diff --git a/CanDI/candi/data.py b/CanDI/candi/data.py
index 66562de..4be05a2 100644
--- a/CanDI/candi/data.py
+++ b/CanDI/candi/data.py
@@ -32,7 +32,7 @@ def _verify_install(self): #ensures data being loaded is present
         try:
             assert "depmap_urls" in self._parser.sections()
         except AssertionError:
-            raise(RuntimeError, "CanDI has not been properly installed. Please run CanDI/install.py prior to import")
+            raise RuntimeError("CanDI has not been properly installed. Please run CanDI/setup/install.py prior to import")
 
     def _init_sources(self):
         """this function creates paths
@@ -59,16 +59,17 @@ def _init_depmap_paths(self):
                 setattr(self, option, new_path)
             except AssertionError:
                 setattr(self, option, None)
+            
 
     def _init_index_tables(self):
 
         for option in self._parser["autoload_info"]:
             try:
-               new_path = self._file_path / self._parser.get("autoload_info", option)
-               assert os.path.exists(new_path)
-               setattr(self, option, self._handle_autoload(option, new_path))
+                new_path = self._file_path / self._parser.get("autoload_info", option)
+                assert os.path.exists(new_path)
+                setattr(self, option, self._handle_autoload(option, new_path))
             except AssertionError:
-               raise(RuntimeError, "You are missing essential index table: {}. exiting...".format(new_path))
+                raise RuntimeError("You are missing essential index table: {}. exiting...".format(new_path))
 
 
     @staticmethod
@@ -102,14 +103,13 @@ def _handle_autoload(method, path):
 
     def load(self, key):
         """This function loads a dataset into memory as a pandas DataFrame.
-
-        Note:
-            If the nth row and mth column is equal to 0 the nth gene is not mutated in the mth cell line.
-            If the nth row and mth column is equal to 1 the nth gene is mutated in the mth cell line.
+        
         Args:
-            key:
+            key: str
+               name of dataset to load into memory
         Returns:
-            [---]
+            pandas.core.frame.DataFrame
+                Pandas DataFrame is returned and saved as an attribute within the data object of the same name as key
         """
         if hasattr(self, key):
 
@@ -137,8 +137,22 @@ def load(self, key):
 
     def unload(self, key):
         """This function removes a dataset from memory
-        Args:
-            key:
+        
+        Args: 
+            key: str
+                name of the dataset to remove from memory
+        Returns:
+            None
+                Data object of with same name as key is removed from memory and attribute is returned to dataset file path.
         """
-        setattr(self, key, self.data_path + self._parser["files"][key])
-        gc.collect()
+        
+        try:
+            assert isinstance(getattr(self, key), pd.core.frame.DataFrame)
+        except AssertionError:
+            raise RuntimeError("{} is not currently loaded into memory".format(key))
+            
+        
+        new_path = self._depmap_path / self._parser.get("depmap_files", key)
+        assert os.path.exists(new_path)
+        setattr(self, key, new_path)
+        
diff --git a/README.md b/README.md
deleted file mode 100644
index 44b9a12..0000000
--- a/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
-# Candi
-[![Documentation Status](https://readthedocs.org/projects/candi/badge/?version=latest)](https://candi.readthedocs.io/en/latest/?badge=latest)
-
-## Installation
-
-
-```Bash
-git clone https://github.com/GilbertLabUCSF/CanDI.git
-conda env create2 -f CaDI/candi.yml -n candi
-python CanDI/CanDI/setup/install.py
-```
diff --git a/README.rst b/README.rst
index 3496a43..6e6e58e 100644
--- a/README.rst
+++ b/README.rst
@@ -62,7 +62,7 @@ cloned folder.
 
 .. code:: python
 
-   import CanDI as can
+   from CanDI import candi
 
 **OR**, you can add path to the `CanDI` directory if you want to use it from other directories.
 
@@ -71,7 +71,7 @@ cloned folder.
    import sys
    sys.path.append("path-to-candi-directory")
 
-   import CanDI as can
+   from CanDI import candi
 
 CanDI Objects
 ~~~~~~~~~~~~~
diff --git a/brca_heatmap.ipynb b/brca_heatmap.ipynb
new file mode 100644
index 0000000..bb63fc1
--- /dev/null
+++ b/brca_heatmap.ipynb
@@ -0,0 +1,671 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "58a5f439",
+   "metadata": {},
+   "source": [
+    "# _BRCA_ Heatmap"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "93b49611",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import CanDI.candi as can\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3f9e2439",
+   "metadata": {},
+   "source": [
+    "### Cancer Object Instantiation\n",
+    "We're interested in cross referencing some data in breast and ovarian cancer so instantiate cancer objects as follows.\n",
+    "To double check the object instantiation I check the length of the depmap_id vectors. This lets me know we're able to index other datasets correctly"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "c220005a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "74\n",
+      "83\n"
+     ]
+    }
+   ],
+   "source": [
+    "ov = can.Cancer(\"Ovarian Cancer\")\n",
+    "br = can.Cancer(\"Breast Cancer\")\n",
+    "\n",
+    "#Number of Ovarian Cell lines\n",
+    "print(len(ov.depmap_ids))\n",
+    "#Number of Breast Cell Lines\n",
+    "print(len(br.depmap_ids))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "659d1805",
+   "metadata": {},
+   "source": [
+    "### Subsetting by mutation status\n",
+    "\n",
+    "Explicitly load mutations into memory.This only needs to be done once\n",
+    "You will be done prompted to load a given dataset if using operations that act\n",
+    "on that dataset and it is not in memory."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "d098ddf9",
+   "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>gene</th>\n",
+       "      <th>Entrez_Gene_Id</th>\n",
+       "      <th>NCBI_Build</th>\n",
+       "      <th>Chromosome</th>\n",
+       "      <th>Start_position</th>\n",
+       "      <th>End_position</th>\n",
+       "      <th>Strand</th>\n",
+       "      <th>Variant_Classification</th>\n",
+       "      <th>Variant_Type</th>\n",
+       "      <th>Reference_Allele</th>\n",
+       "      <th>...</th>\n",
+       "      <th>isCOSMIChotspot</th>\n",
+       "      <th>COSMIChsCnt</th>\n",
+       "      <th>ExAC_AF</th>\n",
+       "      <th>Variant_annotation</th>\n",
+       "      <th>CGA_WES_AC</th>\n",
+       "      <th>HC_AC</th>\n",
+       "      <th>RD_AC</th>\n",
+       "      <th>RNAseq_AC</th>\n",
+       "      <th>SangerWES_AC</th>\n",
+       "      <th>WGS_AC</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>VPS13D</td>\n",
+       "      <td>55187</td>\n",
+       "      <td>37</td>\n",
+       "      <td>1</td>\n",
+       "      <td>12359347</td>\n",
+       "      <td>12359347</td>\n",
+       "      <td>+</td>\n",
+       "      <td>Nonsense_Mutation</td>\n",
+       "      <td>SNP</td>\n",
+       "      <td>C</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>damaging</td>\n",
+       "      <td>34:213</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>34:221</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>AADACL4</td>\n",
+       "      <td>343066</td>\n",
+       "      <td>37</td>\n",
+       "      <td>1</td>\n",
+       "      <td>12726308</td>\n",
+       "      <td>12726322</td>\n",
+       "      <td>+</td>\n",
+       "      <td>In_Frame_Del</td>\n",
+       "      <td>DEL</td>\n",
+       "      <td>CTGGCGTGACGCCAT</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>other non-conserving</td>\n",
+       "      <td>57:141</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9:0</td>\n",
+       "      <td>28:32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>IFNLR1</td>\n",
+       "      <td>163702</td>\n",
+       "      <td>37</td>\n",
+       "      <td>1</td>\n",
+       "      <td>24484172</td>\n",
+       "      <td>24484172</td>\n",
+       "      <td>+</td>\n",
+       "      <td>Silent</td>\n",
+       "      <td>SNP</td>\n",
+       "      <td>G</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>silent</td>\n",
+       "      <td>118:0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>10:0</td>\n",
+       "      <td>118:0</td>\n",
+       "      <td>18:0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>TMEM57</td>\n",
+       "      <td>55219</td>\n",
+       "      <td>37</td>\n",
+       "      <td>1</td>\n",
+       "      <td>25785018</td>\n",
+       "      <td>25785019</td>\n",
+       "      <td>+</td>\n",
+       "      <td>Frame_Shift_Ins</td>\n",
+       "      <td>INS</td>\n",
+       "      <td>-</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>damaging</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6:28</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>ZSCAN20</td>\n",
+       "      <td>7579</td>\n",
+       "      <td>37</td>\n",
+       "      <td>1</td>\n",
+       "      <td>33954141</td>\n",
+       "      <td>33954141</td>\n",
+       "      <td>+</td>\n",
+       "      <td>Missense_Mutation</td>\n",
+       "      <td>SNP</td>\n",
+       "      <td>T</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>other non-conserving</td>\n",
+       "      <td>28:62</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>27:61</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1269994</th>\n",
+       "      <td>EHBP1L1</td>\n",
+       "      <td>254102</td>\n",
+       "      <td>37</td>\n",
+       "      <td>11</td>\n",
+       "      <td>65350600</td>\n",
+       "      <td>65350600</td>\n",
+       "      <td>+</td>\n",
+       "      <td>Frame_Shift_Del</td>\n",
+       "      <td>DEL</td>\n",
+       "      <td>G</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>61:69</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>1269995</th>\n",
+       "      <td>SACS</td>\n",
+       "      <td>26278</td>\n",
+       "      <td>37</td>\n",
+       "      <td>13</td>\n",
+       "      <td>23904582</td>\n",
+       "      <td>23904582</td>\n",
+       "      <td>+</td>\n",
+       "      <td>Frame_Shift_Del</td>\n",
+       "      <td>DEL</td>\n",
+       "      <td>T</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>88:1</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>1269996</th>\n",
+       "      <td>CBFB</td>\n",
+       "      <td>865</td>\n",
+       "      <td>37</td>\n",
+       "      <td>16</td>\n",
+       "      <td>67070637</td>\n",
+       "      <td>67070638</td>\n",
+       "      <td>+</td>\n",
+       "      <td>Frame_Shift_Ins</td>\n",
+       "      <td>INS</td>\n",
+       "      <td>-</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>31:0</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>1269997</th>\n",
+       "      <td>TAF15</td>\n",
+       "      <td>8148</td>\n",
+       "      <td>37</td>\n",
+       "      <td>17</td>\n",
+       "      <td>34171711</td>\n",
+       "      <td>34171734</td>\n",
+       "      <td>+</td>\n",
+       "      <td>In_Frame_Del</td>\n",
+       "      <td>DEL</td>\n",
+       "      <td>GGCTATGGAGGAGACCGAGGAGGT</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24:28</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>1269998</th>\n",
+       "      <td>FRMPD3</td>\n",
+       "      <td>84443</td>\n",
+       "      <td>37</td>\n",
+       "      <td>X</td>\n",
+       "      <td>106846460</td>\n",
+       "      <td>106846461</td>\n",
+       "      <td>+</td>\n",
+       "      <td>In_Frame_Ins</td>\n",
+       "      <td>INS</td>\n",
+       "      <td>-</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6:27</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>1269999 rows × 32 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            gene  Entrez_Gene_Id  NCBI_Build Chromosome  Start_position  \\\n",
+       "0         VPS13D           55187          37          1        12359347   \n",
+       "1        AADACL4          343066          37          1        12726308   \n",
+       "2         IFNLR1          163702          37          1        24484172   \n",
+       "3         TMEM57           55219          37          1        25785018   \n",
+       "4        ZSCAN20            7579          37          1        33954141   \n",
+       "...          ...             ...         ...        ...             ...   \n",
+       "1269994  EHBP1L1          254102          37         11        65350600   \n",
+       "1269995     SACS           26278          37         13        23904582   \n",
+       "1269996     CBFB             865          37         16        67070637   \n",
+       "1269997    TAF15            8148          37         17        34171711   \n",
+       "1269998   FRMPD3           84443          37          X       106846460   \n",
+       "\n",
+       "         End_position Strand Variant_Classification Variant_Type  \\\n",
+       "0            12359347      +      Nonsense_Mutation          SNP   \n",
+       "1            12726322      +           In_Frame_Del          DEL   \n",
+       "2            24484172      +                 Silent          SNP   \n",
+       "3            25785019      +        Frame_Shift_Ins          INS   \n",
+       "4            33954141      +      Missense_Mutation          SNP   \n",
+       "...               ...    ...                    ...          ...   \n",
+       "1269994      65350600      +        Frame_Shift_Del          DEL   \n",
+       "1269995      23904582      +        Frame_Shift_Del          DEL   \n",
+       "1269996      67070638      +        Frame_Shift_Ins          INS   \n",
+       "1269997      34171734      +           In_Frame_Del          DEL   \n",
+       "1269998     106846461      +           In_Frame_Ins          INS   \n",
+       "\n",
+       "                 Reference_Allele  ... isCOSMIChotspot COSMIChsCnt ExAC_AF  \\\n",
+       "0                               C  ...           False         0.0     NaN   \n",
+       "1                 CTGGCGTGACGCCAT  ...           False         3.0     NaN   \n",
+       "2                               G  ...           False         0.0     NaN   \n",
+       "3                               -  ...           False         0.0     NaN   \n",
+       "4                               T  ...           False         0.0     NaN   \n",
+       "...                           ...  ...             ...         ...     ...   \n",
+       "1269994                         G  ...           False         0.0     NaN   \n",
+       "1269995                         T  ...           False         0.0     NaN   \n",
+       "1269996                         -  ...           False         0.0     NaN   \n",
+       "1269997  GGCTATGGAGGAGACCGAGGAGGT  ...           False         0.0     NaN   \n",
+       "1269998                         -  ...           False         0.0     NaN   \n",
+       "\n",
+       "           Variant_annotation CGA_WES_AC HC_AC RD_AC RNAseq_AC SangerWES_AC  \\\n",
+       "0                    damaging     34:213   NaN   NaN       NaN       34:221   \n",
+       "1        other non-conserving     57:141   NaN   NaN       NaN          9:0   \n",
+       "2                      silent      118:0   NaN   NaN      10:0        118:0   \n",
+       "3                    damaging        NaN   NaN   NaN      6:28          NaN   \n",
+       "4        other non-conserving      28:62   NaN   NaN       NaN        27:61   \n",
+       "...                       ...        ...   ...   ...       ...          ...   \n",
+       "1269994                   NaN      61:69   NaN   NaN       NaN          NaN   \n",
+       "1269995                   NaN       88:1   NaN   NaN       NaN          NaN   \n",
+       "1269996                   NaN       31:0   NaN   NaN       NaN          NaN   \n",
+       "1269997                   NaN      24:28   NaN   NaN       NaN          NaN   \n",
+       "1269998                   NaN       6:27   NaN   NaN       NaN          NaN   \n",
+       "\n",
+       "        WGS_AC  \n",
+       "0          NaN  \n",
+       "1        28:32  \n",
+       "2         18:0  \n",
+       "3          NaN  \n",
+       "4          NaN  \n",
+       "...        ...  \n",
+       "1269994    NaN  \n",
+       "1269995    NaN  \n",
+       "1269996    NaN  \n",
+       "1269997    NaN  \n",
+       "1269998    NaN  \n",
+       "\n",
+       "[1269999 rows x 32 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "can.data.load(\"mutations\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2421b512",
+   "metadata": {},
+   "source": [
+    "I want to look at BRCA1 mutations in these types of cancers. I start by using the mutated function to identify ovarian and breast cancer cell lines with BRCA1 mutations. A cancer object's mutated method's default behavior is to output a list of depmap ids corresponding to celllines containing any mutation within the given genes. I then instantiate CellLineCluster objects of exclusively mutated or wild type cell lines for both breast and ovarian cancer. This makes comparing these cell lines easier.\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "28fb0265",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Depmap_ids attribute should be the same as the list used to instantiate the CellLineCluster object\n",
+      "\n",
+      "True\n",
+      "True\n"
+     ]
+    }
+   ],
+   "source": [
+    "ov_mt_list = ov.mutated([\"BRCA1\"]) #List of depmap_ids\n",
+    "br_mt_list = br.mutated([\"BRCA1\"]) #list of depmap_ids\n",
+    "\n",
+    "ov_mt = can.CellLineCluster(ov_mt_list) #CellLineCluster obj\n",
+    "br_mt = can.CellLineCluster(br_mt_list)\n",
+    "\n",
+    "\n",
+    "print(\"Depmap_ids attribute should be the same as the list used to instantiate the CellLineCluster object\\n\")\n",
+    "print(ov_mt.depmap_ids == ov_mt_list)\n",
+    "\n",
+    "#CellLineCluster ojbect must be instantiated with a mutable sequence\n",
+    "#I use set operations to get wild type cell line ids and convert to a list\n",
+    "ov_wt_list = list(set(ov.depmap_ids) - set(ov_mt_list))\n",
+    "br_wt_list = list(set(br.depmap_ids) - set(br_mt_list))\n",
+    "\n",
+    "ov_wt = can.CellLineCluster(ov_wt_list)\n",
+    "br_wt = can.CellLineCluster(br_wt_list)\n",
+    "print(ov_wt.depmap_ids == ov_wt_list)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cb1fd667",
+   "metadata": {},
+   "source": [
+    "### Cross Referencing Mutation and Gene Knockout Data\n",
+    "I'm interested in how the mutation status of BRCA1 effects a cancer's dependency on the fanconi anemia genes.\n",
+    "To visualize this relationship I am going to make a heatmap of fanconi anemia genes sorting the cell lines by their BRCA1 mutation status. The following cell defines a function that plots a heatmap of the gene effect of the fanconi anemia genes separating them by the BRCA1 mutation status of a given cell line. \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "0a51271e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def gene_effect_heatmap(obj1, obj2, genes, name = None):\n",
+    "    \n",
+    "    #Make Figure appropriate size, dpi, and font\n",
+    "    plt.rcParams.update({\"figure.figsize\": (16, 6),\n",
+    "                        \"savefig.dpi\": 300,\n",
+    "                        \"font.size\": 12\n",
+    "                        })\n",
+    "    \n",
+    "    #One figure with one subplot\n",
+    "    fig, ax = plt.subplots(1,1)\n",
+    "    \n",
+    "    #Construcat matrix to make heatmap and cell line labels\n",
+    "    data = pd.concat([obj1.effect_of(genes), obj2.effect_of(genes)], axis=1)\n",
+    "    names = can.data.cell_lines.loc[data.columns, \"cell_line_name\"]\n",
+    "        \n",
+    "    # We want to show all ticks...\n",
+    "    ax.set_xticks(np.arange(len(names)))\n",
+    "    ax.set_yticks(np.arange(len(genes)))\n",
+    "    # ... and label them with the respective list entries\n",
+    "    ax.set_xticklabels(names)\n",
+    "    ax.set_yticklabels(genes)\n",
+    "    \n",
+    "    #make heatmap\n",
+    "    im = ax.imshow(data, cmap=\"RdBu\")\n",
+    "    \n",
+    "    #Make colorbar scale to axis\n",
+    "    divider = make_axes_locatable(ax)\n",
+    "    cax = divider.append_axes(\"right\", size=\"5%\", pad=0.1)\n",
+    "    cbar = ax.figure.colorbar(im, ax = ax, cax = cax)\n",
+    "    cbar.ax.set_ylabel(\"Gene Effect\", rotation=-90, va=\"bottom\")\n",
+    "    \n",
+    "    #Draw Dividing line btween mutant and\n",
+    "    ax.axvline(x=obj1.gene_effect.shape[1] - 0.5, c = \"black\", linewidth = 3)\n",
+    "    plt.setp(ax.get_xticklabels(), rotation=-90, ha=\"left\", va=\"center\",\n",
+    "         rotation_mode=\"anchor\")\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()\n",
+    "    \n",
+    "    if name:\n",
+    "        fig.savefig(name, dpi=300)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9ed37fc0",
+   "metadata": {},
+   "source": [
+    "### Fanconi Anemia Genes Knockout Effect in Ovarian Cancer\n",
+    "BRCA1 Mutant Left of Vertical Line"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "a3adc292",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "gene_effect has not been loaded. Do you want to load, y/n?> y\n",
+      "Load Complete\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x432 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "genes = [\"FANC\" + i for i in [\"A\", \"C\", \"I\", \"M\", \"D2\", \"F\", \"E\"]]\n",
+    "gene_effect_heatmap(ov_mt, ov_wt, genes, name = None)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f8aa60cd",
+   "metadata": {},
+   "source": [
+    "### Fanconi Anemia Genes Knockout Effect in Breast Cancer\n",
+    "BRCA1 Mutant Left of Vertical Line"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "3f3cf06f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x432 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "gene_effect_heatmap(br_mt, br_wt, genes, name = None)"
+   ]
+  }
+ ],
+ "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": "3.9.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/deseq_setup.ipynb b/deseq_setup.ipynb
new file mode 100644
index 0000000..3590ce6
--- /dev/null
+++ b/deseq_setup.ipynb
@@ -0,0 +1,265 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "163bb85c",
+   "metadata": {},
+   "source": [
+    "# CanDI and DESeq2\n",
+    "Let's say I want to look at changes in RNA expression across some cell lines in CCLE. DESeq2 is my preffered tool for doing differential expression analysis, unforutantely it's written in R. CanDI makes it easy to format CCLE read counts data into the shape that DESeq2 expects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "72858c31",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import CanDI.candi as can\n",
+    "import numpy as np\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "319b94c2",
+   "metadata": {},
+   "source": [
+    "#### Object Instantiation\n",
+    "For this example I'm going to do differential expression analysis across male and female KRAS mutant cell lines. The cell below uses CanDI to generate the correct CellLineCluster objects for our purpose."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "e3794753",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "mutations has not been loaded. Do you want to load, y/n?> y\n",
+      "Load Complete\n"
+     ]
+    }
+   ],
+   "source": [
+    "lung = can.Cancer(\"Lung Cancer\", subtype = \"NSCLC\")\n",
+    "lung = can.CellLineCluster(lung.mutated(\"KRAS\", variant = \"Variant_Classification\", item = \"Missense_Mutation\"))\n",
+    "\n",
+    "lung_male = can.CellLineCluster(list(lung._info.loc[lung._info.sex == \"Male\",].index))\n",
+    "lung_female = can.CellLineCluster(list(lung._info.loc[lung._info.sex == \"Female\"].index))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5aae975f",
+   "metadata": {},
+   "source": [
+    "#### Data Munging\n",
+    "The follow function takes two objects that we want to compare and automatically generates the counts and coldata matrices that DESeq2 needs to run. It's typically a good idea to filter our genes/transcripts with consistently low counts prior to running DESeq2. This speeds up analysis and avoids issues related to read count scaling and multiple hypthothesis testing corrections. In this case we don't care about different splicing of the same genes so I sum counts for duplicate indeces for all samples. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "c697995d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "rnaseq_reads has not been loaded. Do you want to load, y/n?> y\n",
+      "Load Complete\n"
+     ]
+    }
+   ],
+   "source": [
+    "def make_counts_coldata(obj1, obj2, condition, factor1, factor2):\n",
+    "    \n",
+    "    counts1 = obj1.rnaseq_reads\n",
+    "    coldat1 = pd.Series(counts1.shape[1] * [factor1], index = counts1.columns, name = condition)\n",
+    "    \n",
+    "    counts2 = obj2.rnaseq_reads\n",
+    "    coldat2 = pd.Series(counts2.shape[1] * [factor2], index = counts2.columns, name = condition)\n",
+    "    \n",
+    "    #Concatenate Column Data\n",
+    "    coldat = pd.concat([coldat1, coldat2], axis = 0)\n",
+    "    #Concatenate read count data \n",
+    "    counts_mat = pd.concat([counts1, counts2], axis = 1)\n",
+    "    #Sum duplicate indeces\n",
+    "    counts_mat = counts_mat.groupby(counts_mat.index).sum().astype(int)\n",
+    "    \n",
+    "    return counts_mat, coldat\n",
+    "    \n",
+    "counts, coldat = make_counts_coldata(lung_male, lung_female, \"sex\", \"male\", \"female\")\n",
+    "\n",
+    "counts.to_csv(\"temp_dat/lung_sex_counts.csv\")\n",
+    "coldat.to_csv(\"temp_dat/lung_sex_coldata.csv\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d148ea96",
+   "metadata": {},
+   "source": [
+    "#### Running DESeq2\n",
+    "In the following cell I use the csvs I just saved as arguments for an r-script that runs DESeq2. The last argument in this script the filname for the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d771eb95",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!Rscript scripts/run_deseq.r temp_dat/lung_sex_counts.csv temp_dat/lung_sex_coldata.csv temp_dat/lung_sex_deseq.csv"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "275e042d",
+   "metadata": {},
+   "source": [
+    "#### Analyzing Results\n",
+    "Now we can read the results of the differential expression analysis back into our python enviroment and continue analysis as necessary. Looking at the genes with the lowest adjusted p-value we see that XIST is the most significant differentially expressed genes. XIST is a lncRNA responsible for X inactivation in females so this a good positive control for our analysis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "f72ee6cd",
+   "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>baseMean</th>\n",
+       "      <th>log2FoldChange</th>\n",
+       "      <th>lfcSE</th>\n",
+       "      <th>stat</th>\n",
+       "      <th>pvalue</th>\n",
+       "      <th>padj</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>XIST</th>\n",
+       "      <td>3936.090666</td>\n",
+       "      <td>-7.030433</td>\n",
+       "      <td>0.708612</td>\n",
+       "      <td>-9.921418</td>\n",
+       "      <td>3.359498e-23</td>\n",
+       "      <td>9.148248e-19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>BCL2L15</th>\n",
+       "      <td>435.882075</td>\n",
+       "      <td>-5.505807</td>\n",
+       "      <td>0.604359</td>\n",
+       "      <td>-9.110166</td>\n",
+       "      <td>8.225616e-20</td>\n",
+       "      <td>7.466391e-16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>FAM224B</th>\n",
+       "      <td>10.235047</td>\n",
+       "      <td>21.650886</td>\n",
+       "      <td>2.367773</td>\n",
+       "      <td>9.143987</td>\n",
+       "      <td>6.019109e-20</td>\n",
+       "      <td>7.466391e-16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CEACAM5</th>\n",
+       "      <td>12273.358936</td>\n",
+       "      <td>-7.444559</td>\n",
+       "      <td>0.859163</td>\n",
+       "      <td>-8.664898</td>\n",
+       "      <td>4.519171e-18</td>\n",
+       "      <td>3.076539e-14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>GJB1</th>\n",
+       "      <td>90.468162</td>\n",
+       "      <td>-6.193651</td>\n",
+       "      <td>0.741574</td>\n",
+       "      <td>-8.352040</td>\n",
+       "      <td>6.709420e-17</td>\n",
+       "      <td>3.654085e-13</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             baseMean  log2FoldChange     lfcSE      stat        pvalue  \\\n",
+       "XIST      3936.090666       -7.030433  0.708612 -9.921418  3.359498e-23   \n",
+       "BCL2L15    435.882075       -5.505807  0.604359 -9.110166  8.225616e-20   \n",
+       "FAM224B     10.235047       21.650886  2.367773  9.143987  6.019109e-20   \n",
+       "CEACAM5  12273.358936       -7.444559  0.859163 -8.664898  4.519171e-18   \n",
+       "GJB1        90.468162       -6.193651  0.741574 -8.352040  6.709420e-17   \n",
+       "\n",
+       "                 padj  \n",
+       "XIST     9.148248e-19  \n",
+       "BCL2L15  7.466391e-16  \n",
+       "FAM224B  7.466391e-16  \n",
+       "CEACAM5  3.076539e-14  \n",
+       "GJB1     3.654085e-13  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "res = pd.read_csv(\"temp_dat/lung_sex_deseq.csv\", index_col = \"Unnamed: 0\")\n",
+    "res.sort_values(\"padj\").head()"
+   ]
+  }
+ ],
+ "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": "3.9.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/source/brca_heatmap.ipynb b/docs/source/brca_heatmap.ipynb
index 10d8fc1..bb63fc1 100644
--- a/docs/source/brca_heatmap.ipynb
+++ b/docs/source/brca_heatmap.ipynb
@@ -2,6 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
+   "id": "58a5f439",
    "metadata": {},
    "source": [
     "# _BRCA_ Heatmap"
@@ -9,11 +10,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
+   "id": "93b49611",
    "metadata": {},
    "outputs": [],
    "source": [
-    "import CanDI as can\n",
+    "import CanDI.candi as can\n",
     "import pandas as pd\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
@@ -22,6 +24,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "3f9e2439",
    "metadata": {},
    "source": [
     "### Cancer Object Instantiation\n",
@@ -31,7 +34,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
+   "id": "c220005a",
    "metadata": {},
    "outputs": [
     {
@@ -55,6 +59,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "659d1805",
    "metadata": {},
    "source": [
     "### Subsetting by mutation status\n",
@@ -66,7 +71,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
+   "id": "d098ddf9",
    "metadata": {},
    "outputs": [
     {
@@ -452,7 +458,7 @@
        "[1269999 rows x 32 columns]"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -463,6 +469,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "2421b512",
    "metadata": {},
    "source": [
     "I want to look at BRCA1 mutations in these types of cancers. I start by using the mutated function to identify ovarian and breast cancer cell lines with BRCA1 mutations. A cancer object's mutated method's default behavior is to output a list of depmap ids corresponding to celllines containing any mutation within the given genes. I then instantiate CellLineCluster objects of exclusively mutated or wild type cell lines for both breast and ovarian cancer. This makes comparing these cell lines easier.\n",
@@ -471,7 +478,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
+   "id": "28fb0265",
    "metadata": {},
    "outputs": [
     {
@@ -508,6 +516,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "cb1fd667",
    "metadata": {},
    "source": [
     "### Cross Referencing Mutation and Gene Knockout Data\n",
@@ -517,7 +526,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
+   "id": "0a51271e",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -526,8 +536,6 @@
     "    #Make Figure appropriate size, dpi, and font\n",
     "    plt.rcParams.update({\"figure.figsize\": (16, 6),\n",
     "                        \"savefig.dpi\": 300,\n",
-    "                        \"font.family\": \"sans-serif\",\n",
-    "                        \"font.sans-serif\": [\"Arial\"],\n",
     "                        \"font.size\": 12\n",
     "                        })\n",
     "    \n",
@@ -567,6 +575,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "9ed37fc0",
    "metadata": {},
    "source": [
     "### Fanconi Anemia Genes Knockout Effect in Ovarian Cancer\n",
@@ -575,7 +584,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
+   "id": "a3adc292",
    "metadata": {},
    "outputs": [
     {
@@ -586,17 +596,9 @@
       "Load Complete\n"
      ]
     },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n",
-      "findfont: Generic family 'sans-serif' not found because none of the following families were found: Arial\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 1152x432 with 2 Axes>"
       ]
@@ -614,6 +616,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "f8aa60cd",
    "metadata": {},
    "source": [
     "### Fanconi Anemia Genes Knockout Effect in Breast Cancer\n",
@@ -622,12 +625,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
+   "id": "3f3cf06f",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1152x432 with 2 Axes>"
       ]
@@ -659,7 +663,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.9.2"
   }
  },
  "nbformat": 4,
diff --git a/docs/source/deseq_setup.ipynb b/docs/source/deseq_setup.ipynb
index fd1f4da..3590ce6 100644
--- a/docs/source/deseq_setup.ipynb
+++ b/docs/source/deseq_setup.ipynb
@@ -2,6 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
+   "id": "163bb85c",
    "metadata": {},
    "source": [
     "# CanDI and DESeq2\n",
@@ -11,16 +12,18 @@
   {
    "cell_type": "code",
    "execution_count": 1,
