe54-3263-4df2-a045-7bd2449a9da8\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"tooltips\":[[\"x\",\"@x\"],[\"y\",\"@y\"],[\"protein\",\"@protein\"]]},\"id\":\"e63ea1cb-f33e-4c48-a281-1347971a4b07\",\"type\":\"HoverTool\"}],\"root_ids\":[\"0681fe54-3263-4df2-a045-7bd2449a9da8\"]},\"title\":\"Bokeh 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   "source": [
    "mds_fit = MDS().fit_transform(transpose_df)\n",
    "mds_df = pd.DataFrame(mds_fit, index=proteins.index, columns=[\"x\", \"y\"]).join(proteins)\n",
    "fig = scatter_with_hover(mds_df, \"x\", \"y\")\n",
    "show(fig, notebook_handle=True);"
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   "metadata": {},
   "source": [
    "Unfortunately the latent structure of the data isn't very interesting in two dimensions.  It is still useful to know which proteins are central and which are peripheral, as this gives you an idea how early in the regulatory cascade they occur.\n",
    "\n",
    "Let's take a look at the list of variables sorted by distance from the center of the distribution, along with their cluster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
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   },
   "outputs": [
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       "      <th>cluster</th>\n",
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       "      <th>H3AcK18_N</th>\n",
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       "      <td>3</td>\n",
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       "      <th>PKCA_N</th>\n",
       "      <td>5.116763</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TRKA_N</th>\n",
       "      <td>5.460736</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NR1_N</th>\n",
       "      <td>6.253063</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pNR1_N</th>\n",
       "      <td>6.367285</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pNR2B_N</th>\n",
       "      <td>6.687251</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bcatenin_N</th>\n",
       "      <td>7.168831</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NR2A_N</th>\n",
       "      <td>7.766819</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ELK_N</th>\n",
       "      <td>7.828060</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ERK_N</th>\n",
       "      <td>8.469671</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pPKCAB_N</th>\n",
       "      <td>8.642955</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JNK_N</th>\n",
       "      <td>1.984242</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MEK_N</th>\n",
       "      <td>2.076306</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAMKII_N</th>\n",
       "      <td>2.487119</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pJNK_N</th>\n",
       "      <td>2.849956</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pMEK_N</th>\n",
       "      <td>3.316666</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RSK_N</th>\n",
       "      <td>4.947737</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pBRAF_N</th>\n",
       "      <td>5.164387</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pAKT_N</th>\n",
       "      <td>5.244980</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CREB_N</th>\n",
       "      <td>5.426041</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             distance  cluster\n",
       "H3AcK18_N   10.147864        0\n",
       "H3MeK4_N    10.410855        0\n",
       "SNCA_N      10.464994        0\n",
       "BAD_N       11.004156        0\n",
       "BCL2_N      11.136682        0\n",
       "EGR1_N      11.355165        0\n",
       "TIAM1_N      2.517704        1\n",
       "NR2B_N       4.157152        1\n",
       "AMPKA_N      4.603190        1\n",
       "RAPTOR_N     4.962855        1\n",
       "MTOR_N       5.625554        1\n",
       "DSCR1_N      5.681683        1\n",
       "pMTOR_N      5.786773        1\n",
       "P38_N        9.410502        1\n",
       "GSK3B_N      8.945928        2\n",
       "ITSN1_N      9.872731        2\n",
       "BRAF_N      10.284238        2\n",
       "DYRK1A_N    10.529294        2\n",
       "pERK_N      10.789086        2\n",
       "BDNF_N       4.261409        3\n",
       "PKCA_N       5.116763        3\n",
       "TRKA_N       5.460736        3\n",
       "NR1_N        6.253063        3\n",
       "pNR1_N       6.367285        3\n",
       "pNR2B_N      6.687251        3\n",
       "Bcatenin_N   7.168831        3\n",
       "NR2A_N       7.766819        3\n",
       "ELK_N        7.828060        3\n",
       "ERK_N        8.469671        3\n",
       "pPKCAB_N     8.642955        3\n",
       "JNK_N        1.984242        4\n",
       "MEK_N        2.076306        4\n",
       "CAMKII_N     2.487119        4\n",
       "pJNK_N       2.849956        4\n",
       "pMEK_N       3.316666        4\n",
       "RSK_N        4.947737        4\n",
       "pBRAF_N      5.164387        4\n",
       "pAKT_N       5.244980        4\n",
       "CREB_N       5.426041        4"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = pd.Series(np.sqrt(mds_df[\"x\"] ** 2 + mds_df[\"y\"] ** 2), index=mds_df.index, name=\"distance\")\n",
    "\n",
    "clustered_only.join(d).sort_values([\"cluster\", \"distance\"])[[\"distance\", \"cluster\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here clusters can be thought of as roughly representing biochemical regulatory pathways.  The proteins with a lower distance are likely to strongly influence activity proteins with a higher distance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>C/S</th>\n",
       "      <th>S/C</th>\n",
       "      <th>mice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Cluster 0</th>\n",
       "      <td>35.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cluster 1</th>\n",
       "      <td>29.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cluster 2</th>\n",
       "      <td>34.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cluster 3</th>\n",
       "      <td>70.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cluster 4</th>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cluster 5</th>\n",
       "      <td>24.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cluster 6</th>\n",
       "      <td>0.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cluster 7</th>\n",
       "      <td>1.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cluster 8</th>\n",
       "      <td>0.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            C/S    S/C  mice\n",
       "Cluster 0  35.0    0.0     3\n",
       "Cluster 1  29.0    0.0     3\n",
       "Cluster 2  34.0    0.0     3\n",
       "Cluster 3  70.0   30.0    13\n",
       "Cluster 4  50.0    0.0     5\n",
       "Cluster 5  24.0   50.0     9\n",
       "Cluster 6   0.0   29.0     4\n",
       "Cluster 7   1.0   28.0     5\n",
