diff --git a/brca_heatmap.ipynb b/brca_heatmap.ipynb
deleted file mode 100644
index bb63fc1..0000000
--- a/brca_heatmap.ipynb
+++ /dev/null
@@ -1,671 +0,0 @@
-{
- "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
deleted file mode 100644
index 3590ce6..0000000
--- a/deseq_setup.ipynb
+++ /dev/null
@@ -1,265 +0,0 @@
-{
- "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 bb63fc1..a127d6d 100644
--- a/docs/source/brca_heatmap.ipynb
+++ b/docs/source/brca_heatmap.ipynb
@@ -1,8 +1,28 @@
 {
  "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6cb97afb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install pycandi"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "feb0be05",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!candi-install"
+   ]
+  },
   {
    "cell_type": "markdown",
-   "id": "58a5f439",
+   "id": "b1ac0947",
    "metadata": {},
    "source": [
     "# _BRCA_ Heatmap"
@@ -10,7 +30,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "id": "93b49611",
    "metadata": {},
    "outputs": [],
@@ -34,7 +54,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "id": "c220005a",
    "metadata": {},
    "outputs": [
@@ -71,7 +91,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "id": "d098ddf9",
    "metadata": {},
    "outputs": [
@@ -265,23 +285,23 @@
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1269994</th>\n",
-       "      <td>EHBP1L1</td>\n",
-       "      <td>254102</td>\n",
+       "      <th>1230240</th>\n",
+       "      <td>SLC39A4</td>\n",
+       "      <td>55630</td>\n",
        "      <td>37</td>\n",
-       "      <td>11</td>\n",
-       "      <td>65350600</td>\n",
-       "      <td>65350600</td>\n",
+       "      <td>8</td>\n",
+       "      <td>145641991</td>\n",
+       "      <td>145641991</td>\n",
        "      <td>+</td>\n",
-       "      <td>Frame_Shift_Del</td>\n",
-       "      <td>DEL</td>\n",
-       "      <td>G</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>NaN</td>\n",
-       "      <td>61:69</td>\n",
+       "      <td>0.000017</td>\n",
+       "      <td>silent</td>\n",
+       "      <td>52:23</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
@@ -289,23 +309,23 @@
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1269995</th>\n",
-       "      <td>SACS</td>\n",
-       "      <td>26278</td>\n",
+       "      <th>1230241</th>\n",
+       "      <td>TAL2</td>\n",
+       "      <td>6887</td>\n",
        "      <td>37</td>\n",
-       "      <td>13</td>\n",
-       "      <td>23904582</td>\n",
-       "      <td>23904582</td>\n",
+       "      <td>9</td>\n",
+       "      <td>108424778</td>\n",
+       "      <td>108424778</td>\n",
        "      <td>+</td>\n",
-       "      <td>Frame_Shift_Del</td>\n",
-       "      <td>DEL</td>\n",
-       "      <td>T</td>\n",
+       "      <td>Start_Codon_SNP</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>NaN</td>\n",
-       "      <td>88:1</td>\n",
+       "      <td>damaging</td>\n",
+       "      <td>27:0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
@@ -313,23 +333,23 @@
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1269996</th>\n",
-       "      <td>CBFB</td>\n",
-       "      <td>865</td>\n",
+       "      <th>1230242</th>\n",
+       "      <td>TRO</td>\n",
+       "      <td>7216</td>\n",
        "      <td>37</td>\n",
-       "      <td>16</td>\n",
-       "      <td>67070637</td>\n",
-       "      <td>67070638</td>\n",
+       "      <td>X</td>\n",
+       "      <td>54955098</td>\n",
+       "      <td>54955098</td>\n",
        "      <td>+</td>\n",
-       "      <td>Frame_Shift_Ins</td>\n",
-       "      <td>INS</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>NaN</td>\n",
-       "      <td>31:0</td>\n",
+       "      <td>silent</td>\n",
+       "      <td>5:16</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
@@ -337,23 +357,23 @@
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1269997</th>\n",
-       "      <td>TAF15</td>\n",
-       "      <td>8148</td>\n",
+       "      <th>1230243</th>\n",