+   "id": "72858c31",
    "metadata": {},
    "outputs": [],
    "source": [
-    "import CanDI as can\n",
+    "import CanDI.candi as can\n",
     "import numpy as np\n",
     "import pandas as pd"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "319b94c2",
    "metadata": {},
    "source": [
     "#### Object Instantiation\n",
@@ -29,9 +32,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 2,
+   "id": "e3794753",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "mutations has not been loaded. Do you want to load, y/n?> y\n",
+      "Load Complete\n"
+     ]
+    }
+   ],
    "source": [
     "lung = can.Cancer(\"Lung Cancer\", subtype = \"NSCLC\")\n",
     "lung = can.CellLineCluster(lung.mutated(\"KRAS\", variant = \"Variant_Classification\", item = \"Missense_Mutation\"))\n",
@@ -42,6 +55,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "5aae975f",
    "metadata": {},
    "source": [
     "#### Data Munging\n",
@@ -50,9 +64,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 3,
+   "id": "c697995d",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "rnaseq_reads has not been loaded. Do you want to load, y/n?> y\n",
+      "Load Complete\n"
+     ]
+    }
+   ],
    "source": [
     "def make_counts_coldata(obj1, obj2, condition, factor1, factor2):\n",
     "    \n",
@@ -79,6 +103,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "d148ea96",
    "metadata": {},
    "source": [
     "#### Running DESeq2\n",
@@ -88,6 +113,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "d771eb95",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -96,6 +122,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "275e042d",
    "metadata": {},
    "source": [
     "#### Analyzing Results\n",
@@ -104,7 +131,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 4,
+   "id": "f72ee6cd",
    "metadata": {},
    "outputs": [
     {
@@ -202,7 +230,7 @@
        "GJB1     3.654085e-13  "
       ]
      },
-     "execution_count": 22,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -229,7 +257,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.9.2"
   }
  },
  "nbformat": 4,
diff --git a/docs/source/get-started.ipynb b/docs/source/get-started.ipynb
index 7f532bb..a30ec58 100644
--- a/docs/source/get-started.ipynb
+++ b/docs/source/get-started.ipynb
@@ -11,39 +11,49 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's go over basic functionality and use cases of CanDI package. "
+    "Let's go over basic functionality and use cases of CanDI package. \n",
+    "\n",
+    "### Importing\n",
+    "\n",
+    "CanDI must be imported from from the main CanDI directory. The core CanDI objects are contained within the CanDI.candi module and are imported as follows. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
-    "import sys\n",
-    "sys.path.append(\"../../\")\n",
-    "\n",
-    "import CanDI as can"
+    "import CanDI.candi as can\n",
+    "#Can also be imported as \n",
+    "from CanDI import candi as can"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Data Object\n",
+    "The Data object is instantiated when CanDI and access as data within the candi module\n",
+    "CanDI dataset paths are defined as attributes within the Data object."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "/Users/abearab/Projects/CanDI/CanDI/data/depmap/CRISPR_gene_effect.csv\n",
-      "/Users/abearab/Projects/CanDI/CanDI/data/depmap/CCLE_expression.csv\n",
-      "/Users/abearab/Projects/CanDI/CanDI/data/depmap/CCLE_gene_cn.csv\n"
+      "/home/cyogodzi/projects/candi-paper/CanDI/CanDI/setup/data/depmap/CRISPR_gene_effect.csv\n",
+      "/home/cyogodzi/projects/candi-paper/CanDI/CanDI/setup/data/depmap/CCLE_expression.csv\n",
+      "/home/cyogodzi/projects/candi-paper/CanDI/CanDI/setup/data/depmap/CCLE_gene_cn.csv\n"
      ]
     }
    ],
    "source": [
-    "#the user can find the dataset location\n",
-    "#datasets are stored as file paths until they're loaded. After which they are pandas dataframes\n",
     "print(can.data.gene_effect) # depmap ceres score\n",
     "print(can.data.expression) # ccle rna seq data\n",
     "print(can.data.gene_cn) # ccle copy number data"
@@ -53,12 +63,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## How to Directly Load a Dataset"
+    "## How to Directly Load a Dataset\n",
+    "The load method of the Data object is used to load specific datasets into memory. The datasets are saved as pandas dataframes as attributes of the data object. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -82,27 +93,27 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>ACH-000001</th>\n",
-       "      <th>ACH-000004</th>\n",
-       "      <th>ACH-000005</th>\n",
-       "      <th>ACH-000007</th>\n",
-       "      <th>ACH-000009</th>\n",
-       "      <th>ACH-000011</th>\n",
-       "      <th>ACH-000012</th>\n",
-       "      <th>ACH-000013</th>\n",
-       "      <th>ACH-000014</th>\n",
-       "      <th>ACH-000015</th>\n",
+       "      <th>ACH-001113</th>\n",
+       "      <th>ACH-001289</th>\n",
+       "      <th>ACH-001339</th>\n",
+       "      <th>ACH-001538</th>\n",
+       "      <th>ACH-000242</th>\n",
+       "      <th>ACH-000708</th>\n",
+       "      <th>ACH-000327</th>\n",
+       "      <th>ACH-000233</th>\n",
+       "      <th>ACH-000461</th>\n",
+       "      <th>ACH-000705</th>\n",
        "      <th>...</th>\n",
-       "      <th>ACH-002458</th>\n",
-       "      <th>ACH-002459</th>\n",
-       "      <th>ACH-002460</th>\n",
-       "      <th>ACH-002462</th>\n",
-       "      <th>ACH-002463</th>\n",
-       "      <th>ACH-002464</th>\n",
-       "      <th>ACH-002467</th>\n",
-       "      <th>ACH-002508</th>\n",
-       "      <th>ACH-002510</th>\n",
-       "      <th>ACH-002512</th>\n",
+       "      <th>ACH-000114</th>\n",
+       "      <th>ACH-000402</th>\n",
+       "      <th>ACH-000036</th>\n",
+       "      <th>ACH-000973</th>\n",
+       "      <th>ACH-001128</th>\n",
+       "      <th>ACH-000750</th>\n",
+       "      <th>ACH-000285</th>\n",
+       "      <th>ACH-001858</th>\n",
+       "      <th>ACH-001997</th>\n",
+       "      <th>ACH-000052</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>gene</th>\n",
@@ -131,124 +142,124 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>A1BG</th>\n",
-       "      <td>-0.334969</td>\n",
-       "      <td>0.020107</td>\n",
-       "      <td>-0.191303</td>\n",
-       "      <td>0.008862</td>\n",
-       "      <td>0.006476</td>\n",
-       "      <td>0.144966</td>\n",
-       "      <td>-0.135015</td>\n",
-       "      <td>-0.093118</td>\n",
-       "      <td>0.009573</td>\n",
-       "      <td>-0.233304</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.164574</td>\n",
-       "      <td>-0.114961</td>\n",
-       "      <td>-0.070382</td>\n",
-       "      <td>-0.055249</td>\n",
-       "      <td>0.012528</td>\n",
-       "      <td>-0.113886</td>\n",
-       "      <td>-0.140471</td>\n",
-       "      <td>-0.079950</td>\n",
-       "      <td>0.005493</td>\n",
-       "      <td>-0.004693</td>\n",
+       "      <th>TSPAN6</th>\n",
+       "      <td>4.990501</td>\n",
+       "      <td>5.209843</td>\n",
+       "      <td>3.779260</td>\n",
+       "      <td>5.726831</td>\n",
+       "      <td>7.465648</td>\n",
+       "      <td>4.914086</td>\n",
+       "      <td>4.032982</td>\n",
+       "      <td>0.097611</td>\n",
+       "      <td>4.712596</td>\n",
+       "      <td>5.101398</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3.793896</td>\n",
+       "      <td>0.070389</td>\n",
+       "      <td>4.692650</td>\n",
+       "      <td>5.026800</td>\n",
+       "      <td>6.699052</td>\n",
+       "      <td>4.173127</td>\n",
+       "      <td>0.097611</td>\n",
+       "      <td>5.045268</td>\n",
+       "      <td>5.805292</td>\n",
+       "      <td>4.870858</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>A1CF</th>\n",
-       "      <td>-0.061580</td>\n",
-       "      <td>-0.000410</td>\n",
-       "      <td>0.086000</td>\n",
-       "      <td>-0.021161</td>\n",
-       "      <td>-0.026033</td>\n",
-       "      <td>-0.104504</td>\n",
-       "      <td>0.118439</td>\n",
-       "      <td>-0.052005</td>\n",
-       "      <td>-0.153536</td>\n",
-       "      <td>-0.080814</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.164348</td>\n",
-       "      <td>-0.054229</td>\n",
-       "      <td>-0.148926</td>\n",
-       "      <td>-0.064067</td>\n",
-       "      <td>0.064360</td>\n",
-       "      <td>0.008348</td>\n",
-       "      <td>0.123300</td>\n",
-       "      <td>0.069270</td>\n",
-       "      <td>0.012978</td>\n",
-       "      <td>0.014939</td>\n",
+       "      <th>TNMD</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.545968</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.176323</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.028569</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>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>A2M</th>\n",
-       "      <td>-0.026897</td>\n",
-       "      <td>-0.055257</td>\n",
-       "      <td>0.235074</td>\n",
-       "      <td>0.102202</td>\n",
-       "      <td>0.116825</td>\n",
-       "      <td>0.121287</td>\n",
-       "      <td>0.253352</td>\n",
-       "      <td>0.093733</td>\n",
-       "      <td>0.137888</td>\n",
-       "      <td>-0.045071</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.124925</td>\n",
-       "      <td>0.199990</td>\n",
-       "      <td>-0.071168</td>\n",
-       "      <td>0.039575</td>\n",
-       "      <td>0.026830</td>\n",
-       "      <td>0.080286</td>\n",
-       "      <td>0.169631</td>\n",
-       "      <td>0.177966</td>\n",
-       "      <td>0.038273</td>\n",
-       "      <td>0.224676</td>\n",
+       "      <th>DPM1</th>\n",
+       "      <td>7.273702</td>\n",
+       "      <td>7.070604</td>\n",
+       "      <td>7.346425</td>\n",
+       "      <td>7.086189</td>\n",
+       "      <td>6.435462</td>\n",
+       "      <td>6.946848</td>\n",
+       "      <td>5.806582</td>\n",
+       "      <td>5.919102</td>\n",
+       "      <td>6.406333</td>\n",
+       "      <td>6.309976</td>\n",
+       "      <td>...</td>\n",
+       "      <td>6.330738</td>\n",
+       "      <td>5.858230</td>\n",
+       "      <td>6.623369</td>\n",
+       "      <td>6.966130</td>\n",
+       "      <td>6.131960</td>\n",
+       "      <td>6.400879</td>\n",
+       "      <td>6.428276</td>\n",
+       "      <td>6.991749</td>\n",
+       "      <td>7.792855</td>\n",
+       "      <td>6.077457</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>A2ML1</th>\n",
-       "      <td>-0.026507</td>\n",
-       "      <td>-0.071736</td>\n",
-       "      <td>0.068524</td>\n",
-       "      <td>0.107526</td>\n",
-       "      <td>0.196238</td>\n",
-       "      <td>0.340482</td>\n",
-       "      <td>0.284129</td>\n",
-       "      <td>0.175221</td>\n",
-       "      <td>0.118795</td>\n",
-       "      <td>0.076349</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.244009</td>\n",
-       "      <td>0.226472</td>\n",
-       "      <td>0.105607</td>\n",
-       "      <td>0.175997</td>\n",
-       "      <td>-0.101228</td>\n",
-       "      <td>0.110665</td>\n",
-       "      <td>0.041703</td>\n",
-       "      <td>0.122893</td>\n",
-       "      <td>0.016146</td>\n",
-       "      <td>0.048566</td>\n",
+       "      <th>SCYL3</th>\n",
+       "      <td>2.765535</td>\n",
+       "      <td>2.538538</td>\n",
+       "      <td>2.339137</td>\n",
+       "      <td>2.543496</td>\n",
+       "      <td>2.414136</td>\n",
+       "      <td>2.577731</td>\n",
+       "      <td>1.948601</td>\n",
+       "      <td>3.983678</td>\n",
+       "      <td>2.247928</td>\n",
+       "      <td>2.361768</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.792855</td>\n",
+       "      <td>2.757023</td>\n",
+       "      <td>2.111031</td>\n",
+       "      <td>1.899176</td>\n",
+       "      <td>2.235727</td>\n",
+       "      <td>1.807355</td>\n",
+       "      <td>3.257011</td>\n",
+       "      <td>1.807355</td>\n",
+       "      <td>2.482848</td>\n",
+       "      <td>2.304511</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>A3GALT2</th>\n",
-       "      <td>-0.129643</td>\n",
-       "      <td>-0.088479</td>\n",
-       "      <td>-0.286711</td>\n",
-       "      <td>-0.045557</td>\n",
-       "      <td>-0.098705</td>\n",
-       "      <td>-0.150980</td>\n",
-       "      <td>-0.037887</td>\n",
-       "      <td>-0.196711</td>\n",
-       "      <td>-0.132300</td>\n",
-       "      <td>-0.139247</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.270227</td>\n",
-       "      <td>-0.251222</td>\n",
-       "      <td>-0.085845</td>\n",
-       "      <td>-0.133148</td>\n",
-       "      <td>-0.137136</td>\n",
-       "      <td>-0.328151</td>\n",
-       "      <td>-0.250064</td>\n",
-       "      <td>-0.297100</td>\n",
-       "      <td>-0.120800</td>\n",
-       "      <td>-0.205108</td>\n",
+       "      <th>C1orf112</th>\n",
+       "      <td>4.480265</td>\n",
+       "      <td>3.510962</td>\n",
+       "      <td>4.254745</td>\n",
+       "      <td>3.102658</td>\n",
+       "      <td>3.864929</td>\n",
+       "      <td>3.853996</td>\n",
+       "      <td>2.684819</td>\n",
+       "      <td>3.733354</td>\n",
+       "      <td>3.032101</td>\n",
+       "      <td>4.280214</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.643856</td>\n",
+       "      <td>5.103078</td>\n",
+       "      <td>2.543496</td>\n",
+       "      <td>3.531069</td>\n",
+       "      <td>3.971773</td>\n",
+       "      <td>3.303050</td>\n",
+       "      <td>4.980482</td>\n",
+       "      <td>3.270529</td>\n",
+       "      <td>3.903038</td>\n",
+       "      <td>3.836934</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -275,209 +286,210 @@
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>ZYG11A</th>\n",
-       "      <td>-0.095770</td>\n",
-       "      <td>0.140520</td>\n",
-       "      <td>-0.139802</td>\n",
-       "      <td>-0.020587</td>\n",
-       "      <td>-0.213931</td>\n",
-       "      <td>0.027516</td>\n",
-       "      <td>0.001441</td>\n",
-       "      <td>-0.121513</td>\n",
-       "      <td>-0.067213</td>\n",
-       "      <td>0.128740</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.065172</td>\n",
-       "      <td>-0.213875</td>\n",
-       "      <td>0.058252</td>\n",
-       "      <td>0.135860</td>\n",
-       "      <td>0.165638</td>\n",
-       "      <td>0.110222</td>\n",
-       "      <td>-0.099656</td>\n",
-       "      <td>0.052434</td>\n",
-       "      <td>-0.137812</td>\n",
-       "      <td>-0.004373</td>\n",
+       "      <th>POLR2J3</th>\n",
+       "      <td>5.781884</td>\n",
+       "      <td>4.704319</td>\n",
+       "      <td>4.931683</td>\n",
+       "      <td>3.858976</td>\n",
+       "      <td>4.990501</td>\n",
+       "      <td>5.303781</td>\n",
+       "      <td>4.996841</td>\n",
+       "      <td>6.839960</td>\n",
+       "      <td>5.529196</td>\n",
+       "      <td>5.860963</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3.793896</td>\n",
+       "      <td>6.669877</td>\n",
+       "      <td>6.191010</td>\n",
+       "      <td>5.934281</td>\n",
+       "      <td>3.097611</td>\n",
+       "      <td>5.102658</td>\n",
+       "      <td>6.341630</td>\n",
+       "      <td>4.607626</td>\n",
+       "      <td>4.787119</td>\n",
+       "      <td>4.452859</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>ZYG11B</th>\n",
-       "      <td>-0.025669</td>\n",
-       "      <td>-0.406777</td>\n",
-       "      <td>-0.096160</td>\n",
-       "      <td>-0.363685</td>\n",
-       "      <td>-0.428286</td>\n",
-       "      <td>-0.216353</td>\n",
-       "      <td>-0.153888</td>\n",
-       "      <td>-0.159908</td>\n",
-       "      <td>-0.198470</td>\n",
-       "      <td>-0.289441</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.002783</td>\n",
-       "      <td>-0.187898</td>\n",
-       "      <td>-0.265942</td>\n",
-       "      <td>-0.120835</td>\n",
-       "      <td>-0.150396</td>\n",
-       "      <td>-0.266665</td>\n",
-       "      <td>-0.159860</td>\n",
-       "      <td>-0.058768</td>\n",
-       "      <td>-0.159896</td>\n",
-       "      <td>-0.053240</td>\n",
+       "      <th>H2BE1</th>\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>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.594549</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.176323</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>0.111031</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>ZYX</th>\n",
-       "      <td>0.215264</td>\n",
-       "      <td>0.152613</td>\n",
-       "      <td>-0.024441</td>\n",
-       "      <td>0.027735</td>\n",
-       "      <td>0.048789</td>\n",
-       "      <td>0.110142</td>\n",
-       "      <td>-0.097014</td>\n",
-       "      <td>0.020813</td>\n",
-       "      <td>-0.170166</td>\n",
-       "      <td>0.026188</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.071612</td>\n",
-       "      <td>-0.143997</td>\n",
-       "      <td>-0.010352</td>\n",
-       "      <td>-0.140926</td>\n",
-       "      <td>-0.224364</td>\n",
-       "      <td>-0.115040</td>\n",
-       "      <td>0.040414</td>\n",
-       "      <td>-0.022447</td>\n",
-       "      <td>-0.085250</td>\n",
-       "      <td>0.115437</td>\n",
+       "      <th>AL445238.1</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.028569</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.042644</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</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>0.097611</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.163499</td>\n",
+       "      <td>0.163499</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>ZZEF1</th>\n",
-       "      <td>0.106057</td>\n",
-       "      <td>0.197995</td>\n",
-       "      <td>0.016988</td>\n",
-       "      <td>-0.334223</td>\n",
-       "      <td>-0.115131</td>\n",
-       "      <td>-0.142422</td>\n",
-       "      <td>-0.127043</td>\n",
-       "      <td>-0.191911</td>\n",
-       "      <td>0.198876</td>\n",
-       "      <td>-0.250152</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.009952</td>\n",
-       "      <td>-0.166538</td>\n",
-       "      <td>-0.166132</td>\n",
-       "      <td>-0.032240</td>\n",
-       "      <td>-0.200218</td>\n",
-       "      <td>-0.058096</td>\n",
-       "      <td>-0.117504</td>\n",
-       "      <td>-0.197666</td>\n",
-       "      <td>-0.188525</td>\n",
-       "      <td>0.006204</td>\n",
+       "      <th>GET1-SH3BGR</th>\n",
+       "      <td>0.799087</td>\n",
+       "      <td>0.464668</td>\n",
+       "      <td>0.263034</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.263034</td>\n",
+       "      <td>0.286881</td>\n",
+       "      <td>2.280956</td>\n",
+       "      <td>0.275007</td>\n",
+       "      <td>0.790772</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.416840</td>\n",
+       "      <td>0.526069</td>\n",
+       "      <td>1.117695</td>\n",
+       "      <td>0.378512</td>\n",
+       "      <td>0.713696</td>\n",
+       "      <td>0.214125</td>\n",
+       "      <td>0.310340</td>\n",
+       "      <td>1.090853</td>\n",
+       "      <td>0.084064</td>\n",
+       "      <td>1.422233</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>ZZZ3</th>\n",
-       "      <td>-0.483079</td>\n",
-       "      <td>-0.199333</td>\n",
-       "      <td>-0.267921</td>\n",
-       "      <td>-0.484621</td>\n",
-       "      <td>-0.411152</td>\n",
-       "      <td>-0.342839</td>\n",
-       "      <td>-0.524093</td>\n",
-       "      <td>-0.634783</td>\n",
-       "      <td>-0.331259</td>\n",
-       "      <td>-0.329825</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.420353</td>\n",
-       "      <td>-0.495791</td>\n",
-       "      <td>-0.355700</td>\n",
-       "      <td>-0.600775</td>\n",
-       "      <td>-0.574311</td>\n",
-       "      <td>-0.491949</td>\n",
-       "      <td>-0.885358</td>\n",
-       "      <td>-0.060559</td>\n",
-       "      <td>-0.404162</td>\n",
-       "      <td>-1.036221</td>\n",
+       "      <th>AC113348.1</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.070389</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.028569</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.084064</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>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>17645 rows × 990 columns</p>\n",
+       "<p>19177 rows × 1379 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "         ACH-000001  ACH-000004  ACH-000005  ACH-000007  ACH-000009  \\\n",
-       "gene                                                                  \n",
-       "A1BG      -0.334969    0.020107   -0.191303    0.008862    0.006476   \n",
-       "A1CF      -0.061580   -0.000410    0.086000   -0.021161   -0.026033   \n",
-       "A2M       -0.026897   -0.055257    0.235074    0.102202    0.116825   \n",
-       "A2ML1     -0.026507   -0.071736    0.068524    0.107526    0.196238   \n",
-       "A3GALT2   -0.129643   -0.088479   -0.286711   -0.045557   -0.098705   \n",
-       "...             ...         ...         ...         ...         ...   \n",
-       "ZYG11A    -0.095770    0.140520   -0.139802   -0.020587   -0.213931   \n",
-       "ZYG11B    -0.025669   -0.406777   -0.096160   -0.363685   -0.428286   \n",
-       "ZYX        0.215264    0.152613   -0.024441    0.027735    0.048789   \n",
-       "ZZEF1      0.106057    0.197995    0.016988   -0.334223   -0.115131   \n",
-       "ZZZ3      -0.483079   -0.199333   -0.267921   -0.484621   -0.411152   \n",
+       "             ACH-001113  ACH-001289  ACH-001339  ACH-001538  ACH-000242  \\\n",
+       "gene                                                                      \n",
+       "TSPAN6         4.990501    5.209843    3.779260    5.726831    7.465648   \n",
+       "TNMD           0.000000    0.545968    0.000000    0.000000    0.000000   \n",
+       "DPM1           7.273702    7.070604    7.346425    7.086189    6.435462   \n",
+       "SCYL3          2.765535    2.538538    2.339137    2.543496    2.414136   \n",
+       "C1orf112       4.480265    3.510962    4.254745    3.102658    3.864929   \n",
+       "...                 ...         ...         ...         ...         ...   \n",
+       "POLR2J3        5.781884    4.704319    4.931683    3.858976    4.990501   \n",
+       "H2BE1          0.000000    0.000000    0.000000    0.000000    0.000000   \n",
+       "AL445238.1     0.000000    0.000000    0.028569    0.000000    0.000000   \n",
+       "GET1-SH3BGR    0.799087    0.464668    0.263034    0.000000    0.000000   \n",
+       "AC113348.1     0.000000    0.070389    0.000000    0.000000    0.000000   \n",
        "\n",
-       "         ACH-000011  ACH-000012  ACH-000013  ACH-000014  ACH-000015  ...  \\\n",
-       "gene                                                                 ...   \n",
-       "A1BG       0.144966   -0.135015   -0.093118    0.009573   -0.233304  ...   \n",
-       "A1CF      -0.104504    0.118439   -0.052005   -0.153536   -0.080814  ...   \n",
-       "A2M        0.121287    0.253352    0.093733    0.137888   -0.045071  ...   \n",
-       "A2ML1      0.340482    0.284129    0.175221    0.118795    0.076349  ...   \n",
-       "A3GALT2   -0.150980   -0.037887   -0.196711   -0.132300   -0.139247  ...   \n",
-       "...             ...         ...         ...         ...         ...  ...   \n",
-       "ZYG11A     0.027516    0.001441   -0.121513   -0.067213    0.128740  ...   \n",
-       "ZYG11B    -0.216353   -0.153888   -0.159908   -0.198470   -0.289441  ...   \n",
-       "ZYX        0.110142   -0.097014    0.020813   -0.170166    0.026188  ...   \n",
-       "ZZEF1     -0.142422   -0.127043   -0.191911    0.198876   -0.250152  ...   \n",
-       "ZZZ3      -0.342839   -0.524093   -0.634783   -0.331259   -0.329825  ...   \n",
+       "             ACH-000708  ACH-000327  ACH-000233  ACH-000461  ACH-000705  ...  \\\n",
+       "gene                                                                     ...   \n",
+       "TSPAN6         4.914086    4.032982    0.097611    4.712596    5.101398  ...   \n",
+       "TNMD           0.176323    0.000000    0.000000    0.000000    0.000000  ...   \n",
+       "DPM1           6.946848    5.806582    5.919102    6.406333    6.309976  ...   \n",
+       "SCYL3          2.577731    1.948601    3.983678    2.247928    2.361768  ...   \n",
+       "C1orf112       3.853996    2.684819    3.733354    3.032101    4.280214  ...   \n",
+       "...                 ...         ...         ...         ...         ...  ...   \n",
+       "POLR2J3        5.303781    4.996841    6.839960    5.529196    5.860963  ...   \n",
+       "H2BE1          0.000000    0.000000    0.000000    0.000000    0.594549  ...   \n",
+       "AL445238.1     0.000000    0.042644    0.000000    0.000000    0.000000  ...   \n",
+       "GET1-SH3BGR    0.263034    0.286881    2.280956    0.275007    0.790772  ...   \n",
+       "AC113348.1     0.000000    0.028569    0.000000    0.000000    0.000000  ...   \n",
        "\n",
-       "         ACH-002458  ACH-002459  ACH-002460  ACH-002462  ACH-002463  \\\n",
-       "gene                                                                  \n",
-       "A1BG      -0.164574   -0.114961   -0.070382   -0.055249    0.012528   \n",
-       "A1CF       0.164348   -0.054229   -0.148926   -0.064067    0.064360   \n",
-       "A2M        0.124925    0.199990   -0.071168    0.039575    0.026830   \n",
-       "A2ML1      0.244009    0.226472    0.105607    0.175997   -0.101228   \n",
-       "A3GALT2   -0.270227   -0.251222   -0.085845   -0.133148   -0.137136   \n",
-       "...             ...         ...         ...         ...         ...   \n",
-       "ZYG11A    -0.065172   -0.213875    0.058252    0.135860    0.165638   \n",
-       "ZYG11B     0.002783   -0.187898   -0.265942   -0.120835   -0.150396   \n",
-       "ZYX       -0.071612   -0.143997   -0.010352   -0.140926   -0.224364   \n",
-       "ZZEF1     -0.009952   -0.166538   -0.166132   -0.032240   -0.200218   \n",
-       "ZZZ3      -0.420353   -0.495791   -0.355700   -0.600775   -0.574311   \n",
+       "             ACH-000114  ACH-000402  ACH-000036  ACH-000973  ACH-001128  \\\n",
+       "gene                                                                      \n",
+       "TSPAN6         3.793896    0.070389    4.692650    5.026800    6.699052   \n",
+       "TNMD           0.028569    0.000000    0.000000    0.000000    0.000000   \n",
+       "DPM1           6.330738    5.858230    6.623369    6.966130    6.131960   \n",
+       "SCYL3          2.792855    2.757023    2.111031    1.899176    2.235727   \n",
+       "C1orf112       2.643856    5.103078    2.543496    3.531069    3.971773   \n",
+       "...                 ...         ...         ...         ...         ...   \n",
+       "POLR2J3        3.793896    6.669877    6.191010    5.934281    3.097611   \n",
+       "H2BE1          0.000000    0.176323    0.000000    0.000000    0.000000   \n",
+       "AL445238.1     0.000000    0.000000    0.000000    0.000000    0.000000   \n",
+       "GET1-SH3BGR    1.416840    0.526069    1.117695    0.378512    0.713696   \n",
+       "AC113348.1     0.084064    0.000000    0.000000    0.000000    0.000000   \n",
        "\n",
-       "         ACH-002464  ACH-002467  ACH-002508  ACH-002510  ACH-002512  \n",
-       "gene                                                                 \n",
-       "A1BG      -0.113886   -0.140471   -0.079950    0.005493   -0.004693  \n",
-       "A1CF       0.008348    0.123300    0.069270    0.012978    0.014939  \n",
-       "A2M        0.080286    0.169631    0.177966    0.038273    0.224676  \n",
-       "A2ML1      0.110665    0.041703    0.122893    0.016146    0.048566  \n",
-       "A3GALT2   -0.328151   -0.250064   -0.297100   -0.120800   -0.205108  \n",
-       "...             ...         ...         ...         ...         ...  \n",
-       "ZYG11A     0.110222   -0.099656    0.052434   -0.137812   -0.004373  \n",
-       "ZYG11B    -0.266665   -0.159860   -0.058768   -0.159896   -0.053240  \n",
-       "ZYX       -0.115040    0.040414   -0.022447   -0.085250    0.115437  \n",
-       "ZZEF1     -0.058096   -0.117504   -0.197666   -0.188525    0.006204  \n",
-       "ZZZ3      -0.491949   -0.885358   -0.060559   -0.404162   -1.036221  \n",
+       "             ACH-000750  ACH-000285  ACH-001858  ACH-001997  ACH-000052  \n",
+       "gene                                                                     \n",
+       "TSPAN6         4.173127    0.097611    5.045268    5.805292    4.870858  \n",
+       "TNMD           0.000000    0.000000    0.000000    0.000000    0.000000  \n",
+       "DPM1           6.400879    6.428276    6.991749    7.792855    6.077457  \n",
+       "SCYL3          1.807355    3.257011    1.807355    2.482848    2.304511  \n",
+       "C1orf112       3.303050    4.980482    3.270529    3.903038    3.836934  \n",
+       "...                 ...         ...         ...         ...         ...  \n",
+       "POLR2J3        5.102658    6.341630    4.607626    4.787119    4.452859  \n",
+       "H2BE1          0.000000    0.000000    0.111031    0.000000    0.000000  \n",
+       "AL445238.1     0.097611    0.000000    0.000000    0.163499    0.163499  \n",
+       "GET1-SH3BGR    0.214125    0.310340    1.090853    0.084064    1.422233  \n",
+       "AC113348.1     0.000000    0.000000    0.000000    0.000000    0.000000  \n",
        "\n",
-       "[17645 rows x 990 columns]"
+       "[19177 rows x 1379 columns]"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "can.data.load(\"gene_effect\")"
+    "can.data.load(\"expression\")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Cell Lines"
+    "## Cell Lines\n",
+    "The Cell Lines dataset contains all cell line metadata. This table is loaded automatically when candi is imported."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -742,7 +754,7 @@
        "[5 rows x 25 columns]"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -755,12 +767,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Genes"
+    "## Genes\n",
+    "The genes dataset contains relevant gene metadata. \n",
+    "The genes dataset is loaded into memory automatically when candi is imported. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -862,7 +876,7 @@
        "CTA-298G8.2                   NaN         NaN        NaN              NaN  "
       ]
      },
-     "execution_count": 18,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -875,12 +889,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Locations"
+    "## Locations\n",
+    "The locations dataset contains location annotations for all genes and their associated confidence scores. Confidence scores were crowd sourced from several protein localization papers and integrated into one scale. This dataset is automatically loaded into memory when candi is imported. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -953,7 +968,7 @@
        "4    AAAS       Cytosol         2.0"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -973,22 +988,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
     "kras = can.Gene(\"KRAS\")\n",
-    "# a549 = can.CellLine(\"A549\")\n",
     "lung = can.Cancer(\"Lung Cancer\")\n",
-    "membrane = can.Organelle(\"Plasma membrane\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
+    "membrane = can.Organelle(\"Plasma membrane\")\n",
     "a549 = can.CellLine(\"A549\") "
    ]
   },
@@ -996,12 +1002,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Gene Object Methods and Attributes\n"
+    "## Gene Object Methods and Attributes\n",
+    "The following function prints the internal attributes and functions of CanDI objects. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -1062,22 +1069,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Gene Indexing examples"
+    "## Gene Indexing examples\n",
+    "If a dataset has not be loaded into memory candi will prompt you.\n",
+    "Once a dataset is loaded, Gene.expression gives all the rna seq transcript data for that specific object.\n",
+    "In this case we have already instantiated a gene object"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "expression has not been loaded. Do you want to load, y/n?> y\n",
-      "Load Complete\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
@@ -1095,23 +1097,27 @@
        "Name: KRAS, Length: 1379, dtype: float64"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "\"\"\"\n",
-    "If a dataset has not be loaded into memory candi will prompt you.\n",
-    "Once a dataset is loaded, Gene.expression gives all the rna seq transcript data for that specific object.\n",
-    "In this case we have already instantiated a gene object\n",
-    "\"\"\"\n",
-    "kras.expression\n"
+    "kras.expression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Basic CanDI filtering\n",
+    "the Gene.expressed() method retrieves cell lines where the user defined gene has above 1 transcript per million\n",
+    "the output is a list of cell line ids which can be used to instantiate CellLine or CellLineClbbuster objects\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -1129,19 +1135,25 @@
        " 'ACH-000705']"
       ]
      },
-     "execution_count": 58,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# the Gene.expressed() method retrieves cell lines where the user defined gene has above 1 transcript per million\n",
-    "kras.expressed()[0:10] # the output is a list of cell line ids which can be used to instantiate CellLine or CellLineClbbuster objects\n"
+    "kras.expressed()[0:10]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The user can specify if they want the tpm values with the depmap ids "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -1161,48 +1173,53 @@
