test_package_KCNK6.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "cd /scratch/groups/mrivas/szainana/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found existing installation: umr-visualizer 0.1.2\n",
      "Uninstalling umr-visualizer-0.1.2:\n",
      "  Successfully uninstalled umr-visualizer-0.1.2\n"
     ]
    }
   ],
   "source": [
    "!pip uninstall umr-visualizer -y\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "! pip list | grep umr-visualizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Obtaining file:///Users/salmazainana/umr-visualizer\n",
      "  Preparing metadata (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25hRequirement already satisfied: numpy in /opt/anaconda3/envs/umr-visualizer/lib/python3.10/site-packages (from umr-visualizer==0.1.2) (2.2.3)\n",
      "Requirement already satisfied: pandas in /opt/anaconda3/envs/umr-visualizer/lib/python3.10/site-packages (from umr-visualizer==0.1.2) (2.2.3)\n",
      "Requirement already satisfied: matplotlib in /opt/anaconda3/envs/umr-visualizer/lib/python3.10/site-packages (from umr-visualizer==0.1.2) (3.10.0)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in /opt/anaconda3/envs/umr-visualizer/lib/python3.10/site-packages (from matplotlib->umr-visualizer==0.1.2) (1.3.1)\n",
      "Requirement already satisfied: cycler>=0.10 in /opt/anaconda3/envs/umr-visualizer/lib/python3.10/site-packages (from matplotlib->umr-visualizer==0.1.2) (0.12.1)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /opt/anaconda3/envs/umr-visualizer/lib/python3.10/site-packages (from matplotlib->umr-visualizer==0.1.2) (4.56.0)\n",
      "Requirement already satisfied: kiwisolver>=1.3.1 in /opt/anaconda3/envs/umr-visualizer/lib/python3.10/site-packages (from matplotlib->umr-visualizer==0.1.2) (1.4.8)\n",
      "Requirement already satisfied: packaging>=20.0 in /opt/anaconda3/envs/umr-visualizer/lib/python3.10/site-packages (from matplotlib->umr-visualizer==0.1.2) (24.2)\n",
      "Requirement already satisfied: pillow>=8 in /opt/anaconda3/envs/umr-visualizer/lib/python3.10/site-packages (from matplotlib->umr-visualizer==0.1.2) (11.1.0)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in /opt/anaconda3/envs/umr-visualizer/lib/python3.10/site-packages (from matplotlib->umr-visualizer==0.1.2) (3.2.1)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /opt/anaconda3/envs/umr-visualizer/lib/python3.10/site-packages (from matplotlib->umr-visualizer==0.1.2) (2.9.0.post0)\n",
      "Requirement already satisfied: pytz>=2020.1 in /opt/anaconda3/envs/umr-visualizer/lib/python3.10/site-packages (from pandas->umr-visualizer==0.1.2) (2025.1)\n",
      "Requirement already satisfied: tzdata>=2022.7 in /opt/anaconda3/envs/umr-visualizer/lib/python3.10/site-packages (from pandas->umr-visualizer==0.1.2) (2025.1)\n",
      "Requirement already satisfied: six>=1.5 in /opt/anaconda3/envs/umr-visualizer/lib/python3.10/site-packages (from python-dateutil>=2.7->matplotlib->umr-visualizer==0.1.2) (1.17.0)\n",
      "Installing collected packages: umr-visualizer\n",
      "\u001b[33m  DEPRECATION: Legacy editable install of umr-visualizer==0.1.2 from file:///Users/salmazainana/umr-visualizer (setup.py develop) is deprecated. pip 25.1 will enforce this behaviour change. A possible replacement is to add a pyproject.toml or enable --use-pep517, and use setuptools >= 64. If the resulting installation is not behaving as expected, try using --config-settings editable_mode=compat. Please consult the setuptools documentation for more information. Discussion can be found at https://github.com/pypa/pip/issues/11457\u001b[0m\u001b[33m\n",
      "\u001b[0m  Running setup.py develop for umr-visualizer\n",
      "Successfully installed umr-visualizer\n",
      "Requirement already satisfied: nbformat in /opt/anaconda3/envs/umr-visualizer/lib/python3.10/site-packages (5.10.4)\n",
      "Requirement already satisfied: fastjsonschema>=2.15 in /opt/anaconda3/envs/umr-visualizer/lib/python3.10/site-packages (from nbformat) (2.21.1)\n",
      "Requirement already satisfied: jsonschema>=2.6 in /opt/anaconda3/envs/umr-visualizer/lib/python3.10/site-packages (from nbformat) (4.23.0)\n",
      "Requirement already satisfied: jupyter-core!=5.0.*,>=4.12 in /opt/anaconda3/envs/umr-visualizer/lib/python3.10/site-packages (from nbformat) (5.7.2)\n",
      "Requirement already satisfied: traitlets>=5.1 in /opt/anaconda3/envs/umr-visualizer/lib/python3.10/site-packages (from nbformat) (5.14.3)\n",
      "Requirement already satisfied: attrs>=22.2.0 in /opt/anaconda3/envs/umr-visualizer/lib/python3.10/site-packages (from jsonschema>=2.6->nbformat) (25.1.0)\n",
      "Requirement already satisfied: jsonschema-specifications>=2023.03.6 in /opt/anaconda3/envs/umr-visualizer/lib/python3.10/site-packages (from jsonschema>=2.6->nbformat) (2024.10.1)\n",
      "Requirement already satisfied: referencing>=0.28.4 in /opt/anaconda3/envs/umr-visualizer/lib/python3.10/site-packages (from jsonschema>=2.6->nbformat) (0.36.2)\n",
      "Requirement already satisfied: rpds-py>=0.7.1 in /opt/anaconda3/envs/umr-visualizer/lib/python3.10/site-packages (from jsonschema>=2.6->nbformat) (0.23.0)\n",
      "Requirement already satisfied: platformdirs>=2.5 in /opt/anaconda3/envs/umr-visualizer/lib/python3.10/site-packages (from jupyter-core!=5.0.*,>=4.12->nbformat) (4.3.6)\n",
      "Requirement already satisfied: typing-extensions>=4.4.0 in /opt/anaconda3/envs/umr-visualizer/lib/python3.10/site-packages (from referencing>=0.28.4->jsonschema>=2.6->nbformat) (4.12.2)\n"
     ]
    }
   ],
   "source": [
    "! pip install -e /Users/salmazainana/umr-visualizer\n",
    "! pip install --upgrade nbformat\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "umr-visualizer            0.1.2       /Users/salmazainana/umr-visualizer\n"
     ]
    }
   ],
   "source": [
    "# Check if package installed\n",
    "! pip list | grep umr-visualizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/umr-visualizer/bin/python\n"
     ]
    }
   ],
   "source": [
    "# # Even if the package is installed, sometimes Jupyter doesn’t recognize it. To fix this:\n",
    "import sys\n",
    "print(sys.executable)\n",
    "sys.path.append(\"/Users/salmazainana/umr-visualizer\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import umr_visualizer as uv\n",
    "import pandas as pd\n",
    "import numpy as np  \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The dataset on the KCNK6 pheno 30680 gene (LDL description)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load data set "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/th/syv18z517n5cjstlw_sv7hz00000gn/T/ipykernel_55924/1615998994.py:12: DtypeWarning:\n",
      "\n",
      "Columns (12) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "\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>locus</th>\n",
       "      <th>alleles</th>\n",
       "      <th>markerID</th>\n",
       "      <th>gene</th>\n",
       "      <th>annotation</th>\n",
       "      <th>call_stats</th>\n",
       "      <th>n_cases</th>\n",
       "      <th>n_controls</th>\n",
       "      <th>heritability</th>\n",
       "      <th>saige_version</th>\n",
       "      <th>...</th>\n",
       "      <th>AF.Controls</th>\n",
       "      <th>Pvalue</th>\n",
       "      <th>chr</th>\n",
       "      <th>pos</th>\n",
