test_package_APOB.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "cd /scratch/groups/mrivas/szainana/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "! pip list | grep umr-visualizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": 5,
   "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": 6,
   "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": 7,
   "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 APOB gene (LDL description)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load data set "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>APOB</td>\n",
       "      <td>0.012711</td>\n",
       "      <td>0.96865</td>\n",
       "      <td>0.042032</td>\n",
       "      <td>0.1485</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>APOB</td>\n",
       "      <td>1.577000</td>\n",
       "      <td>0.96861</td>\n",
       "      <td>0.023355</td>\n",
       "      <td>0.0902</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>APOB</td>\n",
       "      <td>-0.548010</td>\n",
       "      <td>0.96840</td>\n",
       "      <td>0.000067</td>\n",
       "      <td>0.2574</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>APOB</td>\n",
       "      <td>1.034900</td>\n",
       "      <td>0.96867</td>\n",
       "      <td>0.000040</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>APOB</td>\n",
       "      <td>-0.004753</td>\n",
       "      <td>0.14775</td>\n",
       "      <td>0.000040</td>\n",
       "      <td>0.1224</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  locus   alleles  markerID      gene  annotation  call_stats  n_cases  \\\n",
       "0  APOB  0.012711   0.96865  0.042032      0.1485           0        1   \n",
       "1  APOB  1.577000   0.96861  0.023355      0.0902           0        1   \n",
       "2  APOB -0.548010   0.96840  0.000067      0.2574           0        1   \n",
       "3  APOB  1.034900   0.96867  0.000040         NaN           0        0   \n",
       "4  APOB -0.004753   0.14775  0.000040      0.1224           0        1   \n",
       "\n",
       "   n_controls  heritability  saige_version  ...  AF.Controls  Pvalue  chr  \\\n",
       "0         NaN           NaN            NaN  ...          NaN     NaN  NaN   \n",
       "1         NaN           NaN            NaN  ...          NaN     NaN  NaN   \n",
       "2         NaN           NaN            NaN  ...          NaN     NaN  NaN   \n",
       "3         NaN           NaN            NaN  ...          NaN     NaN  NaN   \n",
       "4         NaN           NaN            NaN  ...          NaN     NaN  NaN   \n",
       "\n",
       "   pos  ref  alt  prob_0  am_pathogenicity  pLoF_indicator  missense_indicator  \n",
       "0  NaN  NaN  NaN     NaN               NaN             NaN                 NaN  \n",
       "1  NaN  NaN  NaN     NaN               NaN             NaN                 NaN  \n",
       "2  NaN  NaN  NaN     NaN               NaN             NaN                 NaN  \n",
       "3  NaN  NaN  NaN     NaN               NaN             NaN                 NaN  \n",
       "4  NaN  NaN  NaN     NaN               NaN             NaN                 NaN  \n",
       "\n",
       "[5 rows x 39 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_path = \"/Users/salmazainana/Documents/Rivas/Dataset/filtered_APOB_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": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df = df[df[\"phenocode\"] == 30780]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>0.042032</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.023355</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000067</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000040</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000040</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       gene  BETA  SE  prob_0  am_pathogenicity  pLoF_indicator  \\\n",
       "0  0.042032   NaN NaN     NaN               NaN             NaN   \n",
       "1  0.023355   NaN NaN     NaN               NaN             NaN   \n",
       "2  0.000067   NaN NaN     NaN               NaN             NaN   \n",
       "3  0.000040   NaN NaN     NaN               NaN             NaN   \n",
       "4  0.000040   NaN NaN     NaN               NaN             NaN   \n",
       "\n",
       "   missense_indicator  \n",
       "0                 NaN  \n",
       "1                 NaN  \n",
       "2                 NaN  \n",
       "3                 NaN  \n",
       "4                 NaN  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "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/filtered_APOB_data_2.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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of examples: 1067213\n"
     ]
    }
   ],
   "source": [
    "#print number of examples\n",
    "print(f\"Number of examples: {df.shape[0]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gene                       1\n",
      "BETA                  326120\n",
      "SE                    103251\n",
      "prob_0                  2775\n",
      "am_pathogenicity        1529\n",
      "pLoF_indicator             2\n",
