{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Implimenting bayesian hierarchical modeling on beta arrestin parameter (Kba)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Imports & setup"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The code is based off an example in this cite: https://www.pymc.io/projects/examples/en/latest/generalized_linear_models/multilevel_modeling.html#adding-group-level-predictors"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Running on PyMC v5.11.0\n"
]
}
],
"source": [
"# Imports & set up\n",
"import warnings\n",
"\n",
"import arviz as az\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import pymc as pm\n",
"import xarray as xr\n",
"import seaborn as sns\n",
"import os\n",
"import copy\n",
"\n",
"from scipy.special import expit as logistic\n",
"import pytensor.tensor as at\n",
"\n",
"print(f\"Running on PyMC v{pm.__version__}\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# Initialize random number generator\n",
"RANDOM_SEED = 8927\n",
"az.style.use(\"arviz-darkgrid\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/Users/preetidubey/Library/CloudStorage/OneDrive-SharedLibraries-UW/Cheri Fang - Imoukhuede_Lab_UW/3. Paper Preparation/Preeti Dubey/Dubey et al (2023)/Simulation_code/Final_files_publication/py_scripts\n",
"Directory Set the base directory here/data does not exist.\n",
"/Users/preetidubey/Library/CloudStorage/OneDrive-SharedLibraries-UW/Cheri Fang - Imoukhuede_Lab_UW/3. Paper Preparation/Preeti Dubey/Dubey et al (2023)/Simulation_code/Final_files_publication/py_scripts\n",
"Directory Set the base directory here/results/figures/python does not exist.\n",
"Directory Set the base directory here/results/tables does not exist.\n"
]
}
],
"source": [
"# Set the working directory \n",
"\n",
"# Define the base directory\n",
"base_dir = 'Set the base directory here'\n",
"\n",
"# Change the working directory \n",
"print(os.getcwd()) # Prints the current working directory\n",
"\n",
"# Provide the new path here\n",
"new_dir = os.path.join(base_dir, 'data')\n",
"if os.path.exists(new_dir):\n",
" os.chdir(new_dir)\n",
"else:\n",
" print(f\"Directory {new_dir} does not exist.\")\n",
"\n",
"# Prints the new working directory\n",
"print(os.getcwd()) \n",
"\n",
"# Define the results directories\n",
"results_directory_figures = os.path.join(base_dir, 'results/figures/python')\n",
"results_directory_tables = os.path.join(base_dir, 'results/tables')\n",
"\n",
"# Check if the directories exist\n",
"if not os.path.exists(results_directory_figures):\n",
" print(f\"Directory {results_directory_figures} does not exist.\")\n",
"if not os.path.exists(results_directory_tables):\n",
" print(f\"Directory {results_directory_tables} does not exist.\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Data Set up"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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"\n",
" .dataframe thead th {\n",
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" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Symbol</th>\n",
" <th>Parameter</th>\n",
" <th>Reference</th>\n",
" <th>Binding Partner</th>\n",
" <th>Receptor</th>\n",
" <th>Method</th>\n",
" <th>Ligand/Receptor Source</th>\n",
" <th>association rate</th>\n",
" <th>dissociation rate</th>\n",
" <th>Kd</th>\n",
" <th>Note</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Kon, Koff</td>\n",
" <td>OXT:OXTR Binding Affinity</td>\n",
" <td>Den et al. 1981</td>\n",
" <td>Oxytocin</td>\n",
" <td>Oxytocin receptor</td>\n",
" <td>Radioligand Binding</td>\n",
" <td>Myometrial (Term Human)</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1.87 ± 0.3</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Kon, Koff</td>\n",
" <td>OXT:OXTR Binding Affinity</td>\n",
" <td>Rivera et al. 1990</td>\n",
" <td>Oxytocin</td>\n",
" <td>Oxytocin receptor</td>\n",
" <td>Radioligand Binding</td>\n",
" <td>Myometrial (Term Human)</td>\n",
" <td>-</td>\n",
" <td>-</td>\n",
" <td>0.93 ± 0.29</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Kon, Koff</td>\n",
" <td>OXT:OXTR Binding Affinity</td>\n",
" <td>Rezapour et al. 1996</td>\n",
" <td>Oxytocin</td>\n",
" <td>Oxytocin receptor</td>\n",
" <td>Radioligand Binding</td>\n",
" <td>Myometrial (Term Human)</td>\n",
" <td>-</td>\n",
" <td>-</td>\n",
" <td>1.2 ± 0.21</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Kon, Koff</td>\n",
" <td>OXT:OXTR Binding Affinity</td>\n",
" <td>Phaneuf et al. 1997</td>\n",
" <td>Oxytocin</td>\n",
" <td>Oxytocin receptor</td>\n",
" <td>Radioligand Binding</td>\n",
" <td>Myometrial (Term Human)</td>\n",
" <td>-</td>\n",
" <td>-</td>\n",
" <td>1.6 ± 0.00</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Kon, Koff</td>\n",
" <td>OXT:OXTR Binding Affinity</td>\n",
" <td>Fuchs et al. 1984</td>\n",
" <td>Oxytocin</td>\n",
" <td>Oxytocin receptor</td>\n",
" <td>Radioligand Binding</td>\n",
" <td>Myometrial (Term Human)</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1.7 ± 0.46</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Kon, Koff</td>\n",
" <td>OXT:OXTR Binding Affinity</td>\n",
" <td>Den et al. 1981</td>\n",
" <td>Oxytocin</td>\n",
" <td>Oxytocin receptor</td>\n",
" <td>Radioligand Binding</td>\n",
" <td>Myometrial (1st trimester Human)</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2.71 ± 1.03</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>Kon, Koff</td>\n",
" <td>OXT:OXTR Binding Affinity</td>\n",
" <td>Den et al. 1981</td>\n",
" <td>Oxytocin</td>\n",
" <td>Oxytocin receptor</td>\n",
" <td>Radioligand Binding</td>\n",
" <td>Myometrial (Nonpregnant Human)</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>3.33 ± 0.50</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>Kon, Koff</td>\n",
" <td>OXT:OXTR Binding Affinity</td>\n",
" <td>A. R. Fuchs et al. 1985</td>\n",
" <td>Oxytocin</td>\n",
" <td>Oxytocin receptor</td>\n",
" <td>Radioligand Binding</td>\n",
" <td>Myometrial (Nonpregnant Human)</td>\n",
" <td>-</td>\n",
" <td>-</td>\n",