       "Cluster 8   0.0  177.0    18"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clustered = clustered_df[\"cluster\"] >= 0\n",
    "sorted_clustered = sorted_df[\"cluster\"] >= 0\n",
    "cols = [\"Genotype\", \"Treatment\", \"Behavior\"]\n",
    "in_cluster = df[df_clustered]\n",
    "cluster_conditions = sorted_df[sorted_clustered].loc[:, [\"cluster\"]]\n",
    "cluster_conditions = cluster_conditions.join(in_cluster[cols])\n",
    "\n",
    "group_cluster = cluster_conditions.groupby(\"cluster\")\n",
    "\n",
    "behavior_counts = [pd.Series(group[\"Behavior\"].value_counts(), name=\"Cluster %s\" % idx) \n",
    "                   for (idx, group) in group_cluster]\n",
    "behavior_counts = pd.DataFrame(behavior_counts).fillna(0)\n",
    "\n",
    "def count_mice(group):\n",
    "    return len(set(i.split(\"_\")[0] for i in group.index))\n",
    "\n",
    "mouse_counts = [count_mice(group) for (idx, group) in group_cluster]\n",
    "mice = pd.Series(mouse_counts, name=\"mice\", index=behavior_counts.index)\n",
    "behavior_counts.join(mice)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That separation of condition by cluster appears pretty good.  The next step is to look at genotype and condition separation by cluster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "data": {
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NwO758Vjg8rYZkpaXdKGkeyXdL2mvPH2cpOsk/VHSM5KOlXRCXmaCpMF5ucMl3SfpQUlX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\nMzMzK6H/B9YQJxpDzE0jAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe585ce16a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "group_cluster_behavior = cluster_conditions.groupby([\"cluster\",\"Behavior\"])\n",
    "\n",
    "fig, axes = plt.subplots(3, int(np.ceil(len(group_cluster_behavior) / 3)))\n",
    "\n",
    "colors = [\"#ADD8E6\", \"#9AC7E7\", \"#88B6E9\", \"#76A5EB\", \"#6495ED\", \"#647CD8\", \"#6564C3\", \"#654BAE\", \"#663399\"]\n",
    "\n",
    "my_cmap = ListedColormap(colors, name='my_name')\n",
    "\n",
    "for g, a in zip_longest(group_cluster_behavior, axes.flatten()):\n",
    "    if g is None:\n",
    "        a.axis(\"off\")\n",
    "        break\n",
    "    ((idx, cond), group) = g\n",
    "    x = [1 if l == \"Ts65Dn\" else 0 for l in group[\"Genotype\"]]\n",
    "    y = [1 if l == \"Memantine\" else 0 for l in group[\"Treatment\"]]\n",
    "    res = a.hist2d(x, y, bins=2, range=[[0, 1], [0, 1]], normed=True, cmap=my_cmap)[3]\n",
    "    a.set_title(\"Cluster %s, Behavior %s\" % (idx, cond))\n",
    "    a.set_xticklabels([\"Control\", \"Ts65Dn\"])\n",
    "    a.set_xticks([0.25, 0.75])\n",
    "    a.set_yticklabels([\"Saline\", \"Memantine\"])\n",
    "    a.set_yticks([0.25, 0.75])\n",
    "    a.set_xlim(0, 1)\n",
    "    a.set_ylim(0, 1)\n",
    "\n",
    "fig.subplots_adjust(wspace=0.75, hspace=0.75)\n",
    "cbar_ax = fig.add_axes([1, 0.15, 0.05, 0.7])\n",
    "fig.colorbar(res, cax=cbar_ax);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That separation is excellent.  While not all samples have been clustered, those that have appear to fall into distinct groups.\n",
    "\n",
    "Next let's look at how protein levels change depending on cluster membership."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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isdgoZQW/fgcUHBhTw3DyZyAhpLOAcbIE6y9gOgMQ2IaEGxF3Ndg9gAE2N+II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D9/+Q5xS5trJOoKN7ILHUeXCikkiAZD4M+ioTClxvvc1fqFq09y+Raqww+tzsIfxmwafh4vfd6t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\nVJDDx+Cd09DZhna2QsLW7TjCi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m8ncvIM3stvoGMT6MHda7t7njGA2E6UR07gHT4GgKYS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\nOBwOh8PhcDgcK8D/BWApLs5+yJpvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe5838e2fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def diff_proteins(df):\n",
    "    return df.apply(lambda x: x.mean() - 0.5).sort_values()\n",
    "\n",
    "clusters = list(range(reduced_sample_clusters.max() + 1))\n",
    "diffs = [diff_proteins(sorted_df.filter(like=\"_N\", axis=1)[sorted_df[\"cluster\"] == i]) for i in clusters]\n",
    "\n",
    "titles = [\"Cluster %s\" % c for c in clusters]\n",
    "plot_histogram(*diffs, title=titles);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Identifying significant variables\n",
    "\n",
    "In order to determine how significant a variable is to overall cluster assignment, we can look at the differences between the cluster mean percentiles and overall mean percentiles.  The larger the difference, the more significant that variable is to the clustering.  If we do this for all variables and put the results in a vector, the norm of that vector represents a measure of a variable's overall clustering importance.  We can use that measure to rank and filter variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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cxtl96EWP6V65FipKoa0b56kdWJ83E7j8PvD7sZvWZNaR9Q9CR09mdC0UyNQf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\nBvn/ASGoO0FMjkaIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe581aa0a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "diffs_df = pd.DataFrame(diffs).transpose()\n",
    "norms = pd.Series(np.sqrt(np.square(diffs_df).sum(axis=1)), name=\"norm\")\n",
    "plot_histogram(norms);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "There aren't extreme outliers on the distribution of the norms.  The interesting part of this distribution is the outer tail."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>norm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>pPKCG_N</th>\n",
       "      <td>-0.293995</td>\n",
       "      <td>-0.427490</td>\n",
       "      <td>0.012418</td>\n",
       "      <td>0.208231</td>\n",
       "      <td>-0.416944</td>\n",
       "      <td>0.353141</td>\n",
       "      <td>0.095243</td>\n",
       "      <td>0.398531</td>\n",
       "      <td>-0.154075</td>\n",
       "      <td>0.896038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pCAMKII_N</th>\n",
       "      <td>-0.383386</td>\n",
       "      <td>-0.395019</td>\n",
       "      <td>-0.308932</td>\n",
       "      <td>0.151306</td>\n",
       "      <td>-0.200296</td>\n",
       "      <td>-0.209922</td>\n",
       "      <td>0.357791</td>\n",
       "      <td>0.335792</td>\n",
       "      <td>0.132836</td>\n",
       "      <td>0.874048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P3525_N</th>\n",
       "      <td>-0.451614</td>\n",
       "      <td>-0.337165</td>\n",
       "      <td>-0.260458</td>\n",
       "      <td>0.096148</td>\n",
       "      <td>0.222722</td>\n",
       "      <td>0.235310</td>\n",
       "      <td>-0.119157</td>\n",
       "      <td>0.299425</td>\n",
       "      <td>-0.128714</td>\n",
       "      <td>0.787474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pP70S6_N</th>\n",
       "      <td>-0.176931</td>\n",
       "      <td>-0.457631</td>\n",