      "missense_indicator         2\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df.nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Check if there are NaNs in any columns \n",
    "df.isnull().sum()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": 14,
   "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": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of examples: 1067213\n"
     ]
    }
   ],
   "source": [
    "print(f\"Number of examples: {df.shape[0]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gene                       1\n",
      "BETA                  326120\n",
      "SE                    103251\n",
      "prob_0                  2776\n",
      "am_pathogenicity        1530\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": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>APOB</td>\n",
       "      <td>0.012711</td>\n",
       "      <td>0.96865</td>\n",
       "      <td>0.042032</td>\n",
       "      <td>0.148500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>APOB</td>\n",
       "      <td>1.577000</td>\n",
       "      <td>0.96861</td>\n",
       "      <td>0.023355</td>\n",
       "      <td>0.090200</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>APOB</td>\n",
       "      <td>-0.548010</td>\n",
       "      <td>0.96840</td>\n",
       "      <td>0.000067</td>\n",
       "      <td>0.257400</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>APOB</td>\n",
       "      <td>1.034900</td>\n",
       "      <td>0.96867</td>\n",
       "      <td>0.000040</td>\n",
       "      <td>0.195181</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>APOB</td>\n",
       "      <td>-0.004753</td>\n",
       "      <td>0.14775</td>\n",
       "      <td>0.000040</td>\n",
       "      <td>0.122400</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  APOB  0.012711  0.96865  0.042032          0.148500               0   \n",
       "1  APOB  1.577000  0.96861  0.023355          0.090200               0   \n",
       "2  APOB -0.548010  0.96840  0.000067          0.257400               0   \n",
       "3  APOB  1.034900  0.96867  0.000040          0.195181               0   \n",
       "4  APOB -0.004753  0.14775  0.000040          0.122400               0   \n",
       "\n",
       "   missense_indicator  \n",
       "0                   1  \n",
       "1                   1  \n",
       "2                   1  \n",
       "3                   0  \n",
       "4                   1  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SE min: 0.0\n",
      "SE max: 7758.7\n"
     ]
    }
   ],
   "source": [
    "print(\"SE min:\", np.min(df[\"SE\"]))\n",
    "print(\"SE max:\", np.max(df[\"SE\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "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": 20,
   "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": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SE min: 0.0\n",
      "SE max: 1.7498\n"
     ]
    }
   ],
   "source": [
    "print(\"SE min:\", np.min(df_filtered[\"SE\"]))\n",
    "print(\"SE max:\", np.max(df_filtered[\"SE\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of examples: 1052887\n"
     ]
    }
   ],
   "source": [
    "print(f\"Number of examples: {df_filtered.shape[0]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SE min: 0.010003\n",
      "SE max: 1.7498\n",
      "Number of examples: 1049198\n"
     ]
    }
   ],
   "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": "code",
   "execution_count": 24,
   "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": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w contains NaN: False\n",
      "w contains Inf: False\n",
      "w min: 0.3266052606100299\n",
      "w max: 9994.002698920405\n",
      "the shape of w is: (1049198,)\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": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of X_full: (1049198, 4)\n"
     ]
    }
   ],
   "source": [
    "# Print the shape of matrix X_full\n",
    "print(\"Shape of X_full:\", X_full.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Determinant: 2.182307612322103e+23\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": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "matrix_to_invert = np.linalg.pinv(tmp) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "beta_full = matrix_to_invert @ (X_w @ y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "fitted_values_full = np.dot(X_full, beta_full)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation Matrix:\n",
      "          0         1         2         3\n",
      "0  1.000000 -0.004740  0.018263  0.005628\n",
      "1 -0.004740  1.000000  0.000118 -0.000395\n",
      "2  0.018263  0.000118  1.000000 -0.298417\n",
      "3  0.005628 -0.000395 -0.298417  1.000000\n"
     ]
    }
   ],
   "source": [
    "corr_matrix = pd.DataFrame(X_full).corr()\n",
    "print(\"Correlation Matrix:\")\n",
    "print(corr_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The highest absolute correlation is -0.2984, which is still moderate at best."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    2776\n",
      "1    1530\n",
      "2       2\n",