" <td>0.96 ± 0.48</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>Kon, Koff</td>\n",
" <td>OXT:OXTR Binding Affinity</td>\n",
" <td>Gulliver 2020</td>\n",
" <td>Oxytocin</td>\n",
" <td>Oxytocin receptor</td>\n",
" <td>Radioligand Binding</td>\n",
" <td>Transfected HEK293</td>\n",
" <td>-</td>\n",
" <td>-</td>\n",
" <td>0.56 ± 0.00</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>Kon, Koff</td>\n",
" <td>OXT:OXTR Binding Affinity</td>\n",
" <td>Y. Waltenspühl et al. 2022</td>\n",
" <td>Oxytocin</td>\n",
" <td>Oxytocin receptor</td>\n",
" <td>FRET</td>\n",
" <td>Transfected HEK293</td>\n",
" <td>-</td>\n",
" <td>-</td>\n",
" <td>1.4 ± 0.20</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>Kon, Koff</td>\n",
" <td>OXT:OXTR Binding Affinity</td>\n",
" <td>Kojro et al. 1991</td>\n",
" <td>Oxytocin</td>\n",
" <td>Oxytocin receptor</td>\n",
" <td>Radioligand Binding</td>\n",
" <td>Myometrial (Guinea Pig)</td>\n",
" <td>-</td>\n",
" <td>-</td>\n",
" <td>2.6 ± 0.10</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>Kon, Koff</td>\n",
" <td>OXT:OXTR Binding Affinity</td>\n",
" <td>U. Klein et al 1995</td>\n",
" <td>Oxytocin</td>\n",
" <td>Oxytocin receptor</td>\n",
" <td>Photoaffinity labeling</td>\n",
" <td>Myometrial (Guinea Pig)</td>\n",
" <td>-</td>\n",
" <td>-</td>\n",
" <td>1.5 ± 0.00</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>Kon, Koff</td>\n",
" <td>OXT:OXTR Binding Affinity</td>\n",
" <td>F. Fahrenholz et al. 1988</td>\n",
" <td>Oxytocin</td>\n",
" <td>Oxytocin receptor</td>\n",
" <td>Photoaffinity labeling</td>\n",
" <td>Myometrial (Guinea Pig)</td>\n",
" <td>-</td>\n",
" <td>-</td>\n",
" <td>2.6 ± 0.2</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>Kon, Koff</td>\n",
" <td>OXT:OXTR Binding Affinity</td>\n",
" <td>V. Pliska et al. 1986</td>\n",
" <td>Oxytocin</td>\n",
" <td>Oxytocin receptor</td>\n",
" <td>Binding Isotherm</td>\n",
" <td>Myometrial (Cattle)</td>\n",
" <td>-</td>\n",
" <td>-</td>\n",
" <td>2.52 ± 0.00</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>Kon, Koff</td>\n",
" <td>OXT:OXTR Binding Affinity</td>\n",
" <td>V. Pliska et al. 1986</td>\n",
" <td>Oxytocin</td>\n",
" <td>Oxytocin receptor</td>\n",
" <td>Binding Isotherm</td>\n",
" <td>Myometrial (Sheep)</td>\n",
" <td>-</td>\n",
" <td>-</td>\n",
" <td>4.04 ± 0.00</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>Kon, Koff</td>\n",
" <td>OXT:OXTR Binding Affinity</td>\n",
" <td>Anouar et al. 1996</td>\n",
" <td>Oxytocin</td>\n",
" <td>Oxytocin receptor</td>\n",
" <td>Radioligand Binding</td>\n",
" <td>Myometrial (Rat)</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1.21 ± 0.34</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>Kon, Koff</td>\n",
" <td>OXT:OXTR Binding Affinity</td>\n",
" <td>V. Pliska et al. 1986</td>\n",
" <td>Oxytocin</td>\n",
" <td>Oxytocin receptor</td>\n",
" <td>Binding Isotherm</td>\n",
" <td>Myometrial (Rat)</td>\n",
" <td>-</td>\n",
" <td>-</td>\n",
" <td>9.32 ± 0.00</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Symbol Parameter Reference \\\n",
"0 Kon, Koff OXT:OXTR Binding Affinity Den et al. 1981 \n",
"1 Kon, Koff OXT:OXTR Binding Affinity Rivera et al. 1990 \n",
"2 Kon, Koff OXT:OXTR Binding Affinity Rezapour et al. 1996 \n",
"3 Kon, Koff OXT:OXTR Binding Affinity Phaneuf et al. 1997 \n",
"4 Kon, Koff OXT:OXTR Binding Affinity Fuchs et al. 1984 \n",
"5 Kon, Koff OXT:OXTR Binding Affinity Den et al. 1981 \n",
"6 Kon, Koff OXT:OXTR Binding Affinity Den et al. 1981 \n",
"7 Kon, Koff OXT:OXTR Binding Affinity A. R. Fuchs et al. 1985 \n",
"8 Kon, Koff OXT:OXTR Binding Affinity Gulliver 2020 \n",
"9 Kon, Koff OXT:OXTR Binding Affinity Y. Waltenspühl et al. 2022 \n",
"10 Kon, Koff OXT:OXTR Binding Affinity Kojro et al. 1991 \n",
"11 Kon, Koff OXT:OXTR Binding Affinity U. Klein et al 1995 \n",
"12 Kon, Koff OXT:OXTR Binding Affinity F. Fahrenholz et al. 1988 \n",
"13 Kon, Koff OXT:OXTR Binding Affinity V. Pliska et al. 1986 \n",
"14 Kon, Koff OXT:OXTR Binding Affinity V. Pliska et al. 1986 \n",
"15 Kon, Koff OXT:OXTR Binding Affinity Anouar et al. 1996 \n",
"16 Kon, Koff OXT:OXTR Binding Affinity V. Pliska et al. 1986 \n",
"\n",
" Binding Partner Receptor Method \\\n",
"0 Oxytocin Oxytocin receptor Radioligand Binding \n",
"1 Oxytocin Oxytocin receptor Radioligand Binding \n",
"2 Oxytocin Oxytocin receptor Radioligand Binding \n",
"3 Oxytocin Oxytocin receptor Radioligand Binding \n",
"4 Oxytocin Oxytocin receptor Radioligand Binding \n",
"5 Oxytocin Oxytocin receptor Radioligand Binding \n",
"6 Oxytocin Oxytocin receptor Radioligand Binding \n",
"7 Oxytocin Oxytocin receptor Radioligand Binding \n",
"8 Oxytocin Oxytocin receptor Radioligand Binding \n",
"9 Oxytocin Oxytocin receptor FRET \n",
"10 Oxytocin Oxytocin receptor Radioligand Binding \n",
"11 Oxytocin Oxytocin receptor Photoaffinity labeling \n",
"12 Oxytocin Oxytocin receptor Photoaffinity labeling \n",
"13 Oxytocin Oxytocin receptor Binding Isotherm \n",
"14 Oxytocin Oxytocin receptor Binding Isotherm \n",
"15 Oxytocin Oxytocin receptor Radioligand Binding \n",
"16 Oxytocin Oxytocin receptor Binding Isotherm \n",
"\n",
" Ligand/Receptor Source association rate dissociation rate \\\n",
"0 Myometrial (Term Human) NaN NaN \n",
"1 Myometrial (Term Human) - - \n",
"2 Myometrial (Term Human) - - \n",
"3 Myometrial (Term Human) - - \n",
"4 Myometrial (Term Human) NaN NaN \n",
"5 Myometrial (1st trimester Human) NaN NaN \n",
"6 Myometrial (Nonpregnant Human) NaN NaN \n",
"7 Myometrial (Nonpregnant Human) - - \n",
"8 Transfected HEK293 - - \n",
"9 Transfected HEK293 - - \n",
"10 Myometrial (Guinea Pig) - - \n",
"11 Myometrial (Guinea Pig) - - \n",
"12 Myometrial (Guinea Pig) - - \n",
"13 Myometrial (Cattle) - - \n",
"14 Myometrial (Sheep) - - \n",
"15 Myometrial (Rat) NaN NaN \n",
"16 Myometrial (Rat) - - \n",
"\n",
" Kd Note \n",
"0 1.87 ± 0.3 NaN \n",
"1 0.93 ± 0.29 NaN \n",
"2 1.2 ± 0.21 NaN \n",
"3 1.6 ± 0.00 NaN \n",
"4 1.7 ± 0.46 NaN \n",
"5 2.71 ± 1.03 NaN \n",
"6 3.33 ± 0.50 NaN \n",
"7 0.96 ± 0.48 NaN \n",
"8 0.56 ± 0.00 NaN \n",
"9 1.4 ± 0.20 NaN \n",