       "      <th>ref</th>\n",
       "      <th>alt</th>\n",
       "      <th>prob_0</th>\n",
       "      <th>am_pathogenicity</th>\n",
       "      <th>pLoF_indicator</th>\n",
       "      <th>missense_indicator</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>chr19:38319956</td>\n",
       "      <td>[\"G\",\"A\"]</td>\n",
       "      <td>chr19:38319956_G/A</td>\n",
       "      <td>KCNK6</td>\n",
       "      <td>synonymous</td>\n",
       "      <td>{\"AC\":51,\"AF\":6.458295870996173E-5,\"AN\":789682...</td>\n",
       "      <td>56050</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>SAIGE_0.44.5</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.323320</td>\n",
       "      <td>chr19</td>\n",
       "      <td>38319956</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>chr19:38319980</td>\n",
       "      <td>[\"C\",\"T\"]</td>\n",
       "      <td>chr19:38319980_C/T</td>\n",
       "      <td>KCNK6</td>\n",
       "      <td>synonymous</td>\n",
       "      <td>{\"AC\":3,\"AF\":3.7989975711742196E-6,\"AN\":789682...</td>\n",
       "      <td>56050</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>SAIGE_0.44.5</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.081197</td>\n",
       "      <td>chr19</td>\n",
       "      <td>38319980</td>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>chr19:38319991</td>\n",
       "      <td>[\"A\",\"T\"]</td>\n",
       "      <td>chr19:38319991_A/T</td>\n",
       "      <td>KCNK6</td>\n",
       "      <td>missense</td>\n",
       "      <td>{\"AC\":42,\"AF\":5.318717834420716E-5,\"AN\":789664...</td>\n",
       "      <td>56050</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>SAIGE_0.44.5</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.755360</td>\n",
       "      <td>chr19</td>\n",
       "      <td>38319991</td>\n",
       "      <td>A</td>\n",
       "      <td>T</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.1247</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>chr19:38320009</td>\n",
       "      <td>[\"T\",\"TGGGCGCGCTGTTGG\"]</td>\n",
       "      <td>chr19:38320009_T/TGGGCGCGCTGTTGG</td>\n",
       "      <td>KCNK6</td>\n",
       "      <td>pLoF</td>\n",
       "      <td>{\"AC\":260,\"AF\":3.2924645616843234E-4,\"AN\":7896...</td>\n",
       "      <td>56050</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>SAIGE_0.44.5</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.536840</td>\n",
       "      <td>chr19</td>\n",
       "      <td>38320009</td>\n",
       "      <td>T</td>\n",
       "      <td>TGGGCGCGCTGTTGG</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>chr19:38320030</td>\n",
       "      <td>[\"G\",\"T\"]</td>\n",
       "      <td>chr19:38320030_G/T</td>\n",
       "      <td>KCNK6</td>\n",
       "      <td>missense</td>\n",
       "      <td>{\"AC\":3,\"AF\":3.7990168144484207E-6,\"AN\":789678...</td>\n",
       "      <td>56050</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>SAIGE_0.44.5</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.690820</td>\n",
       "      <td>chr19</td>\n",
       "      <td>38320030</td>\n",
       "      <td>G</td>\n",
       "      <td>T</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.1103</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            locus                  alleles                          markerID  \\\n",
       "0  chr19:38319956                [\"G\",\"A\"]                chr19:38319956_G/A   \n",
       "1  chr19:38319980                [\"C\",\"T\"]                chr19:38319980_C/T   \n",
       "2  chr19:38319991                [\"A\",\"T\"]                chr19:38319991_A/T   \n",
       "3  chr19:38320009  [\"T\",\"TGGGCGCGCTGTTGG\"]  chr19:38320009_T/TGGGCGCGCTGTTGG   \n",
       "4  chr19:38320030                [\"G\",\"T\"]                chr19:38320030_G/T   \n",
       "\n",
       "    gene  annotation                                         call_stats  \\\n",
       "0  KCNK6  synonymous  {\"AC\":51,\"AF\":6.458295870996173E-5,\"AN\":789682...   \n",
       "1  KCNK6  synonymous  {\"AC\":3,\"AF\":3.7989975711742196E-6,\"AN\":789682...   \n",
       "2  KCNK6    missense  {\"AC\":42,\"AF\":5.318717834420716E-5,\"AN\":789664...   \n",
       "3  KCNK6        pLoF  {\"AC\":260,\"AF\":3.2924645616843234E-4,\"AN\":7896...   \n",
       "4  KCNK6    missense  {\"AC\":3,\"AF\":3.7990168144484207E-6,\"AN\":789678...   \n",
       "\n",
       "   n_cases  n_controls  heritability saige_version  ...  AF.Controls  \\\n",
       "0    56050         NaN           0.0  SAIGE_0.44.5  ...          NaN   \n",
       "1    56050         NaN           0.0  SAIGE_0.44.5  ...          NaN   \n",
       "2    56050         NaN           0.0  SAIGE_0.44.5  ...          NaN   \n",
       "3    56050         NaN           0.0  SAIGE_0.44.5  ...          NaN   \n",
       "4    56050         NaN           0.0  SAIGE_0.44.5  ...          NaN   \n",
       "\n",
       "     Pvalue    chr       pos  ref              alt  prob_0  am_pathogenicity  \\\n",
       "0  0.323320  chr19  38319956    G                A     NaN               NaN   \n",
       "1  0.081197  chr19  38319980    C                T     NaN               NaN   \n",
       "2  0.755360  chr19  38319991    A                T     NaN            0.1247   \n",
       "3  0.536840  chr19  38320009    T  TGGGCGCGCTGTTGG     NaN               NaN   \n",
       "4  0.690820  chr19  38320030    G                T     NaN            0.1103   \n",
       "\n",
       "   pLoF_indicator  missense_indicator  \n",
       "0               0                   0  \n",
       "1               0                   0  \n",
       "2               0                   1  \n",
       "3               1                   0  \n",
       "4               0                   1  \n",
       "\n",
       "[5 rows x 39 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_path = \"/Users/salmazainana/Documents/Rivas/Dataset/filtered_KCNK6_data.tsv\"\n",
    "correct_columns = [\n",
    "    \"locus\", \"alleles\", \"markerID\", \"gene\", \"annotation\", \"call_stats\", \"n_cases\",\n",
    "    \"n_controls\", \"heritability\", \"saige_version\", \"inv_normalized\", \"trait_type\",\n",
    "    \"phenocode\", \"pheno_sex\", \"coding\", \"modifier\", \"n_cases_defined\", \n",
    "    \"n_cases_both_sexes\", \"n_cases_females\", \"n_cases_males\", \"description\", \n",
    "    \"description_more\", \"coding_description\", \"category\", \"AC\", \"AF\", \"BETA\", \n",
    "    \"SE\", \"AF.Cases\", \"AF.Controls\", \"Pvalue\", \"chr\", \"pos\", \"ref\", \"alt\", \n",
    "    \"prob_0\", \"am_pathogenicity\", \"pLoF_indicator\", \"missense_indicator\"\n",
    "]\n",
    "\n",
    "df = pd.read_csv(file_path, sep=\"\\t\", skiprows=1, names=correct_columns)\n",
    "df.head()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(101540, 39)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dataset size\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "291"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df[df[\"phenocode\"] == 30680])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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>gene</th>\n",
       "      <th>BETA</th>\n",
       "      <th>SE</th>\n",
       "      <th>prob_0</th>\n",