        "Name: KRAS, Length: 1379, dtype: float64"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "#The user can specify if they want the tpm values with the depmap ids \n",
-    "kras.expressed(style=\"values\")\n"
+    "kras.expressed(style=\"values\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you input a depmap id as an argument to gene.expressed you will get a boolean showing the expression status of your gene"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "expression has not been loaded. Do you want to load, y/n?> y\n",
-      "Load Complete\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
        "True"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# if you input a depmap id as an argument to gene.expressed you will get a boolean showing the expression status of your gene\n",
-    "kras.expressed(a549.depmap_id)\n"
+    "kras.expressed(a549.depmap_id)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The user can use the gene.expression_of() method to check that gene's expression in a specific cell line.\n",
+    "This method only, when called from a Gene object, accepts cell line depmap id's as an argument."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -1211,19 +1228,25 @@
        "4.350497247084133"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# The user can use the gene.expression_of() method to check that gene's expression in a specific cell line\n",
-    "kras.expression_of(a549.depmap_id) # this method only accepts cell line depmap id's as an argument\n"
+    "kras.expression_of(a549.depmap_id)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "CanDI is consistent in the way this works across all classes and data types"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -1617,75 +1640,78 @@
        "[285 rows x 32 columns]"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# CanDI is consistent in the way this works across all classes and data types\n",
-    "kras.mutations\n"
+    "kras.mutations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The gene.mutated() method allows very specific filtering.\n",
+    "Using the variant argument one can select the column on which to filter. Then using the item argument the user can specifiy the specific value in which they're interested. The example below shows retrieval of all cell lines with kras missense mutations."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "['ACH-002140',\n",
-       " 'ACH-000093',\n",
-       " 'ACH-000680',\n",
-       " 'ACH-000243',\n",
-       " 'ACH-000511',\n",
-       " 'ACH-000957',\n",
-       " 'ACH-001108',\n",
-       " 'ACH-000941',\n",
-       " 'ACH-000281',\n",
-       " 'ACH-001852']"
+       "['ACH-000094',\n",
+       " 'ACH-000178',\n",
+       " 'ACH-002186',\n",
+       " 'ACH-000311',\n",
+       " 'ACH-001345',\n",
+       " 'ACH-001843',\n",
+       " 'ACH-001353',\n",
+       " 'ACH-000417',\n",
+       " 'ACH-000347',\n",
+       " 'ACH-000997']"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# the gene.mutated() method allows very specific filtering\n",
-    "# Using the variant argument one can select the column on which to filter\n",
-    "#  - Then using the item argument the user can specifiy the specific value in which they're interested\n",
-    "# the example below shows retrieval of all cell lines with kras missense mutations\n",
     "kras.mutated(variant=\"Variant_Classification\", item=\"Missense_Mutation\")[0:10]"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 27,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "AttributeError",
-     "evalue": "'function' object has no attribute 'mutations'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-27-3ef3f4c24391>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcan\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munload\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmutations\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mAttributeError\u001b[0m: 'function' object has no attribute 'mutations'"
-     ]
-    }
-   ],
    "source": [
-    "can.data.unload.mutations"
+    "Users can use the unload method of the Data object to remove a dataset from memory and return it to a file path string."
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 18,
    "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "PosixPath('/home/cyogodzi/projects/candi-paper/CanDI/CanDI/setup/data/depmap/CCLE_mutations.csv')"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "- [ ] make sure to explain how exactly load an unload data. "
+    "can.data.unload('mutations')\n",
+    "can.data.mutations"
    ]
   },
   {
@@ -1697,7 +1723,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -1741,9 +1767,18 @@
     "pretty_print_attr(a549)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "All methods work in essentially same way regardless of the candi object in use.\n",
+    "The CellLine.expressed() method will return all genes which have expression above 1 transcript per million\n",
+    "in that specific cell line."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -1761,22 +1796,25 @@
        " 'NIPAL3']"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# All methods work in essentially same way regardless of the candi object in use.\n",
-    "# the CellLine.expressed() method will return all genes which have expression above 1 transcript per million\n",
-    "# in that specific cell line.\n",
-    "\n",
-    "a549.expressed()[:10]\n"
+    "a549.expressed()[:10]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Just like gene.expressed() the user can ask for the values"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -1797,19 +1835,25 @@
        "Name: ACH-000681, Length: 11498, dtype: float64"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# Just like gene.expressed() the user can ask for the values\n",
-    "a549.expressed(style=\"values\")\n"
+    "a549.expressed(style=\"values\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And for specific genes expression status"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -1818,19 +1862,25 @@
        "True"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# And for specific genes expression status\n",
-    "a549.expressed(\"KRAS\")\n"
+    "a549.expressed(\"KRAS\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "expressed with style=\"values\" gives the same result as expression_of"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -1839,19 +1889,18 @@
        "4.350497247084133"
       ]
      },
-     "execution_count": 38,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# expressed with style=\"values\" gives the same result as expression_of\n",
-    "a549.expression_of(\"KRAS\")\n"
+    "a549.expression_of(\"KRAS\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -1860,20 +1909,35 @@
        "4.350497247084133"
       ]
      },
-     "execution_count": 39,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "a549.expressed(\"KRAS\", style=\"values\")\n"
+    "a549.expressed(\"KRAS\", style=\"values\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The CellLine.mtuations attribute gives all mutation data for that specific cell line"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "mutations has not been loaded. Do you want to load, y/n?> y\n",
+      "Load Complete\n"
+     ]
+    },
     {
      "data": {
       "text/html": [
@@ -2257,41 +2321,20 @@
        "[758 rows x 32 columns]"
       ]
      },
-     "execution_count": 40,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# the CellLine.mtuations attribute gives all mutation data for that specific cell line\n",
-    "a549.mutations\n"
+    "a549.mutations"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['CCZ1',\n",
-       " 'NEBL',\n",
-       " 'POLD1',\n",
-       " 'FAT4',\n",
-       " 'CTNNA1',\n",
-       " 'ZNF256',\n",
-       " 'FGL1',\n",
-       " 'RP1L1',\n",
-       " 'POLR3B',\n",
-       " 'RNASE3']"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# calling the CellLine.mutated() method works the same way with all CanDI objects\n",
     "a549.mutated(variant=\"Variant_Classification\", item=\"Nonsense_Mutation\")[:10]\n"
@@ -2306,7 +2349,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -2347,12 +2390,20 @@
     }
    ],
    "source": [
-    "pretty_print_attr(lung)\n"
+    "pretty_print_attr(lung)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Cancer objects work essentially works as a group of cell line objects \n",
+    "the Cancer.expression object returns a pandas dataframe rather than a pandas series since there are multiple cell lines to consider."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
@@ -2753,20 +2804,25 @@
        "[19177 rows x 206 columns]"
       ]
      },
-     "execution_count": 44,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "## cancer objects work essentially works as a group of cell line objects\n",
-    "# the Cancer.expression object returns a pandas dataframe rather than a pandas series since there are multiple cell lines to consider\n",
-    "lung.expression\n"
+    "lung.expression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Cancer.expressed method uses an abitrary threshold to filter genes the default is if a gene is expressed in 100 percent of the cell lines within the cancer object it will read out as expressed"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
@@ -2784,21 +2840,25 @@
        " 'RAD52']"
       ]
      },
-     "execution_count": 45,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# Cancer.expressed method uses an abitrary threshold to filter genes\n",
-    "# the default is if a gene is expressed in 100 percent of the cell lines within the cancer object\n",
-    "# it will read out as expressed\n",
-    "lung.expressed()[0:10]\n"
+    "lung.expressed()[0:10]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The user can relax this threshold as necessary"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
@@ -2816,19 +2876,18 @@
        " 'NIPAL3']"
       ]
      },
-     "execution_count": 46,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# The user can relax this threshold as necessary\n",
     "lung.expressed(threshold=0.50)[0:10]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
@@ -3229,18 +3288,26 @@
        "[11827 rows x 206 columns]"
       ]
      },
-     "execution_count": 47,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "lung.expressed(threshold=0.50, style=\"values\")\n"
+    "lung.expressed(threshold=0.50, style=\"values\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Cancer and CellLineCluster objects have an additional method that outputs a binary matrix\n",
+    "of which genes/cell lines have mutations"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
@@ -3433,7 +3500,7 @@
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>ACH-002122</th>\n",
+       "      <th>ACH-000521</th>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
@@ -3457,7 +3524,7 @@
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>ACH-000769</th>\n",
+       "      <th>ACH-000010</th>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
@@ -3481,11 +3548,11 @@
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>ACH-000675</th>\n",
+       "      <th>ACH-000589</th>\n",
+       "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>1</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
@@ -3505,7 +3572,7 @@
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>ACH-000670</th>\n",
+       "      <th>ACH-000575</th>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
@@ -3529,12 +3596,12 @@
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>ACH-000841</th>\n",
+       "      <th>ACH-000587</th>\n",
+       "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>1</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
@@ -3565,11 +3632,11 @@
        "ACH-000852     1     0    1      1        0       0      0     0     0      0   \n",
        "ACH-000867     1     0    0      0        0       0      0     0     0      0   \n",
        "...          ...   ...  ...    ...      ...     ...    ...   ...   ...    ...   \n",
-       "ACH-002122     0     0    0      0        0       0      0     0     0      0   \n",
-       "ACH-000769     0     0    0      0        0       0      0     0     0      0   \n",
-       "ACH-000675     0     0    0      1        0       0      0     0     0      0   \n",
-       "ACH-000670     0     0    0      0        0       0      0     0     0      0   \n",
-       "ACH-000841     0     0    0      0        1       0      0     0     0      0   \n",
+       "ACH-000521     0     0    0      0        0       0      0     0     0      0   \n",
+       "ACH-000010     0     0    0      0        0       0      0     0     0      0   \n",
+       "ACH-000589     0     0    0      0        0       0      0     0     0      0   \n",
+       "ACH-000575     0     0    0      0        0       0      0     0     0      0   \n",
+       "ACH-000587     0     0    0      0        0       0      0     0     0      0   \n",
        "\n",
        "            ...  ZWILCH  ZWINT  ZXDA  ZXDB  ZXDC  ZYG11A  ZYG11B  ZYX  ZZEF1  \\\n",
        "ACH-000523  ...       0      1     0     0     0       0       0    0      0   \n",
@@ -3578,11 +3645,11 @@
        "ACH-000852  ...       0      0     0     0     0       0       1    0      0   \n",
        "ACH-000867  ...       1      0     0     0     0       0       0    0      0   \n",
        "...         ...     ...    ...   ...   ...   ...     ...     ...  ...    ...   \n",
-       "ACH-002122  ...       0      0     0     0     0       0       0    0      0   \n",
-       "ACH-000769  ...       0      0     0     0     0       0       0    0      0   \n",
-       "ACH-000675  ...       0      0     0     0     0       0       0    0      0   \n",
-       "ACH-000670  ...       0      0     0     0     0       0       0    0      0   \n",
-       "ACH-000841  ...       0      0     0     0     0       0       0    0      0   \n",
+       "ACH-000521  ...       0      0     0     0     0       0       0    0      0   \n",
+       "ACH-000010  ...       0      0     0     0     0       0       0    0      0   \n",
+       "ACH-000589  ...       0      0     0     0     0       0       0    0      0   \n",
+       "ACH-000575  ...       0      0     0     0     0       0       0    0      0   \n",
+       "ACH-000587  ...       0      0     0     0     0       0       0    0      0   \n",
        "\n",
        "            ZZZ3  \n",
        "ACH-000523     0  \n",
@@ -3591,24 +3658,22 @@
        "ACH-000852     0  \n",
        "ACH-000867     0  \n",
        "...          ...  \n",
-       "ACH-002122     0  \n",
-       "ACH-000769     0  \n",
-       "ACH-000675     0  \n",
-       "ACH-000670     0  \n",
-       "ACH-000841     0  \n",
+       "ACH-000521     0  \n",
+       "ACH-000010     0  \n",
+       "ACH-000589     0  \n",
+       "ACH-000575     0  \n",
+       "ACH-000587     0  \n",
        "\n",
        "[273 rows x 17376 columns]"
       ]
      },
-     "execution_count": 48,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# Cancer and CellLine objects have an additional method that outputs a binary matrix\n",
-    "# of which genes/cell lines have mutations\n",
-    "lung.mutation_matrix()\n"
+    "lung.mutation_matrix()"
    ]
   },
   {
@@ -3620,7 +3685,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
@@ -3655,15 +3720,15 @@
     }
    ],
    "source": [
-    "pretty_print_attr(membrane)\n"
+    "pretty_print_attr(membrane)"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python [conda env:candi]",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "conda-env-candi-py"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -3675,7 +3740,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.3"
+   "version": "3.9.2"
   }
  },
  "nbformat": 4,
diff --git a/docs/source/kras_egfr_scatter.ipynb b/docs/source/kras_egfr_scatter.ipynb
index cd9cd9b..025208e 100644
--- a/docs/source/kras_egfr_scatter.ipynb
+++ b/docs/source/kras_egfr_scatter.ipynb
@@ -2,6 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
+   "id": "f86433ea",
    "metadata": {},
    "source": [
     "# _KRAS_ and _EGFR_ Scatter plot "
@@ -9,11 +10,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
+   "id": "6dfaefa8",
    "metadata": {},
    "outputs": [],
    "source": [
-    "import CanDI as can\n",
+    "import CanDI.candi as can\n",
     "import pandas as pd\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
@@ -22,6 +24,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "67a24919",
    "metadata": {},
    "source": [
     "## Cancer Object Instantiation\n",
@@ -30,7 +33,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
+   "id": "bc12e739",
    "metadata": {},
    "outputs": [
     {
@@ -45,7 +49,7 @@
        "      dtype=object)"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -57,30 +61,27 @@
   },
   {
    "cell_type": "markdown",
+   "id": "4479b0bd",
    "metadata": {},
    "source": [
-    "I want to look at how oncogenic mutations affect global genetic dependencies. Let's choose KRAS and EGFR as our oncogenic mutations. I'm going to make four CellLineCluster objects per oncogene, eight in total. For each oncogene I want a CellLineCluster that is only mutated cell lines, a CellLineCluster that is only wildtype cell lines, and two additional Mutant/Wild type clusters where cell lines with mutations in the other oncogene have been removed. The CellLineClusters are as follows.\n",
+    "I want to look at how oncogenic mutations affect global genetic dependencies. Let's choose KRAS and EGFR as our oncogenic mutations. I'm going to make two CellLineCluster objects per oncogene, eight in total. For each oncogene I want to make a CellLineCluster where the oncogene of interest is mutated and another where it is wild type.\n",
     "\n",
-    "__To Analyze KRAS and Control for EGFR__\n",
-    "* Lung - KRAS MT - EGFR *\n",
-    "* Lung - KRAS WT - EGFR *\n",
-    "* Lung - KRAS MT - EGFR WT\n",
-    "* Lung - KRAS MT - EGFR WT\n",
+    "__To Analyze KRAS__\n",
+    "* Lung - KRAS MT\n",
+    "* Lung - KRAS WT\n",
     "\n",
-    "__To Analyze EGFR and Control for KRAS__\n",
-    "* Lung - EGFR MT - KRAS *\n",
-    "* Lung - EGFR WT - KRAS *\n",
-    "* Lung - EGFR MT - KRAS WT\n",
-    "* Lung - EGFR WT - KRAS WT\n",
+    "__To Analyze EGFR__\n",
+    "* Lung - EGFR MT\n",
+    "* Lung - EGFR WT\n",
     "\n",
     "MT = Mutant \\\n",
-    "WT = Wild Type \\\n",
-    "\\* = unspecified "
+    "WT = Wild Type"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
+   "id": "e7d7abe9",
    "metadata": {},
    "outputs": [
     {
@@ -100,27 +101,18 @@
     "kras_wt_ids = list(set(lung.depmap_ids) - set(kras_mt_ids))\n",
     "egfr_wt_ids = list(set(lung.depmap_ids) - set(egfr_mt_ids))\n",
     "\n",
-    "kras_mt_ewt_ids = list(set(kras_mt_ids) - set(egfr_mt_ids))\n",
-    "kras_wt_ewt_ids = list(set(kras_wt_ids) - set(egfr_mt_ids))\n",
-    "\n",
-    "egfr_mt_kwt_ids = list(set(egfr_mt_ids) - set(kras_mt_ids))\n",
-    "egfr_wt_kwt_ids = list(set(egfr_wt_ids) - set(kras_mt_ids))\n",
-    "\n",
     "#Instantiate KRAS Clusters\n",
     "kras_mt = can.CellLineCluster(kras_mt_ids)\n",
     "kras_wt = can.CellLineCluster(kras_wt_ids)\n",
-    "kras_mt_ewt = can.CellLineCluster(kras_mt_ewt_ids)\n",
-    "kras_wt_ewt = can.CellLineCluster(kras_wt_ewt_ids)\n",
     "\n",
     "#Instantiate EGFR Clusters\n",
     "egfr_mt = can.CellLineCluster(egfr_mt_ids)\n",
-    "egfr_wt = can.CellLineCluster(egfr_wt_ids)\n",
-    "egfr_mt_kwt = can.CellLineCluster(egfr_mt_kwt_ids)\n",
-    "egfr_wt_kwt = can.CellLineCluster(egfr_wt_kwt_ids)"
+    "egfr_wt = can.CellLineCluster(egfr_wt_ids)"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "2cc87557",
    "metadata": {},
    "source": [
     "## Analyzing Global Gene Dependency\n",
@@ -129,24 +121,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 49,
+   "id": "295b9aba",
    "metadata": {},
    "outputs": [],
    "source": [
-    "def gene_effect_scatter(mt, wt, mt_wt, wt_wt, gene, control, tc1=None, tc2=None, name=None):\n",
+    "def gene_effect_scatter(mt, wt, gene, control, tc1=None, tc2=None, name=None):\n",
     "    \n",
     "    #Average Gene Effect for control agnostic groups\n",
-    "    mt_effect = mt.gene_effect.mean(1)\n",
-    "    wt_effect = wt.gene_effect.mean(1)\n",
-    "    #Average Gene Effect for controlled gorups\n",
-    "    mt_wt_effect = mt_wt.gene_effect.mean(1)\n",
-    "    wt_wt_effect = wt_wt.gene_effect.mean(1)\n",
+    "    mt_effect = mt.gene_dependency.mean(1)\n",
+    "    wt_effect = wt.gene_dependency.mean(1)\n",
     "    \n",
     "    #For Labeling\n",
     "    mt_lab = mt_effect.loc[[gene, control]]\n",
     "    wt_lab = wt_effect.loc[[gene, control]]\n",
-    "    mt_wt_lab = mt_wt_effect.loc[[gene, control]]\n",
-    "    wt_wt_lab = wt_wt_effect.loc[[gene, control]]\n",
     "    \n",
     "    \n",
     "    #Make Figure appropriate size, dpi, and font\n",
@@ -164,50 +152,34 @@
     "    ax.set_ylabel(f\"{gene} WT Average Gene Effect (CERES Score)\")\n",
     "    \n",
     "    #Draw Line at median common essential value\n",
-    "    ax.axhline(y = -1.0,\n",
+    "    ax.axhline(y = 0.50,\n",
     "               c = \"black\",\n",
     "               linewidth=0.5,\n",
-    "               label = \"Median Common Essential Gene Effect\"\n",
+    "               label = \"Minimun Gene Dependencey Probability\"\n",
     "              )\n",
-    "    ax.axvline(x = -1.0,\n",
+    "    \n",
+    "    ax.axvline(x = 0.50,\n",
     "               c= \"black\",\n",
     "               linewidth=0.5)\n",
+    "    \n",
     "    #Plot all genes\n",
     "    ax.scatter(mt_effect,\n",
     "               wt_effect,\n",
-    "               c = \"#e41a1c\",\n",
-    "               alpha = 0.8,\n",
-    "               s = 50,\n",
-    "               label = f\"{control} MT or WT\",\n",
-    "               marker = \"^\"\n",
-    "              )\n",
-    "    #Plot all Genes, control \n",
-    "    ax.scatter(mt_wt_effect,\n",
-    "               wt_wt_effect,\n",
-    "               c = \"#377eb8\",\n",
-    "               s = 35,\n",
-    "               alpha = 0.5,\n",
-    "               label = f\"{control} WT\"\n",
+    "               c = \"#2166ac\",\n",
+    "               alpha = 0.7,\n",
+    "               s = 50\n",
     "              )\n",
+    "    \n",
     "    #Outline Genes To label\n",
     "    ax.scatter(mt_lab,\n",
     "               wt_lab,\n",
-    "               c = \"#e41a1c\",\n",
-    "               alpha = 1,\n",
+    "               c = \"#2166ac\",\n",
     "               s = 50,\n",
     "               edgecolor = \"black\",\n",
     "               linewidth = 2,\n",
-    "               marker = \"^\"\n",
-    "              )\n",
-    "    #Outline Genes to label\n",
-    "    ax.scatter(mt_wt_lab,\n",
-    "               wt_wt_lab,\n",
-    "               c = \"#377eb8\",\n",
-    "               alpha = 1,\n",
-    "               s = 35,\n",
-    "               edgecolor = \"black\",\n",
-    "               linewidth = 2,\n",
+    "               alpha = 0.7\n",
     "              )\n",
+    " \n",
     "    ax.legend()\n",
     "    \n",
     "    #Label control agnostic Series\n",
@@ -220,16 +192,6 @@
     "                        xycoords = \"data\",\n",
     "                        arrowprops = {\"arrowstyle\": \"-\"}\n",
     "                       )\n",
-    "    #Label control wild type series\n",
-    "    if tc2:\n",
-    "        for i in range(mt_wt_lab.shape[0]):\n",
-    "            text = list(mt_wt_lab.index)\n",
-    "            ax.annotate(text[i],\n",
-    "                        xy = (mt_wt_lab[i], wt_wt_lab[i]),\n",
-    "                        xytext = tc2[i],\n",
-    "                        xycoords = \"data\",\n",
-    "                        arrowprops = {\"arrowstyle\": \"-\"}\n",
-    "                       )\n",
     "    \n",
     "    plt.show()\n",
     "    \n",
@@ -242,6 +204,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "0f2e85af",
    "metadata": {},
    "source": [
     "## Note about Gene Effect Scores: Dependency vs Essentiality\n",
@@ -250,6 +213,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "50b32d42",
    "metadata": {},
    "source": [
     "### Average Gene Effect in KRAS Wildtype and KRAS Mutant Cell Lines\n",
@@ -259,20 +223,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 52,
+   "id": "e6b695e6",
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "gene_effect has not been loaded. Do you want to load, y/n?> y\n",
-      "Load Complete\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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\n",
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3UI6r/30zr/zn33LBdT+vObDPNgsxuNeha+1lyn+uH32gEOItQogHhRAPTqWJaqGybt06zjnnnHldw1VXXcUZZ5zBmjVruOyyy6b8i7GaRYsWcd999xGLxbjwwgupr6/nzDPPJJ1O89WvfhXQu8ef/OQnfOYzn2Hx4sWsXLmSf/mXf6lp9nw65/7lX/4l9fX1rFy5kk9/+tP8wz/8w4iu9c997nOsX7+e8847j4aGBi699NJKMxfo9HdzczPLli3j9a9/PV/72tcqu8TJzp2IG264gUwmQ3t7O9dccw1vfOMbK6/V19fz61//mjvuuINly5bR3t7OBz7wgcM6zMeivr6e3/zmN/z0pz+lvb2dE088kd/97ncAvPvd7+blL385l112GfX19Zx33nmVwF/mDW94A4899tikDZ5T4SMf+QjnnHMOp59+Ohs3buTZz372iAmNib7jr3zlK3zsYx+jvr6e66+/nle/+tWHXf/iiy9m/fr1vPCFL+T9738/l1122ZTW94IXvIBTTz2V9vZ2Fi1aVHn+la98ZaWUMZ834Ib54YGdvfSmCuzqzrCrK83eniyP7RtkV1eWrsECW/cMouZ7kVUIXY+fwzcU4lPACqXUNeO8vhX4tFLq+6XHrUAvsEgp1Tfedc855xz14IMPHvb8k08+ycknnzwTSzcc5/z+97/nyiuvrCkzcKzwne98h2984xv88Y9/HPeY48kVbt26dXz961/n0ksvHfcY8zvn2OS2/93FP//wUWxLooBUbvx6+lQ40rS8EOIhpdRhu8KFuHN/Ajij6vEZQPdEgd1gMMw8uVyOr3zlK7zlLW+Z76UsCH74wx8ihOAFL3jBfC/FMA8M5lyC0l54pgL7bDJnwV0IYQshYoAFWEKImBBirIa+7wDXCiFOEUI0Ax8BbpmrdRoMBvjVr37F4sWLWbJkCVdcccV8L2feueSSS3j729/Ol7/8ZeP8dpzSlIiAgqEZDOyfet3pM3at0czlKNxHgI9XPb4S+IQQ4iZgG3CKUmqfUuqXQojPA79Dj8z9cNR5BsO8cMkllxw3KfkXv/jFxpe8ilqVDw3HDtUjb3Uxmy07eij6weQnToErLjlxRq9XzVyOwm0CNo3zct2oY/8V+NdZXpLBYDAYDIfx+N4BPvCdh0jlXFw/pDdVnPFmua++9bwZvuJI5nLnPm8opcacXTUYDIaZZK4blA0zz+N7B7jyS5vxgxABZIszu1sHWFQf4ZKN7ZMfeAQc88Ujx3GOCYc1g8Gw8Mnn8ziOM9/LMEyTghvwge88hB+EOJYkNwuBHWBpc2JWrlvNMR/c29ra6OjoIJfLmbtqg8EwKyilyOVydHR00NbWNt/LMUyRghuweVs3X/zJE3QNZCm4AUM5b1bm1jcsrQcBW3b0Tn7wEXDMp+UbGhoAPYvreQt/fMFgONoYHBxkaGho8gOPcRzHYcmSJZXfOYaFT8ENuPP+fXzzN0+TK/gMZYsU/MnPmy7xiGRpS4LeVIFDQ2NbZc8Ux3xwBx3gzT84g2F22LRp05i+6gbDQqZs8LJ1Vz9FP5wTdbkVrUksKZBS0NYYm/yEI+C4CO4Gg8FgMJQp68Qf7M9R8CeXup4JHAtsKUjnPepiDs85cdHkJx0Bx3zN3WAwGAyGMgU34Kbf7mDvoTQHB+au2VoKQd4LsC3JpteeSdSxJj/pCDA7d4PBYDAcE9TutZ5mIF2syMnONq11Ds31MT7w1xu58OS2WQ/sYIK7wWAwGI4ByoG7e7CA5wc4tsWSphjXv+4sTlzWoMfcvv0gz3Sl8UM1Z4EdYFFjnP9403M5cdnc9X6Z4G4wGAyGo47qXXpLfYT//NlT7O5OgwClQAiP3lSBt3/9Xv72hSfSO1Rke2cKx5bEbUnenZ0Z9moEEHEk7/nLU+Y0sIMJ7gaDwWA4ytjRmWLTHY+QKXiEoSJT8NjfmyMZtYnYOuUdhCHpvM+e7gzf+PXT5IoBeS/AseWcaJ5IAXUxh7amGJnZnK8bBxPcDQaDwXDUUO5094OQlroog1mXVM5DKch7ARFHohRkCz4CCNE7+XjEBlUknfcIZzG2CwEoWLU4yZq2OvrSxVkfexsLE9wNBoPBcNTwwM5eMgWPRMTm0T0D+EFI3vVRgB8oCl6IJQSh0h3qorRLjzkShQ70s4VAj7s5tiQesckW/DkZexsLMwpnMBgMhqOGroE8vh+yvTNFqBQRxyIWGd6n5l2fQOnZdW0aBlHH4pmu9KyvLepIYo5FGCry7tyNvY2F2bkbDAaD4aihvTlO3gvwg5BIKWhGbIklIQj1ztzzQpRSKMCSgl1d6VlNxUsBsYjFykVJlFIEoeKDl5/OBXM09jYWJrgbDAaD4ajh3PWLsKTAC0IQgjAMkVLvmAtugJQCKSEsNcOHczDzJgDPD8m7ActbEmx67Zlz3h0/GpOWNxgMBsNRQyxi8bJzVuL6IdmCR84NyOQ9csWAurhDECiK3tw6gIYKhBAkIjYf+puN8x7YwQR3g8FgMBxFFNyA+7b3kIjaxCMWccdCCFDAUM7T3fFzvCYFnLGmmfq4zWf++zGK3uzP0E+GCe4Gg8FgOGq458lDdA8WKuNlBTeY1Xp6LTiWxPND6uMOmYI3617ttWBq7gaDwWA4KtjRmeKff/goHX1ZLCkI1Nzv0sdCCFXZrYehmnWv9lowwd1gMBgMC57BjMvbv3Yv+3sy+ApCpQjD+Q/uUoAlRKUrfi682mvBBHeDwWAwLGh2dKZ497e2sOdQBlXarc+l8ctoREm/XgrdSBd1LBqTkTnzaq8FU3M3GAwGw4KlLDc7kC0CYFtyXgOXJQW2FERsvYqIJWhtiNKfKc6raM1ozM7dYDAYDAuSghtw0293sL83S1jqmguCkHCe1pOISFa0Jrng5CXYtuDZa1uxbclg1qWtMcZzTly0IAI7mOBuMBgMhgXIjs4UH73tYZ7pSjGQcZElvfj5wrYEKxfVYVuCSza2c9EpS+ZvMTVg0vIGg8FgWFAU3IB/+vaDPHlgkEzBJ1Tgz/O8m0DR2Z9jz6EMj+0dmNe11IIJ7gaDwWCYUwpuwOZt3fzgnj1s3tZNwR0p+nLPU93sPJhCAMF8ds6VsKQgFnGIOBYKuOvBAwtCqGYiTFreYDAYDHPGjs4Um+54hEzBIwwVUgrqYs4IPfb7nu4lCFUpFa+QggUgVCPwg5CobWFJwZYdvQs6NW927gaDwWCYEwpuwMduf5i+dAE/UDi2pLU+ih+EbLrjEYpeQMEN6OjL4gWqkpKf78AuhcILQqQQbFjegFILQ6hmIszO3WAwGAxzwp337+PxvYMIofADhULhSIuTVzaSKXh8/5493LF5N92DOYKqiC6YX7GapmSM5a1xmpIRpBDkiv6CEKqZCBPcDQaDwTDrFNyAG+/ejh+GBKFCKUCBh89Dz/TREHf41Pe3IoUgCEcOu8131b213qGlLgqwoIRqJsIEd4PBYDDMOg/s7MULQnw/RAih6+hAOY4P5rzSkfMdyocR6BG4ghfSPZgf0R+wUObZx8MEd4PBYDBUKLgBD+zspWsgT3tznHPXLyIWOfJA1jWQR4WAECjAXwBd8BMhAMeWnLCkjve94rQFKVQzESa4GwwGgwGorZN9urQ3xwmVImJL8u7CHiMDnT+wLcG/XHMOp65qnu/lTBnTLW8wGAyGioa7H4QsboixpCnO4obYiE72I+Hc9YtoSERGNMrNJ7aljV/GQgCJiMWt77noqAzsYIK7wWAwGNA18UzBoz7ujHi+Pu6QKXhs2dE77WuXU/2XnrGUIJgvZXiNBKK2YF17A2evayXm6DBoS/1fxBKcsKSO//rHiznrhNZ5XeuRYNLyBoPBYKBrIF8xZxlNGE5/rnt0qt+yIPCPZKXTRwDxmEUQ6huOiC05Y20LLzl7BQf6sqDguRsWc8HJbUdFXX0iTHA3GAwGA+3NceQ4eWopxbTmugtuwEdve5jBbBEpBI4lEfM4tS6lYH1bPW6ouOqSdSxvTRw1DXJTxQR3g8FgMHDu+kXUxRzS+ZGp+YnmuifrrP/BPXvYuqcflEIIAUpR9Oev5i4luKHiS9c+54gbBBc6JrgbDAaDgVjEYtNrz2TTHY/Qkyoc1i0/endbTrenci6ZgocfKBoSET73hrM5bXUzj+8d4J9/9BiuHyIBRUm4Zp6wLYEUgsvOXHbMB3Ywwd1gMBgMJU5c1sBN77qALTt6OTRUGHeuu9xZn867dPbn8YOQUCl600Ve/YXf856Xncz3/rAb39cd9vM90m5JQSJi44chv36kk2tesJ5H9w7M+Cz/QsIEd4PBYDBUiDrWpG5nD+zsJZXTgT1UCssSFAoBoYIgVHz2R08A868JDzqwSwFCQNS28IOQK/71D9iWmPFZ/oWEGYUzGAwGw5ToGsiXUvEhlhRkCz5KjZwbXwiBXa9HC+dIIVi/tJ7O/hwF15+VWf6FhAnuBoPBYJgS7c3xkqublpENFQghKtas42jDzAlCwMvOWcHS5ngleLc3xdm4uhkvCAkUNCYiI86ZiVn+hYZJyxsMBsNxynR15DeuakZKoZvlSrtjEJU5+fncsdsCnjwwyKGhAo4lsS1BtujTn3FJRC0sAY3JyGHnHcks/0LEBHeDwWA4DpmOjnzBDbjz/n3cePd2glDh+WElkIcinPc0POiu+P60S9SWuH6IY1koBUU/oOgHLGtJYI0xzz/dWf6FignuBoPBcJwxWke+TDrvsemOR7jpXReMOfr2sdsf5vG9g4RKYUtBXdzG90PyXjivY27V+CF4fkA8aiOlT1tTjIgliToWRT8AxJRm+Y9WTHA3GAyG44yyjnx1YAdde+5JFdiyo5eLTllSSdvv78nyvT/swgsCpISYbQOKghuixMJonisjhcAPFVEABBHbYmlzHIDuwTx/cfYK7nnyUE2z/EczJrgbDAbDcUYtOvLVaftUzqN7qKDr6wp8EZIp+IRqfoVpRhOPWCilUKVFCcD1Ag4O5Ik6EiEEG1c3c+2lJ046y3+0Y4K7wWAwHGdMpiPflHRGpO39QKfhlYK855NboH7srh8CiqhtkS/6uH5Id6lJLlQK25K01EVqmuU/2jGjcAaDwXCcUa0jX0259iwQI+xf9a5X//8CsWMfkzDUmYRQKQpegG3pGxiBFrBZtSjBZ/77sWNqnn08THA3GAyG44yyjrxtSXpSBboH8/SkCtiWZNNrz6QvXRyRtm9KRrClJL9Ad+ygBWuijmRNWx1NyQjLFyU4aXkjK1sTrGuv5/Q1zbQ1xo+5efbxMGl5g8FgOA4ZS0f+9NXNPLp3gO2dKbLFgFAppBAVdbeHnumb72WPiS21neyqxQmWtyZ5umOIwA9pqY8eduyxNs8+Hia4GwwGw3FKde15R2eKd3z9PjIFD88P6RrI0TWQY01bHe3NcbwgxLbkiNn2hYAUkIjahGGIFyg6+3P4gSIaGTsxfazNs4+HCe4Gg8FwnFM9956I2mzvTSGloOAGbO9Msa83S33cKdmmanGY+ay9CwGJiEXBCwEIVUjBC+kZKuCHCiEg70oODeVpa4xXzjuSefbpqvnNFya4GwwGw3FOee69tT7K1t0DhEoRj9jEHN11Xh+zidi6nh2Giu2dKYRgXsbgEhELhLaRjVgShSJXDIjYEgTEHIsNyxvIFnz29WZ1lkFxRPPs01Hzm29McDcYDIbjnPLc+2DWxQ9DIrYOfkIILEvSUhdFSm0MUx6hm4/ALgV89g3PJlMIGMgUqYs7bN3dz/8+0UUy6tBUF6GlLoIUgmRUh7e/ePZy2pri055nn46a30KgpuAuhHCAZwFNwCDwtFLKm+gcg8FgMBwdlOfei6NkZJVSBIEWrEEoNq5q4VePdMxLSl4K+NhrzuBl564ChnfT+3oyZAp6pj2d94hHrEpgV0rR1hTn8vNXT/t9a1XzW2hMGNyFEC8F3ga8EPCANFAPOEKI/wG+ppT62ayv0mAwGI5x5rOmW55770sXECVtGz8IyRQ8lIKuoTy+r9jf2zEn6xlNPCK56V0X8NwNbcDI3fSihhipnEfEtvCDkO0dKc5Y26y7/Gegea4WNb+FyLjBXQhxDzAA3Aa8VSnVWfXaUuAS4G1CiA8qpS6Y7YUaDAbDscp813TLc+8fu/1hOvryZPIurq+GHd/8+emeE+jmuWUtCf7jZ09x07taiTrWiN10EGrlOb/Uze/6AYNZF8eSM2IGM5ma30LtvJ9IxOZtSqmXKaVuqw7sAEqpg0qp25VSLwPeOrtLNBgMhmOX0TXdJU3xkuRryKY7HpkzNbUTlzVwy99fyJtedCJKzY8RjC1FJXMAeg2hgo6+HLu609x09w4KbsD+niypnMfBgTxDOZf1S+uRQuB6Aa4f0psqVgR5jrQePpma30J1kht3566UeqyWCyilHp+55RgMBsPxxXzWdAczLrdt3sWeQxnWtNXx1+et5r7tPSRiNm7WQwrmuL4+0oim7DZX9EIODRW47Q+7+M0jnaTy2simfDNgS8n6pfV4fkhPusDrLlrL31564ow0upWzGpvueOSocpKrtaEuCnwMeB3QqpRqFEJcBmxQSt0wmws0GAyGY5n5qunevbWT99/yIK4fopRCCMF/3vWkft/Scua6cU4IgRBaFQ+ofC9SCpRSRByLZ7rSKBQxR6IUlZT8kweGaK2P0hCPcMXzT5jRoDuWmt9Cd5KrVVv+34DTgNcznK15Anj7bCzKYDAYjhfmo6Y7mHF5/80P4noBthREbYtERFL0QopeSNSee9sRvUtXus7O8K5d/1mHHd8PQYBA0N4YRwpB3vVJ5z2yBZ+O/hxDOY+3fe1ednSmZnR9ZTW/y89fzUWnLFnQgR1qD+6vBK5QSt0LhABKqQ5g+WwtzGAwGI4HZrOmW3ADNm/r5gf37GHztm4KJeOX/7zrSS0zGyjybkC26DOU02l4gCBUjHO/MWsIKOnYa1e3oLRrF6WuuljEAiEq/QBCwGmrmkANz9xbAtJ5l237BvnAtx88LtzfxqNWERt39LFCiMXAwnQRMBgMhqOE2ajpFtyAO+/fx413bycIFXHHwrZ19/gHL9/Izx7ch1JgVUVwPxjujne9AEsKwmDu8vIh+qaiORklHpV4vqI/XSQasREoLClproswmC2C0jvpwZxL0Q+xpN7bRx0bx9Zp+h0H09zz5CFecPrSOfsMC4lag/sPgG8LId4LlVG4LwF3zNK6DAaD4bhhJmu6OzpTfOz2h3l87yChUthSYFuSDcsacP2Av/vG/eSLPkCl1g6MkJMNFQRzGNjLBCFEbMlQzmdxQ5R03iMIQ6K2lpSNRyx2dwECGpMRdnWlCZXCKu34HVt/FtuSFD2P+7f3mOA+CR8CPg88BiSAHcA3gU/M0roMBoPhuKLaoW26lMfqBjJFpISYrX/FlxvOpIBs0Qeld+l+qCiX16ub56K2oDBPs+0FL2BJYwxLSt582QZ+9XAnlhRk8h65os+69noU0J8pki16oEBISMZsdHK/ijkuLSwkJg3uQggL+AjwAaXUe0rp+F6l5kNZ2GAwGAzjUR6rs6QcMVJmScFg1sW2JGGpRu1YAi9Q+KNa4gXMW2AH3WvgBSFhCG2NcX503Z8dltEA2LKjl989dpA7Nu8m6kgsOdxC5gc6Vf+cExfP18eYdyZtqFNKBcA70fKzKKV6phPYhRAtQogfCyGyQoi9QogrxjlOCCE+JYToEEIMCSF+L4Q4darvZzAYDMcb5bG6qCMrYjBBGDKU8wgVuH5IECpCBd44aff53rXFIhYR2yJUihvv3g5wWJd6OcvxT6/cyLOWN+rP5gUUvQDXCwiVYv3SBi48uW2eP838UWu3/LfRGvNHwpfRjXlL0CN1Xx0naL8K+FvgIqAFuBe49Qjf22AwGI5Zyl3x2/YP0pcukisEKAVeEJIt+CykRKs1WdQpLdWWgiBUbNnRO+6hsYjF568+h1NWNNHaEKUhbtPaEOWUFU18/upzFvy42mxSa839OcC7hBD/BOyn6uZOKfX8yU4WQiSBy4HTlFIZ4I9CiP8HXAVcN+rwtcAflVK7Sud+F3hvjes0GAyGY5axzGX292bZdMcj9KWLHOjNkC+puUVsieuGoOZejGY8pNBBe117Hds702Mekyn41AvdFBePWJOK+Jy4rIFb3n3hUSUwMxfUGty/WfpvumwAAqXU9qrntgIXj3HsHcBrhBAbgN3A1cAvj+C9DQbDUcp8OqUtNMYyl0lEbQpugG0JeoYKOLaFY1tkCh6uHyKAoDTyFsxzhBeAY+tegEODhYopzOhlKXT54Iw1TeRcvyYRn5loRjzWqCm4K6W+fYTvUwcMjXpuCG0fO5qDwGbgaSBAZwpeMNZFhRBvAd4CsGrVqiNcosFgWEjMt1PafDHWDQ0wwlymzIG+LB19Oda11+MFIVII/EB7squyvBvAAkjLCwkR2yJb8EjlQxRaE95CVer/ZS37poRDqNSCNmZZ6NS6c0cI8UZ0Gn050AHcqpS6ucbTM8Dof40NaH/40XwcOBdYCXQBVwL/I4Q4VSmVqz5QKfUN4BsA55xzzvz/7TUYDDPCaKe0Mum8x6Y7HuGmd11wTKZdx7uh+ctzV45pLmNJSVDSn8+7PjByhx4pdcTP90SYAMIQPF8rxkkpUJWGPoFtaRGd8rFKiBlzdTteqdU45sPAG4AvAnuB1cA/CSGWKaU+XcMltgO2EOJEpdSO0nNnoPXpR3MG8F9KqQOlx7cIIb4EnAI8WMt6DQbD0c18OqXNFxPd0Nx49w4i9uEhOuro7rT+TBHFcId0WZfdCxURC0qaNbOKpKRNPgGur4+I2JJ8EBCGodbVV3rXLgXYjuTjrzmTP9vYbgL7EVDrzv1NwCVKqb3lJ4QQvwL+AEwa3JVSWSHEj4DrhRBvAs4E/gp43hiHPwC8SghxB9CD7qx3gJ01rtVgMBzlzKZT2kKt44++oQmVYjDrUvRCsgUP3zm8zbwpGQG0kYolqOzayxl5FUJhsog7A4jy/4yTPy0/HSpdZy96IUtb4nQPFkaM5Clg3aI61rXXm8B+hNQa3JPoQFtNHxCfwnu9A7gJOFQ69+1KqSeEEKuAbcApSql9wOeANuCR0vvuBC5XSg1O4b0MBsNRzGw5pS3kOn71DU226LO9I4Uf6vq5H+gIXR93aGsc/rWbLfgsbozRlypgCYlXHDZKmas6pRS6YU8Koe1jJzguGbXJuz6hUmTzHsmopOgrgiDEtiRnr2vF9cNjuvQyV9Q65/5L4HtCiGcJIeJCiJPQs++/qvWNlFL9SqlXKKWSSqlVSqnbSs/vU0rVlQI7SqmCUuqdSqmlSqkGpdSzlVKmW95gOI6YDae00WnvJU1xFjfE8AMdTObbQax8QxMqxfaOFKFSRGwt2OJYkvamOPt6c3QP5ekezNOTKmBbkrf/+Umsba9nVWuykqafa7xAUZwgsMPwzUYi6lAXc8h5IQUvJGJL6uMRzljbQtSxqI87ZArehPPthsmpdef+d8AN6PE1B61W933g72dpXQaD4ThmNpzSFnodv3xD09mfww9DIrb+jH5pV7u6rY66dJG/ePZy2prilXlupeDO+/fx5KHByg5/Lql1wq4p7rC6rY7GZAQh4NE9AyilWN1WR1MyghTDmZojLb0Yah+FSwFvEEJcAyxCa8vP/d8ig8Fw3DCTTmkwu3X8maB8Q/OOr9+H5+t0vICKo5slBUop2priXH7+6hHnvuK5q3h0Tz/hAv6t7IWKurhT6iMIcCxBNOLQUhetHBOEus9gMOvSPZin4AYLoh/iaKTWbvk3AI8opR5F18wRQpwBnK6UMtKwBoNhVphJcZLZquPPJCcua+C6yzfy6R9sJV7SUG9MRiq+6+Ot05KCxmSE7sH5u0GZoJ9Oz68HIY/s7keg3eiEgLwbcmgoT1tjnGzBZ3tniqIfIIBf/qmDe548tCD6IY5Gai3QfBItJlPNfuBTM7scg8FgmB1mo44/G1xwUhtLmxPUxR1a6qOVwD7WOsua8vdv76E3Nb+ZByH0XP1hz6M1dHSN3QcBMcdi4+pmVi1Osq83x8GBHI/vG6DoB0Rti9NWN9PWuHD6IY5Gaq25NwCpUc8NAU0zuhqDwWCYJWajjg8zP1oXi1h88PKNfOA7D9HZn8O29BobEhE2vfZMlILN27p5dHc/P/9TB2EYsqs7zUyW28tKcVNBKZ1ZkCXXufJ1mpIOXqDI5H0sKVm7pJ6WOl1jT0Z1CDppRSPpvM+ihuiI+vtC6Yc4Gqk1uG9DG798v+q5VwJPzviKDAaDYZaY6Tr+bIzW7ehM8c8/fAxLapW2skXrh/5mIwDX3nAP6bzH7u40Qahw/XDGjWGmcz0hOGwt9XEHISSe7yEFRCxZEqsZ3uErpRX0GhMOTclIZbY/6kiakpEF0Q9xNFJrcP8A8HMhxGuAZ4D1wAuBl8zWwgwGg2E2mKk6/mxI5FZfs60xVqmvp/Men/z+o9poJVTYlkAIQVCag59P9Iy7whICSrPuYakZUN+YBNhSIiMChRawqUZKwZq2OrbuGWDr7oHKbL8QWnt+cdX3YKidmmruSqk/Aqei1eOSwBa0fes9s7g2g8FgWLCUR+vq486I549kTnuiax4aytM9WKA+7lD0QoJQB9HxmgRnE0tCIiJxpFbGi9gWQkqkECxvTlAXs4lFLFrqoqxrr+esdS1EbYtQqRGz+OU+gr8+bzVdg3m8IKzM9kdsCy8I6RrMc8aa5jn/jEc7NRvHKKX2A58FEEI0K6UGZm1VBoPBsMCZjdG6ia5Z9AJcX3FwIE/RCwiVTmnP1s693P0uSn+o2LwIbViT97QLXRgobFsQj+gSQrrgYVvawCYZsyujbstbtQiPHyi6B/MjShjbD6ZY0hijN1XE9YKKfK5jSRY1RNm6Z8DU3KfIhMG9NALXrZT6Venx2cCdwDIhxE7g5Uqpp2d9lQaDwbDAmI3RuvGumS369GdclNJBXgBeVfp7Ojglx7jxkFKXMFrropUSwKGhAi31URxLcnAgp28ugFzRp+hBXcwhCBXLWxLs78uSd4NKIK+PR/jee8+mP+Me1u/wyO5+Irbk9DXNDJXm4MtjgL2pgqm5T4PJdu7vQ7vBlfkWcDfwBbRW/L8AL5+dpRkMBsPCpXq0rjqNfiSjdWNdM1SKbfsGcSyBJSVFV+9spdBStdPZuOvyuECIsXf+Aj2uJoTAsSVRx6LoBXqePhFhb09GlwSEQJTWoJS+CYnakqIfcNqqZt582QYGsu6kjYvlmxpLClrqoyNeWygaBEcbkwX3VcBjAEKIlcBpwAuVUv1CiOswTm0Gg+E4ZTZG68a6Zqbg4/oBsYjNUM6b/CKTYEmdMveDw5vxBBCx9Y4+VArPC+kZKmj72CDA9RR7D2VwS+cGatgrXildjnADRSxic/0VZ9U8MTAbN0rHO5MFdx+IAAW0PetTSqn+0ms5puYKZzAYDMcUMz1aV77mV956Ht/7wy4e2zPAfdt7cH1F0R8/sE+kDjeaoKQOZ0ldL69GAUVfIUu2rMmoTaT0WcIwRKFH76Q8fFwuFpH4gWJRfYTb3/d8GhKRmj/zbGkQHM9MFtz/F/i0EOLbwLuAn1a9dhLQNVsLMxgMhqOBsUbrxhO2qUXwZkdnio/d/jBdA3k6+7P4weSBu+LfXsN6pSjtssfIxwsg6kjq4ja5kn1sucYvhKiI24QhCMkILXulwLElf/7sFfzq4c4pC/rMxo3S8cxkwf3dwK3AW4B70V7rZa5CW8EaDAbDMcd0lefGE7Z54wvXc/Nvdx72/Acv38hA1qVrIE9LfYT//NlT7O5O44eqpsBepubjSgeWFe104NbPtzfHedbyBnYeTJOIWLTUxSrNbQPZIvt6cpX3Gm0d5gchUWmxZUcPW7b3TEvQZya9BI53JgzuSqkO4AXjvHbdrKzIYDAY5pnpKs+NJ2wzlHN5380P0N4Yx7IEUceiKRmhL13gyi9tZtWiJErp+vr+3izJqI2UtQdsqG3nLoFw1J+FgHjERghY0hRDCt3Y5oeq0twWhIqdXWndiIe+Eah+L4He0StgUX1shB7+dAV9DEdGrcYxBoPBcFwwOkAvaYqzuKE2E5PxRGiyBZ9MwedAf44DfTl2daXZuqeffT25kl+7YElTvNL9nncD8sWpmaVMFNjLTW8hlORfIRqxSupygoIXYFuCpqSuk9fHHRrikYrJTn+6SFBqoivX2q1Sd7u2pdVz7pYQDGXdyvseiaCP4cgwwd1gMBiqOBLlubFEaIJQsa83C0oHxKhjEXEsPF+RLXgIRkqyli1RZ1KcpvpSjq2V5KRQKKW74pVSxByLwazLUM6lPu7w+avPxrYk+3uzPN05RMELK9dRpc9Vbs5rTGgNeS0vO/KmxGjDzw8muBsMBkMVR6I8N5YIzWDWrWilVxumIHSQrJZkbU5GRgTQ2cASgrNOaOHEZY0sa0no91LQmy7yVEeKHQfTvPGF6zl1VTNfeet52JakORktab2LSsodqKTptUFMuSFvZPrdzKnPDya4GwwGQxVHojw3lmd8eScrhaA6tltClLrch9PhUcealaguALv0235JU4z6uHZgyxZ8rQPvSJY2xTlpWQMnttdz8293UvQCHt07oEsGzXFtWoOWvK2+LkAyZlda9huTwyNwZk59/phMfnYVECqlDpQeJ4APo8Vs7gX+RSk1tcKQwWAwzAHT7XY/EkGVsbzYQQf2U1Y2sb83i+sHlZS7ANoadRNbECp2HEwhBUygCjstdIZA/7lcAhjMuvih1oePRWxOaK+v7MrLHurlLEbe9cccnwsVOBYMZF3WtdejgP5M0cypLwAmG4W7EfgKcKD0+MvA84EfAW8EGoAPzdrqDAaDYRocic/6kQiq7OhM8akfPEqu6JN3A6QQNNdHWNdeT9SRnLG2ueJX7gchfqDF4Xd1Zyh6PqmcN2vpeCEgakkGsy47u9KkcnodiajFhmUNI9Lt5fJDe3McIQTdQ4VxPd6FkLz6grW85bINAGZOfYEwWXA/A/g1gBAiCbwGuEgp9ZAQ4kbg55jgbjAYFhAz4bM+HUGVghvwT99+kF3d6YqLmkDX8Jc1J4hHLfrSelcbhIruoQJLGmPYliBb9OkZKiCEIGIJilXNazOFUmBbEqWU1okv1fwrC62iXH44d/0iglDh+Xq3X5GarfqzFHDKyqbKd2Pm1BcGk9XcI0qpbOnP5wJppdRDAEqppwBTSDEYDAuKmfJZLwuqXH7+ai46ZcmkNwT3PNXNzoMppBBEHN2RrtAqbgf6slz9Z+t5/ytO4/UXr8OSgnVtdSSitk53B1pUJggVXjA9M5jJ0CN2Pn4Y0pRwOG11C3VRGz9UbO9IVVLu1eWHWMTiL569HCgJ3TDS+lUIqI/bDFaNvxkWBpMF991CiEtKf3458LvyC0KIxWh9eYPBYFgwzIbPei3c93RvZTQslfPIllLzBS8g7wb89tGDXHTKEpa3JvCDkD09WXZ1pdnfm6WjPzfcJT9bBu2lGXXXV+ztyZIt+GxY1oBjSfJewO7uDD2pArYlR5QfzljbQntznJgjSUQs4hGLuKNH+uzSnHxT0pnkzQ1zzWRp+U3AnUKIXWgt+UuqXvsrYMvsLMtgMBimx2z4rE9GwQ3o6MsShIp03kehEIiKtKuv4P+e6qboBew6mGZfb7aUgpdIMSz/CszofHs11ZcteCGP7O7nrLUtnL6mmT2H0py3YTEvOWfFYeWHc9cvYmlznIF0sfK9ZoshYaitZ9MFn2/9ZgerFtXVLDNrmH0m3LkrpX4CnA18GjhVKVUdzJ8EPjiLazMYDIYpM9Y4GkxtLKvgBmze1s0P7tnD5m3dFNzxh4J2dKa49oZ7ePLAIH6g6+lhKcWu9eFVyUpV8uP79nHT/+zA9UM8PyRb9MkW/BGBd7Ya6kYThIonO4YAaEhEeMk5Kw4rP5QnDi44qY3FTTFcPyCV9wgCnaGojzlsXN1MGKpJ1fsMc8tkO3eUUs8Az4zx/D1CCFNzNxgMC4ojtQ+dSqd9uXnP9QPqYxHGqlQq9Chc1LG46bc7tPwrUFa2Ga8LfbbRanI+BwdytNbHDrvpGf09xCMWi+pipIs+DXGHproILXWRijBPeXzONNQtDCabc+9XSrVUPf6tUuqFVYfsQo/DGQwGw4JhuvahU+20f2BnL33pIj1DBQpecNiuWwgtVhNxJENZj3jUpikZpXuwoDviZzAHXy4BhKp2+1c/AC8ID7vpGe97yBZ8vGzACe3NI9X2MDKzC43Jdu6juyTOGvV47MKWwWAwzDPTsQ8td9pXBzTQnfZj7Uz392Tp6MtiWxIph/3Oy0QsSTJmk3cDFIpE1KI+7uCV5tslHPHOvRzUhRA4lsD1Q+IRCz8Icf3DO+/LgV8KsCzJK567+rCMxHjfQ2MiQtdggcGsS0tddMRrRmZ2YTFZt/xkf+3mKaFkMBgMM89UO+0Hcy5BSTe+4AaHBeqwNCMuBbz07JVYUtCTKqCUGuGwNlWqd1UKXc+PlPRlbUsgpSAetamPO0TskXuw8ox6ImoTscRhARz0TUsq59LZn6MvXSQoLbQpGcESMJSdfj+DYW6YtOZuMBgMxwtT7bRvSkSQQCbvVbrjoXrXo1jSHKO1Lsbfv+xk3va1e3n6QF9l5zzaF73MeGn18vOi9AcFtNRFuOqSdWxc3YzrB/zr/9uGFLCsJUHeDXhi7yCeP9y0J4XuS3AsyeLGGMtbEyPeY0dniu/9YRfdQ0Vs6ZYsXSUbljWQjNksa0lgW3Ja/QyGuWOy4B4TQnyn6nFy1OPo6BMMBoPhaKTgBrheSK7oc6Avy9LmREWSdbyd6crFSZrqInQP5EEM17yhlC6XEsey+PCrTufRvQPUxxzcknD8RJv20a85FthSUvRDbEsiUAghiNqS1vooP3vwAKetbuaS05ayrr2BTXc8UlHDW7U4yZ6eDE2JCPGIhWNLYiUv94htjfhM5Vp7ImoRdyxCpbAtiR+EbO9MccKSOhY1xPjq285j654BIzO7gJksuH961OPPTPLYYDAYjjqqO8OFEHT05enoy9HeHCcRtYlHbP7y3JX8vy37R5jQnLt+USVgOrZFGIZIKQiCEIRgUUOMy85cxj//8DFSOZftnakpreulZy/nZeesZGdXmu/+/hkODRXwy1KwQo/ZdfTnCEPFp3+wlaXNCTa99szDmglb6iJ85r8fq3S+B6EiHrEP221X19o3LG9ge0eqYnRTDENybsBnrjqbhkTEdMUvcCYM7kqpT8zVQgwGg2E+GKszfEVrgs7+HKGCv3neGn7+0AFu37xrzNG4ay/dwKd/8CiUdtMAUcdmw/IGsgWfXz7cgVKKwayrG+lqRAD7erJcsrGd5a0JvvzzJ4c13UsZAonC9UOitiTu6Ca6clf/6OBby/RAdc9BMmqPMLrJFX2uumSdEao5Sph2zV0I8ZfAR5VSz5nB9RgMBsOETNfKdTzG6gyXQrCiNcmhoQJ3bN5NXcweczTuK289j8akTX3cplD0cRyLqCNpqY/i+zogdg/mkUKQLviTrkWWut4F2l51x8EU//PYQb545xMVq9aK2QulsTelG/eijjVuVz/UNj0wuudAClHpiu9JFQ6rzxsWLpPNubcD/wKcCewA/h5YgbaBbQP+Y5bXZzAYDBWOxMp1PCbqkE/nPfwwZGlzfMTz9XGHA305Lv/c7+gezGvFuUABPgI4lCpiSXAsi6IfYtU4NKxr/PpgocAPQn5y/z4O9GZHSNRWE4SK0ILGZAQ4snnzI/GyNywsJhuF+zI6iH8FqAf+H3B76fFapdTnZ3d5BoPBoBmdPl/SFGdxQ6ySih5L+nQyGdmCG9CTKjCU8+jPFCvOaGWCUGGP0T0fKsWBviwd/Vksqd3fbAmWBAQVr/ZU3sMPFEW/tnR8qLRxTKiU1qcXgs4qU5nx8DxFrqgzA0cyb15W9yt3w3cP5sc0kzEsfCZLy18ErFdKpYQQ3wd6gI1KqSdmf2kGg8EwzFQFZibb5T++d4APfOchUjmX3pLKnGUJVi1K0t4cJ1vwqY87CAG9qSIDJVvT5jq9Q/YDhSUECoFSIIXuYi/PhPtTqK+XCUZlEFSgaIhHkAK8CS6ngG37B1nWkiBUera+4AbTKldMV93PsLCYdBROKZUCUEr1CSGGTGA3GAzzwVQEZiaTkX3fX53C397wf/hBCAo8P9T1ax+e7kyxrzfLhqUNvPMvTuK6Wx+q7IoBOsu7dQXSkoRhWBo+Hw7sM6HuZZXm0ff2ZJjsPkEBuWLA/t4sKxYl+fefbePGu3eMKFdMpVdhOup+hoXFpPKzQog3MiyI5Agh/rb6AKXUTbOyMoPBYKhiKgIzE+3yDw0VeO9ND+AHITHHIpX3kEIg0JKwthQ0JyPYluQ7v3uGMBzugqeUNvcDhSWHZ9kRAX4wvaCuHeOg6v4BAcSjFlJIetNFVA1SdpYlWNNWx7IW3fRWrYe/ryc7470KhoXNZMH9fuANVY8fAK6qeqwAE9wNBsOMMd4OcyrNXpM1yaVzOqB7QVgJ3kLo2rmUkoZEhN50gYIbYFmCxojWg9dz7BIVhnihIlAKW4UE4fQ/r67VWySiWqgmVGXbWEUotLlM1JHk3PHfRABxxxqxEy+XK/74ZDc33b2zZjMcw7HBZHPul8zROgwGg2HSOnmtVq4T7fKDUBFxJG7BJ1/UAi3VbehBqIPpYDYgnfcIQoVAlDTa9a/MoqdoTNhEbYvuI3RCE1JWMgNOSR8+k/dwgxApBGGocCfJCUgpsKQ+trM/R9SxaEpGCEPF/dun1qtgODaYbBTuuUqp+6sex5VS+arHr1RK/Xg2F2gwGI4ParFbrbXZa6Jdfn3cQSmbvrQ75miZH4RYUtCfcSs7aLekCheP2kRtWXoeXnHeKr7/x130pl1sKZCl3X/Bq30r71gC2xJkCz62pYN0sfR+QY2WsEFJdW53d6YidGNbkkUNUW1QMwUzHMOxwWSjcL8Z9bhj1ONvz+BaDAbDcUy5Tl4djEHvMDMFjy07eim4AVt26JR9W2OMc9eP3cU90UjX595wNjB+wAN4Yv8QjiWIORZhyb0tVNrPvD/jUvACckWPn2zZz1DOJ2pb1MUjJGIOyZhDY7y2NLcA3CBEKYg4+qYhXfAmPW8sMgUfyxJEHYuIY+EFId1DBc5e1zIlMxzDscFkNffRfyMme2wwGAzTYrJu+Ef3DPC1Xz5dc1PYWLv801c38+jeARJRe1jKFUY4poUKgiBk48oWnu5MHebRDjqLv7w5ibR1rV7P2OvavZSSsMaNuwI8X+EHOqAvqo/i+gGWYNIO+TJRR+L6IQKdmYhHbISg4vrm2NII0xyHTBbcR//1muyxwWAwTIvx6uShUmQKPrf9YRf1cW05KsWwW9tETWHVI107OlO84+v3kSl4dPTlKsc4lsCxpa5vK0XeDaiPO6UdtaIpGSnt1EcK4OzsTuNYEiVUJRWvbxiCMdP9YyHFsFZ8+TO1N8U5NFRAlcbzJjpXSoEtBS6QjDn4QUhLXZSW+ghNyQg9QwUGs17NvQqGYwfj524wGBYEY9XJs0WfbfsG8UqBNlOw6BkqsmF5A8moXXNTWHU9PxGxR+i0u4EiVCHJmI1E4NiK+phDf6aIG4QgGFP9Tp87vEWPlzzSQ6XIF/0RO+/x/NmVAiH1/0ciFhFbghDYlqzU3cc7FwF1MRvX1413EVuglCAZsyt68OW0uxGmOf6YLLgnhRD7qh43Vj0WgHERMBgMM0K5Tl7eYQahYu+hLFLC0uY4PakiEVs7n23vSHHG2uZKN/mhocKEIi0P7OwllXORUvDMwTQINSL1HYSKdN7DsQSt9VEODeVxg5AwHBa4mYyCG6AcLRtr25KghqY6BZUxuiDQGYAwVJy8opGHdvWh1PjpUcfSNfqIJbGlxA8UQug0PRyedjfCNMcXkwX3F8zJKgwGg4GRdfI/busmV+xk9eI6BrMuvakioLvAXT9gMOvSUhdFSoHnh1x7wz3j1uO37u5nz6EMoYKCF+gUuBBYQne9l6fhvEBxcKAwIqDWkmIvp9aDUM+qW5bA9cPKubVk6QMV0jOYp70kIXvW2hYe3tU/4sbCljpI590AAaxdUk9LXYR8MWDbgUHCEFw/pCdVMGn345zJ5tz/d64WYjAYDDC8w+wayPN/Tx3CkoKmkmKcH4TYlkQpKHoh6bxHImrz43v3MpR3kUJUZrxzRb9iy/qLP3WUBGr0uJoUAqV0hVyhKo11tezQx6IsYBd1ZCWYam15RRCGNanXeX5Zkx52HEzT3hRnSVOcroE8CIjYUu/SLckJ7fXs6kqTynkEQYiUglNWNPFXz12FY0uTdjdMOud+DfDnSqnXjvHa7cBdSqnvztLaDAbDcUx1g50lBRuWNbC9M4XrBfhBSN4NaEpGeM6Ji/jWb7ZXZszLM94bljWQKXh87w+7sKQgalu4/nDtXAhBUGprt6Ruphu9Sy8Lw0wWmJUavnEAPSsfj1gsbYnz4rOWM5gtsrMzxZ929ZMtHl6/LxN1JE1Jh2XNMTIFnxPa6yh6Pg2JCFHHIh7RNy5SCPwg5MJTlrBhWaMJ5obDmGzO/W3A58Z57bPAO2d2OQaDwaCpbrADSMZsTl/TzNKWOMtbE3zkVafzlbeex88ePECoIOJYlRnvUCm2d6bwg5C9PVlCpVjcGMMPtCiNH4ZjBvPRKKUqu3IYe/ZXinJaX1u1ur5O+29Y3oAAlrUk+MdXbMQNFH4QjrheNdrzXeFYgkzBZ/ehDNv2D5ErBvSlihwaLBC1rcoNhG1JLjx5CZefv5qLTlliArthBJPV3NcrpR4e6wWl1FYhxImzsCaDwXCcMrop7oOXb+Sff/jYiBGu1vpYpZa+eVu39ly3RkZM25K4XkDeDaiL2uzuzpRq4CXXNjVS/S1UCseWlS764edHrk+n9iEMS6YxpXS564fEIhYrWhPEIhYN8QipvMtQzqOzP8s3frWdnQdT+vxxZthDQCLYeTBF3i2N1RV9gkDhWFRuWE5f00yu6JsZdcOETBbcLSFEi1Kqf/QLQogWwNwqGgyGKTFeV/t4uvIf+puN9GfcMUe4ugbyJKIWQ9nhenwZv6QJ/8juPoqenj23pACh8Ec1soelGn4thKEO0I4taYg7JGM2gxkX1w/x/BDXD9l5ME0Q6s75r//yafxwWCJ2XBQUS0145aP8QOvc592AeMSiGATsOZQmEXV4ybOXs2VH74TWrYbjl8mC+/8Bfwt8YYzX3gjcO+MrMhgMxyzjBfDyDr1aVz5U2gTlPTc+wHWXb+SlZ684LIi1N8d1PX55A9s7Uri+DuKiJApzxtpmfvmnjkrA9KfbMTeKUIHrhaTxyBR8gjDE8xX7erOV95BCy9falkAEofaOnwDHFpVxNqVKmQH0KF4sYtHWGCNd8HD9kPq44JcPd/DrrZ3GutUwJpMF908AvxVCrAJ+CBwElgKXA9dgRuUMBkONTGQM84HvPIRVpXOeLfps70jhh3o3/OkfbGVpc+KwIFauy/tByBlrmxnMuhS9kCAMaUpE2bqnX7u6SVBHYMs6FuMZxDhSEI3Y+EFAwfNJCP1rtlwFKIvSSMGIUbnRu3rLEghEyQI2xLEl2ULA8tb4CD14Y91qGIsJG+qUUluAy4CzgN8CT5X+/yzgxUqpB2d9hQaD4ZhgPGOYRNTm0FCegwN5+jNFvECxvSNFqBQR28K2JHFHi9dsuuMRhrIum7d184N79vDAzl4+ePlGpBTs7s6yvzdHT6qAFIKXnrtc18aFqFnrvVxHH6/prRYCpUr2sHruPVv0R9wElEN4qHSAr487LKqPkIjaRGxJMmrrHoKqWK8UpcZCxdLmkdpho411yt/N5m3dFNzxO/MNxzaTys8qpe4FLhJCxIFmYKDa9tVgMBgmo+AG/OGJbvpKgbcpGcGS2uZ0e2eKTMkJ7ZkunVZXikoKXqBn3+vjDgf6clzxr3/AtgS+H5L3goota2+6iOfrbvR0zuXLP3+KVM7Fq9WBBR1PRSmtP9UEvkAHaz+ATMGrzK2XSwRKDHfnSwHNSYflLUma66Ps7cmQCBX9GRc/VERti6IfEJY68EHP75fLEKMJQ8Vje6dmrGM4thl35y6EaKt+rJTKK6U6Rwd2IYTRMzQYDOOyozPFtTfcw28fPUh/xmVXV5pH9wyQynls79Q79LL4ixSCIFQUPB9QlSa5xmSEUCk6+rIUXJ9ExKZrsEDvUIEDfTk6+vO6Ia6U6nZ9xaGhIgVv8nG30SimL2bjl2bii56qXCMMIQhHStiGCgazHgrIFX0aYg6DWQ8/CHH9gLzr6wAtBFFb8qG/OZ0P/s3pJKLj78fuevBApeSxpCnO4oZYJdsxlja+4dhmorT874QQXxFCnC+EGHGcEEIKIc4TQnwFnaY3GAyGw6ius69pSxKP2MiSYMxTHUN4JVEZx7I4eUVjZYY7DCFXLM2LL2vAkoLBrEugoC7uVG4KhJQjZs/DUAfOmbKrHGuXPB5lAR1L6p159fOjbxZE6bkn9g8ShgohBasWJ4hHbBIRm2ipHOHYgu/9w/O58pJ1XHBSW2XuP1SK/kyRgwN5DvTpJj5LisNKHtUpe8PxxUTB/SxgG/ANIC2EeEwI8X9CiMeANPA14DHg2bO/TIPBcDRSXWcvC7uUrVWLXkCx5Gi2YXkD9XGH09c0s35pPY4laEzox8mY3q0OZT0kinTOI1fUAc71/GnvsidCUJJ7teWYwjXjnVOurEdsiRSQiFjYpUgvhb5ZkALiUYtkzMa2JKvb6giVoq0xzulrmlnXXs+atiSnrGhk7ZI6MgUfGDbW8fyQB3b08dSBIXZ3p+noy5Er+BTG2Z2XjXUMxxfj5niUUi5wA3CDEGIlsBFoAgaAR5VSHXOyQoPBcNTSNZAnrIq+yahd6Wrf35sDFBtXN1d27JYULG6IUWgLsC1Jf6ZYqR/rOrjg4GAeN1AUff+I1+dYolTXLjfT6XUItOJcWbK2bC4zHlryVtfFFzfEQOjPXt2YZ0mJUvqzxBwLIQReELK/N1v5jiwpaKmPVs452J9j87buiibAxlXNRB2L5a1xrJKOfmMywsGBHB19eVa0Dnvdl5FVUwiG44ea/NyVUvuB/bO8FoPBcBRRFqN5Yt8gm7d1jymm0t4cRwhBf6ZI0QtL2ukRWuqi+IEWdckW/BHp5HTeY1FDjK++7Ty27hng0FCBpqTDN3+1g56hghacURP4nNeALXVa3A/VSOe20gOdXhekCx4TjafroA6tdTFiEZvFjVF2HkzjhyECyLvBcO1dKaTQMrqVmwghWNNWx1MdQ4ddO1vw2debJb/1IPc+dQgp9Ry8H4SsXJQccezS5gQdfTk6+3OsaB1+bbTtq+H4YTJteYPBYDiMcpPcF+58nK17+vnCnY9z7Q33sKMzNeK45mSEfb1Ztnem2N+b5ZmuNFt364BdH3f4/NVnY1uSnlSB7sE8PakCtiXZ9NozaUhEuOgUrZ2OEhxKFVjcGKvMgx9JNt4PqXTlj4VCj7RN9gtSCDhpeRPfeOfzWNIc46kDQ4RK17+9UXcFYaiI2Np33fNDil5AxJa8769OHaGhD3rmfduBQaQQrGlLVhrkCm5AZ3/u8Jl4KWhvjhMqxvwuzfz78UdNO3eDwWAoM1qMZn/MZnFDrCKm8pW3nsejewfY35Ple3/YxYqWBAcH8viBllYt+gH7erN8770Xceqq5op/+1jysqBvJD77o0fpGshhW7I0GjaS6eziRx8vBZX0fNnfPSx5vo81Tdda7/D3LzuVV1+wBqXg5BWNPLyrHykFbqn+LaWgPmqTd31cX5EtBhXvdykEH3316bQ1xdn02jPZdMcjFQ39bNEnDOG01U0j0uyNSYeDg3mGsu6I9D1ovYC/f9kpRGw57ndpOH4wwd1gMEyJcpNctcoccNgceirn0T1UIO5YrG+vxwv0bjXqWHhhSH/GBYb928ei4AZ89LaHKbhBpRu9vGsV6OBZrmP7U5hnH4sR6XmGHeCklKiSCk5Y0qfXvQFxztuwmH09WTbd8Qj7e7OA0vV5pZvmYlXe7lFbW9ImYzYtySgNcYdfPdzJay5cy4nLGkbc5Dx1YIh7nz5EctToW1MygiVgKDcyuJfT7xee3GaCuQGoMbgLId6vlDpMX14I8Q9KqX+d+WUZDIaFyugmuTLlOfS2xhhrl9TjBwq7NPa2syvN6WuaK6Nl3YP5mjq477x/H0/sG6horLteWAny5RWUg+2RBHd9o1Byeyvt4G2rpGwn1HBQFxAv9RXkXZ93f2sLRd/HsSQtdRGGcq6u5QfaQCbmWBURnahjEypFe1OCpc1xQKfQt+zorVi2lm9yNm/rZsuOnsPWKYVgeWsSS4oRTnllsRoT2A1lat25f4yxzWM+ApjgbjAcR7Q3x5FjzH+X59AbExEAoo5EiGH71epU8kQd3OVGvf09Wb78i6cIlU45W5Yklfegao5dC+BYeKNt3qaI1noXCKkqKnUxxyLnBhVr2HLaXgqBH4b0pV1cv4BSiphjVbTgUSFCCJQCL1B4vk7FO7bA9bVfe1+6SNHTYjUdfbnD1lPtZT+62bC1Pjqi2dCk3w1jMWFwF0KUjWEsIcSfwYiRzxPQ8+41UbKIvRGtVd8LfFApdds4x54A/AdwMVAEblJK/VOt72UwGGaPc9cvIhG1OdCXxZKy1BGuGMp6WAIakzq4NyUj2FJbsWrVNl2HnqiDu9o1LpVz6UsXCUJF1NFKdXVRm2xRj8DZlmR5a0IH4aLP3p7sEX2uys6/9Fsu7/qVpjspS/8vABSuF5KIWpVRN8eWFLygtJPWY3RaKc/HkRYyUnJ8Q7CvN0sQqIpL3a2/f4az17WOkIgtz7RX1+Grd+jlZkODYTwm27nfWPr/GHBT1fMK6AbeNYX3+jLgAkuAM4G7hBBblVJPVB8khIgAvykd/xogADZM4X0MBsMssr83S8EN6OjTXdu9qQIP7OhjSXOMZS2JSuq9LE6zvSNFMQzJuT49qcK4KeTRjXqeH2IJhRcqBrMe8YgkakukFASlQFwfd4jakt3dmSP+XGWr1agtCZTC9xW2BQKBV/JmjzgWoYKII3FsnXIPgVTe0w14pdl4x9I3NXHHpq0ppr+rINT1ewWRkhGOY0kSUWtMV7fRdXizQzdMhQmDu1JqLYAQ4jtKqTdM902EEEm0TexpSqkM8EchxP8DrgKuG3X4NUDnqFr+o9N9b4PBMHOUA3DElpyzfhFDWRcvGWF5a5yGeATLEiNSycmozQntdeSKAVddso7lrYlxA9ToRr2CG5D3huvoeTck7w6n30OleLpjCK+kcneklNsI3CCkIe4Q2IqGhIPjSAJf78ibk1Hynr6x8YOQiC3JlEbYpJQoVMVTnpK7XKbgc80L1nP3owfZ25Mt9SEE2FKyYXkDyajNoaECN929g0UNMdqb4xXNgImaDQ2Giai15v6vQoiVJTEbAEqqdS1Kqa01nL8BCJRS26ue24pOu4/mPGCPEOIXwLnA48C7lFKP1bhWg8EwS4wOwC31UerjDitak/SkCrzu/BP46QP7D0slf+bKsyd1Jqtu1HP9kP1j1KKrOaGtjrwbcCil695STd/wZQQKUjmPiC1JRG28IOTcExfx5IEh/FBRcAP8UOFYkrbGGPsD7TkfqnCEtWxZxKboh9z6v7u48uITuOvBAySidkXMRwpBtuizuzvNbZt30ZSIGDc3w4xQa3D/LvDyUc9FgFuB02s4vw4YLcE0BNSPcewK4M9K7/db4N3AT4QQJ5UkcSsIId4CvAVg1apVNSzDYDAcCeN1yoMWaXFseVgq+fTVzTy6d4BHdveP2JWWKTfQbe9MkS36BKFiz6HMYUIt1Qigoz/HspZ4JQWuZsgupvy2RS/kma40tiXw/JCmughBqHjFc1fx60c6SUQsskUfKQSJqD1ChAZ0il8IiEf0nPvPHjhAfcJmSWO86r20d70CFtfHaExGGMy6HBzI8Z4bt3DbPzy/0sNgMEyFWoP7KqXUruonlFLPCCHW1Hh+Bhh9C9rA2A15eeCPSqlfAAghvoDuyj8ZvduvXsM30MY2nHPOObNgH2EwGKoZr1Mehjvgq1PJOzpTvOPr943rMV7dQBeEip6hIn2pInl3Yt14BWSLAXu6syDK43DaS32mKHfNe4EiX/Rw/RAhFL98uINPv/4svviTbfRlivjBcLlgtBCO64fEI6pkZat39tVli8GsS9EPKi5wj+4ZqDQg+mGBK/71D3zp2ueYHbxhytQqP3tACDHC/a30uLPG87cDthDixKrnzgCeGOPYR5k5x0aDwTCDVI9oVTNWB/zoBrnRHuNDWXfE6+1NcU5b3YQfKmqdbPNChRco7eWujrzuPpoQvQPvz/r0pIocGnLZ3pHin77zEB/6m418+FWn01wXxZaSZGx4ZK0yi690F355JPAlZ68YIbfbmyoigLUlffm8FyCEIGJLbCkq36HxYzdMlVqD+7+hU+PvEkK8RAjxLuDH1DjjrpTKAj8CrhdCJIUQFwB/hU7rj+a7wHlCiEuFEBbwHvTo3JM1rtVgMMwS5RGt6gCVLfpjaphX270CFQ/yTMHn4ECe7/zumRGvg27AW7U4iS1Hzt1ORnmsbDYRDO/I9/dkefe3tgCCVz1vNbalW+2r1e0sKSrOb7aUFUvbm951Ae9/xWlc88ITed1Fa2lvjrO9M0Wu6OP5IdmiTzrvoZSiMWn82A3To1ZXuG8KIQaBa4GVaIe49yml/nsK7/UO9DjdIaAPeLtS6gkhxCq0b/wpSql9SqmnhRBXov3i24A/AS8fXW83GAzzw+gRrZ8dbONrpTGucv28ayDP9s4UfmkLni36bO9I4YdhaTcb8t3/3UljUovaBKHSKWov0N3vUiAluP78JPEswWF68tUPvUCxvzfDp3+wlXjEZmlLgrqozVDOZW9PVs/Ho+fdLSlY3pqgPu5UJgXKZYvBjMvXf70dNwgQiErXv/a7D2mIR+hLF6blx179sxir18FwbFOztrxS6gfAD6b7RkqpfuAVYzy/D91wV/3cj9A7fYPBsACpDlCP/aqOqGONqJ9r85OAnqE8dXGHnQfThEoRsXVwEUDEtujsy4FS7O/Vo2UI/Zof6rRiMiLJueGc1umk0BmKbHHiVHh5jYmIxY6uHI1L61m7pJ5FDTGePDBUqtELVi1OUh8fe7b/oWf6qI/ZFL2AkGHTeCn095PKu9PyYx/9szAd+McftWrLC+BNwGuBxUqp04UQzwfalVLfn80FGgyGhc/o+jro3WdfqsDj+waxpNZWB71rty3JkqY4Hf15dnZVCdBURfEQyLo1Ft9ngHLK3S5130/qNKfgUKrIysV1LGmMkSsGuL4eAVzdliQM4SVnr+D0Nc1jzvaX3e760kVsKfDQ31kiahNzJEUvZCjr0d4cn5If+1g/C6Di2jdaLMdwbFLrzv164EXAl9DpcoAD6Fq8Ce4Gw1HCbKVqx3KKk0JwyqomHt7VX5J21RrrtiVZ317PjoOpmqxay8pxs0W5tm9bAimgqS5CIqJ/NXYP5siPkznQWvIwlHWJ2JLXX6xFempRkysHYEsKHEsScXS3fKbgU3ADBLqpsNzjMJVgPJFrX7VRjeHYptbgfg1wllKqVwjx1dJzu9H68gaD4ShgNlO1482/J6M27c1xsgWPtgY9Jlee5S56QU3pdikEgVLIUpCf6ThvWYKYY9GYcIhHbT7w1xu58OQ29vVk+cC3H+SJfYN4oz6bLQV1MRs/UBS9ANvWOve1Bs1yAF7anKBnqFjJZjQmHPKuTzJq01If4/b3PZ+GxNTm3CfTIphO/d5w9FFrt7yFnlWH4X9bdVXPGQyGBcxkY2lHOmo10fx7Q9yhrTFOXdyhpT6KJQVFT6u81dIRr5Qa3r3P8LSbFDpQK6VY0Zrky285jxeevpSoY3HisgZuf//F/N1LT6a1PsKihihRW5KMWjQkHCwptb+8UuMa4YxHOQBbUrBhWQNSCFwvwPV1w2F9IsKXrn3OlAM71KZFYDj2qTW4/xwtQRuFSg3+k8BPZ2thBoNh5hg9llamPj4zo1aj59/LY2+7u7XS3CevGDk+V/ACpBREnMl/BUUdi6gtWd2WJGrX+ivrcGwpcEqpdIDmpMOqRXWsWJTkw686g1vefeFhGYyoY/HmyzawYVkja9vqOPOEFiK2hReE5Io+oVI0xCP85bkr+X9b9rN5WzcFd/IbpeoAnIzZnL6mmXXt9axsTdDeHOcDf71x2tmUqWgRGI5dak3L/wPwHbRkrIPesf8amLaZjMFgmDtmO1VbbVF6oC9HR1+WQOmRsmUtCb74k2186G820jWY5/7tvfhByD1PHeJgf76kyz7+tYteQCxiURdzCFUOWTJkCSbotZMCljRG6c94eKUDy0Ysnh/gBYqmuggf/pvTueDktglr2tWfLVPwWNoSJ1cMsC3BS89ewX3be7h9864plTpG+7VbUtBSHyWd92iqi3LhyW2Tfue1rHcsu1jTTHd8MG5wF0K8XCn1/0oP80qpVwgh2oDVwH6lVNecrNBgMBwxc5GqPXFZA19563lc8a9/oK0xRmMiQmMygiUFQ1mXd39rC0ppn3QhREWq1ZKCcPRQeRWhgnwxYHtnChDEHIElJdmiP+ZNgW0JwlCRiDo0JKLsPKi124NA4RJgW5KTVzSQc30cW9YU7MayXz19dTPv+Pp9hKGaclf6bAdgYxdrmGjn/l2G9eD7gAal1CG0CI3BYDiKGL1TLDPVVO1gxuW2zbvYcyjDmrY6rrhoZE/to3sHkAIaEhEKXoDKujiW5JmuNEN5D0pmKghwpCAoSc2O1TVfvhWJ2PpPdXGbTN6j6INShwd2KXTzHaWmu1zRpzEZwbEltiVZ0Ron5tiVG45MwePQUKHmCYKoY3Hu+kWVYx/e1Udnf55EVKfpyy5vtXalz3YANnaxxzcTBfcuIcTfodXjbCHEnzFGO4tS6n9ma3EGg2FmGG+nmIjalXrxZKNxd2/t5P23PFhq+lIIIfjGr7dzel77PxXcgJ/cv48dnSksKZBS17cLXohlCZQqBWApdJd5VXQea98uRUmjXQgEguZkhIGMvlkIFahgpA9cfdyp+KcToqcClDZtOXVVE8noyF93UgpyBZ/LP/c7UjkX29I754ZEZMy0evW0Qa7os78nixcq4hELS4oR/uy1ljpMADbMFkKNM0Ba0n//BDoNfwKwb4zDlFJqQYzDnXPOOerBBx+c72UYDAuaoazLbX/QO++6uMPjewcoeMGk9eLBjMslH/0lYaiIOhLPV4RK4Qch/Q9+n1/c9p98/sdPsHV3P0U/RAqBEBCxJQU3qIywWaXd+lQRAqK2JCjZylpC4PoBxZI8rZQQK6nf5d0ABDQlHKQU5IsBa9rqaG/WVqtBqDg4kCNb8BnIuChUpfPdtiTLW+PUxyMj0uoFN+DaG+7BD0ISUZtH9wxQ9AMKboAlBQ0JBz/QNxJnrG2mL13k/a84zQRuw6wjhHhIKXXO6Ocn2rlvU0pdWjp5p1Jq/aytzmAwzDrVO0/fD9nXm60IzZR3tePVi2/bvAvXD4k5klTO0zvn0sag6AW88+v3l3bOouSGplAKcsWAUqYcYFqBHfQYXMHTqnEnLa/HsgRFL6Tg+uzrzRKEkKvqUpeKyk1GqODpzhTFkh9s10BeG80EIV6gx9Eitqgo03X05VnWyoi0erUwTF9az6XHHAvXDwlChefrmw7XD+jsz9FaHzNd6YZ5ZaK5kr1Vf94zy+swGAyzyOg594hjIYVOnW/vSBGWAvV4o3F7DmVQSpEt6Fp3GCr9/6VY3TVYYH9vVmukl54vvzaT6nIKSOU9GhMRljbHWbW4jrqojWMJ6qIWEUtglbrpXT8kYkuSMZuoLXUPALC8NcH6pXq2vFyjzxZ9lFKVAJ/JeyPS6tXTBmXxHSFE5abI9YOK6U2oMF3phnlnop17TghxGtpq9Tml2faxau5zJ/5sMBhGUGsz2GhJ0nKAsi292xzMurTURQmVIpXz+Ml9+3hs7wBNiQgrFydZ0ZJElYN2KciN/mVQLAmwzDYd/TlSOY8Ny3THe8EPcWxJU12U3lSBQFHasWuv94itb2IKboBjWaxYnOTgQB79K033DlQfW7aPrZ4gqJ42iDpWlWStJBG1WNIUJ2JJ8l5wRDPqtWDc3gy1MFFw/wSwBYiWHvujXi83uJq/VQbDPFCrnGzBDfjDE930pQpIIWhKRkYEKKWg6IUVW9ac6/PzPx0AISpz6o2JCELowF4tFFcdy+cisJd90ot+wNa9/QR+iK8gHrHoSxdxSxaz5VpAGIaArJQKvFJqPupIbCnwRam8UHVsWZjm9NXNbN7WTddAnpa6KPGIrefQk5HKDh/AsSxWLkqSLfhjzqjPZDA2bm+GWhk3uCulviqE+CbQDjwFnDpnqzIYDBNSq/NXORh09ufoSRXpK3Wbb1jWMOx+JsCxJds7UnhBiB8oorZAlNrVe1IFGuIOixtidPTlgJnXd68VBWxY2oCQgqcPDNGQjOB6IRHHAlSlLGChQICUsqLbXh+3EUJXIuvjkUqDnxcoFIogVORdH9uS/N1LTuIdX79vRBC1hN7VF7wiLfURugbygGBpi67DjzWjPpPB2Li9GabChAp1SikfOCCEOEsptXeiYw0Gw9xRi/PXuesXsemOR0jnPYZyLqGCwA/x/JBH9wzwrOUN7OvNEoYwmHXJuwFK6SBX8BRChICg4OkAYkloTNik8j4CnfoebagyFzQmImSKHkLAovooh6qMV+pien1eSRTHdT1sy6KpziEWsWmMRzg0VKCjL0eoFG6pRi5K392ihhifvOJMvviTbWMGUSkFb3rRBgazLk3JCAIYyLpjzqjPdDA2bm+GqTBhcBdC/EQp9VdKqR2lx59QSn286vUHlFLnzvYiDQbDSGqRk31gZy/pvEdHX06bkcTtSkNcoBTPdKXZuKqZV56/mv99vIvuwXwlrR0qEAosSyu77exKV+xJy28bVOXhx0rTTwVLTiwnW/0+T3cOEovYKKWIRiyWNMXY15ul4AW43siLFAMoBgHuUADo9e85lNGd8qXPKoSWyc0Wfb72+jPJFIIJg2jEllx+/upJ1zrTwdi4vRmmwmTa8n826vG7gI9XPT5pZpdjMBjGYnTdtqUuOqmcbNdAnnTeww9DIqUZ8IaEg+crin5AMmbz1j9/Fs87qY1bf/8MlWK6Gg7WfmkHrBSEgcIPxjZFOdL9ey2Bvfw+A1kPq7Q7392VKa1Z4Xph5SMkYzY51ycMh68/lHPpz7iEKsSWEmyJY1vaTEYI8q7PR773CFdefMKMBNGZDsbG7c0wFWo1jikz+m/WfJXeDIbjhrHqtvGIjSXEhHKyW3b0EoRqVKObwLG1xWldzGYg6/LAzl68IMQLhrvdx/uHPdk/+LgjKPqqsrsfS1Z2NGWN+VqRQvuve75uAmxKRgCBECO79cNRsra63h5S9BShpcfkKp9LKZRSdA3meXh3f6mTfoz3nkIQnelgPFMSwobjg6n6J5pgbjDMIeP5sCulu9alFBUb1Z5UAduSlaauc9cvoj7uVGbYy1Tq03GHtsYY+3uyHOzPjQh2Y1GLlXreU0RtScyRWBLaGqKTnjPVsv2JSxtY3VZHPGLpHXfRp+gNa81LyXDXfAmF7oYvfwYvUOTcgFzRZyjnkcq5FD09337Pk13s6k7zdMcQfeliRXhnqkF0pq1XyxLC1da5o3/mBkOZyXbujhDijQz/u44KIf52CucbDIYjYLK67ZtetIGILcc0HolFLD73hrO58kubybu+loRlWGI1GXNw/ZDNT3bjBYpkyQClnMqujrkCHTRrSZ/nvZAzVzfz9MEhDqWLM/I9lIk5EsfW6nRCCGKOpKVO30B0DxUqgbisnlf5DAqCMKTgVenZK+07X37GluBYknRey/EeHMjTl3axpB4HXNQQm1IQrdX5bSqjcsbtzVArkwXn+xnp2b4FuGrU6waDYZaYrG47mHUnbO46bXUz33vvRfzTtx8ilXexpaAu7mBLrfn+Hz/bRl+qgBeEpPMhUcciVxy7rh7W2PAGcHAwhy0lSoU1peZrQaDla/vSLs11eu5eIGitj9KYjJDOe6QLWmlOWhKqbGT1CNvhq6h+xpIC1w9JRC2SMQfbC1jSGCNU+oboq287j4ZEZEprniwYT2dUzpjNGGphslG4S+ZoHQaDYQzKZid96SJFLyDqWDSVLEtrrdueuqqZH133Z5UA05yM8M1fbydUivq4gyUFgzmPghtoLXgYbqwr1cNrDc7l47qHhnfsQsyMwI1Cp9O7B/OkckXCUGcTyhauz1reyGP7BsgXg8PS8tVrsATUxR0yBX+UKI8g4ggce3gXHHEsljbH6UkV2LpnYFpBdbxgbObWDbOJSasbDAuY5mSE/X05/CA8LK1eH4/UXLetDjCbt3WTc31a6qL0pYsUPB+JIGpL8qWO83KUnpGgrGYuwIPuGUjntXHLmsX19GeKlV3vxlXNbFjewC/+1IFtSdI5l0ApfF/hlxYQi1rYlqQp6ZAvBuTdANsSLGqIMpQbro8LoZXsYHZGzczcumE2McHdYFigFNyAf/7hY6xalKSjL4cf6m7woh+wrzfHTX93Jlt2TF3WtGsgT6bgsbsrgxcE2slN6Fr5WJTT6lNNr5dn16tvFsa67lSQJRvZUCkCpXjTi06kqS5ayUgo4FcPd5CM2qxdUgdogZ6+tEtPqoDrBXheiCDQXfcRi4IXYFuSlvooqVJw9wM9Lqc78Wdn1MzMrRtmExPcDYYFSnln19YYY1FDlMGs7uiOOpJ03ue9Nz1Q2rHq8TDbklx76QZe8dxVFNyA2zZr3/blrQk2LG0gU/Bpb46TLrjsPZQFdM0aoQBVCbZSDBuvjE7Jy9IOvJagPFHzXa2Bvfq4she8bqbTTXOf+/Hj3PyuC2lvildq16mcS/dQUZvLLG+gpS5KUzLCUNal6AX4ocIv+pUblphjsa69vuR0pyVoo7bFhuXaOW62Rs3M3LphNjHB3WBYoFTv7KQQla7wdN5jd/ewYlzRC0AIHEvw6R88yi3/s5OO/hxBqAhCrRUvgBWtSerjNru7Myilg2V5ntsPhrvGdahXh42oKdD66mL6vuyOJXBsSdENqvvdxiXqCIqe9lz3y53wDKf4hzIuf/+t+2mIOSCofEc9Q0Xyrs/TB4Y484QWQK9ZCojZkkDpmwMpBeva67n1vRexdc8Aj+0d4K4HD2BJQSbvkSv6Y2rGzwRmbt0wm9Qc3IUQJwN/A7Qrpd4phDgJiCilHp211RkMxzFj7exCpXjywBAKvePMe0ElQAehwrJgx8EUAmhMOKQLYcXwpLM/x0krGvFDRdnAOSyJt4x2dxsv8E43qFefH3phTbPt2rdGIIWqBPbR5NyAZw6mQcCKlgSDOZcg0BJ7WonP4+mOVCkwK85a24ofhJXmxMZkhP5MsdIsd9EpS7j20hPnZNSs1lE5g2E61BTchRCvAr4M/Ai4AngnUAd8Frh01lZnMBzHjLWzG8y6uH5QSVcrpXf1oAN10Q10l7sUFDxdo69IyYaK/T0ZyvcL8YgNSpEt+jWnyY8ktEsBliwJ5cjabhRsCViSwB87x6+gEvj39GSxBNQnHCLSIuYosgWPIFRcfOoStuzooSHhHHaN0fXt6Y6aTTSvPt5rZm7dMFvUunO/HrhMKfWIEOI1pee2AmfMzrIMBsNYO7uhnKfFWyKy4kNeLR1XUaNTEAQhKlSEDAflVN4vv0zBDSq66pak4qQ2GdNthLOkqFLLqyGwWwLXV9hSHNbUN1JWVj9QgBKQLfjUxx2EEEQci0TUpq0xhm2NrcBXa317ouA90bw6MOEsu5lbN8wGtQb3NnQwB0aV5gwGw6wxemd3aDDPXQ8d4GB/nqIfgACldJo7VFR25YjSjPoY1yz/o9V68qXnRO3jaraEcRrrx0UpSETtiipc0Q8m3bk7lsT1Q2xb4oQhliXx/JKCXtVapRCEpegu0N+DHyiEUNhSkohaNNdFj6i+PVHwXrkoOe68+kdve1j/HEJlZtkNc0qt2vIPMVKZDuC1aMU6g8Ewi5R3dpefv5o3vvBEGhMRlrcmiFiSMFT44bA+e/mOOwjVmDvxEZKyAkpj3CigLlbbvf5UA3v5+um8h+sHeEGI709+F2GVAuiZa1qIRmwEQgdCUbVrL607XtpBh6EOpEVfj7qtX1pPrhjwTFeavzx35YRa/OMxnr6/H4RsuuMR/u+pQ2QKI28aQM+rHxrK0z1YGPO1TMFjy47eqXyNBkPN1Lpz/3vg10KIa4GkEOJXwAbgsllbmcFgOIzqVH17c3yk6AojfdXH2xgLdGCXUhCGquyYSn4c2dnR5043Xad31CFSyJqu4QeKk1c04NiSU1Y08fi+AZrro/hDBYIgJIRS+h3dazBqnctbEzx1YIhQKf7vqW627OghEbF53UUn4Niy5vr2ZGIz9z7dM+68ulvV8HjY92Fm2Q2ziFA1ykYJIRLAy4DVwH7gZ0qpzCyubUqsXbtWXX311fO9DINh2vhBSEd/jkzepy5us7wlMW6d2A9CfvdYF08dGEKNESqV0mIvZRtXUSpMj/4zTF09bvTx+c4niC87tebzyzcVk2FbevwvVApLSvwwZPXiOmKOxfbOIYZynu7sL13LsUQlW6Fn4kMcS7K4MVZxvCt6IVIK/uo5K8f9bkfzxL5Btu7pHzOzkSn4rFyU5EBflmT08NcHMi4AzXWHa9Jniz7PO6mN1YvralqHwTAWn/jEJx5SSp0z+vmag/tC55xzzlEPPvjgfC/DYJgW0zEQed9ND3Dnln1YQozYHQahrsHXx208P8T1w4oAzOiYWu5gD8IQx5aEQTjltPvAg/9F8zmvmfxAdMC2So0Bnj/+SFy5dyBqSyxLlmrpig+/6gxee9Fail7Aj+/bx3/etY3etEvEEkihr93eFCfr+nQNFjh5WQOLRjXL9aQKvP8Vp9XcxLZ5WzdfuPPxw3bu5Wv9/ctO5qa7d+IH4WH1/LL6Xxiqw16zLWlq7oYjRggxZnCv6dZVCLFWCHGbEGKbEGJf9X8zv1SD4fhisppu0Rs7XW5ZJbuTqsBeLT6TLwYV4xcvODyww3CqXAgtiGNZksaEg1WLefs0iEdsIpbEsSSJMXa6oAP7sDe7rrNLqYP3T7bsq8yov+K5q7jsrOXUxyxa6qKsbavjjLUtLGtNUBdzkEI3DR72maeYDp/Ml/3Ck5eM67P+ySvO4vrXnWU82A1zTq0199uAZ4D3AbnZW47BcPxRq4HI6FGsDUsb9I42DJFSAkoLuJSQQjHOfcEIyg14mYIekyt6IRFbsCipNdtFuYGtRtnZ8dC68Fpxrq0xxkDGxZECr1T3F0L3FKCgUFq4lpsNKrvivYcy/O7RLtYtrWfTHY9wcCBPrhjg+Xr98ahNMmYTdSRCiDGD51SlXWsRm5lsXt3MshvmmlqD+6nABUqpafTJGgyGiajFQGSstL0fKJY0xekZKhCUTGXKM+C2jQ7s0yi7KXTXeX+miGOBHx6Zo1u5CU/v1PXMekPcobkuQqbg43ohnh9QF3eIRSzybsC+niyWFDQmHA4O5kFp21ul4L03b2Ht4jrqEw5r2pIMZd2SDr5ie2eK09c0Y0lBxJaVEkCZoayLHygO9GbZvK27ZrOdWsRmJppXN7Pshrmm1uD+B+As9EicwWCYQSYzEGlORsacox7KunT0+5y2qol0waM3VSRfEqbJl5TqphuTvSOUma1GSDhleSOd/XnyrlbD88KQeDTCf7zpbGBY5MXzQ930JmDD8kYe3d2PQFS+n1ApwlDx9MEU65fW4wUh65fWs/NgGj/UsrJ7ezIsbU7whWvO4ebf7qzstl0/pHuoQHtTnNv+sKumvoZqTIA2HE3UGtz3AL8SQvwI6Kp+QSn1sZlelMFwPDGZgYiCMdP2jckISxpjFP2QRNSmKakoenmKXohdSnfPl8yU9p0HKXUzXEe/ruYlYzavft5anrNhEc85cRFK6bLES89ZwVDWpbkuSltjjG/+ejsH+rKVTnnQRi8wbHKz91CGqGNhS1kJ9D1DBV50xjL+4a9OrQTjLTt66ejLcevvn+HEpfU0JoY7142YjOFYpdbgngR+CjjAytlbjsFw/DFRTfeDl2/kJ1v205cqIIWgKRkZkWqO2JJXX7CWdMFjV1eazdu6GcoVjziVfqQkoroJrrkuwr6eHIXSblwKuPHu7diWIBGx+Njtj5DKuXiBHltrSET43BvO5sOvOp2rvrRZN9aFYaXrXCiFkFAuEEZsCz8I2XkwzRlrmwlCxYWnLKkE6nKA37ytG9sSIwI7HN7XYDAcK9QU3JVSb5zthRgMxzNj1XRb6iJ85r8f4+BAnv6MSzrvY1uSDcsaSJZmrgtewI137yjtcHXQd/35/CSaXDEg6kj29egde1kWNlR6VO8/73qSG37+ZGUMT8/f67r66/71D6xbUk/ULu/YQQhFxLYoeiGWgBBVMcyxLYnrB3T252itj40pJVtLX4PBcCwxHcvXJUqpvxNCPAuIGstXg2FmqK7pFtyAa2+4Bz8Ix2waO3VVE3t70hzozWNZAkvouvRC0a1QQKFqYL5aLhZKo26q1OlfmrVXSuEHei5/x8EUzz6hlf5MD0opBIKCF5SmA7RITdSRuF6gneECPTM/3njZZH0NU+meNxiOBmqdc38VuqluOfCG0tP1wL/O0roMhuOSghuweVs3X/zJExwcyJOM2Ugh2LC8QRukKEW26PHAzl729+ZLgU1R9HUzWWE6wu/TpDpU1jIXXy2NS9WfQzXsZhcqvVNXpc958spGROlzhyEEoZ7rP2VFI2eubWVdez0rWxO0N8f5wF9vHLcxbrJZ9cmMYwyGo42pWL6+yFi+GgyzR/W4W1+qQH/GZSjrsmF5A8mozRlrm+lPF3mqI4UtBcXSeeVRsxlscJ+UiKV3u4dSRRAgLUFQgxnMWJTn7KvtXIXQ8/ZLm+M0nRShsz9Hb6qIJWH1ojqa66MAtNRHSec9muqiXHhy27jvUcususFwLGEsXw2GBcBolTopBOm8r9PwHSnOWNuMFIKiH+L5IV5VXX0+/hG6gSIetbGkCwq8UmAfa3deC6MNaSwpiZYs6ywpqI87FL2QF525jCf2DU4rQNcyq24wHCvUGtzLlq/fqXrOWL4aDDNEWaWupS5KX7pI3vX13bNS+EoxmHVpSkbY1ZVaMHfUew9lGG14Vl6blLo2PtZrMKwdX21VWybi6PG5pmSEbNFne0eKoq/r7Vt29FAXc6bs7FbGzKobjheM5avBsADoGsiTK/oc6M3hB2FJJU5R8LWLWc+QTtPXIic7V7iBImYL5BidO6MDexlLQnMyQjrvE4SKiC1w/eFuedsStDXEaK2P0psqsrs7jQKitlWZEkjnPX76wP7KbHq5T6Esy1ur6pzBcCxT6yjcU0KIk9CWrz9jAVq+GgxHMy31EboG8tiWJFK1C7WtANcPuezM5XQO5DjYn6vYmi4E9M574m66mCOxLVkxqDljTStb9/aztCmOkALXC/B87UpX8EI+8qrTueDkNm66ewe3bd7F4voYjVXz/dWz6e1N8Sm76RkMxwM1j8IppXLA92dxLQbD8YsSKKVdzDw/QJU02BEgheCCk9u492k9FlbtmjbfuIGa1JvdKd2w2JYglfN4dN8A+aLPM91pvSMvNQym8x7NdZILTm4j6lgsaojRlIjQUmqeqyYMFR19Ob72y6cPk+U1qnMGwySjcEKIc4UQH6t6/KQQYlfVf4d5yBoMxxLllO8P7tnD5m3dFNzZyYs/eWBQz4a7ATk3JO8G5NyAgqvnuB/bO0B9zKG8Sx5nZPswRtfE5xKBXqfrh7h+QCbvoxTYEk5b1UzUtij6AY/vHeDQ0OE2qJPNpvdnimQKIyV7Qe/sMwWPLTt6Z/sjGgwLlsl27v8I3FH1eBnw16U/nwt8AHjVLKzLYJh3xnJim42Ub8EN+MWfOgD9HiJUlagsAD8M+eovn6KtKYZlge9NeLkRzLemTVjORpREZoTQ7nDJmM3pa5oZyrr0pAv8xbOX87eXnjhip71xVTN+oNjVnaYxEalI75Zn05uTEaM6ZzCMw2TB/TnA1VWPQ6XUbwGEEH8EnpqthRmOP0b7lc9nY9To0bQytaR8a/0c5eP+8EQ3uaKvRWrCcIRRih8qCEvSrENFHEtSnGZXnSUFwRzm88sz65YUJUU6hVLQ3hSvrKelPooXhLQ1xUd8n+UbqyBU9AwV6BosYAlY1pJgUUOMTa89k67BvFGdMxjGYbLg3gpU3/5eUvVnDzCyToYZYa52ybVSHk0b7cQ2mdFIrZ9jLMGaMlqtTRGUOs6FAFlKx+eK0y8LLKqPMpB1K1Ktc4JA36CUiDmSbMEfEchHB+LqG6sVrQmWtcTpz7gcGsyTKfi89+XrWLkoycpFyQnd9IzqnOF4ZjL52V7gpPIDpdTWqtdOBvpmY1GG44vRu+QlTXEWN8Twg5BNdzwy7Z3qkTAdo5FaPkfBDfjt1oO84+v30pcu0FIXZXFjHMeSOiVfSls7lqy4qGmBFy0xO52gXFZ8iziS2Bw0mJX8XojasKatjvamGGva6mhM2AghRvw8xwrE5RurcsDOuwH7e7Jkiz4DmSKf//FjXHvDPezvzbLptWdiW5KeVIHuwTw9qcNr9wbD8chkO/cfA18SQvyVUqry20wIEQe+CPxoNhdnOD6Y7i4ZZi+VPx2jkck+x4/v28dPH9jPwYE8XQM5HEvSM1RkfXs9tiURYUgRffNgWRJ8PTqmSunsqcZ1KfQ5Uui5cxVCoNSs7drLNyPxqE224COlpDHhsLatDoC2xhiP7x0k7wV0l1LqY6nLVd9YlRX6QqVd4ZSCuGNVbphuetcFRnXOYBiDyYL7x4DfAc+UhGu6gKVo8ZqDwMdnd3mG44Hp2nHOZiq/2mhkopRv9c3F9s7UuDVt3w+56bc7aE5GiEesyjy7H4Ts7Eqzrr2eZ7rSWFLglfLxCp3GLnfMT5XyUspj8T3pAp4fIsTMN9pZUo/sJWO2/lkIPdpXrDKyCUPFaaubeNOLNjCYdccNxNU3VoNZFz8Midj6GIFWmRt942dU5wyGkUwY3JVSGSHEBWgnuBeiO+T7gE3Ad5RSxQlONxhqYjq75CNpeKuFsYxGhNANaX/x7OVs2dFLczLCP//wscrNRbbo0zNUpD7ukIyO/KeV9wJAa6R7QVgZUbOkIF/02X0ojSUhYkues2Exp65s4q4HD2BJwf7e7BG7vQkxbItqS4ES2mXtSGJ8edQN9M2CbUuCQGFbkmctT7KzK83eQxn29WRIxhxWL67j+tedNemNV/WNVdELKzcifhBiW5LGZAQwHfEGw0RMKmKjlHKBb5X+MxhmnFp3ydUcSSq/VqqNRh7dM8DPHzqAlPCrhzv49SOd7OvNsmpRgrZG3f0dhIq+dJFt+wY5e30rshTB03mv5D+ubzaakhFsqbve825AECry3nBD3eZt3fQMFbju8o189kePzYiNq1DDO/ggVFjyyAK7FJCM2rpZTuibEgGsWVJHzLZ4bN/AiF17rhiQLXjs7clMGtyrb6zybqCV7QDbkmxY1lBRqjMd8QbD+NTk524wzCblX+ZTaYyabip/qkQdi3PXL+L/njpEXcxmSWOcJU1xbEvgByEdfflKKt6SglNWNBEqxZ5D2RGf49pLN1SCkh8oElGLbMEfM43v+YrH9w7y6e9vZSgzM8mx6tsDBfjj3C+UG/gAHEtUduflHboovR4qyBZ9XF8HXjcICULFYMbl8X39FLwQKbQ6nWNJnaFwA95/84Okcu6k4kAnLmvgK289j1c+dxX1cZuGhMOpq5pIxvR+xHTEGwwTU7P8rMEwm0zVjnM6qfxqptKIV84StNZH6c8UKXohmYKeS/eDkKGsW5FITcZsVi1OcuEpS9iwrLHyOZSCnz6wn329GfZ0ZwlCNebOuWx9qoC9vbk5v/sWYth8ta0xRn+miCW1LrwX6BR5WDmWUnZCELUloYLVbXU6ne57lXn98nUFUPAC/v2n23iqIzVhr0R1P0VjMsrB/jwP7+qjvTlOImobH3aDYRJMcDcsGKZixznVVH51MA9CxZ337yPv+pM24hXcgD880U33YJ7dXRmd30bX3l0/IOpYh43q2ZbkwpMPb/J65188izf8+x8n7FYf/dKRJ+SnRsTWO+1MwUOhP0vEtpBCu7eNZPjmyrEs3CCkLmaj0N39oVKVMTyltCiP5yt+fN8+1i2tH7dXQikO66dY0Zqgsz9HqODvX3YKF5b05w0Gw9hMKbgLISSwRCl1cJbWYzDUxFgNb+ONVlXvAoNQsfdQFinhlBUj07yjG/HK53X25+lJFXWKWgqSUZuoI3H9kIIbYNvDO9Txbi52dKb40Hcfnnc52MnwQ0XMEaxoTWJbgqGsR67oY0uBFNre1fX1h5BCB2yBwg8CwkCRynlkC9qLfrzJgUzRZ1dXpmIYAyN7JYDD+imk0GvqSRWI2NIEdoNhEmoK7kKIJuArwN+glemSQoiXA89RSn1k9pZnMIxPLan80V31/ZkisjS2tb0zxelrmrGkOKwRr/q8xqSD7EP7tJVqzQ1xh3jEIlf0Gcq5I24uPnj5Rv647RD3be8BYMOyBm74+ZMcGswfURPbbFJ2mktELNqaYhS9kH98xWkMZF1uvHs7QenzdfblAO1MF4R6+F4BRV836T20q2/SOXpbisr8+hlrmyuNh+VeiTAc32nOdMgbDLVR6879a8AAsBrYVnruXrSQjQnu88iaNWvo7u7GsoYD2jXXXMMNN9zAwYMH+djHPsZdd91FKpWira2N5z//+Vx33XWcdNJJ7Nmzh7Vr15JMJivnrlu3jq1bt3LLLbdw7bXXEo/HkVKydu1aPv3pT/Oyl71sPj7muEyWyh/dVV8erbJtiesFI+rl1YGj+rzMQJ6oY+GVFOJUqMgXfWIRm7VL6njBxqVsWK7r6y11ET76vYfZcTBNEOoms4Xkvz4eopQ7d/2QfT1ZpIBb/mcn119xFq8879LKDVRd1OZLP9tGwdU9B92DumGwLmZXduqOJSu799HYUqf3g1CL0gxmXVrq9Pdf3SthNOMNhiOj1n6dFwJ/X0rHKwClVA/QNlsLM9TOT3/6UzKZTOW/G264gb6+Pp73vOeRy+XYvHkz6XSaP/3pT1x88cX85je/GXH+4OBg5dytW4cVhs8//3wymQyDg4O84x3v4LWvfS2Dg4Nz/OmOjNFd9VFHVmbMFYyol1cHjurzoo6s7O6TURvHlixqiHH6mmYSUZsLT1nC5eev5tz1i/jUDx7lma40tiWIR+w5DezTdXct78KF0ME35lictqqZUCk23fFIpZM9DBWJmM2/XH0Oy1qSKEp19VJjXfm7i9haOjdqCywx8n1A1+DDUFHw/Mq1q8sZ1f0U1ZgOeYOhdmrduQ+hTWIqtXYhxKrqx4aFxb/927/R0NDArbfeiix1LTc1NfHGN75xyteSUnLVVVfxtre9jR07dnDuuefO9HJnjdFd9eUZ8/LsdDmFPzpwVJ9XPicIFY4tUUrRWh8lV/RHnPPAzl66BwulUTNFrjAFb9YZoHwbUf5cXhAQhFo9zpKC+piNEJJc0afgB9RHbZIxR3f85z2WtyRoiDs0lqxVAfb1Znn5Z35LGCpsS5cd6uIOLzl7BXc/0kk67xMmo5yxtpnBrEtPqoAX6EkAx7awpCJXCuBSCFRpbeXd+0CmiGPLw3olau2nMBgMY1NrcP8W8EMhxIcBKYQ4H/gMOl1vWIDcfffdvPKVr6wE9iMhCAJuvvlmHMdh9erVM7C6uWN0V70Ugg3LG9i2b5BQKYp+QE+qUKmVb9mhO+pb6iMkonblvA3LG9jekSLv+gD0Z10cS3LtpSdWmuT292QZyBTJFv15bZxT6JGzMmGod8qhgpWtcRxbYluyIgP71IEh7n36UMWKNVSK/kyRwazL3p4sthTYUqJQ9Ag9d//43gFa6yNkSz0HeTeo3ATlXb+kaa+V8GDYvEaF+s+JiA7wF57Szsufs/KwXompjkYaDIaR/P/2zjterrLa38/ae08//SQ56QklAYMUIQgIiIpgQcr9oUiRJlauXhG9XvWKcL1eCyJy9QqigIqQRBAIIhYERIqGABpCTyjp7fQyfe/9/v5490zmTE6Zk5yWc97n85lkZtc1+8zM2u961/quSp37d9GtX38MhIBbgBuB/x0huwxD4IwzzsBxdv4pv/e979HS0sL06dOLy377299ywQUX4HkexxxzDA888EBx3ZQpO8OcX/va1/jiF78IwIoVK6irqyOZTOI4DrfddhvTpu1dMzH9ZdUvmlvH6W+dS8ixinPl3/rNc71qr62gNruw36zGOKmcS97VAi1hR1j62Ovc99RGLj5xf371yGt0pfIjnjS3sxK9MgrbtifztCc7CDvCp997IMceOI1o2OaxF7ezcq1O/ktmXdZs7iLv+aSCm5S8p/CVls/1fF0OFwtbVMcitPXkybs+L2zo4PB9G1k4q4bVb7QFQjf6hsIKwvY6/04ny4UdmzlTYpz21jn95kwMpTTSYDD0piLnrpRSwHXBY7cQkQbgZnTTmRbgK0qpJYPs8zDwTiCklHJ399wTneXLl/Pud7+717KbbrqJrVt3zpqcdtppdHR0cNNNN3Hbbbf12ralpaXXzUGBo48+mscff5yenh4uueQSHnvsMc4666yReRMjyGCjwEzO45L/e6JPnXpLhI+fvJD2ZI76RJifPbAGXyniEYeOZI5s3qOlK8MXfv4U02qiev56hL37nh7e9RQ/e2ANK9Y0841z3lKMbnSmcry+rQdf7Wz8UsD3oTQIlMn5vL69C0FnviezLk+ubdZ91qdW0d6TxRIhFrHZ0ZHZmRCgYP/p1YQc3TjHzJ8bDCNDRTFbEXlXP49jRaTSOO2PgRzQBJwH3CAiBw1wzvMwIju7zYknnsjy5cvx/T2XQamqquL666/nV7/6Ff/85z+HwbrRpzAKPPOYeRy/qKlXeLe8f3hBI747nWd7ZxqF4sxj5hFyLFJBlvjqde28vq2bTa0pNrWm6Mm4JDMukZBNP4ne4wLb0pnxCj3ffdWyVYjoOe5UVmvd5/I+qZzbaw5foWvaC+jXur1rNGQTcSxiIZstbSlsG2Jhm+auDBuak+Q9rQeQyXlMqYmgBMIh28yfGwwjSKUTsjcDfwget5U8Xwa8KiLPiMiC/nYWkQRwJnCFUqpHKfU48Fvg/H62r0W3k/1SpW/E0JvLL7+c9vZ2zj//fF577TWUUnR3d7Nq1ardOl5jYyMf+9jH+MY3vjG8hg4jg+mV90dpZnwy4/Zy3Nva03z37udZu6WLbe1pXNdnzZagv3jIJhKydfKZgraeLD66Tev4dfDaVt08RivRrVzbwoKZNZx86AxA4eP3yrzvL1Jgl6S/O7alm8igS+G60y5VUYdo2CZkWyyaXce+06sIORaf+8AibvnssXvcltdgMPRPpSPjm4Fa4OtKqbSIxID/QmfRX4eud78eOKmf/RcCnlJqTcmyZ4ET+tn+W8AN6P7xhkE49dRTe9W5n3TSSdxzzz2sWLGCK664guOOO47u7m6ampo47rjjuOGGG3brPJdddhn77bcfq1ev5pBDDhku83eb3ZWULaeQGe/5qpfjVkrh+YrudJ7Lbl7JZ085kHRedykLl4w4CyIseU8hvjfOVegUiARd6izyrs+OzgyZnMcDz27FsoSQbdNTQaZ/oZTQ9XydQIe+Fu09O3uwh4Bc3sOxhbl1VTR3ZQgZhTmDYcSp1Ll/DphRmPcOHPx/AluUUv8jIl8ANg2wfxX6RqCUTqC6fEMRWQwcG5xz9kBGicgngE8AzJ07t8K3MrFYt25dv+tmzpzJzTff3O/6+fPn9wq1lnLRRRdx0UUX9Vo2e/Zsstnh6VK2p+yupGxfFOact7anio7b83260zrNI5lx6Uzl+eHvXsLzFa6vCCktTuP7Pkrp0i9B13gPR4vWkcLzwRZFxHGoS4Rp7c4yrTbKU6+26CRB26Irne9TZU4pHW53PY+8RzEy4no+nuejLMEObnRKP1alegKDKcwNpaGPwWDon0rD8kmgvLj5CCAVPB/s16wHKB8+1QDdpQsC7frrgc9VkkCnlPqpUmqxUmrx1KlTB9vcMEEol5QNO1YvSdmCUlp1LFQMOw/GqUfOob0nRybvkcl7RQGV6phDJGTjWDrs3pXKk815tCdzJDN5Ujmv2N5UO7Hx69gL+Aqm1UXpTu2s7d/Wngb08oHkY6fUhImGtLZ+JGRTFQ0RDaYmMjkP2xbqE+GiUBDQS09gIIW5tVu6uOT/nuCa5c9z619e5Zrlz3PJ/z3B2i1dw/XWDYZJQ6Uj968DD4jIb4GN6BH1qcBng/UnAr8ZYP81gCMiC5RSa4NlhwIvlG1XAywGfq1bT1K4Zd8kIh9SSj1Wob2GCUx/krK2rXuGv76tm4bqCHWJ8KAjxdIIQDRsIyndxrXQDc31FHnPI531SGW9nY5PR7eJRxxAkcp4hcVFCrXdg2mtjyRhW7BEa78XogsA63YkiYZtrrloMZGQXZyaSKe9klauwR5KBaI4Pk11cWriLlWREGu2dOlRu6+KUxO18RAN1RE2tugoCOjOcrWJ8IAKc+U3bAUqjb4YDIbeVDRyV0rdChwFvIyee18DHBMsRyn1O6XUxwfYPwncDXxDRBIicixwOvCrsk07gZnAYcHj/cHyI4AnK3tLholOX5KyvlJ0pvJkch7b2tO8vq2b1evaybl+vyPFcoeyb1M1iYiDUops3tcj86zOgi+czoLiqNRXkM65uG7fvdlVsE0iYu+2NOzuILLTiec8RabEsVuWEHKE6XUxFsyo5ucPvUo27xWnJnJ5nUxniWCJVdwnGtaj83jEAaX1A2Y3xqlLhImGbeY3VTFvWoKQY9PanaWxJoLrKVzPp6E6TFtPFse2+s2QL69YKDCU6IvBYNhJxaVmSqkX2dk0Zne4FC1+swNoBT6tlHohkLF9EViklNpASRKdiBR+lbebOndDgXJJWdsSUtmdmfGeUmTyHiFfsb0zw6Hz63c5RibncctDa9nQ3MOUmmiQPS7sN72af7zeCuw64vZV4DQDYRadRCZkd+lzvut+jq0T9pTa8zr1wbCAUMjSUwTBPLrrF2rXdZKgbetr1tqd44mXdvCuQ2Zw1dmH8W83PcmOrgye7yOiR/2JoCmMbQmH79vA/U9vYvW69mIiXSbnsaklxZSaCF/6fwcTdix2dGaoT4RRQEcyN6jCXPkNWymmE5zBMHQqdu5Bi9cT0BrzxV9WpdQFleyvlGoDzuhj+QZ0wl1f+6wrPZfBAL0lZRNRh1c2dxVblhbwlEK5PrMa4zy7rr2X0lkhFL+xJUlLt55Hd2yLhTNryOa9AcPoxah88MStIDU+nfOGrCq3J6jg350RBoVSCh9dq+4r2Naewg409r9912rmTEmwYGYNy75wAmd+9y9saUsVnbvn6bua/WfU8OFj9+GWh17VFQnBzYKIkPd8tndmOHL/Rmri4SHbXH7DVorpBGcwDJ1KRWyuRMvNWsCH0CPv9wAdI2aZYVhZt24dN95441ibMSwUJGUd22LdjmQxE9uxhETEIR5xqIqEiAb65