"10 2.6 ± 0.10 NaN \n",
"11 1.5 ± 0.00 NaN \n",
"12 2.6 ± 0.2 NaN \n",
"13 2.52 ± 0.00 NaN \n",
"14 4.04 ± 0.00 NaN \n",
"15 1.21 ± 0.34 NaN \n",
"16 9.32 ± 0.00 NaN "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Import data\n",
"oxtr_data = pd.read_excel(\"OXT_All_Parameters_01122024.xlsx\")\n",
"oxtr_data.columns = oxtr_data.columns.map(str.strip)\n",
"oxtr_data_kd = oxtr_data[oxtr_data.Symbol == \"Kon, Koff\"].copy()\n",
"display(oxtr_data_kd)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Symbol</th>\n",
" <th>Parameter</th>\n",
" <th>Reference</th>\n",
" <th>Binding Partner</th>\n",
" <th>Receptor</th>\n",
" <th>Method</th>\n",
" <th>Source</th>\n",
" <th>Kd</th>\n",
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" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Kon, Koff</td>\n",
" <td>OXT:OXTR Binding Affinity</td>\n",
" <td>Den et al. 1981</td>\n",
" <td>Oxytocin</td>\n",
" <td>Oxytocin receptor</td>\n",
" <td>Radioligand Binding</td>\n",
" <td>Myometrial (Term Human)</td>\n",
" <td>1.87 ± 0.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Kon, Koff</td>\n",
" <td>OXT:OXTR Binding Affinity</td>\n",
" <td>Rivera et al. 1990</td>\n",
" <td>Oxytocin</td>\n",
" <td>Oxytocin receptor</td>\n",
" <td>Radioligand Binding</td>\n",
" <td>Myometrial (Term Human)</td>\n",
" <td>0.93 ± 0.29</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Kon, Koff</td>\n",
" <td>OXT:OXTR Binding Affinity</td>\n",
" <td>Rezapour et al. 1996</td>\n",
" <td>Oxytocin</td>\n",
" <td>Oxytocin receptor</td>\n",
" <td>Radioligand Binding</td>\n",
" <td>Myometrial (Term Human)</td>\n",
" <td>1.2 ± 0.21</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Kon, Koff</td>\n",
" <td>OXT:OXTR Binding Affinity</td>\n",
" <td>Phaneuf et al. 1997</td>\n",
" <td>Oxytocin</td>\n",
" <td>Oxytocin receptor</td>\n",
" <td>Radioligand Binding</td>\n",
" <td>Myometrial (Term Human)</td>\n",
" <td>1.6 ± 0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Kon, Koff</td>\n",
" <td>OXT:OXTR Binding Affinity</td>\n",
" <td>Fuchs et al. 1984</td>\n",
" <td>Oxytocin</td>\n",
" <td>Oxytocin receptor</td>\n",
" <td>Radioligand Binding</td>\n",
" <td>Myometrial (Term Human)</td>\n",
" <td>1.7 ± 0.46</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Kon, Koff</td>\n",
" <td>OXT:OXTR Binding Affinity</td>\n",
" <td>Den et al. 1981</td>\n",
" <td>Oxytocin</td>\n",
" <td>Oxytocin receptor</td>\n",
" <td>Radioligand Binding</td>\n",
" <td>Myometrial (1st trimester Human)</td>\n",
" <td>2.71 ± 1.03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>Kon, Koff</td>\n",
" <td>OXT:OXTR Binding Affinity</td>\n",
" <td>Den et al. 1981</td>\n",
" <td>Oxytocin</td>\n",
" <td>Oxytocin receptor</td>\n",
" <td>Radioligand Binding</td>\n",
" <td>Myometrial (Nonpregnant Human)</td>\n",
" <td>3.33 ± 0.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>Kon, Koff</td>\n",
" <td>OXT:OXTR Binding Affinity</td>\n",
" <td>A. R. Fuchs et al. 1985</td>\n",
" <td>Oxytocin</td>\n",
" <td>Oxytocin receptor</td>\n",
" <td>Radioligand Binding</td>\n",
" <td>Myometrial (Nonpregnant Human)</td>\n",
" <td>0.96 ± 0.48</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>Kon, Koff</td>\n",
" <td>OXT:OXTR Binding Affinity</td>\n",
" <td>Gulliver 2020</td>\n",
" <td>Oxytocin</td>\n",
" <td>Oxytocin receptor</td>\n",
" <td>Radioligand Binding</td>\n",
" <td>Transfected HEK293</td>\n",
" <td>0.56 ± 0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>Kon, Koff</td>\n",
" <td>OXT:OXTR Binding Affinity</td>\n",
" <td>Y. Waltenspühl et al. 2022</td>\n",
" <td>Oxytocin</td>\n",
" <td>Oxytocin receptor</td>\n",
" <td>FRET</td>\n",
" <td>Transfected HEK293</td>\n",
" <td>1.4 ± 0.20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>Kon, Koff</td>\n",
" <td>OXT:OXTR Binding Affinity</td>\n",
" <td>Kojro et al. 1991</td>\n",
" <td>Oxytocin</td>\n",
" <td>Oxytocin receptor</td>\n",
" <td>Radioligand Binding</td>\n",
" <td>Myometrial (Guinea Pig)</td>\n",
" <td>2.6 ± 0.10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>Kon, Koff</td>\n",
" <td>OXT:OXTR Binding Affinity</td>\n",
" <td>U. Klein et al 1995</td>\n",
" <td>Oxytocin</td>\n",
" <td>Oxytocin receptor</td>\n",
" <td>Photoaffinity labeling</td>\n",
" <td>Myometrial (Guinea Pig)</td>\n",
" <td>1.5 ± 0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>Kon, Koff</td>\n",
" <td>OXT:OXTR Binding Affinity</td>\n",
" <td>F. Fahrenholz et al. 1988</td>\n",
" <td>Oxytocin</td>\n",
" <td>Oxytocin receptor</td>\n",
" <td>Photoaffinity labeling</td>\n",
" <td>Myometrial (Guinea Pig)</td>\n",
" <td>2.6 ± 0.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>Kon, Koff</td>\n",
" <td>OXT:OXTR Binding Affinity</td>\n",
" <td>V. Pliska et al. 1986</td>\n",
" <td>Oxytocin</td>\n",
" <td>Oxytocin receptor</td>\n",
" <td>Binding Isotherm</td>\n",
" <td>Myometrial (Cattle)</td>\n",
" <td>2.52 ± 0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>Kon, Koff</td>\n",
" <td>OXT:OXTR Binding Affinity</td>\n",
" <td>V. Pliska et al. 1986</td>\n",
" <td>Oxytocin</td>\n",
" <td>Oxytocin receptor</td>\n",
" <td>Binding Isotherm</td>\n",
" <td>Myometrial (Sheep)</td>\n",
" <td>4.04 ± 0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>Kon, Koff</td>\n",
" <td>OXT:OXTR Binding Affinity</td>\n",
" <td>Anouar et al. 1996</td>\n",
" <td>Oxytocin</td>\n",
" <td>Oxytocin receptor</td>\n",
" <td>Radioligand Binding</td>\n",
" <td>Myometrial (Rat)</td>\n",
" <td>1.21 ± 0.34</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>Kon, Koff</td>\n",
" <td>OXT:OXTR Binding Affinity</td>\n",
" <td>V. Pliska et al. 1986</td>\n",
" <td>Oxytocin</td>\n",
" <td>Oxytocin receptor</td>\n",
" <td>Binding Isotherm</td>\n",
" <td>Myometrial (Rat)</td>\n",
" <td>9.32 ± 0.00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Symbol Parameter Reference \\\n",
"0 Kon, Koff OXT:OXTR Binding Affinity Den et al. 1981 \n",
"1 Kon, Koff OXT:OXTR Binding Affinity Rivera et al. 1990 \n",
"2 Kon, Koff OXT:OXTR Binding Affinity Rezapour et al. 1996 \n",