+       "      <td>USP51</td>\n",
+       "      <td>158880</td>\n",
        "      <td>37</td>\n",
-       "      <td>17</td>\n",
-       "      <td>34171711</td>\n",
-       "      <td>34171734</td>\n",
+       "      <td>X</td>\n",
+       "      <td>55514703</td>\n",
+       "      <td>55514703</td>\n",
        "      <td>+</td>\n",
-       "      <td>In_Frame_Del</td>\n",
-       "      <td>DEL</td>\n",
-       "      <td>GGCTATGGAGGAGACCGAGGAGGT</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>NaN</td>\n",
-       "      <td>24:28</td>\n",
+       "      <td>other non-conserving</td>\n",
+       "      <td>23:0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
@@ -361,23 +381,23 @@
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1269998</th>\n",
-       "      <td>FRMPD3</td>\n",
-       "      <td>84443</td>\n",
+       "      <th>1230244</th>\n",
+       "      <td>C1GALT1C1</td>\n",
+       "      <td>29071</td>\n",
        "      <td>37</td>\n",
        "      <td>X</td>\n",
-       "      <td>106846460</td>\n",
-       "      <td>106846461</td>\n",
+       "      <td>119760406</td>\n",
+       "      <td>119760406</td>\n",
        "      <td>+</td>\n",
-       "      <td>In_Frame_Ins</td>\n",
-       "      <td>INS</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>NaN</td>\n",
-       "      <td>6:27</td>\n",
+       "      <td>other non-conserving</td>\n",
+       "      <td>28:0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
@@ -386,22 +406,22 @@
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>1269999 rows × 32 columns</p>\n",
+       "<p>1230245 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",
+       "              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",
+       "1230240    SLC39A4           55630          37          8       145641991   \n",
+       "1230241       TAL2            6887          37          9       108424778   \n",
+       "1230242        TRO            7216          37          X        54955098   \n",
+       "1230243      USP51          158880          37          X        55514703   \n",
+       "1230244  C1GALT1C1           29071          37          X       119760406   \n",
        "\n",
        "         End_position Strand Variant_Classification Variant_Type  \\\n",
        "0            12359347      +      Nonsense_Mutation          SNP   \n",
@@ -410,24 +430,24 @@
        "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",
+       "1230240     145641991      +                 Silent          SNP   \n",
+       "1230241     108424778      +        Start_Codon_SNP          SNP   \n",
+       "1230242      54955098      +                 Silent          SNP   \n",
+       "1230243      55514703      +      Missense_Mutation          SNP   \n",
+       "1230244     119760406      +      Missense_Mutation          SNP   \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",
+       "        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",
+       "1230240                C  ...           False         0.0  0.000017   \n",
+       "1230241                A  ...           False         0.0       NaN   \n",
+       "1230242                C  ...           False         0.0       NaN   \n",
+       "1230243                G  ...           False         0.0       NaN   \n",
+       "1230244                T  ...           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",
@@ -436,11 +456,11 @@
        "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",
+       "1230240                silent      52:23   NaN   NaN       NaN          NaN   \n",
+       "1230241              damaging       27:0   NaN   NaN       NaN          NaN   \n",
+       "1230242                silent       5:16   NaN   NaN       NaN          NaN   \n",
+       "1230243  other non-conserving       23:0   NaN   NaN       NaN          NaN   \n",
+       "1230244  other non-conserving       28:0   NaN   NaN       NaN          NaN   \n",
        "\n",
        "        WGS_AC  \n",
        "0          NaN  \n",
@@ -449,16 +469,16 @@
