FlyWing_data_comparisons-Copy1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import os\n",
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# define plotting + statistical functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def hierarchical_clustering(data, label, linkage, ax):\n",
    "    import scipy.cluster.hierarchy as shc\n",
    "\n",
    "    dend = shc.dendrogram(shc.linkage(data, method=linkage), \n",
    "                      ax= ax, labels=label,\n",
    "                      leaf_font_size=8, leaf_rotation=90)\n",
    "    \n",
    "    from sklearn.cluster import AgglomerativeClustering\n",
    "    cluster = AgglomerativeClustering(n_clusters=7, \n",
    "                                      affinity = 'euclidean', linkage = linkage)\n",
    "    cluster_labels = cluster.fit_predict(data)\n",
    "    \n",
    "    return dend, cluster_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def do_pearson(x, y):\n",
    "    from scipy.stats import pearsonr\n",
    "    corr, p_val = pearsonr(x, y)\n",
    "    return corr, p_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def capping(input_df):\n",
    "    # cap values above 1 and below 0 to 20% above/below\n",
    "    df_capped = input_df.copy()\n",
    "\n",
    "    for i in range(len(input_df.columns)-1):\n",
    "        i+=1\n",
    "        for j in range(len(input_df)):\n",
    "            if input_df.iloc[j, i] > 1.2:\n",
    "                df_capped.iloc[j, i] = 1.2\n",
    "            elif input_df.iloc[j, i] < -0.2:\n",
    "                df_capped.iloc[j, i ] = -0.2\n",
    "    return df_capped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_heatmap(data, columns, labels, cmap):\n",
    "    fig, ax = plt.subplots(figsize=(10,15), dpi=100)\n",
    "\n",
    "    im = ax.imshow(data, cmap=cmap, aspect='equal')\n",
    "\n",
    "    # show all ticks...\n",
    "    ax.set_xticks(np.arange(len(columns)))\n",
    "    ax.set_yticks(np.arange(len(data)))\n",
    "    ax.tick_params(top=True, bottom=False,\n",
    "                   labeltop=True, labelbottom=False)\n",
    "\n",
    "    # label ticks\n",
    "    ax.set_xticklabels(columns, fontsize=8, rotation=70, \n",
    "                       rotation_mode='anchor', ha='left', va='center')\n",
    "    ax.set_yticklabels(labels, fontsize=8)\n",
    "\n",
    "    # set colorbar\n",
    "    cbar = fig.colorbar(im, cax = fig.add_axes([0.6, 0.5, 0.01, 0.1]), \n",
    "                    ax=ax, orientation='vertical', fraction=.1)\n",
    "    cbar.ax.tick_params(labelsize=6)\n",
    "    \n",
    "    # hatch the background axes patch\n",
    "    #ax.patch.set(hatch='xxx', edgecolor='black')\n",
    "    ax.patch.set(color = 'grey', facecolor = 'grey')\n",
    "    \n",
    "    return fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def scatter_plotting(labels, label_xval, label_yval, data, x_name, y_name):\n",
    "    colors = {0:'cornflowerblue', 1:'darkblue', 2:'darkorange',\n",
    "          3:'gold', 4:'lightsteelblue', 5:'blue',\n",
    "          6:'blueviolet', 7:'cyan', 8:'black'}\n",
    "    \n",
    "    fig = plt.figure(figsize=(10,10), dpi=100)\n",
    "    ax = fig.add_subplot(111)\n",
    "    \n",
    "    for i in range(len(labels)): # plot each point + it's index as text above\n",
    "        ax.text(label_xval[i], label_yval[i], \n",
    "                labels[i], size=8, color='k')\n",
    "\n",
    "    for j in data['cluster_labels'].unique():\n",
    "        df_subset = data[data['cluster_labels']==j]\n",
    "        ax.scatter(df_subset[x_name], df_subset[y_name], \n",
    "                   alpha=0.6, s=80, c=colors[j])\n",
    "\n",
    "    ax.set_xlabel(x_name)\n",
    "    ax.set_ylabel(y_name)\n",
    "\n",
    "    return fig, ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def correlate(df_to_plot, x_name, y_name):\n",
    "\n",
    "    # plot scatter\n",
    "    fig, ax = scatter_plotting(df_to_plot['variant'], df_to_plot[x_name], df_to_plot[y_name], df_to_plot, x_name, y_name)\n",
    "    \n",
    "    return fig, ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def save_fig(fig, title=''):\n",
    "    # output figure\n",
    "    mpl.rcParams['pdf.fonttype'] = 42\n",
    "    plt.savefig(f'{title}.pdf', transparent=True)\n",
    "    return fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# read in datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_wing = pd.read_csv('fly_wing_size.csv', index_col=False)\n",
    "df_eclosion = pd.read_csv('fly_eclosion.csv', index_col=False)\n",
    "df_yeast = pd.read_csv('PTEN_Mighell_2018.csv', index_col=False)\n",
    "df_vac14_hek = pd.read_csv('PTEN_VAC14_HEK.csv', index_col=False)\n",
    "df_mcf10a = pd.read_csv('PTEN_MCF10A.csv', index_col=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## inspect datasets and make adjustments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "''' update 4/23/20: Payel requested to drop variants: \n",
    "eclosion - E256K, F56C, G132D, I135V, M198I, N340D, N340H, N356H, Q396R, R130X\n",
    "wing size - N340H, N340D, C136M fxS44'''\n",
    "\n",
    "'''update 4/9/20: now wing size data is normalized such that\n",
    "0 is loss of function, 1 is functional'''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### inspect wing size data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>variant</th>\n",
       "      <th>wing_size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>control</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>WT</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PTEN- 4Ala</td>\n",
       "      <td>3.463187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>C124S</td>\n",
       "      <td>-0.465176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>D22E</td>\n",
       "      <td>-0.902782</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      variant  wing_size\n",
       "0     control   0.000000\n",
       "1          WT   1.000000\n",
       "2  PTEN- 4Ala   3.463187\n",
       "3       C124S  -0.465176\n",
       "4        D22E  -0.902782"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_wing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   variant  wing_size\n",
      "0  control   0.000000\n",
      "1       WT   1.000000\n",
      "3    C124S  -0.465176\n",
      "4     D22E  -0.902782\n",
      "5     P38H  -0.208456\n"
     ]
    }
   ],
   "source": [
    "# drop 4A so it doesn't skew data\n",
    "df_wing.drop(2, axis=0, inplace=True)\n",
    "print(df_wing.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### inspect eclosion time data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>variant</th>\n",
       "      <th>time</th>\n",
       "      <th>time.adj</th>\n",
       "      <th>sd.time</th>\n",
       "      <th>sd.time.adj</th>\n",
       "      <th>N.time</th>\n",
       "      <th>N.time.adj</th>\n",
       "      <th>sem.time</th>\n",
       "      <th>sem.time.adj</th>\n",
       "      <th>pvalue.from.WT</th>\n",
       "      <th>pvalue.from.NULL</th>\n",
       "      <th>Coefficients</th>\n",
       "      <th>time.scaled.means</th>\n",
       "      <th>time.scaled.sd</th>\n",
       "      <th>time.scaled.sem</th>\n",
       "      <th>time.adj.scaled.means</th>\n",
       "      <th>time.adj.scaled.sd</th>\n",
       "      <th>time.adj.scaled.sem</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A121E</td>\n",
       "      <td>12.571429</td>\n",
       "      <td>9.988761</td>\n",
       "      <td>13.501839</td>\n",