       "      <th>am_pathogenicity</th>\n",
       "      <th>pLoF_indicator</th>\n",
       "      <th>missense_indicator</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KCNK6</td>\n",
       "      <td>-0.619030</td>\n",
       "      <td>0.68271</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KCNK6</td>\n",
       "      <td>0.091654</td>\n",
       "      <td>0.55743</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.1126</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>KCNK6</td>\n",
       "      <td>-0.115640</td>\n",
       "      <td>0.14084</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>KCNK6</td>\n",
       "      <td>-0.743000</td>\n",
       "      <td>0.96548</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.1517</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>KCNK6</td>\n",
       "      <td>-0.001520</td>\n",
       "      <td>0.43178</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.1177</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    gene      BETA       SE  prob_0  am_pathogenicity  pLoF_indicator  \\\n",
       "0  KCNK6 -0.619030  0.68271     NaN               NaN               0   \n",
       "1  KCNK6  0.091654  0.55743     NaN            0.1126               0   \n",
       "2  KCNK6 -0.115640  0.14084     NaN               NaN               0   \n",
       "3  KCNK6 -0.743000  0.96548     NaN            0.1517               0   \n",
       "4  KCNK6 -0.001520  0.43178     NaN            0.1177               0   \n",
       "\n",
       "   missense_indicator  \n",
       "0                   1  \n",
       "1                   1  \n",
       "2                   0  \n",
       "3                   1  \n",
       "4                   1  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Keep rows with phenocode = 30680\n",
    "\n",
    "df = df[df[\"phenocode\"] == 30680]\n",
    "\n",
    "columns_to_keep = [\"gene\", \"BETA\", \"SE\", \"prob_0\", \"am_pathogenicity\", \"pLoF_indicator\", \"missense_indicator\"]\n",
    "\n",
    "# Keep only these columns\n",
    "df_filtered = df[columns_to_keep]\n",
    "df_filtered.head()\n",
    "\n",
    "# Save the filtered DataFrame\n",
    "output_file_path = \"/Users/salmazainana/Documents/Rivas/Dataset/cleaned_KCNK6_data.tsv\"\n",
    "df_filtered.to_csv(output_file_path, sep=\"\\t\", index=False)\n",
    "df = pd.read_csv(output_file_path, sep=\"\\t\")\n",
    "df.head()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of examples: 291\n"
     ]
    }
   ],
   "source": [
    "#print number of examples\n",
    "print(f\"Number of examples: {df.shape[0]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gene                    1\n",
      "BETA                  288\n",
      "SE                     91\n",
      "prob_0                228\n",
      "am_pathogenicity      168\n",
      "pLoF_indicator          2\n",
      "missense_indicator      2\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df.nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gene                    0\n",
       "BETA                    0\n",
       "SE                      0\n",
       "prob_0                 23\n",
       "am_pathogenicity      114\n",
       "pLoF_indicator          0\n",
       "missense_indicator      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# Check if there are NaNs in any columns \n",
    "df.isnull().sum()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # fill the Nan Values with average of the column for prob_0 and am+pathogenicity\n",
    "# df[\"prob_0\"] = df[\"prob_0\"].fillna(df[\"prob_0\"].mean())\n",
    "# df[\"am_pathogenicity\"] = df[\"am_pathogenicity\"].fillna(df[\"am_pathogenicity\"].mean())\n",
    "# df.isnull().sum()\n",
    "\n",
    "# Drop rows with NaN values\n",
    "# df = df.dropna()\n",
    "# df.isnull().sum()\n",
    "\n",
    "\n",
    "# We can't drop them because we will lose a the variety in the two last columns \n",
    "# instead we can fill them with the mean of the column\n",
    "df[\"prob_0\"] = df[\"prob_0\"].fillna(df[\"prob_0\"].mean())\n",
    "df[\"am_pathogenicity\"] = df[\"am_pathogenicity\"].fillna(df[\"am_pathogenicity\"].mean())\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gene                  0\n",
       "BETA                  0\n",
       "SE                    0\n",
       "prob_0                0\n",
       "am_pathogenicity      0\n",
       "pLoF_indicator        0\n",
       "missense_indicator    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of examples: 291\n"
     ]
    }
   ],
   "source": [
    "print(f\"Number of examples: {df.shape[0]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gene                    1\n",
      "BETA                  288\n",
      "SE                     91\n",
      "prob_0                229\n",
      "am_pathogenicity      169\n",
      "pLoF_indicator          2\n",
      "missense_indicator      2\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df.nunique()) # to make sure no constant columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preprocessing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "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>BETA</th>\n",
       "      <th>SE</th>\n",
       "      <th>prob_0</th>\n",
       "      <th>am_pathogenicity</th>\n",
       "      <th>pLoF_indicator</th>\n",
       "      <th>missense_indicator</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KCNK6</td>\n",
       "      <td>-0.619030</td>\n",
       "      <td>0.68271</td>\n",
       "      <td>0.005094</td>\n",
       "      <td>0.259205</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KCNK6</td>\n",
       "      <td>0.091654</td>\n",
       "      <td>0.55743</td>\n",
       "      <td>0.005094</td>\n",
       "      <td>0.112600</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>KCNK6</td>\n",
       "      <td>-0.115640</td>\n",
       "      <td>0.14084</td>\n",
       "      <td>0.005094</td>\n",
       "      <td>0.259205</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>KCNK6</td>\n",
       "      <td>-0.743000</td>\n",
       "      <td>0.96548</td>\n",
       "      <td>0.005094</td>\n",
       "      <td>0.151700</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>KCNK6</td>\n",
       "      <td>-0.001520</td>\n",
       "      <td>0.43178</td>\n",
       "      <td>0.005094</td>\n",
       "      <td>0.117700</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    gene      BETA       SE    prob_0  am_pathogenicity  pLoF_indicator  \\\n",
       "0  KCNK6 -0.619030  0.68271  0.005094          0.259205               0   \n",
       "1  KCNK6  0.091654  0.55743  0.005094          0.112600               0   \n",
       "2  KCNK6 -0.115640  0.14084  0.005094          0.259205               0   \n",
       "3  KCNK6 -0.743000  0.96548  0.005094          0.151700               0   \n",
       "4  KCNK6 -0.001520  0.43178  0.005094          0.117700               0   \n",
       "\n",
       "   missense_indicator  \n",
       "0                   1  \n",
       "1                   1  \n",
       "2                   0  \n",
       "3                   1  \n",
       "4                   1  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SE min: 0.0030202\n",
      "SE max: 0.96562\n"
     ]
    }
   ],
   "source": [