        "3          NaN  \n",
        "4          NaN  \n",
        "...        ...  \n",
-       "1269994    NaN  \n",
-       "1269995    NaN  \n",
-       "1269996    NaN  \n",
-       "1269997    NaN  \n",
-       "1269998    NaN  \n",
+       "1230240    NaN  \n",
+       "1230241    NaN  \n",
+       "1230242    NaN  \n",
+       "1230243    NaN  \n",
+       "1230244    NaN  \n",
        "\n",
-       "[1269999 rows x 32 columns]"
+       "[1230245 rows x 32 columns]"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -478,7 +498,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "id": "28fb0265",
    "metadata": {},
    "outputs": [
@@ -526,7 +546,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "id": "0a51271e",
    "metadata": {},
    "outputs": [],
@@ -584,7 +604,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "id": "a3adc292",
    "metadata": {},
    "outputs": [
@@ -592,20 +612,17 @@
      "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",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 1152x432 with 2 Axes>"
+       "<Figure size 1600x600 with 2 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -625,20 +642,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "id": "3f3cf06f",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 1152x432 with 2 Axes>"
+       "<Figure size 1600x600 with 2 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -663,7 +678,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.2"
+   "version": "3.12.4"
   }
  },
  "nbformat": 4,
diff --git a/docs/source/deseq_setup.ipynb b/docs/source/deseq_setup.ipynb
index 3590ce6..4a73329 100644
--- a/docs/source/deseq_setup.ipynb
+++ b/docs/source/deseq_setup.ipynb
@@ -1,5 +1,25 @@
 {
  "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8493a00f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install pycandi"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "91df1e02",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!candi-install"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "163bb85c",
@@ -16,9 +36,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "import pandas as pd\n",
+    "import anndata as ad\n",
     "import CanDI.candi as can\n",
-    "import numpy as np\n",
-    "import pandas as pd"
+    "\n",
+    "from CanDI.pipelines import diffexp"
    ]
   },
   {
@@ -33,18 +55,20 @@
   {
    "cell_type": "code",
    "execution_count": 2,
+   "id": "ddd21097",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if type(can.data.mutations) != pd.DataFrame:\n",
+    "    can.data.load('mutations')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
    "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"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "lung = can.Cancer(\"Lung Cancer\", subtype = \"NSCLC\")\n",
     "lung = can.CellLineCluster(lung.mutated(\"KRAS\", variant = \"Variant_Classification\", item = \"Missense_Mutation\"))\n",
@@ -64,19 +88,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
+   "id": "29d9ab4c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if type(can.data.rnaseq_reads) != pd.DataFrame:\n",
+    "    can.data.load('rnaseq_reads')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
    "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"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "def make_counts_coldata(obj1, obj2, condition, factor1, factor2):\n",
     "    \n",
@@ -93,12 +119,22 @@
     "    #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",
+    "    adata = ad.AnnData(\n",
+    "        counts_mat.T,\n",
+    "        obs = coldat.to_frame()\n",
+    "    )\n",
     "\n",
-    "counts.to_csv(\"temp_dat/lung_sex_counts.csv\")\n",
-    "coldat.to_csv(\"temp_dat/lung_sex_coldata.csv\")"
+    "    return adata"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "fe08e848",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "adata = make_counts_coldata(lung_male, lung_female, \"sex\", \"male\", \"female\")"
    ]
   },
   {
@@ -106,18 +142,81 @@
    "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."