"3 Kon, Koff OXT:OXTR Binding Affinity Phaneuf et al. 1997 \n",
"4 Kon, Koff OXT:OXTR Binding Affinity Fuchs et al. 1984 \n",
"5 Kon, Koff OXT:OXTR Binding Affinity Den et al. 1981 \n",
"6 Kon, Koff OXT:OXTR Binding Affinity Den et al. 1981 \n",
"7 Kon, Koff OXT:OXTR Binding Affinity A. R. Fuchs et al. 1985 \n",
"8 Kon, Koff OXT:OXTR Binding Affinity Gulliver 2020 \n",
"9 Kon, Koff OXT:OXTR Binding Affinity Y. Waltenspühl et al. 2022 \n",
"10 Kon, Koff OXT:OXTR Binding Affinity Kojro et al. 1991 \n",
"11 Kon, Koff OXT:OXTR Binding Affinity U. Klein et al 1995 \n",
"12 Kon, Koff OXT:OXTR Binding Affinity F. Fahrenholz et al. 1988 \n",
"13 Kon, Koff OXT:OXTR Binding Affinity V. Pliska et al. 1986 \n",
"14 Kon, Koff OXT:OXTR Binding Affinity V. Pliska et al. 1986 \n",
"15 Kon, Koff OXT:OXTR Binding Affinity Anouar et al. 1996 \n",
"16 Kon, Koff OXT:OXTR Binding Affinity V. Pliska et al. 1986 \n",
"\n",
" Binding Partner Receptor Method \\\n",
"0 Oxytocin Oxytocin receptor Radioligand Binding \n",
"1 Oxytocin Oxytocin receptor Radioligand Binding \n",
"2 Oxytocin Oxytocin receptor Radioligand Binding \n",
"3 Oxytocin Oxytocin receptor Radioligand Binding \n",
"4 Oxytocin Oxytocin receptor Radioligand Binding \n",
"5 Oxytocin Oxytocin receptor Radioligand Binding \n",
"6 Oxytocin Oxytocin receptor Radioligand Binding \n",
"7 Oxytocin Oxytocin receptor Radioligand Binding \n",
"8 Oxytocin Oxytocin receptor Radioligand Binding \n",
"9 Oxytocin Oxytocin receptor FRET \n",
"10 Oxytocin Oxytocin receptor Radioligand Binding \n",
"11 Oxytocin Oxytocin receptor Photoaffinity labeling \n",
"12 Oxytocin Oxytocin receptor Photoaffinity labeling \n",
"13 Oxytocin Oxytocin receptor Binding Isotherm \n",
"14 Oxytocin Oxytocin receptor Binding Isotherm \n",
"15 Oxytocin Oxytocin receptor Radioligand Binding \n",
"16 Oxytocin Oxytocin receptor Binding Isotherm \n",
"\n",
" Source Kd \n",
"0 Myometrial (Term Human) 1.87 ± 0.3 \n",
"1 Myometrial (Term Human) 0.93 ± 0.29 \n",
"2 Myometrial (Term Human) 1.2 ± 0.21 \n",
"3 Myometrial (Term Human) 1.6 ± 0.00 \n",
"4 Myometrial (Term Human) 1.7 ± 0.46 \n",
"5 Myometrial (1st trimester Human) 2.71 ± 1.03 \n",
"6 Myometrial (Nonpregnant Human) 3.33 ± 0.50 \n",
"7 Myometrial (Nonpregnant Human) 0.96 ± 0.48 \n",
"8 Transfected HEK293 0.56 ± 0.00 \n",
"9 Transfected HEK293 1.4 ± 0.20 \n",
"10 Myometrial (Guinea Pig) 2.6 ± 0.10 \n",
"11 Myometrial (Guinea Pig) 1.5 ± 0.00 \n",
"12 Myometrial (Guinea Pig) 2.6 ± 0.2 \n",
"13 Myometrial (Cattle) 2.52 ± 0.00 \n",
"14 Myometrial (Sheep) 4.04 ± 0.00 \n",
"15 Myometrial (Rat) 1.21 ± 0.34 \n",
"16 Myometrial (Rat) 9.32 ± 0.00 "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
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" 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>Kd</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1.87</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.93</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1.20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1.60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1.70</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>2.71</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>3.33</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>0.96</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>0.56</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>1.40</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>2.60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>1.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>2.60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>2.52</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>4.04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>1.21</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>9.32</td>\n",
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"text/plain": [
" Kd\n",
"0 1.87\n",
"1 0.93\n",
"2 1.20\n",
"3 1.60\n",
"4 1.70\n",
"5 2.71\n",
"6 3.33\n",
"7 0.96\n",
"8 0.56\n",
"9 1.40\n",
"10 2.60\n",
"11 1.50\n",
"12 2.60\n",
"13 2.52\n",
"14 4.04\n",
"15 1.21\n",
"16 9.32"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Clean the dataframe\n",
"oxtr_data_kd = oxtr_data_kd.drop(['association rate', 'dissociation rate', 'Note'], axis=1) # remove two unnecessary columns\n",
"oxtr_data_kd = oxtr_data_kd.rename(columns={'Ligand/Receptor Source': 'Source'}) # Rename Ligand/Receptor Source to 'Source'\n",
"display(oxtr_data_kd)\n",
"# Extract Data for the Outcome variable \n",
"kd_data = pd.DataFrame(oxtr_data_kd['Kd'])\n",
"kd_data['Kd'] = kd_data['Kd'].str.split(n=1).str[0] # Split the data in the column Kd\n",
"kd_data = kd_data.astype({'Kd': 'float64'}) # Change the data type of the Kd column to float\n",
"kd_mean_binding_affinity = kd_data[\"Kd\"].mean()\n",
"display(kd_data)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The mean G active rate constant is: Kd 2.355882\n",
"dtype: float64\n"
]
}
],
"source": [
"# Define a function to create a lookup dictionary and codes for a column\n",
"def create_lookup(df, column):\n",
" unique_values = df[column].unique()\n",
" lookup = dict(zip(unique_values, range(len(unique_values))))\n",
" codes = df[column].replace(lookup).values\n",
" return unique_values, lookup, codes\n",
"\n",
"# Studies\n",
"studies, study_lookup, study = create_lookup(oxtr_data_kd, 'Reference')\n",
"\n",
"# Sources\n",
"sources, source_lookup, source = create_lookup(oxtr_data_kd, 'Source')\n",
"\n",
"# Methods\n",
"methods, method_lookup, method = create_lookup(oxtr_data_kd, 'Method')\n",
"\n",
"# Receptors\n",
"receptors, receptor_lookup, receptor = create_lookup(oxtr_data_kd, 'Receptor')\n",
"\n",
"# Outcome varibale Binding affinity\n",
"kd = kd_data['Kd'].values\n",
"log_kd = np.log(kd)\n",
"\n",
"# Priors\n",
"kd_mean = kd_data.mean()\n",
"log_kd_mean = np.log(kd_mean)\n",
"print(\"The mean G active rate constant is: \", kd_mean)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Helper Functions"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"def create_table(output, var_name=[\"receptor_intercept\"], mean = False, hdi_interval=0.95, round_to=3, quartile = True):\n",