       "      <td>12.668890</td>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "      <td>2.083379</td>\n",
       "      <td>1.954852</td>\n",
       "      <td>1.090000e-31</td>\n",
       "      <td>0.080943</td>\n",
       "      <td>-35.493288</td>\n",
       "      <td>-0.193717</td>\n",
       "      <td>0.495525</td>\n",
       "      <td>0.076461</td>\n",
       "      <td>-0.175509</td>\n",
       "      <td>0.419584</td>\n",
       "      <td>0.064743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A121P</td>\n",
       "      <td>28.800000</td>\n",
       "      <td>33.989297</td>\n",
       "      <td>13.454686</td>\n",
       "      <td>12.576896</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>3.473985</td>\n",
       "      <td>3.247341</td>\n",
       "      <td>1.401229e-02</td>\n",
       "      <td>0.000068</td>\n",
       "      <td>-11.492753</td>\n",
       "      <td>0.401880</td>\n",
       "      <td>0.493794</td>\n",
       "      <td>0.127497</td>\n",
       "      <td>0.619369</td>\n",
       "      <td>0.416537</td>\n",
       "      <td>0.107549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A126D</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>16.559678</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>11.514632</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>2.502173</td>\n",
       "      <td>2.400967</td>\n",
       "      <td>1.190000e-14</td>\n",
       "      <td>0.735246</td>\n",
       "      <td>-28.922372</td>\n",
       "      <td>-0.214689</td>\n",
       "      <td>0.440407</td>\n",
       "      <td>0.091831</td>\n",
       "      <td>0.042114</td>\n",
       "      <td>0.381355</td>\n",
       "      <td>0.079518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A126P</td>\n",
       "      <td>10.333333</td>\n",
       "      <td>10.750926</td>\n",
       "      <td>11.166276</td>\n",
       "      <td>9.713230</td>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "      <td>1.861046</td>\n",
       "      <td>1.618872</td>\n",
       "      <td>1.060000e-22</td>\n",
       "      <td>0.200159</td>\n",
       "      <td>-34.731124</td>\n",
       "      <td>-0.275857</td>\n",
       "      <td>0.409808</td>\n",
       "      <td>0.068301</td>\n",
       "      <td>-0.150267</td>\n",
       "      <td>0.321694</td>\n",
       "      <td>0.053616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A151P</td>\n",
       "      <td>20.526316</td>\n",
       "      <td>22.873467</td>\n",
       "      <td>7.332773</td>\n",
       "      <td>5.712228</td>\n",
       "      <td>38</td>\n",
       "      <td>38</td>\n",
       "      <td>1.189533</td>\n",
       "      <td>0.926646</td>\n",
       "      <td>3.100000e-12</td>\n",
       "      <td>0.019801</td>\n",
       "      <td>-22.608583</td>\n",
       "      <td>0.098231</td>\n",
       "      <td>0.269117</td>\n",
       "      <td>0.043657</td>\n",
       "      <td>0.251222</td>\n",
       "      <td>0.189184</td>\n",
       "      <td>0.030690</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  variant       time   time.adj    sd.time  sd.time.adj  N.time  N.time.adj  \\\n",
       "0   A121E  12.571429   9.988761  13.501839    12.668890      42          42   \n",
       "1   A121P  28.800000  33.989297  13.454686    12.576896      15          15   \n",
       "2   A126D  12.000000  16.559678  12.000000    11.514632      23          23   \n",
       "3   A126P  10.333333  10.750926  11.166276     9.713230      36          36   \n",
       "4   A151P  20.526316  22.873467   7.332773     5.712228      38          38   \n",
       "\n",
       "   sem.time  sem.time.adj  pvalue.from.WT  pvalue.from.NULL  Coefficients  \\\n",
       "0  2.083379      1.954852    1.090000e-31          0.080943    -35.493288   \n",
       "1  3.473985      3.247341    1.401229e-02          0.000068    -11.492753   \n",
       "2  2.502173      2.400967    1.190000e-14          0.735246    -28.922372   \n",
       "3  1.861046      1.618872    1.060000e-22          0.200159    -34.731124   \n",
       "4  1.189533      0.926646    3.100000e-12          0.019801    -22.608583   \n",
       "\n",
       "   time.scaled.means  time.scaled.sd  time.scaled.sem  time.adj.scaled.means  \\\n",
       "0          -0.193717        0.495525         0.076461              -0.175509   \n",
       "1           0.401880        0.493794         0.127497               0.619369   \n",
       "2          -0.214689        0.440407         0.091831               0.042114   \n",
       "3          -0.275857        0.409808         0.068301              -0.150267   \n",
       "4           0.098231        0.269117         0.043657               0.251222   \n",
       "\n",
       "   time.adj.scaled.sd  time.adj.scaled.sem  \n",
       "0            0.419584             0.064743  \n",
       "1            0.416537             0.107549  \n",
       "2            0.381355             0.079518  \n",
       "3            0.321694             0.053616  \n",
       "4            0.189184             0.030690  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_eclosion.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>variant</th>\n",
       "      <th>eclosion_time</th>\n",
       "      <th>time.adj</th>\n",
       "      <th>sd.time</th>\n",
       "      <th>sd.time.adj</th>\n",
       "      <th>N.time</th>\n",
       "      <th>N.time.adj</th>\n",
       "      <th>sem.time</th>\n",
       "      <th>sem.time.adj</th>\n",
       "      <th>pvalue.from.WT</th>\n",
       "      <th>pvalue.from.NULL</th>\n",
       "      <th>Coefficients</th>\n",
       "      <th>time.scaled.means</th>\n",
       "      <th>time.scaled.sd</th>\n",
       "      <th>time.scaled.sem</th>\n",
       "      <th>time.adj.scaled.means</th>\n",
       "      <th>time.adj.scaled.sd</th>\n",
       "      <th>time.adj.scaled.sem</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A121E</td>\n",
       "      <td>12.571429</td>\n",
       "      <td>9.988761</td>\n",
       "      <td>13.501839</td>\n",
       "      <td>12.668890</td>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "      <td>2.083379</td>\n",
       "      <td>1.954852</td>\n",
       "      <td>1.090000e-31</td>\n",
       "      <td>0.080943</td>\n",
       "      <td>-35.493288</td>\n",
       "      <td>-0.193717</td>\n",
       "      <td>0.495525</td>\n",
       "      <td>0.076461</td>\n",
       "      <td>-0.175509</td>\n",
       "      <td>0.419584</td>\n",
       "      <td>0.064743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A121P</td>\n",
       "      <td>28.800000</td>\n",
       "      <td>33.989297</td>\n",
       "      <td>13.454686</td>\n",
       "      <td>12.576896</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>3.473985</td>\n",
       "      <td>3.247341</td>\n",
       "      <td>1.401229e-02</td>\n",
       "      <td>0.000068</td>\n",
       "      <td>-11.492753</td>\n",
       "      <td>0.401880</td>\n",
       "      <td>0.493794</td>\n",
       "      <td>0.127497</td>\n",
       "      <td>0.619369</td>\n",
       "      <td>0.416537</td>\n",
       "      <td>0.107549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A126D</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>16.559678</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>11.514632</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>2.502173</td>\n",
       "      <td>2.400967</td>\n",
       "      <td>1.190000e-14</td>\n",
       "      <td>0.735246</td>\n",