aWjvtJQ/JSaCOFAKc1XugyurWdkRogKCNmMSv27jg7o7m6gFen8oLa9cOPieto5h2zd8e6qZavI5j1qE2Fu+NQxHDq/gaa6KLXxEI01ERbNruPqCxezZmsX9YkwmZxHTzBtkXO1QE19Isyz69p3y2bTCc5gGF4qHbl/FDhJKfW8iFyslPq8iCzF9HLfa7jxxhuHRa1uvFCQlL1m+QssX7GOVNYjFnGQkjTtbN7D81WvUV9pMp6vVLFDnGPr/u7daX2NBK3mptDlY8NB3hu9MFR9IkxXKo+IEHGChLqSKYG8p/AyWl63NhYuzmsfv6iJBTNr+MXnjutTrveptS20dGVIRB2U0lGBHscmEXVo6cqwuTU1oF390V8PANMJzmDYPSp17nVKqeeD5zkRCSmlVopIfyI0hnGEUoqlS5eyfPnysTZljymvgz76gCmseGUHW9q0kI1T0kDcV4rqWO9RX+ncbqFD3JrNXWTzHtm8hwTurxAi7k8HYHcZjdC8betRe5fkCdlCPBoi7Pl0pnqPikV0otyr27qZXh/tFeHor2lLRyqHpyBq7wz6WZZWqMu6Pu09u6+DYDrBGQzDR6XO/TUROUgp9QLwPPBpEWkHdi8GZxhV/v73vxOPxzn00EPH2pQ9orRsrTCyi0ccHNtiVmOMza1pcnkPX2mhGRE45/h9egmqlM/tJiIO+8+o5qVNWmMpHnHIp9xirfzehACOI0ytjvKfZx3Kc+vauPnBteTyHvmS8EOhRC8asgg7Fumcx47ODNs70mRy3oCiMXXxMLZQjHYUcD0fW6C+KrJH78F0gjMYhodKnfvXgMbg+ZeBJegkuEtHwijD8LJ06VLOOeecXiHrvY3+6qA7kzk9T6ugOu6Qy/u0dWexLGFmQ5w7/7aOP/xjc1GGtry/e97zeWljJznPI+LYHDi7npVrm8e5hGzfKCDi2Nx46TEcNLeesGPx0HNb8TzFa9u6e22nQ+p6Ttv1FZ7n88d/bOaJl3b0kuwtj5Q01UeZ2RCnpStLLu/paQvPxxJ9vWc1xsfkvRsMht4M6twD1bgMsAJAKbUS2H+E7TIME67rcscdd/DEE0+MtSl7RLlwDWhZ2Ne395DOe0yrjSIInak8Mxpi7NNUXRRWKQihXP/Jo1m9vp23HTCV3/9jM209WTa1psi5Phbg24pVb7SOqejMnhANWew7rYrZjQkeWr2Fx1/cQVt3lvZknpBj4SsfCbLfFTqLv9Brfd/p1UyrjfYSjdnQnOwzUhKPOOzbFMLzFdm8R74qwr5NVaaFq8EwjhjUuSulfBG5Vym1iw68Yfzz8MMPM3/+fPbff++6HysfMW5sTvaqgy5t8lLIko+ELJq7MnSlXD3yDgIV1bEQG1uSnHvtozi24PsKAba2p4mHdYvWsGOTyrrkR7oZ+wggAvOnJpg3rYotbWnO/O5f2N6hcxBcz8f1d4biC0p+jiVk8z5h2yIasWmqiwH6WjV3ZXj8pe3c8uCrfSrGCVp1Lue5WJbgo0wLV4NhnFFpWP5RETlaKbViRK0xDDtLlizhnHPOGWszhkRfc+uup3rNg3ckc0GTF4uc69OTcenJ6FGo6/l0JnM0VEfwlaKtO8v65iQNVWEOnF2LJUJrt078ynu69Wkqq28IyuvlxzO2BbZYzG+KM7uxCl8pNrUm8XyfaMghGrbI5D3cjNZ/skQ7Zce2inPwli0cMLN2Z/tWtGjMk2t2jZTATuf/8ZMXEgrEan63dRo/MfKwBsO4olLnvh74g4jci9aWL/78KaW+PhKGGfacdDrNvffey7e//e2xNqViBppbX7utm+qYQ208rMvclKIjmQcUbd0Z8p4i72qHn865JLM2azZ3kcm75F2dLf7sG+0snKXFagph+5zr4/oKW0TnJYzTCXcBxNL1/J6nqI2Hybs+8bCWbNU3PPp9FJPd1M7/tJCedup1sRBtKs/+06uLXfQKWJboOfkBFOPakznOPGYeAM/9qco4doNhnFGpc48By4PnA7ZhNYwffv/733P44YczY8aMsTalYvqaWweoTYRpqo2Synrk3AypnEsm5yGi54HTOa9Yx53N+2xqTbG1PVMMRQvaKaZzLi+s72B+UwJfqaIAjlKVqc2NNqXKdk5Q4mbbgu/rm5JQ0JTFV4qt7WktG2tJsYSv8P4KeAp816e5O0ss7BBy9E2ArxQdyRydSd1A54j9Gli5trlPm4xinMEw/qnIuSulLh5pQwzDz5IlSzj33HPH2oyKKMyx3/fURrpSeRqrI1ii9dg7kjmyeY+c6/GRE/ZjflMVjzy3laWPvUEo6ASn9d4FLD0fn3V9PN8jEbax0J1UUsF2OVzWbuki5+m7gZBjFQV+Co7Ulp2NX4bCcIb1HUvnAnjKpyYWoTrq4KOIhW08H7Z1pJlSE2FzW4qtbWlcX3fHy7kKN5Un4uiGOr0oNHoD6qvCOJbFptYUm1uTeEq/75kNcX7x8GvYIsWqggJGMc5g2DsYirb8m4APAk1Kqc+IyAFARCm1esSsM+w2nZ2dPPjgg9x0001jbcqglM6xd6VybO/Ueu+zp8TZFLQO1WFlxbLH3+C6S97KgbPrmNUYZ3u7VjOTQOfcEoiEbaJhm+50nqm1Udp6cuS8YIQu4Ptasa1Aro/GL5GwTd718YeYYDec8/WuD34w8j737fvwqfce0Evg5dD59Tz9aivfvms1U2sitCez5D3t4D1f6ZuZMgrVkCKQ93wuetf+/PD+l5hWG6U2HqY2Eca2tFNX6FG6UYwzGPY+KnLuIvIh4HrgLuBc4DNANfAd4N0jZp1ht7nnnnt45zvfSX39rh3RxhPlc+wNVRG6Ulqv/KWNnSSiNuGQjevpEHQ8YnPVslV89N0LiEccZjXG2NCcwrYFSwTH1k1M6hJhUllX66r7iljEoTtdeWPBVNbbI6nY4WgUUzh/xLFYsaaZT733gF0EXkKORSzsBLKvQk0sRHcqT1/3JI4tSHBUT/l0JHOsXNuCYwv7NPUuhikkzn3spIXFLm9GMc5g2HuodOT+DbS2/CoR+XCw7Flg75Y8m8AsXbqUj370o2NtRpHy0rYj959CNGzvMsduW8LCmTW8sKEDTylyrsJXHo5lsXBWDYmIQ3NXBgGqoiFa8x6OLYSLTVJ8HMtidmOC7R0Zcq6HQpHN+aOaCb+np7FEy8MKCsex2Nqe5prlL/D2g5qK1w60nG53Ok/e8xCxcAMBe4ve79Wxdjp2pYLkQWBre2rAxLmOksS5AuV/S3e4xPcNBsOwUalzn4Z25lDS9ZLRkco2DJHt27fz5JNPcs8994y1KcCupW0SzKW/7/BZdKbyuGVh8URUj8hf395DfSLMtLoodYlwMbu9kK191dmHccWSf7K5NUUq6+LYgmNZ7D+jmm0dKRqqw3oOOpkv9lIfCmP14bYECORhAXoyLsmMyz1/X8cf/7GJmniY715wBG+eV8/0+hiZnBf0s9/ZrlaC4wg6ic71FbalJ9wt0TKvSimm18fY3NZ3s5e+Euf6KlN8feVGLtzSVVS1MxgMY09FLV+BZ4Dzy5adDawcXnMMw8Gdd97JqaeeSjw+9lKg5WH3qliIza0p1jf3cOOfXuHPz25hQ0uSZKZ3yDwadrBti2l1OlRvlUjnFpzOgpk1/PJzx/GfHzqUWY1xGquj1FeFeXFjJ5tbU4Qdi+qYvn8V2TnfPN7R2f1CMuuRzHpFqdiOVJ72ZI71zT185LrHeGFDOwtm1NCRyvUapRemBFSQLBgJSVGJLmxbREM2IdtiZkOcY980reJWq+V/y6a6mO6u56tiy1iDwTA+qHTk/m/AAyJyCZAQkT8BC4GTR8wyw26zZMkSvva1se3GWwjdPvrCdra2p5g3VYusrNmsVeViYYdc3qMuEaK9O8uLmzo4fN/GopiKYwlhx+olrgK7Op1IyObs4/fhX46eyxMv7eDbd61mZkOMWNgm7ykiIYuptRE2t6ZHrd3qnqLbzO4aNxCBvOtTHQuRyXt89mdP4vkKL5hgL+xSEOcTAdu2CNmC4Ombq6hDJGRjW3oq47g3NTF3SlVFrVb7K1OMhKxeLWMNBsPYU2kp3MsiciDwAeB3aCGb3ymlekbSOMPQeeONN1i7di0nnXTSmNlQGrpt687S2p2lM5mnqS6K6/uEHe0wFJDP+yyaW8fz6ztY39xDIuIUncs1Fy3m5w+9WlG2diRkE3IsbMuiuTMTyK4qPN8n706M2SPPByUK11MoX7F+R1LXvCuFZekqgAKFSEVTbZSptVGeX9+hkw4twVOKWMgpXsdKW62Wtsstx/dVr5axBoNhbKk0W/4MtDO/Y2TNMewpy5Yt44Mf/CChUGjwjUeA8tCtbQld6Ty+UmxoSfYaPQvaKSciDvOmJTj2wGksnFXby7kcv6ip4v7eG5uTbG5NYolo2dWgQUqBieDifQWu55EN8hQcyyKvdFc2ywpa3YIWu7GEmngI31e8eV4dHztpIR3JXJ/XsZJWq+XtcksxwjaGkUAphed52La9V3e1HAsqDctfBdwiIncDtymlHhkxiwx7xJIlS7j++uvH7Pzlodu6hBZK8ZUKFOS0iy30A69NhAGdJX/coqZdHEwkZHPk/lOK2dkr17b0yhYvpSOVw/Mh53v4vpoQzrwv8p4qNsYJORae76OUTlS0LL0u7/k6e95TxMJWrzauu0t5u9wC2bxvhG0Mw0oymWTJ0l9z572/p7WtncaGej50+vs595wPk0gkxtq8vYJKw/KHicgidI37TSISBX4NLFFKPTOSBhoq57nnnqOjo4Njjz12zGwoD91aIiycVcOazV0opWvO0zmXiGOzcGZNUTClP+fQV3Z2ITRf7qzq4mEUapcR+0TEsQVfQdgRbMuhJ+MGTl6PoqfXxTjruH04ZH49h8yrZ/X6dla90darDHGoRMO681v5/LxliRG2MQwbyWSST332ch54YjUZVyeUvrqtm2fX/IxH//YkP/nRtcbBV0DFCnVKqReBrwFfE5Gj0bXvKwHzjR4nLF26lHPOOQfLqrQIYnD6q0/vj75Ct4mIw8Hz6lmzpZNZjXF2dGaoijr0ZPKkcm6/8+j9NZHpTue5Ysk/uehd+/HMa20AHH3AFJrqo9TFw2wfx3O/ttCnwEylOLbQUBWhO53HsfXIvDRaqdAlcNPrYpx82EwALr1xRUU3R5XQ1/z8n9JzTBmcYdhYsvTXPPDEarxILXPedjrRhtlk2jax7Zl7eeCJ1Sxddgcfu8Qoog9Gxc4dQETmoEvgzgXmAT8fCaMMQ0cpxdKlS7n77ruH7ZhDGTUX6Ct0m8y6vLihA18pUlmX2nhY17kfMZuD59X3O4/eX3a2JcLq9e1cfstTxWXLHnudfadXFxuhjFf2xLGLwNSaCFecdSi//MtrJDN5NrWm6E67xUYxjiUcOr8BBK5Y8k9EdLJb+c3RVctWcctutmktn59/yB7f19yw96CU4s57f0/G9ZjzttOJNc4BINY4h+lHnM7GR3/OHcvv55KPXmTm4Aehom+liFwqIo8DLwKLgf8CpiulPjaSxhkqZ8WKFUSjUQ477LBhOV5/Nc2u5/db01wY5R9zwFR6Mi47OjNs60jz3Pp2XF/RVBcjErKZUhOhKurwxEs7BkyQ6ys72/MVr2zpJJvXHeES0RCJaAjHtli3vYfWrvE7ah+ISn6mHEs474T9eO/hs/nGOW+hOhamJh7GEgjZujveW/ZpoCYeojoWYkdnmu0dmV7z46ClZQulawbDeMLzPFrb2lEKog07G5B2vPokYtkoBa1t7Xie0VQYjEpH7qcCNwL3lJa/iciiIFxvGGMKIfnhupvtb9Rc0Bwvr2nedZSvHfHsxjivb+vGsYUdnRmauzLYIjTVx+hJ57n5wbV89MQFxXOWhv/7CvHrDnE6UzxkW4Ai76pi+9a8pwg7Qm6clb9ZAAPI3w5mraBH7QfP070CCuHxa5a/wB+e2ci02lix6UuBXHAD1BemdM0wHrFtm8aGel7d1k2mbVNx5J5PtfPGH3+AHa2mduFbsW0zGzwYlSbUva/wXEQagXOAi4CDgciIWGaoGNd1ueOOO3jssceG7ZhDqWnub268M5XjsRe3IyJEQvqj5no+3RmX7q3a4S977A0eXLUlaK+qeoX/v3LmwURDNq9s7iyKuvjK113cAp/V3pMrysoW+55bFsoZX/XtMgxz7bWJSK+kw0jI5u0HNbFybTMN1bt+DbXevildM+w9iAgfOv39PLvmZ2x75l6mH6Hn3KtmHUTNPkeS3vIC/1jxV26++WYuvvhi4+QHoNI6dwctYHMh8P5gv6vRI3rDGPOXv/yFuXPnsmDBgmE75lBqmvsb5Xu+wlPgBIdRSpHMukV5VEuEhuoIr23rBqGoUOcrxZa2FJ+4/m/FkXq5XxSBrjLJ1AJZ1ycetsm7lXeBG0n21LGDLhW8+sIjdpnCKOQ4dCZzuL6OXkRCNo4lTKuNIYLpyW7Yqzj3nA/z6N+e5IEnVrPx0Z9rOWaBqGNzypn/wic/ej5f/vKX+fGPf8x1113HCSecMNYmj0sGdO4ishjt0M8JFv0GOAm4A/iBUmrHyJpnqIQlS5ZwzjnnDL7hEOivprkvx9DfKD+b9xEUguB6Pr7SeueWCL7v78zyDrx9ZzJHJGyzZnMXedejJ7tzXq3QJa1Qvz5QE5j+Ig5jhdaK3z0RHQmavyyYUcPsxgSPvbh9l8qFi0/cny/+4mly7s5697Bjcc1Fi5k3tTJpWYNhvJBIJPjJj65l6bI7uGP5/cU697POOIVzzj6LRCLBY489xp133smFF17I4sWL+d73vsc+++wz1qaPK0QN8CspIj7QCnwOuEMp5QbLtwKHjifnvnjxYvX000+PtRmjTiaTYebMmTz//PPMnDlzWI9dabb8Yy9u55rlz+8ycm/ryfLSpk6m1kRp686SDyRhdRMYxYIZNYgIG1uSAMxujLG9I4uvFHnXJ5XzejlF29INVdwKnPe4nHfvZ869cI+j8xR2Lg8HrWzfNLuWzlQe29L96sunLr5913PkXA/PV2TzPpGQ1uQPOza3fFZrHlSq8rc7XHXVVVx11VXDdjyDocBgCnXpdJprr72WH/zgB3z84x/nq1/9KtXV1WNg6dghIs8opRaXLx8sLP8N4CPAz4DTROR24A9MDCXPCcHvf/97DjvssGF37NB3TXNfjqG/UX7O9XE9RVt3tjgCBe14IyGH6fUxOpI5Al9P3lNF7fl0TofUSz9o2vFV+tETHEvhjoNW4yI60z0asunO9D9VUBUNkcq6iAhNtVEaayLUBQp+L27sYFptlH2adv5wdafz/Metz2D3M39emvhoGroY9kZEBMfp303FYjH+8z//k4svvpivfvWrHHDAAXzzm9/kwgsvnPTz8QOWwimlrlJK7Q+8D+gBbgO2AQ3oZDrDGLN06VLOPffcETt+oab5zGPmcfyipj5HfAXlMse2aO7KsL0jzfaONJtaU+zbVEUkZGNZQsixECCX91kwowZLRDuvQErVsa0h91yHoAMaO28eBPB8v9/M9NFGKYhHbN3gRXZNcVMAAumsHn17vk/IsYqtbjuSOTwFtfFwr/2qYyG6Urld2rUWMBnxhsnCzJkz+cUvfsG9997LzTffzJFHHjmsCcZ7I5Vmyz8KPCoinwX+H3AB8CcR+YdS6q0jaaChf7q6unjggQf46U9/Otam7DLK396R5o//2My02iizGuM7E+OUYmNLkuauLKmsi2UJ+02vRgGdqTyu5yPo7PBKw+rlWzVUhWnryY2b8JKgHXNLdxboHX4X9HSDQgg5gu8KXuCU505NYInQmcxjC0Ud/lIcW/psD6vPYzLiDZOLI488kscff5xf//rXfOQjH+Goo47i6quvZv78+WNt2qgzJIU6pVQauB24XURmoUP2hjFi+fLlvOMd76C+vn6sTQF6K5fd+cS6omqaJVoytYAIHLeoiYUzd3aAA4r92G1LUArW7eghP8Q0c1ugtSc3TO9oz7GCiETO081VsjkPS4RoxML3FVlXN3hRQWMd29Jz6q7n88b2HmriIaJhm5kN8V1624MO5fvKZMQbDAVEhLPPPpvTTjuN73//+yxevJhPfvKTfOUrX6GqqmqszRs1dls3Uim1WSn13eE0xjA0RiJLfriYXh8DoLU7y5a2FK1BQl1bT5butEtdPMwpR8wuhvojIZu3HTiNC96xP+3dOV7b1j1kxw57XnI23BRK+5QP8bBuw2pZQsi2aKiOFKcQPF8FbWp9osHUx4Gza/niGW9myeVvZ0pNdJfwe3c6T008zNUXHtFrSqS5K4NjWyYj3jCpicfjXHHFFTz77LNs3LiRAw44gF/84hf4/jhIxBkFhjRyN4wfduzYwYoVK7jrrrvG2pQ+qU+E2diawvV0r3FfKXJ5H8cWbEv4/T828fhLO4qZ98+vb+eym1eyqTVF3t21rn1vpdChrjOVY35TA031MdZs7iLreqS7sziORd7ziToWYccm5OjsgXTOZUNzspjA2Fc3ttLKhUoSHw2GycisWbO49dZbWblyJZdddhn/93//x3XXXcdxxx031qaNKMa576XceeedfOADHxiXrQ8zOY9v3/Ucc6ck2Bw4+Gze0wluHhw8t57qeKjYwOSz7z+QS378BJncxHHqpVgCYcdizeYuDt2nnkP3qacjmaO5M0Pe8+lOu8QjO7+KrucTcWwsi2K2+2AOvLyZi8Fg6M1b3/pWnnjiiWIS8jHHHMPVV1/NvHnzxtq0EWHAsLyIjM+Yr6GoJT8eyOQ8HntxO3c+sY7HXtzOEy/toCeTZ1ptlEP3qWdabZSQbVEdDREL2+SDbLKCTv1FP3qc9AR17IUWrI5t4fo+bT25YnKhY1scMrcBS7QOfDbvkcvrOfmFM2tA0SvbvZLKBYPB0D8iwrnnnsvLL7/MokWLOPzww7niiivo6ekZfOe9jMFG7jcCS0fDEEPlrF+/nldeeYWTTz55xM9V3s99wYwa7l6xnnU7epg/rYqjFkzh2t++SE8mj+v6pPMeyUyekG3TWK1LucIhG8e2CDkWmZxLa3eW7kye1q4sHam+y7gmEq6vG96EHYtXt3YVEwZdXze8mV4fozoaKkrHFhrApHKuyXY3GEaAeDzOlVdeySWXXMKXv/xlDjzwQL71rW/xkY98BMuaGC2MB3PupmHuOGTZsmWceeaZhEKhwTfeA8oV6noyLptaU9i2BHXlwg88n32aEjRWRVnT0qVD8K6P6+VI5VwOmFVLJGRpfXXfJ5P32daR3q1kub0ZX0Em75OI2IQdG9fzCdkWU6ojvLa9h7p4mBkN8eL2JtvdYBh5Zs+ezW233caKFSu47LLL+NGPfsR1113HscceO9am7TGDOXdbRN7JAE5eKfXw8JpkGIwlS5bwox/9aETPUd7pLe8pXtm8A6UUvg81sRCup8i6Hm9sT9LcmQH0KD3kWHSmcmRyHq9s7mR2YwLXU/SkXUQgPwGTVSvVjvd8HYJ3bIuFM2tIRB2mZz1SOY+cZ/TfDYax4Oijj+Zvf/sbS5cu5eyzz+a4447j2muvZcaMGWNt2m4zmHOPADfTv3NXwL7DapFhQF544QXa2tpGNNMzk/O45aG1bGxJMqUmgq8Um1uT+EphW5bWfvcUSqmgCYwinfOLddYiQsTRimxdqTyvZDoBBm34srci7KoL3x+xiEVVJERdIkw0rB13yBbOO2E/ZjXGTba7wTBGWJbFeeedxxlnnMH3v/99nnrqKU477bSxNmu3Gcy5J5VSxnmPIwp3liM1L1QIxW9o7qGlO0tnKodjWdh2yf2dAt/3sQMbFDrkXoplgSjBsS0aq8MoBds7MhM2aa7Sm5ZMzifv6qS6Ta0pFs6swbKEWY1xk+1uMIwDEokEX//618fajD3GlMLtRSilWLp0KXfeeedu7V+eHFdoGVq6vhCKn1ITpSuVL84Pp9M7268i+i435AhWDnwoOnrQpVwowRKIhITG6iivbu2akI69QCU69oIuiXNsfa1cz+fFTR0sml1n5tYNBsOwYhLq9iJWrlxJKBTiLW95y5D3raR961OvttCTydNYHSHbncVTikzOIxq28Hwh5+kRulZY0yl1IdvCVx6OBamsq0VqRKiKObR2ZwnZFq9v7yY7HtqzjTFzpyboSuXJuZ7u8S7g+3D6UXNNCN5gMAwrgzn3b4rIUcAzhV7uhrFjyZIlnHvuuX32NR6I8uS4AgURmVs+eyyRkM229jTpnMezb7Tj+j4oyLgumbxgW1p1riOZx7KEZNCaNOxY/M9HDmf1unbuf2YjnqdI53SCmOsrPF+P+CfiXHspgq5nH6gIoKU7y6LZteQ9v9h3PefqDnAGg8EwnAz2q3Ii8CDQISIPisgVInKCiEQG2c8wzHiexx133LFbwjWFEXlpYxHQIjI9mTwr17YA0FAVYWtbGl8pwo5NLOJQGw8TCVmg4IJ37s/fvvM+PnfKmzjjrXP57PsP5OefPZY7nljH06+10FAVIZl18ZVi/rQqrbomk8SxBy1tC68LWBLkH4huwfrq1m7qEmFm1MdoqIr024vdYDCMD+bPn8+DDz5YfL1s2TLq6+v561//iohQVVVFVVUV8+fP5zvf+c4u+z/yyCOICFdfffUu626++WYOPPBAqquraWpq4pRTTqG7u3tY7B5w5K6UOllEbOAI4Pjg8TkgISJPA39VSn1tWCwx9ItSigcffJDZs2ezYMGCIe+/rT2N38+kcK+e36IoNlcPENGhdwEWzaljam2MT7/vQAA6enKc94NHyeQ8ahMhxLGCML1u6xoJWSQnQTReRDvxkG1p5TlLyPuKiBM4fAXJnIdSuttbRzJHQ1XE1LIbDHsZv/zlL7n88su5//77mTlzJgAdHR04jsPTTz/NCSecwBFHHMFJJ53Ua5+GhgZ++ctf8qUvfam4/K9//Stf/epX+eMf/8hb3vIW2trauO+++4bN1kHjgUopTym1Uin1faXUGcD+wJXAAuArw2aJYReSySQ/u+kW3nPahzjvgovpSmX52U23kEwmh3Sc6fUxrJJ2oZ6vit3aklmXuoQe0bd15/S2IrvIoU6vj9GR3NlK9fn17Zz+7YdYu7WLLe0pXt7UwYsbO+jJuKRyHumcR0dyYqvPCbrFrCJo6xo0vMkHN1J5T5HMeCSzHijIuT7JrMuWtpTp3GYw7GX89Kc/5Qtf+AJ/+tOfeNvb3rbL+sWLF3PQQQexatWq4rJUKsVvfvMbfvzjH7N27Vqefvrp4rqnnnqKY445pphD1dDQwIUXXkh1dfWw2DtotryINAJvL3nMAP4OXA08NixWGHYhmUzyqc9ezgNPrCbjenS3tpCNTuNr3/sZj/7tSX7yo2srbhpz5P5TqIrqRi2WCGu2aCU519d16jf9eS1zp1QxvT5GPOIwuzFBezJLe9AXvT4RxlcUw8fPr2/nI9c9Ribn4XkKt3yieYKH4Rfv18Bz69vJuqo4x57OeQjg2ILn697spcGSmpiDY1sksy62JXzuA4s49k3TjGM3GPYCbrjhBh5//HEeeughDj300D63WbFiBc8//zxf+crOMe9dd91FVVUVH/rQh7j99tu59dZbWbx4MQBHHXUUV1xxBVdeeSUnn3wyixcvJhIZvhnvwRrHvAA8BZwGPA+cq5SaqZQ6Uyl1rVLqqWGzxNCLJUt/zQNPrMaL1DLn7RdzyCd+zrx3fRIvUssDT6xm6bI7Kj5WNKxbhooIz29oJ5P3QCAasnnzvDp8X3HVslUcMq+eqmiI1u4sG5tTdCRztPfkeHVbNxtakjRUhcnkPP7j1meCzmVWWRB/4jOrIcbzGzoIOTaN1WFqYiESEe2gFVq5rz4RpiRQgm2BQjv9eNihoTpCyLGMYzcY9hL+/Oc/c/TRR3PwwQfvsm7KlCnEYjGOOeYYLr30Us4444ziul/+8pd8+MMfxrZtzj33XJYuXUo+ryOaxx9/PHfffTf/+Mc/OOWUU2hsbOTyyy/H87xdzrE7DBaW3wIkgHnAHGC2iMQH3sWwpyiluPPe35NxPaYfcTqxxjmE4rXEGucw/YjTybgedyy/HzWETLUFM2v4xMkLmVobY/60KvabXs2h+9STiDjFxLrV69v5ypkHs6ElSbZQrgVEHJu5U+J86zfP8beXdxQjAG4wNJ3gA3VAX4cFM6rxfD1a145Zz6lbllW8wcnkPESEaInj9n3I5d1+u72NJeUd/TK54flhMRgmEj/5yU9Ys2YNH/vYx3b53W1paaGnp4drrrmGRx55pOi8N27cyF/+8hfOO+88AE4//XQymQz3339/cd/3ve993HfffbS1tXHvvffyi1/8gptuumlYbB4soe6kIKHucHRI/rPA7SKyHh2Sf0wpde+wWGIo4nkerW3tKAXRhtm91kUbZqMUtLa143kejlO5DlFrd5ZExKapLrbLutLEujlT4oQsa5cuZc1dGf7+SnMgN+tO+Cz4AgLUxkPUxEJ0pHJI2e2M7/tFbXkvuCiObWFbvpbotYTZjQlmT0mMq25vlWgfGAwGmDZtGg899BAnnHACl156KTfccEOv9bZt84UvfIF77rmH66+/nssuu4xf/epX+L7PqaeeWtwuk8lw66239hrdgxYFO/HEE3nXu97F888/Pyw2V5pQ91RJQt2BwF3AR4C7h8UKQy9s26axoR4RyLRt6rUu07YJEWhsqMe2hxbWLU+sK8UKSrK2tadBQUN1hBkNcRqqdbkW6BsA31d0pHJMpmC8YwvxaAjHsXjPW2ahEDI5VyfQKaWlgIPLIYHfDzlaoU+hpz8Kjn28ZMiXax801cWYWhPF9XyuWraKbN6M4A2GUmbOnMnDDz/MH//4Rz7/+c/3uc2Xv/xlrr766qITv/LKK1m1alXxcdddd3H//ffT2trKvffey7Jly2hvb0cpxcqVK/nrX//K0UcfPSz27k5C3SHocP1DwF+HxQpDL0SED53+fp5d8zO2PXMv0484nWjDbDJtm9j2zL1EHZuzzjhlyGI2pYl1pTXvpQ5n5dqWXTLrO5I5snmPTN4jEbVRSk8dVCK5urdTHQ1RFbUIO0Iq47Lq9TY8zyflgSU+IpCI6K+Rhc5vyOU9FARNdnya6qO0dGXGVbe3gvZBqagRaO2D5q4MK9e2GK17g6GMOXPm8PDDD/P2t7+dbdu27bL+lFNOob6+nu9973usW7eOf/3Xf2Xq1KnF9aeddhr7778/S5cu5ZBDDuGHP/whn/nMZ8hms8yYMYN///d/L4bx95QBnXuQUHcg8AbwKPBD4FGl1BvDcnZDv5x7zod59G9P8sATq9n46M+LcqVRx+bkYw/hnLPPGvIxC4l1Vy1bRXNX3+1F+8usz3u6zGvZY2+Qd71J4dgBZjXGmNkQx/MV/3i9FZSu9395Uyd+0P62K50nFraZOyWBbQs96Tyur6iJhfnmeYfRk/F26fY2mM7/SFOx9oHBMMlZt25dr9f77LMPGzduBHQjr1JEhBdeeAGAK664os/jFdYDPPTQQ8NoaW8GG7n/N1qoZuuIWWDok0QiwU9+dC1Ll93BHcvvp7WtncaGes464xTOOfusisvg+nIit3z2WFaubemzvWjhBuCKJf/k+Q3t+MFNhespwiGLzmSOySQT39qVIRLS7WsLpQGOLbztwGlsbk2Sznn4SnHl2YfxjjdP7/e6ljIe5rormaIxGAx7L4Ml1C0bLUMMu5JIJPjYJRdzyUcvwvM8bNseUih+ICcyUMh1zpQExx44jde2dRELO7T3ZIlHdHa4a/mVtUCbILQnc2TyPp6vSwcsRKvQVQnzplUBsL0jTU/GJRKy+72uhZusjc1Jbn/0deJhe0Cd/5Gmkikag8Gw92Javu4FiMiQsuKh8mYx5RRuCDa2JOnJuCQzHllXK6zJJNCJL8fzIZP3sC0h7ypiYVtr7Zcw2Ei39CarK5Vne2eGWMhm4cwaElH9dx3tue5KpmgMBsPei3HuE5RKEqaO3H9Kr5D9wXPrS/q5R2jvyZLOeUWHPtkcO+y8oXE9H6V0gmFdIlxcP9hIt/wmy/UUjiX4SrFmSxeHzK/vVY0wmnPdC2bWDDhFYzAY9l6Mc5+gDJYw9dz6dn7yx1d6hexdTzc2mTMlQT5o3TqJIvD9otAO3raFKTVRWruzFY90y2+yIiELkUCLPu/RmczRUK0lJweKAIxUAt5AUwkGg2HvZbBs+QSAUioZvBbgY8Cbgb+bOfnxy0AJUwD3P72JqqjTa2T/xvYednSmaaqLsbElOakduyVgiRAL21qcxvdpqIrw9Q8fStixKh7plt9k1SXCOJalIwFQrCcfKAIwHhLwDAbD3sVgI/dlwB3Ar4LX1wAXAQ8DPxSRWUqp74+ceYbdZaCEKddXZHI65T3v+dQlwlgi1CZCbG5PseqNtkkvQyoCiahu9gKQzilq4mGO66PZy0Cj6vKbLEuEhbNqWLO5i6zvk8q5NHdl+o0A7G7uhMFgmNwM5twXA+cDiEgY+DhwulLqLyLyVuBWwDj3cUh/CVO26DrsHZ1aVMUSIewI0+vj+Mon7/r4vj9hR+0FmdjBtomGbHxfkfV1qZtjW1x94RG7ONLBRtV93WQlIg77Tq8ilfU4/x37Masx3m8EwIjNGAyG3WEw5x5XSnUEzxcDrlLqLwBKqZUiMmMkjTPsGeUJU/WJMP9734u09WTxfK0wp5RPOgfd6S5skSB5bKwtH0FEK8n1dfNSdPyiW7SGQ3ZRjObqC4/goLn1vbavZFQ9UFb6tz5yxKBhdSM2YzAYdofBnPsWETlEKbUaOJmS/u0iUgdkR9A2wzBQmjD10OotvL69m5BtkfcUKug7DtrZ+ZMgHV4pnbhWmvpfcOqFJbYIjmPzoWP34eB59Xs8qt6TrHQjNmMwGHaHwZz7NcADIvI34D3A/ytZ9x5g9UgZZhh+VrzSgucromGHhOgmJhPfnfdG0JKyG5tTxfdefg2iYYtpNVGeeGkHl7x7Qb9OeFt7Gs9XtPVkyeZ9IiGrmL9QPqre3ax0IzZjMBh2hwG7wimlbgY+DDwBvEcp9aeS1WngqpEzzTDSTNR59QERaO4sKO71uRpLLKqCHvcr17b0eyjPV6zfkeS1bd1sbNH/P/tGO8msO2yj6kJY37EtmrsybO9I09yVwbEtIzZjMBj6ZdA6d6XUX+m7+9tjwDnA48NtlGFkOHrhVJY99gbprEtqEmbDW6LV/mxL5xYUyt1cX2EJxCM2Yccm7/p0JnMDzmlnch7Ln9yAZeljOI6+T3Y9nxc3dLBobt2wjaqN2IzBYBgqQxKxEREbOAW4IPj/VeD6EbDLMAIc+6Zp7NNUxfMbOsbalDHBV2CL0mHzIBgfdiz8vEciGiIcOOhC/bnjWP2Ovp96tYV0zmXR7DrWbOkqtnkVdO7C6W+dO6zO14jNGAyGoVCRcxeRw9EO/RwgBkSADyql7qv0RCLSANyMTsxrAb6ilFrSx3YXAv8GLAC6gCXAV5VSbqXnmuyUNinpSOWoi4eZMzXBkftP4S37NkxY5y4ldW79zTh4vu5FLyIowFc+lghC7wQ7TynqBpjTLmSxJ6IOh8yvpzPoeR8J2WRdj5Az4IyXwWAwjCiDKdR9EbgQ7WgfAD4H/BZ4DXhyiOf6MZADmoDDgPtF5Fml1Atl28WBy4LjTw3O90XgO0M836SkUHfd2p1lc2sST4EtMLMhzpSaKOnsBL5HCobOYccim/cpJJmX5xb0ZF0KzfVEhANnV7Nue5KeTA6lBMeC+kRkwDnt0ix225KihCxAc1fGZLEbDIYxZbCR+9VAK3rUfqdSun5oKG1Hg+0TwJnAm5VSPcDjIvJbtEDOl0u3VUrdUPJys4jcDrxzSCecBJSroh08t55nXm/hO3c9jyUUk66itpY6benKUhML8dq2rrE2fcRQwT/ZfN8N50O2btgSsi1U0OVuflMVb2zv0XX/vp6Hn94Q52tnHTJgDbrJYjcYDOOZwZz7u9CO/SbgByKyDB0mH2qe9ULAU0qtKVn2LHBCBfu+HSgf3U9qCqPzrlSOnkyedM6jJ+NSEw/R1p3TWuieT3UshFK6lj2Vd2lP5rBtC5gcyXQiQnA/im1JUMuvl8fCNg3VYZQvzGqMY1tWsZQtmXH51m+eG1Da1bRMNRgM45kBnbtS6hHgERH5V+CDaEf/OfS05CdF5HqlVGsF56kCOsuWdQLVA+0kIhejlfE+1s/6TwCfAJg7d24FZuz9FFTRutM5trSlcT2fVNbFB1JZD0GPSH0Fnam8bieqFJ7SjWFEJkdfdt15TRAscq5HPGzjKQUK9ptRTUNVhHU7usnmfRaWjdArlXY1WewGg2G8UlFCnVIqjW4e8ysRmYMOp58PfAU9Rz4YPUB5jLMG6O5vBxE5Az3P/m6lVJ/FxkqpnwI/BVi8ePEEd1eap15toSulHbsfJIYVvTnFaeciXsmEc6F16USn8P7zrk/I0SH4vOcTCzssnFlDIqo/9q6nio1hyqlU2tVksRsMhvHIkPu5K6U2At8Ske8D/17hbmsAR0QWKKXWBssOpZ9wu4i8F/gZcIpS6rmh2ri3M1CXsY3NSZq7MqSyecIhB8/3dei9ZP/JJk5TUJMtvG0R/TxkC44t+L7QWB1h/xk1OpKBnhuviYeLr3c5ppF2NRgMezGDOncRORGd3f6qUupeEXGAS4EvAe3ANwc7hlIqKSJ3A98QkY8FxzsdeFsf53sXcDvwL0qplZW/lYnBQF3GAG5/9HXae3K4vsL1XUBNOmdewLF0aVvYsch5PirIoytcj7yvEE8xZ2qCqdVR2nqyva7pdy84gm/f9ZxJijMYDBOOwUrh/gO4Aj3CPkhErgfegW4Y80ml1P1DONelwC3ADnQG/qeVUi+IyFzgRWCRUmpDcL5a4PclWfmPKaXeN4Rz7ZUM1GXsiiX/RAoqaiEbL+ui1OR17KBH175S5D2d6V6O8iGnfE47cg6ffM8Bfc6Nm6Q4g8EwERls5P5J4ASl1DMicjRaY/6LSqkfDPVESqk24Iw+lm9AJ9wVXk/osreBBGYG6jL2+vZuQGiqi8Ikd+qgQ+91iQjVUYe2niztyTwWIEGYPeJYOLZOpgvZVr9z4yYpzmAwTEQGc+5TlFLPACilVohIFrhuxK2aoBRC7i1dGba0pYoCMzMa4oRsizlT4nSl8jRWR7DKtAS0vKnw/Pr2Ylb8ZPbvSkFdIsyPPn4Uf3t5B9+9W6dmhBybkC2ICK7nY1tCfVWkz2OU5zaccsTsYm6DwWAw7M1UMucu6ARkATLBsmKKsVKqb8UQQy8KIfdc3qOlK1sUmMnlPdZt7yHkWMHoHLpSeRbOqiER2fnnEbHY0ZnG9SazS+/NZ953IAtm1rCtI83cqVU0d2ZwfZ+cq3QpnGUxqzHBrMZdCzoGym0YSLzGYDAY9gYGE8CuAlwgj5aOrSt5XfjfUAGFkLvrK1zPD0qwFJm8h49WTQvbFiHbIu/5rNnchR/UrXWmcnSmsr3K2iYruiUrhB3huQ3tgFaLa6yOsO/0KvabXs2cKQn2m17NvtOraKyO7JIYV57bMLU2im0J29rTXHbzSjqTuTF4ZwaDwTB8DObc9wH2LXns08dzQwUUGo1kg+5hAHlXz50LOjFMAdNqooRsi3Te443tPWzvSLOjM4NtWTj9lG1NJiRo06pf6P8KanFhx8YL2rd6viLs2H0mxhVutKpjIZJZl2ffaOe1bd00d2V4Y0cP5177KGu3TFyZXoPBMPEZLCw/Vyn1WH8rReR/gP8cXpMmJoVGI45tFUVW/BJFGSuQSq2Jh5gzNcG6Hd28aXYt65uT+L4imXXxzcgdEcH3FZ4PEcfmzifWFbUAKk2MK9xo+UoVIyRhJ9gu75HJuVy1bNWA8rMGg8EwnhnMud8rIu9TSu3SAU5ErkU3gzHOvQIKjUZyeQ8naOZiiaBQ2EGvUse2qE2EAxEW4dl17VTHHBqro7T15Mgpb3Jn0bFTcU8EfvfUJhJRu9d8eSVqcYUbrY5kDtf3dzp2dDCgNh6mJ5MfVH7WYDAYxiuDheUvBX4nIm8pXRjUu59OZY1fDJSEjkM2U2oiuJ5PzvVB6aYmtmWxcGYNmbzHM6+2sr0jTXtPlq3taTa09OBY0ks6tpDhCL3lZicype8ZBdGwRVNdjKk1UVzP56plq8jmB2+KU7jR6kzme13TQi5EbSJcsfyswWAwjEcGaxyzTEQiwJ9E5F1KqedF5GbgeHT9+6ZRsXKCUFpTvbk1RXtPlpzr88CqLVgWdGfyrN+RxLJgVn2cHV0Zwo6N6/mICGHHIhO0My0dwE+GRjCxsK07u/mKrKsld/PuzkKNSpu9wM4brctuXonrK8jr0kLH1jdYtiVGftZgMOzVDFoKp5T6ZeDg/ywiK9DtW9+ulNo24tZNQPoSU/nUe7V62iPPbaO1O0tdIkze9Yuj1IIYy/T6GBuak0ES3s5+MRN9Kj4WsogHZYGZnFbmE4GQ0zvwNJTR9oKZNSy5/O2ce+2jZHIutfEwtQmtNW/kZw0Gw97OYPKz7wqevopWp3s38ClgkYgsAlBKPTyiFk4w+msKM70uxqMvbqMzmSOZ0ZrxmZyPQt8QKKW7mBVG6LqZmfRKypuINNWGybpKt7UNetOLQDTkEA7ZtPVkyeZ9IiELkaGNtmsTYa675K3FeveWroyRnzUYDBOCwUbuN5e9bge+XfJaYcrhKqY/4ZSvnHkw377rOWxLdL170al4pHMeXlAb35XK49iC5ysUMiGz53V4XE9BzGqIs6ElGdzACILCEsFV4CnFhuYefS2UrjxwbIuGqvCQzmfkZw0Gw0RksDn3fUbLkInOQE1h/uPWZ7AsaKqLsaUtTSrrErKtkjlmPSccCVmkc4qQY5F3/QmZOF+XcJg3tRrHtnh1WxfZIMdARL/baMgiGrLJ5D0sdPMYQZfFzWqM8a3fPDfkEjbTk91gMEw0htzP3bB7DNQUZmt7Gl8ptrTq/7N5j0xup9iN8rVjz7k+lmUVHd5EpDPpEp/j8NLGTvKBjGws4mCLkPd8LEuYPy3B2q3dzKiPEXZ0U5jCfHmlSXUGg8EwkTHOfZQoCKf0hSXCjo40sYhDLOwQDVm09+RL1uv59h7PxZaJOF7fiQ+8vKmDVM4rJhTaIoQci5Cjtfg7U/rahB2LGQ29deNNCZvBYDAY5z5qFIRT+kKEXuvyruoVclcKLNFzy7mJO2gv0p7UzlvQlQClDfIK10VE+gy9mxI2g8FgGFzExjBMFIRTutO9e+10p/M4tsWMhhiWCNmcG2TL70QBrg+TpSFcQazGsgTbEnoyuvytsC4asgk7OiehFFPCZjAYDBrj3EeJgnCKY1s0d2XY1pHm9e09tPXkOH7RNOJhh/2mV6PQoenJzJSaCPGIQyJiUx3TwaV0ziuWwzVURbjmosWEQzbNXRm2d6Rp7srg2JYpYTMYDAZMWH5UKZRd3bNiAzc/uAZQhB2LlWtaeH17F64PagKWtw2FWNhmZkOciGOzZktXIAkrJKIOdYkwHz1xAf9y9NxihrspYTMYDIZdMc59lFEK7ntqIw1VEd1yNOPy0qZOMvnJ6dQLde118TA9WZdoyKYuEcYS4ZD59XQmczR3Zzj3+H356LsX9HLepoTNYDAY+saE5UeRTM7jlofWsqG5h5yrG8e8vKmDnkx+8J0nGLVxh3jYxnEsZtTHmFYXJeRYzGqMF/u125bOkp87pWoXx24wGAyG/jEj91GioE63sSVJS3eWzmSOvKeboEwmtOCMsM+0amoTYdbt6ObohdN4/+LZNFSF+dZvnqO5K9NLwc/MoxsMBsPQMM59FChVp5tSE6GtJ0sm7+uOZJMIS3QJWzTsFEVnauJh3r94djG8Xi4Fe8i8elavb2fVG229tPgNBoPB0D/GuY8Cpep0ec8nl/cnfMOXvvAVJMI2B8yq7bf7WuGy+L5ic2uK6//wss6SLxvJL5hZM0bvwmAwGMY/xrmPIIUOcPc9tZHOZB4Rob0ni20Lvju5nHtDIkQq66KUT1c6Ryrn7hJyL22s47o+G1qSWCIsmltHImj52p3Oc9WyVUPWjzcYDIbJhHHuI0Spo2rtzrKtPc3mtiSCFqSZLAiwaE4tTXUxfKVYtyPJcYuaOO5NTb1K18ob67R2Z7FEsCxhzeYuDt2nHkuE6ljI6McbDAbDIJhs+RGg1FE1VkdIZVxEBNTkcOyOBbboDm5HLZiCY1tsaUvR3pMjFrJYOLOW4xc19Rp5F6YuqmMhALJ53TjHsS1c36cjmStua/TjDQaDYWDMyH0EKJ1jb+vJ4ilFVdTZRXp2IqJlY7U07P7Tq3klEKJRFLTiFfk+7nDKG+tEQnaxcYxS9OqEZ/TjDQaDYWDMyH0EKHVU2byP7yvynmIi59AJ0Fgd5oBZ1cybmsC2hDd2dOMrRThkEwnZWJZgiXDvyg1k816v/csb69QlwnrU7vmI6Ja3YPTjDQaDoRKMcx8BSh2VUqqoiz6BfTuRkMWcKQlmNiSYMyVBbTyE66lif/qc6xWT41JZl5VrW3rtX95Yx7aEhTNr8JXC9yHn+kY/3mAwGCrEhOVHgIKj6kzm2NaRLi4XmLAO3raEukS417KGqjDT6mJk8z6RkFWUle1J53eZMy801rlq2apeIjaLZtdx+lFzCTmW0Y83GAyGCjHOfQQoOKrLbl5JJu9jW4LvqQnr2AHqqyJF2ViAkGMDuoNbOf3NmRca65hmMAaDwbBnGOc+QiyYWcNHTtiXnz6whmzeY0dnhoksSFeaDNedztNUFwWlnxcy4AvrBpozN81gDAaDYc8xzn2EyOQ82pM58q5Hdzo/IR27oCVlPQWuUmzvSPdSkQN2CbMbrXiDwWAYeYxzHwHWbuni60v/yda2FNs6Ju6I3bIElEKAs4+dz5ypVbuE0k2Y3WAwGEYf49z7oSAdu609PWDDkvLtFsyo4dM/+TubWpK6/G0MbB8tfF9hW8KcxhhzplZx5jHzdtnGhNkNBoNh9DHOvQ9KpWMHalhSvl3O9dncmiKd8ya0Uy+QiNrMn1aFUhhRGYPBYBhHmDr3Mso1zpvqYkytieJ6PlctW1UUXynfbkpNlJauLDl3Yjr2kC27LEtlPV7d2s3G1hQNVeE+9jIYDAbDWGCcexnlGucFqmMhejL5ovhK+XbtPVkyeW/Case7nsLa1b+T9xR18RCX3fwUD63eSibn7bqRwWAwGEYVE5Yvo1zjvJTShiWl2yWzLq9u7Z7Qjk0BYUfIuwoRIeJYiCVkci7NXRmUgv+581lm1MdNv3WDwWAYY8zIvYxyjfNSSsVXCtv5SrFmc5euC+t7t72eWMimoSpEQ1WUSMimLhEiFrHJ5T2UAksEx7aIhexdpi8MBoPBMPoY515GucZ5gXLxlcJ2W9pS5D2PoCpswhENWdQmQtTEwzTVRXFsfReTdxW+AhHBsiwEnRlfPn1hMBgMhtHHOPcyCtKxjm3R3JVhe0e6z4Ylhe3SOY9kxqMnMzFHqtm8z5SaKE21MWxLcCzdqc1XCqUUIiAoHNuiNtCWN/3WDQaDYWwxc+59UKnG+ZwpCeJhZ6/Ojpcg4hB2LHJ9ZANGQro3+9fOOoRv/eY5ptZG2dy6s4Y/bFvYlsX+06vpSObI5j0yeY+6RGjXkxkMBoNhVDDOvR8qEV956tUWOpLZUbJoZCikCfhKYVkQDzt6ZO4rQiGLw/dppCOVo60nV7zh2dyaorkrw/In1xOyLWpiYV7d1o3r+bi+whLhpj+vZe6UKpNYZzAYDGOAce67QSbn8cTL2/nu3c+xvXPvdu6+AsfWGvG26FmakG3hhC0Wzqwh5FjFMHv5Dc973zKLK5b8kxc2tAfHEaIhm4WzavB9xVXLVnHLZ481crMGg8EwyhjnPkTWbuniS798mpc2dZBz9+aAvA7JO5YwvT7GF08/iB/c9yKxkE0kZFObCGMHVQMDtWj9xMkL+eadq4mF7V492wGauzKsXNti5GcNBoNhlDEJdUMgk/O4Ysk/eW1b917v2AEijkXYsTjr2H046bBZzKiPUxUL0VAdKTr2wVq0tnZnSURsZtTHaCjr6W4S6wwGg2FsMM59CDz1qp5v7sm4Y23KHhOyhYWzatmnqZpD5tdXXCVQTqW6AAaDwWAYPUxYvgIKnd+Wr9jA1vbUWJszLEytiWILJGI7R+WVVgmUUqoLUCrZO9iI32AwGAwjh3Hug1Da+W17R3rC9Gbf2pFme2eGK846pJfzHmqL1sKI/6plq2juyuzSRc8k0xkMBsPoY5z7AJR3fsu5Pjv2wux4CyhUsFsC0ZCNY1v4SnHtb1/kjKPmUhPf/a5uuzPiNxgMBsPIYZz7ABQ6v02t0fPGspfqy4ZDWqDGtiyUUuQ8n2jYRsQimXW5/a+v8+n3HbhH5xjqiN9gMBgMI4dJqBuA0s5vr+/o4uUt3WNs0dCJOBZV0Z1z4SKCUrpVK4BSig3NybEyz2AwGAwjgBm5D0AhE/yNbd2sa957EumijuAp8HxFNGzTk+ndBAcFvu8DFiLC3KmJMbHTYDAYDCODce4DcPDcelI5lzf2spGtWBYREfygoYvn61C8r7Q0LAKWZZHNe4Qdi/NO2HesTTYYDAbDMGLC8v2wdksX//rTFWxt2TtG7CL6EbJhSk2Ew/ZtIOLo/uoAM+vjCBQ7uuVcD8sSrrlo8R4l0xkMBoNh/GFG7n1QyJLvTGZJ5XftlDYeiTgWlgi2LTRWRwg7Wht+zZYusr6PbQkLZ9XQk3ZZNKeWg+fVc94J+xrHbjAYDBMQ49z74KlXW+hK5diwl8yzRxyLI/ZtZPWGdlBQF/RVT0Qd9m2qIpXzOP8d+zGrMW5K1AwGg2ESYJx7H2xrT9OTyZN1vbE2ZVAEqIk7dGXy7NtUjaD13kvFZL51/hGm9arBYDBMIoxz74Pp9THyrl8sFxuPREM6XSIRdTjj6Hkc96amotSrEZMxGAyGyY1x7n1w5P5TYPz6dSzRdepKKWYmwlx+2kG9HLgRkzEYDIbJjcmW74ONLUlae8avzKygxWcESETM/ZnBYDAYemOcexmZnMfnbnqS7Djt125b4DhCPOJw+H6NgA7DGwwGg8FQwAz7yvjx71/i5c1dY21Gn4Qdi6a6KI3VEeoSYSwRUlmXHZ2ZsTbNYDAYDOMI49xL6OjJ8dMHXhlrM3bBFvAVxMM2+zZVY1tSXGdZwrTa6BhaZzAYDIbxhgnLl7DksdfJj4PqN1srxBbxlVafy7k+mRIDu9N5qqKhYpa8wWAwGAxgRu69eG3r2IbjQzYcNr+RaNjmH6+3ks1ridiFM2uJhWxe3tLJ8+s7mDctgR3UsF919mGm1M1gMBgMvTDOvQTPHxup2fqqMP/6/gP58z+3kMq57OhK4ymojoVZOKummBF/+L6NrG/u4dgDp3HcoiZTw24wGAyGPjHOvYRXx6Bf++H7NvDtQEHuvLfvy8q1Lfzu6U2sXNPMPk1VuotbgG0JiYjDwlm1ppbdYDAYDP0yanPuItIgIveISFJE1ovIuQNs+3kR2SYinSJyi4hERsPGF0cxS94CLjv1Tdx++duL0rCRkM3xi5r4wOLZ1MRDvRx7cT+TQGcwGAyGQRjNhLofAzmgCTgPuEFEDirfSETeA3wZOBGYD+wL/NfomTnyxELCleccxr99YFGfYfUj959CVTREdzrfa7lJoDMYDAZDJYyKcxeRBHAmcIVSqkcp9TjwW+D8Pja/ELhZKfWCUqod+G/gopG2saMnN9KnwBaoS4TYb0Yt86dV9btdNGxz1dmH4dgWzV0Ztnekae7K4NiWSaAzGAwGw6CM1pz7QsBTSq0pWfYscEIf2x4E3Fu2XZOINCqlWkfKwCWPvT5Shwa0Hnws4jC7MUF1bPDR94KZNdzy2WNNExiDwWAwDJnRcu5VQGfZsk6guoJtC8+rgV7OXUQ+AXwCYO7cuXtk4LodPXu0f38I4NhgWRZzp2rHXunouzAHbzAYDAbDUBgt594DlDcUrwH6Sk8v37bwfJdtlVI/BX4KsHjx4j0Sgx8oTL67RBxhztQqQrbFKYtnc/C8ejP6NhgMBsOIM1rOfQ3giMgCpdTaYNmhwAt9bPtCsO6Oku22j2RIHuDc4/flmuV9mVMZjqVH2iJCTSyEY1tcctIC5k+rMg7dYDAYDKPKqDh3pVRSRO4GviEiHwMOA04H3tbH5rcCvxCR24GtwNeAX4y0jXVVYX566TF84vq/V7R9fTzEYfvWc/QB07h7xQZ6MnkcS6iKhaiJhbnq7MOKJW4Gg8FgMIwmoylicylwC7ADPXf+aaXUCyIyF3gRWKSU2qCU+qOIXA38BYgBdwFXjoaB7z50Jqt+cCqHff6+XdZNrwnx8fe+if1n1OwyEr/gnfubxDeDwWAwjBtGzbkrpdqAM/pYvgGdRFe67Frg2tGxrDc18TCv33jmkPYxiW8Gg8FgGE+YrnAGg8FgMEwwjHM3GAwGg2GCYZy7wWAwGAwTDOPcDQaDwWCYYBjnbjAYDAbDBMM4d4PBYDAYJhjGuRsMBoPBMMEwzt1gMBgMhgmGce4Gg8FgMEwwjHM3GAwGg2GCYZy7wWAwGAwTDOPcDQaDwWCYYBjnbjAYDAbDBMM4d4PBYDAYJhiilBprG4YFEWkG1g/jIacALcN4vMmKuY57jrmGe465hnuOuYZ7zkhcw3lKqanlCyeMcx9uRORppdTisbZjb8dcxz3HXMM9x1zDPcdcwz1nNK+hCcsbDAaDwTDBMM7dYDAYDIYJhnHu/fPTsTZggmCu455jruGeY67hnmOu4Z4zatfQzLkbDAaDwTDBMCN3g8FgMBgmGMa5GwwGg8EwwZjUzl1EGkTkHhFJish6ETl3gG0/LyLbRKRTRG4Rkcho2jpeqfQaisiFIvKMiHSJyCYRuVpEnNG2dzwylM9hyT4Pi4gy11AzxO/yviLyOxHpFpEWEbl6NG0drwzhuywi8k0R2Rz8Hj4iIgeNtr3jERH5jIg8LSJZEfnFINuOqE+Z1M4d+DGQA5qA84Ab+vqQish7gC8DJwLzgX2B/xo9M8c1FV1DIA5chhZxOAp9Lb84SjaOdyq9hgCIyHmAceq9qfS7HAb+DDwMTAdmA7eNop3jmUo/hx8CPgocDzQAfwd+NVpGjnO2AN8Ebhloo9HwKZM2oU5EEkA78Gal1Jpg2a+AzUqpL5dtuwRYp5T6avD6ROB2pdT0UTZ7XDGUa9jHvpcD71RKnTrylo5fhnoNRaQWeAq4AP2jGlJKuaNo8rhjiN/lTwDnK6WOH31Lxy9DvIb/ARyhlDoreH0Q8IxSKjrKZo9bROSbwGyl1EX9rB9xnzKZR+4LAa/wQQ54FujrTvWgYF3pdk0i0jiC9u0NDOUalvN24IURsWrvYqjX8FvADcC2kTZsL2Io1/BoYJ2I/CEIyT8iIgePipXjm6Fcw2XA/iKyUERCwIXAH0fBxonEiPuUyezcq4DOsmWdQHUF2xae97XtZGIo17CIiFwMLAauGSG79iYqvoYishg4FvjRKNi1NzGUz+Fs4Gzgh8BM4H7g3iBcP5kZyjXcCjwGvAKk0WH6z4+odROPEfcpk9m59wA1ZctqgO4Kti0872vbycRQriEAInIG8B3gfUop04SiwmsoIhZwPfC5yR6G74OhfA7TwONKqT8opXLoG8xG4E0ja+K4ZyjX8ErgSGAOEEXPFT8sIvERtXBiMeI+ZTI79zWAIyILSpYdSt+h4heCdaXbbVdKtY6gfXsDQ7mGiMh7gZ8BpyqlnhsF+/YGKr2GNehox69FZBt63h1gk4hM9vnjoXwOVwOTM9FoYIZyDQ8Ffq2U2qSUcpVSvwDqgUUjb+aEYeR9ilJq0j7Qc0dLgQQ63NkJHNTHdu9Fz3EuQn+IHwa+M9b2j4fHEK7hu4BW4O1jbfN4e1RyDQFBZ3cXHkeindQsIDzW72GsH0P4HB4ApIB3AzY6nPyauYZDuoZXAo+js+ot4HwgCdSN9XsY6we6iiUKfBtdQRAFnD62G3GfMuYXY4z/EA3A8uCDuQE4N1g+Fx02mVuy7eXAdqAL+DkQGWv7x8Oj0msI/AVwg2WFxx/G2v7x8BjK57Bkn/mBc9/lh2MyPob4Xf5/wKvBd/mRvhzYZHwM4bscRZfNbQ2u4T+A9461/ePhAVwVfC9LH1eNhU+ZtKVwBoPBYDBMVCbznLvBYDAYDBMS49wNBoPBYJhgGOduMBgMBsMEwzh3g8FgMBgmGMa5GwwGg8EwwTDO3WAwGAyGCYZx7gaDYVwhIgeIyD+Dfuv/JiIxEbkv6Ht95yic/9sictlIn2eiIiIrTX/3scc4d8OQEJF1IvLuktdni0i7iJwgIvNFRIlIT/BYJyJ9tS19R7Ddl/pYd4mIvBz8sG8XkftFpM9mCkFHLyUih5YtXx4sf4eI/KTEnpyI5Ete/2GA97mPiPgicv3QrtD4JfhbPSkiSRHZETy/VERklO24SES8kr9D4TEz2ORLwCNKqWql1A+BD6LV0BqVUh/ag/NeJSID9m4Xkanodro3liyrEZHrRGRDYOerwespwfp1IpIuey//18d77RKRZ0XkAyXHLv/OFB4fDtbPFpG7gg52nSLynIhcNID9XxWRN4JjbBKRX+/u9doDrgG+MQbnNZRgnLthtxGRC9FKVacopf5asqpOKVWF/lG+QkROKtv1QqAt+L/0eCegW5qeo5SqRjfzuGMQM9agf4wLx2hEt/VsBlBKfUopVRXY8y20JnZV8HjfAMe9AN3f+mwRiQxiw5AREWe4jznI+b4A/C/wPbR8bRPwKbTM6Fh0RPt7yd+h8NgSrJtHb03zecAaNToNcy4Cfq+USgME3eIeQrfofC9a4/9taCnlt5bsd2rZe/lMybq/B5+/OnTzn2UiUld23rqy/QtO+VfARvQ1aER/Lrf3ZXjwfTwfeHdwvsWB7cNGhZ/b3wLvFJEZw3luwxAZa7k+89i7HsA6tC73J4AWYHHJuvmUSaICK4F/L3kdR3c+OhvIle3/RWD5EGx5BPg6sAmwg2WfQfc73wS8o2z7q4DbKjz2a8Cn0T+kHwyWnQ08Xbbd54HfBs8j6FHLhmC/nwCxYN07Apv+A60p/Su0pvTv0Dci7cHz2SXH3gd4NLheD6JvpG4rWX808DegA90P+h39vJdatKTomYO850rs/wKwAy09enEl+/ZxnovQndn6Wvcw4AEZtFzn0uBzkg9eXxJs91HgpeC6/QmYV3KMg4A/o28gtwNfRTvm0uM8O8D5P1Ly+mPBMaoG+05U8l7Rn38FHNnfd6Zs/x7gsAo/s/8HXDfA+ga0zOmW4LotL1n3cbQkbxvaOc8sWaeAfwXWAm8Eyz4ArAo+e38DDik715+BCyv9LpvH8D/MyN2wO3wa+G/gRKXU0/1tJCJHA29G/2gUOBP9g3Un+kf5gpJ1TwLvEZH/EpFjKxwxbwFeBE4OXl8A3FrpG+nH7uPRfb+XoSMHBRt/CxwgvTtnnQssCZ5/F1gIHAbsj27q8vWSbaejf2DnoW+OLPSP7Ty09nQa/QNdYAn65qgRfWNyfomNs9C9yL8ZHPOLwF1BWLmcY9DO995B3nol9tcGyy8Bfiwi9RXuWxFKqXehe4V/RukR7Dn0jrjcLLpt8FfRGvFTg+2XAgRTOA8Cf0T3a98feEgp9cey4xxK3xyM7lNe4N3AH5VSPUN9L+WIiA1cjL7BWF/hbivQ1/lsEZlbwbYXiMi/i8ji4Hyl/Ap9c3EQMA34QWDXu9CNTs4CZgS2LSvb9wzgKGCRiBwO3AJ8Ev3ZvBH4bdn39SV6dz0zjDZjfXdhHnvXAz1K6UI7Cqts3Xz0XX4H2lEp9GhOSrZ5kGB0AZyDHrWGSta/D7gvOEYPcC3BqLwPWx5Bj6w+gv5xPwAdvoU9GLkDNxGMatCOMQ9MC17fBnw9eL4APaqOo7u2JYH9So5zDDtHOu9AjxyjA5z3MKA9eD4X3WgnXrL+toL96AjAr8r2/xN9jJaC67OtbFlhxJ8G3l6h/Wl6R2V2oKMHA+7bhz0XBe+to+TxWvnftb+/G/AHghF88NpCd3qbF3ym/tnPeQf9+wd/6wNLXv+ZQbp1ob8TPWXv5+N9vNd8cA3P6uc7U/p4U7C+HvgOeprCQ4+WjxzAlvPQ37Ekeurgy8HyGYAP1Pexz83A1SWvqwJb5wevFfCukvU3AP9ddoxXgBNKXv8PcMtg3zXzGLmHGbkbdodPoUdpN/WTjDUF/QPxRbRTCAGIyBzgncDtwXb3ojtMnVLYUSn1B6XUqejR6OnoH8ePDWLP3eiWsp9Fj052GxGJAR8q2KiU+jtBh6xgkyVoB0KwbLlSKoUeQcaBZ0SkQ0Q60KPH0pF0s1IqU3KuuIjcKCLrRaQLHYKvC0ZcM4G24NgFNpY8nwd8qHCu4HzHoX/Ey2kFppTOlyql3qaUqgvWWRXa36p6z3un0H/nSvYtZ4VSqq7ksd8A25YzD/jfknO1oW8wZgFz0FMqu0s7UJrA2Urf17ScM8rez89K1q0IrnU9OvpzfB/7Tynb/yUApVS7UurLSqmD0HkSq4Dl/SVBKqVuV0q9Gz2//yngGyLyHvR1aVNKtfex20xKIglKRyla0dezQPln7wtln705wXEKVKNvUgxjhHHuht1hB3Ai+keqz2xypZSnlPo+eu700mDx+ejP3H0isg14He3cL+hjf18p9RB6DvTNAxkTOMA/oKcL9si5A/+CTpq6XkS2BXbOKrHxAbSjPAzt5Ash+Rb0qOygkh/oWqUTm4qmlp3rC+how1FKqRr0CBq0o9oKNIhIvGT7OSXPN6JH7qUOIaGU+k4f7+nvQBZ9s9Qfldg/EvvuDhuBT5a995hS6m/Buv5uFCppgbkafeNa4EH0VFFiz0wuOs1LgfNF5C27sX8LOhI2E33zO9C2eaXUnej382b0dWnoI5EP9NTWvMKL4L02AptLD1nyfCPwP2XXP66UWlqyzZvQeSCGMcI4d8NuoXRm87uA94rIDwbY9DvAl0Sk4MT/Cx1+LjzOBE4RkUYROT2YW6wXzVuBE9BziYPxVXRYcN1uvqUCF6LnEw8usfFY4DAROTgYuf4GnXXegA7bopTygZ8BPxCRaaDnxYNRU39Uo51ih4g0AFcWViil1gNPA1eJSFhEjgFOLdn3NuBUEXmPiNgiEhVd+je7/CRKqQ70db9eRD4oIlUiYgU3KIk9sL9w/N3edzf5CfAVCWqpRaRWRAolcr8DpovIZSISEZFqETkqWLcdmC8iA/3u/R79mStQyFa/S0QODK5bY1By9v6hGq6UakVP+1SUjyAi3xWRN4uIE+QTfBp4NThO+bYXicgpwXu2ROR96Pn1J5VSW9E3wNcH36+QiBRuJpcAF4vIYcG8+beCfdb1Y9bPgE+JyFHB9zRROG9gRwQ4guC7YRgbjHM37DZKqY1oB/9BEfl2P5vdjw51/jt6fvHHSqltJY/fohPuzgm2+zg6K7cL7cC+p5S6vc8j97Zli1Lq8T15P0GS2ononIBSG59Bh5kLpXtL0IlWd5aFqf8jeC8rgjD7g+iReX9cB8TQI98VwTlKOQ89d92KTpz7NXoEXrj2p6NvaprRDujf6ec7rZS6GrgcXUO+A+3obgxs/ttu2l/KUPc9Rnat7T6ykhMppe5BJ/AtC871PDpXA6VUN3AS+kZoG/qz9M5g14IATquI/KOfw98KvD+YnkEplUX/rV9GO6sudJLjFHQCaIH7yt7LPQO8heuCcxxSsqyjbP/Lg+Vx4B50iPt19Aj7tH6O24X+PGwItr8a+HTJ9+J89Fz6y+jPwGXBe3wIuAK4Cx0x2g9dGdInSifRfhyd/NmO/rtfVLLJaWidgi277m0YLUSpSiJVBoNhrBEtSPKyUurKQTc27DYi8i1gh1LqurG2ZW9ERJ5EJzw+P9a2TGaMczcYxinBSLYNeANd6rccOEYp9c+xtMtgMIx/RlUly2AwDInp6EqARnRp36eNYzcYDJVgRu4Gg8FgMEwwTEKdwWAwGAwTDOPcDQaDwWCYYBjnbjAYDAbDBMM4d4PBYDAYJhjGuRsMBoPBMMEwzt1gMBgMhgnG/weT7XfFbH3MzgAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 576x576 with 1 Axes>"
       ]
@@ -285,22 +242,20 @@
    ],
    "source": [
     "#Text_coords are custom per graph \n",
-    "text_coords1 = [(-1.35, -0.5), (0.1, -0.4)]\n",
-    "text_coords2 = [(-0.75, -0.3), (0.1, -0.2)]\n",
+    "text_coords1 = [(0.95, 0.25), (0.1, 0.4)]\n",
     "\n",
     "gene_effect_scatter(kras_mt,\n",
     "                    kras_wt,\n",
-    "                    kras_mt_ewt,\n",
-    "                    kras_wt_ewt,\n",
     "                    \"KRAS\",\n",
     "                    \"EGFR\",\n",
     "                    tc1 = text_coords1,\n",
     "                    tc2 = text_coords2,\n",
-    "                    name= None)\n"
+    "                    name= \"figures/kras_gene_dependency_scatter.pdf\")\n"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "11e97be8",
    "metadata": {},
    "source": [
     "### Average Gene Effect of EGFR MT vs EGFR WT Cell Lines\n",
@@ -309,12 +264,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 58,
+   "id": "d658fa0a",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 576x576 with 1 Axes>"
       ]
@@ -326,17 +282,15 @@
     }
    ],
    "source": [
-    "tc1 = [(-0.4, 0), (-0.5, -1)]\n",
-    "tc2 = [(-0.7, -0.1), (0.0,-0.5)]\n",
+    "tc1 = [(0.4, 0.1), (0.2, 0.6)]\n",
+    "\n",
     "gene_effect_scatter(egfr_mt,\n",
     "                    egfr_wt,\n",
-    "                    egfr_mt_kwt,\n",
-    "                    egfr_wt_kwt,\n",
     "                    \"EGFR\",\n",
     "                    \"KRAS\",\n",
     "                    tc1 = tc1,\n",
     "                    tc2 = tc2,\n",
-    "                    name = None\n",
+    "                    name = None#\"figures/egfr_gene_dependency_scatter.pdf\"\n",
     "                   )"
    ]
   }
@@ -357,7 +311,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.9.2"
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  "nbformat": 4,
diff --git a/get-started.ipynb b/get-started.ipynb
new file mode 100644
index 0000000..a30ec58
--- /dev/null
+++ b/get-started.ipynb
@@ -0,0 +1,3748 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Getting Started"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's go over basic functionality and use cases of CanDI package. \n",
+    "\n",
+    "### Importing\n",
+    "\n",
+    "CanDI must be imported from from the main CanDI directory. The core CanDI objects are contained within the CanDI.candi module and are imported as follows. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import CanDI.candi as can\n",
+    "#Can also be imported as \n",
+    "from CanDI import candi as can"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Data Object\n",
+    "The Data object is instantiated when CanDI and access as data within the candi module\n",
+    "CanDI dataset paths are defined as attributes within the Data object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/home/cyogodzi/projects/candi-paper/CanDI/CanDI/setup/data/depmap/CRISPR_gene_effect.csv\n",
+      "/home/cyogodzi/projects/candi-paper/CanDI/CanDI/setup/data/depmap/CCLE_expression.csv\n",
+      "/home/cyogodzi/projects/candi-paper/CanDI/CanDI/setup/data/depmap/CCLE_gene_cn.csv\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(can.data.gene_effect) # depmap ceres score\n",
+    "print(can.data.expression) # ccle rna seq data\n",
+    "print(can.data.gene_cn) # ccle copy number data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## How to Directly Load a Dataset\n",
+    "The load method of the Data object is used to load specific datasets into memory. The datasets are saved as pandas dataframes as attributes of the data object. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "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>ACH-001113</th>\n",
+       "      <th>ACH-001289</th>\n",
+       "      <th>ACH-001339</th>\n",
+       "      <th>ACH-001538</th>\n",
+       "      <th>ACH-000242</th>\n",
+       "      <th>ACH-000708</th>\n",
+       "      <th>ACH-000327</th>\n",
+       "      <th>ACH-000233</th>\n",
+       "      <th>ACH-000461</th>\n",
+       "      <th>ACH-000705</th>\n",
+       "      <th>...</th>\n",
+       "      <th>ACH-000114</th>\n",
