{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Use Case 7: Trans genetic effects"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Trans genetic effects occur when a DNA mutation in one gene affects a different gene. To better understand the effects of DNA mutation, we will investigate downstream proteins potentially influenced by these mutations. Two prominent cancer genes, ARID1A and TP53, will be examined."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part I: ARID1A"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ARID1A, a chromatin remodeling protein, may impact the transcription of numerous genes. We will analyze the proteins interacting with ARID1A to discover possible trans effects."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 1: Import Libraries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Begin by importing standard Python libraries, such as pandas and seaborn for data analysis and visualization, scipy.stats for statistical computations, matplotlib for creating static, animated, and interactive visualizations in Python, numpy for mathematical computations, and CPTAC (Clinical Proteomic Tumor Analysis Consortium) for accessing CPTAC data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy.stats\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import cptac\n",
    "import cptac.utils as ut\n",
    "\n",
    "en = cptac.Ucec()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will conduct our analysis using endometrial cancer data, but the methods used can be applied to other cancer types in the CPTAC dataset as well."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 2: Retrieve Interacting Proteins"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will acquire a list of proteins known to directly interact with ARID1A using the Bioplex process, which identifies proteins in direct physical contact. The CPTAC package offers a function called get_interacting_proteins_bioplex, which yields a list of proteins interacting with a specified gene."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Interacting Proteins:\n",
      "['SMARCC2', 'DPF2', 'DPF1', 'SS18L2', 'SMARCE1', 'TEX13B', 'SMARCD1', 'WWP2', 'BCL7A', 'BCL7C', 'SS18', 'SMARCB1', 'DPF3']\n"
     ]
    }
   ],
   "source": [
    "gene = \"ARID1A\"\n",
    "omics = \"proteomics\"\n",
    "interacting_proteins = ut.get_interacting_proteins_bioplex(gene)\n",
    "print(\"Interacting Proteins:\")\n",
    "print(interacting_proteins)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 3: Obtain Omics Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, create a new dataframe containing protein measurements for ARID1A and its interacting proteins using the en.join_omics_to_mutations method. If the proteomics data doesn't recognize one of the genes in your request, the method will raise a warning and fill the missing values with NaN."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "cptac warning: The following columns were not found in the umich proteomics dataframe, so they were inserted into joined table, but filled with NaN: DPF1, TEX13B (C:\\Users\\sabme\\anaconda3\\lib\\site-packages\\cptac\\cancers\\cancer.py, line 525)\n",
      "cptac warning: Your version of cptac (1.5.1) is out-of-date. Latest is 1.5.0. Please run 'pip install --upgrade cptac' to update it. (C:\\Users\\sabme\\anaconda3\\lib\\threading.py, line 910)\n",
      "cptac warning: In joining the somatic_mutation table, no mutations were found for the following samples, so they were filled with Wildtype_Tumor or Wildtype_Normal: 107 samples for the ARID1A gene (C:\\Users\\sabme\\anaconda3\\lib\\site-packages\\cptac\\cancers\\cancer.py, line 325)\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Name</th>\n",
       "      <th>BCL7A_umich_proteomics</th>\n",
       "      <th>BCL7A_umich_proteomics</th>\n",
       "      <th>BCL7C_umich_proteomics</th>\n",
       "      <th>DPF1_umich_proteomics</th>\n",
       "      <th>DPF2_umich_proteomics</th>\n",
       "      <th>DPF3_umich_proteomics</th>\n",
       "      <th>SMARCB1_umich_proteomics</th>\n",
       "      <th>SMARCC2_umich_proteomics</th>\n",
       "      <th>SMARCC2_umich_proteomics</th>\n",
       "      <th>SMARCD1_umich_proteomics</th>\n",
       "      <th>SMARCE1_umich_proteomics</th>\n",
       "      <th>SS18_umich_proteomics</th>\n",
       "      <th>SS18L2_umich_proteomics</th>\n",
       "      <th>TEX13B_umich_proteomics</th>\n",
       "      <th>WWP2_umich_proteomics</th>\n",
       "      <th>ARID1A_Mutation</th>\n",
       "      <th>ARID1A_Location</th>\n",
       "      <th>ARID1A_Mutation_Status_washu_somatic_mutation</th>\n",
       "      <th>ARID1A_Mutation_Status_washu_somatic_mutation</th>\n",