+    "#### Running pyDESeq2\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",
+   "execution_count": 8,
+   "id": "ceb0a995",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Fitting size factors...\n",
+      "... done in 0.05 seconds.\n",
+      "\n",
+      "Fitting dispersions...\n",
+      "... done in 1.39 seconds.\n",
+      "\n",
+      "Fitting dispersion trend curve...\n",
+      "... done in 0.58 seconds.\n",
+      "\n",
+      "Fitting MAP dispersions...\n",
+      "... done in 1.42 seconds.\n",
+      "\n",
+      "Fitting LFCs...\n",
+      "... done in 2.28 seconds.\n",
+      "\n",
+      "Replacing 5676 outlier genes.\n",
+      "\n",
+      "Fitting dispersions...\n",
+      "... done in 0.28 seconds.\n",
+      "\n",
+      "Fitting MAP dispersions...\n",
+      "... done in 0.28 seconds.\n",
+      "\n",
+      "Fitting LFCs...\n",
+      "... done in 0.36 seconds.\n",
+      "\n",
+      "Running Wald tests...\n",
+      "... done in 1.64 seconds.\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Log2 fold change & Wald test p-value: sex male vs female\n",
+      "              baseMean  log2FoldChange     lfcSE      stat    pvalue      padj\n",
+      "gene                                                                          \n",
+      "A1BG        200.696432        0.027944  0.671884  0.041591  0.966825  0.994527\n",
+      "A1BG-AS1    255.912479        0.202698  0.626175  0.323708  0.746159  0.950009\n",
+      "A1CF         12.057056       -1.367054  0.537503 -2.543342  0.010980  0.236614\n",
+      "A2M          18.685828        0.365012  0.619866  0.588857  0.555957  0.902738\n",
+      "A2M-AS1      49.283425       -0.266289  0.618898 -0.430264  0.667004  0.933027\n",
+      "...                ...             ...       ...       ...       ...       ...\n",
+      "ZYG11AP1      0.038949        0.057154  3.391294  0.016853  0.986554       NaN\n",
+      "ZYG11B     2200.470135       -0.200760  0.214134 -0.937546  0.348478  0.816844\n",
+      "ZYX       11155.922014        0.206356  0.377946  0.545994  0.585070  0.912754\n",
+      "ZZEF1      4400.173132       -0.483351  0.222239 -2.174911  0.029637  0.362384\n",
+      "ZZZ3       3532.326301       -0.212461  0.170959 -1.242756  0.213958  0.722301\n",
+      "\n",
+      "[52443 rows x 6 columns]\n"
+     ]
+    }
+   ],
    "source": [
-    "!Rscript scripts/run_deseq.r temp_dat/lung_sex_counts.csv temp_dat/lung_sex_coldata.csv temp_dat/lung_sex_deseq.csv"
+    "results = diffexp.run_deseq(\n",
+    "    adata,\n",
+    "    design = 'sex',\n",
+    "    tested_level = 'male',\n",
+    "    ref_level = 'female'\n",
+    ")"
    ]
   },
   {
@@ -131,7 +230,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 9,
    "id": "f72ee6cd",
    "metadata": {},
    "outputs": [
@@ -163,81 +262,91 @@
        "      <th>pvalue</th>\n",
        "      <th>padj</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",
+       "    </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",
+       "      <td>4041.971706</td>\n",
+       "      <td>-7.145007</td>\n",
+       "      <td>0.710899</td>\n",
+       "      <td>-10.050660</td>\n",
+       "      <td>9.125401e-24</td>\n",
+       "      <td>2.692176e-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",
+       "      <th>RPS4Y1</th>\n",
+       "      <td>6613.044427</td>\n",
+       "      <td>8.723193</td>\n",
+       "      <td>1.031580</td>\n",
+       "      <td>8.456144</td>\n",
+       "      <td>2.763634e-17</td>\n",
+       "      <td>4.076636e-13</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",
+       "      <th>CEACAM5</th>\n",
+       "      <td>10444.137420</td>\n",
+       "      <td>-7.282857</td>\n",
+       "      <td>0.892029</td>\n",
+       "      <td>-8.164374</td>\n",
+       "      <td>3.231066e-16</td>\n",
+       "      <td>3.177430e-12</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",
+       "      <th>DDX3Y</th>\n",
+       "      <td>1195.388509</td>\n",
+       "      <td>7.642864</td>\n",
+       "      <td>0.983916</td>\n",
+       "      <td>7.767805</td>\n",
+       "      <td>7.985781e-15</td>\n",
+       "      <td>5.889913e-11</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",
+       "      <td>58.891176</td>\n",
+       "      <td>-5.623715</td>\n",
+       "      <td>0.752347</td>\n",
+       "      <td>-7.474896</td>\n",
+       "      <td>7.726502e-14</td>\n",
+       "      <td>4.558945e-10</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",
+       "             baseMean  log2FoldChange     lfcSE       stat        pvalue  \\\n",
+       "gene                                                                       \n",
+       "XIST      4041.971706       -7.145007  0.710899 -10.050660  9.125401e-24   \n",
+       "RPS4Y1    6613.044427        8.723193  1.031580   8.456144  2.763634e-17   \n",
+       "CEACAM5  10444.137420       -7.282857  0.892029  -8.164374  3.231066e-16   \n",
+       "DDX3Y     1195.388509        7.642864  0.983916   7.767805  7.985781e-15   \n",
+       "GJB1        58.891176       -5.623715  0.752347  -7.474896  7.726502e-14   \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  "
+       "gene                   \n",
+       "XIST     2.692176e-19  \n",
+       "RPS4Y1   4.076636e-13  \n",
+       "CEACAM5  3.177430e-12  \n",
+       "DDX3Y    5.889913e-11  \n",
+       "GJB1     4.558945e-10  "
       ]
      },
-     "execution_count": 4,
+     "execution_count": 9,
      "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()"
+    "results.sort_values(\"padj\").head()"
    ]
   }
  ],
@@ -257,7 +366,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.2"
+   "version": "3.12.4"
   }
  },
  "nbformat": 4,
diff --git a/docs/source/get-started.ipynb b/docs/source/get-started.ipynb
index a30ec58..c907c9c 100644
--- a/docs/source/get-started.ipynb
+++ b/docs/source/get-started.ipynb
@@ -1,5 +1,23 @@
 {
  "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install pycandi"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!candi-install"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -20,7 +38,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -40,19 +58,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "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"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(can.data.gene_effect) # depmap ceres score\n",
     "print(can.data.expression) # ccle rna seq data\n",
@@ -69,412 +77,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "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",
-       "      <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",
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-       "      <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"
