CorticalClock

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.CorticalClock)

class CorticalClock(LinearReferenceClock):
    def postprocess(self, x):
        """Horvath anti-logarithmic linear transformation (adult age = 20)."""
        adult_age = 20
        mask_negative = x < 0
        mask_non_negative = ~mask_negative
        age = torch.empty_like(x)
        age[mask_negative] = (1 + adult_age) * torch.exp(x[mask_negative]) - 1
        age[mask_non_negative] = (1 + adult_age) * x[mask_non_negative] + adult_age
        return age

model = pya.models.CorticalClock()

Define clock metadata

model.metadata["clock_name"] = 'corticalclock'
model.metadata["data_type"] = 'methylation'
model.metadata["species"] = 'Homo sapiens'
model.metadata["year"] = 2020
model.metadata["approved_by_author"] = '⌛'
model.metadata["citation"] = "Shireby, Gemma L., et al. \"Recalibrating the epigenetic clock: implications for assessing biological age in the human cortex.\" Brain 143.12 (2020): 3763-3775."
model.metadata["doi"] = "https://doi.org/10.1093/brain/awaa334"
model.metadata["research_only"] = None
model.metadata["notes"] = "Human cortex-specific epigenetic age predictor trained by elastic-net regression on over a thousand post-mortem cortical samples, recalibrated to correct the systematic age underestimation that multi-tissue clocks show in brain tissue."
model.metadata["tissue"] = 'brain cortex (post-mortem human cortical tissue)'
model.metadata["predicts"] = 'chronological age'
model.metadata["unit"] = 'years'
model.metadata["model_type"] = 'Elastic net'
model.metadata["platform"] = 'Illumina 450K/EPIC'
model.metadata["population"] = 'humans, ages 1-108 years'
model.metadata["journal"] = 'Brain'
model.metadata["last_author"] = 'Jonathan Mill'
model.metadata["n_features"] = 347
model.metadata["citations"] = 206
model.metadata["citations_date"] = '2026-07-05'

Download clock dependencies

os.system(f"curl -sL -o CorticalClockCoefs.txt https://raw.githubusercontent.com/gemmashireby/CorticalClock/80c3df19c01d9aac25c9fd5aecd5bbc42aba0939/PredCorticalAge/CorticalClockCoefs.txt")
os.system(f"curl -sL -o Ref_DNAm_brain_values.rdat https://raw.githubusercontent.com/gemmashireby/CorticalClock/80c3df19c01d9aac25c9fd5aecd5bbc42aba0939/PredCorticalAge/Ref_DNAm_brain_values.rdat")

0

%%writefile download.r

library(jsonlite)
coefs <- read.table("CorticalClockCoefs.txt", header = TRUE, stringsAsFactors = FALSE)
coefs <- coefs[tolower(coefs$probe) != "intercept" & tolower(coefs$probe) != "(intercept)", ]
load("Ref_DNAm_brain_values.rdat")
refvals <- as.numeric(ref[coefs$probe])
write_json(list(probe = coefs$probe, coef = coefs$coef, ref = refvals), "cortical.json", digits = 10)

Writing download.r

os.system("Rscript download.r")

0

Load features

d = json.load(open('cortical.json'))
model.features = list(d['probe'])

Load weights into base model

# Intercept 0.577682570446177 hard-coded in the CorticalClock reference script
weights = torch.tensor(d['coef']).unsqueeze(0).float()
intercept = torch.tensor([0.577682570446177]).float()

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 = d['ref']

Load preprocess and postprocess objects

model.preprocess_name = None
model.preprocess_dependencies = None

model.postprocess_name = 'anti_log_linear'
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': 'Shireby, Gemma L., et al. "Recalibrating the epigenetic clock: '
             'implications for assessing biological age in the human cortex." '
             'Brain 143.12 (2020): 3763-3775.',
 'clock_name': 'corticalclock',
 'data_type': 'methylation',
 'doi': 'https://doi.org/10.1093/brain/awaa334',
 'notes': None,
 'research_only': None,
 'species': 'Homo sapiens',
 'version': None,
 'year': 2020}
reference_values: [0.21611617687, 0.15740657645, 0.7341710846, 0.060822631032, 0.10575749654, 0.62440019335, 0.13956151931, 0.93910437046, 0.67922573527, 0.41269620156, 0.18058134103, 0.30387161589, 0.72916401957, 0.16249365613, 0.10514838761, 0.32006057604, 0.41533755409, 0.76224987996, 0.1012865392, 0.068627506888, 0.62711858238, 0.067610974155, 0.73657937789, 0.085680987307, 0.16091221649, 0.5857097145, 0.65691321067, 0.5219473048, 0.14838958913, 0.46603381304]... [Total elements: 347]
preprocess_name: None
preprocess_dependencies: None
postprocess_name: 'anti_log_linear'
postprocess_dependencies: None
features: ['cg00059225', 'cg00088042', 'cg00252534', 'cg00297950', 'cg00384539', 'cg00491255', 'cg00521255', 'cg00648582', 'cg00771642', 'cg00924265', 'cg00935119', 'cg00940577', 'cg01091514', 'cg01122755', 'cg01162920', 'cg01194538', 'cg01264729', 'cg01311102', 'cg01529637', 'cg01532168', 'cg01616394', 'cg01639032', 'cg01641432', 'cg01655150', 'cg01745370', 'cg01899542', 'cg02046143', 'cg02047661', 'cg02357838', 'cg02361903']... [Total elements: 347]
base_model_features: None

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

base_model: LinearModel(
  (linear): Linear(in_features=347, out_features=1, bias=True)
)

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

base_model.linear.weight: [0.24595920741558075, 0.18271556496620178, 0.16822335124015808, 0.14175772666931152, 0.17820188403129578, 0.14450779557228088, -0.2443627119064331, -0.03914619982242584, 0.1277257353067398, -0.06851626932621002, -0.038119036704301834, -0.061217304319143295, -0.03222978115081787, 0.06571090221405029, -0.1973567008972168, -0.151609405875206, -0.15242373943328857, -0.10921627283096313, 0.026809634640812874, 0.3346559405326843, -0.01871008612215519, 0.5150526762008667, -0.0013768351636826992, -0.32761350274086, -0.00586429750546813, -0.050962597131729126, -0.007779109291732311, -0.017116934061050415, 0.2379734218120575, -0.5452234148979187]... [Tensor of shape torch.Size([1, 347])]
base_model.linear.bias: tensor([0.5777])

%==================================== 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([[ 2.8786e+01],
        [-9.7972e-01],
        [ 1.7019e+01],
        [-9.1772e-01],
        [ 8.8761e+00],
        [ 1.7652e+01],
        [ 1.6345e+02],
        [-2.4936e-03],
        [-9.7389e-01],
        [-9.8054e-01]], dtype=torch.float64, grad_fn=<IndexPutBackward0>)

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: CorticalClockCoefs.txt
Deleted file: Ref_DNAm_brain_values.rdat
Deleted file: download.r
Deleted file: cortical.json