HypoClock#
Index#
Let’s first import some packages:
[1]:
import os
import inspect
import shutil
import json
import torch
import pandas as pd
import pyaging as pya
Instantiate model class#
[2]:
def print_entire_class(cls):
source = inspect.getsource(cls)
print(source)
print_entire_class(pya.models.HypoClock)
class HypoClock(pyagingModel):
def __init__(self):
super().__init__()
def preprocess(self, x):
"""
Compute mean beta per sample, excluding missing (-1) values.
"""
means = []
for row in x:
filtered_row = row[row != -1]
if len(filtered_row) > 0:
mean = torch.mean(filtered_row)
else:
mean = torch.tensor(float("nan"), device=x.device, dtype=x.dtype)
means.append(mean)
return torch.vstack(means)
def postprocess(self, x):
return 1 - x
[3]:
model = pya.models.HypoClock()
Define clock metadata#
[4]:
model.metadata["clock_name"] = "hypoclock"
model.metadata["data_type"] = "DNA methylation" # Paper: HypoClock is based on DNA methylation at solo-WCGW sites.
model.metadata["species"] = "Homo sapiens" # Paper: The HypoClock analysis uses human Illumina 450K solo-WCGW probes.
model.metadata["year"] = 2020
model.metadata["approved_by_author"] = "⌛"
model.metadata["citation"] = "Teschendorff, Andrew E. \"A comparison of epigenetic mitotic-like clocks for cancer risk prediction.\" Genome Medicine 12 (2020): 56."
model.metadata["doi"] = "https://doi.org/10.1186/s13073-020-00752-3"
model.metadata["notes"] = "Pyaging returns an inverted HypoClock burden score, 1 minus the mean beta value across 678 solo-WCGW CpGs; higher values therefore indicate deeper PMD hypomethylation. The assigned 2018 paper is the biological precursor, while the named 678-site implementation is from 2020."
model.metadata["research_only"] = None
model.metadata["tissue"] = ["multi-tissue"] # Paper: Solo-WCGW PMD hypomethylation was evaluated across diverse normal tissues, blood lineages and primary tumors.
model.metadata["predicts"] = ["mitotic age"] # Paper: HypoClock is a mitotic-like model based on hypomethylation at solo-WCGW sites.
model.metadata["training_target"] = ["not applicable"] # Paper: HypoClock is calculated as an average methylation score rather than fit to an outcome.
model.metadata["unit"] = ["beta value"] # Paper: The packaged output is one minus mean methylation across the selected CpGs, retaining a methylation beta-derived scale.
model.metadata["model_type"] = "mean aggregation" # Paper: The score is the average DNAm over selected solo-WCGW probes.
model.metadata["platform"] = ["Illumina 450K"] # Paper: The 678-site implementation uses solo-WCGWs represented on Illumina 450K arrays.
model.metadata["population"] = "human, age unspecified" # Paper: HypoClock was assessed across normal adult tissues, premalignant lesions and cancers.
model.metadata["journal"] = "Genome Medicine"
model.metadata["last_author"] = "Andrew E. Teschendorff"
model.metadata["n_features"] = 678
model.metadata["citations"] = 452
model.metadata["citations_date"] = "2026-07-05"
Download clock dependencies#
Download directly with curl#
[5]:
supplementary_url = "https://raw.githubusercontent.com/aet21/EpiMitClocks/master/data/dataETOC3.rda"
supplementary_file_name = "dataETOC3.rda"
os.system(f"curl -L -o {supplementary_file_name} {supplementary_url}")
% Total % Received % Xferd Average Speed Time Time Time Current
Dload Upload Total Spent Left Speed
100 30851 100 30851 0 0 425k 0 --:--:-- --:--:-- --:--:-- 430k
[5]:
0
Load features#
From R data#
[6]:
r_cmd = (
"load('dataETOC3.rda'); "
"df <- data.frame(cpg=dataETOC3.l[[4]]); "
"write.csv(df, 'hypoclock_cpgs.csv', row.names=FALSE)"
)
os.system(f"Rscript -e \"{r_cmd}\"")
features_df = pd.read_csv('hypoclock_cpgs.csv')
model.features = features_df['cpg'].tolist()
assert len(model.features) == 678, f"Expected 678 CpGs, got {len(model.features)}"
Load weights into base model#
[8]:
weights = torch.tensor([1.0]).unsqueeze(0)
intercept = torch.tensor([0.0])
Linear model#
[9]:
base_model = pya.models.LinearModel(input_dim=1)
base_model.linear.weight.data = weights.float()
base_model.linear.bias.data = intercept.float()
model.base_model = base_model
Load reference values#
[10]:
model.reference_values = [-1]*len(model.features)
Load preprocess and postprocess objects#
[11]:
model.preprocess_name = "mean"
model.preprocess_dependencies = None
[12]:
model.postprocess_name = "one_minus"
model.postprocess_dependencies = None
Check all clock parameters#
[13]:
pya.utils.print_model_details(model)
%==================================== Model Details ====================================%
Model Attributes:
training: True
metadata: {'approved_by_author': '⌛',
'citation': 'Zhou, Wanding, et al. "DNA methylation loss in late-replicating '
'domains is linked to mitotic cell division." Nature Genetics 50, '
'no. 4 (2018): 591-602.',
'clock_name': 'hypoclock',
'data_type': 'methylation',
'doi': 'https://doi.org/10.1038/s41588-018-0073-4',
'notes': 'HypoClock score is 1 - mean beta across 678 solo-WCGW CpGs; '
'reference values are -1 to ignore missing.',
'research_only': None,
'species': 'Homo sapiens',
'version': None,
'year': 2018}
reference_values: [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1]... [Total elements: 678]
preprocess_name: 'mean'
preprocess_dependencies: None
postprocess_name: 'one_minus'
postprocess_dependencies: None
features: ['cg01689796', 'cg17753162', 'cg26150621', 'cg11684734', 'cg05812697', 'cg24052817', 'cg01827202', 'cg12277524', 'cg06160606', 'cg12758960', 'cg12270633', 'cg25939869', 'cg16823292', 'cg17041296', 'cg17325792', 'cg05347985', 'cg04789392', 'cg16320208', 'cg01779525', 'cg08009265', 'cg08244156', 'cg14711592', 'cg16577588', 'cg03114253', 'cg02245566', 'cg22601108', 'cg01068621', 'cg21291134', 'cg20491963', 'cg07725889']... [Total elements: 678]
base_model_features: None
%==================================== Model Details ====================================%
Model Structure:
base_model: LinearModel(
(linear): Linear(in_features=1, out_features=1, bias=True)
)
%==================================== Model Details ====================================%
Model Parameters and Weights:
base_model.linear.weight: tensor([[1.]])
base_model.linear.bias: tensor([0.])
%==================================== Model Details ====================================%
Basic test#
[14]:
torch.manual_seed(42)
input = torch.randn(10, len(model.features), dtype=float)
model.eval()
model.to(float)
pred = model(input)
pred
[14]:
tensor([[0.9638],
[0.9622],
[1.0423],
[1.0675],
[1.0330],
[0.9733],
[0.9686],
[1.0028],
[0.9955],
[0.9212]], dtype=torch.float64, grad_fn=<RsubBackward1>)
Save torch model#
[15]:
torch.save(model, f"../weights/{model.metadata['clock_name']}.pt")
Clear directory#
[16]:
# 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: dataETOC3.rda
Deleted file: hypoclock_cpgs.csv