From a26111d66922dd0e01337bca9da352044f8e727b Mon Sep 17 00:00:00 2001 From: Lucas Camillo Date: Sat, 29 Jun 2024 16:02:09 +0100 Subject: [PATCH] Bump to v0.1.7 --- clocks/notebooks/stoch.ipynb | 582 +++++++++++ clocks/notebooks/stocp.ipynb | 582 +++++++++++ clocks/notebooks/stocz.ipynb | 582 +++++++++++ docs/_static/clock_glossary.csv | 3 + docs/source/clock_implementation.rst | 3 + docs/source/clock_notebooks/stoch.ipynb | 582 +++++++++++ docs/source/clock_notebooks/stocp.ipynb | 582 +++++++++++ docs/source/clock_notebooks/stocz.ipynb | 582 +++++++++++ docs/source/index.rst | 6 +- docs/source/installation.rst | 6 +- poetry.lock | 1237 +++++++++++------------ pyaging/__init__.py | 2 +- pyaging/models/_models.py | 32 + pyaging/preprocess/_preprocess.py | 19 +- pyproject.toml | 4 +- tests/predict/test_gold_standard.py | 3 + 16 files changed, 4153 insertions(+), 654 deletions(-) create mode 100644 clocks/notebooks/stoch.ipynb create mode 100644 clocks/notebooks/stocp.ipynb create mode 100644 clocks/notebooks/stocz.ipynb create mode 100644 docs/source/clock_notebooks/stoch.ipynb create mode 100644 docs/source/clock_notebooks/stocp.ipynb create mode 100644 docs/source/clock_notebooks/stocz.ipynb diff --git a/clocks/notebooks/stoch.ipynb b/clocks/notebooks/stoch.ipynb new file mode 100644 index 0000000..477ac91 --- /dev/null +++ b/clocks/notebooks/stoch.ipynb @@ -0,0 +1,582 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "2f04eee0-5928-4e74-a754-6dc2e528810c", + "metadata": {}, + "source": [ + "# StocH" + ] + }, + { + "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": 1, + "id": "4adfb4de-cd79-4913-a1af-9e23e9e236c9", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import inspect\n", + "import shutil\n", + "import json\n", + "import torch\n", + "import pandas as pd\n", + "import pyaging as pya" + ] + }, + { + "cell_type": "markdown", + "id": "145082e5-ced4-47ae-88c0-cb69773e3c5a", + "metadata": {}, + "source": [ + "## Instantiate model class" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "8aa77372-7ed3-4da7-abc9-d30372106139", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "class StocH(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.StocH)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "78536494-f1d9-44de-8583-c89a310d2307", + "metadata": {}, + "outputs": [], + "source": [ + "model = pya.models.StocH()" + ] + }, + { + "cell_type": "markdown", + "id": "51f8615e-01fa-4aa5-b196-3ee2b35d261c", + "metadata": {}, + "source": [ + "## Define clock metadata" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "6601da9e-8adc-44ee-9308-75e3cd31b816", + "metadata": {}, + "outputs": [], + "source": [ + "model.metadata[\"clock_name\"] = 'stoch'\n", + "model.metadata[\"data_type\"] = 'methylation'\n", + "model.metadata[\"species\"] = 'Homo sapiens'\n", + "model.metadata[\"year\"] = 2024\n", + "model.metadata[\"approved_by_author\"] = '⌛'\n", + "model.metadata[\"citation\"] = \"Tong, Huige, et al. \\\"Quantifying the stochastic component of epigenetic aging.\\\" Nature Aging (2024): 1-16.\"\n", + "model.metadata[\"doi\"] = \"https://doi.org/10.1038/s43587-024-00600-8\"\n", + "model.metadata[\"research_only\"] = None\n", + "model.metadata[\"notes\"] = None" + ] + }, + { + "cell_type": "markdown", + "id": "74492239-5aae-4026-9d90-6bc9c574c110", + "metadata": {}, + "source": [ + "## Download clock dependencies" + ] + }, + { + "cell_type": "markdown", + "id": "192924bd-8553-4840-85e9-3ea02b024b79", + "metadata": {}, + "source": [ + "#### Download directly with curl" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "6675770e-a5d1-40ab-93e1-775f288fed1c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "supplementary_url = \"https://figshare.com/ndownloader/files/42406308\"\n", + "supplementary_file_name = \"glmStocAll.Rd\"\n", + "os.system(f\"curl -L -o {supplementary_file_name} {supplementary_url}\")" + ] + }, + { + "cell_type": "markdown", + "id": "aeee6c5a-4e0a-4f2a-acef-d657596f453a", + "metadata": {}, + "source": [ + "#### Download from R package" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "id": "d81f2c9d-362f-43cb-ad52-012e28217164", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Overwriting download.r\n" + ] + } + ], + "source": [ + "%%writefile download.r\n", + "\n", + "options(repos = c(CRAN = \"https://cloud.r-project.org/\"))\n", + "\n", + "# Function to extract and save coefficients and intercepts\n", + "ExtractCoefficients <- function(){\n", + " load(\"glmStocALL.Rd\") # Load in stochastic clock information\n", + "\n", + " # Check the loaded object structure\n", + " if (!exists(\"glmStocALL.lo\")) {\n", + " stop(\"The object glmStocALL.lo was not found in the loaded .Rd file.\")\n", + " }\n", + "\n", + " # List to store coefficients and intercepts for each clock\n", + " coefficients_list <- list()\n", + "\n", + " for (c in 1:length(glmStocALL.lo)) {\n", + " glm.o <- glmStocALL.lo[[c]]\n", + "\n", + " # Ensure glm.o is a glmnet object\n", + " if (!inherits(glm.o, \"glmnet\")) {\n", + " warning(paste(\"Object at index\", c, \"is not a glmnet object. Skipping.\"))\n", + " next\n", + " }\n", + "\n", + " # Extract the coefficients and intercept from the final iteration\n", + " intercept <- glm.o$a0[length(glm.o$a0)]\n", + " coefficients <- as.matrix(glm.o$beta)[, length(glm.o$lambda)]\n", + "\n", + " print(length(coefficients))\n", + " print(length(rownames(coefficients)))\n", + "\n", + " # Create a data frame with feature names and coefficients\n", + " coef_df <- data.frame(\n", + " Feature = rownames(as.matrix(glm.o$beta)),\n", + " Coefficient = as.numeric(coefficients),\n", + " Intercept = rep(intercept, length(coefficients))\n", + " )\n", + " \n", + " # Save each clock's coefficients to a CSV file\n", + " write.csv(coef_df, file = paste0(\"Coefficients_Clock_\", c, \".csv\"), row.names = FALSE)\n", + " \n", + " # Append to the list\n", + " coefficients_list[[c]] <- coef_df\n", + " }\n", + "\n", + " return(coefficients_list) # Return the list for further inspection if needed\n", + "}\n", + "\n", + "# Run the function\n", + "coefficients_list <- ExtractCoefficients()" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "id": "1ce2bac8-dd33-46cb-a7b6-14a1d0976f05", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "os.system(\"Rscript download.r\")" + ] + }, + { + "cell_type": "markdown", + "id": "84930b94-fff8-4e1f-85ef-2194e8a8ee4f", + "metadata": {}, + "source": [ + "## Load features" + ] + }, + { + "cell_type": "markdown", + "id": "dbb77f97-01d4-4c63-aa40-ee24de8bc0e9", + "metadata": {}, + "source": [ + "#### From CSV file" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "id": "110a5ded-d25f-4cef-8e84-4f51210dfc26", + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_csv('Coefficients_Clock_1.csv')\n", + "df['feature'] = df['Feature']\n", + "df['coefficient'] = df['Coefficient']\n", + "model.features = df['feature'].tolist()" + ] + }, + { + "cell_type": "markdown", + "id": "f81dd175-8773-47da-864a-643f45195bf2", + "metadata": {}, + "source": [ + "## Load weights into base model" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "id": "e09b3463-4fd4-41b1-ac21-e63ddd223fe0", + "metadata": {}, + "outputs": [], + "source": [ + "weights = torch.tensor(df['coefficient'].tolist()).unsqueeze(0)\n", + "intercept = torch.tensor([df['Intercept'][0]])" + ] + }, + { + "cell_type": "markdown", + "id": "3bbfeba6-07a3-49c6-b56a-0b9c1c882167", + "metadata": {}, + "source": [ + "#### Linear model" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "id": "d7f43b99-26f2-4622-9a76-316712058877", + "metadata": {}, + "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": "b43d29e6-ec61-450c-be21-989ad9eaebb4", + "metadata": {}, + "source": [ + "## Load reference values" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "id": "ade0f4c9-2298-4fc3-bb72-d200907dd731", + "metadata": {}, + "outputs": [], + "source": [ + "model.reference_values = None" + ] + }, + { + "cell_type": "markdown", + "id": "b42caeae-f255-45aa-9f00-3bed6ef7d26f", + "metadata": {}, + "source": [ + "## Load preprocess and postprocess objects" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "id": "7a22fb20-c605-424d-8efb-7620c2c0755c", + "metadata": {}, + "outputs": [], + "source": [ + "model.preprocess_name = None\n", + "model.preprocess_dependencies = None" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "id": "ff4a21cb-cf41-44dc-9ed1-95cf8aa15772", + "metadata": {}, + "outputs": [], + "source": [ + "model.postprocess_name = None\n", + "model.postprocess_dependencies = None" + ] + }, + { + "cell_type": "markdown", + "id": "2466d7e7-5f1e-4d4a-81f4-721e56958378", + "metadata": {}, + "source": [ + "## Check all clock parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "id": "2168355c-47d9-475d-b816-49f65e74887c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "%==================================== Model Details ====================================%\n", + "Model Attributes:\n", + "\n", + "training: True\n", + "metadata: {'approved_by_author': '⌛',\n", + " 'citation': 'Tong, Huige, et al. \"Quantifying the stochastic component of '\n", + " 'epigenetic aging.