       "      <td>-28.922372</td>\n",
       "      <td>-0.214689</td>\n",
       "      <td>0.440407</td>\n",
       "      <td>0.091831</td>\n",
       "      <td>0.042114</td>\n",
       "      <td>0.381355</td>\n",
       "      <td>0.079518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A126P</td>\n",
       "      <td>10.333333</td>\n",
       "      <td>10.750926</td>\n",
       "      <td>11.166276</td>\n",
       "      <td>9.713230</td>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "      <td>1.861046</td>\n",
       "      <td>1.618872</td>\n",
       "      <td>1.060000e-22</td>\n",
       "      <td>0.200159</td>\n",
       "      <td>-34.731124</td>\n",
       "      <td>-0.275857</td>\n",
       "      <td>0.409808</td>\n",
       "      <td>0.068301</td>\n",
       "      <td>-0.150267</td>\n",
       "      <td>0.321694</td>\n",
       "      <td>0.053616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A151P</td>\n",
       "      <td>20.526316</td>\n",
       "      <td>22.873467</td>\n",
       "      <td>7.332773</td>\n",
       "      <td>5.712228</td>\n",
       "      <td>38</td>\n",
       "      <td>38</td>\n",
       "      <td>1.189533</td>\n",
       "      <td>0.926646</td>\n",
       "      <td>3.100000e-12</td>\n",
       "      <td>0.019801</td>\n",
       "      <td>-22.608583</td>\n",
       "      <td>0.098231</td>\n",
       "      <td>0.269117</td>\n",
       "      <td>0.043657</td>\n",
       "      <td>0.251222</td>\n",
       "      <td>0.189184</td>\n",
       "      <td>0.030690</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  variant  eclosion_time   time.adj    sd.time  sd.time.adj  N.time  \\\n",
       "0   A121E      12.571429   9.988761  13.501839    12.668890      42   \n",
       "1   A121P      28.800000  33.989297  13.454686    12.576896      15   \n",
       "2   A126D      12.000000  16.559678  12.000000    11.514632      23   \n",
       "3   A126P      10.333333  10.750926  11.166276     9.713230      36   \n",
       "4   A151P      20.526316  22.873467   7.332773     5.712228      38   \n",
       "\n",
       "   N.time.adj  sem.time  sem.time.adj  pvalue.from.WT  pvalue.from.NULL  \\\n",
       "0          42  2.083379      1.954852    1.090000e-31          0.080943   \n",
       "1          15  3.473985      3.247341    1.401229e-02          0.000068   \n",
       "2          23  2.502173      2.400967    1.190000e-14          0.735246   \n",
       "3          36  1.861046      1.618872    1.060000e-22          0.200159   \n",
       "4          38  1.189533      0.926646    3.100000e-12          0.019801   \n",
       "\n",
       "   Coefficients  time.scaled.means  time.scaled.sd  time.scaled.sem  \\\n",
       "0    -35.493288          -0.193717        0.495525         0.076461   \n",
       "1    -11.492753           0.401880        0.493794         0.127497   \n",
       "2    -28.922372          -0.214689        0.440407         0.091831   \n",
       "3    -34.731124          -0.275857        0.409808         0.068301   \n",
       "4    -22.608583           0.098231        0.269117         0.043657   \n",
       "\n",
       "   time.adj.scaled.means  time.adj.scaled.sd  time.adj.scaled.sem  \n",
       "0              -0.175509            0.419584             0.064743  \n",
       "1               0.619369            0.416537             0.107549  \n",
       "2               0.042114            0.381355             0.079518  \n",
       "3              -0.150267            0.321694             0.053616  \n",
       "4               0.251222            0.189184             0.030690  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_eclosion.rename(columns={'time':'eclosion_time'}, inplace=True)\n",
    "df_eclosion.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### inspect Mighell (yeast) data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Variant (one letter)</th>\n",
       "      <th>Variant (three letter)</th>\n",
       "      <th>Change</th>\n",
       "      <th>Wt_codon</th>\n",
       "      <th>Mut_codon</th>\n",
       "      <th>Type</th>\n",
       "      <th>Cum_score</th>\n",
       "      <th>Cum_SE</th>\n",
       "      <th>High_conf</th>\n",
       "      <th>High_conf_nature</th>\n",
       "      <th>...</th>\n",
       "      <th>B2_SE</th>\n",
       "      <th>B2_score</th>\n",
       "      <th>A3_SE</th>\n",
       "      <th>A3_score</th>\n",
       "      <th>B3_SE</th>\n",
       "      <th>B3_score</th>\n",
       "      <th>A_score</th>\n",
       "      <th>A_SE</th>\n",
       "      <th>B_score</th>\n",
       "      <th>B_SE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M1A</td>\n",
       "      <td>Met1Ala</td>\n",
       "      <td>A (Ala)</td>\n",
       "      <td>ATG</td>\n",
       "      <td>GCT</td>\n",
       "      <td>missense</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M1V</td>\n",
       "      <td>Met1Val</td>\n",
       "      <td>V (Val)</td>\n",
       "      <td>ATG</td>\n",
       "      <td>GTT</td>\n",
       "      <td>missense</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.894459</td>\n",
       "      <td>-0.151521</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.444826</td>\n",
       "      <td>0.661712</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M1I</td>\n",
       "      <td>Met1Ile</td>\n",
       "      <td>I (Ile)</td>\n",
       "      <td>ATG</td>\n",
       "      <td>ATT</td>\n",
       "      <td>missense</td>\n",
       "      <td>-0.679813</td>\n",
       "      <td>0.929740</td>\n",
       "      <td>False</td>\n",
       "      <td>Fail Both</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.362379</td>\n",
       "      <td>0.377438</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.679813</td>\n",
       "      <td>0.92974</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M1L</td>\n",
       "      <td>Met1Leu</td>\n",
       "      <td>L (Leu)</td>\n",
       "      <td>ATG</td>\n",
       "      <td>TTG</td>\n",
       "      <td>missense</td>\n",
       "      <td>-1.545614</td>\n",
       "      <td>0.883483</td>\n",
       "      <td>False</td>\n",
       "      <td>Fail Both</td>\n",
       "      <td>...</td>\n",
       "      <td>0.692861</td>\n",
       "      <td>-0.151521</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.864111</td>\n",
       "      <td>-2.184489</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.545614</td>\n",
       "      <td>0.883483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M1M</td>\n",
       "      <td>Met1Met</td>\n",
       "      <td>M (Met)</td>\n",
       "      <td>ATG</td>\n",
       "      <td>ATG</td>\n",
       "      <td>WT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Variant (one letter) Variant (three letter)   Change Wt_codon Mut_codon  \\\n",
       "0                  M1A                Met1Ala  A (Ala)      ATG       GCT   \n",
       "1                  M1V                Met1Val  V (Val)      ATG       GTT   \n",
       "2                  M1I                Met1Ile  I (Ile)      ATG       ATT   \n",
       "3                  M1L                Met1Leu  L (Leu)      ATG       TTG   \n",
       "4                  M1M                Met1Met  M (Met)      ATG       ATG   \n",
       "\n",
       "       Type  Cum_score    Cum_SE High_conf High_conf_nature  ...     B2_SE  \\\n",
       "0  missense        NaN       NaN       NaN              NaN  ...       NaN   \n",
       "1  missense        NaN       NaN       NaN              NaN  ...  0.894459   \n",