-    }
-   ],
+   "outputs": [],
    "source": [
     "can.data.load(\"expression\")"
    ]
@@ -489,276 +94,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>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",
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-       "      <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"
-    }
-   ],
+   "outputs": [],
    "source": [
     "can.data.cell_lines.head(5)"
    ]
@@ -774,113 +112,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "      <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",
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-       "    <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"
-    }
-   ],
+   "outputs": [],
    "source": [
     "can.data.genes.head(5)"
    ]
@@ -895,84 +129,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "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"
-    }
-   ],
+   "outputs": [],
    "source": [
     "can.data.locations.head(5)"
    ]
@@ -988,7 +147,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1008,39 +167,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "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"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "def pretty_print_attr(obj):\n",
     "    attr = []\n",
@@ -1077,31 +206,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "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"
-    }
-   ],
+   "outputs": [],
    "source": [
     "kras.expression"
    ]
@@ -1117,29 +224,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "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"
-    }
-   ],
+   "outputs": [],
    "source": [
     "kras.expressed()[0:10]"
    ]
@@ -1153,31 +240,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "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"
-    }
-   ],
+   "outputs": [],
    "source": [
     "kras.expressed(style=\"values\")"
    ]
@@ -1191,20 +256,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "kras.expressed(a549.depmap_id)"
    ]
@@ -1219,20 +273,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "4.350497247084133"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "kras.expression_of(a549.depmap_id)"
    ]
@@ -1246,405 +289,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "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",
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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>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",
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-       "      <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",
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-       "      <td>25398284</td>\n",
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-       "      <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",
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-       "      <td>...</td>\n",
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-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
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-       "      <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"
-    }
-   ],
+   "outputs": [],
    "source": [
     "kras.mutations"
    ]
@@ -1659,29 +306,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "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"
-    }
-   ],
+   "outputs": [],
    "source": [
     "kras.mutated(variant=\"Variant_Classification\", item=\"Missense_Mutation\")[0:10]"
    ]
@@ -1695,20 +322,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "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"
-    }
-   ],
+   "outputs": [],
    "source": [
     "can.data.unload('mutations')\n",
     "can.data.mutations"
@@ -1723,46 +339,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "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"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "pretty_print_attr(a549)"
    ]
@@ -1778,29 +357,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "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"
-    }
-   ],
+   "outputs": [],
    "source": [
     "a549.expressed()[:10]"
    ]
@@ -1814,32 +373,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "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"
-    }
-   ],
+   "outputs": [],
    "source": [
     "a549.expressed(style=\"values\")"
    ]
@@ -1853,20 +389,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "a549.expressed(\"KRAS\")"
    ]
@@ -1880,40 +405,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "4.350497247084133"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "a549.expression_of(\"KRAS\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "4.350497247084133"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "a549.expressed(\"KRAS\", style=\"values\")"
    ]
@@ -1927,405 +430,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
    "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"
-    }
-   ],
+   "outputs": [],
    "source": [
     "a549.mutations"
    ]
@@ -2349,46 +456,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": null,
    "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"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "pretty_print_attr(lung)"
    ]
@@ -2403,412 +473,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
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-       "      <th></th>\n",
-       "      <th>ACH-000010</th>\n",
-       "      <th>ACH-000012</th>\n",
-       "      <th>ACH-000015</th>\n",
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-       "    <tr>\n",
-       "      <th>gene</th>\n",
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-       "      <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",
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-       "    <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",
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-       "      <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",
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-       "    <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",
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-       "      <th>POLR2J3</th>\n",
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-       "      <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",
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-       "      <td>3.759156</td>\n",
-       "      <td>4.130931</td>\n",
-       "      <td>4.688740</td>\n",
-       "      <td>5.414474</td>\n",
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-       "      <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>0.000000</td>\n",