      "3       2\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(pd.DataFrame(X_full).nunique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting of filtered data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Moderators identified: ['prob_0', 'am_pathogenicity', 'pLoF_indicator', 'missense_indicator']\n",
      "Unique genes found: ['APOB']\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: APOB\n",
      "   gene  Proportion_Variance_Explained  R_squared_reduced\n",
      "0  APOB                       0.241823           0.000021\n",
      "   gene  Proportion_Variance_Explained  R_squared_reduced\n",
      "0  APOB                       0.473308           0.000042\n",
      "   gene  Proportion_Variance_Explained  R_squared_reduced\n",
      "0  APOB                       0.410139           0.000036\n",
      "   gene  Proportion_Variance_Explained  R_squared_reduced\n",
      "0  APOB                       0.470665           0.000042\n",
      "Variance data for gene APOB: {'prob_0': np.float64(0.2418231671907962), 'am_pathogenicity': np.float64(0.47330846323472037), 'pLoF_indicator': np.float64(0.4101394359422254), 'missense_indicator': np.float64(0.4706649274084633)}\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=[\"APOB\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Moderators identified: ['prob_0', 'am_pathogenicity', 'pLoF_indicator', 'missense_indicator']\n",
      "Unique genes found: ['APOB']\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: APOB\n",
      "   gene  Proportion_Variance_Explained  R_squared_reduced\n",
      "0  APOB                       0.241823           0.000021\n",
      "   gene  Proportion_Variance_Explained  R_squared_reduced\n",
      "0  APOB                       0.473308           0.000042\n",
      "   gene  Proportion_Variance_Explained  R_squared_reduced\n",
      "0  APOB                       0.410139           0.000036\n",
      "   gene  Proportion_Variance_Explained  R_squared_reduced\n",
      "0  APOB                       0.470665           0.000042\n",
      "Variance data for gene APOB: {'prob_0': np.float64(0.2418231671907962), 'am_pathogenicity': np.float64(0.47330846323472037), 'pLoF_indicator': np.float64(0.4101394359422254), 'missense_indicator': np.float64(0.4706649274084633)}\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=[\"APOB\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If the variance explained by each moderator separately is high, but the full model’s R^2 is low, then:\n",
    "- The moderators might be explaining the same variance, so they don’t add up in the full model.\n",
    "- The variance explained per moderator is overestimated due to shared variance with other factors."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Variance Inflation Factor (VIF) is a measure of multicollinearity among the independent variables in a regression model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              Feature       VIF\n",
      "0              prob_0  1.039968\n",
      "1    am_pathogenicity  1.870392\n",
      "2      pLoF_indicator  1.052101\n",
      "3  missense_indicator  1.831525\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "\n",
    "X = df_filtered[[\"prob_0\", \"am_pathogenicity\", \"pLoF_indicator\", \"missense_indicator\"]]\n",
    "vif_data = pd.DataFrame()\n",
    "vif_data[\"Feature\"] = X.columns\n",
    "vif_data[\"VIF\"] = [variance_inflation_factor(X.values, i) for i in range(X.shape[1])]\n",
    "\n",
    "print(vif_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since all VIF values are below 5, predictors are not highly correlated, meaning they are relatively independent./"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Moderators identified: ['prob_0', 'am_pathogenicity', 'pLoF_indicator', 'missense_indicator']\n",
      "Unique genes found: ['APOB']\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: APOB\n",
      "   gene  Proportion_Variance_Explained  R_squared_reduced\n",
      "0  APOB                       0.241823           0.000021\n",
      "   gene  Proportion_Variance_Explained  R_squared_reduced\n",
      "0  APOB                       0.473308           0.000042\n",
      "   gene  Proportion_Variance_Explained  R_squared_reduced\n",
      "0  APOB                       0.410139           0.000036\n",
      "   gene  Proportion_Variance_Explained  R_squared_reduced\n",
      "0  APOB                       0.470665           0.000042\n",
      "Variance data for gene APOB: {'prob_0': np.float64(0.2418231671907962), 'am_pathogenicity': np.float64(0.47330846323472037), 'pLoF_indicator': np.float64(0.4101394359422254), 'missense_indicator': np.float64(0.4706649274084633)}\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=[\"APOB\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Moderators identified: ['prob_0', 'am_pathogenicity', 'pLoF_indicator', 'missense_indicator']\n",
      "Unique genes found: ['APOB']\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: APOB\n",
      "   gene  Proportion_Variance_Explained  R_squared_reduced\n",
      "0  APOB                       0.241823           0.000021\n",
      "   gene  Proportion_Variance_Explained  R_squared_reduced\n",
      "0  APOB                       0.473308           0.000042\n",
