{ "cells": [ { "cell_type": "markdown", "id": "2f04eee0-5928-4e74-a754-6dc2e528810c", "metadata": {}, "source": [ "# DNAmFitAgeGripM" ] }, { "cell_type": "markdown", "id": "a3f514a3-772c-4a14-afdf-5a8376851ff4", "metadata": {}, "source": [ "## Index\n", "1. [Instantiate model class](#Instantiate-model-class)\n", "2. [Define clock metadata](#Define-clock-metadata)\n", "3. [Download clock dependencies](#Download-clock-dependencies)\n", "5. [Load features](#Load-features)\n", "6. [Load weights into base model](#Load-weights-into-base-model)\n", "7. [Load reference values](#Load-reference-values)\n", "8. [Load preprocess and postprocess objects](#Load-preprocess-and-postprocess-objects)\n", "10. [Check all clock parameters](#Check-all-clock-parameters)\n", "10. [Basic test](#Basic-test)\n", "11. [Save torch model](#Save-torch-model)\n", "12. [Clear directory](#Clear-directory)" ] }, { "cell_type": "markdown", "id": "d95fafdc-643a-40ea-a689-200bd132e90c", "metadata": {}, "source": [ "Let's first import some packages:" ] }, { "cell_type": "code", "execution_count": 18, "id": "4adfb4de-cd79-4913-a1af-9e23e9e236c9", "metadata": { "execution": { "iopub.execute_input": "2025-04-07T17:50:23.621657Z", "iopub.status.busy": "2025-04-07T17:50:23.621346Z", "iopub.status.idle": "2025-04-07T17:50:25.088787Z", "shell.execute_reply": "2025-04-07T17:50:25.088401Z" } }, "outputs": [], "source": [ "import os\n", "import inspect\n", "import shutil\n", "import json\n", "import torch\n", "import pandas as pd\n", "import numpy as np\n", "import pyaging as pya" ] }, { "cell_type": "markdown", "id": "145082e5-ced4-47ae-88c0-cb69773e3c5a", "metadata": {}, "source": [ "## Instantiate model class" ] }, { "cell_type": "code", "execution_count": 19, "id": "8aa77372-7ed3-4da7-abc9-d30372106139", "metadata": { "execution": { "iopub.execute_input": "2025-04-07T17:50:25.090492Z", "iopub.status.busy": "2025-04-07T17:50:25.090265Z", "iopub.status.idle": "2025-04-07T17:50:25.101917Z", "shell.execute_reply": "2025-04-07T17:50:25.101643Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "class DNAmFitAgeGripM(pyagingModel):\n", " def __init__(self):\n", " super().__init__()\n", "\n", " def preprocess(self, x):\n", " return x\n", " \n", " def postprocess(self, x):\n", " return x\n", "\n" ] } ], "source": [ "def print_entire_class(cls):\n", " source = inspect.getsource(cls)\n", " print(source)\n", "\n", "print_entire_class(pya.models.DNAmFitAgeGripM)" ] }, { "cell_type": "code", "execution_count": 20, "id": "914a94cf-bf6c-4b9d-862a-a2787842e07e", "metadata": { "execution": { "iopub.execute_input": "2025-04-07T17:50:25.103160Z", "iopub.status.busy": "2025-04-07T17:50:25.103071Z", "iopub.status.idle": "2025-04-07T17:50:25.104710Z", "shell.execute_reply": "2025-04-07T17:50:25.104466Z" } }, "outputs": [], "source": [ "model = pya.models.DNAmFitAgeGripM()" ] }, { "cell_type": "markdown", "id": "51f8615e-01fa-4aa5-b196-3ee2b35d261c", "metadata": {}, "source": [ "## Define clock metadata" ] }, { "cell_type": "code", "execution_count": 21, "id": "6609d6dc-c0a0-4137-bdf5-9fb31ea85281", "metadata": { "execution": { "iopub.execute_input": "2025-04-07T17:50:25.105997Z", "iopub.status.busy": "2025-04-07T17:50:25.105908Z", "iopub.status.idle": "2025-04-07T17:50:25.107874Z", "shell.execute_reply": "2025-04-07T17:50:25.107633Z" } }, "outputs": [], "source": [ "model.metadata[\"clock_name\"] = \"dnamfitagegripm\"\n", "model.metadata[\"data_type\"] = \"DNA methylation\" # Paper: Blood DNA methylation was used to develop the fitness biomarkers.