\" Nature Aging (2024): 1-16.',\n", + " 'clock_name': 'stoch',\n", + " 'data_type': 'methylation',\n", + " 'doi': 'https://doi.org/10.1038/s43587-024-00600-8',\n", + " 'notes': None,\n", + " 'research_only': None,\n", + " 'species': 'Homo sapiens',\n", + " 'version': None,\n", + " 'year': 2024}\n", + "reference_values: None\n", + "preprocess_name: None\n", + "preprocess_dependencies: None\n", + "postprocess_name: None\n", + "postprocess_dependencies: None\n", + "features: ['cg00075967', 'cg00374717', 'cg00864867', 'cg00945507', 'cg01027739', 'cg01353448', 'cg01584473', 'cg01644850', 'cg01656216', 'cg01873645', 'cg01968178', 'cg02085507', 'cg02154074', 'cg02217159', 'cg02331561', 'cg02332492', 'cg02364642', 'cg02388150', 'cg02479575', 'cg02489552', 'cg02580606', 'cg02654291', 'cg02827112', 'cg02972551', 'cg03103192', 'cg03167275', 'cg03270204', 'cg03565323', 'cg03588357', 'cg03760483']... [Total elements: 353]\n", + "base_model_features: None\n", + "\n", + "%==================================== Model Details ====================================%\n", + "Model Structure:\n", + "\n", + "base_model: LinearModel(\n", + " (linear): Linear(in_features=353, out_features=1, bias=True)\n", + ")\n", + "\n", + "%==================================== Model Details ====================================%\n", + "Model Parameters and Weights:\n", + "\n", + "base_model.linear.weight: [3.585235357284546, 0.29056739807128906, 0.008150330744683743, 7.167356014251709, 0.07318803668022156, 0.14952702820301056, -0.12038026005029678, -0.6500811576843262, -0.24336983263492584, 0.24592465162277222, -8.944670844357461e-05, -0.2048512101173401, 0.3703913986682892, -1.0299664735794067, 1.7403517961502075, -0.7057934403419495, 0.5467443466186523, 5.111024856567383, 7.80165433883667, 4.5496368408203125, 0.3366563022136688, 1.6183545589447021, 0.034926775842905045, -5.03125524520874, 6.2505011558532715, -2.024871349334717, 0.145379438996315, 0.5371573567390442, -0.9422363042831421, 0.05253010243177414]... [Tensor of shape torch.Size([1, 353])]\n", + "base_model.linear.bias: tensor([59.8016])\n", + "\n", + "%==================================== Model Details ====================================%\n", + "\n" + ] + } + ], + "source": [ + "pya.utils.print_model_details(model)" + ] + }, + { + "cell_type": "markdown", + "id": "78e2387c-6de1-4073-b4a5-b8b8e430abdb", + "metadata": {}, + "source": [ + "## Basic test" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "id": "936b9877-d076-4ced-99aa-e8d4c58c5caf", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([[ 18.5127],\n", + " [146.4057],\n", + " [166.5776],\n", + " [ 85.6306],\n", + " [379.5047],\n", + " [-99.5351],\n", + " [ 42.5195],\n", + " [234.2738],\n", + " [213.7955],\n", + " [ 62.3945]], dtype=torch.float64, grad_fn=)" + ] + }, + "execution_count": 72, + "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": "75642087-b880-429b-b19b-c1eaea33614f", + "metadata": {}, + "source": [ + "## Save torch model" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "id": "5ef2fa8d-c80b-4fdd-8555-79c0d541788e", + "metadata": {}, + "outputs": [], + "source": [ + "torch.save(model, f\"../weights/{model.metadata['clock_name']}.pt\")" + ] + }, + { + "cell_type": "markdown", + "id": "f65d9bb5-5baa-486b-bf25-f68f542bd5b7", + "metadata": {}, + "source": [ + "## Clear directory\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "id": "11aeaa70-44c0-42f9-86d7-740e3849a7a6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Deleted file: glmStocAll.Rd\n", + "Deleted file: download.r\n", + "Deleted folder: .ipynb_checkpoints\n", + "Deleted file: Coefficients_Clock_3.csv\n", + "Deleted file: Coefficients_Clock_2.csv\n", + "Deleted file: Coefficients_Clock_1.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": "Python 3", + "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": "3.9.17" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/clocks/notebooks/stocp.ipynb b/clocks/notebooks/stocp.ipynb new file mode 100644 index 0000000..a9f3fcd --- /dev/null +++ b/clocks/notebooks/stocp.ipynb @@ -0,0 +1,582 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "2f04eee0-5928-4e74-a754-6dc2e528810c", + "metadata": {}, + "source": [ + "# StocP" + ] + }, + { + "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": 1, + "id": "4adfb4de-cd79-4913-a1af-9e23e9e236c9", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import inspect\n", + "import shutil\n", + "import json\n", + "import torch\n", + "import pandas as pd\n", + "import pyaging as pya" + ] + }, + { + "cell_type": "markdown", + "id": "145082e5-ced4-47ae-88c0-cb69773e3c5a", + "metadata": {}, + "source": [ + "## Instantiate model class" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "8aa77372-7ed3-4da7-abc9-d30372106139", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "class StocP(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.StocP)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "78536494-f1d9-44de-8583-c89a310d2307", + "metadata": {}, + "outputs": [], + "source": [ + "model = pya.models.StocP()" + ] + }, + { + "cell_type": "markdown", + "id": "51f8615e-01fa-4aa5-b196-3ee2b35d261c", + "metadata": {}, + "source": [ + "## Define clock metadata" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "6601da9e-8adc-44ee-9308-75e3cd31b816", + "metadata": {}, + "outputs": [], + "source": [ + "model.metadata[\"clock_name\"] = 'stocp'\n", + "model.metadata[\"data_type\"] = 'methylation'\n", + "model.metadata[\"species\"] = 'Homo sapiens'\n", + "model.metadata[\"year\"] = 2024\n", + "model.metadata[\"approved_by_author\"] = '⌛'\n", + "model.metadata[\"citation\"] = \"Tong, Huige, et al. \\\"Quantifying the stochastic component of epigenetic aging.\\\" Nature Aging (2024): 1-16.\"\n", + "model.metadata[\"doi\"] = \"https://doi.org/10.1038/s43587-024-00600-8\"\n", + "model.metadata[\"research_only\"] = None\n", + "model.metadata[\"notes\"] = None" + ] + }, + { + "cell_type": "markdown", + "id": "74492239-5aae-4026-9d90-6bc9c574c110", + "metadata": {}, + "source": [ + "## Download clock dependencies" + ] + }, + { + "cell_type": "markdown", + "id": "192924bd-8553-4840-85e9-3ea02b024b79", + "metadata": {}, + "source": [ + "#### Download directly with curl" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "6675770e-a5d1-40ab-93e1-775f288fed1c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "supplementary_url = \"https://figshare.com/ndownloader/files/42406308\"\n", + "supplementary_file_name = \"glmStocAll.Rd\"\n", + "os.system(f\"curl -L -o {supplementary_file_name} {supplementary_url}\")" + ] + }, + { + "cell_type": "markdown", + "id": "aeee6c5a-4e0a-4f2a-acef-d657596f453a", + "metadata": {}, + "source": [ + "#### Download from R package" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "d81f2c9d-362f-43cb-ad52-012e28217164", + "metadata": {}, + "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", + "\n", + "# Function to extract and save coefficients and intercepts\n", + "ExtractCoefficients <- function(){\n", + " load(\"glmStocALL.Rd\") # Load in stochastic clock information\n", + "\n", + " # Check the loaded object structure\n", + " if (!exists(\"glmStocALL.lo\")) {\n", + " stop(\"The object glmStocALL.lo was not found in the loaded .Rd file.\")\n", + " }\n", + "\n", + " # List to store coefficients and intercepts for each clock\n", + " coefficients_list <- list()\n", + "\n", + " for (c in 1:length(glmStocALL.lo)) {\n", + " glm.o <- glmStocALL.lo[[c]]\n", + "\n", + " # Ensure glm.o is a glmnet object\n", + " if (!inherits(glm.o, \"glmnet\")) {\n", + " warning(paste(\"Object at index\", c, \"is not a glmnet object. Skipping.\"))\n", + " next\n", + " }\n", + "\n", + " # Extract the coefficients and intercept from the final iteration\n", + " intercept <- glm.o$a0[length(glm.o$a0)]\n", + " coefficients <- as.matrix(glm.o$beta)[, length(glm.o$lambda)]\n", + "\n", + " print(length(coefficients))\n", + " print(length(rownames(coefficients)))\n", + "\n", + " # Create a data frame with feature names and coefficients\n", + " coef_df <- data.frame(\n", + " Feature = rownames(as.matrix(glm.o$beta)),\n", + " Coefficient = as.numeric(coefficients),\n", + " Intercept = rep(intercept, length(coefficients))\n", + " )\n", + " \n", + " # Save each clock's coefficients to a CSV file\n", + " write.csv(coef_df, file = paste0(\"Coefficients_Clock_\", c, \".csv\"), row.names = FALSE)\n", + " \n", + " # Append to the list\n", + " coefficients_list[[c]] <- coef_df\n", + " }\n", + "\n", + " return(coefficients_list) # Return the list for further inspection if needed\n", + "}\n", + "\n", + "# Run the function\n", + "coefficients_list <- ExtractCoefficients()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "1ce2bac8-dd33-46cb-a7b6-14a1d0976f05", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "os.system(\"Rscript download.r\")" + ] + }, + { + "cell_type": "markdown", + "id": "84930b94-fff8-4e1f-85ef-2194e8a8ee4f", + "metadata": {}, + "source": [ + "## Load features" + ] + }, + { + "cell_type": "markdown", + "id": "dbb77f97-01d4-4c63-aa40-ee24de8bc0e9", + "metadata": {}, + "source": [ + "#### From CSV file" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "110a5ded-d25f-4cef-8e84-4f51210dfc26", + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_csv('Coefficients_Clock_3.csv')\n", + "df['feature'] = df['Feature']\n", + "df['coefficient'] = df['Coefficient']\n", + "model.features = df['feature'].tolist()" + ] + }, + { + "cell_type": "markdown", + "id": "f81dd175-8773-47da-864a-643f45195bf2", + "metadata": {}, + "source": [ + "## Load weights into base model" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "e09b3463-4fd4-41b1-ac21-e63ddd223fe0", + "metadata": {}, + "outputs": [], + "source": [ + "weights = torch.tensor(df['coefficient'].tolist()).unsqueeze(0)\n", + "intercept = torch.tensor([df['Intercept'][0]])" + ] + }, + { + "cell_type": "markdown", + "id": "3bbfeba6-07a3-49c6-b56a-0b9c1c882167", + "metadata": {}, + "source": [ + "#### Linear model" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "d7f43b99-26f2-4622-9a76-316712058877", + "metadata": {}, + "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": "b43d29e6-ec61-450c-be21-989ad9eaebb4", + "metadata": {}, + "source": [ + "## Load reference values" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "ade0f4c9-2298-4fc3-bb72-d200907dd731", + "metadata": {}, + "outputs": [], + "source": [ + "model.reference_values = None" + ] + }, + { + "cell_type": "markdown", + "id": "b42caeae-f255-45aa-9f00-3bed6ef7d26f", + "metadata": {}, + "source": [ + "## Load preprocess and postprocess objects" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "7a22fb20-c605-424d-8efb-7620c2c0755c", + "metadata": {}, + "outputs": [], + "source": [ + "model.preprocess_name = None\n", + "model.preprocess_dependencies = None" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "ff4a21cb-cf41-44dc-9ed1-95cf8aa15772", + "metadata": {}, + "outputs": [], + "source": [ + "model.postprocess_name = None\n", + "model.postprocess_dependencies = None" + ] + }, + { + "cell_type": "markdown", + "id": "2466d7e7-5f1e-4d4a-81f4-721e56958378", + "metadata": {}, + "source": [ + "## Check all clock parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "2168355c-47d9-475d-b816-49f65e74887c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "%==================================== Model Details ====================================%\n", + "Model Attributes:\n", + "\n", + "training: True\n", + "metadata: {'approved_by_author': '⌛',\n", + " 'citation': 'Tong, Huige, et al. \"Quantifying the stochastic component of '\n", + " 'epigenetic aging.