       "2  missense  -0.679813  0.929740     False        Fail Both  ...       NaN   \n",
       "3  missense  -1.545614  0.883483     False        Fail Both  ...  0.692861   \n",
       "4        WT        NaN       NaN       NaN              NaN  ...       NaN   \n",
       "\n",
       "   B2_score     A3_SE  A3_score     B3_SE  B3_score   A_score     A_SE  \\\n",
       "0       NaN       NaN       NaN       NaN       NaN       NaN      NaN   \n",
       "1 -0.151521       NaN       NaN  0.444826  0.661712       NaN      NaN   \n",
       "2       NaN  0.362379  0.377438       NaN       NaN -0.679813  0.92974   \n",
       "3 -0.151521       NaN       NaN  0.864111 -2.184489       NaN      NaN   \n",
       "4       NaN       NaN       NaN       NaN       NaN       NaN      NaN   \n",
       "\n",
       "    B_score      B_SE  \n",
       "0       NaN       NaN  \n",
       "1       NaN       NaN  \n",
       "2       NaN       NaN  \n",
       "3 -1.545614  0.883483  \n",
       "4       NaN       NaN  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_yeast.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# rename col name for indexing the merge letter\n",
    "df_yeast.rename(columns={'Variant (one letter)':'variant','Cum_score':'Mighell_score'}, \n",
    "                inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# keep only two needed columns\n",
    "df_yeast = df_yeast[df_yeast.columns[[0, 6]]]\n",
    "\n",
    "# since Mighell's data is already normalized\n",
    "# I'll put in WT as 0, KO as -4\n",
    "df_yeast.loc[0, :] = ['WT', 0]\n",
    "df_yeast.loc[1, :] = ['control', -4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>variant</th>\n",
       "      <th>Mighell_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>WT</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>control</td>\n",
       "      <td>-4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M1I</td>\n",
       "      <td>-0.679813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M1L</td>\n",
       "      <td>-1.545614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M1M</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   variant  Mighell_score\n",
       "0       WT       0.000000\n",
       "1  control      -4.000000\n",
       "2      M1I      -0.679813\n",
       "3      M1L      -1.545614\n",
       "4      M1M            NaN"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_yeast.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## inspect HEK, VAC14, MCF10A dsets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    variant     VAC14       HEK\n",
      "0   no_PTEN  0.000000  0.000000\n",
      "1        WT  1.000000  1.000000\n",
      "2      P38H -0.257287 -0.378378\n",
      "3     R130L -0.255608 -0.189189\n",
      "4  C124S-4A -0.227811 -0.568047 \n",
      "\n",
      "   variant  MCF10A\n",
      "0  no_PTEN  0.0000\n",
      "1       WT  1.0000\n",
      "2      K6E  0.6689\n",
      "3      K6I  0.5456\n",
      "4     R15S  0.6130\n"
     ]
    }
   ],
   "source": [
    "print(df_vac14_hek.head(), '\\n')\n",
    "print(df_mcf10a.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### merge datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>variant</th>\n",
       "      <th>wing_size</th>\n",
       "      <th>eclosion_time</th>\n",
       "      <th>Mighell_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>no_PTEN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>17.849750</td>\n",
       "      <td>-4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>WT</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>45.097297</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C124S</td>\n",
       "      <td>-0.465176</td>\n",
       "      <td>14.146552</td>\n",
       "      <td>-4.895643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>D22E</td>\n",
       "      <td>-0.902782</td>\n",
       "      <td>16.888889</td>\n",
       "      <td>-1.454357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>P38H</td>\n",
       "      <td>-0.208456</td>\n",
       "      <td>15.287671</td>\n",
       "      <td>-4.783403</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   variant  wing_size  eclosion_time  Mighell_score\n",
       "0  no_PTEN   0.000000      17.849750      -4.000000\n",
       "1       WT   1.000000      45.097297       0.000000\n",
       "2    C124S  -0.465176      14.146552      -4.895643\n",
       "3     D22E  -0.902782      16.888889      -1.454357\n",
       "4     P38H  -0.208456      15.287671      -4.783403"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# start merging different datasets\n",
    "df_merged = df_wing.merge(df_eclosion[['variant', 'eclosion_time']], on='variant', how='left') # merge wing+eclosion\n",
    "df_merged = df_merged.merge(df_yeast, on='variant', how='left') # merge wing/eclosion+yeast\n",
    "\n",
    "# rename control to no_PTEN\n",
    "df_merged['variant'].replace({'control':'no_PTEN'}, inplace=True)\n",
    "\n",
    "df_merged.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### normalize + cap eclosion and Mighell for plotting heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>variant</th>\n",
       "      <th>wing_size</th>\n",
       "      <th>eclosion_time</th>\n",
       "      <th>Mighell_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>no_PTEN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>WT</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C124S</td>\n",
       "      <td>-0.465176</td>\n",
       "      <td>-0.135909</td>\n",
       "      <td>-0.223911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>D22E</td>\n",
       "      <td>-0.902782</td>\n",
       "      <td>-0.035264</td>\n",
       "      <td>0.636411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>P38H</td>\n",
       "      <td>-0.208456</td>\n",
       "      <td>-0.094030</td>\n",
       "      <td>-0.195851</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   variant  wing_size  eclosion_time  Mighell_score\n",
       "0  no_PTEN   0.000000       0.000000       0.000000\n",
       "1       WT   1.000000       1.000000       1.000000\n",
       "2    C124S  -0.465176      -0.135909      -0.223911\n",
       "3     D22E  -0.902782      -0.035264       0.636411\n",
       "4     P38H  -0.208456      -0.094030      -0.195851"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# normalized eclosion and Mighell\n",
    "df_normed = df_merged.copy()\n",
    "\n",
    "for i in range(2):\n",
    "    i+=2\n",
    "    for j in range(len(df_merged)):\n",
    "        df_normed.iloc[j, i] = \\\n",
    "        ((df_merged.iloc[j, i]-df_merged.iloc[0, i])/ (df_merged.iloc[1, i]-df_merged.iloc[0, i]))\n",
    "\n",
    "df_normed.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>variant</th>\n",
       "      <th>wing_size</th>\n",
       "      <th>eclosion_time</th>\n",
       "      <th>Mighell_score</th>\n",
       "      <th>VAC14</th>\n",
       "      <th>HEK</th>\n",
       "      <th>MCF10A</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>no_PTEN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>WT</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C124S</td>\n",
       "      <td>-0.465176</td>\n",
       "      <td>-0.135909</td>\n",
       "      <td>-0.223911</td>\n",
       "      <td>0.039036</td>\n",
       "      <td>-0.586862</td>\n",
       "      <td>0.1203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>D22E</td>\n",