\n", "model.metadata[\"species\"] = \"Homo sapiens\" # Paper: The development cohorts were human adult studies (FHS, BLSA, and Budapest).\n", "model.metadata[\"year\"] = 2023\n", "model.metadata[\"approved_by_author\"] = \"⌛\"\n", "model.metadata[\"citation\"] = \"McGreevy, K. M., et al. “DNAmFitAge: biological age indicator incorporating physical fitness.” Aging 15(10): 3904–3938 (2023).\"\n", "model.metadata[\"doi\"] = \"https://doi.org/10.18632/aging.204538\"\n", "model.metadata[\"notes\"] = \"Male-specific blood DNAm maximum-handgrip-strength estimator without chronological age as an input.\"\n", "model.metadata[\"research_only\"] = None\n", "model.metadata[\"tissue\"] = [\"whole blood\"] # Paper: The biomarkers were developed from blood DNAm data.\n", "model.metadata[\"predicts\"] = [\"grip strength\"] # Paper: The algorithms generate DNAmGaitspeed, DNAmGripmax, or DNAmVO2max estimates in the corresponding physical-fitness scale.\n", "model.metadata[\"training_target\"] = [\"grip strength\"] # Paper: The directly measured fitness parameter was the dependent variable in LASSO regression.\n", "model.metadata[\"unit\"] = [\"kilograms\"] # Paper: Gait speed is measured in m/s, grip force in kg, and VO2max in mL/kg/min.\n", "model.metadata[\"model_type\"] = \"LASSO regression\" # Paper: Each fitness DNAm biomarker was developed using LASSO penalized regression with ten-fold cross-validation.\n", "model.metadata[\"platform\"] = [\"Illumina 450K\"] # Paper: The reported fitness CpG background and fitted loci were on the 450K array.\n", "model.metadata[\"population\"] = \"adult men\" # Paper: The male grip-strength model was fit separately in adult men from the FHS, BLSA, and Budapest development cohorts.\n", "model.metadata[\"journal\"] = \"Aging\"\n", "model.metadata[\"last_author\"] = \"Steve Horvath\"\n", "model.metadata[\"n_features\"] = 93\n", "model.metadata[\"citations\"] = 99\n", "model.metadata[\"citations_date\"] = \"2026-07-05\"\n" ] }, { "cell_type": "markdown", "id": "74492239-5aae-4026-9d90-6bc9c574c110", "metadata": {}, "source": [ "## Download clock dependencies" ] }, { "cell_type": "markdown", "id": "7bec474f-80ce-4884-9472-30c193327117", "metadata": {}, "source": [ "#### Download GitHub repository" ] }, { "cell_type": "code", "execution_count": 22, "id": "aa4a1b59-dda3-4ea8-8f34-b3c53ecbc310", "metadata": { "execution": { "iopub.execute_input": "2025-04-07T17:50:25.109229Z", "iopub.status.busy": "2025-04-07T17:50:25.109147Z", "iopub.status.idle": "2025-04-07T17:50:25.651487Z", "shell.execute_reply": "2025-04-07T17:50:25.650965Z" } }, "outputs": [ { "data": { "text/plain": [ "0" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "github_url = \"https://github.com/kristenmcgreevy/DNAmFitAge.git\"\n", "github_folder_name = github_url.split('/')[-1].split('.')