       "      <th>Sample_Status</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Patient_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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>C3L-00006</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.342403</td>\n",
       "      <td>-0.636899</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.341313</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.276047</td>\n",
       "      <td>-0.518042</td>\n",
       "      <td>-0.147223</td>\n",
       "      <td>-0.180085</td>\n",
       "      <td>-0.255385</td>\n",
       "      <td>-0.184546</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.102838</td>\n",
       "      <td>[Missense_Mutation]</td>\n",
       "      <td>[p.T2121P]</td>\n",
       "      <td>Single_mutation</td>\n",
       "      <td>Single_mutation</td>\n",
       "      <td>Tumor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C3L-00008</th>\n",
       "      <td>0.279261</td>\n",
       "      <td>0.043996</td>\n",
       "      <td>-0.853702</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.613538</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.412291</td>\n",
       "      <td>-0.575983</td>\n",
       "      <td>-0.913941</td>\n",
       "      <td>-0.242887</td>\n",
       "      <td>-0.506047</td>\n",
       "      <td>-0.045882</td>\n",
       "      <td>-0.069976</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.283238</td>\n",
       "      <td>[Nonsense_Mutation, Frame_Shift_Del]</td>\n",
       "      <td>[p.Q403*, p.D1850Tfs*33]</td>\n",
       "      <td>Multiple_mutation</td>\n",
       "      <td>Multiple_mutation</td>\n",
       "      <td>Tumor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C3L-00032</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.012216</td>\n",
       "      <td>-0.405616</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.311407</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.227223</td>\n",
       "      <td>-0.550890</td>\n",
       "      <td>-0.500229</td>\n",
       "      <td>-0.609800</td>\n",
       "      <td>-0.394279</td>\n",
       "      <td>-0.353417</td>\n",
       "      <td>-0.524786</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.225262</td>\n",
       "      <td>[Wildtype_Tumor]</td>\n",
       "      <td>[No_mutation]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tumor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C3L-00084</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.006604</td>\n",
       "      <td>0.209216</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.453395</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.311589</td>\n",
       "      <td>-0.041216</td>\n",
       "      <td>-0.520971</td>\n",
       "      <td>1.456642</td>\n",
       "      <td>0.582811</td>\n",
       "      <td>0.309990</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.191736</td>\n",
       "      <td>[Wildtype_Tumor]</td>\n",
       "      <td>[No_mutation]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tumor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C3L-00090</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.548479</td>\n",
       "      <td>-0.049807</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.201228</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.364734</td>\n",
       "      <td>-0.063142</td>\n",
       "      <td>0.412197</td>\n",
       "      <td>0.210401</td>\n",
       "      <td>0.170752</td>\n",
       "      <td>0.061522</td>\n",
       "      <td>0.056844</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.071053</td>\n",
       "      <td>[Wildtype_Tumor]</td>\n",
       "      <td>[No_mutation]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tumor</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Name        BCL7A_umich_proteomics  BCL7A_umich_proteomics  \\\n",
       "Patient_ID                                                   \n",
       "C3L-00006                      NaN                0.342403   \n",
       "C3L-00008                 0.279261                0.043996   \n",
       "C3L-00032                      NaN                0.012216   \n",
       "C3L-00084                      NaN                0.006604   \n",
       "C3L-00090                      NaN                0.548479   \n",
       "\n",
       "Name        BCL7C_umich_proteomics  DPF1_umich_proteomics  \\\n",
       "Patient_ID                                                  \n",
       "C3L-00006                -0.636899                    NaN   \n",
       "C3L-00008                -0.853702                    NaN   \n",
       "C3L-00032                -0.405616                    NaN   \n",