       "      <td>-0.902782</td>\n",
       "      <td>-0.035264</td>\n",
       "      <td>0.636411</td>\n",
       "      <td>0.203508</td>\n",
       "      <td>-0.088757</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>P38H</td>\n",
       "      <td>-0.208456</td>\n",
       "      <td>-0.094030</td>\n",
       "      <td>-0.195851</td>\n",
       "      <td>-0.257287</td>\n",
       "      <td>-0.378378</td>\n",
       "      <td>0.6479</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   variant  wing_size  eclosion_time  Mighell_score     VAC14       HEK  \\\n",
       "0  no_PTEN   0.000000       0.000000       0.000000  0.000000  0.000000   \n",
       "1       WT   1.000000       1.000000       1.000000  1.000000  1.000000   \n",
       "2    C124S  -0.465176      -0.135909      -0.223911  0.039036 -0.586862   \n",
       "3     D22E  -0.902782      -0.035264       0.636411  0.203508 -0.088757   \n",
       "4     P38H  -0.208456      -0.094030      -0.195851 -0.257287 -0.378378   \n",
       "\n",
       "   MCF10A  \n",
       "0  0.0000  \n",
       "1  1.0000  \n",
       "2  0.1203  \n",
       "3     NaN  \n",
       "4  0.6479  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#merge in VAC14, HEK and MCF10A dsets\n",
    "\n",
    "df_all = df_normed.copy()\n",
    "\n",
    "for i in [df_vac14_hek, df_mcf10a]:\n",
    "    df_all = df_all.merge(i, how='left', on='variant')\n",
    "\n",
    "df_all.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>variant</th>\n",
       "      <th>wing_size</th>\n",
       "      <th>eclosion_time</th>\n",
       "      <th>Mighell_score</th>\n",
       "      <th>VAC14</th>\n",
       "      <th>HEK</th>\n",
       "      <th>MCF10A</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>no_PTEN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>WT</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C124S</td>\n",
       "      <td>-0.200000</td>\n",
       "      <td>-0.135909</td>\n",
       "      <td>-0.200000</td>\n",
       "      <td>0.039036</td>\n",
       "      <td>-2.000000e-01</td>\n",
       "      <td>0.1203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>D22E</td>\n",
       "      <td>-0.200000</td>\n",
       "      <td>-0.035264</td>\n",
       "      <td>0.636411</td>\n",
       "      <td>0.203508</td>\n",
       "      <td>-8.875740e-02</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>P38H</td>\n",
       "      <td>-0.200000</td>\n",
       "      <td>-0.094030</td>\n",
       "      <td>-0.195851</td>\n",
       "      <td>-0.200000</td>\n",
       "      <td>-2.000000e-01</td>\n",
       "      <td>0.6479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>P354Q</td>\n",
       "      <td>0.759744</td>\n",
       "      <td>1.171776</td>\n",
       "      <td>1.022765</td>\n",
       "      <td>1.061492</td>\n",
       "      <td>1.200000e+00</td>\n",
       "      <td>0.8232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>I400V</td>\n",
       "      <td>0.754183</td>\n",
       "      <td>0.428982</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.006796</td>\n",
       "      <td>1.011905e+00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>K402N</td>\n",
       "      <td>0.126912</td>\n",
       "      <td>0.776226</td>\n",
       "      <td>0.683824</td>\n",
       "      <td>0.723201</td>\n",
       "      <td>1.023669e+00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>G44D</td>\n",
       "      <td>0.390293</td>\n",
       "      <td>-0.200000</td>\n",
       "      <td>0.229917</td>\n",
       "      <td>0.163274</td>\n",
       "      <td>1.730000e-18</td>\n",
       "      <td>0.3768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>P246L</td>\n",
       "      <td>0.508052</td>\n",
       "      <td>0.331032</td>\n",
       "      <td>0.594587</td>\n",
       "      <td>0.834561</td>\n",
       "      <td>8.066429e-02</td>\n",
       "      <td>0.3838</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>98 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    variant  wing_size  eclosion_time  Mighell_score     VAC14           HEK  \\\n",
       "0   no_PTEN   0.000000       0.000000       0.000000  0.000000  0.000000e+00   \n",
       "1        WT   1.000000       1.000000       1.000000  1.000000  1.000000e+00   \n",
       "2     C124S  -0.200000      -0.135909      -0.200000  0.039036 -2.000000e-01   \n",
       "3      D22E  -0.200000      -0.035264       0.636411  0.203508 -8.875740e-02   \n",
       "4      P38H  -0.200000      -0.094030      -0.195851 -0.200000 -2.000000e-01   \n",
       "..      ...        ...            ...            ...       ...           ...   \n",
       "93    P354Q   0.759744       1.171776       1.022765  1.061492  1.200000e+00   \n",
       "94    I400V   0.754183       0.428982            NaN  1.006796  1.011905e+00   \n",
       "95    K402N   0.126912       0.776226       0.683824  0.723201  1.023669e+00   \n",
       "96     G44D   0.390293      -0.200000       0.229917  0.163274  1.730000e-18   \n",
       "97    P246L   0.508052       0.331032       0.594587  0.834561  8.066429e-02   \n",
       "\n",
       "    MCF10A  \n",
       "0   0.0000  \n",
       "1   1.0000  \n",
       "2   0.1203  \n",
       "3      NaN  \n",
       "4   0.6479  \n",
       "..     ...  \n",
       "93  0.8232  \n",
       "94     NaN  \n",
       "95     NaN  \n",
       "96  0.3768  \n",
       "97  0.3838  \n",
       "\n",
       "[98 rows x 7 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cap values above 1 and below 0 to 20% above/below for plotting heatmap\n",
    "df_capped = capping(df_all)\n",
    "df_capped"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# analyze all data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## heatmap for all data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# grab AA positions\n",
    "AA_position = []\n",
    "for i in df_all['variant']:\n",
    "    try:\n",
    "        AA_position.append(int(i[1:-1]))\n",
    "    except ValueError:\n",
    "        AA_position.append(0)\n",
    "\n",
    "df_all['AA_position'] = AA_position\n",
    "\n",
    "# sort by aa position\n",
    "df_all = df_all.sort_values(by=['AA_position'], axis=0).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>variant</th>\n",
       "      <th>wing_size</th>\n",
       "      <th>eclosion_time</th>\n",
       "      <th>Mighell_score</th>\n",
       "      <th>VAC14</th>\n",
       "      <th>HEK</th>\n",
       "      <th>MCF10A</th>\n",
       "      <th>AA_position</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>no_PTEN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>WT</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>I135fs</td>\n",
       "      <td>0.109975</td>\n",
       "      <td>-0.096580</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>C124S-4A</td>\n",
       "      <td>-0.139094</td>\n",
       "      <td>-0.125417</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.200000</td>\n",
       "      <td>-0.200000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M1I</td>\n",
       "      <td>-0.057725</td>\n",
       "      <td>-0.106281</td>\n",
       "      <td>0.830047</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>P354Q</td>\n",
       "      <td>0.759744</td>\n",
       "      <td>1.171776</td>\n",
       "      <td>1.022765</td>\n",
       "      <td>1.061492</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>0.8232</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>P357S</td>\n",