      "   gene  Proportion_Variance_Explained  R_squared_reduced\n",
      "0  APOB                       0.410139           0.000036\n",
      "   gene  Proportion_Variance_Explained  R_squared_reduced\n",
      "0  APOB                       0.470665           0.000042\n",
      "Variance data for gene APOB: {'prob_0': np.float64(0.2418231671907962), 'am_pathogenicity': np.float64(0.47330846323472037), 'pLoF_indicator': np.float64(0.4101394359422254), 'missense_indicator': np.float64(0.4706649274084633)}\n"
     ]
    },
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "hovertemplate": "Proportion of Variance Explained (%)=%{marker.color}<br>Moderator=%{y}<extra></extra>",
         "legendgroup": "",
         "marker": {
          "color": {
           "bdata": "A/b5TqwuOEA5qB0sWapHQFaWWOfIgURAnq2J1YKIR0A=",
           "dtype": "f8"
          },
          "coloraxis": "coloraxis",
          "pattern": {
           "shape": ""
          }
         },
         "name": "",
         "orientation": "h",
         "showlegend": false,
         "text": {
          "bdata": "A/b5TqwuOEA5qB0sWapHQFaWWOfIgURAnq2J1YKIR0A=",
          "dtype": "f8"
         },
         "textposition": "inside",
         "texttemplate": "%{text:.1f}%",
         "type": "bar",
         "x": {
          "bdata": "A/b5TqwuOEA5qB0sWapHQFaWWOfIgURAnq2J1YKIR0A=",
          "dtype": "f8"
         },
         "xaxis": "x",
         "y": [
          "prob_0",
          "am_pathogenicity",
          "pLoF_indicator",
          "missense_indicator"
         ],
         "yaxis": "y"
        }
       ],
       "layout": {
        "barmode": "relative",
        "coloraxis": {
         "colorbar": {
          "title": {
           "text": "Proportion of Variance Explained (%)"
          }
         },
         "colorscale": [
          [
           0,
           "rgb(247,251,255)"
          ],
          [
           0.125,
           "rgb(222,235,247)"
          ],
          [
           0.25,
           "rgb(198,219,239)"
          ],
          [
           0.375,
           "rgb(158,202,225)"
          ],
          [
           0.5,
           "rgb(107,174,214)"
          ],
          [
           0.625,
           "rgb(66,146,198)"
          ],
          [
           0.75,
           "rgb(33,113,181)"
          ],
          [
           0.875,
           "rgb(8,81,156)"
          ],
          [
           1,
           "rgb(8,48,107)"
          ]
         ],
         "showscale": false
        },
        "legend": {
         "tracegroupgap": 0
        },
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
            },
            "marker": {
             "line": {
              "color": "white",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "bar"
           }
          ],
          "barpolar": [
           {
            "marker": {
             "line": {
              "color": "white",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "barpolar"
           }
          ],
          "carpet": [
           {
            "aaxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "#C8D4E3",
             "linecolor": "#C8D4E3",
             "minorgridcolor": "#C8D4E3",
             "startlinecolor": "#2a3f5f"
            },
            "baxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "#C8D4E3",
             "linecolor": "#C8D4E3",
             "minorgridcolor": "#C8D4E3",
             "startlinecolor": "#2a3f5f"
            },
            "type": "carpet"
           }
          ],
          "choropleth": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "choropleth"
           }
          ],
          "contour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "contour"
           }
          ],
          "contourcarpet": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "contourcarpet"
           }
          ],
          "heatmap": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmap"
           }
          ],
          "histogram": [
           {
            "marker": {
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "histogram"
           }
          ],
          "histogram2d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2d"
           }
          ],
          "histogram2dcontour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2dcontour"
           }
          ],
          "mesh3d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "mesh3d"
           }
          ],
          "parcoords": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "parcoords"
           }
          ],
          "pie": [
           {
            "automargin": true,
            "type": "pie"
           }
          ],
          "scatter": [
           {
            "fillpattern": {
             "fillmode": "overlay",
             "size": 10,
             "solidity": 0.2
            },
            "type": "scatter"
           }
          ],
          "scatter3d": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatter3d"
           }
          ],
          "scattercarpet": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattercarpet"
           }
          ],
          "scattergeo": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergeo"
           }
          ],