    "print(\"SE min:\", np.min(df[\"SE\"]))\n",
    "print(\"SE max:\", np.max(df[\"SE\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4uDjxlV27dvlsX/AN+tQ+9Kl96FP70Kf+P567du2SsLAwn7TBnUHPgAi9W7ZskdDQUFm0aJFMnjxZ9u3b57wvPj5ekpOTs83IUKpUKalXr56sX7/e46FX21GrVi3xNv30op2pIT88PNzr+4P30af2oU/tQ5/ahz71jrS0tAI/Rvugbt26Xu/THTt2uLXdgAi97du3N7fcHDhwwHnVNVcVKlRw3udJOlyuV3XzFe1MX+4P3kef2oc+tQ99ah/61LPC3fgA4ek+yGt77pQ2BMU8vY56jpzD2CVKlJD09HQ/tQoAAADBJOBDr6M2RGt7XWng5WsMAAAAWBF6HWUNhw4dyrZcf4+JifFTqwAAABBMAj701qlTRyIjI83MDg4nTpyQrVu3mvl6AQAAgKA4ke1ctJa3Z8+eMn78eImKipLKlSubeXp1vt4OHTr4u3kAAAAIAgEfetVjjz0mmZmZ5jLDOoWGjvDOmDHDTC8GAAAABF3o1auu5VS0aFEZOnSouQEAAADW1fQCAAAAhUXoBQAAgPUIvQAAALAeoRcAAADWI/QCAADAeoReAAAAWI/QCwAAAOsRegEAAGA9Qi8AAACsR+gFAACA9Qi9AAAAsB6hFwAAANYj9AIAAMB6hF4AAABYj9ALAAAA6xF6AQAAYD1CLwAAAKxH6AUAAID1CL0AAACwHqEXAAAA1iP0AgAAwHqEXgAAAFiP0AsAAADrEXoBAABgPUIvAAAArEfoBQAAgPUIvQAAALAeoRcAAADWI/QCAADAeoReAAAAWI/QCwAAAOsRegEAAGA9Qi8AAACsR+gFAACA9Qi9AAAAsB6hFwAAANYj9AIAAMB6hF4AAABYj9ALAAAA6xF6AQAAYD1CLwAAAKxH6AUAAID1CL0AAACwHqEXAAAA1iP0AgAAwHqEXgAAAFiP0AsAAADrEXoBAABgPUIvAAAArEfoBQAAgPUIvQAAALAeoRcAAADWI/QCAADAeoReAAAAWI/QCwAAAOsRegEAAGA9Qi8AAACsR+gFAACA9Qi9AAAAsB6hFwAAANYj9AIAAMB6hF4AAABYj9ALAAAA6xF6AQAAYD1CLwAAAKxH6AUAAID1CL0AAACwHqEXAAAA1iP0AgAAwHqEXgAAAFiP0AsAAADrEXoBAABgPUIvAAAArEfoBQAAgPUIvQAAALAeoRcAAADWI/QCAADAeoReAAAAWI/QCwAAAOsRegEAAGA9Qi8AAACsR+gFAACA9Yr5uwEAgAvD7t27JSEhId/rR0dHS7Vq1bzaJgAXjkKF3vT0dElKSpIyZcpIsWLkZwBA3oG3dp26kpaaku/HhIVHyLb4OIIvAI8ocFJduXKlLF68WNauXStHjhwxy0JCQswn8tatW0unTp2kVatWnmkdAMAKOsKrgbdhp0FSMqrKeddPStwrm5bEmscRegH4NPRqyH3ppZdk+/bt0rBhQ+nSpYtUrlxZwsPD5cSJE3LgwAHZsGGDLFiwQGrXri1PPvmktGzZ0iONBADYQQNv6Zia/m6G1aUhlIUAhQi9zz33nKxYsUJ69+5twm5MTEye6x4+fFg++eQTGT58uFx77bUyatSo/OwCAAB4oDSEshCgEKG3bNmy8vXXX0tYWNh51y1fvrwMGDBA7rvvPpk+fXp+Ng8AADxQGkJZCFDI0PvYY49JQV100UXyxBNPFPhxAADgbJSGAIXj9pQLf/75p6xatUoOHTokvXr1kj179kidOnUkMjKykE0CAAAA/Bx6z5w5IyNGjJD58+dLVlaWmblBZ2yYMmWKqTv64IMPpGLFih5uJgAAAODDK7JpuNUpy1544QUz0qvBVw0dOtQE4tjY2EI0BwAAAAiA0KsjvFrj2717d3NRCoe6deua5RqEAQAAgKAOvXpGqAbc3OhUZjpnLwAAABDUobd69ermqmy5+emnn8z9AAAAQFCfyKYXqNAT2U6dOiXt2rUzJ7L9/fffsm7dOpk5c6a5KAUAAAAQ1KH39ttvl8TERHnzzTflww8/NCeyDR48WEJDQ6Vv375y1113eaelAAAAgC/n6e3Xr5/cc889snHjRjl+/LiUKlVKGjRokO3ENgAAACDoL06hF6Fo06aNZ1sDAAAA+Cv0tm/f3tTu5oeut2zZssK2CwAAAPBt6G3WrFm+Qy8AAAAQlKF37Nix3m8JAAAAEEg1venp6bJt2zbJyMhwXoZYL0GcmpoqP//8swwZMsTT7QQAAAB8F3p1Pt7HH3/czNqQm4suuojQCwAAgOAOvbGxsVK2bFl5/vnnZdGiRVKkSBHp1q2bfP/992be3unTp3unpQAAAICvQq+WNbzwwgty/fXXS1JSknz00UdyzTXXmJtepU0vWvHWW2+52x4AAADA/6FXa3djYmLMv6tXry7bt2933nfDDTfIsGHDPNtCAAhiu3fvloSEhHytGx0dLdWqVfN6mwDgQlTg0KtvyDra26RJE7nkkkvMyWt//fWXXHrppZKZmSnJycneaSkABGHgrV2nrqSlpuRr/bDwCNkWH0fwBYBACL033nijjB8/3sza0LNnT/n3v/9t6nt79eolU6dOlVq1anmjnQAQdHSEVwNvw06DpGRUlXOum5S4VzYtiTWPIfQCQACE3r59+8rRo0fl119/NaF35MiR8uCDD0r//v3NpYm1phcA8D8aeEvH1PR3MwDgglbg0KuzNbjW7V5xxRXmssOOEgcNvgAAAEAgKeLOg7788ksZMWKE8/c//vhDRo8eLT/99JMn2wYAAAD4J/QuWLBABg8eLMeOHXMuK1OmjJQvX14GDhxoRn0BAACAoA69M2bMkD59+sjEiROdy7SsQWt5e/fuLVOmTPF0GwEAAADfhl6dgkcvRJGbNm3amNpeAAAAIKhDr5Yx/Pbbb7neFx8fby5RDAAAAAT17A1du3Y1pQwRERHmUsRRUVGSmJgo3377rbzxxhtmvl4AAAAgqEPvgAEDTAnDCy+8IGPGjHEu14tVdOzYUR599FFPtxEAAADwbegNDQ01J7HpNGUbN240sziULFlSrrzySqlTp07hWgMAAAAEQuh1uOyyy8xNHT58WA4dOiSnT5+WokWLerJ9AAAAgO9PZDt58qQ8/fTTMnv2bPP7kiVLpF27dnLbbbeZet9//vmn8K0CAAAA/Bl6X331Vfn666+ldOnS5vfx48ebsoZJkyZJsWLFzO8AAABAUJc3LF++XIYPH25GdTdv3iz79u2Tp556Sq699lrJzMyUkSNHeqelAAAAgK9GevXENb0Cm1q5cqUZ3W3ZsqX5XUd/09PT3W0LAAAAEBiht3LlyrJt2zbz72XLlknDhg0lMjLSGYKrVKni+VYCAAAAvgy9PXr0kLFjx0rnzp0lLi5O7r77brN84MCB8u6775r7AQAAgKCu6e3du7eUK1dO1q9fb4Kuhl/H/L2jRo2SO++80xvtBAAAAHw7T6+exKY3V7Gxse63AkCh7d69WxISEvK1bnR0tFSrVs3rbQIAIOgvTgEgsAJv7Tp1JS01JV/rh4VHyLb4OIIvAOCCQegFLKAjvBp4G3YaJCWjzn0yaVLiXtm0JNY8htALALhQEHoBi2jgLR1T09/NAAAg+GdvAAAAAIINoRcAAADWK3B5Q/v27SUkJCTX+4oUKSIRERFSvXp16dWrlzRt2tQTbQQAAAB8O9J74403yuHDhyUlJUWaNWtm5um96qqrzOWH9+/fLzVq1JB//vnHzOe7Zs2awrUOAAAA8MdI77Fjx6RevXoyY8YMueiii5zL09LSpF+/flK+fHl5/fXX5ZlnnpEpU6ZIixYtPNFOAAAAwHcjvV999ZU89NBD2QKvCgsLk/vuu08WL15sftcR4K1bt7rfMgAAAMCfJ7IlJyfnujwpKUkyMzPNv4sVK5Zn7S8AAAAQ0KH36quvltdee03i4uKyLY+Pj5cJEyZIy5Ytze/ffPON1KzJfKEAAAAIwpperdW99957pVu3blK1alWJioqSI0eOyN69e+XSSy+V//znP7J06VKZM2eOqe0FAAAAgi706olqCxculEWLFsm6deskMTHRjOgOGDDAzOxQtGhRE34//vhjqV+/vndaDQAAAHj7MsTFixeX2267Tbp06SInT56UMmXKSGhoqPP+WrVqubNZAAAAIHBC788//yyvvPKKbN68WbKysswyHdUdNGiQNG/e3NNtBAAAAHwbejdu3GimJtN63v79+0t0dLQcOnRIvvjiC+nbt6/MmjVLGjVqVLhWAQAAAP4MvTpDQ5MmTczFKbR+12HgwIHywAMPyBtvvCEzZ870ZBsBAAAA305Z9vvvv5vZG1wDr9lQkSLSs2dP+e233wrXIgAAAMDfoVevxOa4AEVOutxR4wsAAAAEbeht3LixvPXWW5KamppteUpKilmupQ8AAABAUNf0Pvnkk+bCFNdee620bdvWzNt7+PBh+e677yQtLU3GjBnjnZYCAAAAvgq91atXNxeemDRpkqxcuVKOHz8upUuXlmbNmpmT2ZijFwAAAFbM06vBVmdxAAAAAKwJvQsWLCjQRm+55RZ32wMAAAD4J/QOHz483xsMCQkh9AIAACD4Qu/y5cu93xIAAADAn6E3IiJCypYtW+CNHz161K3HFcbBgwfNRTK++eYbn+4XAAAAQT5Pb/fu3WXWrFly6tSpfG1U5/DVyxTfeuut4ktr1qyR3r17S0JCgk/3CwAAAAtC7/vvv29GTtu0aSMjR46U1atXy8mTJ7Otk5SUZObqfe6558x6y5YtM0HZl+bPn8+sEgAAAHCvvKFKlSom+C5dutRcdU3n6dUT1kqVKiXh4eFy4sQJM7qrlyCuV6+euUBFhw4dxNfGjx/v830CAADAsnl6NcjqbefOnbJ27VrZs2ePGfHVut2LL75YWrZsaQIyAAAAEPQXp7jkkkvMDQAAALCmphcAAAAIZoReAAAAWM+t8gZvmzZtmvz444/ZZn84c+aMTJo0SebOnWtmimjatKmMGDFCqlatetbjf/nlF7f3rSfjpaSkiLfpiX+uPxH8/Nmn7uxz06ZN+X5cdHR0rn9rtitsn7rzOH2ML96DfK0wx9CTxyMY33t5HdnXp8Eg1Y+vu/P1qWY1nVAh6EPv7NmzzbRjTZo0ybZ8ypQpMmfOHBk7dqxUrFhRxo0bJ3379pXFixdL8eLFPbZ/nYs4Li5OfGXXrl0+2xfs7dOC7DMt+aheL1weeOCBfD+mRIkwmT9/nvnbuxC526fuPE4fExYWJrYpzDH0xvEIpvdeXkf29Wkw2BUAr7tztcGd7Ffg0Pv555+bGRw8GTQdV1LTOYDXrVsnNWrUyHZfRkaGzJw5U4YMGSJt27Y1y2JjY6V169ZmGrWuXbt6rB2hoaFSq1Yt8Tb99KKdqc9Vp31D8PNnn6alpeV73cz0ZP2YLA07DZKSUeefbSUpca9sWhIrZcqUkbp168qFpLB9WpB+cdB92Xic3TkW3jgewfjey+vIvj4NBml+fN2dr0937Njh1nYLHHqfeuopcwGKLl26SLdu3aR+/friCVu2bDGBc9GiRTJ58mTZt2+f8774+HhJTk6WFi1aOJfpHME6J/D69es9Gnp1uFwvu+wr2pm+3B/s7FN33ug18JaOqVmgfVyor1V3n7s7/WLrcXY3jHjreATTceZ1lD8X4nO2/XUXnsf23CltcCv0rlixQj777DNZuHChuUiFTl2mlym+6aabpHz58uKu9u3bm1tuDhw4YH5WqlQp2/IKFSo47wMAAAA8NnuD1vQ98sgj8tVXX5n6W629nT59urRr104efvhhU26QmZkpnuQoZM5ZUlGiRAlJT0/36L4AAABgn0KdyNa4cWNzu/322+WVV16R7777ztz0TO/evXvL/fffL0WLFi10Ix1F0Vrb61ogrYGX+h0AAAB4LfRqza2WOOht9+7dUq1aNRk8eLA50UyDr9blaqHxyy+/LIXlKGs4dOiQ2Y+D/l67du1Cbx8AAAB2K3Do1XlyNehu3LjRlBd07NhRxowZk22Kscsuu0yOHj0qH330kUdCb506dSQyMtLM7OAIvSdOnJCtW7dKz549C719AAAA2K3Aofe///2vNGjQQEaNGiWdO3c2YTQ3OgJ75513eqKNppZXw+348eMlKipKKleubObp1fpinT4NAAAA8Pg8vTqP7enTp531ujqXm17UoWTJks71brnlFvGkxx57zJwg9+yzz5r96RXZZsyYYaY5AwAAADwaenWiYL2IxObNm2X+/PlmmZY6PPTQQ9KrVy8ZOnSoFClS4EkhstGrruWkAVu3rTcAAACgIAqcTidOnGguIOF6QQi9SIReLe2TTz6Rt99+u6CbBAAAAAJrpHfx4sUybNgw6dGjh3OZXp70vvvuk2LFisn7779vRn0BAACAoB3p1VkZqlatmut9l156KVdIAwAAQPCHXg22X3/9dZ6XKK5evbon2gUAAAD4r7zh3nvvleHDh8uxY8fkuuuuk3LlykliYqJ8++23smTJEnnppZc81zoAAADAH6FXpyJLTk6WKVOmyNKlS53Ly5Yta+bw9fRUZYBt9AqGCQkJ+VpXL+ntehVC+LZPUlNTZdeuXWaaRMclz+kTALiALkN8zz33yN133y07d+40I76lSpUyZQ+FnaoMuBDCVe06dSUtNSVf64eFR8i2+DhClhfRJwBwYXAr9KqQkBATdAHkn44marhq2GmQlIyqcs51kxL3yqYlseYxBCzvoU8A4MJQ4NCr9btjxoyR7777znz1l5WVdVYY3rp1qyfbCFhHw1XpmJr+bgZc0CcAYLcCh97Ro0ebk9a6dOkiFStWpKQBAAAA9oXe77//Xp555hm58847vdMiAAAAwMMKPEwbGhqa58UpAAAAACtC7/XXXy+ff/65d1oDAAAABEJ5Q7169WTChAmyZ88eadCggYSFhZ11ItuAAQM82UYAAADA9yeyqfXr15tbToReAAAABH3ojY+P905LLmBcoSvwjl0g9UlcXJxH1rFBIPWLv3EsAMBHF6dQSUlJcujQIXNiW9GiRc0NBaNlIg0bNeZqUAF0JS13t6vBwpPSko/qVyfSs2dPj243WHHltP/hWACAj0LvunXrZPz48bJ582ZTzjB37lyZPn26mbd3+PDh7mzygsXVoALv2Lm7XU+H3sz0ZJGsrHy14+DODfLH6jliM/5W/odjAQA+CL1r1qyRBx98UBo1aiRDhgwx4VfVqVNHJk6cKDExMdKnTx83mnJh42pQgXfsAqVP8tOOk4l