[0]\n", "os.system(f\"git clone {github_url}\")" ] }, { "cell_type": "markdown", "id": "6bd15521-363f-4029-99ff-9f0b2ae0ed2e", "metadata": {}, "source": [ "#### Download from R package" ] }, { "cell_type": "code", "execution_count": 23, "id": "f1f9bbe4-cfc8-494c-b910-c96da88afb2b", "metadata": { "execution": { "iopub.execute_input": "2025-04-07T17:50:25.653928Z", "iopub.status.busy": "2025-04-07T17:50:25.653723Z", "iopub.status.idle": "2025-04-07T17:50:25.657586Z", "shell.execute_reply": "2025-04-07T17:50:25.657192Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Writing download.r\n" ] } ], "source": [ "%%writefile download.r\n", "\n", "options(repos = c(CRAN = \"https://cloud.r-project.org/\"))\n", "library(jsonlite)\n", "\n", "DNAmFitnessModels <- readRDS(\"DNAmFitAge/DNAmFitnessModelsandFitAge_Oct2022.rds\")\n", "\n", "AllCpGs <- DNAmFitnessModels$AllCpGs\n", "write_json(AllCpGs, \"AllCpGs.json\")\n", "\n", "MaleMedians <- DNAmFitnessModels$Male_Medians_All\n", "write.csv(MaleMedians, \"MaleMedians.csv\")\n", "FemaleMedians <- DNAmFitnessModels$Female_Medians_All\n", "write.csv(FemaleMedians, \"FemaleMedians.csv\")\n", "\n", "Gait_noAge_Females <- DNAmFitnessModels$Gait_noAge_Females\n", "Gait_noAge_Males <- DNAmFitnessModels$Gait_noAge_Males\n", "Grip_noAge_Females <- DNAmFitnessModels$Grip_noAge_Females\n", "Grip_noAge_Males <- DNAmFitnessModels$Grip_noAge_Males\n", "VO2maxModel <- DNAmFitnessModels$VO2maxModel\n", "write.csv(Gait_noAge_Females, \"Gait_noAge_Females.csv\")\n", "write.csv(Gait_noAge_Males, \"Gait_noAge_Males.csv\")\n", "write.csv(Grip_noAge_Females, \"Grip_noAge_Females.csv\")\n", "write.csv(Grip_noAge_Males, \"Grip_noAge_Males.csv\")\n", "write.csv(VO2maxModel, \"VO2maxModel.csv\")" ] }, { "cell_type": "code", "execution_count": 24, "id": "f1965587-a6ac-47ce-bd7a-bb98ca1d91b5", "metadata": { "execution": { "iopub.execute_input": "2025-04-07T17:50:25.659496Z", "iopub.status.busy": "2025-04-07T17:50:25.659322Z", "iopub.status.idle": "2025-04-07T17:50:27.279878Z", "shell.execute_reply": "2025-04-07T17:50:27.279562Z" } }, "outputs": [ { "data": { "text/plain": [ "0" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "os.system(\"Rscript download.r\")" ] }, { "cell_type": "markdown", "id": "5035b180-3d1b-4432-8ebe-b9c92bd93a7f", "metadata": {}, "source": [ "## Load features" ] }, { "cell_type": "markdown", "id": "d8025ed7-0013-419b-8cb5-1a2db98f9eba", "metadata": {}, "source": [ "#### From JSON file" ] }, { "cell_type": "code", "execution_count": 25, "id": "c0b63afa", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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termstepestimatelambdadev.ratiofeaturecoefficient
1(Intercept)143.0197541.0890440.479362(Intercept)43.019754
2cg167366301-0.8928441.0890440.479362cg16736630-0.892844
3cg2622430511.6379541.0890440.479362cg262243051.637954
4cg2082299011.5265461.0890440.479362cg208229901.526546
5cg037722531-0.3347031.0890440.479362cg03772253-0.334703
\n", "
" ], "text/plain": [ " term step estimate lambda dev.ratio feature coefficient\n", "1 (Intercept) 1 43.019754 1.089044 0.479362 (Intercept) 43.019754\n", "2 cg16736630 1 -0.892844 1.089044 0.479362 cg16736630 -0.892844\n", "3 cg26224305 1 1.637954 1.089044 0.479362 cg26224305 1.637954\n", "4 cg20822990 1 1.526546 1.089044 0.479362 cg20822990 1.526546\n", "5 cg03772253 1 -0.334703 1.089044 0.479362 cg03772253 -0.334703" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv('Grip_noAge_Males.csv', index_col=0)\n", "df['feature'] = df['term']\n", "df['coefficient'] = df['estimate']\n", "model.features = df['feature'][1:].tolist()\n", "df.head()" ] }, { "cell_type": "markdown", "id": "ee6d8fa0-4767-4c45-9717-eb1c95e2ddc0", "metadata": {}, "source": [ "## Load weights into base model" ] }, { "cell_type": "code", "execution_count": 27, "id": "8d58875d", "metadata": {}, "outputs": [], "source": [ "weights = torch.tensor(df['coefficient'][1:].tolist()).unsqueeze(0)\n", "intercept = torch.tensor([df['coefficient'].iloc[0]])" ] }, { "cell_type": "markdown", "id": "69901c2b-9584-4de3-a642-ddb6b43d923a", "metadata": {}, "source": [ "#### Linear model" ] }, { "cell_type": "code", "execution_count": 28, "id": "5fb10110-a89a-4caa-a62a-59899ebccd23", "metadata": { "execution": { "iopub.execute_input": "2025-04-07T17:50:27.292804Z", "iopub.status.busy": "2025-04-07T17:50:27.292717Z", "iopub.status.idle": "2025-04-07T17:50:27.302996Z", "shell.execute_reply": "2025-04-07T17:50:27.302732Z" } }, "outputs": [], "source": [ "base_model = pya.models.LinearModel(input_dim=len(model.features))\n", "\n", "base_model.linear.weight.data = weights.float()\n", "base_model.linear.bias.data = intercept.float()\n", "\n", "model.base_model = base_model" ] }, { "cell_type": "markdown", "id": "ad8b4c1d-9d57-48b7-9a30-bcfea7b747b1", "metadata": {}, "source": [ "## Load reference values" ] }, { "cell_type": "markdown", "id": "f7fdae64-096a-4640-ade7-6a17b78a01d5", "metadata": {}, "source": [ "#### From CSV file" ] }, { "cell_type": "code", "execution_count": 29, "id": "e1dc004f-06b7-4e24-a937-00736e93765f", "metadata": { "execution": { "iopub.execute_input": "2025-04-07T17:50:27.308644Z", "iopub.status.busy": "2025-04-07T17:50:27.308553Z", "iopub.status.idle": "2025-04-07T17:50:27.323332Z", "shell.execute_reply": "2025-04-07T17:50:27.323061Z" } }, "outputs": [], "source": [ "reference_df = pd.read_csv('MaleMedians.csv', index_col=0)\n", "model.reference_values = list(reference_df.loc[1, model.features])" ] }, { "cell_type": "markdown", "id": "af3bcf7b-74a8-4d21-9ccb-4de0c2b0516b", "metadata": {}, "source": [ "## Load preprocess and postprocess objects" ] }, { "cell_type": "code", "execution_count": 30, "id": "79a1b3a2-00f1-42b1-9fcd-f919343391d7", "metadata": { "execution": { "iopub.execute_input": "2025-04-07T17:50:27.324794Z", "iopub.status.busy": "2025-04-07T17:50:27.324706Z", "iopub.status.idle": "2025-04-07T17:50:27.326219Z", "shell.execute_reply": "2025-04-07T17:50:27.325910Z" } }, "outputs": [], "source": [ "model.preprocess_name = None\n", "model.preprocess_dependencies = None" ] }, { "cell_type": "code", "execution_count": 31, "id": "ff4a21cb-cf41-44dc-9ed1-95cf8aa15772", "metadata": { "execution": { "iopub.execute_input": "2025-04-07T17:50:27.327714Z", "iopub.status.busy": "2025-04-07T17:50:27.327614Z", "iopub.status.idle": "2025-04-07T17:50:27.329350Z", "shell.execute_reply": "2025-04-07T17:50:27.329050Z" } }, "outputs": [], "source": [ "model.postprocess_name = None\n", "model.postprocess_dependencies = None" ] }, { "cell_type": "markdown", "id": "86e3d6b1-e67e-4f3d-bd39-0ebec5726c3c", "metadata": {}, "source": [ "## Check all clock parameters" ] }, { "cell_type": "code", "execution_count": 