"\n",
" # if mean is not false then create a new mean dataset\n",
" if mean != False:\n",
" mean_output = az.summary(output, var_names=mean, hdi_prob=hdi_interval)\n",
"\n",
" # Create a table of the results\n",
" table = az.summary(output, var_names=var_name, hdi_prob=hdi_interval)\n",
" \n",
" # Create a deep copy of the output\n",
" output_copy = copy.deepcopy(output)\n",
" mean_output_copy = copy.deepcopy(output)\n",
" # Modify the output to get the original values\n",
" output_copy.posterior[var_name[0]] = np.exp(output.posterior[var_name[0]])\n",
" mean_output_copy.posterior[mean[0]] = np.exp(mean_output_copy.posterior[mean[0]])\n",
" # Get the hdi values from the modified output\n",
" hdi = az.hdi(output_copy, hdi_prob=hdi_interval, var_names=var_name)\n",
" mean_hdi = az.hdi(mean_output_copy, hdi_prob=hdi_interval, var_names=mean)\n",
"\n",
" # Convert the hdi to a dataframe\n",
" hdi = hdi.to_dataframe()\n",
" # Extract the lower and upper values\n",
" lower_values = hdi.xs('lower', level='hdi')\n",
" upper_values = hdi.xs('higher', level='hdi')\n",
" \n",
" # convert mean_hdi to a dataframe\n",
" mean_hdi = mean_hdi.to_dataframe()\n",
" # Extract the mean lower and upper values\n",
" if isinstance(mean_hdi.index, pd.MultiIndex):\n",
" mean_lower_values = mean_hdi.xs('lower', level='hdi')\n",
" mean_upper_values = mean_hdi.xs('higher', level='hdi')\n",
" else:\n",
" mean_lower_values = mean_hdi.loc['lower']\n",
" mean_upper_values = mean_hdi.loc['higher']\n",
" \n",
" # extract the data from the thickest lines and convert to a dataframe\n",
" interval_25 = output.posterior[var_name].quantile([0.25], dim=[\"chain\", \"draw\"]).to_dataframe()\n",
" interval_75 = output.posterior[var_name].quantile([0.75], dim=[\"chain\", \"draw\"]).to_dataframe() \n",
" mean_interval_25 = output.posterior[mean].quantile([0.25], dim=[\"chain\", \"draw\"]).to_dataframe()\n",
" mean_interval_75 = output.posterior[mean].quantile([0.75], dim=[\"chain\", \"draw\"]).to_dataframe()\n",
" # Create a dataframe of the means\n",
" Result = np.round(np.exp(table[['mean']]), round_to)\n",
" Result.insert(1, 'Standard deviation', np.round(np.exp(table['mean'].values + table['sd'].values) - np.exp(table['mean'].values), round_to))\n",
" # Use the lower and upper values from the az.hdi() \n",
" Result.insert(2, 'HDI(2.5%) Minimum', np.round((lower_values.values), round_to))\n",
" Result.insert(3, 'HDI(97.5%) Maximum', np.round((upper_values.values), round_to))\n",
" if quartile == True:\n",
" # Adding the data from the Quartile interval to the table.\n",
" Result.insert(4, 'Quartile Minumum', np.round(np.exp(interval_25).values, round_to))\n",
" Result.insert(5, 'Quartile Maximum', np.round(np.exp(interval_75).values, round_to))\n",
"\n",
" # create a dataframe for the mean\n",
" mean_result = np.round(np.exp(mean_output[['mean']]), round_to)\n",
" mean_result.insert(1, 'Standard deviation', np.round(np.exp(mean_output['mean'].values + mean_output['sd'].values) - np.exp(mean_output['mean'].values), round_to))\n",
" # Use the lower and upper values from the az.hdi()\n",
" mean_result.insert(2, 'HDI(2.5%) Minimum', np.round((mean_lower_values.values), round_to))\n",
" mean_result.insert(3, 'HDI(97.5%) Maximum', np.round((mean_upper_values.values), round_to))\n",
" if quartile == True:\n",
" # Adding the data from the Quartile interval to the table.\n",
" mean_result.insert(4, 'Quartile Minumum', np.round(np.exp(mean_interval_25).values, round_to))\n",
" mean_result.insert(5, 'Quartile Maximum', np.round(np.exp(mean_interval_75).values, round_to))\n",
"\n",
" # Get the first index label\n",
" first_index_label = mean_result.index[0]\n",
" # Rename the row label\n",
" mean_result.rename(index={str(first_index_label): 'Global Mean'}, inplace=True)\n",
" # Combine the two dataframes into one\n",
" Result = pd.concat([Result, mean_result])\n",
" return Result"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Model For Binding affinity"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Hierarchical model with linear regression + varying intercepts\n",
"In this hierarchical model with linear regression and varying intercepts, each group (in this case, each receptor) has its own baseline rate constant (intercept), while the effect of the predictor variable (beta-arrestin type) on the rate constant is assumed to be consistent across all groups. This means that while the starting point (intercept) can vary between different receptors, the influence of beta-arrestin type (slope) on the rate constant is fixed. In other words, regardless of the receptor type, a change in beta-arrestin type will have the same effect on the rate constant."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Model"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"# Centered Parameterization\n",
"coords = {\"Methods\": [\"0\", \"1\", \"2\", \"3\", \"4\"], \"obs_id\": np.arange(method.size)}\n",
"coords[\"Source\"] = sources\n",
"\n",
"with pm.Model(coords=coords) as varying_intercept:\n",
" method_idx = pm.MutableData(\"method_idx\", method, dims=\"obs_id\")\n",
" source_idx = pm.MutableData(\"source_idx\", source, dims=\"obs_id\")\n",
" \n",
" # HyperPriors\n",
"\n",
" # A normal prior distribution for a parameter \"mu_a\". This prior distribution has a mean (mu) of 2 and a standard deviation (sigma) of 1.\n",
" mu0 = pm.Normal(\"mu0\", mu=log_kd_mean, sigma=1) \n",
" # A half-cauchy/Exponential prior distribution for a parameter \"ic_sigma_a\". This prior distribution has a rate parameter of 1, which is equivalent to a mean of 1. SD of receptor_intercept\n",
" sigma0 = pm.HalfCauchy(\"sigma0\", 1.0) \n",
"\n",
" # Varying intercepts\n",
"\n",
" # A normal prior distribution for a group-level parameter named \"receptor_intercept\". This prior distribution has a mean (mu) equal to the value of the \"ic_mu\" parameter and a standard deviation (sigma) equal to the value of the \"ic_sigma\" parameter. \n",