75UIRKP0SCDgWAODFKct05oZrr71WZs2aJb1793Zehvjhhx+Wvn37mlFfAAAAIKhDr54w0717d/NvLW1w1bJlS9m3b5/nWgcAAAD4I/SWLFlSDh8+nOt9//zzj7kfAAAACOrQq6UNsbGx8vvvvzuX6YjvgQMHZOrUqdK2bVtPtxEAAADw7YlsTz75pPz6669yxx13OM9WHzx4sAm9lSpVMv8GAAAAgjr0li5d2pystmDBAlm7dq0cO3bMlDT06tVLunXrJuHh4d5pKQAAAODLeXqLFy9uRnr1BgAAAFgXenWE93xuueUWd9sDAAAA+D/05nXFNT2ZzXEpYkIvAAAAgjr0Ll++/KxlKSkp8vPPP5tLEU+ePNlTbQMAAAD8E3orV66c6/J//etfcurUKXn++edlzpw5nmgbAAAA4J95es+ldu3asmXLFk9uEgAAAAic0JuRkSHz5s2TcuXKeWqTAAAAgH/KG9q3b29OWnN15swZOXr0qKSnp8uwYcM80zIAAADAX6G3WbNmZ4VeFRkZKe3atZOrr77aU20DAAAA/BN6x44d65k9AwAAAIEaevfv31+g9S+++OKC7gIAAAAIvJrec4mLiyvoLgAAAAD/ht4JEybIyJEj5fLLL5ebbrpJYmJizElsK1askCVLlsgjjzyS51y+AAAAQFCE3oULF5oT1nLW9nbu3NlMV7Zx40YZOHCgJ9sIAAAA+Hae3jVr1kjXrl1zva9NmzayYcOGwrUIAAAA8PdIb9myZeXXX3+VVq1a5RqItdwBCFT5qTGnDr1wxyU6OlqqVavm9fYAAODV0HvbbbfJm2++KampqeaktqioKElISJCvvvpKPvzwQ/nvf/9b0E0CXpeWfFQkJER69uzp76ZYf+zCwiNkW3wcwRcAENyht3///pKUlCTvvvuuzJgxwyzLysqS8PBwGTRokPTo0cMb7QQKJTM9WV+o0rDTICkZVeWc6x7cuUH+WD3HZ22z6dglJe6VTUtizQdhQi8AIKhDr05XNnz4cBN+N23aJMePHzclDw0bNjRXZQMCmYa20jE1z7nOycS9PmuPbccOAABrQq+DBtwKFSqYf2vgzczM9GS7AAAAAP+GXp227NVXX5XDhw+bkd+5c+fKG2+8IaGhoWZ58eLFPddCAAAAwNdTln355ZcybNgwad68ubz22mty5swZs/z666+XlStXypQpUwrbJgAAAMC/I71Tp041J6uNGjVKTp8+7VzevXt3SUxMlE8++USeeOIJz7YSAAAA8OVI786dO82obm4aNGggBw8eLEx7AAAAAP+HXr3U8J9//pnrfbpc7wcAAACCOvR27txZJk6caC5GkZGRYZbpyWybN2829bwdO3b0RjsBAAAA39X0ar3uH3/8YX4WKfJ/mblXr16SkpIiTZo0kccff9z91gAAAACBEHp1OrK3335bVq1aJWvXrpVjx45JyZIlpVmzZnLNNdeYUV8AAAAgqEPvAw88IH379pWWLVuaGwAAAGBdTe/GjRsZzQUAAIDdobd169ayaNEiOXXqlHdaBAAAAPi7vKFEiRIm9C5ZskRq1qwpERER2e7XUeD33nvPk20EAAAAfBt6Dxw4II0aNXL+npWVle3+nL8jOOzevVsSEhLyvX50dLRUq1bNq20CAADwW+idNWuWx3aOwAm8tevUlbTUlHw/Jiw8QrbFxxF8AQCAPaF36dKl0rx5cylVqpT3WwSf0xFeDbwNOw2SklFVzrt+UuJe2bQk1jyO0AsAAKwJvXrBiY8//ljq16/vXDZ9+nTp1q0blx22iAbe0jE1/d0MAAAA/8zekLNO9/Tp0/Laa6+Z+l4AAADAuinLHDhhDQAAANaHXgAAACBYEHoBAABgvUKFXi5HDAAAAKvm6R0wYIAUL14827KHH35YQkNDzwrCy5Yt81wLAQAAAF+E3ltvvbWw+wEAAAACO/S+9NJL3m8JAAAA4CWcyAYAAADrEXoBAABgPUIvAAAArEfoBQAAgPUIvQAAALAeoRcAAADWI/QCAADAeoReAAAAWI/QCwAAAOsRegEAAGA9Qi8AAACsR+gFAACA9Qi9AAAAsB6hFwAAANYj9AIAAMB6hF4AAABYj9ALAAAA6xF6AQAAYD1CLwAAAKxH6AUAAID1CL0AAACwHqEXAAAA1iP0AgAAwHqEXgAAAFiP0AsAAADrEXoBAABgPUIvAAAArEfoBQAAgPUIvQAAALAeoRcAAADWI/QCAADAeoReAAAAWI/QCwAAAOsRegEAAGA9Qi8AAACsR+gFAACA9Qi9AAAAsB6hFwAAANYj9AIAAMB6hF4AAABYj9ALAAAA6xXzdwMQvOLi4vK1XnR0tFSrVk1sPxapqamya9cuSUtLk/Dw8DzXuxDk93mmp6dLiRIlPLpNAAByQ+hFgaUlHxUJCZGePXvma/2w8AjZFh9nZfAt6LGwXUGPR0hIEcnKOuP1dgEAQOhFgWWmJ4tkZUnDToOkZFSVc66blLhXNi2JlYSEBCtDb0GOhTq4c4P8sXqO2Kogx8NxLDh2AABfIPTCbRpUSsfU9HczgupYnEzcKxeC/BwPx7Hg2AEAfIET2QAAAGA9Qi8AAACsR+gFAACA9Qi9AAAAsB6hFwAAANYj9AIAAMB6hF4AAABYj9ALAAAA6xF6AQAAYD1CLwAAAKxH6AUAAID1CL0AAACwHqEXAAAA1iP0AgAAwHqEXgAAAFiP0AsAAADrEXoBAABgPUIvAAAArEfoBQAAgPUIvQAAALAeoRcAAADWI/QCAADAeoReAAAAWI/QCwAAAOsRegEAAGA9Qi8AAACsR+gFAACA9Qi9AAAAsB6hFwAAANYj9AIAAMB6hF4AAABYj9ALAAAA6xF6AQAAYD1CLwAAAKxH6AUAAID1CL0AAACwHqEXAAAA1iP0AgAAwHqEXgAAAFjPmtD72WefSefOneWGG26QZcuW+bs5AAAACCDFxAIHDx6UadOmybx58yQjI0Puuusuad68uURGRvq7aQAAAAgAVoz0rl69Wlq1amVCblRUlDRp0kR++OEHfzcLAAAAAcKK0Hvo0CGpUKGC8/fo6Gg5fPiwX9sEAACAwGFF6M3KyjprWZEiVjw1AAAAeIAVyVBHeRMSEpy/HzlyJNvILwAAAC5sVoTeFi1amBrepKQkOXbsmPz000+mrhcAAACwZvaGSpUqSd++faVHjx6SmZkpAwcONCe0AQAAAAEZenXqsR9//FFmzZrlXHbmzBmZNGmSzJ0714zmNm3aVEaMGCFVq1Z1rtO9e3dzAwAAAAI69M6ePVsmTJhwVmnClClTZM6cOTJ27FipWLGijBs3zozsLl68WIoXL+7RNuhJcSkpKeJtqamp5md6enqBH7tp0ybn489FZ7Fw/WBwvrZ4U37brPMs57dPt23b5oGWwWb6mjvf37M7r//8bNeb2/Zmm73F3fcZT7fb0Q5fvO95SjD2ty8FY58Gg1Q/vu7O16ea1UJCQoIz9OrFJUaOHCnr1q2TGjVqnBWCZs6cKUOGDJG2bduaZbGxsdK6dWtZunSpdO3a1aNtOXXqlMTFxYmv/PPPP/leNy35qEhIiDzwwAP5Wr9EiTCZP3+e+aBwLrt27RJvKWibQ0KKSFbWGa+1BxcWfW2HhYWddx1vbNeb2/Zmm73F3fcZb7Xbm+97nhaM/e0PwdSnwWBXALzuztUGdwY9AyL0btmyRUJDQ2XRokUyefJk2bdvn/O++Ph4SU5ONierOZQqVUrq1asn69ev93jo1XbUqlVLvE0/vWhnaj1yfmWmJ+vHG2nYaZCUjKpyznWTEvfKpiWxUqZMGalbt+45101LSxNvKUibD+7cIH+snpOvdV3XB/KiH6K98frPz3a9uW1vttlb3H2f8XS7He+9ut3w8HAJBsHY374UjH0aDNL8+Lo7X5/u2LHDre0GROht3769ueXmwIED5mfOcKhTkjnu8yQdLo+IiBBfKVGiRIEfo4GwdEzNfK2rL5bzPR9fvEnkp80nE/fme13X9QFfv/7zs11vbtubbfYWd99nvNVufx+PggjG/vaHC/E52/66C89je+6UNgTFlGWOeo6cw9gaFt2phwUAAMCFJ+BDr6M2RGt7XWng5WsMAAAAWBF6HWUNhw4dyrZcf4+JifFTqwAAABBMAj701qlTRyIjI83MDg4nTpyQrVu3mvl6AQAAgKA4ke1ctJa3Z8+eMn78eHOVtcqVK5t5enUarg4dOvi7eQAAAAgCAR961WOPPWYuL/zss8+aKTR0hHfGjBlmejEAAAAg6EKvXnUtp6JFi8rQoUPNDQAAALCuphcAAAAoLEIvAAAArEfoBQAAgPUIvQAAALAeoRcAAADWI/QCAADAeiFZWVlZ/m5EoNi4caPo4dALYnib7ufUqVNy5swZ2b9/v4SXLCdFip573uHMjFRJTzmer3XPnD4lqUlHpEqVKud9PhkZGbJ37958bbeg7fDWuoHSDtocmO3w1uu/INv15ra92WZvKej7jLfa7Xjv1XneQ0JCJBgEY3/7UjD2aTDI8OPr7nx9qm3T5Y0bNy7Qdgm9Ln755RdzoLnoBQAAQGDSQKyht1GjRgV6HKEXAAAA1qOmFwAAANYj9AIAAMB6hF4AAABYj9ALAAAA6xF6AQAAYD1CLwAAAKxH6AUAAID1CL0AAACwHqEXAAAA1iP0AgAAwHqEXgAAAFiP0AsAAADrEXq95MyZMzJx4kRp3bq1NGzYUB588EHZs2dPnusfPXpUnnzySWnatKk0a9ZMnnvuOUlNTfVpm+HZPt2+fbs89NBDctVVV0mLFi3ksccek/379/u0zfBsn7patGiR1K5dW/bu3ev1dsJ7fXrq1Cl59dVXnev37NlT4uLifNpmeLZPjxw5Yv5/2rx5c/P+O2jQIDl48KBP24z8mzZtmvTq1euc63gqIxF6vWTKlCkyZ84cef755+Wjjz4yf7R9+/aVjIyMXNfXQPT333/Lu+++K6+//rqsXLlSRo0a5fN2wzN9qn+gffr0kbCwMJk1a5ZMnz5dEhMTzfrp6el+aT8K/3fqsG/fPhk9erTP2gnv9am+z3766afy4osvyvz58yUqKsqEqqSkJJ+3HZ7p0yeeeMIMMLzzzjvmpv8eMGCAz9uN85s9e7ZMmDDhvOt5LCNlwePS09OzGjVqlDV79mznsuPHj2fVr18/a/HixWetv3HjxqzLLrssa8eOHc5lP/zwQ1bt2rWzDhw44LN2w3N9+sknn5j1U1NTncv2799v+nn16tU+azc816cOp0+fzrrrrruy7r33XtOfe/bs8VGL4ek+3b17t3mf/fbbb7Ot365dO/5Og7RP9T79u1y+fLlz2bJly8yyo0eP+qzdODfNNv369ctq2LBhVseOHbN69uyZ57qezEiM9HpBfHy8JCcnm6+0HUqVKiX16tWT9evXn7X+zz//LOXLl5eaNWs6l+nwfUhIiGzYsMFn7Ybn+lTX09EJHel1KFLk//7cTpw44aNWw5N96jB16lTzlXi/fv181FJ4q09XrVolJUuWlDZt2mRbf8WKFdm2geDpU33Pveiii2TBggVy8uRJc1u4cKFccskl5nEIDFu2bJHQ0FBTJtagQYNzruvJjFTM7RYjTwcOHDA/K1WqlG15hQoVnPe50lqjnOsWL15cypQpI//884+XWwtv9GmVKlXMzdVbb71l3pC1JgnB16fqt99+k5kzZ8q8efOoEbSgT3fu3ClVq1aVpUuXmr9P7VMNU8OHD8/2P1gET5/q/zvHjh0rI0aMkCZNmphgpOt+8MEHzoEH+F/79u3NLT88mZF4BXiBo7haO8VViRIlcq3n1PVzrnuu9RH4fZqT1vXqm+6QIUNMzSCCr09TUlJM/+mtRo0aPmsnvNenOgqodYL6rczgwYPlzTfflGLFisndd99tToZC8PVpVlaWORGxUaNGpl70vffek4svvlj69+9v+hvBx5MZidDrBY6vtHMW2WvnhIeH57p+bgX5un5ERIQXWwpv9anrG7AW6b/wwgvyyCOPnPcMVQRun2of6lekPXr08Fkb4d0+1YCrQSg2NlZatWol9evXN/9Wn332mY9aDU/26ZIlS8wAw7hx4+TKK680X4NrSZKefKrf0CD4eDIjEXq9wDEMf+jQoWzL9feYmJiz1q9YseJZ62oHHzt2zHwtg+DrU6V1n0OHDjVvuE8//bQ5oxjB26d6Zv/q1avNCJLe9Ax/1bVrV9PHCM73Xg2+rqUM+j9YLXlgKrrg7FOt/9QPp5GRkc5lpUuXNst0VB/Bx5MZidDrBXXq1DF/cOvWrXMu05OXtm7dmms9py7T2iTXP8iffvrJ/NRPqgi+PlVPPfWUfPXVV2YO0Pvuu8+HrYU3+lTrPj///HNzgozedORXaS0oo7/B+96bmZkpv//+u3NZWlqamQO2evXqPms3PNenGpD0/6WuX3traZJ+iKEsKTh5MiNxIpsXaO2JTnA+fvx4U79ZuXJl81WL/jF26NBBTp8+beZs1bOGdVRBz1xs3LixmUBb553TP1Atwr/lllvyHEVEYPepzvv55ZdfmuCrX68dPnzYuS3HOgiuPs0Zghwn0Wi9oJ5QgeDrUz3R6eqrr5Zhw4aZeZe1H/UiCEWLFpWbb77Z308HbvSp/n9zxowZ5pu1xx9/3GxDS8y0/rNbt27+fjrIB69mpAJNcIZ8y8zMzHrllVeymjdvbuahe/DBB53zeepPnXNu/vz5zvUTEhKyHn30UbPuVVddlTVy5MistLQ0Pz4DFKZP+/TpY37P7eba7wiuv1NXa9euZZ5eC/o0KSnJvN/q+26DBg3M3+727dv9+AxQ2D7V+Vx1DthmzZqZxwwcOJC/0wA2bNiwbPP0ejMjheh/ChaTAQAAgOBCTS8AAACsR+gFAACA9Qi9AAAAsB6hFwAAANYj9AIAAMB6hF4AAABYj9ALAAAA63FFNgDwgj/++EPefPNNc7nM48ePm6t96RXAHn74YXNpVYc33nhDJk2adM5t/fbbb+aKUt7iaMO2bdu8tg8A8DdCLwB42Pbt2+XOO++Uhg0byrPPPivlypUzly3+4IMP5I477pD333/f3Ofq448/PuelWAEAhUPoBQAPe+edd6Rs2bIyffp0KVbsf2+z1113nXTs2FGmTJkib731VrbH5AzBAADPoqYXADwsISFB9ArvZ86cybY8IiJCnnnmGenUqZNH9nP//fdLt27dzlrev39/uemmm5y/z50716ynwbp+/fpy8803y5IlS/Lcbvv27WX48OHZln366adSu3Zt2bt3b7YSjn79+knjxo3NbcCAAbJnz55sj3vvvfdM0L/iiiukdevWMmrUKDl58mQhnzkAFByhFwA8rG3btrJ//37p0aOHzJ49W/78808TgpUGwFtvvfWsx2RmZuZ6yxmcXWmw3bJli/z999/OZSdOnJDvv//eBFul+x8xYoQZZZ42bZqMHz/elEsMGTLElFy4a+fOneb5HTlyRF5++WUZM2aMCbx33XWXWaY+//xzGTdunNxzzz0yY8YME4oXLlwozz//vNv7BQB3Ud4AAB529913y+HDh03QGz16tFmm5Q6tWrWSe++914y25nT55Zfnui0NjBpac9OhQwd57rnnTLjUQKmWLl0qp0+flq5du5rfNYg+8MADZvTXoXLlymbkd8OGDdKlSxe3nqOe+BYeHi7vvvuuREZGmmUtWrQw4frtt9+WYcOGmZP4qlSpYp5DkSJFpFmzZma0W0/sAwBfI/QCgBc8/vjjct9998kPP/wga9askXXr1snixYtNQNUSBw2/rubNm5frdvQkuLxogNSQ+eWXXzpD7xdffGHCZ0xMjPndUaagI8B//fWXGRXWtqiMjAy3n9/atWtNiA0LCzMj0krDr85QsXr1avN78+bNzQl6GrC1nddcc43ceOONEhIS4vZ+AcBdhF4A8JLSpUubEVfHqOvWrVtl6NCh5it/DX86+uugNa/u0DKGRYsWSXx8vERHR5tA++KLLzrv3717txkp1uAdGhoql156qXPKNEfJhTuOHTtmwrbecoqKijI/O3fubMoz5syZY07e06nRdJRZSyv0PgDwJUIvAHjQwYMHpXv37mak9/bbb892X7169WTQoEHOE75cQ6+7dFS3fPny5sQ0/anz+WrZg9LA+dBDD5mwqyPJdevWNbNJ7Nixw9TWnouWSLhKSUnJ9nvJkiXl6quvlj59+pz1WNcZKxyhPykpSX788Uczo4UG/yuvvNI5Gg0AvkDoBQAP0tFWDX06uqknmuW8qISWGOiy6tWre2R/RYsWNaPG3377rZQqVcqUEWjZgzp69Kg54UzLKVxHkvVEN5XXSXJappDzJDet/3WlpQ0anh1B2jFyrKO4+tx0+RNPPCGnTp2SyZMnm5Css1ZoANfQf+jQIUIvAJ8i9AKAB2kI1Wm5NNjpiK+exFWzZk1JTU2VVatWmdkUdBRYSx9cbdq0Kc9tXnLJJWetn7PEYebMmeZkMR1Jda0H1nIC3WfFihVNKNYaY704htI25aZdu3Zmpge9NWjQQFasWGFqeF3piXE6e4NOWaYzNmiQ1/rdZcuWycSJE501vSNHjjSzO7Rp08bUFesJcDVq1Mh2VToA8IWQrMIUdQEAcqVTiensDTpCmpiYaKYJ0/KGXr16OcsP8nsZYh0p1RHcc9HRXh3ZXblypQneDlrrq9OJbd682bShVq1a5lLIWvd72WWXyeuvv37WZYi1lOGll16Sr7/+2ozU6hRsuv1HHnlEli9fbmZkcDzH2NhY2bhxoxnl1e1pOcW1117r3P+sWbPko48+MvP76klvWo6h5Q0axgHAlwi9AAAAsB4XpwAAAID1CL0AAACwHqEXAAAA1iP0AgAAwHqEXgAAAFiP0AsAAADrEXoBAABgPUIvAAAArEfoBQAAgPUIvQAAALAeoRcAAADWI/QCAABAbPf/AKIF17+x2c8aAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Assuming SE values are stored in a variable called se_values\n",
    "se_values = df[\"SE\"].values\n",
    "\n",
    "# Plot histogram of SE values\n",
    "plt.figure(figsize=(8, 5))\n",
    "plt.hist(se_values, bins=50, edgecolor=\"black\", log=True)  # Log scale for better visibility\n",
    "plt.xlabel(\"SE values\")\n",
    "plt.ylabel(\"Frequency (log scale)\")\n",
    "plt.title(\"Distribution of SE values\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute IQR\n",
    "Q1 = np.percentile(se_values, 25)\n",
    "Q3 = np.percentile(se_values, 75)\n",
    "IQR = Q3 - Q1\n",
    "\n",
    "# Define outlier thresholds\n",
    "lower_bound = Q1 - 1.5 * IQR\n",
    "upper_bound = Q3 + 1.5 * IQR\n",
    "\n",
    "# Filter out rows where SE is outside the bounds\n",
    "valid_indices = (se_values >= lower_bound) & (se_values <= upper_bound)\n",
    "df_filtered = df[valid_indices]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SE min: 0.0030202\n",
      "SE max: 0.96562\n"
     ]
    }
   ],
   "source": [
    "print(\"SE min:\", np.min(df_filtered[\"SE\"]))\n",
    "print(\"SE max:\", np.max(df_filtered[\"SE\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of examples: 291\n"
     ]
    }
   ],
   "source": [
    "print(f\"Number of examples: {df_filtered.shape[0]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Clip very small SE values to avoid extreme w values\n",
    "# SE_min_threshold = 1e-2 # Adjust as needed\n",
    "# df_filtered = df_filtered[df_filtered[\"SE\"] >= SE_min_threshold]\n",
    "# print(\"SE min:\", np.min(df_filtered[\"SE\"]))\n",
    "# print(\"SE max:\", np.max(df_filtered[\"SE\"]))\n",
    "# print(f\"Number of examples: {df_filtered.shape[0]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting Fitted values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_full = df_filtered[[\"prob_0\", \"am_pathogenicity\", \"pLoF_indicator\", \"missense_indicator\"]].values\n",
    "y = df_filtered['BETA'].values\n",
    "w = 1 / (df_filtered['SE'].values ** 2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_full contains NaN: False\n"
     ]
    }
   ],
   "source": [
    "# Does X_full contain any NaN values?\n",
    "print(\"X_full contains NaN:\", np.isnan(X_full).any())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w contains NaN: False\n",
      "w contains Inf: False\n",
      "w min: 1.0724757853532223\n",
      "w max: 109629.79286270672\n",
      "the shape of w is: (291,)\n"
     ]
    }
   ],
   "source": [
    "print(\"w contains NaN:\", np.isnan(w).any())\n",
    "print(\"w contains Inf:\", np.isinf(w).any())\n",
    "print(\"w min:\", np.min(w))\n",
    "print(\"w max:\", np.max(w))\n",
    "print(\"the shape of w is:\", w.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of X_full: (291, 4)\n"
     ]
    }
   ],
   "source": [
    "# Print the shape of matrix X_full\n",
    "print(\"Shape of X_full:\", X_full.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_w = X_full.T * w\n",
    "tmp = X_w @ X_full# Equivalent to X_full.T @ np.diag(w) @ X_full"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Determinant: 8551311048071.711\n",
      "Rank: 4 / 4\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print(\"Determinant:\", np.linalg.det(tmp))\n",
    "print(\"Rank:\", np.linalg.matrix_rank(tmp), \"/\", tmp.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "matrix_to_invert = np.linalg.pinv(tmp) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "beta_full = matrix_to_invert @ (X_w @ y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "fitted_values_full = np.dot(X_full, beta_full)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation Matrix:\n",
      "          0             1             2             3\n",
      "0  1.000000  5.026081e-02  1.879281e-02 -5.757537e-02\n",
      "1  0.050261  1.000000e+00 -9.376585e-18 -1.925302e-17\n",
      "2  0.018793 -9.376585e-18  1.000000e+00 -3.484763e-01\n",
      "3 -0.057575 -1.925302e-17 -3.484763e-01  1.000000e+00\n"
     ]
    }
   ],
   "source": [
    "corr_matrix = pd.DataFrame(X_full).corr()\n",
    "print(\"Correlation Matrix:\")\n",
    "print(corr_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    229\n",
      "1    169\n",
      "2      2\n",
      "3      2\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(pd.DataFrame(X_full).nunique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Testing functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm\n",
    "X = df_filtered[[\"prob_0\", \"am_pathogenicity\", \"pLoF_indicator\", \"missense_indicator\"]].values\n",
    "y = df_filtered['BETA'].values\n",
    "w = 1 / (df_filtered['SE'].values ** 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Testing SSR:\n",
    "\n",
    "SSR = sum wi(beta_i - predicted_beta_i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(449.5294078798596)"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uv.ssr_mod_all(df_filtered, fitted_values_full)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SSR_full (from statsmodels): 449.5294078798596\n"
     ]
    }
   ],
   "source": [
    "# Fit WLS model\n",
    "model = sm.WLS(y, X, weights=w).fit()\n",
    "\n",
    "fitted_vals = model.fittedvalues  # This is X * Bhat\n",
    "residuals = model.resid          # y_i - fitted_vals\n",
    "\n",
    "# Weighted residual sum of squares, i.e. SSR_full\n",
    "ssr_full_sm = np.sum(w * residuals**2)\n",
    "r2_total = model.rsquared\n",
    "print(\"SSR_full (from statsmodels):\", ssr_full_sm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Testing SST:\n",
    "\n",