       "C3L-00084                 0.209216                    NaN   \n",
       "C3L-00090                -0.049807                    NaN   \n",
       "\n",
       "Name        DPF2_umich_proteomics  DPF3_umich_proteomics  \\\n",
       "Patient_ID                                                 \n",
       "C3L-00006               -0.341313                    NaN   \n",
       "C3L-00008               -0.613538                    NaN   \n",
       "C3L-00032               -0.311407                    NaN   \n",
       "C3L-00084                0.453395                    NaN   \n",
       "C3L-00090                0.201228                    NaN   \n",
       "\n",
       "Name        SMARCB1_umich_proteomics  SMARCC2_umich_proteomics  \\\n",
       "Patient_ID                                                       \n",
       "C3L-00006                  -0.276047                 -0.518042   \n",
       "C3L-00008                  -0.412291                 -0.575983   \n",
       "C3L-00032                  -0.227223                 -0.550890   \n",
       "C3L-00084                   0.311589                 -0.041216   \n",
       "C3L-00090                   0.364734                 -0.063142   \n",
       "\n",
       "Name        SMARCC2_umich_proteomics  SMARCD1_umich_proteomics  \\\n",
       "Patient_ID                                                       \n",
       "C3L-00006                  -0.147223                 -0.180085   \n",
       "C3L-00008                  -0.913941                 -0.242887   \n",
       "C3L-00032                  -0.500229                 -0.609800   \n",
       "C3L-00084                  -0.520971                  1.456642   \n",
       "C3L-00090                   0.412197                  0.210401   \n",
       "\n",
       "Name        SMARCE1_umich_proteomics  SS18_umich_proteomics  \\\n",
       "Patient_ID                                                    \n",
       "C3L-00006                  -0.255385              -0.184546   \n",
       "C3L-00008                  -0.506047              -0.045882   \n",
       "C3L-00032                  -0.394279              -0.353417   \n",
       "C3L-00084                   0.582811               0.309990   \n",
       "C3L-00090                   0.170752               0.061522   \n",
       "\n",
       "Name        SS18L2_umich_proteomics  TEX13B_umich_proteomics  \\\n",
       "Patient_ID                                                     \n",
       "C3L-00006                       NaN                      NaN   \n",
       "C3L-00008                 -0.069976                      NaN   \n",
       "C3L-00032                 -0.524786                      NaN   \n",
       "C3L-00084                       NaN                      NaN   \n",
       "C3L-00090                  0.056844                      NaN   \n",
       "\n",
       "Name        WWP2_umich_proteomics                       ARID1A_Mutation  \\\n",
       "Patient_ID                                                                \n",
       "C3L-00006                0.102838                   [Missense_Mutation]   \n",
       "C3L-00008                0.283238  [Nonsense_Mutation, Frame_Shift_Del]   \n",
       "C3L-00032                0.225262                      [Wildtype_Tumor]   \n",
       "C3L-00084               -0.191736                      [Wildtype_Tumor]   \n",
       "C3L-00090                0.071053                      [Wildtype_Tumor]   \n",
       "\n",
       "Name                 ARID1A_Location  \\\n",
       "Patient_ID                             \n",
       "C3L-00006                 [p.T2121P]   \n",
       "C3L-00008   [p.Q403*, p.D1850Tfs*33]   \n",
       "C3L-00032              [No_mutation]   \n",
       "C3L-00084              [No_mutation]   \n",
       "C3L-00090              [No_mutation]   \n",
       "\n",
       "Name       ARID1A_Mutation_Status_washu_somatic_mutation  \\\n",
       "Patient_ID                                                 \n",
       "C3L-00006                                Single_mutation   \n",
       "C3L-00008                              Multiple_mutation   \n",
       "C3L-00032                                            NaN   \n",
       "C3L-00084                                            NaN   \n",
       "C3L-00090                                            NaN   \n",
       "\n",
       "Name       ARID1A_Mutation_Status_washu_somatic_mutation Sample_Status  \n",
       "Patient_ID                                                              \n",
       "C3L-00006                                Single_mutation         Tumor  \n",
       "C3L-00008                              Multiple_mutation         Tumor  \n",