" # The dimensions of this parameter are specified as \"county\", indicating that there is one \"alpha\" value for each county.\n",
" source_intercept = pm.Normal(\"source_intercept\", mu=mu0, sigma=sigma0, dims=\"Source\")\n",
"\n",
" # Common slope\n",
" beta = pm.Normal(\"beta\", mu=0, sigma=1) # consider adding two slopes for beta arrestin one and two\n",
" \n",
" # Expected value per receptor\n",
"\n",
" # Computes the expected value of the response variable by indexing the \"receptor_intercept\" parameter using the \"Receptor_idx\" variable. \n",
" # This means that each observation is associated with the \"receptor_intercept\" value corresponding to its receptor.\n",
" theta = source_intercept[source_idx] + beta * method_idx\n",
"\n",
" # Model error\n",
"\n",
" # This line defines an HalfCauchy prior distribution for a parameter named \"sigma\". \n",
" # A prior distribution has a rate parameter of 1, which is equivalent to a mean of 1.\n",
" sigma = pm.HalfCauchy(\"sigma\", 1.0) # HalfCauchy/Exponential is a distribution over positive values\n",
"\n",
" # Data likelihood or Outcome\n",
"\n",
" # Defines the likelihood of the observed data using a normal distribution. The mean (mu) of this distribution is set to the expected value theta, & its standard deviation (sigma) is set to the value of the \"sigma\" parameter. \n",
" # The observed data is specified using the observed=kba_data_log, and the dimensions of this variable are specified as \"obs_id\".\n",
" y = pm.Normal(\"y\", mu=theta, sigma=sigma, observed=log_kd, dims=\"obs_id\")"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Auto-assigning NUTS sampler...\n",
"Initializing NUTS using jitter+adapt_diag...\n",
"Multiprocess sampling (4 chains in 4 jobs)\n",
"NUTS: [mu0, sigma0, source_intercept, beta, sigma]\n"
]
},
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" background-size: auto;\n",
" }\n",
" progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
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"name": "stderr",
"output_type": "stream",
"text": [
"Sampling 4 chains for 2_000 tune and 1_000 draw iterations (8_000 + 4_000 draws total) took 28 seconds.\n",
"There were 98 divergences after tuning. Increase `target_accept` or reparameterize.\n",
"The effective sample size per chain is smaller than 100 for some parameters. A higher number is needed for reliable rhat and ess computation. See https://arxiv.org/abs/1903.08008 for details\n"
]
}
],
"source": [
"# Bayesian model run\n",
"with varying_intercept:\n",
" varying_intercept_trace_kd = pm.sample(tune=2000, random_seed=RANDOM_SEED)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Results"
]
},
{
"cell_type": "code",
"execution_count": 12,
"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>mean</th>\n",
" <th>Standard deviation</th>\n",
" <th>HDI(2.5%) Minimum</th>\n",
" <th>HDI(97.5%) Maximum</th>\n",
" <th>Quartile Minumum</th>\n",
" <th>Quartile Maximum</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>source_intercept[Myometrial (Term Human)]</th>\n",
" <td>1.4492</td>\n",
" <td>0.3226</td>\n",
" <td>0.9149</td>\n",
" <td>2.0668</td>\n",
" <td>1.2806</td>\n",
" <td>1.6579</td>\n",
" </tr>\n",
" <tr>\n",
" <th>source_intercept[Myometrial (1st trimester Human)]</th>\n",
" <td>1.6905</td>\n",
" <td>0.6562</td>\n",
" <td>0.7955</td>\n",
" <td>3.2322</td>\n",
" <td>1.3818</td>\n",
" <td>1.9975</td>\n",
" </tr>\n",
" <tr>\n",
" <th>source_intercept[Myometrial (Nonpregnant Human)]</th>\n",
" <td>1.5793</td>\n",
" <td>0.4710</td>\n",
" <td>0.8496</td>\n",
" <td>2.4848</td>\n",
" <td>1.3340</td>\n",
" <td>1.8592</td>\n",
" </tr>\n",
" <tr>\n",
" <th>source_intercept[Transfected HEK293]</th>\n",
" <td>1.2008</td>\n",
" <td>0.4348</td>\n",
" <td>0.5841</td>\n",
" <td>1.9804</td>\n",
" <td>0.9913</td>\n",
" <td>1.5010</td>\n",
" </tr>\n",
" <tr>\n",
" <th>source_intercept[Myometrial (Guinea Pig)]</th>\n",
" <td>1.4434</td>\n",
" <td>0.4361</td>\n",
" <td>0.7582</td>\n",
" <td>2.2661</td>\n",
" <td>1.2287</td>\n",
" <td>1.7011</td>\n",
" </tr>\n",
" <tr>\n",
" <th>source_intercept[Myometrial (Cattle)]</th>\n",
" <td>1.3499</td>\n",
" <td>0.5791</td>\n",
" <td>0.4788</td>\n",
" <td>2.2851</td>\n",
" <td>1.1271</td>\n",
" <td>1.6996</td>\n",
" </tr>\n",
" <tr>\n",
" <th>source_intercept[Myometrial (Sheep)]</th>\n",
" <td>1.4888</td>\n",
" <td>0.5925</td>\n",
" <td>0.6771</td>\n",
" <td>2.7149</td>\n",
" <td>1.2262</td>\n",
" <td>1.8107</td>\n",
" </tr>\n",
" <tr>\n",
" <th>source_intercept[Myometrial (Rat)]</th>\n",
" <td>1.6454</td>\n",
" <td>0.5558</td>\n",
" <td>0.8459</td>\n",
" <td>2.7802</td>\n",
" <td>1.3685</td>\n",
" <td>1.9691</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Global Mean</th>\n",
" <td>1.4874</td>\n",
" <td>0.3623</td>\n",
" <td>0.8692</td>\n",
" <td>2.1530</td>\n",
" <td>1.2974</td>\n",
" <td>1.7071</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" mean \\\n",
"source_intercept[Myometrial (Term Human)] 1.4492 \n",
"source_intercept[Myometrial (1st trimester Human)] 1.6905 \n",
"source_intercept[Myometrial (Nonpregnant Human)] 1.5793 \n",
"source_intercept[Transfected HEK293] 1.2008 \n",
"source_intercept[Myometrial (Guinea Pig)] 1.4434 \n",
"source_intercept[Myometrial (Cattle)] 1.3499 \n",
"source_intercept[Myometrial (Sheep)] 1.4888 \n",
"source_intercept[Myometrial (Rat)] 1.6454 \n",
"Global Mean 1.4874 \n",
"\n",
" Standard deviation \\\n",
"source_intercept[Myometrial (Term Human)] 0.3226 \n",
"source_intercept[Myometrial (1st trimester Human)] 0.6562 \n",
"source_intercept[Myometrial (Nonpregnant Human)] 0.4710 \n",
"source_intercept[Transfected HEK293] 0.4348 \n",
"source_intercept[Myometrial (Guinea Pig)] 0.4361 \n",
"source_intercept[Myometrial (Cattle)] 0.5791 \n",
"source_intercept[Myometrial (Sheep)] 0.5925 \n",
"source_intercept[Myometrial (Rat)] 0.5558 \n",
"Global Mean 0.3623 \n",
"\n",
" HDI(2.5%) Minimum \\\n",
"source_intercept[Myometrial (Term Human)] 0.9149 \n",
"source_intercept[Myometrial (1st trimester Human)] 0.7955 \n",