    "SST = sum wi(beta_i - beta_i_bar)  with beta_i_bar = sum wiyi / sum wi\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(471.72055515586567)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uv.sst_all(df_filtered)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SST (from statsmodels): 471.72055515586567\n"
     ]
    }
   ],
   "source": [
    "# Weighted mean of y\n",
    "y_w = np.sum(w * y) / np.sum(w)\n",
    "\n",
    "# Weighted total sum of squares, i.e. SST\n",
    "sst_sm = np.sum(w * (y - y_w)**2)\n",
    "print(\"SST (from statsmodels):\", sst_sm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Testing R2 total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0470429940638768\n"
     ]
    }
   ],
   "source": [
    "r = 1 - uv.ssr_mod_all(df_filtered, fitted_values_full)/uv.sst_all(df_filtered)\n",
    "print(r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2 (from statsmodels): 0.0470429940638768\n"
     ]
    }
   ],
   "source": [
    "r2 = 1 - ssr_full_sm / sst_sm\n",
    "print(\"R^2 (from statsmodels):\", r2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2_total (from statsmodels): 0.16604034630072118\n"
     ]
    }
   ],
   "source": [
    "print(\"R2_total (from statsmodels):\", r2_total)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "### Testing SSR_reduced_j:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      " For reduced model:  prob_0\n",
      " SSR reduced is : 461.5607530143726\n",
      "\n",
      "\n",
      " For reduced model:  am_pathogenicity\n",
      " SSR reduced is : 458.9088964625057\n",
      "\n",
      "\n",
      " For reduced model:  pLoF_indicator\n",
      " SSR reduced is : 451.50773353325883\n",
      "\n",
      "\n",
      " For reduced model:  missense_indicator\n",
      " SSR reduced is : 499.56621889470944\n"
     ]
    }
   ],
   "source": [
    "for mod in [\"prob_0\", \"am_pathogenicity\", \"pLoF_indicator\", \"missense_indicator\"]:\n",
    "    print(\"\\n\")\n",
    "    print(\" For reduced model: \", mod)\n",
    "    print(\" SSR reduced is :\",     uv.ssr_mod_reduced(df_filtered, fitted_values_full, mod))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      " For reduced model:  prob_0\n",
      "SSR_full (from statsmodels): 461.5607530143726\n",
      "\n",
      "\n",
      " For reduced model:  am_pathogenicity\n",
      "SSR_full (from statsmodels): 458.90889646250577\n",
      "\n",
      "\n",
      " For reduced model:  pLoF_indicator\n",
      "SSR_full (from statsmodels): 451.50773353325883\n",
      "\n",
      "\n",
      " For reduced model:  missense_indicator\n",
      "SSR_full (from statsmodels): 499.5662188947095\n"
     ]
    }
   ],
   "source": [
    "for mod in [\"prob_0\", \"am_pathogenicity\", \"pLoF_indicator\", \"missense_indicator\"]:\n",
    "    print(\"\\n\")\n",
    "    print(\" For reduced model: \", mod)\n",
    "    X_temp = df_filtered[[\"prob_0\", \"am_pathogenicity\", \"pLoF_indicator\", \"missense_indicator\"]].drop(mod, axis=1)\n",
    "    y = df_filtered['BETA']\n",
    "    w = 1 / (df_filtered['SE'].values ** 2)\n",
    "    reduced_model = sm.WLS(y, X_temp, weights=w).fit()\n",
    "    fitted_vals = reduced_model.fittedvalues  \n",
    "    residuals = reduced_model.resid          \n",
    "    ssr_red_sm = np.sum(w * residuals**2)\n",
    "    print(\"SSR_full (from statsmodels):\", ssr_red_sm)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "### Testing R^2_j:\n",
    "\n",
    "R^2_j = (SSR_red_j - SSR_full )/SSR_red_j\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      " For reduced model:  prob_0\n",
      "    gene  Proportion_Variance_Explained  R_squared_reduced\n",
      "0  KCNK6                       0.554103           0.026067\n",
      "\n",
      "\n",
      " For reduced model:  am_pathogenicity\n",
      "    gene  Proportion_Variance_Explained  R_squared_reduced\n",
      "0  KCNK6                       0.434468           0.020439\n",
      "\n",
      "\n",
      " For reduced model:  pLoF_indicator\n",
      "    gene  Proportion_Variance_Explained  R_squared_reduced\n",
      "0  KCNK6                        0.09314           0.004382\n",
      "\n",
      "\n",
      " For reduced model:  missense_indicator\n",
      "    gene  Proportion_Variance_Explained  R_squared_reduced\n",
      "0  KCNK6                       2.129127           0.100161\n"
     ]
    }
   ],
   "source": [
    "for mod in [\"prob_0\", \"am_pathogenicity\", \"pLoF_indicator\", \"missense_indicator\"]:\n",
    "    print(\"\\n\")\n",
    "    print(\" For reduced model: \", mod)\n",
    "    result = uv.proportion_variance_explained_per_gene(df_filtered, fitted_values_full, mod)\n",
    "    print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      " For reduced model:  prob_0\n",
      "R² for reduced model prob_0: 0.0215\n",
      "Partial R² for prob_0: 0.0261\n",
      "Proportion of variance explained by prob_0: 0.5541\n",
      "\n",
      "\n",
      " For reduced model:  am_pathogenicity\n",
      "R² for reduced model am_pathogenicity: 0.0272\n",
      "Partial R² for am_pathogenicity: 0.0204\n",
      "Proportion of variance explained by am_pathogenicity: 0.4345\n",
      "\n",
      "\n",
      " For reduced model:  pLoF_indicator\n",
      "R² for reduced model pLoF_indicator: 0.0428\n",
      "Partial R² for pLoF_indicator: 0.0044\n",
      "Proportion of variance explained by pLoF_indicator: 0.0931\n",
      "\n",
      "\n",
      " For reduced model:  missense_indicator\n",
      "R² for reduced model missense_indicator: -0.0590\n",
      "Partial R² for missense_indicator: 0.1002\n",
      "Proportion of variance explained by missense_indicator: 2.1291\n"
     ]
    }
   ],
   "source": [
    "# R^2 of reduced mod is 1 - (ssr_reduced / sst)\t and means Variance explained without mod1\n",
    "# Partial R² for mod1\t(ssr_reduced - ssr_full) / ssr_reduced\tHow much mod1 improves model fit\n",
    "\n",
    "\n",
    "for mod in [\"prob_0\", \"am_pathogenicity\", \"pLoF_indicator\", \"missense_indicator\"]:\n",
    "    print(\"\\n\")\n",
    "    print(\" For reduced model: \", mod)\n",
    "    X_temp = df_filtered[[\"prob_0\", \"am_pathogenicity\", \"pLoF_indicator\", \"missense_indicator\"]].drop(mod, axis=1)\n",
    "    y = df_filtered['BETA']\n",
    "    w = 1 / (df_filtered['SE'].values ** 2)\n",
    "    reduced_model = sm.WLS(y, X_temp, weights=w).fit()\n",
    "    ssr_reduced = np.sum(w * reduced_model.resid ** 2)\n",
    "\n",
    "    # Partial R² for mod1\n",
    "    r2_red_mod = 1 - ssr_reduced / sst_sm\n",
    "    partial_r2_mod1 = (ssr_reduced - ssr_full_sm) / ssr_reduced\n",
    "    prop = partial_r2_mod1/r2\n",
    "    print(f\"R² for reduced model {mod}: {r2_red_mod:.4f}\")\n",
    "    print(f\"Partial R² for {mod}: {partial_r2_mod1:.4f}\")\n",
    "    print(f\"Proportion of variance explained by {mod}: {prop:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting of filtered data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Moderators identified: ['prob_0', 'am_pathogenicity', 'pLoF_indicator', 'missense_indicator']\n",
      "Unique genes found: ['KCNK6']\n",
      "Computing variance explained for moderator: prob_0\n",
      "Computing variance explained for moderator: am_pathogenicity\n",
      "Computing variance explained for moderator: pLoF_indicator\n",
      "Computing variance explained for moderator: missense_indicator\n",