          "scattergl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergl"
           }
          ],
          "scattermap": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattermap"
           }
          ],
          "scattermapbox": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattermapbox"
           }
          ],
          "scatterpolar": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolar"
           }
          ],
          "scatterpolargl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolargl"
           }
          ],
          "scatterternary": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterternary"
           }
          ],
          "surface": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "surface"
           }
          ],
          "table": [
           {
            "cells": {
             "fill": {
              "color": "#EBF0F8"
             },
             "line": {
              "color": "white"
             }
            },
            "header": {
             "fill": {
              "color": "#C8D4E3"
             },
             "line": {
              "color": "white"
             }
            },
            "type": "table"
           }
          ]
         },
         "layout": {
          "annotationdefaults": {
           "arrowcolor": "#2a3f5f",
           "arrowhead": 0,
           "arrowwidth": 1
          },
          "autotypenumbers": "strict",
          "coloraxis": {
           "colorbar": {
            "outlinewidth": 0,
            "ticks": ""
           }
          },
          "colorscale": {
           "diverging": [
            [
             0,
             "#8e0152"
            ],
            [
             0.1,
             "#c51b7d"
            ],
            [
             0.2,
             "#de77ae"
            ],
            [
             0.3,
             "#f1b6da"
            ],
            [
             0.4,
             "#fde0ef"
            ],
            [
             0.5,
             "#f7f7f7"
            ],
            [
             0.6,
             "#e6f5d0"
            ],
            [
             0.7,
             "#b8e186"
            ],
            [
             0.8,
             "#7fbc41"
            ],
            [
             0.9,
             "#4d9221"
            ],
            [
             1,
             "#276419"
            ]
           ],
           "sequential": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ],
           "sequentialminus": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ]
          },
          "colorway": [
           "#636efa",
           "#EF553B",
           "#00cc96",
           "#ab63fa",
           "#FFA15A",
           "#19d3f3",
           "#FF6692",
           "#B6E880",
           "#FF97FF",
           "#FECB52"
          ],
          "font": {
           "color": "#2a3f5f"
          },
          "geo": {
           "bgcolor": "white",
           "lakecolor": "white",
           "landcolor": "white",
           "showlakes": true,
           "showland": true,
           "subunitcolor": "#C8D4E3"
          },
          "hoverlabel": {
           "align": "left"
          },
          "hovermode": "closest",
          "mapbox": {
           "style": "light"
          },
          "paper_bgcolor": "white",
          "plot_bgcolor": "white",
          "polar": {
           "angularaxis": {
            "gridcolor": "#EBF0F8",
            "linecolor": "#EBF0F8",
            "ticks": ""
           },
           "bgcolor": "white",
           "radialaxis": {
            "gridcolor": "#EBF0F8",
            "linecolor": "#EBF0F8",
            "ticks": ""
           }
          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "white",
            "gridcolor": "#DFE8F3",
            "gridwidth": 2,
            "linecolor": "#EBF0F8",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "#EBF0F8"
           },
           "yaxis": {
            "backgroundcolor": "white",
            "gridcolor": "#DFE8F3",
            "gridwidth": 2,
            "linecolor": "#EBF0F8",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "#EBF0F8"
           },
           "zaxis": {
            "backgroundcolor": "white",
            "gridcolor": "#DFE8F3",
            "gridwidth": 2,
            "linecolor": "#EBF0F8",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "#EBF0F8"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "#DFE8F3",
            "linecolor": "#A2B1C6",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "#DFE8F3",
            "linecolor": "#A2B1C6",
            "ticks": ""
           },
           "bgcolor": "white",
           "caxis": {
            "gridcolor": "#DFE8F3",
            "linecolor": "#A2B1C6",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "#EBF0F8",
           "linecolor": "#EBF0F8",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "#EBF0F8",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "#EBF0F8",
           "linecolor": "#EBF0F8",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "#EBF0F8",
           "zerolinewidth": 2
          }
         }
        },
        "title": {
         "text": "Proportion of Variance Explained per Moderator and Full Model"
        },