       "C3L-00032                                            NaN         Tumor  \n",
       "C3L-00084                                            NaN         Tumor  \n",
       "C3L-00090                                            NaN         Tumor  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "protdf = en.join_omics_to_mutations(mutations_genes=gene,\n",
    "                                    mutations_source='washu',\n",
    "                                    omics_name=omics,\n",
    "                                    omics_source='umich',\n",
    "                                    omics_genes=interacting_proteins)\n",
    "protdf = protdf.loc[protdf['Sample_Status'] == 'Tumor']\n",
    "protdf.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 4: Simplify Mutation Status"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When observing the mutations, we are not concerned about the number of mutations a sample has, so we want to simplify the mutation status to either \"Mutated\" or \"Wildtype\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "protdf = protdf.loc[:,~protdf.columns.duplicated()]\n",
    "for ind, row in protdf.iterrows():\n",
    "    mutation_status = row[\"ARID1A_Mutation_Status_washu_somatic_mutation\"]\n",
    "    if mutation_status == 'Single_mutation' or mutation_status == 'Multiple_mutation':\n",
    "        protdf.at[ind,'Gene Mutation Status'] = 'Mutated'\n",
    "    else:\n",
    "        protdf.at[ind,'Gene Mutation Status'] = 'Wildtype'\n",
    "protdf = protdf.drop(gene+\"_Mutation\",axis=1)\n",
    "protdf = protdf.drop(gene+\"_Location\",axis=1)\n",
    "protdf = protdf.drop(gene+\"_Mutation_Status_washu_somatic_mutation\", axis=1)\n",
    "protdf = protdf.drop(\"Sample_Status\",axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 5: Conduct T-tests"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Carry out t-tests for each protein to identify significant differences in protein abundances between the wildtype and mutated samples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 Comparison   P_Value\n",
      "0     DPF2_umich_proteomics  0.000017\n",
      "1    BCL7C_umich_proteomics  0.000035\n",
      "2  SMARCC2_umich_proteomics  0.000413\n",
      "3  SMARCB1_umich_proteomics  0.004456\n",
      "4  SMARCE1_umich_proteomics  0.004670\n",
      "5  SMARCD1_umich_proteomics  0.015895\n"
     ]
    }
   ],
   "source": [
    "col_list = list(protdf.columns)\n",
    "col_list.remove('Gene Mutation Status')\n",
    "\n",
    "wrap_results = ut.wrap_ttest(protdf, 'Gene Mutation Status', col_list)\n",
    "\n",
    "if wrap_results is not None:\n",
    "    print(wrap_results)\n",
    "else:\n",
    "    print(\"No significant comparisons found\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 6: Visualize the Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The final step involves visualizing the results with a plot. Here, a volcano plot is used to represent the differentially expressed proteins between the mutated and wildtype groups."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if wrap_results is not None:\n",
    "    wrap_results = wrap_results.set_index('Comparison')\n",
    "\n",
    "    # Correct p-values using Bonferroni adjustment method\n",
    "    wrap_results['P_Value'] = wrap_results['P_Value'].apply(lambda x: x*len(col_list))\n",
    "    \n",
    "    # Create a significance threshold line for the plot (default p-value = 0.05)\n",
    "    wrap_results['P_Value'][wrap_results['P_Value'] > 0.05] = np.nan\n",
    "\n",
    "    # Create a dataframe for mutated and wildtype data\n",
    "    sig_cols = list(wrap_results.index)\n",
    "    sig_mut_and_wildtype = protdf.loc[:, sig_cols + ['Gene Mutation Status']]\n",
    "\n",
    "    # Melt the dataframe to long-form for easier plotting\n",
    "    long_df = sig_mut_and_wildtype.melt(id_vars = 'Gene Mutation Status', var_name = 'Protein', value_name = 'Abundance')\n",
    "\n",
    "    # Create the plot\n",
    "    plt.figure(figsize=(10,10))\n",
    "    sns.set(style=\"whitegrid\", palette=\"muted\")\n",
    "    volcano_plot = sns.scatterplot(x=long_df['Protein'], y=long_df['Abundance'], hue=long_df['Gene Mutation Status'])\n",
    "    volcano_plot.set_title(\"Differentially Expressed Proteins in ARID1A Mutated vs Wildtype Samples\")\n",