      "Generating plot for gene: KCNK6\n",
      "    gene  Proportion_Variance_Explained  R_squared_reduced\n",
      "0  KCNK6                       0.554103           0.026067\n",
      "    gene  Proportion_Variance_Explained  R_squared_reduced\n",
      "0  KCNK6                       0.434468           0.020439\n",
      "    gene  Proportion_Variance_Explained  R_squared_reduced\n",
      "0  KCNK6                        0.09314           0.004382\n",
      "    gene  Proportion_Variance_Explained  R_squared_reduced\n",
      "0  KCNK6                       2.129127           0.100161\n",
      "Variance data for gene KCNK6: {'prob_0': np.float64(0.5541028160374821), 'am_pathogenicity': np.float64(0.43446793251763577), 'pLoF_indicator': np.float64(0.09314030043725424), 'missense_indicator': np.float64(2.1291271846702933)}\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "uv.plot_proportion_variance_explained(df_filtered, fitted_values_full, style=\"matplotlib\", gene_filter=[\"KCNK6\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Moderators identified: ['prob_0', 'am_pathogenicity', 'pLoF_indicator', 'missense_indicator']\n",
      "Unique genes found: ['KCNK6']\n",
      "Computing variance explained for moderator: prob_0\n",
      "Computing variance explained for moderator: am_pathogenicity\n",
      "Computing variance explained for moderator: pLoF_indicator\n",
      "Computing variance explained for moderator: missense_indicator\n",
      "Generating plot for gene: KCNK6\n",
      "    gene  Proportion_Variance_Explained  R_squared_reduced\n",
      "0  KCNK6                       0.554103           0.026067\n",
      "    gene  Proportion_Variance_Explained  R_squared_reduced\n",
      "0  KCNK6                       0.434468           0.020439\n",
      "    gene  Proportion_Variance_Explained  R_squared_reduced\n",
      "0  KCNK6                        0.09314           0.004382\n",
      "    gene  Proportion_Variance_Explained  R_squared_reduced\n",
      "0  KCNK6                       2.129127           0.100161\n",
      "Variance data for gene KCNK6: {'prob_0': np.float64(0.5541028160374821), 'am_pathogenicity': np.float64(0.43446793251763577), 'pLoF_indicator': np.float64(0.09314030043725424), 'missense_indicator': np.float64(2.1291271846702933)}\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "uv.plot_proportion_variance_explained(df_filtered, fitted_values_full, style=\"seaborn\", gene_filter=[\"KCNK6\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Moderators identified: ['prob_0', 'am_pathogenicity', 'pLoF_indicator', 'missense_indicator']\n",
      "Unique genes found: ['KCNK6']\n",
      "Computing variance explained for moderator: prob_0\n",
      "Computing variance explained for moderator: am_pathogenicity\n",
      "Computing variance explained for moderator: pLoF_indicator\n",
      "Computing variance explained for moderator: missense_indicator\n",
      "Generating plot for gene: KCNK6\n",
      "    gene  Proportion_Variance_Explained  R_squared_reduced\n",
      "0  KCNK6                       0.554103           0.026067\n",
      "    gene  Proportion_Variance_Explained  R_squared_reduced\n",
      "0  KCNK6                       0.434468           0.020439\n",
      "    gene  Proportion_Variance_Explained  R_squared_reduced\n",
      "0  KCNK6                        0.09314           0.004382\n",
      "    gene  Proportion_Variance_Explained  R_squared_reduced\n",
      "0  KCNK6                       2.129127           0.100161\n",
      "Variance data for gene KCNK6: {'prob_0': np.float64(0.5541028160374821), 'am_pathogenicity': np.float64(0.43446793251763577), 'pLoF_indicator': np.float64(0.09314030043725424), 'missense_indicator': np.float64(2.1291271846702933)}\n"
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      "Moderators identified: ['prob_0', 'am_pathogenicity', 'pLoF_indicator', 'missense_indicator']\n",
      "Unique genes found: ['KCNK6']\n",
      "Computing variance explained for moderator: prob_0\n",
      "Computing variance explained for moderator: am_pathogenicity\n",
      "Computing variance explained for moderator: pLoF_indicator\n",
      "Computing variance explained for moderator: missense_indicator\n",
      "Generating plot for gene: KCNK6\n",
      "    gene  Proportion_Variance_Explained  R_squared_reduced\n",
      "0  KCNK6                       0.554103           0.026067\n",
      "    gene  Proportion_Variance_Explained  R_squared_reduced\n",
      "0  KCNK6                       0.434468           0.020439\n",
      "    gene  Proportion_Variance_Explained  R_squared_reduced\n",
      "0  KCNK6                        0.09314           0.004382\n",
      "    gene  Proportion_Variance_Explained  R_squared_reduced\n",
      "0  KCNK6                       2.129127           0.100161\n",
      "Variance data for gene KCNK6: {'prob_0': np.float64(0.5541028160374821), 'am_pathogenicity': np.float64(0.43446793251763577), 'pLoF_indicator': np.float64(0.09314030043725424), 'missense_indicator': np.float64(2.1291271846702933)}\n"
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  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
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     "text": [
      "Moderators identified: ['prob_0', 'am_pathogenicity', 'pLoF_indicator', 'missense_indicator']\n",
      "Unique genes found: ['KCNK6']\n",
      "Processing full model R² for gene: KCNK6\n",
      "For gene KCNK6 : SSR Full= 449.5294078798596, SST= 471.72055515586567\n",
      "Computed Full Model R² values: {'KCNK6': np.float64(0.0470429940638768)}\n"
     ]
    },
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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "uv.plot_r2(df_filtered, fitted_values_full, style=\"matplotlib\", gene_filter=[\"KCNK6\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Moderators identified: ['prob_0', 'am_pathogenicity', 'pLoF_indicator', 'missense_indicator']\n",
      "Unique genes found: ['KCNK6']\n",
      "Processing full model R² for gene: KCNK6\n",
      "For gene KCNK6 : SSR Full= 449.5294078798596, SST= 471.72055515586567\n",
      "Computed Full Model R² values: {'KCNK6': np.float64(0.0470429940638768)}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/salmazainana/umr-visualizer/umr_visualizer/plots.py:178: FutureWarning:\n",
      "\n",
      "\n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "uv.plot_r2(df_filtered, fitted_values_full, style=\"seaborn\", gene_filter=[\"KCNK6\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Moderators identified: ['prob_0', 'am_pathogenicity', 'pLoF_indicator', 'missense_indicator']\n",
      "Unique genes found: ['KCNK6']\n",
      "Processing full model R² for gene: KCNK6\n",
      "For gene KCNK6 : SSR Full= 449.5294078798596, SST= 471.72055515586567\n",
      "Computed Full Model R² values: {'KCNK6': np.float64(0.0470429940638768)}\n"
     ]
    },
    {
     "data": {
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         "y": [
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         "yaxis": "y"
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       "layout": {
        "barmode": "relative",
        "coloraxis": {
         "colorbar": {
          "title": {
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