        "xaxis": {
         "anchor": "y",
         "domain": [
          0,
          1
         ],
         "range": [
          0,
          100
         ],
         "title": {
          "text": "Proportion of Variance Explained (%)"
         }
        },
        "yaxis": {
         "anchor": "x",
         "categoryarray": [
          "prob_0",
          "am_pathogenicity",
          "pLoF_indicator",
          "missense_indicator"
         ],
         "categoryorder": "array",
         "domain": [
          0,
          1
         ],
         "title": {
          "text": "Moderator"
         }
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "uv.plot_proportion_variance_explained(df_filtered, fitted_values_full, style=\"plotly\", gene_filter=[\"APOB\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Moderators identified: ['prob_0', 'am_pathogenicity', 'pLoF_indicator', 'missense_indicator']\n",
      "Unique genes found: ['APOB']\n",
      "Processing full model R² for gene: APOB\n",
      "For gene APOB : SSR Full= 1103241.0900303237, SST= 1103338.7102968858\n",
      "Computed Full Model R² values: {'APOB': np.float64(8.8477151803934e-05)}\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=\"matplotlib\", gene_filter=[\"APOB\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Moderators identified: ['prob_0', 'am_pathogenicity', 'pLoF_indicator', 'missense_indicator']\n",
      "Unique genes found: ['APOB']\n",
      "Processing full model R² for gene: APOB\n",
      "For gene APOB : SSR Full= 1103241.0900303237, SST= 1103338.7102968858\n",
      "Computed Full Model R² values: {'APOB': np.float64(8.8477151803934e-05)}\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=[\"APOB\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Moderators identified: ['prob_0', 'am_pathogenicity', 'pLoF_indicator', 'missense_indicator']\n",
      "Unique genes found: ['APOB']\n",
      "Processing full model R² for gene: APOB\n",
      "For gene APOB : SSR Full= 1103241.0900303237, SST= 1103338.7102968858\n",
      "Computed Full Model R² values: {'APOB': np.float64(8.8477151803934e-05)}\n"
     ]
    },
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "hovertemplate": "R2 Values=%{marker.color}<br>Moderator=%{y}<extra></extra>",
         "legendgroup": "",
         "marker": {
          "color": {
           "bdata": "NCpD52Vv9j7mll09ovQFPxJmGCB9BgM/Ukj/wD3VBT8AwM3kmTEXPw==",
           "dtype": "f8"
          },
          "coloraxis": "coloraxis",
          "pattern": {
           "shape": ""
          }
         },
         "name": "",
         "orientation": "h",
         "showlegend": false,
         "textposition": "auto",
         "type": "bar",
         "x": {
          "bdata": "NCpD52Vv9j7mll09ovQFPxJmGCB9BgM/Ukj/wD3VBT8AwM3kmTEXPw==",
          "dtype": "f8"
         },
         "xaxis": "x",
         "y": [
          "prob_0",
          "am_pathogenicity",
          "pLoF_indicator",
          "missense_indicator",
          "Full Model"
         ],
         "yaxis": "y"
        }
       ],
       "layout": {
        "barmode": "relative",
        "coloraxis": {
         "colorbar": {
          "title": {
           "text": "R2 Values"
          }
         },
         "colorscale": [
          [
           0,
           "#0d0887"
          ],
          [
           0.1111111111111111,
           "#46039f"
          ],
          [
           0.2222222222222222,
           "#7201a8"
          ],
          [
           0.3333333333333333,
           "#9c179e"
          ],
          [
           0.4444444444444444,
           "#bd3786"
          ],
          [
           0.5555555555555556,
           "#d8576b"
          ],
          [
           0.6666666666666666,
           "#ed7953"
          ],
          [
           0.7777777777777778,
           "#fb9f3a"
          ],
          [
           0.8888888888888888,
           "#fdca26"
          ],
          [
           1,
           "#f0f921"
          ]
         ]
        },
        "legend": {
         "tracegroupgap": 0
        },
        "margin": {
         "t": 60
        },
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
            },
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "bar"
           }
          ],
          "barpolar": [
           {
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "barpolar"
           }
          ],
          "carpet": [
           {
            "aaxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "baxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "type": "carpet"
           }
          ],
          "choropleth": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "choropleth"
           }
          ],
          "contour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "contour"
           }
          ],
          "contourcarpet": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "contourcarpet"
           }
          ],
          "heatmap": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmap"
           }
          ],
          "histogram": [
           {
            "marker": {
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "histogram"
           }
          ],
          "histogram2d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2d"