\" Nature Aging (2024): 1-16.',\n", + " 'clock_name': 'stocp',\n", + " 'data_type': 'methylation',\n", + " 'doi': 'https://doi.org/10.1038/s43587-024-00600-8',\n", + " 'notes': None,\n", + " 'research_only': None,\n", + " 'species': 'Homo sapiens',\n", + " 'version': None,\n", + " 'year': 2024}\n", + "reference_values: None\n", + "preprocess_name: None\n", + "preprocess_dependencies: None\n", + "postprocess_name: None\n", + "postprocess_dependencies: None\n", + "features: ['cg15611364', 'cg17605084', 'cg26382071', 'cg12743894', 'cg19287114', 'cg12985418', 'cg19398783', 'cg15963417', 'cg27187881', 'cg09892203', 'cg00943950', 'cg18996776', 'cg16340918', 'cg23832061', 'cg22736354', 'cg04084157', 'cg07265300', 'cg02503970', 'cg11426590', 'cg23710218', 'cg02802055', 'cg13631913', 'cg06493994', 'cg24304712', 'cg01131735', 'cg24208206', 'cg01930621', 'cg19104072', 'cg07850604', 'cg27493997']... [Total elements: 513]\n", + "base_model_features: None\n", + "\n", + "%==================================== Model Details ====================================%\n", + "Model Structure:\n", + "\n", + "base_model: LinearModel(\n", + " (linear): Linear(in_features=513, out_features=1, bias=True)\n", + ")\n", + "\n", + "%==================================== Model Details ====================================%\n", + "Model Parameters and Weights:\n", + "\n", + "base_model.linear.weight: [-0.26072555780410767, -0.4015197157859802, 0.04175494238734245, 0.08702494204044342, -0.13476140797138214, 0.016701284795999527, 0.07106481492519379, 0.23266158998012543, -0.01433953084051609, -0.9317121505737305, -0.005014453548938036, 0.4903888404369354, -1.6926921606063843, -0.523848831653595, 4.922214031219482, 1.7051185369491577, -0.011739220470190048, -0.7235432863235474, -0.06498837471008301, -0.6380035281181335, 0.22486011683940887, 0.013822232373058796, 10.945517539978027, 0.04070192202925682, -0.028888650238513947, 0.16922490298748016, 0.6264103651046753, -0.018560025840997696, -1.6304043531417847, 0.083981454372406]... [Tensor of shape torch.Size([1, 513])]\n", + "base_model.linear.bias: tensor([92.8311])\n", + "\n", + "%==================================== Model Details ====================================%\n", + "\n" + ] + } + ], + "source": [ + "pya.utils.print_model_details(model)" + ] + }, + { + "cell_type": "markdown", + "id": "78e2387c-6de1-4073-b4a5-b8b8e430abdb", + "metadata": {}, + "source": [ + "## Basic test" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "936b9877-d076-4ced-99aa-e8d4c58c5caf", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([[ 272.9167],\n", + " [ -8.4764],\n", + " [-152.8284],\n", + " [ 167.9197],\n", + " [ -3.3252],\n", + " [ 154.7005],\n", + " [ 2.9908],\n", + " [ 57.1228],\n", + " [ 123.9416],\n", + " [ 83.7115]], dtype=torch.float64, grad_fn=)" + ] + }, + "execution_count": 15, + "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": "75642087-b880-429b-b19b-c1eaea33614f", + "metadata": {}, + "source": [ + "## Save torch model" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "5ef2fa8d-c80b-4fdd-8555-79c0d541788e", + "metadata": {}, + "outputs": [], + "source": [ + "torch.save(model, f\"../weights/{model.metadata['clock_name']}.pt\")" + ] + }, + { + "cell_type": "markdown", + "id": "f65d9bb5-5baa-486b-bf25-f68f542bd5b7", + "metadata": {}, + "source": [ + "## Clear directory\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "11aeaa70-44c0-42f9-86d7-740e3849a7a6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Deleted file: glmStocAll.Rd\n", + "Deleted file: download.r\n", + "Deleted folder: .ipynb_checkpoints\n", + "Deleted file: Coefficients_Clock_3.csv\n", + "Deleted file: Coefficients_Clock_2.csv\n", + "Deleted file: Coefficients_Clock_1.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": "Python 3", + "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": "3.9.17" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/clocks/notebooks/stocz.ipynb b/clocks/notebooks/stocz.ipynb new file mode 100644 index 0000000..a2b108e --- /dev/null +++ b/clocks/notebooks/stocz.ipynb @@ -0,0 +1,582 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "2f04eee0-5928-4e74-a754-6dc2e528810c", + "metadata": {}, + "source": [ + "# StocZ" + ] + }, + { + "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": 1, + "id": "4adfb4de-cd79-4913-a1af-9e23e9e236c9", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import inspect\n", + "import shutil\n", + "import json\n", + "import torch\n", + "import pandas as pd\n", + "import pyaging as pya" + ] + }, + { + "cell_type": "markdown", + "id": "145082e5-ced4-47ae-88c0-cb69773e3c5a", + "metadata": {}, + "source": [ + "## Instantiate model class" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "8aa77372-7ed3-4da7-abc9-d30372106139", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "class StocZ(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.StocZ)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "78536494-f1d9-44de-8583-c89a310d2307", + "metadata": {}, + "outputs": [], + "source": [ + "model = pya.models.StocZ()" + ] + }, + { + "cell_type": "markdown", + "id": "51f8615e-01fa-4aa5-b196-3ee2b35d261c", + "metadata": {}, + "source": [ + "## Define clock metadata" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "6601da9e-8adc-44ee-9308-75e3cd31b816", + "metadata": {}, + "outputs": [], + "source": [ + "model.metadata[\"clock_name\"] = 'stocz'\n", + "model.metadata[\"data_type\"] = 'methylation'\n", + "model.metadata[\"species\"] = 'Homo sapiens'\n", + "model.metadata[\"year\"] = 2024\n", + "model.metadata[\"approved_by_author\"] = '⌛'\n", + "model.metadata[\"citation\"] = \"Tong, Huige, et al. \\\"Quantifying the stochastic component of epigenetic aging.\\\" Nature Aging (2024): 1-16.\"\n", + "model.metadata[\"doi\"] = \"https://doi.org/10.1038/s43587-024-00600-8\"\n", + "model.metadata[\"research_only\"] = None\n", + "model.metadata[\"notes\"] = None" + ] + }, + { + "cell_type": "markdown", + "id": "74492239-5aae-4026-9d90-6bc9c574c110", + "metadata": {}, + "source": [ + "## Download clock dependencies" + ] + }, + { + "cell_type": "markdown", + "id": "192924bd-8553-4840-85e9-3ea02b024b79", + "metadata": {}, + "source": [ + "#### Download directly with curl" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "6675770e-a5d1-40ab-93e1-775f288fed1c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "supplementary_url = \"https://figshare.com/ndownloader/files/42406308\"\n", + "supplementary_file_name = \"glmStocAll.Rd\"\n", + "os.system(f\"curl -L -o {supplementary_file_name} {supplementary_url}\")" + ] + }, + { + "cell_type": "markdown", + "id": "aeee6c5a-4e0a-4f2a-acef-d657596f453a", + "metadata": {}, + "source": [ + "#### Download from R package" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "d81f2c9d-362f-43cb-ad52-012e28217164", + "metadata": {}, + "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", + "\n", + "# Function to extract and save coefficients and intercepts\n", + "ExtractCoefficients <- function(){\n", + " load(\"glmStocALL.Rd\") # Load in stochastic clock information\n", + "\n", + " # Check the loaded object structure\n", + " if (!exists(\"glmStocALL.lo\")) {\n", + " stop(\"The object glmStocALL.lo was not found in the loaded .Rd file.\")\n", + " }\n", + "\n", + " # List to store coefficients and intercepts for each clock\n", + " coefficients_list <- list()\n", + "\n", + " for (c in 1:length(glmStocALL.lo)) {\n", + " glm.o <- glmStocALL.lo[[c]]\n", + "\n", + " # Ensure glm.o is a glmnet object\n", + " if (!inherits(glm.o, \"glmnet\")) {\n", + " warning(paste(\"Object at index\", c, \"is not a glmnet object. Skipping.\"))\n", + " next\n", + " }\n", + "\n", + " # Extract the coefficients and intercept from the final iteration\n", + " intercept <- glm.o$a0[length(glm.o$a0)]\n", + " coefficients <- as.matrix(glm.o$beta)[, length(glm.o$lambda)]\n", + "\n", + " print(length(coefficients))\n", + " print(length(rownames(coefficients)))\n", + "\n", + " # Create a data frame with feature names and coefficients\n", + " coef_df <- data.frame(\n", + " Feature = rownames(as.matrix(glm.o$beta)),\n", + " Coefficient = as.numeric(coefficients),\n", + " Intercept = rep(intercept, length(coefficients))\n", + " )\n", + " \n", + " # Save each clock's coefficients to a CSV file\n", + " write.csv(coef_df, file = paste0(\"Coefficients_Clock_\", c, \".csv\"), row.names = FALSE)\n", + " \n", + " # Append to the list\n", + " coefficients_list[[c]] <- coef_df\n", + " }\n", + "\n", + " return(coefficients_list) # Return the list for further inspection if needed\n", + "}\n", + "\n", + "# Run the function\n", + "coefficients_list <- ExtractCoefficients()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "1ce2bac8-dd33-46cb-a7b6-14a1d0976f05", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "os.system(\"Rscript download.r\")" + ] + }, + { + "cell_type": "markdown", + "id": "84930b94-fff8-4e1f-85ef-2194e8a8ee4f", + "metadata": {}, + "source": [ + "## Load features" + ] + }, + { + "cell_type": "markdown", + "id": "dbb77f97-01d4-4c63-aa40-ee24de8bc0e9", + "metadata": {}, + "source": [ + "#### From CSV file" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "110a5ded-d25f-4cef-8e84-4f51210dfc26", + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_csv('Coefficients_Clock_2.csv')\n", + "df['feature'] = df['Feature']\n", + "df['coefficient'] = df['Coefficient']\n", + "model.features = df['feature'].tolist()" + ] + }, + { + "cell_type": "markdown", + "id": "f81dd175-8773-47da-864a-643f45195bf2", + "metadata": {}, + "source": [ + "## Load weights into base model" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "e09b3463-4fd4-41b1-ac21-e63ddd223fe0", + "metadata": {}, + "outputs": [], + "source": [ + "weights = torch.tensor(df['coefficient'].tolist()).unsqueeze(0)\n", + "intercept = torch.tensor([df['Intercept'][0]])" + ] + }, + { + "cell_type": "markdown", + "id": "3bbfeba6-07a3-49c6-b56a-0b9c1c882167", + "metadata": {}, + "source": [ + "#### Linear model" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "d7f43b99-26f2-4622-9a76-316712058877", + "metadata": {}, + "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": "b43d29e6-ec61-450c-be21-989ad9eaebb4", + "metadata": {}, + "source": [ + "## Load reference values" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "ade0f4c9-2298-4fc3-bb72-d200907dd731", + "metadata": {}, + "outputs": [], + "source": [ + "model.reference_values = None" + ] + }, + { + "cell_type": "markdown", + "id": "b42caeae-f255-45aa-9f00-3bed6ef7d26f", + "metadata": {}, + "source": [ + "## Load preprocess and postprocess objects" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "7a22fb20-c605-424d-8efb-7620c2c0755c", + "metadata": {}, + "outputs": [], + "source": [ + "model.preprocess_name = None\n", + "model.preprocess_dependencies = None" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "ff4a21cb-cf41-44dc-9ed1-95cf8aa15772", + "metadata": {}, + "outputs": [], + "source": [ + "model.postprocess_name = None\n", + "model.postprocess_dependencies = None" + ] + }, + { + "cell_type": "markdown", + "id": "2466d7e7-5f1e-4d4a-81f4-721e56958378", + "metadata": {}, + "source": [ + "## Check all clock parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "2168355c-47d9-475d-b816-49f65e74887c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "%==================================== Model Details ====================================%\n", + "Model Attributes:\n", + "\n", + "training: True\n", + "metadata: {'approved_by_author': '⌛',\n", + " 'citation': 'Tong, Huige, et al. \"Quantifying the stochastic component of '\n", + " 'epigenetic aging.\" Nature Aging (2024): 1-16.',\n", + " 'clock_name': 'stocz',\n", + " 'data_type': 'methylation',\n", + " 'doi': 'https://doi.org/10.1038/s43587-024-00600-8',\n", + " 'notes': None,\n", + " 'research_only': None,\n", + " 'species': 'Homo sapiens',\n", + " 'version': None,\n", + " 'year': 2024}\n", + "reference_values: None\n", + "preprocess_name: None\n", + "preprocess_dependencies: None\n", + "postprocess_name: None\n", + "postprocess_dependencies: None\n", + "features: ['cg24611351', 'cg24173182', 'cg09604333', 'cg13617776', 'cg09432590', 'cg05516505', 'cg12757684', 'cg23606718', 'cg20050761', 'cg22452230', 'cg05898618', 'cg01620164', 'cg06758350', 'cg23615741', 'cg09692396', 'cg02046143', 'cg08540945', 'cg11714320', 'cg22708738', 'cg21567504', 'cg08313880', 'cg03527802', 'cg23995914', 'cg04027548', 'cg07077459', 'cg03025830', 'cg07978099', 'cg24349631', 'cg04218760', 'cg24788483']... [Total elements: 514]\n", + "base_model_features: None\n", + "\n", + "%==================================== Model Details ====================================%\n", + "Model Structure:\n", + "\n", + "base_model: LinearModel(\n", + " (linear): Linear(in_features=514, out_features=1, bias=True)\n", + ")\n", + "\n", + "%==================================== Model Details ====================================%\n", + "Model Parameters and Weights:\n", + "\n", + "base_model.linear.weight: [-3.350179433822632, -0.036642078310251236, -0.05118434131145477, 0.13607171177864075, -0.6929044127464294, -8.596583366394043, -0.023057660087943077, 22.69631576538086, 0.00651215435937047, -1.4592914581298828, -0.09501103311777115, -0.12129057198762894, -0.08514729142189026, -0.0008293375140056014, 0.4517977833747864, -0.3498646914958954, 15.742756843566895, -0.005218011327087879, -0.37894847989082336, -0.02003936469554901, 0.29712849855422974, -0.18342454731464386, -0.31919535994529724, -2.756777286529541, -2.310011625289917, 0.009594087488949299, 0.0197058767080307, 0.18678680062294006, 0.004259498789906502, -3.520700216293335]... [Tensor of shape torch.Size([1, 514])]\n", + "base_model.linear.bias: tensor([64.8077])\n", + "\n", + "%==================================== Model Details ====================================%\n", + "\n" + ] + } + ], + "source": [ + "pya.utils.print_model_details(model)" + ] + }, + { + "cell_type": "markdown", + "id": "78e2387c-6de1-4073-b4a5-b8b8e430abdb", + "metadata": {}, + "source": [ + "## Basic test" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "936b9877-d076-4ced-99aa-e8d4c58c5caf", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([[ -65.2707],\n", + " [-228.6931],\n", + " [ 81.9692],\n", + " [ 186.4775],\n", + " [ -30.6134],\n", + " [ 20.5118],\n", + " [ 50.0692],\n", + " [ 127.1051],\n", + " [ 150.1440],\n", + " [ -14.1187]], dtype=torch.float64, grad_fn=)" + ] + }, + "execution_count": 15, + "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": "75642087-b880-429b-b19b-c1eaea33614f", + "metadata": {}, + "source": [ + "## Save torch model" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "5ef2fa8d-c80b-4fdd-8555-79c0d541788e", + "metadata": {}, + "outputs": [], + "source": [ + "torch.save(model, f\"../weights/{model.metadata['clock_name']}.pt\")" + ] + }, + { + "cell_type": "markdown", + "id": "f65d9bb5-5baa-486b-bf25-f68f542bd5b7", + "metadata": {}, + "source": [ + "## Clear directory\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "11aeaa70-44c0-42f9-86d7-740e3849a7a6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Deleted file: glmStocAll.Rd\n", + "Deleted file: download.r\n", + "Deleted folder: .ipynb_checkpoints\n", + "Deleted file: Coefficients_Clock_3.csv\n", + "Deleted file: Coefficients_Clock_2.csv\n", + "Deleted file: Coefficients_Clock_1.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": "Python 3", + "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": "3.9.17" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/_static/clock_glossary.csv b/docs/_static/clock_glossary.csv index a31d81b..6938038 100644 --- a/docs/_static/clock_glossary.csv +++ b/docs/_static/clock_glossary.csv @@ -50,6 +50,9 @@ petkovich,methylation,Mus musculus,2017,⌛,https://doi.org/10.1016/j.cmet.2017. phenoage,blood chemistry,Homo sapiens,2018,⌛,https://doi.org/10.18632%2Faging.101414,,,mortality_to_phenoage, replitali,methylation,Homo sapiens,2022,⌛,https://doi.org/10.1038/s41467-022-34268-8,,,, skinandblood,methylation,Homo sapiens,2018,⌛,https://doi.org/10.18632/aging.101508,,,anti_log_linear, +stoch,methylation,Homo sapiens,2024,⌛,https://doi.org/10.1038/s43587-024-00600-8,,,, +stocp,methylation,Homo sapiens,2024,⌛,https://doi.org/10.1038/s43587-024-00600-8,,,, +stocz,methylation,Homo sapiens,2024,⌛,https://doi.org/10.1038/s43587-024-00600-8,,,, stubbs,methylation,Mus musculus,2017,⌛,https://doi.org/10.1186/s13059-017-1203-5,,quantile_normalization_and_scale_with_gold_standard,stubbs,True yingadaptage,methylation,Homo sapiens,2024,⌛,https://doi.org/10.1038/s43587-023-00557-0,,,, yingcausage,methylation,Homo sapiens,2024,⌛,https://doi.org/10.1038/s43587-023-00557-0,,,, diff --git a/docs/source/clock_implementation.rst b/docs/source/clock_implementation.rst index 664bd2c..fc38ff2 100644 --- a/docs/source/clock_implementation.rst +++ b/docs/source/clock_implementation.rst @@ -56,6 +56,9 @@ The following collection of Jupyter Notebooks provides a comprehensive guide to clock_notebooks/phenoage clock_notebooks/replitali clock_notebooks/skinandblood + clock_notebooks/stoch + clock_notebooks/stocp + clock_notebooks/stocz clock_notebooks/stubbs clock_notebooks/thompson clock_notebooks/yingadaptage diff --git a/docs/source/clock_notebooks/stoch.ipynb b/docs/source/clock_notebooks/stoch.ipynb new file mode 100644 index 0000000..477ac91 --- /dev/null +++ b/docs/source/clock_notebooks/stoch.ipynb @@ -0,0 +1,582 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "2f04eee0-5928-4e74-a754-6dc2e528810c", + "metadata": {}, + "source": [ + "# StocH" + ] + }, + { + "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": 1, + "id": "4adfb4de-cd79-4913-a1af-9e23e9e236c9", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import inspect\n", + "import shutil\n", + "import json\n", + "import torch\n", + "import pandas as pd\n", + "import pyaging as pya" + ] + }, + { + "cell_type": "markdown", + "id": "145082e5-ced4-47ae-88c0-cb69773e3c5a", + "metadata": {}, + "source": [ + "## Instantiate model class" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "8aa77372-7ed3-4da7-abc9-d30372106139", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "class StocH(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.StocH)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "78536494-f1d9-44de-8583-c89a310d2307", + "metadata": {}, + "outputs": [], + "source": [ + "model = pya.models.StocH()" + ] + }, + { + "cell_type": "markdown", + "id": "51f8615e-01fa-4aa5-b196-3ee2b35d261c", + "metadata": {}, + "source": [ + "## Define clock metadata" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "6601da9e-8adc-44ee-9308-75e3cd31b816", + "metadata": {}, + "outputs": [], + "source": [ + "model.metadata[\"clock_name\"] = 'stoch'\n", + "model.metadata[\"data_type\"] = 'methylation'\n", + "model.metadata[\"species\"] = 'Homo sapiens'\n", + "model.metadata[\"year\"] = 2024\n", + "model.metadata[\"approved_by_author\"] = '⌛'\n", + "model.metadata[\"citation\"] = \"Tong, Huige, et al. \\\"Quantifying the stochastic component of epigenetic aging.\\\" Nature Aging (2024): 1-16.