       "      <td>-0.119499</td>\n",
       "      <td>0.149111</td>\n",
       "      <td>-0.414889</td>\n",
       "      <td>0.176602</td>\n",
       "      <td>0.174393</td>\n",
       "      <td>0.299489</td>\n",
       "      <td>0.017279</td>\n",
       "      <td>0.775223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pERK_N</th>\n",
       "      <td>0.281190</td>\n",
       "      <td>0.435185</td>\n",
       "      <td>0.355692</td>\n",
       "      <td>0.079185</td>\n",
       "      <td>0.239537</td>\n",
       "      <td>-0.248286</td>\n",
       "      <td>-0.139208</td>\n",
       "      <td>0.030268</td>\n",
       "      <td>-0.211519</td>\n",
       "      <td>0.765049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RAPTOR_N</th>\n",
       "      <td>0.296138</td>\n",
       "      <td>-0.364623</td>\n",
       "      <td>-0.272549</td>\n",
       "      <td>0.068519</td>\n",
       "      <td>0.130815</td>\n",
       "      <td>-0.316617</td>\n",
       "      <td>0.237516</td>\n",
       "      <td>0.319700</td>\n",
       "      <td>0.043220</td>\n",
       "      <td>0.759917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pRSK_N</th>\n",
       "      <td>-0.079815</td>\n",
       "      <td>-0.427075</td>\n",
       "      <td>0.153540</td>\n",
       "      <td>0.236833</td>\n",
       "      <td>-0.325167</td>\n",
       "      <td>-0.020946</td>\n",
       "      <td>-0.061622</td>\n",
       "      <td>0.419636</td>\n",
       "      <td>-0.094947</td>\n",
       "      <td>0.750671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BRAF_N</th>\n",
       "      <td>0.233492</td>\n",
       "      <td>0.406960</td>\n",
       "      <td>0.404575</td>\n",
       "      <td>0.042667</td>\n",
       "      <td>0.117574</td>\n",
       "      <td>-0.207845</td>\n",
       "      <td>-0.294189</td>\n",
       "      <td>0.041443</td>\n",
       "      <td>-0.159285</td>\n",
       "      <td>0.745853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DYRK1A_N</th>\n",
       "      <td>0.262513</td>\n",
       "      <td>0.428097</td>\n",
       "      <td>0.402315</td>\n",
       "      <td>0.081111</td>\n",
       "      <td>0.128833</td>\n",
       "      <td>-0.153829</td>\n",
       "      <td>-0.207312</td>\n",
       "      <td>0.123180</td>\n",
       "      <td>-0.184003</td>\n",
       "      <td>0.743563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P38_N</th>\n",
       "      <td>-0.088175</td>\n",
       "      <td>-0.356801</td>\n",
       "      <td>-0.402723</td>\n",
       "      <td>0.037046</td>\n",
       "      <td>-0.004259</td>\n",
       "      <td>-0.230531</td>\n",
       "      <td>0.277331</td>\n",
       "      <td>0.209483</td>\n",
       "      <td>0.227992</td>\n",
       "      <td>0.724278</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  0         1         2         3         4         5  \\\n",
       "pPKCG_N   -0.293995 -0.427490  0.012418  0.208231 -0.416944  0.353141   \n",
       "pCAMKII_N -0.383386 -0.395019 -0.308932  0.151306 -0.200296 -0.209922   \n",
       "P3525_N   -0.451614 -0.337165 -0.260458  0.096148  0.222722  0.235310   \n",
       "pP70S6_N  -0.176931 -0.457631 -0.119499  0.149111 -0.414889  0.176602   \n",
       "pERK_N     0.281190  0.435185  0.355692  0.079185  0.239537 -0.248286   \n",
       "RAPTOR_N   0.296138 -0.364623 -0.272549  0.068519  0.130815 -0.316617   \n",
       "pRSK_N    -0.079815 -0.427075  0.153540  0.236833 -0.325167 -0.020946   \n",
       "BRAF_N     0.233492  0.406960  0.404575  0.042667  0.117574 -0.207845   \n",
       "DYRK1A_N   0.262513  0.428097  0.402315  0.081111  0.128833 -0.153829   \n",
       "P38_N     -0.088175 -0.356801 -0.402723  0.037046 -0.004259 -0.230531   \n",
       "\n",
       "                  6         7         8      norm  \n",
       "pPKCG_N    0.095243  0.398531 -0.154075  0.896038  \n",
       "pCAMKII_N  0.357791  0.335792  0.132836  0.874048  \n",
       "P3525_N   -0.119157  0.299425 -0.128714  0.787474  \n",
       "pP70S6_N   0.174393  0.299489  0.017279  0.775223  \n",
       "pERK_N    -0.139208  0.030268 -0.211519  0.765049  \n",
       "RAPTOR_N   0.237516  0.319700  0.043220  0.759917  \n",
       "pRSK_N    -0.061622  0.419636 -0.094947  0.750671  \n",
       "BRAF_N    -0.294189  0.041443 -0.159285  0.745853  \n",
       "DYRK1A_N  -0.207312  0.123180 -0.184003  0.743563  \n",
       "P38_N      0.277331  0.209483  0.227992  0.724278  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "high_clustered_norms = diffs_df.join(norms).sort_values('norm', ascending=False).head(n=10)\n",
    "high_clustered_norms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "These proteins represent the largest differentiators between clusters.\n",
    "\n",
    "Let's see how this list compares with the list that results from examining the norms of data grouped by class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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k53Q6Ma/sBalOfiLyZihgLWT5IRKlhckW0gpXIrPHGMgMQjwBe4/gaOvCVpQk\nNyj2uFNdnciCEY/HaW9v59SpUzQ1NXHjjTfi9/t56aWXePDBBwkEAhQWFlJTU0NhYSHDw8N4PB4u\nvfTSyTAlIjLdFLAWolOtEM6G5nYozNOaK5FUcTqgqgwLcLIF09aJ3boJSotSXZnIvGOtJZFI4HQ6\nOXXqFD/5yU9oamoiFosB4PV6qampoaCggKqqKj75yU++ao8pEZHZoIC10JxowvHMi9iiPOySQoUr\nkVRzGGx1OeSFMMcacDy+i8S6WlhXm+rKROa0SCTCqVOnJm8nT57kpptuYv369YyNjTE2Nsb69esp\nLCyksLCQ3NzcyTDl92uPRxFJHQWsheRUC+aZF7GZQWxRvsKVyFySGcRuWpNclxWLQe8AZKWD9skR\nIRaL0draijGG0tJShoaG+PznPz95fzgcpqqqirS0NPr7+8nIyOCDH/yg9pkSkTlJAWuhaG7HPPkc\nBAPJhhYBLdAVmXMcjuRoVjwOzW2YX7dBZgZ2y9pUVyYy6/bv309DQwMnT56cnOq3cuVKbrvtNsbG\nxrjuuusIh8MUFRURCARIS9Mli4jMD3q3WggSCczz+8HvS4ardE2NEJnTnE5ID0CaExqaMK0dHNl8\nNbWbl6S6MpFpNzExQUtLC42NjYyOjvL2t7+dWCzGQw89RGtrK4WFhaxfv56ioiJKSkqIxWK43W62\nbt2a6tJFRC6KAtYCYVdWw0gUMtJTXYqITIUBu6wS8sKYYw38w8cf5vIba7jts5fjDajToMx/u3fv\nZteuXZw6dYp4PA4kp/qtXZscsb3xxhsJBoN4vV5N9RORBUUBaz7rG8CcOIUtCMPEBGQFU12RiFyo\n7AzspWtYNtrGS483suUdy6heW6AW0jIvJBIJOjo6qK+vp7GxkcbGRj73uc/hcrno7u4mGo2yYcMG\nSkpKWLJkyWRXP2OM9poSkQVLAWu+GhjCPPI0JGyyFXRWRqorEpGL5XSy9eYVrN9RSf3eDoZ6Rmg/\ndYYdH1iD1+9KdXUik8bHxwFwu90cPHiQ733ve0SjUQACgQDFxcWcPn2arKwsNm7cyObNmzU6JSKL\njgLWPNTfFcE8vBPiieSC+ayM5MamIjKvBYIe/Olujr3Uxs7/OsrOnx7lji9dRe1lJakuTRapSCRC\nQ0MD9fX1NDQ00NTUxHvf+17WrFmD0+lk2bJlLFmyhCVLlhAOh7XnlIgICljzzsjQGP/7Ew/B2Bi2\nuiK5obA76YHJAAAgAElEQVTClciCYYyhem0h/nQ3z9x/jH/42ENc/o4abv3s5fjStTZLZlZ/fz8T\nExPk5eUxMDDA//gf/wMAh8NBUVERmzZtwu/3MzQ0RCgU4t3vfrems4qI/A4FrHmm5UQfPW1D2Mol\nkKtwJbJQFS8N8e67LuO5/z7Brl8cp/1kP5//3jt1MSvTqqurixMnTkze+vv7WbNmDbfeeivRaJRr\nrrmGoqIiiouL1YxCRGSKFLDmmcywnx3vX82Dx52gaRgiC5rLncbWm1dQdUk+kYExXnq8kep1hbi9\naRrNkgtmraWjo4Pu7m7WrFmDtZZ7772X1tZW/H4/paWlbNiwgfLycsbGxnC5XFx11VWpLltEZN5R\nwJoHrLX8+Gu7yc4NYK0lVBSE+miqyxKRWVJUmQPAme4I373nMTpPD/AHX9zOmq1lKa5M5rru7m6O\nHDnC8ePHOX78OJFIBJfLxRe+8AXGx8e5+uqr8Xq9FBQUaP2UiMg0UcCaBx75/j6e+M9D1Gwo5NLr\nl+L26D+byGKUkeOnZkMRHU0DfPvTj7Lx2kre9/krSc/ypro0mQPOjlAdP36czZs343K52LVrF48+\n+iiZmZmUl5dTVlZGaWkp8Xgcr9dLTU1NqssWEVlwdKU+x+36RR0/+/aLlNWGWX9NBR61bBZZ1Aor\nsnnPXZt5/pET7Hm8kcPPtfCnX9nBCnUaXJSGhobYv38/dXV1HD9+nKGhIQC8Xi/FxcVUV1dTVVVF\nKBTC5XJpDZ+IyCxQwJrDDj57mn/7q6cpqMhk03VVBDL0LbWIgDPNweW/t4zqtYX8+oFjnK7rYcmy\nsEayFoEzZ85QV1dHQUEBpaWldHd3c99995Genk5ZWdnkLRwO43K5tJmviEgKKGDNYS0neskpSOey\nt1WTGQ6kuhwRmWNySzJ458cuZfhMlGd/dpT6fR2s2VbO1puXa6RigUgkEpMjVMeOHaOrqwuALVu2\ncM0115CWlsaf/umfkpeXpxEqEZE5QgFrDrLWYhOWwopsrnznCsJFwVSXJCJzWHqWl+iIk7bGMxx8\n9hmef+gEd3zpKnJLMlJdmlygiYkJGhsbiUQirF+/nkQiwY9+9CNGR0cpLS1l9erVlJWVUVhYiMvl\nwuPxEAjoCzgRkblEAWuOGT4T5VuffoRN11YyPhYnVJie6pJEZB7w+l3c9JGNHHjmNPuePMn/vOXH\n3HTnRnZ8YA3ONHWGm8va2to4fPgwx44d48SJE8RiMbKysigsLCQWi/H+97+fnJwcPB6PuvyJiMwD\nClhzyMR4nH+6+zGajnRRXJVD9fpCTfcQkSkzxnDJtjIqVuXyzE+P8uC9L1O7uYQly8KpLk1+y8DA\nAMeOHWPTpk0APPHEE+zatYvc3FzWrl1LeXk5FRUVOJ1O3G43fr8/xRWLiMiFUMCaI6y1/OCvnubE\ny+1s2FHJ0rUFOBwKVyJy4TJy/Lz9g+vpax/m0K5m+ruGaa3v5+r3rsLjUyfS2TY+Pk59fT1Hjx7l\n6NGjtLW1AeD3+8nJyWHdunVs3LiR7Oxs0tL0sSwiMt/pnXyOePDel3n+4XpqNxdTu7lEU3pE5E0y\n5BQGsQnL/p1NPHt/HU/+6BC3f3E7q68oTXVxC9rZ/ah8Ph9ZWVnU1dXxj//4jzgcDkpLS7n66qup\nqKggFArhdrspKChIdckiIjKNFLDmgETC0lzXQ/mqXNZeVY7bq/8sIjI9jMOwbEMx/qCXXT+v41t3\nPcK6q8t5/+evJCOkqWfTZXR0lLq6Oo4cOcLhw4fp7+9nx44dbN26lfT0dG677TbKy8vx+Xw4nc5U\nlysiIjNIV/JzQHwizpqtpYxGJvCle1JdjogsQEtqQrznU5t54ZET7HvqFN0tg3zhB+/C4dRo+cWw\n1jI8PEwwGCQej/OFL3yBaDSK2+2moqKCzZs3U1VVRSKRICMjg1WrVqW6ZBERmSUKWCnU2XSGH/z1\nTjZeX8X4aIysXLXaFZGZ40xzsOX3llGzoYgzXRF2P3icpWsLmBiPU1IdSnV5c140GuXYsWMcPnyY\ngwcPEgwG+eQnP8nIyAg7duwgKyuL0tJSdfsTEVnkFLBSZPhMlG/e9TDDZ6KUr8yjdLm6fInI7AgV\nBgkVBhkZGuMH/+sZTrzcztW3reKmj27C61cTjNfy4IMP8sgjj5BIJHC73VRWVrJ06VIGBgZwuVxc\ndtllqS5RRETmCAWsFJgYi/Gdzz5Kb8cwV9y4nCU1+uZYRGafP+hh03VVTERjPPHDQ7z4y3ref8+V\nrHtLxaLdImJiYoL6+noOHjzIoUOH+NjHPkYgECAYDLJp0yaWLl1KWVmZRqlEROR1KWDNMmstP/jr\nnTTs72TjdVVUrcnHqB27iKSIL+DmmvetprW+j12/OMY/3/04192+hnfftTnVpc2qzs5OHnjgAY4c\nOcL4+DhOp5OKigra2trIzc1l+fLlWkclIiJTooA1y0aHxzld10vt5mJWXFasduwiMicUL83h3Xdt\n4eVfNZKwlhN728ktySCQ4cHlWVgfFdZa2traOHDgAIWFhaxatQprLY2NjaxcuZKqqiqqqqrw+/0a\npRIRkQu2sD415wGH08HWdy7H6TK4F9hFi4jMbw6HYeO1VSTiCRoPdPKff/trIgNR3n/PlVyyrTzV\n5b1pdXV17N+/nwMHDtDX1wfA5s2bCYVCGGO46667cLlci3Z6pIiITA9d4c+S1vo+Hv7eXpZvKsZa\niz/oS3VJIiKvyeF0kJ2fzvKNRTz/cD3f+cwvWbG5mA/cs5XckoxUlzdlIyMjtLS0UFNTQyKR4P77\n76e1tZXKykouu+wyampqyMrKIi1NH4UiIjJ99KkyC4b6R/nWpx4mGpkgXBSkqCon1SWJiJzX2Q6n\nL/+qkUO7Wvh/3v1DPvi/drDhmspUl/a6zpw5w/79+9m/fz91dXU4HA7+/M//nEQiwQ033EBGRgaB\nQEBT/0REZMZMKWAZY34K/H/Aw9baxMyWtLBMjMf5pz97jIGeEa68aTmFFdmpLklEZMocTgcbr1vK\n8kuLef7herpOD9B+sp9gjo9Ahifl0+mstVhrcTgcPPvss9x3330A5OTkTI5SJRIJPB4PxcXFKa1V\nREQWh6mOYH0H+CPgfxtj/i/wPWtt3cyVtTBYa7nv/32G+n0dbLi2korV6hgoIvNTepaPa963mvFo\njL1PnOTZnx0juyCd93/+SoqXzu6ovLWWpqYm9u3bx969e7npppuorq4mOzubq666imXLlpGfn4/L\npT29RERk9k0pYFlrHwceN8ZkAu975ffNwL3Av1trJ2awxnnrTPcI+3c2sfzSYmovK1HHQBGZ99ze\nNEKFQcpqwxx8tpm/fO9/ccVNy7j545cSzJ7ZtaVjY2M88MAD7N27l4GBAQDKy8uJRCIMDAwQDoe5\n5pprZrQGERGR85nyGixjTAj4feB2YC/wH8CVwB8CV81EcfOdP+jmutsvweNPw+3VcjcRWRiMw7Du\nLZUsv7SE5x86wa9/XseLv2zgM//4dipW5U/bz5mYmOD48eMMDw9z6aWXAnDo0CHy8/PZtm0bNTU1\nBINBnE7ntP1MERGRN2uqa7DuB5YBPwDeYa1tf+WuHxlj9sxUcfNV+8l+9jzWSF5pBk6Xg0CGN9Ul\niYhMO1/AzVW3rGTVFUvY99Qpmo72kJ7lxeVJIzPsv6j1WRMTExw9epSXX36ZAwcOEI1GycnJoaSk\nhEQiwYc//GE8Ho+aVIiIyJw11WGVe621D/32AWOMx1o7Zq3dOAN1zVuRgSjf+tQjRAbGeMuttRRW\nqmOgiCxs4aIMdrx/DdHIOM/99wme+OEhSmpCvO/uK6bUNXViYoK0tDSMMfzkJz9h586deL1eqqur\nWbZsGUuXLsXtditUiYjIvDDVgPXXwEO/c2w3sH56y5nf4rEE/+cLT9DbPsSV71xOQbk6BorI4uEN\nuHF506jZUMShXaf5i9v+iy3vqOHmj11KZth/zrnj4+McPnyYvXv3cuDAAT760Y8SDodZtmwZBQUF\nVFVV4fV6FapERGTeecOAZYwpAIoBnzFmHXB2vkcG4H/dBy5S93/7BY4818Laq8qoVMdAEVmEnE4H\nG3ZUsmJzCS88coLdDx7nxUfr+fMfvIviqhwGBgb46U9/yv79+xkfH8fv97Ny5UpGRkYYGxujpKSE\n0tLSVL8MERGRi3a+EazrgTuAEuBrv3V8CPjzGappXupuGeRX9x2k6pJ8Vl1eSppLi65FZPHyp7u5\n6j0rWXXlEl7aeZQnHniRq9+5kYG+QY4fP0FtbS3Lly+nsrJSa6pERGRBecOAZa39V+BfjTHvttb+\nZJZqmpey8wPc8Mfr8Ka78fi194qILF7WWtrb2zl58iQnT51kfHyMkeM5cL9l1y/qyMytonbbRpbX\nFqd8o2IREZHpdr4pgr9vrf13oNwY85nfvd9a+7XXeNiiMtg3SsP+DpxpDtzeNDJDmjkpIouR5ews\n8p07n6ahsQGny0lhUT7FxcUU5BXgcrtYf1U1h3e38IO/2klpbS6/9yfrKKkJp7Z0ERGRaXS+KYKB\nV35Nn+lC5qPYRJx//vxjnDrUxTUfWE3xFLpliYgsJP39/TQ2NtB48iTXXXctLreL4tJCcvIyKcgv\nwuPxnDNKVbkmn/LaXI4838KJlzv4zmd/yV3fuoH8sqwUvgoREZHpc74pgv/8yq9/MTvlzC8//upu\n6vd2sGFHJYXl2ZrqIiKLwvj4GMeO1dHQ2EB/fx/GWHLzw/QPdJORmUVeXh7GvP6aKkeag1VXlFK9\noYjTR7qp29NGZHCMzqYzrLishKzcwOs+VkREZK4z1trzn2TM35Js1T4KPAKsAT79yvTBC5Jbtta+\n8/NPXOjD5p5jjTh2vYwtyMVWLYG0qXa8FxGZhxLjkBiDtCDER3B2/ALrzgBfAQlvARg/vEGoekM2\nAUMRzLGG5J+XlmFrq8HjvuCn+srtGgkTEZGZUVtbe7izs3PV+c6baiq4zlp7tzHmZuAU8C5gJ3DB\nAWtBGBzG7H4ZmxnElhUrXInIwmQTmGg7ZqQJM9qK9WSTCF0KZoJ4/tZXQtU0dEw1DsgIYtcsx5xu\ngxNNmMZm7PIqWFah91gREZlXpvqpdfa8twP/11o7sKinwwUD2BVLk9+uei/8G1YRkbnOMXQMM3gE\n7AQ4Xdj0Iqy3KDnaZNzgmIH3Pq8XW1MJw8OY0x2Yo/XYvBCEskH7CoqIyDwx1YD1oDHmGMkpgh8x\nxuQC0Zkra46Kx2EoAmPj4PNCwJfqikREpkdsGMfIaRLp1WAM1pEAXw7WV4B15wIumK0v1tLTsbVL\nYXQUevpgcAjT3IktyoOqJeDUPoMiIjJ3TSlgWWvveWUd1oC1Nm6MiQA3zWxpc4y1mN37oP4UdmU1\nZGXM3sWGiMhMSExgRpsxkSbMeBcYi3GlYT05WG8B1lt88euqpoPvlS+xxsZgOIJj3xHs0frke3Dl\nEtDmxCIiMgddyMT25ST3w/rtx/zbNNczd9WdxBw/iS3MhYygPthFZH6Lj+Js/28wcUjzYTMqSHiL\nwBkEHGe3tJobPB7s6mXYvn5MSweOlw9jjzZgt26CrGCqqxMRETnHlAKWMeYHQBWwD4i/ctiyWAJW\nZ0+yqUVWBra0GNI0PUVE5pnYMI7IKSBGImMVmAQ2owzrzsG6QtPTrGKm5WRjc7KxPX2Yrl7oOwOu\nNEgkklO29cWXiIjMAVMdwdoI1Nqp9HRfaEajmF/tTn6DWlGiphYiMn/YGGakBRM5OTkF0PrywA6A\nNSQCy+bnVOdwDjacA+Pj0NSCOd4ELie2dinxWAbONAUtERFJnal+Ch0CCmaykDnL44bCXGzlEghq\n80sRmT8cg4dx9D+PscPYzArieVtJZG4A3GBmsWnFTHG7IRDAluRDPIHjxYP8/Yd+zou/rCceS6S6\nOhERWaSmOoIVBo4YY14Axs4etNbeOCNVzRUTMRgdxeZkgt83/y9GRGThSozhiJzCRE6SyFyN9WSR\n8IUx7g1YdwjMAt5LKjeEzQ1hu3tJDPRw/zdfAGPYuKOSRb2liIiIpMRUP3G/NJNFzEl1JzF7j2Cr\nyyDdr7n9IjIHWUy0ExNpxIy2golj3ZlABKwPnEFsWkaqi5w9uSHe9q4Smo/1Mtw/ysu/Okl/1zCB\nDA8br6vC5V7AIVNEROaMqbZpf9oYUwZUW2sfN8b4gXmwIvoidfVidr0MGeng8UCaPpRFZA6xseSI\nlE3g6HsBTAybXkzCVwRpWfOjYcWMMSxZHgZgLDLO/p1N9LYO8fh9B9n+npVc9raleHyuFNcoIiIL\n2ZSGZYwxHwL+C/jnVw4VAz+bqaJSarKphRtbWQI+T6orEhEBEpjRNhw9z+LseBgSI2AHiYfWEM/d\nRiK4CuZLN8BZ4gm4ueZ9q7j8pmV4A24e+d5evnLHz9i/synVpYmIyAI21aGZjwGXAs8DWGtPGGPy\nZqyqVEkkME88B9Ex7PJKCKanuiIRWeziURyRBsxwAyRGwenC+osgMQTGmwxV8gYMJdUhSqpDdJw8\nw5HnWmhr6CO/NBNvwIXT5SSY5U11kSIisoBMNWCNWWvHzy4WfmWz4YXXsn0iBok4tqwIwtlqaiEi\nKWIhEQdHGmaiHzN0EOvJwQaWYt35wALoAJgCBRVZFFRkMTEWp25P2ythq5eN11Wx7d21ZOfpSzUR\nEXnzphqwnjbG/DngM8ZcC3wU+MXMlZU6tnwJuNPU1EJEZl88iiPSiIk0YP3FJII1WLeLeO6V4AyC\n0fvSdHB5nGTm+ll+aTGxWJwXHqnn+YfrWX1lKW+5dRUF5VmpLlFEROaxqQase4A/AQ4Cfwo8BPyf\nmSpq1vX2Y148iF1SAA6jphYiMqvMWDdmuAEz2pzsBOjJwbr9YCcAN6RpLehMyM4PsPXmFQz1jXLk\nuRaO7G5hYjzBrZ/ejDegTeVFROTiTLWLYMIY8zPgZ9ba7hmuaXZFxzCP74JYDHJzIGsRtTQWkdQ5\n2wkQMJEGzFhLshOgvwScGWpWMYuCOT4uu6GaS7aXMXQmyvOP1ONwGA4+e5q33LaK5ZuKtJ+WiIhM\n2RsGLJP8RPmfwMd5peOgMSYOfNNa+5czX94MS1jMU89DZDTZ1CIzmOqKRGShmxjAMVyPGTlFPLQV\n3B4SwVLIqEw2rdCFfMp4A268ATc2YWk82EVP6xA/+KunCRdnsP09tVyyvYw0l4KviIi8sfNN6P80\ncAWwyVqbY63NAS4DrjDGfHrGq5th5qVDmLYubFlxcvRKFzYiMiMSmNEWHN1P4ex8BDPSgPWHwTEG\n1ibXVzl8eg+aI4zDUHVJPr/34fWsvaqcaGScn/zDc3zzkw8TjydSXZ6IiMxx55sieDtwrbW25+wB\na22jMeb3gV8CX5/J4mbUxAScbMbmhqA4X00tRGQGWMBAPIaj73lwpmEzK0n4Sl8ZrdL7zlzmcDqo\n2VhEzcZCTh/tYXhwjD2/bKB0eZjDu5vZeG0VWbmBVJcpIiJzzPkCluu3