+       "      <th>ACH-000402</th>\n",
+       "      <th>ACH-000036</th>\n",
+       "      <th>ACH-000973</th>\n",
+       "      <th>ACH-001128</th>\n",
+       "      <th>ACH-000750</th>\n",
+       "      <th>ACH-000285</th>\n",
+       "      <th>ACH-001858</th>\n",
+       "      <th>ACH-001997</th>\n",
+       "      <th>ACH-000052</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>gene</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>TSPAN6</th>\n",
+       "      <td>4.990501</td>\n",
+       "      <td>5.209843</td>\n",
+       "      <td>3.779260</td>\n",
+       "      <td>5.726831</td>\n",
+       "      <td>7.465648</td>\n",
+       "      <td>4.914086</td>\n",
+       "      <td>4.032982</td>\n",
+       "      <td>0.097611</td>\n",
+       "      <td>4.712596</td>\n",
+       "      <td>5.101398</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3.793896</td>\n",
+       "      <td>0.070389</td>\n",
+       "      <td>4.692650</td>\n",
+       "      <td>5.026800</td>\n",
+       "      <td>6.699052</td>\n",
+       "      <td>4.173127</td>\n",
+       "      <td>0.097611</td>\n",
+       "      <td>5.045268</td>\n",
+       "      <td>5.805292</td>\n",
+       "      <td>4.870858</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TNMD</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.545968</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.176323</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.028569</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>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DPM1</th>\n",
+       "      <td>7.273702</td>\n",
+       "      <td>7.070604</td>\n",
+       "      <td>7.346425</td>\n",
+       "      <td>7.086189</td>\n",
+       "      <td>6.435462</td>\n",
+       "      <td>6.946848</td>\n",
+       "      <td>5.806582</td>\n",
+       "      <td>5.919102</td>\n",
+       "      <td>6.406333</td>\n",
+       "      <td>6.309976</td>\n",
+       "      <td>...</td>\n",
+       "      <td>6.330738</td>\n",
+       "      <td>5.858230</td>\n",
+       "      <td>6.623369</td>\n",
+       "      <td>6.966130</td>\n",
+       "      <td>6.131960</td>\n",
+       "      <td>6.400879</td>\n",
+       "      <td>6.428276</td>\n",
+       "      <td>6.991749</td>\n",
+       "      <td>7.792855</td>\n",
+       "      <td>6.077457</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SCYL3</th>\n",
+       "      <td>2.765535</td>\n",
+       "      <td>2.538538</td>\n",
+       "      <td>2.339137</td>\n",
+       "      <td>2.543496</td>\n",
+       "      <td>2.414136</td>\n",
+       "      <td>2.577731</td>\n",
+       "      <td>1.948601</td>\n",
+       "      <td>3.983678</td>\n",
+       "      <td>2.247928</td>\n",
+       "      <td>2.361768</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.792855</td>\n",
+       "      <td>2.757023</td>\n",
+       "      <td>2.111031</td>\n",
+       "      <td>1.899176</td>\n",
+       "      <td>2.235727</td>\n",
+       "      <td>1.807355</td>\n",
+       "      <td>3.257011</td>\n",
+       "      <td>1.807355</td>\n",
+       "      <td>2.482848</td>\n",
+       "      <td>2.304511</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>C1orf112</th>\n",
+       "      <td>4.480265</td>\n",
+       "      <td>3.510962</td>\n",
+       "      <td>4.254745</td>\n",
+       "      <td>3.102658</td>\n",
+       "      <td>3.864929</td>\n",
+       "      <td>3.853996</td>\n",
+       "      <td>2.684819</td>\n",
+       "      <td>3.733354</td>\n",
+       "      <td>3.032101</td>\n",
+       "      <td>4.280214</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.643856</td>\n",
+       "      <td>5.103078</td>\n",
+       "      <td>2.543496</td>\n",
+       "      <td>3.531069</td>\n",
+       "      <td>3.971773</td>\n",
+       "      <td>3.303050</td>\n",
+       "      <td>4.980482</td>\n",
+       "      <td>3.270529</td>\n",
+       "      <td>3.903038</td>\n",
+       "      <td>3.836934</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>POLR2J3</th>\n",
+       "      <td>5.781884</td>\n",
+       "      <td>4.704319</td>\n",
+       "      <td>4.931683</td>\n",
+       "      <td>3.858976</td>\n",
+       "      <td>4.990501</td>\n",
+       "      <td>5.303781</td>\n",
+       "      <td>4.996841</td>\n",
+       "      <td>6.839960</td>\n",
+       "      <td>5.529196</td>\n",
+       "      <td>5.860963</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3.793896</td>\n",
+       "      <td>6.669877</td>\n",
+       "      <td>6.191010</td>\n",
+       "      <td>5.934281</td>\n",
+       "      <td>3.097611</td>\n",
+       "      <td>5.102658</td>\n",
+       "      <td>6.341630</td>\n",
+       "      <td>4.607626</td>\n",
+       "      <td>4.787119</td>\n",
+       "      <td>4.452859</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>H2BE1</th>\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>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.594549</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.176323</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>0.111031</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AL445238.1</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.028569</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.042644</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</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>0.097611</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.163499</td>\n",
+       "      <td>0.163499</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>GET1-SH3BGR</th>\n",
+       "      <td>0.799087</td>\n",
+       "      <td>0.464668</td>\n",
+       "      <td>0.263034</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.263034</td>\n",
+       "      <td>0.286881</td>\n",
+       "      <td>2.280956</td>\n",
+       "      <td>0.275007</td>\n",
+       "      <td>0.790772</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.416840</td>\n",
+       "      <td>0.526069</td>\n",
+       "      <td>1.117695</td>\n",
+       "      <td>0.378512</td>\n",
+       "      <td>0.713696</td>\n",
+       "      <td>0.214125</td>\n",
+       "      <td>0.310340</td>\n",
+       "      <td>1.090853</td>\n",
+       "      <td>0.084064</td>\n",
+       "      <td>1.422233</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AC113348.1</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.070389</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
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+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.084064</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>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>19177 rows × 1379 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             ACH-001113  ACH-001289  ACH-001339  ACH-001538  ACH-000242  \\\n",
+       "gene                                                                      \n",
+       "TSPAN6         4.990501    5.209843    3.779260    5.726831    7.465648   \n",
+       "TNMD           0.000000    0.545968    0.000000    0.000000    0.000000   \n",
+       "DPM1           7.273702    7.070604    7.346425    7.086189    6.435462   \n",
+       "SCYL3          2.765535    2.538538    2.339137    2.543496    2.414136   \n",
+       "C1orf112       4.480265    3.510962    4.254745    3.102658    3.864929   \n",
+       "...                 ...         ...         ...         ...         ...   \n",
+       "POLR2J3        5.781884    4.704319    4.931683    3.858976    4.990501   \n",
+       "H2BE1          0.000000    0.000000    0.000000    0.000000    0.000000   \n",
+       "AL445238.1     0.000000    0.000000    0.028569    0.000000    0.000000   \n",
+       "GET1-SH3BGR    0.799087    0.464668    0.263034    0.000000    0.000000   \n",
+       "AC113348.1     0.000000    0.070389    0.000000    0.000000    0.000000   \n",
+       "\n",
+       "             ACH-000708  ACH-000327  ACH-000233  ACH-000461  ACH-000705  ...  \\\n",
+       "gene                                                                     ...   \n",
+       "TSPAN6         4.914086    4.032982    0.097611    4.712596    5.101398  ...   \n",
+       "TNMD           0.176323    0.000000    0.000000    0.000000    0.000000  ...   \n",
+       "DPM1           6.946848    5.806582    5.919102    6.406333    6.309976  ...   \n",
+       "SCYL3          2.577731    1.948601    3.983678    2.247928    2.361768  ...   \n",
+       "C1orf112       3.853996    2.684819    3.733354    3.032101    4.280214  ...   \n",
+       "...                 ...         ...         ...         ...         ...  ...   \n",
+       "POLR2J3        5.303781    4.996841    6.839960    5.529196    5.860963  ...   \n",
+       "H2BE1          0.000000    0.000000    0.000000    0.000000    0.594549  ...   \n",
+       "AL445238.1     0.000000    0.042644    0.000000    0.000000    0.000000  ...   \n",
+       "GET1-SH3BGR    0.263034    0.286881    2.280956    0.275007    0.790772  ...   \n",
+       "AC113348.1     0.000000    0.028569    0.000000    0.000000    0.000000  ...   \n",
+       "\n",
+       "             ACH-000114  ACH-000402  ACH-000036  ACH-000973  ACH-001128  \\\n",
+       "gene                                                                      \n",
+       "TSPAN6         3.793896    0.070389    4.692650    5.026800    6.699052   \n",
+       "TNMD           0.028569    0.000000    0.000000    0.000000    0.000000   \n",
+       "DPM1           6.330738    5.858230    6.623369    6.966130    6.131960   \n",
+       "SCYL3          2.792855    2.757023    2.111031    1.899176    2.235727   \n",
+       "C1orf112       2.643856    5.103078    2.543496    3.531069    3.971773   \n",
+       "...                 ...         ...         ...         ...         ...   \n",
+       "POLR2J3        3.793896    6.669877    6.191010    5.934281    3.097611   \n",
+       "H2BE1          0.000000    0.176323    0.000000    0.000000    0.000000   \n",
+       "AL445238.1     0.000000    0.000000    0.000000    0.000000    0.000000   \n",
+       "GET1-SH3BGR    1.416840    0.526069    1.117695    0.378512    0.713696   \n",
+       "AC113348.1     0.084064    0.000000    0.000000    0.000000    0.000000   \n",
+       "\n",
+       "             ACH-000750  ACH-000285  ACH-001858  ACH-001997  ACH-000052  \n",
+       "gene                                                                     \n",
+       "TSPAN6         4.173127    0.097611    5.045268    5.805292    4.870858  \n",
+       "TNMD           0.000000    0.000000    0.000000    0.000000    0.000000  \n",
+       "DPM1           6.400879    6.428276    6.991749    7.792855    6.077457  \n",
+       "SCYL3          1.807355    3.257011    1.807355    2.482848    2.304511  \n",
+       "C1orf112       3.303050    4.980482    3.270529    3.903038    3.836934  \n",
+       "...                 ...         ...         ...         ...         ...  \n",
+       "POLR2J3        5.102658    6.341630    4.607626    4.787119    4.452859  \n",
+       "H2BE1          0.000000    0.000000    0.111031    0.000000    0.000000  \n",
+       "AL445238.1     0.097611    0.000000    0.000000    0.163499    0.163499  \n",
+       "GET1-SH3BGR    0.214125    0.310340    1.090853    0.084064    1.422233  \n",
+       "AC113348.1     0.000000    0.000000    0.000000    0.000000    0.000000  \n",
+       "\n",
+       "[19177 rows x 1379 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "can.data.load(\"expression\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Cell Lines\n",
+    "The Cell Lines dataset contains all cell line metadata. This table is loaded automatically when candi is imported."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "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>cell_line_name</th>\n",
+       "      <th>stripped_cell_line_name</th>\n",
+       "      <th>CCLE_Name</th>\n",
+       "      <th>alias</th>\n",
+       "      <th>COSMICID</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>source</th>\n",
+       "      <th>Achilles_n_replicates</th>\n",
+       "      <th>cell_line_NNMD</th>\n",
+       "      <th>culture_type</th>\n",
+       "      <th>...</th>\n",
+       "      <th>primary_or_metastasis</th>\n",
+       "      <th>primary_disease</th>\n",
+       "      <th>Subtype</th>\n",
+       "      <th>age</th>\n",
+       "      <th>Sanger_Model_ID</th>\n",
+       "      <th>depmap_public_comments</th>\n",
+       "      <th>lineage</th>\n",
+       "      <th>lineage_subtype</th>\n",
+       "      <th>lineage_sub_subtype</th>\n",
+       "      <th>lineage_molecular_subtype</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DepMap_ID</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>ACH-000001</th>\n",
+       "      <td>NIH:OVCAR-3</td>\n",
+       "      <td>NIHOVCAR3</td>\n",
+       "      <td>NIHOVCAR3_OVARY</td>\n",
+       "      <td>OVCAR3</td>\n",
+       "      <td>905933.0</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>ATCC</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Metastasis</td>\n",
+       "      <td>Ovarian Cancer</td>\n",
+       "      <td>Adenocarcinoma, high grade serous</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>SIDM00105</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>ovary</td>\n",
+       "      <td>ovary_adenocarcinoma</td>\n",
+       "      <td>high_grade_serous</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ACH-000002</th>\n",
+       "      <td>HL-60</td>\n",
+       "      <td>HL60</td>\n",
+       "      <td>HL60_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>905938.0</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>ATCC</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Primary</td>\n",
+       "      <td>Leukemia</td>\n",
+       "      <td>Acute Myelogenous Leukemia (AML), M3 (Promyelo...</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>SIDM00829</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>AML</td>\n",
+       "      <td>M3</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ACH-000003</th>\n",
+       "      <td>CACO2</td>\n",
+       "      <td>CACO2</td>\n",
+       "      <td>CACO2_LARGE_INTESTINE</td>\n",
+       "      <td>CACO2, CaCo-2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>ATCC</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Colon/Colorectal Cancer</td>\n",
+       "      <td>Adenocarcinoma</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>SIDM00891</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>colorectal</td>\n",
+       "      <td>colorectal_adenocarcinoma</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ACH-000004</th>\n",
+       "      <td>HEL</td>\n",
+       "      <td>HEL</td>\n",
+       "      <td>HEL_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>907053.0</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>DSMZ</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>-3.079202</td>\n",
+       "      <td>Suspension</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Leukemia</td>\n",
+       "      <td>Acute Myelogenous Leukemia (AML), M6 (Erythrol...</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>SIDM00594</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>AML</td>\n",
+       "      <td>M6</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ACH-000005</th>\n",
+       "      <td>HEL 92.1.7</td>\n",
+       "      <td>HEL9217</td>\n",
+       "      <td>HEL9217_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>ATCC</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>-2.404409</td>\n",
+       "      <td>Suspension</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Leukemia</td>\n",
+       "      <td>Acute Myelogenous Leukemia (AML), M6 (Erythrol...</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>SIDM00593</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>AML</td>\n",
+       "      <td>M6</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 25 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           cell_line_name stripped_cell_line_name  \\\n",
+       "DepMap_ID                                           \n",
+       "ACH-000001    NIH:OVCAR-3               NIHOVCAR3   \n",
+       "ACH-000002          HL-60                    HL60   \n",
+       "ACH-000003          CACO2                   CACO2   \n",
+       "ACH-000004            HEL                     HEL   \n",
+       "ACH-000005     HEL 92.1.7                 HEL9217   \n",
+       "\n",
+       "                                             CCLE_Name          alias  \\\n",
+       "DepMap_ID                                                               \n",
+       "ACH-000001                             NIHOVCAR3_OVARY         OVCAR3   \n",
+       "ACH-000002     HL60_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE            NaN   \n",
+       "ACH-000003                       CACO2_LARGE_INTESTINE  CACO2, CaCo-2   \n",
+       "ACH-000004      HEL_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE            NaN   \n",
+       "ACH-000005  HEL9217_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE            NaN   \n",
+       "\n",
+       "            COSMICID     sex source  Achilles_n_replicates  cell_line_NNMD  \\\n",
+       "DepMap_ID                                                                    \n",
+       "ACH-000001  905933.0  Female   ATCC                    NaN             NaN   \n",
+       "ACH-000002  905938.0  Female   ATCC                    NaN             NaN   \n",
+       "ACH-000003       NaN    Male   ATCC                    NaN             NaN   \n",
+       "ACH-000004  907053.0    Male   DSMZ                    2.0       -3.079202   \n",
+       "ACH-000005       NaN    Male   ATCC                    2.0       -2.404409   \n",
+       "\n",
+       "           culture_type  ... primary_or_metastasis          primary_disease  \\\n",
+       "DepMap_ID                ...                                                  \n",
+       "ACH-000001          NaN  ...            Metastasis           Ovarian Cancer   \n",
+       "ACH-000002          NaN  ...               Primary                 Leukemia   \n",
+       "ACH-000003          NaN  ...                   NaN  Colon/Colorectal Cancer   \n",
+       "ACH-000004   Suspension  ...                   NaN                 Leukemia   \n",
+       "ACH-000005   Suspension  ...                   NaN                 Leukemia   \n",
+       "\n",
+       "                                                      Subtype   age  \\\n",
+       "DepMap_ID                                                             \n",
+       "ACH-000001                  Adenocarcinoma, high grade serous  60.0   \n",
+       "ACH-000002  Acute Myelogenous Leukemia (AML), M3 (Promyelo...  35.0   \n",
+       "ACH-000003                                     Adenocarcinoma   NaN   \n",
+       "ACH-000004  Acute Myelogenous Leukemia (AML), M6 (Erythrol...  30.0   \n",
+       "ACH-000005  Acute Myelogenous Leukemia (AML), M6 (Erythrol...  30.0   \n",
+       "\n",
+       "           Sanger_Model_ID depmap_public_comments     lineage  \\\n",
+       "DepMap_ID                                                       \n",
+       "ACH-000001       SIDM00105                    NaN       ovary   \n",
+       "ACH-000002       SIDM00829                    NaN       blood   \n",
+       "ACH-000003       SIDM00891                    NaN  colorectal   \n",
+       "ACH-000004       SIDM00594                    NaN       blood   \n",
+       "ACH-000005       SIDM00593                    NaN       blood   \n",
+       "\n",
+       "                      lineage_subtype  lineage_sub_subtype  \\\n",
+       "DepMap_ID                                                    \n",
+       "ACH-000001       ovary_adenocarcinoma    high_grade_serous   \n",
+       "ACH-000002                        AML                   M3   \n",
+       "ACH-000003  colorectal_adenocarcinoma                  NaN   \n",
+       "ACH-000004                        AML                   M6   \n",
+       "ACH-000005                        AML                   M6   \n",
+       "\n",
+       "           lineage_molecular_subtype  \n",
+       "DepMap_ID                             \n",
+       "ACH-000001                       NaN  \n",
+       "ACH-000002                       NaN  \n",
+       "ACH-000003                       NaN  \n",
+       "ACH-000004                       NaN  \n",
+       "ACH-000005                       NaN  \n",
+       "\n",
+       "[5 rows x 25 columns]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "can.data.cell_lines.head(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Genes\n",
+    "The genes dataset contains relevant gene metadata. \n",
+    "The genes dataset is loaded into memory automatically when candi is imported. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "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>Approved name</th>\n",
+       "      <th>Accession numbers</th>\n",
+       "      <th>UniProt ID</th>\n",
+       "      <th>ENTREZ ID</th>\n",
+       "      <th>Ensembl ID</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Approved symbol</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>PINLYP</th>\n",
+       "      <td>phospholipase A2 inhibitor and LY6/PLAUR domai...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>A6NC86</td>\n",
+       "      <td>390940</td>\n",
+       "      <td>ENSG00000234465</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ARL6IP1P1</th>\n",
+       "      <td>ADP ribosylation factor like GTPase 6 interact...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>100288702</td>\n",
+       "      <td>ENSG00000255664</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>PRAMEF33</th>\n",
+       "      <td>PRAME family member 33</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>A0A0G2JMD5</td>\n",
+       "      <td>645382</td>\n",
+       "      <td>ENSG00000237700</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AL353354.2</th>\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>CTA-298G8.2</th>\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",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                     Approved name  \\\n",
+       "Approved symbol                                                      \n",
+       "PINLYP           phospholipase A2 inhibitor and LY6/PLAUR domai...   \n",
+       "ARL6IP1P1        ADP ribosylation factor like GTPase 6 interact...   \n",
+       "PRAMEF33                                    PRAME family member 33   \n",
+       "AL353354.2                                                     NaN   \n",
+       "CTA-298G8.2                                                    NaN   \n",
+       "\n",
+       "                Accession numbers  UniProt ID  ENTREZ ID       Ensembl ID  \n",
+       "Approved symbol                                                            \n",
+       "PINLYP                        NaN      A6NC86     390940  ENSG00000234465  \n",
+       "ARL6IP1P1                     NaN         NaN  100288702  ENSG00000255664  \n",
+       "PRAMEF33                      NaN  A0A0G2JMD5     645382  ENSG00000237700  \n",
+       "AL353354.2                    NaN         NaN        NaN              NaN  \n",
+       "CTA-298G8.2                   NaN         NaN        NaN              NaN  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "can.data.genes.head(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Locations\n",
+    "The locations dataset contains location annotations for all genes and their associated confidence scores. Confidence scores were crowd sourced from several protein localization papers and integrated into one scale. This dataset is automatically loaded into memory when candi is imported. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "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>gene</th>\n",
+       "      <th>location</th>\n",
+       "      <th>confidence</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>A1CF</td>\n",
+       "      <td>Nucleus</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>A4GALT</td>\n",
+       "      <td>Mitochondria</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>AAAS</td>\n",
+       "      <td>Nucleus</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>AAAS</td>\n",
+       "      <td>Cytoskeleton</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>AAAS</td>\n",
+       "      <td>Cytosol</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     gene      location  confidence\n",
+       "0    A1CF       Nucleus         1.0\n",
+       "1  A4GALT  Mitochondria         1.0\n",
+       "2    AAAS       Nucleus         2.0\n",
+       "3    AAAS  Cytoskeleton         2.0\n",
+       "4    AAAS       Cytosol         2.0"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "can.data.locations.head(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Basic Object Instantiation\n",
+    "- The user input for object instantiation is used directly for indexing\n",
+    "- This means if it is misspelled candi will not be able to retrieve the data in which the user is interested\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kras = can.Gene(\"KRAS\")\n",
+    "lung = can.Cancer(\"Lung Cancer\")\n",
+    "membrane = can.Organelle(\"Plasma membrane\")\n",
+    "a549 = can.CellLine(\"A549\") "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Gene Object Methods and Attributes\n",
+    "The following function prints the internal attributes and functions of CanDI objects. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Attributes:\n",
+      "\n",
+      "ensembl: ENSG00000133703\n",
+      "entrez: 3845\n",
+      "get_name: KRAS\n",
+      "name: KRAS proto-oncogene, GTPase\n",
+      "symbol: KRAS\n",
+      "\n",
+      "Methods:\n",
+      "\n",
+      "cn_normal\n",
+      "deletion\n",
+      "dependency_of\n",
+      "dependent\n",
+      "duplication\n",
+      "effect_of\n",
+      "essential\n",
+      "expressed\n",
+      "expression_of\n",
+      "mutated\n",
+      "non_dependent\n",
+      "non_essential\n",
+      "unexpressed\n"
+     ]
+    }
+   ],
+   "source": [
+    "def pretty_print_attr(obj):\n",
+    "    attr = []\n",
+    "    ls_attr = []\n",
+    "    meth = []\n",
+    "    for i in dir(obj):\n",
+    "        if \"_\" != i[0]:\n",
+    "            if type(getattr(obj, i)) == str or type(getattr(obj, i)) == int:\n",
+    "                attr.append(i)\n",
+    "            elif type(getattr(obj, i)) == list:\n",
+    "                ls_attr.append(i)\n",
+    "            else:\n",
+    "                meth.append(i)\n",
+    "                \n",
+    "    print(\"Attributes:\\n\")\n",
+    "    for i in attr: print(i+\":\", getattr(obj, i))\n",
+    "    for i in ls_attr: print(i+\" list first item:\", getattr(obj, i)[0])\n",
+    "    for i in ls_attr: print(i+\" length:\", len(getattr(obj, i)))\n",
+    "    print(\"\\nMethods:\\n\")\n",
+    "    for i in meth: print(i)\n",
+    "\n",
+    "pretty_print_attr(kras)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Gene Indexing examples\n",
+    "If a dataset has not be loaded into memory candi will prompt you.\n",
+    "Once a dataset is loaded, Gene.expression gives all the rna seq transcript data for that specific object.\n",
+    "In this case we have already instantiated a gene object"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ACH-001113    4.568640\n",
+       "ACH-001289    4.554589\n",
+       "ACH-001339    3.955127\n",
+       "ACH-001538    5.593354\n",
+       "ACH-000242    3.845992\n",
+       "                ...   \n",
+       "ACH-000750    3.729009\n",
+       "ACH-000285    5.389567\n",
+       "ACH-001858    4.014355\n",
+       "ACH-001997    3.455492\n",
+       "ACH-000052    3.587365\n",
+       "Name: KRAS, Length: 1379, dtype: float64"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "kras.expression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Basic CanDI filtering\n",
+    "the Gene.expressed() method retrieves cell lines where the user defined gene has above 1 transcript per million\n",
+    "the output is a list of cell line ids which can be used to instantiate CellLine or CellLineClbbuster objects\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['ACH-001113',\n",
+       " 'ACH-001289',\n",
+       " 'ACH-001339',\n",
+       " 'ACH-001538',\n",
+       " 'ACH-000242',\n",
+       " 'ACH-000708',\n",
+       " 'ACH-000327',\n",
+       " 'ACH-000233',\n",
+       " 'ACH-000461',\n",
+       " 'ACH-000705']"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "kras.expressed()[0:10]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The user can specify if they want the tpm values with the depmap ids "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ACH-001113    4.568640\n",
+       "ACH-001289    4.554589\n",
+       "ACH-001339    3.955127\n",
+       "ACH-001538    5.593354\n",
+       "ACH-000242    3.845992\n",
+       "                ...   \n",
+       "ACH-000750    3.729009\n",
+       "ACH-000285    5.389567\n",
+       "ACH-001858    4.014355\n",
+       "ACH-001997    3.455492\n",
+       "ACH-000052    3.587365\n",
+       "Name: KRAS, Length: 1379, dtype: float64"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "kras.expressed(style=\"values\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you input a depmap id as an argument to gene.expressed you will get a boolean showing the expression status of your gene"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "kras.expressed(a549.depmap_id)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The user can use the gene.expression_of() method to check that gene's expression in a specific cell line.\n",
+    "This method only, when called from a Gene object, accepts cell line depmap id's as an argument."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4.350497247084133"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "kras.expression_of(a549.depmap_id)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "CanDI is consistent in the way this works across all classes and data types"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "mutations has not been loaded. Do you want to load, y/n?> y\n",
+      "Load Complete\n"
+     ]
+    },
+    {
+     "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>gene</th>\n",
+       "      <th>Entrez_Gene_Id</th>\n",
+       "      <th>NCBI_Build</th>\n",
+       "      <th>Chromosome</th>\n",
+       "      <th>Start_position</th>\n",
+       "      <th>End_position</th>\n",
+       "      <th>Strand</th>\n",
+       "      <th>Variant_Classification</th>\n",
+       "      <th>Variant_Type</th>\n",
+       "      <th>Reference_Allele</th>\n",
+       "      <th>...</th>\n",
+       "      <th>isCOSMIChotspot</th>\n",
+       "      <th>COSMIChsCnt</th>\n",
+       "      <th>ExAC_AF</th>\n",
+       "      <th>Variant_annotation</th>\n",
+       "      <th>CGA_WES_AC</th>\n",
+       "      <th>HC_AC</th>\n",
+       "      <th>RD_AC</th>\n",
+       "      <th>RNAseq_AC</th>\n",
+       "      <th>SangerWES_AC</th>\n",
+       "      <th>WGS_AC</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1543</th>\n",
+       "      <td>KRAS</td>\n",
+       "      <td>3845</td>\n",
+       "      <td>37</td>\n",
+       "      <td>12</td>\n",
+       "      <td>25398284</td>\n",
+       "      <td>25398284</td>\n",
+       "      <td>+</td>\n",
+       "      <td>Missense_Mutation</td>\n",
+       "      <td>SNP</td>\n",
+       "      <td>C</td>\n",
+       "      <td>...</td>\n",
+       "      <td>True</td>\n",
+       "      <td>15813.0</td>\n",
+       "      <td>0.000016</td>\n",
+       "      <td>other non-conserving</td>\n",
+       "      <td>187:172</td>\n",
+       "      <td>26:35</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>90:89</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>17:12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7075</th>\n",
+       "      <td>KRAS</td>\n",
+       "      <td>3845</td>\n",
+       "      <td>37</td>\n",
+       "      <td>12</td>\n",
+       "      <td>25398284</td>\n",
+       "      <td>25398284</td>\n",
+       "      <td>+</td>\n",
+       "      <td>Missense_Mutation</td>\n",
+       "      <td>SNP</td>\n",
+       "      <td>C</td>\n",
+       "      <td>...</td>\n",
+       "      <td>True</td>\n",
+       "      <td>15813.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>other non-conserving</td>\n",
+       "      <td>144:0</td>\n",
+       "      <td>184:2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>155:2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7340</th>\n",
+       "      <td>KRAS</td>\n",
+       "      <td>3845</td>\n",
+       "      <td>37</td>\n",
+       "      <td>12</td>\n",
+       "      <td>25398284</td>\n",
+       "      <td>25398284</td>\n",
+       "      <td>+</td>\n",
+       "      <td>Missense_Mutation</td>\n",
+       "      <td>SNP</td>\n",
+       "      <td>C</td>\n",
+       "      <td>...</td>\n",
+       "      <td>True</td>\n",
+       "      <td>15813.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>other non-conserving</td>\n",
+       "      <td>14:0</td>\n",
+       "      <td>157:1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>106:1</td>\n",
+       "      <td>16:0</td>\n",
+       "      <td>24:0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10322</th>\n",
+       "      <td>KRAS</td>\n",
+       "      <td>3845</td>\n",
+       "      <td>37</td>\n",
+       "      <td>12</td>\n",
+       "      <td>25380276</td>\n",
+       "      <td>25380276</td>\n",
+       "      <td>+</td>\n",
+       "      <td>Missense_Mutation</td>\n",
+       "      <td>SNP</td>\n",
+       "      <td>T</td>\n",
+       "      <td>...</td>\n",
+       "      <td>True</td>\n",
+       "      <td>141.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>other non-conserving</td>\n",
+       "      <td>34:30</td>\n",