\"\n", + "model.metadata[\"doi\"] = \"https://doi.org/10.1038/s43587-024-00600-8\"\n", + "model.metadata[\"research_only\"] = None\n", + "model.metadata[\"notes\"] = None" + ] + }, + { + "cell_type": "markdown", + "id": "74492239-5aae-4026-9d90-6bc9c574c110", + "metadata": {}, + "source": [ + "## Download clock dependencies" + ] + }, + { + "cell_type": "markdown", + "id": "192924bd-8553-4840-85e9-3ea02b024b79", + "metadata": {}, + "source": [ + "#### Download directly with curl" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "6675770e-a5d1-40ab-93e1-775f288fed1c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "supplementary_url = \"https://figshare.com/ndownloader/files/42406308\"\n", + "supplementary_file_name = \"glmStocAll.Rd\"\n", + "os.system(f\"curl -L -o {supplementary_file_name} {supplementary_url}\")" + ] + }, + { + "cell_type": "markdown", + "id": "aeee6c5a-4e0a-4f2a-acef-d657596f453a", + "metadata": {}, + "source": [ + "#### Download from R package" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "id": "d81f2c9d-362f-43cb-ad52-012e28217164", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Overwriting download.r\n" + ] + } + ], + "source": [ + "%%writefile download.r\n", + "\n", + "options(repos = c(CRAN = \"https://cloud.r-project.org/\"))\n", + "\n", + "# Function to extract and save coefficients and intercepts\n", + "ExtractCoefficients <- function(){\n", + " load(\"glmStocALL.Rd\") # Load in stochastic clock information\n", + "\n", + " # Check the loaded object structure\n", + " if (!exists(\"glmStocALL.lo\")) {\n", + " stop(\"The object glmStocALL.lo was not found in the loaded .Rd file.\")\n", + " }\n", + "\n", + " # List to store coefficients and intercepts for each clock\n", + " coefficients_list <- list()\n", + "\n", + " for (c in 1:length(glmStocALL.lo)) {\n", + " glm.o <- glmStocALL.lo[[c]]\n", + "\n", + " # Ensure glm.o is a glmnet object\n", + " if (!inherits(glm.o, \"glmnet\")) {\n", + " warning(paste(\"Object at index\", c, \"is not a glmnet object. Skipping.\"))\n", + " next\n", + " }\n", + "\n", + " # Extract the coefficients and intercept from the final iteration\n", + " intercept <- glm.o$a0[length(glm.o$a0)]\n", + " coefficients <- as.matrix(glm.o$beta)[, length(glm.o$lambda)]\n", + "\n", + " print(length(coefficients))\n", + " print(length(rownames(coefficients)))\n", + "\n", + " # Create a data frame with feature names and coefficients\n", + " coef_df <- data.frame(\n", + " Feature = rownames(as.matrix(glm.o$beta)),\n", + " Coefficient = as.numeric(coefficients),\n", + " Intercept = rep(intercept, length(coefficients))\n", + " )\n", + " \n", + " # Save each clock's coefficients to a CSV file\n", + " write.csv(coef_df, file = paste0(\"Coefficients_Clock_\", c, \".csv\"), row.names = FALSE)\n", + " \n", + " # Append to the list\n", + " coefficients_list[[c]] <- coef_df\n", + " }\n", + "\n", + " return(coefficients_list) # Return the list for further inspection if needed\n", + "}\n", + "\n", + "# Run the function\n", + "coefficients_list <- ExtractCoefficients()" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "id": "1ce2bac8-dd33-46cb-a7b6-14a1d0976f05", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "os.system(\"Rscript download.r\")" + ] + }, + { + "cell_type": "markdown", + "id": "84930b94-fff8-4e1f-85ef-2194e8a8ee4f", + "metadata": {}, + "source": [ + "## Load features" + ] + }, + { + "cell_type": "markdown", + "id": "dbb77f97-01d4-4c63-aa40-ee24de8bc0e9", + "metadata": {}, + "source": [ + "#### From CSV file" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "id": "110a5ded-d25f-4cef-8e84-4f51210dfc26", + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_csv('Coefficients_Clock_1.csv')\n", + "df['feature'] = df['Feature']\n", + "df['coefficient'] = df['Coefficient']\n", + "model.features = df['feature'].tolist()" + ] + }, + { + "cell_type": "markdown", + "id": "f81dd175-8773-47da-864a-643f45195bf2", + "metadata": {}, + "source": [ + "## Load weights into base model" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "id": "e09b3463-4fd4-41b1-ac21-e63ddd223fe0", + "metadata": {}, + "outputs": [], + "source": [ + "weights = torch.tensor(df['coefficient'].tolist()).unsqueeze(0)\n", + "intercept = torch.tensor([df['Intercept'][0]])" + ] + }, + { + "cell_type": "markdown", + "id": "3bbfeba6-07a3-49c6-b56a-0b9c1c882167", + "metadata": {}, + "source": [ + "#### Linear model" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "id": "d7f43b99-26f2-4622-9a76-316712058877", + "metadata": {}, + "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": "b43d29e6-ec61-450c-be21-989ad9eaebb4", + "metadata": {}, + "source": [ + "## Load reference values" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "id": "ade0f4c9-2298-4fc3-bb72-d200907dd731", + "metadata": {}, + "outputs": [], + "source": [ + "model.reference_values = None" + ] + }, + { + "cell_type": "markdown", + "id": "b42caeae-f255-45aa-9f00-3bed6ef7d26f", + "metadata": {}, + "source": [ + "## Load preprocess and postprocess objects" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "id": "7a22fb20-c605-424d-8efb-7620c2c0755c", + "metadata": {}, + "outputs": [], + "source": [ + "model.preprocess_name = None\n", + "model.preprocess_dependencies = None" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "id": "ff4a21cb-cf41-44dc-9ed1-95cf8aa15772", + "metadata": {}, + "outputs": [], + "source": [ + "model.postprocess_name = None\n", + "model.postprocess_dependencies = None" + ] + }, + { + "cell_type": "markdown", + "id": "2466d7e7-5f1e-4d4a-81f4-721e56958378", + "metadata": {}, + "source": [ + "## Check all clock parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "id": "2168355c-47d9-475d-b816-49f65e74887c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "%==================================== Model Details ====================================%\n", + "Model Attributes:\n", + "\n", + "training: True\n", + "metadata: {'approved_by_author': '⌛',\n", + " 'citation': 'Tong, Huige, et al. \"Quantifying the stochastic component of '\n", + " 'epigenetic aging.\" Nature Aging (2024): 1-16.',\n", + " 'clock_name': 'stoch',\n", + " 'data_type': 'methylation',\n", + " 'doi': 'https://doi.org/10.1038/s43587-024-00600-8',\n", + " 'notes': None,\n", + " 'research_only': None,\n", + " 'species': 'Homo sapiens',\n", + " 'version': None,\n", + " 'year': 2024}\n", + "reference_values: None\n", + "preprocess_name: None\n", + "preprocess_dependencies: None\n", + "postprocess_name: None\n", + "postprocess_dependencies: None\n", + "features: ['cg00075967', 'cg00374717', 'cg00864867', 'cg00945507', 'cg01027739', 'cg01353448', 'cg01584473', 'cg01644850', 'cg01656216', 'cg01873645', 'cg01968178', 'cg02085507', 'cg02154074', 'cg02217159', 'cg02331561', 'cg02332492', 'cg02364642', 'cg02388150', 'cg02479575', 'cg02489552', 'cg02580606', 'cg02654291', 'cg02827112', 'cg02972551', 'cg03103192', 'cg03167275', 'cg03270204', 'cg03565323', 'cg03588357', 'cg03760483']... [Total elements: 353]\n", + "base_model_features: None\n", + "\n", + "%==================================== Model Details ====================================%\n", + "Model Structure:\n", + "\n", + "base_model: LinearModel(\n", + " (linear): Linear(in_features=353, out_features=1, bias=True)\n", + ")\n", + "\n", + "%==================================== Model Details ====================================%\n", + "Model Parameters and Weights:\n", + "\n", + "base_model.linear.weight: [3.585235357284546, 0.29056739807128906, 0.008150330744683743, 7.167356014251709, 0.07318803668022156, 0.14952702820301056, -0.12038026005029678, -0.6500811576843262, -0.24336983263492584, 0.24592465162277222, -8.944670844357461e-05, -0.2048512101173401, 0.3703913986682892, -1.0299664735794067, 1.7403517961502075, -0.7057934403419495, 0.5467443466186523, 5.111024856567383, 7.80165433883667, 4.5496368408203125, 0.3366563022136688, 1.6183545589447021, 0.034926775842905045, -5.03125524520874, 6.2505011558532715, -2.024871349334717, 0.145379438996315, 0.5371573567390442, -0.9422363042831421, 0.05253010243177414]... [Tensor of shape torch.Size([1, 353])]\n", + "base_model.linear.bias: tensor([59.8016])\n", + "\n", + "%==================================== Model Details ====================================%\n", + "\n" + ] + } + ], + "source": [ + "pya.utils.print_model_details(model)" + ] + }, + { + "cell_type": "markdown", + "id": "78e2387c-6de1-4073-b4a5-b8b8e430abdb", + "metadata": {}, + "source": [ + "## Basic test" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "id": "936b9877-d076-4ced-99aa-e8d4c58c5caf", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([[ 18.5127],\n", + " [146.4057],\n", + " [166.5776],\n", + " [ 85.6306],\n", + " [379.5047],\n", + " [-99.5351],\n", + " [ 42.5195],\n", + " [234.2738],\n", + " [213.7955],\n", + " [ 62.3945]], dtype=torch.float64, grad_fn=)" + ] + }, + "execution_count": 72, + "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": "75642087-b880-429b-b19b-c1eaea33614f", + "metadata": {}, + "source": [ + "## Save torch model" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "id": "5ef2fa8d-c80b-4fdd-8555-79c0d541788e", + "metadata": {}, + "outputs": [], + "source": [ + "torch.save(model, f\"../weights/{model.metadata['clock_name']}.pt\")" + ] + }, + { + "cell_type": "markdown", + "id": "f65d9bb5-5baa-486b-bf25-f68f542bd5b7", + "metadata": {}, + "source": [ + "## Clear directory\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "id": "11aeaa70-44c0-42f9-86d7-740e3849a7a6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Deleted file: glmStocAll.Rd\n", + "Deleted file: download.r\n", + "Deleted folder: .ipynb_checkpoints\n", + "Deleted file: Coefficients_Clock_3.csv\n", + "Deleted file: Coefficients_Clock_2.csv\n", + "Deleted file: Coefficients_Clock_1.