       "      <td>0.834664</td>\n",
       "      <td>0.686143</td>\n",
       "      <td>0.590567</td>\n",
       "      <td>1.008465</td>\n",
       "      <td>1.059172</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>T363N</td>\n",
       "      <td>0.998301</td>\n",
       "      <td>0.338380</td>\n",
       "      <td>0.858815</td>\n",
       "      <td>1.059160</td>\n",
       "      <td>0.905325</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>I400V</td>\n",
       "      <td>0.754183</td>\n",
       "      <td>0.428982</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.006796</td>\n",
       "      <td>1.011905</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>K402N</td>\n",
       "      <td>0.126912</td>\n",
       "      <td>0.776226</td>\n",
       "      <td>0.683824</td>\n",
       "      <td>0.723201</td>\n",
       "      <td>1.023669</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>98 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     variant  wing_size  eclosion_time  Mighell_score     VAC14       HEK  \\\n",
       "0    no_PTEN   0.000000       0.000000       0.000000  0.000000  0.000000   \n",
       "1         WT   1.000000       1.000000       1.000000  1.000000  1.000000   \n",
       "2     I135fs   0.109975      -0.096580            NaN       NaN       NaN   \n",
       "3   C124S-4A  -0.139094      -0.125417            NaN -0.200000 -0.200000   \n",
       "4        M1I  -0.057725      -0.106281       0.830047       NaN       NaN   \n",
       "..       ...        ...            ...            ...       ...       ...   \n",
       "93     P354Q   0.759744       1.171776       1.022765  1.061492  1.200000   \n",
       "94     P357S   0.834664       0.686143       0.590567  1.008465  1.059172   \n",
       "95     T363N   0.998301       0.338380       0.858815  1.059160  0.905325   \n",
       "96     I400V   0.754183       0.428982            NaN  1.006796  1.011905   \n",
       "97     K402N   0.126912       0.776226       0.683824  0.723201  1.023669   \n",
       "\n",
       "    MCF10A  AA_position  \n",
       "0   0.0000          0.0  \n",
       "1   1.0000          0.0  \n",
       "2      NaN          0.0  \n",
       "3      NaN          0.0  \n",
       "4      NaN          1.0  \n",
       "..     ...          ...  \n",
       "93  0.8232          1.2  \n",
       "94     NaN          1.2  \n",
       "95     NaN          1.2  \n",
       "96     NaN          1.2  \n",
       "97     NaN          1.2  \n",
       "\n",
       "[98 rows x 8 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# capping values above/blow 1/0 to 1.2/-0.2\n",
    "df_all_capped = capping(df_all)\n",
    "df_all_capped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "#all_data_scaled = MinMaxScaler().fit_transform(df_all.iloc[:, 1:-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1500 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "heatmap_data = df_all_capped.iloc[:, 1:-1]\n",
    "labels = df_all_capped['variant'].tolist()\n",
    "\n",
    "#heatmap_data = all_data_scaled\n",
    "#labels = df_all['variant'].tolist()\n",
    "\n",
    "\n",
    "fig = plot_heatmap(heatmap_data, df_all_capped.columns[1:-1], labels, 'Reds_r')\n",
    "\n",
    "# save fig\n",
    "mpl.rcParams['ps.fonttype'] = 42\n",
    "fig.savefig('data_comparison_heatmap_by_aa.pdf', transparent=True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## clustering all data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "linkage = 'complete'\n",
    "\n",
    "fig = plt.figure(figsize=(12,5))\n",
    "ax = fig.add_subplot(111)\n",
    "# hierarchical_clustering(data, label, linkage, ax)\n",
    "dend, cluster_labels = hierarchical_clustering(\n",
    "                                data = df_all.iloc[:, 1:-2].dropna().reset_index(drop=True), \n",
    "                                label = df_all.iloc[:, :-2].dropna()['variant'].to_list(), \n",
    "                                linkage = linkage,\n",
    "                                ax = ax)\n",
    "\n",
    "ax.set_title(f'wing_size, eclosion_time, Mighell, VAC14, HEK clustering by \"{linkage} linkage\"')\n",
    "\n",
    "# output figure\n",
    "mpl.rcParams['pdf.fonttype'] = 42\n",
    "plt.savefig(f'all_data_dendrogram ({linkage}).pdf', transparent=True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>variant</th>\n",
       "      <th>wing_size</th>\n",
       "      <th>eclosion_time</th>\n",
       "      <th>Mighell_score</th>\n",
       "      <th>VAC14</th>\n",
       "      <th>HEK</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>H118P</td>\n",
       "      <td>-0.070744</td>\n",
       "      <td>0.293472</td>\n",
       "      <td>0.601692</td>\n",
       "      <td>0.350474</td>\n",
       "      <td>0.404762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>N12T</td>\n",
       "      <td>0.130851</td>\n",
       "      <td>0.104226</td>\n",
       "      <td>0.919713</td>\n",
       "      <td>0.726773</td>\n",
       "      <td>0.284024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>L70V</td>\n",
       "      <td>0.014905</td>\n",
       "      <td>0.470813</td>\n",
       "      <td>1.039242</td>\n",
       "      <td>0.648596</td>\n",
       "      <td>0.461342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>T167N</td>\n",
       "      <td>-0.171130</td>\n",
       "      <td>0.380455</td>\n",
       "      <td>0.917410</td>\n",
       "      <td>0.821710</td>\n",
       "      <td>0.270270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>Y180H</td>\n",
       "      <td>-0.200000</td>\n",
       "      <td>0.852450</td>\n",
       "      <td>1.084011</td>\n",
       "      <td>0.943517</td>\n",
       "      <td>0.750391</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   variant  wing_size  eclosion_time  Mighell_score     VAC14       HEK\n",
       "30   H118P  -0.070744       0.293472       0.601692  0.350474  0.404762\n",
       "4     N12T   0.130851       0.104226       0.919713  0.726773  0.284024\n",
       "19    L70V   0.014905       0.470813       1.039242  0.648596  0.461342\n",
       "51   T167N  -0.171130       0.380455       0.917410  0.821710  0.270270\n",
       "56   Y180H  -0.200000       0.852450       1.084011  0.943517  0.750391"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# re-index data according to cluster labels\n",
    "data_clustered = df_all_capped.iloc[:, :-2].dropna().reset_index(drop=True).reindex(dend['leaves'])\n",
    "data_clustered.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1500 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot heatmap in order of cluster labels\n",
    "# plot_heatmap(data, columns, labels, cmap)\n",
    "fig = plot_heatmap(data_clustered.iloc[:,1:], data_clustered.columns[1:], data_clustered['variant'], 'Reds_r')\n",
    "\n",
    "# save fig\n",
    "mpl.rcParams['ps.fonttype'] = 42\n",
    "plt.savefig(f'all_data_heatmap_clustered ({linkage}).pdf', transparent=True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3-way comparisons (wing vs HEK vs Mighell)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>variant</th>\n",
       "      <th>wing_size</th>\n",
       "      <th>eclosion_time</th>\n",
       "      <th>Mighell_score</th>\n",
       "      <th>VAC14</th>\n",
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       "  <tbody>\n",
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       "      <td>no_PTEN</td>\n",
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       "      <th>1</th>\n",
