Stubbs

Index

1. Instantiate model class

2. Define clock metadata

3. Download clock dependencies

4. Load features

5. Load weights into base model

6. Load reference values

7. Load preprocess and postprocess objects

8. Check all clock parameters

9. Basic test

10. Save torch model

11. Clear directory

Let’s first import some packages:

import os
import inspect
import shutil
import json
import torch
import pandas as pd
import pyaging as pya

Instantiate model class

def print_entire_class(cls):
    source = inspect.getsource(cls)
    print(source)

print_entire_class(pya.models.Stubbs)

class Stubbs(pyagingModel):
    def __init__(self):
        super().__init__()

    def preprocess(self, x):
        """
        Apply quantile normalization on x using gold standard means
        and then scale with the means and standard deviation.
        """

        gold_standard_means = torch.tensor(
            self.reference_values, device=x.device, dtype=x.dtype
        )

        # Ensure gold_standard_means is a 1D tensor and sorted
        sorted_gold_standard = torch.sort(gold_standard_means)[0]

        # Pre-compute the quantile indices
        quantile_indices = torch.linspace(
            0, len(sorted_gold_standard) - 1, steps=x.size(1)
        ).long()

        # Prepare a tensor to hold normalized data
        normalized_data = torch.empty_like(x, device=x.device, dtype=x.dtype)

        for i in range(x.size(0)):
            sorted_indices = torch.argsort(x[i, :])
            normalized_data[i, sorted_indices] = sorted_gold_standard[quantile_indices]

        gold_standard_stds = torch.tensor(
            self.preprocess_dependencies[0], device=x.device, dtype=x.dtype
        )

        # Avoid division by zero in case of a column with constant value
        gold_standard_stds[torch.abs(gold_standard_stds) < 10e-10] = 1.0

        normalized_data = (normalized_data - gold_standard_means) / gold_standard_stds

        # Return only the subset from x that is used in the base model
        return normalized_data[:, self.preprocess_dependencies[1]]

    def postprocess(self, x):
        """
        Applies a convertion from the output of an ElasticNet to mouse age in months.
        """
        age = torch.exp(0.1207 * (x**2) + 1.2424 * x + 2.5440) - 3
        age = age * (7 / 30.5)  # weeks to months
        return age

model = pya.models.Stubbs()

Define clock metadata

model.metadata["clock_name"] = 'stubbs'
model.metadata["data_type"] = 'methylation'
model.metadata["species"] = 'Mus musculus'
model.metadata["year"] = 2017
model.metadata["approved_by_author"] = '⌛'
model.metadata["citation"] = "Stubbs, Thomas M., et al. \"Multi-tissue DNA methylation age predictor in mouse.\" Genome biology 18 (2017): 1-14."
model.metadata["doi"] = "https://doi.org/10.1186/s13059-017-1203-5"
model.metadata["research_only"] = None
model.metadata["notes"] = None

Download clock dependencies

Download directly with curl

supplementary_url = "https://elifesciences.org/download/aHR0cHM6Ly9jZG4uZWxpZmVzY2llbmNlcy5vcmcvYXJ0aWNsZXMvNDA2NzUvZWxpZmUtNDA2NzUtc3VwcDMtdjIueGxzeA--/elife-40675-supp3-v2.xlsx?_hash=qzOMc4yUFACfDFG%2FlgxkFTHWt%2BSXSmP9zz1BM3oOTRM%3D"
supplementary_file_name = "coefficients.xlsx"
os.system(f"curl -o {supplementary_file_name} {supplementary_url}")

0

Download GitHub repository

github_url = "https://github.com/EpigenomeClock/MouseEpigeneticClock.git"
github_folder_name = github_url.split('/')[-1].split('.')[0]
os.system(f"git clone {github_url}")

0

Load features

df = pd.read_excel('coefficients.xlsx', sheet_name='Young age multi-tissue', nrows=329)
df['feature'] = df['Chromosome'].astype(str) + ':' + df['Position'].astype(int).astype(str)
df['coefficient'] = df['Weight']

reference_feature_values_df = pd.read_table('MouseEpigeneticClock/TrainingMatrix/TrainingData_Babraham_Reizel_Cannon.txt', index_col=0)
reference_feature_values_df.index = ['chr' + index for index in reference_feature_values_df.index]
reference_feature_values_df = reference_feature_values_df.T

model.features = reference_feature_values_df.columns.tolist()
model.base_model_features = df['feature'].tolist()

Load weights into base model

weights = torch.tensor(df['coefficient'].tolist()).unsqueeze(0)
intercept = torch.tensor([0.0])

Linear model

base_model = pya.models.LinearModel(input_dim=len(model.features))

base_model.linear.weight.data = weights.float()
base_model.linear.bias.data = intercept.float()

model.base_model = base_model

Load reference values

model.reference_values = reference_feature_values_df.mean().tolist()

Load preprocess and postprocess objects

model.preprocess_name = 'quantile_normalization_and_scale_with_gold_standard'
gold_standard_stds = reference_feature_values_df.std().tolist()
indices = [model.features.index(item) for item in model.base_model_features]
model.preprocess_dependencies = [gold_standard_stds, indices]

model.postprocess_name = 'stubbs'
model.postprocess_dependencies = None

Check all clock parameters

pya.utils.print_model_details(model)