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": "Python 3", + "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": "3.9.17" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/source/clock_notebooks/stocp.ipynb b/docs/source/clock_notebooks/stocp.ipynb new file mode 100644 index 0000000..a9f3fcd --- /dev/null +++ b/docs/source/clock_notebooks/stocp.ipynb @@ -0,0 +1,582 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "2f04eee0-5928-4e74-a754-6dc2e528810c", + "metadata": {}, + "source": [ + "# StocP" + ] + }, + { + "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": 1, + "id": "4adfb4de-cd79-4913-a1af-9e23e9e236c9", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import inspect\n", + "import shutil\n", + "import json\n", + "import torch\n", + "import pandas as pd\n", + "import pyaging as pya" + ] + }, + { + "cell_type": "markdown", + "id": "145082e5-ced4-47ae-88c0-cb69773e3c5a", + "metadata": {}, + "source": [ + "## Instantiate model class" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "8aa77372-7ed3-4da7-abc9-d30372106139", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "class StocP(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.StocP)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "78536494-f1d9-44de-8583-c89a310d2307", + "metadata": {}, + "outputs": [], + "source": [ + "model = pya.models.StocP()" + ] + }, + { + "cell_type": "markdown", + "id": "51f8615e-01fa-4aa5-b196-3ee2b35d261c", + "metadata": {}, + "source": [ + "## Define clock metadata" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "6601da9e-8adc-44ee-9308-75e3cd31b816", + "metadata": {}, + "outputs": [], + "source": [ + "model.metadata[\"clock_name\"] = 'stocp'\n", + "model.metadata[\"data_type\"] = 'methylation'\n", + "model.metadata[\"species\"] = 'Homo sapiens'\n", + "model.metadata[\"year\"] = 2024\n", + "model.metadata[\"approved_by_author\"] = '⌛'\n", + "model.metadata[\"citation\"] = \"Tong, Huige, et al. \\\"Quantifying the stochastic component of epigenetic aging.\\\" Nature Aging (2024): 1-16.\"\n", + "model.metadata[\"doi\"] = \"https://doi.org/10.1038/s43587-024-00600-8\"\n", + "model.metadata[\"research_only\"] = None\n", + "model.metadata[\"notes\"] = None" + ] + }, + { + "cell_type": "markdown", + "id": "74492239-5aae-4026-9d90-6bc9c574c110", + "metadata": {}, + "source": [ + "## Download clock dependencies" + ] + }, + { + "cell_type": "markdown", + "id": "192924bd-8553-4840-85e9-3ea02b024b79", + "metadata": {}, + "source": [ + "#### Download directly with curl" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "6675770e-a5d1-40ab-93e1-775f288fed1c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "supplementary_url = \"https://figshare.com/ndownloader/files/42406308\"\n", + "supplementary_file_name = \"glmStocAll.Rd\"\n", + "os.system(f\"curl -L -o {supplementary_file_name} {supplementary_url}\")" + ] + }, + { + "cell_type": "markdown", + "id": "aeee6c5a-4e0a-4f2a-acef-d657596f453a", + "metadata": {}, + "source": [ + "#### Download from R package" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "d81f2c9d-362f-43cb-ad52-012e28217164", + "metadata": {}, + "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", + "\n", + "# Function to extract and save coefficients and intercepts\n", + "ExtractCoefficients <- function(){\n", + " load(\"glmStocALL.Rd\") # Load in stochastic clock information\n", + "\n", + " # Check the loaded object structure\n", + " if (!exists(\"glmStocALL.lo\")) {\n", + " stop(\"The object glmStocALL.lo was not found in the loaded .Rd file.\")\n", + " }\n", + "\n", + " # List to store coefficients and intercepts for each clock\n", + " coefficients_list <- list()\n", + "\n", + " for (c in 1:length(glmStocALL.lo)) {\n", + " glm.o <- glmStocALL.lo[[c]]\n", + "\n", + " # Ensure glm.o is a glmnet object\n", + " if (!inherits(glm.o, \"glmnet\")) {\n", + " warning(paste(\"Object at index\", c, \"is not a glmnet object. Skipping.\"))\n", + " next\n", + " }\n", + "\n", + " # Extract the coefficients and intercept from the final iteration\n", + " intercept <- glm.o$a0[length(glm.o$a0)]\n", + " coefficients <- as.matrix(glm.o$beta)[, length(glm.o$lambda)]\n", + "\n", + " print(length(coefficients))\n", + " print(length(rownames(coefficients)))\n", + "\n", + " # Create a data frame with feature names and coefficients\n", + " coef_df <- data.frame(\n", + " Feature = rownames(as.matrix(glm.o$beta)),\n", + " Coefficient = as.numeric(coefficients),\n", + " Intercept = rep(intercept, length(coefficients))\n", + " )\n", + " \n", + " # Save each clock's coefficients to a CSV file\n", + " write.csv(coef_df, file = paste0(\"Coefficients_Clock_\", c, \".csv\"), row.names = FALSE)\n", + " \n", + " # Append to the list\n", + " coefficients_list[[c]] <- coef_df\n", + " }\n", + "\n", + " return(coefficients_list) # Return the list for further inspection if needed\n", + "}\n", + "\n", + "# Run the function\n", + "coefficients_list <- ExtractCoefficients()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "1ce2bac8-dd33-46cb-a7b6-14a1d0976f05", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "os.system(\"Rscript download.r\")" + ] + }, + { + "cell_type": "markdown", + "id": "84930b94-fff8-4e1f-85ef-2194e8a8ee4f", + "metadata": {}, + "source": [ + "## Load features" + ] + }, + { + "cell_type": "markdown", + "id": "dbb77f97-01d4-4c63-aa40-ee24de8bc0e9", + "metadata": {}, + "source": [ + "#### From CSV file" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "110a5ded-d25f-4cef-8e84-4f51210dfc26", + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_csv('Coefficients_Clock_3.csv')\n", + "df['feature'] = df['Feature']\n", + "df['coefficient'] = df['Coefficient']\n", + "model.features = df['feature'].tolist()" + ] + }, + { + "cell_type": "markdown", + "id": "f81dd175-8773-47da-864a-643f45195bf2", + "metadata": {}, + "source": [ + "## Load weights into base model" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "e09b3463-4fd4-41b1-ac21-e63ddd223fe0", + "metadata": {}, + "outputs": [], + "source": [ + "weights = torch.tensor(df['coefficient'].tolist()).unsqueeze(0)\n", + "intercept = torch.tensor([df['Intercept'][0]])" + ] + }, + { + "cell_type": "markdown", + "id": "3bbfeba6-07a3-49c6-b56a-0b9c1c882167", + "metadata": {}, + "source": [ + "#### Linear model" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "d7f43b99-26f2-4622-9a76-316712058877", + "metadata": {}, + "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": "b43d29e6-ec61-450c-be21-989ad9eaebb4", + "metadata": {}, + "source": [ + "## Load reference values" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "ade0f4c9-2298-4fc3-bb72-d200907dd731", + "metadata": {}, + "outputs": [], + "source": [ + "model.reference_values = None" + ] + }, + { + "cell_type": "markdown", + "id": "b42caeae-f255-45aa-9f00-3bed6ef7d26f", + "metadata": {}, + "source": [ + "## Load preprocess and postprocess objects" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "7a22fb20-c605-424d-8efb-7620c2c0755c", + "metadata": {}, + "outputs": [], + "source": [ + "model.preprocess_name = None\n", + "model.preprocess_dependencies = None" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "ff4a21cb-cf41-44dc-9ed1-95cf8aa15772", + "metadata": {}, + "outputs": [], + "source": [ + "model.postprocess_name = None\n", + "model.postprocess_dependencies = None" + ] + }, + { + "cell_type": "markdown", + "id": "2466d7e7-5f1e-4d4a-81f4-721e56958378", + "metadata": {}, + "source": [ + "## Check all clock parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "2168355c-47d9-475d-b816-49f65e74887c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "%==================================== Model Details ====================================%\n", + "Model Attributes:\n", + "\n", + "training: True\n", + "metadata: {'approved_by_author': '⌛',\n", + " 'citation': 'Tong, Huige, et al. \"Quantifying the stochastic component of '\n", + " 'epigenetic aging.\" Nature Aging (2024): 1-16.',\n", + " 'clock_name': 'stocp',\n", + " 'data_type': 'methylation',\n", + " 'doi': 'https://doi.org/10.1038/s43587-024-00600-8',\n", + " 'notes': None,\n", + " 'research_only': None,\n", + " 'species': 'Homo sapiens',\n", + " 'version': None,\n", + " 'year': 2024}\n", + "reference_values: None\n", + "preprocess_name: None\n", + "preprocess_dependencies: None\n", + "postprocess_name: None\n", + "postprocess_dependencies: None\n", + "features: ['cg15611364', 'cg17605084', 'cg26382071', 'cg12743894', 'cg19287114', 'cg12985418', 'cg19398783', 'cg15963417', 'cg27187881', 'cg09892203', 'cg00943950', 'cg18996776', 'cg16340918', 'cg23832061', 'cg22736354', 'cg04084157', 'cg07265300', 'cg02503970', 'cg11426590', 'cg23710218', 'cg02802055', 'cg13631913', 'cg06493994', 'cg24304712', 'cg01131735', 'cg24208206', 'cg01930621', 'cg19104072', 'cg07850604', 'cg27493997']... [Total elements: 513]\n", + "base_model_features: None\n", + "\n", + "%==================================== Model Details ====================================%\n", + "Model Structure:\n", + "\n", + "base_model: LinearModel(\n", + " (linear): Linear(in_features=513, out_features=1, bias=True)\n", + ")\n", + "\n", + "%==================================== Model Details ====================================%\n", + "Model Parameters and Weights:\n", + "\n", + "base_model.linear.weight: [-0.26072555780410767, -0.4015197157859802, 0.04175494238734245, 0.08702494204044342, -0.13476140797138214, 0.016701284795999527, 0.07106481492519379, 0.23266158998012543, -0.01433953084051609, -0.9317121505737305, -0.005014453548938036, 0.4903888404369354, -1.6926921606063843, -0.523848831653595, 4.922214031219482, 1.7051185369491577, -0.011739220470190048, -0.7235432863235474, -0.06498837471008301, -0.6380035281181335, 0.22486011683940887, 0.013822232373058796, 10.945517539978027, 0.04070192202925682, -0.028888650238513947, 0.16922490298748016, 0.6264103651046753, -0.018560025840997696, -1.6304043531417847, 0.083981454372406]... [Tensor of shape torch.Size([1, 513])]\n", + "base_model.linear.bias: tensor([92.8311])\n", + "\n", + "%==================================== Model Details ====================================%\n", + "\n" + ] + } + ], + "source": [ + "pya.utils.print_model_details(model)" + ] + }, + { + "cell_type": "markdown", + "id": "78e2387c-6de1-4073-b4a5-b8b8e430abdb", + "metadata": {}, + "source": [ + "## Basic test" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "936b9877-d076-4ced-99aa-e8d4c58c5caf", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([[ 272.9167],\n", + " [ -8.4764],\n", + " [-152.8284],\n", + " [ 167.9197],\n", + " [ -3.3252],\n", + " [ 154.7005],\n", + " [ 2.9908],\n", + " [ 57.1228],\n", + " [ 123.9416],\n", + " [ 83.7115]], dtype=torch.float64, grad_fn=)" + ] + }, + "execution_count": 15, + "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": "75642087-b880-429b-b19b-c1eaea33614f", + "metadata": {}, + "source": [ + "## Save torch model" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "5ef2fa8d-c80b-4fdd-8555-79c0d541788e", + "metadata": {}, + "outputs": [], + "source": [ + "torch.save(model, f\"../weights/{model.metadata['clock_name']}.pt\")" + ] + }, + { + "cell_type": "markdown", + "id": "f65d9bb5-5baa-486b-bf25-f68f542bd5b7", + "metadata": {}, + "source": [ + "## Clear directory\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "11aeaa70-44c0-42f9-86d7-740e3849a7a6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Deleted file: glmStocAll.Rd\n", + "Deleted file: download.r\n", + "Deleted folder: .ipynb_checkpoints\n", + "Deleted file: Coefficients_Clock_3.csv\n", + "Deleted file: Coefficients_Clock_2.csv\n", + "Deleted file: Coefficients_Clock_1.