       "      <td>WT</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
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       "      <td>-0.586862</td>\n",
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       "      <td>0.203508</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>-0.257287</td>\n",
       "      <td>-0.378378</td>\n",
       "      <td>0.6479</td>\n",
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      ],
      "text/plain": [
       "   variant  wing_size  eclosion_time  Mighell_score     VAC14       HEK  \\\n",
       "0  no_PTEN   0.000000       0.000000       0.000000  0.000000  0.000000   \n",
       "1       WT   1.000000       1.000000       1.000000  1.000000  1.000000   \n",
       "2    C124S  -0.465176      -0.135909      -0.223911  0.039036 -0.586862   \n",
       "3     D22E  -0.902782      -0.035264       0.636411  0.203508 -0.088757   \n",
       "4     P38H  -0.208456      -0.094030      -0.195851 -0.257287 -0.378378   \n",
       "\n",
       "   MCF10A  \n",
       "0  0.0000  \n",
       "1  1.0000  \n",
       "2  0.1203  \n",
       "3     NaN  \n",
       "4  0.6479  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>variant</th>\n",
       "      <th>wing_size</th>\n",
       "      <th>Mighell_score</th>\n",
       "      <th>HEK</th>\n",
       "    </tr>\n",
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       "      <td>WT</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
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       "      <th>2</th>\n",
       "      <td>C124S</td>\n",
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       "      <td>-5.868615e-01</td>\n",
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       "      <td>D22E</td>\n",
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       "      <td>-8.875740e-02</td>\n",
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       "      <th>4</th>\n",
       "      <td>P38H</td>\n",
       "      <td>-0.208456</td>\n",
       "      <td>-0.195851</td>\n",
       "      <td>-3.783784e-01</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <th>77</th>\n",
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       "      <td>0.522926</td>\n",
       "      <td>8.757396e-01</td>\n",
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       "      <th>78</th>\n",
       "      <td>P354Q</td>\n",
       "      <td>0.759744</td>\n",
       "      <td>1.022765</td>\n",
       "      <td>1.391753e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>K402N</td>\n",
       "      <td>0.126912</td>\n",
       "      <td>0.683824</td>\n",
       "      <td>1.023669e+00</td>\n",
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       "      <td>P246L</td>\n",
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       "      <td>0.594587</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>82 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    variant  wing_size  Mighell_score           HEK\n",
       "0   no_PTEN   0.000000       0.000000  0.000000e+00\n",
       "1        WT   1.000000       1.000000  1.000000e+00\n",
       "2     C124S  -0.465176      -0.223911 -5.868615e-01\n",
       "3      D22E  -0.902782       0.636411 -8.875740e-02\n",
       "4      P38H  -0.208456      -0.195851 -3.783784e-01\n",
       "..      ...        ...            ...           ...\n",
       "77    K342N   0.679118       0.522926  8.757396e-01\n",
       "78    P354Q   0.759744       1.022765  1.391753e+00\n",
       "79    K402N   0.126912       0.683824  1.023669e+00\n",
       "80     G44D   0.390293       0.229917  1.730000e-18\n",
       "81    P246L   0.508052       0.594587  8.066429e-02\n",
       "\n",
       "[82 rows x 4 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clustering_data = df_all.drop(columns=['eclosion_time','VAC14','MCF10A']).dropna().reset_index(drop=True)\n",
    "clustering_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>wing_size</th>\n",
       "      <th>HEK</th>\n",
       "      <th>Mighell_score</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>WT</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>D22E</td>\n",
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       "      <td>0.636411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>P38H</td>\n",
       "      <td>-0.208456</td>\n",
       "      <td>-0.378378</td>\n",
       "      <td>-0.195851</td>\n",
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       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "   variant  wing_size       HEK  Mighell_score\n",
       "0  no_PTEN   0.000000  0.000000       0.000000\n",
       "1       WT   1.000000  1.000000       1.000000\n",
       "2    C124S  -0.465176 -0.586862      -0.223911\n",
       "3     D22E  -0.902782 -0.088757       0.636411\n",
       "4     P38H  -0.208456 -0.378378      -0.195851"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = ['variant', 'wing_size', 'HEK', 'Mighell_score']\n",
    "clustering_data = clustering_data[cols]\n",
    "clustering_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "linkage_method = 'complete'\n",
    "\n",
    "fig = plt.figure(figsize=(10,5), dpi=100)\n",
    "ax = fig.add_subplot(111)\n",
    "dend, cluster_labels = hierarchical_clustering(\n",
    "                              clustering_data.iloc[:, 1:], \n",
    "                              clustering_data['variant'].tolist(),\n",
    "                              linkage_method, ax)\n",
    "\n",
    "ax.set_title(f\"3-way clustering wing/HEK/Mighell (by {linkage_method} linkage)\")\n",
    "\n",
    "#save_fig(fig, title=f'WHM_dendrogram ({linkage_method})')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>variant</th>\n",
       "      <th>wing_size</th>\n",
       "      <th>Mighell_score</th>\n",
       "      <th>HEK</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>E256K</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>0.728004</td>\n",
       "      <td>1.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>K330E</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>0.976313</td>\n",
       "      <td>1.178571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>K322E</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>0.958300</td>\n",
       "      <td>0.607143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Y176C</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>0.677128</td>\n",
       "      <td>0.958580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>F279L</td>\n",
       "      <td>0.931094</td>\n",
       "      <td>0.740493</td>\n",
       "      <td>0.833333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   variant  wing_size  Mighell_score       HEK\n",
       "70   E256K   1.200000       0.728004  1.200000\n",
       "73   K330E   1.200000       0.976313  1.178571\n",
       "39   K322E   1.200000       0.958300  0.607143\n",
       "20   Y176C   1.200000       0.677128  0.958580\n",
       "36   F279L   0.931094       0.740493  0.833333"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# re-index data according to cluster labels\n",
    "heatmap_data = df_capped[['variant', 'wing_size', 'Mighell_score', 'HEK']].dropna().reset_index(drop=True).reindex(dend['leaves'])\n",
    "heatmap_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "heatmap_data = heatmap_data[cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1500 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot heatmap in order of cluster labels\n",