w9VZ1tpuY4xrhmqaHU4n\ndvWyZGt2l5paiMj0MeP9mOETEBsgEd4KZpR4eAOkZQBpGqmaQT98ZmSGnjkABDjw/Cg8ehjTcJpf\n/echWFKIXVG14NbvfuV2dVIUEblY50sW4xd539w1No45dBybm5McxQr4U12RiCwICcxoK2boOGa8\nBxwOrK8Q7CDg1obAC0VaGoRzsOk+TGsXtHbgaG7H5uZgt6wDrzo+iogsducLWJcYYwZf47gBvDNQ\nz8yyFrPzRWhuh+WVkKcLHhGZHmakGUffc+DyYTOXkvCVgNG6qgXL68NWlUFpIba9GzM4DN39EM6C\n0ShkBCFNDTFERBajNwxY1tqF9emw7yimuZ3EkkIIq6mFiLwJE4M4ho+DK5OEvxTrCZIIXYJ158E8\nn0EtF8DlhtJirLXJYNXYjKlrTI5gLi2DpWUa1RIRWWQWz+Kj02049h7BhrNhSQE4tbhcRC6UxUQ7\nktMAxzrAGGx6GdhsIA3rKU51gZIqxoDHDR43tmIJpqMbx5F67NF6KCvB1lZBuhpiiIgsBosjYMXj\nmF+/jE33Y8tLwKVvl0Xkwjn69mBGGsHpxmZUJjcFNn51A5Rz5WRhc7KwkQimrQuaWiA7A8rc4DDg\ndGoGhYjIArY4ApbTiV23EoaGIeBLdTUiMl8kojiGGkgEysHpwvrCWK8X6ykE3LpIljcWCGCrK2Bi\nHGJxaDyN6eqD4Qi2uhwqSpJNM0REZEFZ2O/s1kJLB/h9EI0mv0EUETmfiQEcQ8cxI6fAxDFpCayv\nCOvOBhNOdXUy37jc4AKsxXo9mIHB5JT1g3VQVYpdWq4v/0REFpCFHbAOHMPx0mES1eWQH9K3zSJy\nHhZHz68x0dbk+qpAIYlAGTgzAUeyf6rIxTIG8sPY/DD2zACmvRvqTmKGItjNa5NTB8+eJyIi89bC\nDVgtHZiXDmNDWRDO/s0Hl4jIORKYsR6sJw9sHNLStL5KZl5WJjYrE6KjMBaD+tPgMJiTLdhlFVBS\nqDbvIiLz1MIMWIPDmCefg4Av2dTCraYWIvI7EjEckUbM8HGIR4jnbgWXm0SwBkjTKILMDq8vuauk\ntdDTB9FxHC8cwO47AlVlyb22/PNv20kRkcVs4QWshMU8sRsSFluxBNL9qa5IROaSxDiOoTpMpB7s\nGNadhc1aA2lewKVpgJIaxkBuCJsbwvaewXT2wNEGTEMT9u1vUfdbEZF5ZOEFLIfB1lRATz/kZKa6\nGhGZK2wiOd3PJjCROqw3B+svxbrCYDQVS+aQUFZyevvoKAxGoKEZMtMxJ5qwJflQWqTugyIic9jC\neoeOjCb3GInFoSCsKT4iAuP9OIb+//buPEbyM7/r+Pv7q6ur6+j7Pqfn8oxnfNvrXdvrXW8SIgIb\nRBAkQES4JI6QP4IgCPiH8E8ACSQQIgGUTRRpsxAkYEnIOntn4z18znoO23P1zPQcfR9VXV1d1VW/\nL3/8xvas2bV77J6uqu7PSypNd011z3c0z/zq9+nneb7Pm1BfJ+x9CqxMvf8p7a+S5pdORw8PYWkV\nVgsEswv4qTfgwBh+eEKHF4uINKG9E7Cuz2JffiHaHNzVoaYWIvucVeaxwptY5RYEMTwzAl4CkhAk\nG12eyPZZALks/uAxfGUNm1uCC1cILlwhfOYxGOpvdIUiInKHvRGw1opRU4v2Nsi0q6mFyD5n5esE\nSy9EhwN3HCRMj4O1aVZbWl9XB97VAZUKPrsIqwUIQ1hbj35/agzaUo2tUURkn2v9gLW1hX31O9EB\njlNqaiGyP4VY6Vo0U9U2hCdzhF334alhsJSClew9qRRMjETdB0ub2MwstlbAz5yH0UH88GR0RInG\nvojIrmvtgOWOffMlWC3ghyaipYEisn94naA0jRXfhHoJT/fiqTQQw9NTja5O5N4zg1QSv28KL5aw\n+UW4OU9wfRafGsUfe6DRFYqI7DutHbAA78xCOKimFiL7jJVnCFZehXATT+Xxzgfw5CDRGVaNrk6k\nAXIZPJeBiRo+twQEcGUGUinsxiw+NaHuuiIiu6B1A1YYQr0O7e2Qyaiphch+EFajXy0B1PFkGs8c\nw5P9arUu8rZ4HEYGoo+3tmD2BszMEly+jnfk8UPjMKFW7yIi90prXl1XC9jz34rWmCeTkFZHMJE9\nLdwkKJ7H1i/g2QOEuQN4Kosnn1CwEnk/iQQMDeC9XfjcEra4SvDKGfz1N/E/8xwkWvM2QESkmbXe\nlbVSxb78AlS3oB5CWt2SRPasepmg+Ba2fhGshqcHCNu6wQMwLQUU2bZEEkaH8NEhfLUA6yWYnoHO\nPHZpBu/vjg4wVuASEfnIWutKGjr2xy9CsYQfmYSufKMrEpF7KFh9DducwTODhJkDEOvQ4cAiH1Vn\nPnp4CIsrsLBEMHMTf/UsjA/jU2O4d2Da1ywi8qG0VMCyV85gM7OE40PQr6YWIntObZ2g+AZh9gjE\nkoS5cciNQSyvYCWy0yyATHt0gPFaAZtfgplbBFdv8J0jW3z8p44oZImIfAitE7DcoVDE+3tgdBBi\nutkS2TNq6wSFc9jGVTDHkm142zDEcgpWIruhI4935KFew+eW2ShUePH5S6zOrXNzeoUn/tQhjj42\nTDyhPY8iIh+kdQJWGOJjQ9Heq0Si0dWIyA4JVl7GSpfBwLOjhJkJCLIKViKNEIvDcD+9I+1UN2ss\nXC9w9dwCF165RTqb5KFnJ3nkx6cYOdjd6EpFRJpW8wesjTL2zRfxA6OwVYP2dKMrEpGPqlaCeCba\nAxK8HawmIcgoWIk0iWRbnBNPj3P8yVGuvLnAzBuLfO8Pz3P53Dx/41efI9fZxmapSltGnXxFRO7U\n3AGrVse+8m1YXoOuDujtanRFIvJRbBWipYDla4Q9H8dTacLsASChPZUiTSqIB0ydGGDqxADlYoXV\nhTKvfuUSsWSM5z/3GgdODvDojx3k+JMjJJLNfVshIrIbmvdK6I698AosruBTY9DTqRswkVZVKxKs\nncXK16KlgLkxPBEDT+r/tUgLSedSpHPR8SjF5TLjx/uYOb/ExddmSaYTnPjEGJ/+S/fTM5RrcKUi\nIo3TvAHr9FvYpWuEIwMw1A+Blg2JtKaQ2PzXwCt4bixaCmgZBSuRFpfrTvP4TxzCayEzF5a49uYi\n3//WFaZO9hOLB6yvbmIGwwe71Y1QRPaV5gxYYYhNX8d7OqODD+PqWiTSUmrrBKVLhPkT4BXCruN4\nIgOWVbAS2WMsHjB+rI/xY33UqnU2ihW+/80rnPrjq8xNr9I1lOWRTx/goU9NamZLRPaF5gxYgB87\nCJsVSKpjoEjLeE+7dVJ5SOTx5ICClcg+EE/GyCajZlRP/MRBps/McePiCl/9/Gm++vnT3P/xUf7K\nP/1kg6sUEbm3mitgbVawV87gwwOwWYVMe6MrEpHtCLcI1k5hpStgrq6AIkI6l+L4x8c5/vFx1hY3\nuMZCqHwAAB/lSURBVHJmHsx4+cuX6BvN86XfOsWxj41y8ukxuvqzjS5XRGTHNE/Aqtexr30H5haj\nzwd6G1uPiHwwD6MAZQFWXcQzw1FXQAUrEblDR287D35qEoDqZo1z373BynyJL33uNb70udcYPtTN\ng5+c4KFPHyDX2dbYYkVEPqLmCFju2AuvYrOLhJMj0N+j5UQizay+SVB8A9uYod7/HAQV6r2PE7Vb\nV7ASkR8t2RZnaKqToamHWJld58q5BeaurPKHv/kamY4U9z0+wkaxSq1aZ2CiQw0yRKTlNEfAev0t\n7OJVwuF+GBlUx0CRZhVWCIpvYsULYHW8fQgogrcrWInIXesazNI1GC0PXJldp7hc5uU/usSbL93g\nytkFOvsznHhqjPs/Ps7Y0R6CQGFLRJpf4wNWdQs7eyHqGDihjoEiTau+QWz2D4EtPDNEmJmCWF7B\nSkR2xNtBC+C+j42SyaeYvbbGC//7Tf7kf75J/1ieX/oPf5ogFhDWQ4KYrj0i0pwaH7BiMfyBoxA6\nJJONrkZE7hTWor1VqQGwGJ6bIEz1QrxLwUpE7pl8d5r7nxrn/qdgc73C1TcW2arU+fbvn6d3OMcX\nf/1l+oZzHHtylKOPD6tJhog0lcYFrPUNuHAFujuicJVJN6wUEXkPrxOsX8KK58Cr1AeehSBGmDms\nYCUiu6otm+Lo4yMAhLWQ+ZkC+Z52rl9a4cKpWfh16BvN82N/9QFOPjXe4GpFRBoVsLa2sC+/AIVi\ndN5Vd2dDyhCR9/CQYGMaWzsLYRlv68KzJ8BSQAy0/UFEGiiIB+R70jz5U4fBQxZvlrh+cZmFmTUu\nfX+WTD5FbavOd//gIkcfHeLQw4M63FhEdt3uBywH+8b3YGUNPzQehSt1CBJpDvUStvIqnsrh2fvw\nZD+Y9kWKSBOygN6RHL0jUYCqVevcuLjM3LUCV8/N8+aL1wHo7M9w5NEhPvOXH9j1FvD/5HdWd/XP\n26t+7ef1g3hpLbsfsDbK2Mws4fgQDPYpXIk0lGPlG1hlkbDjfghC6n2PQ7xTwUpEWko8GSPXnSbX\nnebQA30s3lrn5qVVFm8WeO1r0xx+eIiB8U4uvHaT4nKZqQcGGD/WRzLV+O3oIrK37PpVJVExfKAH\nRocgphs4kUaxzVsEa2dgaxkSGagPgSUh0dPo0kREPpogoHckT+9IHohmt1YXSizeLHL22zNcv7DE\nN37vHLG4MXK4h+MfG+OTP3OswUWLyF6x6wGrr+8oPj4KCf3ESKQhakWC5Zew6gLE2wi7juFto2CJ\nRlcmInJPxJMx4snoh7qf+OxRNosVbk6vsnCjwMrcOq99Y5rRI9109mf46udP09mfYfJ4H+NHe0i1\nq8OxiNydXU85QTwJCc1ciew6r0fL/iyO+QbeeYSwbQyCVKMrExHZVW25FFMPDDD1wAAA1XKNGxeX\nufbmIpden6WwWI5eaDA42ckzf+4+Hn5uqoEVi0gr0TSSyF5XKxKsncZq69T7PgFWod77CSCuPZAi\nIkAyHSeZjm6JfvIXHqZcrDB3dY3FW0VW5kqcf/UW6VyKWCLgf/777zF+vI+J+3oZO9rL0IEuYnEd\nXyEi71LAEtmrahsEhbPYxjQEhmfGwMtASsFKROR9pHMpJk/0M3miH4B6NWR5dp2V+RLJdII3X7zJ\n69+8CkAsGfCXf+UZjj0xQqlQoVatke9px3SdFdm3FLBE9iCrLBIsfB0sxLNjhNkDYBkFKxGRDyGW\nDGhPpmjPpxg51I3XQ1YXN1iYKbAyX2LmzQUKixvcnF7hpS9dpL0jxejhHihnoLsD+nvU2EtkH1HA\nEtkrwi2sVsQTXXi8Hc+NEqbHIZYF0/IVEZGdYrGAroEsXQPZd56rVUMy+RT3PTHC2tIGNy4sYWs3\nAfAffxra22B2Ecpl6OyArjyk1EBDZC9SwBJpdV4nWL+IFc8BRn3wGcAJs8cVrEREdkk8GdA/3kH/\neEf0hDtf+NoalDZgbhFwbOYWtlJ452s8nYL+XvxjD0ZP1GrRTJdWG4i0NAUskZYVYqUr0VlWYRlv\n6ybMHgSPK1iJiDSaGSST0eM2P3wAr1ahUIJyGStXoLAO09ehPY29egaqW9CZh64OvDMP3XnIZhr4\nFxGRu6WAJdKibHOeYOUlPJXHs8fwZF/Uhl1ERJqTGaRS0Bcdj+EA7hDWYa2A57PYRhnWijC3SAD4\nYB/+2AlIJLCz5/FcJgpgHTmI6zZOpBnpf6ZIC7HNWaxeImyfwJPthD0P48l+BSsRkVZlBrF49Bgd\nejd01er4xgaEIVy7CbU6XLpGUA+B2+Es244fOwQHRqPXlSvRXi8tMRRpKAUskRZg1SVs7TRWmYNE\nFtJ58DieGmp0aSIistPMIBGHjvwPPO2PnsA3yrBehvJmtMRwcRkMqNUIXj2Hv/113floieFAH6R1\noLzIblLAEmlmtXWC1e9jm9chlsA7DxO2jYMn9RNKEZH9xgLIZKIHdywxrNehskU40h+FrlIJllYI\n3AkfuR+G+qFQxK7dwrvyUQDrykMy0dC/jshepYAl0szCLax6C88fIGyfBNPSDxERuYNZtBcrG4ds\nJgpdAPU6XtqImmbM3ISVAszOE1y98c6XeroNf+5jUWArbQCmJYYiO0ABS6SZhBWCwhsQVgk7T0I8\npD7wSUBveCIichdiMcjn3v08ncaH+vDNCqxvvLvE8NostKWw67ewWwt4MhE10OjuxLs6YGxQ7z8i\nd0kBS6QZhDWC9bew4ltAFW8fBi8BCTAdRCkiIjvAAkinowc/2MXQO3J4YNhGBdZLsLCMpZJ4PgOp\nJHbxKoQeha6uPGTbFbxEfgQFLJEGs8o8wdK3wTfxdD9hZgriXTrLSkRE7r23uxh2RHuzfmBfV3kT\nbi1Er7kxB4V13n5n8kQcxkfwR++PnqhuaU+XyG0KWCIN4RBWwFJ4LI2ncnjmJJ7oUct1ERFprLf3\ndeWy7zzlxw5BrYaXSlDcwMqbURv5y9cg3YZ9+9XoUOXuDry3C7o6o0OSdVaX7EMa9SK7zMo3CdZO\n40GMsPdxCLYIOx9VsBKRpvGFb200ugRpRvE4dHRAR8cdywtDKKzj/T3R8sKFZYIbcwCEhyfhxJGo\njfzNBejpVBMN2RcUsER2iVUWorOsqguQaMdzB8FDsFT05iMiItJKzKJmGrFYtFwQwB3frEKxCIHB\npWtQ2iC4dA0Ab0tBTxfe1wVjQ5Bua+hfQeReUMAS2QVWvk6w9ALEkoRd9+Fto2peISIie49ZdLDx\nnYcbJxOER6eguI5tbML8EsGNWcJkAvp6oFiClTXo64KujiiwibQwBSyRe6W2jtXLeLIHT3bgHYcI\n02M6y0pERPaXWAw689D5bhMNL29CuQozt2BukWBuEQAPDLq7oL8bP3ZQYUtakgKWyE6rbxIUzmKl\ny5Bop973McAI2w8rWImIiJhBe/rdzydGCIf6YKWIlUpQKsHFYjS7lU3z9f9+gyAwpk72M3KomyCm\nLrvS3BSwRHZKWCUo3j7Lyup4ZoQwOwWeVLASERH5UcwglYLBFE5v1DyjVoPVAqys8eprN1i6WQQg\nmU5w4GQ/D35ygoeenWxs3SI/ggKWyA6xyhy2fhZvHyTMHoRYXmdZiYiI3C0zSCTg9rFan/m5ExRX\nNrl5eYWlmwWuvbFIrVqjdyhLrjvNN37vLFMnBzn44CDtOe1vlsZTwBL50EKsdAXzOmFmAk9lqfc+\nqUOCRUREdpIZue40R7vTwDCEzmapyuXTC5QKm7z8lWm+938vAjByuJujjw3z8KcP0DOUa2zdsm8p\nYIncNcc2ZggKZ6FWwNt6oL0LPA6JnkYXJyIisrcFRlsuRVsO8r1pfvrvPM7s1RXmrqyyeKPI1373\nDKl0gpNPj1NerzJ3dZXDjwyTyac++HuL7AAFLJG7YNUlgpVXYWsZEhnCngfw5BAQ01lWIiIiDRBP\nBowe7mH0cPRDzlJhk3o15Pt/fJVLp2a5eGoWgLGjPRx/coyjjw0zMNGBaX+03CMKWCLbEgIBeB18\ng7D7OJ4aAUs0ujARERG5Qyb/7uHFD31qksEDXdy8vMzC9QLP//YpvvK7p/nl//RTdPRmWJlbJ9/T\nTiKpdvCycxSwRN7P1hrB2mmIJQg77scTAfW+p3VIsIiISAsI4gHDB7sYPtgFQGGpzML1Aq9/6xrx\nRMAL//stSoUKRx8d4vjHx7jvsWHaMnqPl49GAUvkh6mtE6ydwcrXIAjw3CR4FVDLdRERkVaV70mT\n74nO4AprIYceGuTW9CpvvXKLMy/MYAE8+xeO8xM//1CDK5VWpoAl8h62cZVg+UUwx3NjhJkpsLSC\nlYiIyB4SxAOmHhhg6oEBwlrIzekVbl5cZm2xzKlvXiGdTfBHv/M6Dz4zwf2fGFNXQtk2BSwRgLAa\nPWLteDyHZ4YJswcgyCpYiYiI7HFB/I5GGe5USlvMvLHI+uomX/qtU3zpt04xMNHByacn+NifPqyO\nhPK+FLBkfwtrBOvnseKbeLKTsOchiDlh/oTOshIREdmPzEhlEowd62XsWC+r8yWuvrHI3JUVvvL5\n1xk53M3IoW5WZteJJWIMHehUR0L5AQpYsj95nWD9ElY8B76Jp/ujpYAeU7ASERGRd3T2Z+jsz8Cz\nE6yvbjJ3ZY25K6u8/JXLLF4v0D2U5cFPTnDy6QkGJzsbXa40AQUs2ZeC9fPY2ut4WxeePYknesDU\nolVERER+tGznuy3gn/jJg1w9u8DNK6t8/b+d5ev/7SzHnxzlr/zTZzSjtc8pYMk+4VjpKsTa8FQP\nYbofiz+CJ/sUrEREROSuZTvT3P/UOPc/NU5xuczVcwskUnFe/NJFeoay/N/PneL+J0d58NlJeofV\nIGM/UcCSPc/K1wnWzkJtFW8fxJP3g8Xx1GCjSxMREZE9INed5sTT4wBsVepceG2WzfUqX/38ab76\n+dMMTnXx8KcmeeQzU2qQsQ8oYMmeZZV5gtXvw9YyJDKEPSfx5CCQAM3ci4iItIQvfGuj0SV8CCk4\ncBgGN2FhiVs3i8z+5mv8wRvAYB9UqxCLQ9vuHGr8az+vvWG7SQFL9iAHDGpF8A3CrmN42yhYotGF\niYiIyH6SboPxEXwcfGMDSmW4dBW7MQdLq9Dfg0+OwugAxHVbvlfoX1L2DKssYoUz0DZImBnB03nq\nbU+BaSpeREREGqy9PfrVHe/vwYIAVtcIXlzCXzKYGsMfPdHYGmVHKGBJy7PqCrZ2GqvcgliCMOgG\n3wJSOiRYREREmosZ5HN4PheFrdU1bHEVL5Xh5hzkstiFK/joIPR1616mBSlgSUsL1s5gxbMQS+Ad\nBwnT42BtuhiJiIhI8zODrk68qxPcYb0Mc0tw+RrBxat4KgUTw/jECHTmdH/TIhSwpPVsrUEsBZbE\nk3nIHyBsn1SwEhERkdZlBqkkpJJ4PosvrmDLa3B+muD8NOFTj8Jw/7uvlaalgCWtY6tAUDiHla/h\n2QOE+Sk8mcGT9+lCIyIiIntHPA6DffhgH1Sq+MIyrK3DZgVbXIH19dvNMYaiUCZNRQFLml+tSLB2\nFitfg8Dw3DhhZhw8oWAlIiIie1sqCaO3z+6s1/DaFrZeJnjlLP7KWRjqw6fGYWSgsXXKOxSwpOkF\na2ewzRk8N0aYmQTLKFiJiIjI/hOLw+gwPjKEF9exhWVYXMG2anhHFtJpWF6Fni4IdK/UKApY0nxq\nRYLCG4TZwxBPEebHoWMCLKtgJSIiIvKeToRsbsL1WdisEpyfxpMpmBjCJ0egM9/oavcdBSxpHrUi\nwdo5rHwVDCyVxmPDEKhrjoiIiMgPZRbNXAEkE4STo9jSKly4QnDhCp5r59YzzzJ0oKuxde4jCljS\nFIKVV7DSJTBuLwXUjJWIiIjIXYnFYaAXH+iFahWfX8LWikyfmadeC5m9sspWpc7JZ8bJdrQ1uto9\nSwFLGqe2DvEseAixQMFKREREZKckkzA6hI8OkUjFufz6HK998wpz06v8n//8MocfGuTh5w5w/MlR\nkm2JRle7pyhgye7bWrvdbn2GsOcJPJWNugKiroAiIiIiOy2RipFItfPsnz/GwvUCV87Oc+38Ehde\nm2XyRB9//V88RyIZI6yHBLGg0eW2PAUs2TVWXcEK57DN6xDE8Nw4nkiBJxWsRERERO41M/rGOugb\n68DrITcurVCr1fnO758n15ni9//ra5z4+CgPfvoAk8f7CNSJ8ENRwJJdEhIsfgtsC89PErZPgqUV\nrEREREQawGIBo0d6APB6yOLNIh19aV752jQv/dElct1tPPDsJM/89H3ke9obXG1rUcCSe8Yq89j6\nZcKuR4EqYfdJPN6uYCUiIiLSRCwWvDOzVd2sceXMPDcuLvOdL77FxNFeJo73UVgpk0zG6RtV2/cP\nooAlO8yx8i2s+AZWXYRYArYGIZ7HEz0KViIiIiJNLNkW58hjwxx5bJhKqcryXIml2XVOfX2auatr\nDEx08NCnDnDymXG6B7KNLrcpKWDJzqlvElv4JtRWId6Gdx0lTI2ApRSsRERERFpMKpMklYk+fuQz\nU0yfmefm9ArP//Ypnv/tU5x8Zpyf+8dPN7bIJqSAJR+N17GtNTzZHTWuSKbw/HE8NYy6AoqIiIjs\nDbnuNA98coIHPjnB6nyJ6TPzeOi8+pXL9I7kef53TnHiE2Oc+MQ4ue50o8ttKAUs+XDCLYLSZaz4\nFniN+sCzEDhhx4NgGlYiIiIizeIL39rY4e9okBiAGrz+J0VYn8WurDJ9ep4v/sYr0NuJj4/A+DAk\n998ZW7oTlrsTbhIUL2DrF4AqnurGMxNgMSAGmrASERER2T/iCejsxB/swIslWFrF1goEr54lTCZg\nuB8qWxALoC3V6Gp3hQKWbJMDBlsb2PpZPD2At4/fblwRa3RxIiIiItJIZpDPQj6Lu+PrJdgow4Wr\n2Ow8LKxEM1ujwzA6AO17dxmhApa8L6suRcsALU7YeQISTr3/GQiyYDrpW0RERETewwxytzsMuuO9\n3ZgFsFYkOHUOTp3Dh/vxpx9rbJ33iAKW/BBvt1p/C6vOR80rsmPgZSAOMZ1/ICIiIiLbYAb5HJ7P\nRWGrtAFLKxA6TM9ARw47fR7v74aRQchlWr5JmgKW/H+CwjmscAbiKbzjEGF6DKyt5Qe7iIiIiDSQ\nGWQz0QOgVoObc1BcJ5hbhNPn8UwaRofwqbEobLUgBSyB+ibB+kU81Y+nOgnburH4/XhqCLVaFxER\nEZF7Ih6HXA4/eR9eLt9ukFGEty5DMg4To1CvQ2kD+nqiRhktQAFrP9taIyiexzaugNXBDuHJGMTS\neHyi0dWJiIiIyH6RTsNoGh8dgmoVanW4dgObW8LmFvF4DAb78JFBGOpr6vbvClj7VLD8IrYxDWZ4\nZpgwMw6xDiBQq3URERERaZxkEpLRhz42iGfasJUCzC4QXJ/FYwH+Z5+LXrdVg0RzRZrmqkbunXCL\nYOMqYXYKPIRkGk9MEabHwdJaBigiIiIizScWh75evK83apKxWoDyJlyagXQKO3cJcBgZwIcHoKcT\ngsYuJVTA2utqRYLiRWzjMrCFxRxPdRGmR4kOBlawEhEREZEWYAZdHdHDHeo1PJ/BCuvw1jTBW9N4\nIo7fdxCOHWxYmQpYe1VYJVj6Lla5BQbePhh1A0x0EwWrRhcoIiIiIvIhmUE8AWPDOEC1ii8XsGIB\n1oowcwsScez1t2C4Hx/uj4LZLkwuKGDtJWEF21rDk323B88mnp8kbB8Dy+hgYBERERHZm5JJGOzF\nB6OlhGxWYGE5aphx7iLBuYt4MgEDvfjJI++2ir8HFLD2AKsuYeuXsI1rEBj1gU+DhYTdjwFxLQMU\nERERkf3DLApcPUm8pwsqVXx5NVpKeGseBnqgHsLyGlYo4oP90NsJsdiO/PEKWC3MKgsEq6dgaxmC\nAM8MRbNVBETnVzW6QhERERGRBkslYagfH+qPZre2tuDWPHZrERaWor1bsQD6evDBPjg88ZEmKBSw\nWs3WGlgMYhmgDlTwziOEbSNgbZqtEhERERH5Ud6e3QJ8agzGBvCVQjS7tbyGFdfx3k5It8H09ejX\n/h7IpLf9RyhgtYKwhpWvYaXLWHUJz0wQdhzBE3HqvU9HgUtERERERO5OIgn9vXj/7b1b1a1o71bo\n2PlprLoFgGfat/0tFbCaXLD2fWz9IlCDeAbvOBS1WPcYmJYBioiIiIjsCLNoOeFt/tBxvLgOa0Ws\nuMFUx+Nd2/k2CljNJqxg5Rt4Zgq8BlTx9h48PYonejVbJSIiIiKyG8wgn4N8DgeS19q2dSOugNUU\nQqw8i21cwco3wOrUY3FIZgizB4nOrVKLdRERERGRZqeA1Whba8QWvgHhJsQSeHaEMD0C8Tw6EFhE\nREREpLUoYO22cJNgYwa3BN4+BkEMb+vA00fwZD9Re3WlKhERERGRVqSAtRu8hpVvYKWrWGUOrA5t\n/Xg6Cxhhx8NaAigiIiIisgcoYN0zztvr+4LlF7HyDMRTeG6MsG0Q4p1oCaCIiIiIyN6igLWjHKss\nYhszWPka9b5PQSxBmBnC2vvxZDcQ1xJAEREREZE9SgFrJ9Q3CQpvRLNUYRnM8HQf+Bp4DhI9uEKV\niIiIiMiep4D1oThWXQYP8WQvUZv1S3hbN56ewhP9YCntqxIRERER2WcUsLYtxCoL2MYNrHwdwjKe\n6sF7HgFz6v3PgiUVqkRERERE9jEFrPfj4TuBKVj4E6xyK1r+19aDpw9EbdX99iHAylUiIiIiIvue\nAtZ7hZtYeTZqq16Zoz74p8Aczw7i2V480avlfyIiIiIi8kMpYN1m1SVs9bVob5WFEKTw9gEI1yBI\n48lBdf8TEREREZH3tT8DVljDKnPY5k28bRhvG8AJMavi+UnCZB8k3j6nSqFKRERERES2Zx8FLCco\nvgmbc1hlAQghiEEigXsbxALC7qe09E9ERERERD60vRuw6mVscw7zLcLMIaCObVwCA8+N4Mm+2wf/\nJgADTVSJiIiIiMhHtKcCllXmo+YUm/NQWwVziOegvRuAes8Tt1upxxpcqYiIiIiI7EWtG7DCKlZZ\nxCoLhB0nAcfKM1jpEp7qgMxBwmQvxDtut1I3tVIXEREREZF7qrUC1laBoHQJqyzC1ko0Q4VBugsS\nGcLsCGQnweLaSyUiIiIiIruuSQOWR2Gqugibi3hmEk/1YvV1rHQhmqFKH8ATnXiiOwpUBBAkG124\niIiIiIjsY00SsEIggLBKsPRdrLoIvhXNUAVJPN0OHsMTCeoDn9EMlYiIiIiINKXdD1gOVl3ENteg\nuoxVl/C2AcLOB4EqUMLb+/BkJ57ohFgWiPFOpz91+xMRERERkSa16wHL6gVs4QUIahBL44ksHktA\ndQkwwvxjvJOkHKiFRDNcIiIiIiIiDWLbm+nZ9YC1uHpt7aVX6mtme2MuKvR6JrBYqdF1SGvTOJKd\noHEkH5XGkOwEjSPZCc04jsrVUud2Xmfufq9r2dPM7GV3f6zRdUhr0ziSnaBxJB+VxpDsBI0j2Qmt\nPI7UKUJERERERGSHKGCJiIiIiIjsEAWsj+4/N7oA2RM0jmQnaBzJR6UxJDtB40h2QsuOI+3BEhER\nERER2SGawRIREREREdkhClgiIiIiIiI7RAFrm8zsJ83sLTO7aGb/5If8/i+b2Tkze93MvmpmE42o\nU5rbNsbR3zGz02Z2ysz+xMyON6JOaW4fNI7ueN3PmJmbWUu2uZV7ZxvXol8ws4Xb16JTZva3GlGn\nNLftXIvM7C/evj86a2af3+0apflt43r07+64Fp03s9VG1Hk3tAdrG8wsBpwHfhy4DrwE/Jy7n7vj\nNZ8GvufuG2b2d4FPuftfakjB0pS2OY7y7l64/fFngb/n7j/ZiHqlOW1nHN1+XQ74AyAJ/KK7v7zb\ntUpz2ua16BeAx9z9FxtSpDS9bY6jw8B/B55z9xUz63f3+YYULE1pu+9pd7z+HwAPu/vf2L0q755m\nsLbnCeCiu1929yrwBeCn73yBu3/d3Tduf/pdYHSXa5Tmt51xVLjj0wygn4DIe33gOLrtXwL/Ctjc\nzeKkJWx3DIm8n+2Mo78N/Ed3XwFQuJIf4m6vRz8H/O6uVPYRKGBtzwgwc8fn128/96P8TeAP72lF\n0oq2NY7M7O+b2SXgXwO/tEu1Sev4wHFkZo8AY+7+B7tZmLSM7b6n/cztZe//w8zGdqc0aSHbGUdH\ngCNm9oKZfdfMtCJD3mvb99i3t98cAL62C3V9JApYO8zM/irwGPBvGl2LtCZ3/4/ufhD4FeCfN7oe\naS1mFgD/FviHja5FWtr/ASbd/QHgy8BvN7geaU1x4DDwKaKZh/9iZp0NrUha2c8C/8Pd640u5IMo\nYG3PDeDOn96N3n7uB5jZjwH/DPisu1d2qTZpHdsaR3f4AvDn7mlF0oo+aBzlgBPAN8zsCvAk8EU1\nupA7fOC1yN2X7ngf+6/Ao7tUm7SO7bynXQe+6O5b7j5NtNfm8C7VJ63hbu6NfpYWWB4ICljb9RJw\n2MwOmFmS6B/4i3e+wMweBn6DKFxpjbH8MNsZR3e+8fwUcGEX65PW8L7jyN3X3L3X3SfdfZJoT+hn\n1eRC7rCda9HQHZ9+FnhjF+uT1vCB4wj4X0SzV5hZL9GSwcu7WaQ0ve2MI8zsPqAL+M4u1/ehxBtd\nQCtw95qZ/SLwPBADftPdz5rZrwIvu/sXiZYEZoHfMzOAa+7+2YYVLU1nm+PoF2/PhG4BK8Bfa1zF\n0oy2OY5EfqRtjqFfut3JtAYsA7/QsIKlKW1zHD0P/ISZnQPqwD9y96XGVS3N5i7e034W+IK3SPtz\ntWkXERERERHZIVoiKCIiIiIiskMUsERERERERHaIApaIiIiIiMgOUcASERERERHZIQpYIiIiIiIi\nO0QBS0REREREZIcoYImIiIiIiOyQ/weP7RYfM2iNiAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe581697828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "categorical_cols = [\"Genotype\", \"Behavior\", \"Treatment\"]\n",