    "    plt.xticks(rotation=90)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This will generate a scatter plot of proteins whose abundances significantly differ between ARID1A mutated and wildtype samples. The proteins above the line are those with p-values less than the significance threshold of 0.05, following Bonferroni correction for multiple comparisons."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that this example examines ARID1A's trans-effects on the protein level, but this concept could be applied to mRNA, methylation, or other omic levels. Understanding trans-genetic effects is crucial to delineate the complexity of disease, with applications in drug target identification and personalized medicine."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part II: Example with TP53"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will now look at TP53: a tumor suppressing gene that is very important in helping prevent mutation, as well as repairing damaged DNA in cells [(Wikipedia)](https://en.wikipedia.org/wiki/P53)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use the same steps as above, this time without separated code blocks. Because we are not sure which proteins might be affected, we will look for trans effects in all the proteomics data instead of just the interacting proteins from bioplex. Due to the high number of comparisons, this will take a little bit of time. In the end, we will have a list of genes affected by TP53."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Gene:  TP53\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "cptac warning: In joining the somatic_mutation table, no mutations were found for the following samples, so they were filled with Wildtype_Tumor or Wildtype_Normal: 129 samples for the TP53 gene (C:\\Users\\sabme\\anaconda3\\lib\\site-packages\\cptac\\cancers\\cancer.py, line 325)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Doing t-test comparisons\n",
      "\n",
      "                   Comparison   P_Value\n",
      "0      STAT2_umich_proteomics  0.000002\n",
      "1     DLGAP5_umich_proteomics  0.000030\n",
      "2   SLC4A1AP_umich_proteomics  0.000053\n",
      "3      CMTR1_umich_proteomics  0.000126\n",
      "4      TRMT6_umich_proteomics  0.000165\n",
      "..                        ...       ...\n",
      "72     RRP36_umich_proteomics  0.047035\n",
      "73    TOM1L1_umich_proteomics  0.048238\n",
      "74    KIF13B_umich_proteomics  0.048484\n",
      "75    PARP14_umich_proteomics  0.048569\n",
      "76      BDH2_umich_proteomics  0.049898\n",
      "\n",
      "[77 rows x 2 columns]\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "gene = \"TP53\"\n",
    "print(\"\\nGene: \", gene)\n",
    "\n",
    "# Step 1: Create dataframe in order to do comparisons with wrap_ttest\n",
    "# Note that here we do not pass any values for 'omics_genes', so it will use all available genes\n",
    "protdf = en.join_omics_to_mutations(mutations_genes=[gene],\n",
    "                                    mutations_source=\"washu\",\n",
    "                                    omics_name=omics,\n",
    "                                    omics_source=\"umich\")\n",
    "protdf = protdf.loc[protdf['Sample_Status'] == 'Tumor']\n",
    "\n",
    "protdf = protdf.loc[:,~protdf.columns.duplicated()]\n",
    "\n",
    "for ind, row in protdf.iterrows():\n",
    "    mutation_status = row[\"TP53_Mutation_Status_washu_somatic_mutation\"]\n",
    "    if mutation_status == 'Single_mutation' or mutation_status == 'Multiple_mutation':\n",
    "        protdf.at[ind,'Gene Mutation Status'] = 'Mutated'\n",
    "    else:\n",
    "        protdf.at[ind,'Gene Mutation Status'] = 'Wildtype'\n",
    "\n",
    "        \n",
    "# Step 2: Format the dataframe to set it up properly for our t-test\n",
    "protdf = protdf.drop(gene+\"_Mutation\",axis=1)\n",
    "protdf = protdf.drop(gene+\"_Location\",axis=1)\n",
    "protdf = protdf.drop(gene+\"_Mutation_Status_washu_somatic_mutation\", axis=1)\n",
    "protdf = protdf.drop(\"Sample_Status\",axis=1)\n",
    "\n",
    "\n",
    "#Step 3: Make list of columns to be compared using t-tests\n",
    "col_list = list(protdf.columns)\n",
    "col_list.remove('Gene Mutation Status')\n",
    "\n",
    "    \n",
    "#Step 4: Call wrap_ttest, pass in formatted dataframe, print results\n",
    "print(\"Doing t-test comparisons\\n\")\n",
    "wrap_results = ut.wrap_ttest(protdf, 'Gene Mutation Status', col_list)\n",
    "\n",
    "if wrap_results is not None:\n",
    "        print(wrap_results)\n",
    "        print(\"\\n\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each of the proteins shown can be individually analyzed in the same manner that we showed above. It's worth noting that with some genes this list might be relatively small because our wrap_ttest does Bonferroni correction, so the cutoff for \"significance\" is very strict. For further ideas about how to analyze larger lists of genes, refer to usecase 5."
   ]
  }
 ],
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