    "# plot_heatmap(data, columns, labels, cmap)\n",
    "fig = plot_heatmap(heatmap_data.iloc[:,1:], heatmap_data.columns[1:], heatmap_data['variant'], 'Reds_r')\n",
    "\n",
    "save_fig(fig, title=f'WHM_heatmap_clustered ({linkage_method})')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## plot correlation (scatter) plots using cluster assignments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    variant  cluster_labels\n",
      "0   no_PTEN               5\n",
      "1        WT               1\n",
      "2     C124S               0\n",
      "3      D22E               0\n",
      "4      P38H               0\n",
      "..      ...             ...\n",
      "77    K342N               1\n",
      "78    P354Q               1\n",
      "79    K402N               3\n",
      "80     G44D               6\n",
      "81    P246L               6\n",
      "\n",
      "[82 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "# prep the data for scatter plotting\n",
    "d = {'variant':clustering_data['variant'], 'cluster_labels':cluster_labels}\n",
    "df_labels = pd.DataFrame(d)\n",
    "print(df_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(cluster_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>variant</th>\n",
       "      <th>wing_size</th>\n",
       "      <th>eclosion_time</th>\n",
       "      <th>Mighell_score</th>\n",
       "      <th>VAC14</th>\n",
       "      <th>HEK</th>\n",
       "      <th>MCF10A</th>\n",
       "      <th>cluster_labels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>no_PTEN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>WT</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C124S</td>\n",
       "      <td>-0.465176</td>\n",
       "      <td>-0.135909</td>\n",
       "      <td>-0.223911</td>\n",
       "      <td>0.039036</td>\n",
       "      <td>-5.868615e-01</td>\n",
       "      <td>0.1203</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>D22E</td>\n",
       "      <td>-0.902782</td>\n",
       "      <td>-0.035264</td>\n",
       "      <td>0.636411</td>\n",
       "      <td>0.203508</td>\n",
       "      <td>-8.875740e-02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>P38H</td>\n",
       "      <td>-0.208456</td>\n",
       "      <td>-0.094030</td>\n",
       "      <td>-0.195851</td>\n",
       "      <td>-0.257287</td>\n",
       "      <td>-3.783784e-01</td>\n",
       "      <td>0.6479</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>P354Q</td>\n",
       "      <td>0.759744</td>\n",
       "      <td>1.171776</td>\n",
       "      <td>1.022765</td>\n",
       "      <td>1.061492</td>\n",
       "      <td>1.391753e+00</td>\n",
       "      <td>0.8232</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>I400V</td>\n",
       "      <td>0.754183</td>\n",
       "      <td>0.428982</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.006796</td>\n",
       "      <td>1.011905e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>K402N</td>\n",
       "      <td>0.126912</td>\n",
       "      <td>0.776226</td>\n",
       "      <td>0.683824</td>\n",
       "      <td>0.723201</td>\n",
       "      <td>1.023669e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>G44D</td>\n",
       "      <td>0.390293</td>\n",
       "      <td>-0.401015</td>\n",
       "      <td>0.229917</td>\n",
       "      <td>0.163274</td>\n",
       "      <td>1.730000e-18</td>\n",
       "      <td>0.3768</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>P246L</td>\n",
       "      <td>0.508052</td>\n",
       "      <td>0.331032</td>\n",
       "      <td>0.594587</td>\n",
       "      <td>0.834561</td>\n",
       "      <td>8.066429e-02</td>\n",
       "      <td>0.3838</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>98 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    variant  wing_size  eclosion_time  Mighell_score     VAC14           HEK  \\\n",
       "0   no_PTEN   0.000000       0.000000       0.000000  0.000000  0.000000e+00   \n",
       "1        WT   1.000000       1.000000       1.000000  1.000000  1.000000e+00   \n",
       "2     C124S  -0.465176      -0.135909      -0.223911  0.039036 -5.868615e-01   \n",
       "3      D22E  -0.902782      -0.035264       0.636411  0.203508 -8.875740e-02   \n",
       "4      P38H  -0.208456      -0.094030      -0.195851 -0.257287 -3.783784e-01   \n",
       "..      ...        ...            ...            ...       ...           ...   \n",
       "93    P354Q   0.759744       1.171776       1.022765  1.061492  1.391753e+00   \n",
       "94    I400V   0.754183       0.428982            NaN  1.006796  1.011905e+00   \n",
       "95    K402N   0.126912       0.776226       0.683824  0.723201  1.023669e+00   \n",
       "96     G44D   0.390293      -0.401015       0.229917  0.163274  1.730000e-18   \n",
       "97    P246L   0.508052       0.331032       0.594587  0.834561  8.066429e-02   \n",
       "\n",
       "    MCF10A  cluster_labels  \n",
       "0   0.0000             5.0  \n",
       "1   1.0000             1.0  \n",
       "2   0.1203             0.0  \n",
       "3      NaN             0.0  \n",
       "4   0.6479             0.0  \n",
       "..     ...             ...  \n",
       "93  0.8232             1.0  \n",
       "94     NaN             NaN  \n",
       "95     NaN             3.0  \n",
       "96  0.3768             6.0  \n",
       "97  0.3838             6.0  \n",
       "\n",
       "[98 rows x 8 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_to_plot = df_all.merge(df_labels, on='variant', how='left')\n",
    "display(df_to_plot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>variant</th>\n",
       "      <th>wing_size</th>\n",
       "      <th>eclosion_time</th>\n",
       "      <th>Mighell_score</th>\n",
       "      <th>VAC14</th>\n",
       "      <th>HEK</th>\n",
       "      <th>MCF10A</th>\n",
       "      <th>cluster_labels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>I203V</td>\n",
       "      <td>0.821931</td>\n",
       "      <td>1.137988</td>\n",
       "      <td>1.112498</td>\n",
       "      <td>0.997972</td>\n",
       "      <td>1.015697</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   variant  wing_size  eclosion_time  Mighell_score     VAC14       HEK  \\\n",
       "38   I203V   0.821931       1.137988       1.112498  0.997972  1.015697   \n",
       "\n",
       "    MCF10A  cluster_labels  \n",
       "38     NaN             1.0  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_to_plot[df_to_plot['variant']=='I203V']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fill na with 8 to plot them gray later in the scatterplot\n",
    "filler = {'cluster_labels':8}\n",
    "df_to_plot.fillna(value=filler, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = 'Mighell_score'\n",
    "y = 'HEK'\n",
    "df_temp = df_to_plot[['variant', x, y, 'cluster_labels']].dropna().reset_index(drop=True)\n",
    "\n",
    "# calculate overall Pearson's r\n",
    "corr, p = do_pearson(df_temp[x], df_temp[y])\n",
    "pear_r = f\"Pearson's r={corr:.4f} (p={p:.4f})\"\n",
    "\n",
    "# plot scatterplot\n",
    "fig, ax = correlate(df_temp, x, y)\n",
    "text_x = ax.get_xlim()[0]*0.9\n",
    "text_y = ax.get_ylim()[1]*0.95\n",
    "ax.text(text_x, text_y, pear_r)\n",
    "title = f'{y} vs {x}, colored by W-H-M clustering ({linkage_method} linkage)'\n",
    "ax.set_title(title)\n",
    "\n",
    "save_fig(fig, title=f\"{y}-{x} (WHM clustering)\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}