    "means = ecdf_normalized_df.join(df[categorical_cols]).groupby(categorical_cols).mean().apply(lambda c: c - 0.5)\n",
    "t_means = means.transpose()\n",
    "unclustered_norms = pd.Series(np.sqrt(np.square(t_means).sum(axis=1)), name=\"norm\")\n",
    "\n",
    "plot_histogram(unclustered_norms);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The upper tail of the distribution for norms here is about 0.55.  Let's take a look at those proteins."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>(Control, C/S, Memantine)</th>\n",
       "      <th>(Control, C/S, Saline)</th>\n",
       "      <th>(Control, S/C, Memantine)</th>\n",
       "      <th>(Control, S/C, Saline)</th>\n",
       "      <th>(Ts65Dn, C/S, Memantine)</th>\n",
       "      <th>(Ts65Dn, C/S, Saline)</th>\n",
       "      <th>(Ts65Dn, S/C, Memantine)</th>\n",
       "      <th>(Ts65Dn, S/C, Saline)</th>\n",
       "      <th>norm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SOD1_N</th>\n",
       "      <td>-0.213580</td>\n",
       "      <td>-0.243930</td>\n",
       "      <td>0.164827</td>\n",
       "      <td>0.318374</td>\n",
       "      <td>-0.299540</td>\n",
       "      <td>-0.276728</td>\n",
       "      <td>0.283429</td>\n",
       "      <td>0.214794</td>\n",
       "      <td>0.725548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pERK_N</th>\n",
       "      <td>0.234302</td>\n",
       "      <td>0.196379</td>\n",
       "      <td>-0.211340</td>\n",
       "      <td>-0.291584</td>\n",
       "      <td>0.317394</td>\n",
       "      <td>0.229506</td>\n",
       "      <td>-0.222339</td>\n",
       "      <td>-0.200144</td>\n",
       "      <td>0.682680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CaNA_N</th>\n",
       "      <td>0.188290</td>\n",
       "      <td>0.241927</td>\n",
       "      <td>-0.313506</td>\n",
       "      <td>-0.166955</td>\n",
       "      <td>0.270926</td>\n",
       "      <td>0.204638</td>\n",
       "      <td>-0.274472</td>\n",
       "      <td>-0.087757</td>\n",
       "      <td>0.646882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DYRK1A_N</th>\n",
       "      <td>0.173043</td>\n",
       "      <td>0.123649</td>\n",
       "      <td>-0.289340</td>\n",
       "      <td>-0.288519</td>\n",
       "      <td>0.336036</td>\n",
       "      <td>0.249127</td>\n",
       "      <td>-0.134259</td>\n",
       "      <td>-0.097730</td>\n",
       "      <td>0.644014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P38_N</th>\n",
       "      <td>-0.023623</td>\n",
       "      <td>-0.172888</td>\n",
       "      <td>0.228407</td>\n",
       "      <td>0.179657</td>\n",
       "      <td>-0.245082</td>\n",
       "      <td>-0.372143</td>\n",
       "      <td>0.254636</td>\n",
       "      <td>0.049321</td>\n",
       "      <td>0.617027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BRAF_N</th>\n",
       "      <td>0.170846</td>\n",
       "      <td>0.104794</td>\n",
       "      <td>-0.237389</td>\n",
       "      <td>-0.236351</td>\n",
       "      <td>0.341077</td>\n",
       "      <td>0.208519</td>\n",
       "      <td>-0.135357</td>\n",
       "      <td>-0.158683</td>\n",
       "      <td>0.596406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ARC_N</th>\n",
       "      <td>-0.024549</td>\n",
       "      <td>-0.194870</td>\n",
       "      <td>0.147833</td>\n",
       "      <td>0.252462</td>\n",
       "      <td>-0.283745</td>\n",
       "      <td>-0.211314</td>\n",
       "      <td>0.312490</td>\n",
       "      <td>-0.055261</td>\n",
       "      <td>0.591639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pS6_N</th>\n",
       "      <td>-0.024549</td>\n",
       "      <td>-0.194870</td>\n",
       "      <td>0.147833</td>\n",
       "      <td>0.252462</td>\n",
       "      <td>-0.283745</td>\n",
       "      <td>-0.211314</td>\n",
       "      <td>0.312490</td>\n",
       "      <td>-0.055261</td>\n",
       "      <td>0.591639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ITSN1_N</th>\n",
       "      <td>0.107512</td>\n",
       "      <td>0.090583</td>\n",
       "      <td>-0.309710</td>\n",
       "      <td>-0.280343</td>\n",
       "      <td>0.281564</td>\n",
       "      <td>0.266975</td>\n",
       "      <td>-0.013567</td>\n",
       "      <td>-0.057490</td>\n",
       "      <td>0.590186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ubiquitin_N</th>\n",
       "      <td>-0.098907</td>\n",
       "      <td>-0.296872</td>\n",
       "      <td>0.276401</td>\n",
       "      <td>0.070720</td>\n",
       "      <td>-0.264588</td>\n",
       "      <td>-0.080988</td>\n",
       "      <td>0.296180</td>\n",
       "      <td>0.064040</td>\n",
       "      <td>0.589665</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             (Control, C/S, Memantine)  (Control, C/S, Saline)  \\\n",
       "SOD1_N                       -0.213580               -0.243930   \n",
       "pERK_N                        0.234302                0.196379   \n",
       "CaNA_N                        0.188290                0.241927   \n",
       "DYRK1A_N                      0.173043                0.123649   \n",
       "P38_N                        -0.023623               -0.172888   \n",
       "BRAF_N                        0.170846                0.104794   \n",
       "ARC_N                        -0.024549               -0.194870   \n",
       "pS6_N                        -0.024549               -0.194870   \n",
       "ITSN1_N                       0.107512                0.090583   \n",
       "Ubiquitin_N                  -0.098907               -0.296872   \n",
       "\n",
       "             (Control, S/C, Memantine)  (Control, S/C, Saline)  \\\n",
       "SOD1_N                        0.164827                0.318374   \n",
       "pERK_N                       -0.211340               -0.291584   \n",
       "CaNA_N                       -0.313506               -0.166955   \n",
       "DYRK1A_N                     -0.289340               -0.288519   \n",