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": "Python 3", + "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": "3.9.17" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/source/clock_notebooks/stocz.ipynb b/docs/source/clock_notebooks/stocz.ipynb new file mode 100644 index 0000000..a2b108e --- /dev/null +++ b/docs/source/clock_notebooks/stocz.ipynb @@ -0,0 +1,582 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "2f04eee0-5928-4e74-a754-6dc2e528810c", + "metadata": {}, + "source": [ + "# StocZ" + ] + }, + { + "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": 1, + "id": "4adfb4de-cd79-4913-a1af-9e23e9e236c9", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import inspect\n", + "import shutil\n", + "import json\n", + "import torch\n", + "import pandas as pd\n", + "import pyaging as pya" + ] + }, + { + "cell_type": "markdown", + "id": "145082e5-ced4-47ae-88c0-cb69773e3c5a", + "metadata": {}, + "source": [ + "## Instantiate model class" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "8aa77372-7ed3-4da7-abc9-d30372106139", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "class StocZ(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.StocZ)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "78536494-f1d9-44de-8583-c89a310d2307", + "metadata": {}, + "outputs": [], + "source": [ + "model = pya.models.StocZ()" + ] + }, + { + "cell_type": "markdown", + "id": "51f8615e-01fa-4aa5-b196-3ee2b35d261c", + "metadata": {}, + "source": [ + "## Define clock metadata" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "6601da9e-8adc-44ee-9308-75e3cd31b816", + "metadata": {}, + "outputs": [], + "source": [ + "model.metadata[\"clock_name\"] = 'stocz'\n", + "model.metadata[\"data_type\"] = 'methylation'\n", + "model.metadata[\"species\"] = 'Homo sapiens'\n", + "model.metadata[\"year\"] = 2024\n", + "model.metadata[\"approved_by_author\"] = '⌛'\n", + "model.metadata[\"citation\"] = \"Tong, Huige, et al. \\\"Quantifying the stochastic component of epigenetic aging.\\\" Nature Aging (2024): 1-16.\"\n", + "model.metadata[\"doi\"] = \"https://doi.org/10.1038/s43587-024-00600-8\"\n", + "model.metadata[\"research_only\"] = None\n", + "model.metadata[\"notes\"] = None" + ] + }, + { + "cell_type": "markdown", + "id": "74492239-5aae-4026-9d90-6bc9c574c110", + "metadata": {}, + "source": [ + "## Download clock dependencies" + ] + }, + { + "cell_type": "markdown", + "id": "192924bd-8553-4840-85e9-3ea02b024b79", + "metadata": {}, + "source": [ + "#### Download directly with curl" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "6675770e-a5d1-40ab-93e1-775f288fed1c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "supplementary_url = \"https://figshare.com/ndownloader/files/42406308\"\n", + "supplementary_file_name = \"glmStocAll.Rd\"\n", + "os.system(f\"curl -L -o {supplementary_file_name} {supplementary_url}\")" + ] + }, + { + "cell_type": "markdown", + "id": "aeee6c5a-4e0a-4f2a-acef-d657596f453a", + "metadata": {}, + "source": [ + "#### Download from R package" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "d81f2c9d-362f-43cb-ad52-012e28217164", + "metadata": {}, + "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", + "\n", + "# Function to extract and save coefficients and intercepts\n", + "ExtractCoefficients <- function(){\n", + " load(\"glmStocALL.Rd\") # Load in stochastic clock information\n", + "\n", + " # Check the loaded object structure\n", + " if (!exists(\"glmStocALL.lo\")) {\n", + " stop(\"The object glmStocALL.lo was not found in the loaded .Rd file.\")\n", + " }\n", + "\n", + " # List to store coefficients and intercepts for each clock\n", + " coefficients_list <- list()\n", + "\n", + " for (c in 1:length(glmStocALL.lo)) {\n", + " glm.o <- glmStocALL.lo[[c]]\n", + "\n", + " # Ensure glm.o is a glmnet object\n", + " if (!inherits(glm.o, \"glmnet\")) {\n", + " warning(paste(\"Object at index\", c, \"is not a glmnet object. Skipping.\"))\n", + " next\n", + " }\n", + "\n", + " # Extract the coefficients and intercept from the final iteration\n", + " intercept <- glm.o$a0[length(glm.o$a0)]\n", + " coefficients <- as.matrix(glm.o$beta)[, length(glm.o$lambda)]\n", + "\n", + " print(length(coefficients))\n", + " print(length(rownames(coefficients)))\n", + "\n", + " # Create a data frame with feature names and coefficients\n", + " coef_df <- data.frame(\n", + " Feature = rownames(as.matrix(glm.o$beta)),\n", + " Coefficient = as.numeric(coefficients),\n", + " Intercept = rep(intercept, length(coefficients))\n", + " )\n", + " \n", + " # Save each clock's coefficients to a CSV file\n", + " write.csv(coef_df, file = paste0(\"Coefficients_Clock_\", c, \".csv\"), row.names = FALSE)\n", + " \n", + " # Append to the list\n", + " coefficients_list[[c]] <- coef_df\n", + " }\n", + "\n", + " return(coefficients_list) # Return the list for further inspection if needed\n", + "}\n", + "\n", + "# Run the function\n", + "coefficients_list <- ExtractCoefficients()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "1ce2bac8-dd33-46cb-a7b6-14a1d0976f05", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "os.system(\"Rscript download.r\")" + ] + }, + { + "cell_type": "markdown", + "id": "84930b94-fff8-4e1f-85ef-2194e8a8ee4f", + "metadata": {}, + "source": [ + "## Load features" + ] + }, + { + "cell_type": "markdown", + "id": "dbb77f97-01d4-4c63-aa40-ee24de8bc0e9", + "metadata": {}, + "source": [ + "#### From CSV file" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "110a5ded-d25f-4cef-8e84-4f51210dfc26", + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_csv('Coefficients_Clock_2.csv')\n", + "df['feature'] = df['Feature']\n", + "df['coefficient'] = df['Coefficient']\n", + "model.features = df['feature'].tolist()" + ] + }, + { + "cell_type": "markdown", + "id": "f81dd175-8773-47da-864a-643f45195bf2", + "metadata": {}, + "source": [ + "## Load weights into base model" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "e09b3463-4fd4-41b1-ac21-e63ddd223fe0", + "metadata": {}, + "outputs": [], + "source": [ + "weights = torch.tensor(df['coefficient'].tolist()).unsqueeze(0)\n", + "intercept = torch.tensor([df['Intercept'][0]])" + ] + }, + { + "cell_type": "markdown", + "id": "3bbfeba6-07a3-49c6-b56a-0b9c1c882167", + "metadata": {}, + "source": [ + "#### Linear model" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "d7f43b99-26f2-4622-9a76-316712058877", + "metadata": {}, + "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": "b43d29e6-ec61-450c-be21-989ad9eaebb4", + "metadata": {}, + "source": [ + "## Load reference values" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "ade0f4c9-2298-4fc3-bb72-d200907dd731", + "metadata": {}, + "outputs": [], + "source": [ + "model.reference_values = None" + ] + }, + { + "cell_type": "markdown", + "id": "b42caeae-f255-45aa-9f00-3bed6ef7d26f", + "metadata": {}, + "source": [ + "## Load preprocess and postprocess objects" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "7a22fb20-c605-424d-8efb-7620c2c0755c", + "metadata": {}, + "outputs": [], + "source": [ + "model.preprocess_name = None\n", + "model.preprocess_dependencies = None" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "ff4a21cb-cf41-44dc-9ed1-95cf8aa15772", + "metadata": {}, + "outputs": [], + "source": [ + "model.postprocess_name = None\n", + "model.postprocess_dependencies = None" + ] + }, + { + "cell_type": "markdown", + "id": "2466d7e7-5f1e-4d4a-81f4-721e56958378", + "metadata": {}, + "source": [ + "## Check all clock parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "2168355c-47d9-475d-b816-49f65e74887c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "%==================================== Model Details ====================================%\n", + "Model Attributes:\n", + "\n", + "training: True\n", + "metadata: {'approved_by_author': '⌛',\n", + " 'citation': 'Tong, Huige, et al. \"Quantifying the stochastic component of '\n", + " 'epigenetic aging.