           }
          ],
          "histogram2dcontour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2dcontour"
           }
          ],
          "mesh3d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "mesh3d"
           }
          ],
          "parcoords": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "parcoords"
           }
          ],
          "pie": [
           {
            "automargin": true,
            "type": "pie"
           }
          ],
          "scatter": [
           {
            "fillpattern": {
             "fillmode": "overlay",
             "size": 10,
             "solidity": 0.2
            },
            "type": "scatter"
           }
          ],
          "scatter3d": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatter3d"
           }
          ],
          "scattercarpet": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattercarpet"
           }
          ],
          "scattergeo": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergeo"
           }
          ],
          "scattergl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergl"
           }
          ],
          "scattermap": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattermap"
           }
          ],
          "scattermapbox": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattermapbox"
           }
          ],
          "scatterpolar": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolar"
           }
          ],
          "scatterpolargl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolargl"
           }
          ],
          "scatterternary": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterternary"
           }
          ],
          "surface": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "surface"
           }
          ],
          "table": [
           {
            "cells": {
             "fill": {
              "color": "#EBF0F8"
             },
             "line": {
              "color": "white"
             }
            },
            "header": {
             "fill": {
              "color": "#C8D4E3"
             },
             "line": {
              "color": "white"
             }
            },
            "type": "table"
           }
          ]
         },
         "layout": {
          "annotationdefaults": {
           "arrowcolor": "#2a3f5f",
           "arrowhead": 0,
           "arrowwidth": 1
          },
          "autotypenumbers": "strict",
          "coloraxis": {
           "colorbar": {
            "outlinewidth": 0,
            "ticks": ""
           }
          },
          "colorscale": {
           "diverging": [
            [
             0,
             "#8e0152"
            ],
            [
             0.1,
             "#c51b7d"
            ],
            [
             0.2,
             "#de77ae"
            ],
            [
             0.3,
             "#f1b6da"
            ],
            [
             0.4,
             "#fde0ef"
            ],
            [
             0.5,
             "#f7f7f7"
            ],
            [
             0.6,
             "#e6f5d0"
            ],
            [
             0.7,
             "#b8e186"
            ],
            [
             0.8,
             "#7fbc41"
            ],
            [
             0.9,
             "#4d9221"
            ],
            [
             1,
             "#276419"
            ]
           ],
           "sequential": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ],
           "sequentialminus": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ]
          },
          "colorway": [
           "#636efa",
           "#EF553B",
           "#00cc96",
           "#ab63fa",
           "#FFA15A",
           "#19d3f3",
           "#FF6692",
           "#B6E880",
           "#FF97FF",
           "#FECB52"
          ],
          "font": {
           "color": "#2a3f5f"
          },
          "geo": {
           "bgcolor": "white",
           "lakecolor": "white",
           "landcolor": "#E5ECF6",
           "showlakes": true,
           "showland": true,
           "subunitcolor": "white"
          },
          "hoverlabel": {
           "align": "left"
          },
          "hovermode": "closest",
          "mapbox": {
           "style": "light"
          },
          "paper_bgcolor": "white",
          "plot_bgcolor": "#E5ECF6",
          "polar": {
           "angularaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "radialaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "yaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "title": {
         "text": "R^2 Values for Gene: APOB"
        },
        "xaxis": {
         "anchor": "y",
         "domain": [
          0,
          1
         ],
         "title": {
          "text": "R2 Values"
         }
        },
        "yaxis": {
         "anchor": "x",
         "domain": [
          0,
          1
         ],
         "title": {
          "text": "Moderator"
         }