       "P38_N                         0.228407                0.179657   \n",
       "BRAF_N                       -0.237389               -0.236351   \n",
       "ARC_N                         0.147833                0.252462   \n",
       "pS6_N                         0.147833                0.252462   \n",
       "ITSN1_N                      -0.309710               -0.280343   \n",
       "Ubiquitin_N                   0.276401                0.070720   \n",
       "\n",
       "             (Ts65Dn, C/S, Memantine)  (Ts65Dn, C/S, Saline)  \\\n",
       "SOD1_N                      -0.299540              -0.276728   \n",
       "pERK_N                       0.317394               0.229506   \n",
       "CaNA_N                       0.270926               0.204638   \n",
       "DYRK1A_N                     0.336036               0.249127   \n",
       "P38_N                       -0.245082              -0.372143   \n",
       "BRAF_N                       0.341077               0.208519   \n",
       "ARC_N                       -0.283745              -0.211314   \n",
       "pS6_N                       -0.283745              -0.211314   \n",
       "ITSN1_N                      0.281564               0.266975   \n",
       "Ubiquitin_N                 -0.264588              -0.080988   \n",
       "\n",
       "             (Ts65Dn, S/C, Memantine)  (Ts65Dn, S/C, Saline)      norm  \n",
       "SOD1_N                       0.283429               0.214794  0.725548  \n",
       "pERK_N                      -0.222339              -0.200144  0.682680  \n",
       "CaNA_N                      -0.274472              -0.087757  0.646882  \n",
       "DYRK1A_N                    -0.134259              -0.097730  0.644014  \n",
       "P38_N                        0.254636               0.049321  0.617027  \n",
       "BRAF_N                      -0.135357              -0.158683  0.596406  \n",
       "ARC_N                        0.312490              -0.055261  0.591639  \n",
       "pS6_N                        0.312490              -0.055261  0.591639  \n",
       "ITSN1_N                     -0.013567              -0.057490  0.590186  \n",
       "Ubiquitin_N                  0.296180               0.064040  0.589665  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "high_unclustered_norms = t_means.join(unclustered_norms).sort_values(\"norm\", ascending=False).head(n=10)\n",
    "high_unclustered_norms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This list is different from  the clustered list because it isn't influenced by data separability. As a result, more general variation is incorporated.  The proteins only present here are likely to be important in multiple pathways.\n",
    "\n",
    "Let's take a look at the similarities and differences in these groups."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Both: pERK, BRAF, P38, DYRK1A\n",
      "Clustered only: pP70S6, pRSK, P3525, RAPTOR, pCAMKII, pPKCG\n",
      "Unclustered only: SOD1, ARC, pS6, ITSN1, CaNA, Ubiquitin\n"
     ]
    }
   ],
   "source": [
    "s_clu_norms = set(high_clustered_norms.index)\n",
    "s_unc_norms = set(high_unclustered_norms.index)\n",
    "\n",
    "print(\"Both: \" + \", \".join(g.split(\"_\")[0] for g in s_clu_norms & s_unc_norms))\n",
    "print(\"Clustered only: \" + \", \".join(n.split(\"_\")[0] for n in s_clu_norms if n not in s_unc_norms))\n",
    "print(\"Unclustered only: \" + \", \".join(n.split(\"_\")[0] for n in s_unc_norms if n not in s_clu_norms))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The regulatory cascade is probably driven by proteins present in both groups.  Proteins present only in the unclustered group probably exert a weak, non-specific regulatory influence on the cell.  Proteins present in the clustered group only probably play a role in controlling the magnitude of the effect."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true,
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [],
   "source": [
    "categorical_columns = [\"Genotype\", \"Treatment\", \"Behavior\"]\n",
    "small_df = t_sorted_df.drop(\"cluster\", 1).loc[:,df_clustered]\n",
    "conditions = df.loc[:, categorical_columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def only_samples_where(df, genotype=None, treatment=None, behavior=None):\n",
    "    genotype = genotype or set()\n",
    "    treatment = treatment or set()\n",
    "    behavior = behavior or set()\n",
    "    df = df.transpose().join(conditions)\n",
    "    \n",
    "    def mask(value=False):\n",
    "        return pd.Series([value for _ in df.index], dtype=bool, index=df.index)\n",
    "    \n",
    "    m = mask(True)\n",
    "    \n",
    "    if genotype:\n",
    "        genotype_mask = mask(False)\n",
    "        if \"Control\" in genotype:\n",
    "            genotype_mask |= df[\"Genotype\"] == \"Control\"\n",
    "        if \"Ts65Dn\" in genotype:\n",
    "            genotype_mask |= df[\"Genotype\"] == \"Ts65Dn\"\n",
    "        m &= genotype_mask\n",
    "    \n",
    "    if treatment:\n",
    "        treatment_mask = mask(False)\n",
    "        if \"Memantine\" in treatment:\n",
    "            treatment_mask |= df[\"Treatment\"] == \"Memantine\"\n",
    "        if \"Saline\" in treatment:\n",
    "            treatment_mask |= df[\"Treatment\"] == \"Saline\"\n",
    "        m &= treatment_mask\n",
    "        \n",
    "    if behavior:\n",
    "        behavior_mask = mask(False)\n",
    "        if \"C/S\" in behavior:\n",
    "            behavior_mask |= df[\"Behavior\"] == \"C/S\"\n",
    "        if \"S/C\" in behavior:\n",
    "            behavior_mask |= df[\"Behavior\"] == \"S/C\"\n",
    "        m &= behavior_mask\n",
    "        \n",
    "        return df[m].drop(categorical_columns, axis=1).transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Widget Javascript not detected.  It may not be installed or enabled properly.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5fc6ab46440a4c11ad555c50c3bd1d68"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def filtered_heatmap(genotype, treatment, behavior):\n",
    "    filtered_df = only_samples_where(small_df, genotype=genotype, treatment=treatment, behavior=behavior)\n",
    "    fig = block_heatmap(filtered_df, width=875)\n",
    "    show(fig, notebook_handle=True)\n",
    "\n",
    "genotype_opts = {\"All\": \"Control Ts65Dn\", \"Control\": \"Control\", \"Ts65Dn\": \"Ts65Dn\"}\n",
    "treatment_opts = {\"All\": \"Memantine Saline\", \"Saline\": \"Saline\", \"Memantine\": \"Memantine\"}\n",
    "behavior_opts = {\"All\": \"C/S S/C\", \"Learning\": \"C/S\", \"No learning\": \"S/C\"}\n",
    "interact(filtered_heatmap, genotype=genotype_opts, treatment=treatment_opts, behavior=behavior_opts);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "This beast of a plot shows patterns of activity for every protein in the database."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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