"source_intercept[Myometrial (Nonpregnant Human)] 0.8496 \n",
"source_intercept[Transfected HEK293] 0.5841 \n",
"source_intercept[Myometrial (Guinea Pig)] 0.7582 \n",
"source_intercept[Myometrial (Cattle)] 0.4788 \n",
"source_intercept[Myometrial (Sheep)] 0.6771 \n",
"source_intercept[Myometrial (Rat)] 0.8459 \n",
"Global Mean 0.8692 \n",
"\n",
" HDI(97.5%) Maximum \\\n",
"source_intercept[Myometrial (Term Human)] 2.0668 \n",
"source_intercept[Myometrial (1st trimester Human)] 3.2322 \n",
"source_intercept[Myometrial (Nonpregnant Human)] 2.4848 \n",
"source_intercept[Transfected HEK293] 1.9804 \n",
"source_intercept[Myometrial (Guinea Pig)] 2.2661 \n",
"source_intercept[Myometrial (Cattle)] 2.2851 \n",
"source_intercept[Myometrial (Sheep)] 2.7149 \n",
"source_intercept[Myometrial (Rat)] 2.7802 \n",
"Global Mean 2.1530 \n",
"\n",
" Quartile Minumum \\\n",
"source_intercept[Myometrial (Term Human)] 1.2806 \n",
"source_intercept[Myometrial (1st trimester Human)] 1.3818 \n",
"source_intercept[Myometrial (Nonpregnant Human)] 1.3340 \n",
"source_intercept[Transfected HEK293] 0.9913 \n",
"source_intercept[Myometrial (Guinea Pig)] 1.2287 \n",
"source_intercept[Myometrial (Cattle)] 1.1271 \n",
"source_intercept[Myometrial (Sheep)] 1.2262 \n",
"source_intercept[Myometrial (Rat)] 1.3685 \n",
"Global Mean 1.2974 \n",
"\n",
" Quartile Maximum \n",
"source_intercept[Myometrial (Term Human)] 1.6579 \n",
"source_intercept[Myometrial (1st trimester Human)] 1.9975 \n",
"source_intercept[Myometrial (Nonpregnant Human)] 1.8592 \n",
"source_intercept[Transfected HEK293] 1.5010 \n",
"source_intercept[Myometrial (Guinea Pig)] 1.7011 \n",
"source_intercept[Myometrial (Cattle)] 1.6996 \n",
"source_intercept[Myometrial (Sheep)] 1.8107 \n",
"source_intercept[Myometrial (Rat)] 1.9691 \n",
"Global Mean 1.7071 "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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",
"text/plain": [
"<Figure size 1200x500 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Plot the result \n",
"ax = pm.plot_forest(\n",
" varying_intercept_trace_kd,\n",
" var_names=[\"source_intercept\", \"mu0\"],\n",
" transform=np.exp,\n",
" hdi_prob= 0.95,\n",
" quartiles = False,\n",
" figsize=(12, 5),\n",
" combined=True,\n",
" r_hat=False,\n",
" labeller=az.labels.NoVarLabeller(),\n",
" linewidth=2,\n",
" markersize=14,\n",
")\n",
"# Make y-axis labels bigger and bolder\n",
"for label in ax[0].get_yticklabels():\n",
" label.set_fontsize(15) # Change the number to the size you want\n",
" label.set_weight('bold') # Change 'bold' to 'normal' to make the labels not bold\n",
"#ax[0].set_ylabel(\"Cell assayed\", weight='bold')\n",
"ax[0].set_xlabel('Equilibrium Dissociation Constant (Kd, nM)', weight='bold')\n",
"#ax[0].set_title('95% Highest Density Interval', weight='bold')\n",
"ax[0].set_title('')\n",
"# change labels\n",
"labels = [\n",
" 'Myometrial (Term Human)',\n",
" 'Myometrial (1st trimester Human)',\n",
" 'Myometrial (Nonpregnant Human)',\n",
" 'Transfected HEK293',\n",
" 'Myometrial (Guinea Pig)',\n",
" 'Myometrial (Cattle)',\n",
" 'Myometrial (Sheep)',\n",
" 'Myometrial (Rat)',\n",
" 'Global Mean Kd'\n",
"]\n",
"# Reverse the order of the list\n",
"labels.reverse()\n",
"# Change the y-axis labels\n",
"ax[0].set_yticklabels(labels) \n",
"# Change the color of the first HDI\n",
"ax[0].get_children()[0].set_edgecolor('gray')\n",
"# Change the color of the marker\n",
"ax[0].get_children()[1].set_color('gray')\n",
"# Change the type of the marker\n",
"ax[0].get_children()[1].set_marker('d')\n",
"# Change the size of the marker\n",
"ax[0].get_children()[1].set_markersize(15)\n",
"# Save the figure\n",
"plt.savefig(\"Equilibrium_Dissociation_Constant_All_cells.png\")\n",
"output = varying_intercept_trace_kd\n",
"# Create a table of the results\n",
"Result = create_table(output, var_name=[\"source_intercept\"], mean = [\"mu0\"], hdi_interval=0.95, round_to=4)\n",
"display(Result)\n",
"# Save the table\n",
"Result.to_excel('Equilibrium_Dissociation_Constant_All_cells.xlsx')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Human"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Data set up"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0.62593843, -0.07257069, 0.18232156, 0.47000363, 0.53062825,\n",
" 0.99694863, 1.2029723 , -0.04082199])"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Human Data likelihood or Outcome\n",
"log_kd_Human_m = log_kd[:-9]\n",
"display(log_kd_Human_m)\n",
"# Mean human binding affinity\n",
"mean_human_binding_affinity = np.exp(log_kd_Human_m.mean())\n",
"# Creating data human dataframe\n",
"oxtr_data_kd_human_m = oxtr_data_kd[:-9]\n",
"# Sources\n",
"sources_human, source_lookup_human, source_human = create_lookup(oxtr_data_kd_human_m, 'Source')\n",
"# Methods\n",
"methods_human, method_lookup_human, method_human = create_lookup(oxtr_data_kd_human_m, 'Method')\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Model"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"# Centered Parameterization\n",
"coords = {\"Methods\": [\"0\", \"1\", \"2\", \"3\", \"4\"], \"obs_id\": np.arange(method_human.size)}\n",
"coords[\"Source\"] = sources_human\n",
"\n",
"with pm.Model(coords=coords) as varying_intercept:\n",
" method_idx = pm.MutableData(\"method_idx\", method_human, dims=\"obs_id\")\n",
" source_idx = pm.MutableData(\"source_idx\", source_human, dims=\"obs_id\")\n",
" \n",
" # HyperPriors\n",
" mu0 = pm.Normal(\"mu0\", mu=log_kd_mean, sigma=1) \n",
" sigma0 = pm.HalfCauchy(\"sigma0\", 1.0) \n",
"\n",
" # Varying intercepts\n",
" source_intercept = pm.Normal(\"source_intercept\", mu=mu0, sigma=sigma0, dims=\"Source\")\n",
"\n",
" # Common slope\n",
" beta = pm.Normal(\"beta\", mu=0, sigma=1) # consider adding two slopes for beta arrestin one and two\n",
" \n",
" # Expected value per receptor\n",
" theta = source_intercept[source_idx] + beta * method_idx\n",
"\n",
" # Model error\n",
" sigma = pm.HalfCauchy(\"sigma\", 1.0) # HalfCauchy/Exponential is a distribution over positive values\n",