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "uv.plot_r2(df_filtered, fitted_values_full, style=\"plotly\", gene_filter=[\"APOB\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "EditThe very small R² values you're seeing (around 0.00008 for the full model) indicate that your model is explaining an extremely tiny fraction of the variance in the Beta values, despite the gene being statistically significant.\n",
    "This situation is actually not uncommon in genetics studies and meta-regression models for several reasons:\n",
    "\n",
    "Statistical significance vs. effect size: P-values measure statistical significance, which can be very strong even when the amount of variance explained is small, especially with large sample sizes.\n",
    "Rare variant context: In rare variant association studies, individual variants often have small effects, resulting in low R² values even when the association is statistically robust.\n",
    "Weight structure: The 1/SE² weighting means that variants with small standard errors have outsized influence on the model. This can lead to statistically significant results with small R² values.\n",
    "Proportion of variance explained vs. absolute R²: Notice that while your full model R² is tiny (0.00008), the proportion of variance explained by each moderator is substantial (e.g., am_pathogenicity explains about 47% of that small amount of explained variance).\n",
    "\n",
    "To better understand what's happening:\n",
    "\n",
    "Check variant counts: How many variants are you including for this gene? A small number could influence the reliability of R² calculations.\n",
    "Examine the distribution of Beta values: Are most Beta values clustered tightly? This could make the total variance small.\n",
    "Look at moderator distributions: Are your moderators well-distributed or are they heavily skewed?\n",
    "Consider transformations: Log transformations on some variables might improve the model fit.\n",
    "Examine model residuals: Plot residuals to check for patterns suggesting model misspecification."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting scikit-learn\n",
      "  Downloading scikit_learn-1.6.1-cp310-cp310-macosx_12_0_arm64.whl.metadata (31 kB)\n",
      "Requirement already satisfied: numpy>=1.19.5 in /opt/anaconda3/envs/umr-visualizer/lib/python3.10/site-packages (from scikit-learn) (2.2.3)\n",
      "Requirement already satisfied: scipy>=1.6.0 in /opt/anaconda3/envs/umr-visualizer/lib/python3.10/site-packages (from scikit-learn) (1.15.2)\n",
      "Collecting joblib>=1.2.0 (from scikit-learn)\n",
      "  Using cached joblib-1.4.2-py3-none-any.whl.metadata (5.4 kB)\n",
      "Collecting threadpoolctl>=3.1.0 (from scikit-learn)\n",
      "  Using cached threadpoolctl-3.5.0-py3-none-any.whl.metadata (13 kB)\n",
      "Downloading scikit_learn-1.6.1-cp310-cp310-macosx_12_0_arm64.whl (11.1 MB)\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m11.1/11.1 MB\u001b[0m \u001b[31m4.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m \u001b[36m0:00:01\u001b[0m\n",
      "\u001b[?25hUsing cached joblib-1.4.2-py3-none-any.whl (301 kB)\n",
      "Using cached threadpoolctl-3.5.0-py3-none-any.whl (18 kB)\n",
      "Installing collected packages: threadpoolctl, joblib, scikit-learn\n",
      "Successfully installed joblib-1.4.2 scikit-learn-1.6.1 threadpoolctl-3.5.0\n"
     ]
    }
   ],
   "source": [
    "!pip install scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# test \n",
    "# This do not work because y_bar_w = sum (y)/n \n",
    "# from sklearn.metrics import r2_score\n",
    "# r_squared_full = r2_score(df_filtered['BETA'], fitted_values_full, sample_weight=1 / (df_filtered['SE'] ** 2))  \n",
    "# r_squared_full"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Full Model R² : 0.0001128681873381332\n",
      "Reduced Model R² (without prob_0): 9.147431943179463e-05\n",
      "Reduced Model R² (without am_pathogenicity): 7.099417560885168e-05\n",
      "Reduced Model R² (without pLoF_indicator): 7.658299724322415e-05\n",
      "Reduced Model R² (without missense_indicator): 7.122806126014236e-05\n",
      "la somme est  0.0003102795535440128\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "# Full model (with all moderators)\n",
    "X_full = df_filtered[[\"prob_0\", \"am_pathogenicity\", \"pLoF_indicator\", \"missense_indicator\"]]\n",
    "wls_full = sm.WLS(df_filtered['BETA'], X_full, weights=1 / (df_filtered['SE'] ** 2)).fit()\n",
    "print(f\"Full Model R² : {wls_full.rsquared}\")\n",
    "# Reduced models\n",
    "for mod in [\"prob_0\", \"am_pathogenicity\", \"pLoF_indicator\", \"missense_indicator\"]:\n",
    "    X_full = df_filtered[[\"prob_0\", \"am_pathogenicity\", \"pLoF_indicator\", \"missense_indicator\"]]\n",
    "    X_reduced = X_full.drop(columns=mod)  \n",
    "    wls_reduced = sm.WLS(df_filtered['BETA'], X_reduced, weights=1/(df_filtered['SE'] ** 2)).fit()\n",
    "\n",
    "    # Compute R² for reduced models\n",
    "    r_squared_reduced = wls_reduced.rsquared\n",
    "    print(f\"Reduced Model R² (without {mod}): {r_squared_reduced}\")\n",
    "print(\"la somme est \", somme)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Test functions + Do same for gene KCNK6 phenotype 30680 expect Rj piss < Rj prob0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "umr-visualizer",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}