+       "      <td>97:47</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>52:41</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15559</th>\n",
+       "      <td>KRAS</td>\n",
+       "      <td>3845</td>\n",
+       "      <td>37</td>\n",
+       "      <td>12</td>\n",
+       "      <td>25398284</td>\n",
+       "      <td>25398284</td>\n",
+       "      <td>+</td>\n",
+       "      <td>Missense_Mutation</td>\n",
+       "      <td>SNP</td>\n",
+       "      <td>C</td>\n",
+       "      <td>...</td>\n",
+       "      <td>True</td>\n",
+       "      <td>15813.0</td>\n",
+       "      <td>0.000016</td>\n",
+       "      <td>other non-conserving</td>\n",
+       "      <td>14:20</td>\n",
+       "      <td>39:45</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>91:89</td>\n",
+       "      <td>23:30</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1265558</th>\n",
+       "      <td>KRAS</td>\n",
+       "      <td>3845</td>\n",
+       "      <td>37</td>\n",
+       "      <td>12</td>\n",
+       "      <td>25398283</td>\n",
+       "      <td>25398284</td>\n",
+       "      <td>+</td>\n",
+       "      <td>In_Frame_Ins</td>\n",
+       "      <td>INS</td>\n",
+       "      <td>-</td>\n",
+       "      <td>...</td>\n",
+       "      <td>True</td>\n",
+       "      <td>15827.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>other non-conserving</td>\n",
+       "      <td>71:112</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>1265728</th>\n",
+       "      <td>KRAS</td>\n",
+       "      <td>3845</td>\n",
+       "      <td>37</td>\n",
+       "      <td>12</td>\n",
+       "      <td>25378562</td>\n",
+       "      <td>25378562</td>\n",
+       "      <td>+</td>\n",
+       "      <td>Missense_Mutation</td>\n",
+       "      <td>SNP</td>\n",
+       "      <td>C</td>\n",
+       "      <td>...</td>\n",
+       "      <td>True</td>\n",
+       "      <td>82.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>other non-conserving</td>\n",
+       "      <td>58:71</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>1265729</th>\n",
+       "      <td>KRAS</td>\n",
+       "      <td>3845</td>\n",
+       "      <td>37</td>\n",
+       "      <td>12</td>\n",
+       "      <td>25398283</td>\n",
+       "      <td>25398284</td>\n",
+       "      <td>+</td>\n",
+       "      <td>In_Frame_Ins</td>\n",
+       "      <td>INS</td>\n",
+       "      <td>-</td>\n",
+       "      <td>...</td>\n",
+       "      <td>True</td>\n",
+       "      <td>15827.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>other non-conserving</td>\n",
+       "      <td>76:106</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>1265899</th>\n",
+       "      <td>KRAS</td>\n",
+       "      <td>3845</td>\n",
+       "      <td>37</td>\n",
+       "      <td>12</td>\n",
+       "      <td>25398283</td>\n",
+       "      <td>25398284</td>\n",
+       "      <td>+</td>\n",
+       "      <td>In_Frame_Ins</td>\n",
+       "      <td>INS</td>\n",
+       "      <td>-</td>\n",
+       "      <td>...</td>\n",
+       "      <td>True</td>\n",
+       "      <td>15827.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>other non-conserving</td>\n",
+       "      <td>55:70</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>1266065</th>\n",
+       "      <td>KRAS</td>\n",
+       "      <td>3845</td>\n",
+       "      <td>37</td>\n",
+       "      <td>12</td>\n",
+       "      <td>25398283</td>\n",
+       "      <td>25398284</td>\n",
+       "      <td>+</td>\n",
+       "      <td>In_Frame_Ins</td>\n",
+       "      <td>INS</td>\n",
+       "      <td>-</td>\n",
+       "      <td>...</td>\n",
+       "      <td>True</td>\n",
+       "      <td>15827.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>other non-conserving</td>\n",
+       "      <td>67:78</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>285 rows × 32 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         gene  Entrez_Gene_Id  NCBI_Build Chromosome  Start_position  \\\n",
+       "1543     KRAS            3845          37         12        25398284   \n",
+       "7075     KRAS            3845          37         12        25398284   \n",
+       "7340     KRAS            3845          37         12        25398284   \n",
+       "10322    KRAS            3845          37         12        25380276   \n",
+       "15559    KRAS            3845          37         12        25398284   \n",
+       "...       ...             ...         ...        ...             ...   \n",
+       "1265558  KRAS            3845          37         12        25398283   \n",
+       "1265728  KRAS            3845          37         12        25378562   \n",
+       "1265729  KRAS            3845          37         12        25398283   \n",
+       "1265899  KRAS            3845          37         12        25398283   \n",
+       "1266065  KRAS            3845          37         12        25398283   \n",
+       "\n",
+       "         End_position Strand Variant_Classification Variant_Type  \\\n",
+       "1543         25398284      +      Missense_Mutation          SNP   \n",
+       "7075         25398284      +      Missense_Mutation          SNP   \n",
+       "7340         25398284      +      Missense_Mutation          SNP   \n",
+       "10322        25380276      +      Missense_Mutation          SNP   \n",
+       "15559        25398284      +      Missense_Mutation          SNP   \n",
+       "...               ...    ...                    ...          ...   \n",
+       "1265558      25398284      +           In_Frame_Ins          INS   \n",
+       "1265728      25378562      +      Missense_Mutation          SNP   \n",
+       "1265729      25398284      +           In_Frame_Ins          INS   \n",
+       "1265899      25398284      +           In_Frame_Ins          INS   \n",
+       "1266065      25398284      +           In_Frame_Ins          INS   \n",
+       "\n",
+       "        Reference_Allele  ... isCOSMIChotspot COSMIChsCnt   ExAC_AF  \\\n",
+       "1543                   C  ...            True     15813.0  0.000016   \n",
+       "7075                   C  ...            True     15813.0       NaN   \n",
+       "7340                   C  ...            True     15813.0       NaN   \n",
+       "10322                  T  ...            True       141.0       NaN   \n",
+       "15559                  C  ...            True     15813.0  0.000016   \n",
+       "...                  ...  ...             ...         ...       ...   \n",
+       "1265558                -  ...            True     15827.0       NaN   \n",
+       "1265728                C  ...            True        82.0       NaN   \n",
+       "1265729                -  ...            True     15827.0       NaN   \n",
+       "1265899                -  ...            True     15827.0       NaN   \n",
+       "1266065                -  ...            True     15827.0       NaN   \n",
+       "\n",
+       "           Variant_annotation CGA_WES_AC  HC_AC RD_AC RNAseq_AC SangerWES_AC  \\\n",
+       "1543     other non-conserving    187:172  26:35   NaN     90:89          NaN   \n",
+       "7075     other non-conserving      144:0  184:2   NaN     155:2          NaN   \n",
+       "7340     other non-conserving       14:0  157:1   NaN     106:1         16:0   \n",
+       "10322    other non-conserving      34:30  97:47   NaN     52:41          NaN   \n",
+       "15559    other non-conserving      14:20  39:45   NaN     91:89        23:30   \n",
+       "...                       ...        ...    ...   ...       ...          ...   \n",
+       "1265558  other non-conserving     71:112    NaN   NaN       NaN          NaN   \n",
+       "1265728  other non-conserving      58:71    NaN   NaN       NaN          NaN   \n",
+       "1265729  other non-conserving     76:106    NaN   NaN       NaN          NaN   \n",
+       "1265899  other non-conserving      55:70    NaN   NaN       NaN          NaN   \n",
+       "1266065  other non-conserving      67:78    NaN   NaN       NaN          NaN   \n",
+       "\n",
+       "        WGS_AC  \n",
+       "1543     17:12  \n",
+       "7075       NaN  \n",
+       "7340      24:0  \n",
+       "10322      NaN  \n",
+       "15559      NaN  \n",
+       "...        ...  \n",
+       "1265558    NaN  \n",
+       "1265728    NaN  \n",
+       "1265729    NaN  \n",
+       "1265899    NaN  \n",
+       "1266065    NaN  \n",
+       "\n",
+       "[285 rows x 32 columns]"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "kras.mutations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The gene.mutated() method allows very specific filtering.\n",
+    "Using the variant argument one can select the column on which to filter. Then using the item argument the user can specifiy the specific value in which they're interested. The example below shows retrieval of all cell lines with kras missense mutations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['ACH-000094',\n",
+       " 'ACH-000178',\n",
+       " 'ACH-002186',\n",
+       " 'ACH-000311',\n",
+       " 'ACH-001345',\n",
+       " 'ACH-001843',\n",
+       " 'ACH-001353',\n",
+       " 'ACH-000417',\n",
+       " 'ACH-000347',\n",
+       " 'ACH-000997']"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "kras.mutated(variant=\"Variant_Classification\", item=\"Missense_Mutation\")[0:10]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Users can use the unload method of the Data object to remove a dataset from memory and return it to a file path string."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "PosixPath('/home/cyogodzi/projects/candi-paper/CanDI/CanDI/setup/data/depmap/CCLE_mutations.csv')"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "can.data.unload('mutations')\n",
+    "can.data.mutations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## CellLine Methods and Attributes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Attributes:\n",
+      "\n",
+      "ccle_name: A549_LUNG\n",
+      "depmap_id: ACH-000681\n",
+      "get_name: ACH-000681\n",
+      "lineage: lung\n",
+      "name: A549\n",
+      "sanger_id: SIDM00903\n",
+      "sex: Male\n",
+      "source: ATCC\n",
+      "subtype: NSCLC\n",
+      "tissue: lung\n",
+      "\n",
+      "Methods:\n",
+      "\n",
+      "aliases\n",
+      "cn_normal\n",
+      "cosmic_id\n",
+      "deletion\n",
+      "dependency_of\n",
+      "dependent\n",
+      "duplication\n",
+      "effect_of\n",
+      "essential\n",
+      "expressed\n",
+      "expression_of\n",
+      "mutated\n",
+      "non_dependent\n",
+      "non_essential\n",
+      "unexpressed\n"
+     ]
+    }
+   ],
+   "source": [
+    "pretty_print_attr(a549)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "All methods work in essentially same way regardless of the candi object in use.\n",
+    "The CellLine.expressed() method will return all genes which have expression above 1 transcript per million\n",
+    "in that specific cell line."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['TSPAN6',\n",
+       " 'DPM1',\n",
+       " 'SCYL3',\n",
+       " 'C1orf112',\n",
+       " 'CFH',\n",
+       " 'FUCA2',\n",
+       " 'GCLC',\n",
+       " 'NFYA',\n",
+       " 'STPG1',\n",
+       " 'NIPAL3']"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a549.expressed()[:10]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Just like gene.expressed() the user can ask for the values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "gene\n",
+       "TSPAN6         5.176323\n",
+       "DPM1           6.310522\n",
+       "SCYL3          2.017922\n",
+       "C1orf112       4.058316\n",
+       "CFH            3.772941\n",
+       "                 ...   \n",
+       "UPK3BL2        1.367371\n",
+       "AC093512.2     4.087463\n",
+       "ARHGAP11B      1.531069\n",
+       "ABCF2-H2BE1    1.891419\n",
+       "POLR2J3        3.372952\n",
+       "Name: ACH-000681, Length: 11498, dtype: float64"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a549.expressed(style=\"values\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And for specific genes expression status"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a549.expressed(\"KRAS\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "expressed with style=\"values\" gives the same result as expression_of"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4.350497247084133"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a549.expression_of(\"KRAS\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4.350497247084133"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a549.expressed(\"KRAS\", style=\"values\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The CellLine.mtuations attribute gives all mutation data for that specific cell line"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "mutations has not been loaded. Do you want to load, y/n?> y\n",
+      "Load Complete\n"
+     ]
+    },
+    {
+     "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>gene</th>\n",
+       "      <th>Entrez_Gene_Id</th>\n",
+       "      <th>NCBI_Build</th>\n",
+       "      <th>Chromosome</th>\n",
+       "      <th>Start_position</th>\n",
+       "      <th>End_position</th>\n",
+       "      <th>Strand</th>\n",
+       "      <th>Variant_Classification</th>\n",
+       "      <th>Variant_Type</th>\n",
+       "      <th>Reference_Allele</th>\n",
+       "      <th>...</th>\n",
+       "      <th>isCOSMIChotspot</th>\n",
+       "      <th>COSMIChsCnt</th>\n",
+       "      <th>ExAC_AF</th>\n",
+       "      <th>Variant_annotation</th>\n",
+       "      <th>CGA_WES_AC</th>\n",
+       "      <th>HC_AC</th>\n",
+       "      <th>RD_AC</th>\n",
+       "      <th>RNAseq_AC</th>\n",
+       "      <th>SangerWES_AC</th>\n",
+       "      <th>WGS_AC</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>244692</th>\n",
+       "      <td>TPRG1L</td>\n",
+       "      <td>127262</td>\n",
+       "      <td>37</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3542384</td>\n",
+       "      <td>3542384</td>\n",
+       "      <td>+</td>\n",
+       "      <td>Missense_Mutation</td>\n",
+       "      <td>SNP</td>\n",
+       "      <td>G</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>other non-conserving</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>17:28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>244693</th>\n",
+       "      <td>ENO1</td>\n",
+       "      <td>2023</td>\n",
+       "      <td>37</td>\n",
+       "      <td>1</td>\n",
+       "      <td>8925414</td>\n",
+       "      <td>8925414</td>\n",
+       "      <td>+</td>\n",
+       "      <td>Missense_Mutation</td>\n",
+       "      <td>SNP</td>\n",
+       "      <td>A</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>other non-conserving</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>22:30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>244694</th>\n",
+       "      <td>NMNAT1</td>\n",
+       "      <td>64802</td>\n",
+       "      <td>37</td>\n",
+       "      <td>1</td>\n",
+       "      <td>10042579</td>\n",
+       "      <td>10042579</td>\n",
+       "      <td>+</td>\n",
+       "      <td>Missense_Mutation</td>\n",
+       "      <td>SNP</td>\n",
+       "      <td>C</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>other non-conserving</td>\n",
+       "      <td>33:30</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>13:33</td>\n",
+       "      <td>33:31</td>\n",
+       "      <td>20:32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>244695</th>\n",
+       "      <td>MFN2</td>\n",
+       "      <td>9927</td>\n",
+       "      <td>37</td>\n",
+       "      <td>1</td>\n",
+       "      <td>12058908</td>\n",
+       "      <td>12058908</td>\n",
+       "      <td>+</td>\n",
+       "      <td>Silent</td>\n",
+       "      <td>SNP</td>\n",
+       "      <td>C</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>silent</td>\n",
+       "      <td>19:91</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>20:93</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>244696</th>\n",
+       "      <td>PRAMEF4</td>\n",
+       "      <td>400735</td>\n",
+       "      <td>37</td>\n",
+       "      <td>1</td>\n",
+       "      <td>12942971</td>\n",
+       "      <td>12942971</td>\n",
+       "      <td>+</td>\n",
+       "      <td>Missense_Mutation</td>\n",
+       "      <td>SNP</td>\n",
+       "      <td>G</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>other non-conserving</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>29:39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>245445</th>\n",
+       "      <td>IGSF1</td>\n",
+       "      <td>3547</td>\n",
+       "      <td>37</td>\n",
+       "      <td>X</td>\n",
+       "      <td>130411178</td>\n",
+       "      <td>130411178</td>\n",
+       "      <td>+</td>\n",
+       "      <td>Missense_Mutation</td>\n",
+       "      <td>SNP</td>\n",
+       "      <td>G</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>other non-conserving</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>19:12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>245446</th>\n",
+       "      <td>HS6ST2</td>\n",
+       "      <td>90161</td>\n",
+       "      <td>37</td>\n",
+       "      <td>X</td>\n",
+       "      <td>132091282</td>\n",
+       "      <td>132091282</td>\n",
+       "      <td>+</td>\n",
+       "      <td>Silent</td>\n",
+       "      <td>SNP</td>\n",
+       "      <td>G</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>silent</td>\n",
+       "      <td>35:30</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>35:32</td>\n",
+       "      <td>16:10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>245447</th>\n",
+       "      <td>SLITRK4</td>\n",
+       "      <td>139065</td>\n",
+       "      <td>37</td>\n",
+       "      <td>X</td>\n",
+       "      <td>142717709</td>\n",
+       "      <td>142717709</td>\n",
+       "      <td>+</td>\n",
+       "      <td>Missense_Mutation</td>\n",
+       "      <td>SNP</td>\n",
+       "      <td>G</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>other non-conserving</td>\n",
+       "      <td>125:0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>128:0</td>\n",
+       "      <td>37:0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>245448</th>\n",
+       "      <td>MAGEA11</td>\n",
+       "      <td>4110</td>\n",
+       "      <td>37</td>\n",
+       "      <td>X</td>\n",
+       "      <td>148798368</td>\n",
+       "      <td>148798368</td>\n",
+       "      <td>+</td>\n",
+       "      <td>Missense_Mutation</td>\n",
+       "      <td>SNP</td>\n",
+       "      <td>G</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>other non-conserving</td>\n",
+       "      <td>96:1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>69:1</td>\n",
+       "      <td>47:0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>245449</th>\n",
+       "      <td>MAMLD1</td>\n",
+       "      <td>10046</td>\n",
+       "      <td>37</td>\n",
+       "      <td>X</td>\n",
+       "      <td>149639149</td>\n",
+       "      <td>149639149</td>\n",
+       "      <td>+</td>\n",
+       "      <td>Missense_Mutation</td>\n",
+       "      <td>SNP</td>\n",
+       "      <td>C</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>other non-conserving</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>14:26</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>758 rows × 32 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           gene  Entrez_Gene_Id  NCBI_Build Chromosome  Start_position  \\\n",
+       "244692   TPRG1L          127262          37          1         3542384   \n",
+       "244693     ENO1            2023          37          1         8925414   \n",
+       "244694   NMNAT1           64802          37          1        10042579   \n",
+       "244695     MFN2            9927          37          1        12058908   \n",
+       "244696  PRAMEF4          400735          37          1        12942971   \n",
+       "...         ...             ...         ...        ...             ...   \n",
+       "245445    IGSF1            3547          37          X       130411178   \n",
+       "245446   HS6ST2           90161          37          X       132091282   \n",
+       "245447  SLITRK4          139065          37          X       142717709   \n",
+       "245448  MAGEA11            4110          37          X       148798368   \n",
+       "245449   MAMLD1           10046          37          X       149639149   \n",
+       "\n",
+       "        End_position Strand Variant_Classification Variant_Type  \\\n",
+       "244692       3542384      +      Missense_Mutation          SNP   \n",
+       "244693       8925414      +      Missense_Mutation          SNP   \n",
+       "244694      10042579      +      Missense_Mutation          SNP   \n",
+       "244695      12058908      +                 Silent          SNP   \n",
+       "244696      12942971      +      Missense_Mutation          SNP   \n",
+       "...              ...    ...                    ...          ...   \n",
+       "245445     130411178      +      Missense_Mutation          SNP   \n",
+       "245446     132091282      +                 Silent          SNP   \n",
+       "245447     142717709      +      Missense_Mutation          SNP   \n",
+       "245448     148798368      +      Missense_Mutation          SNP   \n",
+       "245449     149639149      +      Missense_Mutation          SNP   \n",
+       "\n",
+       "       Reference_Allele  ... isCOSMIChotspot COSMIChsCnt ExAC_AF  \\\n",
+       "244692                G  ...           False         0.0     NaN   \n",
+       "244693                A  ...           False         0.0     NaN   \n",
+       "244694                C  ...           False         0.0     NaN   \n",
+       "244695                C  ...           False         0.0     NaN   \n",
+       "244696                G  ...           False         0.0     NaN   \n",
+       "...                 ...  ...             ...         ...     ...   \n",
+       "245445                G  ...           False         0.0     NaN   \n",
+       "245446                G  ...           False         0.0     NaN   \n",
+       "245447                G  ...           False         0.0     NaN   \n",
+       "245448                G  ...           False         0.0     NaN   \n",
+       "245449                C  ...           False         0.0     NaN   \n",
+       "\n",
+       "          Variant_annotation CGA_WES_AC HC_AC RD_AC RNAseq_AC SangerWES_AC  \\\n",
+       "244692  other non-conserving        NaN   NaN   NaN       NaN          NaN   \n",
+       "244693  other non-conserving        NaN   NaN   NaN       NaN          NaN   \n",
+       "244694  other non-conserving      33:30   NaN   NaN     13:33        33:31   \n",
+       "244695                silent      19:91   NaN   NaN       NaN        20:93   \n",
+       "244696  other non-conserving        NaN   NaN   NaN       NaN          NaN   \n",
+       "...                      ...        ...   ...   ...       ...          ...   \n",
+       "245445  other non-conserving        NaN   NaN   NaN       NaN          NaN   \n",
+       "245446                silent      35:30   NaN   NaN       NaN        35:32   \n",
+       "245447  other non-conserving      125:0   NaN   NaN       NaN        128:0   \n",
+       "245448  other non-conserving       96:1   NaN   NaN       NaN         69:1   \n",
+       "245449  other non-conserving        NaN   NaN   NaN       NaN          NaN   \n",
+       "\n",
+       "       WGS_AC  \n",
+       "244692  17:28  \n",
+       "244693  22:30  \n",
+       "244694  20:32  \n",
+       "244695    NaN  \n",
+       "244696  29:39  \n",
+       "...       ...  \n",
+       "245445  19:12  \n",
+       "245446  16:10  \n",
+       "245447   37:0  \n",
+       "245448   47:0  \n",
+       "245449  14:26  \n",
+       "\n",
+       "[758 rows x 32 columns]"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a549.mutations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# calling the CellLine.mutated() method works the same way with all CanDI objects\n",
+    "a549.mutated(variant=\"Variant_Classification\", item=\"Nonsense_Mutation\")[:10]\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Cancer Methods and Attributes\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Attributes:\n",
+      "\n",
+      "disease: Lung Cancer\n",
+      "get_name: Lung Cancer\n",
+      "ccle_names list first item: NCIH2077_LUNG\n",
+      "depmap_ids list first item: ACH-000010\n",
+      "names list first item: NCI-H2077\n",
+      "ccle_names length: 273\n",
+      "depmap_ids length: 273\n",
+      "names length: 273\n",
+      "\n",
+      "Methods:\n",
+      "\n",
+      "cn_normal\n",
+      "deletion\n",
+      "dependency_of\n",
+      "dependent\n",
+      "duplication\n",
+      "effect_of\n",
+      "essential\n",
+      "expressed\n",
+      "expression_of\n",
+      "mutated\n",
+      "mutation_matrix\n",
+      "non_dependent\n",
+      "non_essential\n",
+      "sexes\n",
+      "sources\n",
+      "subtypes\n",
+      "unexpressed\n"
+     ]
+    }
+   ],
+   "source": [
+    "pretty_print_attr(lung)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Cancer objects work essentially works as a group of cell line objects \n",
+    "the Cancer.expression object returns a pandas dataframe rather than a pandas series since there are multiple cell lines to consider."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "    .dataframe thead th {\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ACH-000010</th>\n",
+       "      <th>ACH-000012</th>\n",
+       "      <th>ACH-000015</th>\n",
+       "      <th>ACH-000021</th>\n",
+       "      <th>ACH-000029</th>\n",
+       "      <th>ACH-000030</th>\n",
+       "      <th>ACH-000033</th>\n",
+       "      <th>ACH-000035</th>\n",
+       "      <th>ACH-000062</th>\n",
+       "      <th>ACH-000066</th>\n",
+       "      <th>...</th>\n",
+       "      <th>ACH-001386</th>\n",
+       "      <th>ACH-001549</th>\n",
+       "      <th>ACH-001555</th>\n",
+       "      <th>ACH-001556</th>\n",
+       "      <th>ACH-001557</th>\n",
+       "      <th>ACH-001558</th>\n",
+       "      <th>ACH-001559</th>\n",
+       "      <th>ACH-001560</th>\n",
+       "      <th>ACH-001561</th>\n",
+       "      <th>ACH-001562</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>gene</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>TSPAN6</th>\n",
+       "      <td>4.447579</td>\n",
+       "      <td>5.802452</td>\n",
+       "      <td>4.794936</td>\n",
+       "      <td>4.831371</td>\n",
+       "      <td>6.491532</td>\n",
+       "      <td>5.399855</td>\n",
+       "      <td>5.391974</td>\n",
+       "      <td>4.888013</td>\n",
+       "      <td>4.731726</td>\n",
+       "      <td>5.753818</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3.060047</td>\n",
+       "      <td>0.773996</td>\n",
+       "      <td>2.769772</td>\n",
+       "      <td>4.280956</td>\n",
+       "      <td>4.089159</td>\n",
+       "      <td>4.799087</td>\n",
+       "      <td>4.275007</td>\n",
+       "      <td>4.621173</td>\n",
+       "      <td>4.399855</td>\n",
+       "      <td>4.628774</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TNMD</th>\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>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.028569</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.028569</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.028569</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.028569</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DPM1</th>\n",
+       "      <td>7.227760</td>\n",
+       "      <td>5.997744</td>\n",
+       "      <td>6.929436</td>\n",
+       "      <td>6.498570</td>\n",
+       "      <td>6.672850</td>\n",
+       "      <td>6.307246</td>\n",
+       "      <td>6.866661</td>\n",
+       "      <td>7.254840</td>\n",
+       "      <td>5.849499</td>\n",
+       "      <td>5.514438</td>\n",
+       "      <td>...</td>\n",
+       "      <td>6.837691</td>\n",
+       "      <td>5.908333</td>\n",
+       "      <td>7.010220</td>\n",
+       "      <td>7.745170</td>\n",
+       "      <td>7.149137</td>\n",
+       "      <td>7.057992</td>\n",
+       "      <td>7.379725</td>\n",
+       "      <td>7.168923</td>\n",
+       "      <td>7.191109</td>\n",
+       "      <td>7.704941</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SCYL3</th>\n",
+       "      <td>2.405992</td>\n",
+       "      <td>1.970854</td>\n",
+       "      <td>2.952334</td>\n",
+       "      <td>2.414136</td>\n",
+       "      <td>2.475085</td>\n",
+       "      <td>2.017922</td>\n",
+       "      <td>1.803227</td>\n",
+       "      <td>2.289834</td>\n",
+       "      <td>2.503349</td>\n",
+       "      <td>2.536053</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.472488</td>\n",
+       "      <td>1.831877</td>\n",
+       "      <td>1.895303</td>\n",
+       "      <td>2.304511</td>\n",
+       "      <td>2.220330</td>\n",
+       "      <td>2.204767</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>1.871844</td>\n",
+       "      <td>2.060047</td>\n",
+       "      <td>2.589763</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>C1orf112</th>\n",
+       "      <td>4.594549</td>\n",
+       "      <td>3.784504</td>\n",
+       "      <td>3.709291</td>\n",
+       "      <td>4.527946</td>\n",
+       "      <td>4.486714</td>\n",
+       "      <td>3.671293</td>\n",
+       "      <td>3.841973</td>\n",
+       "      <td>3.795975</td>\n",
+       "      <td>3.761285</td>\n",
+       "      <td>4.566206</td>\n",
+       "      <td>...</td>\n",
+       "      <td>4.571677</td>\n",
+       "      <td>4.060912</td>\n",
+       "      <td>1.550901</td>\n",
+       "      <td>4.306700</td>\n",
+       "      <td>4.551516</td>\n",
+       "      <td>3.526069</td>\n",
+       "      <td>4.251719</td>\n",
+       "      <td>2.153805</td>\n",
+       "      <td>3.231125</td>\n",
+       "      <td>3.066950</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>POLR2J3</th>\n",
+       "      <td>4.516646</td>\n",
+       "      <td>5.238023</td>\n",
+       "      <td>5.458119</td>\n",
+       "      <td>6.648465</td>\n",
+       "      <td>5.239933</td>\n",
+       "      <td>5.650190</td>\n",
+       "      <td>6.129489</td>\n",
+       "      <td>4.511595</td>\n",
+       "      <td>5.559798</td>\n",
+       "      <td>5.768449</td>\n",
+       "      <td>...</td>\n",
+       "      <td>5.689020</td>\n",
+       "      <td>3.447579</td>\n",
+       "      <td>5.375735</td>\n",
+       "      <td>3.456806</td>\n",
+       "      <td>4.397118</td>\n",
+       "      <td>5.673556</td>\n",
+       "      <td>3.759156</td>\n",
+       "      <td>4.130931</td>\n",
+       "      <td>4.688740</td>\n",
+       "      <td>5.414474</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>H2BE1</th>\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>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</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>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AL445238.1</th>\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>0.124328</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.298658</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</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>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>GET1-SH3BGR</th>\n",
+       "      <td>0.807355</td>\n",
+       "      <td>0.536053</td>\n",
+       "      <td>0.545968</td>\n",
+       "      <td>0.757023</td>\n",
+       "      <td>0.150560</td>\n",
+       "      <td>0.650765</td>\n",
+       "      <td>0.941106</td>\n",
+       "      <td>0.432959</td>\n",
+       "      <td>0.748461</td>\n",
+       "      <td>0.536053</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.321928</td>\n",
+       "      <td>0.739848</td>\n",
+       "      <td>0.739848</td>\n",
+       "      <td>0.495695</td>\n",
+       "      <td>1.659925</td>\n",
+       "      <td>1.028569</td>\n",
+       "      <td>0.389567</td>\n",
+       "      <td>1.327687</td>\n",
+       "      <td>0.650765</td>\n",
+       "      <td>0.807355</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AC113348.1</th>\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>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.042644</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.042644</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.432959</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.189034</td>\n",
+       "      <td>0.042644</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>19177 rows × 206 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             ACH-000010  ACH-000012  ACH-000015  ACH-000021  ACH-000029  \\\n",
+       "gene                                                                      \n",
+       "TSPAN6         4.447579    5.802452    4.794936    4.831371    6.491532   \n",
+       "TNMD           0.000000    0.000000    0.000000    0.000000    0.000000   \n",
+       "DPM1           7.227760    5.997744    6.929436    6.498570    6.672850   \n",
+       "SCYL3          2.405992    1.970854    2.952334    2.414136    2.475085   \n",
+       "C1orf112       4.594549    3.784504    3.709291    4.527946    4.486714   \n",
+       "...                 ...         ...         ...         ...         ...   \n",
+       "POLR2J3        4.516646    5.238023    5.458119    6.648465    5.239933   \n",
+       "H2BE1          0.000000    0.000000    0.000000    0.000000    0.000000   \n",
+       "AL445238.1     0.000000    0.000000    0.000000    0.000000    0.000000   \n",
+       "GET1-SH3BGR    0.807355    0.536053    0.545968    0.757023    0.150560   \n",
+       "AC113348.1     0.000000    0.000000    0.000000    0.000000    0.000000   \n",
+       "\n",