"\n",
" # Data likelihood or Outcome\n",
" y = pm.Normal(\"y\", mu=theta, sigma=sigma, observed=log_kd_Human_m, dims=\"obs_id\")"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Auto-assigning NUTS sampler...\n",
"Initializing NUTS using jitter+adapt_diag...\n",
"Multiprocess sampling (4 chains in 4 jobs)\n",
"NUTS: [mu0, sigma0, source_intercept, beta, sigma]\n"
]
},
{
"data": {
"text/html": [
"\n",
"<style>\n",
" /* Turns off some styling */\n",
" progress {\n",
" /* gets rid of default border in Firefox and Opera. */\n",
" border: none;\n",
" /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
" background-size: auto;\n",
" }\n",
" progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
" background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
" }\n",
" .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
" background: #F44336;\n",
" }\n",
"</style>\n"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"\n",
" <div>\n",
" <progress value='12000' class='' max='12000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" 100.00% [12000/12000 00:07<00:00 Sampling 4 chains, 163 divergences]\n",
" </div>\n",
" "
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Sampling 4 chains for 2_000 tune and 1_000 draw iterations (8_000 + 4_000 draws total) took 20 seconds.\n",
"There were 163 divergences after tuning. Increase `target_accept` or reparameterize.\n"
]
}
],
"source": [
"# Bayesian model run\n",
"with varying_intercept:\n",
" varying_intercept_trace_hm = pm.sample(tune=2000, random_seed=RANDOM_SEED)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Results"
]
},
{
"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>mean</th>\n",
" <th>Standard deviation</th>\n",
" <th>HDI(2.5%) Minimum</th>\n",
" <th>HDI(97.5%) Maximum</th>\n",
" <th>Quartile Minumum</th>\n",
" <th>Quartile Maximum</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>source_intercept[Myometrial (Term Human)]</th>\n",
" <td>1.5144</td>\n",
" <td>0.4108</td>\n",
" <td>0.9033</td>\n",
" <td>2.2841</td>\n",
" <td>1.3087</td>\n",
" <td>1.7512</td>\n",
" </tr>\n",
" <tr>\n",
" <th>source_intercept[Myometrial (1st trimester Human)]</th>\n",
" <td>2.0897</td>\n",
" <td>1.0496</td>\n",
" <td>0.7849</td>\n",
" <td>4.4092</td>\n",
" <td>1.5841</td>\n",
" <td>2.6501</td>\n",
" </tr>\n",
" <tr>\n",
" <th>source_intercept[Myometrial (Nonpregnant Human)]</th>\n",
" <td>1.7594</td>\n",
" <td>0.6394</td>\n",
" <td>0.8658</td>\n",
" <td>3.0183</td>\n",
" <td>1.4483</td>\n",
" <td>2.1344</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Global Mean</th>\n",
" <td>1.8004</td>\n",
" <td>0.8035</td>\n",
" <td>0.6633</td>\n",
" <td>3.4328</td>\n",
" <td>1.4445</td>\n",
" <td>2.1982</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" mean \\\n",
"source_intercept[Myometrial (Term Human)] 1.5144 \n",
"source_intercept[Myometrial (1st trimester Human)] 2.0897 \n",
"source_intercept[Myometrial (Nonpregnant Human)] 1.7594 \n",
"Global Mean 1.8004 \n",
"\n",
" Standard deviation \\\n",
"source_intercept[Myometrial (Term Human)] 0.4108 \n",
"source_intercept[Myometrial (1st trimester Human)] 1.0496 \n",
"source_intercept[Myometrial (Nonpregnant Human)] 0.6394 \n",
"Global Mean 0.8035 \n",
"\n",
" HDI(2.5%) Minimum \\\n",
"source_intercept[Myometrial (Term Human)] 0.9033 \n",
"source_intercept[Myometrial (1st trimester Human)] 0.7849 \n",
"source_intercept[Myometrial (Nonpregnant Human)] 0.8658 \n",
"Global Mean 0.6633 \n",
"\n",
" HDI(97.5%) Maximum \\\n",
"source_intercept[Myometrial (Term Human)] 2.2841 \n",
"source_intercept[Myometrial (1st trimester Human)] 4.4092 \n",
"source_intercept[Myometrial (Nonpregnant Human)] 3.0183 \n",
"Global Mean 3.4328 \n",
"\n",
" Quartile Minumum \\\n",
"source_intercept[Myometrial (Term Human)] 1.3087 \n",
"source_intercept[Myometrial (1st trimester Human)] 1.5841 \n",
"source_intercept[Myometrial (Nonpregnant Human)] 1.4483 \n",
"Global Mean 1.4445 \n",
"\n",
" Quartile Maximum \n",
"source_intercept[Myometrial (Term Human)] 1.7512 \n",
"source_intercept[Myometrial (1st trimester Human)] 2.6501 \n",
"source_intercept[Myometrial (Nonpregnant Human)] 2.1344 \n",
"Global Mean 2.1982 "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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",
"text/plain": [
"<Figure size 1100x300 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Plot the result\n",
"ax = pm.plot_forest(\n",
" varying_intercept_trace_hm,\n",
" var_names=[\"source_intercept\", \"mu0\"],\n",
" textsize=15,\n",
" transform=np.exp,\n",
" hdi_prob= 0.95,\n",
" quartiles = False, # This removes the quartiles\n",
" figsize=(11, 3),\n",
" combined=True,\n",
" r_hat=False,\n",
" labeller=az.labels.NoVarLabeller(),\n",
" linewidth=2,\n",
" markersize=13, \n",
")\n",
"#ax[0].set_ylabel(\"Cell assayed\", weight='bold')\n",
"ax[0].set_xlabel('Equilibrium Dissociation Constant (Kd, nM)', weight='bold')\n",
"#ax[0].set_title('95% Highest Density Interval', weight='bold')\n",
"ax[0].set_title('')\n",
"ax[0].set_yticklabels(['Global Mean Kd', 'Myometrial (Nonpregnant Human)', 'Myometrial (1st trimester Human)', 'Myometrial (Term Human)'], weight='bold')\n",
"# Change the color of the first HDI\n",
"ax[0].get_children()[0].set_edgecolor('gray')\n",
"# Change the color of the marker\n",
"ax[0].get_children()[1].set_color('gray')\n",
"# Change the type of the marker\n",
"ax[0].get_children()[1].set_marker('d')\n",
"# Change the size of the marker\n",
"ax[0].get_children()[1].set_markersize(15)\n",
"# Save the plot\n",
"plt.savefig(\"Equilibrium_Dissociation_Constant_Human_Myometrial_cells.png\")\n",
"output = varying_intercept_trace_hm\n",
"# Create a table of the results\n",
"Result = create_table(output, var_name=[\"source_intercept\"], mean = [\"mu0\"], hdi_interval=0.95, round_to=4)\n",
"display(Result)\n",
"Result.to_excel('Equilibrium_Dissociation_Constant_Human_Myometrial_cells.xlsx')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.11.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}