32, "id": "2168355c-47d9-475d-b816-49f65e74887c", "metadata": { "execution": { "iopub.execute_input": "2025-04-07T17:50:27.330820Z", "iopub.status.busy": "2025-04-07T17:50:27.330730Z", "iopub.status.idle": "2025-04-07T17:50:27.336089Z", "shell.execute_reply": "2025-04-07T17:50:27.335809Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "%==================================== Model Details ====================================%\n", "Model Attributes:\n", "\n", "training: True\n", "metadata: {'approved_by_author': '⌛',\n", " 'citation': 'McGreevy, Kristen M., et al. \"DNAmFitAge: biological age '\n", " 'indicator incorporating physical fitness.\" Aging (Albany NY) '\n", " '15.10 (2023): 3904.',\n", " 'clock_name': 'dnamfitagegripm',\n", " 'data_type': 'methylation',\n", " 'doi': 'https://doi.org/10.18632/aging.204538',\n", " 'notes': 'Reference values is mean between male and female training medians',\n", " 'research_only': None,\n", " 'species': 'Homo sapiens',\n", " 'version': None,\n", " 'year': 2023}\n", "reference_values: [0.510108256486195, 0.0174923251251838, 0.282280857535263, 0.0528901870575477, 0.597656383314558, 0.248233103255157, 0.852384268050395, 0.941476851346533, 0.734221050058483, 0.190121263555547, 0.31940105422856, 0.814431141757476, 0.19176405807589, 0.0479152480674275, 0.953607748943679, 0.492922877265799, 0.185486398346001, 0.0841236306449339, 0.621103094526097, 0.87518246696653, 0.663858799750913, 0.556449656236505, 0.623096676798486, 0.345448583792196, 0.527682477978777, 0.212507683830467, 0.485466327170399, 0.846662732645798, 0.851402366282247, 0.839862295544553]... [Total elements: 93]\n", "preprocess_name: None\n", "preprocess_dependencies: None\n", "postprocess_name: None\n", "postprocess_dependencies: None\n", "features: ['cg16736630', 'cg26224305', 'cg20822990', 'cg03772253', 'cg25410668', 'cg06335143', 'cg24152080', 'cg13783177', 'cg01233392', 'cg09789768', 'cg22431262', 'cg01791778', 'cg19516340', 'cg26248645', 'cg17082856', 'cg06639320', 'cg26158023', 'cg14507891', 'cg13287247', 'cg01552919', 'cg17315281', 'cg21397124', 'cg14069287', 'cg03614721', 'cg12655768', 'cg08206318', 'cg23500537', 'cg26960988', 'cg15971074', 'cg23069046']... [Total elements: 93]\n", "base_model_features: None\n", "\n", "%==================================== Model Details ====================================%\n", "Model Structure:\n", "\n", "base_model: LinearModel(\n", " (linear): Linear(in_features=93, out_features=1, bias=True)\n", ")\n", "\n", "%==================================== Model Details ====================================%\n", "Model Parameters and Weights:\n", "\n", "base_model.linear.weight: [-0.892844021320343, 1.6379542350769043, 1.5265462398529053, -0.3347032964229584, -1.9029316902160645, -0.2647155225276947, -6.30814266204834, 14.954833984375, 1.178484320640564, 3.5211784839630127, -0.1861504316329956, -1.6255935430526733, 4.550158977508545, -1.587499976158142, -0.449662446975708, -8.599822998046875, 25.895660400390625, 4.368823051452637, 3.992393970489502, 1.3252184391021729, -2.2360410690307617, -0.6896253228187561, 1.5932470560073853, 1.5443568229675293, -0.7052236795425415, -3.0787854194641113, -0.2242996096611023, -0.23673297464847565, 2.1442930698394775, -0.3954241871833801]... [Tensor of shape torch.Size([1, 93])]\n", "base_model.linear.bias: tensor([43.0198])\n", "\n", "%==================================== Model Details ====================================%\n", "\n" ] } ], "source": [ "pya.utils.print_model_details(model)" ] }, { "cell_type": "markdown", "id": "986d0262-e0c7-4036-b687-dee53ba392fb", "metadata": {}, "source": [ "## Basic test" ] }, { "cell_type": "code", "execution_count": 33, "id": "352cffb0-c5a8-4c82-8f61-fce35baf5a22", "metadata": { "execution": { "iopub.execute_input": "2025-04-07T17:50:27.337690Z", "iopub.status.busy": "2025-04-07T17:50:27.337490Z", "iopub.status.idle": "2025-04-07T17:50:27.346106Z", "shell.execute_reply": "2025-04-07T17:50:27.345820Z" } }, "outputs": [ { "data": { "text/plain": [ "tensor([[ 41.6172],\n", " [ 21.7346],\n", " [ 54.7012],\n", " [ 53.8505],\n", " [ 71.7095],\n", " [203.5236],\n", " [ 58.8656],\n", " [ 38.5258],\n", " [ 34.2558],\n", " [109.6429]], dtype=torch.float64, grad_fn=)" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "torch.manual_seed(42)\n", "input = torch.randn(10, len(model.features), dtype=float)\n", "model.eval()\n", "model.to(float)\n", "pred = model(input)\n", "pred" ] }, { "cell_type": "markdown", "id": "fe8299d7-9285-4e22-82fd-b664434b4369", "metadata": {}, "source": [ "## Save torch model" ] }, { "cell_type": "code", "execution_count": 34, "id": "0c3a2d80-1b5f-458a-926c-cbc0aa9416e1", "metadata": { "execution": { "iopub.execute_input": "2025-04-07T17:50:27.347459Z", "iopub.status.busy": "2025-04-07T17:50:27.347364Z", "iopub.status.idle": "2025-04-07T17:50:27.350847Z", "shell.execute_reply": "2025-04-07T17:50:27.350573Z" } }, "outputs": [], "source": [ "torch.save(model, f\"../weights/{model.metadata['clock_name']}.pt\")" ] }, { "cell_type": "markdown", "id": "bac6257b-8d08-4a90-8d0b-7f745dc11ac1", "metadata": {}, "source": [ "## Clear directory\n", "" ] }, { "cell_type": "code", "execution_count": 35, "id": "11aeaa70-44c0-42f9-86d7-740e3849a7a6", "metadata": { "execution": { "iopub.execute_input": "2025-04-07T17:50:27.352136Z", "iopub.status.busy": "2025-04-07T17:50:27.352040Z", "iopub.status.idle": "2025-04-07T17:50:27.360420Z", "shell.execute_reply": "2025-04-07T17:50:27.360132Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Deleted file: Grip_noAge_Females.csv\n", "Deleted file: Grip_noAge_Males.csv\n", "Deleted file: Gait_noAge_Females.csv\n", "Deleted file: VO2maxModel.csv\n", "Deleted file: AllCpGs.json\n", "Deleted file: Gait_noAge_Males.csv\n", "Deleted folder: DNAmFitAge\n", "Deleted file: download.r\n", "Deleted file: FemaleMedians.csv\n", "Deleted file: MaleMedians.csv\n" ] } ], "source": [ "# Function to remove a folder and all its contents\n", "def remove_folder(path):\n", " try:\n", " shutil.rmtree(path)\n", " print(f\"Deleted folder: {path}\")\n", " except Exception as e:\n", " print(f\"Error deleting folder {path}: {e}\")\n", "\n", "# Get a list of all files and folders in the current directory\n", "all_items = os.listdir('.')\n", "\n", "# Loop through the items\n", "for item in all_items:\n", " # Check if it's a file and does not end with .ipynb\n", " if os.path.isfile(item) and not item.endswith('.ipynb'):\n", " os.remove(item)\n", " print(f\"Deleted file: {item}\")\n", " # Check if it's a folder\n", " elif os.path.isdir(item):\n", " remove_folder(item)" ] } ], "metadata": { "kernelspec": { "display_name": "research", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.7" } }, "nbformat": 4, "nbformat_minor": 5 }