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-       "      <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>0.000000</td>\n",
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-       "    <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"
-    }
-   ],
+   "outputs": [],
    "source": [
     "lung.expression"
    ]
@@ -2822,29 +489,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": null,
    "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"
-    }
-   ],
+   "outputs": [],
    "source": [
     "lung.expressed()[0:10]"
    ]
@@ -2858,441 +505,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": null,
    "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"
-    }
-   ],
+   "outputs": [],
    "source": [
     "lung.expressed(threshold=0.50)[0:10]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "\n",
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-       "\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>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"
-    }
-   ],
+   "outputs": [],
    "source": [
     "lung.expressed(threshold=0.50, style=\"values\")"
    ]
@@ -3307,371 +531,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>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",
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-       "      <td>0</td>\n",
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-       "    <tr>\n",
-       "      <th>ACH-000749</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
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-       "    </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",
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-       "      <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",
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-       "      <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",
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-       "      <td>...</td>\n",
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-       "      <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",
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-       "      <td>0</td>\n",
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-       "      <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",
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-       "      <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",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ACH-000589</th>\n",
-       "      <td>0</td>\n",
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-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ACH-000575</th>\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",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ACH-000587</th>\n",
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-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>273 rows × 17376 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
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-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "lung.mutation_matrix()"
    ]
@@ -3685,40 +547,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": null,
    "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"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "pretty_print_attr(membrane)"
    ]
@@ -3740,7 +571,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.2"
+   "version": "3.12.4"
   }
  },
  "nbformat": 4,
diff --git a/get-started.ipynb b/get-started.ipynb
deleted file mode 100644
index a30ec58..0000000
--- a/get-started.ipynb
+++ /dev/null
@@ -1,3748 +0,0 @@
-{
- "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": [
-    {
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-       "  </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": {
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-       "\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": {
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-       "</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",
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-       "</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": {
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-       "  <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",
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-       "      <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",
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-       "    <tr>\n",
-       "      <th>gene</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
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-       "      <th></th>\n",
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-       "      <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",
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-       "\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": {
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-       "<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",
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-       "      <td>0</td>\n",
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-       "      <td>0</td>\n",
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-       "    <tr>\n",
-       "      <th>ACH-000749</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
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-       "      <td>0</td>\n",
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-       "    </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",
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-       "      <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",
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-       "      <td>0</td>\n",
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-       "      <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",
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-       "      <td>0</td>\n",
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-       "      <td>0</td>\n",
-       "    </tr>\n",
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-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
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-       "      <td>...</td>\n",
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-       "      <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",
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-       "      <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",
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-       "      <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
deleted file mode 100644
index 025208e..0000000
--- a/kras_egfr_scatter.ipynb
+++ /dev/null
@@ -1,319 +0,0 @@
-{
- "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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