%==================================== Model Details ====================================%
Model Attributes:

training: True
metadata: {'approved_by_author': '⌛',
 'citation': 'Stubbs, Thomas M., et al. "Multi-tissue DNA methylation age '
             'predictor in mouse." Genome biology 18 (2017): 1-14.',
 'clock_name': 'stubbs',
 'data_type': 'methylation',
 'doi': 'https://doi.org/10.1186/s13059-017-1203-5',
 'notes': None,
 'research_only': None,
 'species': 'Mus musculus',
 'version': None,
 'year': 2017}
reference_values: [0.009419308625383253, 0.9454021653384829, 0.9262220310728511, 0.01115736080094597, 0.005828992981096152, 0.015486742789977479, 0.10632124338173232, 0.008327731508385066, 0.9470653205543639, 0.7441407980821007, 0.9443701038672639, 0.7338555884793152, 0.7296056309787987, 0.9211735929020528, 0.012150415029205755, 0.01607368220739321, 0.011272227879684186, 0.012648110923722247, 0.17279958253325498, 0.606354333653314, 0.612341969307046, 0.0016412203744236714, 0.0016069770681773007, 0.0009744614615922172, 0.0031840144988144236, 0.0010236321557703402, 0.00775896971942178, 0.018119994693935132, 0.0023590301620859755, 0.0022931444123898757]... [Total elements: 17992]
preprocess_name: 'quantile_normalization_and_scale_with_gold_standard'
preprocess_dependencies: [[0.023642310306951884,
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postprocess_name: 'stubbs'
postprocess_dependencies: None
features: ['chr1:10038066', 'chr1:101374410', 'chr1:101374418', 'chr1:105356622', 'chr1:105356633', 'chr1:105780925', 'chr1:106173313', 'chr1:106759301', 'chr1:109034542', 'chr1:110429621', 'chr1:110429664', 'chr1:113816340', 'chr1:113816377', 'chr1:118453160', 'chr1:118628354', 'chr1:119047941', 'chr1:119048037', 'chr1:119054228', 'chr1:119054537', 'chr1:119471763', 'chr1:119471798', 'chr1:119836882', 'chr1:119836891', 'chr1:119836906', 'chr1:119836918', 'chr1:119836920', 'chr1:119836928', 'chr1:120073681', 'chr1:120121455', 'chr1:120121465']... [Total elements: 17992]
base_model_features: ['chr1:73924900', 'chr1:153890242', 'chr1:180350659', 'chr2:25579878', 'chr2:131180254', 'chr2:162944571', 'chr2:164678867', 'chr2:164833043', 'chr2:168893991', 'chr2:173184335', 'chr2:173184340', 'chr2:180702779', 'chr3:53477767', 'chr3:53477794', 'chr3:94399354', 'chr3:96219156', 'chr3:97767095', 'chr3:105052161', 'chr3:107758395', 'chr4:46581731', 'chr4:56740768', 'chr4:78212397', 'chr4:88641066', 'chr4:119415073', 'chr4:137522100', 'chr4:138453876', 'chr5:30647717', 'chr5:32133020', 'chr5:113101756', 'chr5:115632768']... [Total elements: 329]

%==================================== Model Details ====================================%
Model Structure:

base_model: LinearModel(
  (linear): Linear(in_features=17992, out_features=1, bias=True)
)

%==================================== Model Details ====================================%
Model Parameters and Weights:

base_model.linear.weight: [-0.0009982034098356962, -0.00023933289048727602, -0.0028150584548711777, -0.007451509591192007, -0.0022230781614780426, -0.001344481366686523, -0.00554255023598671, -0.0003380068519618362, -0.007523837964981794, -0.0013427615631371737, -0.0005768182454630733, -0.01288866437971592, -0.009710701182484627, -8.49335701786913e-05, -0.008504485711455345, -0.0033585140481591225, -0.001603029086254537, -0.00699494918808341, -0.007361156865954399, -0.00831840094178915, -0.005099604371935129, -0.012041870504617691, -0.0066992854699492455, -0.0028678528033196926, -0.006041061598807573, -8.668371447129175e-05, -0.010333182290196419, -0.0035897456109523773, -0.012446772307157516, -0.0065109808929264545]... [Tensor of shape torch.Size([1, 329])]
base_model.linear.bias: tensor([0.])

%==================================== Model Details ====================================%

Basic test

torch.manual_seed(42)
input = torch.randn(10, len(model.features), dtype=float)
model.eval()
model.to(float)
pred = model(input)
pred

tensor([[ 1.1474],
        [-0.2128],
        [-0.1608],
        [ 2.1481],
        [-0.2222],
        [ 0.8489],
        [ 3.7938],
        [ 0.6316],
        [-0.1613],
        [ 2.0362]], dtype=torch.float64, grad_fn=<MulBackward0>)

Save torch model

torch.save(model, f"../weights/{model.metadata['clock_name']}.pt")

Clear directory

# Function to remove a folder and all its contents
def remove_folder(path):
    try:
        shutil.rmtree(path)
        print(f"Deleted folder: {path}")
    except Exception as e:
        print(f"Error deleting folder {path}: {e}")

# Get a list of all files and folders in the current directory
all_items = os.listdir('.')

# Loop through the items
for item in all_items:
    # Check if it's a file and does not end with .ipynb
    if os.path.isfile(item) and not item.endswith('.ipynb'):
        os.remove(item)
        print(f"Deleted file: {item}")
    # Check if it's a folder
    elif os.path.isdir(item):
        remove_folder(item)

Deleted file: coefficients.xlsx
Deleted folder: MouseEpigeneticClock