\" Nature Aging (2024): 1-16.',\n", + " 'clock_name': 'stocz',\n", + " 'data_type': 'methylation',\n", + " 'doi': 'https://doi.org/10.1038/s43587-024-00600-8',\n", + " 'notes': None,\n", + " 'research_only': None,\n", + " 'species': 'Homo sapiens',\n", + " 'version': None,\n", + " 'year': 2024}\n", + "reference_values: None\n", + "preprocess_name: None\n", + "preprocess_dependencies: None\n", + "postprocess_name: None\n", + "postprocess_dependencies: None\n", + "features: ['cg24611351', 'cg24173182', 'cg09604333', 'cg13617776', 'cg09432590', 'cg05516505', 'cg12757684', 'cg23606718', 'cg20050761', 'cg22452230', 'cg05898618', 'cg01620164', 'cg06758350', 'cg23615741', 'cg09692396', 'cg02046143', 'cg08540945', 'cg11714320', 'cg22708738', 'cg21567504', 'cg08313880', 'cg03527802', 'cg23995914', 'cg04027548', 'cg07077459', 'cg03025830', 'cg07978099', 'cg24349631', 'cg04218760', 'cg24788483']... [Total elements: 514]\n", + "base_model_features: None\n", + "\n", + "%==================================== Model Details ====================================%\n", + "Model Structure:\n", + "\n", + "base_model: LinearModel(\n", + " (linear): Linear(in_features=514, out_features=1, bias=True)\n", + ")\n", + "\n", + "%==================================== Model Details ====================================%\n", + "Model Parameters and Weights:\n", + "\n", + "base_model.linear.weight: [-3.350179433822632, -0.036642078310251236, -0.05118434131145477, 0.13607171177864075, -0.6929044127464294, -8.596583366394043, -0.023057660087943077, 22.69631576538086, 0.00651215435937047, -1.4592914581298828, -0.09501103311777115, -0.12129057198762894, -0.08514729142189026, -0.0008293375140056014, 0.4517977833747864, -0.3498646914958954, 15.742756843566895, -0.005218011327087879, -0.37894847989082336, -0.02003936469554901, 0.29712849855422974, -0.18342454731464386, -0.31919535994529724, -2.756777286529541, -2.310011625289917, 0.009594087488949299, 0.0197058767080307, 0.18678680062294006, 0.004259498789906502, -3.520700216293335]... [Tensor of shape torch.Size([1, 514])]\n", + "base_model.linear.bias: tensor([64.8077])\n", + "\n", + "%==================================== Model Details ====================================%\n", + "\n" + ] + } + ], + "source": [ + "pya.utils.print_model_details(model)" + ] + }, + { + "cell_type": "markdown", + "id": "78e2387c-6de1-4073-b4a5-b8b8e430abdb", + "metadata": {}, + "source": [ + "## Basic test" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "936b9877-d076-4ced-99aa-e8d4c58c5caf", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([[ -65.2707],\n", + " [-228.6931],\n", + " [ 81.9692],\n", + " [ 186.4775],\n", + " [ -30.6134],\n", + " [ 20.5118],\n", + " [ 50.0692],\n", + " [ 127.1051],\n", + " [ 150.1440],\n", + " [ -14.1187]], dtype=torch.float64, grad_fn=)" + ] + }, + "execution_count": 15, + "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": "75642087-b880-429b-b19b-c1eaea33614f", + "metadata": {}, + "source": [ + "## Save torch model" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "5ef2fa8d-c80b-4fdd-8555-79c0d541788e", + "metadata": {}, + "outputs": [], + "source": [ + "torch.save(model, f\"../weights/{model.metadata['clock_name']}.pt\")" + ] + }, + { + "cell_type": "markdown", + "id": "f65d9bb5-5baa-486b-bf25-f68f542bd5b7", + "metadata": {}, + "source": [ + "## Clear directory\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "11aeaa70-44c0-42f9-86d7-740e3849a7a6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Deleted file: glmStocAll.Rd\n", + "Deleted file: download.r\n", + "Deleted folder: .ipynb_checkpoints\n", + "Deleted file: Coefficients_Clock_3.csv\n", + "Deleted file: Coefficients_Clock_2.csv\n", + "Deleted file: Coefficients_Clock_1.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": "Python 3", + "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": "3.9.17" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/source/index.rst b/docs/source/index.rst index 8845b7a..5d1dd21 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -18,9 +18,9 @@ :target: https://github.com/rsinghlab/pyaging/blob/main/LICENSE :alt: License -.. image:: https://img.shields.io/badge/bioRxiv-DOI-purple.svg - :target: https://doi.org/10.1101/2023.11.28.569069 - :alt: bioRxiv +.. image:: https://img.shields.io/badge/DOI-10.1093%2Fbioinformatics%2Fbtae200-blue.svg + :target: https://doi.org/10.1093/bioinformatics/btae200 + :alt: DOI .. raw:: html diff --git a/docs/source/installation.rst b/docs/source/installation.rst index f0b6013..78a8353 100644 --- a/docs/source/installation.rst +++ b/docs/source/installation.rst @@ -28,4 +28,8 @@ Lastly, it can be installed from source: .. code-block:: bash - pip install git+https://github.com/rsinghlab/pyaging \ No newline at end of file + pip install git+https://github.com/rsinghlab/pyaging + +.. note:: + + The histone mark clocks can only be used when the optional dependency pyBigWig is also installed. Currently, pyBigWig is not supported on Windows. \ No newline at end of file diff --git a/poetry.lock b/poetry.lock index 3c1d129..29a2bc9 100644 --- a/poetry.lock +++ b/poetry.lock @@ -2,18 +2,22 @@ [[package]] name = "accessible-pygments" -version = "0.0.4" +version = "0.0.5" description = "A collection of accessible pygments styles" optional = false -python-versions = "*" +python-versions = ">=3.9" files = [ - {file = "accessible-pygments-0.0.4.tar.gz", hash = "sha256:e7b57a9b15958e9601c7e9eb07a440c813283545a20973f2574a5f453d0e953e"}, - {file = "accessible_pygments-0.0.4-py2.py3-none-any.whl", hash = "sha256:416c6d8c1ea1c5ad8701903a20fcedf953c6e720d64f33dc47bfb2d3f2fa4e8d"}, + {file = "accessible_pygments-0.0.5-py3-none-any.whl", hash = "sha256:88ae3211e68a1d0b011504b2ffc1691feafce124b845bd072ab6f9f66f34d4b7"}, + {file = "accessible_pygments-0.0.5.tar.gz", hash = "sha256:40918d3e6a2b619ad424cb91e556bd3bd8865443d9f22f1dcdf79e33c8046872"}, ] [package.dependencies] pygments = ">=1.5" +[package.extras] +dev = ["pillow", "pkginfo (>=1.10)", "playwright", "pre-commit", "setuptools", "twine (>=5.0)"] +tests = ["hypothesis", "pytest"] + [[package]] name = "alabaster" version = "0.7.16" @@ -27,30 +31,30 @@ files = [ [[package]] name = "anndata" -version = "0.10.5.post1" +version = "0.10.8" description = "Annotated data." optional = false python-versions = ">=3.9" files = [ - 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">=3.9,<3.12" -content-hash = "8255f11e8ff2613493802e7e1e58e336628acfa657a664879eb970756f3f5033" +content-hash = "0055bc97fd74d23c1f9027d72e2afeaec5e1d47403c9a36466607ce9e48f00e1" diff --git a/pyaging/__init__.py b/pyaging/__init__.py index 91b0a2a..bfb1f56 100644 --- a/pyaging/__init__.py +++ b/pyaging/__init__.py @@ -4,4 +4,4 @@ from . import predict as pred from . import preprocess as pp -__version__ = "0.1.6" +__version__ = "0.1.7" diff --git a/pyaging/models/_models.py b/pyaging/models/_models.py index ada7bd9..a38bc0a 100644 --- a/pyaging/models/_models.py +++ b/pyaging/models/_models.py @@ -1347,3 +1347,35 @@ def preprocess(self, x): def postprocess(self, x): return x + +class StocH(pyagingModel): + def __init__(self): + super().__init__() + + def preprocess(self, x): + return x + + def postprocess(self, x): + return x + +class StocZ(pyagingModel): + def __init__(self): + super().__init__() + + def preprocess(self, x): + return x + + def postprocess(self, x): + return x + +class StocP(pyagingModel): + def __init__(self): + super().__init__() + + def preprocess(self, x): + return x + + def postprocess(self, x): + return x + + diff --git a/pyaging/preprocess/_preprocess.py b/pyaging/preprocess/_preprocess.py index 022e297..6d6fe8a 100644 --- a/pyaging/preprocess/_preprocess.py +++ b/pyaging/preprocess/_preprocess.py @@ -3,8 +3,13 @@ import anndata import numpy as np import pandas as pd -from pyBigWig import open as open_bw +try: + from pyBigWig import open as open_bw + PYBIGWIG_AVAILABLE = True +except ImportError: + PYBIGWIG_AVAILABLE = False + from ..logger import LoggerManager, main_tqdm, silence_logger from ._preprocess_utils import * @@ -35,11 +40,18 @@ def bigwig_to_df(bw_files: Union[str, List[str]], dir: str = "pyaging_data", ver A DataFrame where each row represents a bigWig file and each column corresponds to a gene. The values in the DataFrame are the transformed signal data for each gene in each bigWig file. + Raises + ------ + ImportError + If pyBigWig is not installed and the function is called. + Notes ----- The function utilizes Ensembl gene annotations and assumes the presence of genes on standard chromosomes (1-22, X). Non-standard chromosomes or regions outside annotated genes are not processed. The signal - transformation uses the arcsinh function for normalization. + transformation uses the arcsinh function for normalization. This function requires pyBigWig to be installed. + If pyBigWig is not available, an ImportError will be raised. To use this function, ensure you have installed + pyaging with the 'bigwig' extra: pip install pyaging[bigwig] Examples -------- @@ -48,6 +60,9 @@ def bigwig_to_df(bw_files: Union[str, List[str]], dir: str = "pyaging_data", ver # This returns a DataFrame where rows are bigWig files and columns are genes, with signal values. """ + if not PYBIGWIG_AVAILABLE: + raise ImportError("pyBigWig is not installed. To use this function, install please install it.") + logger = LoggerManager.gen_logger("bigwig_to_df") if not verbose: silence_logger("bigwig_to_df") diff --git a/pyproject.toml b/pyproject.toml index ae3f636..0929dfb 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "pyaging" -version = "v0.1.6" +version = "v0.1.7" description = "A Python-based compendium of GPU-optimized aging clocks." authors = ["Lucas Paulo de Lima Camillo "] license = "BSD" @@ -16,7 +16,6 @@ scikit-learn = "^1.3.2" pandas = "^2.1.3" torch = "^2.1.1" numpy = "^1.26.2" -pyBigWig = "^0.3.22" anndata = "^0.10.3" [tool.poetry.dev-dependencies] @@ -34,6 +33,7 @@ nbsphinx = "^0.8.11" pygments = "^2.6.1" pytest = "^7.2.0" nbmake = "^1.4.6" +pyBigWig = {version = "^0.3.22", optional = true} [tool.poetry.scripts] pyaging = "pyaging:main" diff --git a/tests/predict/test_gold_standard.py b/tests/predict/test_gold_standard.py index 9897672..434d3fa 100644 --- a/tests/predict/test_gold_standard.py +++ b/tests/predict/test_gold_standard.py @@ -62,6 +62,9 @@ "yingcausage": 195.3013578758023, "yingadaptage": 173.48314231920278, "yingdamage": -53.509282005508, + "stoch": 136.48186449945413, + "stocz": -21.238430415750855, + "stocp": -48.95705647484499, }