+       "             ACH-000030  ACH-000033  ACH-000035  ACH-000062  ACH-000066  ...  \\\n",
+       "gene                                                                     ...   \n",
+       "TSPAN6         5.399855    5.391974    4.888013    4.731726    5.753818  ...   \n",
+       "TNMD           0.000000    0.000000    0.028569    0.000000    0.000000  ...   \n",
+       "DPM1           6.307246    6.866661    7.254840    5.849499    5.514438  ...   \n",
+       "SCYL3          2.017922    1.803227    2.289834    2.503349    2.536053  ...   \n",
+       "C1orf112       3.671293    3.841973    3.795975    3.761285    4.566206  ...   \n",
+       "...                 ...         ...         ...         ...         ...  ...   \n",
+       "POLR2J3        5.650190    6.129489    4.511595    5.559798    5.768449  ...   \n",
+       "H2BE1          0.000000    0.000000    0.000000    0.000000    0.000000  ...   \n",
+       "AL445238.1     0.124328    0.000000    0.000000    0.298658    0.000000  ...   \n",
+       "GET1-SH3BGR    0.650765    0.941106    0.432959    0.748461    0.536053  ...   \n",
+       "AC113348.1     0.000000    0.000000    0.042644    0.000000    0.000000  ...   \n",
+       "\n",
+       "             ACH-001386  ACH-001549  ACH-001555  ACH-001556  ACH-001557  \\\n",
+       "gene                                                                      \n",
+       "TSPAN6         3.060047    0.773996    2.769772    4.280956    4.089159   \n",
+       "TNMD           0.000000    0.028569    0.000000    0.000000    0.028569   \n",
+       "DPM1           6.837691    5.908333    7.010220    7.745170    7.149137   \n",
+       "SCYL3          2.472488    1.831877    1.895303    2.304511    2.220330   \n",
+       "C1orf112       4.571677    4.060912    1.550901    4.306700    4.551516   \n",
+       "...                 ...         ...         ...         ...         ...   \n",
+       "POLR2J3        5.689020    3.447579    5.375735    3.456806    4.397118   \n",
+       "H2BE1          0.000000    0.000000    0.000000    0.000000    0.000000   \n",
+       "AL445238.1     0.000000    0.000000    0.000000    0.000000    0.000000   \n",
+       "GET1-SH3BGR    0.321928    0.739848    0.739848    0.495695    1.659925   \n",
+       "AC113348.1     0.042644    0.000000    0.000000    0.000000    0.000000   \n",
+       "\n",
+       "             ACH-001558  ACH-001559  ACH-001560  ACH-001561  ACH-001562  \n",
+       "gene                                                                     \n",
+       "TSPAN6         4.799087    4.275007    4.621173    4.399855    4.628774  \n",
+       "TNMD           0.000000    0.000000    0.000000    0.028569    0.000000  \n",
+       "DPM1           7.057992    7.379725    7.168923    7.191109    7.704941  \n",
+       "SCYL3          2.204767    2.000000    1.871844    2.060047    2.589763  \n",
+       "C1orf112       3.526069    4.251719    2.153805    3.231125    3.066950  \n",
+       "...                 ...         ...         ...         ...         ...  \n",
+       "POLR2J3        5.673556    3.759156    4.130931    4.688740    5.414474  \n",
+       "H2BE1          0.000000    0.000000    0.000000    0.000000    0.000000  \n",
+       "AL445238.1     0.000000    0.000000    0.000000    0.000000    0.000000  \n",
+       "GET1-SH3BGR    1.028569    0.389567    1.327687    0.650765    0.807355  \n",
+       "AC113348.1     0.432959    0.000000    0.000000    0.189034    0.042644  \n",
+       "\n",
+       "[19177 rows x 206 columns]"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lung.expression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Cancer.expressed method uses an abitrary threshold to filter genes the default is if a gene is expressed in 100 percent of the cell lines within the cancer object it will read out as expressed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['DPM1',\n",
+       " 'SCYL3',\n",
+       " 'C1orf112',\n",
+       " 'GCLC',\n",
+       " 'NFYA',\n",
+       " 'LAS1L',\n",
+       " 'ANKIB1',\n",
+       " 'CYP51A1',\n",
+       " 'KRIT1',\n",
+       " 'RAD52']"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lung.expressed()[0:10]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The user can relax this threshold as necessary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['TSPAN6',\n",
+       " 'DPM1',\n",
+       " 'SCYL3',\n",
+       " 'C1orf112',\n",
+       " 'CFH',\n",
+       " 'FUCA2',\n",
+       " 'GCLC',\n",
+       " 'NFYA',\n",
+       " 'STPG1',\n",
+       " 'NIPAL3']"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lung.expressed(threshold=0.50)[0:10]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "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>ACH-000010</th>\n",
+       "      <th>ACH-000012</th>\n",
+       "      <th>ACH-000015</th>\n",
+       "      <th>ACH-000021</th>\n",
+       "      <th>ACH-000029</th>\n",
+       "      <th>ACH-000030</th>\n",
+       "      <th>ACH-000033</th>\n",
+       "      <th>ACH-000035</th>\n",
+       "      <th>ACH-000062</th>\n",
+       "      <th>ACH-000066</th>\n",
+       "      <th>...</th>\n",
+       "      <th>ACH-001386</th>\n",
+       "      <th>ACH-001549</th>\n",
+       "      <th>ACH-001555</th>\n",
+       "      <th>ACH-001556</th>\n",
+       "      <th>ACH-001557</th>\n",
+       "      <th>ACH-001558</th>\n",
+       "      <th>ACH-001559</th>\n",
+       "      <th>ACH-001560</th>\n",
+       "      <th>ACH-001561</th>\n",
+       "      <th>ACH-001562</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>gene</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>TSPAN6</th>\n",
+       "      <td>4.447579</td>\n",
+       "      <td>5.802452</td>\n",
+       "      <td>4.794936</td>\n",
+       "      <td>4.831371</td>\n",
+       "      <td>6.491532</td>\n",
+       "      <td>5.399855</td>\n",
+       "      <td>5.391974</td>\n",
+       "      <td>4.888013</td>\n",
+       "      <td>4.731726</td>\n",
+       "      <td>5.753818</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3.060047</td>\n",
+       "      <td>0.773996</td>\n",
+       "      <td>2.769772</td>\n",
+       "      <td>4.280956</td>\n",
+       "      <td>4.089159</td>\n",
+       "      <td>4.799087</td>\n",
+       "      <td>4.275007</td>\n",
+       "      <td>4.621173</td>\n",
+       "      <td>4.399855</td>\n",
+       "      <td>4.628774</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DPM1</th>\n",
+       "      <td>7.227760</td>\n",
+       "      <td>5.997744</td>\n",
+       "      <td>6.929436</td>\n",
+       "      <td>6.498570</td>\n",
+       "      <td>6.672850</td>\n",
+       "      <td>6.307246</td>\n",
+       "      <td>6.866661</td>\n",
+       "      <td>7.254840</td>\n",
+       "      <td>5.849499</td>\n",
+       "      <td>5.514438</td>\n",
+       "      <td>...</td>\n",
+       "      <td>6.837691</td>\n",
+       "      <td>5.908333</td>\n",
+       "      <td>7.010220</td>\n",
+       "      <td>7.745170</td>\n",
+       "      <td>7.149137</td>\n",
+       "      <td>7.057992</td>\n",
+       "      <td>7.379725</td>\n",
+       "      <td>7.168923</td>\n",
+       "      <td>7.191109</td>\n",
+       "      <td>7.704941</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SCYL3</th>\n",
+       "      <td>2.405992</td>\n",
+       "      <td>1.970854</td>\n",
+       "      <td>2.952334</td>\n",
+       "      <td>2.414136</td>\n",
+       "      <td>2.475085</td>\n",
+       "      <td>2.017922</td>\n",
+       "      <td>1.803227</td>\n",
+       "      <td>2.289834</td>\n",
+       "      <td>2.503349</td>\n",
+       "      <td>2.536053</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.472488</td>\n",
+       "      <td>1.831877</td>\n",
+       "      <td>1.895303</td>\n",
+       "      <td>2.304511</td>\n",
+       "      <td>2.220330</td>\n",
+       "      <td>2.204767</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>1.871844</td>\n",
+       "      <td>2.060047</td>\n",
+       "      <td>2.589763</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>C1orf112</th>\n",
+       "      <td>4.594549</td>\n",
+       "      <td>3.784504</td>\n",
+       "      <td>3.709291</td>\n",
+       "      <td>4.527946</td>\n",
+       "      <td>4.486714</td>\n",
+       "      <td>3.671293</td>\n",
+       "      <td>3.841973</td>\n",
+       "      <td>3.795975</td>\n",
+       "      <td>3.761285</td>\n",
+       "      <td>4.566206</td>\n",
+       "      <td>...</td>\n",
+       "      <td>4.571677</td>\n",
+       "      <td>4.060912</td>\n",
+       "      <td>1.550901</td>\n",
+       "      <td>4.306700</td>\n",
+       "      <td>4.551516</td>\n",
+       "      <td>3.526069</td>\n",
+       "      <td>4.251719</td>\n",
+       "      <td>2.153805</td>\n",
+       "      <td>3.231125</td>\n",
+       "      <td>3.066950</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CFH</th>\n",
+       "      <td>3.404631</td>\n",
+       "      <td>2.750607</td>\n",
+       "      <td>3.560715</td>\n",
+       "      <td>2.153805</td>\n",
+       "      <td>0.014355</td>\n",
+       "      <td>1.214125</td>\n",
+       "      <td>0.807355</td>\n",
+       "      <td>0.367371</td>\n",
+       "      <td>5.036503</td>\n",
+       "      <td>3.881665</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.056584</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.704872</td>\n",
+       "      <td>2.901108</td>\n",
+       "      <td>5.655638</td>\n",
+       "      <td>3.142413</td>\n",
+       "      <td>3.300124</td>\n",
+       "      <td>0.111031</td>\n",
+       "      <td>2.857981</td>\n",
+       "      <td>6.017254</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>UPK3BL2</th>\n",
+       "      <td>3.279471</td>\n",
+       "      <td>2.729009</td>\n",
+       "      <td>3.976364</td>\n",
+       "      <td>2.643856</td>\n",
+       "      <td>5.422233</td>\n",
+       "      <td>2.073820</td>\n",
+       "      <td>3.065228</td>\n",
+       "      <td>3.572890</td>\n",
+       "      <td>3.241840</td>\n",
+       "      <td>3.084064</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.353323</td>\n",
+       "      <td>0.863938</td>\n",
+       "      <td>0.565597</td>\n",
+       "      <td>0.516015</td>\n",
+       "      <td>0.650765</td>\n",
+       "      <td>1.565597</td>\n",
+       "      <td>0.163499</td>\n",
+       "      <td>0.887525</td>\n",
+       "      <td>0.137504</td>\n",
+       "      <td>2.063503</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AC093512.2</th>\n",
+       "      <td>2.653060</td>\n",
+       "      <td>3.700440</td>\n",
+       "      <td>2.214125</td>\n",
+       "      <td>2.403268</td>\n",
+       "      <td>2.933573</td>\n",
+       "      <td>3.944858</td>\n",
+       "      <td>2.965323</td>\n",
+       "      <td>3.026800</td>\n",
+       "      <td>2.778209</td>\n",
+       "      <td>4.265287</td>\n",
+       "      <td>...</td>\n",
+       "      <td>4.765535</td>\n",
+       "      <td>5.610877</td>\n",
+       "      <td>2.424922</td>\n",
+       "      <td>4.426265</td>\n",
+       "      <td>2.324811</td>\n",
+       "      <td>2.344828</td>\n",
+       "      <td>1.799087</td>\n",
+       "      <td>1.899176</td>\n",
+       "      <td>2.904966</td>\n",
+       "      <td>2.720278</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ARHGAP11B</th>\n",
+       "      <td>1.827819</td>\n",
+       "      <td>1.695994</td>\n",
+       "      <td>0.903038</td>\n",
+       "      <td>2.666757</td>\n",
+       "      <td>2.195348</td>\n",
+       "      <td>1.056584</td>\n",
+       "      <td>1.655352</td>\n",
+       "      <td>2.996389</td>\n",
+       "      <td>1.682573</td>\n",
+       "      <td>2.364572</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.807355</td>\n",
+       "      <td>1.541019</td>\n",
+       "      <td>0.613532</td>\n",
+       "      <td>0.773996</td>\n",
+       "      <td>1.176323</td>\n",
+       "      <td>0.565597</td>\n",
+       "      <td>1.744161</td>\n",
+       "      <td>0.137504</td>\n",
+       "      <td>0.956057</td>\n",
+       "      <td>1.550901</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ABCF2-H2BE1</th>\n",
+       "      <td>1.521051</td>\n",
+       "      <td>3.132577</td>\n",
+       "      <td>1.646163</td>\n",
+       "      <td>0.855990</td>\n",
+       "      <td>2.100978</td>\n",
+       "      <td>2.797013</td>\n",
+       "      <td>2.419539</td>\n",
+       "      <td>0.344828</td>\n",
+       "      <td>2.981853</td>\n",
+       "      <td>2.482848</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.823749</td>\n",
+       "      <td>0.189034</td>\n",
+       "      <td>3.712596</td>\n",
+       "      <td>1.007196</td>\n",
+       "      <td>3.446256</td>\n",
+       "      <td>2.592158</td>\n",
+       "      <td>1.150560</td>\n",
+       "      <td>2.754888</td>\n",
+       "      <td>1.765535</td>\n",
+       "      <td>3.129283</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>POLR2J3</th>\n",
+       "      <td>4.516646</td>\n",
+       "      <td>5.238023</td>\n",
+       "      <td>5.458119</td>\n",
+       "      <td>6.648465</td>\n",
+       "      <td>5.239933</td>\n",
+       "      <td>5.650190</td>\n",
+       "      <td>6.129489</td>\n",
+       "      <td>4.511595</td>\n",
+       "      <td>5.559798</td>\n",
+       "      <td>5.768449</td>\n",
+       "      <td>...</td>\n",
+       "      <td>5.689020</td>\n",
+       "      <td>3.447579</td>\n",
+       "      <td>5.375735</td>\n",
+       "      <td>3.456806</td>\n",
+       "      <td>4.397118</td>\n",
+       "      <td>5.673556</td>\n",
+       "      <td>3.759156</td>\n",
+       "      <td>4.130931</td>\n",
+       "      <td>4.688740</td>\n",
+       "      <td>5.414474</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>11827 rows × 206 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             ACH-000010  ACH-000012  ACH-000015  ACH-000021  ACH-000029  \\\n",
+       "gene                                                                      \n",
+       "TSPAN6         4.447579    5.802452    4.794936    4.831371    6.491532   \n",
+       "DPM1           7.227760    5.997744    6.929436    6.498570    6.672850   \n",
+       "SCYL3          2.405992    1.970854    2.952334    2.414136    2.475085   \n",
+       "C1orf112       4.594549    3.784504    3.709291    4.527946    4.486714   \n",
+       "CFH            3.404631    2.750607    3.560715    2.153805    0.014355   \n",
+       "...                 ...         ...         ...         ...         ...   \n",
+       "UPK3BL2        3.279471    2.729009    3.976364    2.643856    5.422233   \n",
+       "AC093512.2     2.653060    3.700440    2.214125    2.403268    2.933573   \n",
+       "ARHGAP11B      1.827819    1.695994    0.903038    2.666757    2.195348   \n",
+       "ABCF2-H2BE1    1.521051    3.132577    1.646163    0.855990    2.100978   \n",
+       "POLR2J3        4.516646    5.238023    5.458119    6.648465    5.239933   \n",
+       "\n",
+       "             ACH-000030  ACH-000033  ACH-000035  ACH-000062  ACH-000066  ...  \\\n",
+       "gene                                                                     ...   \n",
+       "TSPAN6         5.399855    5.391974    4.888013    4.731726    5.753818  ...   \n",
+       "DPM1           6.307246    6.866661    7.254840    5.849499    5.514438  ...   \n",
+       "SCYL3          2.017922    1.803227    2.289834    2.503349    2.536053  ...   \n",
+       "C1orf112       3.671293    3.841973    3.795975    3.761285    4.566206  ...   \n",
+       "CFH            1.214125    0.807355    0.367371    5.036503    3.881665  ...   \n",
+       "...                 ...         ...         ...         ...         ...  ...   \n",
+       "UPK3BL2        2.073820    3.065228    3.572890    3.241840    3.084064  ...   \n",
+       "AC093512.2     3.944858    2.965323    3.026800    2.778209    4.265287  ...   \n",
+       "ARHGAP11B      1.056584    1.655352    2.996389    1.682573    2.364572  ...   \n",
+       "ABCF2-H2BE1    2.797013    2.419539    0.344828    2.981853    2.482848  ...   \n",
+       "POLR2J3        5.650190    6.129489    4.511595    5.559798    5.768449  ...   \n",
+       "\n",
+       "             ACH-001386  ACH-001549  ACH-001555  ACH-001556  ACH-001557  \\\n",
+       "gene                                                                      \n",
+       "TSPAN6         3.060047    0.773996    2.769772    4.280956    4.089159   \n",
+       "DPM1           6.837691    5.908333    7.010220    7.745170    7.149137   \n",
+       "SCYL3          2.472488    1.831877    1.895303    2.304511    2.220330   \n",
+       "C1orf112       4.571677    4.060912    1.550901    4.306700    4.551516   \n",
+       "CFH            0.056584    0.000000    1.704872    2.901108    5.655638   \n",
+       "...                 ...         ...         ...         ...         ...   \n",
+       "UPK3BL2        2.353323    0.863938    0.565597    0.516015    0.650765   \n",
+       "AC093512.2     4.765535    5.610877    2.424922    4.426265    2.324811   \n",
+       "ARHGAP11B      1.807355    1.541019    0.613532    0.773996    1.176323   \n",
+       "ABCF2-H2BE1    0.823749    0.189034    3.712596    1.007196    3.446256   \n",
+       "POLR2J3        5.689020    3.447579    5.375735    3.456806    4.397118   \n",
+       "\n",
+       "             ACH-001558  ACH-001559  ACH-001560  ACH-001561  ACH-001562  \n",
+       "gene                                                                     \n",
+       "TSPAN6         4.799087    4.275007    4.621173    4.399855    4.628774  \n",
+       "DPM1           7.057992    7.379725    7.168923    7.191109    7.704941  \n",
+       "SCYL3          2.204767    2.000000    1.871844    2.060047    2.589763  \n",
+       "C1orf112       3.526069    4.251719    2.153805    3.231125    3.066950  \n",
+       "CFH            3.142413    3.300124    0.111031    2.857981    6.017254  \n",
+       "...                 ...         ...         ...         ...         ...  \n",
+       "UPK3BL2        1.565597    0.163499    0.887525    0.137504    2.063503  \n",
+       "AC093512.2     2.344828    1.799087    1.899176    2.904966    2.720278  \n",
+       "ARHGAP11B      0.565597    1.744161    0.137504    0.956057    1.550901  \n",
+       "ABCF2-H2BE1    2.592158    1.150560    2.754888    1.765535    3.129283  \n",
+       "POLR2J3        5.673556    3.759156    4.130931    4.688740    5.414474  \n",
+       "\n",
+       "[11827 rows x 206 columns]"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lung.expressed(threshold=0.50, style=\"values\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Cancer and CellLineCluster objects have an additional method that outputs a binary matrix\n",
+    "of which genes/cell lines have mutations"
+   ]
+  },
+  {
+   "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>A1BG</th>\n",
+       "      <th>A1CF</th>\n",
+       "      <th>A2M</th>\n",
+       "      <th>A2ML1</th>\n",
+       "      <th>A3GALT2</th>\n",
+       "      <th>A4GALT</th>\n",
+       "      <th>A4GNT</th>\n",
+       "      <th>AAAS</th>\n",
+       "      <th>AACS</th>\n",
+       "      <th>AADAC</th>\n",
+       "      <th>...</th>\n",
+       "      <th>ZWILCH</th>\n",
+       "      <th>ZWINT</th>\n",
+       "      <th>ZXDA</th>\n",
+       "      <th>ZXDB</th>\n",
+       "      <th>ZXDC</th>\n",
+       "      <th>ZYG11A</th>\n",
+       "      <th>ZYG11B</th>\n",
+       "      <th>ZYX</th>\n",
+       "      <th>ZZEF1</th>\n",
+       "      <th>ZZZ3</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>ACH-000523</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
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+       "      <td>0</td>\n",
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+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ACH-000749</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
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+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ACH-000787</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ACH-000852</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ACH-000867</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>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",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ACH-000521</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ACH-000010</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ACH-000589</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ACH-000575</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ACH-000587</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>273 rows × 17376 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            A1BG  A1CF  A2M  A2ML1  A3GALT2  A4GALT  A4GNT  AAAS  AACS  AADAC  \\\n",
+       "ACH-000523     1     0    0      0        0       0      0     0     0      0   \n",
+       "ACH-000749     1     0    0      0        0       0      0     0     0      0   \n",
+       "ACH-000787     1     1    0      0        0       0      0     0     0      0   \n",
+       "ACH-000852     1     0    1      1        0       0      0     0     0      0   \n",
+       "ACH-000867     1     0    0      0        0       0      0     0     0      0   \n",
+       "...          ...   ...  ...    ...      ...     ...    ...   ...   ...    ...   \n",
+       "ACH-000521     0     0    0      0        0       0      0     0     0      0   \n",
+       "ACH-000010     0     0    0      0        0       0      0     0     0      0   \n",
+       "ACH-000589     0     0    0      0        0       0      0     0     0      0   \n",
+       "ACH-000575     0     0    0      0        0       0      0     0     0      0   \n",
+       "ACH-000587     0     0    0      0        0       0      0     0     0      0   \n",
+       "\n",
+       "            ...  ZWILCH  ZWINT  ZXDA  ZXDB  ZXDC  ZYG11A  ZYG11B  ZYX  ZZEF1  \\\n",
+       "ACH-000523  ...       0      1     0     0     0       0       0    0      0   \n",
+       "ACH-000749  ...       0      0     0     0     0       0       0    0      0   \n",
+       "ACH-000787  ...       0      0     0     0     0       0       0    0      0   \n",
+       "ACH-000852  ...       0      0     0     0     0       0       1    0      0   \n",
+       "ACH-000867  ...       1      0     0     0     0       0       0    0      0   \n",
+       "...         ...     ...    ...   ...   ...   ...     ...     ...  ...    ...   \n",
+       "ACH-000521  ...       0      0     0     0     0       0       0    0      0   \n",
+       "ACH-000010  ...       0      0     0     0     0       0       0    0      0   \n",
+       "ACH-000589  ...       0      0     0     0     0       0       0    0      0   \n",
+       "ACH-000575  ...       0      0     0     0     0       0       0    0      0   \n",
+       "ACH-000587  ...       0      0     0     0     0       0       0    0      0   \n",
+       "\n",
+       "            ZZZ3  \n",
+       "ACH-000523     0  \n",
+       "ACH-000749     0  \n",
+       "ACH-000787     0  \n",
+       "ACH-000852     0  \n",
+       "ACH-000867     0  \n",
+       "...          ...  \n",
+       "ACH-000521     0  \n",
+       "ACH-000010     0  \n",
+       "ACH-000589     0  \n",
+       "ACH-000575     0  \n",
+       "ACH-000587     0  \n",
+       "\n",
+       "[273 rows x 17376 columns]"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lung.mutation_matrix()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Organelle Methods and Attributes\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Attributes:\n",
+      "\n",
+      "conf: 3\n",
+      "get_name: Plasma membrane\n",
+      "location: Plasma membrane\n",
+      "genes list first item: ABCA7\n",
+      "genes length: 1547\n",
+      "\n",
+      "Methods:\n",
+      "\n",
+      "cn_normal\n",
+      "deletion\n",
+      "dependency_of\n",
+      "dependent\n",
+      "duplication\n",
+      "effect_of\n",
+      "essential\n",
+      "expressed\n",
+      "expression_of\n",
+      "genes_and_conf\n",
+      "mutated\n",
+      "non_dependent\n",
+      "non_essential\n",
+      "unexpressed\n"
+     ]
+    }
+   ],
+   "source": [
+    "pretty_print_attr(membrane)"
+   ]
+  }
+ ],
+ "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": "3.9.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/kras_egfr_scatter.ipynb b/kras_egfr_scatter.ipynb
new file mode 100644
index 0000000..025208e
--- /dev/null
+++ b/kras_egfr_scatter.ipynb
@@ -0,0 +1,319 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f86433ea",
+   "metadata": {},
+   "source": [
+    "# _KRAS_ and _EGFR_ Scatter plot "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "6dfaefa8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import CanDI.candi as can\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn import cluster, decomposition, preprocessing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "67a24919",
+   "metadata": {},
+   "source": [
+    "## Cancer Object Instantiation\n",
+    "I'm interested in studying non-small cell lung cancer using the data in depmap and ccle. I start by instantiating a cancer object that will allow me to explore the data space of non-small cell lung cancer cell lines. Since I don't want any small cell lung cancer cell lines included I will specify a disease subtype during instantiation. The subtype argument of Cancer object instantiation works by string matching in the lineage_subtype collumn of the cell_lines dataset. Below you can see that we have a variety of cell types within a given lineage subtype."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "bc12e739",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Non-Small Cell Lung Cancer (NSCLC), Adenocarcinoma',\n",
+       "       'Non-Small Cell Lung Cancer (NSCLC), Large Cell Carcinoma',\n",
+       "       'Non-Small Cell Lung Cancer (NSCLC), unspecified',\n",
+       "       'Non-Small Cell Lung Cancer (NSCLC), Squamous Cell Carcinoma',\n",
+       "       'Non-Small Cell Lung Cancer (NSCLC), Adenosquamous Carcinoma',\n",
+       "       'Non-Small Cell Lung Cancer (NSCLC), Mucoepidermoid Carcinoma'],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lung = can.Cancer(\"Lung Cancer\", subtype=\"NSCLC\")\n",
+    "lung.subtypes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4479b0bd",
+   "metadata": {},
+   "source": [
+    "I want to look at how oncogenic mutations affect global genetic dependencies. Let's choose KRAS and EGFR as our oncogenic mutations. I'm going to make two CellLineCluster objects per oncogene, eight in total. For each oncogene I want to make a CellLineCluster where the oncogene of interest is mutated and another where it is wild type.\n",
+    "\n",
+    "__To Analyze KRAS__\n",
+    "* Lung - KRAS MT\n",
+    "* Lung - KRAS WT\n",
+    "\n",
+    "__To Analyze EGFR__\n",
+    "* Lung - EGFR MT\n",
+    "* Lung - EGFR WT\n",
+    "\n",
+    "MT = Mutant \\\n",
+    "WT = Wild Type"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "e7d7abe9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "mutations has not been loaded. Do you want to load, y/n?> y\n",
+      "Load Complete\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Mutated function automatically ignores silent mutations\n",
+    "kras_mt_ids = lung.mutated(\"KRAS\", variant=\"Variant_Classification\", item = \"Missense_Mutation\")\n",
+    "egfr_mt_ids = lung.mutated(\"EGFR\", variant=\"Variant_Classification\")\n",
+    "\n",
+    "kras_wt_ids = list(set(lung.depmap_ids) - set(kras_mt_ids))\n",
+    "egfr_wt_ids = list(set(lung.depmap_ids) - set(egfr_mt_ids))\n",
+    "\n",
+    "#Instantiate KRAS Clusters\n",
+    "kras_mt = can.CellLineCluster(kras_mt_ids)\n",
+    "kras_wt = can.CellLineCluster(kras_wt_ids)\n",
+    "\n",
+    "#Instantiate EGFR Clusters\n",
+    "egfr_mt = can.CellLineCluster(egfr_mt_ids)\n",
+    "egfr_wt = can.CellLineCluster(egfr_wt_ids)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2cc87557",
+   "metadata": {},
+   "source": [
+    "## Analyzing Global Gene Dependency\n",
+    "To see how KRAS and EGFR mutations affect global gene dependency I'm going to plot the average gene effect for every gene of the mutant and wildtype clusters against each other. This if gene effect skews towards wildtype or mutation status for any give gene. The Function below will be used to make this plot. Unless you are interested in specifically how this plot is made you can skip the following cell."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "295b9aba",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def gene_effect_scatter(mt, wt, gene, control, tc1=None, tc2=None, name=None):\n",
+    "    \n",
+    "    #Average Gene Effect for control agnostic groups\n",
+    "    mt_effect = mt.gene_dependency.mean(1)\n",
+    "    wt_effect = wt.gene_dependency.mean(1)\n",
+    "    \n",
+    "    #For Labeling\n",
+    "    mt_lab = mt_effect.loc[[gene, control]]\n",
+    "    wt_lab = wt_effect.loc[[gene, control]]\n",
+    "    \n",
+    "    \n",
+    "    #Make Figure appropriate size, dpi, and font\n",
+    "    plt.rcParams.update({\"figure.figsize\": (8, 8),\n",
+    "                        \"savefig.dpi\": 300,\n",
+    "                        \"font.family\": \"sans-serif\",\n",
+    "                        \"font.size\": 12\n",
+    "                        })\n",
+    "    \n",
+    "    #Generate Figure and Axis objects\n",
+    "    fig, ax = plt.subplots(1,1)\n",
+    "    \n",
+    "    #Label Axes\n",
+    "    ax.set_xlabel(f\"{gene} MT Average Gene Effect (CERES Score)\")\n",
+    "    ax.set_ylabel(f\"{gene} WT Average Gene Effect (CERES Score)\")\n",
+    "    \n",
+    "    #Draw Line at median common essential value\n",
+    "    ax.axhline(y = 0.50,\n",
+    "               c = \"black\",\n",
+    "               linewidth=0.5,\n",
+    "               label = \"Minimun Gene Dependencey Probability\"\n",
+    "              )\n",
+    "    \n",
+    "    ax.axvline(x = 0.50,\n",
+    "               c= \"black\",\n",
+    "               linewidth=0.5)\n",
+    "    \n",
+    "    #Plot all genes\n",
+    "    ax.scatter(mt_effect,\n",
+    "               wt_effect,\n",
+    "               c = \"#2166ac\",\n",
+    "               alpha = 0.7,\n",
+    "               s = 50\n",
+    "              )\n",
+    "    \n",
+    "    #Outline Genes To label\n",
+    "    ax.scatter(mt_lab,\n",
+    "               wt_lab,\n",
+    "               c = \"#2166ac\",\n",
+    "               s = 50,\n",
+    "               edgecolor = \"black\",\n",
+    "               linewidth = 2,\n",
+    "               alpha = 0.7\n",
+    "              )\n",
+    " \n",
+    "    ax.legend()\n",
+    "    \n",
+    "    #Label control agnostic Series\n",
+    "    if tc1:\n",
+    "        for i in range(mt_lab.shape[0]):\n",
+    "            text = list(mt_lab.index)\n",
+    "            ax.annotate(text[i],\n",
+    "                        xy = (mt_lab[i], wt_lab[i]),\n",
+    "                        xytext = tc1[i],\n",
+    "                        xycoords = \"data\",\n",
+    "                        arrowprops = {\"arrowstyle\": \"-\"}\n",
+    "                       )\n",
+    "    \n",
+    "    plt.show()\n",
+    "    \n",
+    "    if name:\n",
+    "        fig.savefig(name, dpi=300)\n",
+    "        \n",
+    "    return\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0f2e85af",
+   "metadata": {},
+   "source": [
+    "## Note about Gene Effect Scores: Dependency vs Essentiality\n",
+    "A more negative gene effect means more dependent. A gene effect of -1.0 is the median gene effect of all common essential genes. If a gene has a gene effect of -1.0 or lower it then that gene is essential. A cell line can still be dependent on a gene with a lower gene effect if knocking out that gene slows growth/proliferation. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "50b32d42",
+   "metadata": {},
+   "source": [
+    "### Average Gene Effect in KRAS Wildtype and KRAS Mutant Cell Lines\n",
+    "\n",
+    "KRAS dependency heavily favors KRAS mutant cell lines. No other gene's depedencies are as skewed toward KRAS mutant cell lines. KRAS mutations appear to be self essentializing."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "e6b695e6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Text_coords are custom per graph \n",
+    "text_coords1 = [(0.95, 0.25), (0.1, 0.4)]\n",
+    "\n",
+    "gene_effect_scatter(kras_mt,\n",
+    "                    kras_wt,\n",
+    "                    \"KRAS\",\n",
+    "                    \"EGFR\",\n",
+    "                    tc1 = text_coords1,\n",
+    "                    tc2 = text_coords2,\n",
+    "                    name= \"figures/kras_gene_dependency_scatter.pdf\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "11e97be8",
+   "metadata": {},
+   "source": [
+    "### Average Gene Effect of EGFR MT vs EGFR WT Cell Lines\n",
+    "We don't observed the same self esentializing effect with EGFR and EGFR mutations as we do with KRAS and KRAS mutations. The KRAS point moves upwards along the diaganol when KRAS mutant cell lines are removed, indicating the self essentializing effect of KRAS mutations was represented equally in both EGFR mutant and EGFR wild type cell lines. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "d658fa0a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tc1 = [(0.4, 0.1), (0.2, 0.6)]\n",
+    "\n",
+    "gene_effect_scatter(egfr_mt,\n",
+    "                    egfr_wt,\n",
+    "                    \"EGFR\",\n",
+    "                    \"KRAS\",\n",
+    "                    tc1 = tc1,\n",
+    "                    tc2 = tc2,\n",
+    "                    name = None#\"figures/egfr_gene_dependency_scatter.pdf\"\n",
+    "                   )"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
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+}